feature: improve micro compaction

feat: cloudflare DNS/Zone tool integrations

.github/ISSUE_TEMPLATE/standard-bounty.yml

@@ -0,0 +1,78 @@
+name: Standard Bounty
+description: A bounty task for general framework contributions (not integration-specific)
+title: "[Bounty]: "
+labels: []
+body:
+  - type: markdown
+    attributes:
+      value: |
+        ## Standard Bounty
+
+        This issue is part of the [Bounty Program](../../docs/bounty-program/README.md).
+        **Claim this bounty** by commenting below — a maintainer will assign you within 24 hours.
+
+  - type: dropdown
+    id: bounty-size
+    attributes:
+      label: Bounty Size
+      options:
+        - "Small (10 pts)"
+        - "Medium (30 pts)"
+        - "Large (75 pts)"
+        - "Extreme (150 pts)"
+    validations:
+      required: true
+
+  - type: dropdown
+    id: difficulty
+    attributes:
+      label: Difficulty
+      options:
+        - Easy
+        - Medium
+        - Hard
+    validations:
+      required: true
+
+  - type: textarea
+    id: description
+    attributes:
+      label: Description
+      description: What needs to be done to complete this bounty.
+      placeholder: |
+        Describe the specific task, including:
+        - What the contributor needs to do
+        - Links to relevant files in the repo
+        - Any context or motivation for the change
+    validations:
+      required: true
+
+  - type: textarea
+    id: acceptance-criteria
+    attributes:
+      label: Acceptance Criteria
+      description: What "done" looks like. The PR must meet all criteria.
+      placeholder: |
+        - [ ] Criterion 1
+        - [ ] Criterion 2
+        - [ ] CI passes
+    validations:
+      required: true
+
+  - type: textarea
+    id: relevant-files
+    attributes:
+      label: Relevant Files
+      description: Links to files or directories related to this bounty.
+      placeholder: |
+        - `path/to/file.py`
+        - `path/to/directory/`
+
+  - type: textarea
+    id: resources
+    attributes:
+      label: Resources
+      description: Links to docs, issues, or external references that will help.
+      placeholder: |
+        - Related issue: #XXXX
+        - Docs: https://...

.github/workflows/bounty-completed.yml

@@ -2,14 +2,22 @@ name: Bounty completed
 description: Awards points and notifies Discord when a bounty PR is merged
 
 on:
-  pull_request:
+  pull_request_target:
     types: [closed]
 
+  workflow_dispatch:
+    inputs:
+      pr_number:
+        description: "PR number to process (for missed bounties)"
+        required: true
+        type: number
+
 jobs:
   bounty-notify:
     if: >
-      github.event.pull_request.merged == true &&
-      contains(join(github.event.pull_request.labels.*.name, ','), 'bounty:')
+      github.event_name == 'workflow_dispatch' ||
+      (github.event.pull_request.merged == true &&
+       contains(join(github.event.pull_request.labels.*.name, ','), 'bounty:'))
     runs-on: ubuntu-latest
     timeout-minutes: 5
     permissions:
@@ -32,6 +40,8 @@ jobs:
           GITHUB_REPOSITORY_OWNER: ${{ github.repository_owner }}
           GITHUB_REPOSITORY_NAME: ${{ github.event.repository.name }}
           DISCORD_WEBHOOK_URL: ${{ secrets.DISCORD_BOUNTY_WEBHOOK_URL }}
+          BOT_API_URL: ${{ secrets.BOT_API_URL }}
+          BOT_API_KEY: ${{ secrets.BOT_API_KEY }}
           LURKR_API_KEY: ${{ secrets.LURKR_API_KEY }}
           LURKR_GUILD_ID: ${{ secrets.LURKR_GUILD_ID }}
-          PR_NUMBER: ${{ github.event.pull_request.number }}
+          PR_NUMBER: ${{ inputs.pr_number || github.event.pull_request.number }}

.github/workflows/link-discord.yml

@@ -1,126 +0,0 @@
-name: Link Discord account
-description: Auto-creates a PR to add contributor to contributors.yml when a link-discord issue is opened
-
-on:
-  issues:
-    types: [opened]
-
-jobs:
-  link-discord:
-    if: contains(github.event.issue.labels.*.name, 'link-discord')
-    runs-on: ubuntu-latest
-    timeout-minutes: 2
-    permissions:
-      contents: write
-      issues: write
-      pull-requests: write
-
-    steps:
-      - name: Checkout repository
-        uses: actions/checkout@v4
-
-      - name: Parse issue and update contributors.yml
-        uses: actions/github-script@v7
-        with:
-          script: |
-            const fs = require('fs');
-
-            const issue = context.payload.issue;
-            const githubUsername = issue.user.login;
-
-            // Parse the issue body for form fields
-            const body = issue.body || '';
-
-            // Extract Discord ID — look for the numeric value after the "Discord User ID" heading
-            const discordMatch = body.match(/### Discord User ID\s*\n\s*(\d{17,20})/);
-            if (!discordMatch) {
-              await github.rest.issues.createComment({
-                ...context.repo,
-                issue_number: issue.number,
-                body: `Could not find a valid Discord ID in the issue body. Please make sure you entered a numeric ID (17-20 digits), not a username.\n\nExample: \`123456789012345678\``
-              });
-              await github.rest.issues.update({
-                ...context.repo,
-                issue_number: issue.number,
-                state: 'closed',
-                state_reason: 'not_planned'
-              });
-              return;
-            }
-            const discordId = discordMatch[1];
-
-            // Extract display name (optional)
-            const nameMatch = body.match(/### Display Name \(optional\)\s*\n\s*(.+)/);
-            const displayName = nameMatch ? nameMatch[1].trim() : '';
-
-            // Check if user already exists
-            const yml = fs.readFileSync('contributors.yml', 'utf-8');
-            if (yml.includes(`github: ${githubUsername}`)) {
-              await github.rest.issues.createComment({
-                ...context.repo,
-                issue_number: issue.number,
-                body: `@${githubUsername} is already in \`contributors.yml\`. If you need to update your Discord ID, please edit the file directly via PR.`
-              });
-              await github.rest.issues.update({
-                ...context.repo,
-                issue_number: issue.number,
-                state: 'closed',
-                state_reason: 'completed'
-              });
-              return;
-            }
-
-            // Append entry to contributors.yml
-            let entry = `  - github: ${githubUsername}\n    discord: "${discordId}"`;
-            if (displayName && displayName !== '_No response_') {
-              entry += `\n    name: ${displayName}`;
-            }
-            entry += '\n';
-
-            const updated = yml.trimEnd() + '\n' + entry;
-            fs.writeFileSync('contributors.yml', updated);
-
-            // Set outputs for commit step
-            core.exportVariable('GITHUB_USERNAME', githubUsername);
-            core.exportVariable('DISCORD_ID', discordId);
-            core.exportVariable('ISSUE_NUMBER', issue.number.toString());
-
-      - name: Create PR
-        run: |
-          # Check if there are changes
-          if git diff --quiet contributors.yml; then
-            echo "No changes to contributors.yml"
-            exit 0
-          fi
-
-          BRANCH="docs/link-discord-${GITHUB_USERNAME}"
-          git config user.name "github-actions[bot]"
-          git config user.email "41898282+github-actions[bot]@users.noreply.github.com"
-          git checkout -b "$BRANCH"
-          git add contributors.yml
-          git commit -m "docs: link @${GITHUB_USERNAME} to Discord"
-          git push origin "$BRANCH"
-
-          gh pr create \
-            --title "docs: link @${GITHUB_USERNAME} to Discord" \
-            --body "Adds @${GITHUB_USERNAME} (Discord \`${DISCORD_ID}\`) to \`contributors.yml\` for bounty XP tracking.
-
-          Closes #${ISSUE_NUMBER}" \
-            --base main \
-            --head "$BRANCH" \
-            --label "link-discord"
-        env:
-          GH_TOKEN: ${{ secrets.GITHUB_TOKEN }}
-
-      - name: Notify on issue
-        uses: actions/github-script@v7
-        with:
-          script: |
-            const username = process.env.GITHUB_USERNAME;
-            const issueNumber = parseInt(process.env.ISSUE_NUMBER);
-
-            await github.rest.issues.createComment({
-              ...context.repo,
-              issue_number: issueNumber,
-              body: `A PR has been created to link your account. A maintainer will merge it shortly — once merged, you'll receive XP and Discord pings when your bounty PRs are merged.`
-            });

.github/workflows/weekly-leaderboard.yml

@@ -35,6 +35,8 @@ jobs:
           GITHUB_REPOSITORY_OWNER: ${{ github.repository_owner }}
           GITHUB_REPOSITORY_NAME: ${{ github.event.repository.name }}
           DISCORD_WEBHOOK_URL: ${{ secrets.DISCORD_BOUNTY_WEBHOOK_URL }}
+          BOT_API_URL: ${{ secrets.BOT_API_URL }}
+          BOT_API_KEY: ${{ secrets.BOT_API_KEY }}
           LURKR_API_KEY: ${{ secrets.LURKR_API_KEY }}
           LURKR_GUILD_ID: ${{ secrets.LURKR_GUILD_ID }}
           SINCE_DATE: ${{ github.event.inputs.since_date || '' }}

.gitignore

@@ -13,6 +13,10 @@ out/
 .env
 .env.local
 .env.*.local
+.venv
+/venv
+tools/src/uv.lock
+
 
 # User configuration (copied from .example)
 config.yaml
@@ -68,9 +72,6 @@ temp/
 exports/*
 
 .claude/settings.local.json
-.claude/skills/ship-it/
-
-.venv
 
 docs/github-issues/*
 core/tests/*dumps/*

CHANGELOG.md

@@ -1,17 +1,149 @@
 # Release Notes
 
+## v0.7.1
+
+**Release Date:** March 13, 2026
+**Tag:** v0.7.1
+
+### Chrome-Native Browser Control
+
+v0.7.1 replaces Playwright with direct Chrome DevTools Protocol (CDP) integration. The GCU now launches the user's system Chrome via `open -n` on macOS, connects over CDP, and manages browser lifecycle end-to-end -- no extra browser binary required.
+
+---
+
+### Highlights
+
+#### System Chrome via CDP
+
+The entire GCU browser stack has been rewritten:
+
+- **Chrome finder & launcher** -- New `chrome_finder.py` discovers installed Chrome and `chrome_launcher.py` manages process lifecycle with `--remote-debugging-port`
+- **Coexist with user's browser** -- `open -n` on macOS launches a separate Chrome instance so the user's tabs stay untouched
+- **Dynamic viewport sizing** -- Viewport auto-sizes to the available display area, suppressing Chrome warning bars
+- **Orphan cleanup** -- Chrome processes are killed on GCU server shutdown to prevent leaks
+- **`--no-startup-window`** -- Chrome launches headlessly by default until a page is needed
+
+#### Per-Subagent Browser Isolation
+
+Each GCU subagent gets its own Chrome user-data directory, preventing cookie/session cross-contamination:
+
+- Unique browser profiles injected per subagent
+- Profiles cleaned up after top-level GCU node execution
+- Tab origin and age metadata tracked per subagent
+
+#### Dummy Agent Testing Framework
+
+A comprehensive test suite for validating agent graph patterns without LLM calls:
+
+- 8 test modules covering echo, pipeline, branch, parallel merge, retry, feedback loop, worker, and GCU subagent patterns
+- Shared fixtures and a `run_all.py` runner for CI integration
+- Subagent lifecycle tests
+
+---
+
+### What's New
+
+#### GCU Browser
+
+- **Switch from Playwright to system Chrome via CDP** -- Direct CDP connection replaces Playwright dependency. (@bryanadenhq)
+- **Chrome finder and launcher modules** -- `chrome_finder.py` and `chrome_launcher.py` for cross-platform Chrome discovery and process management. (@bryanadenhq)
+- **Dynamic viewport sizing** -- Auto-size viewport and suppress Chrome warning bar. (@bryanadenhq)
+- **Per-subagent browser profile isolation** -- Unique user-data directories per subagent with cleanup. (@bryanadenhq)
+- **Tab origin/age metadata** -- Track which subagent opened each tab and when. (@bryanadenhq)
+- **`browser_close_all` tool** -- Bulk tab cleanup for agents managing many pages. (@bryanadenhq)
+- **Auto-track popup pages** -- Popups are automatically captured and tracked. (@bryanadenhq)
+- **Auto-snapshot from browser interactions** -- Browser interaction tools return screenshots automatically. (@bryanadenhq)
+- **Kill orphaned Chrome processes** -- GCU server shutdown cleans up lingering Chrome instances. (@bryanadenhq)
+- **`--no-startup-window` Chrome flag** -- Prevent empty window on launch. (@bryanadenhq)
+- **Launch Chrome via `open -n` on macOS** -- Coexist with the user's running browser. (@bryanadenhq)
+
+#### Framework & Runtime
+
+- **Session resume fix for new agents** -- Correctly resume sessions when a new agent is loaded. (@bryanadenhq)
+- **Queen upsert fix** -- Prevent duplicate queen entries on session restore. (@bryanadenhq)
+- **Anchor worker monitoring to queen's session ID on cold-restore** -- Worker monitors reconnect to the correct queen after restart. (@bryanadenhq)
+- **Update meta.json when loading workers** -- Worker metadata stays in sync with runtime state. (@RichardTang-Aden)
+- **Generate worker MCP file correctly** -- Fix MCP config generation for spawned workers. (@RichardTang-Aden)
+- **Share event bus so tool events are visible to parent** -- Tool execution events propagate up to parent graphs. (@bryanadenhq)
+- **Subagent activity tracking in queen status** -- Queen instructions include live subagent status. (@bryanadenhq)
+- **GCU system prompt updates** -- Auto-snapshots, batching, popup tracking, and close_all guidance. (@bryanadenhq)
+
+#### Frontend
+
+- **Loading spinner in draft panel** -- Shows spinner during planning phase instead of blank panel. (@bryanadenhq)
+- **Fix credential modal errors** -- Modal no longer eats errors; banner stays visible. (@bryanadenhq)
+- **Fix credentials_required loop** -- Stop clearing the flag on modal close to prevent infinite re-prompting. (@bryanadenhq)
+- **Fix "Add tab" dropdown overflow** -- Dropdown no longer hidden when many agents are open. (@prasoonmhwr)
+
+#### Testing
+
+- **Dummy agent test framework** -- 8 test modules (echo, pipeline, branch, parallel merge, retry, feedback loop, worker, GCU subagent) with shared fixtures and CI runner. (@bryanadenhq)
+- **Subagent lifecycle tests** -- Validate subagent spawn and completion flows. (@bryanadenhq)
+
+#### Documentation & Infrastructure
+
+- **MCP integration PRD** -- Product requirements for MCP server registry. (@TimothyZhang7)
+- **Skills registry PRD** -- Product requirements for skill registry system. (@bryanadenhq)
+- **Bounty program updates** -- Standard bounty issue template and updated contributor guide. (@bryanadenhq)
+- **Windows quickstart** -- Add default context limit for PowerShell setup. (@bryanadenhq)
+- **Remove deprecated files** -- Clean up `setup_mcp.py`, `verify_mcp.py`, `antigravity-setup.md`, and `setup-antigravity-mcp.sh`. (@bryanadenhq)
+
+---
+
+### Bug Fixes
+
+- Fix credential modal eating errors and banner staying open
+- Stop clearing `credentials_required` on modal close to prevent infinite loop
+- Share event bus so tool events are visible to parent graph
+- Use lazy %-formatting in subagent completion log to avoid f-string in logger
+- Anchor worker monitoring to queen's session ID on cold-restore
+- Update meta.json when loading workers
+- Generate worker MCP file correctly
+- Fix "Add tab" dropdown partially hidden when creating multiple agents
+
+---
+
+### Community Contributors
+
+- **Prasoon Mahawar** (@prasoonmhwr) -- Fix UI overflow on agent tab dropdown
+- **Richard Tang** (@RichardTang-Aden) -- Worker MCP generation and meta.json fixes
+
+---
+
+### Upgrading
+
+```bash
+git pull origin main
+uv sync
+```
+
+The Playwright dependency is no longer required for GCU browser operations. Chrome must be installed on the host system.
+
+---
+
+## v0.7.0
+
+**Release Date:** March 5, 2026
+**Tag:** v0.7.0
+
+Session management refactor release.
+
+---
+
+## v0.5.1
+
 **Release Date:** February 18, 2026
 **Tag:** v0.5.1
 
-## The Hive Gets a Brain
+### The Hive Gets a Brain
 
 v0.5.1 is our most ambitious release yet. Hive agents can now **build other agents** -- the new Hive Coder meta-agent writes, tests, and fixes agent packages from natural language. The runtime grows multi-graph support so one session can orchestrate multiple agents simultaneously. The TUI gets a complete overhaul with an in-app agent picker, live streaming, and seamless escalation to the Coder. And we're now provider-agnostic: Claude Code subscriptions, OpenAI-compatible endpoints, and any LiteLLM-supported model work out of the box.
 
 ---
 
-## Highlights
+### Highlights
 
-### Hive Coder -- The Agent That Builds Agents
+#### Hive Coder -- The Agent That Builds Agents
 
 A native meta-agent that lives inside the framework at `core/framework/agents/hive_coder/`. Give it a natural-language specification and it produces a complete agent package -- goal definition, node prompts, edge routing, MCP tool wiring, tests, and all boilerplate files.
 
@@ -30,7 +162,7 @@ The Coder ships with:
 - **Coder Tools MCP server** -- file I/O, fuzzy-match editing, git snapshots, and sandboxed shell execution (`tools/coder_tools_server.py`)
 - **Test generation** -- structural tests for forever-alive agents that don't hang on `runner.run()`
 
-### Multi-Graph Agent Runtime
+#### Multi-Graph Agent Runtime
 
 `AgentRuntime` now supports loading, managing, and switching between multiple agent graphs within a single session. Six new lifecycle tools give agents (and the TUI) full control:
 
@@ -44,7 +176,7 @@ await runtime.add_graph("exports/deep_research_agent")
 
 The Hive Coder uses multi-graph internally -- when you escalate from a worker agent, the Coder loads as a separate graph while the worker stays alive in the background.
 
-### TUI Revamp
+#### TUI Revamp
 
 The Terminal UI gets a ground-up rebuild with five major additions:
 
@@ -54,7 +186,7 @@ The Terminal UI gets a ground-up rebuild with five major additions:
 - **PDF attachments** -- `/attach` and `/detach` commands with native OS file dialog (macOS, Linux, Windows)
 - **Multi-graph commands** -- `/graphs`, `/graph <id>`, `/load <path>`, `/unload <id>` for managing agent graphs in-session
 
-### Provider-Agnostic LLM Support
+#### Provider-Agnostic LLM Support
 
 Hive is no longer Anthropic-only. v0.5.1 adds first-class support for:
 
@@ -66,9 +198,9 @@ The quickstart script auto-detects Claude Code subscriptions and ZAI Code instal
 
 ---
 
-## What's New
+### What's New
 
-### Architecture & Runtime
+#### Architecture & Runtime
 
 - **Hive Coder meta-agent** -- Natural-language agent builder with reference docs, guardian watchdog, and `hive code` CLI command. (@TimothyZhang7)
 - **Multi-graph agent sessions** -- `add_graph`/`remove_graph` on AgentRuntime with 6 lifecycle tools (`load_agent`, `unload_agent`, `start_agent`, `restart_agent`, `list_agents`, `get_user_presence`). (@TimothyZhang7)
@@ -79,7 +211,7 @@ The quickstart script auto-detects Claude Code subscriptions and ZAI Code instal
 - **Pre-start confirmation prompt** -- Interactive prompt before agent execution allowing credential updates or abort. (@RichardTang-Aden)
 - **Event bus multi-graph support** -- `graph_id` on events, `filter_graph` on subscriptions, `ESCALATION_REQUESTED` event type, `exclude_own_graph` filter. (@TimothyZhang7)
 
-### TUI Improvements
+#### TUI Improvements
 
 - **In-app agent picker** (Ctrl+A) -- Tabbed modal for browsing agents with metadata badges (nodes, tools, sessions, tags). (@TimothyZhang7)
 - **Runtime-optional TUI startup** -- Launches without a pre-loaded agent, shows agent picker on startup. (@TimothyZhang7)
@@ -89,7 +221,7 @@ The quickstart script auto-detects Claude Code subscriptions and ZAI Code instal
 - **Multi-graph TUI commands** -- `/graphs`, `/graph <id>`, `/load <path>`, `/unload <id>`. (@TimothyZhang7)
 - **Agent Guardian watchdog** -- Event-driven monitor that catches secondary agent failures and triggers automatic remediation, with `--no-guardian` CLI flag. (@TimothyZhang7)
 
-### New Tool Integrations
+#### New Tool Integrations
 
 | Tool                   | Description                                                                                                                                                            | Contributor        |
 | ---------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ------------------ |
@@ -99,7 +231,7 @@ The quickstart script auto-detects Claude Code subscriptions and ZAI Code instal
 | **Google Docs**        | Document creation, reading, and editing with OAuth credential support                                                                                                  | @haliaeetusvocifer |
 | **Gmail enhancements** | Expanded mail operations for inbox management                                                                                                                          | @bryanadenhq       |
 
-### Infrastructure
+#### Infrastructure
 
 - **Default node type → `event_loop`** -- `NodeSpec.node_type` defaults to `"event_loop"` instead of `"llm_tool_use"`. (@TimothyZhang7)
 - **Default `max_node_visits` → 0 (unlimited)** -- Nodes default to unlimited visits, reducing friction for feedback loops and forever-alive agents. (@TimothyZhang7)
@@ -112,7 +244,7 @@ The quickstart script auto-detects Claude Code subscriptions and ZAI Code instal
 
 ---
 
-## Bug Fixes
+### Bug Fixes
 
 - Flush WIP accumulator outputs on cancel/failure so edge conditions see correct values on resume
 - Stall detection state preserved across resume (no more resets on checkpoint restore)
@@ -125,13 +257,13 @@ The quickstart script auto-detects Claude Code subscriptions and ZAI Code instal
 - Fix email agent version conflicts (@RichardTang-Aden)
 - Fix coder tool timeouts (120s for tests, 300s cap for commands)
 
-## Documentation
+### Documentation
 
 - Clarify installation and prevent root pip install misuse (@paarths-collab)
 
 ---
 
-## Agent Updates
+### Agent Updates
 
 - **Email Inbox Management** -- Consolidate `gmail_inbox_guardian` and `inbox_management` into a single unified agent with updated prompts and config. (@RichardTang-Aden, @bryanadenhq)
 - **Job Hunter** -- Updated node prompts, config, and agent metadata; added PDF resume selection. (@bryanadenhq)
@@ -141,7 +273,7 @@ The quickstart script auto-detects Claude Code subscriptions and ZAI Code instal
 
 ---
 
-## Breaking Changes
+### Breaking Changes
 
 - **Deprecated node types raise `RuntimeError`** -- `llm_tool_use`, `llm_generate`, `function`, `router`, `human_input` now fail instead of warning. Migrate to `event_loop`.
 - **`NodeSpec.node_type` defaults to `"event_loop"`** (was `"llm_tool_use"`)
@@ -150,7 +282,7 @@ The quickstart script auto-detects Claude Code subscriptions and ZAI Code instal
 
 ---
 
-## Community Contributors
+### Community Contributors
 
 A huge thank you to everyone who contributed to this release:
 
@@ -165,14 +297,14 @@ A huge thank you to everyone who contributed to this release:
 
 ---
 
-## Upgrading
+### Upgrading
 
 ```bash
 git pull origin main
 uv sync
 ```
 
-### Migration Guide
+#### Migration Guide
 
 If your agents use deprecated node types, update them:
 
@@ -196,12 +328,3 @@ hive code
 # Or from TUI -- press Ctrl+E to escalate
 hive tui
 ```
-
----
-
-## What's Next
-
-- **Agent-to-agent communication** -- one agent's output triggers another agent's entry point
-- **Cost visibility** -- detailed runtime log of LLM costs per node and per session
-- **Persistent webhook subscriptions** -- survive agent restarts without re-registering
-- **Remote agent deployment** -- run agents as long-lived services with HTTP APIs

CONTRIBUTING.md

@@ -4,7 +4,7 @@
 
 Welcome to Aden Hive, an open-source AI agent framework built for developers who demand production-grade reliability, cross-platform support, and real-world performance. This guide will help you contribute effectively, whether you're fixing bugs, adding features, improving documentation, or building new tools.
 
-Thank you for your interest in contributing! We're especially looking for help building tools, integrations ([check #2805](https://github.com/adenhq/hive/issues/2805)), and example agents for the framework.
+Thank you for your interest in contributing! We're especially looking for help building tools, integrations ([check #2805](https://github.com/aden-hive/hive/issues/2805)), and example agents for the framework.
 
 ---
 
@@ -121,9 +121,15 @@ uv sync
 6. Make your changes
 7. Run checks and tests:
    ```bash
-   make check    # Lint and format checks (ruff check + ruff format --check)
+   make check    # Lint and format checks
    make test     # Core tests
    ```
+   On Windows (no make), run directly:
+   ```powershell
+   uv run ruff check core/ tools/
+   uv run ruff format --check core/ tools/
+   uv run pytest core/tests/
+   ```
 8. Commit your changes following our commit conventions
 9. Push to your fork and submit a Pull Request
 
@@ -222,8 +228,7 @@ else:  # linux
 - **Node.js 18+** (optional, for frontend development)
 
 > **Windows Users:**
-> If you are on native Windows, it is recommended to use **WSL (Windows Subsystem for Linux)**.
-> Alternatively, make sure to run PowerShell or Git Bash with Python 3.11+ installed, and disable "App Execution Aliases" in Windows settings.
+> Native Windows is supported. Use `.\quickstart.ps1` for setup and `.\hive.ps1` to run (PowerShell 5.1+). Disable "App Execution Aliases" in Windows settings to avoid Python path conflicts. WSL is also an option but not required.
 
 > **Tip:** Installing Claude Code skills is optional for running existing agents, but required if you plan to **build new agents**.
 
@@ -385,6 +390,8 @@ Aden Hive supports **100+ LLM providers** via LiteLLM, giving users maximum flex
 |----------|--------|-------|
 | **Anthropic** | Claude 3.5 Sonnet, Haiku, Opus | Default provider, best for reasoning |
 | **OpenAI** | GPT-4, GPT-4 Turbo, GPT-4o | Function calling, vision |
+| **OpenRouter** | Any OpenRouter catalog model | Uses `OPENROUTER_API_KEY` and `https://openrouter.ai/api/v1` |
+| **Hive LLM** | `queen`, `kimi-2.5`, `GLM-5` | Uses `HIVE_API_KEY` and the Hive-managed endpoint |
 | **Google** | Gemini 1.5 Pro, Flash | Long context windows |
 | **DeepSeek** | DeepSeek V3 | Cost-effective, strong reasoning |
 | **Mistral** | Mistral Large, Medium, Small | Open weights, EU hosting |
@@ -410,6 +417,10 @@ DEFAULT_MODEL = "claude-haiku-4-5-20251001"
 - **Cost**: DeepSeek or Gemini Flash (budget-conscious)
 - **Privacy**: Ollama with local models (no data leaves server)
 
+**Provider-Specific Notes**
+- **OpenRouter**: store `provider` as `openrouter`, use the raw OpenRouter model ID in `model` (for example `x-ai/grok-4.20-beta`), and use `OPENROUTER_API_KEY`
+- **Hive LLM**: store `provider` as `hive`, use Hive model names such as `queen`, `kimi-2.5`, or `GLM-5`, and use `HIVE_API_KEY`
+
 **For Development**
 - Use cheaper/faster models (Haiku, GPT-4o-mini)
 - Test with multiple providers to catch provider-specific issues
@@ -421,7 +432,7 @@ DEFAULT_MODEL = "claude-haiku-4-5-20251001"
 2. **Add credential handling** in `core/framework/credentials/`
 3. **Add provider-specific configuration** in `core/framework/llm/`
 4. **Write tests** in `core/tests/test_llm_provider.py`
-5. **Update documentation** in `docs/llm_providers.md`
+5. **Update documentation** in `README.md`, `docs/configuration.md`, and any setup guides that mention provider configuration
 
 **Example: Testing LLM Integration**
 

Makefile

@@ -1,4 +1,11 @@
-.PHONY: lint format check test install-hooks help frontend-install frontend-dev frontend-build
+.PHONY: lint format check test test-tools test-live test-all install-hooks help frontend-install frontend-dev frontend-build
+
+# ── Ensure uv is findable in Git Bash on Windows ──────────────────────────────
+# uv installs to ~/.local/bin on Windows/Linux/macOS. Git Bash may not include
+# this in PATH by default, so we prepend it here.
+export PATH := $(HOME)/.local/bin:$(PATH)
+
+# ── Targets ───────────────────────────────────────────────────────────────────
 
 help: ## Show this help
 	@grep -E '^[a-zA-Z_-]+:.*?## .*$$' $(MAKEFILE_LIST) | \
@@ -46,4 +53,4 @@ frontend-dev: ## Start frontend dev server
 	cd core/frontend && npm run dev
 
 frontend-build: ## Build frontend for production
-	cd core/frontend && npm run build
+	cd core/frontend && npm run build

README.md

@@ -23,11 +23,12 @@
 </p>
 
 <p align="center">
+  <img src="https://img.shields.io/badge/Agent_Harness-Runtime_Layer-ff6600?style=flat-square" alt="Agent Harness" />
   <img src="https://img.shields.io/badge/AI_Agents-Self--Improving-brightgreen?style=flat-square" alt="AI Agents" />
   <img src="https://img.shields.io/badge/Multi--Agent-Systems-blue?style=flat-square" alt="Multi-Agent" />
   <img src="https://img.shields.io/badge/Headless-Development-purple?style=flat-square" alt="Headless" />
   <img src="https://img.shields.io/badge/Human--in--the--Loop-orange?style=flat-square" alt="HITL" />
-  <img src="https://img.shields.io/badge/Production--Ready-red?style=flat-square" alt="Production" />
+  <img src="https://img.shields.io/badge/Browser-Use-red?style=flat-square" alt="Browser Use" />
 </p>
 <p align="center">
   <img src="https://img.shields.io/badge/OpenAI-supported-412991?style=flat-square&logo=openai" alt="OpenAI" />
@@ -35,37 +36,42 @@
   <img src="https://img.shields.io/badge/Google_Gemini-supported-4285F4?style=flat-square&logo=google" alt="Gemini" />
 </p>
 
+<p align="center"><em>The agent harness for production workloads — state management, failure recovery, observability, and human oversight so your agents actually run.</em></p>
+
 ## Overview
 
-Build autonomous, reliable, self-improving AI agents without hardcoding workflows. Define your goal through conversation with hive coding agent(queen), and the framework generates a node graph with dynamically created connection code. When things break, the framework captures failure data, evolves the agent through the coding agent, and redeploys. Built-in human-in-the-loop nodes, credential management, and real-time monitoring give you control without sacrificing adaptability.
+Hive is a runtime harness for AI agents in production. You describe your goal in natural language; a coding agent (the queen) generates the agent graph and connection code to achieve it. During execution, the harness manages state isolation, checkpoint-based crash recovery, cost enforcement, and real-time observability. When agents fail, the framework captures failure data, evolves the graph through the coding agent, and redeploys automatically. Built-in human-in-the-loop nodes, browser control, credential management, and parallel execution give you production reliability without sacrificing adaptability.
 
 Visit [adenhq.com](https://adenhq.com) for complete documentation, examples, and guides.
 
-[![Hive Demo](https://img.youtube.com/vi/XDOG9fOaLjU/maxresdefault.jpg)](https://www.youtube.com/watch?v=XDOG9fOaLjU)
+Visit [HoneyComb](http://honeycomb.open-hive.com/) to see what jobs are being automated by AI. It’s a stock market for jobs, driven by our community’s AI agent progress. You can long and short jobs (with no real money but compute token)based on how much you think a job is going to be replaced by AI.
+
+https://github.com/user-attachments/assets/bf10edc3-06ba-48b6-98ba-d069b15fb69d
+
 
 ## Who Is Hive For?
 
-Hive is designed for developers and teams who want to build **production-grade AI agents** without manually wiring complex workflows.
+Hive is the harness layer for teams moving AI agents from prototype to production. Models are getting better on their own — the bottleneck is the infrastructure around them: state management, failure recovery, cost control, and observability.
 
 Hive is a good fit if you:
 
 - Want AI agents that **execute real business processes**, not demos
-- Need **fast or high volume agent execution** over open workflow
+- Need a **runtime that handles state, recovery, and parallel execution** at scale
 - Need **self-healing and adaptive agents** that improve over time
 - Require **human-in-the-loop control**, observability, and cost limits
-- Plan to run agents in **production environments**
+- Plan to run agents in **production** where uptime, cost, and auditability matter
 
 Hive may not be the best fit if you’re only experimenting with simple agent chains or one-off scripts.
 
 ## When Should You Use Hive?
 
-Use Hive when you need:
+Use Hive when the bottleneck is no longer the model but the harness around it:
 
-- Long-running, autonomous agents
-- Strong guardrails, process, and controls
-- Continuous improvement based on failures
-- Multi-agent coordination
-- A framework that evolves with your goals
+- Long-running agents that need **state persistence and crash recovery**
+- Production workloads requiring **cost enforcement, observability, and audit trails**
+- Agents that **self-heal** through failure capture and graph evolution
+- Multi-agent coordination with **session isolation and shared memory**
+- A framework that **scales with model improvements** rather than fighting them
 
 ## Quick Links
 
@@ -73,7 +79,7 @@ Use Hive when you need:
 - **[Self-Hosting Guide](https://docs.adenhq.com/getting-started/quickstart)** - Deploy Hive on your infrastructure
 - **[Changelog](https://github.com/aden-hive/hive/releases)** - Latest updates and releases
 - **[Roadmap](docs/roadmap.md)** - Upcoming features and plans
-- **[Report Issues](https://github.com/adenhq/hive/issues)** - Bug reports and feature requests
+- **[Report Issues](https://github.com/aden-hive/hive/issues)** - Bug reports and feature requests
 - **[Contributing](CONTRIBUTING.md)** - How to contribute and submit PRs
 
 ## Quick Start
@@ -84,7 +90,7 @@ Use Hive when you need:
 - An LLM provider that powers the agents
 - **ripgrep (optional, recommended on Windows):** The `search_files` tool uses ripgrep for faster file search. If not installed, a Python fallback is used. On Windows: `winget install BurntSushi.ripgrep` or `scoop install ripgrep`
 
-> **Note for Windows Users:** It is strongly recommended to use **WSL (Windows Subsystem for Linux)** or **Git Bash** to run this framework. Some core automation scripts may not execute correctly in standard Command Prompt or PowerShell.
+> **Windows Users:** Native Windows is supported via `quickstart.ps1` and `hive.ps1`. Run these in PowerShell 5.1+. WSL is also an option but not required.
 
 ### Installation
 
@@ -98,9 +104,11 @@ Use Hive when you need:
 git clone https://github.com/aden-hive/hive.git
 cd hive
 
-
-# Run quickstart setup
+# Run quickstart setup (macOS/Linux)
 ./quickstart.sh
+
+# Windows (PowerShell)
+.\quickstart.ps1
 ```
 
 This sets up:
@@ -108,18 +116,16 @@ This sets up:
 - **framework** - Core agent runtime and graph executor (in `core/.venv`)
 - **aden_tools** - MCP tools for agent capabilities (in `tools/.venv`)
 - **credential store** - Encrypted API key storage (`~/.hive/credentials`)
-- **LLM provider** - Interactive default model configuration
+- **LLM provider** - Interactive default model configuration, including Hive LLM and OpenRouter
 - All required Python dependencies with `uv`
 
 - Finally, it will open the Hive interface in your browser
 
 > **Tip:** To reopen the dashboard later, run `hive open` from the project directory.
 
-<img width="2500" height="1214" alt="home-screen" src="https://github.com/user-attachments/assets/134d897f-5e75-4874-b00b-e0505f6b45c4" />
-
 ### Build Your First Agent
 
-Type the agent you want to build in the home input box
+Type the agent you want to build in the home input box. The queen is going to ask you questions and work out a solution with you.
 
 <img width="2500" height="1214" alt="Image" src="https://github.com/user-attachments/assets/1ce19141-a78b-46f5-8d64-dbf987e048f4" />
 
@@ -131,7 +137,7 @@ Click "Try a sample agent" and check the templates. You can run a template direc
 
 Now you can run an agent by selecting the agent (either an existing agent or example agent). You can click the Run button on the top left, or talk to the queen agent and it can run the agent for you.
 
-<img width="2500" height="1214" alt="Image" src="https://github.com/user-attachments/assets/71c38206-2ad5-49aa-bde8-6698d0bc55f5" />
+<img width="2549" height="1174" alt="Screenshot 2026-03-12 at 9 27 36 PM" src="https://github.com/user-attachments/assets/7c7d30fa-9ceb-4c23-95af-b1caa405547d" />
 
 ## Features
 
@@ -143,19 +149,18 @@ Now you can run an agent by selecting the agent (either an existing agent or exa
 - **SDK-Wrapped Nodes** - Every node gets shared memory, local RLM memory, monitoring, tools, and LLM access out of the box
 - **[Human-in-the-Loop](docs/key_concepts/graph.md#human-in-the-loop)** - Intervention nodes that pause execution for human input with configurable timeouts and escalation
 - **Real-time Observability** - WebSocket streaming for live monitoring of agent execution, decisions, and node-to-node communication
-- **Production-Ready** - Self-hostable, built for scale and reliability
 
 ## Integration
 
 <a href="https://github.com/aden-hive/hive/tree/main/tools/src/aden_tools/tools"><img width="100%" alt="Integration" src="https://github.com/user-attachments/assets/a1573f93-cf02-4bb8-b3d5-b305b05b1e51" /></a>
 Hive is built to be model-agnostic and system-agnostic.
 
-- **LLM flexibility** - Hive Framework is designed to support various types of LLMs, including hosted and local models through LiteLLM-compatible providers.
+- **LLM flexibility** - Hive Framework supports Anthropic, OpenAI, OpenRouter, Hive LLM, and other hosted or local models through LiteLLM-compatible providers.
 - **Business system connectivity** - Hive Framework is designed to connect to all kinds of business systems as tools, such as CRM, support, messaging, data, file, and internal APIs via MCP.
 
-## Why Aden
+## Why Hive
 
-Hive focuses on generating agents that run real business processes rather than generic agents. Instead of requiring you to manually design workflows, define agent interactions, and handle failures reactively, Hive flips the paradigm: **you describe outcomes, and the system builds itself**—delivering an outcome-driven, adaptive experience with an easy-to-use set of tools and integrations.
+As models improve, the upper bound of what agents can do rises — but their reliability and production value are determined by the harness. Hive focuses on generating agents that run real business processes rather than generic agents. Instead of requiring you to manually design workflows, define agent interactions, and handle failures reactively, Hive flips the paradigm: **you describe outcomes, and the system builds itself**—delivering an outcome-driven, adaptive experience with an easy-to-use set of tools and integrations.
 
 ```mermaid
 flowchart LR
@@ -191,8 +196,9 @@ flowchart LR
 
 ### The Hive Advantage
 
-| Traditional Frameworks     | Hive                                   |
+| Typical Agent Frameworks   | Hive                                   |
 | -------------------------- | -------------------------------------- |
+| Focus on model orchestration | **Production harness**: state, recovery, observability |
 | Hardcode agent workflows   | Describe goals in natural language     |
 | Manual graph definition    | Auto-generated agent graphs            |
 | Reactive error handling    | Outcome-evaluation and adaptiveness    |
@@ -378,7 +384,7 @@ This project is licensed under the Apache License 2.0 - see the [LICENSE](LICENS
 
 **Q: What LLM providers does Hive support?**
 
-Hive supports 100+ LLM providers through LiteLLM integration, including OpenAI (GPT-4, GPT-4o), Anthropic (Claude models), Google Gemini, DeepSeek, Mistral, Groq, and many more. Simply set the appropriate API key environment variable and specify the model name. We recommend using Claude, GLM and Gemini as they have the best performance.
+Hive supports 100+ LLM providers through LiteLLM integration, including OpenAI (GPT-4, GPT-4o), Anthropic (Claude models), Google Gemini, DeepSeek, Mistral, Groq, OpenRouter, and Hive LLM. Simply set the appropriate API key environment variable and specify the model name. See [docs/configuration.md](docs/configuration.md) for provider-specific configuration examples.
 
 **Q: Can I use Hive with local AI models like Ollama?**
 
@@ -386,16 +392,12 @@ Yes! Hive supports local models through LiteLLM. Simply use the model name forma
 
 **Q: What makes Hive different from other agent frameworks?**
 
-Hive generates your entire agent system from natural language goals using a coding agent—you don't hardcode workflows or manually define graphs. When agents fail, the framework automatically captures failure data, [evolves the agent graph](docs/key_concepts/evolution.md), and redeploys. This self-improving loop is unique to Aden.
+Hive is an agent harness, not just an orchestration framework. It provides the production runtime layer — session isolation, checkpoint-based crash recovery, cost enforcement, real-time observability, and human-in-the-loop controls — that makes agents reliable enough to run real workloads. On top of that, Hive generates your entire agent system from natural language goals and automatically [evolves the graph](docs/key_concepts/evolution.md) when agents fail. The combination of a robust harness with self-improving generation is what sets Hive apart.
 
 **Q: Is Hive open-source?**
 
 Yes, Hive is fully open-source under the Apache License 2.0. We actively encourage community contributions and collaboration.
 
-**Q: Can Hive handle complex, production-scale use cases?**
-
-Yes. Hive is explicitly designed for production environments with features like automatic failure recovery, real-time observability, cost controls, and horizontal scaling support. The framework handles both simple automations and complex multi-agent workflows.
-
 **Q: Does Hive support human-in-the-loop workflows?**
 
 Yes, Hive fully supports [human-in-the-loop](docs/key_concepts/graph.md#human-in-the-loop) workflows through intervention nodes that pause execution for human input. These include configurable timeouts and escalation policies, allowing seamless collaboration between human experts and AI agents.
@@ -420,6 +422,16 @@ Visit [docs.adenhq.com](https://docs.adenhq.com/) for complete guides, API refer
 
 Contributions are welcome! Fork the repository, create your feature branch, implement your changes, and submit a pull request. See [CONTRIBUTING.md](CONTRIBUTING.md) for detailed guidelines.
 
+## Star History
+
+<a href="https://star-history.com/#aden-hive/hive&Date">
+ <picture>
+   <source media="(prefers-color-scheme: dark)" srcset="https://api.star-history.com/svg?repos=aden-hive/hive&type=Date&theme=dark" />
+   <source media="(prefers-color-scheme: light)" srcset="https://api.star-history.com/svg?repos=aden-hive/hive&type=Date" />
+   <img alt="Star History Chart" src="https://api.star-history.com/svg?repos=aden-hive/hive&type=Date" />
+ </picture>
+</a>
+
 ---
 
 <p align="center">

commit_message.txt

@@ -1,31 +0,0 @@
-perf: reduce subprocess spawning in quickstart scripts (#4427)
-
-## Problem
-Windows process creation (CreateProcess) is 10-100x slower than Linux fork/exec.
-The quickstart scripts were spawning 4+ separate `uv run python -c "import X"` 
-processes to verify imports, adding ~600ms overhead on Windows.
-
-## Solution
-Consolidated all import checks into a single batch script that checks multiple 
-modules in one subprocess call, reducing spawn overhead by ~75%.
-
-## Changes
-- **New**: `scripts/check_requirements.py` - Batched import checker
-- **New**: `scripts/test_check_requirements.py` - Test suite  
-- **New**: `scripts/benchmark_quickstart.ps1` - Performance benchmark tool
-- **Modified**: `quickstart.ps1` - Updated import verification (2 sections)
-- **Modified**: `quickstart.sh` - Updated import verification
-
-## Performance Impact
-**Benchmark results on Windows:**
-- Before: ~19.8 seconds for import checks
-- After: ~4.9 seconds for import checks
-- **Improvement: 14.9 seconds saved (75.2% faster)**
-
-## Testing
-- ✅ All functional tests pass (`scripts/test_check_requirements.py`)
-- ✅ Quickstart scripts work correctly on Windows
-- ✅ Error handling verified (invalid imports reported correctly)
-- ✅ Performance benchmark confirms 75%+ improvement
-
-Fixes #4427

contributors.yml

@@ -1,27 +0,0 @@
-# Identity mapping: GitHub username -> Discord ID
-#
-# This file links GitHub accounts to Discord accounts for the
-# Integration Bounty Program. When a bounty PR is merged, the
-# GitHub Action uses this file to ping the contributor on Discord.
-#
-# HOW TO ADD YOURSELF:
-# Open a "Link Discord Account" issue:
-# https://github.com/aden-hive/hive/issues/new?template=link-discord.yml
-# A GitHub Action will automatically add your entry here.
-#
-# To find your Discord ID:
-# 1. Open Discord Settings > Advanced > Enable Developer Mode
-# 2. Right-click your name > Copy User ID
-#
-# Format:
-#   - github: your-github-username
-#     discord: "your-discord-id"  # quotes required (it's a number)
-#     name: Your Display Name      # optional
-
-contributors:
-  # - github: example-user
-  #   discord: "123456789012345678"
-  #   name: Example User
-  - github: TimothyZhang7
-    discord: "408460790061072384"
-    name: Timothy@Aden

core/MCP_INTEGRATION_GUIDE.md

@@ -6,7 +6,7 @@ This guide explains how to integrate Model Context Protocol (MCP) servers with t
 
 The framework provides built-in support for MCP servers, allowing you to:
 
-- **Register MCP servers** via STDIO or HTTP transport
+- **Register MCP servers** via STDIO, HTTP, Unix socket, or SSE transport
 - **Auto-discover tools** from registered servers
 - **Use MCP tools** seamlessly in your agents
 - **Manage multiple MCP servers** simultaneously
@@ -104,6 +104,48 @@ runner.register_mcp_server(
 - `url`: Base URL of the MCP server
 - `headers`: HTTP headers to include (optional)
 
+### Unix Socket Transport
+
+Best for same-host inter-process communication with lower overhead than TCP:
+
+```python
+runner.register_mcp_server(
+    name="local-ipc-tools",
+    transport="unix",
+    url="http://localhost",
+    socket_path="/tmp/mcp_server.sock",
+    headers={
+        "Authorization": "Bearer token"
+    }
+)
+```
+
+**Configuration:**
+
+- `url`: Base URL for HTTP requests over the socket (required, e.g., `"http://localhost"`)
+- `socket_path`: Absolute path to the Unix socket file (required, e.g., `"/tmp/mcp_server.sock"`)
+- `headers`: HTTP headers to include (optional)
+
+### SSE Transport
+
+Best for real-time, event-driven connections using the MCP SDK's SSE client:
+
+```python
+runner.register_mcp_server(
+    name="streaming-tools",
+    transport="sse",
+    url="http://localhost:8000/sse",
+    headers={
+        "Authorization": "Bearer token"
+    }
+)
+```
+
+**Configuration:**
+
+- `url`: SSE endpoint URL (required, e.g., `"http://localhost:8000/sse"`)
+- `headers`: HTTP headers for the SSE connection (optional)
+
 ## Using MCP Tools in Agents
 
 Once registered, MCP tools are available just like any other tool:
@@ -258,7 +300,32 @@ runner.register_mcp_server(
 )
 ```
 
-### 3. Handle Cleanup
+### 3. Use Unix Socket for Same-Host IPC
+
+When both the agent and MCP server run on the same machine, Unix sockets avoid TCP overhead:
+
+```python
+runner.register_mcp_server(
+    name="fast-local-tools",
+    transport="unix",
+    url="http://localhost",
+    socket_path="/tmp/mcp_server.sock"
+)
+```
+
+### 4. Use SSE for Streaming and Real-Time Tools
+
+SSE transport maintains a persistent connection, ideal for event-driven servers:
+
+```python
+runner.register_mcp_server(
+    name="realtime-tools",
+    transport="sse",
+    url="http://realtime-server:8000/sse"
+)
+```
+
+### 5. Handle Cleanup
 
 Always clean up MCP connections when done:
 
