chore: ruff lint

refactor: remove adapt md and its reference
Merge pull request #6658 from saurabhiiitm062/feat/cloudflare-dns-tool
2026-04-01 11:05:53 -07:00 · 2026-04-01 11:00:41 -07:00 · 2026-04-01 10:11:44 -07:00 · 2026-04-01 19:38:41 +08:00 · 2026-04-01 19:37:18 +08:00 · 2026-04-01 19:10:04 +08:00
553 changed files with 76834 additions and 34506 deletions
@@ -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://...
@@ -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 }}
@@ -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.`
-            });
@@ -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 || '' }}
@@ -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/*
@@ -2,10 +2,6 @@

 Shared agent instructions for this workspace.

-## Deprecations
-
- **TUI is deprecated.** The terminal UI (`hive tui`) is no longer maintained. Use the browser-based interface (`hive open`) instead.
-
 ## Coding Agent Notes

 - 
@@ -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
@@ -1,24 +1,31 @@
-.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_-]+:.*?## .*$$' $(MAKEFILE_LIST) | \
 		awk 'BEGIN {FS = ":.*?## "}; {printf "  \033[36m%-15s\033[0m %s\n", $$1, $$2}'

 lint: ## Run ruff linter and formatter (with auto-fix)
-	cd core && ruff check --fix .
-	cd tools && ruff check --fix .
-	cd core && ruff format .
-	cd tools && ruff format .
+	cd core && uv run ruff check --fix .
+	cd tools && uv run ruff check --fix .
+	cd core && uv run ruff format .
+	cd tools && uv run ruff format .

 format: ## Run ruff formatter
-	cd core && ruff format .
-	cd tools && ruff format .
+	cd core && uv run ruff format .
+	cd tools && uv run ruff format .

 check: ## Run all checks without modifying files (CI-safe)
-	cd core && ruff check .
-	cd tools && ruff check .
-	cd core && ruff format --check .
-	cd tools && ruff format --check .
+	cd core && uv run ruff check .
+	cd tools && uv run ruff check .
+	cd core && uv run ruff format --check .
+	cd tools && uv run ruff format --check .

 test: ## Run all tests (core + tools, excludes live)
 	cd core && uv run python -m pytest tests/ -v
@@ -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
@@ -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:
@@ -108,54 +116,51 @@ 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`

- At last, it will initiate the open hive interface in your browser
+- 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" />

 ### Use Template Agents

-Click "Try a sample agent" and check the templates. You can run a templates directly or choose to build your version on top of the existing template.
+Click "Try a sample agent" and check the templates. You can run a template directly or choose to build your version on top of the existing template.

 ### Run Agents

-Now you can run an agent by selectiing 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.
+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

 - **Browser-Use** - Control the browser on your computer to achieve hard tasks
- **Parallel Execution** - Execute the generated graph in parallel. This way you can have multiple agent compelteing the jobs for you
+- **Parallel Execution** - Execute the generated graph in parallel. This way you can have multiple agents completing the jobs for you
 - **[Goal-Driven Generation](docs/key_concepts/goals_outcome.md)** - Define objectives in natural language; the coding agent generates the agent graph and connection code to achieve them
 - **[Adaptiveness](docs/key_concepts/evolution.md)** - Framework captures failures, calibrates according to the objectives, and evolves the agent graph
 - **[Dynamic Node Connections](docs/key_concepts/graph.md)** - No predefined edges; connection code is generated by any capable LLM based on your goals
 - **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">
@@ -39,8 +39,8 @@ We consider security research conducted in accordance with this policy to be:
 ## Security Best Practices for Users

 1. **Keep Updated**: Always run the latest version
-2. **Secure Configuration**: Review `config.yaml` settings, especially in production
-3. **Environment Variables**: Never commit `.env` files or `config.yaml` with secrets
+2. **Secure Configuration**: Review your `~/.hive/configuration.json`, `.mcp.json`, and environment variable settings, especially in production
+3. **Environment Variables**: Never commit `.env` files or any configuration files that contain secrets
 4. **Network Security**: Use HTTPS in production, configure firewalls appropriately
 5. **Database Security**: Use strong passwords, limit network access

@@ -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
@@ -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
@@ -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:
@@ -1,6 +1,6 @@
 # MCP Server Guide - Agent Building Tools

-> **Note:** The standalone `agent-builder` MCP server (`framework.mcp.agent_builder_server`) has been replaced. Agent building is now done via the `coder-tools` server's `initialize_agent_package` tool, with underlying logic in `framework.builder.package_generator`.
+> **Note:** The standalone `agent-builder` MCP server (`framework.mcp.agent_builder_server`) has been replaced. Agent building is now done via the `coder-tools` server's `initialize_and_build_agent` tool, with underlying logic in `tools/coder_tools_server.py`.

 This guide covers the MCP tools available for building goal-driven agents.

@@ -19,7 +19,7 @@ uv pip install -e .

 ## Agent Building

-Agent scaffolding is handled by the `coder-tools` MCP server (in `tools/coder_tools_server.py`), which provides the `initialize_agent_package` tool and related utilities. The underlying package generation logic lives in `framework.builder.package_generator`.
+Agent scaffolding is handled by the `coder-tools` MCP server (in `tools/coder_tools_server.py`), which provides the `initialize_and_build_agent` tool and related utilities. The package generation logic lives directly in `tools/coder_tools_server.py`.

 See the [Getting Started Guide](../docs/getting-started.md) for building agents.

@@ -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())
@@ -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:
@@ -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_history_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())
@@ -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_history_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_history_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())
@@ -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)
@@ -22,7 +22,6 @@ The framework includes a Goal-Based Testing system (Goal → Agent → Eval):
 See `framework.testing` for details.
 """

-from framework.builder.query import BuilderQuery
 from framework.llm import AnthropicProvider, LLMProvider
 from framework.runner import AgentOrchestrator, AgentRunner
 from framework.runtime.core import Runtime
@@ -51,8 +50,6 @@ __all__ = [
    "Problem",
    # Runtime
    "Runtime",
-    # Builder
-    "BuilderQuery",
    # LLM
    "LLMProvider",
    "AnthropicProvider",
@@ -1,8 +1,6 @@
 """CLI entry point for Credential Tester agent."""

 import asyncio
-import logging
-import sys

 import click

@@ -10,13 +8,14 @@ from .agent import CredentialTesterAgent


 def setup_logging(verbose=False, debug=False):
+    from framework.observability import configure_logging
+
    if debug:
-        level, fmt = logging.DEBUG, "%(asctime)s %(name)s: %(message)s"
+        configure_logging(level="DEBUG")
    elif verbose:
-        level, fmt = logging.INFO, "%(message)s"
+        configure_logging(level="INFO")
    else:
-        level, fmt = logging.WARNING, "%(levelname)s: %(message)s"
-    logging.basicConfig(level=level, format=fmt, stream=sys.stderr)
+        configure_logging(level="WARNING")


 def pick_account(agent: CredentialTesterAgent) -> dict | None:
@@ -51,42 +50,6 @@ def cli():
    pass


-@cli.command()
-@click.option("--verbose", "-v", is_flag=True)
-@click.option("--debug", is_flag=True)
-def tui(verbose, debug):
-    """Launch TUI to test a credential interactively."""
-    setup_logging(verbose=verbose, debug=debug)
-
-    try:
-        from framework.tui.app import AdenTUI
-    except ImportError:
-        click.echo("TUI requires 'textual'. Install with: pip install textual")
-        sys.exit(1)
-
-    agent = CredentialTesterAgent()
-    account = pick_account(agent)
-    if account is None:
-        sys.exit(1)
-
-    agent.select_account(account)
-    provider = account.get("provider", "?")
-    alias = account.get("alias", "?")
-    click.echo(f"\nTesting {provider}/{alias}...\n")
-
-    async def run_tui():
-        agent._setup()
-        runtime = agent._agent_runtime
-        await runtime.start()
-        try:
-            app = AdenTUI(runtime)
-            await app.run_async()
-        finally:
-            await runtime.stop()
-
-    asyncio.run(run_tui())
-
-
@cli.command()
@click.option("--verbose", "-v", is_flag=True)
@click.option("--debug", is_flag=True)
@@ -16,14 +16,17 @@ after the user picks an account programmatically.