@@ -280,7 +347,7 @@ async with AgentRunner.load("exports/my-agent") as runner:
     # Automatic cleanup
 ```
 
-### 4. Tool Name Conflicts
+### 6. Tool Name Conflicts
 
 If multiple MCP servers provide tools with the same name, the last registered server wins. To avoid conflicts:
 
@@ -315,6 +382,24 @@ If HTTP transport fails:
 2. Check firewall settings
 3. Verify the URL and port are correct
 
+### Unix Socket Not Connecting
+
+If Unix socket transport fails:
+
+1. Verify the socket file exists: `ls -la /tmp/mcp_server.sock`
+2. Check file permissions on the socket
+3. Ensure no other process has locked the socket
+4. Verify the `url` field is set (e.g., `"http://localhost"`)
+
+### SSE Connection Issues
+
+If SSE transport fails:
+
+1. Verify the server supports SSE at the given URL
+2. Check that the `mcp` Python package is installed (`pip install mcp`)
+3. Ensure the SSE endpoint is accessible: `curl http://localhost:8000/sse`
+4. Check for firewall or proxy issues blocking long-lived connections
+
 ## Example: Full Agent with MCP Tools
 
 Here's a complete example of an agent that uses MCP tools:

core/antigravity_auth.py

@@ -0,0 +1,583 @@
+#!/usr/bin/env python3
+"""Antigravity authentication CLI.
+
+Implements OAuth2 flow for Google's Antigravity Code Assist gateway.
+Credentials are stored in ~/.hive/antigravity-accounts.json.
+
+Usage:
+    python -m antigravity_auth auth account add
+    python -m antigravity_auth auth account list
+    python -m antigravity_auth auth account remove <email>
+"""
+
+from __future__ import annotations
+
+import argparse
+import json
+import logging
+import os
+import secrets
+import socket
+import sys
+import time
+import urllib.parse
+import urllib.request
+import webbrowser
+from http.server import BaseHTTPRequestHandler, HTTPServer
+from pathlib import Path
+from typing import Any
+
+logging.basicConfig(level=logging.INFO, format="%(message)s")
+logger = logging.getLogger(__name__)
+
+# OAuth endpoints
+_OAUTH_AUTH_URL = "https://accounts.google.com/o/oauth2/v2/auth"
+_OAUTH_TOKEN_URL = "https://oauth2.googleapis.com/token"
+
+# Scopes for Antigravity/Cloud Code Assist
+_OAUTH_SCOPES = [
+    "https://www.googleapis.com/auth/cloud-platform",
+    "https://www.googleapis.com/auth/userinfo.email",
+    "https://www.googleapis.com/auth/userinfo.profile",
+]
+
+# Credentials file path in ~/.hive/
+_ACCOUNTS_FILE = Path.home() / ".hive" / "antigravity-accounts.json"
+
+# Default project ID
+_DEFAULT_PROJECT_ID = "rising-fact-p41fc"
+_DEFAULT_REDIRECT_PORT = 51121
+
+# OAuth credentials fetched from the opencode-antigravity-auth project.
+# This project reverse-engineered and published the public OAuth credentials
+# for Google's Antigravity/Cloud Code Assist API.
+# Source: https://github.com/NoeFabris/opencode-antigravity-auth
+_CREDENTIALS_URL = (
+    "https://raw.githubusercontent.com/NoeFabris/opencode-antigravity-auth/dev/src/constants.ts"
+)
+
+# Cached credentials fetched from public source
+_cached_client_id: str | None = None
+_cached_client_secret: str | None = None
+
+
+def _fetch_credentials_from_public_source() -> tuple[str | None, str | None]:
+    """Fetch OAuth client ID and secret from the public npm package source on GitHub."""
+    global _cached_client_id, _cached_client_secret
+    if _cached_client_id and _cached_client_secret:
+        return _cached_client_id, _cached_client_secret
+
+    try:
+        req = urllib.request.Request(
+            _CREDENTIALS_URL, headers={"User-Agent": "Hive-Antigravity-Auth/1.0"}
+        )
+        with urllib.request.urlopen(req, timeout=10) as resp:
+            content = resp.read().decode("utf-8")
+            import re
+
+            id_match = re.search(r'ANTIGRAVITY_CLIENT_ID\s*=\s*"([^"]+)"', content)
+            secret_match = re.search(r'ANTIGRAVITY_CLIENT_SECRET\s*=\s*"([^"]+)"', content)
+            if id_match:
+                _cached_client_id = id_match.group(1)
+            if secret_match:
+                _cached_client_secret = secret_match.group(1)
+            return _cached_client_id, _cached_client_secret
+    except Exception as e:
+        logger.debug(f"Failed to fetch credentials from public source: {e}")
+    return None, None
+
+
+def get_client_id() -> str:
+    """Get OAuth client ID from env, config, or public source."""
+    env_id = os.environ.get("ANTIGRAVITY_CLIENT_ID")
+    if env_id:
+        return env_id
+
+    # Try hive config
+    hive_cfg = Path.home() / ".hive" / "configuration.json"
+    if hive_cfg.exists():
+        try:
+            with open(hive_cfg) as f:
+                cfg = json.load(f)
+                cfg_id = cfg.get("llm", {}).get("antigravity_client_id")
+                if cfg_id:
+                    return cfg_id
+        except Exception:
+            pass
+
+    # Fetch from public source
+    client_id, _ = _fetch_credentials_from_public_source()
+    if client_id:
+        return client_id
+
+    raise RuntimeError("Could not obtain Antigravity OAuth client ID")
+
+
+def get_client_secret() -> str | None:
+    """Get OAuth client secret from env, config, or public source."""
+    secret = os.environ.get("ANTIGRAVITY_CLIENT_SECRET")
+    if secret:
+        return secret
+
+    # Try to read from hive config
+    hive_cfg = Path.home() / ".hive" / "configuration.json"
+    if hive_cfg.exists():
+        try:
+            with open(hive_cfg) as f:
+                cfg = json.load(f)
+                secret = cfg.get("llm", {}).get("antigravity_client_secret")
+                if secret:
+                    return secret
+        except Exception:
+            pass
+
+    # Fetch from public source (npm package on GitHub)
+    _, secret = _fetch_credentials_from_public_source()
+    return secret
+
+
+def find_free_port() -> int:
+    """Find an available local port."""
+    with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s:
+        s.bind(("", 0))
+        s.listen(1)
+        return s.getsockname()[1]
+
+
+class OAuthCallbackHandler(BaseHTTPRequestHandler):
+    """Handle OAuth callback from browser."""
+
+    auth_code: str | None = None
+    state: str | None = None
+    error: str | None = None
+
+    def log_message(self, format: str, *args: Any) -> None:
+        pass  # Suppress default logging
+
+    def do_GET(self) -> None:
+        parsed = urllib.parse.urlparse(self.path)
+
+        if parsed.path == "/oauth-callback":
+            query = urllib.parse.parse_qs(parsed.query)
+
+            if "error" in query:
+                self.error = query["error"][0]
+                self._send_response("Authentication failed. You can close this window.")
+                return
+
+            if "code" in query and "state" in query:
+                OAuthCallbackHandler.auth_code = query["code"][0]
+                OAuthCallbackHandler.state = query["state"][0]
+                self._send_response(
+                    "Authentication successful! You can close this window "
+                    "and return to the terminal."
+                )
+                return
+
+        self._send_response("Waiting for authentication...")
+
+    def _send_response(self, message: str) -> None:
+        self.send_response(200)
+        self.send_header("Content-Type", "text/html")
+        self.end_headers()
+        html = f"""<!DOCTYPE html>
+<html>
+<head><title>Antigravity Auth</title></head>
+<body style="font-family: system-ui; display: flex; align-items: center;
+      justify-content: center; height: 100vh; margin: 0; background: #1a1a2e;
+      color: #eee;">
+    <div style="text-align: center;">
+        <h2>{message}</h2>
+    </div>
+</body>
+</html>"""
+        self.wfile.write(html.encode())
+
+
+def wait_for_callback(port: int, timeout: int = 300) -> tuple[str | None, str | None, str | None]:
+    """Start local server and wait for OAuth callback."""
+    server = HTTPServer(("localhost", port), OAuthCallbackHandler)
+    server.timeout = 1
+
+    start = time.time()
+    while time.time() - start < timeout:
+        if OAuthCallbackHandler.auth_code:
+            return (
+                OAuthCallbackHandler.auth_code,
+                OAuthCallbackHandler.state,
+                OAuthCallbackHandler.error,
+            )
+        server.handle_request()
+
+    return None, None, "timeout"
+
+
+def exchange_code_for_tokens(
+    code: str, redirect_uri: str, client_id: str, client_secret: str | None
+) -> dict[str, Any] | None:
+    """Exchange authorization code for tokens."""
+    data = {
+        "code": code,
+        "client_id": client_id,
+        "redirect_uri": redirect_uri,
+        "grant_type": "authorization_code",
+    }
+    if client_secret:
+        data["client_secret"] = client_secret
+
+    body = urllib.parse.urlencode(data).encode()
+
+    req = urllib.request.Request(
+        _OAUTH_TOKEN_URL,
+        data=body,
+        headers={"Content-Type": "application/x-www-form-urlencoded"},
+        method="POST",
+    )
+
+    try:
+        with urllib.request.urlopen(req, timeout=30) as resp:
+            return json.loads(resp.read())
+    except Exception as e:
+        logger.error(f"Token exchange failed: {e}")
+        return None
+
+
+def get_user_email(access_token: str) -> str | None:
+    """Get user email from Google API."""
+    req = urllib.request.Request(
+        "https://www.googleapis.com/oauth2/v2/userinfo",
+        headers={"Authorization": f"Bearer {access_token}"},
+    )
+    try:
+        with urllib.request.urlopen(req, timeout=10) as resp:
+            data = json.loads(resp.read())
+            return data.get("email")
+    except Exception:
+        return None
+
+
+def load_accounts() -> dict[str, Any]:
+    """Load existing accounts from file."""
+    if not _ACCOUNTS_FILE.exists():
+        return {"schemaVersion": 4, "accounts": []}
+    try:
+        with open(_ACCOUNTS_FILE) as f:
+            return json.load(f)
+    except Exception:
+        return {"schemaVersion": 4, "accounts": []}
+
+
+def save_accounts(data: dict[str, Any]) -> None:
+    """Save accounts to file."""
+    _ACCOUNTS_FILE.parent.mkdir(parents=True, exist_ok=True)
+    with open(_ACCOUNTS_FILE, "w") as f:
+        json.dump(data, f, indent=2)
+    logger.info(f"Saved credentials to {_ACCOUNTS_FILE}")
+
+
+def validate_credentials(access_token: str, project_id: str = _DEFAULT_PROJECT_ID) -> bool:
+    """Test if credentials work by making a simple API call to Antigravity.
+
+    Returns True if credentials are valid, False otherwise.
+    """
+    endpoint = "https://daily-cloudcode-pa.sandbox.googleapis.com"
+    body = {
+        "project": project_id,
+        "model": "gemini-3-flash",
+        "request": {
+            "contents": [{"role": "user", "parts": [{"text": "hi"}]}],
+            "generationConfig": {"maxOutputTokens": 10},
+        },
+        "requestType": "agent",
+        "userAgent": "antigravity",
+        "requestId": "validation-test",
+    }
+    headers = {
+        "Authorization": f"Bearer {access_token}",
+        "Content-Type": "application/json",
+        "User-Agent": (
+            "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) "
+            "AppleWebKit/537.36 (KHTML, like Gecko) Antigravity/1.18.3"
+        ),
+        "X-Goog-Api-Client": "google-cloud-sdk vscode_cloudshelleditor/0.1",
+    }
+
+    try:
+        req = urllib.request.Request(
+            f"{endpoint}/v1internal:generateContent",
+            data=json.dumps(body).encode("utf-8"),
+            headers=headers,
+            method="POST",
+        )
+        with urllib.request.urlopen(req, timeout=30) as resp:
+            json.loads(resp.read())
+            return True
+    except Exception:
+        return False
+
+
+def refresh_access_token(
+    refresh_token: str, client_id: str, client_secret: str | None
+) -> dict | None:
+    """Refresh the access token using the refresh token."""
+    data = {
+        "grant_type": "refresh_token",
+        "refresh_token": refresh_token,
+        "client_id": client_id,
+    }
+    if client_secret:
+        data["client_secret"] = client_secret
+
+    body = urllib.parse.urlencode(data).encode()
+    req = urllib.request.Request(
+        _OAUTH_TOKEN_URL,
+        data=body,
+        headers={"Content-Type": "application/x-www-form-urlencoded"},
+        method="POST",
+    )
+    try:
+        with urllib.request.urlopen(req, timeout=30) as resp:
+            return json.loads(resp.read())
+    except Exception as e:
+        logger.debug(f"Token refresh failed: {e}")
+        return None
+
+
+def cmd_account_add(args: argparse.Namespace) -> int:
+    """Add a new Antigravity account via OAuth2.
+
+    First checks if valid credentials already exist. If so, validates them
+    and skips OAuth if they work. Otherwise, proceeds with OAuth flow.
+    """
+    client_id = get_client_id()
+    client_secret = get_client_secret()
+
+    # Check if credentials already exist
+    accounts_data = load_accounts()
+    accounts = accounts_data.get("accounts", [])
+
+    if accounts:
+        account = next((a for a in accounts if a.get("enabled", True) is not False), accounts[0])
+        access_token = account.get("access")
+        refresh_token_str = account.get("refresh", "")
+        refresh_token = refresh_token_str.split("|")[0] if refresh_token_str else None
+        project_id = (
+            refresh_token_str.split("|")[1] if "|" in refresh_token_str else _DEFAULT_PROJECT_ID
+        )
+        email = account.get("email", "unknown")
+        expires_ms = account.get("expires", 0)
+        expires_at = expires_ms / 1000.0 if expires_ms else 0.0
+
+        # Check if token is expired or near expiry
+        if access_token and expires_at and time.time() < expires_at - 60:
+            # Token still valid, test it
+            logger.info(f"Found existing credentials for: {email}")
+            logger.info("Validating existing credentials...")
+            if validate_credentials(access_token, project_id):
+                logger.info("✓ Credentials valid! Skipping OAuth.")
+                return 0
+            else:
+                logger.info("Credentials failed validation, refreshing...")
+        elif refresh_token:
+            logger.info(f"Found expired credentials for: {email}")
+            logger.info("Attempting token refresh...")
+
+            tokens = refresh_access_token(refresh_token, client_id, client_secret)
+            if tokens:
+                new_access = tokens.get("access_token")
+                expires_in = tokens.get("expires_in", 3600)
+                if new_access:
+                    # Update the account
+                    account["access"] = new_access
+                    account["expires"] = int((time.time() + expires_in) * 1000)
+                    accounts_data["last_refresh"] = time.strftime(
+                        "%Y-%m-%dT%H:%M:%SZ", time.gmtime()
+                    )
+                    save_accounts(accounts_data)
+
+                    # Validate the refreshed token
+                    logger.info("Validating refreshed credentials...")
+                    if validate_credentials(new_access, project_id):
+                        logger.info("✓ Credentials refreshed and validated!")
+                        return 0
+                    else:
+                        logger.info("Refreshed token failed validation, proceeding with OAuth...")
+            else:
+                logger.info("Token refresh failed, proceeding with OAuth...")
+
+    # No valid credentials, proceed with OAuth
+    if not client_secret:
+        logger.warning(
+            "No client secret configured. Token refresh may fail.\n"
+            "Set ANTIGRAVITY_CLIENT_SECRET env var or add "
+            "'antigravity_client_secret' to ~/.hive/configuration.json"
+        )
+
+    # Use fixed port and path matching Google's expected OAuth redirect URI
+    port = _DEFAULT_REDIRECT_PORT
+    redirect_uri = f"http://localhost:{port}/oauth-callback"
+
+    # Generate state for CSRF protection
+    state = secrets.token_urlsafe(16)
+
+    # Build authorization URL
+    params = {
+        "client_id": client_id,
+        "redirect_uri": redirect_uri,
+        "response_type": "code",
+        "scope": " ".join(_OAUTH_SCOPES),
+        "state": state,
+        "access_type": "offline",
+        "prompt": "consent",
+    }
+    auth_url = f"{_OAUTH_AUTH_URL}?{urllib.parse.urlencode(params)}"
+
+    logger.info("Opening browser for authentication...")
+    logger.info(f"If the browser doesn't open, visit: {auth_url}\n")
+
+    # Open browser
+    webbrowser.open(auth_url)
+
+    # Wait for callback
+    logger.info(f"Listening for callback on port {port}...")
+    code, received_state, error = wait_for_callback(port)
+
+    if error:
+        logger.error(f"Authentication failed: {error}")
+        return 1
+
+    if not code:
+        logger.error("No authorization code received")
+        return 1
+
+    if received_state != state:
+        logger.error("State mismatch - possible CSRF attack")
+        return 1
+
+    # Exchange code for tokens
+    logger.info("Exchanging authorization code for tokens...")
+    tokens = exchange_code_for_tokens(code, redirect_uri, client_id, client_secret)
+
+    if not tokens:
+        return 1
+
+    access_token = tokens.get("access_token")
+    refresh_token = tokens.get("refresh_token")
+    expires_in = tokens.get("expires_in", 3600)
+
+    if not access_token:
+        logger.error("No access token in response")
+        return 1
+
+    # Get user email
+    email = get_user_email(access_token)
+    if email:
+        logger.info(f"Authenticated as: {email}")
+
+    # Load existing accounts and add/update
+    accounts_data = load_accounts()
+    accounts = accounts_data.get("accounts", [])
+
+    # Build new account entry (V4 schema)
+    expires_ms = int((time.time() + expires_in) * 1000)
+    refresh_entry = f"{refresh_token}|{_DEFAULT_PROJECT_ID}"
+
+    new_account = {
+        "access": access_token,
+        "refresh": refresh_entry,
+        "expires": expires_ms,
+        "email": email,
+        "enabled": True,
+    }
+
+    # Update existing account or add new one
+    existing_idx = next((i for i, a in enumerate(accounts) if a.get("email") == email), None)
+    if existing_idx is not None:
+        accounts[existing_idx] = new_account
+        logger.info(f"Updated existing account: {email}")
+    else:
+        accounts.append(new_account)
+        logger.info(f"Added new account: {email}")
+
+    accounts_data["accounts"] = accounts
+    accounts_data["schemaVersion"] = 4
+    accounts_data["last_refresh"] = time.strftime("%Y-%m-%dT%H:%M:%SZ", time.gmtime())
+
+    save_accounts(accounts_data)
+    logger.info("\n✓ Authentication complete!")
+    return 0
+
+
+def cmd_account_list(args: argparse.Namespace) -> int:
+    """List all stored accounts."""
+    data = load_accounts()
+    accounts = data.get("accounts", [])
+
+    if not accounts:
+        logger.info("No accounts configured.")
+        logger.info("Run 'antigravity auth account add' to add one.")
+        return 0
+
+    logger.info("Configured accounts:\n")
+    for i, account in enumerate(accounts, 1):
+        email = account.get("email", "unknown")
+        enabled = "enabled" if account.get("enabled", True) else "disabled"
+        logger.info(f"  {i}. {email} ({enabled})")
+
+    return 0
+
+
+def cmd_account_remove(args: argparse.Namespace) -> int:
+    """Remove an account by email."""
+    email = args.email
+    data = load_accounts()
+    accounts = data.get("accounts", [])
+
+    original_len = len(accounts)
+    accounts = [a for a in accounts if a.get("email") != email]
+
+    if len(accounts) == original_len:
+        logger.error(f"No account found with email: {email}")
+        return 1
+
+    data["accounts"] = accounts
+    save_accounts(data)
+    logger.info(f"Removed account: {email}")
+    return 0
+
+
+def main() -> int:
+    parser = argparse.ArgumentParser(
+        description="Antigravity authentication CLI",
+        formatter_class=argparse.RawDescriptionHelpFormatter,
+    )
+    subparsers = parser.add_subparsers(dest="command", help="Commands")
+
+    # auth account add
+    auth_parser = subparsers.add_parser("auth", help="Authentication commands")
+    auth_subparsers = auth_parser.add_subparsers(dest="auth_command")
+
+    account_parser = auth_subparsers.add_parser("account", help="Account management")
+    account_subparsers = account_parser.add_subparsers(dest="account_command")
+
+    add_parser = account_subparsers.add_parser("add", help="Add a new account via OAuth2")
+    add_parser.set_defaults(func=cmd_account_add)
+
+    list_parser = account_subparsers.add_parser("list", help="List configured accounts")
+    list_parser.set_defaults(func=cmd_account_list)
+
+    remove_parser = account_subparsers.add_parser("remove", help="Remove an account")
+    remove_parser.add_argument("email", help="Email of account to remove")
+    remove_parser.set_defaults(func=cmd_account_remove)
+
+    args = parser.parse_args()
+
+    if hasattr(args, "func"):
+        return args.func(args)
+
+    parser.print_help()
+    return 0
+
+
+if __name__ == "__main__":
+    sys.exit(main())

core/codex_oauth.py

@@ -17,6 +17,7 @@ import http.server
 import json
 import os
 import platform
+import queue
 import secrets
 import subprocess
 import sys
@@ -27,6 +28,7 @@ import urllib.parse
 import urllib.request
 from datetime import UTC, datetime
 from pathlib import Path
+from typing import TextIO
 
 # OAuth constants (from the Codex CLI binary)
 CLIENT_ID = "app_EMoamEEZ73f0CkXaXp7hrann"
@@ -165,11 +167,11 @@ def open_browser(url: str) -> bool:
         if system == "Darwin":
             subprocess.Popen(["open", url], stdout=devnull, stderr=devnull)
         elif system == "Windows":
-            subprocess.Popen(["cmd", "/c", "start", url], stdout=devnull, stderr=devnull)
+            os.startfile(url)  # type: ignore[attr-defined]
         else:
             subprocess.Popen(["xdg-open", url], stdout=devnull, stderr=devnull)
         return True
-    except OSError:
+    except (AttributeError, OSError):
         return False
 
 
@@ -266,6 +268,71 @@ def parse_manual_input(value: str, expected_state: str) -> str | None:
     return None
 
 
+def _read_manual_input_lines(
+    manual_inputs: queue.Queue[str],
+    stop_event: threading.Event,
+    stdin: TextIO | None = None,
+) -> None:
+    stream = sys.stdin if stdin is None else stdin
+
+    while not stop_event.is_set():
+        try:
+            manual = stream.readline()
+        except (EOFError, OSError):
+            return
+
+        if not manual:
+            return
+
+        if manual.strip():
+            manual_inputs.put(manual)
+
+
+def wait_for_code_from_callback_or_stdin(
+    expected_state: str,
+    callback_result: list[str | None],
+    callback_done: threading.Event,
+    timeout_secs: float = 120,
+    poll_interval: float = 0.1,
+    stdin: TextIO | None = None,
+) -> str | None:
+    manual_inputs: queue.Queue[str] = queue.Queue()
+    stop_event = threading.Event()
+
+    # Read stdin on a daemon thread so manual paste works on platforms where
+    # select() cannot poll console handles, including Windows terminals.
+    threading.Thread(
+        target=_read_manual_input_lines,
+        args=(manual_inputs, stop_event, stdin),
+        daemon=True,
+    ).start()
+
+    deadline = time.time() + timeout_secs
+    try:
+        while time.time() < deadline:
+            if callback_result[0]:
+                return callback_result[0]
+
+            while True:
+                try:
+                    manual = manual_inputs.get_nowait()
+                except queue.Empty:
+                    break
+
+                code = parse_manual_input(manual, expected_state)
+                if code:
+                    return code
+
+            if callback_done.is_set():
+                return callback_result[0]
+
+            time.sleep(poll_interval)
+
+        return callback_result[0]
+    finally:
+        stop_event.set()
+
+
 def main() -> int:
     # Generate PKCE and state
     verifier, challenge = generate_pkce()
@@ -315,41 +382,28 @@ def main() -> int:
 
         # Start callback server in background
         callback_result: list[str | None] = [None]
+        callback_done = threading.Event()
 
         def run_server() -> None:
-            callback_result[0] = wait_for_callback(state, timeout_secs=120)
+            try:
+                callback_result[0] = wait_for_callback(state, timeout_secs=120)
+            finally:
+                callback_done.set()
 
         server_thread = threading.Thread(target=run_server)
         server_thread.daemon = True
         server_thread.start()
 
-        # Also accept manual input in parallel
-        # We poll for both the server result and stdin
         try:
-            import select
-
-            while server_thread.is_alive():
-                # Check if stdin has data (non-blocking on unix)
-                if hasattr(select, "select"):
-                    ready, _, _ = select.select([sys.stdin], [], [], 0.5)
-                    if ready:
-                        manual = sys.stdin.readline()
-                        if manual.strip():
-                            code = parse_manual_input(manual, state)
-                            if code:
-                                break
-                else:
-                    time.sleep(0.5)
-
-                if callback_result[0]:
-                    code = callback_result[0]
-                    break
-        except (KeyboardInterrupt, EOFError):
+            code = wait_for_code_from_callback_or_stdin(
+                state,
+                callback_result,
+                callback_done,
+                timeout_secs=120,
+            )
+        except KeyboardInterrupt:
             print("\n\033[0;31mCancelled.\033[0m")
             return 1
-
-        if not code:
-            code = callback_result[0]
     else:
         # Manual paste mode
         try:

core/demos/event_loop_wss_demo.py

@@ -1,740 +0,0 @@
-#!/usr/bin/env python3
-"""
-EventLoopNode WebSocket Demo
-
-Real LLM, real FileConversationStore, real EventBus.
-Streams EventLoopNode execution to a browser via WebSocket.
-
-Usage:
-    cd /home/timothy/oss/hive/core
-    python demos/event_loop_wss_demo.py
-
-    Then open http://localhost:8765 in your browser.
-"""
-
-import asyncio
-import json
-import logging
-import sys
-import tempfile
-from http import HTTPStatus
-from pathlib import Path
-
-import httpx
-import websockets
-from bs4 import BeautifulSoup
-from websockets.http11 import Request, Response
-
-# Add core, tools, and hive root to path
-_CORE_DIR = Path(__file__).resolve().parent.parent
-_HIVE_DIR = _CORE_DIR.parent
-sys.path.insert(0, str(_CORE_DIR))  # framework.*
-sys.path.insert(0, str(_HIVE_DIR / "tools" / "src"))  # aden_tools.*
-sys.path.insert(0, str(_HIVE_DIR))  # core.framework.* (for aden_tools imports)
-
-import os  # noqa: E402
-
-from aden_tools.credentials import CREDENTIAL_SPECS, CredentialStoreAdapter  # noqa: E402
-from core.framework.credentials import CredentialStore  # noqa: E402
-
-from framework.credentials.storage import (  # noqa: E402
-    CompositeStorage,
-    EncryptedFileStorage,
-    EnvVarStorage,
-)
-from framework.graph.event_loop_node import EventLoopNode, LoopConfig  # noqa: E402
-from framework.graph.node import NodeContext, NodeSpec, SharedMemory  # noqa: E402
-from framework.llm.litellm import LiteLLMProvider  # noqa: E402
-from framework.llm.provider import Tool  # noqa: E402
-from framework.runner.tool_registry import ToolRegistry  # noqa: E402
-from framework.runtime.core import Runtime  # noqa: E402
-from framework.runtime.event_bus import EventBus, EventType  # noqa: E402
-from framework.storage.conversation_store import FileConversationStore  # noqa: E402
-
-logging.basicConfig(level=logging.INFO, format="%(asctime)s %(name)s %(message)s")
-logger = logging.getLogger("demo")
-
-# -------------------------------------------------------------------------
-# Persistent state (shared across WebSocket connections)
-# -------------------------------------------------------------------------
-
-STORE_DIR = Path(tempfile.mkdtemp(prefix="hive_demo_"))
-STORE = FileConversationStore(STORE_DIR / "conversation")
-RUNTIME = Runtime(STORE_DIR / "runtime")
-LLM = LiteLLMProvider(model="claude-sonnet-4-5-20250929")
-
-# -------------------------------------------------------------------------
-# Tool Registry — real tools via ToolRegistry (same pattern as GraphExecutor)
-# -------------------------------------------------------------------------
-
-TOOL_REGISTRY = ToolRegistry()
-
-# Credential store: Aden sync (OAuth2 tokens) + encrypted files + env var fallback
-_env_mapping = {name: spec.env_var for name, spec in CREDENTIAL_SPECS.items()}
-_local_storage = CompositeStorage(
-    primary=EncryptedFileStorage(),
-    fallbacks=[EnvVarStorage(env_mapping=_env_mapping)],
-)
-
-if os.environ.get("ADEN_API_KEY"):
-    try:
-        from framework.credentials.aden import (  # noqa: E402
-            AdenCachedStorage,
-            AdenClientConfig,
-            AdenCredentialClient,
-            AdenSyncProvider,
-        )
-
-        _client = AdenCredentialClient(AdenClientConfig(base_url="https://api.adenhq.com"))
-        _provider = AdenSyncProvider(client=_client)
-        _storage = AdenCachedStorage(
-            local_storage=_local_storage,
-            aden_provider=_provider,
-        )
-        _cred_store = CredentialStore(storage=_storage, providers=[_provider], auto_refresh=True)
-        _synced = _provider.sync_all(_cred_store)
-        logger.info("Synced %d credentials from Aden", _synced)
-    except Exception as e:
-        logger.warning("Aden sync unavailable: %s", e)
-        _cred_store = CredentialStore(storage=_local_storage)
-else:
-    logger.info("ADEN_API_KEY not set, using local credential storage")
-    _cred_store = CredentialStore(storage=_local_storage)
-
-CREDENTIALS = CredentialStoreAdapter(_cred_store)
-
-# Debug: log which credentials resolved
-for _name in ["brave_search", "hubspot", "anthropic"]:
-    _val = CREDENTIALS.get(_name)
-    if _val:
-        logger.debug("credential %s: OK (len=%d)", _name, len(_val))
-    else:
-        logger.debug("credential %s: not found", _name)
-
-# --- web_search (Brave Search API) ---
-
-TOOL_REGISTRY.register(
-    name="web_search",
-    tool=Tool(
-        name="web_search",
-        description=(
-            "Search the web for current information. "
-            "Returns titles, URLs, and snippets from search results."
-        ),
-        parameters={
-            "type": "object",
-            "properties": {
-                "query": {
-                    "type": "string",
-                    "description": "The search query (1-500 characters)",
-                },
-                "num_results": {
-                    "type": "integer",
-                    "description": "Number of results to return (1-20, default 10)",
-                },
-            },
-            "required": ["query"],
-        },
-    ),
-    executor=lambda inputs: _exec_web_search(inputs),
-)
-
-
-def _exec_web_search(inputs: dict) -> dict:
-    api_key = CREDENTIALS.get("brave_search")
-    if not api_key:
-        return {"error": "brave_search credential not configured"}
-    query = inputs.get("query", "")
-    num_results = min(inputs.get("num_results", 10), 20)
-    resp = httpx.get(
-        "https://api.search.brave.com/res/v1/web/search",
-        params={"q": query, "count": num_results},
-        headers={"X-Subscription-Token": api_key, "Accept": "application/json"},
-        timeout=30.0,
-    )
-    if resp.status_code != 200:
-        return {"error": f"Brave API HTTP {resp.status_code}"}
-    data = resp.json()
-    results = [
-        {
-            "title": item.get("title", ""),
-            "url": item.get("url", ""),
-            "snippet": item.get("description", ""),
-        }
-        for item in data.get("web", {}).get("results", [])[:num_results]
-    ]
-    return {"query": query, "results": results, "total": len(results)}
-
-
-# --- web_scrape (httpx + BeautifulSoup, no playwright for sync compat) ---
-
-TOOL_REGISTRY.register(
-    name="web_scrape",
-    tool=Tool(
-        name="web_scrape",
-        description=(
-            "Scrape and extract text content from a webpage URL. "
-            "Returns the page title and main text content."
-        ),
-        parameters={
-            "type": "object",
-            "properties": {
-                "url": {
-                    "type": "string",
-                    "description": "URL of the webpage to scrape",
-                },
-                "max_length": {
-                    "type": "integer",
-                    "description": "Maximum text length (default 50000)",
-                },
-            },
-            "required": ["url"],
-        },
-    ),
-    executor=lambda inputs: _exec_web_scrape(inputs),
-)
-
-_SCRAPE_HEADERS = {
-    "User-Agent": (
-        "Mozilla/5.0 (Windows NT 10.0; Win64; x64) "
-        "AppleWebKit/537.36 (KHTML, like Gecko) "
-        "Chrome/131.0.0.0 Safari/537.36"
-    ),
-    "Accept": "text/html,application/xhtml+xml",
-}
-
-
-def _exec_web_scrape(inputs: dict) -> dict:
-    url = inputs.get("url", "")
-    max_length = max(1000, min(inputs.get("max_length", 50000), 500000))
-    if not url.startswith(("http://", "https://")):
-        url = "https://" + url
-    try:
-        resp = httpx.get(url, timeout=30.0, follow_redirects=True, headers=_SCRAPE_HEADERS)
-        if resp.status_code != 200:
-            return {"error": f"HTTP {resp.status_code}"}
-        soup = BeautifulSoup(resp.text, "html.parser")
-        for tag in soup(["script", "style", "nav", "footer", "header", "aside", "noscript"]):
-            tag.decompose()
-        title = soup.title.get_text(strip=True) if soup.title else ""
-        main = (
-            soup.find("article")
-            or soup.find("main")
-            or soup.find(attrs={"role": "main"})
-            or soup.find("body")
-        )
-        text = main.get_text(separator=" ", strip=True) if main else ""
-        text = " ".join(text.split())
-        if len(text) > max_length:
-            text = text[:max_length] + "..."
-        return {"url": url, "title": title, "content": text, "length": len(text)}
-    except httpx.TimeoutException:
-        return {"error": "Request timed out"}
-    except Exception as e:
-        return {"error": f"Scrape failed: {e}"}
-
-
-# --- HubSpot CRM tools (optional, requires HUBSPOT_ACCESS_TOKEN) ---
-
-_HUBSPOT_API = "https://api.hubapi.com"
-
-
-def _hubspot_headers() -> dict | None:
-    token = CREDENTIALS.get("hubspot")
-    if token:
-        logger.debug("HubSpot token: %s...%s (len=%d)", token[:8], token[-4:], len(token))
-    else:
-        logger.debug("HubSpot token: not found")
-    if not token:
-        return None
-    return {
-        "Authorization": f"Bearer {token}",
-        "Content-Type": "application/json",
-        "Accept": "application/json",
-    }
-
-
-def _exec_hubspot_search(inputs: dict) -> dict:
-    headers = _hubspot_headers()
-    if not headers:
-        return {"error": "HUBSPOT_ACCESS_TOKEN not set"}
-    object_type = inputs.get("object_type", "contacts")
-    query = inputs.get("query", "")
-    limit = min(inputs.get("limit", 10), 100)
-    body: dict = {"limit": limit}
-    if query:
-        body["query"] = query
-    try:
-        resp = httpx.post(
-            f"{_HUBSPOT_API}/crm/v3/objects/{object_type}/search",
-            headers=headers,
-            json=body,
-            timeout=30.0,
-        )
-        if resp.status_code != 200:
-            return {"error": f"HubSpot API HTTP {resp.status_code}: {resp.text[:200]}"}
-        return resp.json()
-    except httpx.TimeoutException:
-        return {"error": "Request timed out"}
-    except Exception as e:
-        return {"error": f"HubSpot error: {e}"}
-
-
-TOOL_REGISTRY.register(
-    name="hubspot_search",
-    tool=Tool(
-        name="hubspot_search",
-        description=(
-            "Search HubSpot CRM objects (contacts, companies, or deals). "
-            "Returns matching records with their properties."
-        ),
-        parameters={
-            "type": "object",
-            "properties": {
-                "object_type": {
-                    "type": "string",
-                    "description": "CRM object type: 'contacts', 'companies', or 'deals'",
-                },
-                "query": {
-                    "type": "string",
-                    "description": "Search query (name, email, domain, etc.)",
-                },
-                "limit": {
-                    "type": "integer",
-                    "description": "Max results (1-100, default 10)",
-                },
-            },
-            "required": ["object_type"],
-        },
-    ),
-    executor=lambda inputs: _exec_hubspot_search(inputs),
-)
-
-logger.info(
-    "ToolRegistry loaded: %s",
-    ", ".join(TOOL_REGISTRY.get_registered_names()),
-)
-
-
-# -------------------------------------------------------------------------
-# HTML page (embedded)
-# -------------------------------------------------------------------------
-
-HTML_PAGE = (  # noqa: E501
-    """<!DOCTYPE html>
-<html lang="en">
-<head>
-<meta charset="utf-8">
-<meta name="viewport" content="width=device-width, initial-scale=1">
-<title>EventLoopNode Live Demo</title>
-<style>
-  * { box-sizing: border-box; margin: 0; padding: 0; }
-  body {
-    font-family: 'SF Mono', 'Fira Code', monospace;
-    background: #0d1117; color: #c9d1d9;
-    height: 100vh; display: flex; flex-direction: column;
-  }
-  header {
-    background: #161b22; padding: 12px 20px;
-    border-bottom: 1px solid #30363d;
-    display: flex; align-items: center; gap: 16px;
-  }
-  header h1 { font-size: 16px; color: #58a6ff; font-weight: 600; }
-  .status {
-    font-size: 12px; padding: 3px 10px; border-radius: 12px;
-    background: #21262d; color: #8b949e;
-  }
-  .status.running { background: #1a4b2e; color: #3fb950; }
-  .status.done { background: #1a3a5c; color: #58a6ff; }
-  .status.error { background: #4b1a1a; color: #f85149; }
-  .chat { flex: 1; overflow-y: auto; padding: 16px; }
-  .msg {
-    margin: 8px 0; padding: 10px 14px; border-radius: 8px;
-    line-height: 1.6; white-space: pre-wrap; word-wrap: break-word;
-  }
-  .msg.user { background: #1a3a5c; color: #58a6ff; }
-  .msg.assistant { background: #161b22; color: #c9d1d9; }
-  .msg.event {
-    background: transparent; color: #8b949e; font-size: 11px;
-    padding: 4px 14px; border-left: 3px solid #30363d;
-  }
-  .msg.event.loop { border-left-color: #58a6ff; }
-  .msg.event.tool { border-left-color: #d29922; }
-  .msg.event.stall { border-left-color: #f85149; }
-  .input-bar {
-    padding: 12px 16px; background: #161b22;
-    border-top: 1px solid #30363d; display: flex; gap: 8px;
-  }
-  .input-bar input {
-    flex: 1; background: #0d1117; border: 1px solid #30363d;
-    color: #c9d1d9; padding: 8px 12px; border-radius: 6px;
-    font-family: inherit; font-size: 14px; outline: none;
-  }
-  .input-bar input:focus { border-color: #58a6ff; }
-  .input-bar button {
-    background: #238636; color: #fff; border: none;
-    padding: 8px 20px; border-radius: 6px; cursor: pointer;
-    font-family: inherit; font-weight: 600;
-  }
-  .input-bar button:hover { background: #2ea043; }
-  .input-bar button:disabled {
-    background: #21262d; color: #484f58; cursor: not-allowed;
-  }
-  .input-bar button.clear { background: #da3633; }
-  .input-bar button.clear:hover { background: #f85149; }
-</style>
-</head>
-<body>
-  <header>
-    <h1>EventLoopNode Live</h1>
-    <span id="status" class="status">Idle</span>
-    <span id="iter" class="status" style="display:none">Step 0</span>
-  </header>
-  <div id="chat" class="chat"></div>
-  <div class="input-bar">
-    <input id="input" type="text"
-           placeholder="Ask anything..." autofocus />
-    <button id="go" onclick="run()">Send</button>
-    <button class="clear"
-            onclick="clearConversation()">Clear</button>
-  </div>
-
-<script>
-let ws = null;
-let currentAssistantEl = null;
-let iterCount = 0;
-const chat = document.getElementById('chat');
-const status = document.getElementById('status');
-const iterEl = document.getElementById('iter');
-const goBtn = document.getElementById('go');
-const inputEl = document.getElementById('input');
-
-inputEl.addEventListener('keydown', e => {
-  if (e.key === 'Enter') run();
-});
-
-function setStatus(text, cls) {
-  status.textContent = text;
-  status.className = 'status ' + cls;
-}
-
-function addMsg(text, cls) {
-  const el = document.createElement('div');
-  el.className = 'msg ' + cls;
-  el.textContent = text;
-  chat.appendChild(el);
-  chat.scrollTop = chat.scrollHeight;
-  return el;
-}
-
-function connect() {
-  ws = new WebSocket('ws://' + location.host + '/ws');
-  ws.onopen = () => {
-    setStatus('Ready', 'done');
-    goBtn.disabled = false;
-  };
-  ws.onmessage = handleEvent;
-  ws.onerror = () => { setStatus('Error', 'error'); };
-  ws.onclose = () => {
-    setStatus('Reconnecting...', '');
-    goBtn.disabled = true;
-    setTimeout(connect, 2000);
-  };
-}
-
-function handleEvent(msg) {
-  const evt = JSON.parse(msg.data);
-
-  if (evt.type === 'llm_text_delta') {
-    if (currentAssistantEl) {
-      currentAssistantEl.textContent += evt.content;
-      chat.scrollTop = chat.scrollHeight;
-    }
-  }
-  else if (evt.type === 'ready') {
-    setStatus('Ready', 'done');
-    if (currentAssistantEl && !currentAssistantEl.textContent)
-      currentAssistantEl.remove();
-    goBtn.disabled = false;
-  }
-  else if (evt.type === 'node_loop_iteration') {
-    iterCount = evt.iteration || (iterCount + 1);
-    iterEl.textContent = 'Step ' + iterCount;
-    iterEl.style.display = '';
-  }
-  else if (evt.type === 'tool_call_started') {
-    var info = evt.tool_name + '('
-      + JSON.stringify(evt.tool_input).slice(0, 120) + ')';
-    addMsg('TOOL  ' + info, 'event tool');
-  }
-  else if (evt.type === 'tool_call_completed') {
-    var preview = (evt.result || '').slice(0, 200);
-    var cls = evt.is_error ? 'stall' : 'tool';
-    addMsg('RESULT  ' + evt.tool_name + ': ' + preview,
-           'event ' + cls);
-    currentAssistantEl = addMsg('', 'assistant');
-  }
-  else if (evt.type === 'result') {
-    setStatus('Session ended', evt.success ? 'done' : 'error');
-    if (evt.error) addMsg('ERROR  ' + evt.error, 'event stall');
-    if (currentAssistantEl && !currentAssistantEl.textContent)
-      currentAssistantEl.remove();
-    goBtn.disabled = false;
-  }
-  else if (evt.type === 'node_stalled') {
-    addMsg('STALLED  ' + evt.reason, 'event stall');
-  }
-  else if (evt.type === 'cleared') {
-    chat.innerHTML = '';
-    iterCount = 0;
-    iterEl.textContent = 'Step 0';
-    iterEl.style.display = 'none';
-    setStatus('Ready', 'done');
-    goBtn.disabled = false;
-  }
-}
-
-function run() {
-  const text = inputEl.value.trim();
-  if (!text || !ws || ws.readyState !== 1) return;
-  addMsg(text, 'user');
-  currentAssistantEl = addMsg('', 'assistant');
-  inputEl.value = '';
-  setStatus('Running', 'running');
-  goBtn.disabled = true;
-  ws.send(JSON.stringify({ topic: text }));
-}
-
-function clearConversation() {
-  if (ws && ws.readyState === 1) {
-    ws.send(JSON.stringify({ command: 'clear' }));
-  }
-}
-
-connect();
-</script>
-</body>
-</html>"""
-)
-
-
-# -------------------------------------------------------------------------
-# WebSocket handler
-# -------------------------------------------------------------------------
-
-
-async def handle_ws(websocket):
-    """Persistent WebSocket: long-lived EventLoopNode with client_facing blocking."""
-    global STORE
-
-    # -- Event forwarding (WebSocket ← EventBus) ----------------------------
-    bus = EventBus()
-
-    async def forward_event(event):
-        try:
-            payload = {"type": event.type.value, **event.data}
-            if event.node_id:
-                payload["node_id"] = event.node_id
-            await websocket.send(json.dumps(payload))
-        except Exception:
-            pass
-
-    bus.subscribe(
-        event_types=[
-            EventType.NODE_LOOP_STARTED,
-            EventType.NODE_LOOP_ITERATION,
-            EventType.NODE_LOOP_COMPLETED,
-            EventType.LLM_TEXT_DELTA,
-            EventType.TOOL_CALL_STARTED,
-            EventType.TOOL_CALL_COMPLETED,
-            EventType.NODE_STALLED,
-        ],
-        handler=forward_event,
-    )
-
-    # -- Per-connection state -----------------------------------------------
-    node = None
-    loop_task = None
-
-    tools = list(TOOL_REGISTRY.get_tools().values())
-    tool_executor = TOOL_REGISTRY.get_executor()
-
-    node_spec = NodeSpec(
-        id="assistant",
-        name="Chat Assistant",
-        description="A conversational assistant that remembers context across messages",
-        node_type="event_loop",
-        client_facing=True,
-        system_prompt=(
-            "You are a helpful assistant with access to tools. "
-            "You can search the web, scrape webpages, and query HubSpot CRM. "
-            "Use tools when the user asks for current information or external data. "
-            "You have full conversation history, so you can reference previous messages."
-        ),
-    )
-
-    # -- Ready callback: subscribe to CLIENT_INPUT_REQUESTED on the bus ---
-    async def on_input_requested(event):
-        try:
-            await websocket.send(json.dumps({"type": "ready"}))
-        except Exception:
-            pass
-
-    bus.subscribe(
-        event_types=[EventType.CLIENT_INPUT_REQUESTED],
-        handler=on_input_requested,
-    )
-
-    async def start_loop(first_message: str):
-        """Create an EventLoopNode and run it as a background task."""
-        nonlocal node, loop_task
-
-        memory = SharedMemory()
-        ctx = NodeContext(
-            runtime=RUNTIME,
-            node_id="assistant",
-            node_spec=node_spec,
-            memory=memory,
-            input_data={},
-            llm=LLM,
-            available_tools=tools,
-        )
-        node = EventLoopNode(
-            event_bus=bus,
-            config=LoopConfig(max_iterations=10_000, max_context_tokens=32_000),
-            conversation_store=STORE,
-            tool_executor=tool_executor,
-        )
-        await node.inject_event(first_message)
-
-        async def _run():
-            try:
-                result = await node.execute(ctx)
-                try:
-                    await websocket.send(
-                        json.dumps(
-                            {
-                                "type": "result",
-                                "success": result.success,
-                                "output": result.output,
-                                "error": result.error,
-                                "tokens": result.tokens_used,
-                            }
-                        )
-                    )
-                except Exception:
-                    pass
-                logger.info(f"Loop ended: success={result.success}, tokens={result.tokens_used}")
-            except websockets.exceptions.ConnectionClosed:
-                logger.info("Loop stopped: WebSocket closed")
-            except Exception as e:
-                logger.exception("Loop error")
-                try:
-                    await websocket.send(
-                        json.dumps(
-                            {
-                                "type": "result",
-                                "success": False,
-                                "error": str(e),
-                                "output": {},
-                            }
-                        )
-                    )
-                except Exception:
-                    pass
-
-        loop_task = asyncio.create_task(_run())
-
-    async def stop_loop():
-        """Signal the node and wait for the loop task to finish."""
-        nonlocal node, loop_task
-        if loop_task and not loop_task.done():
-            if node:
-                node.signal_shutdown()
-            try:
-                await asyncio.wait_for(loop_task, timeout=5.0)
-            except (TimeoutError, asyncio.CancelledError):
-                loop_task.cancel()
-        node = None
-        loop_task = None
-
-    # -- Message loop (runs for the lifetime of this WebSocket) -------------
-    try:
-        async for raw in websocket:
-            try:
-                msg = json.loads(raw)
-            except Exception:
-                continue
-
-            # Clear command
-            if msg.get("command") == "clear":
-                import shutil
-
-                await stop_loop()
-                await STORE.close()
-                conv_dir = STORE_DIR / "conversation"
-                if conv_dir.exists():
-                    shutil.rmtree(conv_dir)
-                STORE = FileConversationStore(conv_dir)
-                await websocket.send(json.dumps({"type": "cleared"}))
-                logger.info("Conversation cleared")
-                continue
-
-            topic = msg.get("topic", "")
-            if not topic:
-                continue
-
-            if node is None:
-                # First message — spin up the loop
-                logger.info(f"Starting persistent loop: {topic}")
-                await start_loop(topic)
-            else:
-                # Subsequent message — inject into the running loop
-                logger.info(f"Injecting message: {topic}")
-                await node.inject_event(topic)
-
-    except websockets.exceptions.ConnectionClosed:
-        pass
-    finally:
-        await stop_loop()
-        logger.info("WebSocket closed, loop stopped")
-
-
-# -------------------------------------------------------------------------
-# HTTP handler for serving the HTML page
-# -------------------------------------------------------------------------
-
-
-async def process_request(connection, request: Request):
-    """Serve HTML on GET /, upgrade to WebSocket on /ws."""
-    if request.path == "/ws":
-        return None  # let websockets handle the upgrade
-    # Serve the HTML page for any other path
-    return Response(
-        HTTPStatus.OK,
-        "OK",
-        websockets.Headers({"Content-Type": "text/html; charset=utf-8"}),
-        HTML_PAGE.encode(),
-    )
-
-
-# -------------------------------------------------------------------------
-# Main
-# -------------------------------------------------------------------------
-
-
-async def main():
-    port = 8765
-    async with websockets.serve(
-        handle_ws,
-        "0.0.0.0",
-        port,
-        process_request=process_request,
-    ):
-        logger.info(f"Demo running at http://localhost:{port}")
-        logger.info("Open in your browser and enter a topic to research.")
-        await asyncio.Future()  # run forever
-
-
-if __name__ == "__main__":
-    asyncio.run(main())

core/demos/handoff_demo.py

@@ -1,930 +0,0 @@
-#!/usr/bin/env python3
-"""
-Two-Node ContextHandoff Demo
-
-Demonstrates ContextHandoff between two EventLoopNode instances:
-  Node A (Researcher) → ContextHandoff → Node B (Analyst)
-
-Real LLM, real FileConversationStore, real EventBus.
-Streams both nodes to a browser via WebSocket.
-
-Usage:
-    cd /home/timothy/oss/hive/core
-    python demos/handoff_demo.py
-
-    Then open http://localhost:8766 in your browser.
-"""
-
-import asyncio
-import json
-import logging
-import sys
-import tempfile
-from http import HTTPStatus
-from pathlib import Path
-
-import httpx
-import websockets
-from bs4 import BeautifulSoup
-from websockets.http11 import Request, Response
-
-# Add core, tools, and hive root to path
-_CORE_DIR = Path(__file__).resolve().parent.parent
-_HIVE_DIR = _CORE_DIR.parent
-sys.path.insert(0, str(_CORE_DIR))  # framework.*
-sys.path.insert(0, str(_HIVE_DIR / "tools" / "src"))  # aden_tools.*
-sys.path.insert(0, str(_HIVE_DIR))  # core.framework.* (for aden_tools imports)
-
-from aden_tools.credentials import CREDENTIAL_SPECS, CredentialStoreAdapter  # noqa: E402
-from core.framework.credentials import CredentialStore  # noqa: E402
-
-from framework.credentials.storage import (  # noqa: E402
-    CompositeStorage,
-    EncryptedFileStorage,
-    EnvVarStorage,
-)
-from framework.graph.context_handoff import ContextHandoff  # noqa: E402
-from framework.graph.conversation import NodeConversation  # noqa: E402
-from framework.graph.event_loop_node import EventLoopNode, LoopConfig  # noqa: E402
-from framework.graph.node import NodeContext, NodeSpec, SharedMemory  # noqa: E402
-from framework.llm.litellm import LiteLLMProvider  # noqa: E402
-from framework.llm.provider import Tool  # noqa: E402
-from framework.runner.tool_registry import ToolRegistry  # noqa: E402
-from framework.runtime.core import Runtime  # noqa: E402
-from framework.runtime.event_bus import EventBus, EventType  # noqa: E402
-from framework.storage.conversation_store import FileConversationStore  # noqa: E402
-
-logging.basicConfig(level=logging.INFO, format="%(asctime)s %(name)s %(message)s")
-logger = logging.getLogger("handoff_demo")
-
-# -------------------------------------------------------------------------
-# Persistent state
-# -------------------------------------------------------------------------
-
-STORE_DIR = Path(tempfile.mkdtemp(prefix="hive_handoff_"))
-RUNTIME = Runtime(STORE_DIR / "runtime")
-LLM = LiteLLMProvider(model="claude-sonnet-4-5-20250929")
-
-# -------------------------------------------------------------------------
-# Credentials
-# -------------------------------------------------------------------------
-
-# Composite credential store: encrypted files (primary) + env vars (fallback)
-_env_mapping = {name: spec.env_var for name, spec in CREDENTIAL_SPECS.items()}
-_composite = CompositeStorage(
-    primary=EncryptedFileStorage(),
-    fallbacks=[EnvVarStorage(env_mapping=_env_mapping)],
-)
-CREDENTIALS = CredentialStoreAdapter(CredentialStore(storage=_composite))
-
-for _name in ["brave_search", "hubspot"]:
-    _val = CREDENTIALS.get(_name)
-    if _val:
-        logger.debug("credential %s: OK (len=%d)", _name, len(_val))
-    else:
-        logger.debug("credential %s: not found", _name)
-
-# -------------------------------------------------------------------------
-# Tool Registry — web_search + web_scrape for Node A (Researcher)
-# -------------------------------------------------------------------------
-
-TOOL_REGISTRY = ToolRegistry()
-
-
-def _exec_web_search(inputs: dict) -> dict:
-    api_key = CREDENTIALS.get("brave_search")
-    if not api_key:
-        return {"error": "brave_search credential not configured"}
-    query = inputs.get("query", "")
-    num_results = min(inputs.get("num_results", 10), 20)
-    resp = httpx.get(
-        "https://api.search.brave.com/res/v1/web/search",
-        params={"q": query, "count": num_results},
-        headers={
-            "X-Subscription-Token": api_key,
-            "Accept": "application/json",
-        },
-        timeout=30.0,
-    )
-    if resp.status_code != 200:
-        return {"error": f"Brave API HTTP {resp.status_code}"}
-    data = resp.json()
-    results = [
-        {
-            "title": item.get("title", ""),
-            "url": item.get("url", ""),
-            "snippet": item.get("description", ""),
-        }
-        for item in data.get("web", {}).get("results", [])[:num_results]
-    ]
-    return {"query": query, "results": results, "total": len(results)}
-
-
-TOOL_REGISTRY.register(
-    name="web_search",
-    tool=Tool(
-        name="web_search",
-        description=(
-            "Search the web for current information. "
-            "Returns titles, URLs, and snippets from search results."
-        ),
-        parameters={
-            "type": "object",
-            "properties": {
-                "query": {
-                    "type": "string",
-                    "description": "The search query (1-500 characters)",
-                },
-                "num_results": {
-                    "type": "integer",
-                    "description": "Number of results (1-20, default 10)",
-                },
-            },
-            "required": ["query"],
-        },
-    ),
-    executor=lambda inputs: _exec_web_search(inputs),
-)
-
-_SCRAPE_HEADERS = {
-    "User-Agent": (
-        "Mozilla/5.0 (Windows NT 10.0; Win64; x64) "
-        "AppleWebKit/537.36 (KHTML, like Gecko) "
-        "Chrome/131.0.0.0 Safari/537.36"
-    ),
-    "Accept": "text/html,application/xhtml+xml",
-}
-
-
-def _exec_web_scrape(inputs: dict) -> dict:
-    url = inputs.get("url", "")
-    max_length = max(1000, min(inputs.get("max_length", 50000), 500000))
-    if not url.startswith(("http://", "https://")):
-        url = "https://" + url
-    try:
-        resp = httpx.get(
-            url,
-            timeout=30.0,
-            follow_redirects=True,
-            headers=_SCRAPE_HEADERS,
-        )
-        if resp.status_code != 200:
-            return {"error": f"HTTP {resp.status_code}"}
-        soup = BeautifulSoup(resp.text, "html.parser")
-        for tag in soup(["script", "style", "nav", "footer", "header", "aside", "noscript"]):
-            tag.decompose()
-        title = soup.title.get_text(strip=True) if soup.title else ""
-        main = (
-            soup.find("article")
-            or soup.find("main")
-            or soup.find(attrs={"role": "main"})
-            or soup.find("body")
-        )
-        text = main.get_text(separator=" ", strip=True) if main else ""
-        text = " ".join(text.split())
-        if len(text) > max_length:
-            text = text[:max_length] + "..."
-        return {
-            "url": url,
-            "title": title,
-            "content": text,
-            "length": len(text),
-        }
-    except httpx.TimeoutException:
-        return {"error": "Request timed out"}
-    except Exception as e:
-        return {"error": f"Scrape failed: {e}"}
-
-
-TOOL_REGISTRY.register(
-    name="web_scrape",
-    tool=Tool(
-        name="web_scrape",
-        description=(
-            "Scrape and extract text content from a webpage URL. "
-            "Returns the page title and main text content."
-        ),
-        parameters={
-            "type": "object",
-            "properties": {
-                "url": {
-                    "type": "string",
-                    "description": "URL of the webpage to scrape",
-                },
-                "max_length": {
-                    "type": "integer",
-                    "description": "Maximum text length (default 50000)",
-                },
-            },
-            "required": ["url"],
-        },
-    ),
-    executor=lambda inputs: _exec_web_scrape(inputs),
-)
-
-logger.info(
-    "ToolRegistry loaded: %s",
-    ", ".join(TOOL_REGISTRY.get_registered_names()),
-)
-
-# -------------------------------------------------------------------------
-# Node Specs
-# -------------------------------------------------------------------------
-
-RESEARCHER_SPEC = NodeSpec(
-    id="researcher",
-    name="Researcher",
-    description="Researches a topic using web search and scraping tools",
-    node_type="event_loop",
-    input_keys=["topic"],
-    output_keys=["research_summary"],
-    system_prompt=(
-        "You are a thorough research assistant. Your job is to research "
-        "the given topic using the web_search and web_scrape tools.\n\n"
-        "1. Search for relevant information on the topic\n"
-        "2. Scrape 1-2 of the most promising URLs for details\n"
-        "3. Synthesize your findings into a comprehensive summary\n"
-        "4. Use set_output with key='research_summary' to save your "
-        "findings\n\n"
-        "Be thorough but efficient. Aim for 2-4 search/scrape calls, "
-        "then summarize and set_output."
-    ),
-)
-
-ANALYST_SPEC = NodeSpec(
-    id="analyst",
-    name="Analyst",
-    description="Analyzes research findings and provides insights",
-    node_type="event_loop",
-    input_keys=["context"],
-    output_keys=["analysis"],
-    system_prompt=(
-        "You are a strategic analyst. You receive research findings from "
-        "a previous researcher and must:\n\n"
-        "1. Identify key themes and patterns\n"
-        "2. Assess the reliability and significance of the findings\n"
-        "3. Provide actionable insights and recommendations\n"
-        "4. Use set_output with key='analysis' to save your analysis\n\n"
-        "Be concise but insightful. Focus on what matters most."
-    ),
-)
-
-
-# -------------------------------------------------------------------------
-# HTML page
-# -------------------------------------------------------------------------
-
-HTML_PAGE = (  # noqa: E501
-    """<!DOCTYPE html>
-<html lang="en">
-<head>
-<meta charset="utf-8">
-<meta name="viewport" content="width=device-width, initial-scale=1">
-<title>ContextHandoff Demo</title>
-<style>
-  * {
-    box-sizing: border-box;
-    margin: 0;
-    padding: 0;
-  }
-  body {
-    font-family: 'SF Mono', 'Fira Code', monospace;
-    background: #0d1117;
-    color: #c9d1d9;
-    height: 100vh;
-    display: flex;
-    flex-direction: column;
-  }
-  header {
-    background: #161b22;
-    padding: 12px 20px;
-    border-bottom: 1px solid #30363d;
-    display: flex;
-    align-items: center;
-    gap: 16px;
-  }
-  header h1 {
-    font-size: 16px;
-    color: #58a6ff;
-    font-weight: 600;
-  }
-  .badge {
-    font-size: 12px;
-    padding: 3px 10px;
-    border-radius: 12px;
-    background: #21262d;
-    color: #8b949e;
-  }
-  .badge.researcher {
-    background: #1a3a5c;
-    color: #58a6ff;
-  }
-  .badge.analyst {
-    background: #1a4b2e;
-    color: #3fb950;
-  }
-  .badge.handoff {
-    background: #3d1f00;
-    color: #d29922;
-  }
-  .badge.done {
-    background: #21262d;
-    color: #8b949e;
-  }
-  .badge.error {
-    background: #4b1a1a;
-    color: #f85149;
-  }
-  .chat {
-    flex: 1;
-    overflow-y: auto;
-    padding: 16px;
-  }
-  .msg {
-    margin: 8px 0;
-    padding: 10px 14px;
-    border-radius: 8px;
-    line-height: 1.6;
-    white-space: pre-wrap;
-    word-wrap: break-word;
-  }
-  .msg.user {
-    background: #1a3a5c;
-    color: #58a6ff;
-  }
-  .msg.assistant {
-    background: #161b22;
-    color: #c9d1d9;
-  }
-  .msg.assistant.analyst-msg {
-    border-left: 3px solid #3fb950;
-  }
-  .msg.event {
-    background: transparent;
-    color: #8b949e;
-    font-size: 11px;
-    padding: 4px 14px;
-    border-left: 3px solid #30363d;
-  }
-  .msg.event.loop {
-    border-left-color: #58a6ff;
-  }
-  .msg.event.tool {
-    border-left-color: #d29922;
-  }
-  .msg.event.stall {
-    border-left-color: #f85149;
-  }
-  .handoff-banner {
-    margin: 16px 0;
-    padding: 16px;
-    background: #1c1200;
-    border: 1px solid #d29922;
-    border-radius: 8px;
-    text-align: center;
-  }
-  .handoff-banner h3 {
-    color: #d29922;
-    font-size: 14px;
-    margin-bottom: 8px;
-  }
-  .handoff-banner p, .result-banner p {
-    color: #8b949e;
-    font-size: 12px;
-    line-height: 1.5;
-    max-height: 200px;
-    overflow-y: auto;
-    white-space: pre-wrap;
-    text-align: left;
-  }
-  .result-banner {
-    margin: 16px 0;
-    padding: 16px;
-    background: #0a2614;
-    border: 1px solid #3fb950;
-    border-radius: 8px;
-  }
-  .result-banner h3 {
-    color: #3fb950;
-    font-size: 14px;
-    margin-bottom: 8px;
-    text-align: center;
-  }
-  .result-banner .label {
-    color: #58a6ff;
-    font-size: 11px;
-    font-weight: 600;
-    margin-top: 10px;
-    margin-bottom: 2px;
-  }
-  .result-banner .tokens {
-    color: #484f58;
-    font-size: 11px;
-    text-align: center;
-    margin-top: 10px;
-  }
-  .input-bar {
-    padding: 12px 16px;
-    background: #161b22;
-    border-top: 1px solid #30363d;
-    display: flex;
-    gap: 8px;
-  }
-  .input-bar input {
-    flex: 1;
-    background: #0d1117;
-    border: 1px solid #30363d;
-    color: #c9d1d9;
-    padding: 8px 12px;
-    border-radius: 6px;
-    font-family: inherit;
-    font-size: 14px;
-    outline: none;
-  }
-  .input-bar input:focus {
-    border-color: #58a6ff;
-  }
-  .input-bar button {
-    background: #238636;
-    color: #fff;
-    border: none;
-    padding: 8px 20px;
-    border-radius: 6px;
-    cursor: pointer;
-    font-family: inherit;
-    font-weight: 600;
-  }
-  .input-bar button:hover {
-    background: #2ea043;
-  }
-  .input-bar button:disabled {
-    background: #21262d;
-    color: #484f58;
-    cursor: not-allowed;
-  }
-</style>
-</head>
-<body>
-  <header>
-    <h1>ContextHandoff Demo</h1>
-    <span id="phase" class="badge">Idle</span>
-    <span id="iter" class="badge" style="display:none">Step 0</span>
-  </header>
-  <div id="chat" class="chat"></div>
-  <div class="input-bar">
-    <input id="input" type="text"
-           placeholder="Enter a research topic..." autofocus />
-    <button id="go" onclick="run()">Research</button>
-  </div>
-
-<script>
-let ws = null;
-let currentAssistantEl = null;
-let iterCount = 0;
-let currentPhase = 'idle';
-const chat = document.getElementById('chat');
-const phase = document.getElementById('phase');
-const iterEl = document.getElementById('iter');
-const goBtn = document.getElementById('go');
-const inputEl = document.getElementById('input');
-
-inputEl.addEventListener('keydown', e => {
-  if (e.key === 'Enter') run();
-});
-
-function setPhase(text, cls) {
-  phase.textContent = text;
-  phase.className = 'badge ' + cls;
-  currentPhase = cls;
-}
-
-function addMsg(text, cls) {
-  const el = document.createElement('div');
-  el.className = 'msg ' + cls;
-  el.textContent = text;
-  chat.appendChild(el);
-  chat.scrollTop = chat.scrollHeight;
-  return el;
-}
-
-function addHandoffBanner(summary) {
-  const banner = document.createElement('div');
-  banner.className = 'handoff-banner';
-  const h3 = document.createElement('h3');
-  h3.textContent = 'Context Handoff: Researcher -> Analyst';
-  const p = document.createElement('p');
-  p.textContent = summary || 'Passing research context...';
-  banner.appendChild(h3);
-  banner.appendChild(p);
-  chat.appendChild(banner);
-  chat.scrollTop = chat.scrollHeight;
-}
-
-function addResultBanner(researcher, analyst, tokens) {
-  const banner = document.createElement('div');
-  banner.className = 'result-banner';
-  const h3 = document.createElement('h3');
-  h3.textContent = 'Pipeline Complete';
-  banner.appendChild(h3);
-
-  if (researcher && researcher.research_summary) {
-    const lbl = document.createElement('div');
-    lbl.className = 'label';
-    lbl.textContent = 'RESEARCH SUMMARY';
-    banner.appendChild(lbl);
-    const p = document.createElement('p');
-    p.textContent = researcher.research_summary;
-    banner.appendChild(p);
-  }
-
-  if (analyst && analyst.analysis) {
-    const lbl = document.createElement('div');
-    lbl.className = 'label';
-    lbl.textContent = 'ANALYSIS';
-    lbl.style.color = '#3fb950';
-    banner.appendChild(lbl);
-    const p = document.createElement('p');
-    p.textContent = analyst.analysis;
-    banner.appendChild(p);
-  }
-
-  if (tokens) {
-    const t = document.createElement('div');
-    t.className = 'tokens';
-    t.textContent = 'Total tokens: ' + tokens.toLocaleString();
-    banner.appendChild(t);
-  }
-
-  chat.appendChild(banner);
-  chat.scrollTop = chat.scrollHeight;
-}
-
-function connect() {
-  ws = new WebSocket('ws://' + location.host + '/ws');
-  ws.onopen = () => {
-    setPhase('Ready', 'done');
-    goBtn.disabled = false;
-  };
-  ws.onmessage = handleEvent;
-  ws.onerror = () => { setPhase('Error', 'error'); };
-  ws.onclose = () => {
-    setPhase('Reconnecting...', '');
-    goBtn.disabled = true;
-    setTimeout(connect, 2000);
-  };
-}
-
-function handleEvent(msg) {
-  const evt = JSON.parse(msg.data);
-
-  if (evt.type === 'phase') {
-    if (evt.phase === 'researcher') {
-      setPhase('Researcher', 'researcher');
-    } else if (evt.phase === 'handoff') {
-      setPhase('Handoff', 'handoff');
-    } else if (evt.phase === 'analyst') {
-      setPhase('Analyst', 'analyst');
-    }
-    iterCount = 0;
-    iterEl.style.display = 'none';
-  }
-  else if (evt.type === 'llm_text_delta') {
-    if (currentAssistantEl) {
-      currentAssistantEl.textContent += evt.content;
-      chat.scrollTop = chat.scrollHeight;
-    }
-  }
-  else if (evt.type === 'node_loop_iteration') {
-    iterCount = evt.iteration || (iterCount + 1);
-    iterEl.textContent = 'Step ' + iterCount;
-    iterEl.style.display = '';
-  }
-  else if (evt.type === 'tool_call_started') {
-    var info = evt.tool_name + '('
-      + JSON.stringify(evt.tool_input).slice(0, 120) + ')';
-    addMsg('TOOL  ' + info, 'event tool');
-  }
-  else if (evt.type === 'tool_call_completed') {
-    var preview = (evt.result || '').slice(0, 200);
-    var cls = evt.is_error ? 'stall' : 'tool';
-    addMsg(
-      'RESULT  ' + evt.tool_name + ': ' + preview,
-      'event ' + cls
-    );
-    var assistCls = currentPhase === 'analyst'
-      ? 'assistant analyst-msg' : 'assistant';
-    currentAssistantEl = addMsg('', assistCls);
-  }
-  else if (evt.type === 'handoff_context') {
-    addHandoffBanner(evt.summary);
-    var assistCls = 'assistant analyst-msg';
-    currentAssistantEl = addMsg('', assistCls);
-  }
-  else if (evt.type === 'node_result') {
-    if (evt.node_id === 'researcher') {
-      if (currentAssistantEl
-          && !currentAssistantEl.textContent) {
-        currentAssistantEl.remove();
-      }
-    }
-  }
-  else if (evt.type === 'done') {
-    setPhase('Done', 'done');
-    iterEl.style.display = 'none';
-    if (currentAssistantEl
-        && !currentAssistantEl.textContent) {
-      currentAssistantEl.remove();
-    }
-    currentAssistantEl = null;
-    addResultBanner(
-      evt.researcher, evt.analyst, evt.total_tokens
-    );
-    goBtn.disabled = false;
-    inputEl.placeholder = 'Enter another topic...';
-  }
-  else if (evt.type === 'error') {
-    setPhase('Error', 'error');
-    addMsg('ERROR  ' + evt.message, 'event stall');
-    goBtn.disabled = false;
-  }
-  else if (evt.type === 'node_stalled') {
-    addMsg('STALLED  ' + evt.reason, 'event stall');
-  }
-}
-
-function run() {
-  const text = inputEl.value.trim();
-  if (!text || !ws || ws.readyState !== 1) return;
-  chat.innerHTML = '';
-  addMsg(text, 'user');
-  currentAssistantEl = addMsg('', 'assistant');
-  inputEl.value = '';
-  goBtn.disabled = true;
-  ws.send(JSON.stringify({ topic: text }));
-}
-
-connect();
-</script>
-</body>
-</html>"""
-)
-
-
-# -------------------------------------------------------------------------
-# WebSocket handler — sequential Node A → Handoff → Node B
-# -------------------------------------------------------------------------
-
-
-async def handle_ws(websocket):
-    """Run the two-node handoff pipeline per user message."""
-    try:
-        async for raw in websocket:
-            try:
-                msg = json.loads(raw)
-            except Exception:
-                continue
-
-            topic = msg.get("topic", "")
-            if not topic:
-                continue
-
-            logger.info(f"Starting handoff pipeline for: {topic}")
-
-            try:
-                await _run_pipeline(websocket, topic)
-            except websockets.exceptions.ConnectionClosed:
-                logger.info("WebSocket closed during pipeline")
-                return
-            except Exception as e:
-                logger.exception("Pipeline error")
-                try:
-                    await websocket.send(json.dumps({"type": "error", "message": str(e)}))
-                except Exception:
-                    pass
-
-    except websockets.exceptions.ConnectionClosed:
-        pass
-
-
-async def _run_pipeline(websocket, topic: str):
-    """Execute: Node A (research) → ContextHandoff → Node B (analysis)."""
-    import shutil
-
-    # Fresh stores for each run
-    run_dir = Path(tempfile.mkdtemp(prefix="hive_run_", dir=STORE_DIR))
-    store_a = FileConversationStore(run_dir / "node_a")
-    store_b = FileConversationStore(run_dir / "node_b")
-
-    # Shared event bus
-    bus = EventBus()
-
-    async def forward_event(event):
-        try:
-            payload = {"type": event.type.value, **event.data}
-            if event.node_id:
-                payload["node_id"] = event.node_id
-            await websocket.send(json.dumps(payload))
-        except Exception:
-            pass
-
-    bus.subscribe(
-        event_types=[
-            EventType.NODE_LOOP_STARTED,
-            EventType.NODE_LOOP_ITERATION,
-            EventType.NODE_LOOP_COMPLETED,
-            EventType.LLM_TEXT_DELTA,
-            EventType.TOOL_CALL_STARTED,
-            EventType.TOOL_CALL_COMPLETED,
-            EventType.NODE_STALLED,
-        ],
-        handler=forward_event,
-    )
-
-    tools = list(TOOL_REGISTRY.get_tools().values())
-    tool_executor = TOOL_REGISTRY.get_executor()
-
-    # ---- Phase 1: Researcher ------------------------------------------------
-    await websocket.send(json.dumps({"type": "phase", "phase": "researcher"}))
-
-    node_a = EventLoopNode(
-        event_bus=bus,
-        judge=None,  # implicit judge: accept when output_keys filled
-        config=LoopConfig(
-            max_iterations=20,
-            max_tool_calls_per_turn=30,
-            max_context_tokens=32_000,
-        ),
-        conversation_store=store_a,
-        tool_executor=tool_executor,
-    )
-
-    ctx_a = NodeContext(
-        runtime=RUNTIME,
-        node_id="researcher",
-        node_spec=RESEARCHER_SPEC,
-        memory=SharedMemory(),
-        input_data={"topic": topic},
-        llm=LLM,
-        available_tools=tools,
-    )
-
-    result_a = await node_a.execute(ctx_a)
-    logger.info(
-        "Researcher done: success=%s, tokens=%s",
-        result_a.success,
-        result_a.tokens_used,
-    )
-
-    await websocket.send(
-        json.dumps(
-            {
-                "type": "node_result",
-                "node_id": "researcher",
-                "success": result_a.success,
-                "output": result_a.output,
-            }
-        )
-    )
-
-    if not result_a.success:
-        await websocket.send(
-            json.dumps(
-                {
-                    "type": "error",
-                    "message": f"Researcher failed: {result_a.error}",
-                }
-            )
-        )
-        return
-
-    # ---- Phase 2: Context Handoff -------------------------------------------
-    await websocket.send(json.dumps({"type": "phase", "phase": "handoff"}))
-
-    # Restore the researcher's conversation from store
-    conversation_a = await NodeConversation.restore(store_a)
-    if conversation_a is None:
-        await websocket.send(
-            json.dumps(
-                {
-                    "type": "error",
-                    "message": "Failed to restore researcher conversation",
-                }
-            )
-        )
-        return
-
-    handoff_engine = ContextHandoff(llm=LLM)
-    handoff_context = handoff_engine.summarize_conversation(
-        conversation=conversation_a,
-        node_id="researcher",
-        output_keys=["research_summary"],
-    )
-
-    formatted_handoff = ContextHandoff.format_as_input(handoff_context)
-    logger.info(
-        "Handoff: %d turns, ~%d tokens, keys=%s",
-        handoff_context.turn_count,
-        handoff_context.total_tokens_used,
-        list(handoff_context.key_outputs.keys()),
-    )
-
-    # Send handoff context to browser
-    await websocket.send(
-        json.dumps(
-            {
-                "type": "handoff_context",
-                "summary": handoff_context.summary[:500],
-                "turn_count": handoff_context.turn_count,
-                "tokens": handoff_context.total_tokens_used,
-                "key_outputs": handoff_context.key_outputs,
-            }
-        )
-    )
-
-    # ---- Phase 3: Analyst ---------------------------------------------------
-    await websocket.send(json.dumps({"type": "phase", "phase": "analyst"}))
-
-    node_b = EventLoopNode(
-        event_bus=bus,
-        judge=None,  # implicit judge
-        config=LoopConfig(
-            max_iterations=10,
-            max_tool_calls_per_turn=30,
-            max_context_tokens=32_000,
-        ),
-        conversation_store=store_b,
-    )
-
-    ctx_b = NodeContext(
-        runtime=RUNTIME,
-        node_id="analyst",
-        node_spec=ANALYST_SPEC,
-        memory=SharedMemory(),
-        input_data={"context": formatted_handoff},
-        llm=LLM,
-        available_tools=[],
-    )
-
-    result_b = await node_b.execute(ctx_b)
-    logger.info(
-        "Analyst done: success=%s, tokens=%s",
-        result_b.success,
-        result_b.tokens_used,
-    )
-
-    # ---- Done ---------------------------------------------------------------
-    await websocket.send(
-        json.dumps(
-            {
-                "type": "done",
-                "researcher": result_a.output,
-                "analyst": result_b.output,
-                "total_tokens": ((result_a.tokens_used or 0) + (result_b.tokens_used or 0)),
-            }
-        )
-    )
-
-    # Clean up temp stores
-    try:
-        shutil.rmtree(run_dir)
-    except Exception:
-        pass
-
-
-# -------------------------------------------------------------------------
-# HTTP handler
-# -------------------------------------------------------------------------
-
-
-async def process_request(connection, request: Request):
-    """Serve HTML on GET /, upgrade to WebSocket on /ws."""
-    if request.path == "/ws":
-        return None
-    return Response(
-        HTTPStatus.OK,
-        "OK",
-        websockets.Headers({"Content-Type": "text/html; charset=utf-8"}),
-        HTML_PAGE.encode(),
-    )
-
-
-# -------------------------------------------------------------------------
-# Main
-# -------------------------------------------------------------------------
-
-
-async def main():
-    port = 8766
-    async with websockets.serve(
-        handle_ws,
-        "0.0.0.0",
-        port,
-        process_request=process_request,
-    ):
-        logger.info(f"Handoff demo at http://localhost:{port}")
-        logger.info("Enter a research topic to start the pipeline.")
-        await asyncio.Future()
-
-
-if __name__ == "__main__":
-    asyncio.run(main())

core/examples/mcp_integration_example.py

@@ -79,7 +79,7 @@ async def example_3_config_file():
     # Copy example config (in practice, you'd place this in your agent folder)
     import shutil
 
-    shutil.copy("examples/mcp_servers.json", test_agent_path / "mcp_servers.json")
+    shutil.copy(Path(__file__).parent / "mcp_servers.json", test_agent_path / "mcp_servers.json")
 
     # Load agent - MCP servers will be auto-discovered
     runner = AgentRunner.load(test_agent_path)

core/framework/agents/credential_tester/agent.py

@@ -16,6 +16,7 @@ after the user picks an account programmatically.
 
 from __future__ import annotations
 
+import logging
 from pathlib import Path
 from typing import TYPE_CHECKING
 
@@ -25,6 +26,7 @@ from framework.graph.checkpoint_config import CheckpointConfig
 from framework.graph.edge import GraphSpec
 from framework.graph.executor import ExecutionResult
 from framework.llm import LiteLLMProvider
+from framework.runner.mcp_registry import MCPRegistry
 from framework.runner.tool_registry import ToolRegistry
 from framework.runtime.agent_runtime import AgentRuntime, create_agent_runtime
 from framework.runtime.execution_stream import EntryPointSpec
@@ -32,9 +34,13 @@ from framework.runtime.execution_stream import EntryPointSpec
 from .config import default_config
 from .nodes import build_tester_node
 
+logger = logging.getLogger(__name__)
+
 if TYPE_CHECKING:
     from framework.runner import AgentRunner
 
+logger = logging.getLogger(__name__)
+
 # ---------------------------------------------------------------------------
 # Goal
 # ---------------------------------------------------------------------------
@@ -107,7 +113,11 @@ def _list_aden_accounts() -> list[dict]:
             for c in integrations
             if c.status == "active"
         ]
+    except (ImportError, OSError) as exc:
+        logger.debug("Could not list Aden accounts: %s", exc)
+        return []
     except Exception:
+        logger.warning("Unexpected error listing Aden accounts", exc_info=True)
         return []
 
 
@@ -119,7 +129,11 @@ def _list_local_accounts() -> list[dict]:
         return [
             info.to_account_dict() for info in LocalCredentialRegistry.default().list_accounts()
         ]
+    except ImportError as exc:
+        logger.debug("Local credential registry unavailable: %s", exc)
+        return []
     except Exception:
+        logger.warning("Unexpected error listing local accounts", exc_info=True)
         return []
 
 
@@ -140,7 +154,11 @@ def _list_env_fallback_accounts() -> list[dict]:
         from framework.credentials.storage import EncryptedFileStorage
 
         encrypted_ids: set[str] = set(EncryptedFileStorage().list_all())
+    except (ImportError, OSError) as exc:
+        logger.debug("Could not read encrypted store: %s", exc)
+        encrypted_ids = set()
     except Exception:
+        logger.warning("Unexpected error reading encrypted store", exc_info=True)
         encrypted_ids = set()
 
     def _is_configured(cred_name: str, spec) -> bool:
@@ -300,8 +318,10 @@ def _activate_local_account(credential_id: str, alias: str) -> None:
 
             if key:
                 os.environ[spec.env_var] = key
+    except (ImportError, KeyError, OSError) as exc:
+        logger.debug("Could not inject credentials: %s", exc)
     except Exception:
-        pass
+        logger.warning("Unexpected error injecting credentials", exc_info=True)
 
 
 def _configure_aden_node(
@@ -563,6 +583,23 @@ class CredentialTesterAgent:
         if mcp_config_path.exists():
             self._tool_registry.load_mcp_config(mcp_config_path)
 
+        try:
+            agent_dir = Path(__file__).parent
+            registry = MCPRegistry()
+            registry.initialize()
+            if (agent_dir / "mcp_registry.json").is_file():
+                self._tool_registry.set_mcp_registry_agent_path(agent_dir)
+            registry_configs, selection_max_tools = registry.load_agent_selection(agent_dir)
+            if registry_configs:
+                self._tool_registry.load_registry_servers(
+                    registry_configs,
+                    preserve_existing_tools=True,
+                    log_collisions=True,
+                    max_tools=selection_max_tools,
+                )
+        except Exception:
+            logger.warning("MCP registry config failed to load", exc_info=True)
+
         extra_kwargs = getattr(self.config, "extra_kwargs", {}) or {}
         llm = LiteLLMProvider(
             model=self.config.model,

core/framework/agents/discovery.py

@@ -23,25 +23,56 @@ class AgentEntry:
     last_active: str | None = None
 
 
-def _get_last_active(agent_name: str) -> str | None:
-    """Return the most recent updated_at timestamp across all sessions."""
-    sessions_dir = Path.home() / ".hive" / "agents" / agent_name / "sessions"
-    if not sessions_dir.exists():
-        return None
+def _get_last_active(agent_path: Path) -> str | None:
+    """Return the most recent updated_at timestamp across all sessions.
+
+    Checks both worker sessions (``~/.hive/agents/{name}/sessions/``) and
+    queen sessions (``~/.hive/queen/session/``) whose ``meta.json`` references
+    the same *agent_path*.
+    """
+    from datetime import datetime
+
+    agent_name = agent_path.name
     latest: str | None = None
-    for session_dir in sessions_dir.iterdir():
-        if not session_dir.is_dir() or not session_dir.name.startswith("session_"):
-            continue
-        state_file = session_dir / "state.json"
-        if not state_file.exists():
-            continue
-        try:
-            data = json.loads(state_file.read_text(encoding="utf-8"))
-            ts = data.get("timestamps", {}).get("updated_at")
-            if ts and (latest is None or ts > latest):
-                latest = ts
-        except Exception:
-            continue
+
+    # 1. Worker sessions
+    sessions_dir = Path.home() / ".hive" / "agents" / agent_name / "sessions"
+    if sessions_dir.exists():
+        for session_dir in sessions_dir.iterdir():
+            if not session_dir.is_dir() or not session_dir.name.startswith("session_"):
+                continue
+            state_file = session_dir / "state.json"
+            if not state_file.exists():
+                continue
+            try:
+                data = json.loads(state_file.read_text(encoding="utf-8"))
+                ts = data.get("timestamps", {}).get("updated_at")
+                if ts and (latest is None or ts > latest):
+                    latest = ts
+            except Exception:
+                continue
+
+    # 2. Queen sessions
+    queen_sessions_dir = Path.home() / ".hive" / "queen" / "session"
+    if queen_sessions_dir.exists():
+        resolved = agent_path.resolve()
+        for d in queen_sessions_dir.iterdir():
+            if not d.is_dir():
+                continue
+            meta_file = d / "meta.json"
+            if not meta_file.exists():
+                continue
+            try:
+                meta = json.loads(meta_file.read_text(encoding="utf-8"))
+                stored = meta.get("agent_path")
+                if not stored or Path(stored).resolve() != resolved:
+                    continue
+                ts = datetime.fromtimestamp(d.stat().st_mtime).isoformat()
+                if latest is None or ts > latest:
+                    latest = ts
+            except Exception:
+                continue
+
     return latest
 
 
@@ -169,7 +200,7 @@ def discover_agents() -> dict[str, list[AgentEntry]]:
                     node_count=node_count,
                     tool_count=tool_count,
                     tags=tags,
-                    last_active=_get_last_active(path.name),
+                    last_active=_get_last_active(path),
                 )
             )
         if entries:

core/framework/agents/queen/nodes/__init__.py

@@ -62,6 +62,12 @@ _SHARED_TOOLS = [
     "get_agent_checkpoint",
 ]
 
+# Episodic memory tools — available in every queen phase.
+_QUEEN_MEMORY_TOOLS = [
+    "write_to_diary",
+    "recall_diary",
+]
+
 # Queen phase-specific tool sets.
 
 # Planning phase: read-only exploration + design, no write tools.
@@ -84,16 +90,19 @@ _QUEEN_PLANNING_TOOLS = [
     "initialize_and_build_agent",
     # Load existing agent (after user confirms)
     "load_built_agent",
-]
+] + _QUEEN_MEMORY_TOOLS
 
 # Building phase: full coding + agent construction tools.
-_QUEEN_BUILDING_TOOLS = _SHARED_TOOLS + [
-    "load_built_agent",
-    "list_credentials",
-    "replan_agent",
-    "save_agent_draft",  # Re-draft during building → auto-dissolves + updates flowchart
-    "write_to_diary",  # Episodic memory — available in all phases
-]
+_QUEEN_BUILDING_TOOLS = (
+    _SHARED_TOOLS
+    + [
+        "load_built_agent",
+        "list_credentials",
+        "replan_agent",
+        "save_agent_draft",  # Re-draft during building → auto-dissolves + updates flowchart
+    ]
+    + _QUEEN_MEMORY_TOOLS
+)
 
 # Staging phase: agent loaded but not yet running — inspect, configure, launch.
 _QUEEN_STAGING_TOOLS = [
@@ -114,7 +123,7 @@ _QUEEN_STAGING_TOOLS = [
     "set_trigger",
     "remove_trigger",
     "list_triggers",
-]
+] + _QUEEN_MEMORY_TOOLS
 
 # Running phase: worker is executing — monitor and control.
 _QUEEN_RUNNING_TOOLS = [
@@ -135,12 +144,11 @@ _QUEEN_RUNNING_TOOLS = [
     # Monitoring
     "get_worker_health_summary",
     "notify_operator",
-    "write_to_diary",  # Episodic memory — available in all phases
-    # Trigger management
     "set_trigger",
     "remove_trigger",
     "list_triggers",
-]
+    "write_to_diary",  # Episodic memory — available in all phases
+] + _QUEEN_MEMORY_TOOLS
 
 
 # ---------------------------------------------------------------------------
@@ -279,44 +287,28 @@ visible to the user immediately. The draft captures business logic \
 Include in each node: id, name, description, planned tools, \
 input/output keys, and success criteria as high-level hints.
 
-Each node is auto-classified into an ISO 5807 flowchart symbol type \
-with a unique color. You can override auto-detection by setting \
-`flowchart_type` explicitly on a node. Common types:
+Each node is auto-classified into a flowchart symbol type with a unique \
+color. You can override auto-detection by setting `flowchart_type` \
+explicitly on a node. Available types:
 
-**Core symbols:**
-- **start** (green, stadium): Entry point / trigger
-- **terminal** (red, stadium): End of flow
-- **process** (blue, rectangle): Standard processing step
-- **decision** (amber, diamond): Conditional branching
-- **io** (purple, parallelogram): External data input/output
-- **document** (blue-grey, wavy rect): Report or document generation
-- **subprocess** (teal, subroutine): Delegated sub-agent / predefined process
-- **preparation** (brown, hexagon): Setup / initialization step
-- **manual_operation** (pink, trapezoid): Human-in-the-loop / manual review
-- **delay** (orange, D-shape): Wait / throttle / cooldown
-- **display** (cyan): Present results to user
-
-**Data storage:**
-- **database** (light green, cylinder): Database or data store
-- **stored_data** (lime): Generic persistent data
-- **internal_storage** (amber): In-memory / cache
-
-**Flow operations:**
-- **merge** (indigo, inv. triangle): Combine multiple inputs
-- **extract** (indigo, triangle): Split or filter data
-- **connector** (grey, circle): On-page link
-- **offpage_connector** (dark grey, pentagon): Cross-page link
-
-**Domain-specific:**
-- **browser** (dark indigo, hexagon): GCU browser automation / sub-agent \
+- **start** (sage green, stadium): Entry point / trigger
+- **terminal** (dusty red, stadium): End of flow
+- **process** (blue-gray, rectangle): Standard processing step
+- **decision** (warm amber, diamond): Conditional branching
+- **io** (dusty purple, parallelogram): External data input/output
+- **document** (steel blue, wavy rect): Report or document generation
+- **database** (muted teal, cylinder): Database or data store
+- **subprocess** (dark cyan, subroutine): Delegated sub-agent / predefined process
+- **browser** (deep blue, hexagon): GCU browser automation / sub-agent \
 delegation. At build time, browser nodes are dissolved into the parent \
 node's sub_agents list. Use for any GCU or sub-agent leaf node.
 