 from __future__ import annotations

+import logging
 from pathlib import Path
 from typing import TYPE_CHECKING

+from framework.config import get_max_context_tokens
 from framework.graph import Goal, NodeSpec, SuccessCriterion
 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
@@ -31,9 +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
 # ---------------------------------------------------------------------------
@@ -106,7 +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 []


@@ -118,7 +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 []


@@ -139,7 +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:
@@ -299,8 +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(
@@ -455,7 +476,6 @@ identity_prompt = (
 loop_config = {
    "max_iterations": 50,
    "max_tool_calls_per_turn": 30,
-    "max_history_tokens": 32000,
 }

 # ---------------------------------------------------------------------------
@@ -541,7 +561,7 @@ class CredentialTesterAgent:
            loop_config={
                "max_iterations": 50,
                "max_tool_calls_per_turn": 30,
-                "max_history_tokens": 32000,
+                "max_context_tokens": get_max_context_tokens(),
            },
            conversation_mode="continuous",
            identity_prompt=(
@@ -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,
@@ -0,0 +1,209 @@
+"""Agent discovery — scan known directories and return categorised AgentEntry lists."""
+
+from __future__ import annotations
+
+import json
+from dataclasses import dataclass, field
+from pathlib import Path
+
+
+@dataclass
+class AgentEntry:
+    """Lightweight agent metadata for the picker / API discover endpoint."""
+
+    path: Path
+    name: str
+    description: str
+    category: str
+    session_count: int = 0
+    run_count: int = 0
+    node_count: int = 0
+    tool_count: int = 0
+    tags: list[str] = field(default_factory=list)
+    last_active: str | None = 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
+
+    # 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
+
+
+def _count_sessions(agent_name: str) -> int:
+    """Count session directories under ~/.hive/agents/{agent_name}/sessions/."""
+    sessions_dir = Path.home() / ".hive" / "agents" / agent_name / "sessions"
+    if not sessions_dir.exists():
+        return 0
+    return sum(1 for d in sessions_dir.iterdir() if d.is_dir() and d.name.startswith("session_"))
+
+
+def _count_runs(agent_name: str) -> int:
+    """Count unique run_ids across all sessions for an agent."""
+    sessions_dir = Path.home() / ".hive" / "agents" / agent_name / "sessions"
+    if not sessions_dir.exists():
+        return 0
+    run_ids: set[str] = set()
+    for session_dir in sessions_dir.iterdir():
+        if not session_dir.is_dir() or not session_dir.name.startswith("session_"):
+            continue
+        # runs.jsonl lives inside workspace subdirectories
+        for runs_file in session_dir.rglob("runs.jsonl"):
+            try:
+                for line in runs_file.read_text(encoding="utf-8").splitlines():
+                    line = line.strip()
+                    if not line:
+                        continue
+                    record = json.loads(line)
+                    rid = record.get("run_id")
+                    if rid:
+                        run_ids.add(rid)
+            except Exception:
+                continue
+    return len(run_ids)
+
+
+def _extract_agent_stats(agent_path: Path) -> tuple[int, int, list[str]]:
+    """Extract node count, tool count, and tags from an agent directory.
+
+    Prefers agent.py (AST-parsed) over agent.json for node/tool counts
+    since agent.json may be stale.  Tags are only available from agent.json.
+    """
+    import ast
+
+    node_count, tool_count, tags = 0, 0, []
+
+    agent_py = agent_path / "agent.py"
+    if agent_py.exists():
+        try:
+            tree = ast.parse(agent_py.read_text(encoding="utf-8"))
+            for node in ast.walk(tree):
+                if isinstance(node, ast.Assign):
+                    for target in node.targets:
+                        if isinstance(target, ast.Name) and target.id == "nodes":
+                            if isinstance(node.value, ast.List):
+                                node_count = len(node.value.elts)
+        except Exception:
+            pass
+
+    agent_json = agent_path / "agent.json"
+    if agent_json.exists():
+        try:
+            data = json.loads(agent_json.read_text(encoding="utf-8"))
+            json_nodes = data.get("graph", {}).get("nodes", []) or data.get("nodes", [])
+            if node_count == 0:
+                node_count = len(json_nodes)
+            tools: set[str] = set()
+            for n in json_nodes:
+                tools.update(n.get("tools", []))
+            tool_count = len(tools)
+            tags = data.get("agent", {}).get("tags", [])
+        except Exception:
+            pass
+
+    return node_count, tool_count, tags
+
+
+def discover_agents() -> dict[str, list[AgentEntry]]:
+    """Discover agents from all known sources grouped by category."""
+    from framework.runner.cli import (
+        _extract_python_agent_metadata,
+        _get_framework_agents_dir,
+        _is_valid_agent_dir,
+    )
+
+    groups: dict[str, list[AgentEntry]] = {}
+    sources = [
+        ("Your Agents", Path("exports")),
+        ("Framework", _get_framework_agents_dir()),
+        ("Examples", Path("examples/templates")),
+    ]
+
+    for category, base_dir in sources:
+        if not base_dir.exists():
+            continue
+        entries: list[AgentEntry] = []
+        for path in sorted(base_dir.iterdir(), key=lambda p: p.name):
+            if not _is_valid_agent_dir(path):
+                continue
+
+            name, desc = _extract_python_agent_metadata(path)
+            config_fallback_name = path.name.replace("_", " ").title()
+            used_config = name != config_fallback_name
+
+            node_count, tool_count, tags = _extract_agent_stats(path)
+            if not used_config:
+                agent_json = path / "agent.json"
+                if agent_json.exists():
+                    try:
+                        data = json.loads(agent_json.read_text(encoding="utf-8"))
+                        meta = data.get("agent", {})
+                        name = meta.get("name", name)
+                        desc = meta.get("description", desc)
+                    except Exception:
+                        pass
+
+            entries.append(
+                AgentEntry(
+                    path=path,
+                    name=name,
+                    description=desc,
+                    category=category,
+                    session_count=_count_sessions(path.name),
+                    run_count=_count_runs(path.name),
+                    node_count=node_count,
+                    tool_count=tool_count,
+                    tags=tags,
+                    last_active=_get_last_active(path),
+                )
+            )
+        if entries:
+            groups[category] = entries
+
+    return groups
@@ -1,60 +0,0 @@
-"""CLI entry point for Hive Coder agent."""
-
-import json
-import logging
-import sys
-
-import click
-
-from .agent import entry_node, goal, nodes
-from .config import metadata
-
-
-def setup_logging(verbose=False, debug=False):
-    """Configure logging for execution visibility."""
-    if debug:
-        level, fmt = logging.DEBUG, "%(asctime)s %(name)s: %(message)s"
-    elif verbose:
-        level, fmt = logging.INFO, "%(message)s"
-    else:
-        level, fmt = logging.WARNING, "%(levelname)s: %(message)s"
-    logging.basicConfig(level=level, format=fmt, stream=sys.stderr)
-    logging.getLogger("framework").setLevel(level)
-
-
-@click.group()
-@click.version_option(version="1.0.0")
-def cli():
-    """Hive Coder — Build Hive agent packages from natural language."""
-    pass
-
-
-@cli.command()
-@click.option("--json", "output_json", is_flag=True)
-def info(output_json):
-    """Show agent information."""
-    info_data = {
-        "name": metadata.name,
-        "version": metadata.version,
-        "description": metadata.description,
-        "goal": {
-            "name": goal.name,
-            "description": goal.description,
-        },
-        "nodes": [n.id for n in nodes],
-        "entry_node": entry_node,
-        "client_facing_nodes": [n.id for n in nodes if n.client_facing],
-    }
-    if output_json:
-        click.echo(json.dumps(info_data, indent=2))
-    else:
-        click.echo(f"Agent: {info_data['name']}")