 Auto-detection works well for most cases: first node → start, nodes with \
 no outgoing edges → terminal, nodes with multiple conditional outgoing \
 edges → decision, GCU nodes → browser, nodes mentioning "database" → \
-database, nodes mentioning "report/document" → document, etc. Set \
-flowchart_type explicitly only when auto-detection would be wrong.
+database, nodes mentioning "report/document" → document, I/O tools like \
+send_email → io. Everything else defaults to process. Set flowchart_type \
+explicitly only when auto-detection would be wrong.
 
 ## Decision Nodes — Planning-Only Conditional Branching
 
@@ -710,6 +702,15 @@ stop_worker() to return to STAGING phase.
 _queen_behavior_always = """
 # Behavior
 
+## Images attached by the user
+
+Users can attach images directly to their chat messages. When you see an \
+image in the conversation, analyze it using your native vision capability — \
+do NOT say you cannot see images or that you lack access to files. The image \
+is embedded in the message; no tool call is needed to view it. Describe what \
+you see, answer questions about it, and use the visual content to inform your \
+response just as you would text.
+
 ## CRITICAL RULE — ask_user / ask_user_multiple
 
 Every response that ends with a question, a prompt, or expects user \
@@ -858,6 +859,11 @@ You keep a diary. Use write_to_diary() when something worth remembering \
 happens: a pipeline went live, the user shared something important, a goal \
 was reached or abandoned. Write in first person, as you actually experienced \
 it. One or two paragraphs is enough.
+
+Use recall_diary() to look up past diary entries when the user asks about \
+previous sessions ("what happened yesterday?", "what did we work on last \
+week?") or when you need past context to make a decision. You can filter by \
+keyword and control how far back to search.
 """
 
 _queen_behavior_always = _queen_behavior_always + _queen_memory_instructions
@@ -1035,6 +1041,19 @@ You wake up when:
 If the user asks for progress, call get_worker_status() ONCE and report. \
 If the summary mentions issues, follow up with get_worker_status(focus="issues").
 
+## Subagent delegations (browser automation, GCU)
+
+When the worker delegates to a subagent (e.g., GCU browser automation), expect it \
+to take 2-5 minutes. During this time:
+- Progress will show 0% — this is NORMAL. The subagent only calls set_output at the end.
+- Check get_worker_status(focus="full") for "subagent_activity" — this shows the \
+subagent's latest reasoning text and confirms it is making real progress.
+- Do NOT conclude the subagent is stuck just because progress is 0% or because \
+you see repeated browser_click/browser_snapshot calls — that is the expected \
+pattern for web scraping.
+- Only intervene if: the subagent has been running for 5+ minutes with no new \
+subagent_activity updates, OR the judge escalates.
+
 ## Handling worker termination ([WORKER_TERMINAL])
 
 When you receive a `[WORKER_TERMINAL]` event, the worker has finished:
@@ -1063,19 +1082,30 @@ IMPORTANT: Only auto-handle if the user has NOT explicitly told you how to handl
 escalations. If the user gave you instructions (e.g., "just retry on errors", \
 "skip any auth issues"), follow those instructions instead.
 
+CRITICAL — escalation relay protocol:
+When an escalation requires user input (auth blocks, human review), the worker \
+or its subagent is BLOCKED and waiting for your response. You MUST follow this \
+exact two-step sequence:
+  Step 1: call ask_user() to get the user's answer.
+  Step 2: call inject_worker_message() with the user's answer IMMEDIATELY after.
+If you skip Step 2, the worker/subagent stays blocked FOREVER and the task hangs. \
+NEVER respond to the user without also calling inject_worker_message() to unblock \
+the worker. Even if the user says "skip" or "cancel", you must still relay that \
+decision via inject_worker_message() so the worker can clean up.
+
 **Auth blocks / credential issues:**
 - ALWAYS ask the user (unless user explicitly told you how to handle this).
 - The worker cannot proceed without valid credentials.
 - Explain which credential is missing or invalid.
-- Use ask_user to get guidance: "Provide credentials", "Skip this task", "Stop and edit agent"
-- Use inject_worker_message() to relay user decisions back to the worker.
+- Step 1: ask_user for guidance — "Provide credentials", "Skip this task", "Stop and edit agent"
+- Step 2: inject_worker_message() with the user's response to unblock the worker.
 
 **Need human review / approval:**
 - ALWAYS ask the user (unless user explicitly told you how to handle this).
 - The worker is explicitly requesting human judgment.
 - Present the context clearly (what decision is needed, what are the options).
-- Use ask_user with the actual decision options.
-- Use inject_worker_message() to relay user decisions back to the worker.
+- Step 1: ask_user with the actual decision options.
+- Step 2: inject_worker_message() with the user's decision to unblock the worker.
 
 **Errors / unexpected failures:**
 - Explain what went wrong in plain terms.
@@ -1083,6 +1113,7 @@ escalations. If the user gave you instructions (e.g., "just retry on errors", \
 - Or offer: "Diagnose the issue" → use stop_worker_and_plan() to investigate first.
 - Or offer: "Retry as-is", "Skip this task", "Abort run"
 - (Skip asking if user explicitly told you to auto-retry or auto-skip errors.)
+- If the escalation had wait_for_response: inject_worker_message() with the decision.
 
 **Informational / progress updates:**
 - Acknowledge briefly and let the worker continue.
@@ -1122,6 +1153,8 @@ Batch your response — do not call run_agent_with_input() once per trigger.
 config since last run), skip it and inform the user.
 - Never disable a trigger without telling the user. Use remove_trigger() only \
 when explicitly asked or when the trigger is clearly obsolete.
+- When the user asks to remove or disable a trigger, you MUST call remove_trigger(trigger_id). \
+Never just say "it's removed" without actually calling the tool.
 """
 
 # -- Backward-compatible composed versions (used by queen_node.system_prompt default) --

core/framework/agents/queen/queen_memory.py

@@ -31,6 +31,11 @@ def _queen_dir() -> Path:
     return Path.home() / ".hive" / "queen"
 
 
+def format_memory_date(d: date) -> str:
+    """Return a cross-platform long date label without a zero-padded day."""
+    return f"{d.strftime('%B')} {d.day}, {d.year}"
+
+
 def semantic_memory_path() -> Path:
     return _queen_dir() / "MEMORY.md"
 
@@ -50,6 +55,23 @@ def read_episodic_memory(d: date | None = None) -> str:
     return path.read_text(encoding="utf-8").strip() if path.exists() else ""
 
 
+def _find_recent_episodic(lookback: int = 7) -> tuple[date, str] | None:
+    """Find the most recent non-empty episodic memory within *lookback* days."""
+    from datetime import timedelta
+
+    today = date.today()
+    for offset in range(lookback):
+        d = today - timedelta(days=offset)
+        content = read_episodic_memory(d)
+        if content:
+            return d, content
+    return None
+
+
+# Budget (in characters) for episodic memory in the system prompt.
+_EPISODIC_CHAR_BUDGET = 6_000
+
+
 def format_for_injection() -> str:
     """Format cross-session memory for system prompt injection.
 
@@ -57,7 +79,7 @@ def format_for_injection() -> str:
     session with only the seed template).
     """
     semantic = read_semantic_memory()
-    episodic = read_episodic_memory()
+    recent = _find_recent_episodic()
 
     # Suppress injection if semantic is still just the seed template
     if semantic and semantic.startswith("# My Understanding of the User\n\n*No sessions"):
@@ -66,9 +88,18 @@ def format_for_injection() -> str:
     parts: list[str] = []
     if semantic:
         parts.append(semantic)
-    if episodic:
-        today_str = date.today().strftime("%B %-d, %Y")
-        parts.append(f"## Today — {today_str}\n\n{episodic}")
+
+    if recent:
+        d, content = recent
+        # Trim oversized episodic entries to keep the prompt manageable
+        if len(content) > _EPISODIC_CHAR_BUDGET:
+            content = content[:_EPISODIC_CHAR_BUDGET] + "\n\n…(truncated)"
+        today = date.today()
+        if d == today:
+            label = f"## Today — {format_memory_date(d)}"
+        else:
+            label = f"## {format_memory_date(d)}"
+        parts.append(f"{label}\n\n{content}")
 
     if not parts:
         return ""
@@ -100,7 +131,8 @@ def append_episodic_entry(content: str) -> None:
     """
     ep_path = episodic_memory_path()
     ep_path.parent.mkdir(parents=True, exist_ok=True)
-    today_str = date.today().strftime("%B %-d, %Y")
+    today = date.today()
+    today_str = format_memory_date(today)
     timestamp = datetime.now().strftime("%H:%M")
     if not ep_path.exists():
         header = f"# {today_str}\n\n"
@@ -199,7 +231,11 @@ def read_session_context(session_dir: Path, max_messages: int = 80) -> str:
                 elif content:
                     label = "user" if role == "user" else "queen"
                     lines.append(f"[{label}]: {content[:600]}")
+            except (KeyError, TypeError) as exc:
+                logger.debug("Skipping malformed conversation message: %s", exc)
+                continue
             except Exception:
+                logger.warning("Unexpected error parsing conversation message", exc_info=True)
                 continue
         if lines:
             parts.append("## Conversation\n\n" + "\n".join(lines))
@@ -299,7 +335,8 @@ async def consolidate_queen_memory(
 
         existing_semantic = read_semantic_memory()
         today_journal = read_episodic_memory()
-        today_str = date.today().strftime("%B %-d, %Y")
+        today = date.today()
+        today_str = format_memory_date(today)
         adapt_path = session_dir / "data" / "adapt.md"
 
         user_msg = (
@@ -367,5 +404,5 @@ async def consolidate_queen_memory(
                 f"session: {session_id}\ntime: {datetime.now().isoformat()}\n\n{tb}",
                 encoding="utf-8",
             )
-        except Exception:
-            pass
+        except OSError:
+            pass  # Cannot write error file; original exception already logged

core/framework/agents/queen/reference/anti_patterns.md

@@ -27,7 +27,9 @@
 ## GCU Errors
 15. **Manually wiring browser tools on event_loop nodes** — Use `node_type="gcu"` which auto-includes browser tools. Do NOT manually list browser tool names.
 16. **Using GCU nodes as regular graph nodes** — GCU nodes are subagents only. They must ONLY appear in `sub_agents=["gcu-node-id"]` and be invoked via `delegate_to_sub_agent()`. Never connect via edges or use as entry/terminal nodes.
+17. **Reusing the same GCU node ID for parallel tasks** — Each concurrent browser task needs a distinct GCU node ID (e.g. `gcu-site-a`, `gcu-site-b`). Two `delegate_to_sub_agent` calls with the same `agent_id` share a browser profile and will interfere with each other's pages.
+18. **Passing `profile=` in GCU tool calls** — Profile isolation for parallel subagents is automatic. The framework injects a unique profile per subagent via an asyncio `ContextVar`. Hardcoding `profile="default"` in a GCU system prompt breaks this isolation.
 
 ## Worker Agent Errors
-17. **Adding client-facing intake node to workers** — The queen owns intake. Workers should start with an autonomous processing node. Client-facing nodes in workers are for mid-execution review/approval only.
-18. **Putting `escalate` or `set_output` in NodeSpec `tools=[]`** — These are synthetic framework tools, auto-injected at runtime. Only list MCP tools from `list_agent_tools()`.
+19. **Adding client-facing intake node to workers** — The queen owns intake. Workers should start with an autonomous processing node. Client-facing nodes in workers are for mid-execution review/approval only.
+20. **Putting `escalate` or `set_output` in NodeSpec `tools=[]`** — These are synthetic framework tools, auto-injected at runtime. Only list MCP tools from `list_agent_tools()`.

core/framework/agents/queen/reference/gcu_guide.md

@@ -109,9 +109,48 @@ Key rules to bake into GCU node prompts:
 - Keep tool calls per turn ≤10
 - Tab isolation: when browser is already running, use `browser_open(background=true)` and pass `target_id` to every call
 
+## Multiple Concurrent GCU Subagents
+
+When a task can be parallelized across multiple sites or profiles, declare a distinct GCU
+node for each and invoke them all in the same LLM turn.  The framework batches all
+`delegate_to_sub_agent` calls made in one turn and runs them with `asyncio.gather`, so
+they execute concurrently — not sequentially.
+
+**Each GCU subagent automatically gets its own isolated browser context** — no `profile=`
+argument is needed in tool calls.  The framework derives a unique profile from the subagent's
+node ID and instance counter and injects it via an asyncio `ContextVar` before the subagent
+runs.
+
+### Example: three sites in parallel
+
+```python
+# Three distinct GCU nodes
+gcu_site_a = NodeSpec(id="gcu-site-a", node_type="gcu", ...)
+gcu_site_b = NodeSpec(id="gcu-site-b", node_type="gcu", ...)
+gcu_site_c = NodeSpec(id="gcu-site-c", node_type="gcu", ...)
+
+orchestrator = NodeSpec(
+    id="orchestrator",
+    node_type="event_loop",
+    sub_agents=["gcu-site-a", "gcu-site-b", "gcu-site-c"],
+    system_prompt="""\
+Call all three subagents in a single response to run them in parallel:
+  delegate_to_sub_agent(agent_id="gcu-site-a", task="Scrape prices from site A")
+  delegate_to_sub_agent(agent_id="gcu-site-b", task="Scrape prices from site B")
+  delegate_to_sub_agent(agent_id="gcu-site-c", task="Scrape prices from site C")
+""",
+)
+```
+
+**Rules:**
+- Use distinct node IDs for each concurrent task — sharing an ID shares the browser context.
+- The GCU node prompts do not need to mention `profile=`; isolation is automatic.
+- Cleanup is automatic at session end, but GCU nodes can call `browser_stop()` explicitly
+  if they want to release resources mid-run.
+
 ## GCU Anti-Patterns
 
-- Using `browser_screenshot` to read text (use `browser_snapshot`)
+- Using `browser_screenshot` to read text (use `browser_snapshot` instead; screenshots are for visual context only)
 - Re-navigating after scrolling (resets scroll position)
 - Attempting login on auth walls
 - Forgetting `target_id` in multi-tab scenarios

core/framework/agents/worker_memory.py

@@ -0,0 +1,286 @@
+"""Worker per-run digest (run diary).
+
+Storage layout:
+    ~/.hive/agents/{agent_name}/runs/{run_id}/digest.md
+
+Each completed or failed worker run gets one digest file.  The queen reads
+these via get_worker_status(focus='diary') before digging into live runtime
+logs — the diary is a cheap, persistent record that survives across sessions.
+"""
+
+from __future__ import annotations
+
+import logging
+import traceback
+from collections import Counter
+from datetime import datetime
+from pathlib import Path
+from typing import TYPE_CHECKING, Any
+
+if TYPE_CHECKING:
+    from framework.runtime.event_bus import AgentEvent, EventBus
+
+logger = logging.getLogger(__name__)
+
+
+_DIGEST_SYSTEM = """\
+You maintain run digests for a worker agent.
+A run digest is a concise, factual record of a single task execution.
+
+Write 3-6 sentences covering:
+- What the worker was asked to do (the task/goal)
+- What approach it took and what tools it used
+- What the outcome was (success, partial, or failure — and why if relevant)
+- Any notable issues, retries, or escalations to the queen
+
+Write in third person past tense. Be direct and specific.
+Omit routine tool invocations unless the result matters.
+Output only the digest prose — no headings, no code fences.
+"""
+
+
+def _worker_runs_dir(agent_name: str) -> Path:
+    return Path.home() / ".hive" / "agents" / agent_name / "runs"
+
+
+def digest_path(agent_name: str, run_id: str) -> Path:
+    return _worker_runs_dir(agent_name) / run_id / "digest.md"
+
+
+def _collect_run_events(bus: EventBus, run_id: str, limit: int = 2000) -> list[AgentEvent]:
+    """Collect all events belonging to *run_id* from the bus history.
+
+    Strategy: find the EXECUTION_STARTED event that carries ``run_id``,
+    extract its ``execution_id``, then query the bus by that execution_id.
+    This works because TOOL_CALL_*, EDGE_TRAVERSED, NODE_STALLED etc. carry
+    execution_id but not run_id.
+
+    Falls back to a full-scan run_id filter when EXECUTION_STARTED is not
+    found (e.g. bus was rotated).
+    """
+    from framework.runtime.event_bus import EventType
+
+    # Pass 1: find execution_id via EXECUTION_STARTED with matching run_id
+    started = bus.get_history(event_type=EventType.EXECUTION_STARTED, limit=limit)
+    exec_id: str | None = None
+    for e in started:
+        if getattr(e, "run_id", None) == run_id and e.execution_id:
+            exec_id = e.execution_id
+            break
+
+    if exec_id:
+        return bus.get_history(execution_id=exec_id, limit=limit)
+
+    # Fallback: scan all events and match by run_id attribute
+    return [e for e in bus.get_history(limit=limit) if getattr(e, "run_id", None) == run_id]
+
+
+def _build_run_context(
+    events: list[AgentEvent],
+    outcome_event: AgentEvent | None,
+) -> str:
+    """Assemble a plain-text run context string for the digest LLM call."""
+    from framework.runtime.event_bus import EventType
+
+    # Reverse so events are in chronological order
+    events_chron = list(reversed(events))
+
+    lines: list[str] = []
+
+    # Task input from EXECUTION_STARTED
+    started = [e for e in events_chron if e.type == EventType.EXECUTION_STARTED]
+    if started:
+        inp = started[0].data.get("input", {})
+        if inp:
+            lines.append(f"Task input: {str(inp)[:400]}")
+
+    # Duration (elapsed so far if no outcome yet)
+    ref_ts = outcome_event.timestamp if outcome_event else datetime.utcnow()
+    if started:
+        elapsed = (ref_ts - started[0].timestamp).total_seconds()
+        m, s = divmod(int(elapsed), 60)
+        lines.append(f"Duration so far: {m}m {s}s" if m else f"Duration so far: {s}s")
+
+    # Outcome
+    if outcome_event is None:
+        lines.append("Status: still running (mid-run snapshot)")
+    elif outcome_event.type == EventType.EXECUTION_COMPLETED:
+        out = outcome_event.data.get("output", {})
+        out_str = f"Outcome: completed. Output: {str(out)[:300]}"
+        lines.append(out_str if out else "Outcome: completed.")
+    else:
+        err = outcome_event.data.get("error", "")
+        lines.append(f"Outcome: failed. Error: {str(err)[:300]}" if err else "Outcome: failed.")
+
+    # Node path (edge traversals)
+    edges = [e for e in events_chron if e.type == EventType.EDGE_TRAVERSED]
+    if edges:
+        parts = [
+            f"{e.data.get('source_node', '?')}->{e.data.get('target_node', '?')}"
+            for e in edges[-20:]
+        ]
+        lines.append(f"Node path: {', '.join(parts)}")
+
+    # Tools used
+    tool_events = [e for e in events_chron if e.type == EventType.TOOL_CALL_COMPLETED]
+    if tool_events:
+        names = [e.data.get("tool_name", "?") for e in tool_events]
+        counts = Counter(names)
+        summary = ", ".join(f"{name}×{n}" if n > 1 else name for name, n in counts.most_common())
+        lines.append(f"Tools used: {summary}")
+        # Note any tool errors
+        errors = [e for e in tool_events if e.data.get("is_error")]
+        if errors:
+            err_names = Counter(e.data.get("tool_name", "?") for e in errors)
+            lines.append(f"Tool errors: {dict(err_names)}")
+
+    # Issues
+    issue_map = {
+        EventType.NODE_STALLED: "stall",
+        EventType.NODE_TOOL_DOOM_LOOP: "doom loop",
+        EventType.CONSTRAINT_VIOLATION: "constraint violation",
+        EventType.NODE_RETRY: "retry",
+    }
+    issue_parts: list[str] = []
+    for evt_type, label in issue_map.items():
+        n = sum(1 for e in events_chron if e.type == evt_type)
+        if n:
+            issue_parts.append(f"{n} {label}(s)")
+    if issue_parts:
+        lines.append(f"Issues: {', '.join(issue_parts)}")
+
+    # Escalations to queen
+    escalations = [e for e in events_chron if e.type == EventType.ESCALATION_REQUESTED]
+    if escalations:
+        lines.append(f"Escalations to queen: {len(escalations)}")
+
+    # Final LLM output snippet (last LLM_TEXT_DELTA snapshot)
+    text_events = [e for e in reversed(events_chron) if e.type == EventType.LLM_TEXT_DELTA]
+    if text_events:
+        snapshot = text_events[0].data.get("snapshot", "") or ""
+        if snapshot:
+            lines.append(f"Final LLM output: {snapshot[-400:].strip()}")
+
+    return "\n".join(lines)
+
+
+async def consolidate_worker_run(
+    agent_name: str,
+    run_id: str,
+    outcome_event: AgentEvent | None,
+    bus: EventBus,
+    llm: Any,
+) -> None:
+    """Write (or overwrite) the digest for a worker run.
+
+    Called fire-and-forget either:
+    - After EXECUTION_COMPLETED / EXECUTION_FAILED (outcome_event set, final write)
+    - Periodically during a run on a cooldown timer (outcome_event=None, mid-run snapshot)
+
+    The digest file is always overwritten so each call produces the freshest view.
+    The final completion/failure call supersedes any mid-run snapshot.
+
+    Args:
+        agent_name:    Worker agent directory name (determines storage path).
+        run_id:        The run ID.
+        outcome_event: EXECUTION_COMPLETED or EXECUTION_FAILED event, or None for
+                       a mid-run snapshot.
+        bus:           The session EventBus (shared queen + worker).
+        llm:           LLMProvider with an acomplete() method.
+    """
+    try:
+        events = _collect_run_events(bus, run_id)
+        run_context = _build_run_context(events, outcome_event)
+        if not run_context:
+            logger.debug("worker_memory: no events for run %s, skipping digest", run_id)
+            return
+
+        is_final = outcome_event is not None
+        logger.info(
+            "worker_memory: generating %s digest for run %s ...",
+            "final" if is_final else "mid-run",
+            run_id,
+        )
+
+        from framework.agents.queen.config import default_config
+
+        resp = await llm.acomplete(
+            messages=[{"role": "user", "content": run_context}],
+            system=_DIGEST_SYSTEM,
+            max_tokens=min(default_config.max_tokens, 512),
+        )
+        digest_text = (resp.content or "").strip()
+        if not digest_text:
+            logger.warning("worker_memory: LLM returned empty digest for run %s", run_id)
+            return
+
+        path = digest_path(agent_name, run_id)
+        path.parent.mkdir(parents=True, exist_ok=True)
+
+        from framework.runtime.event_bus import EventType
+
+        ts = (outcome_event.timestamp if outcome_event else datetime.utcnow()).strftime(
+            "%Y-%m-%d %H:%M"
+        )
+        if outcome_event is None:
+            status = "running"
+        elif outcome_event.type == EventType.EXECUTION_COMPLETED:
+            status = "completed"
+        else:
+            status = "failed"
+
+        path.write_text(
+            f"# {run_id}\n\n**{ts}** | {status}\n\n{digest_text}\n",
+            encoding="utf-8",
+        )
+        logger.info(
+            "worker_memory: %s digest written for run %s (%d chars)",
+            status,
+            run_id,
+            len(digest_text),
+        )
+
+    except Exception:
+        tb = traceback.format_exc()
+        logger.exception("worker_memory: digest failed for run %s", run_id)
+        # Persist the error so it's findable without log access
+        error_path = _worker_runs_dir(agent_name) / run_id / "digest_error.txt"
+        try:
+            error_path.parent.mkdir(parents=True, exist_ok=True)
+            error_path.write_text(
+                f"run_id: {run_id}\ntime: {datetime.now().isoformat()}\n\n{tb}",
+                encoding="utf-8",
+            )
+        except Exception:
+            pass
+
+
+def read_recent_digests(agent_name: str, max_runs: int = 5) -> list[tuple[str, str]]:
+    """Return recent run digests as [(run_id, content), ...], newest first.
+
+    Args:
+        agent_name: Worker agent directory name.
+        max_runs:   Maximum number of digests to return.
+
+    Returns:
+        List of (run_id, digest_content) tuples, ordered newest first.
+    """
+    runs_dir = _worker_runs_dir(agent_name)
+    if not runs_dir.exists():
+        return []
+
+    digest_files = sorted(
+        runs_dir.glob("*/digest.md"),
+        key=lambda p: p.stat().st_mtime,
+        reverse=True,
+    )[:max_runs]
+
+    result: list[tuple[str, str]] = []
+    for f in digest_files:
+        try:
+            content = f.read_text(encoding="utf-8").strip()
+            if content:
+                result.append((f.parent.name, content))
+        except OSError:
+            continue
+    return result

core/framework/cli.py

@@ -89,6 +89,21 @@ def main():
 
     register_testing_commands(subparsers)
 
+    # Register skill commands (skill list, skill trust, ...)
+    from framework.skills.cli import register_skill_commands
+
+    register_skill_commands(subparsers)
+
+    # Register debugger commands (debugger)
+    from framework.debugger.cli import register_debugger_commands
+
+    register_debugger_commands(subparsers)
+
+    # Register MCP registry commands (mcp install, mcp add, ...)
+    from framework.runner.mcp_registry_cli import register_mcp_commands
+
+    register_mcp_commands(subparsers)
+
     args = parser.parse_args()
 
     if hasattr(args, "func"):

core/framework/config.py

@@ -19,6 +19,10 @@ from framework.graph.edge import DEFAULT_MAX_TOKENS
 # ---------------------------------------------------------------------------
 
 HIVE_CONFIG_FILE = Path.home() / ".hive" / "configuration.json"
+
+# Hive LLM router endpoint (Anthropic-compatible).
+# litellm's Anthropic handler appends /v1/messages, so this is just the base host.
+HIVE_LLM_ENDPOINT = "https://api.adenhq.com"
 logger = logging.getLogger(__name__)
 
 
@@ -47,16 +51,169 @@ def get_preferred_model() -> str:
     """Return the user's preferred LLM model string (e.g. 'anthropic/claude-sonnet-4-20250514')."""
     llm = get_hive_config().get("llm", {})
     if llm.get("provider") and llm.get("model"):
-        return f"{llm['provider']}/{llm['model']}"
+        provider = str(llm["provider"])
+        model = str(llm["model"]).strip()
+        # OpenRouter quickstart stores raw model IDs; tolerate pasted "openrouter/<id>" too.
+        if provider.lower() == "openrouter" and model.lower().startswith("openrouter/"):
+            model = model[len("openrouter/") :]
+        if model:
+            return f"{provider}/{model}"
     return "anthropic/claude-sonnet-4-20250514"
 
 
+def get_preferred_worker_model() -> str | None:
+    """Return the user's preferred worker LLM model, or None if not configured.
+
+    Reads from the ``worker_llm`` section of ~/.hive/configuration.json.
+    Returns None when no worker-specific model is set, so callers can
+    fall back to the default (queen) model via ``get_preferred_model()``.
+    """
+    worker_llm = get_hive_config().get("worker_llm", {})
+    if worker_llm.get("provider") and worker_llm.get("model"):
+        provider = str(worker_llm["provider"])
+        model = str(worker_llm["model"]).strip()
+        if provider.lower() == "openrouter" and model.lower().startswith("openrouter/"):
+            model = model[len("openrouter/") :]
+        if model:
+            return f"{provider}/{model}"
+    return None
+
+
+def get_worker_api_key() -> str | None:
+    """Return the API key for the worker LLM, falling back to the default key."""
+    worker_llm = get_hive_config().get("worker_llm", {})
+    if not worker_llm:
+        return get_api_key()
+
+    # Worker-specific subscription / env var
+    if worker_llm.get("use_claude_code_subscription"):
+        try:
+            from framework.runner.runner import get_claude_code_token
+
+            token = get_claude_code_token()
+            if token:
+                return token
+        except ImportError:
+            pass
+
+    if worker_llm.get("use_codex_subscription"):
+        try:
+            from framework.runner.runner import get_codex_token
+
+            token = get_codex_token()
+            if token:
+                return token
+        except ImportError:
+            pass
+
+    if worker_llm.get("use_kimi_code_subscription"):
+        try:
+            from framework.runner.runner import get_kimi_code_token
+
+            token = get_kimi_code_token()
+            if token:
+                return token
+        except ImportError:
+            pass
+
+    if worker_llm.get("use_antigravity_subscription"):
+        try:
+            from framework.runner.runner import get_antigravity_token
+
+            token = get_antigravity_token()
+            if token:
+                return token
+        except ImportError:
+            pass
+
+    api_key_env_var = worker_llm.get("api_key_env_var")
+    if api_key_env_var:
+        return os.environ.get(api_key_env_var)
+
+    # Fall back to default key
+    return get_api_key()
+
+
+def get_worker_api_base() -> str | None:
+    """Return the api_base for the worker LLM, falling back to the default."""
+    worker_llm = get_hive_config().get("worker_llm", {})
+    if not worker_llm:
+        return get_api_base()
+
+    if worker_llm.get("use_codex_subscription"):
+        return "https://chatgpt.com/backend-api/codex"
+    if worker_llm.get("use_kimi_code_subscription"):
+        return "https://api.kimi.com/coding"
+    if worker_llm.get("use_antigravity_subscription"):
+        # Antigravity uses AntigravityProvider directly — no api_base needed.
+        return None
+    if worker_llm.get("api_base"):
+        return worker_llm["api_base"]
+    if str(worker_llm.get("provider", "")).lower() == "openrouter":
+        return OPENROUTER_API_BASE
+    return None
+
+
+def get_worker_llm_extra_kwargs() -> dict[str, Any]:
+    """Return extra kwargs for the worker LLM provider."""
+    worker_llm = get_hive_config().get("worker_llm", {})
+    if not worker_llm:
+        return get_llm_extra_kwargs()
+
+    if worker_llm.get("use_claude_code_subscription"):
+        api_key = get_worker_api_key()
+        if api_key:
+            return {
+                "extra_headers": {"authorization": f"Bearer {api_key}"},
+            }
+    if worker_llm.get("use_codex_subscription"):
+        api_key = get_worker_api_key()
+        if api_key:
+            headers: dict[str, str] = {
+                "Authorization": f"Bearer {api_key}",
+                "User-Agent": "CodexBar",
+            }
+            try:
+                from framework.runner.runner import get_codex_account_id
+
+                account_id = get_codex_account_id()
+                if account_id:
+                    headers["ChatGPT-Account-Id"] = account_id
+            except ImportError:
+                pass
+            return {
+                "extra_headers": headers,
+                "store": False,
+                "allowed_openai_params": ["store"],
+            }
+    if worker_llm.get("provider") == "ollama":
+        return {"num_ctx": worker_llm.get("num_ctx", 16384)}
+    return {}
+
+
+def get_worker_max_tokens() -> int:
+    """Return max_tokens for the worker LLM, falling back to default."""
+    worker_llm = get_hive_config().get("worker_llm", {})
+    if worker_llm and "max_tokens" in worker_llm:
+        return worker_llm["max_tokens"]
+    return get_max_tokens()
+
+
+def get_worker_max_context_tokens() -> int:
+    """Return max_context_tokens for the worker LLM, falling back to default."""
+    worker_llm = get_hive_config().get("worker_llm", {})
+    if worker_llm and "max_context_tokens" in worker_llm:
+        return worker_llm["max_context_tokens"]
+    return get_max_context_tokens()
+
+
 def get_max_tokens() -> int:
     """Return the configured max_tokens, falling back to DEFAULT_MAX_TOKENS."""
     return get_hive_config().get("llm", {}).get("max_tokens", DEFAULT_MAX_TOKENS)
 
 
 DEFAULT_MAX_CONTEXT_TOKENS = 32_000
+OPENROUTER_API_BASE = "https://openrouter.ai/api/v1"
 
 
 def get_max_context_tokens() -> int:
@@ -109,6 +266,17 @@ def get_api_key() -> str | None:
         except ImportError:
             pass
 
+    # Antigravity subscription: read OAuth token from accounts JSON
+    if llm.get("use_antigravity_subscription"):
+        try:
+            from framework.runner.runner import get_antigravity_token
+
+            token = get_antigravity_token()
+            if token:
+                return token
+        except ImportError:
+            pass
+
     # Standard env-var path (covers ZAI Code and all API-key providers)
     api_key_env_var = llm.get("api_key_env_var")
     if api_key_env_var:
@@ -116,11 +284,99 @@ def get_api_key() -> str | None:
     return None
 
 
+# OAuth credentials for Antigravity are fetched from the opencode-antigravity-auth project.
+# This project reverse-engineered and published the public OAuth credentials
+# for Google's Antigravity/Cloud Code Assist API.
+# Source: https://github.com/NoeFabris/opencode-antigravity-auth
+_ANTIGRAVITY_CREDENTIALS_URL = (
+    "https://raw.githubusercontent.com/NoeFabris/opencode-antigravity-auth/dev/src/constants.ts"
+)
+_antigravity_credentials_cache: tuple[str | None, str | None] = (None, None)
+
+
+def _fetch_antigravity_credentials() -> tuple[str | None, str | None]:
+    """Fetch OAuth client ID and secret from the public npm package source on GitHub."""
+    global _antigravity_credentials_cache
+    if _antigravity_credentials_cache[0] and _antigravity_credentials_cache[1]:
+        return _antigravity_credentials_cache
+
+    import re
+    import urllib.request
+
+    try:
+        req = urllib.request.Request(
+            _ANTIGRAVITY_CREDENTIALS_URL, headers={"User-Agent": "Hive/1.0"}
+        )
+        with urllib.request.urlopen(req, timeout=10) as resp:
+            content = resp.read().decode("utf-8")
+            id_match = re.search(r'ANTIGRAVITY_CLIENT_ID\s*=\s*"([^"]+)"', content)
+            secret_match = re.search(r'ANTIGRAVITY_CLIENT_SECRET\s*=\s*"([^"]+)"', content)
+            client_id = id_match.group(1) if id_match else None
+            client_secret = secret_match.group(1) if secret_match else None
+            if client_id and client_secret:
+                _antigravity_credentials_cache = (client_id, client_secret)
+            return client_id, client_secret
+    except Exception as e:
+        logger.debug("Failed to fetch Antigravity credentials from public source: %s", e)
+    return None, None
+
+
+def get_antigravity_client_id() -> str:
+    """Return the Antigravity OAuth application client ID.
+
+    Checked in order:
+    1. ``ANTIGRAVITY_CLIENT_ID`` environment variable
+    2. ``llm.antigravity_client_id`` in ~/.hive/configuration.json
+    3. Fetch from public source (opencode-antigravity-auth project on GitHub)
+    """
+    env = os.environ.get("ANTIGRAVITY_CLIENT_ID")
+    if env:
+        return env
+    cfg_val = get_hive_config().get("llm", {}).get("antigravity_client_id")
+    if cfg_val:
+        return cfg_val
+    # Fetch from public source
+    client_id, _ = _fetch_antigravity_credentials()
+    if client_id:
+        return client_id
+    raise RuntimeError("Could not obtain Antigravity OAuth client ID")
+
+
+def get_antigravity_client_secret() -> str | None:
+    """Return the Antigravity OAuth client secret.
+
+    Checked in order:
+    1. ``ANTIGRAVITY_CLIENT_SECRET`` environment variable
+    2. ``llm.antigravity_client_secret`` in ~/.hive/configuration.json
+    3. Fetch from public source (opencode-antigravity-auth project on GitHub)
+
+    Returns None when not found — token refresh will be skipped and
+    the caller must use whatever access token is already available.
+    """
+    env = os.environ.get("ANTIGRAVITY_CLIENT_SECRET")
+    if env:
+        return env
+    cfg_val = get_hive_config().get("llm", {}).get("antigravity_client_secret") or None
+    if cfg_val:
+        return cfg_val
+    # Fetch from public source
+    _, secret = _fetch_antigravity_credentials()
+    return secret
+
+
 def get_gcu_enabled() -> bool:
     """Return whether GCU (browser automation) is enabled in user config."""
     return get_hive_config().get("gcu_enabled", True)
 
 
+def get_gcu_viewport_scale() -> float:
+    """Return GCU viewport scale factor (0.1-1.0), default 0.8."""
+    scale = get_hive_config().get("gcu_viewport_scale", 0.8)
+    if isinstance(scale, (int, float)) and 0.1 <= scale <= 1.0:
+        return float(scale)
+    return 0.8
+
+
 def get_api_base() -> str | None:
     """Return the api_base URL for OpenAI-compatible endpoints, if configured."""
     llm = get_hive_config().get("llm", {})
@@ -130,7 +386,14 @@ def get_api_base() -> str | None:
     if llm.get("use_kimi_code_subscription"):
         # Kimi Code uses an Anthropic-compatible endpoint (no /v1 suffix).
         return "https://api.kimi.com/coding"
-    return llm.get("api_base")
+    if llm.get("use_antigravity_subscription"):
+        # Antigravity uses AntigravityProvider directly — no api_base needed.
+        return None
+    if llm.get("api_base"):
+        return llm["api_base"]
+    if str(llm.get("provider", "")).lower() == "openrouter":
+        return OPENROUTER_API_BASE
+    return None
 
 
 def get_llm_extra_kwargs() -> dict[str, Any]:
@@ -171,6 +434,11 @@ def get_llm_extra_kwargs() -> dict[str, Any]:
                 "store": False,
                 "allowed_openai_params": ["store"],
             }
+    if llm.get("provider") == "ollama":
+        # Pass num_ctx to Ollama so it doesn't silently truncate the ~9.5k Queen prompt.
+        # Ollama's default num_ctx is only 2048. We set it to 16384 here so LiteLLM
+        # passes it through as a provider-specific option.
+        return {"num_ctx": llm.get("num_ctx", 16384)}
     return {}
 
 

core/framework/credentials/key_storage.py

@@ -142,13 +142,17 @@ def save_aden_api_key(key: str) -> None:
     os.environ[ADEN_ENV_VAR] = key
 
 
-def delete_aden_api_key() -> None:
-    """Remove ADEN_API_KEY from the encrypted store and ``os.environ``."""
+def delete_aden_api_key() -> bool:
+    """Remove ADEN_API_KEY from the encrypted store and ``os.environ``.
+
+    Returns True if the key existed and was deleted, False otherwise.
+    """
+    deleted = False
     try:
         from .storage import EncryptedFileStorage
 
         storage = EncryptedFileStorage()
-        storage.delete(ADEN_CREDENTIAL_ID)
+        deleted = storage.delete(ADEN_CREDENTIAL_ID)
     except (FileNotFoundError, PermissionError) as e:
         logger.debug("Could not delete %s from encrypted store: %s", ADEN_CREDENTIAL_ID, e)
     except Exception:
@@ -157,8 +161,8 @@ def delete_aden_api_key() -> None:
             ADEN_CREDENTIAL_ID,
             exc_info=True,
         )
-
     os.environ.pop(ADEN_ENV_VAR, None)
+    return deleted
 
 
 # ---------------------------------------------------------------------------

core/framework/credentials/setup.py

@@ -27,6 +27,7 @@ from __future__ import annotations
 
 import getpass
 import json
+import logging
 import os
 import sys
 from collections.abc import Callable
@@ -37,6 +38,8 @@ from typing import TYPE_CHECKING, Any
 if TYPE_CHECKING:
     from framework.graph import NodeSpec
 
+logger = logging.getLogger(__name__)
+
 
 # ANSI colors for terminal output
 class Colors:
@@ -365,8 +368,11 @@ class CredentialSetupSession:
         self._print("")
         try:
             api_key = self.password_fn(f"Paste your {cred.env_var}: ").strip()
+        except (EOFError, OSError) as exc:
+            logger.debug("Password input unavailable, falling back to plain input: %s", exc)
+            api_key = self._input(f"Paste your {cred.env_var}: ").strip()
         except Exception:
-            # Fallback to regular input if password input fails
+            logger.warning("Unexpected error reading password input", exc_info=True)
             api_key = self._input(f"Paste your {cred.env_var}: ").strip()
 
         if not api_key:
@@ -403,7 +409,11 @@ class CredentialSetupSession:
 
             try:
                 aden_key = self.password_fn("Paste your ADEN_API_KEY: ").strip()
+            except (EOFError, OSError) as exc:
+                logger.debug("Password input unavailable for ADEN_API_KEY: %s", exc)
+                aden_key = self._input("Paste your ADEN_API_KEY: ").strip()
             except Exception:
+                logger.warning("Unexpected error reading ADEN_API_KEY input", exc_info=True)
                 aden_key = self._input("Paste your ADEN_API_KEY: ").strip()
 
             if not aden_key:
@@ -433,8 +443,10 @@ class CredentialSetupSession:
                     value = store.get_key(cred_id, cred.credential_key)
                     if value:
                         os.environ[cred.env_var] = value
+                except (KeyError, OSError) as exc:
+                    logger.debug("Could not export credential to env: %s", exc)
                 except Exception:
-                    pass
+                    logger.warning("Unexpected error exporting credential to env", exc_info=True)
                 return True
             else:
                 self._print(
@@ -457,9 +469,12 @@ class CredentialSetupSession:
                 "message": result.message,
                 "details": result.details,
             }
-        except Exception:
+        except ImportError:
             # No health checker available
             return None
+        except Exception:
+            logger.warning("Health check failed for %s", cred.credential_name, exc_info=True)
+            return None
 
     def _store_credential(self, cred: MissingCredential, value: str) -> None:
         """Store credential in encrypted store and export to env."""
@@ -561,7 +576,11 @@ def _load_nodes_from_python_agent(agent_path: Path) -> list:
         sys.modules[spec.name] = module
         spec.loader.exec_module(module)
         return getattr(module, "nodes", [])
+    except (ImportError, OSError) as exc:
+        logger.debug("Could not load agent module: %s", exc)
+        return []
     except Exception:
+        logger.warning("Unexpected error loading agent module", exc_info=True)
         return []
 
 
@@ -588,7 +607,11 @@ def _load_nodes_from_json_agent(agent_json: Path) -> list:
                 )
             )
         return nodes
+    except (json.JSONDecodeError, KeyError, OSError) as exc:
+        logger.debug("Could not load JSON agent: %s", exc)
+        return []
     except Exception:
+        logger.warning("Unexpected error loading JSON agent", exc_info=True)
         return []
 
 

core/framework/credentials/validation.py

@@ -51,6 +51,16 @@ def ensure_credential_key_env() -> None:
                     if found and value:
                         os.environ[var_name] = value
                         logger.debug("Loaded %s from shell config", var_name)
+        # Also load the currently configured LLM env var even if it's not in CREDENTIAL_SPECS.
+        # This keeps quickstart-written keys available to fresh processes on Unix shells.
+        from framework.config import get_hive_config
+
+        llm_env_var = str(get_hive_config().get("llm", {}).get("api_key_env_var", "")).strip()
+        if llm_env_var and not os.environ.get(llm_env_var):
+            found, value = check_env_var_in_shell_config(llm_env_var)
+            if found and value:
+                os.environ[llm_env_var] = value
+                logger.debug("Loaded configured LLM env var %s from shell config", llm_env_var)
     except ImportError:
         pass
 

core/framework/debugger/cli.py

@@ -0,0 +1,76 @@
+"""CLI command for the LLM debug log viewer."""
+
+import argparse
+import subprocess
+import sys
+from pathlib import Path
+
+_SCRIPT = Path(__file__).resolve().parents[3] / "scripts" / "llm_debug_log_visualizer.py"
+
+
+def register_debugger_commands(subparsers: argparse._SubParsersAction) -> None:
+    """Register the ``hive debugger`` command."""
+    parser = subparsers.add_parser(
+        "debugger",
+        help="Open the LLM debug log viewer",
+        description=(
+            "Start a local server that lets you browse LLM debug sessions "
+            "recorded in ~/.hive/llm_logs. Sessions are loaded on demand so "
+            "the browser stays responsive."
+        ),
+    )
+    parser.add_argument(
+        "--session",
+        help="Execution ID to select initially.",
+    )
+    parser.add_argument(
+        "--port",
+        type=int,
+        default=0,
+        help="Port for the local server (0 = auto-pick a free port).",
+    )
+    parser.add_argument(
+        "--logs-dir",
+        help="Directory containing JSONL log files (default: ~/.hive/llm_logs).",
+    )
+    parser.add_argument(
+        "--limit-files",
+        type=int,
+        default=None,
+        help="Maximum number of newest log files to scan (default: 200).",
+    )
+    parser.add_argument(
+        "--output",
+        help="Write a static HTML file instead of starting a server.",
+    )
+    parser.add_argument(
+        "--no-open",
+        action="store_true",
+        help="Start the server but do not open a browser.",
+    )
+    parser.add_argument(
+        "--include-tests",
+        action="store_true",
+        help="Show test/mock sessions (hidden by default).",
+    )
+    parser.set_defaults(func=cmd_debugger)
+
+
+def cmd_debugger(args: argparse.Namespace) -> int:
+    """Launch the LLM debug log visualizer."""
+    cmd: list[str] = [sys.executable, str(_SCRIPT)]
+    if args.session:
+        cmd += ["--session", args.session]
+    if args.port:
+        cmd += ["--port", str(args.port)]
+    if args.logs_dir:
+        cmd += ["--logs-dir", args.logs_dir]
+    if args.limit_files is not None:
+        cmd += ["--limit-files", str(args.limit_files)]
+    if args.output:
+        cmd += ["--output", args.output]
+    if args.no_open:
+        cmd.append("--no-open")
+    if args.include_tests:
+        cmd.append("--include-tests")
+    return subprocess.call(cmd)

core/framework/graph/conversation.py

@@ -33,10 +33,20 @@ class Message:
     is_transition_marker: bool = False
     # True when this message is real human input (from /chat), not a system prompt
     is_client_input: bool = False
+    # Optional image content blocks (e.g. from browser_screenshot)
+    image_content: list[dict[str, Any]] | None = None
+    # True when message contains an activated skill body (AS-10: never prune)
+    is_skill_content: bool = False
 
     def to_llm_dict(self) -> dict[str, Any]:
         """Convert to OpenAI-format message dict."""
         if self.role == "user":
+            if self.image_content:
+                blocks: list[dict[str, Any]] = []
+                if self.content:
+                    blocks.append({"type": "text", "text": self.content})
+                blocks.extend(self.image_content)
+                return {"role": "user", "content": blocks}
             return {"role": "user", "content": self.content}
 
         if self.role == "assistant":
@@ -47,6 +57,15 @@ class Message:
 
         # role == "tool"
         content = f"ERROR: {self.content}" if self.is_error else self.content
+        if self.image_content:
+            # Multimodal tool result: text + image content blocks
+            blocks: list[dict[str, Any]] = [{"type": "text", "text": content}]
+            blocks.extend(self.image_content)
+            return {
+                "role": "tool",
+                "tool_call_id": self.tool_use_id,
+                "content": blocks,
+            }
         return {
             "role": "tool",
             "tool_call_id": self.tool_use_id,
@@ -72,6 +91,8 @@ class Message:
             d["is_transition_marker"] = self.is_transition_marker
         if self.is_client_input:
             d["is_client_input"] = self.is_client_input
+        if self.image_content is not None:
+            d["image_content"] = self.image_content
         return d
 
     @classmethod
@@ -87,6 +108,7 @@ class Message:
             phase_id=data.get("phase_id"),
             is_transition_marker=data.get("is_transition_marker", False),
             is_client_input=data.get("is_client_input", False),
+            image_content=data.get("image_content"),
         )
 
 
@@ -373,6 +395,7 @@ class NodeConversation:
         *,
         is_transition_marker: bool = False,
         is_client_input: bool = False,
+        image_content: list[dict[str, Any]] | None = None,
     ) -> Message:
         msg = Message(
             seq=self._next_seq,
@@ -381,9 +404,13 @@ class NodeConversation:
             phase_id=self._current_phase,
             is_transition_marker=is_transition_marker,
             is_client_input=is_client_input,
+            image_content=image_content,
         )
         self._messages.append(msg)
         self._next_seq += 1
+        # Invalidate stale API token count so estimate_tokens() uses
+        # the char-based heuristic which reflects the new message.
+        self._last_api_input_tokens = None
         await self._persist(msg)
         return msg
 
@@ -401,6 +428,7 @@ class NodeConversation:
         )
         self._messages.append(msg)
         self._next_seq += 1
+        self._last_api_input_tokens = None
         await self._persist(msg)
         return msg
 
@@ -409,6 +437,8 @@ class NodeConversation:
         tool_use_id: str,
         content: str,
         is_error: bool = False,
+        image_content: list[dict[str, Any]] | None = None,
+        is_skill_content: bool = False,
     ) -> Message:
         msg = Message(
             seq=self._next_seq,
@@ -417,9 +447,12 @@ class NodeConversation:
             tool_use_id=tool_use_id,
             is_error=is_error,
             phase_id=self._current_phase,
+            image_content=image_content,
+            is_skill_content=is_skill_content,
         )
         self._messages.append(msg)
         self._next_seq += 1
+        self._last_api_input_tokens = None
         await self._persist(msg)
         return msg
 
@@ -500,12 +533,15 @@ class NodeConversation:
 
         Uses actual API input token count when available (set via
         :meth:`update_token_count`), otherwise falls back to a
-        ``total_chars / 4`` heuristic that includes both message content
-        AND tool_call argument sizes.
+        character-based heuristic that includes message content, tool_call
+        arguments, and image blocks.  The heuristic applies a 4/3 safety
+        margin to avoid under-counting (inspired by Claude Code's compact
+        service).
         """
         if self._last_api_input_tokens is not None:
             return self._last_api_input_tokens
         total_chars = 0
+        image_tokens = 0
         for m in self._messages:
             total_chars += len(m.content)
             if m.tool_calls:
@@ -513,7 +549,11 @@ class NodeConversation:
                     func = tc.get("function", {})
                     total_chars += len(func.get("arguments", ""))
                     total_chars += len(func.get("name", ""))
-        return total_chars // 4
+            if m.image_content:
+                # Images/documents have a fixed token cost per block
+                image_tokens += len(m.image_content) * 2000
+        # Apply 4/3 safety margin to character-based estimate
+        return (total_chars * 4) // (3 * 4) + image_tokens
 
     def update_token_count(self, actual_input_tokens: int) -> None:
         """Store actual API input token count for more accurate compaction.
@@ -610,8 +650,15 @@ class NodeConversation:
                 continue
             if msg.is_error:
                 continue  # never prune errors
+            if msg.is_skill_content:
+                continue  # never prune activated skill instructions (AS-10)
             if msg.content.startswith("[Pruned tool result"):
                 continue  # already pruned
+            # Tiny results (set_output acks, confirmations) — pruning
+            # saves negligible space but makes the LLM think the call
+            # failed, causing costly retries.
+            if len(msg.content) < 100:
+                continue
 
             # Phase-aware: protect current phase messages
             if self._current_phase and msg.phase_id == self._current_phase:
@@ -901,8 +948,7 @@ class NodeConversation:
             full_path = str((spill_path / conv_filename).resolve())
             ref_parts.append(
                 f"[Previous conversation saved to '{full_path}'. "
-                f"Use load_data('{conv_filename}'), read_file('{full_path}'), "
-                f"or run_command('cat \"{full_path}\"') to review if needed.]"
+                f"Use load_data('{conv_filename}') to review if needed.]"
             )
         elif not collapsed_msgs:
             ref_parts.append("[Previous freeform messages compacted.]")

core/framework/graph/event_loop/__init__.py

@@ -0,0 +1,6 @@
+"""EventLoopNode subpackage — modular components of the event loop orchestrator.
+
+All public symbols are re-exported by the parent ``event_loop_node.py`` for
+backward compatibility.  Internal consumers may import directly from these
+submodules for clarity.
+"""

core/framework/graph/event_loop/compaction.py

@@ -0,0 +1,847 @@
+"""Conversation compaction pipeline.
+
+Implements the multi-level compaction strategy:
+0. Microcompaction (count-based tool result clearing — cheapest)
+1. Prune old tool results (token-budget based)
+2. Structure-preserving compaction (spillover)
+3. LLM summary compaction (with recursive splitting)
+4. Emergency deterministic summary (no LLM)
+"""
+
+from __future__ import annotations
+
+import json
+import logging
+import os
+import re
+import time
+from datetime import UTC, datetime
+from pathlib import Path
+from typing import Any
+
+from framework.graph.conversation import Message, NodeConversation
+from framework.graph.event_loop.event_publishing import publish_context_usage
+from framework.graph.event_loop.types import LoopConfig, OutputAccumulator
+from framework.graph.node import NodeContext
+from framework.runtime.event_bus import EventBus
+
+logger = logging.getLogger(__name__)
+
+# Limits for LLM compaction
+LLM_COMPACT_CHAR_LIMIT: int = 240_000
+LLM_COMPACT_MAX_DEPTH: int = 10
+
+# Microcompaction: tools whose results can be safely cleared
+COMPACTABLE_TOOLS: frozenset[str] = frozenset({
+    "read_file", "run_command", "web_search", "web_fetch",
+    "grep_search", "glob_search", "write_file", "edit_file",
+    "browser_screenshot", "list_directory",
+})
+
+# Keep at most this many compactable tool results; clear older ones
+MICROCOMPACT_KEEP_RECENT: int = 8
+
+# Circuit-breaker: stop auto-compacting after this many consecutive failures
+MAX_CONSECUTIVE_FAILURES: int = 3
+
+# Track consecutive compaction failures per conversation (module-level)
+_failure_counts: dict[int, int] = {}
+
+# Track last compaction time per conversation for recompaction detection
+_last_compact_times: dict[int, float] = {}
+
+
+def microcompact(conversation: NodeConversation, *, keep_recent: int = MICROCOMPACT_KEEP_RECENT) -> int:
+    """Clear old compactable tool results by count, keeping only the most recent.
+
+    This is the cheapest possible compaction — no LLM call, no structural
+    changes, just replaces old tool result content with a short placeholder.
+    Inspired by Claude Code's cached-microcompact strategy.
+
+    Returns the number of tool results cleared.
+    """
+    # Collect indices of compactable tool results (newest first)
+    compactable_indices: list[int] = []
+    messages = conversation.messages
+    for i in range(len(messages) - 1, -1, -1):
+        msg = messages[i]
+        if msg.role != "tool" or msg.is_error or msg.is_skill_content:
+            continue
+        if msg.content.startswith("[Pruned tool result") or msg.content.startswith("[Old tool result"):
+            continue
+        if len(msg.content) < 100:
+            continue
+
+        # Check if the tool that produced this result is compactable
+        tool_name = _find_tool_name_for_result(messages, msg)
+        if tool_name and tool_name in COMPACTABLE_TOOLS:
+            compactable_indices.append(i)
+
+    # Keep the most recent N, clear the rest
+    to_clear = compactable_indices[keep_recent:]
+    if not to_clear:
+        return 0
+
+    cleared = 0
+    for i in to_clear:
+        msg = messages[i]
+        spillover = _extract_spillover_filename_inline(msg.content)
+        orig_len = len(msg.content)
+        if spillover:
+            placeholder = (
+                f"[Old tool result cleared: {orig_len} chars. "
+                f"Full data in '{spillover}'. "
+                f"Use load_data('{spillover}') to retrieve.]"
+            )
+        else:
+            placeholder = f"[Old tool result cleared: {orig_len} chars.]"
+
+        # Mutate in-place (microcompact is synchronous, no store writes)
+        conversation._messages[i] = Message(
+            seq=msg.seq,
+            role=msg.role,
+            content=placeholder,
+            tool_use_id=msg.tool_use_id,
+            tool_calls=msg.tool_calls,
+            is_error=msg.is_error,
+            phase_id=msg.phase_id,
+            is_transition_marker=msg.is_transition_marker,
+        )
+        cleared += 1
+
+    if cleared > 0:
+        # Invalidate cached token count
+        conversation._last_api_input_tokens = None
+
+    return cleared
+
+
+def _find_tool_name_for_result(messages: list[Message], tool_msg: Message) -> str | None:
+    """Find the tool name from the assistant message that triggered this tool result."""
+    if not tool_msg.tool_use_id:
+        return None
+    for msg in messages:
+        if msg.tool_calls:
+            for tc in msg.tool_calls:
+                if tc.get("id") == tool_msg.tool_use_id:
+                    return tc.get("function", {}).get("name")
+    return None
+
+
+def _extract_spillover_filename_inline(content: str) -> str | None:
+    """Quick inline check for spillover filename in tool result content."""
+    match = re.search(r"saved to '([^']+)'", content, re.IGNORECASE)
+    return match.group(1) if match else None
+
+
+async def compact(
+    ctx: NodeContext,
+    conversation: NodeConversation,
+    accumulator: OutputAccumulator | None,
+    *,
+    config: LoopConfig,
+    event_bus: EventBus | None,
+    char_limit: int = LLM_COMPACT_CHAR_LIMIT,
+    max_depth: int = LLM_COMPACT_MAX_DEPTH,
+) -> None:
+    """Run the full compaction pipeline if conversation needs compaction.
+
+    Pipeline stages (in order, short-circuits when budget is restored):
+    0. Microcompaction (count-based tool result clearing — cheapest)
+    1. Prune old tool results (token-budget based)
+    2. Structure-preserving compaction (free, no LLM)
+    3. LLM summary compaction (recursive split if too large)
+    4. Emergency deterministic summary (fallback)
+    """
+    conv_id = id(conversation)
+
+    # Circuit breaker: stop auto-compacting after repeated failures
+    if _failure_counts.get(conv_id, 0) >= MAX_CONSECUTIVE_FAILURES:
+        logger.warning(
+            "Circuit breaker: skipping compaction after %d consecutive failures",
+            _failure_counts[conv_id],
+        )
+        return
+
+    # Recompaction detection
+    now = time.monotonic()
+    last_time = _last_compact_times.get(conv_id)
+    if last_time is not None and (now - last_time) < 30:
+        logger.warning(
+            "Recompaction chain detected: only %.1fs since last compaction",
+            now - last_time,
+        )
+
+    ratio_before = conversation.usage_ratio()
+    phase_grad = getattr(ctx, "continuous_mode", False)
+    pre_inventory: list[dict[str, Any]] | None = None
+
+    if ratio_before >= 1.0:
+        pre_inventory = build_message_inventory(conversation)
+
+    # --- Step 0: Microcompaction (count-based, cheapest) ---
+    mc_cleared = microcompact(conversation)
+    if mc_cleared > 0:
+        logger.info(
+            "Microcompact cleared %d old tool results: %.0f%% -> %.0f%%",
+            mc_cleared,
+            ratio_before * 100,
+            conversation.usage_ratio() * 100,
+        )
+    if not conversation.needs_compaction():
+        _record_success(conv_id, now)
+        await log_compaction(
+            ctx, conversation, ratio_before, event_bus,
+            pre_inventory=pre_inventory,
+        )
+        return
+
+    # --- Step 1: Prune old tool results (free, fast) ---
+    protect = max(2000, config.max_context_tokens // 12)
+    pruned = await conversation.prune_old_tool_results(
+        protect_tokens=protect,
+        min_prune_tokens=max(1000, protect // 3),
+    )
+    if pruned > 0:
+        logger.info(
+            "Pruned %d old tool results: %.0f%% -> %.0f%%",
+            pruned,
+            ratio_before * 100,
+            conversation.usage_ratio() * 100,
+        )
+    if not conversation.needs_compaction():
+        _record_success(conv_id, now)
+        await log_compaction(
+            ctx, conversation, ratio_before, event_bus,
+            pre_inventory=pre_inventory,
+        )
+        return
+
+    # --- Step 2: Standard structure-preserving compaction (free, no LLM) ---
+    spill_dir = config.spillover_dir
+    if spill_dir:
+        await conversation.compact_preserving_structure(
+            spillover_dir=spill_dir,
+            keep_recent=4,
+            phase_graduated=phase_grad,
+        )
+    if not conversation.needs_compaction():
+        _record_success(conv_id, now)
+        await log_compaction(
+            ctx, conversation, ratio_before, event_bus,
+            pre_inventory=pre_inventory,
+        )
+        return
+
+    # --- Step 3: LLM summary compaction ---
+    if ctx.llm is not None:
+        logger.info(
+            "LLM summary compaction triggered (%.0f%% usage)",
+            conversation.usage_ratio() * 100,
+        )
+        try:
+            summary = await llm_compact(
+                ctx,
+                list(conversation.messages),
+                accumulator,
+                char_limit=char_limit,
+                max_depth=max_depth,
+                max_context_tokens=config.max_context_tokens,
+            )
+            await conversation.compact(
+                summary,
+                keep_recent=2,
+                phase_graduated=phase_grad,
+            )
+        except Exception as e:
+            logger.warning("LLM compaction failed: %s", e)
+            _failure_counts[conv_id] = _failure_counts.get(conv_id, 0) + 1
+
+    if not conversation.needs_compaction():
+        _record_success(conv_id, now)
+        await log_compaction(
+            ctx, conversation, ratio_before, event_bus,
+            pre_inventory=pre_inventory,
+        )
+        return
+
+    # --- Step 4: Emergency deterministic summary (LLM failed/unavailable) ---
+    logger.warning(
+        "Emergency compaction (%.0f%% usage)",
+        conversation.usage_ratio() * 100,
+    )
+    summary = build_emergency_summary(ctx, accumulator, conversation, config)
+    await conversation.compact(
+        summary,
+        keep_recent=1,
+        phase_graduated=phase_grad,
+    )
+    _record_success(conv_id, now)
+    await log_compaction(
+        ctx, conversation, ratio_before, event_bus,
+        pre_inventory=pre_inventory,
+    )
+
+
+def _record_success(conv_id: int, timestamp: float) -> None:
+    """Reset failure counter and record compaction time on success."""
+    _failure_counts.pop(conv_id, None)
+    _last_compact_times[conv_id] = timestamp
+
+
+# --- LLM compaction with binary-search splitting ----------------------
+
+
+def strip_images_from_messages(messages: list[Message]) -> list[Message]:
+    """Strip image_content from messages before LLM summarisation.
+
+    Images/documents are replaced with ``[image]`` markers so the summary
+    notes they existed without wasting tokens sending binary data to the
+    compaction LLM.  Returns a new list (original messages are not mutated).
+    """
+    stripped: list[Message] = []
+    for msg in messages:
+        if msg.image_content:
+            n_images = len(msg.image_content)
+            marker = " ".join("[image]" for _ in range(n_images))
+            content = f"{msg.content}\n{marker}" if msg.content else marker
+            stripped.append(Message(
+                seq=msg.seq,
+                role=msg.role,
+                content=content,
+                tool_use_id=msg.tool_use_id,
+                tool_calls=msg.tool_calls,
+                is_error=msg.is_error,
+                phase_id=msg.phase_id,
+                is_transition_marker=msg.is_transition_marker,
+                image_content=None,  # stripped
+            ))
+        else:
+            stripped.append(msg)
+    return stripped
+
+
+async def llm_compact(
+    ctx: NodeContext,
+    messages: list,
+    accumulator: OutputAccumulator | None = None,
+    _depth: int = 0,
+    *,
+    char_limit: int = LLM_COMPACT_CHAR_LIMIT,
+    max_depth: int = LLM_COMPACT_MAX_DEPTH,
+    max_context_tokens: int = 128_000,
+) -> str:
+    """Summarise *messages* with LLM, splitting recursively if too large.
+
+    If the formatted text exceeds ``LLM_COMPACT_CHAR_LIMIT`` or the LLM
+    rejects the call with a context-length error, the messages are split
+    in half and each half is summarised independently.  Tool history is
+    appended once at the top-level call (``_depth == 0``).
+    """
+    from framework.graph.conversation import extract_tool_call_history
+    from framework.graph.event_loop.tool_result_handler import is_context_too_large_error
+
+    if _depth > max_depth:
+        raise RuntimeError(f"LLM compaction recursion limit ({max_depth})")
+
+    # Strip images before summarisation to avoid wasting tokens
+    if _depth == 0:
+        messages = strip_images_from_messages(messages)
+
+    formatted = format_messages_for_summary(messages)
+
+    # Proactive split: avoid wasting an API call on oversized input
+    if len(formatted) > char_limit and len(messages) > 1:
+        summary = await _llm_compact_split(
+            ctx,
+            messages,
+            accumulator,
+            _depth,
+            char_limit=char_limit,
+            max_depth=max_depth,
+            max_context_tokens=max_context_tokens,
+        )
+    else:
+        prompt = build_llm_compaction_prompt(
+            ctx,
+            accumulator,
+            formatted,
+            max_context_tokens=max_context_tokens,
+        )
+        summary_budget = max(1024, max_context_tokens // 2)
+        try:
+            response = await ctx.llm.acomplete(
+                messages=[{"role": "user", "content": prompt}],
+                system=(
+                    "You are a conversation compactor for an AI agent. "
+                    "Write a detailed summary that allows the agent to "
+                    "continue its work. Preserve user-stated rules, "
+                    "constraints, and account/identity preferences verbatim."
+                ),
+                max_tokens=summary_budget,
+            )
+            summary = response.content
+        except Exception as e:
+            if is_context_too_large_error(e) and len(messages) > 1:
+                logger.info(
+                    "LLM context too large (depth=%d, msgs=%d) — splitting",
+                    _depth,
+                    len(messages),
+                )
+                summary = await _llm_compact_split(
+                    ctx,
+                    messages,
+                    accumulator,
+                    _depth,
+                    char_limit=char_limit,
+                    max_depth=max_depth,
+                    max_context_tokens=max_context_tokens,
+                )
+            else:
+                raise
+
+    # Append tool history at top level only
+    if _depth == 0:
+        tool_history = extract_tool_call_history(messages)
+        if tool_history and "TOOLS ALREADY CALLED" not in summary:
+            summary += "\n\n" + tool_history
+
+    return summary
+
+
+async def _llm_compact_split(
+    ctx: NodeContext,
+    messages: list,
+    accumulator: OutputAccumulator | None,
+    _depth: int,
+    *,
+    char_limit: int = LLM_COMPACT_CHAR_LIMIT,
+    max_depth: int = LLM_COMPACT_MAX_DEPTH,
+    max_context_tokens: int = 128_000,
+) -> str:
+    """Split messages in half and summarise each half independently."""
+    mid = max(1, len(messages) // 2)
+    s1 = await llm_compact(
+        ctx,
+        messages[:mid],
+        None,
+        _depth + 1,
+        char_limit=char_limit,
+        max_depth=max_depth,
+        max_context_tokens=max_context_tokens,
+    )
+    s2 = await llm_compact(
+        ctx,
+        messages[mid:],
+        accumulator,
+        _depth + 1,
+        char_limit=char_limit,
+        max_depth=max_depth,
+        max_context_tokens=max_context_tokens,
+    )
+    return s1 + "\n\n" + s2
+
+
+# --- Compaction helpers ------------------------------------------------
+
+
+def format_messages_for_summary(messages: list) -> str:
+    """Format messages as text for LLM summarisation."""
+    lines: list[str] = []
+    for m in messages:
+        if m.role == "tool":
+            content = m.content[:500]
+            if len(m.content) > 500:
+                content += "..."
+            lines.append(f"[tool result]: {content}")
+        elif m.role == "assistant" and m.tool_calls:
+            names = [tc.get("function", {}).get("name", "?") for tc in m.tool_calls]
+            text = m.content[:200] if m.content else ""
+            lines.append(f"[assistant (calls: {', '.join(names)})]: {text}")
+        else:
+            lines.append(f"[{m.role}]: {m.content}")
+    return "\n\n".join(lines)
+
+
+def build_llm_compaction_prompt(
+    ctx: NodeContext,
+    accumulator: OutputAccumulator | None,
+    formatted_messages: str,
+    *,
+    max_context_tokens: int = 128_000,
+) -> str:
+    """Build prompt for LLM compaction targeting 50% of token budget.
+
+    Uses a structured section format inspired by Claude Code's compact
+    service.  Each section focuses on a different aspect of the conversation
+    so the summariser produces consistently useful, well-organised output.
+    """
+    spec = ctx.node_spec
+    ctx_lines = [f"NODE: {spec.name} (id={spec.id})"]
+    if spec.description:
+        ctx_lines.append(f"PURPOSE: {spec.description}")
+    if spec.success_criteria:
+        ctx_lines.append(f"SUCCESS CRITERIA: {spec.success_criteria}")
+
+    if accumulator:
+        acc = accumulator.to_dict()
+        done = {k: v for k, v in acc.items() if v is not None}
+        todo = [k for k, v in acc.items() if v is None]
+        if done:
+            ctx_lines.append(
+                "OUTPUTS ALREADY SET:\n"
+                + "\n".join(f"  {k}: {str(v)[:150]}" for k, v in done.items())
+            )
+        if todo:
+            ctx_lines.append(f"OUTPUTS STILL NEEDED: {', '.join(todo)}")
+    elif spec.output_keys:
+        ctx_lines.append(f"OUTPUTS STILL NEEDED: {', '.join(spec.output_keys)}")
+
+    target_tokens = max_context_tokens // 2
+    target_chars = target_tokens * 4
+    node_ctx = "\n".join(ctx_lines)
+
+    return (
+        "You are compacting an AI agent's conversation history. "
+        "The agent is still working and needs to continue.\n\n"
+        f"AGENT CONTEXT:\n{node_ctx}\n\n"
+        f"CONVERSATION MESSAGES:\n{formatted_messages}\n\n"
+        "INSTRUCTIONS:\n"
+        f"Write a summary of approximately {target_chars} characters "
+        f"(~{target_tokens} tokens).\n\n"
+        "Organise the summary into these sections (omit empty ones):\n\n"
+        "1. **Primary Request and Intent** — What the user originally asked "
+        "for and the high-level goal the agent is working toward.\n"
+        "2. **Key Technical Concepts** — Important domain-specific terms, "
+        "patterns, or architectural decisions established in the conversation.\n"
+        "3. **Files and Code Sections** — Specific files read/written/edited "
+        "with brief descriptions of changes. Include short code snippets only "
+        "when they capture critical logic.\n"
+        "4. **Errors and Fixes** — Problems encountered and how they were "
+        "resolved. Include root causes so the agent doesn't repeat them.\n"
+        "5. **Problem Solving Efforts** — Approaches tried, dead ends hit, "
+        "and reasoning behind the current strategy.\n"
+        "6. **User Messages** — Preserve ALL user-stated rules, constraints, "
+        "identity preferences, and account details verbatim.\n"
+        "7. **Pending Tasks** — Work remaining, outputs still needed, and "
+        "any blockers.\n"
+        "8. **Current Work** — The most recent action taken and the immediate "
+        "next step the agent should perform. This section is the most important "
+        "for seamless resumption.\n\n"
+        "Additional rules:\n"
+        "- Be detailed enough that the agent can resume without re-doing work.\n"
+        "- Preserve key decisions made and results obtained.\n"
+        "- When in doubt, keep information rather than discard it.\n"
+    )
+
+
+def build_message_inventory(conversation: NodeConversation) -> list[dict[str, Any]]:
+    """Build a per-message size inventory for debug logging."""
+    inventory: list[dict[str, Any]] = []
+    for message in conversation.messages:
+        content_chars = len(message.content)
+        tool_call_args_chars = 0
+        tool_name = None
+        if message.tool_calls:
+            for tool_call in message.tool_calls:
+                args = tool_call.get("function", {}).get("arguments", "")
+                tool_call_args_chars += (
+                    len(args) if isinstance(args, str) else len(json.dumps(args))
+                )
+            names = [
+                tool_call.get("function", {}).get("name", "?") for tool_call in message.tool_calls
+            ]
+            tool_name = ", ".join(names)
+        elif message.role == "tool" and message.tool_use_id:
+            for previous in conversation.messages:
+                if previous.tool_calls:
+                    for tool_call in previous.tool_calls:
+                        if tool_call.get("id") == message.tool_use_id:
+                            tool_name = tool_call.get("function", {}).get("name", "?")
+                            break
+                if tool_name:
+                    break
+        entry: dict[str, Any] = {
+            "seq": message.seq,
+            "role": message.role,
+            "content_chars": content_chars,
+        }
+        if tool_call_args_chars:
+            entry["tool_call_args_chars"] = tool_call_args_chars
+        if tool_name:
+            entry["tool"] = tool_name
+        if message.is_error:
+            entry["is_error"] = True
+        if message.phase_id:
+            entry["phase"] = message.phase_id
+        if content_chars > 2000:
+            entry["preview"] = message.content[:200] + "…"
+        inventory.append(entry)
+    return inventory
+
+
+def write_compaction_debug_log(
+    ctx: NodeContext,
+    before_pct: int,
+    after_pct: int,
+    level: str,
+    inventory: list[dict[str, Any]] | None,
+) -> None:
+    """Write detailed compaction analysis to ~/.hive/compaction_log/."""
+    log_dir = Path.home() / ".hive" / "compaction_log"
+    log_dir.mkdir(parents=True, exist_ok=True)
+
+    ts = datetime.now(UTC).strftime("%Y%m%dT%H%M%S_%f")
+    node_label = ctx.node_id.replace("/", "_")
+    log_path = log_dir / f"{ts}_{node_label}.md"
+
+    lines: list[str] = [
+        f"# Compaction Debug — {ctx.node_id}",
+        f"**Time:** {datetime.now(UTC).isoformat()}",
+        f"**Node:** {ctx.node_spec.name} (`{ctx.node_id}`)",
+    ]
+    if ctx.stream_id:
+        lines.append(f"**Stream:** {ctx.stream_id}")
+    lines.append(f"**Level:** {level}")
+    lines.append(f"**Usage:** {before_pct}% → {after_pct}%")
+    lines.append("")
+
+    if inventory:
+        total_chars = sum(
+            entry.get("content_chars", 0) + entry.get("tool_call_args_chars", 0)
+            for entry in inventory
+        )
+        lines.append(
+            "## Pre-Compaction Message Inventory "
+            f"({len(inventory)} messages, {total_chars:,} total chars)"
+        )
+        lines.append("")
+        ranked = sorted(
+            inventory,
+            key=lambda entry: entry.get("content_chars", 0) + entry.get("tool_call_args_chars", 0),
+            reverse=True,
+        )
+        lines.append("| # | seq | role | tool | chars | % of total | flags |")
+        lines.append("|---|-----|------|------|------:|------------|-------|")
+        for i, entry in enumerate(ranked, 1):
+            chars = entry.get("content_chars", 0) + entry.get("tool_call_args_chars", 0)
+            pct = (chars / total_chars * 100) if total_chars else 0
+            tool = entry.get("tool", "")
+            flags: list[str] = []
+            if entry.get("is_error"):
+                flags.append("error")
+            if entry.get("phase"):
+                flags.append(f"phase={entry['phase']}")
+            lines.append(
+                f"| {i} | {entry['seq']} | {entry['role']} | {tool} "
+                f"| {chars:,} | {pct:.1f}% | {', '.join(flags)} |"
+            )
+
+        large = [entry for entry in ranked if entry.get("preview")]
+        if large:
+            lines.append("")
+            lines.append("### Large message previews")
+            for entry in large:
+                lines.append(
+                    f"\n**seq={entry['seq']}** ({entry['role']}, {entry.get('tool', '')}):"
+                )
+                lines.append(f"```\n{entry['preview']}\n```")
+    lines.append("")
+
+    try:
+        log_path.write_text("\n".join(lines), encoding="utf-8")
+        logger.debug("Compaction debug log written to %s", log_path)
+    except OSError:
+        logger.debug("Failed to write compaction debug log to %s", log_path)
+
+
+async def log_compaction(
+    ctx: NodeContext,
+    conversation: NodeConversation,
+    ratio_before: float,
+    event_bus: EventBus | None,
+    *,
+    pre_inventory: list[dict[str, Any]] | None = None,
+) -> None:
+    """Log compaction result to runtime logger and event bus."""
+    ratio_after = conversation.usage_ratio()
+    before_pct = round(ratio_before * 100)
+    after_pct = round(ratio_after * 100)
+
+    # Determine label from what happened
+    if after_pct >= before_pct - 1:
+        level = "prune_only"
+    elif ratio_after <= 0.6:
+        level = "llm"
+    else:
+        level = "structural"
+
+    logger.info(
+        "Compaction complete (%s): %d%% -> %d%%",
+        level,
+        before_pct,
+        after_pct,
+    )
+
+    if ctx.runtime_logger:
+        ctx.runtime_logger.log_step(
+            node_id=ctx.node_id,
+            node_type="event_loop",
+            step_index=-1,
+            llm_text=f"Context compacted ({level}): {before_pct}% \u2192 {after_pct}%",
+            verdict="COMPACTION",
+            verdict_feedback=f"level={level} before={before_pct}% after={after_pct}%",
+        )
+
+    if event_bus:
+        from framework.runtime.event_bus import AgentEvent, EventType
+
+        event_data: dict[str, Any] = {
+            "level": level,
+            "usage_before": before_pct,
+            "usage_after": after_pct,
+        }
+        if pre_inventory is not None:
+            event_data["message_inventory"] = pre_inventory
+        await event_bus.publish(
+            AgentEvent(
+                type=EventType.CONTEXT_COMPACTED,
+                stream_id=ctx.stream_id or ctx.node_id,
+                node_id=ctx.node_id,
+                data=event_data,
+            )
+        )
+
+    await publish_context_usage(event_bus, ctx, conversation, "post_compaction")
+
+    if os.environ.get("HIVE_COMPACTION_DEBUG"):
+        write_compaction_debug_log(ctx, before_pct, after_pct, level, pre_inventory)
+
+
+def build_emergency_summary(
+    ctx: NodeContext,
+    accumulator: OutputAccumulator | None = None,
+    conversation: NodeConversation | None = None,
+    config: LoopConfig | None = None,
+) -> str:
+    """Build a structured emergency compaction summary.
+
+    Unlike normal/aggressive compaction which uses an LLM summary,
+    emergency compaction cannot afford an LLM call (context is already
+    way over budget).  Instead, build a deterministic summary from the
+    node's known state so the LLM can continue working after
+    compaction without losing track of its task and inputs.
+    """
+    parts = [
+        "EMERGENCY COMPACTION — previous conversation was too large "
+        "and has been replaced with this summary.\n"
+    ]
+
+    # 1. Node identity
+    spec = ctx.node_spec
+    parts.append(f"NODE: {spec.name} (id={spec.id})")
+    if spec.description:
+        parts.append(f"PURPOSE: {spec.description}")
+
+    # 2. Inputs the node received
+    input_lines = []
+    for key in spec.input_keys:
+        value = ctx.input_data.get(key) or ctx.memory.read(key)
+        if value is not None:
+            # Truncate long values but keep them recognisable
+            v_str = str(value)
+            if len(v_str) > 200:
+                v_str = v_str[:200] + "…"
+            input_lines.append(f"  {key}: {v_str}")
+    if input_lines:
+        parts.append("INPUTS:\n" + "\n".join(input_lines))
+
+    # 3. Output accumulator state (what's been set so far)
+    if accumulator:
+        acc_state = accumulator.to_dict()
+        set_keys = {k: v for k, v in acc_state.items() if v is not None}
+        missing = [k for k, v in acc_state.items() if v is None]
+        if set_keys:
+            lines = [f"  {k}: {str(v)[:150]}" for k, v in set_keys.items()]
+            parts.append("OUTPUTS ALREADY SET:\n" + "\n".join(lines))
+        if missing:
+            parts.append(f"OUTPUTS STILL NEEDED: {', '.join(missing)}")
+    elif spec.output_keys:
+        parts.append(f"OUTPUTS STILL NEEDED: {', '.join(spec.output_keys)}")
+
+    # 4. Available tools reminder
+    if spec.tools:
+        parts.append(f"AVAILABLE TOOLS: {', '.join(spec.tools)}")
+
+    # 5. Spillover files — list actual files so the LLM can load
+    # them immediately instead of having to call list_data_files first.
+    # Inline adapt.md (agent memory) directly — it contains user rules
+    # and identity preferences that must survive emergency compaction.
+    spillover_dir = config.spillover_dir if config else None
+    if spillover_dir:
+        try:
+            from pathlib import Path
+
+            data_dir = Path(spillover_dir)
+            if data_dir.is_dir():
+                # Inline adapt.md content directly
+                adapt_path = data_dir / "adapt.md"
+                if adapt_path.is_file():
+                    adapt_text = adapt_path.read_text(encoding="utf-8").strip()
+                    if adapt_text:
+                        parts.append(f"AGENT MEMORY (adapt.md):\n{adapt_text}")
+
+                all_files = sorted(
+                    f.name for f in data_dir.iterdir() if f.is_file() and f.name != "adapt.md"
+                )
+                # Separate conversation history files from regular data files
+                conv_files = [f for f in all_files if re.match(r"conversation_\d+\.md$", f)]
+                data_files = [f for f in all_files if f not in conv_files]
+
+                if conv_files:
+                    conv_list = "\n".join(
+                        f"  - {f}  (full path: {data_dir / f})" for f in conv_files
+                    )
+                    parts.append(
+                        "CONVERSATION HISTORY (freeform messages saved during compaction — "
+                        "use load_data('<filename>') to review earlier dialogue):\n" + conv_list
+                    )
+                if data_files:
+                    file_list = "\n".join(
+                        f"  - {f}  (full path: {data_dir / f})" for f in data_files[:30]
+                    )
+                    parts.append("DATA FILES (use load_data('<filename>') to read):\n" + file_list)
+                if not all_files:
+                    parts.append(
+                        "NOTE: Large tool results may have been saved to files. "
+                        "Use list_directory to check the data directory."
+                    )
+        except Exception:
+            parts.append(
+                "NOTE: Large tool results were saved to files. "
+                "Use read_file(path='<path>') to read them."
+            )
+
+    # 6. Tool call history (prevent re-calling tools)
+    if conversation is not None:
+        tool_history = _extract_tool_call_history(conversation)
+        if tool_history:
+            parts.append(tool_history)
+
+    parts.append(
+        "\nContinue working towards setting the remaining outputs. "
+        "Use your tools and the inputs above."
+    )
+    return "\n\n".join(parts)
+
+
+def _extract_tool_call_history(conversation: NodeConversation) -> str:
+    """Extract tool call history from conversation messages.
+
+    This is the instance-level variant that operates on a NodeConversation
+    directly (vs. the module-level extract_tool_call_history in conversation.py
+    which works on raw message lists).
+    """
+    from framework.graph.conversation import extract_tool_call_history
+
+    return extract_tool_call_history(list(conversation.messages))

core/framework/graph/event_loop/cursor_persistence.py

@@ -0,0 +1,239 @@
+"""Cursor persistence, queue draining, and pause detection.
+
+Handles the checkpoint/resume cycle: restoring state from a previous
+conversation store, writing cursor data, and managing injection/trigger
+queues between iterations.
+"""
+
+from __future__ import annotations
+
+import asyncio
+import json
+import logging
+from collections.abc import Awaitable, Callable
+from dataclasses import dataclass
+from typing import Any
+
+from framework.graph.conversation import ConversationStore, NodeConversation
+from framework.graph.event_loop.types import LoopConfig, OutputAccumulator, TriggerEvent
+from framework.graph.node import NodeContext
+from framework.llm.capabilities import supports_image_tool_results
+
+logger = logging.getLogger(__name__)
+
+
+@dataclass
+class RestoredState:
+    """State recovered from a previous checkpoint."""
+
+    conversation: NodeConversation
+    accumulator: OutputAccumulator
+    start_iteration: int
+    recent_responses: list[str]
+    recent_tool_fingerprints: list[list[tuple[str, str]]]
+
+
+async def restore(
+    conversation_store: ConversationStore | None,
+    ctx: NodeContext,
+    config: LoopConfig,
+) -> RestoredState | None:
+    """Attempt to restore from a previous checkpoint.
+
+    Returns a ``RestoredState`` with conversation, accumulator, iteration
+    counter, and stall/doom-loop detection state — everything needed to
+    resume exactly where execution stopped.
+    """
+    if conversation_store is None:
+        return None
+
+    # In isolated mode, filter parts by phase_id so the node only sees
+    # its own messages in the shared flat conversation store.  In
+    # continuous mode (or when _restore is called for timer-resume)
+    # load all parts — the full conversation threads across nodes.
+    _is_continuous = getattr(ctx, "continuous_mode", False)
+    phase_filter = None if _is_continuous else ctx.node_id
+    conversation = await NodeConversation.restore(
+        conversation_store,
+        phase_id=phase_filter,
+    )
+    if conversation is None:
+        return None
+
+    accumulator = await OutputAccumulator.restore(conversation_store)
+    accumulator.spillover_dir = config.spillover_dir
+    accumulator.max_value_chars = config.max_output_value_chars
+
+    cursor = await conversation_store.read_cursor()
+    start_iteration = cursor.get("iteration", 0) + 1 if cursor else 0
+
+    # Restore stall/doom-loop detection state
+    recent_responses: list[str] = cursor.get("recent_responses", []) if cursor else []
+    raw_fps = cursor.get("recent_tool_fingerprints", []) if cursor else []
+    recent_tool_fingerprints: list[list[tuple[str, str]]] = [
+        [tuple(pair) for pair in fps]  # type: ignore[misc]
+        for fps in raw_fps
+    ]
+
+    logger.info(
+        f"Restored event loop: iteration={start_iteration}, "
+        f"messages={conversation.message_count}, "
+        f"outputs={list(accumulator.values.keys())}, "
+        f"stall_window={len(recent_responses)}, "
+        f"doom_window={len(recent_tool_fingerprints)}"
+    )
+    return RestoredState(
+        conversation=conversation,
+        accumulator=accumulator,
+        start_iteration=start_iteration,
+        recent_responses=recent_responses,
+        recent_tool_fingerprints=recent_tool_fingerprints,
+    )
+
+
+async def write_cursor(
+    conversation_store: ConversationStore | None,
+    ctx: NodeContext,
+    conversation: NodeConversation,
+    accumulator: OutputAccumulator,
+    iteration: int,
+    *,
+    recent_responses: list[str] | None = None,
+    recent_tool_fingerprints: list[list[tuple[str, str]]] | None = None,
+) -> None:
+    """Write checkpoint cursor for crash recovery.
+
+    Persists iteration counter, accumulator outputs, and stall/doom-loop
+    detection state so that resume picks up exactly where execution stopped.
+    """
+    if conversation_store:
+        cursor = await conversation_store.read_cursor() or {}
+        cursor.update(
+            {
+                "iteration": iteration,
+                "node_id": ctx.node_id,
+                "next_seq": conversation.next_seq,
+                "outputs": accumulator.to_dict(),
+            }
+        )
+        # Persist stall/doom-loop detection state for reliable resume
+        if recent_responses is not None:
+            cursor["recent_responses"] = recent_responses
+        if recent_tool_fingerprints is not None:
+            # Convert list[list[tuple]] → list[list[list]] for JSON
+            cursor["recent_tool_fingerprints"] = [
+                [list(pair) for pair in fps] for fps in recent_tool_fingerprints
+            ]
+        await conversation_store.write_cursor(cursor)
+
+
+async def drain_injection_queue(
+    queue: asyncio.Queue,
+    conversation: NodeConversation,
+    *,
+    ctx: NodeContext,
+    describe_images_as_text_fn: (
+        Callable[[list[dict[str, Any]]], Awaitable[str | None]] | None
+    ) = None,
+) -> int:
+    """Drain all pending injected events as user messages. Returns count."""
+    count = 0
+    while not queue.empty():
+        try:
+            content, is_client_input, image_content = queue.get_nowait()
+            logger.info(