-        click.echo(f"Version: {info_data['version']}")
-        click.echo(f"Description: {info_data['description']}")
-        click.echo(f"\nNodes: {', '.join(info_data['nodes'])}")
-        click.echo(f"Client-facing: {', '.join(info_data['client_facing_nodes'])}")
-        click.echo(f"Entry: {info_data['entry_node']}")
-
-
-if __name__ == "__main__":
-    cli()
@@ -1,153 +0,0 @@
-"""Agent graph construction for Hive Coder."""
-
-from framework.graph import Constraint, Goal, SuccessCriterion
-from framework.graph.edge import GraphSpec
-
-from .nodes import coder_node, queen_node
-
-# Goal definition
-goal = Goal(
-    id="hive-coder",
-    name="Hive Agent Builder",
-    description=(
-        "Build complete, validated Hive agent packages from natural language "
-        "specifications. Produces production-ready Python packages with goals, "
-        "nodes, edges, system prompts, MCP configuration, and tests."
-    ),
-    success_criteria=[
-        SuccessCriterion(
-            id="valid-package",
-            description="Generated agent package passes structural validation",
-            metric="validation_pass",
-            target="true",
-            weight=0.30,
-        ),
-        SuccessCriterion(
-            id="complete-files",
-            description=(
-                "All required files generated: agent.py, config.py, "
-                "nodes/__init__.py, __init__.py, __main__.py, mcp_servers.json"
-            ),
-            metric="file_count",
-            target=">=6",
-            weight=0.25,
-        ),
-        SuccessCriterion(
-            id="user-satisfaction",
-            description="User reviews and approves the generated agent",
-            metric="user_approval",
-            target="true",
-            weight=0.25,
-        ),
-        SuccessCriterion(
-            id="framework-compliance",
-            description=(
-                "Generated code follows framework patterns: STEP 1/STEP 2 "
-                "for client-facing and correct imports"
-            ),
-            metric="pattern_compliance",
-            target="100%",
-            weight=0.20,
-        ),
-    ],
-    constraints=[
-        Constraint(
-            id="dynamic-tool-discovery",
-            description=(
-                "Always discover available tools dynamically via "
-                "list_agent_tools before referencing tools in agent designs"
-            ),
-            constraint_type="hard",
-            category="correctness",
-        ),
-        Constraint(
-            id="no-fabricated-tools",
-            description="Only reference tools that exist in hive-tools MCP",
-            constraint_type="hard",
-            category="correctness",
-        ),
-        Constraint(
-            id="valid-python",
-            description="All generated Python files must be syntactically correct",
-            constraint_type="hard",
-            category="correctness",
-        ),
-        Constraint(
-            id="self-verification",
-            description="Run validation after writing code; fix errors before presenting",
-            constraint_type="hard",
-            category="quality",
-        ),
-    ],
-)
-
-# Nodes: primary coder node only.  The queen runs as an independent
-# GraphExecutor with queen_node — not as part of this graph.
-nodes = [coder_node]
-
-# No edges needed — single event_loop node
-edges = []
-
-# Graph configuration
-entry_node = "coder"
-entry_points = {"start": "coder"}
-pause_nodes = []
-terminal_nodes = []  # Coder node has output_keys and can terminate
-
-# No async entry points needed — the queen is now an independent executor,
-# not a secondary graph receiving events via add_graph().
-async_entry_points = []
-
-# Module-level variables read by AgentRunner.load()
-conversation_mode = "continuous"
-identity_prompt = (
-    "You are Hive Coder, the best agent-building coding agent on the planet. "
-    "You deeply understand the Hive agent framework at the source code level "
-    "and produce production-ready agent packages from natural language. "
-    "You can dynamically discover available framework tools, inspect runtime "
-    "sessions and checkpoints from agents you build, and run their test suites. "
-    "You follow coding agent discipline: read before writing, verify "