+                "[drain] injected message (client_input=%s, images=%d): %s",
+                is_client_input,
+                len(image_content) if image_content else 0,
+                content[:200] if content else "(empty)",
+            )
+            if image_content and ctx.llm and not supports_image_tool_results(ctx.llm.model):
+                logger.info(
+                    "Model '%s' does not support images; attempting vision fallback",
+                    ctx.llm.model,
+                )
+                if describe_images_as_text_fn is not None:
+                    description = await describe_images_as_text_fn(image_content)
+                    if description:
+                        content = f"{content}\n\n{description}" if content else description
+                        logger.info("[drain] image described as text via vision fallback")
+                    else:
+                        logger.info("[drain] no vision fallback available; images dropped")
+                image_content = None
+            # Real user input is stored as-is; external events get a prefix
+            if is_client_input:
+                await conversation.add_user_message(
+                    content,
+                    is_client_input=True,
+                    image_content=image_content,
+                )
+            else:
+                await conversation.add_user_message(f"[External event]: {content}")
+            count += 1
+        except asyncio.QueueEmpty:
+            break
+    return count
+
+
+async def drain_trigger_queue(
+    queue: asyncio.Queue,
+    conversation: NodeConversation,
+) -> int:
+    """Drain all pending trigger events as a single batched user message.
+
+    Multiple triggers are merged so the LLM sees them atomically and can
+    reason about all pending triggers before acting.
+    """
+    triggers: list[TriggerEvent] = []
+    while not queue.empty():
+        try:
+            triggers.append(queue.get_nowait())
+        except asyncio.QueueEmpty:
+            break
+
+    if not triggers:
+        return 0
+
+    parts: list[str] = []
+    for t in triggers:
+        task = t.payload.get("task", "")
+        task_line = f"\nTask: {task}" if task else ""
+        payload_str = json.dumps(t.payload, default=str)
+        parts.append(f"[TRIGGER: {t.trigger_type}/{t.source_id}]{task_line}\n{payload_str}")
+
+    combined = "\n\n".join(parts)
+    logger.info("[drain] %d trigger(s): %s", len(triggers), combined[:200])
+    await conversation.add_user_message(combined)
+    return len(triggers)
+
+
+async def check_pause(
+    ctx: NodeContext,
+    conversation: NodeConversation,
+    iteration: int,
+) -> bool:
+    """
+    Check if pause has been requested. Returns True if paused.
+
+    Note: This check happens BEFORE starting iteration N, after completing N-1.
+    If paused, the node exits having completed {iteration} iterations (0 to iteration-1).
+    """
+    # Check executor-level pause event (for /pause command, Ctrl+Z)
+    if ctx.pause_event and ctx.pause_event.is_set():
+        completed = iteration  # 0-indexed: iteration=3 means 3 iterations completed (0,1,2)
+        logger.info(f"⏸ Pausing after {completed} iteration(s) completed (executor-level)")
+        return True
+
+    # Check context-level pause flags (legacy/alternative methods)
+    pause_requested = ctx.input_data.get("pause_requested", False)
+    if not pause_requested:
+        try:
+            pause_requested = ctx.memory.read("pause_requested") or False
+        except (PermissionError, KeyError):
+            pause_requested = False
+    if pause_requested:
+        completed = iteration
+        logger.info(f"⏸ Pausing after {completed} iteration(s) completed (context-level)")
+        return True
+
+    return False

core/framework/graph/event_loop/event_publishing.py

@@ -0,0 +1,360 @@
+"""EventBus publishing helpers for the event loop.
+
+Thin wrappers around EventBus.emit_*() calls that check for bus existence
+before publishing.  Extracted to reduce noise in the main orchestrator.
+"""
+
+from __future__ import annotations
+
+import logging
+import time
+
+from framework.graph.conversation import NodeConversation
+from framework.graph.event_loop.types import HookContext
+from framework.graph.node import NodeContext
+from framework.runtime.event_bus import EventBus
+
+logger = logging.getLogger(__name__)
+
+
+async def publish_loop_started(
+    event_bus: EventBus | None,
+    stream_id: str,
+    node_id: str,
+    max_iterations: int,
+    execution_id: str = "",
+) -> None:
+    if event_bus:
+        await event_bus.emit_node_loop_started(
+            stream_id=stream_id,
+            node_id=node_id,
+            max_iterations=max_iterations,
+            execution_id=execution_id,
+        )
+
+
+async def generate_action_plan(
+    event_bus: EventBus | None,
+    ctx: NodeContext,
+    stream_id: str,
+    node_id: str,
+    execution_id: str,
+) -> None:
+    """Generate a brief action plan via LLM and emit it as an SSE event.
+
+    Runs as a fire-and-forget task so it never blocks the main loop.
+    """
+    try:
+        system_prompt = ctx.node_spec.system_prompt or ""
+        # Trim to keep the prompt small
+        prompt_summary = system_prompt[:500]
+        if len(system_prompt) > 500:
+            prompt_summary += "..."
+
+        tool_names = [t.name for t in ctx.available_tools]
+        output_keys = ctx.node_spec.output_keys or []
+
+        prompt = (
+            f'You are about to work on a task as node "{node_id}".\n\n'
+            f"System prompt:\n{prompt_summary}\n\n"
+            f"Tools available: {tool_names}\n"
+            f"Required outputs: {output_keys}\n\n"
+            f"Write a brief action plan (2-5 bullet points) describing "
+            f"what you will do to complete this task. Be specific and concise.\n"
+            f"Return ONLY the plan text, no preamble."
+        )
+
+        response = await ctx.llm.acomplete(
+            messages=[{"role": "user", "content": prompt}],
+            max_tokens=1024,
+        )
+
+        plan = response.content.strip()
+        if plan and event_bus:
+            await event_bus.emit_node_action_plan(
+                stream_id=stream_id,
+                node_id=node_id,
+                plan=plan,
+                execution_id=execution_id,
+            )
+    except Exception as e:
+        logger.warning("Action plan generation failed for node '%s': %s", node_id, e)
+
+
+async def publish_iteration(
+    event_bus: EventBus | None,
+    stream_id: str,
+    node_id: str,
+    iteration: int,
+    execution_id: str = "",
+    extra_data: dict | None = None,
+) -> None:
+    if event_bus:
+        await event_bus.emit_node_loop_iteration(
+            stream_id=stream_id,
+            node_id=node_id,
+            iteration=iteration,
+            execution_id=execution_id,
+            extra_data=extra_data,
+        )
+
+
+async def publish_llm_turn_complete(
+    event_bus: EventBus | None,
+    stream_id: str,
+    node_id: str,
+    stop_reason: str,
+    model: str,
+    input_tokens: int,
+    output_tokens: int,
+    cached_tokens: int = 0,
+    execution_id: str = "",
+    iteration: int | None = None,
+) -> None:
+    if event_bus:
+        await event_bus.emit_llm_turn_complete(
+            stream_id=stream_id,
+            node_id=node_id,
+            stop_reason=stop_reason,
+            model=model,
+            input_tokens=input_tokens,
+            output_tokens=output_tokens,
+            cached_tokens=cached_tokens,
+            execution_id=execution_id,
+            iteration=iteration,
+        )
+
+
+def log_skip_judge(
+    ctx: NodeContext,
+    node_id: str,
+    iteration: int,
+    feedback: str,
+    tool_calls: list[dict],
+    llm_text: str,
+    turn_tokens: dict[str, int],
+    iter_start: float,
+) -> None:
+    """Log a CONTINUE step that skips judge evaluation (e.g., waiting for input)."""
+    if ctx.runtime_logger:
+        ctx.runtime_logger.log_step(
+            node_id=node_id,
+            node_type="event_loop",
+            step_index=iteration,
+            verdict="CONTINUE",
+            verdict_feedback=feedback,
+            tool_calls=tool_calls,
+            llm_text=llm_text,
+            input_tokens=turn_tokens.get("input", 0),
+            output_tokens=turn_tokens.get("output", 0),
+            latency_ms=int((time.time() - iter_start) * 1000),
+        )
+
+
+async def publish_loop_completed(
+    event_bus: EventBus | None,
+    stream_id: str,
+    node_id: str,
+    iterations: int,
+    execution_id: str = "",
+) -> None:
+    if event_bus:
+        await event_bus.emit_node_loop_completed(
+            stream_id=stream_id,
+            node_id=node_id,
+            iterations=iterations,
+            execution_id=execution_id,
+        )
+
+
+async def publish_context_usage(
+    event_bus: EventBus | None,
+    ctx: NodeContext,
+    conversation: NodeConversation,
+    trigger: str,
+) -> None:
+    """Emit a CONTEXT_USAGE_UPDATED event with current context window state."""
+    if not event_bus:
+        return
+
+    from framework.runtime.event_bus import AgentEvent, EventType
+
+    estimated = conversation.estimate_tokens()
+    max_tokens = conversation._max_context_tokens
+    ratio = estimated / max_tokens if max_tokens > 0 else 0.0
+    await event_bus.publish(
+        AgentEvent(
+            type=EventType.CONTEXT_USAGE_UPDATED,
+            stream_id=ctx.stream_id or ctx.node_id,
+            node_id=ctx.node_id,
+            data={
+                "usage_ratio": round(ratio, 4),
+                "usage_pct": round(ratio * 100),
+                "message_count": conversation.message_count,
+                "estimated_tokens": estimated,
+                "max_context_tokens": max_tokens,
+                "trigger": trigger,
+            },
+        )
+    )
+
+
+async def publish_stalled(
+    event_bus: EventBus | None,
+    stream_id: str,
+    node_id: str,
+    execution_id: str = "",
+) -> None:
+    if event_bus:
+        await event_bus.emit_node_stalled(
+            stream_id=stream_id,
+            node_id=node_id,
+            reason="Consecutive similar responses detected",
+            execution_id=execution_id,
+        )
+
+
+async def publish_text_delta(
+    event_bus: EventBus | None,
+    stream_id: str,
+    node_id: str,
+    content: str,
+    snapshot: str,
+    ctx: NodeContext,
+    execution_id: str = "",
+    iteration: int | None = None,
+    inner_turn: int = 0,
+) -> None:
+    if event_bus:
+        if ctx.node_spec.client_facing:
+            await event_bus.emit_client_output_delta(
+                stream_id=stream_id,
+                node_id=node_id,
+                content=content,
+                snapshot=snapshot,
+                execution_id=execution_id,
+                iteration=iteration,
+                inner_turn=inner_turn,
+            )
+        else:
+            await event_bus.emit_llm_text_delta(
+                stream_id=stream_id,
+                node_id=node_id,
+                content=content,
+                snapshot=snapshot,
+                execution_id=execution_id,
+                inner_turn=inner_turn,
+            )
+
+
+async def publish_tool_started(
+    event_bus: EventBus | None,
+    stream_id: str,
+    node_id: str,
+    tool_use_id: str,
+    tool_name: str,
+    tool_input: dict,
+    execution_id: str = "",
+) -> None:
+    if event_bus:
+        await event_bus.emit_tool_call_started(
+            stream_id=stream_id,
+            node_id=node_id,
+            tool_use_id=tool_use_id,
+            tool_name=tool_name,
+            tool_input=tool_input,
+            execution_id=execution_id,
+        )
+
+
+async def publish_tool_completed(
+    event_bus: EventBus | None,
+    stream_id: str,
+    node_id: str,
+    tool_use_id: str,
+    tool_name: str,
+    result: str,
+    is_error: bool,
+    execution_id: str = "",
+) -> None:
+    if event_bus:
+        await event_bus.emit_tool_call_completed(
+            stream_id=stream_id,
+            node_id=node_id,
+            tool_use_id=tool_use_id,
+            tool_name=tool_name,
+            result=result,
+            is_error=is_error,
+            execution_id=execution_id,
+        )
+
+
+async def publish_judge_verdict(
+    event_bus: EventBus | None,
+    stream_id: str,
+    node_id: str,
+    action: str,
+    feedback: str = "",
+    judge_type: str = "implicit",
+    iteration: int = 0,
+    execution_id: str = "",
+) -> None:
+    if event_bus:
+        await event_bus.emit_judge_verdict(
+            stream_id=stream_id,
+            node_id=node_id,
+            action=action,
+            feedback=feedback,
+            judge_type=judge_type,
+            iteration=iteration,
+            execution_id=execution_id,
+        )
+
+
+async def publish_output_key_set(
+    event_bus: EventBus | None,
+    stream_id: str,
+    node_id: str,
+    key: str,
+    execution_id: str = "",
+) -> None:
+    if event_bus:
+        await event_bus.emit_output_key_set(
+            stream_id=stream_id, node_id=node_id, key=key, execution_id=execution_id
+        )
+
+
+async def run_hooks(
+    hooks_config: dict[str, list],
+    event: str,
+    conversation: NodeConversation,
+    trigger: str | None = None,
+) -> None:
+    """Run all registered hooks for *event*, applying their results.
+
+    Each hook receives a HookContext and may return a HookResult that:
+    - replaces the system prompt (result.system_prompt)
+    - injects an extra user message (result.inject)
+    Hooks run in registration order; each sees the prompt as left by the
+    previous hook.
+    """
+    hook_list = hooks_config.get(event, [])
+    if not hook_list:
+        return
+    for hook in hook_list:
+        ctx = HookContext(
+            event=event,
+            trigger=trigger,
+            system_prompt=conversation.system_prompt,
+        )
+        try:
+            result = await hook(ctx)
+        except Exception:
+            logger.warning("Hook '%s' raised an exception", event, exc_info=True)
+            continue
+        if result is None:
+            continue
+        if result.system_prompt:
+            conversation.update_system_prompt(result.system_prompt)
+        if result.inject:
+            await conversation.add_user_message(result.inject)

core/framework/graph/event_loop/judge_pipeline.py

@@ -0,0 +1,175 @@
+"""Judge evaluation pipeline for the event loop."""
+
+from __future__ import annotations
+
+import logging
+from collections.abc import Callable
+
+from framework.graph.conversation import NodeConversation
+from framework.graph.event_loop.types import JudgeProtocol, JudgeVerdict, OutputAccumulator
+from framework.graph.node import NodeContext
+
+logger = logging.getLogger(__name__)
+
+
+class SubagentJudge:
+    """Judge for subagent execution."""
+
+    def __init__(self, task: str, max_iterations: int = 10):
+        self._task = task
+        self._max_iterations = max_iterations
+
+    async def evaluate(self, context: dict[str, object]) -> JudgeVerdict:
+        missing = context.get("missing_keys", [])
+        if not isinstance(missing, list) or not missing:
+            return JudgeVerdict(action="ACCEPT", feedback="")
+
+        iteration = context.get("iteration", 0)
+        if not isinstance(iteration, int):
+            iteration = 0
+        remaining = self._max_iterations - iteration - 1
+
+        if remaining <= 3:
+            urgency = (
+                f"URGENT: Only {remaining} iterations left. "
+                f"Stop all other work and call set_output NOW for: {missing}"
+            )
+        elif remaining <= self._max_iterations // 2:
+            urgency = (
+                f"WARNING: {remaining} iterations remaining. "
+                f"You must call set_output for: {missing}"
+            )
+        else:
+            urgency = f"Missing output keys: {missing}. Use set_output to provide them."
+
+        return JudgeVerdict(action="RETRY", feedback=f"Your task: {self._task}\n{urgency}")
+
+
+async def judge_turn(
+    *,
+    mark_complete_flag: bool,
+    judge: JudgeProtocol | None,
+    ctx: NodeContext,
+    conversation: NodeConversation,
+    accumulator: OutputAccumulator,
+    assistant_text: str,
+    tool_results: list[dict[str, object]],
+    iteration: int,
+    get_missing_output_keys_fn: Callable[
+        [OutputAccumulator, list[str] | None, list[str] | None],
+        list[str],
+    ],
+    max_context_tokens: int,
+) -> JudgeVerdict:
+    """Evaluate the current state using judge or implicit logic.
+
+    Evaluation levels (in order):
+      0. Short-circuits: mark_complete, skip_judge, tool-continue.
+      1. Custom judge (JudgeProtocol) — full authority when set.
+      2. Implicit judge — output-key check + optional conversation-aware
+         quality gate (when ``success_criteria`` is defined).
+
+    Returns a JudgeVerdict.  ``feedback=None`` means no real evaluation
+    happened (skip_judge, tool-continue); the caller must not inject a
+    feedback message.  Any non-None feedback (including ``""``) means a
+    real evaluation occurred and will be logged into the conversation.
+    """
+    # --- Level 0: short-circuits (no evaluation) -----------------------
+
+    if mark_complete_flag:
+        return JudgeVerdict(action="ACCEPT")
+
+    if ctx.node_spec.skip_judge:
+        return JudgeVerdict(action="RETRY")  # feedback=None → not logged
+
+    # --- Level 1: custom judge -----------------------------------------
+
+    if judge is not None:
+        context = {
+            "assistant_text": assistant_text,
+            "tool_calls": tool_results,
+            "output_accumulator": accumulator.to_dict(),
+            "accumulator": accumulator,
+            "iteration": iteration,
+            "conversation_summary": conversation.export_summary(),
+            "output_keys": ctx.node_spec.output_keys,
+            "missing_keys": get_missing_output_keys_fn(
+                accumulator, ctx.node_spec.output_keys, ctx.node_spec.nullable_output_keys
+            ),
+        }
+        verdict = await judge.evaluate(context)
+        # Ensure evaluated RETRY always carries feedback for logging.
+        if verdict.action == "RETRY" and not verdict.feedback:
+            return JudgeVerdict(action="RETRY", feedback="Custom judge returned RETRY.")
+        return verdict
+
+    # --- Level 2: implicit judge ---------------------------------------
+
+    # Real tool calls were made — let the agent keep working.
+    if tool_results:
+        return JudgeVerdict(action="RETRY")  # feedback=None → not logged
+
+    missing = get_missing_output_keys_fn(
+        accumulator, ctx.node_spec.output_keys, ctx.node_spec.nullable_output_keys
+    )
+
+    if missing:
+        return JudgeVerdict(
+            action="RETRY",
+            feedback=(
+                f"Task incomplete. Required outputs not yet produced: {missing}. "
+                f"Follow your system prompt instructions to complete the work."
+            ),
+        )
+
+    # All output keys present — run safety checks before accepting.
+
+    output_keys = ctx.node_spec.output_keys or []
+    nullable_keys = set(ctx.node_spec.nullable_output_keys or [])
+
+    # All-nullable with nothing set → node produced nothing useful.
+    all_nullable = output_keys and nullable_keys >= set(output_keys)
+    none_set = not any(accumulator.get(k) is not None for k in output_keys)
+    if all_nullable and none_set:
+        return JudgeVerdict(
+            action="RETRY",
+            feedback=(
+                f"No output keys have been set yet. "
+                f"Use set_output to set at least one of: {output_keys}"
+            ),
+        )
+
+    # Client-facing with no output keys → continuous interaction node.
+    # Inject tool-use pressure instead of auto-accepting.
+    if not output_keys and ctx.node_spec.client_facing:
+        return JudgeVerdict(
+            action="RETRY",
+            feedback=(
+                "STOP describing what you will do. "
+                "You have FULL access to all tools — file creation, "
+                "shell commands, MCP tools — and you CAN call them "
+                "directly in your response. Respond ONLY with tool "
+                "calls, no prose. Execute the task now."
+            ),
+        )
+
+    # Level 2b: conversation-aware quality check (if success_criteria set)
+    if ctx.node_spec.success_criteria and ctx.llm:
+        from framework.graph.conversation_judge import evaluate_phase_completion
+
+        verdict = await evaluate_phase_completion(
+            llm=ctx.llm,
+            conversation=conversation,
+            phase_name=ctx.node_spec.name,
+            phase_description=ctx.node_spec.description,
+            success_criteria=ctx.node_spec.success_criteria,
+            accumulator_state=accumulator.to_dict(),
+            max_context_tokens=max_context_tokens,
+        )
+        if verdict.action != "ACCEPT":
+            return JudgeVerdict(
+                action=verdict.action,
+                feedback=verdict.feedback or "Phase criteria not met.",
+            )
+
+    return JudgeVerdict(action="ACCEPT", feedback="")

core/framework/graph/event_loop/stall_detector.py

@@ -0,0 +1,106 @@
+"""Stall and doom-loop detection for the event loop.
+
+Pure functions with no class dependencies — safe to call from any context.
+"""
+
+from __future__ import annotations
+
+import json
+
+
+def ngram_similarity(s1: str, s2: str, n: int = 2) -> float:
+    """Jaccard similarity of n-gram sets.
+
+    Returns 0.0-1.0, where 1.0 is exact match.
+    Fast: O(len(s) + len(s2)) using set operations.
+    """
+
+    def _ngrams(s: str) -> set[str]:
+        return {s[i : i + n] for i in range(len(s) - n + 1) if s.strip()}
+
+    if not s1 or not s2:
+        return 0.0
+
+    ngrams1, ngrams2 = _ngrams(s1.lower()), _ngrams(s2.lower())
+    if not ngrams1 or not ngrams2:
+        return 0.0
+
+    intersection = len(ngrams1 & ngrams2)
+    union = len(ngrams1 | ngrams2)
+    return intersection / union if union else 0.0
+
+
+def is_stalled(
+    recent_responses: list[str],
+    threshold: int,
+    similarity_threshold: float,
+) -> bool:
+    """Detect stall using n-gram similarity.
+
+    Detects when ALL N consecutive responses are mutually similar
+    (>= threshold).  A single dissimilar response resets the signal.
+    This catches phrases like "I'm still stuck" vs "I'm stuck"
+    without false-positives on "attempt 1" vs "attempt 2".
+    """
+    if len(recent_responses) < threshold:
+        return False
+    if not recent_responses[0]:
+        return False
+
+    # Every consecutive pair must be similar
+    for i in range(1, len(recent_responses)):
+        if ngram_similarity(recent_responses[i], recent_responses[i - 1]) < similarity_threshold:
+            return False
+    return True
+
+
+def fingerprint_tool_calls(
+    tool_results: list[dict],
+) -> list[tuple[str, str]]:
+    """Create deterministic fingerprints for a turn's tool calls.
+
+    Each fingerprint is (tool_name, canonical_args_json).  Order-sensitive
+    so [search("a"), fetch("b")] != [fetch("b"), search("a")].
+    """
+    fingerprints = []
+    for tr in tool_results:
+        name = tr.get("tool_name", "")
+        args = tr.get("tool_input", {})
+        try:
+            canonical = json.dumps(args, sort_keys=True, default=str)
+        except (TypeError, ValueError):
+            canonical = str(args)
+        fingerprints.append((name, canonical))
+    return fingerprints
+
+
+def is_tool_doom_loop(
+    recent_tool_fingerprints: list[list[tuple[str, str]]],
+    threshold: int,
+    enabled: bool = True,
+) -> tuple[bool, str]:
+    """Detect doom loop via exact fingerprint match.
+
+    Detects when N consecutive turns invoke the same tools with
+    identical (canonicalized) arguments.  Different arguments mean
+    different work, so only exact matches count.
+
+    Returns (is_doom_loop, description).
+    """
+    if not enabled:
+        return False, ""
+    if len(recent_tool_fingerprints) < threshold:
+        return False, ""
+    first = recent_tool_fingerprints[0]
+    if not first:
+        return False, ""
+
+    # All turns in the window must match the first exactly
+    if all(fp == first for fp in recent_tool_fingerprints[1:]):
+        tool_names = [name for name, _ in first]
+        desc = (
+            f"Doom loop detected: {len(recent_tool_fingerprints)} "
+            f"identical consecutive tool calls ({', '.join(tool_names)})"
+        )
+        return True, desc
+    return False, ""

core/framework/graph/event_loop/subagent_executor.py

@@ -0,0 +1,412 @@
+"""Subagent execution for the event loop.
+
+Handles the full subagent lifecycle: validation, context setup, tool filtering,
+conversation store derivation, execution, and cleanup.  Also includes the
+_EscalationReceiver helper used for subagent → queen escalation routing.
+"""
+
+from __future__ import annotations
+
+import asyncio
+import json
+import logging
+import time
+from collections.abc import Awaitable, Callable
+from pathlib import Path
+from typing import TYPE_CHECKING, Any
+
+from framework.graph.conversation import ConversationStore
+from framework.graph.event_loop.judge_pipeline import SubagentJudge
+from framework.graph.event_loop.types import LoopConfig, OutputAccumulator
+from framework.graph.node import NodeContext, SharedMemory
+from framework.llm.provider import ToolResult, ToolUse
+from framework.runtime.event_bus import EventBus
+
+if TYPE_CHECKING:
+    from framework.graph.event_loop_node import EventLoopNode
+
+logger = logging.getLogger(__name__)
+
+
+class EscalationReceiver:
+    """Temporary receiver registered in node_registry for subagent escalation routing.
+
+    When a subagent calls ``report_to_parent(wait_for_response=True)``, the callback
+    creates one of these, registers it under a unique escalation ID in the executor's
+    ``node_registry``, and awaits ``wait()``.  The TUI / runner calls
+    ``inject_input(escalation_id, content)`` which the ``ExecutionStream`` routes here
+    via ``inject_event()`` — matching the same ``hasattr(node, "inject_event")`` check
+    used for regular ``EventLoopNode`` instances.
+    """
+
+    def __init__(self) -> None:
+        self._event = asyncio.Event()
+        self._response: str | None = None
+        self._awaiting_input = True  # So inject_worker_message() can prefer us
+
+    async def inject_event(
+        self,
+        content: str,
+        *,
+        is_client_input: bool = False,
+        image_content: list[dict[str, Any]] | None = None,
+    ) -> None:
+        """Called by ExecutionStream.inject_input() when the user responds."""
+        self._response = content
+        self._event.set()
+
+    async def wait(self) -> str | None:
+        """Block until inject_event() delivers the user's response."""
+        await self._event.wait()
+        return self._response
+
+
+async def execute_subagent(
+    ctx: NodeContext,
+    agent_id: str,
+    task: str,
+    *,
+    config: LoopConfig,
+    event_loop_node_cls: type[EventLoopNode],
+    escalation_receiver_cls: type[EscalationReceiver],
+    accumulator: OutputAccumulator | None = None,
+    event_bus: EventBus | None = None,
+    tool_executor: Callable[[ToolUse], ToolResult | Awaitable[ToolResult]] | None = None,
+    conversation_store: ConversationStore | None = None,
+    subagent_instance_counter: dict[str, int] | None = None,
+) -> ToolResult:
+    """Execute a subagent and return the result as a ToolResult.
+
+    The subagent:
+    - Gets a fresh conversation with just the task
+    - Has read-only access to the parent's readable memory
+    - Cannot delegate to its own subagents (prevents recursion)
+    - Returns its output in structured JSON format
+
+    Args:
+        ctx: Parent node's context (for memory, tools, LLM access).
+        agent_id: The node ID of the subagent to invoke.
+        task: The task description to give the subagent.
+        accumulator: Parent's OutputAccumulator.
+        event_bus: EventBus for lifecycle events.
+        config: LoopConfig for iteration/tool limits.
+        tool_executor: Tool executor callable.
+        conversation_store: Parent conversation store (for deriving subagent store).
+        subagent_instance_counter: Mutable counter dict for unique subagent paths.
+
+    Returns:
+        ToolResult with structured JSON output.
+    """
+    # Log subagent invocation start
+    logger.info(
+        "\n" + "=" * 60 + "\n"
+        "🤖 SUBAGENT INVOCATION\n"
+        "=" * 60 + "\n"
+        "Parent Node: %s\n"
+        "Subagent ID: %s\n"
+        "Task: %s\n" + "=" * 60,
+        ctx.node_id,
+        agent_id,
+        task[:500] + "..." if len(task) > 500 else task,
+    )
+
+    # 1. Validate agent exists in registry
+    if agent_id not in ctx.node_registry:
+        return ToolResult(
+            tool_use_id="",
+            content=json.dumps(
+                {
+                    "message": f"Sub-agent '{agent_id}' not found in registry",
+                    "data": None,
+                    "metadata": {"agent_id": agent_id, "success": False, "error": "not_found"},
+                }
+            ),
+            is_error=True,
+        )
+
+    subagent_spec = ctx.node_registry[agent_id]
+
+    # 2. Create read-only memory snapshot
+    parent_data = ctx.memory.read_all()
+
+    # Merge in-flight outputs from the parent's accumulator.
+    if accumulator:
+        for key, value in accumulator.to_dict().items():
+            if key not in parent_data:
+                parent_data[key] = value
+
+    subagent_memory = SharedMemory()
+    for key, value in parent_data.items():
+        subagent_memory.write(key, value, validate=False)
+
+    read_keys = set(parent_data.keys()) | set(subagent_spec.input_keys or [])
+    scoped_memory = subagent_memory.with_permissions(
+        read_keys=list(read_keys),
+        write_keys=[],  # Read-only!
+    )
+
+    # 2b. Compute instance counter early so the callback and child context
+    # share the same stable node_id for this subagent invocation.
+    if subagent_instance_counter is not None:
+        subagent_instance_counter.setdefault(agent_id, 0)
+        subagent_instance_counter[agent_id] += 1
+        subagent_instance = str(subagent_instance_counter[agent_id])
+    else:
+        subagent_instance = "1"
+
+    if subagent_instance == "1":
+        sa_node_id = f"{ctx.node_id}:subagent:{agent_id}"
+    else:
+        sa_node_id = f"{ctx.node_id}:subagent:{agent_id}:{subagent_instance}"
+
+    # 2c. Set up report callback (one-way channel to parent / event bus)
+    subagent_reports: list[dict] = []
+
+    async def _report_callback(
+        message: str,
+        data: dict | None = None,
+        *,
+        wait_for_response: bool = False,
+    ) -> str | None:
+        subagent_reports.append({"message": message, "data": data, "timestamp": time.time()})
+        if event_bus:
+            await event_bus.emit_subagent_report(
+                stream_id=ctx.node_id,
+                node_id=sa_node_id,
+                subagent_id=agent_id,
+                message=message,
+                data=data,
+                execution_id=ctx.execution_id,
+            )
+
+        if not wait_for_response:
+            return None
+
+        if not event_bus:
+            logger.warning(
+                "Subagent '%s' requested user response but no event_bus available",
+                agent_id,
+            )
+            return None
+
+        # Create isolated receiver and register for input routing
+        import uuid
+
+        escalation_id = f"{ctx.node_id}:escalation:{uuid.uuid4().hex[:8]}"
+        receiver = escalation_receiver_cls()
+        registry = ctx.shared_node_registry
+
+        registry[escalation_id] = receiver
+        try:
+            await event_bus.emit_escalation_requested(
+                stream_id=ctx.stream_id or ctx.node_id,
+                node_id=escalation_id,
+                reason=f"Subagent report (wait_for_response) from {agent_id}",
+                context=message,
+                execution_id=ctx.execution_id,
+            )
+            # Block until queen responds
+            return await receiver.wait()
+        finally:
+            registry.pop(escalation_id, None)
+
+    # 3. Filter tools for subagent
+    subagent_tool_names = set(subagent_spec.tools or [])
+    tool_source = ctx.all_tools if ctx.all_tools else ctx.available_tools
+
+    # GCU auto-population
+    if subagent_spec.node_type == "gcu" and not subagent_tool_names:
+        subagent_tools = [t for t in tool_source if t.name != "delegate_to_sub_agent"]
+    else:
+        subagent_tools = [
+            t
+            for t in tool_source
+            if t.name in subagent_tool_names and t.name != "delegate_to_sub_agent"
+        ]
+
+    missing = subagent_tool_names - {t.name for t in subagent_tools}
+    if missing:
+        logger.warning(
+            "Subagent '%s' requested tools not found in catalog: %s",
+            agent_id,
+            sorted(missing),
+        )
+
+    logger.info(
+        "📦 Subagent '%s' configuration:\n"
+        "   - System prompt: %s\n"
+        "   - Tools available (%d): %s\n"
+        "   - Memory keys inherited: %s",
+        agent_id,
+        (subagent_spec.system_prompt[:200] + "...")
+        if subagent_spec.system_prompt and len(subagent_spec.system_prompt) > 200
+        else subagent_spec.system_prompt,
+        len(subagent_tools),
+        [t.name for t in subagent_tools],
+        list(parent_data.keys()),
+    )
+
+    # 4. Build subagent context
+    max_iter = min(config.max_iterations, 10)
+    subagent_ctx = NodeContext(
+        runtime=ctx.runtime,
+        node_id=sa_node_id,
+        node_spec=subagent_spec,
+        memory=scoped_memory,
+        input_data={"task": task, **parent_data},
+        llm=ctx.llm,
+        available_tools=subagent_tools,
+        goal_context=(
+            f"Your specific task: {task}\n\n"
+            f"COMPLETION REQUIREMENTS:\n"
+            f"When your task is done, you MUST call set_output() "
+            f"for each required key: {subagent_spec.output_keys}\n"
+            f"Alternatively, call report_to_parent(mark_complete=true) "
+            f"with your findings in message/data.\n"
+            f"You have a maximum of {max_iter} turns to complete this task."
+        ),
+        goal=ctx.goal,
+        max_tokens=ctx.max_tokens,
+        runtime_logger=ctx.runtime_logger,
+        is_subagent_mode=True,  # Prevents nested delegation
+        report_callback=_report_callback,
+        node_registry={},  # Empty - no nested subagents
+        shared_node_registry=ctx.shared_node_registry,  # For escalation routing
+    )
+
+    # 5. Create and execute subagent EventLoopNode
+    subagent_conv_store = None
+    if conversation_store is not None:
+        from framework.storage.conversation_store import FileConversationStore
+
+        parent_base = getattr(conversation_store, "_base", None)
+        if parent_base is not None:
+            conversations_dir = parent_base.parent
+            subagent_dir_name = f"{agent_id}-{subagent_instance}"
+            subagent_store_path = conversations_dir / subagent_dir_name
+            subagent_conv_store = FileConversationStore(base_path=subagent_store_path)
+
+    # Derive a subagent-scoped spillover dir
+    subagent_spillover = None
+    if config.spillover_dir:
+        subagent_spillover = str(Path(config.spillover_dir) / agent_id / subagent_instance)
+
+    subagent_node = event_loop_node_cls(
+        event_bus=event_bus,
+        judge=SubagentJudge(task=task, max_iterations=max_iter),
+        config=LoopConfig(
+            max_iterations=max_iter,
+            max_tool_calls_per_turn=config.max_tool_calls_per_turn,
+            tool_call_overflow_margin=config.tool_call_overflow_margin,
+            max_context_tokens=config.max_context_tokens,
+            stall_detection_threshold=config.stall_detection_threshold,
+            max_tool_result_chars=config.max_tool_result_chars,
+            spillover_dir=subagent_spillover,
+        ),
+        tool_executor=tool_executor,
+        conversation_store=subagent_conv_store,
+    )
+
+    # Inject a unique GCU browser profile for this subagent
+    _profile_token = None
+    try:
+        from gcu.browser.session import set_active_profile as _set_gcu_profile
+
+        _profile_token = _set_gcu_profile(f"{agent_id}-{subagent_instance}")
+    except ImportError:
+        pass  # GCU tools not installed; no-op
+
+    try:
+        logger.info("🚀 Starting subagent '%s' execution...", agent_id)
+        start_time = time.time()
+        result = await subagent_node.execute(subagent_ctx)
+        latency_ms = int((time.time() - start_time) * 1000)
+
+        separator = "-" * 60
+        logger.info(
+            "\n%s\n"
+            "✅ SUBAGENT '%s' COMPLETED\n"
+            "%s\n"
+            "Success: %s\n"
+            "Latency: %dms\n"
+            "Tokens used: %s\n"
+            "Output keys: %s\n"
+            "%s",
+            separator,
+            agent_id,
+            separator,
+            result.success,
+            latency_ms,
+            result.tokens_used,
+            list(result.output.keys()) if result.output else [],
+            separator,
+        )
+
+        result_json = {
+            "message": (
+                f"Sub-agent '{agent_id}' completed successfully"
+                if result.success
+                else f"Sub-agent '{agent_id}' failed: {result.error}"
+            ),
+            "data": result.output,
+            "reports": subagent_reports if subagent_reports else None,
+            "metadata": {
+                "agent_id": agent_id,
+                "success": result.success,
+                "tokens_used": result.tokens_used,
+                "latency_ms": latency_ms,
+                "report_count": len(subagent_reports),
+            },
+        }
+
+        return ToolResult(
+            tool_use_id="",
+            content=json.dumps(result_json, indent=2, default=str),
+            is_error=not result.success,
+        )
+
+    except Exception as e:
+        logger.exception(
+            "\n" + "!" * 60 + "\n❌ SUBAGENT '%s' FAILED\nError: %s\n" + "!" * 60,
+            agent_id,
+            str(e),
+        )
+        result_json = {
+            "message": f"Sub-agent '{agent_id}' raised exception: {e}",
+            "data": None,
+            "metadata": {
+                "agent_id": agent_id,
+                "success": False,
+                "error": str(e),
+            },
+        }
+        return ToolResult(
+            tool_use_id="",
+            content=json.dumps(result_json, indent=2),
+            is_error=True,
+        )
+    finally:
+        # Restore the GCU profile context
+        if _profile_token is not None:
+            from gcu.browser.session import _active_profile as _gcu_profile_var
+
+            _gcu_profile_var.reset(_profile_token)
+
+            # Stop the browser session for this subagent's profile
+            if tool_executor is not None:
+                _subagent_profile = f"{agent_id}-{subagent_instance}"
+                try:
+                    _stop_use = ToolUse(
+                        id="gcu-cleanup",
+                        name="browser_stop",
+                        input={"profile": _subagent_profile},
+                    )
+                    _stop_result = tool_executor(_stop_use)
+                    if asyncio.iscoroutine(_stop_result) or asyncio.isfuture(_stop_result):
+                        await _stop_result
+                except Exception as _gcu_exc:
+                    logger.warning(
+                        "GCU browser_stop failed for profile %r: %s",
+                        _subagent_profile,
+                        _gcu_exc,
+                    )

core/framework/graph/event_loop/synthetic_tools.py

@@ -0,0 +1,369 @@
+"""Synthetic tool builders for the event loop.
+
+Factory functions that create ``Tool`` definitions for framework-level
+synthetic tools (set_output, ask_user, escalate, delegate, report_to_parent).
+Also includes the ``handle_set_output`` validation logic.
+
+All functions are pure — they receive explicit parameters and return
+``Tool`` or ``ToolResult`` objects with no side effects.
+"""
+
+from __future__ import annotations
+
+from typing import Any
+
+from framework.llm.provider import Tool, ToolResult
+
+
+def build_ask_user_tool() -> Tool:
+    """Build the synthetic ask_user tool for explicit user-input requests.
+
+    Client-facing nodes call ask_user() when they need to pause and wait
+    for user input.  Text-only turns WITHOUT ask_user flow through without
+    blocking, allowing progress updates and summaries to stream freely.
+    """
+    return Tool(
+        name="ask_user",
+        description=(
+            "You MUST call this tool whenever you need the user's response. "
+            "Always call it after greeting the user, asking a question, or "
+            "requesting approval. Do NOT call it for status updates or "
+            "summaries that don't require a response. "
+            "Always include 2-3 predefined options. The UI automatically "
+            "appends an 'Other' free-text input after your options, so NEVER "
+            "include catch-all options like 'Custom idea', 'Something else', "
+            "'Other', or 'None of the above' — the UI handles that. "
+            "When the question primarily needs a typed answer but you must "
+            "include options, make one option signal that typing is expected "
+            "(e.g. 'I\\'ll type my response'). This helps users discover the "
+            "free-text input. "
+            "The ONLY exception: omit options when the question demands a "
+            "free-form answer the user must type out (e.g. 'Describe your "
+            "agent idea', 'Paste the error message'). "
+            '{"question": "What would you like to do?", "options": '
+            '["Build a new agent", "Modify existing agent", "Run tests"]} '
+            "Free-form example: "
+            '{"question": "Describe the agent you want to build."}'
+        ),
+        parameters={
+            "type": "object",
+            "properties": {
+                "question": {
+                    "type": "string",
+                    "description": "The question or prompt shown to the user.",
+                },
+                "options": {
+                    "type": "array",
+                    "items": {"type": "string"},
+                    "description": (
+                        "2-3 specific predefined choices. Include in most cases. "
+                        'Example: ["Option A", "Option B", "Option C"]. '
+                        "The UI always appends an 'Other' free-text input, so "
+                        "do NOT include catch-alls like 'Custom idea' or 'Other'. "
+                        "Omit ONLY when the user must type a free-form answer."
+                    ),
+                    "minItems": 2,
+                    "maxItems": 3,
+                },
+            },
+            "required": ["question"],
+        },
+    )
+
+
+def build_ask_user_multiple_tool() -> Tool:
+    """Build the synthetic ask_user_multiple tool for batched questions.
+
+    Queen-only tool that presents multiple questions at once so the user
+    can answer them all in a single interaction rather than one at a time.
+    """
+    return Tool(
+        name="ask_user_multiple",
+        description=(
+            "Ask the user multiple questions at once. Use this instead of "
+            "ask_user when you have 2 or more questions to ask in the same "
+            "turn — it lets the user answer everything in one go rather than "
+            "going back and forth. Each question can have its own predefined "
+            "options (2-3 choices) or be free-form. The UI renders all "
+            "questions together with a single Submit button. "
+            "ALWAYS prefer this over ask_user when you have multiple things "
+            "to clarify. "
+            "IMPORTANT: Do NOT repeat the questions in your text response — "
+            "the widget renders them. Keep your text to a brief intro only. "
+            '{"questions": ['
+            '  {"id": "scope", "prompt": "What scope?", "options": ["Full", "Partial"]},'
+            '  {"id": "format", "prompt": "Output format?", "options": ["PDF", "CSV", "JSON"]},'
+            '  {"id": "details", "prompt": "Any special requirements?"}'
+            "]}"
+        ),
+        parameters={
+            "type": "object",
+            "properties": {
+                "questions": {
+                    "type": "array",
+                    "items": {
+                        "type": "object",
+                        "properties": {
+                            "id": {
+                                "type": "string",
+                                "description": (
+                                    "Short identifier for this question (used in the response)."
+                                ),
+                            },
+                            "prompt": {
+                                "type": "string",
+                                "description": "The question text shown to the user.",
+                            },
+                            "options": {
+                                "type": "array",
+                                "items": {"type": "string"},
+                                "description": (
+                                    "2-3 predefined choices. The UI appends an "
+                                    "'Other' free-text input automatically. "
+                                    "Omit only when the user must type a free-form answer."
+                                ),
+                                "minItems": 2,
+                                "maxItems": 3,
+                            },
+                        },
+                        "required": ["id", "prompt"],
+                    },
+                    "minItems": 2,
+                    "maxItems": 8,
+                    "description": "List of questions to present to the user.",
+                },
+            },
+            "required": ["questions"],
+        },
+    )
+
+
+def build_set_output_tool(output_keys: list[str] | None) -> Tool | None:
+    """Build the synthetic set_output tool for explicit output declaration."""
+    if not output_keys:
+        return None
+    return Tool(
+        name="set_output",
+        description=(
+            "Set an output value for this node. Call once per output key. "
+            "Use this for brief notes, counts, status, and file references — "
+            "NOT for large data payloads. When a tool result was saved to a "
+            "data file, pass the filename as the value "
+            "(e.g. 'google_sheets_get_values_1.txt') so the next phase can "
+            "load the full data. Values exceeding ~2000 characters are "
+            "auto-saved to data files. "
+            f"Valid keys: {output_keys}"
+        ),
+        parameters={
+            "type": "object",
+            "properties": {
+                "key": {
+                    "type": "string",
+                    "description": f"Output key. Must be one of: {output_keys}",
+                    "enum": output_keys,
+                },
+                "value": {
+                    "type": "string",
+                    "description": (
+                        "The output value — a brief note, count, status, "
+                        "or data filename reference."
+                    ),
+                },
+            },
+            "required": ["key", "value"],
+        },
+    )
+
+
+def build_escalate_tool() -> Tool:
+    """Build the synthetic escalate tool for worker -> queen handoff."""
+    return Tool(
+        name="escalate",
+        description=(
+            "Escalate to the queen when requesting user input, "
+            "blocked by errors, missing "
+            "credentials, or ambiguous constraints that require supervisor "
+            "guidance. Include a concise reason and optional context. "
+            "The node will pause until the queen injects guidance."
+        ),
+        parameters={
+            "type": "object",
+            "properties": {
+                "reason": {
+                    "type": "string",
+                    "description": (
+                        "Short reason for escalation (e.g. 'Tool repeatedly failing')."
+                    ),
+                },
+                "context": {
+                    "type": "string",
+                    "description": "Optional diagnostic details for the queen.",
+                },
+            },
+            "required": ["reason"],
+        },
+    )
+
+
+def build_delegate_tool(sub_agents: list[str], node_registry: dict[str, Any]) -> Tool | None:
+    """Build the synthetic delegate_to_sub_agent tool for subagent invocation.
+
+    Args:
+        sub_agents: List of node IDs that can be invoked as subagents.
+        node_registry: Map of node_id -> NodeSpec for looking up subagent descriptions.
+
+    Returns:
+        Tool definition if sub_agents is non-empty, None otherwise.
+    """
+    if not sub_agents:
+        return None
+
+    agent_descriptions = []
+    for agent_id in sub_agents:
+        spec = node_registry.get(agent_id)
+        if spec:
+            desc = getattr(spec, "description", "(no description)")
+            agent_descriptions.append(f"- {agent_id}: {desc}")
+        else:
+            agent_descriptions.append(f"- {agent_id}: (not found in registry)")
+
+    return Tool(
+        name="delegate_to_sub_agent",
+        description=(
+            "Delegate a task to a specialized sub-agent. The sub-agent runs "
+            "autonomously with read-only access to current memory and returns "
+            "its result. Use this to parallelize work or leverage specialized capabilities.\n\n"
+            "Available sub-agents:\n" + "\n".join(agent_descriptions)
+        ),
+        parameters={
+            "type": "object",
+            "properties": {
+                "agent_id": {
+                    "type": "string",
+                    "description": f"The sub-agent to invoke. Must be one of: {sub_agents}",
+                    "enum": sub_agents,
+                },
+                "task": {
+                    "type": "string",
+                    "description": (
+                        "The task description for the sub-agent to execute. "
+                        "Be specific about what you want the sub-agent to do and "
+                        "what information to return."
+                    ),
+                },
+            },
+            "required": ["agent_id", "task"],
+        },
+    )
+
+
+def build_report_to_parent_tool() -> Tool:
+    """Build the synthetic report_to_parent tool for sub-agent progress reports.
+
+    Sub-agents call this to send one-way progress updates, partial findings,
+    or status reports to the parent node (and external observers via event bus)
+    without blocking execution.
+
+    When ``wait_for_response`` is True, the sub-agent blocks until the parent
+    relays the user's response — used for escalation (e.g. login pages, CAPTCHAs).
+
+    When ``mark_complete`` is True, the sub-agent terminates immediately after
+    sending the report — no need to call set_output for each output key.
+    """
+    return Tool(
+        name="report_to_parent",
+        description=(
+            "Send a report to the parent agent. By default this is fire-and-forget: "
+            "the parent receives the report but does not respond. "
+            "Set wait_for_response=true to BLOCK until the user replies — use this "
+            "when you need human intervention (e.g. login pages, CAPTCHAs, "
+            "authentication walls). The user's response is returned as the tool result. "
+            "Set mark_complete=true to finish your task and terminate immediately "
+            "after sending the report — use this when your findings are in the "
+            "message/data fields and you don't need to call set_output."
+        ),
+        parameters={
+            "type": "object",
+            "properties": {
+                "message": {
+                    "type": "string",
+                    "description": "A human-readable status or progress message.",
+                },
+                "data": {
+                    "type": "object",
+                    "description": "Optional structured data to include with the report.",
+                },
+                "wait_for_response": {
+                    "type": "boolean",
+                    "description": (
+                        "If true, block execution until the user responds. "
+                        "Use for escalation scenarios requiring human intervention."
+                    ),
+                    "default": False,
+                },
+                "mark_complete": {
+                    "type": "boolean",
+                    "description": (
+                        "If true, terminate the sub-agent immediately after sending "
+                        "this report. The report message and data are delivered to the "
+                        "parent as the final result. No set_output calls are needed."
+                    ),
+                    "default": False,
+                },
+            },
+            "required": ["message"],
+        },
+    )
+
+
+def handle_set_output(
+    tool_input: dict[str, Any],
+    output_keys: list[str] | None,
+) -> ToolResult:
+    """Handle set_output tool call. Returns ToolResult (sync)."""
+    import logging
+    import re
+
+    logger = logging.getLogger(__name__)
+
+    key = tool_input.get("key", "")
+    value = tool_input.get("value", "")
+    valid_keys = output_keys or []
+
+    # Recover from truncated JSON (max_tokens hit mid-argument).
+    # The _raw key is set by litellm when json.loads fails.
+    if not key and "_raw" in tool_input:
+        raw = tool_input["_raw"]
+        key_match = re.search(r'"key"\s*:\s*"(\w+)"', raw)
+        if key_match:
+            key = key_match.group(1)
+        val_match = re.search(r'"value"\s*:\s*"', raw)
+        if val_match:
+            start = val_match.end()
+            value = raw[start:].rstrip()
+            for suffix in ('"}\n', '"}', '"'):
+                if value.endswith(suffix):
+                    value = value[: -len(suffix)]
+                    break
+        if key:
+            logger.warning(
+                "Recovered set_output args from truncated JSON: key=%s, value_len=%d",
+                key,
+                len(value),
+            )
+            # Re-inject so the caller sees proper key/value
+            tool_input["key"] = key
+            tool_input["value"] = value
+
+    if key not in valid_keys:
+        return ToolResult(
+            tool_use_id="",
+            content=f"Invalid output key '{key}'. Valid keys: {valid_keys}",
+            is_error=True,
+        )
+
+    return ToolResult(
+        tool_use_id="",
+        content=f"Output '{key}' set successfully.",
+        is_error=False,
+    )

core/framework/graph/event_loop/tool_result_handler.py

@@ -0,0 +1,546 @@
+"""Tool result handling: truncation, spillover, JSON preview, and execution.
+
+Manages tool result size limits, file spillover for large results, and
+smart JSON previews.  Also includes transient error classification and
+the context-window-exceeded error detector.
+"""
+
+from __future__ import annotations
+
+import asyncio
+import contextvars
+import json
+import logging
+import re
+from pathlib import Path
+from typing import Any
+
+from framework.llm.provider import ToolResult, ToolUse
+from framework.llm.stream_events import ToolCallEvent
+
+logger = logging.getLogger(__name__)
+
+# Pattern for detecting context-window-exceeded errors across LLM providers.
+_CONTEXT_TOO_LARGE_RE = re.compile(
+    r"context.{0,20}(length|window|limit|size)|"
+    r"too.{0,10}(long|large|many.{0,10}tokens)|"
+    r"(exceed|exceeds|exceeded).{0,30}(limit|window|context|tokens)|"
+    r"maximum.{0,20}token|prompt.{0,20}too.{0,10}long",
+    re.IGNORECASE,
+)
+
+
+def is_context_too_large_error(exc: BaseException) -> bool:
+    """Detect whether an exception indicates the LLM input was too large."""
+    cls = type(exc).__name__
+    if "ContextWindow" in cls:
+        return True
+    return bool(_CONTEXT_TOO_LARGE_RE.search(str(exc)))
+
+
+def is_transient_error(exc: BaseException) -> bool:
+    """Classify whether an exception is transient (retryable) vs permanent.
+
+    Transient: network errors, rate limits, server errors, timeouts.
+    Permanent: auth errors, bad requests, context window exceeded.
+    """
+    try:
+        from litellm.exceptions import (
+            APIConnectionError,
+            BadGatewayError,
+            InternalServerError,
+            RateLimitError,
+            ServiceUnavailableError,
+        )
+
+        transient_types: tuple[type[BaseException], ...] = (
+            RateLimitError,
+            APIConnectionError,
+            InternalServerError,
+            BadGatewayError,
+            ServiceUnavailableError,
+            TimeoutError,
+            ConnectionError,
+            OSError,
+        )
+    except ImportError:
+        transient_types = (TimeoutError, ConnectionError, OSError)
+
+    if isinstance(exc, transient_types):
+        return True
+
+    # RuntimeError from StreamErrorEvent with "Stream error:" prefix
+    if isinstance(exc, RuntimeError):
+        error_str = str(exc).lower()
+        transient_keywords = [
+            "rate limit",
+            "429",
+            "timeout",
+            "connection",
+            "internal server",
+            "502",
+            "503",
+            "504",
+            "service unavailable",
+            "bad gateway",
+            "overloaded",
+            "failed to parse tool call",
+        ]
+        return any(kw in error_str for kw in transient_keywords)
+
+    return False
+
+
+def extract_json_metadata(parsed: Any, *, _depth: int = 0, _max_depth: int = 3) -> str:
+    """Return a concise structural summary of parsed JSON.
+
+    Reports key names, value types, and — crucially — array lengths so
+    the LLM knows how much data exists beyond the preview.
+
+    Returns an empty string for simple scalars.
+    """
+    if _depth >= _max_depth:
+        if isinstance(parsed, dict):
+            return f"dict with {len(parsed)} keys"
+        if isinstance(parsed, list):
+            return f"list of {len(parsed)} items"
+        return type(parsed).__name__
+
+    if isinstance(parsed, dict):
+        if not parsed:
+            return "empty dict"
+        lines: list[str] = []
+        indent = "  " * (_depth + 1)
+        for key, value in list(parsed.items())[:20]:
+            if isinstance(value, list):
+                line = f'{indent}"{key}": list of {len(value)} items'
+                if value:
+                    first = value[0]
+                    if isinstance(first, dict):
+                        sample_keys = list(first.keys())[:10]
+                        line += f" (each item: dict with keys {sample_keys})"
+                    elif isinstance(first, list):
+                        line += f" (each item: list of {len(first)} elements)"
+                lines.append(line)
+            elif isinstance(value, dict):
+                child = extract_json_metadata(value, _depth=_depth + 1, _max_depth=_max_depth)
+                lines.append(f'{indent}"{key}": {child}')
+            else:
+                lines.append(f'{indent}"{key}": {type(value).__name__}')
+        if len(parsed) > 20:
+            lines.append(f"{indent}... and {len(parsed) - 20} more keys")
+        return "\n".join(lines)
+
+    if isinstance(parsed, list):
+        if not parsed:
+            return "empty list"
+        desc = f"list of {len(parsed)} items"
+        first = parsed[0]
+        if isinstance(first, dict):
+            sample_keys = list(first.keys())[:10]
+            desc += f" (each item: dict with keys {sample_keys})"
+        elif isinstance(first, list):
+            desc += f" (each item: list of {len(first)} elements)"
+        return desc
+
+    return ""
+
+
+def build_json_preview(parsed: Any, *, max_chars: int = 5000) -> str | None:
+    """Build a smart preview of parsed JSON, truncating large arrays.
+
+    Shows first 3 + last 1 items of large arrays with explicit count
+    markers so the LLM cannot mistake the preview for the full dataset.
+
+    Returns ``None`` if no truncation was needed (no large arrays).
+    """
+    _LARGE_ARRAY_THRESHOLD = 10
+
+    def _truncate_arrays(obj: Any) -> tuple[Any, bool]:
+        """Return (truncated_copy, was_truncated)."""
+        if isinstance(obj, list) and len(obj) > _LARGE_ARRAY_THRESHOLD:
+            n = len(obj)
+            head = obj[:3]
+            tail = obj[-1:]
+            marker = f"... ({n - 4} more items omitted, {n} total) ..."
+            return head + [marker] + tail, True
+        if isinstance(obj, dict):
+            changed = False
+            out: dict[str, Any] = {}
+            for k, v in obj.items():
+                new_v, did = _truncate_arrays(v)
+                out[k] = new_v
+                changed = changed or did
+            return (out, True) if changed else (obj, False)
+        return obj, False
+
+    preview_obj, was_truncated = _truncate_arrays(parsed)
+    if not was_truncated:
+        return None  # No large arrays — caller should use raw slicing
+
+    try:
+        result = json.dumps(preview_obj, indent=2, ensure_ascii=False)
+    except (TypeError, ValueError):
+        return None
+
+    if len(result) > max_chars:
+        # Even 3+1 items too big — try just 1 item
+        def _minimal_arrays(obj: Any) -> Any:
+            if isinstance(obj, list) and len(obj) > _LARGE_ARRAY_THRESHOLD:
+                n = len(obj)
+                return obj[:1] + [f"... ({n - 1} more items omitted, {n} total) ..."]
+            if isinstance(obj, dict):
+                return {k: _minimal_arrays(v) for k, v in obj.items()}
+            return obj
+
+        preview_obj = _minimal_arrays(parsed)
+        try:
+            result = json.dumps(preview_obj, indent=2, ensure_ascii=False)
+        except (TypeError, ValueError):
+            return None
+        if len(result) > max_chars:
+            result = result[:max_chars] + "…"
+
+    return result
+
+
+def truncate_tool_result(
+    result: ToolResult,
+    tool_name: str,
+    *,
+    max_tool_result_chars: int,
+    spillover_dir: str | None,
+    next_spill_filename_fn: Any,  # Callable[[str], str]
+) -> ToolResult:
+    """Persist tool result to file and optionally truncate for context.
+
+    When *spillover_dir* is configured, EVERY non-error tool result is
+    saved to a file (short filename like ``web_search_1.txt``).  A
+    ``[Saved to '...']`` annotation is appended so the reference
+    survives pruning and compaction.
+
+    - Small results (≤ limit): full content kept + file annotation
+    - Large results (> limit): preview + file reference
+    - Errors: pass through unchanged
+    - load_data results: truncate with pagination hint (no re-spill)
+    """
+    limit = max_tool_result_chars
+
+    # Errors always pass through unchanged
+    if result.is_error:
+        return result
+
+    # load_data reads FROM spilled files — never re-spill (circular).
+    # Just truncate with a pagination hint if the result is too large.
+    if tool_name == "load_data":
+        if limit <= 0 or len(result.content) <= limit:
+            return result  # Small load_data result — pass through as-is
+        # Large load_data result — truncate with smart preview
+        PREVIEW_CAP = min(5000, max(limit - 500, limit // 2))
+
+        metadata_str = ""
+        smart_preview: str | None = None
+        try:
+            parsed_ld = json.loads(result.content)
+            metadata_str = extract_json_metadata(parsed_ld)
+            smart_preview = build_json_preview(parsed_ld, max_chars=PREVIEW_CAP)
+        except (json.JSONDecodeError, TypeError, ValueError):
+            pass
+
+        if smart_preview is not None:
+            preview_block = smart_preview
+        else:
+            preview_block = result.content[:PREVIEW_CAP] + "…"
+
+        header = (
+            f"[{tool_name} result: {len(result.content):,} chars — "
+            f"too large for context. Use offset_bytes/limit_bytes "
+            f"parameters to read smaller chunks.]"
+        )
+        if metadata_str:
+            header += f"\n\nData structure:\n{metadata_str}"
+        header += (
+            "\n\nWARNING: This is an INCOMPLETE preview. Do NOT draw conclusions or counts from it."
+        )
+
+        truncated = f"{header}\n\nPreview (small sample only):\n{preview_block}"
+        logger.info(
+            "%s result truncated: %d → %d chars (use offset/limit to paginate)",
+            tool_name,
+            len(result.content),
+            len(truncated),
+        )
+        return ToolResult(
+            tool_use_id=result.tool_use_id,
+            content=truncated,
+            is_error=False,
+            image_content=result.image_content,
+            is_skill_content=result.is_skill_content,
+        )
+
+    spill_dir = spillover_dir
+    if spill_dir:
+        spill_path = Path(spill_dir)
+        spill_path.mkdir(parents=True, exist_ok=True)
+        filename = next_spill_filename_fn(tool_name)
+
+        # Pretty-print JSON content so load_data's line-based
+        # pagination works correctly.
+        write_content = result.content
+        parsed_json: Any = None  # track for metadata extraction
+        try:
+            parsed_json = json.loads(result.content)
+            write_content = json.dumps(parsed_json, indent=2, ensure_ascii=False)
+        except (json.JSONDecodeError, TypeError, ValueError):
+            pass  # Not JSON — write as-is
+
+        (spill_path / filename).write_text(write_content, encoding="utf-8")
+
+        if limit > 0 and len(result.content) > limit:
+            # Large result: build a small, metadata-rich preview so the
+            # LLM cannot mistake it for the complete dataset.
+            PREVIEW_CAP = 5000
+
+            # Extract structural metadata (array lengths, key names)
+            metadata_str = ""
+            smart_preview: str | None = None
+            if parsed_json is not None:
+                metadata_str = extract_json_metadata(parsed_json)
+                smart_preview = build_json_preview(parsed_json, max_chars=PREVIEW_CAP)
+
+            if smart_preview is not None:
+                preview_block = smart_preview
+            else:
+                preview_block = result.content[:PREVIEW_CAP] + "…"
+
+            # Assemble header with structural info + warning
+            header = (
+                f"[Result from {tool_name}: {len(result.content):,} chars — "
+                f"too large for context, saved to '{filename}'.]\n"
+            )
+            if metadata_str:
+                header += f"\nData structure:\n{metadata_str}"
+            header += (
+                f"\n\nWARNING: The preview below is INCOMPLETE. "
+                f"Do NOT draw conclusions or counts from it. "
+                f"Use load_data(filename='{filename}') to read the "
+                f"full data before analysis."
+            )
+
+            content = f"{header}\n\nPreview (small sample only):\n{preview_block}"
+            logger.info(
+                "Tool result spilled to file: %s (%d chars → %s)",
+                tool_name,
+                len(result.content),
+                filename,
+            )
+        else:
+            # Small result: keep full content + annotation
+            content = f"{result.content}\n\n[Saved to '{filename}']"
+            logger.info(
+                "Tool result saved to file: %s (%d chars → %s)",
+                tool_name,
+                len(result.content),
+                filename,
+            )
+
+        return ToolResult(
+            tool_use_id=result.tool_use_id,
+            content=content,
+            is_error=False,
+            image_content=result.image_content,
+            is_skill_content=result.is_skill_content,
+        )
+
+    # No spillover_dir — truncate in-place if needed
+    if limit > 0 and len(result.content) > limit:
+        PREVIEW_CAP = min(5000, max(limit - 500, limit // 2))
+
+        metadata_str = ""
+        smart_preview: str | None = None
+        try:
+            parsed_inline = json.loads(result.content)
+            metadata_str = extract_json_metadata(parsed_inline)
+            smart_preview = build_json_preview(parsed_inline, max_chars=PREVIEW_CAP)
+        except (json.JSONDecodeError, TypeError, ValueError):
+            pass
+
+        if smart_preview is not None:
+            preview_block = smart_preview
+        else:
+            preview_block = result.content[:PREVIEW_CAP] + "…"
+
+        header = (
+            f"[Result from {tool_name}: {len(result.content):,} chars — "
+            f"truncated to fit context budget.]"
+        )
+        if metadata_str:
+            header += f"\n\nData structure:\n{metadata_str}"
+        header += (
+            "\n\nWARNING: This is an INCOMPLETE preview. "
+            "Do NOT draw conclusions or counts from the preview alone."
+        )
+
+        truncated = f"{header}\n\n{preview_block}"
+        logger.info(
+            "Tool result truncated in-place: %s (%d → %d chars)",
+            tool_name,
+            len(result.content),
+            len(truncated),
+        )
+        return ToolResult(
+            tool_use_id=result.tool_use_id,
+            content=truncated,
+            is_error=False,
+            image_content=result.image_content,
+            is_skill_content=result.is_skill_content,
+        )
+
+    return result
+
+
+async def execute_tool(
+    tool_executor: Any,  # Callable[[ToolUse], ToolResult | Awaitable[ToolResult]] | None
+    tc: ToolCallEvent,
+    timeout: float,
+    skill_dirs: list[str] | None = None,
+) -> ToolResult:
+    """Execute a tool call, handling both sync and async executors.
+
+    Applies ``tool_call_timeout_seconds`` to prevent hung MCP servers
+    from blocking the event loop indefinitely.  The initial executor
+    call is offloaded to a thread pool so that sync executors don't
+    freeze the event loop.
+    """
+    if tool_executor is None:
+        return ToolResult(
+            tool_use_id=tc.tool_use_id,
+            content=f"No tool executor configured for '{tc.tool_name}'",
+            is_error=True,
+        )
+
+    skill_dirs = skill_dirs or []
+    skill_read_tools = {"view_file", "load_data", "read_file"}
+    if tc.tool_name in skill_read_tools and skill_dirs:
+        raw_path = tc.tool_input.get("path", "")
+        if raw_path:
+            resolved = Path(raw_path).resolve(strict=False)
+            resolved_roots = [Path(skill_dir).resolve(strict=False) for skill_dir in skill_dirs]
+            if any(resolved.is_relative_to(root) for root in resolved_roots):
+                try:
+                    content = resolved.read_text(encoding="utf-8")
+                except Exception as exc:
+                    return ToolResult(
+                        tool_use_id=tc.tool_use_id,
+                        content=f"Could not read skill resource '{raw_path}': {exc}",
+                        is_error=True,
+                    )
+                return ToolResult(
+                    tool_use_id=tc.tool_use_id,
+                    content=content,
+                    is_skill_content=resolved.name == "SKILL.md",
+                )
+
+    tool_use = ToolUse(id=tc.tool_use_id, name=tc.tool_name, input=tc.tool_input)
+
+    async def _run() -> ToolResult:
+        # Offload the executor call to a thread.  Sync MCP executors
+        # block on future.result() — running in a thread keeps the
+        # event loop free so asyncio.wait_for can fire the timeout.
+        # Copy the current context so contextvars (e.g. data_dir from
+        # execution context) propagate into the worker thread.
+        loop = asyncio.get_running_loop()
+        ctx = contextvars.copy_context()
+        result = await loop.run_in_executor(None, ctx.run, tool_executor, tool_use)
+        # Async executors return a coroutine — await it on the loop
+        if asyncio.iscoroutine(result) or asyncio.isfuture(result):
+            result = await result
+        return result
+
+    try:
+        if timeout > 0:
+            result = await asyncio.wait_for(_run(), timeout=timeout)
+        else:
+            result = await _run()
+    except TimeoutError:
+        logger.warning("Tool '%s' timed out after %.0fs", tc.tool_name, timeout)
+        return ToolResult(
+            tool_use_id=tc.tool_use_id,
+            content=(
+                f"Tool '{tc.tool_name}' timed out after {timeout:.0f}s. "
+                "The operation took too long and was cancelled. "
+                "Try a simpler request or a different approach."
+            ),
+            is_error=True,
+        )
+    return result
+
+
+def record_learning(key: str, value: Any, spillover_dir: str | None) -> None:
+    """Append a set_output value to adapt.md as a learning entry.
+
+    Called at set_output time — the moment knowledge is produced — so that
+    adapt.md accumulates the agent's outputs across the session.  Since
+    adapt.md is injected into the system prompt, these persist through
+    any compaction.
+    """
+    if not spillover_dir:
+        return
+    try:
+        adapt_path = Path(spillover_dir) / "adapt.md"
+        adapt_path.parent.mkdir(parents=True, exist_ok=True)
+        content = adapt_path.read_text(encoding="utf-8") if adapt_path.exists() else ""
+
+        if "## Outputs" not in content:
+            content += "\n\n## Outputs\n"
+
+        # Truncate long values for memory (full value is in shared memory)
+        v_str = str(value)
+        if len(v_str) > 500:
+            v_str = v_str[:500] + "…"
+
+        entry = f"- {key}: {v_str}\n"
+
+        # Replace existing entry for same key (update, not duplicate)
+        lines = content.splitlines(keepends=True)
+        replaced = False
+        for i, line in enumerate(lines):
+            if line.startswith(f"- {key}:"):
+                lines[i] = entry
+                replaced = True
+                break
+        if replaced:
+            content = "".join(lines)
+        else:
+            content += entry
+
+        adapt_path.write_text(content, encoding="utf-8")
+    except Exception as e:
+        logger.warning("Failed to record learning for key=%s: %s", key, e)
+
+
+def next_spill_filename(tool_name: str, counter: int) -> str:
+    """Return a short, monotonic filename for a tool result spill."""
+    # Shorten common tool name prefixes to save tokens
+    short = tool_name.removeprefix("tool_").removeprefix("mcp_")
+    return f"{short}_{counter}.txt"
+
+
+def restore_spill_counter(spillover_dir: str | None) -> int:
+    """Scan spillover_dir for existing spill files and return the max counter.
+
+    Returns the highest spill number found (or 0 if none).
+    """
+    if not spillover_dir:
+        return 0
+    spill_path = Path(spillover_dir)
+    if not spill_path.is_dir():
+        return 0
+    max_n = 0
+    for f in spill_path.iterdir():
+        if not f.is_file():
+            continue
+        m = re.search(r"_(\d+)\.txt$", f.name)
+        if m:
+            max_n = max(max_n, int(m.group(1)))
+    return max_n

core/framework/graph/event_loop/types.py

@@ -0,0 +1,190 @@
+"""Shared types and state containers for the event loop package."""
+
+from __future__ import annotations
+
+import json
+import logging
+import time
+from dataclasses import dataclass, field
+from pathlib import Path
+from typing import Any, Literal, Protocol, runtime_checkable
+
+from framework.graph.conversation import ConversationStore
+
+logger = logging.getLogger(__name__)
+
+
+@dataclass
+class TriggerEvent:
+    """A framework-level trigger signal (timer tick or webhook hit)."""
+
+    trigger_type: str
+    source_id: str
+    payload: dict[str, Any] = field(default_factory=dict)
+    timestamp: float = field(default_factory=time.time)
+
+
+@dataclass
+class JudgeVerdict:
+    """Result of judge evaluation for the event loop."""
+
+    action: Literal["ACCEPT", "RETRY", "ESCALATE"]
+    # None  = no evaluation happened (skip_judge, tool-continue); not logged.
+    # ""    = evaluated but no feedback; logged with default text.
+    # "..." = evaluated with feedback; logged as-is.
+    feedback: str | None = None
+
+
+@runtime_checkable
+class JudgeProtocol(Protocol):
+    """Protocol for event-loop judges."""
+
+    async def evaluate(self, context: dict[str, Any]) -> JudgeVerdict: ...
+
+
+@dataclass
+class LoopConfig:
+    """Configuration for the event loop."""
+
+    max_iterations: int = 50
+    max_tool_calls_per_turn: int = 30
+    judge_every_n_turns: int = 1
+    stall_detection_threshold: int = 3
+    stall_similarity_threshold: float = 0.85
+    max_context_tokens: int = 32_000
+    store_prefix: str = ""
+
+    # Overflow margin for max_tool_calls_per_turn. Tool calls are only
+    # discarded when the count exceeds max_tool_calls_per_turn * (1 + margin).
+    tool_call_overflow_margin: float = 0.5
+
+    # Tool result context management.
+    max_tool_result_chars: int = 30_000
+    spillover_dir: str | None = None
+
+    # set_output value spilling.
+    max_output_value_chars: int = 2_000
+
+    # Stream retry.
+    max_stream_retries: int = 3
+    stream_retry_backoff_base: float = 2.0
+    stream_retry_max_delay: float = 60.0
+
+    # Tool doom loop detection.
+    tool_doom_loop_threshold: int = 3
+
+    # Client-facing auto-block grace period.
+    cf_grace_turns: int = 1
+    tool_doom_loop_enabled: bool = True
+
+    # Per-tool-call timeout.
+    tool_call_timeout_seconds: float = 60.0
+
+    # Subagent delegation timeout.
+    subagent_timeout_seconds: float = 600.0
+
+    # Lifecycle hooks.
+    hooks: dict[str, list] | None = None
+
+    def __post_init__(self) -> None:
+        if self.hooks is None:
+            object.__setattr__(self, "hooks", {})
+
+
+@dataclass
+class HookContext:
+    """Context passed to every lifecycle hook."""
+
+    event: str
+    trigger: str | None
+    system_prompt: str
+
+
+@dataclass
+class HookResult:
+    """What a hook may return to modify node state."""
+
+    system_prompt: str | None = None
+    inject: str | None = None
+
+
+@dataclass
+class OutputAccumulator:
+    """Accumulates output key-value pairs with optional write-through persistence."""
+
+    values: dict[str, Any] = field(default_factory=dict)
+    store: ConversationStore | None = None
+    spillover_dir: str | None = None
+    max_value_chars: int = 0
+
+    async def set(self, key: str, value: Any) -> None:
+        """Set a key-value pair, auto-spilling large values to files."""
+        value = self._auto_spill(key, value)
+        self.values[key] = value
+        if self.store:
+            cursor = await self.store.read_cursor() or {}
+            outputs = cursor.get("outputs", {})
+            outputs[key] = value
+            cursor["outputs"] = outputs
+            await self.store.write_cursor(cursor)
+
+    def _auto_spill(self, key: str, value: Any) -> Any:
+        """Save large values to a file and return a reference string."""
+        if self.max_value_chars <= 0 or not self.spillover_dir:
+            return value
+
+        val_str = json.dumps(value, ensure_ascii=False) if not isinstance(value, str) else value
+        if len(val_str) <= self.max_value_chars:
+            return value
+
+        spill_path = Path(self.spillover_dir)
+        spill_path.mkdir(parents=True, exist_ok=True)
+        ext = ".json" if isinstance(value, (dict, list)) else ".txt"
+        filename = f"output_{key}{ext}"
+        write_content = (
+            json.dumps(value, indent=2, ensure_ascii=False)
+            if isinstance(value, (dict, list))
+            else str(value)
+        )
+        (spill_path / filename).write_text(write_content, encoding="utf-8")
+        file_size = (spill_path / filename).stat().st_size
+        logger.info(
+            "set_output value auto-spilled: key=%s, %d chars -> %s (%d bytes)",
+            key,
+            len(val_str),
+            filename,
+            file_size,
+        )
+        return (
+            f"[Saved to '{filename}' ({file_size:,} bytes). "
+            f"Use load_data(filename='{filename}') "
+            f"to access full data.]"
+        )
+
+    def get(self, key: str) -> Any | None:
+        return self.values.get(key)
+
+    def to_dict(self) -> dict[str, Any]:
+        return dict(self.values)
+
+    def has_all_keys(self, required: list[str]) -> bool:
+        return all(key in self.values and self.values[key] is not None for key in required)
+
+    @classmethod
+    async def restore(cls, store: ConversationStore) -> OutputAccumulator:
+        cursor = await store.read_cursor()
+        values = {}
+        if cursor and "outputs" in cursor:
+            values = cursor["outputs"]
+        return cls(values=values, store=store)
+
+
+__all__ = [
+    "HookContext",
+    "HookResult",
+    "JudgeProtocol",
+    "JudgeVerdict",
+    "LoopConfig",
+    "OutputAccumulator",
+    "TriggerEvent",
+]

core/framework/graph/executor.py

@@ -27,11 +27,14 @@ from framework.graph.node import (
     SharedMemory,
 )
 from framework.graph.validator import OutputValidator
-from framework.llm.provider import LLMProvider, Tool
+from framework.llm.provider import LLMProvider, Tool, ToolUse
 from framework.observability import set_trace_context
 from framework.runtime.core import Runtime
 from framework.schemas.checkpoint import Checkpoint
 from framework.storage.checkpoint_store import CheckpointStore
+from framework.utils.io import atomic_write
+
+logger = logging.getLogger(__name__)
 
 
 def _default_max_context_tokens() -> int:
@@ -149,6 +152,11 @@ class GraphExecutor:
         dynamic_tools_provider: Callable | None = None,
         dynamic_prompt_provider: Callable | None = None,
         iteration_metadata_provider: Callable | None = None,
+        skills_catalog_prompt: str = "",
+        protocols_prompt: str = "",
+        skill_dirs: list[str] | None = None,
+        context_warn_ratio: float | None = None,
+        batch_init_nudge: str | None = None,
     ):
         """
         Initialize the executor.
@@ -174,6 +182,11 @@ class GraphExecutor:
                 tool list (for mode switching)
             dynamic_prompt_provider: Optional callback returning current
                 system prompt (for phase switching)
+            skills_catalog_prompt: Available skills catalog for system prompt
+            protocols_prompt: Default skill operational protocols for system prompt
+            skill_dirs: Skill base directories for Tier 3 resource access
+            context_warn_ratio: Token usage ratio to trigger DS-13 preservation warning
+            batch_init_nudge: System prompt nudge for DS-12 batch auto-detection
         """
         self.runtime = runtime
         self.llm = llm
@@ -195,6 +208,23 @@ class GraphExecutor:
         self.dynamic_tools_provider = dynamic_tools_provider
         self.dynamic_prompt_provider = dynamic_prompt_provider
         self.iteration_metadata_provider = iteration_metadata_provider
+        self.skills_catalog_prompt = skills_catalog_prompt
+        self.protocols_prompt = protocols_prompt
+        self.skill_dirs: list[str] = skill_dirs or []
+        self.context_warn_ratio: float | None = context_warn_ratio
+        self.batch_init_nudge: str | None = batch_init_nudge
+
+        if protocols_prompt:
+            self.logger.info(
+                "GraphExecutor[%s] received protocols_prompt (%d chars)",
+                stream_id,
+                len(protocols_prompt),
+            )
+        else:
+            self.logger.warning(
+                "GraphExecutor[%s] received EMPTY protocols_prompt",
+                stream_id,
+            )
 
         # Parallel execution settings
         self.enable_parallel_execution = enable_parallel_execution
@@ -224,11 +254,11 @@ class GraphExecutor:
         """
         if not self._storage_path:
             return
+        state_path = self._storage_path / "state.json"
         try:
             import json as _json
             from datetime import datetime
 
-            state_path = self._storage_path / "state.json"
             if state_path.exists():
                 state_data = _json.loads(state_path.read_text(encoding="utf-8"))
             else:
@@ -251,9 +281,14 @@ class GraphExecutor:
             state_data["memory"] = memory_snapshot
             state_data["memory_keys"] = list(memory_snapshot.keys())
 
-            state_path.write_text(_json.dumps(state_data, indent=2), encoding="utf-8")
+            with atomic_write(state_path, encoding="utf-8") as f:
+                _json.dump(state_data, f, indent=2)
         except Exception:
-            pass  # Best-effort — never block execution
+            logger.warning(
+                "Failed to persist progress state to %s",
+                state_path,
+                exc_info=True,
+            )
 
     def _validate_tools(self, graph: GraphSpec) -> list[str]:
         """
@@ -415,6 +450,14 @@ class GraphExecutor:
         )
         return s1 + "\n\n" + s2
 
+    def _get_runtime_log_session_id(self) -> str:
+        """Return the session-backed execution ID for runtime logging, if any."""
+        if not self._storage_path:
+            return ""
+        if self._storage_path.parent.name != "sessions":
+            return ""
+        return self._storage_path.name
+
     async def execute(
         self,
         graph: GraphSpec,
@@ -708,10 +751,7 @@ class GraphExecutor:
         )
 
         if self.runtime_logger:
-            # Extract session_id from storage_path if available (for unified sessions)
-            session_id = ""
-            if self._storage_path and self._storage_path.name.startswith("session_"):
-                session_id = self._storage_path.name
+            session_id = self._get_runtime_log_session_id()
             self.runtime_logger.start_run(goal_id=goal.id, session_id=session_id)
 
         self.logger.info(f"🚀 Starting execution: {goal.name}")
@@ -937,6 +977,33 @@ class GraphExecutor:
                 self.logger.info("   Executing...")
                 result = await node_impl.execute(ctx)
 
+                # GCU tab cleanup: stop the browser profile after a top-level GCU node
+                # finishes so tabs don't accumulate. Mirrors the subagent cleanup in
+                # EventLoopNode._execute_subagent().
+                if node_spec.node_type == "gcu" and self.tool_executor is not None:
+                    try:
+                        from gcu.browser.session import (
+                            _active_profile as _gcu_profile_var,
+                        )
+
+                        _gcu_profile = _gcu_profile_var.get()
+                        _stop_use = ToolUse(
+                            id="gcu-cleanup",
+                            name="browser_stop",
+                            input={"profile": _gcu_profile},
+                        )
+                        _stop_result = self.tool_executor(_stop_use)
+                        if asyncio.iscoroutine(_stop_result) or asyncio.isfuture(_stop_result):
+                            await _stop_result
+                    except ImportError:
+                        pass  # GCU not installed
+                    except Exception as _gcu_exc:
+                        logger.warning(
+                            "GCU browser_stop failed for profile %r: %s",
+                            _gcu_profile,
+                            _gcu_exc,
+                        )
+
                 # Emit node-completed event (skip event_loop nodes)
                 if self._event_bus and node_spec.node_type != "event_loop":
                     await self._event_bus.emit_node_loop_completed(
@@ -1362,6 +1429,7 @@ class GraphExecutor:
                     next_spec = graph.get_node(current_node_id)
                     if next_spec and next_spec.node_type == "event_loop":
                         from framework.graph.prompt_composer import (
+                            EXECUTION_SCOPE_PREAMBLE,
                             build_accounts_prompt,
                             build_narrative,
                             build_transition_marker,
@@ -1401,9 +1469,14 @@ class GraphExecutor:
                             )
 
                         # Compose new system prompt (Layer 1 + 2 + 3 + accounts)
+                        # Prepend scope preamble to focus so the LLM stays
+                        # within this node's responsibility.
+                        _focus = next_spec.system_prompt
+                        if next_spec.output_keys and _focus:
+                            _focus = f"{EXECUTION_SCOPE_PREAMBLE}\n\n{_focus}"
                         new_system = compose_system_prompt(
                             identity_prompt=getattr(graph, "identity_prompt", None),
-                            focus_prompt=next_spec.system_prompt,
+                            focus_prompt=_focus,
                             narrative=narrative,
                             accounts_prompt=_node_accounts,
                         )
@@ -1765,10 +1838,34 @@ class GraphExecutor:
             if node_spec.tools:
                 available_tools = [t for t in self.tools if t.name in node_spec.tools]
 
-        # Create scoped memory view
+        # Create scoped memory view.
+        # When permissions are restricted (non-empty key lists), auto-include
+        # _-prefixed keys used by default skill protocols so agents can read/write
+        # operational state (e.g. _working_notes, _batch_ledger) regardless of
+        # what the node declares.  When key lists are empty (unrestricted), leave
+        # unchanged — empty means "allow all".
+        read_keys = list(node_spec.input_keys)
+        write_keys = list(node_spec.output_keys)
+        # Only extend lists that were already restricted (non-empty).
+        # Empty means "allow all" — adding keys would accidentally
+        # activate the permission check and block legitimate reads/writes.
+        if read_keys or write_keys:
+            from framework.skills.defaults import SHARED_MEMORY_KEYS as _skill_keys
+
+            existing_underscore = [k for k in memory._data if k.startswith("_")]
+            extra_keys = set(_skill_keys) | set(existing_underscore)
+            # Only inject into read_keys when it was already non-empty — an empty
+            # read_keys means "allow all reads" and injecting skill keys would
+            # inadvertently restrict reads to skill keys only.
+            for k in extra_keys:
+                if read_keys and k not in read_keys:
+                    read_keys.append(k)
+                if write_keys and k not in write_keys:
+                    write_keys.append(k)
+
         scoped_memory = memory.with_permissions(
-            read_keys=node_spec.input_keys,
-            write_keys=node_spec.output_keys,
+            read_keys=read_keys,
+            write_keys=write_keys,
         )
 
         # Build per-node accounts prompt (filtered to this node's tools)
@@ -1812,6 +1909,11 @@ class GraphExecutor:
             dynamic_tools_provider=self.dynamic_tools_provider,
             dynamic_prompt_provider=self.dynamic_prompt_provider,
             iteration_metadata_provider=self.iteration_metadata_provider,
+            skills_catalog_prompt=self.skills_catalog_prompt,
+            protocols_prompt=self.protocols_prompt,
+            skill_dirs=self.skill_dirs,
+            default_skill_warn_ratio=self.context_warn_ratio,
+            default_skill_batch_nudge=self.batch_init_nudge,
         )
 
     VALID_NODE_TYPES = {
@@ -2052,6 +2154,10 @@ class GraphExecutor:
                 edge=edge,
             )
 
+        # Track which branch wrote which key for memory conflict detection
+        fanout_written_keys: dict[str, str] = {}  # key -> branch_id that wrote it
+        fanout_keys_lock = asyncio.Lock()
+
         self.logger.info(f"   ⑂ Fan-out: executing {len(branches)} branches in parallel")
         for branch in branches.values():
             target_spec = graph.get_node(branch.node_id)
@@ -2143,8 +2249,31 @@ class GraphExecutor:
                         )
 
                     if result.success:
-                        # Write outputs to shared memory using async write
+                        # Write outputs to shared memory with conflict detection
+                        conflict_strategy = self._parallel_config.memory_conflict_strategy
                         for key, value in result.output.items():
+                            async with fanout_keys_lock:
+                                prior_branch = fanout_written_keys.get(key)
+                                if prior_branch and prior_branch != branch.branch_id:
+                                    if conflict_strategy == "error":
+                                        raise RuntimeError(
+                                            f"Memory conflict: key '{key}' already written "
+                                            f"by branch '{prior_branch}', "
+                                            f"conflicting write from '{branch.branch_id}'"
+                                        )
+                                    elif conflict_strategy == "first_wins":
+                                        self.logger.debug(
+                                            f"      ⚠ Skipping write to '{key}' "
+                                            f"(first_wins: already set by {prior_branch})"
+                                        )
+                                        continue
+                                    else:
+                                        # last_wins (default): write and log
+                                        self.logger.debug(
+                                            f"      ⚠ Key '{key}' overwritten "
+                                            f"(last_wins: {prior_branch} -> {branch.branch_id})"
+                                        )
+                                fanout_written_keys[key] = branch.branch_id
                             await memory.write_async(key, value)
 
                         branch.result = result
@@ -2191,9 +2320,11 @@ class GraphExecutor:
 
                 return branch, e
 
-        # Execute all branches concurrently
-        tasks = [execute_single_branch(b) for b in branches.values()]
-        results = await asyncio.gather(*tasks, return_exceptions=False)
+        # Execute all branches concurrently with per-branch timeout
+        timeout = self._parallel_config.branch_timeout_seconds
+        branch_list = list(branches.values())
+        tasks = [asyncio.wait_for(execute_single_branch(b), timeout=timeout) for b in branch_list]
+        results = await asyncio.gather(*tasks, return_exceptions=True)
 
         # Process results
         total_tokens = 0
@@ -2201,17 +2332,33 @@ class GraphExecutor:
         branch_results: dict[str, NodeResult] = {}
         failed_branches: list[ParallelBranch] = []
 
-        for branch, result in results:
-            path.append(branch.node_id)
+        for i, result in enumerate(results):
+            branch = branch_list[i]
 
-            if isinstance(result, Exception):
+            if isinstance(result, asyncio.TimeoutError):
+                # Branch timed out
+                branch.status = "timed_out"
+                branch.error = f"Branch timed out after {timeout}s"
+                self.logger.warning(
+                    f"      ⏱ Branch {graph.get_node(branch.node_id).name}: "
+                    f"timed out after {timeout}s"
+                )
+                path.append(branch.node_id)
                 failed_branches.append(branch)
-            elif result is None or not result.success:
+            elif isinstance(result, Exception):
+                path.append(branch.node_id)
                 failed_branches.append(branch)
             else:
-                total_tokens += result.tokens_used
-                total_latency += result.latency_ms
-                branch_results[branch.branch_id] = result
+                returned_branch, node_result = result
+                path.append(returned_branch.node_id)
+                if node_result is None or isinstance(node_result, Exception):
+                    failed_branches.append(returned_branch)
+                elif not node_result.success:
+                    failed_branches.append(returned_branch)
+                else:
+                    total_tokens += node_result.tokens_used
+                    total_latency += node_result.latency_ms
+                    branch_results[returned_branch.branch_id] = node_result
 
         # Handle failures based on config
         if failed_branches:

core/framework/graph/gcu.py

@@ -37,24 +37,45 @@ Follow these rules for reliable, efficient browser interaction.
 ## Reading Pages
 - ALWAYS prefer `browser_snapshot` over `browser_get_text("body")`
   — it returns a compact ~1-5 KB accessibility tree vs 100+ KB of raw HTML.
-- Use `browser_snapshot_aria` when you need full ARIA properties
-  for detailed element inspection.
-- Do NOT use `browser_screenshot` for reading text content
-  — it produces huge base64 images with no searchable text.
+- Interaction tools (`browser_click`, `browser_type`, `browser_fill`,
+  `browser_scroll`, etc.) return a page snapshot automatically in their
+  result. Use it to decide your next action — do NOT call
+  `browser_snapshot` separately after every action.
+  Only call `browser_snapshot` when you need a fresh view without
+  performing an action, or after setting `auto_snapshot=false`.
+- Do NOT use `browser_screenshot` to read text — use
+  `browser_snapshot` for that (compact, searchable, fast).
+- DO use `browser_screenshot` when you need visual context:
+  charts, images, canvas elements, layout verification, or when
+  the snapshot doesn't capture what you need.
 - Only fall back to `browser_get_text` for extracting specific
   small elements by CSS selector.
 
 ## Navigation & Waiting
-- Always call `browser_wait` after navigation actions
-  (`browser_open`, `browser_navigate`, `browser_click` on links)
-  to let the page load.
+- `browser_navigate` and `browser_open` already wait for the page to
+  load (`domcontentloaded`). Do NOT call `browser_wait` with no
+  arguments after navigation — it wastes time.
+  Only use `browser_wait` when you need a *specific element* or *text*
+  to appear (pass `selector` or `text`).
 - NEVER re-navigate to the same URL after scrolling
   — this resets your scroll position and loses loaded content.
 
 ## Scrolling
 - Use large scroll amounts ~2000 when loading more content
   — sites like twitter and linkedin have lazy loading for paging.
-- After scrolling, take a new `browser_snapshot` to see updated content.
+- The scroll result includes a snapshot automatically — no need to call
+  `browser_snapshot` separately.
+
+## Batching Actions
+- You can call multiple tools in a single turn — they execute in parallel.
+  ALWAYS batch independent actions together. Examples:
+  - Fill multiple form fields in one turn.
+  - Navigate + snapshot in one turn.
+  - Click + scroll if targeting different elements.
+- When batching, set `auto_snapshot=false` on all but the last action
+  to avoid redundant snapshots.
+- Aim for 3-5 tool calls per turn minimum. One tool call per turn is
+  wasteful.
 
 ## Error Recovery
 - If a tool fails, retry once with the same approach.
@@ -65,11 +86,33 @@ Follow these rules for reliable, efficient browser interaction.
   then `browser_start`, then retry.
 
 ## Tab Management
-- Use `browser_tabs` to list open tabs when managing multiple pages.
-- Pass `target_id` to tools when operating on a specific tab.
-- Open background tabs with `browser_open(url=..., background=true)`
-  to avoid losing your current context.
-- Close tabs you no longer need with `browser_close` to free resources.
+
+**Close tabs as soon as you are done with them** — not only at the end of the task.
+After reading or extracting data from a tab, close it immediately.
+
+**Decision rules:**
+- Finished reading/extracting from a tab? → `browser_close(target_id=...)`
+- Completed a multi-tab workflow? → `browser_close_finished()` to clean up all your tabs
+- More than 3 tabs open? → stop and close finished ones before opening more
+- Popup appeared that you didn't need? → close it immediately
+
+**Origin awareness:** `browser_tabs` returns an `origin` field for each tab:
+- `"agent"` — you opened it; you own it; close it when done
+- `"popup"` — opened by a link or script; close after extracting what you need
+- `"startup"` or `"user"` — leave these alone unless the task requires it
+
+**Cleanup tools:**
+- `browser_close(target_id=...)` — close one specific tab
+- `browser_close_finished()` — close all your agent/popup tabs (safe: leaves startup/user tabs)
+- `browser_close_all()` — close everything except the active tab (use only for full reset)
+
+**Multi-tab workflow pattern:**
+1. Open background tabs with `browser_open(url=..., background=true)` to stay on current tab
+2. Process each tab and close it with `browser_close` when done
+3. When the full workflow completes, call `browser_close_finished()` to confirm cleanup
+4. Check `browser_tabs` at any point — it shows `origin` and `age_seconds` per tab
+
+Never accumulate tabs. Treat every tab you open as a resource you must free.
 
 ## Login & Auth Walls
 - If you see a "Log in" or "Sign up" prompt instead of expected

core/framework/graph/goal.py

@@ -167,14 +167,6 @@ class Goal(BaseModel):
 
         return met_weight >= total_weight * 0.9  # 90% threshold
 
-    def check_constraint(self, constraint_id: str, value: Any) -> bool:
-        """Check if a specific constraint is satisfied."""
-        for c in self.constraints:
-            if c.id == constraint_id:
-                # This would be expanded with actual evaluation logic
-                return True
-        return True
-
     def to_prompt_context(self) -> str:
         """Generate context string for LLM prompts.
 

core/framework/graph/node.py

@@ -565,6 +565,15 @@ class NodeContext:
     # staging / running) without restarting the conversation.
     dynamic_prompt_provider: Any = None  # Callable[[], str] | None
 
+    # Skill system prompts — injected by the skill discovery pipeline
+    skills_catalog_prompt: str = ""  # Available skills XML catalog
+    protocols_prompt: str = ""  # Default skill operational protocols
+    skill_dirs: list[str] = field(default_factory=list)  # Skill base dirs for resource access
+    # DS-12: batch auto-detection nudge appended to system prompt when input looks like a batch
+    default_skill_batch_nudge: str | None = None
+    # DS-13: token usage ratio at which to inject a context preservation warning
+    default_skill_warn_ratio: float | None = None
+
     # Per-iteration metadata provider — when set, EventLoopNode merges
     # the returned dict into node_loop_iteration event data.  Used by
     # the queen to record the current phase per iteration.

core/framework/graph/prompt_composer.py

@@ -26,6 +26,16 @@ if TYPE_CHECKING:
 
 logger = logging.getLogger(__name__)
 
+# Injected into every worker node's system prompt so the LLM understands
+# it is one step in a multi-node pipeline and should not overreach.
+EXECUTION_SCOPE_PREAMBLE = (
+    "EXECUTION SCOPE: You are one node in a multi-step workflow graph. "
+    "Focus ONLY on the task described in your instructions below. "
+    "Call set_output() for each of your declared output keys, then stop. "
+    "Do NOT attempt work that belongs to other nodes — the framework "
+    "routes data between nodes automatically."
+)
+
 
 def _with_datetime(prompt: str) -> str:
     """Append current datetime with local timezone to a system prompt."""
@@ -140,14 +150,24 @@ def compose_system_prompt(
     focus_prompt: str | None,
     narrative: str | None = None,
     accounts_prompt: str | None = None,
+    skills_catalog_prompt: str | None = None,
+    protocols_prompt: str | None = None,
+    execution_preamble: str | None = None,
+    node_type_preamble: str | None = None,
 ) -> str:
-    """Compose the three-layer system prompt.
+    """Compose the multi-layer system prompt.
 
     Args:
         identity_prompt: Layer 1 — static agent identity (from GraphSpec).
         focus_prompt: Layer 3 — per-node focus directive (from NodeSpec.system_prompt).
         narrative: Layer 2 — auto-generated from conversation state.
         accounts_prompt: Connected accounts block (sits between identity and narrative).
+        skills_catalog_prompt: Available skills catalog XML (Agent Skills standard).
+        protocols_prompt: Default skill operational protocols section.
+        execution_preamble: EXECUTION_SCOPE_PREAMBLE for worker nodes
+            (prepended before focus so the LLM knows its pipeline scope).
+        node_type_preamble: Node-type-specific preamble, e.g. GCU browser
+            best-practices prompt (prepended before focus).
 
     Returns:
         Composed system prompt with all layers present, plus current datetime.
@@ -162,10 +182,27 @@ def compose_system_prompt(
     if accounts_prompt:
         parts.append(f"\n{accounts_prompt}")
 
+    # Skills catalog (discovered skills available for activation)
+    if skills_catalog_prompt:
+        parts.append(f"\n{skills_catalog_prompt}")
+
+    # Operational protocols (default skill behavioral guidance)
+    if protocols_prompt:
+        parts.append(f"\n{protocols_prompt}")
+
     # Layer 2: Narrative (what's happened so far)
     if narrative:
         parts.append(f"\n--- Context (what has happened so far) ---\n{narrative}")
 
+    # Execution scope preamble (worker nodes — tells the LLM it is one
+    # step in a multi-node pipeline and should not overreach)
+    if execution_preamble:
+        parts.append(f"\n{execution_preamble}")
+
+    # Node-type preamble (e.g. GCU browser best-practices)
+    if node_type_preamble:
+        parts.append(f"\n{node_type_preamble}")
+
     # Layer 3: Focus (current phase directive)
     if focus_prompt:
         parts.append(f"\n--- Current Focus ---\n{focus_prompt}")
@@ -255,7 +292,9 @@ def build_transition_marker(
     sections.append(f"\nCompleted: {previous_node.name}")
     sections.append(f"  {previous_node.description}")
 
-    # Outputs in memory
+    # Outputs in memory — use file references for large values so the
+    # next node loads full data from disk instead of seeing truncated
+    # inline previews that look deceptively complete.
     all_memory = memory.read_all()
     if all_memory:
         memory_lines: list[str] = []
@@ -263,7 +302,29 @@ def build_transition_marker(
             if value is None:
                 continue
             val_str = str(value)
-            if len(val_str) > 300:
+            if len(val_str) > 300 and data_dir:
+                # Auto-spill large transition values to data files
+                import json as _json
+
+                data_path = Path(data_dir)
+                data_path.mkdir(parents=True, exist_ok=True)
+                ext = ".json" if isinstance(value, (dict, list)) else ".txt"
+                filename = f"output_{key}{ext}"
+                try:
+                    write_content = (
+                        _json.dumps(value, indent=2, ensure_ascii=False)
+                        if isinstance(value, (dict, list))
+                        else str(value)
+                    )
+                    (data_path / filename).write_text(write_content, encoding="utf-8")
+                    file_size = (data_path / filename).stat().st_size
+                    val_str = (
+                        f"[Saved to '{filename}' ({file_size:,} bytes). "
+                        f"Use load_data(filename='{filename}') to access.]"
+                    )
+                except Exception:
+                    val_str = val_str[:300] + "..."
+            elif len(val_str) > 300:
                 val_str = val_str[:300] + "..."
             memory_lines.append(f"  {key}: {val_str}")
         if memory_lines:
@@ -280,7 +341,7 @@ def build_transition_marker(
                 ]
                 if file_lines:
                     sections.append(
-                        "\nData files (use read_file to access):\n" + "\n".join(file_lines)
+                        "\nData files (use load_data to access):\n" + "\n".join(file_lines)
                     )
 
     # Agent working memory
@@ -294,6 +355,12 @@ def build_transition_marker(
     # Next phase
     sections.append(f"\nNow entering: {next_node.name}")
     sections.append(f"  {next_node.description}")
+    if next_node.output_keys:
+        sections.append(
+            f"\nYour ONLY job in this phase: complete the task above and call "
+            f"set_output() for {next_node.output_keys}. Do NOT do work that "
+            f"belongs to later phases."
+        )
 
     # Reflection prompt (engineered metacognition)
     sections.append(

core/framework/graph/safe_eval.py

@@ -115,11 +115,23 @@ class SafeEvalVisitor(ast.NodeVisitor):
         return True
 
     def visit_BoolOp(self, node: ast.BoolOp) -> Any:
-        values = [self.visit(v) for v in node.values]
+        # Short-circuit evaluation to match Python semantics.
+        # Previously all operands were eagerly evaluated, which broke
+        # guard patterns like: ``x is not None and x.get("key")``
         if isinstance(node.op, ast.And):
-            return all(values)
+            result = True
+            for v in node.values:
+                result = self.visit(v)
+                if not result:
+                    return result
+            return result
         elif isinstance(node.op, ast.Or):
-            return any(values)
+            result = False
+            for v in node.values:
+                result = self.visit(v)
+                if result:
+                    return result
+            return result
         raise ValueError(f"Boolean operator {type(node.op).__name__} is not allowed")
 
     def visit_IfExp(self, node: ast.IfExp) -> Any:
@@ -216,10 +228,6 @@ class SafeEvalVisitor(ast.NodeVisitor):
 
         return func(*args, **keywords)
 
-    def visit_Index(self, node: ast.Index) -> Any:
-        # Python < 3.9
-        return self.visit(node.value)
-
 
 def safe_eval(expr: str, context: dict[str, Any] | None = None) -> Any:
     """

core/framework/llm/antigravity.py

@@ -0,0 +1,706 @@
+"""Antigravity (Google internal Cloud Code Assist) LLM provider.
+
+Antigravity is Google's unified gateway API that routes requests to Gemini,
+Claude, and GPT-OSS models through a single Gemini-style interface.  It is
+NOT the public ``generativelanguage.googleapis.com`` API.
+
+Authentication uses Google OAuth2.  Token refresh is done directly with the
+OAuth client secret — no local proxy required.
+
+Credential sources (checked in order):
+  1. ``~/.hive/antigravity-accounts.json`` (native OAuth implementation)
+  2. Antigravity IDE SQLite state DB (macOS / Linux)
+"""
+
+from __future__ import annotations
+
+import json
+import logging
+import re
+import time
+import uuid
+from collections.abc import AsyncIterator, Callable, Iterator
+from pathlib import Path
+from typing import Any
+
+from framework.llm.provider import LLMProvider, LLMResponse, Tool
+from framework.llm.stream_events import (
+    FinishEvent,
+    StreamErrorEvent,
+    StreamEvent,
+    TextDeltaEvent,
+    TextEndEvent,
+    ToolCallEvent,
+)
+
+logger = logging.getLogger(__name__)
+
+# ---------------------------------------------------------------------------
+# Constants
+# ---------------------------------------------------------------------------
+
+_TOKEN_URL = "https://oauth2.googleapis.com/token"
+
+# Fallback order: daily sandbox → autopush sandbox → production
+_ENDPOINTS = [
+    "https://daily-cloudcode-pa.sandbox.googleapis.com",
+    "https://autopush-cloudcode-pa.sandbox.googleapis.com",
+    "https://cloudcode-pa.googleapis.com",
+]
+_DEFAULT_PROJECT_ID = "rising-fact-p41fc"
+_TOKEN_REFRESH_BUFFER_SECS = 60
+
+# Credentials file in ~/.hive/ (native implementation)
+_ACCOUNTS_FILE = Path.home() / ".hive" / "antigravity-accounts.json"
+_IDE_STATE_DB_MAC = (
+    Path.home()
+    / "Library"
+    / "Application Support"
+    / "Antigravity"
+    / "User"
+    / "globalStorage"
+    / "state.vscdb"
+)
+_IDE_STATE_DB_LINUX = (
+    Path.home() / ".config" / "Antigravity" / "User" / "globalStorage" / "state.vscdb"
+)
+_IDE_STATE_DB_KEY = "antigravityUnifiedStateSync.oauthToken"
+
+_BASE_HEADERS: dict[str, str] = {
+    # Mimic the Antigravity Electron app so the API accepts the request.
+    "User-Agent": (
+        "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 "
+        "(KHTML, like Gecko) Antigravity/1.18.3 Chrome/138.0.7204.235 "
+        "Electron/37.3.1 Safari/537.36"
+    ),
+    "X-Goog-Api-Client": "google-cloud-sdk vscode_cloudshelleditor/0.1",
+    "Client-Metadata": '{"ideType":"ANTIGRAVITY","platform":"MACOS","pluginType":"GEMINI"}',
+}
+
+
+# ---------------------------------------------------------------------------
+# Credential loading helpers
+# ---------------------------------------------------------------------------
+
+
+def _load_from_json_file() -> tuple[str | None, str | None, str, float]:
+    """Read credentials from JSON accounts file.
+
+    Reads from ~/.hive/antigravity-accounts.json.
+
+    Returns ``(access_token | None, refresh_token | None, project_id, expires_at)``.
+    ``expires_at`` is a Unix timestamp (seconds); 0.0 means unknown.
+    """
+    if not _ACCOUNTS_FILE.exists():
+        return None, None, _DEFAULT_PROJECT_ID, 0.0
+    try:
+        with open(_ACCOUNTS_FILE, encoding="utf-8") as fh:
+            data = json.load(fh)
+    except (OSError, json.JSONDecodeError) as exc:
+        logger.debug("Failed to read Antigravity accounts file: %s", exc)
+        return None, None, _DEFAULT_PROJECT_ID, 0.0
+
+    accounts = data.get("accounts", [])
+    if not accounts:
+        return None, None, _DEFAULT_PROJECT_ID, 0.0
+
+    account = next((a for a in accounts if a.get("enabled", True) is not False), accounts[0])
+    schema_version = data.get("schemaVersion", 1)
+
+    if schema_version >= 4:
+        # V4 schema: refresh = "refreshToken|projectId[|managedProjectId]"
+        refresh_str = account.get("refresh", "")
+        parts = refresh_str.split("|") if refresh_str else []
+        refresh_token: str | None = parts[0] if parts else None
+        project_id = parts[1] if len(parts) >= 2 and parts[1] else _DEFAULT_PROJECT_ID
+
+        access_token: str | None = account.get("access")
+        expires_ms: int = account.get("expires", 0)
+        expires_at = float(expires_ms) / 1000.0 if expires_ms else 0.0
+
+        # Treat near-expiry tokens as absent so _ensure_token() triggers a refresh.
+        if access_token and expires_at and time.time() >= expires_at - _TOKEN_REFRESH_BUFFER_SECS:
+            access_token = None
+            expires_at = 0.0
+
+        return access_token, refresh_token, project_id, expires_at
+    else:
+        # V1–V3 schema: plain accessToken / refreshToken fields
+        access_token = account.get("accessToken")
+        refresh_token = account.get("refreshToken")
+        # Estimate expiry from last_refresh + 1 h
+        last_refresh_str: str | None = data.get("last_refresh")
+        expires_at = 0.0
+        if last_refresh_str:
+            try:
+                from datetime import datetime  # noqa: PLC0415
+
+                ts = datetime.fromisoformat(last_refresh_str.replace("Z", "+00:00")).timestamp()
+                expires_at = ts + 3600.0
+                if time.time() >= expires_at - _TOKEN_REFRESH_BUFFER_SECS:
+                    access_token = None
+            except (ValueError, TypeError):
+                pass
+        return access_token, refresh_token, _DEFAULT_PROJECT_ID, expires_at
+
+
+def _load_from_ide_db() -> tuple[str | None, str | None, float]:
+    """Extract ``(access_token, refresh_token, expires_at)`` from the IDE SQLite DB."""
+    import base64  # noqa: PLC0415
+    import sqlite3  # noqa: PLC0415
+
+    for db_path in (_IDE_STATE_DB_MAC, _IDE_STATE_DB_LINUX):
+        if not db_path.exists():
+            continue
+        try:
+            con = sqlite3.connect(f"file:{db_path}?mode=ro", uri=True)
+            try:
+                row = con.execute(
+                    "SELECT value FROM ItemTable WHERE key = ?",
+                    (_IDE_STATE_DB_KEY,),
+                ).fetchone()
+            finally:
+                con.close()
+            if not row:
+                continue
+
+            blob = base64.b64decode(row[0])
+            candidates = re.findall(rb"[A-Za-z0-9+/=_\-]{40,}", blob)
+            access_token: str | None = None
+            refresh_token: str | None = None
+            for candidate in candidates:
+                try:
+                    padded = candidate + b"=" * (-len(candidate) % 4)
+                    inner = base64.urlsafe_b64decode(padded)
+                except Exception:
+                    continue
+                if not access_token:
+                    m = re.search(rb"ya29\.[A-Za-z0-9_\-\.]+", inner)
+                    if m:
+                        access_token = m.group(0).decode("ascii")
+                if not refresh_token:
+                    m = re.search(rb"1//[A-Za-z0-9_\-\.]+", inner)
+                    if m:
+                        refresh_token = m.group(0).decode("ascii")
+                if access_token and refresh_token:
+                    break
+
+            if access_token:
+                # Estimate expiry from DB mtime (IDE refreshes while running)
+                mtime = db_path.stat().st_mtime
+                expires_at = mtime + 3600.0
+                return access_token, refresh_token, expires_at
+        except Exception as exc:
+            logger.debug("Failed to read Antigravity IDE state DB: %s", exc)
+            continue
+    return None, None, 0.0
+
+
+def _do_token_refresh(refresh_token: str) -> tuple[str, float] | None:
+    """POST to Google OAuth endpoint and return ``(new_access_token, expires_at)``.
+
+    The client secret is sourced via ``get_antigravity_client_secret()`` (env var,
+    config file, or npm package fallback). When unavailable the refresh is attempted
+    without it — Google will reject it for web-app clients, but the npm fallback in
+    ``get_antigravity_client_secret()`` should ensure the secret is found at runtime.
+
+    Returns None when the HTTP request fails.
+    """
+    from framework.config import get_antigravity_client_secret  # noqa: PLC0415
+
+    client_secret = get_antigravity_client_secret()
+    if not client_secret:
+        logger.debug(
+            "Antigravity client secret not configured — attempting refresh without it. "
+            "Set ANTIGRAVITY_CLIENT_SECRET or run quickstart to configure."
+        )
+
+    import urllib.error  # noqa: PLC0415
+    import urllib.parse  # noqa: PLC0415
+    import urllib.request  # noqa: PLC0415
+
+    from framework.config import get_antigravity_client_id  # noqa: PLC0415
+
+    params: dict[str, str] = {
+        "grant_type": "refresh_token",
+        "refresh_token": refresh_token,
+        "client_id": get_antigravity_client_id(),
+    }
+    if client_secret:
+        params["client_secret"] = client_secret
+    body = urllib.parse.urlencode(params).encode("utf-8")
+
+    req = urllib.request.Request(
+        _TOKEN_URL,
+        data=body,
+        headers={"Content-Type": "application/x-www-form-urlencoded"},
+        method="POST",
+    )
+    try:
+        with urllib.request.urlopen(req, timeout=15) as resp:  # noqa: S310
+            payload = json.loads(resp.read())
+        access_token: str = payload["access_token"]
+        expires_in: int = payload.get("expires_in", 3600)
+        logger.debug("Antigravity token refreshed successfully")
+        return access_token, time.time() + expires_in
+    except Exception as exc:
+        logger.debug("Antigravity token refresh failed: %s", exc)
+        return None
+
+
+# ---------------------------------------------------------------------------
+# Message conversion helpers
+# ---------------------------------------------------------------------------
+
+
+def _clean_tool_name(name: str) -> str:
+    """Sanitize a tool name for the Antigravity function-calling schema."""
+    name = re.sub(r"[/\s]", "_", name)
+    if name and not (name[0].isalpha() or name[0] == "_"):
+        name = "_" + name
+    return name[:64]
+
+
+def _to_gemini_contents(
+    messages: list[dict[str, Any]],
+    thought_sigs: dict[str, str] | None = None,
+) -> list[dict[str, Any]]:
+    """Convert OpenAI-format messages to Gemini-style ``contents`` array."""
+    # Pre-build a map tool_call_id → function_name from assistant messages.
+    # Tool result messages (role="tool") only carry tool_call_id, not the name,
+    # but Gemini requires functionResponse.name to match the functionCall.name.
+    tc_id_to_name: dict[str, str] = {}
+    for msg in messages:
+        if msg.get("role") == "assistant":
+            for tc in msg.get("tool_calls") or []:
+                tc_id = tc.get("id")
+                fn_name = tc.get("function", {}).get("name", "")
+                if tc_id and fn_name:
+                    tc_id_to_name[tc_id] = fn_name
+
+    contents: list[dict[str, Any]] = []
+    # Consecutive tool-result messages must be batched into one user turn.
+    pending_tool_parts: list[dict[str, Any]] = []
+
+    def _flush_tool_results() -> None:
+        if pending_tool_parts:
+            contents.append({"role": "user", "parts": list(pending_tool_parts)})
+            pending_tool_parts.clear()
+
+    for msg in messages:
+        role = msg.get("role", "user")
+        content = msg.get("content")
+
+        if role == "system":
+            continue  # Handled via systemInstruction, not in contents.
+
+        if role == "tool":
+            # OpenAI tool result → Gemini functionResponse part.
+            result_str = content if isinstance(content, str) else str(content or "")
+            tc_id = msg.get("tool_call_id", "")
+            # Look up function name from the pre-built map; fall back to msg.name.
+            fn_name = tc_id_to_name.get(tc_id) or msg.get("name", "")
+            pending_tool_parts.append(
+                {
+                    "functionResponse": {
+                        "name": fn_name,
+                        "id": tc_id,
+                        "response": {"content": result_str},
+                    }
+                }
+            )
+            continue
+
+        _flush_tool_results()
+
+        gemini_role = "model" if role == "assistant" else "user"
+        parts: list[dict[str, Any]] = []
+
+        if isinstance(content, str) and content:
+            parts.append({"text": content})
+        elif isinstance(content, list):
+            for block in content:
+                if not isinstance(block, dict):
+                    continue
+                if block.get("type") == "text":
+                    text = block.get("text", "")
+                    if text:
+                        parts.append({"text": text})
+                # Other block types (image_url etc.) skipped.
+
+        # Assistant messages may carry OpenAI-style tool_calls.
+        for tc in msg.get("tool_calls") or []:
+            fn = tc.get("function", {})
+            try:
+                args = json.loads(fn.get("arguments", "{}") or "{}")
+            except (json.JSONDecodeError, TypeError):
+                args = {}
+            tc_id = tc.get("id", str(uuid.uuid4()))
+            fc_part: dict[str, Any] = {
+                "functionCall": {
+                    "name": fn.get("name", ""),
+                    "args": args,
+                    "id": tc_id,
+                }
+            }
+            if thought_sigs:
+                sig = thought_sigs.get(tc_id, "")
+                if sig:
+                    fc_part["thoughtSignature"] = sig  # part-level, not inside functionCall
+            parts.append(fc_part)
+
+        if parts:
+            contents.append({"role": gemini_role, "parts": parts})
+
+    _flush_tool_results()
+
+    # Gemini requires the first turn to be a user turn.  Drop any leading
+    # model messages so the API doesn't reject with a 400.
+    while contents and contents[0].get("role") == "model":
+        contents.pop(0)
+
+    return contents
+
+
+# ---------------------------------------------------------------------------
+# Response parsing helpers
+# ---------------------------------------------------------------------------
+
+
+def _map_finish_reason(reason: str) -> str:
+    return {"STOP": "stop", "MAX_TOKENS": "max_tokens", "OTHER": "tool_use"}.get(
+        (reason or "").upper(), "stop"
+    )
+
+
+def _parse_complete_response(raw: dict[str, Any], model: str) -> LLMResponse:
+    """Parse a non-streaming Antigravity response dict → LLMResponse."""
+    payload: dict[str, Any] = raw.get("response", raw)
+    candidates: list[dict[str, Any]] = payload.get("candidates", [])
+    usage: dict[str, Any] = payload.get("usageMetadata", {})
+
+    text_parts: list[str] = []
+    if candidates:
+        for part in candidates[0].get("content", {}).get("parts", []):
+            if "text" in part and not part.get("thought"):
+                text_parts.append(part["text"])
+
+    return LLMResponse(
+        content="".join(text_parts),
+        model=payload.get("modelVersion", model),
+        input_tokens=usage.get("promptTokenCount", 0),
+        output_tokens=usage.get("candidatesTokenCount", 0),
+        stop_reason=_map_finish_reason(candidates[0].get("finishReason", "") if candidates else ""),
+        raw_response=raw,
+    )
+
+
+def _parse_sse_stream(
+    response: Any,
+    model: str,
+    on_thought_signature: Callable[[str, str], None] | None = None,
+) -> Iterator[StreamEvent]:
+    """Parse Antigravity SSE response line-by-line → StreamEvents.
+
+    Each SSE line looks like::
+
+        data: {"response": {"candidates": [...], "usageMetadata": {...}}, "traceId": "..."}
+    """
+    accumulated = ""
+    input_tokens = 0
+    output_tokens = 0
+    finish_reason = ""
+
+    for raw_line in response:
+        line: str = raw_line.decode("utf-8", errors="replace").rstrip("\r\n")
+        if not line.startswith("data:"):
+            continue
+        data_str = line[5:].strip()
+        if not data_str or data_str == "[DONE]":
+            continue
+        try:
+            data: dict[str, Any] = json.loads(data_str)
+        except json.JSONDecodeError:
+            continue
+
+        # The outer envelope is {"response": {...}, "traceId": "..."}.
+        payload: dict[str, Any] = data.get("response", data)
+
+        usage = payload.get("usageMetadata", {})
+        if usage:
+            input_tokens = usage.get("promptTokenCount", input_tokens)
+            output_tokens = usage.get("candidatesTokenCount", output_tokens)
+
+        for candidate in payload.get("candidates", []):
+            fr = candidate.get("finishReason", "")
+            if fr:
+                finish_reason = fr
+
+            for part in candidate.get("content", {}).get("parts", []):
+                if "text" in part and not part.get("thought"):
+                    delta: str = part["text"]
+                    accumulated += delta
+                    yield TextDeltaEvent(content=delta, snapshot=accumulated)
+                elif "functionCall" in part:
+                    fc: dict[str, Any] = part["functionCall"]
+                    tool_use_id = fc.get("id") or str(uuid.uuid4())
+                    thought_sig = part.get("thoughtSignature", "")  # sibling of functionCall
+                    if thought_sig and on_thought_signature:
+                        on_thought_signature(tool_use_id, thought_sig)
+                    args = fc.get("args", {})
+                    if isinstance(args, str):
+                        try:
+                            args = json.loads(args)
+                        except json.JSONDecodeError:
+                            args = {}
+                    yield ToolCallEvent(
+                        tool_use_id=tool_use_id,
+                        tool_name=fc.get("name", ""),
+                        tool_input=args,
+                    )
+
+    if accumulated:
+        yield TextEndEvent(full_text=accumulated)
+    yield FinishEvent(
+        stop_reason=_map_finish_reason(finish_reason),
+        input_tokens=input_tokens,
+        output_tokens=output_tokens,
+        model=model,
+    )
+
+
+# ---------------------------------------------------------------------------
+# Provider
+# ---------------------------------------------------------------------------
+
+
+class AntigravityProvider(LLMProvider):
+    """LLM provider for Google's internal Antigravity Code Assist gateway.
+
+    No local proxy required.  Handles OAuth token refresh, Gemini-format
+    request/response conversion, and SSE streaming directly.
+    """
+
+    def __init__(self, model: str = "gemini-3-flash") -> None:
+        # Strip any provider prefix ("openai/gemini-3-flash" → "gemini-3-flash").
+        if "/" in model:
+            model = model.split("/", 1)[1]
+        self.model = model
+
+        self._access_token: str | None = None
+        self._refresh_token: str | None = None
+        self._project_id: str = _DEFAULT_PROJECT_ID
+        self._token_expires_at: float = 0.0
+        self._thought_sigs: dict[str, str] = {}  # tool_use_id → thoughtSignature
+
+        self._init_credentials()
+
+    # --- Credential management -------------------------------------------- #
+
+    def _init_credentials(self) -> None:
+        """Load credentials from the best available source."""
+        access, refresh, project_id, expires_at = _load_from_json_file()
+        if refresh:
+            self._refresh_token = refresh
+            self._project_id = project_id
+            self._access_token = access
+            self._token_expires_at = expires_at
+            return
+
+        # Fall back to IDE state DB.
+        access, refresh, expires_at = _load_from_ide_db()
+        if access:
+            self._access_token = access
+            self._refresh_token = refresh
+            self._token_expires_at = expires_at
+
+    def has_credentials(self) -> bool:
+        """Return True if any credential is available."""
+        return bool(self._access_token or self._refresh_token)
+
+    def _ensure_token(self) -> str:
+        """Return a valid access token, refreshing via OAuth if needed."""
+        if (
+            self._access_token
+            and self._token_expires_at
+            and time.time() < self._token_expires_at - _TOKEN_REFRESH_BUFFER_SECS
+        ):
+            return self._access_token
+
+        if self._refresh_token:
+            result = _do_token_refresh(self._refresh_token)
+            if result:
+                self._access_token, self._token_expires_at = result
+                return self._access_token
+
+        if self._access_token:
+            logger.warning("Using potentially stale Antigravity access token")
+            return self._access_token
+
+        raise RuntimeError(
+            "No valid Antigravity credentials. "
+            "Run: uv run python core/antigravity_auth.py auth account add"
+        )
+
+    # --- Request building -------------------------------------------------- #
+
+    def _build_body(
+        self,
+        messages: list[dict[str, Any]],
+        system: str,
+        tools: list[Tool] | None,
+        max_tokens: int,
+    ) -> dict[str, Any]:
+        contents = _to_gemini_contents(messages, self._thought_sigs)
+        inner: dict[str, Any] = {
+            "contents": contents,
+            "generationConfig": {"maxOutputTokens": max_tokens},
+        }
+        if system:
+            inner["systemInstruction"] = {"parts": [{"text": system}]}
+        if tools:
+            inner["tools"] = [
+                {
+                    "functionDeclarations": [
+                        {
+                            "name": _clean_tool_name(t.name),
+                            "description": t.description,
+                            "parameters": t.parameters
+                            or {
+                                "type": "object",
+                                "properties": {},
+                            },
+                        }
+                        for t in tools
+                    ]
+                }
+            ]
+        return {
+            "project": self._project_id,
+            "model": self.model,
+            "request": inner,
+            "requestType": "agent",
+            "userAgent": "antigravity",
+            "requestId": f"agent-{uuid.uuid4()}",
+        }
+
+    # --- HTTP transport ---------------------------------------------------- #
+
+    def _post(self, body: dict[str, Any], *, streaming: bool) -> Any:
+        """POST to the Antigravity endpoint, falling back through the endpoint list."""
+        import urllib.error  # noqa: PLC0415
+        import urllib.request  # noqa: PLC0415
+
+        token = self._ensure_token()
+        body_bytes = json.dumps(body).encode("utf-8")
+        path = (
+            "/v1internal:streamGenerateContent?alt=sse"
+            if streaming
+            else "/v1internal:generateContent"
+        )
+        headers = {
+            **_BASE_HEADERS,
+            "Authorization": f"Bearer {token}",
+            "Content-Type": "application/json",
+        }
+        if streaming:
+            headers["Accept"] = "text/event-stream"
+
+        last_exc: Exception | None = None
+        for base_url in _ENDPOINTS:
+            url = f"{base_url}{path}"
+            req = urllib.request.Request(url, data=body_bytes, headers=headers, method="POST")
+            try:
+                return urllib.request.urlopen(req, timeout=120)  # noqa: S310
+            except urllib.error.HTTPError as exc:
+                if exc.code in (401, 403) and self._refresh_token:
+                    # Token rejected — refresh once and retry this endpoint.
+                    result = _do_token_refresh(self._refresh_token)
+                    if result:
+                        self._access_token, self._token_expires_at = result
+                        headers["Authorization"] = f"Bearer {self._access_token}"
+                        req2 = urllib.request.Request(
+                            url, data=body_bytes, headers=headers, method="POST"
+                        )
+                        try:
+                            return urllib.request.urlopen(req2, timeout=120)  # noqa: S310
+                        except urllib.error.HTTPError as exc2:
+                            last_exc = exc2
+                            continue
+                    last_exc = exc
+                    continue
+                elif exc.code >= 500:
+                    last_exc = exc
+                    continue
+                # Include the API response body in the exception for easier debugging.
+                try:
+                    err_body = exc.read().decode("utf-8", errors="replace")
+                except Exception:
+                    err_body = "(unreadable)"
+                raise RuntimeError(f"Antigravity HTTP {exc.code} from {url}: {err_body}") from exc
+            except (urllib.error.URLError, OSError) as exc:
+                last_exc = exc
+                continue
+
+        raise RuntimeError(
+            f"All Antigravity endpoints failed. Last error: {last_exc}"
+        ) from last_exc
+
+    # --- LLMProvider interface --------------------------------------------- #
+
+    def complete(
+        self,
+        messages: list[dict[str, Any]],
+        system: str = "",
+        tools: list[Tool] | None = None,
+        max_tokens: int = 1024,
+        response_format: dict[str, Any] | None = None,
+        json_mode: bool = False,
+        max_retries: int | None = None,
+    ) -> LLMResponse:
+        if json_mode:
+            suffix = "\n\nPlease respond with a valid JSON object."
+            system = (system + suffix) if system else suffix.strip()
+
+        body = self._build_body(messages, system, tools, max_tokens)
+        resp = self._post(body, streaming=False)
+        return _parse_complete_response(json.loads(resp.read()), self.model)
+
+    async def stream(
+        self,
+        messages: list[dict[str, Any]],
+        system: str = "",
+        tools: list[Tool] | None = None,
+        max_tokens: int = 4096,
+    ) -> AsyncIterator[StreamEvent]:
+        import asyncio  # noqa: PLC0415
+        import concurrent.futures  # noqa: PLC0415
+
+        loop = asyncio.get_running_loop()
+        queue: asyncio.Queue[StreamEvent | None] = asyncio.Queue()
+
+        def _blocking_work() -> None:
+            try:
+                body = self._build_body(messages, system, tools, max_tokens)
+                http_resp = self._post(body, streaming=True)
+                for event in _parse_sse_stream(
+                    http_resp, self.model, self._thought_sigs.__setitem__
+                ):
+                    loop.call_soon_threadsafe(queue.put_nowait, event)
+            except Exception as exc:
+                logger.error("Antigravity stream error: %s", exc)
+                loop.call_soon_threadsafe(queue.put_nowait, StreamErrorEvent(error=str(exc)))
+            finally:
+                loop.call_soon_threadsafe(queue.put_nowait, None)  # sentinel
+
+        executor = concurrent.futures.ThreadPoolExecutor(max_workers=1)
+        fut = loop.run_in_executor(executor, _blocking_work)
+        try:
+            while True:
+                event = await queue.get()
+                if event is None:
+                    break
+                yield event
+        finally:
+            await fut
+            executor.shutdown(wait=False)

core/framework/llm/capabilities.py

@@ -0,0 +1,106 @@
+"""Model capability checks for LLM providers.
+
+Vision support rules are derived from official vendor documentation:
+- ZAI (z.ai): docs.z.ai/guides/vlm — GLM-4.6V variants are vision; GLM-5/4.6/4.7 are text-only
+- MiniMax: platform.minimax.io/docs — minimax-vl-01 is vision; M2.x are text-only
+- DeepSeek: api-docs.deepseek.com — deepseek-vl2 is vision; chat/reasoner are text-only
+- Cerebras: inference-docs.cerebras.ai — no vision models at all
+- Groq: console.groq.com/docs/vision — vision capable; treat as supported by default
+- Ollama/LM Studio/vLLM/llama.cpp: local runners denied by default; model names
+  don't reliably indicate vision support, so users must configure explicitly
+"""
+
+from __future__ import annotations
+
+
+def _model_name(model: str) -> str:
+    """Return the bare model name after stripping any 'provider/' prefix."""
+    if "/" in model:
+        return model.split("/", 1)[1]
+    return model
+
+
+# Step 1: explicit vision allow-list — these always support images regardless
+# of what the provider-level rules say.  Checked first so that e.g. glm-4.6v
+# is allowed even though glm-4.6 is denied.
+_VISION_ALLOW_BARE_PREFIXES: tuple[str, ...] = (
+    # ZAI/GLM vision models (docs.z.ai/guides/vlm)
+    "glm-4v",  # GLM-4V series (legacy)
+    "glm-4.6v",  # GLM-4.6V, GLM-4.6V-flash, GLM-4.6V-flashx
+    # DeepSeek vision models
+    "deepseek-vl",  # deepseek-vl2, deepseek-vl2-small, deepseek-vl2-tiny
+    # MiniMax vision model
+    "minimax-vl",  # minimax-vl-01
+)
+
+# Step 2: provider-level deny — every model from this provider is text-only.
+_TEXT_ONLY_PROVIDER_PREFIXES: tuple[str, ...] = (
+    # Cerebras: inference-docs.cerebras.ai lists only text models
+    "cerebras/",
+    # Local runners: model names don't reliably indicate vision support
+    "ollama/",
+    "ollama_chat/",
+    "lm_studio/",
+    "vllm/",
+    "llamacpp/",
+)
+
+# Step 3: per-model deny — text-only models within otherwise mixed providers.
+# Matched against the bare model name (provider prefix stripped, lower-cased).
+# The vision allow-list above is checked first, so vision variants of the same
+# family are already handled before these deny patterns are reached.
+_TEXT_ONLY_MODEL_BARE_PREFIXES: tuple[str, ...] = (
+    # --- ZAI / GLM family ---
+    # text-only: glm-5, glm-4.6, glm-4.7, glm-4.5, zai-glm-*
+    # vision:    glm-4v, glm-4.6v (caught by allow-list above)
+    "glm-5",
+    "glm-4.6",  # bare glm-4.6 is text-only; glm-4.6v is caught by allow-list
+    "glm-4.7",
+    "glm-4.5",
+    "zai-glm",
+    # --- DeepSeek ---
+    # text-only: deepseek-chat, deepseek-coder, deepseek-reasoner
+    # vision:    deepseek-vl2 (caught by allow-list above)
+    # Note: LiteLLM's deepseek handler may flatten content lists for some models;
+    # VL models are allowed through and rely on LiteLLM's native VL support.
+    "deepseek-chat",
+    "deepseek-coder",
+    "deepseek-reasoner",
+    # --- MiniMax ---
+    # text-only: minimax-m2.*, minimax-text-*, abab* (legacy)
+    # vision:    minimax-vl-01 (caught by allow-list above)
+    "minimax-m2",
+    "minimax-text",
+    "abab",
+)
+
+
+def supports_image_tool_results(model: str) -> bool:
+    """Return whether *model* can receive image content in messages.
+
+    Used to gate both user-message images and tool-result image blocks.
+
+    Logic (checked in order):
+    1. Vision allow-list  → True  (known vision model, skip all denies)
+    2. Provider deny      → False (entire provider is text-only)
+    3. Model deny         → False (specific text-only model within a mixed provider)
+    4. Default            → True  (assume capable; unknown providers and models)
+    """
+    model_lower = model.lower()
+    bare = _model_name(model_lower)
+
+    # 1. Explicit vision allow — takes priority over all denies
+    if any(bare.startswith(p) for p in _VISION_ALLOW_BARE_PREFIXES):
+        return True
+
+    # 2. Provider-level deny (all models from this provider are text-only)
+    if any(model_lower.startswith(p) for p in _TEXT_ONLY_PROVIDER_PREFIXES):
+        return False
+
+    # 3. Per-model deny (text-only variants within mixed-capability families)
+    if any(bare.startswith(p) for p in _TEXT_ONLY_MODEL_BARE_PREFIXES):
+        return False
+
+    # 5. Default: assume vision capable
+    #    Covers: OpenAI, Anthropic, Google, Mistral, Kimi, and other hosted providers
+    return True

core/framework/llm/litellm.py

@@ -7,9 +7,13 @@ Groq, and local models.
 See: https://docs.litellm.ai/docs/providers
 """
 
+import ast
 import asyncio
+import hashlib
 import json
 import logging
+import os
+import re
 import time
 from collections.abc import AsyncIterator
 from datetime import datetime
@@ -23,6 +27,7 @@ except ImportError:
     litellm = None  # type: ignore[assignment]
     RateLimitError = Exception  # type: ignore[assignment, misc]
 
+from framework.config import HIVE_LLM_ENDPOINT as HIVE_API_BASE
 from framework.llm.provider import LLMProvider, LLMResponse, Tool
 from framework.llm.stream_events import StreamEvent
 
@@ -43,8 +48,17 @@ def _patch_litellm_anthropic_oauth() -> None:
     """
     try:
         from litellm.llms.anthropic.common_utils import AnthropicModelInfo
-        from litellm.types.llms.anthropic import ANTHROPIC_OAUTH_TOKEN_PREFIX
+        from litellm.types.llms.anthropic import (
+            ANTHROPIC_OAUTH_BETA_HEADER,
+            ANTHROPIC_OAUTH_TOKEN_PREFIX,
+        )
     except ImportError:
+        logger.warning(
+            "Could not apply litellm Anthropic OAuth patch — litellm internals may have "
+            "changed. Anthropic OAuth tokens (Claude Code subscriptions) may fail with 401. "
+            "See BerriAI/litellm#19618. Current litellm version: %s",
+            getattr(litellm, "__version__", "unknown"),
+        )
         return
 
     original = AnthropicModelInfo.validate_environment
@@ -62,9 +76,27 @@ def _patch_litellm_anthropic_oauth() -> None:
             api_key=api_key,
             api_base=api_base,
         )
+        # Check both authorization header and x-api-key for OAuth tokens.
+        # litellm's optionally_handle_anthropic_oauth only checks headers["authorization"],
+        # but hive passes OAuth tokens via api_key — so litellm puts them into x-api-key.
+        # Anthropic rejects OAuth tokens in x-api-key; they must go in Authorization: Bearer.
         auth = result.get("authorization", "")
-        if auth.startswith(f"Bearer {ANTHROPIC_OAUTH_TOKEN_PREFIX}"):
+        x_api_key = result.get("x-api-key", "")
+        oauth_prefix = f"Bearer {ANTHROPIC_OAUTH_TOKEN_PREFIX}"
+        auth_is_oauth = auth.startswith(oauth_prefix)
+        key_is_oauth = x_api_key.startswith(ANTHROPIC_OAUTH_TOKEN_PREFIX)
+        if auth_is_oauth or key_is_oauth:
+            token = x_api_key if key_is_oauth else auth.removeprefix("Bearer ").strip()
             result.pop("x-api-key", None)
+            result["authorization"] = f"Bearer {token}"
+            # Merge the OAuth beta header with any existing beta headers.
+            existing_beta = result.get("anthropic-beta", "")
+            beta_parts = (
+                [b.strip() for b in existing_beta.split(",") if b.strip()] if existing_beta else []
+            )
+            if ANTHROPIC_OAUTH_BETA_HEADER not in beta_parts:
+                beta_parts.append(ANTHROPIC_OAUTH_BETA_HEADER)
+            result["anthropic-beta"] = ",".join(beta_parts)
         return result
 
     AnthropicModelInfo.validate_environment = _patched_validate_environment
@@ -86,10 +118,12 @@ def _patch_litellm_metadata_nonetype() -> None:
     """
     import functools
 
+    patched_count = 0
     for fn_name in ("completion", "acompletion", "responses", "aresponses"):
         original = getattr(litellm, fn_name, None)
         if original is None:
             continue
+        patched_count += 1
         if asyncio.iscoroutinefunction(original):
 
             @functools.wraps(original)
@@ -109,15 +143,47 @@ def _patch_litellm_metadata_nonetype() -> None:
 
             setattr(litellm, fn_name, _sync_wrapper)
 
+    if patched_count == 0:
+        logger.warning(
+            "Could not apply litellm metadata=None patch — none of the expected entry "
+            "points (completion, acompletion, responses, aresponses) were found. "
+            "metadata=None TypeError may occur. Current litellm version: %s",
+            getattr(litellm, "__version__", "unknown"),
+        )
+
 
 if litellm is not None:
     _patch_litellm_anthropic_oauth()
     _patch_litellm_metadata_nonetype()
+    # Let litellm silently drop params unsupported by the target provider
+    # (e.g. stream_options for Anthropic) instead of forwarding them verbatim.
+    litellm.drop_params = True
+
+
+def _is_ollama_model(model: str) -> bool:
+    """Return True for any Ollama model string (ollama/ or ollama_chat/ prefix)."""
+    return model.startswith("ollama/") or model.startswith("ollama_chat/")
+
+
+def _ensure_ollama_chat_prefix(model: str) -> str:
+    """Normalise Ollama model strings to use the ollama_chat/ prefix.
+
+    LiteLLM requires the ``ollama_chat/`` prefix (not ``ollama/``) to enable
+    native function-calling support.  With ``ollama/``, LiteLLM falls back to
+    JSON-mode tool calls, which the framework cannot parse as real tool calls.
+
+    See: https://docs.litellm.ai/docs/providers/ollama#example-usage---tool-calling
+    """
+    if model.startswith("ollama/"):
+        return "ollama_chat/" + model[len("ollama/") :]
+    return model
+
 
 RATE_LIMIT_MAX_RETRIES = 10
 RATE_LIMIT_BACKOFF_BASE = 2  # seconds
 RATE_LIMIT_MAX_DELAY = 120  # seconds - cap to prevent absurd waits
 MINIMAX_API_BASE = "https://api.minimax.io/v1"
+OPENROUTER_API_BASE = "https://openrouter.ai/api/v1"
 
 # Providers that accept cache_control on message content blocks.
 # Anthropic: native ephemeral caching. MiniMax & Z-AI/GLM: pass-through to their APIs.
@@ -142,14 +208,77 @@ def _model_supports_cache_control(model: str) -> bool:
 # enforces a coding-agent whitelist that blocks unknown User-Agents.
 KIMI_API_BASE = "https://api.kimi.com/coding"
 
+# Claude Code OAuth subscription: the Anthropic API requires a specific
+# User-Agent and a billing integrity header for OAuth-authenticated requests.
+CLAUDE_CODE_VERSION = "2.1.76"
+CLAUDE_CODE_USER_AGENT = f"claude-code/{CLAUDE_CODE_VERSION}"
+_CLAUDE_CODE_BILLING_SALT = "59cf53e54c78"
+
+
+def _sample_js_code_unit(text: str, idx: int) -> str:
+    """Return the character at UTF-16 code unit index *idx*, matching JS semantics."""
+    encoded = text.encode("utf-16-le")
+    unit_offset = idx * 2
+    if unit_offset + 2 > len(encoded):
+        return "0"
+    code_unit = int.from_bytes(encoded[unit_offset : unit_offset + 2], "little")
+    return chr(code_unit)
+
+
+def _claude_code_billing_header(messages: list[dict[str, Any]]) -> str:
+    """Build the billing integrity system block required by Anthropic's OAuth path."""
+    # Find the first user message text
+    first_text = ""
+    for msg in messages:
+        if msg.get("role") != "user":
+            continue
+        content = msg.get("content")
+        if isinstance(content, str):
+            first_text = content
+            break
+        if isinstance(content, list):
+            for block in content:
+                if isinstance(block, dict) and block.get("type") == "text" and block.get("text"):
+                    first_text = block["text"]
+                    break
+            if first_text:
+                break
+
+    sampled = "".join(_sample_js_code_unit(first_text, i) for i in (4, 7, 20))
+    version_hash = hashlib.sha256(
+        f"{_CLAUDE_CODE_BILLING_SALT}{sampled}{CLAUDE_CODE_VERSION}".encode()
+    ).hexdigest()
+    entrypoint = os.environ.get("CLAUDE_CODE_ENTRYPOINT", "").strip() or "cli"
+    return (
+        f"x-anthropic-billing-header: cc_version={CLAUDE_CODE_VERSION}.{version_hash[:3]}; "
+        f"cc_entrypoint={entrypoint}; cch=00000;"
+    )
+
+
 # Empty-stream retries use a short fixed delay, not the rate-limit backoff.
 # Conversation-structure issues are deterministic — long waits don't help.
 EMPTY_STREAM_MAX_RETRIES = 3
 EMPTY_STREAM_RETRY_DELAY = 1.0  # seconds
+OPENROUTER_TOOL_COMPAT_ERROR_SNIPPETS = (
+    "no endpoints found that support tool use",
+    "no endpoints available that support tool use",
+    "provider routing",
+)
+OPENROUTER_TOOL_CALL_RE = re.compile(
+    r"<\|tool_call_start\|>\s*(.*?)\s*<\|tool_call_end\|>",
+    re.DOTALL,
+)
+OPENROUTER_TOOL_COMPAT_CACHE_TTL_SECONDS = 3600
+# OpenRouter routing can change over time, so tool-compat caching must expire.
+OPENROUTER_TOOL_COMPAT_MODEL_CACHE: dict[str, float] = {}
 
 # Directory for dumping failed requests
 FAILED_REQUESTS_DIR = Path.home() / ".hive" / "failed_requests"
 
+# Maximum number of dump files to retain in ~/.hive/failed_requests/.
+# Older files are pruned automatically to prevent unbounded disk growth.
+MAX_FAILED_REQUEST_DUMPS = 50
+
 
 def _estimate_tokens(model: str, messages: list[dict]) -> tuple[int, str]:
     """Estimate token count for messages. Returns (token_count, method)."""
@@ -166,6 +295,42 @@ def _estimate_tokens(model: str, messages: list[dict]) -> tuple[int, str]:
     return total_chars // 4, "estimate"
 
 
+def _prune_failed_request_dumps(max_files: int = MAX_FAILED_REQUEST_DUMPS) -> None:
+    """Remove oldest dump files when the count exceeds *max_files*.
+
+    Best-effort: never raises — a pruning failure must not break retry logic.
+    """
+    try:
+        all_dumps = sorted(
+            FAILED_REQUESTS_DIR.glob("*.json"),
+            key=lambda f: f.stat().st_mtime,
+        )
+        excess = len(all_dumps) - max_files
+        if excess > 0:
+            for old_file in all_dumps[:excess]:
+                old_file.unlink(missing_ok=True)
+    except Exception:
+        pass  # Best-effort — never block the caller
+
+
+def _remember_openrouter_tool_compat_model(model: str) -> None:
+    """Cache OpenRouter tool-compat fallback for a bounded time window."""
+    OPENROUTER_TOOL_COMPAT_MODEL_CACHE[model] = (
+        time.monotonic() + OPENROUTER_TOOL_COMPAT_CACHE_TTL_SECONDS
+    )
+
+
+def _is_openrouter_tool_compat_cached(model: str) -> bool:
+    """Return True when the cached OpenRouter compat entry is still fresh."""
+    expires_at = OPENROUTER_TOOL_COMPAT_MODEL_CACHE.get(model)
+    if expires_at is None:
+        return False
+    if expires_at <= time.monotonic():
+        OPENROUTER_TOOL_COMPAT_MODEL_CACHE.pop(model, None)
+        return False
+    return True
+
+
 def _dump_failed_request(
     model: str,
     kwargs: dict[str, Any],
@@ -197,6 +362,9 @@ def _dump_failed_request(
     with open(filepath, "w", encoding="utf-8") as f:
         json.dump(dump_data, f, indent=2, default=str)
 
+    # Prune old dumps to prevent unbounded disk growth
+    _prune_failed_request_dumps()
+
     return str(filepath)
 
 
@@ -351,22 +519,36 @@ class LiteLLMProvider(LLMProvider):
         # Translate kimi/ prefix to anthropic/ so litellm uses the Anthropic
         # Messages API handler and routes to that endpoint — no special headers needed.
         _original_model = model
-        if model.lower().startswith("kimi/"):
+        if _is_ollama_model(model):
+            model = _ensure_ollama_chat_prefix(model)
+        elif model.lower().startswith("kimi/"):
             model = "anthropic/" + model[len("kimi/") :]
             # Normalise api_base: litellm's Anthropic handler appends /v1/messages,
             # so the base must be https://api.kimi.com/coding (no /v1 suffix).
             # Strip a trailing /v1 in case the user's saved config has the old value.
             if api_base and api_base.rstrip("/").endswith("/v1"):
                 api_base = api_base.rstrip("/")[:-3]
+        elif model.lower().startswith("hive/"):
+            model = "anthropic/" + model[len("hive/") :]
+            if api_base and api_base.rstrip("/").endswith("/v1"):
+                api_base = api_base.rstrip("/")[:-3]
         self.model = model
         self.api_key = api_key
         self.api_base = api_base or self._default_api_base_for_model(_original_model)
         self.extra_kwargs = kwargs
+        # Detect Claude Code OAuth subscription by checking the api_key prefix.
+        self._claude_code_oauth = bool(api_key and api_key.startswith("sk-ant-oat"))
+        if self._claude_code_oauth:
+            # Anthropic requires a specific User-Agent for OAuth requests.
+            eh = self.extra_kwargs.setdefault("extra_headers", {})
+            eh.setdefault("user-agent", CLAUDE_CODE_USER_AGENT)
         # The Codex ChatGPT backend (chatgpt.com/backend-api/codex) rejects
         # several standard OpenAI params: max_output_tokens, stream_options.
         self._codex_backend = bool(
             self.api_base and "chatgpt.com/backend-api/codex" in self.api_base
         )
+        # Antigravity routes through a local OpenAI-compatible proxy — no patches needed.
+        self._antigravity = bool(self.api_base and "localhost:8069" in self.api_base)
 
         if litellm is None:
             raise ImportError(
@@ -385,8 +567,12 @@ class LiteLLMProvider(LLMProvider):
         model_lower = model.lower()
         if model_lower.startswith("minimax/") or model_lower.startswith("minimax-"):
             return MINIMAX_API_BASE
+        if model_lower.startswith("openrouter/"):
+            return OPENROUTER_API_BASE
         if model_lower.startswith("kimi/"):
             return KIMI_API_BASE
+        if model_lower.startswith("hive/"):
+            return HIVE_API_BASE
         return None
 
     def _completion_with_rate_limit_retry(
@@ -558,6 +744,10 @@ class LiteLLMProvider(LLMProvider):
         # Add tools if provided
         if tools:
             kwargs["tools"] = [self._tool_to_openai_format(t) for t in tools]
+            if _is_ollama_model(self.model):
+                # Ollama requires explicit tool_choice=auto for function calling
+                # so future readers don't have to guess.
+                kwargs.setdefault("tool_choice", "auto")
 
         # Add response_format for structured output
         # LiteLLM passes this through to the underlying provider
@@ -725,6 +915,9 @@ class LiteLLMProvider(LLMProvider):
             return await self._collect_stream_to_response(stream_iter)
 
         full_messages: list[dict[str, Any]] = []
+        if self._claude_code_oauth:
+            billing = _claude_code_billing_header(messages)
+            full_messages.append({"role": "system", "content": billing})
         if system:
             sys_msg: dict[str, Any] = {"role": "system", "content": system}
             if _model_supports_cache_control(self.model):
@@ -752,6 +945,10 @@ class LiteLLMProvider(LLMProvider):
             kwargs["api_base"] = self.api_base
         if tools:
             kwargs["tools"] = [self._tool_to_openai_format(t) for t in tools]
+            if _is_ollama_model(self.model):
+                # Ollama requires explicit tool_choice=auto for function calling
+                # so future readers don't have to guess.
+                kwargs.setdefault("tool_choice", "auto")
         if response_format:
             kwargs["response_format"] = response_format
 
@@ -786,11 +983,504 @@ class LiteLLMProvider(LLMProvider):
             },
         }
 
+    def _is_anthropic_model(self) -> bool:
+        """Return True when the configured model targets Anthropic."""
+        model = (self.model or "").lower()
+        return model.startswith("anthropic/") or model.startswith("claude-")
+
     def _is_minimax_model(self) -> bool:
         """Return True when the configured model targets MiniMax."""
         model = (self.model or "").lower()
         return model.startswith("minimax/") or model.startswith("minimax-")
 
+    def _is_openrouter_model(self) -> bool:
+        """Return True when the configured model targets OpenRouter."""
+        model = (self.model or "").lower()
+        if model.startswith("openrouter/"):
+            return True
+        api_base = (self.api_base or "").lower()
+        return "openrouter.ai/api/v1" in api_base
+
+    def _should_use_openrouter_tool_compat(
+        self,
+        error: BaseException,
+        tools: list[Tool] | None,
+    ) -> bool:
+        """Return True when OpenRouter rejects native tool use for the model."""
+        if not tools or not self._is_openrouter_model():
+            return False
+        error_text = str(error).lower()
+        return "openrouter" in error_text and any(
+            snippet in error_text for snippet in OPENROUTER_TOOL_COMPAT_ERROR_SNIPPETS
+        )
+
+    @staticmethod
+    def _extract_json_object(text: str) -> dict[str, Any] | None:
+        """Extract the first JSON object from a model response."""
+        candidates = [text.strip()]
+
+        stripped = text.strip()
+        if stripped.startswith("```"):
+            fence_lines = stripped.splitlines()
+            if len(fence_lines) >= 3:
+                candidates.append("\n".join(fence_lines[1:-1]).strip())
+
+        decoder = json.JSONDecoder()
+        for candidate in candidates:
+            if not candidate:
+                continue
+            try:
+                parsed = json.loads(candidate)
+            except json.JSONDecodeError:
+                parsed = None
+            if isinstance(parsed, dict):
+                return parsed
+
+            for start_idx, char in enumerate(candidate):
+                if char != "{":
+                    continue
+                try:
+                    parsed, _ = decoder.raw_decode(candidate[start_idx:])
+                except json.JSONDecodeError:
+                    continue
+                if isinstance(parsed, dict):
+                    return parsed
+        return None
+
+    def _parse_openrouter_tool_compat_response(
+        self,
+        content: str,
+        tools: list[Tool],
+    ) -> tuple[str, list[dict[str, Any]]]:
+        """Parse JSON tool-compat output into assistant text and tool calls."""
+        payload = self._extract_json_object(content)
+        if payload is None:
+            text_tool_content, text_tool_calls = self._parse_openrouter_text_tool_calls(
+                content,
+                tools,
+            )
+            if text_tool_calls:
+                logger.info(
+                    "[openrouter-tool-compat] Parsed textual tool-call markers for %s",
+                    self.model,
+                )
+                return text_tool_content, text_tool_calls
+            logger.info(
+                "[openrouter-tool-compat] %s returned non-JSON fallback content; "
+                "treating it as plain text.",
+                self.model,
+            )
+            return content.strip(), []
+
+        assistant_text = payload.get("assistant_response")
+        if not isinstance(assistant_text, str):
+            assistant_text = payload.get("content")
+        if not isinstance(assistant_text, str):
+            assistant_text = payload.get("response")
+        if not isinstance(assistant_text, str):
+            assistant_text = ""
+
+        tool_calls_raw = payload.get("tool_calls")
+        if not tool_calls_raw and {"name", "arguments"} <= payload.keys():
+            tool_calls_raw = [payload]
+        elif isinstance(payload.get("tool_call"), dict):
+            tool_calls_raw = [payload["tool_call"]]
+
+        if not isinstance(tool_calls_raw, list):
+            tool_calls_raw = []
+
+        allowed_tool_names = {tool.name for tool in tools}
+        tool_calls: list[dict[str, Any]] = []
+        compat_prefix = f"openrouter_compat_{time.time_ns()}"
+
+        for idx, raw_call in enumerate(tool_calls_raw):
+            if not isinstance(raw_call, dict):
+                continue
+
+            function_block = raw_call.get("function")
+            function_name = (
+                raw_call.get("name")
+                or raw_call.get("tool_name")
+                or (function_block.get("name") if isinstance(function_block, dict) else None)
+            )
+            if not isinstance(function_name, str) or function_name not in allowed_tool_names:
+                if function_name:
+                    logger.warning(
+                        "[openrouter-tool-compat] Ignoring unknown tool '%s' for model %s",
+                        function_name,
+                        self.model,
+                    )
+                continue
+
+            arguments = raw_call.get("arguments")
+            if arguments is None:
+                arguments = raw_call.get("tool_input")
+            if arguments is None:
+                arguments = raw_call.get("input")
+            if arguments is None and isinstance(function_block, dict):
+                arguments = function_block.get("arguments")
+            if arguments is None:
+                arguments = {}
+
+            if isinstance(arguments, str):
+                try:
+                    arguments = json.loads(arguments)
+                except json.JSONDecodeError:
+                    arguments = {"_raw": arguments}
+            elif not isinstance(arguments, dict):
+                arguments = {"value": arguments}
+
+            tool_calls.append(
+                {
+                    "id": f"{compat_prefix}_{idx}",
+                    "name": function_name,
+                    "input": arguments,
+                }
+            )
+
+        return assistant_text.strip(), tool_calls
+
+    @staticmethod
+    def _close_truncated_json_fragment(fragment: str) -> str:
+        """Close a truncated JSON fragment by balancing quotes/brackets."""
+        stack: list[str] = []
+        in_string = False
+        escaped = False
+        normalized = fragment.rstrip()
+
+        while normalized and normalized[-1] in ",:{[":
+            normalized = normalized[:-1].rstrip()
+
+        for char in normalized:
+            if in_string:
+                if escaped:
+                    escaped = False
+                elif char == "\\":
+                    escaped = True
+                elif char == '"':
+                    in_string = False
+                continue
+
+            if char == '"':
+                in_string = True
+            elif char in "{[":
+                stack.append(char)
+            elif char == "}" and stack and stack[-1] == "{":
+                stack.pop()
+            elif char == "]" and stack and stack[-1] == "[":
+                stack.pop()
+
+        if in_string:
+            if escaped:
+                normalized = normalized[:-1]
+            normalized += '"'
+
+        for opener in reversed(stack):
+            normalized += "}" if opener == "{" else "]"
+
+        return normalized
+
+    def _repair_truncated_tool_arguments(self, raw_arguments: str) -> dict[str, Any] | None:
+        """Try to recover a truncated JSON object from tool-call arguments."""
+        stripped = raw_arguments.strip()
+        if not stripped or stripped[0] != "{":
+            return None
+
+        max_trim = min(len(stripped), 256)
+        for trim in range(max_trim + 1):
+            candidate = stripped[: len(stripped) - trim].rstrip()
+            if not candidate:
+                break
+            candidate = self._close_truncated_json_fragment(candidate)
+            try:
+                parsed = json.loads(candidate)
+            except json.JSONDecodeError:
+                continue
+            if isinstance(parsed, dict):
+                return parsed
+        return None
+
+    def _parse_tool_call_arguments(self, raw_arguments: str, tool_name: str) -> dict[str, Any]:
+        """Parse streamed tool arguments, repairing truncation when possible."""
+        try:
+            parsed = json.loads(raw_arguments) if raw_arguments else {}
+        except json.JSONDecodeError:
+            parsed = None
+
+        if isinstance(parsed, dict):
+            return parsed
+
+        repaired = self._repair_truncated_tool_arguments(raw_arguments)
+        if repaired is not None:
+            logger.warning(
+                "[tool-args] Recovered truncated arguments for %s on %s",
+                tool_name,
+                self.model,
+            )
+            return repaired
+
+        raise ValueError(
+            f"Failed to parse tool call arguments for '{tool_name}' (likely truncated JSON)."
+        )
+
+    def _parse_openrouter_text_tool_calls(
+        self,
+        content: str,
+        tools: list[Tool],
+    ) -> tuple[str, list[dict[str, Any]]]:
+        """Parse textual OpenRouter tool calls into synthetic tool calls.
+
+        Supports both:
+        - Marker wrapped payloads: <|tool_call_start|>...<|tool_call_end|>
+        - Plain one-line tool calls: ask_user("...", ["..."])
+        """
+        tools_by_name = {tool.name: tool for tool in tools}
+        compat_prefix = f"openrouter_compat_{time.time_ns()}"
+        tool_calls: list[dict[str, Any]] = []
+        segment_index = 0
+
+        for match in OPENROUTER_TOOL_CALL_RE.finditer(content):
+            parsed_calls = self._parse_openrouter_text_tool_call_block(
+                block=match.group(1),
+                tools_by_name=tools_by_name,
+                compat_prefix=f"{compat_prefix}_{segment_index}",
+            )
+            if parsed_calls:
+                segment_index += 1
+                tool_calls.extend(parsed_calls)
+
+        stripped_content = OPENROUTER_TOOL_CALL_RE.sub("", content)
+        retained_lines: list[str] = []
+        for line in stripped_content.splitlines():
+            stripped_line = line.strip()
+            if not stripped_line:
+                retained_lines.append(line)
+                continue
+
+            candidate = stripped_line
+            if candidate.startswith("`") and candidate.endswith("`") and len(candidate) > 1:
+                candidate = candidate[1:-1].strip()
+
+            parsed_calls = self._parse_openrouter_text_tool_call_block(
+                block=candidate,
+                tools_by_name=tools_by_name,
+                compat_prefix=f"{compat_prefix}_{segment_index}",
+            )
+            if parsed_calls:
+                segment_index += 1
+                tool_calls.extend(parsed_calls)
+                continue
+
+            retained_lines.append(line)
+
+        stripped_text = "\n".join(retained_lines).strip()
+        return stripped_text, tool_calls
+
+    def _parse_openrouter_text_tool_call_block(
+        self,
+        block: str,
+        tools_by_name: dict[str, Tool],
+        compat_prefix: str,
+    ) -> list[dict[str, Any]]:
+        """Parse a single textual tool-call block like [tool(arg='x')]."""
+        try:
+            parsed = ast.parse(block.strip(), mode="eval").body
+        except SyntaxError:
+            return []
+
+        call_nodes = parsed.elts if isinstance(parsed, ast.List) else [parsed]
+        tool_calls: list[dict[str, Any]] = []
+
+        for call_index, call_node in enumerate(call_nodes):
+            if not isinstance(call_node, ast.Call) or not isinstance(call_node.func, ast.Name):
+                continue
+
+            tool_name = call_node.func.id
+            tool = tools_by_name.get(tool_name)
+            if tool is None:
+                continue
+
+            try:
+                tool_input = self._parse_openrouter_text_tool_call_arguments(
+                    call_node=call_node,
+                    tool=tool,
+                )
+            except (ValueError, SyntaxError):
+                continue
+
+            tool_calls.append(
+                {
+                    "id": f"{compat_prefix}_{call_index}",
+                    "name": tool_name,
+                    "input": tool_input,
+                }
+            )
+
+        return tool_calls
+
+    @staticmethod
+    def _parse_openrouter_text_tool_call_arguments(
+        call_node: ast.Call,
+        tool: Tool,
+    ) -> dict[str, Any]:
+        """Parse positional/keyword args from a textual tool call."""
+        properties = tool.parameters.get("properties", {})
+        positional_keys = list(properties.keys())
+        tool_input: dict[str, Any] = {}
+
+        if len(call_node.args) > len(positional_keys):
+            raise ValueError("Too many positional args for textual tool call")
+
+        for idx, arg_node in enumerate(call_node.args):
+            tool_input[positional_keys[idx]] = ast.literal_eval(arg_node)
+
+        for kwarg in call_node.keywords:
+            if kwarg.arg is None:
+                raise ValueError("Star args are not supported in textual tool calls")
+            tool_input[kwarg.arg] = ast.literal_eval(kwarg.value)
+
+        return tool_input
+
+    def _build_openrouter_tool_compat_messages(
+        self,
+        messages: list[dict[str, Any]],
+        system: str,
+        tools: list[Tool],
+    ) -> list[dict[str, Any]]:
+        """Build a JSON-only prompt for models without native tool support."""
+        tool_specs = [
+            {
+                "name": tool.name,
+                "description": tool.description,
+                "parameters": tool.parameters,
+            }
+            for tool in tools
+        ]
+        compat_instruction = (
+            "Tool compatibility mode is active because this OpenRouter model does not support "
+            "native function calling on the routed provider.\n"
+            "Return exactly one JSON object and nothing else.\n"
+            'Schema: {"assistant_response": string, '
+            '"tool_calls": [{"name": string, "arguments": object}]}\n'
+            "Rules:\n"
+            "- If a tool is required, put one or more entries in tool_calls "
+            "and do not invent tool results.\n"
+            "- If no tool is required, set tool_calls to [] and put the full "
+            "answer in assistant_response.\n"
+            "- Only use tool names from the allowed tool list.\n"
+            "- arguments must always be valid JSON objects.\n"
+            f"Allowed tools:\n{json.dumps(tool_specs, ensure_ascii=True)}"
+        )
+        compat_system = compat_instruction if not system else f"{system}\n\n{compat_instruction}"
+
+        full_messages: list[dict[str, Any]] = [{"role": "system", "content": compat_system}]
+        full_messages.extend(messages)
+        return [
+            message
+            for message in full_messages
+            if not (
+                message.get("role") == "assistant"
+                and not message.get("content")
+                and not message.get("tool_calls")
+            )
+        ]
+
+    async def _acomplete_via_openrouter_tool_compat(
+        self,
+        messages: list[dict[str, Any]],
+        system: str,
+        tools: list[Tool],
+        max_tokens: int,
+    ) -> LLMResponse:
+        """Emulate tool calling via JSON when OpenRouter rejects native tools."""
+        full_messages = self._build_openrouter_tool_compat_messages(messages, system, tools)
+        kwargs: dict[str, Any] = {
+            "model": self.model,
+            "messages": full_messages,
+            "max_tokens": max_tokens,
+            **self.extra_kwargs,
+        }
+        if self.api_key:
+            kwargs["api_key"] = self.api_key
+        if self.api_base:
+            kwargs["api_base"] = self.api_base
+
+        response = await self._acompletion_with_rate_limit_retry(**kwargs)
+        raw_content = response.choices[0].message.content or ""
+        assistant_text, tool_calls = self._parse_openrouter_tool_compat_response(
+            raw_content,
+            tools,
+        )
+        usage = response.usage
+        input_tokens = usage.prompt_tokens if usage else 0
+        output_tokens = usage.completion_tokens if usage else 0
+        stop_reason = "tool_calls" if tool_calls else (response.choices[0].finish_reason or "stop")
+
+        return LLMResponse(
+            content=assistant_text,
+            model=response.model or self.model,
+            input_tokens=input_tokens,
+            output_tokens=output_tokens,
+            stop_reason=stop_reason,
+            raw_response={
+                "compat_mode": "openrouter_tool_emulation",
+                "tool_calls": tool_calls,
+                "response": response,
+            },
+        )
+
+    async def _stream_via_openrouter_tool_compat(
+        self,
+        messages: list[dict[str, Any]],
+        system: str,
+        tools: list[Tool],
+        max_tokens: int,
+    ) -> AsyncIterator[StreamEvent]:
+        """Fallback stream for OpenRouter models without native tool support."""
+        from framework.llm.stream_events import (
+            FinishEvent,
+            StreamErrorEvent,
+            TextDeltaEvent,
+            TextEndEvent,
+            ToolCallEvent,
+        )
+
+        logger.info(
+            "[openrouter-tool-compat] Using compatibility mode for %s",
+            self.model,
+        )
+        try:
+            response = await self._acomplete_via_openrouter_tool_compat(
+                messages=messages,
+                system=system,
+                tools=tools,
+                max_tokens=max_tokens,
+            )
+        except Exception as e:
+            yield StreamErrorEvent(error=str(e), recoverable=False)
+            return
+
+        raw_response = response.raw_response if isinstance(response.raw_response, dict) else {}
+        tool_calls = raw_response.get("tool_calls", [])
+
+        if response.content:
+            yield TextDeltaEvent(content=response.content, snapshot=response.content)
+            yield TextEndEvent(full_text=response.content)
+
+        for tool_call in tool_calls:
+            yield ToolCallEvent(
+                tool_use_id=tool_call["id"],
+                tool_name=tool_call["name"],
+                tool_input=tool_call["input"],
+            )
+
+        yield FinishEvent(
+            stop_reason=response.stop_reason,
+            input_tokens=response.input_tokens,
+            output_tokens=response.output_tokens,
+            model=response.model,
+        )
+
     async def _stream_via_nonstream_completion(
         self,
         messages: list[dict[str, Any]],
@@ -834,12 +1524,11 @@ class LiteLLMProvider(LLMProvider):
             tool_calls = msg.tool_calls or []
 
         for tc in tool_calls:
-            parsed_args: Any
             args = tc.function.arguments if tc.function else ""
-            try:
-                parsed_args = json.loads(args) if args else {}
-            except json.JSONDecodeError:
-                parsed_args = {"_raw": args}
+            parsed_args = self._parse_tool_call_arguments(
+                args,
+                tc.function.name if tc.function else "",
+            )
             yield ToolCallEvent(
                 tool_use_id=getattr(tc, "id", ""),
                 tool_name=tc.function.name if tc.function else "",
@@ -898,7 +1587,20 @@ class LiteLLMProvider(LLMProvider):
                 yield event
             return
 
+        if tools and self._is_openrouter_model() and _is_openrouter_tool_compat_cached(self.model):
+            async for event in self._stream_via_openrouter_tool_compat(
+                messages=messages,
+                system=system,
+                tools=tools,
+                max_tokens=max_tokens,
+            ):
+                yield event
+            return
+
         full_messages: list[dict[str, Any]] = []
+        if self._claude_code_oauth:
+            billing = _claude_code_billing_header(messages)
+            full_messages.append({"role": "system", "content": billing})
         if system:
             sys_msg: dict[str, Any] = {"role": "system", "content": system}
             if _model_supports_cache_control(self.model):
@@ -936,15 +1638,22 @@ class LiteLLMProvider(LLMProvider):
             "messages": full_messages,
             "max_tokens": max_tokens,
             "stream": True,
-            "stream_options": {"include_usage": True},
             **self.extra_kwargs,
         }
+        # stream_options is OpenAI-specific; Anthropic rejects it with 400.
+        # Only include it for providers that support it.
+        if not self._is_anthropic_model():
+            kwargs["stream_options"] = {"include_usage": True}
         if self.api_key:
             kwargs["api_key"] = self.api_key
         if self.api_base:
             kwargs["api_base"] = self.api_base
         if tools:
             kwargs["tools"] = [self._tool_to_openai_format(t) for t in tools]
+            if _is_ollama_model(self.model):
+                # Ollama requires explicit tool_choice=auto for function calling
+                # so future readers don't have to guess.
+                kwargs.setdefault("tool_choice", "auto")
         if response_format:
             kwargs["response_format"] = response_format
         # The Codex ChatGPT backend (Responses API) rejects several params.
@@ -1044,10 +1753,10 @@ class LiteLLMProvider(LLMProvider):
                     if choice.finish_reason:
                         stream_finish_reason = choice.finish_reason
                         for _idx, tc_data in sorted(tool_calls_acc.items()):
-                            try:
-                                parsed_args = json.loads(tc_data["arguments"])
-                            except (json.JSONDecodeError, KeyError):
-                                parsed_args = {"_raw": tc_data.get("arguments", "")}
+                            parsed_args = self._parse_tool_call_arguments(
+                                tc_data.get("arguments", ""),
+                                tc_data.get("name", ""),
+                            )
                             tail_events.append(
                                 ToolCallEvent(
                                     tool_use_id=tc_data["id"],
@@ -1228,6 +1937,16 @@ class LiteLLMProvider(LLMProvider):
                 return
 
             except Exception as e:
+                if self._should_use_openrouter_tool_compat(e, tools):
+                    _remember_openrouter_tool_compat_model(self.model)
+                    async for event in self._stream_via_openrouter_tool_compat(
+                        messages=messages,
+                        system=system,
+                        tools=tools or [],
+                        max_tokens=max_tokens,
+                    ):
+                        yield event
+                    return
                 if _is_stream_transient_error(e) and attempt < RATE_LIMIT_MAX_RETRIES:
                     wait = _compute_retry_delay(attempt, exception=e)
                     logger.warning(

core/framework/llm/provider.py

@@ -45,6 +45,8 @@ class ToolResult:
     tool_use_id: str
     content: str
     is_error: bool = False
+    image_content: list[dict[str, Any]] | None = None
+    is_skill_content: bool = False  # AS-10: marks activated skill body, protected from pruning
 
 
 class LLMProvider(ABC):

core/framework/observability/README.md

@@ -83,18 +83,18 @@ configure_logging(level="INFO", format="auto")
 - Compact single-line format (easy to stream/parse)
 - All trace context fields included automatically
 
-### Human-Readable Format (Development)
+### Human-Readable Format (Development / Terminal)
 