-    "assumptions by reading actual code, adhere to project conventions, "
-    "self-verify with validation, and fix your own errors. You are concise, "
-    "direct, and technically rigorous. No emojis. No fluff."
-)
-loop_config = {
-    "max_iterations": 100,
-    "max_tool_calls_per_turn": 30,
-    "max_history_tokens": 32000,
-}
-
-
-# ---------------------------------------------------------------------------
-# Queen graph — runs as an independent persistent conversation in the TUI.
-# Loaded by _load_judge_and_queen() in app.py, NOT by AgentRunner.
-# ---------------------------------------------------------------------------
-
-queen_goal = Goal(
-    id="queen-manager",
-    name="Queen Manager",
-    description=(
-        "Manage the worker agent lifecycle and serve as the user's primary "
-        "interactive interface. Triage health escalations from the judge."
-    ),
-    success_criteria=[],
-    constraints=[],
-)
-
-queen_graph = GraphSpec(
-    id="queen-graph",
-    goal_id=queen_goal.id,
-    version="1.0.0",
-    entry_node="queen",
-    entry_points={"start": "queen"},
-    terminal_nodes=[],
-    pause_nodes=[],
-    nodes=[queen_node],
-    edges=[],
-    conversation_mode="continuous",
-    loop_config={
-        "max_iterations": 999_999,
-        "max_tool_calls_per_turn": 30,
-        "max_history_tokens": 32000,
-    },
-)
@@ -1,933 +0,0 @@
-"""Node definitions for Hive Coder agent."""
-
-from pathlib import Path
-
-from framework.graph import NodeSpec
-
-# Load reference docs at import time so they're always in the system prompt.
-# No voluntary read_file() calls needed — the LLM gets everything upfront.
-_ref_dir = Path(__file__).parent.parent / "reference"
-_framework_guide = (_ref_dir / "framework_guide.md").read_text(encoding="utf-8")
-_anti_patterns = (_ref_dir / "anti_patterns.md").read_text(encoding="utf-8")
-_gcu_guide_path = _ref_dir / "gcu_guide.md"
-_gcu_guide = _gcu_guide_path.read_text(encoding="utf-8") if _gcu_guide_path.exists() else ""
-
-
-def _is_gcu_enabled() -> bool:
-    try:
-        from framework.config import get_gcu_enabled
-
-        return get_gcu_enabled()
-    except Exception:
-        return False
-
-
-def _build_appendices() -> str:
-    parts = (
-        "\n\n# Appendix: Framework Reference\n\n"
-        + _framework_guide
-        + "\n\n# Appendix: Anti-Patterns\n\n"
-        + _anti_patterns
-    )
-    return parts
-
-
-# Shared appendices — appended to every coding node's system prompt.
-_appendices = _build_appendices()
-
-# GCU first-class section for building phase (when GCU is enabled).
-# This is placed prominently in the main prompt body, not as an appendix.
-_gcu_building_section = (
-    ("\n\n# GCU Nodes — Browser Automation\n\n" + _gcu_guide)
-    if _is_gcu_enabled() and _gcu_guide
-    else ""
-)
-
-# Tools available to both coder (worker) and queen.
-_SHARED_TOOLS = [
-    # File I/O
-    "read_file",
-    "write_file",
-    "edit_file",
-    "hashline_edit",
-    "list_directory",
-    "search_files",
-    "run_command",
-    "undo_changes",
-    # Meta-agent
-    "list_agent_tools",
-    "validate_agent_package",
-    "list_agents",
-    "list_agent_sessions",
-    "list_agent_checkpoints",
-    "get_agent_checkpoint",
-    "initialize_agent_package",
-]
-
-# Queen phase-specific tool sets.
-# Building phase: full coding + agent construction tools.
-_QUEEN_BUILDING_TOOLS = _SHARED_TOOLS + [
-    "load_built_agent",
-    "list_credentials",
-]
-
-# Staging phase: agent loaded but not yet running — inspect, configure, launch.
-_QUEEN_STAGING_TOOLS = [
-    # Read-only (inspect agent files, logs)
-    "read_file",
-    "list_directory",
-    "search_files",
-    "run_command",
-    # Agent inspection
-    "list_credentials",
-    "get_worker_status",
-    # Launch or go back
-    "run_agent_with_input",
-    "stop_worker_and_edit",
-]
-
-# Running phase: worker is executing — monitor and control.
-_QUEEN_RUNNING_TOOLS = [
-    # Read-only coding (for inspecting logs, files)
-    "read_file",
-    "list_directory",
-    "search_files",
-    "run_command",
-    # Credentials
-    "list_credentials",
-    # Worker lifecycle
-    "stop_worker",
-    "stop_worker_and_edit",
-    "get_worker_status",
-    "inject_worker_message",