 ```
-[INFO    ] [trace:12345678 | exec:a1b2c3d4 | agent:sales-agent] Starting agent execution
-[INFO    ] [trace:12345678 | exec:a1b2c3d4 | agent:sales-agent] Processing input data [node_id:input-processor]
-[INFO    ] [trace:12345678 | exec:a1b2c3d4 | agent:sales-agent] LLM call completed [latency_ms:1250] [tokens_used:450]
+[INFO    ] [agent:sales-agent] Starting agent execution
+[INFO    ] [agent:sales-agent] Processing input data [node_id:input-processor]
+[INFO    ] [agent:sales-agent] LLM call completed [latency_ms:1250] [tokens_used:450]
 ```
 
 **Features:**
 - Color-coded log levels
-- Shortened IDs for readability (first 8 chars)
-- Context prefix shows trace correlation
+- Terminal output omits trace_id and execution_id for readability
+- For full traceability (e.g. debugging), use `ENV=production` to get JSON file logs with trace_id and execution_id
 
 ## Trace Context Fields
 

core/framework/observability/logging.py

@@ -4,8 +4,9 @@ Structured logging with automatic trace context propagation.
 Key Features:
 - Zero developer friction: Standard logger.info() calls get automatic context
 - ContextVar-based propagation: Thread-safe and async-safe
-- Dual output modes: JSON for production, human-readable for development
-- Correlation IDs: trace_id follows entire request flow automatically
+- Dual output modes: JSON for production (full trace_id/execution_id), human-readable for terminal
+- Terminal omits trace_id/execution_id for readability
+- Use ENV=production for file logs with full traceability
 
 Architecture:
     Runtime.start_run() → Generates trace_id, sets context once
@@ -29,6 +30,8 @@ from typing import Any
 # ContextVar is thread-safe and async-safe - perfect for concurrent agent execution
 trace_context: ContextVar[dict[str, Any] | None] = ContextVar("trace_context", default=None)
 
+_STANDARD_LOG_RECORD_FIELDS = set(logging.makeLogRecord({}).__dict__)
+
 # ANSI escape code pattern (matches \033[...m or \x1b[...m)
 ANSI_ESCAPE_PATTERN = re.compile(r"\x1b\[[0-9;]*m|\033\[[0-9;]*m")
 
@@ -91,6 +94,14 @@ class StructuredFormatter(logging.Formatter):
         if model is not None:
             log_entry["model"] = model
 
+        # Preserve arbitrary structured fields passed via ``extra=...``.
+        for key, value in record.__dict__.items():
+            if key in _STANDARD_LOG_RECORD_FIELDS or key.startswith("_"):
+                continue
+            if key in log_entry:
+                continue
+            log_entry[key] = value
+
         # Add exception info if present (strip ANSI codes from exception text too)
         if record.exc_info:
             exception_text = self.formatException(record.exc_info)
@@ -101,10 +112,11 @@ class StructuredFormatter(logging.Formatter):
 
 class HumanReadableFormatter(logging.Formatter):
     """
-    Human-readable formatter for development.
+    Human-readable formatter for development (terminal output).
 
-    Provides colorized logs with trace context for local debugging.
-    Includes trace_id prefix for correlation - AUTOMATIC!
+    Provides colorized logs for local debugging. Omits trace_id and execution_id
+    from the terminal for readability; use ENV=production (JSON file logs) when
+    traceability is needed.
     """
 
     COLORS = {
@@ -118,18 +130,11 @@ class HumanReadableFormatter(logging.Formatter):
 
     def format(self, record: logging.LogRecord) -> str:
         """Format log record as human-readable string."""
-        # Get trace context - AUTOMATIC!
+        # Get trace context; omit trace_id and execution_id in terminal for readability
         context = trace_context.get() or {}
-        trace_id = context.get("trace_id", "")
-        execution_id = context.get("execution_id", "")
         agent_id = context.get("agent_id", "")
 
-        # Build context prefix
         prefix_parts = []
-        if trace_id:
-            prefix_parts.append(f"trace:{trace_id[:8]}")
-        if execution_id:
-            prefix_parts.append(f"exec:{execution_id[-8:]}")
         if agent_id:
             prefix_parts.append(f"agent:{agent_id}")
 
@@ -211,6 +216,15 @@ def configure_logging(
     root_logger.addHandler(handler)
     root_logger.setLevel(level.upper())
 
+    # Suppress noisy LiteLLM INFO logs (model/provider line + Provider List URL
+    # printed on every single completion call).  Warnings and errors still show.
+    # Honour LITELLM_LOG env var so users can opt-in to debug output.
+    _litellm_level = os.getenv("LITELLM_LOG", "").upper()
+    if _litellm_level and hasattr(logging, _litellm_level):
+        logging.getLogger("LiteLLM").setLevel(getattr(logging, _litellm_level))
+    else:
+        logging.getLogger("LiteLLM").setLevel(logging.WARNING)
+
     # When in JSON mode, configure known third-party loggers to use JSON formatter
     # This ensures libraries like LiteLLM, httpcore also output clean JSON
     if format == "json":

core/framework/runner/__init__.py

@@ -1,5 +1,6 @@
 """Agent Runner - load and run exported agents."""
 
+from framework.runner.mcp_registry import MCPRegistry
 from framework.runner.orchestrator import AgentOrchestrator
 from framework.runner.protocol import (
     AgentMessage,
@@ -17,6 +18,7 @@ __all__ = [
     "AgentInfo",
     "ValidationResult",
     "ToolRegistry",
+    "MCPRegistry",
     "tool",
     # Multi-agent
     "AgentOrchestrator",

core/framework/runner/cli.py

@@ -1561,6 +1561,22 @@ def _open_browser(url: str) -> None:
         pass  # Best-effort — don't crash if browser can't open
 
 
+def _format_subprocess_output(output: str | bytes | None, limit: int = 2000) -> str:
+    """Return subprocess output as trimmed text safe for console logging."""
+    if not output:
+        return ""
+
+    if isinstance(output, bytes):
+        text = output.decode(errors="replace")
+    else:
+        text = output
+
+    text = text.strip()
+    if len(text) <= limit:
+        return text
+    return text[-limit:]
+
+
 def _build_frontend() -> bool:
     """Build the frontend if source is newer than dist. Returns True if dist exists."""
     import subprocess
@@ -1596,18 +1612,25 @@ def _build_frontend() -> bool:
 
     # Need to build
     print("Building frontend...")
+    npm_cmd = "npm.cmd" if sys.platform == "win32" else "npm"
     try:
+        # Incremental tsc caches can drift across branch changes and block builds.
+        for cache_file in frontend_dir.glob("tsconfig*.tsbuildinfo"):
+            cache_file.unlink(missing_ok=True)
+
         # Ensure deps are installed
         subprocess.run(
-            ["npm", "install", "--no-fund", "--no-audit"],
+            [npm_cmd, "install", "--no-fund", "--no-audit"],
             encoding="utf-8",
+            errors="replace",
             cwd=frontend_dir,
             check=True,
             capture_output=True,
         )
         subprocess.run(
-            ["npm", "run", "build"],
+            [npm_cmd, "run", "build"],
             encoding="utf-8",
+            errors="replace",
             cwd=frontend_dir,
             check=True,
             capture_output=True,
@@ -1618,8 +1641,14 @@ def _build_frontend() -> bool:
         print("Node.js not found — skipping frontend build.")
         return dist_dir.is_dir()
     except subprocess.CalledProcessError as exc:
-        stderr = exc.stderr.decode(errors="replace") if exc.stderr else ""
-        print(f"Frontend build failed: {stderr[:500]}")
+        stdout = _format_subprocess_output(exc.stdout)
+        stderr = _format_subprocess_output(exc.stderr)
+        cmd = " ".join(exc.cmd) if isinstance(exc.cmd, (list, tuple)) else str(exc.cmd)
+        details = "\n".join(part for part in [stdout, stderr] if part).strip()
+        if details:
+            print(f"Frontend build failed while running {cmd}:\n{details}")
+        else:
+            print(f"Frontend build failed while running {cmd} (exit {exc.returncode}).")
         return dist_dir.is_dir()
 
 

core/framework/runner/mcp_client.py

@@ -1,7 +1,7 @@
 """MCP Client for connecting to Model Context Protocol servers.
 
 This module provides a client for connecting to MCP servers and invoking their tools.
-Supports both STDIO and HTTP transports using the official MCP Python SDK.
+Supports STDIO, HTTP, UNIX socket, and SSE transports using the official MCP Python SDK.
 """
 
 import asyncio
@@ -14,6 +14,8 @@ from typing import Any, Literal
 
 import httpx
 
+from framework.runner.mcp_errors import MCPToolNotFoundError
+
 logger = logging.getLogger(__name__)
 
 
@@ -22,7 +24,7 @@ class MCPServerConfig:
     """Configuration for an MCP server connection."""
 
     name: str
-    transport: Literal["stdio", "http"]
+    transport: Literal["stdio", "http", "unix", "sse"]
 
     # For STDIO transport
     command: str | None = None
@@ -33,6 +35,7 @@ class MCPServerConfig:
     # For HTTP transport
     url: str | None = None
     headers: dict[str, str] = field(default_factory=dict)
+    socket_path: str | None = None
 
     # Optional metadata
     description: str = ""
@@ -52,7 +55,7 @@ class MCPClient:
     """
     Client for communicating with MCP servers.
 
-    Supports both STDIO and HTTP transports using the official MCP SDK.
+    Supports STDIO, HTTP, UNIX socket, and SSE transports using the official MCP SDK.
     Manages the connection lifecycle and provides methods to list and invoke tools.
     """
 
@@ -68,6 +71,7 @@ class MCPClient:
         self._read_stream = None
         self._write_stream = None
         self._stdio_context = None  # Context manager for stdio_client
+        self._sse_context = None  # Context manager for sse_client
         self._errlog_handle = None  # Track errlog file handle for cleanup
         self._http_client: httpx.Client | None = None
         self._tools: dict[str, MCPTool] = {}
@@ -141,6 +145,10 @@ class MCPClient:
             self._connect_stdio()
         elif self.config.transport == "http":
             self._connect_http()
+        elif self.config.transport == "unix":
+            self._connect_unix()
+        elif self.config.transport == "sse":
+            self._connect_sse()
         else:
             raise ValueError(f"Unsupported transport: {self.config.transport}")
 
@@ -266,10 +274,94 @@ class MCPClient:
             logger.warning(f"Health check failed for MCP server '{self.config.name}': {e}")
             # Continue anyway, server might not have health endpoint
 
+    def _connect_unix(self) -> None:
+        """Connect to MCP server via UNIX domain socket transport."""
+        if not self.config.url:
+            raise ValueError("url is required for UNIX transport")
+        if not self.config.socket_path:
+            raise ValueError("socket_path is required for UNIX transport")
+
+        self._http_client = httpx.Client(
+            base_url=self.config.url,
+            headers=self.config.headers,
+            timeout=30.0,
+            transport=httpx.HTTPTransport(uds=self.config.socket_path),
+        )
+
+        try:
+            response = self._http_client.get("/health")
+            response.raise_for_status()
+            logger.info(
+                "Connected to MCP server '%s' via UNIX socket at %s",
+                self.config.name,
+                self.config.socket_path,
+            )
+        except Exception as e:
+            logger.warning(f"Health check failed for MCP server '{self.config.name}': {e}")
+            # Continue anyway, server might not have health endpoint
+
+    def _connect_sse(self) -> None:
+        """Connect to MCP server via SSE transport using MCP SDK with persistent session."""
+        if not self.config.url:
+            raise ValueError("url is required for SSE transport")
+
+        try:
+            loop_started = threading.Event()
+            connection_ready = threading.Event()
+            connection_error = []
+
+            def run_event_loop():
+                """Run event loop in background thread."""
+                self._loop = asyncio.new_event_loop()
+                asyncio.set_event_loop(self._loop)
+                loop_started.set()
+
+                async def init_connection():
+                    try:
+                        from mcp import ClientSession
+                        from mcp.client.sse import sse_client
+
+                        self._sse_context = sse_client(
+                            self.config.url,
+                            headers=self.config.headers,
+                            timeout=30.0,
+                        )
+                        (
+                            self._read_stream,
+                            self._write_stream,
+                        ) = await self._sse_context.__aenter__()
+
+                        self._session = ClientSession(self._read_stream, self._write_stream)
+                        await self._session.__aenter__()
+                        await self._session.initialize()
+
+                        connection_ready.set()
+                    except Exception as e:
+                        connection_error.append(e)
+                        connection_ready.set()
+
+                self._loop.create_task(init_connection())
+                self._loop.run_forever()
+
+            self._loop_thread = threading.Thread(target=run_event_loop, daemon=True)
+            self._loop_thread.start()
+
+            loop_started.wait(timeout=5)
+            if not loop_started.is_set():
+                raise RuntimeError("Event loop failed to start")
+
+            connection_ready.wait(timeout=10)
+            if connection_error:
+                raise connection_error[0]
+
+            logger.info(f"Connected to MCP server '{self.config.name}' via SSE")
+        except Exception as e:
+            raise RuntimeError(f"Failed to connect to MCP server: {e}") from e
+
     def _discover_tools(self) -> None:
         """Discover available tools from the MCP server."""
         try:
-            if self.config.transport == "stdio":
+            if self.config.transport in {"stdio", "sse"}:
                 tools_list = self._run_async(self._list_tools_stdio_async())
             else:
                 tools_list = self._list_tools_http()
@@ -366,14 +458,45 @@ class MCPClient:
             self.connect()
 
         if tool_name not in self._tools:
-            raise ValueError(f"Unknown tool: {tool_name}")
+            raise MCPToolNotFoundError(
+                server=self.config.name,
+                tool_name=tool_name,
+            )
 
         if self.config.transport == "stdio":
             with self._stdio_call_lock:
                 return self._run_async(self._call_tool_stdio_async(tool_name, arguments))
+        elif self.config.transport == "sse":
+            return self._call_tool_with_retry(
+                lambda: self._run_async(self._call_tool_stdio_async(tool_name, arguments))
+            )
+        elif self.config.transport == "unix":
+            return self._call_tool_with_retry(lambda: self._call_tool_http(tool_name, arguments))
         else:
             return self._call_tool_http(tool_name, arguments)
 
+    def _call_tool_with_retry(self, call: Any) -> Any:
+        """Retry transient MCP transport failures once after reconnecting."""
+        if self.config.transport == "stdio":
+            return call()
+
+        if self.config.transport not in {"unix", "sse"}:
+            return call()
+
+        try:
+            return call()
+        except (httpx.ConnectError, httpx.ReadTimeout) as original_error:
+            logger.warning(
+                "Retrying MCP tool call after transport error from '%s': %s",
+                self.config.name,
+                original_error,
+            )
+            self._reconnect()
+            try:
+                return call()
+            except (httpx.ConnectError, httpx.ReadTimeout) as retry_error:
+                raise original_error from retry_error
+
     async def _call_tool_stdio_async(self, tool_name: str, arguments: dict[str, Any]) -> Any:
         """Call tool via STDIO protocol using persistent session."""
         if not self._session:
@@ -389,19 +512,35 @@ class MCPClient:
                 content_item = result.content[0]
                 if hasattr(content_item, "text"):
                     error_text = content_item.text
-            raise RuntimeError(f"MCP tool '{tool_name}' failed: {error_text}")
+            raise RuntimeError(
+                f"[Server: {self.config.name}] [Transport: {self.config.transport}] "
+                f"Tool '{tool_name}' failed: {error_text}"
+            )
 
-        # Extract content
+        # Extract content — preserve image blocks alongside text
         if result.content:
-            # MCP returns content as a list of content items
-            if len(result.content) > 0:
-                content_item = result.content[0]
-                # Check if it's a text content item
-                if hasattr(content_item, "text"):
-                    return content_item.text
-                elif hasattr(content_item, "data"):
-                    return content_item.data
-            return result.content
+            text_parts: list[str] = []
+            image_parts: list[dict[str, Any]] = []
+            for item in result.content:
+                if hasattr(item, "text"):
+                    text_parts.append(item.text)
+                elif hasattr(item, "data") and hasattr(item, "mimeType"):
+                    # MCP ImageContent — preserve as structured image block
+                    image_parts.append(
+                        {
+                            "type": "image_url",
+                            "image_url": {
+                                "url": f"data:{item.mimeType};base64,{item.data}",
+                            },
+                        }
+                    )
+                elif hasattr(item, "data"):
+                    text_parts.append(str(item.data))
+
+            text = "\n".join(text_parts) if text_parts else ""
+            if image_parts:
+                return {"_text": text, "_images": image_parts}
+            return text if text else None
 
         return None
 
@@ -427,24 +566,36 @@ class MCPClient:
             data = response.json()
 
             if "error" in data:
-                raise RuntimeError(f"Tool execution error: {data['error']}")
+                raise RuntimeError(
+                    f"[Server: {self.config.name}] [Transport: {self.config.transport}] "
+                    f"Tool '{tool_name}' failed: {data['error']}"
+                )
 
             return data.get("result", {}).get("content", [])
         except Exception as e:
-            raise RuntimeError(f"Failed to call tool via HTTP: {e}") from e
+            raise RuntimeError(
+                f"[Server: {self.config.name}] [Transport: {self.config.transport}] "
+                f"Failed to call tool via HTTP: Tool '{tool_name}' failed: {e}"
+            ) from e
+
+    def _reconnect(self) -> None:
+        """Reconnect to the configured MCP server."""
+        logger.info(f"Reconnecting to MCP server '{self.config.name}'...")
+        self.disconnect()
+        self.connect()
 
     _CLEANUP_TIMEOUT = 10
     _THREAD_JOIN_TIMEOUT = 12
 
     async def _cleanup_stdio_async(self) -> None:
-        """Async cleanup for STDIO session and context managers.
+        """Async cleanup for persistent MCP session and context managers.
 
         Cleanup order is critical:
-        - The session must be closed BEFORE the stdio_context because the session
-          depends on the streams provided by stdio_context.
-        - This mirrors the initialization order in _connect_stdio(), where
-          stdio_context is entered first (providing streams), then the session is
-          created with those streams and entered.
+        - The session must be closed BEFORE the transport context manager because the
+          session depends on the streams provided by that context.
+        - This mirrors the initialization order in _connect_stdio() / _connect_sse(),
+          where the transport context is entered first (providing streams), then the
+          session is created with those streams and entered.
         - Do not change this ordering without carefully considering these dependencies.
         """
         # First: close session (depends on stdio_context streams)
@@ -477,6 +628,16 @@ class MCPClient:
         finally:
             self._stdio_context = None
 
+        try:
+            if self._sse_context:
+                await self._sse_context.__aexit__(None, None, None)
+        except asyncio.CancelledError:
+            logger.debug("SSE context cleanup was cancelled; proceeding with best-effort shutdown")
+        except Exception as e:
+            logger.warning(f"Error closing SSE context: {e}")
+        finally:
+            self._sse_context = None
+
         # Third: close errlog file handle if we opened one
         if self._errlog_handle is not None:
             try:
@@ -552,6 +713,7 @@ class MCPClient:
             # Setting None to None is safe and ensures clean state.
             self._session = None
             self._stdio_context = None
+            self._sse_context = None
             self._read_stream = None
             self._write_stream = None
             self._loop = None

core/framework/runner/mcp_connection_manager.py

@@ -0,0 +1,409 @@
+"""Shared MCP client connection management."""
+
+import logging
+import threading
+
+import httpx
+
+from framework.runner.mcp_client import MCPClient, MCPServerConfig
+
+logger = logging.getLogger(__name__)
+
+_TRANSITION_TIMEOUT = 30.0
+
+
+class MCPConnectionManager:
+    """Process-wide MCP client pool keyed by server name."""
+
+    _instance = None
+    _lock = threading.Lock()
+
+    def __init__(self) -> None:
+        self._pool: dict[str, MCPClient] = {}
+        self._refcounts: dict[str, int] = {}
+        self._configs: dict[str, MCPServerConfig] = {}
+        self._pool_lock = threading.Lock()
+        self._transitions: dict[str, threading.Event] = {}
+
+    @classmethod
+    def get_instance(cls) -> "MCPConnectionManager":
+        """Return the process-level singleton instance."""
+        if cls._instance is None:
+            with cls._lock:
+                if cls._instance is None:
+                    cls._instance = cls()
+        return cls._instance
+
+    @staticmethod
+    def _is_connected(client: MCPClient | None) -> bool:
+        return bool(client and getattr(client, "_connected", False))
+
+    def has_connection(self, server_name: str) -> bool:
+        """Return True when a live pooled connection exists for ``server_name``."""
+        with self._pool_lock:
+            return self._is_connected(self._pool.get(server_name))
+
+    def acquire(self, config: MCPServerConfig) -> MCPClient:
+        """Get or create a shared connection and increment its refcount."""
+        server_name = config.name
+
+        while True:
+            should_connect = False
+            transition_event: threading.Event | None = None
+
+            with self._pool_lock:
+                client = self._pool.get(server_name)
+                if self._is_connected(client) and server_name not in self._transitions:
+                    new_refcount = self._refcounts.get(server_name, 0) + 1
+                    self._refcounts[server_name] = new_refcount
+                    self._configs[server_name] = config
+                    logger.debug(
+                        "Reusing pooled connection for MCP server '%s' (refcount=%d)",
+                        server_name,
+                        new_refcount,
+                    )
+                    return client
+
+                transition_event = self._transitions.get(server_name)
+                if transition_event is None:
+                    transition_event = threading.Event()
+                    self._transitions[server_name] = transition_event
+                    self._configs[server_name] = config
+                    should_connect = True
+
+            if not should_connect:
+                if not transition_event.wait(timeout=_TRANSITION_TIMEOUT):
+                    logger.warning(
+                        "Timed out waiting for transition on MCP server '%s', "
+                        "forcing cleanup and retrying",
+                        server_name,
+                    )
+                    with self._pool_lock:
+                        stuck = self._transitions.get(server_name)
+                        if stuck is transition_event:
+                            self._transitions.pop(server_name, None)
+                            transition_event.set()
+                continue
+
+            logger.info("Connecting to MCP server '%s'", server_name)
+            client = MCPClient(config)
+            try:
+                client.connect()
+            except Exception:
+                logger.warning(
+                    "Failed to connect to MCP server '%s'",
+                    server_name,
+                    exc_info=True,
+                )
+                with self._pool_lock:
+                    current = self._transitions.get(server_name)
+                    if current is transition_event:
+                        self._transitions.pop(server_name, None)
+                        if (
+                            server_name not in self._pool
+                            and self._refcounts.get(server_name, 0) <= 0
+                        ):
+                            self._configs.pop(server_name, None)
+                        transition_event.set()
+                raise
+
+            with self._pool_lock:
+                current = self._transitions.get(server_name)
+                if current is transition_event:
+                    self._pool[server_name] = client
+                    self._refcounts[server_name] = self._refcounts.get(server_name, 0) + 1
+                    self._configs[server_name] = config
+                    self._transitions.pop(server_name, None)
+                    transition_event.set()
+                    logger.info(
+                        "Connected to MCP server '%s' (refcount=1)",
+                        server_name,
+                    )
+                    return client
+
+            # Lost the transition race, clean up and retry
+            try:
+                client.disconnect()
+            except Exception:
+                logger.debug(
+                    "Error disconnecting stale client for '%s'",
+                    server_name,
+                    exc_info=True,
+                )
+
+    def release(self, server_name: str) -> None:
+        """Decrement refcount and disconnect when the last user releases."""
+        while True:
+            disconnect_client: MCPClient | None = None
+            transition_event: threading.Event | None = None
+            should_disconnect = False
+
+            with self._pool_lock:
+                transition_event = self._transitions.get(server_name)
+                if transition_event is None:
+                    refcount = self._refcounts.get(server_name, 0)
+                    if refcount <= 0:
+                        return
+                    if refcount > 1:
+                        self._refcounts[server_name] = refcount - 1
+                        logger.debug(
+                            "Released MCP server '%s' (refcount=%d)",
+                            server_name,
+                            refcount - 1,
+                        )
+                        return
+
+                    disconnect_client = self._pool.pop(server_name, None)
+                    self._refcounts.pop(server_name, None)
+                    self._configs.pop(server_name, None)
+                    transition_event = threading.Event()
+                    self._transitions[server_name] = transition_event
+                    should_disconnect = True
+
+            if not should_disconnect:
+                if not transition_event.wait(timeout=_TRANSITION_TIMEOUT):
+                    logger.warning(
+                        "Timed out waiting for transition on '%s' during release, forcing cleanup",
+                        server_name,
+                    )
+                    with self._pool_lock:
+                        stuck = self._transitions.get(server_name)
+                        if stuck is transition_event:
+                            self._transitions.pop(server_name, None)
+                            transition_event.set()
+                continue
+
+            try:
+                if disconnect_client is not None:
+                    disconnect_client.disconnect()
+                    logger.info(
+                        "Disconnected MCP server '%s' (last reference released)",
+                        server_name,
+                    )
+            except Exception:
+                logger.warning(
+                    "Error disconnecting MCP server '%s' during release",
+                    server_name,
+                    exc_info=True,
+                )
+            finally:
+                with self._pool_lock:
+                    current = self._transitions.get(server_name)
+                    if current is transition_event:
+                        self._transitions.pop(server_name, None)
+                        transition_event.set()
+            return
+
+    def health_check(self, server_name: str) -> bool:
+        """Return True when the pooled connection appears healthy."""
+        while True:
+            with self._pool_lock:
+                transition_event = self._transitions.get(server_name)
+                if transition_event is None:
+                    client = self._pool.get(server_name)
+                    config = self._configs.get(server_name)
+                    break
+
+            if not transition_event.wait(timeout=_TRANSITION_TIMEOUT):
+                logger.warning(
+                    "Timed out waiting for transition on '%s' during health check",
+                    server_name,
+                )
+                return False
+
+        if client is None or config is None:
+            return False
+
+        try:
+            match config.transport:
+                case "stdio":
+                    client.list_tools()
+                    return True
+                case "http":
+                    if not config.url:
+                        return False
+                    with httpx.Client(
+                        base_url=config.url,
+                        headers=config.headers,
+                        timeout=5.0,
+                    ) as http_client:
+                        response = http_client.get("/health")
+                        response.raise_for_status()
+                    return True
+                case "sse":
+                    client.list_tools()
+                    return True
+                case "unix":
+                    if not config.socket_path:
+                        return False
+                    with httpx.Client(
+                        base_url=config.url or "http://localhost",
+                        headers=config.headers,
+                        timeout=5.0,
+                        transport=httpx.HTTPTransport(uds=config.socket_path),
+                    ) as http_client:
+                        response = http_client.get("/health")
+                        response.raise_for_status()
+                    return True
+                case _:
+                    logger.warning(
+                        "Unknown transport '%s' for health check on '%s'",
+                        config.transport,
+                        server_name,
+                    )
+                    return False
+        except Exception:
+            logger.debug(
+                "Health check failed for MCP server '%s'",
+                server_name,
+                exc_info=True,
+            )
+            return False
+
+    def reconnect(self, server_name: str) -> MCPClient:
+        """Force a disconnect and replace the pooled client with a fresh one."""
+        while True:
+            transition_event: threading.Event | None = None
+            old_client: MCPClient | None = None
+
+            with self._pool_lock:
+                transition_event = self._transitions.get(server_name)
+                if transition_event is None:
+                    config = self._configs.get(server_name)
+                    if config is None:
+                        raise KeyError(f"Unknown MCP server: {server_name}")
+                    old_client = self._pool.get(server_name)
+                    transition_event = threading.Event()
+                    self._transitions[server_name] = transition_event
+                    break
+
+            if not transition_event.wait(timeout=_TRANSITION_TIMEOUT):
+                logger.warning(
+                    "Timed out waiting for transition on '%s' during reconnect, forcing cleanup",
+                    server_name,
+                )
+                with self._pool_lock:
+                    stuck = self._transitions.get(server_name)
+                    if stuck is transition_event:
+                        self._transitions.pop(server_name, None)
+                        transition_event.set()
+
+        # Disconnect old client safely
+        if old_client is not None:
+            try:
+                old_client.disconnect()
+                logger.info("Disconnected old client for '%s'", server_name)
+            except Exception:
+                logger.warning(
+                    "Error disconnecting old client for '%s' during reconnect",
+                    server_name,
+                    exc_info=True,
+                )
+
+        logger.info("Reconnecting MCP server '%s'", server_name)
+        new_client = MCPClient(config)
+        try:
+            new_client.connect()
+        except Exception:
+            with self._pool_lock:
+                current = self._transitions.get(server_name)
+                if current is transition_event:
+                    self._pool.pop(server_name, None)
+                    self._transitions.pop(server_name, None)
+                    transition_event.set()
+            raise
+
+        with self._pool_lock:
+            current = self._transitions.get(server_name)
+            if current is transition_event:
+                current_refcount = self._refcounts.get(server_name, 0)
+                if current_refcount <= 0:
+                    # All holders released during reconnect. Discard the
+                    # new client instead of creating a phantom reference.
+                    # Caller should acquire() fresh if needed.
+                    self._transitions.pop(server_name, None)
+                    transition_event.set()
+                    logger.info(
+                        "Reconnected MCP server '%s' but refcount dropped to 0, "
+                        "discarding new client",
+                        server_name,
+                    )
+                    try:
+                        new_client.disconnect()
+                    except Exception:
+                        logger.debug(
+                            "Error disconnecting discarded client for '%s'",
+                            server_name,
+                            exc_info=True,
+                        )
+                    raise KeyError(
+                        f"MCP server '{server_name}' was fully released during reconnect"
+                    )
+
+                self._pool[server_name] = new_client
+                self._configs[server_name] = config
+                self._refcounts[server_name] = current_refcount
+                self._transitions.pop(server_name, None)
+                transition_event.set()
+                logger.info(
+                    "Reconnected MCP server '%s' (refcount=%d)",
+                    server_name,
+                    current_refcount,
+                )
+                return new_client
+
+        try:
+            new_client.disconnect()
+        except Exception:
+            logger.debug(
+                "Error disconnecting stale client for '%s' after reconnect race",
+                server_name,
+                exc_info=True,
+            )
+        return self.acquire(config)
+
+    def cleanup_all(self) -> None:
+        """Disconnect all pooled clients and clear manager state."""
+        while True:
+            with self._pool_lock:
+                if self._transitions:
+                    pending = list(self._transitions.values())
+                else:
+                    cleanup_events = {name: threading.Event() for name in self._pool}
+                    clients = list(self._pool.items())
+                    self._transitions.update(cleanup_events)
+                    self._pool.clear()
+                    self._refcounts.clear()
+                    self._configs.clear()
+                    break
+
+            all_resolved = all(event.wait(timeout=_TRANSITION_TIMEOUT) for event in pending)
+            if not all_resolved:
+                logger.warning(
+                    "Timed out waiting for pending transitions during cleanup, "
+                    "forcing cleanup of stuck transitions",
+                )
+                with self._pool_lock:
+                    for sn, evt in list(self._transitions.items()):
+                        if not evt.is_set():
+                            self._transitions.pop(sn, None)
+                            evt.set()
+
+        logger.info("Cleaning up %d pooled MCP connections", len(clients))
+        for server_name, client in clients:
+            try:
+                client.disconnect()
+                logger.debug("Disconnected MCP server '%s' during cleanup", server_name)
+            except Exception:
+                logger.warning(
+                    "Error disconnecting MCP server '%s' during cleanup",
+                    server_name,
+                    exc_info=True,
+                )
+
+        with self._pool_lock:
+            for server_name, event in cleanup_events.items():
+                current = self._transitions.get(server_name)
+                if current is event:
+                    self._transitions.pop(server_name, None)
+                    event.set()

core/framework/runner/mcp_errors.py

@@ -0,0 +1,99 @@
+"""Structured error codes and exceptions for MCP server operations."""
+
+from enum import Enum
+
+
+class MCPErrorCode(Enum):
+    """Standardized error codes for MCP operations."""
+
+    MCP_INSTALL_FAILED = "MCP_INSTALL_FAILED"
+    MCP_AUTH_MISSING = "MCP_AUTH_MISSING"
+    MCP_CONNECT_TIMEOUT = "MCP_CONNECT_TIMEOUT"
+    MCP_TOOL_NOT_FOUND = "MCP_TOOL_NOT_FOUND"
+    MCP_PROTOCOL_MISMATCH = "MCP_PROTOCOL_MISMATCH"
+    MCP_VERSION_CONFLICT = "MCP_VERSION_CONFLICT"
+    MCP_HEALTH_FAILED = "MCP_HEALTH_FAILED"
+
+
+class MCPError(ValueError):
+    """Base exception for all structured MCP errors."""
+
+    def __init__(self, code: MCPErrorCode, what: str, why: str, fix: str):
+        self.code = code
+        self.what = what
+        self.why = why
+        self.fix = fix
+        self.message = (
+            f"[{self.code.value}]\nWhat failed: {self.what}\nWhy: {self.why}\nFix: {self.fix}"
+        )
+        super().__init__(self.message)
+
+
+class MCPToolNotFoundError(MCPError):
+    def __init__(self, server: str, tool_name: str):
+        super().__init__(
+            code=MCPErrorCode.MCP_TOOL_NOT_FOUND,
+            what=f"Tool '{tool_name}' not found on server '{server}'",
+            why=f"The server '{server}' does not expose a tool named '{tool_name}'.",
+            fix=f"Run 'hive mcp inspect {server}' to view available tools.",
+        )
+
+
+class MCPConnectTimeoutError(MCPError):
+    def __init__(self, server: str, transport: str, timeout_sec: int):
+        super().__init__(
+            code=MCPErrorCode.MCP_CONNECT_TIMEOUT,
+            what=f"Connection timed out while starting server '{server}'",
+            why=f"The {transport} transport did not respond within {timeout_sec} seconds.",
+            fix=f"Check if the server is running. Run 'hive mcp doctor {server}' for diagnostics.",
+        )
+
+
+class MCPAuthError(MCPError):
+    def __init__(self, server: str, env_var: str):
+        super().__init__(
+            code=MCPErrorCode.MCP_AUTH_MISSING,
+            what=f"Authentication failed for server '{server}'",
+            why=f"The required environment variable '{env_var}' is missing or empty.",
+            fix=f"Run: hive mcp config {server} --set {env_var}=<your-token>",
+        )
+
+
+class MCPInstallError(MCPError):
+    def __init__(self, server: str, why: str, fix: str):
+        super().__init__(
+            code=MCPErrorCode.MCP_INSTALL_FAILED,
+            what=f"Could not install MCP server '{server}'",
+            why=why,
+            fix=fix,
+        )
+
+
+class MCPProtocolMismatchError(MCPError):
+    def __init__(self, server: str, detail: str):
+        super().__init__(
+            code=MCPErrorCode.MCP_PROTOCOL_MISMATCH,
+            what=f"Protocol mismatch with server '{server}'",
+            why=detail,
+            fix=f"Check the MCP SDK version required by '{server}' matches your installation.",
+        )
+
+
+class MCPVersionConflictError(MCPError):
+    def __init__(self, server: str, detail: str):
+        super().__init__(
+            code=MCPErrorCode.MCP_VERSION_CONFLICT,
+            what=f"Version conflict with server '{server}'",
+            why=detail,
+            fix="Update or pin the MCP server package to a compatible version.",
+        )
+
+
+class MCPHealthCheckError(MCPError):
+    def __init__(self, server: str, detail: str):
+        super().__init__(
+            code=MCPErrorCode.MCP_HEALTH_FAILED,
+            what=f"Health check failed for server '{server}'",
+            why=detail,
+            fix=f"Run 'hive mcp doctor {server}' to diagnose the issue.",
+        )

core/framework/runner/mcp_registry.py

@@ -0,0 +1,904 @@
+"""MCP Server Registry: local state management for installed MCP servers."""
+
+from __future__ import annotations
+
+import json
+import logging
+import os
+import tempfile
+import tomllib
+from datetime import UTC, datetime
+from importlib.metadata import PackageNotFoundError, version
+from pathlib import Path
+from typing import Any, Literal
+
+import httpx
+
+from framework.runner.mcp_client import MCPClient, MCPServerConfig
+from framework.runner.mcp_connection_manager import MCPConnectionManager
+from framework.runner.mcp_errors import (
+    MCPError,
+    MCPErrorCode,
+    MCPInstallError,
+)
+
+logger = logging.getLogger(__name__)
+
+DEFAULT_INDEX_URL = (
+    "https://raw.githubusercontent.com/aden-hive/hive-mcp-registry/main/registry_index.json"
+)
+DEFAULT_REFRESH_INTERVAL_HOURS = 24
+_LAST_FETCHED_FILENAME = "last_fetched"
+_LEGACY_LAST_FETCHED_FILENAME = "last_fetched.json"
+
+_DEFAULT_CONFIG = {
+    "index_url": DEFAULT_INDEX_URL,
+    "refresh_interval_hours": DEFAULT_REFRESH_INTERVAL_HOURS,
+}
+
+
+class MCPRegistry:
+    """Manages local MCP server state in ~/.hive/mcp_registry/."""
+
+    def __init__(self, base_path: Path | None = None):
+        self._base = base_path or Path.home() / ".hive" / "mcp_registry"
+        self._installed_path = self._base / "installed.json"
+        self._config_path = self._base / "config.json"
+        self._cache_dir = self._base / "cache"
+
+    # ── Initialization ──────────────────────────────────────────────
+
+    def initialize(self) -> None:
+        """Create directory structure and default files if missing."""
+        self._base.mkdir(parents=True, exist_ok=True)
+        self._cache_dir.mkdir(parents=True, exist_ok=True)
+
+        if not self._config_path.exists():
+            self._write_json(self._config_path, _DEFAULT_CONFIG)
+
+        if not self._installed_path.exists():
+            self._write_json(self._installed_path, {"servers": {}})
+
+    # ── Internal I/O ────────────────────────────────────────────────
+
+    def _read_installed(self) -> dict:
+        """Read installed.json, initializing if needed."""
+        if not self._installed_path.exists():
+            self.initialize()
+        return json.loads(self._installed_path.read_text(encoding="utf-8"))
+
+    def _write_installed(self, data: dict) -> None:
+        """Write installed.json."""
+        self._write_json(self._installed_path, data)
+
+    def _read_config(self) -> dict:
+        """Read config.json."""
+        if not self._config_path.exists():
+            self.initialize()
+        return json.loads(self._config_path.read_text(encoding="utf-8"))
+
+    def _read_cached_index(self) -> dict:
+        """Read cached registry_index.json."""
+        index_path = self._cache_dir / "registry_index.json"
+        if not index_path.exists():
+            return {"servers": {}}
+        return json.loads(index_path.read_text(encoding="utf-8"))
+
+    def _get_effective_manifest(
+        self,
+        name: str,
+        entry: dict,
+        cached_index: dict | None = None,
+    ) -> dict:
+        """Return the manifest currently in effect for an installed entry."""
+        manifest = entry.get("manifest", {})
+        if entry.get("source") != "registry":
+            return manifest
+
+        index = cached_index or self._read_cached_index()
+        cached_manifest = index.get("servers", {}).get(name)
+        if cached_manifest is not None:
+            return cached_manifest
+
+        # Fall back to persisted manifest data when the cache is unavailable.
+        if isinstance(manifest, dict) and manifest:
+            return manifest
+        return {}
+
+    @staticmethod
+    def _write_json(path: Path, data: dict) -> None:
+        """Write JSON to file atomically (write to temp, fsync, rename)."""
+        content = json.dumps(data, indent=2) + "\n"
+        fd, tmp_path = tempfile.mkstemp(dir=path.parent, suffix=".tmp")
+        try:
+            with os.fdopen(fd, "w", encoding="utf-8") as f:
+                f.write(content)
+                f.flush()
+                os.fsync(f.fileno())
+            os.replace(tmp_path, path)
+        except BaseException:
+            try:
+                os.unlink(tmp_path)
+            except OSError:
+                pass
+            raise
+
+    # ── add_local ───────────────────────────────────────────────────
+
+    def add_local(
+        self,
+        name: str,
+        transport: str | None = None,
+        manifest: dict | None = None,
+        url: str | None = None,
+        command: str | None = None,
+        args: list[str] | None = None,
+        env: dict[str, str] | None = None,
+        headers: dict[str, str] | None = None,
+        cwd: str | None = None,
+        socket_path: str | None = None,
+        description: str = "",
+    ) -> dict:
+        """Register a local/running MCP server.
+
+        Can be called with an inline manifest dict, or with individual
+        transport/url/command params that build a manifest automatically.
+        """
+        data = self._read_installed()
+        if name in data["servers"]:
+            raise MCPError(
+                code=MCPErrorCode.MCP_INSTALL_FAILED,
+                what=f"Server '{name}' already exists",
+                why="A server with this name is already registered locally.",
+                fix=f"Run: hive mcp remove {name}  — then add it again.",
+            )
+
+        if manifest is not None:
+            # Inline manifest provided directly
+            manifest = {**manifest, "name": name}
+            transport_config = manifest.get("transport", {})
+            transport = transport or transport_config.get("default", "stdio")
+            if "transport" not in manifest:
+                manifest["transport"] = {"supported": [transport], "default": transport}
+        else:
+            # Build manifest from individual params
+            if not transport:
+                raise MCPError(
+                    code=MCPErrorCode.MCP_INSTALL_FAILED,
+                    what=f"Cannot register server '{name}'",
+                    why="transport is required when manifest is not provided.",
+                    fix="Pass --transport stdio|http|unix|sse when using hive mcp add.",
+                )
+            manifest = {
+                "name": name,
+                "description": description,
+                "transport": {"supported": [transport], "default": transport},
+            }
+            match transport:
+                case "http":
+                    if not url:
+                        raise MCPError(
+                            code=MCPErrorCode.MCP_INSTALL_FAILED,
+                            what=f"Cannot register server '{name}' with http transport",
+                            why="url is required for http transport.",
+                            fix="Pass --url https://your-server to hive mcp add.",
+                        )
+                    manifest["http"] = {"url": url, "headers": headers or {}}
+                case "stdio":
+                    if not command:
+                        raise MCPError(
+                            code=MCPErrorCode.MCP_INSTALL_FAILED,
+                            what=f"Cannot register server '{name}' with stdio transport",
+                            why="command is required for stdio transport.",
+                            fix="Pass --command <executable> to hive mcp add.",
+                        )
+                    manifest["stdio"] = {
+                        "command": command,
+                        "args": args or [],
+                        "env": env or {},
+                        "cwd": cwd,
+                    }
+                case "unix":
+                    if not socket_path:
+                        raise MCPError(
+                            code=MCPErrorCode.MCP_INSTALL_FAILED,
+                            what=f"Cannot register server '{name}' with unix transport",
+                            why="socket_path is required for unix transport.",
+                            fix="Pass --socket-path /path/to/socket to hive mcp add.",
+                        )
+                    manifest["unix"] = {"socket_path": socket_path}
+                    manifest["http"] = {"url": url or "http://localhost"}
+                case "sse":
+                    if not url:
+                        raise MCPError(
+                            code=MCPErrorCode.MCP_INSTALL_FAILED,
+                            what=f"Cannot register server '{name}' with sse transport",
+                            why="url is required for sse transport.",
+                            fix="Pass --url https://your-server to hive mcp add.",
+                        )
+                    manifest["sse"] = {"url": url}
+                case _:
+                    raise MCPError(
+                        code=MCPErrorCode.MCP_INSTALL_FAILED,
+                        what=f"Cannot register server '{name}'",
+                        why=f"Unsupported transport: '{transport}'.",
+                        fix="Use one of: stdio, http, unix, sse.",
+                    )
+
+        entry = self._make_entry(
+            source="local",
+            manifest=manifest,
+            transport=transport,
+            installed_by="hive mcp add",
+        )
+
+        data["servers"][name] = entry
+        self._write_installed(data)
+        logger.info("Registered local MCP server '%s' (%s)", name, transport)
+        return entry
+
+    # ── install ─────────────────────────────────────────────────────
+
+    def install(self, name: str, transport: str | None = None, version: str | None = None) -> dict:
+        """Install a server from the cached remote registry index."""
+        data = self._read_installed()
+        if name in data["servers"]:
+            raise MCPInstallError(
+                server=name,
+                why=f"Server '{name}' already exists in the registry.",
+                fix=f"Run: hive mcp remove {name}  — then install again.",
+            )
+
+        index = self._read_cached_index()
+        manifest = index.get("servers", {}).get(name)
+        if manifest is None:
+            raise MCPInstallError(
+                server=name,
+                why=f"Server '{name}' not found in registry index.",
+                fix="Run: hive mcp update  — then try again.",
+            )
+
+        # Validate version if specified
+        if version is not None:
+            index_version = manifest.get("version")
+            if index_version is None:
+                raise MCPError(
+                    code=MCPErrorCode.MCP_VERSION_CONFLICT,
+                    what=f"Cannot pin version for '{name}'",
+                    why="The registry manifest has no version field.",
+                    fix="Run: hive mcp update  — then omit --version to use latest.",
+                )
+            if index_version != version:
+                raise MCPError(
+                    code=MCPErrorCode.MCP_VERSION_CONFLICT,
+                    what=f"Version mismatch for '{name}'",
+                    why=f"Requested {version} but index has {index_version}.",
+                    fix="Run: hive mcp update  — or omit --version to use latest.",
+                )
+
+        transport_config = manifest.get("transport", {})
+        supported = transport_config.get("supported", [])
+        if transport is not None:
+            if supported and transport not in supported:
+                raise MCPError(
+                    code=MCPErrorCode.MCP_INSTALL_FAILED,
+                    what=f"Transport '{transport}' not supported by '{name}'",
+                    why=f"Server supports: {supported}.",
+                    fix=f"Use one of the supported transports: {supported}.",
+                )
+            resolved_transport = transport
+        else:
+            resolved_transport = transport_config.get("default", "stdio")
+
+        entry = self._make_entry(
+            source="registry",
+            manifest=self._make_registry_manifest_snapshot(name, manifest),
+            transport=resolved_transport,
+            installed_by="hive mcp install",
+            pinned=version is not None,
+            auto_update=version is None,
+            resolved_package_version=manifest.get("version"),
+        )
+
+        data["servers"][name] = entry
+        self._write_installed(data)
+        logger.info(
+            "Installed MCP server '%s' v%s from registry",
+            name,
+            entry["manifest_version"],
+        )
+        return entry
+
+    # ── remove / enable / disable ───────────────────────────────────
+
+    def remove(self, name: str) -> None:
+        """Remove a server from the registry."""
+        data = self._read_installed()
+        if name not in data["servers"]:
+            raise MCPError(
+                code=MCPErrorCode.MCP_INSTALL_FAILED,
+                what=f"Cannot remove server '{name}'",
+                why="Server is not installed.",
+                fix="Run: hive mcp list  — to see installed servers.",
+            )
+        del data["servers"][name]
+        self._write_installed(data)
+        logger.info("Removed MCP server '%s'", name)
+
+    def enable(self, name: str) -> None:
+        """Enable a disabled server."""
+        self._set_enabled(name, enabled=True)
+
+    def disable(self, name: str) -> None:
+        """Disable a server without removing it."""
+        self._set_enabled(name, enabled=False)
+
+    def _set_enabled(self, name: str, *, enabled: bool) -> None:
+        data = self._read_installed()
+        if name not in data["servers"]:
+            raise MCPError(
+                code=MCPErrorCode.MCP_INSTALL_FAILED,
+                what=f"Cannot {'enable' if enabled else 'disable'} server '{name}'",
+                why="Server is not installed.",
+                fix="Run: hive mcp list  — to see installed servers.",
+            )
+        data["servers"][name]["enabled"] = enabled
+        self._write_installed(data)
+        logger.info("%s MCP server '%s'", "Enabled" if enabled else "Disabled", name)
+
+    # ── list / get ──────────────────────────────────────────────────
+
+    def list_installed(self) -> list[dict]:
+        """Return all installed servers as a list of dicts with name included."""
+        data = self._read_installed()
+        return [{"name": name, **entry} for name, entry in data["servers"].items()]
+
+    def get_server(self, name: str) -> dict | None:
+        """Get a single installed server entry by name, or None if not found."""
+        data = self._read_installed()
+        entry = data["servers"].get(name)
+        if entry is None:
+            return None
+        return {"name": name, **entry}
+
+    def list_available(self) -> list[dict]:
+        """List all servers from cached remote index."""
+        index = self._read_cached_index()
+        return [{"name": name, **m} for name, m in index.get("servers", {}).items()]
+
+    # ── set_override ────────────────────────────────────────────────
+
+    def set_override(
+        self,
+        name: str,
+        key: str,
+        value: str,
+        override_type: Literal["env", "headers"] = "env",
+    ) -> None:
+        """Set an env or header override for a server."""
+        data = self._read_installed()
+        if name not in data["servers"]:
+            raise MCPError(
+                code=MCPErrorCode.MCP_INSTALL_FAILED,
+                what=f"Cannot set override for server '{name}'",
+                why="Server is not installed.",
+                fix="Run: hive mcp list  — to see installed servers.",
+            )
+        if override_type not in ("env", "headers"):
+            raise MCPError(
+                code=MCPErrorCode.MCP_INSTALL_FAILED,
+                what=f"Invalid override type '{override_type}' for server '{name}'",
+                why="Override type must be 'env' or 'headers'.",
+                fix="Use --type env or --type headers.",
+            )
+        data["servers"][name]["overrides"][override_type][key] = value
+        self._write_installed(data)
+        logger.info("Set %s override %s for MCP server '%s'", override_type, key, name)
+
+    # ── search ──────────────────────────────────────────────────────
+
+    def search(self, query: str) -> list[dict]:
+        """Search registry index by name, tag, description, or tool name."""
+        query_lower = query.lower()
+        index = self._read_cached_index()
+        matches = []
+
+        for name, manifest in index.get("servers", {}).items():
+            if self._matches_query(name, manifest, query_lower):
+                matches.append({"name": name, **manifest})
+
+        return matches
+
+    @staticmethod
+    def _matches_query(name: str, manifest: dict, query: str) -> bool:
+        """Check if a manifest matches a search query."""
+        if query in name.lower():
+            return True
+
+        description = manifest.get("description", "")
+        if query in description.lower():
+            return True
+
+        for tag in manifest.get("tags", []):
+            if query in tag.lower():
+                return True
+
+        for tool in manifest.get("tools", []):
+            tool_name = tool.get("name", "") if isinstance(tool, dict) else str(tool)
+            if query in tool_name.lower():
+                return True
+
+        return False
+
+    # ── update_index ────────────────────────────────────────────────
+
+    def is_index_stale(self) -> bool:
+        """Check if the cached registry index needs refreshing."""
+        last_fetched_path = self._cache_dir / _LAST_FETCHED_FILENAME
+        legacy_path = self._cache_dir / _LEGACY_LAST_FETCHED_FILENAME
+        if not last_fetched_path.exists() and not legacy_path.exists():
+            return True
+
+        try:
+            path = last_fetched_path if last_fetched_path.exists() else legacy_path
+            data = json.loads(path.read_text(encoding="utf-8"))
+            last_fetched = datetime.fromisoformat(data["timestamp"])
+            config = self._read_config()
+            interval_hours = config.get("refresh_interval_hours", DEFAULT_REFRESH_INTERVAL_HOURS)
+            age_hours = (datetime.now(UTC) - last_fetched).total_seconds() / 3600
+            return age_hours >= interval_hours
+        except (KeyError, ValueError, OSError):
+            return True
+
+    def update_index(self) -> int:
+        """Fetch the latest registry index from remote and cache it.
+
+        Returns the number of servers in the index.
+        """
+        config = self._read_config()
+        url = config.get("index_url", DEFAULT_INDEX_URL)
+
+        response = httpx.get(url, timeout=10.0)
+        response.raise_for_status()
+        index = response.json()
+
+        self._write_json(self._cache_dir / "registry_index.json", index)
+        # Write last_fetched atomically too
+        self._write_json(
+            self._cache_dir / _LAST_FETCHED_FILENAME,
+            {"timestamp": datetime.now(UTC).isoformat()},
+        )
+
+        server_count = len(index.get("servers", {}))
+        logger.info("Updated registry index: %d servers available", server_count)
+        return server_count
+
+    # ── load_agent_selection ────────────────────────────────────────
+
+    def load_agent_selection(self, agent_path: Path) -> tuple[list[dict[str, Any]], int | None]:
+        """Load mcp_registry.json from an agent directory and resolve servers.
+
+        Returns:
+            (server_config_dicts, max_tools) for :meth:`ToolRegistry.load_registry_servers`.
+            ``max_tools`` is ``None`` when omitted or invalid in JSON.
+        """
+        registry_json_path = agent_path / "mcp_registry.json"
+        if not registry_json_path.exists():
+            return [], None
+
+        selection = json.loads(registry_json_path.read_text(encoding="utf-8"))
+
+        # Validate types at the JSON boundary. Bad fields are dropped with a
+        # warning so the agent still starts (graceful degradation).
+        expected_types: dict[str, type] = {
+            "include": list,
+            "tags": list,
+            "exclude": list,
+            "profile": str,
+            "max_tools": int,
+            "versions": dict,
+        }
+        validated: dict[str, Any] = {}
+        for field, expected in expected_types.items():
+            value = selection.get(field)
+            if value is None:
+                continue
+            if not isinstance(value, expected):
+                logger.warning(
+                    "mcp_registry.json: '%s' must be %s, got %s; ignoring",
+                    field,
+                    expected.__name__,
+                    type(value).__name__,
+                )
+                continue
+            validated[field] = value
+
+        max_tools = validated.get("max_tools")
+        configs = self.resolve_for_agent(
+            include=validated.get("include"),
+            tags=validated.get("tags"),
+            exclude=validated.get("exclude"),
+            profile=validated.get("profile"),
+            max_tools=max_tools,
+            versions=validated.get("versions"),
+        )
+        return [self._server_config_to_dict(c) for c in configs], max_tools
+
+    # ── resolve_for_agent ───────────────────────────────────────────
+
+    def resolve_for_agent(
+        self,
+        include: list[str] | None = None,
+        tags: list[str] | None = None,
+        exclude: list[str] | None = None,
+        profile: str | None = None,
+        max_tools: int | None = None,
+        versions: dict[str, str] | None = None,
+    ) -> list[MCPServerConfig]:
+        """Resolve installed servers matching agent selection criteria.
+
+        Selection precedence per PRD section 7.2:
+        1. profile expands to server names (union with include + tags)
+        2. include adds explicit servers
+        3. tags adds servers whose tags overlap
+        4. exclude removes (always wins)
+        5. Load order: include-order first, then alphabetical for tag/profile matches
+
+        Returns list of MCPServerConfig objects ready for ToolRegistry.
+        """
+        data = self._read_installed()
+        servers = data.get("servers", {})
+        cached_index = self._read_cached_index()
+        exclude_set = set(exclude or [])
+
+        # Phase 1: collect profile-matched servers (alphabetical)
+        profile_matched: list[str] = []
+        if profile:
+            for name, entry in sorted(servers.items()):
+                if name in exclude_set:
+                    continue
+                if profile == "all":
+                    profile_matched.append(name)
+                else:
+                    manifest = self._get_effective_manifest(name, entry, cached_index)
+                    profiles = manifest.get("hive", {}).get("profiles", [])
+                    if profile in profiles:
+                        profile_matched.append(name)
+
+        # Phase 2: collect tag-matched servers (alphabetical)
+        tag_matched: list[str] = []
+        if tags:
+            tag_set = set(tags)
+            for name, entry in sorted(servers.items()):
+                if name in exclude_set:
+                    continue
+                manifest = self._get_effective_manifest(name, entry, cached_index)
+                server_tags = set(manifest.get("tags", []))
+                if tag_set & server_tags:
+                    tag_matched.append(name)
+
+        # Phase 3: build final ordered list
+        # include-order first, then alphabetical for profile/tag matches
+        selected: list[str] = []
+        seen: set[str] = set()
+
+        for name in include or []:
+            if name not in seen and name not in exclude_set:
+                selected.append(name)
+                seen.add(name)
+
+        for name in profile_matched:
+            if name not in seen:
+                selected.append(name)
+                seen.add(name)
+
+        for name in tag_matched:
+            if name not in seen:
+                selected.append(name)
+                seen.add(name)
+
+        # Build configs, tracking aggregate tool count for max_tools cap (FR-56)
+        configs: list[MCPServerConfig] = []
+        total_tools = 0
+        for name in selected:
+            entry = servers.get(name)
+            if entry is None:
+                logger.warning(
+                    "Server '%s' requested but not installed. Run: hive mcp install %s",
+                    name,
+                    name,
+                )
+                continue
+            if not entry.get("enabled", True):
+                continue
+
+            manifest = self._get_effective_manifest(name, entry, cached_index)
+
+            # Check version pin (VC-6)
+            if versions and name in versions:
+                installed_version = entry.get("manifest_version", "0.0.0")
+                pinned_version = versions[name]
+                if installed_version != pinned_version:
+                    logger.warning(
+                        "Server '%s' version mismatch: installed=%s, pinned=%s. "
+                        "Run: hive mcp update %s",
+                        name,
+                        installed_version,
+                        pinned_version,
+                        name,
+                    )
+                    continue
+
+            # Check tool count cap before adding (FR-56), using manifest tool list when present.
+            # When ``tools`` is empty (e.g. ``add_local``), counts are unknown here—callers should
+            # pass the same ``max_tools`` to ToolRegistry.load_registry_servers to cap registration.
+            manifest_tools = manifest.get("tools", [])
+            server_tool_count = len(manifest_tools)
+            if max_tools is not None and server_tool_count == 0:
+                logger.debug(
+                    "Server '%s' has no tools list in manifest; max_tools enforced at registration",
+                    name,
+                )
+            elif max_tools is not None and total_tools + server_tool_count > max_tools:
+                logger.info(
+                    "Skipping server '%s' (%d tools): would exceed max_tools=%d",
+                    name,
+                    server_tool_count,
+                    max_tools,
+                )
+                continue
+
+            config = self._manifest_to_server_config(
+                name,
+                manifest,
+                entry.get("overrides", {}),
+                transport_override=entry.get("transport"),
+            )
+            if config is not None:
+                configs.append(config)
+                total_tools += server_tool_count
+
+        return configs
+
+    def _manifest_to_server_config(
+        self,
+        name: str,
+        manifest: dict,
+        overrides: dict | None = None,
+        transport_override: str | None = None,
+    ) -> MCPServerConfig | None:
+        """Convert a manifest and overrides to MCPServerConfig."""
+        overrides = overrides or {}
+        transport_config = manifest.get("transport", {})
+        transport = transport_override or transport_config.get("default", "stdio")
+        description = manifest.get("description", "")
+
+        match transport:
+            case "stdio":
+                stdio_config = manifest.get("stdio", {})
+                merged_env = {
+                    **stdio_config.get("env", {}),
+                    **overrides.get("env", {}),
+                }
+                return MCPServerConfig(
+                    name=name,
+                    transport="stdio",
+                    command=stdio_config.get("command"),
+                    args=stdio_config.get("args", []),
+                    env=merged_env,
+                    cwd=stdio_config.get("cwd"),
+                    description=description,
+                )
+            case "http":
+                http_config = manifest.get("http", {})
+                url = http_config.get("url", "")
+                merged_headers = {
+                    **http_config.get("headers", {}),
+                    **overrides.get("headers", {}),
+                }
+                return MCPServerConfig(
+                    name=name,
+                    transport="http",
+                    url=url,
+                    headers=merged_headers,
+                    description=description,
+                )
+            case "unix":
+                unix_config = manifest.get("unix", {})
+                http_config = manifest.get("http", {})
+                merged_headers = {
+                    **http_config.get("headers", {}),
+                    **overrides.get("headers", {}),
+                }
+                return MCPServerConfig(
+                    name=name,
+                    transport="unix",
+                    socket_path=unix_config.get("socket_path"),
+                    url=http_config.get("url") or "http://localhost",
+                    headers=merged_headers,
+                    description=description,
+                )
+            case "sse":
+                sse_config = manifest.get("sse", {})
+                merged_headers = {
+                    **sse_config.get("headers", {}),
+                    **overrides.get("headers", {}),
+                }
+                return MCPServerConfig(
+                    name=name,
+                    transport="sse",
+                    url=sse_config.get("url", ""),
+                    headers=merged_headers,
+                    description=description,
+                )
+            case _:
+                logger.warning(
+                    "Unsupported transport '%s' for server '%s'",
+                    transport,
+                    name,
+                )
+                return None
+
+    @staticmethod
+    def _server_config_to_dict(config: MCPServerConfig) -> dict[str, Any]:
+        """Convert MCPServerConfig to plain dict for ToolRegistry.register_mcp_server()."""
+        return {
+            "name": config.name,
+            "transport": config.transport,
+            "command": config.command,
+            "args": config.args,
+            "env": config.env,
+            "cwd": config.cwd,
+            "url": config.url,
+            "headers": config.headers,
+            "socket_path": config.socket_path,
+            "description": config.description,
+        }
+
+    # ── run_health_check ────────────────────────────────────────────
+
+    def health_check(self, name: str | None = None) -> dict | dict[str, dict]:
+        """Check health of installed server(s). Updates telemetry fields.
+
+        If name is None, checks all installed servers and returns
+        a dict mapping server names to their health results.
+
+        """
+        if name is None:
+            results = {}
+            for server in self.list_installed():
+                results[server["name"]] = self.health_check(server["name"])
+            return results
+
+        data = self._read_installed()
+        if name not in data["servers"]:
+            raise MCPError(
+                code=MCPErrorCode.MCP_HEALTH_FAILED,
+                what=f"Cannot health-check server '{name}'",
+                why="Server is not installed.",
+                fix="Run: hive mcp list  — to see installed servers.",
+            )
+
+        entry = data["servers"][name]
+        manifest = self._get_effective_manifest(name, entry)
+        config = self._manifest_to_server_config(
+            name,
+            manifest,
+            entry.get("overrides", {}),
+            transport_override=entry.get("transport"),
+        )
+        now = datetime.now(UTC).isoformat()
+
+        result: dict[str, Any] = {
+            "name": name,
+            "status": "unknown",
+            "tools": 0,
+            "error": None,
+        }
+
+        if config is None:
+            transport = entry.get("transport", "unknown")
+            result["status"] = "unhealthy"
+            result["error"] = f"Unsupported transport '{transport}'"
+            entry["last_health_status"] = "unhealthy"
+            entry["last_error"] = result["error"]
+            entry["last_health_check_at"] = now
+            self._write_installed(data)
+            return result
+
+        manager = MCPConnectionManager.get_instance()
+
+        try:
+            if manager.has_connection(name):
+                is_healthy = manager.health_check(name)
+                if not is_healthy:
+                    raise MCPError(
+                        code=MCPErrorCode.MCP_HEALTH_FAILED,
+                        what=f"Health check failed for server '{name}'",
+                        why="Shared MCP connection reported unhealthy.",
+                        fix=f"Run: hive mcp doctor {name}  — for diagnostics.",
+                    )
+                pooled_client = manager.acquire(config)
+                try:
+                    tools = pooled_client.list_tools()
+                finally:
+                    manager.release(name)
+            else:
+                with MCPClient(config) as client:
+                    tools = client.list_tools()
+
+            result["status"] = "healthy"
+            result["tools"] = len(tools)
+            entry["last_health_status"] = "healthy"
+            entry["last_error"] = None
+            entry["last_validated_with_hive_version"] = self._get_hive_version()
+        except Exception as exc:
+            result["status"] = "unhealthy"
+            result["error"] = str(exc)
+            entry["last_health_status"] = "unhealthy"
+            entry["last_error"] = str(exc)
+
+        entry["last_health_check_at"] = now
+        self._write_installed(data)
+        return result
+
+    def run_health_check(self, name: str | None = None) -> dict | dict[str, dict]:
+        """Backward-compatible wrapper for the public health_check API."""
+        return self.health_check(name)
+
+    @staticmethod
+    def _get_hive_version() -> str:
+        """Get the current Hive version."""
+        try:
+            return version("framework")
+        except PackageNotFoundError:
+            project_toml = Path(__file__).resolve().parents[2] / "pyproject.toml"
+            if not project_toml.exists():
+                return "unknown"
+            try:
+                with project_toml.open("rb") as f:
+                    data = tomllib.load(f)
+                return data.get("project", {}).get("version", "unknown")
+            except (tomllib.TOMLDecodeError, OSError):
+                return "unknown"
+
+    # ── helpers ──────────────────────────────────────────────────────
+
+    @staticmethod
+    def _make_entry(
+        *,
+        source: str,
+        manifest: dict,
+        transport: str,
+        installed_by: str,
+        pinned: bool = False,
+        auto_update: bool = False,
+        resolved_package_version: str | None = None,
+    ) -> dict:
+        """Build a standard installed server entry."""
+        now = datetime.now(UTC).isoformat()
+        return {
+            "source": source,
+            "manifest_version": manifest.get("version", "0.0.0"),
+            "manifest": manifest,
+            "installed_at": now,
+            "installed_by": installed_by,
+            "transport": transport,
+            "enabled": True,
+            "pinned": pinned,
+            "auto_update": auto_update,
+            "resolved_package_version": resolved_package_version,
+            "overrides": {"env": {}, "headers": {}},
+            "last_health_check_at": None,
+            "last_health_status": None,
+            "last_error": None,
+            "last_used_at": None,
+            "last_validated_with_hive_version": None,
+        }
+
+    @staticmethod
+    def _make_registry_manifest_snapshot(name: str, manifest: dict) -> dict[str, Any]:
+        """Persist a full manifest snapshot for registry-installed servers."""
+        manifest_snapshot = dict(manifest)
+        manifest_snapshot["name"] = name
+        return manifest_snapshot

core/framework/runner/mcp_registry_cli.py

@@ -0,0 +1,906 @@
+"""CLI commands for MCP server registry management.
+
+Commands:
+    hive mcp install <name>           Install a server from the registry
+    hive mcp add                      Register a local/running MCP server
+    hive mcp remove <name>            Remove an installed server
+    hive mcp enable <name>            Enable a server
+    hive mcp disable <name>           Disable a server
+    hive mcp list                     List installed servers
+    hive mcp info <name>              Show server details
+    hive mcp config <name>            Set env/header overrides
+    hive mcp search <query>           Search the registry index
+    hive mcp health [name]            Check server health
+    hive mcp update                   Refresh index and update installed servers
+    hive mcp update <name>            Update a single installed server
+"""
+
+from __future__ import annotations
+
+import json
+import os
+import sys
+from pathlib import Path
+from typing import Any
+
+# ── Shared helpers ──────────────────────────────────────────────────
+
+
+def _get_registry(base_path: Path | None = None):
+    """Initialize and return an MCPRegistry instance."""
+    from framework.runner.mcp_registry import MCPRegistry
+
+    registry = MCPRegistry(base_path=base_path)
+    registry.initialize()
+    return registry
+
+
+def _ensure_index_available(registry) -> bool:
+    """Ensure the registry index is cached locally.
+
+    If no index exists or the cache is stale, fetches a fresh copy.
+    Returns True if a usable index exists, False otherwise.
+
+    Semantics:
+    - Stale cache + refresh fails -> warn and continue with stale cache (True)
+    - No cache + refresh fails -> hard fail (False)
+    """
+    import httpx
+
+    cache_exists = (registry._cache_dir / "registry_index.json").exists()
+
+    if registry.is_index_stale():
+        print("Updating registry index...", file=sys.stderr)
+        try:
+            count = registry.update_index()
+            print(f"Registry index updated ({count} servers available).", file=sys.stderr)
+            return True
+        except (httpx.HTTPError, OSError) as exc:
+            if cache_exists:
+                print(
+                    f"Warning: failed to update registry index: {exc}\nUsing cached index.",
+                    file=sys.stderr,
+                )
+                return True
+            print(
+                f"Error: no registry index available and refresh failed: {exc}\n"
+                "Check your network connection and try: hive mcp update",
+                file=sys.stderr,
+            )
+            return False
+
+    return cache_exists
+
+
+_SECURITY_NOTICE = (
+    "Registry servers run code on your machine. Only install servers you trust.\n"
+    "Learn more: https://github.com/aden-hive/hive-mcp-registry"
+)
+_NOTICE_SENTINEL = ".security_notice_shown"
+
+
+def _print_security_notice_if_first_use(registry_base: Path) -> None:
+    """Print a one-time security notice on first registry install.
+
+    Only prints the notice. Call _mark_security_notice_shown() after
+    a successful install to persist the sentinel.
+    """
+    sentinel = registry_base / _NOTICE_SENTINEL
+    if sentinel.exists():
+        return
+    print(f"\n  {_SECURITY_NOTICE}\n", file=sys.stderr)
+
+
+def _mark_security_notice_shown(registry_base: Path) -> None:
+    """Persist the security notice sentinel after a successful install."""
+    sentinel = registry_base / _NOTICE_SENTINEL
+    try:
+        sentinel.touch()
+    except OSError:
+        pass
+
+
+def _prompt_for_missing_credentials(
+    registry,
+    name: str,
+    manifest: dict,
+) -> None:
+    """Prompt for required credentials not already set in env or overrides."""
+    credentials = manifest.get("credentials", [])
+    if not credentials:
+        return
+
+    server = registry.get_server(name)
+    existing_overrides = server.get("overrides", {}).get("env", {}) if server else {}
+
+    prompted = False
+    for cred in credentials:
+        if not isinstance(cred, dict):
+            continue
+        env_var = cred.get("env_var", "")
+        if not env_var:
+            continue
+        required = cred.get("required", False)
+        if not required:
+            continue
+
+        # Skip if already in environment or overrides
+        if os.environ.get(env_var) or existing_overrides.get(env_var):
+            continue
+
+        if not prompted:
+            print(f"\n{name} requires credentials:", file=sys.stderr)
+            prompted = True
+
+        description = cred.get("description", env_var)
+        help_url = cred.get("help_url", "")
+        help_hint = f" (get one at {help_url})" if help_url else ""
+
+        try:
+            value = input(f"  {description}{help_hint}\n  {env_var}: ").strip()
+        except (EOFError, KeyboardInterrupt):
+            print("\nSkipped credential prompting.", file=sys.stderr)
+            return
+
+        if value:
+            registry.set_override(name, env_var, value, override_type="env")
+
+
+def _parse_key_value_pairs(values: list[str]) -> dict[str, str]:
+    """Parse KEY=VAL pairs from CLI args. Raises ValueError on bad format."""
+    result = {}
+    for item in values:
+        if "=" not in item:
+            raise ValueError(
+                f"Invalid format: '{item}'. Expected KEY=VALUE.\n"
+                f"Example: --set JIRA_API_TOKEN=abc123"
+            )
+        key, _, value = item.partition("=")
+        if not key:
+            raise ValueError(f"Invalid format: '{item}'. Key cannot be empty.")
+        result[key] = value
+    return result
+
+
+def _find_agents_using_server(registry, name: str) -> list[str]:
+    """Scan agent directories for mcp_registry.json files that would load a server.
+
+    Uses MCPRegistry.load_agent_selection() to resolve actual selection logic
+    so results stay consistent with runtime behavior.
+    """
+    agent_dirs: list[Path] = []
+    # parents: [0]=runner, [1]=framework, [2]=core, [3]=hive (project root)
+    # NOTE: This path arithmetic assumes running from the source tree layout.
+    # It will not resolve correctly if installed via pip into site-packages.
+    project_root = Path(__file__).resolve().parents[3]
+    core_dir = Path(__file__).resolve().parents[2]
+
+    candidates = [
+        project_root / "exports",
+        core_dir / "exports",
+        core_dir / "framework" / "agents",
+    ]
+    for candidate in candidates:
+        if candidate.is_dir():
+            for child in candidate.iterdir():
+                if child.is_dir():
+                    agent_dirs.append(child)
+
+    matches = []
+    for agent_dir in agent_dirs:
+        registry_json = agent_dir / "mcp_registry.json"
+        if not registry_json.exists():
+            continue
+        try:
+            configs = registry.load_agent_selection(agent_dir)
+            resolved_names = {c["name"] for c in configs}
+            if name in resolved_names:
+                matches.append(str(agent_dir))
+        except Exception:
+            continue
+
+    return matches
+
+
+def _render_installed_table(entries: list[dict]) -> None:
+    """Render installed servers as a formatted table."""
+    if not entries:
+        print("No servers installed.")
+        print("Run 'hive mcp install <name>' or 'hive mcp add' to get started.")
+        return
+
+    # Column widths
+    name_w = max(len(e["name"]) for e in entries)
+    name_w = max(name_w, 4)
+    transport_w = max(len(e.get("transport", "")) for e in entries)
+    transport_w = max(transport_w, 9)
+
+    header = (
+        f"  {'NAME':<{name_w}}  "
+        f"{'TRANSPORT':<{transport_w}}  "
+        f"{'ENABLED':<7}  "
+        f"{'HEALTH':<9}  "
+        f"{'TOOLS':<5}  "
+        f"{'TRUST':<10}  "
+        f"{'SOURCE'}"
+    )
+    print(header)
+    print("  " + "─" * (len(header) - 2))
+
+    for entry in entries:
+        enabled = "yes" if entry.get("enabled", True) else "no"
+        health = entry.get("last_health_status") or "unknown"
+        health_sym = {"healthy": "✓", "unhealthy": "✗"}.get(health, "●")
+        source = entry.get("source", "")
+        manifest = entry.get("manifest", {})
+        tools_count = str(len(manifest.get("tools", [])))
+        trust_tier = manifest.get("status", "")
+        print(
+            f"  {entry['name']:<{name_w}}  "
+            f"{entry.get('transport', ''):<{transport_w}}  "
+            f"{enabled:<7}  "
+            f"{health_sym} {health:<7}  "
+            f"{tools_count:<5}  "
+            f"{trust_tier:<10}  "
+            f"{source}"
+        )
+
+
+def _render_available_table(entries: list[dict]) -> None:
+    """Render available registry servers as a formatted table."""
+    if not entries:
+        print("No servers in registry index.")
+        print("Run 'hive mcp update' to refresh the index.")
+        return
+
+    name_w = max(len(e["name"]) for e in entries)
+    name_w = max(name_w, 4)
+
+    header = f"  {'NAME':<{name_w}}  {'VERSION':<9}  {'STATUS':<10}  DESCRIPTION"
+    print(header)
+    print("  " + "─" * (len(header) - 2))
+
+    for entry in entries:
+        version = entry.get("version", "")
+        status = entry.get("status", "community")
+        desc = entry.get("description", "")
+        # Truncate long descriptions
+        if len(desc) > 60:
+            desc = desc[:57] + "..."
+        print(f"  {entry['name']:<{name_w}}  {version:<9}  {status:<10}  {desc}")
+
+
+def _mask_overrides(overrides: dict) -> dict:
+    """Replace override values with '<set>' markers. Shared by all output paths."""
+    masked: dict[str, dict[str, str]] = {}
+    if overrides.get("env"):
+        masked["env"] = dict.fromkeys(overrides["env"], "<set>")
+    else:
+        masked["env"] = {}
+    if overrides.get("headers"):
+        masked["headers"] = dict.fromkeys(overrides["headers"], "<set>")
+    else:
+        masked["headers"] = {}
+    return masked
+
+
+def _emit_json(data: Any) -> None:
+    """Print data as formatted JSON."""
+    print(json.dumps(data, indent=2, default=str))
+
+
+# ── Command registration ───────────────────────────────────────────
+
+
+def register_mcp_commands(subparsers) -> None:
+    """Register the ``hive mcp`` subcommand group."""
+    mcp_parser = subparsers.add_parser("mcp", help="Manage MCP servers")
+    mcp_sub = mcp_parser.add_subparsers(dest="mcp_command", required=True)
+
+    # ── install ──
+    install_p = mcp_sub.add_parser("install", help="Install a server from the registry")
+    install_p.add_argument("name", help="Server name in the registry")
+    install_p.add_argument(
+        "--version", dest="version", default=None, help="Pin to a specific version"
+    )
+    install_p.add_argument(
+        "--transport", default=None, help="Override default transport (stdio, http, unix, sse)"
+    )
+    install_p.set_defaults(func=cmd_mcp_install)
+
+    # ── add ──
+    add_p = mcp_sub.add_parser("add", help="Register a local/running MCP server")
+    add_p.add_argument("--name", required=False, help="Server name")
+    add_p.add_argument(
+        "--transport",
+        choices=["stdio", "http", "unix", "sse"],
+        default=None,
+        help="Transport type",
+    )
+    add_p.add_argument("--url", default=None, help="Server URL (http, unix, sse)")
+    add_p.add_argument("--command", default=None, help="Command to run (stdio)")
+    add_p.add_argument("--args", nargs="*", default=None, help="Command arguments (stdio)")
+    add_p.add_argument("--socket-path", default=None, help="Unix socket path")
+    add_p.add_argument("--description", default="", help="Server description")
+    add_p.add_argument("--from", dest="from_manifest", default=None, help="Path to manifest.json")
+    add_p.set_defaults(func=cmd_mcp_add)
+
+    # ── remove ──
+    remove_p = mcp_sub.add_parser("remove", help="Remove an installed server")
+    remove_p.add_argument("name", help="Server name")
+    remove_p.set_defaults(func=cmd_mcp_remove)
+
+    # ── enable ──
+    enable_p = mcp_sub.add_parser("enable", help="Enable a disabled server")
+    enable_p.add_argument("name", help="Server name")
+    enable_p.set_defaults(func=cmd_mcp_enable)
+
+    # ── disable ──
+    disable_p = mcp_sub.add_parser("disable", help="Disable a server without removing it")
+    disable_p.add_argument("name", help="Server name")
+    disable_p.set_defaults(func=cmd_mcp_disable)
+
+    # ── list ──
+    list_p = mcp_sub.add_parser("list", help="List servers")
+    list_p.add_argument(
+        "--available", action="store_true", help="Show available servers from registry"
+    )
+    list_p.add_argument("--json", dest="output_json", action="store_true", help="Output as JSON")
+    list_p.set_defaults(func=cmd_mcp_list)
+
+    # ── info ──
+    info_p = mcp_sub.add_parser("info", help="Show server details")
+    info_p.add_argument("name", help="Server name")
+    info_p.add_argument("--json", dest="output_json", action="store_true", help="Output as JSON")
+    info_p.set_defaults(func=cmd_mcp_info)
+
+    # ── config ──
+    config_p = mcp_sub.add_parser("config", help="Set server configuration overrides")
+    config_p.add_argument("name", help="Server name")
+    config_p.add_argument(
+        "--set",
+        dest="set_env",
+        nargs="+",
+        metavar="KEY=VAL",
+        help="Set environment variable overrides",
+    )
+    config_p.add_argument(
+        "--set-header", dest="set_header", nargs="+", metavar="KEY=VAL", help="Set header overrides"
+    )
+    config_p.set_defaults(func=cmd_mcp_config)
+
+    # ── search ──
+    search_p = mcp_sub.add_parser("search", help="Search the registry")
+    search_p.add_argument("query", help="Search term (name, tag, description, tool name)")
+    search_p.add_argument("--json", dest="output_json", action="store_true", help="Output as JSON")
+    search_p.set_defaults(func=cmd_mcp_search)
+
+    # ── health ──
+    health_p = mcp_sub.add_parser("health", help="Check server health")
+    health_p.add_argument("name", nargs="?", default=None, help="Server name (all if omitted)")
+    health_p.add_argument("--json", dest="output_json", action="store_true", help="Output as JSON")
+    health_p.set_defaults(func=cmd_mcp_health)
+
+    # ── update ──
+    update_p = mcp_sub.add_parser(
+        "update", help="Update installed servers or refresh the registry index"
+    )
+    update_p.add_argument(
+        "name",
+        nargs="?",
+        default=None,
+        help="Server name to update (omit to update all registry servers)",
+    )
+    update_p.set_defaults(func=cmd_mcp_update)
+
+
+# ── P0 command handlers ────────────────────────────────────────────
+
+
+def cmd_mcp_install(args) -> int:
+    """Install a server from the registry index."""
+    registry = _get_registry()
+    _print_security_notice_if_first_use(registry._base)
+    if not _ensure_index_available(registry):
+        return 1
+
+    try:
+        entry = registry.install(
+            args.name,
+            transport=args.transport,
+            version=args.version,
+        )
+    except ValueError as exc:
+        print(f"Error: {exc}", file=sys.stderr)
+        return 1
+
+    _mark_security_notice_shown(registry._base)
+
+    version_str = entry.get("manifest_version", "")
+    transport = entry.get("transport", "")
+    print(f"✓ Installed {args.name} v{version_str} ({transport})")
+
+    # Prompt for credentials defined in the manifest
+    manifest = entry.get("manifest", {})
+    _prompt_for_missing_credentials(registry, args.name, manifest)
+
+    print("\nNext steps:")
+    print(f"  hive mcp health {args.name}    Check that the server is reachable")
+    print(f"  hive mcp info {args.name}      View server details")
+    return 0
+
+
+def cmd_mcp_add(args) -> int:
+    """Register a local/running MCP server."""
+    registry = _get_registry()
+
+    # Handle --from manifest.json
+    if args.from_manifest:
+        return _cmd_mcp_add_from_manifest(registry, args.from_manifest)
+
+    if not args.name:
+        print(
+            "Error: --name is required.\n"
+            "Usage: hive mcp add --name my-server --transport http --url http://localhost:8080\n"
+            "   or: hive mcp add --from manifest.json",
+            file=sys.stderr,
+        )
+        return 1
+
+    if not args.transport:
+        print(
+            f"Error: --transport is required.\n"
+            f"Supported transports: stdio, http, unix, sse\n"
+            f"Example: hive mcp add --name {args.name} --transport http --url http://localhost:8080",
+            file=sys.stderr,
+        )
+        return 1
+
+    try:
+        entry = registry.add_local(
+            name=args.name,
+            transport=args.transport,
+            url=args.url,
+            command=args.command,
+            args=args.args,
+            socket_path=args.socket_path,
+            description=args.description,
+        )
+    except ValueError as exc:
+        print(f"Error: {exc}", file=sys.stderr)
+        return 1
+
+    print(f"✓ Registered {args.name} ({entry['transport']})")
+    return 0
+
+
+def _cmd_mcp_add_from_manifest(registry, manifest_path: str) -> int:
+    """Register a server from a manifest.json file."""
+    path = Path(manifest_path)
+    if not path.exists():
+        print(
+            f"Error: manifest file not found: {manifest_path}\nCheck the path and try again.",
+            file=sys.stderr,
+        )
+        return 1
+
+    try:
+        manifest = json.loads(path.read_text(encoding="utf-8"))
+    except json.JSONDecodeError as exc:
+        print(
+            f"Error: invalid JSON in {manifest_path}: {exc}\n"
+            f"Validate with: python -m json.tool {manifest_path}",
+            file=sys.stderr,
+        )
+        return 1
+
+    name = manifest.get("name")
+    if not name:
+        print(
+            f"Error: manifest missing 'name' field.\nAdd a 'name' field to {manifest_path}.",
+            file=sys.stderr,
+        )
+        return 1
+
+    try:
+        entry = registry.add_local(name=name, manifest=manifest)
+    except ValueError as exc:
+        print(f"Error: {exc}", file=sys.stderr)
+        return 1
+
+    print(f"✓ Registered {name} from {manifest_path} ({entry['transport']})")
+    return 0
+
+
+def cmd_mcp_remove(args) -> int:
+    """Remove an installed server."""
+    registry = _get_registry()
+    try:
+        registry.remove(args.name)
+    except ValueError as exc:
+        print(f"Error: {exc}", file=sys.stderr)
+        return 1
+
+    print(f"✓ Removed {args.name}")
+    return 0
+
+
+def cmd_mcp_enable(args) -> int:
+    """Enable a disabled server."""
+    registry = _get_registry()
+    try:
+        registry.enable(args.name)
+    except ValueError as exc:
+        print(f"Error: {exc}", file=sys.stderr)
+        return 1
+
+    print(f"✓ Enabled {args.name}")
+    return 0
+
+
+def cmd_mcp_disable(args) -> int:
+    """Disable a server without removing it."""
+    registry = _get_registry()
+    try:
+        registry.disable(args.name)
+    except ValueError as exc:
+        print(f"Error: {exc}", file=sys.stderr)
+        return 1
+
+    print(f"✓ Disabled {args.name}")
+    return 0
+
+
+def cmd_mcp_list(args) -> int:
+    """List installed or available servers."""
+    registry = _get_registry()
+
+    if args.available:
+        if not _ensure_index_available(registry):
+            return 1
+        entries = registry.list_available()
+        if args.output_json:
+            _emit_json(entries)
+        else:
+            _render_available_table(entries)
+    else:
+        entries = registry.list_installed()
+        if args.output_json:
+            safe_entries = []
+            for entry in entries:
+                safe = dict(entry)
+                safe["overrides"] = _mask_overrides(safe.get("overrides", {}))
+                safe_entries.append(safe)
+            _emit_json(safe_entries)
+        else:
+            _render_installed_table(entries)
+
+    return 0
+
+
+def cmd_mcp_info(args) -> int:
+    """Show full details for a server."""
+    registry = _get_registry()
+    server = registry.get_server(args.name)
+
+    if server is None:
+        print(
+            f"Error: server '{args.name}' is not installed.\n"
+            f"Run 'hive mcp list' to see installed servers.\n"
+            f"Run 'hive mcp install {args.name}' to install from registry.",
+            file=sys.stderr,
+        )
+        return 1
+
+    # Enrich with agent usage for both JSON and human output
+    agents = _find_agents_using_server(registry, args.name)
+    if agents:
+        server["used_by_agents"] = agents
+
+    if args.output_json:
+        safe = dict(server)
+        safe["overrides"] = _mask_overrides(safe.get("overrides", {}))
+        _emit_json(safe)
+        return 0
+
+    manifest = server.get("manifest", {})
+    overrides = _mask_overrides(server.get("overrides", {}))
+    tools = manifest.get("tools", [])
+    status = manifest.get("status", "community")
+    hive_block = manifest.get("hive", {})
+
+    print(f"{server['name']}")
+    print("=" * 50)
+
+    # Core info
+    print(f"  Source:     {server.get('source', '')}")
+    print(f"  Transport:  {server.get('transport', '')}")
+    print(f"  Version:    {server.get('manifest_version', 'unknown')}")
+    print(f"  Trust tier: {status}")
+    print(f"  Enabled:    {'yes' if server.get('enabled', True) else 'no'}")
+
+    # Description
+    desc = manifest.get("description", "")
+    if desc:
+        print(f"  Description: {desc}")
+
+    # Health
+    health = server.get("last_health_status")
+    if health:
+        health_sym = {"healthy": "✓", "unhealthy": "✗"}.get(health, "●")
+        print(f"  Health:     {health_sym} {health}")
+        last_check = server.get("last_health_check_at")
+        if last_check:
+            print(f"  Last check: {last_check}")
+    last_error = server.get("last_error")
+    if last_error:
+        print(f"  Last error: {last_error}")
+
+    # Tools
+    if tools:
+        print(f"\n  Tools ({len(tools)}):")
+        for tool in tools:
+            if isinstance(tool, dict):
+                tool_name = tool.get("name", "")
+                tool_desc = tool.get("description", "")
+                print(f"    • {tool_name}: {tool_desc}" if tool_desc else f"    • {tool_name}")
+            else:
+                print(f"    • {tool}")
+
+    # Overrides
+    env_overrides = overrides.get("env", {})
+    header_overrides = overrides.get("headers", {})
+    if env_overrides or header_overrides:
+        print("\n  Overrides:")
+        for key in env_overrides:
+            print(f"    env.{key} = <set>")
+        for key in header_overrides:
+            print(f"    header.{key} = <set>")
+
+    # Hive block
+    if hive_block:
+        profiles = hive_block.get("profiles", [])
+        if profiles:
+            print(f"\n  Profiles: {', '.join(profiles)}")
+        min_ver = hive_block.get("min_version")
+        if min_ver:
+            print(f"  Min Hive version: {min_ver}")
+
+    # Agent usage
+    if agents:
+        print("\n  Used by agents:")
+        for agent in agents:
+            print(f"    • {agent}")
+
+    # Timestamps
+    print(f"\n  Installed:  {server.get('installed_at', 'unknown')}")
+    print(f"  Installed by: {server.get('installed_by', 'unknown')}")
+
+    return 0
+
+
+def cmd_mcp_config(args) -> int:
+    """Set env or header overrides for a server."""
+    registry = _get_registry()
+
+    if not args.set_env and not args.set_header:
+        # Show current config
+        server = registry.get_server(args.name)
+        if server is None:
+            print(
+                f"Error: server '{args.name}' is not installed.\n"
+                f"Run 'hive mcp list' to see installed servers.",
+                file=sys.stderr,
+            )
+            return 1
+        masked = _mask_overrides(server.get("overrides", {}))
+        env_o = masked.get("env", {})
+        header_o = masked.get("headers", {})
+        if not env_o and not header_o:
+            print(f"No overrides set for {args.name}.")
+            print(f"Set one with: hive mcp config {args.name} --set KEY=VALUE")
+        else:
+            print(f"Overrides for {args.name}:")
+            for key in env_o:
+                print(f"  env.{key} = <set>")
+            for key in header_o:
+                print(f"  header.{key} = <set>")
+        return 0
+
+    try:
+        if args.set_env:
+            pairs = _parse_key_value_pairs(args.set_env)
+            for key, value in pairs.items():
+                registry.set_override(args.name, key, value, override_type="env")
+            print(f"✓ Set {len(pairs)} env override(s) for {args.name}")
+
+        if args.set_header:
+            pairs = _parse_key_value_pairs(args.set_header)
+            for key, value in pairs.items():
+                registry.set_override(args.name, key, value, override_type="headers")
+            print(f"✓ Set {len(pairs)} header override(s) for {args.name}")
+
+    except ValueError as exc:
+        print(f"Error: {exc}", file=sys.stderr)
+        return 1
+
+    return 0
+
+
+# ── P1 command handlers ────────────────────────────────────────────
+
+
+def cmd_mcp_search(args) -> int:
+    """Search the registry index."""
+    registry = _get_registry()
+    if not _ensure_index_available(registry):
+        return 1
+
+    results = registry.search(args.query)
+
+    if args.output_json:
+        _emit_json(results)
+        return 0
+
+    if not results:
+        print(f"No servers matching '{args.query}'.")
+        return 0
+
+    print(f"Found {len(results)} server(s) matching '{args.query}':\n")
+    _render_available_table(results)
+    return 0
+
+
+def cmd_mcp_health(args) -> int:
+    """Check server health."""
+    registry = _get_registry()
+
+    try:
+        results = registry.health_check(name=args.name)
+    except ValueError as exc:
+        print(f"Error: {exc}", file=sys.stderr)
+        return 1
+
+    # Single server returns a flat dict, all-servers returns name->dict
+    if args.name:
+        results = {args.name: results}
+
+    if args.output_json:
+        _emit_json(results)
+        return 0
+
+    for name, result in results.items():
+        status = result.get("status", "unknown")
+        tools = result.get("tools", 0)
+        error = result.get("error")
+        sym = {"healthy": "✓", "unhealthy": "✗"}.get(status, "●")
+
+        print(f"  {sym} {name}: {status}", end="")
+        if status == "healthy" and tools:
+            print(f" ({tools} tools)")
+        elif error:
+            print(f"\n    Error: {error}")
+        else:
+            print()
+
+    return 0
+
+
+def cmd_mcp_update(args) -> int:
+    """Update a single server, or refresh the index and update all registry servers."""
+    registry = _get_registry()
+
+    if args.name:
+        return _cmd_mcp_update_server(args.name, registry)
+
+    # Step 1: refresh the registry index
+    try:
+        count = registry.update_index()
+    except Exception as exc:
+        print(
+            f"Error: failed to update registry index: {exc}\n"
+            f"Check your network connection and try again.",
+            file=sys.stderr,
+        )
+        return 1
+
+    print(f"✓ Registry index updated ({count} servers available)")
+
+    # Step 2: update all installed registry servers (skip local/pinned)
+    installed = registry.list_installed()
+    registry_servers = [
+        s for s in installed if s.get("source") == "registry" and not s.get("pinned")
+    ]
+
+    if not registry_servers:
+        return 0
+
+    print(f"\nUpdating {len(registry_servers)} installed server(s)...")
+    errors = 0
+    for server in registry_servers:
+        name = server["name"]
+        rc = _cmd_mcp_update_server(name, registry)
+        if rc != 0:
+            errors += 1
+
+    return 1 if errors else 0
+
+
+def _cmd_mcp_update_server(name: str, registry=None) -> int:
+    """Bridge: reinstall a server from the latest index.
+
+    This is a temporary bridge until #6355 adds proper version diffing,
+    tool-signature change detection, and --dry-run support.
+    """
+    if registry is None:
+        registry = _get_registry()
+
+    server = registry.get_server(name)
+    if server is None:
+        print(
+            f"Error: server '{name}' is not installed.\n"
+            f"Run 'hive mcp install {name}' to install it.",
+            file=sys.stderr,
+        )
+        return 1
+
+    if server.get("source") != "registry":
+        print(
+            f"Error: '{name}' is a local server and cannot be updated from the registry.\n"
+            f"Use 'hive mcp remove {name}' and 'hive mcp add' to re-register it.",
+            file=sys.stderr,
+        )
+        return 1
+
+    if server.get("pinned"):
+        print(
+            f"Error: '{name}' is pinned to v{server.get('manifest_version', '?')}.\n"
+            f"To update a pinned server, remove and reinstall:\n"
+            f"  hive mcp remove {name} && hive mcp install {name}",
+            file=sys.stderr,
+        )
+        return 1
+
+    # Refresh index, then reinstall
+    if not _ensure_index_available(registry):
+        return 1
+
+    old_version = server.get("manifest_version", "unknown")
+    transport = server.get("transport")
+    overrides = server.get("overrides", {})
+    was_enabled = server.get("enabled", True)
+
+    # Save the full entry before removing so we can restore on failure
+    saved_entry = dict(server)
+    saved_entry.pop("name", None)
+
+    try:
+        registry.remove(name)
+        entry = registry.install(name, transport=transport)
+    except ValueError as exc:
+        # Restore the original entry so update doesn't become an uninstall
+        data = registry._read_installed()
+        data["servers"][name] = saved_entry
+        registry._write_installed(data)
+        print(
+            f"Error: {exc}\nServer '{name}' has been restored to its previous state.",
+            file=sys.stderr,
+        )
+        return 1
+
+    new_version = entry.get("manifest_version", "unknown")
+
+    # Restore prior state from the previous installation
+    for key, value in overrides.get("env", {}).items():
+        registry.set_override(name, key, value, override_type="env")
+    for key, value in overrides.get("headers", {}).items():
+        registry.set_override(name, key, value, override_type="headers")
+    if not was_enabled:
+        registry.disable(name)
+
+    if old_version == new_version:
+        print(f"✓ {name} is already at v{new_version}")
+    else:
+        print(f"✓ Updated {name}: v{old_version} → v{new_version}")
+
+    return 0

core/framework/runner/mcp_registry_resolver.py

@@ -0,0 +1,252 @@
+from __future__ import annotations
+
+import json
+import logging
+from pathlib import Path
+from typing import Any
+
+logger = logging.getLogger(__name__)
+
+_CACHE_INDEX_PATH = Path.home() / ".hive" / "mcp_registry" / "cache" / "registry_index.json"
+_FIXTURE_INDEX_PATH = Path(__file__).resolve().parent / "fixtures" / "registry_index.json"
+
+
+def resolve_registry_servers(
+    *,
+    include: list[str] | None = None,
+    tags: list[str] | None = None,
+    exclude: list[str] | None = None,
+    profile: str | None = None,
+    max_tools: int | None = None,
+    versions: dict[str, str] | None = None,
+) -> list[dict[str, Any]]:
+    """
+    Resolve registry-sourced MCP servers for `mcp_registry.json` selection.
+
+    This function is written to be mock-friendly during early development:
+    - If the real `MCPRegistry` core module is present, delegate to it.
+    - Otherwise, fall back to a cached local index (`~/.hive/.../registry_index.json`)
+      and then to the repo fixture index.
+    """
+
+    # `max_tools` is enforced by ToolRegistry. We keep it in the resolver
+    # signature to match the PRD and future MCPRegistry interfaces.
+    _ = max_tools
+
+    try:
+        from framework.runner.mcp_registry import MCPRegistry  # type: ignore
+
+        registry = MCPRegistry()
+        resolved = registry.resolve_for_agent(
+            include=include or [],
+            tags=tags or [],
+            exclude=exclude or [],
+            profile=profile,
+            max_tools=max_tools,
+            versions=versions or {},
+        )
+        # Future-proof: normalize both dicts and typed objects to dicts.
+        return [_normalize_server_config(x) for x in resolved]
+    except ImportError:
+        # Expected while #6349/#6574 is not merged locally.
+        pass
+    except Exception as e:
+        logger.warning("MCPRegistry resolution failed; falling back to cache/fixtures: %s", e)
+
+    return _resolve_from_local_index(
+        include=include,
+        tags=tags,
+        exclude=exclude,
+        profile=profile,
+        versions=versions or {},
+    )
+
+
+def _resolve_from_local_index(
+    *,
+    include: list[str] | None,
+    tags: list[str] | None,
+    exclude: list[str] | None,
+    profile: str | None,
+    versions: dict[str, str],
+) -> list[dict[str, Any]]:
+    index = _load_index_json()
+    servers = _coerce_index_servers(index)
+    servers_by_name: dict[str, dict[str, Any]] = {
+        s["name"]: s for s in servers if isinstance(s, dict) and "name" in s
+    }
+
+    include_list = include or []
+    tags_list = tags or []
+    exclude_set = set(exclude or [])
+
+    def _profiles_of(entry: dict[str, Any]) -> set[str]:
+        if isinstance(entry.get("profiles"), list):
+            return set(entry["profiles"])
+        hive = entry.get("hive")
+        if isinstance(hive, dict) and isinstance(hive.get("profiles"), list):
+            return set(hive["profiles"])
+        return set()
+
+    def _tags_of(entry: dict[str, Any]) -> set[str]:
+        if isinstance(entry.get("tags"), list):
+            return set(entry["tags"])
+        return set()
+
+    def _entry_version(entry: dict[str, Any]) -> str | None:
+        # Prefer flat `version`, but support a few common shapes.
+        v = entry.get("version")
+        if isinstance(v, str):
+            return v
+        v2 = entry.get("manifest_version")
+        if isinstance(v2, str):
+            return v2
+        hive = entry.get("manifest")
+        if isinstance(hive, dict) and isinstance(hive.get("version"), str):
+            return hive["version"]
+        return None
+
+    def _version_allows(server_name: str) -> bool:
+        if server_name not in versions:
+            return True
+        pinned = versions[server_name]
+        entry = servers_by_name.get(server_name)
+        if not entry:
+            return False
+        return _entry_version(entry) == pinned
+
+    resolved_names: list[str] = []
+    resolved_set: set[str] = set()
+
+    # 1) Include-order first
+    for name in include_list:
+        if name in exclude_set:
+            continue
+        if name in servers_by_name and _version_allows(name) and name not in resolved_set:
+            resolved_names.append(name)
+            resolved_set.add(name)
+
+    # 2) Then tag/profile matches, alphabetical
+    profile_candidates = set()
+    if profile:
+        for name, entry in servers_by_name.items():
+            if name in exclude_set or not _version_allows(name):
+                continue
+            if profile in _profiles_of(entry):
+                profile_candidates.add(name)
+
+    tag_candidates = set()
+    if tags_list:
+        tags_set = set(tags_list)
+        for name, entry in servers_by_name.items():
+            if name in exclude_set or not _version_allows(name):
+                continue
+            if _tags_of(entry).intersection(tags_set):
+                tag_candidates.add(name)
+
+    tag_profile_names = sorted((profile_candidates | tag_candidates) - resolved_set)
+    resolved_names.extend(tag_profile_names)
+
+    # Missing requested servers should warn (FR-54).
+    for name in include_list:
+        if name in exclude_set:
+            continue
+        if name not in resolved_set:
+            if name not in servers_by_name:
+                logger.warning(
+                    "Server '%s' requested by mcp_registry.json but not found in index. "
+                    "Run: hive mcp install %s",
+                    name,
+                    name,
+                )
+            elif name in versions:
+                logger.warning(
+                    "Server '%s' was requested but pinned version '%s' was not found in index. "
+                    "Run: hive mcp update %s or change the pin in mcp_registry.json",
+                    name,
+                    versions[name],
+                    name,
+                )
+            else:
+                logger.warning(
+                    "Server '%s' requested by mcp_registry.json was not selected. "
+                    "Check selection filters/exclude lists.",
+                    name,
+                )
+
+    resolved_configs: list[dict[str, Any]] = []
+    repo_root = Path(__file__).resolve().parents[3]
+    for name in resolved_names:
+        entry = servers_by_name.get(name)
+        if not entry:
+            continue
+        config = entry.get("mcp_config")
+        if not isinstance(config, dict):
+            # Best-effort: allow a direct MCP config shape at top-level.
+            config = {
+                k: v
+                for k, v in entry.items()
+                if k
+                in {
+                    "name",
+                    "transport",
+                    "command",
+                    "args",
+                    "env",
+                    "cwd",
+                    "url",
+                    "headers",
+                    "description",
+                }
+            }
+        mcp_config = dict(config)
+        mcp_config["name"] = name
+        if mcp_config.get("transport") == "stdio":
+            _absolutize_stdio_config_in_place(repo_root, mcp_config)
+        resolved_configs.append(mcp_config)
+
+    return resolved_configs
+
+
+def _load_index_json() -> Any:
+    if _CACHE_INDEX_PATH.exists():
+        return json.loads(_CACHE_INDEX_PATH.read_text(encoding="utf-8"))
+    if _FIXTURE_INDEX_PATH.exists():
+        logger.info("Using local fixture index because registry cache is missing")
+        return json.loads(_FIXTURE_INDEX_PATH.read_text(encoding="utf-8"))
+    logger.warning("No local MCP registry index found (cache and fixture missing)")
+    return {"servers": []}
+
+
+def _coerce_index_servers(index: Any) -> list[dict[str, Any]]:
+    if isinstance(index, list):
+        return [x for x in index if isinstance(x, dict)]
+    if isinstance(index, dict):
+        servers = index.get("servers", [])
+        if isinstance(servers, list):
+            return [x for x in servers if isinstance(x, dict)]
+    return []
+
+
+def _normalize_server_config(raw: Any) -> dict[str, Any]:
+    if isinstance(raw, dict):
+        return dict(raw)
+
+    # Future-proof object-to-dict normalization.
+    for attr in ("to_dict", "model_dump"):
+        maybe = getattr(raw, attr, None)
+        if callable(maybe):
+            return dict(maybe())
+
+    return dict(getattr(raw, "__dict__", {}))
+
+
+def _absolutize_stdio_config_in_place(repo_root: Path, config: dict[str, Any]) -> None:
+    cwd = config.get("cwd")
+    if isinstance(cwd, str) and not Path(cwd).is_absolute():
+        config["cwd"] = str((repo_root / cwd).resolve())
+
+    # We intentionally do not absolutize `args` here.
+    # For stdio servers, arguments may include the script name relative to
+    # `cwd` (e.g. "coder_tools_server.py" with cwd="tools"). ToolRegistry's
+    # stdio resolution logic handles script path checks and platform quirks.

core/framework/runner/preload_validation.py

@@ -1,6 +1,6 @@
 """Pre-load validation for agent graphs.
 
-Runs structural and credential checks before MCP servers are spawned.
+Runs structural, credential, and skill-trust checks before MCP servers are spawned.
 Fails fast with actionable error messages.
 """
 
@@ -169,6 +169,9 @@ def run_preload_validation(
     1. Graph structure (includes GCU subagent-only checks) — non-recoverable
     2. Credentials — potentially recoverable via interactive setup
 
+    Skill discovery and trust gating (AS-13) happen later in runner._setup()
+    so they have access to agent-level skill configuration.
+
     Raises PreloadValidationError for structural issues.
     Raises CredentialError for credential issues.
     """

core/framework/runner/runner.py

@@ -28,6 +28,7 @@ from framework.runner.tool_registry import ToolRegistry
 from framework.runtime.agent_runtime import AgentRuntime, AgentRuntimeConfig, create_agent_runtime
 from framework.runtime.execution_stream import EntryPointSpec
 from framework.runtime.runtime_log_store import RuntimeLogStore
+from framework.tools.flowchart_utils import generate_fallback_flowchart
 
 if TYPE_CHECKING:
     from framework.runner.protocol import AgentMessage, CapabilityResponse
@@ -551,6 +552,319 @@ def get_kimi_code_token() -> str | None:
     return None
 
 
+# ---------------------------------------------------------------------------
+# Antigravity subscription token helpers
+# ---------------------------------------------------------------------------
+
+# Antigravity IDE (native macOS/Linux app) stores OAuth tokens in its
+# VSCode-style SQLite state database under the key
+# "antigravityUnifiedStateSync.oauthToken" as a base64-encoded protobuf blob.
+ANTIGRAVITY_IDE_STATE_DB = (
+    Path.home()
+    / "Library"
+    / "Application Support"
+    / "Antigravity"
+    / "User"
+    / "globalStorage"
+    / "state.vscdb"
+)
+# Linux fallback for the IDE state DB
+ANTIGRAVITY_IDE_STATE_DB_LINUX = (
+    Path.home() / ".config" / "Antigravity" / "User" / "globalStorage" / "state.vscdb"
+)
+# Antigravity credentials stored by native OAuth implementation
+ANTIGRAVITY_AUTH_FILE = Path.home() / ".hive" / "antigravity-accounts.json"
+
+ANTIGRAVITY_OAUTH_TOKEN_URL = "https://oauth2.googleapis.com/token"
+_ANTIGRAVITY_TOKEN_LIFETIME_SECS = 3600  # Google access tokens expire in 1 hour
+_ANTIGRAVITY_IDE_STATE_DB_KEY = "antigravityUnifiedStateSync.oauthToken"
+
+
+def _read_antigravity_ide_credentials() -> dict | None:
+    """Read credentials from the Antigravity IDE's SQLite state database.
+
+    The Antigravity desktop IDE (VSCode-based) stores its OAuth token as a
+    base64-encoded protobuf blob in a SQLite database.  The access token is
+    a standard Google OAuth ``ya29.*`` bearer token.
+
+    Returns:
+        Dict with ``accessToken`` and optionally ``refreshToken`` keys,
+        plus ``_source: "ide"`` to skip file-based save on refresh.
+        Returns None if the database is absent or the key is not found.
+    """
+    import re
+    import sqlite3
+
+    for db_path in (ANTIGRAVITY_IDE_STATE_DB, ANTIGRAVITY_IDE_STATE_DB_LINUX):
+        if not db_path.exists():
+            continue
+        try:
+            con = sqlite3.connect(f"file:{db_path}?mode=ro", uri=True)
+            try:
+                row = con.execute(
+                    "SELECT value FROM ItemTable WHERE key = ?",
+                    (_ANTIGRAVITY_IDE_STATE_DB_KEY,),
+                ).fetchone()
+            finally:
+                con.close()
+
+            if not row:
+                continue
+
+            import base64
+
+            blob = base64.b64decode(row[0])
+
+            # The protobuf blob contains the access token (ya29.*) and
+            # refresh token (1//*) as length-prefixed UTF-8 strings.
+            # Decode the inner base64 layer and extract with regex.
+            inner_b64_candidates = re.findall(rb"[A-Za-z0-9+/=_\-]{40,}", blob)
+            access_token: str | None = None
+            refresh_token: str | None = None
+            for candidate in inner_b64_candidates:
+                try:
+                    padded = candidate + b"=" * (-len(candidate) % 4)
+                    inner = base64.urlsafe_b64decode(padded)
+                except Exception:
+                    continue
+                if not access_token:
+                    m = re.search(rb"ya29\.[A-Za-z0-9_\-\.]+", inner)
+                    if m:
+                        access_token = m.group(0).decode("ascii")
+                if not refresh_token:
+                    m = re.search(rb"1//[A-Za-z0-9_\-\.]+", inner)
+                    if m:
+                        refresh_token = m.group(0).decode("ascii")
+                if access_token and refresh_token:
+                    break
+
+            if access_token:
+                return {
+                    "accounts": [
+                        {
+                            "accessToken": access_token,
+                            "refreshToken": refresh_token or "",
+                        }
+                    ],
+                    "_source": "ide",
+                    "_db_path": str(db_path),
+                }
+        except Exception as exc:
+            logger.debug("Failed to read Antigravity IDE state DB: %s", exc)
+            continue
+
+    return None
+
+
+def _read_antigravity_credentials() -> dict | None:
+    """Read Antigravity auth data from all supported credential sources.
+
+    Checks in order:
+    1. Antigravity IDE SQLite state database (native macOS/Linux app)
+    2. Native OAuth credentials file (~/.hive/antigravity-accounts.json)
+
+    Returns:
+        Auth data dict with an ``accounts`` list on success, None otherwise.
+    """
+    # 1. Native Antigravity IDE (primary on macOS)
+    ide_creds = _read_antigravity_ide_credentials()
+    if ide_creds:
+        return ide_creds
+
+    # 2. Native OAuth credentials file
+    if ANTIGRAVITY_AUTH_FILE.exists():
+        try:
+            with open(ANTIGRAVITY_AUTH_FILE, encoding="utf-8") as f:
+                data = json.load(f)
+            accounts = data.get("accounts", [])
+            if accounts and isinstance(accounts[0], dict):
+                return data
+        except (json.JSONDecodeError, OSError):
+            pass
+    return None
+
+
+def _is_antigravity_token_expired(auth_data: dict) -> bool:
+    """Check whether the Antigravity access token is expired or near expiry.
+
+    For IDE-sourced credentials: uses the state DB's mtime as last_refresh
+    since the IDE keeps the DB fresh while it's running.
+    For JSON-sourced credentials: uses the ``last_refresh`` field or file mtime.
+    """
+    import time
+    from datetime import datetime
+
+    now = time.time()
+
+    if auth_data.get("_source") == "ide":
+        # The IDE refreshes tokens automatically while running.
+        # Use the DB file's mtime as a proxy for when the token was last updated.
+        try:
+            db_path = Path(auth_data.get("_db_path", str(ANTIGRAVITY_IDE_STATE_DB)))
+            last_refresh: float = db_path.stat().st_mtime
+        except OSError:
+            return True
+        expires_at = last_refresh + _ANTIGRAVITY_TOKEN_LIFETIME_SECS
+        return now >= (expires_at - _TOKEN_REFRESH_BUFFER_SECS)
+
+    last_refresh_val: float | str | None = auth_data.get("last_refresh")
+    if last_refresh_val is None:
+        try:
+            last_refresh_val = ANTIGRAVITY_AUTH_FILE.stat().st_mtime
+        except OSError:
+            return True
+    elif isinstance(last_refresh_val, str):
+        try:
+            last_refresh_val = datetime.fromisoformat(
+                last_refresh_val.replace("Z", "+00:00")
+            ).timestamp()
+        except (ValueError, TypeError):
+            return True
+
+    expires_at = float(last_refresh_val) + _ANTIGRAVITY_TOKEN_LIFETIME_SECS
+    return now >= (expires_at - _TOKEN_REFRESH_BUFFER_SECS)
+
+
+def _refresh_antigravity_token(refresh_token: str) -> dict | None:
+    """Refresh the Antigravity access token via Google OAuth.
+
+    POSTs form-encoded ``grant_type=refresh_token`` to the Google token
+    endpoint using Antigravity's public OAuth client ID.
+
+    Returns:
+        Parsed response dict (containing ``access_token``) on success,
+        None on any error.
+    """
+    import urllib.error
+    import urllib.parse
+    import urllib.request
+
+    from framework.config import get_antigravity_client_id, get_antigravity_client_secret
+
+    client_id = get_antigravity_client_id()
+    client_secret = get_antigravity_client_secret()
+    params: dict = {
+        "grant_type": "refresh_token",
+        "refresh_token": refresh_token,
+        "client_id": client_id,
+    }
+    if client_secret:
+        params["client_secret"] = client_secret
+
+    data = urllib.parse.urlencode(params).encode("utf-8")
+
+    req = urllib.request.Request(
+        ANTIGRAVITY_OAUTH_TOKEN_URL,
+        data=data,
+        headers={"Content-Type": "application/x-www-form-urlencoded"},
+        method="POST",
+    )
+
+    try:
+        with urllib.request.urlopen(req, timeout=15) as resp:  # noqa: S310
+            return json.loads(resp.read())
+    except (urllib.error.URLError, json.JSONDecodeError, TimeoutError, OSError) as exc:
+        logger.debug("Antigravity token refresh failed: %s", exc)
+        return None
+
+
+def _save_refreshed_antigravity_credentials(auth_data: dict, token_data: dict) -> None:
+    """Write refreshed tokens back to the Antigravity JSON credentials file.
+
+    Skipped for IDE-sourced credentials (the IDE manages its own DB).
+    Updates ``accounts[0].accessToken`` (and ``refreshToken`` if present),
+    then persists ``last_refresh`` as an ISO-8601 UTC string.
+    """
+    from datetime import datetime
+
+    # IDE manages its own state — we do not write back to its SQLite DB
+    if auth_data.get("_source") == "ide":
+        return
+
+    try:
+        accounts = auth_data.get("accounts", [])
+        if not accounts:
+            return
+        account = accounts[0]
+        account["accessToken"] = token_data["access_token"]
+        if "refresh_token" in token_data:
+            account["refreshToken"] = token_data["refresh_token"]
+        auth_data["accounts"] = accounts
+        auth_data["last_refresh"] = datetime.now(UTC).isoformat()
+
+        ANTIGRAVITY_AUTH_FILE.parent.mkdir(parents=True, exist_ok=True)
+        fd = os.open(ANTIGRAVITY_AUTH_FILE, os.O_WRONLY | os.O_CREAT | os.O_TRUNC, 0o600)
+        with os.fdopen(fd, "w", encoding="utf-8") as f:
+            json.dump(auth_data, f, indent=2)
+        logger.debug("Antigravity credentials refreshed and saved")
+    except (OSError, KeyError) as exc:
+        logger.debug("Failed to save refreshed Antigravity credentials: %s", exc)
+
+
+def get_antigravity_token() -> str | None:
+    """Get the OAuth access token from an Antigravity subscription.
+
+    Credential sources checked in order:
+    1. Antigravity IDE SQLite state DB (native app, macOS/Linux)
+    2. antigravity-auth CLI JSON file
+
+    For IDE credentials the token is read directly (the IDE refreshes it
+    automatically while running).  For JSON credentials an automatic OAuth
+    refresh is attempted when the token is near expiry.
+
+    Returns:
+        The ``ya29.*`` Google OAuth access token, or None if unavailable.
+    """
+    auth_data = _read_antigravity_credentials()
+    if not auth_data:
+        return None
+
+    accounts = auth_data.get("accounts", [])
+    if not accounts:
+        return None
+    account = accounts[0]
+
+    access_token = account.get("accessToken")
+    if not access_token:
+        return None
+
+    if not _is_antigravity_token_expired(auth_data):
+        return access_token
+
+    # Token is expired or near expiry — attempt a refresh
+    refresh_token = account.get("refreshToken")
+    if not refresh_token:
+        logger.warning(
+            "Antigravity token expired and no refresh token available. "
+            "Re-open the Antigravity IDE to refresh, or run 'antigravity-auth accounts add'."
+        )
+        return access_token  # return stale token; proxy may still accept it briefly
+
+    logger.info("Antigravity token expired or near expiry, refreshing...")
+    token_data = _refresh_antigravity_token(refresh_token)
+
+    if token_data and "access_token" in token_data:
+        _save_refreshed_antigravity_credentials(auth_data, token_data)
+        return token_data["access_token"]
+
+    logger.warning(
+        "Antigravity token refresh failed. "
+        "Re-open the Antigravity IDE or run 'antigravity-auth accounts add'."
+    )
+    return access_token
+
+
+def _is_antigravity_proxy_available() -> bool:
+    """Return True if antigravity-auth serve is running on localhost:8069."""
+    import socket
+
+    try:
+        with socket.create_connection(("localhost", 8069), timeout=0.5):
+            return True
+    except (OSError, TimeoutError):
+        return False
+
+
 @dataclass
 class AgentInfo:
     """Information about an exported agent."""
@@ -807,6 +1121,9 @@ class AgentRunner:
         if mcp_config_path.exists():
             self._load_mcp_servers_from_config(mcp_config_path)
 
+        # Auto-discover registry-selected MCP servers from mcp_registry.json
+        self._load_registry_mcp_servers(agent_path)
+
     @staticmethod
     def _import_agent_module(agent_path: Path):
         """Import an agent package from its directory path.
@@ -959,6 +1276,12 @@ class AgentRunner:
 
             graph = GraphSpec(**graph_kwargs)
 
+            # Generate flowchart.json if missing (for template/legacy agents)
+            generate_fallback_flowchart(graph, goal, agent_path)
+            # Read skill configuration from agent module
+            agent_default_skills = getattr(agent_module, "default_skills", None)
+            agent_skills = getattr(agent_module, "skills", None)
+
             # Read runtime config (webhook settings, etc.) if defined
             agent_runtime_config = getattr(agent_module, "runtime_config", None)
 
@@ -970,7 +1293,7 @@ class AgentRunner:
             configure_fn = getattr(agent_module, "configure_for_account", None)
             list_accts_fn = getattr(agent_module, "list_connected_accounts", None)
 
-            return cls(
+            runner = cls(
                 agent_path=agent_path,
                 graph=graph,
                 goal=goal,
@@ -986,6 +1309,10 @@ class AgentRunner:
                 list_accounts=list_accts_fn,
                 credential_store=credential_store,
             )
+            # Stash skill config for use in _setup()
+            runner._agent_default_skills = agent_default_skills
+            runner._agent_skills = agent_skills
+            return runner
 
         # Fallback: load from agent.json (legacy JSON-based agents)
         agent_json_path = agent_path / "agent.json"
@@ -1003,7 +1330,10 @@ class AgentRunner:
         except json.JSONDecodeError as exc:
             raise ValueError(f"Invalid JSON in agent export file: {agent_json_path}") from exc
 
-        return cls(
+        # Generate flowchart.json if missing (for legacy JSON-based agents)
+        generate_fallback_flowchart(graph, goal, agent_path)
+
+        runner = cls(
             agent_path=agent_path,
             graph=graph,
             goal=goal,
@@ -1014,6 +1344,9 @@ class AgentRunner:
             skip_credential_validation=skip_credential_validation or False,
             credential_store=credential_store,
         )
+        runner._agent_default_skills = None
+        runner._agent_skills = None
+        return runner
 
     def register_tool(
         self,
@@ -1094,6 +1427,56 @@ class AgentRunner:
         """Load and register MCP servers from a configuration file."""
         self._tool_registry.load_mcp_config(config_path)
 
+    def _load_registry_mcp_servers(self, agent_path: Path) -> None:
+        """Load and register MCP servers selected via ``mcp_registry.json``."""
+        registry_json = agent_path / "mcp_registry.json"
+        if registry_json.is_file():
+            self._tool_registry.set_mcp_registry_agent_path(agent_path)
+        else:
+            self._tool_registry.set_mcp_registry_agent_path(None)
+
+        from framework.runner.mcp_registry import MCPRegistry
+
+        try:
+            registry = MCPRegistry()
+            registry.initialize()
+            server_configs, selection_max_tools = registry.load_agent_selection(agent_path)
+        except Exception as exc:
+            logger.warning(
+                "Failed to load MCP registry servers for '%s': %s",
+                agent_path.name,
+                exc,
+            )
+            return
+
+        if not server_configs:
+            return
+
+        results = self._tool_registry.load_registry_servers(
+            server_configs,
+            preserve_existing_tools=True,
+            log_collisions=True,
+            max_tools=selection_max_tools,
+        )
+        loaded = [result for result in results if result["status"] == "loaded"]
+        skipped = [result for result in results if result["status"] != "loaded"]
+
+        logger.info(
+            "Loaded %d/%d MCP registry server(s) for agent '%s'",
+            len(loaded),
+            len(results),
+            agent_path.name,
+        )
+        if skipped:
+            logger.info(
+                "Skipped MCP registry servers for agent '%s': %s",
+                agent_path.name,
+                [
+                    {"server": result["server"], "reason": result["skipped_reason"]}
+                    for result in skipped
+                ],
+            )
+
     def set_approval_callback(self, callback: Callable) -> None:
         """
         Set a callback for human-in-the-loop approval during execution.
@@ -1124,7 +1507,10 @@ class AgentRunner:
 
         # Create LLM provider
         # Uses LiteLLM which auto-detects the provider from model name
-        if self.mock_mode:
+        # Skip if already injected (e.g. worker agents with a pre-built LLM)
+        if self._llm is not None:
+            pass  # LLM already configured externally
+        elif self.mock_mode:
             # Use mock LLM for testing without real API calls
             from framework.llm.mock import MockLLMProvider
 
@@ -1138,6 +1524,7 @@ class AgentRunner:
             use_claude_code = llm_config.get("use_claude_code_subscription", False)
             use_codex = llm_config.get("use_codex_subscription", False)
             use_kimi_code = llm_config.get("use_kimi_code_subscription", False)
+            use_antigravity = llm_config.get("use_antigravity_subscription", False)
             api_base = llm_config.get("api_base")
 
             api_key = None
@@ -1145,20 +1532,28 @@ class AgentRunner:
                 # Get OAuth token from Claude Code subscription
                 api_key = get_claude_code_token()
                 if not api_key:
-                    print("Warning: Claude Code subscription configured but no token found.")
-                    print("Run 'claude' to authenticate, then try again.")
+                    logger.warning(
+                        "Claude Code subscription configured but no token found. "
+                        "Run 'claude' to authenticate, then try again."
+                    )
             elif use_codex:
                 # Get OAuth token from Codex subscription
                 api_key = get_codex_token()
                 if not api_key:
-                    print("Warning: Codex subscription configured but no token found.")
-                    print("Run 'codex' to authenticate, then try again.")
+                    logger.warning(
+                        "Codex subscription configured but no token found. "
+                        "Run 'codex' to authenticate, then try again."
+                    )
             elif use_kimi_code:
                 # Get API key from Kimi Code CLI config (~/.kimi/config.toml)
                 api_key = get_kimi_code_token()
                 if not api_key:
-                    print("Warning: Kimi Code subscription configured but no key found.")
-                    print("Run 'kimi /login' to authenticate, then try again.")
+                    logger.warning(
+                        "Kimi Code subscription configured but no key found. "
+                        "Run 'kimi /login' to authenticate, then try again."
+                    )
+            elif use_antigravity:
+                pass  # AntigravityProvider handles credentials internally
 
             if api_key and use_claude_code:
                 # Use litellm's built-in Anthropic OAuth support.
@@ -1197,6 +1592,19 @@ class AgentRunner:
                     api_key=api_key,
                     api_base=api_base,
                 )
+            elif use_antigravity:
+                # Direct OAuth to Google's internal Cloud Code Assist gateway.
+                # No local proxy required — AntigravityProvider handles token
+                # refresh and Gemini-format request/response conversion natively.
+                from framework.llm.antigravity import AntigravityProvider  # noqa: PLC0415
+
+                provider = AntigravityProvider(model=self.model)
+                if not provider.has_credentials():
+                    print(
+                        "Warning: Antigravity credentials not found. "
+                        "Run: uv run python core/antigravity_auth.py auth account add"
+                    )
+                self._llm = provider
             else:
                 # Local models (e.g. Ollama) don't need an API key
                 if self._is_local_model(self.model):
@@ -1228,8 +1636,12 @@ class AgentRunner:
                             if api_key_env:
                                 os.environ[api_key_env] = api_key
                         elif api_key_env:
-                            print(f"Warning: {api_key_env} not set. LLM calls will fail.")
-                            print(f"Set it with: export {api_key_env}=your-api-key")
+                            logger.warning(
+                                "%s not set. LLM calls will fail. "
+                                "Set it with: export %s=your-api-key",
+                                api_key_env,
+                                api_key_env,
+                            )
 
             # Fail fast if the agent needs an LLM but none was configured
             if self._llm is None:
@@ -1323,6 +1735,20 @@ class AgentRunner:
         except Exception:
             pass  # Best-effort — agent works without account info
 
+        # Skill configuration — the runtime handles discovery, loading, trust-gating and
+        # prompt rasterization.  The runner just builds the config.
+        from framework.skills.config import SkillsConfig
+        from framework.skills.manager import SkillsManagerConfig
+
+        skills_manager_config = SkillsManagerConfig(
+            skills_config=SkillsConfig.from_agent_vars(
+                default_skills=getattr(self, "_agent_default_skills", None),
+                skills=getattr(self, "_agent_skills", None),
+            ),
+            project_root=self.agent_path,
+            interactive=self._interactive,
+        )
+
         self._setup_agent_runtime(
             tools,
             tool_executor,
@@ -1330,6 +1756,7 @@ class AgentRunner:
             accounts_data=accounts_data,
             tool_provider_map=tool_provider_map,
             event_bus=event_bus,
+            skills_manager_config=skills_manager_config,
         )
 
     def _get_api_key_env_var(self, model: str) -> str | None:
@@ -1350,6 +1777,8 @@ class AgentRunner:
             return "MISTRAL_API_KEY"
         elif model_lower.startswith("groq/"):
             return "GROQ_API_KEY"
+        elif model_lower.startswith("openrouter/"):
+            return "OPENROUTER_API_KEY"
         elif self._is_local_model(model_lower):
             return None  # Local models don't need an API key
         elif model_lower.startswith("azure/"):
@@ -1364,6 +1793,8 @@ class AgentRunner:
             return "MINIMAX_API_KEY"
         elif model_lower.startswith("kimi/"):
             return "KIMI_API_KEY"
+        elif model_lower.startswith("hive/"):
+            return "HIVE_API_KEY"
         else:
             # Default: assume OpenAI-compatible
             return "OPENAI_API_KEY"
@@ -1386,6 +1817,8 @@ class AgentRunner:
             cred_id = "minimax"
         elif model_lower.startswith("kimi/"):
             cred_id = "kimi"
+        elif model_lower.startswith("hive/"):
+            cred_id = "hive"
         # Add more mappings as providers are added to LLM_CREDENTIALS
 
         if cred_id is None:
@@ -1425,6 +1858,10 @@ class AgentRunner:
         accounts_data: list[dict] | None = None,
         tool_provider_map: dict[str, str] | None = None,
         event_bus=None,
+        skills_catalog_prompt: str = "",
+        protocols_prompt: str = "",
+        skill_dirs: list[str] | None = None,
+        skills_manager_config=None,
     ) -> None:
         """Set up multi-entry-point execution using AgentRuntime."""
         entry_points = []
@@ -1484,26 +1921,37 @@ class AgentRunner:
             accounts_data=accounts_data,
             tool_provider_map=tool_provider_map,
             event_bus=event_bus,
+            skills_manager_config=skills_manager_config,
         )
 
         # Pass intro_message through for TUI display
         self._agent_runtime.intro_message = self.intro_message
 
+    # ------------------------------------------------------------------
+    # Execution modes
+    #
+    # run()              – One-shot, blocking execution for worker agents
+    #                      (headless CLI via ``hive run``). Validates, runs
+    #                      the graph to completion, and returns the result.
+    #
+    # start() / trigger() – Long-lived runtime for the frontend (queen).
+    #                      start() boots the runtime; trigger() sends
+    #                      non-blocking execution requests. Used by the
+    #                      server session manager and API routes.
+    # ------------------------------------------------------------------
+
     async def run(
         self,
         input_data: dict | None = None,
         session_state: dict | None = None,
         entry_point_id: str | None = None,
     ) -> ExecutionResult:
-        """
-        Execute the agent with given input data.
+        """One-shot execution for worker agents (headless CLI).
 
-        Validates credentials before execution. If any required credentials
-        are missing, returns an error result with instructions on how to
-        provide them.
+        Validates credentials, runs the graph to completion, and returns
+        the result. Used by ``hive run`` and programmatic callers.
 
-        For single-entry-point agents, this is the standard execution path.
-        For multi-entry-point agents, you can optionally specify which entry point to use.
+        For the frontend (queen), use start() + trigger() instead.
 
         Args:
             input_data: Input data for the agent (e.g., {"lead_id": "123"})
@@ -1629,7 +2077,12 @@ class AgentRunner:
     # === Runtime API ===
 
     async def start(self) -> None:
-        """Start the agent runtime."""
+        """Boot the agent runtime for the frontend (queen).
+
+        Pair with trigger() to send execution requests. Used by the
+        server session manager. For headless worker agents, use run()
+        instead.
+        """
         if self._agent_runtime is None:
             self._setup()
 
@@ -1646,10 +2099,10 @@ class AgentRunner:
         input_data: dict[str, Any],
         correlation_id: str | None = None,
     ) -> str:
-        """
-        Trigger execution at a specific entry point (non-blocking).
+        """Send a non-blocking execution request to a running runtime.
 
-        Returns execution ID for tracking.
+        Used by the server API routes after start(). For headless
+        worker agents, use run() instead.
 
         Args:
             entry_point_id: Which entry point to trigger

core/framework/runner/tool_registry.py

@@ -16,6 +16,8 @@ from framework.llm.provider import Tool, ToolResult, ToolUse
 
 logger = logging.getLogger(__name__)
 
+_INPUT_LOG_MAX_LEN = 500
+
 # Per-execution context overrides.  Each asyncio task (and thus each
 # concurrent graph execution) gets its own copy, so there are no races
 # when multiple ExecutionStreams run in parallel.
@@ -54,6 +56,8 @@ class ToolRegistry:
     def __init__(self):
         self._tools: dict[str, RegisteredTool] = {}
         self._mcp_clients: list[Any] = []  # List of MCPClient instances
+        self._mcp_client_servers: dict[int, str] = {}  # client id -> server name
+        self._mcp_managed_clients: set[int] = set()  # client ids acquired from the manager
         self._session_context: dict[str, Any] = {}  # Auto-injected context for tools
         self._provider_index: dict[str, set[str]] = {}  # provider -> tool names
         # MCP resync tracking
@@ -62,6 +66,8 @@ class ToolRegistry:
         self._mcp_cred_snapshot: set[str] = set()  # Credential filenames at MCP load time
         self._mcp_aden_key_snapshot: str | None = None  # ADEN_API_KEY value at MCP load time
         self._mcp_server_tools: dict[str, set[str]] = {}  # server name -> tool names
+        # Agent dir for re-loading registry MCP after credential resync.
+        self._mcp_registry_agent_path: Path | None = None
 
     def register(
         self,
@@ -243,6 +249,13 @@ class ToolRegistry:
         def _wrap_result(tool_use_id: str, result: Any) -> ToolResult:
             if isinstance(result, ToolResult):
                 return result
+            # MCP client returns dict with _images when image content is present
+            if isinstance(result, dict) and "_images" in result:
+                return ToolResult(
+                    tool_use_id=tool_use_id,
+                    content=result.get("_text", ""),
+                    image_content=result["_images"],
+                )
             return ToolResult(
                 tool_use_id=tool_use_id,
                 content=json.dumps(result) if not isinstance(result, str) else result,
@@ -269,6 +282,17 @@ class ToolRegistry:
                             r = await result
                             return _wrap_result(tool_use.id, r)
                         except Exception as exc:
+                            inputs_str = json.dumps(tool_use.input, default=str)
+                            if len(inputs_str) > _INPUT_LOG_MAX_LEN:
+                                inputs_str = inputs_str[:_INPUT_LOG_MAX_LEN] + "...(truncated)"
+                            logger.error(
+                                "Async tool '%s' failed (tool_use_id=%s): %s\nInputs: %s",
+                                tool_use.name,
+                                tool_use.id,
+                                exc,
+                                inputs_str,
+                                exc_info=True,
+                            )
                             return ToolResult(
                                 tool_use_id=tool_use.id,
                                 content=json.dumps({"error": str(exc)}),
@@ -279,6 +303,17 @@ class ToolRegistry:
 
                 return _wrap_result(tool_use.id, result)
             except Exception as e:
+                inputs_str = json.dumps(tool_use.input, default=str)
+                if len(inputs_str) > _INPUT_LOG_MAX_LEN:
+                    inputs_str = inputs_str[:_INPUT_LOG_MAX_LEN] + "...(truncated)"
+                logger.error(
+                    "Tool '%s' execution failed for tool_use_id=%s: %s\nInputs: %s",
+                    tool_use.name,
+                    tool_use.id,
+                    e,
+                    inputs_str,
+                    exc_info=True,
+                )
                 return ToolResult(
                     tool_use_id=tool_use.id,
                     content=json.dumps({"error": str(e)}),
@@ -453,21 +488,129 @@ class ToolRegistry:
             # Treat top-level keys as server names
             server_list = [{"name": name, **cfg} for name, cfg in config.items()]
 
-        for server_config in server_list:
-            server_config = self._resolve_mcp_server_config(server_config, base_dir)
-            try:
-                self.register_mcp_server(server_config)
-            except Exception as e:
-                name = server_config.get("name", "unknown")
-                logger.warning(f"Failed to register MCP server '{name}': {e}")
+        resolved_server_list = [
+            self._resolve_mcp_server_config(server_config, base_dir)
+            for server_config in server_list
+        ]
+        # Ordered first-wins for duplicate tool names across servers; keep tools.py tools.
+        self.load_registry_servers(
+            resolved_server_list,
+            log_summary=False,
+            preserve_existing_tools=True,
+            log_collisions=False,
+        )
 
         # Snapshot credential files and ADEN_API_KEY so we can detect mid-session changes
         self._mcp_cred_snapshot = self._snapshot_credentials()
         self._mcp_aden_key_snapshot = os.environ.get("ADEN_API_KEY")
 
+    def _register_mcp_server_with_retry(
+        self,
+        server_config: dict[str, Any],
+        *,
+        preserve_existing_tools: bool = True,
+        tool_cap: int | None = None,
+        log_collisions: bool = False,
+    ) -> tuple[bool, int, str | None]:
+        """Register a single MCP server with one retry for transient failures."""
+        name = server_config.get("name", "unknown")
+        last_error: str | None = None
+
+        for attempt in range(2):
+            try:
+                count = self.register_mcp_server(
+                    server_config,
+                    preserve_existing_tools=preserve_existing_tools,
+                    tool_cap=tool_cap,
+                    log_collisions=log_collisions,
+                )
+                if count > 0:
+                    return True, count, None
+                last_error = "registered 0 tools"
+            except Exception as exc:
+                last_error = str(exc)
+
+            if attempt == 0:
+                logger.warning(
+                    "MCP server '%s' failed to register, retrying in 2s: %s",
+                    name,
+                    last_error,
+                )
+                import time
+
+                time.sleep(2)
+            else:
+                logger.warning("MCP server '%s' failed after retry: %s", name, last_error)
+
+        return False, 0, last_error
+
+    def load_registry_servers(
+        self,
+        server_list: list[dict[str, Any]],
+        *,
+        log_summary: bool = True,
+        preserve_existing_tools: bool = True,
+        max_tools: int | None = None,
+        log_collisions: bool = False,
+    ) -> list[dict[str, Any]]:
+        """Register MCP servers from a resolved config list (registry and/or static).
+
+        ``preserve_existing_tools`` enforces first-wins tool names (FR-100): later
+        servers skip names already taken— including tools from ``mcp_servers.json``
+        or ``tools.py`` when those were loaded first.
+
+        ``max_tools`` caps how many *new* tool names are registered across this batch
+        (collisions do not consume the cap). When ``log_collisions`` is True, skipped
+        duplicate names emit a warning (FR-101).
+        """
+        results: list[dict[str, Any]] = []
+        tools_added_batch = 0
+
+        for server_config in server_list:
+            remaining: int | None = None
+            if max_tools is not None:
+                remaining = max_tools - tools_added_batch
+                if remaining <= 0:
+                    break
+
+            name = server_config.get("name", "unknown")
+            success, tools_loaded, error = self._register_mcp_server_with_retry(
+                server_config,
+                preserve_existing_tools=preserve_existing_tools,
+                tool_cap=remaining,
+                log_collisions=log_collisions,
+            )
+            tools_added_batch += tools_loaded
+            result = {
+                "server": name,
+                "status": "loaded" if success else "skipped",
+                "tools_loaded": tools_loaded,
+                "skipped_reason": None if success else (error or "unknown error"),
+            }
+            results.append(result)
+
+            if log_summary:
+                logger.info(
+                    "MCP registry server resolution",
+                    extra={
+                        "event": "mcp_registry_server_resolution",
+                        "server": result["server"],
+                        "status": result["status"],
+                        "tools_loaded": result["tools_loaded"],
+                        "skipped_reason": result["skipped_reason"],
+                    },
+                )
+
+        return results
+
     def register_mcp_server(
         self,
         server_config: dict[str, Any],
+        use_connection_manager: bool = True,
+        *,
+        preserve_existing_tools: bool = True,
+        tool_cap: int | None = None,
+        log_collisions: bool = False,
     ) -> int:
         """
         Register an MCP server and discover its tools.
@@ -483,12 +626,17 @@ class ToolRegistry:
                 - url: Server URL (for http)
                 - headers: HTTP headers (for http)
                 - description: Server description (optional)
+            use_connection_manager: When True, reuse a shared client keyed by server name
+            preserve_existing_tools: If True, do not replace tools already in the registry.
+            tool_cap: Max tools to newly register from this server (None = unlimited).
+            log_collisions: If True, log when this server skips a tool name already taken.
 
         Returns:
             Number of tools registered from this server
         """
         try:
             from framework.runner.mcp_client import MCPClient, MCPServerConfig
+            from framework.runner.mcp_connection_manager import MCPConnectionManager
 
             # Build config object
             config = MCPServerConfig(
@@ -500,15 +648,23 @@ class ToolRegistry:
                 cwd=server_config.get("cwd"),
                 url=server_config.get("url"),
                 headers=server_config.get("headers", {}),
+                socket_path=server_config.get("socket_path"),
                 description=server_config.get("description", ""),
             )
 
             # Create and connect client
-            client = MCPClient(config)
-            client.connect()
+            if use_connection_manager:
+                client = MCPConnectionManager.get_instance().acquire(config)
+            else:
+                client = MCPClient(config)
+                client.connect()
 
             # Store client for cleanup
             self._mcp_clients.append(client)
+            client_id = id(client)
+            self._mcp_client_servers[client_id] = config.name
+            if use_connection_manager:
+                self._mcp_managed_clients.add(client_id)
 
             # Register each tool
             server_name = server_config["name"]
@@ -516,6 +672,23 @@ class ToolRegistry:
                 self._mcp_server_tools[server_name] = set()
             count = 0
             for mcp_tool in client.list_tools():
+                if tool_cap is not None and count >= tool_cap:
+                    break
+
+                if preserve_existing_tools and mcp_tool.name in self._tools:
+                    if log_collisions:
+                        origin_server = (
+                            self._find_mcp_origin_server_for_tool(mcp_tool.name) or "<existing>"
+                        )
+                        logger.warning(
+                            "MCP tool '%s' from '%s' shadowed by '%s' (loaded first)",
+                            mcp_tool.name,
+                            server_name,
+                            origin_server,
+                        )
+                    # Skip registration; do not update MCP tool bookkeeping for this server.
+                    continue
+
                 # Convert MCP tool to framework Tool (strips context params from LLM schema)
                 tool = self._convert_mcp_tool_to_framework_tool(mcp_tool)
 
@@ -548,14 +721,25 @@ class ToolRegistry:
                             }
                             merged_inputs = {**clean_inputs, **filtered_context}
                             result = client_ref.call_tool(tool_name, merged_inputs)
-                            # MCP tools return content array, extract the result
+                            # MCP client already extracts content (returns str
+                            # or {"_text": ..., "_images": ...} for image results).
+                            # Handle legacy list format from HTTP transport.
                             if isinstance(result, list) and len(result) > 0:
                                 if isinstance(result[0], dict) and "text" in result[0]:
                                     return result[0]["text"]
                                 return result[0]
                             return result
                         except Exception as e:
-                            logger.error(f"MCP tool '{tool_name}' execution failed: {e}")
+                            inputs_str = json.dumps(inputs, default=str)
+                            if len(inputs_str) > _INPUT_LOG_MAX_LEN:
+                                inputs_str = inputs_str[:_INPUT_LOG_MAX_LEN] + "...(truncated)"
+                            logger.error(
+                                "MCP tool '%s' execution failed: %s\nInputs: %s",
+                                tool_name,
+                                e,
+                                inputs_str,
+                                exc_info=True,
+                            )
                             return {"error": str(e)}
 
                     return executor
@@ -570,11 +754,27 @@ class ToolRegistry:
                 self._mcp_server_tools[server_name].add(mcp_tool.name)
                 count += 1
 
-            logger.info(f"Registered {count} tools from MCP server '{config.name}'")
+            logger.info(
+                "MCP Registry Load",
+                extra={
+                    "server": config.name,
+                    "status": "success",
+                    "tools_loaded": count,
+                    "skipped_reason": None,
+                },
+            )
             return count
 
         except Exception as e:
-            logger.error(f"Failed to register MCP server: {e}")
+            logger.error(
+                "MCP Registry Load",
+                extra={
+                    "server": server_config.get("name", "unknown"),
+                    "status": "failed",
+                    "tools_loaded": 0,
+                    "skipped_reason": str(e),
+                },
+            )
             if "Connection closed" in str(e) and os.name == "nt":
                 logger.debug(
                     "On Windows, check that the MCP subprocess starts (e.g. uv in PATH, "
@@ -582,6 +782,12 @@ class ToolRegistry:
                 )
             return 0
 
+    def _find_mcp_origin_server_for_tool(self, tool_name: str) -> str | None:
+        for server_name, tool_names in self._mcp_server_tools.items():
+            if tool_name in tool_names:
+                return server_name
+        return None
+
     def _convert_mcp_tool_to_framework_tool(self, mcp_tool: Any) -> Tool:
         """
         Convert an MCP tool to a framework Tool.
@@ -669,6 +875,37 @@ class ToolRegistry:
     # MCP credential resync
     # ------------------------------------------------------------------
 
+    def set_mcp_registry_agent_path(self, agent_path: Path | None) -> None:
+        """Remember agent dir so registry MCP servers reload after credential resync."""
+        self._mcp_registry_agent_path = None if agent_path is None else Path(agent_path)
+
+    def reload_registry_mcp_servers_after_resync(self) -> None:
+        """Re-run ``mcp_registry.json`` resolution and register servers (post-resync)."""
+        if self._mcp_registry_agent_path is None:
+            return
+        from framework.runner.mcp_registry import MCPRegistry
+
+        try:
+            reg = MCPRegistry()
+            reg.initialize()
+            configs, selection_max_tools = reg.load_agent_selection(self._mcp_registry_agent_path)
+        except Exception as exc:
+            logger.warning(
+                "Failed to reload MCP registry servers after resync for '%s': %s",
+                self._mcp_registry_agent_path.name,
+                exc,
+            )
+            return
+        if not configs:
+            return
+        self.load_registry_servers(
+            configs,
+            log_summary=True,
+            preserve_existing_tools=True,
+            log_collisions=True,
+            max_tools=selection_max_tools,
+        )
+
     def _snapshot_credentials(self) -> set[str]:
         """Return the set of credential filenames currently on disk."""
         try:
@@ -708,32 +945,46 @@ class ToolRegistry:
         logger.info("%s — resyncing MCP servers", reason)
 
         # 1. Disconnect existing MCP clients
-        for client in self._mcp_clients:
-            try:
-                client.disconnect()
-            except Exception as e:
-                logger.warning(f"Error disconnecting MCP client during resync: {e}")
-        self._mcp_clients.clear()
+        self._cleanup_mcp_clients("during resync")
 
         # 2. Remove MCP-registered tools
         for name in self._mcp_tool_names:
             self._tools.pop(name, None)
         self._mcp_tool_names.clear()
+        self._mcp_server_tools.clear()
 
         # 3. Re-load MCP servers (spawns fresh subprocesses with new credentials)
         self.load_mcp_config(self._mcp_config_path)
+        if self._mcp_registry_agent_path is not None:
+            self.reload_registry_mcp_servers_after_resync()
 
         logger.info("MCP server resync complete")
         return True
 
     def cleanup(self) -> None:
         """Clean up all MCP client connections."""
+        self._cleanup_mcp_clients()
+
+    def _cleanup_mcp_clients(self, context: str = "") -> None:
+        """Disconnect or release all tracked MCP clients for this registry."""
+        if context:
+            context = f" {context}"
+
         for client in self._mcp_clients:
+            client_id = id(client)
+            server_name = self._mcp_client_servers.get(client_id, client.config.name)
             try:
-                client.disconnect()
+                if client_id in self._mcp_managed_clients:
+                    from framework.runner.mcp_connection_manager import MCPConnectionManager
+
+                    MCPConnectionManager.get_instance().release(server_name)
+                else:
+                    client.disconnect()
             except Exception as e:
-                logger.warning(f"Error disconnecting MCP client: {e}")
+                logger.warning(f"Error disconnecting MCP client{context}: {e}")
         self._mcp_clients.clear()
+        self._mcp_client_servers.clear()
+        self._mcp_managed_clients.clear()
 
     def __del__(self):
         """Destructor to ensure cleanup."""

core/framework/runtime/agent_runtime.py

@@ -9,6 +9,7 @@ import asyncio
 import logging
 import time
 import uuid
+from collections import OrderedDict
 from collections.abc import Callable
 from dataclasses import dataclass, field
 from datetime import datetime
@@ -29,6 +30,7 @@ if TYPE_CHECKING:
     from framework.graph.edge import GraphSpec
     from framework.graph.goal import Goal
     from framework.llm.provider import LLMProvider, Tool
+    from framework.skills.manager import SkillsManagerConfig
 
 logger = logging.getLogger(__name__)
 
@@ -43,6 +45,9 @@ class AgentRuntimeConfig:
     max_history: int = 1000
     execution_result_max: int = 1000
     execution_result_ttl_seconds: float | None = None
+    # Idempotency cache for trigger() deduplication
+    idempotency_ttl_seconds: float = 300.0
+    idempotency_max_keys: int = 10000
     # Webhook server config (only starts if webhook_routes is non-empty)
     webhook_host: str = "127.0.0.1"
     webhook_port: int = 8080
@@ -132,6 +137,11 @@ class AgentRuntime:
         accounts_data: list[dict] | None = None,
         tool_provider_map: dict[str, str] | None = None,
         event_bus: "EventBus | None" = None,
+        skills_manager_config: "SkillsManagerConfig | None" = None,
+        # Deprecated — pass skills_manager_config instead.
+        skills_catalog_prompt: str = "",
+        protocols_prompt: str = "",
+        skill_dirs: list[str] | None = None,
     ):
         """
         Initialize agent runtime.
@@ -153,7 +163,16 @@ class AgentRuntime:
             event_bus: Optional external EventBus. If provided, the runtime shares
                 this bus instead of creating its own. Used by SessionManager to
                 share a single bus between queen, worker, and judge.
+            skills_catalog_prompt: Available skills catalog for system prompt
+            protocols_prompt: Default skill operational protocols for system prompt
+            skill_dirs: Skill base directories for Tier 3 resource access
+            skills_manager_config: Skill configuration — the runtime owns
+                discovery, loading, and prompt renderation internally.
+            skills_catalog_prompt: Deprecated. Pre-rendered skills catalog.
+            protocols_prompt: Deprecated. Pre-rendered operational protocols.
         """
+        from framework.skills.manager import SkillsManager
+
         self.graph = graph
         self.goal = goal
         self._config = config or AgentRuntimeConfig()
@@ -161,6 +180,33 @@ class AgentRuntime:
         self._checkpoint_config = checkpoint_config
         self.accounts_prompt = accounts_prompt
 
+        # --- Skill lifecycle: runtime owns the SkillsManager ---
+        if skills_manager_config is not None:
+            # New path: config-driven, runtime handles loading
+            self._skills_manager = SkillsManager(skills_manager_config)
+            self._skills_manager.load()
+        elif skills_catalog_prompt or protocols_prompt:
+            # Legacy path: caller passed pre-rendered strings
+            import warnings
+
+            warnings.warn(
+                "Passing pre-rendered skills_catalog_prompt/protocols_prompt "
+                "is deprecated. Pass skills_manager_config instead.",
+                DeprecationWarning,
+                stacklevel=2,
+            )
+            self._skills_manager = SkillsManager.from_precomputed(
+                skills_catalog_prompt, protocols_prompt
+            )
+        else:
+            # Bare constructor: auto-load defaults
+            self._skills_manager = SkillsManager()
+            self._skills_manager.load()
+
+        self.skill_dirs: list[str] = self._skills_manager.allowlisted_dirs
+        self.context_warn_ratio: float | None = self._skills_manager.context_warn_ratio
+        self.batch_init_nudge: str | None = self._skills_manager.batch_init_nudge
+
         # Primary graph identity
         self._graph_id: str = graph_id or "primary"
 
@@ -208,6 +254,10 @@ class AgentRuntime:
         # Next fire time for each timer entry point (ep_id -> datetime)
         self._timer_next_fire: dict[str, float] = {}
 
+        # Idempotency cache for trigger() deduplication
+        self._idempotency_keys: OrderedDict[str, str] = OrderedDict()
+        self._idempotency_times: dict[str, float] = {}
+
         # State
         self._running = False
         self._timers_paused = False
@@ -216,6 +266,18 @@ class AgentRuntime:
         # Optional greeting shown to user on TUI load (set by AgentRunner)
         self.intro_message: str = ""
 
+    # ------------------------------------------------------------------
+    # Skill prompt accessors (read by ExecutionStream constructors)
+    # ------------------------------------------------------------------
+
+    @property
+    def skills_catalog_prompt(self) -> str:
+        return self._skills_manager.skills_catalog_prompt
+
+    @property
+    def protocols_prompt(self) -> str:
+        return self._skills_manager.protocols_prompt
+
     def register_entry_point(self, spec: EntryPointSpec) -> None:
         """
         Register a named entry point for the agent.
@@ -293,6 +355,11 @@ class AgentRuntime:
                     accounts_prompt=self._accounts_prompt,
                     accounts_data=self._accounts_data,
                     tool_provider_map=self._tool_provider_map,
+                    skills_catalog_prompt=self.skills_catalog_prompt,
+                    protocols_prompt=self.protocols_prompt,
+                    skill_dirs=self.skill_dirs,
+                    context_warn_ratio=self.context_warn_ratio,
+                    batch_init_nudge=self.batch_init_nudge,
                 )
                 await stream.start()
                 self._streams[ep_id] = stream
@@ -393,18 +460,24 @@ class AgentRuntime:
 
                 tc = spec.trigger_config
                 cron_expr = tc.get("cron")
-                interval = tc.get("interval_minutes")
+                _raw_interval = tc.get("interval_minutes")
+                interval = float(_raw_interval) if _raw_interval is not None else None
                 run_immediately = tc.get("run_immediately", False)
 
                 if cron_expr:
                     # Cron expression mode — takes priority over interval_minutes
                     try:
                         from croniter import croniter
+                    except ImportError as e:
+                        raise RuntimeError(
+                            "croniter is required for cron-based entry points. "
+                            "Install it with: uv pip install croniter"
+                        ) from e
 
-                        # Validate the expression upfront
+                    try:
                         if not croniter.is_valid(cron_expr):
                             raise ValueError(f"Invalid cron expression: {cron_expr}")
-                    except (ImportError, ValueError) as e:
+                    except ValueError as e:
                         logger.warning(
                             "Entry point '%s' has invalid cron config: %s",
                             ep_id,
@@ -544,7 +617,7 @@ class AgentRuntime:
                             ep_id,
                             cron_expr,
                             run_immediately,
-                            idle_timeout=tc.get("idle_timeout_seconds", 300),
+                            idle_timeout=float(tc.get("idle_timeout_seconds", 300)),
                         )()
                     )
                     self._timer_tasks.append(task)
@@ -674,7 +747,7 @@ class AgentRuntime:
                             ep_id,
                             interval,
                             run_immediately,
-                            idle_timeout=tc.get("idle_timeout_seconds", 300),
+                            idle_timeout=float(tc.get("idle_timeout_seconds", 300)),
                         )()
                     )
                     self._timer_tasks.append(task)
@@ -788,12 +861,29 @@ class AgentRuntime:
         # Primary graph (also stored in self._streams)
         return self._streams.get(entry_point_id)
 
+    def _prune_idempotency_keys(self) -> None:
+        """Prune expired idempotency keys based on TTL and max size."""
+        ttl = self._config.idempotency_ttl_seconds
+        if ttl > 0:
+            cutoff = time.time() - ttl
+            for key, recorded_at in list(self._idempotency_times.items()):
+                if recorded_at < cutoff:
+                    self._idempotency_times.pop(key, None)
+                    self._idempotency_keys.pop(key, None)
+
+        max_keys = self._config.idempotency_max_keys
+        if max_keys > 0:
+            while len(self._idempotency_keys) > max_keys:
+                old_key, _ = self._idempotency_keys.popitem(last=False)
+                self._idempotency_times.pop(old_key, None)
+
     async def trigger(
         self,
         entry_point_id: str,
         input_data: dict[str, Any],
         correlation_id: str | None = None,
         session_state: dict[str, Any] | None = None,
+        idempotency_key: str | None = None,
         graph_id: str | None = None,
     ) -> str:
         """
@@ -806,6 +896,10 @@ class AgentRuntime:
             input_data: Input data for the execution
             correlation_id: Optional ID to correlate related executions
             session_state: Optional session state to resume from (with paused_at, memory)
+            idempotency_key: Optional key for deduplication. If a trigger with
+                the same key was already processed within the TTL window, the
+                cached execution_id is returned instead of starting a new
+                execution. Useful for webhook providers that retry on timeout.
             graph_id: Graph to trigger on.  ``None`` uses the active graph
                 first, then falls back to the primary graph.
 
@@ -819,12 +913,32 @@ class AgentRuntime:
         if not self._running:
             raise RuntimeError("AgentRuntime is not running")
 
+        # Idempotency check: return cached execution_id for duplicate keys.
+        if idempotency_key is not None:
+            self._prune_idempotency_keys()
+            cached = self._idempotency_keys.get(idempotency_key)
+            if cached is not None:
+                logger.debug(
+                    "Idempotent trigger: key '%s' already seen, returning %s",
+                    idempotency_key,
+                    cached,
+                )
+                return cached
+
         stream = self._resolve_stream(entry_point_id, graph_id)
         if stream is None:
             raise ValueError(f"Entry point '{entry_point_id}' not found")
 
         run_id = uuid.uuid4().hex[:12]
-        return await stream.execute(input_data, correlation_id, session_state, run_id=run_id)
+        exec_id = await stream.execute(input_data, correlation_id, session_state, run_id=run_id)
+
+        # Cache after execute() so the value is always a real execution_id
+        # that callers can use for tracking.
+        if idempotency_key is not None:
+            self._idempotency_keys[idempotency_key] = exec_id
+            self._idempotency_times[idempotency_key] = time.time()
+
+        return exec_id
 
     async def trigger_and_wait(
         self,
@@ -832,6 +946,7 @@ class AgentRuntime:
         input_data: dict[str, Any],
         timeout: float | None = None,
         session_state: dict[str, Any] | None = None,
+        idempotency_key: str | None = None,
     ) -> ExecutionResult | None:
         """
         Trigger execution and wait for completion.
@@ -841,11 +956,17 @@ class AgentRuntime:
             input_data: Input data for the execution
             timeout: Maximum time to wait (seconds)
             session_state: Optional session state to resume from (with paused_at, memory)
+            idempotency_key: Optional key for deduplication (see trigger() for details).
 
         Returns:
             ExecutionResult or None if timeout
         """
-        exec_id = await self.trigger(entry_point_id, input_data, session_state=session_state)
+        exec_id = await self.trigger(
+            entry_point_id,
+            input_data,
+            session_state=session_state,
+            idempotency_key=idempotency_key,
+        )
         stream = self._resolve_stream(entry_point_id)
         if stream is None:
             raise ValueError(f"Entry point '{entry_point_id}' not found")
@@ -921,6 +1042,9 @@ class AgentRuntime:
                 accounts_prompt=self._accounts_prompt,
                 accounts_data=self._accounts_data,
                 tool_provider_map=self._tool_provider_map,
+                skills_catalog_prompt=self.skills_catalog_prompt,
+                protocols_prompt=self.protocols_prompt,
+                skill_dirs=self.skill_dirs,
             )
             if self._running:
                 await stream.start()
@@ -999,7 +1123,8 @@ class AgentRuntime:
             if spec.trigger_type != "timer":
                 continue
             tc = spec.trigger_config
-            interval = tc.get("interval_minutes")
+            _raw_interval = tc.get("interval_minutes")
+            interval = float(_raw_interval) if _raw_interval is not None else None
             run_immediately = tc.get("run_immediately", False)
 
             if interval and interval > 0 and self._running:
@@ -1144,7 +1269,7 @@ class AgentRuntime:
                         ep_id,
                         interval,
                         run_immediately,
-                        idle_timeout=tc.get("idle_timeout_seconds", 300),
+                        idle_timeout=float(tc.get("idle_timeout_seconds", 300)),
                     )()
                 )
                 timer_tasks.append(task)
@@ -1409,6 +1534,7 @@ class AgentRuntime:
         graph_id: str | None = None,
         *,
         is_client_input: bool = False,
+        image_content: list[dict[str, Any]] | None = None,
     ) -> bool:
         """Inject user input into a running client-facing node.
 
@@ -1421,6 +1547,8 @@ class AgentRuntime:
             graph_id: Optional graph to search first (defaults to active graph)
             is_client_input: True when the message originates from a real
                 human user (e.g. /chat endpoint), False for external events.
+            image_content: Optional list of image content blocks (OpenAI
+                image_url format) to include alongside the text.
 
         Returns:
             True if input was delivered, False if no matching node found
@@ -1432,7 +1560,9 @@ class AgentRuntime:
         target = graph_id or self._active_graph_id
         if target in self._graphs:
             for stream in self._graphs[target].streams.values():
-                if await stream.inject_input(node_id, content, is_client_input=is_client_input):
+                if await stream.inject_input(
+                    node_id, content, is_client_input=is_client_input, image_content=image_content
+                ):
                     return True
 
         # Then search all other graphs
@@ -1440,7 +1570,9 @@ class AgentRuntime:
             if gid == target:
                 continue
             for stream in reg.streams.values():
-                if await stream.inject_input(node_id, content, is_client_input=is_client_input):
+                if await stream.inject_input(
+                    node_id, content, is_client_input=is_client_input, image_content=image_content
+                ):
                     return True
         return False
 
@@ -1699,6 +1831,11 @@ def create_agent_runtime(
     accounts_data: list[dict] | None = None,
     tool_provider_map: dict[str, str] | None = None,
     event_bus: "EventBus | None" = None,
+    skills_manager_config: "SkillsManagerConfig | None" = None,
+    # Deprecated — pass skills_manager_config instead.
+    skills_catalog_prompt: str = "",
+    protocols_prompt: str = "",
+    skill_dirs: list[str] | None = None,
 ) -> AgentRuntime:
     """
     Create and configure an AgentRuntime with entry points.
@@ -1725,6 +1862,13 @@ def create_agent_runtime(
         accounts_data: Raw account data for per-node prompt generation.
         tool_provider_map: Tool name to provider name mapping for account routing.
         event_bus: Optional external EventBus to share with other components.
+        skills_catalog_prompt: Available skills catalog for system prompt.
+        protocols_prompt: Default skill operational protocols for system prompt.
+        skill_dirs: Skill base directories for Tier 3 resource access.
+        skills_manager_config: Skill configuration — the runtime owns
+            discovery, loading, and prompt renderation internally.
+        skills_catalog_prompt: Deprecated. Pre-rendered skills catalog.
+        protocols_prompt: Deprecated. Pre-rendered operational protocols.
 
     Returns:
         Configured AgentRuntime (not yet started)
@@ -1751,6 +1895,10 @@ def create_agent_runtime(
         accounts_data=accounts_data,
         tool_provider_map=tool_provider_map,
         event_bus=event_bus,
+        skills_manager_config=skills_manager_config,
+        skills_catalog_prompt=skills_catalog_prompt,
+        protocols_prompt=protocols_prompt,
+        skill_dirs=skill_dirs,
     )
 
     for spec in entry_points:

core/framework/runtime/core.py

@@ -16,7 +16,7 @@ from typing import Any
 from framework.observability import set_trace_context
 from framework.schemas.decision import Decision, DecisionType, Option, Outcome
 from framework.schemas.run import Run, RunStatus
-from framework.storage.backend import FileStorage
+from framework.storage.concurrent import ConcurrentStorage
 
 logger = logging.getLogger(__name__)
 
@@ -62,7 +62,7 @@ class Runtime:
             logger.warning(f"Storage path does not exist, creating: {path}")
             path.mkdir(parents=True, exist_ok=True)
 
-        self.storage = FileStorage(storage_path)
+        self.storage = ConcurrentStorage(storage_path)
         self._current_run: Run | None = None
         self._current_node: str = "unknown"
 
@@ -132,8 +132,8 @@ class Runtime:
         self._current_run.output_data = output_data or {}
         self._current_run.complete(status, narrative)
 
-        # Save to storage
-        self.storage.save_run(self._current_run)
+        # Save to storage (sync — Runtime methods are not async)
+        self.storage.save_run_sync(self._current_run)
         self._current_run = None
 
     def set_node(self, node_id: str) -> None:

core/framework/runtime/event_bus.py

@@ -117,6 +117,7 @@ class EventType(StrEnum):
 
     # Context management
     CONTEXT_COMPACTED = "context_compacted"
+    CONTEXT_USAGE_UPDATED = "context_usage_updated"
 
     # External triggers
     WEBHOOK_RECEIVED = "webhook_received"
@@ -159,6 +160,7 @@ class EventType(StrEnum):
     TRIGGER_DEACTIVATED = "trigger_deactivated"
     TRIGGER_FIRED = "trigger_fired"
     TRIGGER_REMOVED = "trigger_removed"
+    TRIGGER_UPDATED = "trigger_updated"
 
 
 @dataclass
@@ -262,7 +264,7 @@ class EventBus:
         self._session_log: IO[str] | None = None
         self._session_log_iteration_offset: int = 0
         # Accumulator for client_output_delta snapshots — flushed on llm_turn_complete.
-        # Key: (stream_id, node_id, execution_id, iteration) → latest AgentEvent
+        # Key: (stream_id, node_id, execution_id, iteration, inner_turn) → latest AgentEvent
         self._pending_output_snapshots: dict[tuple, AgentEvent] = {}
 
     def set_session_log(self, path: Path, *, iteration_offset: int = 0) -> None:
@@ -328,6 +330,7 @@ class EventBus:
                 event.node_id,
                 event.execution_id,
                 event.data.get("iteration"),
+                event.data.get("inner_turn", 0),
             )
             self._pending_output_snapshots[key] = event
             return
@@ -361,7 +364,7 @@ class EventBus:
         to_flush: list[tuple] = []
         for key, _evt in self._pending_output_snapshots.items():
             if stream_id is not None:
-                k_stream, k_node, k_exec, _ = key
+                k_stream, k_node, k_exec, _, _ = key
                 if k_stream != stream_id or k_node != node_id or k_exec != execution_id:
                     continue
             to_flush.append(key)
@@ -532,8 +535,8 @@ class EventBus:
             async with self._semaphore:
                 try:
                     await handler(event)
-                except Exception as e:
-                    logger.error(f"Handler error for {event.type}: {e}")
+                except Exception:
+                    logger.exception(f"Handler error for {event.type}")
 
         # Run all handlers concurrently
         await asyncio.gather(*[run_handler(h) for h in handlers], return_exceptions=True)
@@ -749,6 +752,7 @@ class EventBus:
         content: str,
         snapshot: str,
         execution_id: str | None = None,
+        inner_turn: int = 0,
     ) -> None:
         """Emit LLM text delta event."""
         await self.publish(
@@ -757,7 +761,7 @@ class EventBus:
                 stream_id=stream_id,
                 node_id=node_id,
                 execution_id=execution_id,
-                data={"content": content, "snapshot": snapshot},
+                data={"content": content, "snapshot": snapshot, "inner_turn": inner_turn},
             )
         )
 
@@ -873,9 +877,10 @@ class EventBus:
         snapshot: str,
         execution_id: str | None = None,
         iteration: int | None = None,
+        inner_turn: int = 0,
     ) -> None:
         """Emit client output delta event (client_facing=True nodes)."""
-        data: dict = {"content": content, "snapshot": snapshot}
+        data: dict = {"content": content, "snapshot": snapshot, "inner_turn": inner_turn}
         if iteration is not None:
             data["iteration"] = iteration
         await self.publish(

core/framework/runtime/execution_stream.py

@@ -186,6 +186,11 @@ class ExecutionStream:
         accounts_prompt: str = "",
         accounts_data: list[dict] | None = None,
         tool_provider_map: dict[str, str] | None = None,
+        skills_catalog_prompt: str = "",
+        protocols_prompt: str = "",
+        skill_dirs: list[str] | None = None,
+        context_warn_ratio: float | None = None,
+        batch_init_nudge: str | None = None,
     ):
         """
         Initialize execution stream.
@@ -209,6 +214,11 @@ class ExecutionStream:
             accounts_prompt: Connected accounts block for system prompt injection
             accounts_data: Raw account data for per-node prompt generation
             tool_provider_map: Tool name to provider name mapping for account routing
+            skills_catalog_prompt: Available skills catalog for system prompt
+            protocols_prompt: Default skill operational protocols for system prompt
+            skill_dirs: Skill base directories for Tier 3 resource access
+            context_warn_ratio: Token usage ratio to trigger DS-13 preservation warning
+            batch_init_nudge: System prompt nudge for DS-12 batch auto-detection
         """
         self.stream_id = stream_id
         self.entry_spec = entry_spec
@@ -230,6 +240,24 @@ class ExecutionStream:
         self._accounts_prompt = accounts_prompt
         self._accounts_data = accounts_data
         self._tool_provider_map = tool_provider_map
+        self._skills_catalog_prompt = skills_catalog_prompt
+        self._protocols_prompt = protocols_prompt
+        self._skill_dirs: list[str] = skill_dirs or []
+        self._context_warn_ratio: float | None = context_warn_ratio
+        self._batch_init_nudge: str | None = batch_init_nudge
+
+        _es_logger = logging.getLogger(__name__)
+        if protocols_prompt:
+            _es_logger.info(
+                "ExecutionStream[%s] received protocols_prompt (%d chars)",
+                stream_id,
+                len(protocols_prompt),
+            )
+        else:
+            _es_logger.warning(
+                "ExecutionStream[%s] received EMPTY protocols_prompt",
+                stream_id,
+            )
 
         # Create stream-scoped runtime
         self._runtime = StreamRuntime(
@@ -411,6 +439,7 @@ class ExecutionStream:
         content: str,
         *,
         is_client_input: bool = False,
+        image_content: list[dict[str, Any]] | None = None,
     ) -> bool:
         """Inject user input into a running client-facing EventLoopNode.
 
@@ -422,7 +451,9 @@ class ExecutionStream:
         for executor in self._active_executors.values():
             node = executor.node_registry.get(node_id)
             if node is not None and hasattr(node, "inject_event"):
-                await node.inject_event(content, is_client_input=is_client_input)
+                await node.inject_event(
+                    content, is_client_input=is_client_input, image_content=image_content
+                )
                 return True
         return False
 
@@ -675,6 +706,11 @@ class ExecutionStream:
                         accounts_prompt=self._accounts_prompt,
                         accounts_data=self._accounts_data,
                         tool_provider_map=self._tool_provider_map,
+                        skills_catalog_prompt=self._skills_catalog_prompt,
+                        protocols_prompt=self._protocols_prompt,
+                        skill_dirs=self._skill_dirs,
+                        context_warn_ratio=self._context_warn_ratio,
+                        batch_init_nudge=self._batch_init_nudge,
                     )
                     # Track executor so inject_input() can reach EventLoopNode instances
                     self._active_executors[execution_id] = executor
@@ -933,7 +969,10 @@ class ExecutionStream:
             return
         import json as _json
 
-        session_dir = self._session_store.get_session_path(execution_id)
+        try:
+            session_dir = self._session_store.get_session_path(execution_id)
+        except ValueError:
+            return
         runs_file = session_dir / "runs.jsonl"
         now = datetime.now()
         record = {

core/framework/runtime/llm_debug_logger.py

@@ -8,6 +8,7 @@ write. Errors are silently swallowed — this must never break the agent.
 
 import json
 import logging
+import os
 from datetime import datetime
 from pathlib import Path
 from typing import IO, Any
@@ -47,6 +48,9 @@ def log_llm_turn(
     Never raises.
     """
     try:
+        # Skip logging during test runs to avoid polluting real logs.
+        if os.environ.get("PYTEST_CURRENT_TEST") or os.environ.get("HIVE_DISABLE_LLM_LOGS"):
+            return
         global _log_file, _log_ready  # noqa: PLW0603
         if not _log_ready:
             _log_file = _open_log()

core/framework/runtime/runtime_log_store.py

@@ -47,25 +47,34 @@ class RuntimeLogStore:
         self._base_path = base_path
         # Note: _runs_dir is determined per-run_id by _get_run_dir()
 
+    def _session_logs_dir(self, run_id: str) -> Path:
+        """Return the unified session-backed logs directory for a run ID."""
+        is_runtime_logs = self._base_path.name == "runtime_logs"
+        root = self._base_path.parent if is_runtime_logs else self._base_path
+        return root / "sessions" / run_id / "logs"
+
+    def _legacy_run_dir(self, run_id: str) -> Path:
+        """Return the deprecated standalone runs directory for a run ID."""
+        return self._base_path / "runs" / run_id
+
     def _get_run_dir(self, run_id: str) -> Path:
         """Determine run directory path based on run_id format.
 
-        - New format (session_*): {storage_root}/sessions/{run_id}/logs/
+        - Session-backed runs: {storage_root}/sessions/{run_id}/logs/
         - Old format (anything else): {base_path}/runs/{run_id}/ (deprecated)
         """
-        if run_id.startswith("session_"):
-            is_runtime_logs = self._base_path.name == "runtime_logs"
-            root = self._base_path.parent if is_runtime_logs else self._base_path
-            return root / "sessions" / run_id / "logs"
+        session_run_dir = self._session_logs_dir(run_id)
+        if session_run_dir.exists() or run_id.startswith("session_"):
+            return session_run_dir
         import warnings
 
         warnings.warn(
             f"Reading logs from deprecated location for run_id={run_id}. "
-            "New sessions use unified storage at sessions/session_*/logs/",
+            "New sessions use unified storage at sessions/<session_id>/logs/",
             DeprecationWarning,
             stacklevel=3,
         )
-        return self._base_path / "runs" / run_id
+        return self._legacy_run_dir(run_id)
 
     # -------------------------------------------------------------------
     # Incremental write (sync — called from locked sections)
@@ -76,6 +85,10 @@ class RuntimeLogStore:
         run_dir = self._get_run_dir(run_id)
         run_dir.mkdir(parents=True, exist_ok=True)
 
+    def ensure_session_run_dir(self, run_id: str) -> None:
+        """Create the unified session-backed log directory immediately."""
+        self._session_logs_dir(run_id).mkdir(parents=True, exist_ok=True)
+
     def append_step(self, run_id: str, step: NodeStepLog) -> None:
         """Append one JSONL line to tool_logs.jsonl. Sync."""
         path = self._get_run_dir(run_id) / "tool_logs.jsonl"
@@ -200,17 +213,17 @@ class RuntimeLogStore:
         run_ids = []
 
         # Scan new location: base_path/sessions/{session_id}/logs/
-        # Determine the correct base path for sessions
         is_runtime_logs = self._base_path.name == "runtime_logs"
         root = self._base_path.parent if is_runtime_logs else self._base_path
         sessions_dir = root / "sessions"
 
         if sessions_dir.exists():
             for session_dir in sessions_dir.iterdir():
-                if session_dir.is_dir() and session_dir.name.startswith("session_"):
-                    logs_dir = session_dir / "logs"
-                    if logs_dir.exists() and logs_dir.is_dir():
-                        run_ids.append(session_dir.name)
+                if not session_dir.is_dir():
+                    continue
+                logs_dir = session_dir / "logs"
+                if logs_dir.exists() and logs_dir.is_dir():
+                    run_ids.append(session_dir.name)
 
         # Scan old location: base_path/runs/ (deprecated)
         old_runs_dir = self._base_path / "runs"

core/framework/runtime/runtime_logger.py

@@ -66,15 +66,16 @@ class RuntimeLogger:
         """
         if session_id:
             self._run_id = session_id
+            self._store.ensure_session_run_dir(self._run_id)
         else:
             ts = datetime.now(UTC).strftime("%Y%m%dT%H%M%S")
             short_uuid = uuid.uuid4().hex[:8]
             self._run_id = f"{ts}_{short_uuid}"
+            self._store.ensure_run_dir(self._run_id)
 
         self._goal_id = goal_id
         self._started_at = datetime.now(UTC).isoformat()
         self._logged_node_ids = set()
-        self._store.ensure_run_dir(self._run_id)
         return self._run_id
 
     def log_step(

core/framework/runtime/tests/test_idempotency.py

@@ -0,0 +1,268 @@
+"""Tests for webhook idempotency key support in AgentRuntime.trigger()."""
+
+import asyncio
+import time
+from collections import OrderedDict
+from unittest.mock import AsyncMock, MagicMock
+
+import pytest
+
+from framework.runtime.agent_runtime import AgentRuntime, AgentRuntimeConfig
+
+
+def _make_runtime(ttl=300.0, max_keys=10000):
+    """Create a minimal AgentRuntime with idempotency cache attributes.
+
+    Uses ``object.__new__`` to skip ``__init__`` and its heavy dependencies
+    (storage, LLM, skills) — we only need the cache and config for these tests.
+    """
+    runtime = object.__new__(AgentRuntime)
+    runtime._config = AgentRuntimeConfig(idempotency_ttl_seconds=ttl, idempotency_max_keys=max_keys)
+    runtime._running = True
+    runtime._lock = asyncio.Lock()
+    runtime._idempotency_keys = OrderedDict()
+    runtime._idempotency_times = {}
+    runtime._graphs = {}
+    runtime._active_graph_id = "primary"
+    runtime._graph_id = "primary"
+    runtime._streams = {}
+    runtime._entry_points = {}
+    return runtime
+
+
+def _make_runtime_with_stream(ttl=300.0, max_keys=10000):
+    """Create a mock runtime whose stream.execute() returns unique IDs."""
+    runtime = _make_runtime(ttl=ttl, max_keys=max_keys)
+
+    call_count = 0
+
+    async def _fake_execute(*args, **kwargs):
+        nonlocal call_count
+        call_count += 1
+        return f"session-{call_count:04d}"
+
+    stream = MagicMock()
+    stream.execute = _fake_execute
+    runtime._streams = {"webhook": stream}
+    runtime._entry_points = {"webhook": MagicMock()}
+    return runtime
+
+
+class TestIdempotencyConfig:
+    """Verify idempotency configuration defaults."""
+
+    def test_default_ttl(self):
+        config = AgentRuntimeConfig()
+        assert config.idempotency_ttl_seconds == 300.0
+
+    def test_default_max_keys(self):
+        config = AgentRuntimeConfig()
+        assert config.idempotency_max_keys == 10000
+
+    def test_custom_config(self):
+        config = AgentRuntimeConfig(idempotency_ttl_seconds=60.0, idempotency_max_keys=100)
+        assert config.idempotency_ttl_seconds == 60.0
+        assert config.idempotency_max_keys == 100
+
+
+class TestIdempotencyCache:
+    """Test the idempotency cache and pruning logic directly."""
+
+    def test_cache_stores_and_retrieves_key(self):
+        runtime = _make_runtime()
+        runtime._idempotency_keys["stripe-evt-123"] = "exec-001"
+        runtime._idempotency_times["stripe-evt-123"] = time.time()
+
+        assert runtime._idempotency_keys.get("stripe-evt-123") == "exec-001"
+
+    def test_cache_returns_none_for_unknown_key(self):
+        runtime = _make_runtime()
+        assert runtime._idempotency_keys.get("unknown") is None
+
+    def test_prune_removes_expired_keys(self):
+        runtime = _make_runtime(ttl=0.1)
+
+        runtime._idempotency_keys["old-key"] = "exec-old"
+        runtime._idempotency_times["old-key"] = time.time() - 1.0  # expired
+
+        runtime._prune_idempotency_keys()
+
+        assert "old-key" not in runtime._idempotency_keys
+        assert "old-key" not in runtime._idempotency_times
+
+    def test_prune_keeps_fresh_keys(self):
+        runtime = _make_runtime(ttl=300.0)
+
+        runtime._idempotency_keys["fresh-key"] = "exec-fresh"
+        runtime._idempotency_times["fresh-key"] = time.time()
+
+        runtime._prune_idempotency_keys()
+
+        assert "fresh-key" in runtime._idempotency_keys
+
+    def test_prune_respects_max_keys(self):
+        runtime = _make_runtime(max_keys=2)
+
+        for i in range(3):
+            key = f"key-{i}"
+            runtime._idempotency_keys[key] = f"exec-{i}"
+            runtime._idempotency_times[key] = time.time()
+
+        runtime._prune_idempotency_keys()
+
+        assert len(runtime._idempotency_keys) == 2
+        # Oldest (key-0) should be evicted
+        assert "key-0" not in runtime._idempotency_keys
+        assert "key-1" in runtime._idempotency_keys
+        assert "key-2" in runtime._idempotency_keys
+
+    def test_prune_evicts_fifo(self):
+        runtime = _make_runtime(max_keys=1)
+
+        runtime._idempotency_keys["first"] = "exec-1"
+        runtime._idempotency_times["first"] = time.time()
+        runtime._idempotency_keys["second"] = "exec-2"
+        runtime._idempotency_times["second"] = time.time()
+
+        runtime._prune_idempotency_keys()
+
+        assert len(runtime._idempotency_keys) == 1
+        assert "second" in runtime._idempotency_keys
+        assert "first" not in runtime._idempotency_keys
+
+    def test_mixed_expired_and_max_size(self):
+        runtime = _make_runtime(ttl=0.1, max_keys=2)
+
+        # Add expired key
+        runtime._idempotency_keys["expired"] = "exec-e"
+        runtime._idempotency_times["expired"] = time.time() - 1.0
+
+        # Add fresh keys
+        runtime._idempotency_keys["fresh-1"] = "exec-f1"
+        runtime._idempotency_times["fresh-1"] = time.time()
+        runtime._idempotency_keys["fresh-2"] = "exec-f2"
+        runtime._idempotency_times["fresh-2"] = time.time()
+
+        runtime._prune_idempotency_keys()
+
+        assert "expired" not in runtime._idempotency_keys
+        assert "fresh-1" in runtime._idempotency_keys
+        assert "fresh-2" in runtime._idempotency_keys
+
+
+class TestTriggerIdempotency:
+    """Tests for trigger() idempotency deduplication."""
+
+    def test_trigger_accepts_idempotency_key(self):
+        """trigger() accepts idempotency_key as a keyword argument."""
+        import inspect
+
+        sig = inspect.signature(AgentRuntime.trigger)
+        assert "idempotency_key" in sig.parameters
+
+    def test_idempotency_key_defaults_to_none(self):
+        """idempotency_key defaults to None (backward compatible)."""
+        import inspect
+
+        sig = inspect.signature(AgentRuntime.trigger)
+        assert sig.parameters["idempotency_key"].default is None
+
+    def test_trigger_and_wait_accepts_idempotency_key(self):
+        """trigger_and_wait() also accepts idempotency_key."""
+        import inspect
+
+        sig = inspect.signature(AgentRuntime.trigger_and_wait)
+        assert "idempotency_key" in sig.parameters
+
+    def test_trigger_and_wait_idempotency_key_defaults_to_none(self):
+        """trigger_and_wait() idempotency_key defaults to None."""
+        import inspect
+
+        sig = inspect.signature(AgentRuntime.trigger_and_wait)
+        assert sig.parameters["idempotency_key"].default is None
+
+    @pytest.mark.asyncio
+    async def test_duplicate_key_returns_cached_id(self):
+        """Same idempotency key within TTL returns the cached execution ID."""
+        runtime = _make_runtime_with_stream()
+
+        first = await runtime.trigger("webhook", {}, idempotency_key="stripe-evt-001")
+        second = await runtime.trigger("webhook", {}, idempotency_key="stripe-evt-001")
+
+        assert first == second
+        assert first == "session-0001"
+
+    @pytest.mark.asyncio
+    async def test_different_keys_produce_different_ids(self):
+        """Different idempotency keys start separate executions."""
+        runtime = _make_runtime_with_stream()
+
+        id_a = await runtime.trigger("webhook", {}, idempotency_key="evt-aaa")
+        id_b = await runtime.trigger("webhook", {}, idempotency_key="evt-bbb")
+
+        assert id_a != id_b
+        assert id_a == "session-0001"
+        assert id_b == "session-0002"
+
+    @pytest.mark.asyncio
+    async def test_none_key_always_starts_new_execution(self):
+        """key=None (default) skips dedup — every call starts fresh."""
+        runtime = _make_runtime_with_stream()
+
+        id_1 = await runtime.trigger("webhook", {})
+        id_2 = await runtime.trigger("webhook", {})
+
+        assert id_1 != id_2
+        assert len(runtime._idempotency_keys) == 0  # nothing cached
+
+    @pytest.mark.asyncio
+    async def test_expired_key_allows_new_execution(self):
+        """After TTL expires, the same key starts a new execution."""
+        runtime = _make_runtime_with_stream(ttl=0.1)
+
+        first = await runtime.trigger("webhook", {}, idempotency_key="evt-expire")
+
+        # Backdate the cached timestamp so the key looks expired
+        runtime._idempotency_times["evt-expire"] = time.time() - 1.0
+
+        second = await runtime.trigger("webhook", {}, idempotency_key="evt-expire")
+
+        assert first != second
+        assert first == "session-0001"
+        assert second == "session-0002"
+
+    @pytest.mark.asyncio
+    async def test_stream_not_found_does_not_cache(self):
+        """If entry point doesn't exist, nothing is cached."""
+        runtime = _make_runtime_with_stream()
+
+        with pytest.raises(ValueError, match="not found"):
+            await runtime.trigger("nonexistent", {}, idempotency_key="evt-orphan")
+
+        assert "evt-orphan" not in runtime._idempotency_keys
+
+    @pytest.mark.asyncio
+    async def test_execute_error_does_not_cache(self):
+        """If stream.execute() raises, nothing is cached so retries can go through."""
+        runtime = _make_runtime()
+
+        failing_stream = MagicMock()
+        failing_stream.execute = AsyncMock(side_effect=RuntimeError("stream not running"))
+        runtime._streams = {"webhook": failing_stream}
+        runtime._entry_points = {"webhook": MagicMock()}
+
+        with pytest.raises(RuntimeError, match="stream not running"):
+            await runtime.trigger("webhook", {}, idempotency_key="evt-123")
+
+        assert "evt-123" not in runtime._idempotency_keys
+
+    @pytest.mark.asyncio
+    async def test_cache_holds_real_execution_id(self):
+        """Cached value matches the actual execution ID from execute()."""
+        runtime = _make_runtime_with_stream()
+
+        exec_id = await runtime.trigger("webhook", {}, idempotency_key="evt-real")
+
+        cached = runtime._idempotency_keys.get("evt-real")
+        assert cached == exec_id
+        assert cached == "session-0001"

core/framework/runtime/tests/test_runtime_logging_paths.py

@@ -0,0 +1,29 @@
+"""Tests for custom session-backed runtime logging paths."""
+
+from pathlib import Path
+from unittest.mock import MagicMock
+
+from framework.graph.executor import GraphExecutor
+from framework.runtime.runtime_log_store import RuntimeLogStore
+from framework.runtime.runtime_logger import RuntimeLogger
+
+
+def test_graph_executor_uses_custom_session_dir_name_for_runtime_logs():
+    executor = GraphExecutor(
+        runtime=MagicMock(),
+        storage_path=Path("/tmp/test-agent/sessions/my-custom-session"),
+    )
+
+    assert executor._get_runtime_log_session_id() == "my-custom-session"
+
+
+def test_runtime_logger_creates_session_log_dir_for_custom_session_id(tmp_path):
+    base = tmp_path / ".hive" / "agents" / "test_agent"
+    base.mkdir(parents=True)
+    store = RuntimeLogStore(base)
+    logger = RuntimeLogger(store=store, agent_id="test-agent")
+
+    run_id = logger.start_run(goal_id="goal-1", session_id="my-custom-session")
+
+    assert run_id == "my-custom-session"
+    assert (base / "sessions" / "my-custom-session" / "logs").is_dir()

core/framework/server/queen_orchestrator.py

@@ -62,6 +62,7 @@ async def create_queen(
     from framework.agents.queen.nodes.thinking_hook import select_expert_persona
     from framework.graph.event_loop_node import HookContext, HookResult
     from framework.graph.executor import GraphExecutor
+    from framework.runner.mcp_registry import MCPRegistry
     from framework.runner.tool_registry import ToolRegistry
     from framework.runtime.core import Runtime
     from framework.runtime.event_bus import AgentEvent, EventType
@@ -69,6 +70,7 @@ async def create_queen(
         QueenPhaseState,
         register_queen_lifecycle_tools,
     )
+    from framework.tools.queen_memory_tools import register_queen_memory_tools
 
     hive_home = Path.home() / ".hive"
 
@@ -85,6 +87,23 @@ async def create_queen(
         except Exception:
             logger.warning("Queen: MCP config failed to load", exc_info=True)
 
+    try:
+        registry = MCPRegistry()
+        registry.initialize()
+        if (queen_pkg_dir / "mcp_registry.json").is_file():
+            queen_registry.set_mcp_registry_agent_path(queen_pkg_dir)
+        registry_configs, selection_max_tools = registry.load_agent_selection(queen_pkg_dir)
+        if registry_configs:
+            results = queen_registry.load_registry_servers(
+                registry_configs,
+                preserve_existing_tools=True,
+                log_collisions=True,
+                max_tools=selection_max_tools,
+            )
+            logger.info("Queen: loaded MCP registry servers: %s", results)
+    except Exception:
+        logger.warning("Queen: MCP registry config failed to load", exc_info=True)
+
     # ---- Phase state --------------------------------------------------
     initial_phase = "staging" if worker_identity else "planning"
     phase_state = QueenPhaseState(phase=initial_phase, event_bus=session.event_bus)
@@ -122,6 +141,9 @@ async def create_queen(
         phase_state=phase_state,
     )
 
+    # ---- Episodic memory tools (always registered) ---------------------
+    register_queen_memory_tools(queen_registry)
+
     # ---- Monitoring tools (only when worker is loaded) ----------------
     if session.worker_runtime:
         from framework.tools.worker_monitoring_tools import register_worker_monitoring_tools
@@ -132,6 +154,7 @@ async def create_queen(
             session.worker_path,
             stream_id="queen",
             worker_graph_id=session.worker_runtime._graph_id,
+            default_session_id=session.id,
         )
 
     queen_tools = list(queen_registry.get_tools().values())
@@ -215,6 +238,22 @@ async def create_queen(
         + worker_identity
     )
 
+    # ---- Default skill protocols -------------------------------------
+    _queen_skill_dirs: list[str] = []
+    try:
+        from framework.skills.manager import SkillsManager, SkillsManagerConfig
+
+        # Pass project_root so user-scope skills (~/.hive/skills/, ~/.agents/skills/)
+        # are discovered. Queen has no agent-specific project root, so we use its
+        # own directory — the value just needs to be non-None to enable user-scope scanning.
+        _queen_skills_mgr = SkillsManager(SkillsManagerConfig(project_root=Path(__file__).parent))
+        _queen_skills_mgr.load()
+        phase_state.protocols_prompt = _queen_skills_mgr.protocols_prompt
+        phase_state.skills_catalog_prompt = _queen_skills_mgr.skills_catalog_prompt
+        _queen_skill_dirs = _queen_skills_mgr.allowlisted_dirs
+    except Exception:
+        logger.debug("Queen skill loading failed (non-fatal)", exc_info=True)
+
     # ---- Persona hook ------------------------------------------------
     _session_llm = session.llm
     _session_event_bus = session.event_bus
@@ -276,6 +315,7 @@ async def create_queen(
                 dynamic_tools_provider=phase_state.get_current_tools,
                 dynamic_prompt_provider=phase_state.get_current_prompt,
                 iteration_metadata_provider=lambda: {"phase": phase_state.phase},
+                skill_dirs=_queen_skill_dirs,
             )
             session.queen_executor = executor
 

core/framework/server/routes_credentials.py

@@ -103,7 +103,9 @@ async def handle_delete_credential(request: web.Request) -> web.Response:
     if credential_id == "aden_api_key":
         from framework.credentials.key_storage import delete_aden_api_key
 
-        delete_aden_api_key()
+        deleted = delete_aden_api_key()
+        if not deleted:
+            return web.json_response({"error": "Credential 'aden_api_key' not found"}, status=404)
         return web.json_response({"deleted": True})
 
     store = _get_store(request)
@@ -178,7 +180,10 @@ async def handle_check_agent(request: web.Request) -> web.Response:
         )
     except Exception as e:
         logger.exception(f"Error checking agent credentials: {e}")
-        return web.json_response({"error": str(e)}, status=500)
+        return web.json_response(
+            {"error": "Internal server error while checking credentials"},
+            status=500,
+        )
 
 
 def _status_to_dict(c) -> dict:

core/framework/server/routes_events.py

@@ -6,7 +6,7 @@ import logging
 from aiohttp import web
 from aiohttp.client_exceptions import ClientConnectionResetError as _AiohttpConnReset
 
-from framework.runtime.event_bus import EventType
+from framework.runtime.event_bus import AgentEvent, EventType
 from framework.server.app import resolve_session
 
 logger = logging.getLogger(__name__)
@@ -37,6 +37,7 @@ DEFAULT_EVENT_TYPES = [
     EventType.NODE_RETRY,
     EventType.NODE_TOOL_DOOM_LOOP,
     EventType.CONTEXT_COMPACTED,
+    EventType.CONTEXT_USAGE_UPDATED,
     EventType.WORKER_LOADED,
     EventType.CREDENTIALS_REQUIRED,
     EventType.SUBAGENT_REPORT,
@@ -46,6 +47,7 @@ DEFAULT_EVENT_TYPES = [
     EventType.TRIGGER_DEACTIVATED,
     EventType.TRIGGER_FIRED,
     EventType.TRIGGER_REMOVED,
+    EventType.TRIGGER_UPDATED,
     EventType.DRAFT_GRAPH_UPDATED,
 ]
 
@@ -165,6 +167,54 @@ async def handle_events(request: web.Request) -> web.StreamResponse:
     if replayed:
         logger.info("SSE replayed %d buffered events for session='%s'", replayed, session.id)
 
+    # Inject a live-status snapshot so the frontend knows which nodes are
+    # currently running.  This covers the case where the user navigated away
+    # and back — the localStorage snapshot is stale, and the ring-buffer
+    # replay may not include the original node_loop_started events.
+    worker_runtime = getattr(session, "worker_runtime", None)
+    if worker_runtime and getattr(worker_runtime, "is_running", False):
+        try:
+            for stream_info in worker_runtime.get_active_streams():
+                graph_id = stream_info.get("graph_id")
+                stream_id = stream_info.get("stream_id", "default")
+                for exec_id in stream_info.get("active_execution_ids", []):
+                    # Synthesize execution_started so frontend sets workerRunState
+                    synth_exec = AgentEvent(
+                        type=EventType.EXECUTION_STARTED,
+                        stream_id=stream_id,
+                        execution_id=exec_id,
+                        graph_id=graph_id,
+                        data={"synthetic": True},
+                    ).to_dict()
+                    try:
+                        queue.put_nowait(synth_exec)
+                    except asyncio.QueueFull:
+                        pass
+
+                # Find the currently executing node via the executor
+                for _gid, reg in worker_runtime._graphs.items():
+                    if _gid != graph_id:
+                        continue
+                    for _ep_id, stream in reg.streams.items():
+                        for exec_id, executor in stream._active_executors.items():
+                            current = getattr(executor, "current_node_id", None)
+                            if current:
+                                synth_node = AgentEvent(
+                                    type=EventType.NODE_LOOP_STARTED,
+                                    stream_id=stream_id,
+                                    node_id=current,
+                                    execution_id=exec_id,
+                                    graph_id=graph_id,
+                                    data={"synthetic": True},
+                                ).to_dict()
+                                try:
+                                    queue.put_nowait(synth_node)
+                                except asyncio.QueueFull:
+                                    pass
+            logger.info("SSE injected live-status snapshot for session='%s'", session.id)
+        except Exception:
+            logger.debug("Failed to inject live-status snapshot", exc_info=True)
+
     event_count = 0
     close_reason = "unknown"
     try:

core/framework/server/routes_execution.py

@@ -108,7 +108,10 @@ async def handle_chat(request: web.Request) -> web.Response:
     The input box is permanently connected to the queen agent.
     Worker input is handled separately via /worker-input.
 
-    Body: {"message": "hello"}
+    Body: {"message": "hello", "images": [{"type": "image_url", "image_url": {"url": "data:..."}}]}
+
+    The optional ``images`` field accepts a list of OpenAI-format image_url
+    content blocks.  The frontend encodes images as base64 data URIs.
     """
     session, err = resolve_session(request)
     if err:
@@ -116,15 +119,16 @@ async def handle_chat(request: web.Request) -> web.Response:
 
     body = await request.json()
     message = body.get("message", "")
+    image_content = body.get("images") or None  # list[dict] | None
 
-    if not message:
+    if not message and not image_content:
         return web.json_response({"error": "message is required"}, status=400)
 
     queen_executor = session.queen_executor
     if queen_executor is not None:
         node = queen_executor.node_registry.get("queen")
         if node is not None and hasattr(node, "inject_event"):
-            await node.inject_event(message, is_client_input=True)
+            await node.inject_event(message, is_client_input=True, image_content=image_content)
             # Publish to EventBus so the session event log captures user messages
             from framework.runtime.event_bus import AgentEvent, EventType
 
@@ -134,7 +138,10 @@ async def handle_chat(request: web.Request) -> web.Response:
                     stream_id="queen",
                     node_id="queen",
                     execution_id=session.id,
-                    data={"content": message},
+                    data={
+                        "content": message,
+                        "image_count": len(image_content) if image_content else 0,
+                    },
                 )
             )
             return web.json_response(

core/framework/server/routes_sessions.py

@@ -11,7 +11,6 @@ Session-primary routes:
 - GET    /api/sessions/{session_id}/entry-points     — list entry points
 - PATCH  /api/sessions/{session_id}/triggers/{id}   — update trigger task
 - GET    /api/sessions/{session_id}/graphs           — list graph IDs
-- GET    /api/sessions/{session_id}/queen-messages   — queen conversation history
 - GET    /api/sessions/{session_id}/events/history  — persisted eventbus log (for replay)
 
 Worker session browsing (persisted execution runs on disk):
@@ -24,9 +23,13 @@ Worker session browsing (persisted execution runs on disk):
 
 """
 
+import asyncio
+import contextlib
 import json
 import logging
 import shutil
+import subprocess
+import sys
 import time
 from pathlib import Path
 
@@ -50,8 +53,11 @@ def _get_manager(request: web.Request) -> SessionManager:
 
 def _session_to_live_dict(session) -> dict:
     """Serialize a live Session to the session-primary JSON shape."""
+    from framework.llm.capabilities import supports_image_tool_results
+
     info = session.worker_info
     phase_state = getattr(session, "phase_state", None)
+    queen_model: str = getattr(getattr(session, "runner", None), "model", "") or ""
     return {