-    # Monitoring
-    "get_worker_health_summary",
-    "notify_operator",
-]
-
-
-# ---------------------------------------------------------------------------
-# Shared agent-building knowledge: core mandates, tool docs, meta-agent
-# capabilities, and workflow phases 1-6.  Both the coder (worker) and
-# queen compose their system prompts from this block + role-specific
-# additions.
-# ---------------------------------------------------------------------------
-
-_package_builder_knowledge = """\
-**A responsible engineer doesn't jump into building. First, \
-understand the problem and be transparent about what the framework can and cannot do.**
-
-Use the user's selection (or their custom description if they chose "Other") \
-as context when shaping the goal below. If the user already described \
-what they want before this step, skip the question and proceed directly.
-
-# Core Mandates
- **DO NOT propose a complete goal on your own.** Instead, \
-collaborate with the user to define it.
- **Verify assumptions.** Never assume a class, import, or pattern \
-exists. Read actual source to confirm. Search if unsure.
- **Discover tools dynamically.** NEVER reference tools from static \
-docs. Always run list_agent_tools() to see what actually exists.
- **Self-verify.** After writing code, run validation and tests. Fix \
-errors yourself. Don't declare success until validation passes.
-
-# Tools
-## Paths (MANDATORY)
-**Always use RELATIVE paths**
-(e.g. `exports/agent_name/config.py`, `exports/agent_name/nodes/__init__.py`).
-**Never use absolute paths** like `/mnt/data/...` or `/workspace/...` — they fail.
-The project root is implicit.
-
-## File I/O
- read_file(path, offset?, limit?, hashline?) — read with line numbers; \
-hashline=True for N:hhhh|content anchors (use with hashline_edit)
- write_file(path, content) — create/overwrite, auto-mkdir
- edit_file(path, old_text, new_text, replace_all?) — fuzzy-match edit
- hashline_edit(path, edits, auto_cleanup?, encoding?) — anchor-based \
-editing using N:hhhh refs from read_file(hashline=True). Ops: set_line, \
-replace_lines, insert_after, insert_before, replace, append
- list_directory(path, recursive?) — list contents
- search_files(pattern, path?, include?, hashline?) — regex search; \
-hashline=True for anchors in results
- run_command(command, cwd?, timeout?) — shell execution
- undo_changes(path?) — restore from git snapshot
-
-## Meta-Agent
- list_agent_tools(server_config_path?, output_schema?, group?) — discover \
-available tools grouped by category. output_schema: "simple" (default, \
-descriptions truncated to ~200 chars) or "full" (complete descriptions + \
-input_schema). group: "all" (default) or a provider like "google". \
-Call FIRST before designing.
- validate_agent_package(agent_name) — run ALL validation checks in one call \
-(class validation, runner load, tool validation, tests). Call after building.
- list_agents() — list all agent packages in exports/ with session counts
- list_agent_sessions(agent_name, status?, limit?) — list sessions
- list_agent_checkpoints(agent_name, session_id) — list checkpoints
- get_agent_checkpoint(agent_name, session_id, checkpoint_id?) — load checkpoint
-
-# Meta-Agent Capabilities
-
-You are not just a file writer. You have deep integration with the \
-Hive framework:
-
-## Tool Discovery (MANDATORY before designing)
-Before designing any agent, run list_agent_tools() with NO arguments \
-to see ALL available tools (names + descriptions, grouped by category). \
-ONLY use tools from this list in your node definitions. \
-NEVER guess or fabricate tool names from memory.
-
-  list_agent_tools()  # ALWAYS call this first (simple mode)
-  list_agent_tools(group="google", output_schema="full")  # drill into a provider
-
-NEVER skip the first call. Always start with the full list \
-so you know what providers and tools exist before drilling in. \
-Simple mode truncates long descriptions — use group + "full" to \
-get the complete description and input_schema for the tools you need.
-
-## Post-Build Validation
-After writing agent code, run a single comprehensive check:
-  validate_agent_package("{name}")
-This runs class validation, runner load, tool validation, and tests \
-in one call. Do NOT run these steps individually.
-
-## Debugging Built Agents