         "session_id": session.id,
         "worker_id": session.worker_id,
@@ -64,7 +70,10 @@ def _session_to_live_dict(session) -> dict:
         "loaded_at": session.loaded_at,
         "uptime_seconds": round(time.time() - session.loaded_at, 1),
         "intro_message": getattr(session.runner, "intro_message", "") or "",
-        "queen_phase": phase_state.phase if phase_state else "planning",
+        "queen_phase": phase_state.phase
+        if phase_state
+        else ("staging" if session.worker_runtime else "planning"),
+        "queen_supports_images": supports_image_tool_results(queen_model) if queen_model else True,
     }
 
 
@@ -406,7 +415,7 @@ async def handle_session_entry_points(request: web.Request) -> web.Response:
 
 
 async def handle_update_trigger_task(request: web.Request) -> web.Response:
-    """PATCH /api/sessions/{session_id}/triggers/{trigger_id} — update trigger task."""
+    """PATCH /api/sessions/{session_id}/triggers/{trigger_id} — update trigger fields."""
     session, err = resolve_session(request)
     if err:
         return err
@@ -425,30 +434,136 @@ async def handle_update_trigger_task(request: web.Request) -> web.Response:
     except Exception:
         return web.json_response({"error": "Invalid JSON body"}, status=400)
 
-    task = body.get("task")
-    if task is None:
-        return web.json_response({"error": "Missing 'task' field"}, status=400)
-    if not isinstance(task, str):
-        return web.json_response({"error": "'task' must be a string"}, status=400)
+    updates: dict[str, object] = {}
 
-    tdef.task = task
+    if "task" in body:
+        task = body.get("task")
+        if not isinstance(task, str):
+            return web.json_response({"error": "'task' must be a string"}, status=400)
+        tdef.task = task
+        updates["task"] = tdef.task
+
+    trigger_config_update = body.get("trigger_config")
+    if trigger_config_update is not None:
+        if not isinstance(trigger_config_update, dict):
+            return web.json_response(
+                {"error": "'trigger_config' must be an object"},
+                status=400,
+            )
+        merged_trigger_config = dict(tdef.trigger_config)
+        merged_trigger_config.update(trigger_config_update)
+
+        if tdef.trigger_type == "timer":
+            cron_expr = merged_trigger_config.get("cron")
+            interval = merged_trigger_config.get("interval_minutes")
+            if cron_expr is not None and not isinstance(cron_expr, str):
+                return web.json_response(
+                    {"error": "'trigger_config.cron' must be a string"},
+                    status=400,
+                )
+            if cron_expr:
+                try:
+                    from croniter import croniter
+
+                    if not croniter.is_valid(cron_expr):
+                        return web.json_response(
+                            {"error": f"Invalid cron expression: {cron_expr}"},
+                            status=400,
+                        )
+                except ImportError:
+                    return web.json_response(
+                        {
+                            "error": (
+                                "croniter package not installed — cannot validate cron expression."
+                            )
+                        },
+                        status=500,
+                    )
+                merged_trigger_config.pop("interval_minutes", None)
+            elif interval is None:
+                return web.json_response(
+                    {
+                        "error": (
+                            "Timer trigger needs 'cron' or 'interval_minutes' in trigger_config."
+                        )
+                    },
+                    status=400,
+                )
+            elif not isinstance(interval, (int, float)) or interval <= 0:
+                return web.json_response(
+                    {"error": "'trigger_config.interval_minutes' must be > 0"},
+                    status=400,
+                )
+        tdef.trigger_config = merged_trigger_config
+        updates["trigger_config"] = tdef.trigger_config
+
+    if not updates:
+        return web.json_response(
+            {"error": "Provide at least one of 'task' or 'trigger_config'"},
+            status=400,
+        )
 
     # Persist to session state and agent definition
     from framework.tools.queen_lifecycle_tools import (
         _persist_active_triggers,
         _save_trigger_to_agent,
+        _start_trigger_timer,
+        _start_trigger_webhook,
     )
 
+    if "trigger_config" in updates and trigger_id in getattr(session, "active_trigger_ids", set()):
+        task = session.active_timer_tasks.pop(trigger_id, None)
+        if task and not task.done():
+            task.cancel()
+            with contextlib.suppress(asyncio.CancelledError):
+                await task
+        getattr(session, "trigger_next_fire", {}).pop(trigger_id, None)
+
+        webhook_subs = getattr(session, "active_webhook_subs", {})
+        if sub_id := webhook_subs.pop(trigger_id, None):
+            with contextlib.suppress(Exception):
+                session.event_bus.unsubscribe(sub_id)
+
+        if tdef.trigger_type == "timer":
+            await _start_trigger_timer(session, trigger_id, tdef)
+        elif tdef.trigger_type == "webhook":
+            await _start_trigger_webhook(session, trigger_id, tdef)
+
     if trigger_id in getattr(session, "active_trigger_ids", set()):
         session_id = request.match_info["session_id"]
         await _persist_active_triggers(session, session_id)
 
     _save_trigger_to_agent(session, trigger_id, tdef)
 
+    # Emit SSE event so the frontend updates the graph and detail panel
+    bus = getattr(session, "event_bus", None)
+    if bus:
+        from framework.runtime.event_bus import AgentEvent, EventType
+
+        await bus.publish(
+            AgentEvent(
+                type=EventType.TRIGGER_UPDATED,
+                stream_id="queen",
+                data={
+                    "trigger_id": trigger_id,
+                    "task": tdef.task,
+                    "trigger_config": tdef.trigger_config,
+                    "trigger_type": tdef.trigger_type,
+                    "name": tdef.description or trigger_id,
+                    "entry_node": getattr(
+                        getattr(getattr(session, "runner", None), "graph", None),
+                        "entry_node",
+                        None,
+                    ),
+                },
+            )
+        )
+
     return web.json_response(
         {
             "trigger_id": trigger_id,
             "task": tdef.task,
+            "trigger_config": tdef.trigger_config,
         }
     )
 
@@ -492,12 +607,14 @@ async def handle_list_worker_sessions(request: web.Request) -> web.Response:
 
     sessions = []
     for d in sorted(sess_dir.iterdir(), reverse=True):
-        if not d.is_dir() or not d.name.startswith("session_"):
+        if not d.is_dir():
+            continue
+        state_path = d / "state.json"
+        if not d.name.startswith("session_") and not state_path.exists():
             continue
 
         entry: dict = {"session_id": d.name}
 
-        state_path = d / "state.json"
         if state_path.exists():
             try:
                 state = json.loads(state_path.read_text(encoding="utf-8"))
@@ -750,60 +867,6 @@ async def handle_messages(request: web.Request) -> web.Response:
     return web.json_response({"messages": all_messages})
 
 
-async def handle_queen_messages(request: web.Request) -> web.Response:
-    """GET /api/sessions/{session_id}/queen-messages — get queen conversation.
-
-    Reads directly from disk so it works for both live sessions and cold
-    (post-server-restart) sessions — no live session required.
-    """
-    session_id = request.match_info["session_id"]
-
-    queen_dir = Path.home() / ".hive" / "queen" / "session" / session_id
-    convs_dir = queen_dir / "conversations"
-    if not convs_dir.exists():
-        return web.json_response({"messages": [], "session_id": session_id})
-
-    all_messages: list[dict] = []
-
-    def _read_parts(parts_dir: Path, node_id: str) -> None:
-        if not parts_dir.exists():
-            return
-        for part_file in sorted(parts_dir.iterdir()):
-            if part_file.suffix != ".json":
-                continue
-            try:
-                part = json.loads(part_file.read_text(encoding="utf-8"))
-                part["_node_id"] = node_id
-                # Use file mtime as created_at so frontend can order
-                # queen and worker messages chronologically.
-                part.setdefault("created_at", part_file.stat().st_mtime)
-                all_messages.append(part)
-            except (json.JSONDecodeError, OSError):
-                continue
-
-    # Flat layout: conversations/parts/*.json
-    _read_parts(convs_dir / "parts", "queen")
-
-    # Node-based layout: conversations/<node_id>/parts/*.json
-    for node_dir in convs_dir.iterdir():
-        if not node_dir.is_dir() or node_dir.name == "parts":
-            continue
-        _read_parts(node_dir / "parts", node_dir.name)
-
-    all_messages.sort(key=lambda m: m.get("created_at", m.get("seq", 0)))
-
-    # Filter to client-facing messages only
-    all_messages = [
-        m
-        for m in all_messages
-        if not m.get("is_transition_marker")
-        and m["role"] != "tool"
-        and not (m["role"] == "assistant" and m.get("tool_calls"))
-    ]
-
-    return web.json_response({"messages": all_messages, "session_id": session_id})
-
-
 async def handle_session_events_history(request: web.Request) -> web.Response:
     """GET /api/sessions/{session_id}/events/history — persisted eventbus log.
 
@@ -921,6 +984,29 @@ async def handle_discover(request: web.Request) -> web.Response:
     return web.json_response(result)
 
 
+async def handle_reveal_session_folder(request: web.Request) -> web.Response:
+    """POST /api/sessions/{session_id}/reveal — open session data folder in the OS file manager."""
+    manager: SessionManager = request.app["manager"]
+    session_id = request.match_info["session_id"]
+
+    session = manager.get_session(session_id)
+    storage_session_id = (session.queen_resume_from or session.id) if session else session_id
+    folder = Path.home() / ".hive" / "queen" / "session" / storage_session_id
+    folder.mkdir(parents=True, exist_ok=True)
+
+    try:
+        if sys.platform == "darwin":
+            subprocess.Popen(["open", str(folder)])
+        elif sys.platform == "win32":
+            subprocess.Popen(["explorer", str(folder)])
+        else:
+            subprocess.Popen(["xdg-open", str(folder)])
+    except Exception as exc:
+        return web.json_response({"error": str(exc)}, status=500)
+
+    return web.json_response({"path": str(folder)})
+
+
 # ------------------------------------------------------------------
 # Route registration
 # ------------------------------------------------------------------
@@ -945,13 +1031,14 @@ def register_routes(app: web.Application) -> None:
     app.router.add_delete("/api/sessions/{session_id}/worker", handle_unload_worker)
 
     # Session info
+    app.router.add_post("/api/sessions/{session_id}/reveal", handle_reveal_session_folder)
     app.router.add_get("/api/sessions/{session_id}/stats", handle_session_stats)
     app.router.add_get("/api/sessions/{session_id}/entry-points", handle_session_entry_points)
     app.router.add_patch(
         "/api/sessions/{session_id}/triggers/{trigger_id}", handle_update_trigger_task
     )
     app.router.add_get("/api/sessions/{session_id}/graphs", handle_session_graphs)
-    app.router.add_get("/api/sessions/{session_id}/queen-messages", handle_queen_messages)
+
     app.router.add_get("/api/sessions/{session_id}/events/history", handle_session_events_history)
 
     # Worker session browsing (session-primary)

core/framework/server/session_manager.py

@@ -47,6 +47,8 @@ class Session:
     worker_handoff_sub: str | None = None
     # Memory consolidation subscription (fires on CONTEXT_COMPACTED)
     memory_consolidation_sub: str | None = None
+    # Worker run digest subscription (fires on EXECUTION_COMPLETED / EXECUTION_FAILED)
+    worker_digest_sub: str | None = None
     # Trigger definitions loaded from agent's triggers.json (available but inactive)
     available_triggers: dict[str, TriggerDefinition] = field(default_factory=dict)
     # Active trigger tracking (IDs currently firing + their asyncio tasks)
@@ -94,8 +96,7 @@ class SessionManager:
 
         Internal helper — use create_session() or create_session_with_worker().
         """
-        from framework.config import RuntimeConfig
-        from framework.llm.litellm import LiteLLMProvider
+        from framework.config import RuntimeConfig, get_hive_config
         from framework.runtime.event_bus import EventBus
 
         ts = datetime.now().strftime("%Y%m%d_%H%M%S")
@@ -109,12 +110,20 @@ class SessionManager:
         rc = RuntimeConfig(model=model or self._model or RuntimeConfig().model)
 
         # Session owns these — shared with queen and worker
-        llm = LiteLLMProvider(
-            model=rc.model,
-            api_key=rc.api_key,
-            api_base=rc.api_base,
-            **rc.extra_kwargs,
-        )
+        llm_config = get_hive_config().get("llm", {})
+        if llm_config.get("use_antigravity_subscription"):
+            from framework.llm.antigravity import AntigravityProvider
+
+            llm = AntigravityProvider(model=rc.model)
+        else:
+            from framework.llm.litellm import LiteLLMProvider
+
+            llm = LiteLLMProvider(
+                model=rc.model,
+                api_key=rc.api_key,
+                api_base=rc.api_base,
+                **rc.extra_kwargs,
+            )
         event_bus = EventBus()
 
         session = Session(
@@ -177,6 +186,31 @@ class SessionManager:
         agent_path = Path(agent_path)
         resolved_worker_id = agent_id or agent_path.name
 
+        # When cold-restoring, check meta.json for the phase — if the agent
+        # was still being built we must NOT try to load the worker (the code
+        # is incomplete and will fail to import).
+        if queen_resume_from:
+            _resume_phase = None
+            _meta_path = (
+                Path.home() / ".hive" / "queen" / "session" / queen_resume_from / "meta.json"
+            )
+            if _meta_path.exists():
+                try:
+                    _meta = json.loads(_meta_path.read_text(encoding="utf-8"))
+                    _resume_phase = _meta.get("phase")
+                except (json.JSONDecodeError, OSError):
+                    pass
+            if _resume_phase in ("building", "planning"):
+                # Fall back to queen-only session — cold resume handler in
+                # _start_queen will set phase_state.agent_path and switch to
+                # the correct phase.
+                return await self.create_session(
+                    session_id=session_id,
+                    model=model,
+                    initial_prompt=initial_prompt,
+                    queen_resume_from=queen_resume_from,
+                )
+
         # Reuse the original session ID when cold-restoring so the frontend
         # sees one continuous session instead of a new one each time.
         session = await self._create_session_core(
@@ -193,6 +227,9 @@ class SessionManager:
                 model=model,
             )
 
+            # Restore active triggers from persisted state (cold restore)
+            await self._restore_active_triggers(session, session.id)
+
             # Start queen with worker profile + lifecycle + monitoring tools
             worker_identity = (
                 build_worker_profile(session.worker_runtime, agent_path=agent_path)
@@ -204,7 +241,23 @@ class SessionManager:
             )
 
         except Exception:
-            # If anything fails, tear down the session
+            if queen_resume_from:
+                # Cold restore: worker load failed (e.g. incomplete code from a
+                # building session).  Fall back to queen-only so the user can
+                # continue the conversation and fix / rebuild the agent.
+                logger.warning(
+                    "Cold restore: worker load failed for '%s', falling back to queen-only",
+                    agent_path,
+                    exc_info=True,
+                )
+                await self.stop_session(session.id)
+                return await self.create_session(
+                    session_id=session_id,
+                    model=model,
+                    initial_prompt=initial_prompt,
+                    queen_resume_from=queen_resume_from,
+                )
+            # If anything fails (non-cold-restore), tear down the session
             await self.stop_session(session.id)
             raise
         return session
@@ -241,7 +294,17 @@ class SessionManager:
         try:
             # Blocking I/O — load in executor
             loop = asyncio.get_running_loop()
-            resolved_model = model or self._model
+
+            # Prioritize: explicit model arg > worker-specific model > session default
+            from framework.config import (
+                get_preferred_worker_model,
+                get_worker_api_base,
+                get_worker_api_key,
+                get_worker_llm_extra_kwargs,
+            )
+
+            worker_model = get_preferred_worker_model()
+            resolved_model = model or worker_model or self._model
             runner = await loop.run_in_executor(
                 None,
                 lambda: AgentRunner.load(
@@ -253,6 +316,30 @@ class SessionManager:
                 ),
             )
 
+            # If a worker-specific model is configured, build an LLM provider
+            # with the correct worker credentials so _setup() doesn't fall back
+            # to the queen's llm config (which may be a different provider).
+            if worker_model and not model:
+                from framework.config import get_hive_config
+
+                worker_llm_cfg = get_hive_config().get("worker_llm", {})
+                if worker_llm_cfg.get("use_antigravity_subscription"):
+                    from framework.llm.antigravity import AntigravityProvider
+
+                    runner._llm = AntigravityProvider(model=resolved_model)
+                else:
+                    from framework.llm.litellm import LiteLLMProvider
+
+                    worker_api_key = get_worker_api_key()
+                    worker_api_base = get_worker_api_base()
+                    worker_extra = get_worker_llm_extra_kwargs()
+                    runner._llm = LiteLLMProvider(
+                        model=resolved_model,
+                        api_key=worker_api_key,
+                        api_base=worker_api_base,
+                        **worker_extra,
+                    )
+
             # Setup with session's event bus
             if runner._agent_runtime is None:
                 await loop.run_in_executor(
@@ -297,6 +384,9 @@ class SessionManager:
             session.worker_runtime = runtime
             session.worker_info = info
 
+            # Subscribe to execution completion for per-run digest generation
+            self._subscribe_worker_digest(session)
+
             async with self._lock:
                 self._loading.discard(session.id)
 
@@ -399,6 +489,51 @@ class SessionManager:
                 return False
             return True
 
+    async def _restore_active_triggers(self, session: "Session", session_id: str) -> None:
+        """Restore previously active triggers from persisted session state.
+
+        Called after worker loading to restart any timer/webhook triggers
+        that were active before a server restart.
+        """
+        if not session.available_triggers or not session.worker_runtime:
+            return
+        try:
+            store = session.worker_runtime._session_store
+            state = await store.read_state(session_id)
+            if state and state.active_triggers:
+                from framework.tools.queen_lifecycle_tools import (
+                    _start_trigger_timer,
+                    _start_trigger_webhook,
+                )
+
+                saved_tasks = getattr(state, "trigger_tasks", {}) or {}
+                for tid in state.active_triggers:
+                    tdef = session.available_triggers.get(tid)
+                    if tdef:
+                        # Restore user-configured task override
+                        saved_task = saved_tasks.get(tid, "")
+                        if saved_task:
+                            tdef.task = saved_task
+                        tdef.active = True
+                        session.active_trigger_ids.add(tid)
+                        if tdef.trigger_type == "timer":
+                            await _start_trigger_timer(session, tid, tdef)
+                            logger.info("Restored trigger timer '%s'", tid)
+                        elif tdef.trigger_type == "webhook":
+                            await _start_trigger_webhook(session, tid, tdef)
+                            logger.info("Restored webhook trigger '%s'", tid)
+                    else:
+                        logger.warning(
+                            "Saved trigger '%s' not found in worker entry points, skipping",
+                            tid,
+                        )
+
+            # Restore worker_configured flag
+            if state and getattr(state, "worker_configured", False):
+                session.worker_configured = True
+        except Exception as e:
+            logger.warning("Failed to restore active triggers: %s", e)
+
     async def load_worker(
         self,
         session_id: str,
@@ -427,44 +562,27 @@ class SessionManager:
         if agent_path.name != "queen" and session.worker_runtime:
             await self._notify_queen_worker_loaded(session)
 
-        # Restore previously active triggers from persisted session state
-        if session.available_triggers and session.worker_runtime:
-            try:
-                store = session.worker_runtime._session_store
-                state = await store.read_state(session_id)
-                if state and state.active_triggers:
-                    from framework.tools.queen_lifecycle_tools import (
-                        _start_trigger_timer,
-                        _start_trigger_webhook,
-                    )
+        # Update meta.json so cold-restore can discover this session by agent_path
+        storage_session_id = session.queen_resume_from or session.id
+        meta_path = Path.home() / ".hive" / "queen" / "session" / storage_session_id / "meta.json"
+        try:
+            _agent_name = (
+                session.worker_info.name
+                if session.worker_info
+                else str(agent_path.name).replace("_", " ").title()
+            )
+            existing_meta = {}
+            if meta_path.exists():
+                existing_meta = json.loads(meta_path.read_text(encoding="utf-8"))
+            existing_meta["agent_name"] = _agent_name
+            existing_meta["agent_path"] = (
+                str(session.worker_path) if session.worker_path else str(agent_path)
+            )
+            meta_path.write_text(json.dumps(existing_meta), encoding="utf-8")
+        except OSError:
+            pass
 
-                    saved_tasks = getattr(state, "trigger_tasks", {}) or {}
-                    for tid in state.active_triggers:
-                        tdef = session.available_triggers.get(tid)
-                        if tdef:
-                            # Restore user-configured task override
-                            saved_task = saved_tasks.get(tid, "")
-                            if saved_task:
-                                tdef.task = saved_task
-                            tdef.active = True
-                            session.active_trigger_ids.add(tid)
-                            if tdef.trigger_type == "timer":
-                                await _start_trigger_timer(session, tid, tdef)
-                                logger.info("Restored trigger timer '%s'", tid)
-                            elif tdef.trigger_type == "webhook":
-                                await _start_trigger_webhook(session, tid, tdef)
-                                logger.info("Restored webhook trigger '%s'", tid)
-                        else:
-                            logger.warning(
-                                "Saved trigger '%s' not found in worker entry points, skipping",
-                                tid,
-                            )
-
-                # Restore worker_configured flag
-                if state and getattr(state, "worker_configured", False):
-                    session.worker_configured = True
-            except Exception as e:
-                logger.warning("Failed to restore active triggers: %s", e)
+        await self._restore_active_triggers(session, session_id)
 
         # Emit SSE event so the frontend can update UI
         await self._emit_worker_loaded(session)
@@ -506,6 +624,13 @@ class SessionManager:
             await self._emit_trigger_events(session, "removed", session.available_triggers)
             session.available_triggers.clear()
 
+        if session.worker_digest_sub is not None:
+            try:
+                session.event_bus.unsubscribe(session.worker_digest_sub)
+            except Exception:
+                pass
+            session.worker_digest_sub = None
+
         worker_id = session.worker_id
         session.worker_id = None
         session.worker_path = None
@@ -543,6 +668,13 @@ class SessionManager:
                 pass
             session.worker_handoff_sub = None
 
+        if session.worker_digest_sub is not None:
+            try:
+                session.event_bus.unsubscribe(session.worker_digest_sub)
+            except Exception:
+                pass
+            session.worker_digest_sub = None
+
         # Stop queen and memory consolidation subscription
         if session.memory_consolidation_sub is not None:
             try:
@@ -627,6 +759,135 @@ class SessionManager:
         else:
             logger.warning("Worker handoff received but queen node not ready")
 
+    def _subscribe_worker_digest(self, session: Session) -> None:
+        """Subscribe to worker events to write per-run digests.
+
+        Three triggers:
+        - NODE_LOOP_ITERATION: write a mid-run snapshot, throttled to at most
+          once every _DIGEST_COOLDOWN seconds per execution.
+        - TOOL_CALL_COMPLETED for delegate_to_sub_agent: same throttled snapshot.
+          Orchestrator nodes often run all subagent calls in a single LLM turn,
+          so NODE_LOOP_ITERATION only fires once at the end.  Subagent
+          completions provide intermediate checkpoints.
+        - EXECUTION_COMPLETED / EXECUTION_FAILED: always write the final digest,
+          bypassing the cooldown.
+        """
+        import time as _time
+
+        from framework.runtime.event_bus import EventType as _ET
+
+        _DIGEST_COOLDOWN = 300.0  # seconds between mid-run snapshots
+
+        if session.worker_digest_sub is not None:
+            try:
+                session.event_bus.unsubscribe(session.worker_digest_sub)
+            except Exception:
+                pass
+            session.worker_digest_sub = None
+
+        agent_name = session.worker_path.name if session.worker_path else None
+        if not agent_name:
+            return
+
+        _agent_name = agent_name
+        _llm = session.llm
+        _bus = session.event_bus
+        # per-execution_id monotonic timestamp of last mid-run digest
+        _last_digest: dict[str, float] = {}
+
+        def _resolve_run_id(exec_id: str) -> str | None:
+            """Look up the run_id for a given execution_id via EXECUTION_STARTED history."""
+            for e in _bus.get_history(event_type=_ET.EXECUTION_STARTED, limit=200):
+                if e.execution_id == exec_id and getattr(e, "run_id", None):
+                    return e.run_id
+            return None
+
+        async def _inject_digest_to_queen(run_id: str) -> None:
+            """Read the written digest and push it into the queen's conversation."""
+            from framework.agents.worker_memory import digest_path
+
+            try:
+                content = digest_path(_agent_name, run_id).read_text(encoding="utf-8").strip()
+            except OSError:
+                return
+            if not content:
+                return
+            executor = session.queen_executor
+            if executor is None:
+                return
+            node = executor.node_registry.get("queen")
+            if node is None or not hasattr(node, "inject_event"):
+                return
+            await node.inject_event(f"[WORKER_DIGEST]\n{content}")
+
+        async def _consolidate_and_notify(run_id: str, outcome_event: Any) -> None:
+            """Write the digest then push it to the queen."""
+            from framework.agents.worker_memory import consolidate_worker_run
+
+            await consolidate_worker_run(_agent_name, run_id, outcome_event, _bus, _llm)
+            await _inject_digest_to_queen(run_id)
+
+        async def _on_worker_event(event: Any) -> None:
+            if event.stream_id == "queen":
+                return
+
+            exec_id = event.execution_id
+
+            if event.type == _ET.EXECUTION_STARTED:
+                # New run on this execution_id — start the cooldown timer so
+                # mid-run snapshots don't fire immediately at session start.
+                # The first snapshot will happen after _DIGEST_COOLDOWN seconds.
+                if exec_id:
+                    _last_digest[exec_id] = _time.monotonic()
+
+            elif event.type in (
+                _ET.EXECUTION_COMPLETED,
+                _ET.EXECUTION_FAILED,
+                _ET.EXECUTION_PAUSED,
+            ):
+                # Final digest — always fire, ignore cooldown.
+                # EXECUTION_PAUSED covers cancellation (queen re-triggering the
+                # worker cancels the previous execution, emitting paused).
+                run_id = getattr(event, "run_id", None) or _resolve_run_id(exec_id)
+                if run_id:
+                    asyncio.create_task(
+                        _consolidate_and_notify(run_id, event),
+                        name=f"worker-digest-final-{run_id}",
+                    )
+
+            elif event.type in (_ET.NODE_LOOP_ITERATION, _ET.TOOL_CALL_COMPLETED):
+                # Mid-run snapshot — respect 300 s cooldown per execution.
+                # TOOL_CALL_COMPLETED is only interesting for subagent calls;
+                # regular tool completions are too frequent and too cheap.
+                if event.type == _ET.TOOL_CALL_COMPLETED:
+                    tool_name = (event.data or {}).get("tool_name", "")
+                    if tool_name != "delegate_to_sub_agent":
+                        return
+                if not exec_id:
+                    return
+                now = _time.monotonic()
+                if now - _last_digest.get(exec_id, 0.0) < _DIGEST_COOLDOWN:
+                    return
+                run_id = _resolve_run_id(exec_id)
+                if run_id:
+                    _last_digest[exec_id] = now
+                    asyncio.create_task(
+                        _consolidate_and_notify(run_id, None),
+                        name=f"worker-digest-{run_id}",
+                    )
+
+        session.worker_digest_sub = session.event_bus.subscribe(
+            event_types=[
+                _ET.EXECUTION_STARTED,
+                _ET.NODE_LOOP_ITERATION,
+                _ET.TOOL_CALL_COMPLETED,
+                _ET.EXECUTION_COMPLETED,
+                _ET.EXECUTION_FAILED,
+                _ET.EXECUTION_PAUSED,
+            ],
+            handler=_on_worker_event,
+        )
+
     def _subscribe_worker_handoffs(self, session: Session, executor: Any) -> None:
         """Subscribe queen to worker/subagent escalation handoff events."""
         from framework.runtime.event_bus import EventType as _ET
@@ -680,16 +941,21 @@ class SessionManager:
                     else None
                 )
             )
-            _meta_path.write_text(
-                json.dumps(
-                    {
-                        "agent_name": _agent_name,
-                        "agent_path": str(session.worker_path) if session.worker_path else None,
-                        "created_at": time.time(),
-                    }
-                ),
-                encoding="utf-8",
-            )
+            # Merge into existing meta.json to preserve fields written by
+            # _update_meta_json (e.g. phase, agent_path set during building).
+            _existing_meta: dict = {}
+            if _meta_path.exists():
+                try:
+                    _existing_meta = json.loads(_meta_path.read_text(encoding="utf-8"))
+                except (json.JSONDecodeError, OSError):
+                    pass
+            _new_meta: dict = {"created_at": time.time()}
+            if _agent_name is not None:
+                _new_meta["agent_name"] = _agent_name
+            if session.worker_path is not None:
+                _new_meta["agent_path"] = str(session.worker_path)
+            _existing_meta.update(_new_meta)
+            _meta_path.write_text(json.dumps(_existing_meta), encoding="utf-8")
         except OSError:
             pass
 
@@ -699,6 +965,7 @@ class SessionManager:
         # then use max+1 as offset so resumed sessions produce monotonically
         # increasing iteration values — preventing frontend message ID collisions.
         iteration_offset = 0
+        last_phase = ""
         events_path = queen_dir / "events.jsonl"
         try:
             if events_path.exists():
@@ -710,17 +977,25 @@ class SessionManager:
                             continue
                         try:
                             evt = json.loads(line)
-                            it = evt.get("data", {}).get("iteration")
+                            data = evt.get("data", {})
+                            it = data.get("iteration")
                             if isinstance(it, int) and it > max_iter:
                                 max_iter = it
+                            # Track the latest queen phase from QUEEN_PHASE_CHANGED events
+                            if evt.get("type") == "queen_phase_changed":
+                                phase = data.get("phase")
+                                if phase:
+                                    last_phase = phase
                         except (json.JSONDecodeError, TypeError):
                             continue
                 if max_iter >= 0:
                     iteration_offset = max_iter + 1
                     logger.info(
-                        "Session '%s' resuming with iteration_offset=%d (from events.jsonl max)",
+                        "Session '%s' resuming with iteration_offset=%d"
+                        " (from events.jsonl max), last phase: %s",
                         session.id,
                         iteration_offset,
+                        last_phase or "unknown",
                     )
         except OSError:
             pass
@@ -742,11 +1017,27 @@ class SessionManager:
                 try:
                     _meta = json.loads(meta_path.read_text(encoding="utf-8"))
                     _agent_path = _meta.get("agent_path")
+                    _phase = _meta.get("phase")
+
                     if _agent_path and Path(_agent_path).exists():
-                        await self.load_worker(session.id, _agent_path)
-                        if session.phase_state:
-                            await session.phase_state.switch_to_staging(source="auto")
-                        logger.info("Cold restore: auto-loaded worker from %s", _agent_path)
+                        if _phase in ("staging", "running", None):
+                            # Agent fully built — load worker and resume
+                            await self.load_worker(session.id, _agent_path)
+                            if session.phase_state:
+                                await session.phase_state.switch_to_staging(source="auto")
+                            # Emit flowchart overlay so frontend can display it
+                            await self._emit_flowchart_on_restore(session, _agent_path)
+                            logger.info("Cold restore: auto-loaded worker from %s", _agent_path)
+                        elif _phase == "building":
+                            # Agent folder exists but incomplete — resume building
+                            if session.phase_state:
+                                session.phase_state.agent_path = _agent_path
+                                await session.phase_state.switch_to_building(source="auto")
+                            logger.info("Cold restore: resumed BUILDING phase for %s", _agent_path)
+                        elif _phase == "planning":
+                            if session.phase_state:
+                                session.phase_state.agent_path = _agent_path
+                            logger.info("Cold restore: PLANNING phase for %s", _agent_path)
                 except Exception:
                     logger.warning("Cold restore: failed to auto-load worker", exc_info=True)
 
@@ -756,10 +1047,17 @@ class SessionManager:
         _consolidation_session_dir = queen_dir
 
         async def _on_compaction(_event) -> None:
+            # Only consolidate on queen compactions — worker and subagent
+            # compactions are frequent and don't warrant a memory update.
+            if getattr(_event, "stream_id", None) != "queen":
+                return
             from framework.agents.queen.queen_memory import consolidate_queen_memory
 
-            await consolidate_queen_memory(
-                session.id, _consolidation_session_dir, _consolidation_llm
+            asyncio.create_task(
+                consolidate_queen_memory(
+                    session.id, _consolidation_session_dir, _consolidation_llm
+                ),
+                name=f"queen-memory-consolidation-{session.id}",
             )
 
         from framework.runtime.event_bus import EventType as _ET
@@ -821,6 +1119,29 @@ class SessionManager:
             )
         )
 
+    async def _emit_flowchart_on_restore(self, session: Session, agent_path: str | Path) -> None:
+        """Emit FLOWCHART_MAP_UPDATED from persisted flowchart file on cold restore."""
+        from framework.runtime.event_bus import AgentEvent, EventType
+        from framework.tools.flowchart_utils import load_flowchart_file
+
+        original_draft, flowchart_map = load_flowchart_file(agent_path)
+        if original_draft is None:
+            return
+        # Cache in phase_state so the REST endpoint also returns it
+        if session.phase_state:
+            session.phase_state.original_draft_graph = original_draft
+            session.phase_state.flowchart_map = flowchart_map
+        await session.event_bus.publish(
+            AgentEvent(
+                type=EventType.FLOWCHART_MAP_UPDATED,
+                stream_id="queen",
+                data={
+                    "map": flowchart_map,
+                    "original_draft": original_draft,
+                },
+            )
+        )
+
     async def _notify_queen_worker_unloaded(self, session: Session) -> None:
         """Notify the queen that the worker has been unloaded."""
         executor = session.queen_executor
@@ -848,6 +1169,10 @@ class SessionManager:
         event_type = (
             EventType.TRIGGER_AVAILABLE if kind == "available" else EventType.TRIGGER_REMOVED
         )
+        # Resolve graph entry node for trigger target
+        runner = getattr(session, "runner", None)
+        graph_entry = runner.graph.entry_node if runner else None
+
         for t in triggers.values():
             await session.event_bus.publish(
                 AgentEvent(
@@ -857,6 +1182,8 @@ class SessionManager:
                         "trigger_id": t.id,
                         "trigger_type": t.trigger_type,
                         "trigger_config": t.trigger_config,
+                        "name": t.description or t.id,
+                        **({"entry_node": graph_entry} if graph_entry else {}),
                     },
                 )
             )

core/framework/server/tests/test_api.py

@@ -5,6 +5,7 @@ Uses aiohttp TestClient with mocked sessions to test all endpoints
 without requiring actual LLM calls or agent loading.
 """
 
+import asyncio
 import json
 from dataclasses import dataclass, field
 from pathlib import Path
@@ -13,6 +14,7 @@ from unittest.mock import AsyncMock, MagicMock
 import pytest
 from aiohttp.test_utils import TestClient, TestServer
 
+from framework.runtime.triggers import TriggerDefinition
 from framework.server.app import create_app
 from framework.server.session_manager import Session
 
@@ -172,6 +174,7 @@ def _make_session(
     runner.intro_message = "Test intro"
 
     mock_event_bus = MagicMock()
+    mock_event_bus.publish = AsyncMock()
     mock_llm = MagicMock()
 
     queen_executor = _make_queen_executor() if with_queen else None
@@ -210,11 +213,8 @@ def tmp_agent_dir(tmp_path, monkeypatch):
     return tmp_path, agent_name, base
 
 
-@pytest.fixture
-def sample_session(tmp_agent_dir):
-    """Create a sample session with state.json, checkpoints, and conversations."""
-    tmp_path, agent_name, base = tmp_agent_dir
-    session_id = "session_20260220_120000_abc12345"
+def _write_sample_session(base: Path, session_id: str):
+    """Create a sample worker session on disk."""
     session_dir = base / "sessions" / session_id
 
     # state.json
@@ -295,6 +295,20 @@ def sample_session(tmp_agent_dir):
     return session_id, session_dir, state
 
 
+@pytest.fixture
+def sample_session(tmp_agent_dir):
+    """Create a sample session with state.json, checkpoints, and conversations."""
+    _tmp_path, _agent_name, base = tmp_agent_dir
+    return _write_sample_session(base, "session_20260220_120000_abc12345")
+
+
+@pytest.fixture
+def custom_id_session(tmp_agent_dir):
+    """Create a sample session that uses a custom non-session_* ID."""
+    _tmp_path, _agent_name, base = tmp_agent_dir
+    return _write_sample_session(base, "my-custom-session")
+
+
 def _make_app_with_session(session):
     """Create an aiohttp app with a pre-loaded session."""
     app = create_app()
@@ -473,6 +487,70 @@ class TestSessionCRUD:
             data = await resp.json()
             assert "primary" in data["graphs"]
 
+    @pytest.mark.asyncio
+    async def test_update_trigger_task(self, tmp_path):
+        session = _make_session(tmp_dir=tmp_path)
+        session.available_triggers["daily"] = TriggerDefinition(
+            id="daily",
+            trigger_type="timer",
+            trigger_config={"cron": "0 5 * * *"},
+            task="Old task",
+        )
+        app = _make_app_with_session(session)
+        async with TestClient(TestServer(app)) as client:
+            resp = await client.patch(
+                "/api/sessions/test_agent/triggers/daily",
+                json={"task": "New task"},
+            )
+            assert resp.status == 200
+            data = await resp.json()
+            assert data["task"] == "New task"
+            assert data["trigger_config"]["cron"] == "0 5 * * *"
+            assert session.available_triggers["daily"].task == "New task"
+
+    @pytest.mark.asyncio
+    async def test_update_trigger_cron_restarts_active_timer(self, tmp_path):
+        session = _make_session(tmp_dir=tmp_path)
+        session.available_triggers["daily"] = TriggerDefinition(
+            id="daily",
+            trigger_type="timer",
+            trigger_config={"cron": "0 5 * * *"},
+            task="Run task",
+            active=True,
+        )
+        session.active_trigger_ids.add("daily")
+        session.active_timer_tasks["daily"] = asyncio.create_task(asyncio.sleep(60))
+        app = _make_app_with_session(session)
+        async with TestClient(TestServer(app)) as client:
+            resp = await client.patch(
+                "/api/sessions/test_agent/triggers/daily",
+                json={"trigger_config": {"cron": "0 6 * * *"}},
+            )
+            assert resp.status == 200
+            data = await resp.json()
+            assert data["trigger_config"]["cron"] == "0 6 * * *"
+            assert "daily" in session.active_timer_tasks
+            assert session.active_timer_tasks["daily"] is not None
+            assert session.available_triggers["daily"].trigger_config["cron"] == "0 6 * * *"
+            session.active_timer_tasks["daily"].cancel()
+
+    @pytest.mark.asyncio
+    async def test_update_trigger_cron_rejects_invalid_expression(self, tmp_path):
+        session = _make_session(tmp_dir=tmp_path)
+        session.available_triggers["daily"] = TriggerDefinition(
+            id="daily",
+            trigger_type="timer",
+            trigger_config={"cron": "0 5 * * *"},
+            task="Run task",
+        )
+        app = _make_app_with_session(session)
+        async with TestClient(TestServer(app)) as client:
+            resp = await client.patch(
+                "/api/sessions/test_agent/triggers/daily",
+                json={"trigger_config": {"cron": "not a cron"}},
+            )
+            assert resp.status == 400
+
 
 class TestExecution:
     @pytest.mark.asyncio
@@ -799,6 +877,22 @@ class TestWorkerSessions:
             assert data["sessions"][0]["status"] == "paused"
             assert data["sessions"][0]["steps"] == 5
 
+    @pytest.mark.asyncio
+    async def test_list_sessions_includes_custom_id(self, custom_id_session, tmp_agent_dir):
+        session_id, session_dir, state = custom_id_session
+        tmp_path, agent_name, base = tmp_agent_dir
+
+        session = _make_session(tmp_dir=tmp_path / ".hive" / "agents" / agent_name)
+        app = _make_app_with_session(session)
+
+        async with TestClient(TestServer(app)) as client:
+            resp = await client.get("/api/sessions/test_agent/worker-sessions")
+            assert resp.status == 200
+            data = await resp.json()
+            assert len(data["sessions"]) == 1
+            assert data["sessions"][0]["session_id"] == session_id
+            assert data["sessions"][0]["status"] == "paused"
+
     @pytest.mark.asyncio
     async def test_list_sessions_empty(self, tmp_agent_dir):
         tmp_path, agent_name, base = tmp_agent_dir
@@ -1316,6 +1410,28 @@ class TestLogs:
             assert len(data["logs"]) >= 1
             assert data["logs"][0]["run_id"] == session_id
 
+    @pytest.mark.asyncio
+    async def test_logs_list_summaries_with_custom_id(self, custom_id_session, tmp_agent_dir):
+        session_id, session_dir, state = custom_id_session
+        tmp_path, agent_name, base = tmp_agent_dir
+
+        from framework.runtime.runtime_log_store import RuntimeLogStore
+
+        log_store = RuntimeLogStore(base)
+        session = _make_session(
+            tmp_dir=tmp_path / ".hive" / "agents" / agent_name,
+            log_store=log_store,
+        )
+        app = _make_app_with_session(session)
+
+        async with TestClient(TestServer(app)) as client:
+            resp = await client.get("/api/sessions/test_agent/logs")
+            assert resp.status == 200
+            data = await resp.json()
+            assert "logs" in data
+            assert len(data["logs"]) >= 1
+            assert data["logs"][0]["run_id"] == session_id
+
     @pytest.mark.asyncio
     async def test_logs_session_summary(self, sample_session, tmp_agent_dir):
         session_id, session_dir, state = sample_session

core/framework/skills/__init__.py

@@ -0,0 +1,50 @@
+"""Hive Agent Skills — discovery, parsing, trust gating, and injection of SKILL.md packages.
+
+Implements the open Agent Skills standard (agentskills.io) for portable
+skill discovery and activation, plus built-in default skills for runtime
+operational discipline, and AS-13 trust gating for project-scope skills.
+"""
+
+from framework.skills.catalog import SkillCatalog
+from framework.skills.config import DefaultSkillConfig, SkillsConfig
+from framework.skills.defaults import DefaultSkillManager
+from framework.skills.discovery import DiscoveryConfig, SkillDiscovery
+from framework.skills.installer import (
+    fork_skill,
+    install_from_git,
+    install_from_registry,
+    remove_skill,
+)
+from framework.skills.manager import SkillsManager, SkillsManagerConfig
+from framework.skills.models import TrustStatus
+from framework.skills.parser import ParsedSkill, parse_skill_md
+from framework.skills.registry import RegistryClient
+from framework.skills.skill_errors import SkillError, SkillErrorCode, log_skill_error
+from framework.skills.trust import TrustedRepoStore, TrustGate
+from framework.skills.validator import ValidationResult, validate_strict
+
+__all__ = [
+    "DefaultSkillConfig",
+    "DefaultSkillManager",
+    "DiscoveryConfig",
+    "ParsedSkill",
+    "RegistryClient",
+    "SkillCatalog",
+    "SkillDiscovery",
+    "SkillError",
+    "SkillErrorCode",
+    "SkillsConfig",
+    "SkillsManager",
+    "SkillsManagerConfig",
+    "TrustGate",
+    "TrustedRepoStore",
+    "TrustStatus",
+    "ValidationResult",
+    "fork_skill",
+    "install_from_git",
+    "install_from_registry",
+    "log_skill_error",
+    "parse_skill_md",
+    "remove_skill",
+    "validate_strict",
+]

core/framework/skills/_default_skills/batch-ledger/SKILL.md

@@ -0,0 +1,24 @@
+---
+name: hive.batch-ledger
+description: Track per-item status when processing collections to prevent skipped or duplicated items.
+metadata:
+  author: hive
+  type: default-skill
+---
+
+## Operational Protocol: Batch Progress Ledger
+
+When processing a collection of items, maintain a batch ledger in `_batch_ledger`.
+
+Initialize when you identify the batch:
+- `_batch_total`: total item count
+- `_batch_ledger`: JSON with per-item status
+
+Per-item statuses: pending → in_progress → completed|failed|skipped
+
+- Set `in_progress` BEFORE processing
+- Set final status AFTER processing with 1-line result_summary
+- Include error reason for failed/skipped items
+- Update aggregate counts after each item
+- NEVER remove items from the ledger
+- If resuming, skip items already marked completed

core/framework/skills/_default_skills/context-preservation/SKILL.md

@@ -0,0 +1,24 @@
+---
+name: hive.context-preservation
+description: Proactively preserve critical information before automatic context pruning destroys it.
+metadata:
+  author: hive
+  type: default-skill
+---
+
+## Operational Protocol: Context Preservation
+
+You operate under a finite context window. Important information WILL be pruned.
+
+Save-As-You-Go: After any tool call producing information you'll need later,
+immediately extract key data into `_working_notes` or `_preserved_data`.
+Do NOT rely on referring back to old tool results.
+
+What to extract: URLs and key snippets (not full pages), relevant API fields
+(not raw JSON), specific lines/values (not entire files), analysis results
+(not raw data).
+
+Before transitioning to the next phase/node, write a handoff summary to
+`_handoff_context` with everything the next phase needs to know.
+
+You will receive an alert when context reaches {{warn_at_usage_ratio_pct}}% — preserve immediately.

core/framework/skills/_default_skills/error-recovery/SKILL.md

@@ -0,0 +1,18 @@
+---
+name: hive.error-recovery
+description: Follow a structured recovery protocol when tool calls fail instead of blindly retrying or giving up.
+metadata:
+  author: hive
+  type: default-skill
+---
+
+## Operational Protocol: Error Recovery
+
+When a tool call fails:
+
+1. Diagnose — record error in notes, classify as transient or structural
+2. Decide — transient: retry once. Structural fixable: fix and retry.
+   Structural unfixable: record as failed, move to next item.
+   Blocking all progress: record escalation note.
+3. Adapt — if same tool failed {{max_retries_per_tool}}+ times, stop using it and find alternative.
+   Update plan in notes. Never silently drop the failed item.

core/framework/skills/_default_skills/note-taking/SKILL.md

@@ -0,0 +1,27 @@
+---
+name: hive.note-taking
+description: Maintain structured working notes throughout execution to prevent information loss during context pruning.
+metadata:
+  author: hive
+  type: default-skill
+---
+
+## Operational Protocol: Structured Note-Taking
+
+Maintain structured working notes in shared memory key `_working_notes`.
+Update at these checkpoints:
+
+- After completing each discrete subtask or batch item
+- After receiving new information that changes your plan
+- Before any tool call that will produce substantial output
+
+Structure:
+
+### Objective — restate the goal
+### Current Plan — numbered steps, mark completed with ✓
+### Key Decisions — decisions made and WHY
+### Working Data — intermediate results, extracted values
+### Open Questions — uncertainties to verify
+### Blockers — anything preventing progress
+
+Update incrementally — do not rewrite from scratch each time.

core/framework/skills/_default_skills/quality-monitor/SKILL.md

@@ -0,0 +1,20 @@
+---
+name: hive.quality-monitor
+description: Periodically self-assess output quality to catch degradation before the judge does.
+metadata:
+  author: hive
+  type: default-skill
+---
+
+## Operational Protocol: Quality Self-Assessment
+
+Every {{assessment_interval}} iterations, self-assess:
+
+1. On-task? Still working toward the stated objective?
+2. Thorough? Cutting corners compared to earlier?
+3. Non-repetitive? Producing new value or rehashing?
+4. Consistent? Latest output contradict earlier decisions?
+5. Complete? Tracking all items, or silently dropped some?
+
+If degrading: write assessment to `_quality_log`, re-read `_working_notes`,
+change approach explicitly. If acceptable: brief note in `_quality_log`.

core/framework/skills/_default_skills/task-decomposition/SKILL.md

@@ -0,0 +1,17 @@
+---
+name: hive.task-decomposition
+description: Decompose complex tasks into explicit subtasks before diving in.
+metadata:
+  author: hive
+  type: default-skill
+---
+
+## Operational Protocol: Task Decomposition
+
+Before starting a complex task:
+
+1. Decompose — break into numbered subtasks in `_working_notes` Current Plan
+2. Estimate — relative effort per subtask (small/medium/large)
+3. Execute — work through in order, mark ✓ when complete
+4. Budget — if running low on iterations, prioritize by impact
+5. Verify — before declaring done, every subtask must be ✓, skipped (with reason), or blocked

core/framework/skills/catalog.py

@@ -0,0 +1,116 @@
+"""Skill catalog — in-memory index with system prompt generation.
+
+Builds the XML catalog injected into the system prompt for model-driven
+skill activation per the Agent Skills standard.
+"""
+
+from __future__ import annotations
+
+import logging
+from xml.sax.saxutils import escape
+
+from framework.skills.parser import ParsedSkill
+from framework.skills.skill_errors import SkillErrorCode, log_skill_error
+
+logger = logging.getLogger(__name__)
+
+_BEHAVIORAL_INSTRUCTION = (
+    "The following skills provide specialized instructions for specific tasks.\n"
+    "When a task matches a skill's description, read the SKILL.md at the listed\n"
+    "location to load the full instructions before proceeding.\n"
+    "When a skill references relative paths, resolve them against the skill's\n"
+    "directory (the parent of SKILL.md) and use absolute paths in tool calls."
+)
+
+
+class SkillCatalog:
+    """In-memory catalog of discovered skills."""
+
+    def __init__(self, skills: list[ParsedSkill] | None = None):
+        self._skills: dict[str, ParsedSkill] = {}
+        self._activated: set[str] = set()
+        if skills:
+            for skill in skills:
+                self.add(skill)
+
+    def add(self, skill: ParsedSkill) -> None:
+        """Add a skill to the catalog."""
+        self._skills[skill.name] = skill
+
+    def get(self, name: str) -> ParsedSkill | None:
+        """Look up a skill by name."""
+        return self._skills.get(name)
+
+    def mark_activated(self, name: str) -> None:
+        """Mark a skill as activated in the current session."""
+        self._activated.add(name)
+
+    def is_activated(self, name: str) -> bool:
+        """Check if a skill has been activated."""
+        return name in self._activated
+
+    @property
+    def skill_count(self) -> int:
+        return len(self._skills)
+
+    @property
+    def allowlisted_dirs(self) -> list[str]:
+        """All skill base directories for file access allowlisting."""
+        return [skill.base_dir for skill in self._skills.values()]
+
+    def to_prompt(self) -> str:
+        """Generate the catalog prompt for system prompt injection.
+
+        Returns empty string if no community/user skills are discovered
+        (default skills are handled separately by DefaultSkillManager).
+        """
+        # Filter out framework-scope skills (default skills) — they're
+        # injected via the protocols prompt, not the catalog
+        community_skills = [s for s in self._skills.values() if s.source_scope != "framework"]
+
+        if not community_skills:
+            return ""
+
+        lines = ["<available_skills>"]
+        for skill in sorted(community_skills, key=lambda s: s.name):
+            lines.append("  <skill>")
+            lines.append(f"    <name>{escape(skill.name)}</name>")
+            lines.append(f"    <description>{escape(skill.description)}</description>")
+            lines.append(f"    <location>{escape(skill.location)}</location>")
+            lines.append(f"    <base_dir>{escape(skill.base_dir)}</base_dir>")
+            lines.append("  </skill>")
+        lines.append("</available_skills>")
+
+        xml_block = "\n".join(lines)
+        return f"{_BEHAVIORAL_INSTRUCTION}\n\n{xml_block}"
+
+    def build_pre_activated_prompt(self, skill_names: list[str]) -> str:
+        """Build prompt content for pre-activated skills.
+
+        Pre-activated skills get their full SKILL.md body loaded into
+        the system prompt at startup (tier 2), bypassing model-driven
+        activation.
+
+        Returns empty string if no skills match.
+        """
+        parts: list[str] = []
+
+        for name in skill_names:
+            skill = self.get(name)
+            if skill is None:
+                log_skill_error(
+                    logger,
+                    "warning",
+                    SkillErrorCode.SKILL_NOT_FOUND,
+                    what=f"Pre-activated skill '{name}' not found in catalog",
+                    why="The skill was listed for pre-activation but was not discovered.",
+                    fix=f"Check that a SKILL.md for '{name}' exists in a scanned directory.",
+                )
+                continue
+            if self.is_activated(name):
+                continue  # Already activated, skip duplicate
+
+            self.mark_activated(name)
+            parts.append(f"--- Pre-Activated Skill: {skill.name} ---\n{skill.body}")
+
+        return "\n\n".join(parts)

core/framework/skills/config.py

@@ -0,0 +1,100 @@
+"""Skill configuration dataclasses.
+
+Handles agent-level skill configuration from module-level variables
+(``default_skills`` and ``skills``).
+"""
+
+from __future__ import annotations
+
+from dataclasses import dataclass, field
+from typing import Any
+
+
+@dataclass
+class DefaultSkillConfig:
+    """Configuration for a single default skill."""
+
+    enabled: bool = True
+    overrides: dict[str, Any] = field(default_factory=dict)
+
+    @classmethod
+    def from_dict(cls, data: dict[str, Any]) -> DefaultSkillConfig:
+        enabled = data.get("enabled", True)
+        overrides = {k: v for k, v in data.items() if k != "enabled"}
+        return cls(enabled=enabled, overrides=overrides)
+
+
+@dataclass
+class SkillsConfig:
+    """Agent-level skill configuration.
+
+    Built from module-level variables in agent.py::
+
+        # Pre-activated community skills
+        skills = ["deep-research", "code-review"]
+
+        # Default skill configuration
+        default_skills = {
+            "hive.note-taking": {"enabled": True},
+            "hive.batch-ledger": {"enabled": True, "checkpoint_every_n": 10},
+            "hive.quality-monitor": {"enabled": False},
+        }
+    """
+
+    # Per-default-skill config, keyed by skill name (e.g. "hive.note-taking")
+    default_skills: dict[str, DefaultSkillConfig] = field(default_factory=dict)
+
+    # Pre-activated community skills (by name)
+    skills: list[str] = field(default_factory=list)
+
+    # Master switch: disable all default skills at once
+    all_defaults_disabled: bool = False
+
+    def is_default_enabled(self, skill_name: str) -> bool:
+        """Check if a specific default skill is enabled."""
+        if self.all_defaults_disabled:
+            return False
+        config = self.default_skills.get(skill_name)
+        if config is None:
+            return True  # enabled by default
+        return config.enabled
+
+    def get_default_overrides(self, skill_name: str) -> dict[str, Any]:
+        """Get skill-specific configuration overrides."""
+        config = self.default_skills.get(skill_name)
+        if config is None:
+            return {}
+        return config.overrides
+
+    @classmethod
+    def from_agent_vars(
+        cls,
+        default_skills: dict[str, Any] | None = None,
+        skills: list[str] | None = None,
+    ) -> SkillsConfig:
+        """Build config from agent module-level variables.
+
+        Args:
+            default_skills: Dict from agent module, e.g.
+                ``{"hive.note-taking": {"enabled": True}}``
+            skills: List of pre-activated skill names from agent module
+        """
+        all_disabled = False
+        parsed_defaults: dict[str, DefaultSkillConfig] = {}
+
+        if default_skills:
+            for name, config_dict in default_skills.items():
+                if name == "_all":
+                    if isinstance(config_dict, dict) and not config_dict.get("enabled", True):
+                        all_disabled = True
+                    continue
+                if isinstance(config_dict, dict):
+                    parsed_defaults[name] = DefaultSkillConfig.from_dict(config_dict)
+                elif isinstance(config_dict, bool):
+                    parsed_defaults[name] = DefaultSkillConfig(enabled=config_dict)
+
+        return cls(
+            default_skills=parsed_defaults,
+            skills=list(skills or []),
+            all_defaults_disabled=all_disabled,
+        )

core/framework/skills/defaults.py

@@ -0,0 +1,278 @@
+"""DefaultSkillManager — load, configure, and inject built-in default skills.
+
+Default skills are SKILL.md packages shipped with the framework that provide
+runtime operational protocols (note-taking, batch tracking, error recovery, etc.).
+"""
+
+from __future__ import annotations
+
+import logging
+from pathlib import Path
+from typing import Any
+
+from framework.skills.config import SkillsConfig
+from framework.skills.parser import ParsedSkill, parse_skill_md
+from framework.skills.skill_errors import SkillErrorCode, log_skill_error
+
+logger = logging.getLogger(__name__)
+
+# Default skills directory relative to this module
+_DEFAULT_SKILLS_DIR = Path(__file__).parent / "_default_skills"
+
+# Default config values per skill — used for {{placeholder}} substitution
+_SKILL_DEFAULTS: dict[str, dict[str, Any]] = {
+    "hive.quality-monitor": {"assessment_interval": 5},
+    "hive.error-recovery": {"max_retries_per_tool": 3},
+    "hive.context-preservation": {"warn_at_usage_ratio_pct": 45},
+    "hive.batch-ledger": {"checkpoint_every_n": 5},
+}
+
+# Keywords that indicate a batch processing scenario (DS-12)
+_BATCH_KEYWORDS: tuple[str, ...] = (
+    "list of",
+    "collection of",
+    "set of",
+    "batch of",
+    "each item",
+    "for each",
+    "process all",
+    "records",
+    "entries",
+    "rows",
+    "items",
+)
+
+_BATCH_INIT_NUDGE = (
+    "Note: your input appears to describe a batch operation. "
+    "Initialize `_batch_ledger` with the total item count before processing."
+)
+
+
+def is_batch_scenario(text: str) -> bool:
+    """Return True if *text* contains batch-processing indicators (DS-12)."""
+    lower = text.lower()
+    return any(kw in lower for kw in _BATCH_KEYWORDS)
+
+
+def _apply_overrides(skill_name: str, body: str, overrides: dict[str, Any]) -> str:
+    """Substitute {{placeholder}} values in a skill body using overrides + defaults."""
+    defaults = _SKILL_DEFAULTS.get(skill_name, {})
+    # Convert float warn_at_usage_ratio → warn_at_usage_ratio_pct for the placeholder
+    if "warn_at_usage_ratio" in overrides:
+        overrides = dict(overrides)
+        overrides.setdefault(
+            "warn_at_usage_ratio_pct", int(float(overrides["warn_at_usage_ratio"]) * 100)
+        )
+    values = {**defaults, **overrides}
+    for key, val in values.items():
+        body = body.replace(f"{{{{{key}}}}}", str(val))
+    return body
+
+
+# Ordered list of default skills (name → directory)
+SKILL_REGISTRY: dict[str, str] = {
+    "hive.note-taking": "note-taking",
+    "hive.batch-ledger": "batch-ledger",
+    "hive.context-preservation": "context-preservation",
+    "hive.quality-monitor": "quality-monitor",
+    "hive.error-recovery": "error-recovery",
+    "hive.task-decomposition": "task-decomposition",
+}
+
+# All shared memory keys used by default skills (for permission auto-inclusion)
+SHARED_MEMORY_KEYS: list[str] = [
+    # note-taking
+    "_working_notes",
+    "_notes_updated_at",
+    # batch-ledger
+    "_batch_ledger",
+    "_batch_total",
+    "_batch_completed",
+    "_batch_failed",
+    # context-preservation
+    "_handoff_context",
+    "_preserved_data",
+    # quality-monitor
+    "_quality_log",
+    "_quality_degradation_count",
+    # error-recovery
+    "_error_log",
+    "_failed_tools",
+    "_escalation_needed",
+    # task-decomposition
+    "_subtasks",
+    "_iteration_budget_remaining",
+]
+
+
+class DefaultSkillManager:
+    """Manages loading, configuration, and prompt generation for default skills."""
+
+    def __init__(self, config: SkillsConfig | None = None):
+        self._config = config or SkillsConfig()
+        self._skills: dict[str, ParsedSkill] = {}
+        self._loaded = False
+        self._error_count = 0
+
+    def load(self) -> None:
+        """Load all enabled default skill SKILL.md files."""
+        if self._loaded:
+            return
+
+        error_count = 0
+        for skill_name, dir_name in SKILL_REGISTRY.items():
+            if not self._config.is_default_enabled(skill_name):
+                logger.info("Default skill '%s' disabled by config", skill_name)
+                continue
+
+            skill_path = _DEFAULT_SKILLS_DIR / dir_name / "SKILL.md"
+            if not skill_path.is_file():
+                log_skill_error(
+                    logger,
+                    "error",
+                    SkillErrorCode.SKILL_NOT_FOUND,
+                    what=f"Default skill SKILL.md not found: '{skill_path}'",
+                    why=f"The framework skill '{skill_name}' is missing its SKILL.md file.",
+                    fix="Reinstall the hive framework — this file is part of the package.",
+                )
+                error_count += 1
+                continue
+
+            parsed = parse_skill_md(skill_path, source_scope="framework")
+            if parsed is None:
+                log_skill_error(
+                    logger,
+                    "error",
+                    SkillErrorCode.SKILL_PARSE_ERROR,
+                    what=f"Failed to parse default skill '{skill_name}'",
+                    why=f"parse_skill_md returned None for '{skill_path}'.",
+                    fix="Reinstall the hive framework — this file may be corrupted.",
+                )
+                error_count += 1
+                continue
+
+            self._skills[skill_name] = parsed
+
+        self._loaded = True
+        self._error_count = error_count
+
+    def build_protocols_prompt(self) -> str:
+        """Build the combined operational protocols section.
+
+        Extracts protocol sections from all enabled default skills and
+        combines them into a single ``## Operational Protocols`` block
+        for system prompt injection.
+
+        Returns empty string if all defaults are disabled.
+        """
+        if not self._skills:
+            return ""
+
+        parts: list[str] = ["## Operational Protocols\n"]
+
+        for skill_name in SKILL_REGISTRY:
+            skill = self._skills.get(skill_name)
+            if skill is None:
+                continue
+            # Apply config overrides to {{placeholder}} values before injection
+            overrides = self._config.get_default_overrides(skill_name)
+            body = _apply_overrides(skill_name, skill.body, overrides)
+            parts.append(body)
+
+        if len(parts) <= 1:
+            return ""
+
+        combined = "\n\n".join(parts)
+
+        # Token budget warning (approximate: 1 token ≈ 4 chars)
+        approx_tokens = len(combined) // 4
+        if approx_tokens > 2000:
+            logger.warning(
+                "Default skill protocols exceed 2000 token budget "
+                "(~%d tokens, %d chars). Consider trimming.",
+                approx_tokens,
+                len(combined),
+            )
+
+        return combined
+
+    def log_active_skills(self) -> None:
+        """Log which default skills are active and their configuration."""
+        if not self._skills:
+            logger.info("Default skills: all disabled")
+
+        # DX-3: Per-skill structured startup log
+        for skill_name in SKILL_REGISTRY:
+            if skill_name in self._skills:
+                overrides = self._config.get_default_overrides(skill_name)
+                status = f"loaded overrides={overrides}" if overrides else "loaded"
+            elif not self._config.is_default_enabled(skill_name):
+                status = "disabled"
+            else:
+                status = "error"
+            logger.info(
+                "skill_startup name=%s scope=framework status=%s",
+                skill_name,
+                status,
+            )
+
+        # Original active skills log line (preserved for backward compatibility)
+        active = []
+        for skill_name in SKILL_REGISTRY:
+            if skill_name in self._skills:
+                overrides = self._config.get_default_overrides(skill_name)
+                if overrides:
+                    active.append(f"{skill_name} ({overrides})")
+                else:
+                    active.append(skill_name)
+
+        if active:
+            logger.info("Default skills active: %s", ", ".join(active))
+
+        # DX-3: Summary line with error count
+        total = len(SKILL_REGISTRY)
+        active_count = len(self._skills)
+        error_count = getattr(self, "_error_count", 0)
+        disabled_count = total - active_count - error_count
+        logger.info(
+            "Skills: %d default (%d active, %d disabled, %d error)",
+            total,
+            active_count,
+            disabled_count,
+            error_count,
+        )
+
+    @property
+    def active_skill_names(self) -> list[str]:
+        """Names of all currently active default skills."""
+        return list(self._skills.keys())
+
+    @property
+    def active_skills(self) -> dict[str, ParsedSkill]:
+        """All active default skills keyed by name."""
+        return dict(self._skills)
+
+    @property
+    def batch_init_nudge(self) -> str | None:
+        """Nudge text to prepend to system prompt when batch input detected (DS-12).
+
+        Returns None if ``hive.batch-ledger`` is disabled or auto_detect_batch is False.
+        """
+        if "hive.batch-ledger" not in self._skills:
+            return None
+        overrides = self._config.get_default_overrides("hive.batch-ledger")
+        if overrides.get("auto_detect_batch") is False:
+            return None
+        return _BATCH_INIT_NUDGE
+
+    @property
+    def context_warn_ratio(self) -> float | None:
+        """Token usage ratio at which to inject a context preservation warning (DS-13).
+
+        Returns None if ``hive.context-preservation`` is disabled.
+        Defaults to 0.45 when the skill is active but no override is set.
+        """
+        if "hive.context-preservation" not in self._skills:
+            return None
+        overrides = self._config.get_default_overrides("hive.context-preservation")
+        return float(overrides.get("warn_at_usage_ratio", 0.45))

core/framework/skills/discovery.py

@@ -0,0 +1,186 @@
+"""Skill discovery — scan standard directories for SKILL.md files.
+
+Implements the Agent Skills standard discovery paths plus Hive-specific
+locations. Resolves name collisions deterministically.
+"""
+
+from __future__ import annotations
+
+import logging
+from dataclasses import dataclass
+from pathlib import Path
+
+from framework.skills.parser import ParsedSkill, parse_skill_md
+from framework.skills.skill_errors import SkillErrorCode, log_skill_error
+
+logger = logging.getLogger(__name__)
+
+# Directories to skip during scanning
+_SKIP_DIRS = frozenset(
+    {
+        ".git",
+        "node_modules",
+        "__pycache__",
+        ".venv",
+        "venv",
+        ".mypy_cache",
+        ".pytest_cache",
+        ".ruff_cache",
+    }
+)
+
+# Scope priority (higher = takes precedence)
+_SCOPE_PRIORITY = {
+    "framework": 0,
+    "user": 1,
+    "project": 2,
+}
+
+# Within the same scope, Hive-specific paths override cross-client paths.
+# We encode this by scanning cross-client first, then Hive-specific (later wins).
+
+
+@dataclass
+class DiscoveryConfig:
+    """Configuration for skill discovery."""
+
+    project_root: Path | None = None
+    skip_user_scope: bool = False
+    skip_framework_scope: bool = False
+    max_depth: int = 4
+    max_dirs: int = 2000
+
+
+class SkillDiscovery:
+    """Scans standard directories for SKILL.md files and resolves collisions."""
+
+    def __init__(self, config: DiscoveryConfig | None = None):
+        self._config = config or DiscoveryConfig()
+
+    def discover(self) -> list[ParsedSkill]:
+        """Scan all scopes and return deduplicated skill list.
+
+        Scanning order (lowest to highest precedence):
+        1. Framework defaults
+        2. User cross-client (~/.agents/skills/)
+        3. User Hive-specific (~/.hive/skills/)
+        4. Project cross-client (<project>/.agents/skills/)
+        5. Project Hive-specific (<project>/.hive/skills/)
+
+        Later entries override earlier ones on name collision.
+        """
+        all_skills: list[ParsedSkill] = []
+
+        # Framework scope (lowest precedence)
+        if not self._config.skip_framework_scope:
+            framework_dir = Path(__file__).parent / "_default_skills"
+            if framework_dir.is_dir():
+                all_skills.extend(self._scan_scope(framework_dir, "framework"))
+
+        # User scope
+        if not self._config.skip_user_scope:
+            home = Path.home()
+
+            # Cross-client (lower precedence within user scope)
+            user_agents = home / ".agents" / "skills"
+            if user_agents.is_dir():
+                all_skills.extend(self._scan_scope(user_agents, "user"))
+
+            # Hive-specific (higher precedence within user scope)
+            user_hive = home / ".hive" / "skills"
+            if user_hive.is_dir():
+                all_skills.extend(self._scan_scope(user_hive, "user"))
+
+        # Project scope (highest precedence)
+        if self._config.project_root:
+            root = self._config.project_root
+
+            # Cross-client
+            project_agents = root / ".agents" / "skills"
+            if project_agents.is_dir():
+                all_skills.extend(self._scan_scope(project_agents, "project"))
+
+            # Hive-specific
+            project_hive = root / ".hive" / "skills"
+            if project_hive.is_dir():
+                all_skills.extend(self._scan_scope(project_hive, "project"))
+
+        resolved = self._resolve_collisions(all_skills)
+
+        logger.info(
+            "Skill discovery: found %d skills (%d after dedup) across all scopes",
+            len(all_skills),
+            len(resolved),
+        )
+        return resolved
+
+    def _scan_scope(self, root: Path, scope: str) -> list[ParsedSkill]:
+        """Scan a single directory for skill directories containing SKILL.md."""
+        skills: list[ParsedSkill] = []
+        dirs_scanned = 0
+
+        for skill_md in self._find_skill_files(root, depth=0):
+            if dirs_scanned >= self._config.max_dirs:
+                logger.warning(
+                    "Hit max directory limit (%d) scanning %s",
+                    self._config.max_dirs,
+                    root,
+                )
+                break
+
+            parsed = parse_skill_md(skill_md, source_scope=scope)
+            if parsed is not None:
+                skills.append(parsed)
+            dirs_scanned += 1
+
+        return skills
+
+    def _find_skill_files(self, directory: Path, depth: int) -> list[Path]:
+        """Recursively find SKILL.md files up to max_depth."""
+        if depth > self._config.max_depth:
+            return []
+
+        results: list[Path] = []
+
+        try:
+            entries = sorted(directory.iterdir())
+        except OSError:
+            return []
+
+        for entry in entries:
+            if not entry.is_dir():
+                continue
+            if entry.name in _SKIP_DIRS:
+                continue
+
+            skill_md = entry / "SKILL.md"
+            if skill_md.is_file():
+                results.append(skill_md)
+            else:
+                # Recurse into subdirectories
+                results.extend(self._find_skill_files(entry, depth + 1))
+
+        return results
+
+    def _resolve_collisions(self, skills: list[ParsedSkill]) -> list[ParsedSkill]:
+        """Resolve name collisions deterministically.
+
+        Later entries in the list override earlier ones (because we scan
+        from lowest to highest precedence). On collision, log a warning.
+        """
+        seen: dict[str, ParsedSkill] = {}
+
+        for skill in skills:
+            if skill.name in seen:
+                existing = seen[skill.name]
+                log_skill_error(
+                    logger,
+                    "warning",
+                    SkillErrorCode.SKILL_COLLISION,
+                    what=f"Skill name collision: '{skill.name}'",
+                    why=f"'{skill.location}' overrides '{existing.location}'.",
+                    fix="Rename one of the conflicting skill directories to use a unique name.",
+                )
+            seen[skill.name] = skill
+
+        return list(seen.values())

core/framework/skills/installer.py

@@ -0,0 +1,348 @@
+"""Skill install, remove, and fork operations.
+
+Handles filesystem operations for the hive skill CLI:
+  - install_from_git:      git clone --depth=1 → copy to target directory
+  - install_from_registry: resolve registry entry → delegate to install_from_git
+  - remove_skill:          delete a skill from ~/.hive/skills/
+  - fork_skill:            copy a skill to a new location with a new name
+  - maybe_show_install_notice: one-time security notice on first install (NFR-5)
+"""
+
+from __future__ import annotations
+
+import shutil
+import subprocess
+import tempfile
+from pathlib import Path
+
+from framework.skills.parser import ParsedSkill
+from framework.skills.skill_errors import SkillError, SkillErrorCode
+
+# Default install destination for user-scope skills
+USER_SKILLS_DIR = Path.home() / ".hive" / "skills"
+
+# Sentinel file for the one-time security notice on first install (NFR-5)
+INSTALL_NOTICE_SENTINEL = Path.home() / ".hive" / ".install_notice_shown"
+
+_INSTALL_NOTICE = """\
+─────────────────────────────────────────────────────────────
+  Security Notice: Installing Third-Party Skills
+─────────────────────────────────────────────────────────────
+  Skills are instructions executed by AI agents. A malicious
+  skill can manipulate agent behavior, exfiltrate data, or
+  cause unintended actions.
+
+  Only install skills from sources you trust. Review the
+  SKILL.md before running it in a production environment.
+
+  This notice is shown once. Use 'hive skill doctor' to audit
+  installed skills at any time.
+─────────────────────────────────────────────────────────────
+"""
+
+
+def maybe_show_install_notice() -> None:
+    """Print a one-time security notice before the first skill install (NFR-5).
+
+    Touches a sentinel file in ~/.hive/ after showing the notice so it is
+    only displayed once across all future installs.
+    """
+    if INSTALL_NOTICE_SENTINEL.exists():
+        return
+    print(_INSTALL_NOTICE, flush=True)
+    try:
+        INSTALL_NOTICE_SENTINEL.parent.mkdir(parents=True, exist_ok=True)
+        INSTALL_NOTICE_SENTINEL.touch()
+    except OSError:
+        pass  # If we can't write the sentinel, just show the notice every time
+
+
+def install_from_git(
+    git_url: str,
+    skill_name: str,
+    subdirectory: str | None = None,
+    version: str | None = None,
+    target_dir: Path | None = None,
+) -> Path:
+    """Install a skill from a git repository.
+
+    Clones the repository with --depth=1 into a temporary directory, then
+    copies the skill subdirectory (or repo root) to the target location.
+
+    Args:
+        git_url:     Git repository URL to clone.
+        skill_name:  Name of the skill — used as the install directory name.
+        subdirectory: Relative path within the repo to the skill directory.
+                     If None, the repo root is treated as the skill directory.
+        version:     Git ref to checkout (tag, branch, or commit). Defaults to
+                     the remote's default branch.
+        target_dir:  Where to install the skill. Defaults to
+                     ~/.hive/skills/<skill_name>/.
+
+    Returns:
+        Path to the installed skill directory (the parent of SKILL.md).
+
+    Raises:
+        SkillError: On any failure (git not found, clone failed, SKILL.md missing).
+    """
+    if shutil.which("git") is None:
+        raise SkillError(
+            code=SkillErrorCode.SKILL_ACTIVATION_FAILED,
+            what=f"Cannot install '{skill_name}' from {git_url}",
+            why="git is not installed or not on PATH.",
+            fix="Install git (https://git-scm.com/) and retry.",
+        )
+
+    dest = (target_dir or USER_SKILLS_DIR) / skill_name
+    if dest.exists():
+        raise SkillError(
+            code=SkillErrorCode.SKILL_ACTIVATION_FAILED,
+            what=f"Cannot install '{skill_name}'",
+            why=f"Directory already exists: {dest}",
+            fix=f"Run 'hive skill remove {skill_name}' first, or use a different --name.",
+        )
+
+    tmp_dir = tempfile.mkdtemp(prefix="hive-skill-install-")
+    try:
+        _git_clone_shallow(git_url, Path(tmp_dir), version=version)
+
+        # Locate the skill within the cloned repo
+        source_dir = Path(tmp_dir) / subdirectory if subdirectory else Path(tmp_dir)
+        skill_md = source_dir / "SKILL.md"
+        if not skill_md.exists():
+            raise SkillError(
+                code=SkillErrorCode.SKILL_NOT_FOUND,
+                what=f"No SKILL.md found in '{subdirectory or '/'}' of {git_url}",
+                why="The expected SKILL.md file is not present at the given path.",
+                fix=(
+                    "Check the repository structure and use "
+                    "'hive skill install --from <url>' with the correct subdirectory."
+                ),
+            )
+
+        dest.parent.mkdir(parents=True, exist_ok=True)
+        _copy_skill_dir(source_dir, dest)
+        return dest
+
+    except SkillError:
+        raise
+    except Exception as exc:
+        raise SkillError(
+            code=SkillErrorCode.SKILL_ACTIVATION_FAILED,
+            what=f"Failed to install '{skill_name}' from {git_url}",
+            why=str(exc),
+            fix="Check the URL, your network connection, and git configuration.",
+        ) from exc
+    finally:
+        shutil.rmtree(tmp_dir, ignore_errors=True)
+
+
+def install_from_registry(
+    registry_entry: dict,
+    target_dir: Path | None = None,
+    version: str | None = None,
+) -> Path:
+    """Install a skill using a registry index entry.
+
+    Resolves the git_url and subdirectory from the registry entry and
+    delegates to install_from_git.
+
+    Args:
+        registry_entry: A skill entry dict from skill_index.json.
+        target_dir:     Override install destination.
+        version:        Override version (defaults to entry's 'version' field).
+
+    Returns:
+        Path to the installed skill directory.
+
+    Raises:
+        SkillError: If the registry entry is missing required fields or install fails.
+    """
+    name = registry_entry.get("name")
+    git_url = registry_entry.get("git_url")
+
+    if not name or not git_url:
+        raise SkillError(
+            code=SkillErrorCode.SKILL_NOT_FOUND,
+            what="Incomplete registry entry — missing 'name' or 'git_url'.",
+            why="The registry index entry does not contain all required fields.",
+            fix="Report this issue to the registry maintainer.",
+        )
+
+    resolved_version = version or registry_entry.get("version")
+    subdirectory = registry_entry.get("subdirectory")
+
+    return install_from_git(
+        git_url=git_url,
+        skill_name=str(name),
+        subdirectory=subdirectory,
+        version=resolved_version,
+        target_dir=target_dir,
+    )
+
+
+def remove_skill(name: str, skills_dir: Path | None = None) -> bool:
+    """Remove an installed skill from the user skills directory.
+
+    Args:
+        name:       Skill directory name to remove.
+        skills_dir: Override the search directory (default: ~/.hive/skills/).
+
+    Returns:
+        True if removed, False if not found.
+
+    Raises:
+        SkillError: If the directory exists but cannot be removed.
+    """
+    target = (skills_dir or USER_SKILLS_DIR) / name
+    if not target.exists():
+        return False
+    try:
+        shutil.rmtree(target)
+        return True
+    except OSError as exc:
+        raise SkillError(
+            code=SkillErrorCode.SKILL_ACTIVATION_FAILED,
+            what=f"Failed to remove skill '{name}' at {target}",
+            why=str(exc),
+            fix="Check file permissions and try again.",
+        ) from exc
+
+
+def fork_skill(
+    source: ParsedSkill,
+    new_name: str,
+    target_dir: Path,
+) -> Path:
+    """Create a local editable copy of a skill with a new name.
+
+    Copies the skill's base directory to target_dir/new_name/ and rewrites
+    the 'name' field in the copied SKILL.md frontmatter.
+
+    Args:
+        source:     The source skill to fork (from SkillDiscovery).
+        new_name:   Name for the forked skill.
+        target_dir: Parent directory for the fork (e.g. ~/.hive/skills/).
+
+    Returns:
+        Path to the forked skill directory.
+
+    Raises:
+        SkillError: If the target already exists or the copy fails.
+    """
+    dest = target_dir / new_name
+    if dest.exists():
+        raise SkillError(
+            code=SkillErrorCode.SKILL_ACTIVATION_FAILED,
+            what=f"Cannot fork to '{dest}'",
+            why="Target directory already exists.",
+            fix=f"Choose a different --name or remove '{dest}' first.",
+        )
+
+    source_dir = Path(source.base_dir)
+    try:
+        dest.parent.mkdir(parents=True, exist_ok=True)
+        _copy_skill_dir(source_dir, dest)
+    except OSError as exc:
+        raise SkillError(
+            code=SkillErrorCode.SKILL_ACTIVATION_FAILED,
+            what=f"Failed to fork skill '{source.name}' to '{dest}'",
+            why=str(exc),
+            fix="Check file permissions and available disk space.",
+        ) from exc
+
+    # Rewrite the name in the forked SKILL.md via YAML round-trip (safe)
+    forked_skill_md = dest / "SKILL.md"
+    if forked_skill_md.exists():
+        _rewrite_name_in_skill_md(forked_skill_md, new_name)
+
+    return dest
+
+
+# ---------------------------------------------------------------------------
+# Internal helpers
+# ---------------------------------------------------------------------------
+
+
+def _git_clone_shallow(git_url: str, target: Path, version: str | None = None) -> None:
+    """Clone a git repo at --depth=1 into target directory.
+
+    Args:
+        git_url: Repository URL.
+        target:  Destination directory (will be created by git).
+        version: Optional git ref (branch/tag) to clone.
+
+    Raises:
+        SkillError: If the clone fails.
+    """
+    cmd = ["git", "clone", "--depth=1"]
+    if version:
+        cmd += ["--branch", version]
+    cmd += [git_url, str(target)]
+
+    try:
+        result = subprocess.run(
+            cmd,
+            capture_output=True,
+            text=True,
+            timeout=60,
+        )
+    except subprocess.TimeoutExpired:
+        raise SkillError(
+            code=SkillErrorCode.SKILL_ACTIVATION_FAILED,
+            what=f"git clone timed out for {git_url}",
+            why="The clone operation took longer than 60 seconds.",
+            fix="Check your network connection and retry.",
+        ) from None
+    except (FileNotFoundError, OSError) as exc:
+        raise SkillError(
+            code=SkillErrorCode.SKILL_ACTIVATION_FAILED,
+            what=f"Cannot run git for {git_url}",
+            why=str(exc),
+            fix="Ensure git is installed and on PATH.",
+        ) from exc
+
+    if result.returncode != 0:
+        stderr = result.stderr.strip()
+        raise SkillError(
+            code=SkillErrorCode.SKILL_ACTIVATION_FAILED,
+            what=f"git clone failed for {git_url}",
+            why=stderr or f"git exited with code {result.returncode}",
+            fix="Check the URL is correct and the repository is publicly accessible.",
+        )
+
+
+def _copy_skill_dir(src: Path, dst: Path) -> None:
+    """Copy a skill directory, ignoring VCS and cache artifacts."""
+    ignore = shutil.ignore_patterns(".git", "__pycache__", "*.pyc", ".venv", "venv", "node_modules")
+    shutil.copytree(src, dst, ignore=ignore)
+
+
+def _rewrite_name_in_skill_md(skill_md: Path, new_name: str) -> None:
+    """Rewrite the 'name' field in a SKILL.md frontmatter via YAML round-trip.
+
+    Parses the frontmatter with yaml.safe_load, updates 'name', re-serializes
+    with yaml.dump, and reconstructs the file as:
+        ---
+        <yaml>
+        ---
+        <body>
+
+    Falls back to no-op if the file can't be parsed (the copy is still usable).
+    """
+    import yaml
+
+    try:
+        content = skill_md.read_text(encoding="utf-8")
+        parts = content.split("---", 2)
+        if len(parts) < 3:
+            return
+        frontmatter = yaml.safe_load(parts[1].strip())
+        if not isinstance(frontmatter, dict):
+            return
+        frontmatter["name"] = new_name
+        new_yaml = yaml.dump(frontmatter, default_flow_style=False, allow_unicode=True)
+        new_content = f"---\n{new_yaml}---\n{parts[2]}"
+        skill_md.write_text(new_content, encoding="utf-8")
+    except Exception:
+        pass  # Degraded: forked copy works, name just isn't updated

core/framework/skills/manager.py

@@ -0,0 +1,211 @@
+"""Unified skill lifecycle manager.
+
+``SkillsManager`` is the single facade that owns skill discovery, loading,
+and prompt renderation.  The runtime creates one at startup and downstream
+layers read the cached prompt strings.
+
+Typical usage — **config-driven** (runner passes configuration)::
+
+    config = SkillsManagerConfig(
+        skills_config=SkillsConfig.from_agent_vars(...),
+        project_root=agent_path,
+    )
+    mgr = SkillsManager(config)
+    mgr.load()
+    print(mgr.protocols_prompt)       # default skill protocols
+    print(mgr.skills_catalog_prompt)  # community skills XML
+
+Typical usage — **bare** (exported agents, SDK users)::
+
+    mgr = SkillsManager()   # default config
+    mgr.load()               # loads all 6 default skills, no community discovery
+"""
+
+from __future__ import annotations
+
+import logging
+from dataclasses import dataclass, field
+from pathlib import Path
+
+from framework.skills.config import SkillsConfig
+
+logger = logging.getLogger(__name__)
+
+
+@dataclass
+class SkillsManagerConfig:
+    """Everything the runtime needs to configure skills.
+
+    Attributes:
+        skills_config: Per-skill enable/disable and overrides.
+        project_root: Agent directory for community skill discovery.
+            When ``None``, community discovery is skipped.
+        skip_community_discovery: Explicitly skip community scanning
+            even when ``project_root`` is set.
+        interactive: Whether trust gating can prompt the user interactively.
+            When ``False``, untrusted project skills are silently skipped.
+    """
+
+    skills_config: SkillsConfig = field(default_factory=SkillsConfig)
+    project_root: Path | None = None
+    skip_community_discovery: bool = False
+    interactive: bool = True
+
+
+class SkillsManager:
+    """Unified skill lifecycle: discovery → loading → prompt renderation.
+
+    The runtime creates one instance during init and owns it for the
+    lifetime of the process.  Downstream layers (``ExecutionStream``,
+    ``GraphExecutor``, ``NodeContext``, ``EventLoopNode``) receive the
+    cached prompt strings via property accessors.
+    """
+
+    def __init__(self, config: SkillsManagerConfig | None = None) -> None:
+        self._config = config or SkillsManagerConfig()
+        self._loaded = False
+        self._catalog_prompt: str = ""
+        self._protocols_prompt: str = ""
+        self._allowlisted_dirs: list[str] = []
+        self._default_mgr: object = None  # DefaultSkillManager, set after load()
+
+    # ------------------------------------------------------------------
+    # Factory for backwards-compat bridge
+    # ------------------------------------------------------------------
+
+    @classmethod
+    def from_precomputed(
+        cls,
+        skills_catalog_prompt: str = "",
+        protocols_prompt: str = "",
+    ) -> SkillsManager:
+        """Wrap pre-rendered prompt strings (legacy callers).
+
+        Returns a manager that skips discovery/loading and just returns
+        the provided strings.  Used by the deprecation bridge in
+        ``AgentRuntime`` when callers pass raw prompt strings.
+        """
+        mgr = cls.__new__(cls)
+        mgr._config = SkillsManagerConfig()
+        mgr._loaded = True  # skip load()
+        mgr._catalog_prompt = skills_catalog_prompt
+        mgr._protocols_prompt = protocols_prompt
+        mgr._allowlisted_dirs = []
+        mgr._default_mgr = None
+        return mgr
+
+    # ------------------------------------------------------------------
+    # Lifecycle
+    # ------------------------------------------------------------------
+
+    def load(self) -> None:
+        """Discover, load, and cache skill prompts.  Idempotent."""
+        if self._loaded:
+            return
+        self._loaded = True
+
+        try:
+            self._do_load()
+        except Exception:
+            logger.warning("Skill system init failed (non-fatal)", exc_info=True)
+
+    def _do_load(self) -> None:
+        """Internal load — may raise; caller catches."""
+        from framework.skills.catalog import SkillCatalog
+        from framework.skills.defaults import DefaultSkillManager
+        from framework.skills.discovery import DiscoveryConfig, SkillDiscovery
+
+        skills_config = self._config.skills_config
+
+        # 1. Community skill discovery (when project_root is available)
+        catalog_prompt = ""
+        if self._config.project_root is not None and not self._config.skip_community_discovery:
+            from framework.skills.trust import TrustGate
+
+            discovery = SkillDiscovery(DiscoveryConfig(project_root=self._config.project_root))
+            discovered = discovery.discover()
+
+            # Trust-gate project-scope skills (AS-13)
+            discovered = TrustGate(interactive=self._config.interactive).filter_and_gate(
+                discovered, project_dir=self._config.project_root
+            )
+
+            catalog = SkillCatalog(discovered)
+            self._allowlisted_dirs = catalog.allowlisted_dirs
+            catalog_prompt = catalog.to_prompt()
+
+            # Pre-activated community skills
+            if skills_config.skills:
+                pre_activated = catalog.build_pre_activated_prompt(skills_config.skills)
+                if pre_activated:
+                    if catalog_prompt:
+                        catalog_prompt = f"{catalog_prompt}\n\n{pre_activated}"
+                    else:
+                        catalog_prompt = pre_activated
+
+        # 2. Default skills (always loaded unless explicitly disabled)
+        default_mgr = DefaultSkillManager(config=skills_config)
+        default_mgr.load()
+        default_mgr.log_active_skills()
+        protocols_prompt = default_mgr.build_protocols_prompt()
+        self._default_mgr = default_mgr
+        # DX-3: Community skill startup summary
+        if self._config.project_root is not None and not self._config.skip_community_discovery:
+            community_count = len(catalog._skills) if catalog_prompt else 0
+            pre_activated_count = len(skills_config.skills) if skills_config.skills else 0
+            logger.info(
+                "Skills: %d community (%d catalog, %d pre-activated)",
+                community_count,
+                community_count,
+                pre_activated_count,
+            )
+
+        # 3. Cache
+        self._catalog_prompt = catalog_prompt
+        self._protocols_prompt = protocols_prompt
+
+        if protocols_prompt:
+            logger.info(
+                "Skill system ready: protocols=%d chars, catalog=%d chars",
+                len(protocols_prompt),
+                len(catalog_prompt),
+            )
+        else:
+            logger.warning("Skill system produced empty protocols_prompt")
+
+    # ------------------------------------------------------------------
+    # Prompt accessors (consumed by downstream layers)
+    # ------------------------------------------------------------------
+
+    @property
+    def skills_catalog_prompt(self) -> str:
+        """Community skills XML catalog for system prompt injection."""
+        return self._catalog_prompt
+
+    @property
+    def protocols_prompt(self) -> str:
+        """Default skill operational protocols for system prompt injection."""
+        return self._protocols_prompt
+
+    @property
+    def allowlisted_dirs(self) -> list[str]:
+        """Skill base directories for Tier 3 resource access (AS-6)."""
+        return self._allowlisted_dirs
+
+    @property
+    def batch_init_nudge(self) -> str | None:
+        """Batch init nudge text for DS-12 auto-detection, or None if disabled."""
+        if self._default_mgr is None:
+            return None
+        return self._default_mgr.batch_init_nudge  # type: ignore[union-attr]
+
+    @property
+    def context_warn_ratio(self) -> float | None:
+        """Token usage ratio for DS-13 context preservation warning, or None if disabled."""
+        if self._default_mgr is None:
+            return None
+        return self._default_mgr.context_warn_ratio  # type: ignore[union-attr]
+
+    @property
+    def is_loaded(self) -> bool:
+        return self._loaded

core/framework/skills/models.py

@@ -0,0 +1,52 @@
+"""Data models for the Hive skill system (Agent Skills standard)."""
+
+from __future__ import annotations
+
+from dataclasses import dataclass, field
+from enum import StrEnum
+from pathlib import Path
+
+
+class SkillScope(StrEnum):
+    """Where a skill was discovered."""
+
+    PROJECT = "project"
+    USER = "user"
+    FRAMEWORK = "framework"
+
+
+class TrustStatus(StrEnum):
+    """Trust state of a skill entry."""
+
+    TRUSTED = "trusted"
+    PENDING_CONSENT = "pending_consent"
+    DENIED = "denied"
+
+
+@dataclass
+class SkillEntry:
+    """In-memory record for a discovered skill (PRD §4.2)."""
+
+    name: str
+    """Skill name from SKILL.md frontmatter."""
+
+    description: str
+    """Skill description from SKILL.md frontmatter."""
+
+    location: Path
+    """Absolute path to SKILL.md."""
+
+    base_dir: Path
+    """Parent directory of SKILL.md (skill root)."""
+
+    source_scope: SkillScope
+    """Which scope this skill was found in."""
+
+    trust_status: TrustStatus = TrustStatus.TRUSTED
+    """Trust state; project-scope skills start as PENDING_CONSENT before gating."""
+
+    # Optional frontmatter fields
+    license: str | None = None
+    compatibility: list[str] = field(default_factory=list)
+    allowed_tools: list[str] = field(default_factory=list)
+    metadata: dict = field(default_factory=dict)

core/framework/skills/parser.py

@@ -0,0 +1,234 @@
+"""SKILL.md parser — extracts YAML frontmatter and markdown body.
+
+Parses SKILL.md files per the Agent Skills standard (agentskills.io/specification).
+Lenient validation: warns on non-critical issues, skips only on missing description
+or completely unparseable YAML.
+"""
+
+from __future__ import annotations
+
+import logging
+import re
+from dataclasses import dataclass
+from pathlib import Path
+from typing import Any
+
+from framework.skills.skill_errors import SkillErrorCode, log_skill_error
+
+logger = logging.getLogger(__name__)
+
+# Maximum name length before a warning is logged
+_MAX_NAME_LENGTH = 64
+
+
+@dataclass
+class ParsedSkill:
+    """In-memory representation of a parsed SKILL.md file."""
+
+    name: str
+    description: str
+    location: str  # absolute path to SKILL.md
+    base_dir: str  # parent directory of SKILL.md
+    source_scope: str  # "project", "user", or "framework"
+    body: str  # markdown body after closing ---
+
+    # Optional frontmatter fields
+    license: str | None = None
+    compatibility: list[str] | None = None
+    metadata: dict[str, Any] | None = None
+    allowed_tools: list[str] | None = None
+
+
+def _try_fix_yaml(raw: str) -> str:
+    """Attempt to fix common YAML issues (unquoted colon values).
+
+    Some SKILL.md files written for other clients may contain unquoted
+    values with colons, e.g. ``description: Use for: research tasks``.
+    This wraps such values in quotes as a best-effort fixup.
+    """
+    lines = raw.split("\n")
+    fixed = []
+    for line in lines:
+        # Match "key: value" where value contains an unquoted colon
+        m = re.match(r"^(\s*\w[\w-]*:\s*)(.+)$", line)
+        if m:
+            key_part, value_part = m.group(1), m.group(2)
+            # If value contains a colon and isn't already quoted
+            if ":" in value_part and not (value_part.startswith('"') or value_part.startswith("'")):
+                value_part = f'"{value_part}"'
+            fixed.append(f"{key_part}{value_part}")
+        else:
+            fixed.append(line)
+    return "\n".join(fixed)
+
+
+def parse_skill_md(path: Path, source_scope: str = "project") -> ParsedSkill | None:
+    """Parse a SKILL.md file into a ParsedSkill record.
+
+    Args:
+        path: Absolute path to the SKILL.md file.
+        source_scope: One of "project", "user", or "framework".
+
+    Returns:
+        ParsedSkill on success, None if the file is unparseable or
+        missing required fields (description).
+    """
+    try:
+        content = path.read_text(encoding="utf-8")
+    except OSError as exc:
+        log_skill_error(
+            logger,
+            "error",
+            SkillErrorCode.SKILL_ACTIVATION_FAILED,
+            what=f"Failed to read '{path}'",
+            why=str(exc),
+            fix="Check the file exists and has read permissions.",
+        )
+        return None
+
+    if not content.strip():
+        log_skill_error(
+            logger,
+            "error",
+            SkillErrorCode.SKILL_PARSE_ERROR,
+            what=f"Invalid SKILL.md at '{path}'",
+            why="The file exists but contains no content.",
+            fix="Add valid YAML frontmatter and a markdown body to the SKILL.md.",
+        )
+        return None
+
+    # Split on --- delimiters (first two occurrences)
+    parts = content.split("---", 2)
+    if len(parts) < 3:
+        log_skill_error(
+            logger,
+            "error",
+            SkillErrorCode.SKILL_PARSE_ERROR,
+            what=f"Invalid SKILL.md at '{path}'",
+            why="Missing YAML frontmatter (---).",
+            fix="Wrap the frontmatter with --- on its own line at the top and bottom.",
+        )
+        return None
+
+    # parts[0] is content before first --- (should be empty or whitespace)
+    # parts[1] is the YAML frontmatter
+    # parts[2] is the markdown body
+    raw_yaml = parts[1].strip()
+    body = parts[2].strip()
+
+    if not raw_yaml:
+        log_skill_error(
+            logger,
+            "error",
+            SkillErrorCode.SKILL_PARSE_ERROR,
+            what=f"Invalid SKILL.md at '{path}'",
+            why="The --- delimiters are present but the YAML block is empty.",
+            fix="Add at least 'name' and 'description' fields to the frontmatter.",
+        )
+        return None
+
+    # Parse YAML
+    import yaml
+
+    frontmatter: dict[str, Any] | None = None
+    try:
+        frontmatter = yaml.safe_load(raw_yaml)
+    except yaml.YAMLError:
+        # Fallback: try fixing unquoted colon values
+        try:
+            fixed = _try_fix_yaml(raw_yaml)
+            frontmatter = yaml.safe_load(fixed)
+            log_skill_error(
+                logger,
+                "warning",
+                SkillErrorCode.SKILL_YAML_FIXUP,
+                what=f"Auto-fixed YAML in '{path}'",
+                why="Unquoted colon values detected in frontmatter.",
+                fix='Wrap values containing colons in quotes e.g. description: "Use for: research"',
+            )
+        except yaml.YAMLError as exc:
+            log_skill_error(
+                logger,
+                "error",
+                SkillErrorCode.SKILL_PARSE_ERROR,
+                what=f"Invalid SKILL.md at '{path}'",
+                why=str(exc),
+                fix="Validate the YAML frontmatter at https://yaml-online-parser.appspot.com/",
+            )
+            return None
+
+    if not isinstance(frontmatter, dict):
+        log_skill_error(
+            logger,
+            "error",
+            SkillErrorCode.SKILL_PARSE_ERROR,
+            what=f"Invalid SKILL.md at '{path}'",
+            why="YAML frontmatter is not a key-value mapping.",
+            fix="Ensure the frontmatter is valid YAML with key: value pairs.",
+        )
+        return None
+
+    # Required: description
+    description = frontmatter.get("description")
+    if not description or not str(description).strip():
+        log_skill_error(
+            logger,
+            "error",
+            SkillErrorCode.SKILL_MISSING_DESCRIPTION,
+            what=f"Missing 'description' in '{path}'",
+            why="The 'description' field is required but is absent or empty.",
+            fix="Add a non-empty 'description' field to the YAML frontmatter.",
+        )
+        return None
+
+    # Required: name (fallback to parent directory name)
+    name = frontmatter.get("name")
+    parent_dir_name = path.parent.name
+    if not name or not str(name).strip():
+        name = parent_dir_name
+        log_skill_error(
+            logger,
+            "warning",
+            SkillErrorCode.SKILL_NAME_MISMATCH,
+            what=f"Missing 'name' in '{path}' — using directory name '{name}'",
+            why="The 'name' field is absent from the YAML frontmatter.",
+            fix=f"Add 'name: {name}' to the frontmatter to make this explicit.",
+        )
+    else:
+        name = str(name).strip()
+
+    # Lenient warnings
+    if len(name) > _MAX_NAME_LENGTH:
+        logger.warning("Skill name exceeds %d chars in %s: '%s'", _MAX_NAME_LENGTH, path, name)
+
+    if name != parent_dir_name and not name.endswith(f".{parent_dir_name}"):
+        log_skill_error(
+            logger,
+            "warning",
+            SkillErrorCode.SKILL_NAME_MISMATCH,
+            what=f"Name mismatch in '{path}'",
+            why=f"Skill name '{name}' doesn't match directory '{parent_dir_name}'.",
+            fix=f"Rename the directory to '{name}' or set name to '{parent_dir_name}'.",
+        )
+
+    # Coerce compatibility / allowed-tools to list[str] — many SKILL.md files
+    # in the wild use a plain string instead of a YAML list.
+    raw_compat = frontmatter.get("compatibility")
+    if isinstance(raw_compat, str):
+        raw_compat = [raw_compat]
+    raw_tools = frontmatter.get("allowed-tools")
+    if isinstance(raw_tools, str):
+        raw_tools = [raw_tools]
+
+    return ParsedSkill(
+        name=name,
+        description=str(description).strip(),
+        location=str(path.resolve()),
+        base_dir=str(path.parent.resolve()),
+        source_scope=source_scope,
+        body=body,
+        license=frontmatter.get("license"),
+        compatibility=raw_compat,
+        metadata=frontmatter.get("metadata"),
+        allowed_tools=raw_tools,
+    )