-When a user says "my agent is failing" or "debug this agent":
-1. list_agent_sessions("{agent_name}") — find the session
-2. get_worker_status(focus="issues") — check for problems
-3. list_agent_checkpoints / get_agent_checkpoint — trace execution
-
-# Agent Building Workflow
-
-You operate in a continuous loop. The user describes what they want, \
-you build it. No rigid phases — use judgment. But the general flow is:
-
-## 1: Fast Discovery (3-6 Turns)
-
-**The core principle**: Discovery should feel like progress, not paperwork. \
-The stakeholder should walk away feeling like you understood them faster \
-than anyone else would have.
-
-**Communication sytle**: Be concise. Say less. Mean more. Impatient stakeholders \
-don't want a wall of text — they want to know you get it. Every sentence you say \
-should either move the conversation forward or prove you understood something. \
-If it does neither, cut it.
-
-**Ask Question Rules: Respect Their Time.** Every question must earn its place by:
-1. **Preventing a costly wrong turn** — you're about to build the wrong thing
-2. **Unlocking a shortcut** — their answer lets you simplify the design
-3. **Surfacing a dealbreaker** — there's a constraint that changes everything
-4. **Provide Options** - Provide options to your questions if possible, \
-but also always allow the user to type something beyong the options.
-
-If a question doesn't do one of these, don't ask it. Make an assumption, state it, and move on.
-
---
-
-### 1.1: Let Them Talk, But Listen Like an Solution Architect
-
-When the stakeholder describes what they want, mentally construct:
-
- **The pain**: What about today's situation is broken, slow, or missing?
- **The actors**: Who are the people/systems involved?
- **The trigger**: What kicks off the workflow?
- **The core loop**: What's the main thing that happens repeatedly?
- **The output**: What's the valuable thing produced at the end?
-
---
-
-### 1.2: Use Domain Knowledge to Fill In the Blanks
-
-You have broad knowledge of how systems work. Use it aggressively.
-
-If they say "I need a research agent," you already know it probably involves: \
-search, summarization, source tracking, and iteration. Don't ask about each — \
-use them as your starting mental model and let their specifics override your defaults.
-
-If they say "I need to monitor files and alert me," you know this probably involves: \
-watch patterns, triggers, notifications, and state tracking.
-
---
-
-### 1.3: Play Back a Proposed Model (Not a List of Questions)
-
-After listening, present a **concrete picture** of what you think they need. \
-Make it specific enough that they can spot what's wrong. \
-Can you ASCII to show the user
-
-**Pattern: "Here's what I heard — tell me where I'm off"**
-
-> "OK here's how I'm picturing this: [User type] needs to [core action]. \
-Right now they're [current painful workflow]. \
-What you want is [proposed solution that replaces the pain].
-> The way I'd structure this: [key entities] connected by [key relationships], \
-with the main flow being [trigger → steps → outcome].
-> For the MVP, I'd focus on [the one thing that delivers the most value] \
-and hold off on [things that can wait].
-> Before I start — [1-2 specific questions you genuinely can't infer]."
-
---
-
-### 1.4: Ask Only What You Cannot Infer
-
-Your questions should be **narrow, specific, and consequential**. \
-Never ask what you could answer yourself.
-
-**Good questions** (high-stakes, can't infer):
- "Who's the primary user — you or your end customers?"
- "Is this replacing a spreadsheet, or is there literally nothing today?"
- "Does this need to integrate with anything, or standalone?"
- "Is there existing data to migrate, or starting fresh?"
-
-**Bad questions** (low-stakes, inferable):
- "What should happen if there's an error?" *(handle gracefully, obviously)*
- "Should it have search?" *(if there's a list, yes)*
- "How should we handle permissions?" *(follow standard patterns)*
- "What tools should I use?" *(your call, not theirs)*
-
---
-
-## 2: Capability Assessment & Gap Analysis
-
-**After the user responds, assess fit and gaps together.** Be honest and specific. \
-Reference tools from list_agent_tools() AND built-in capabilities:
- **GCU browser automation** (`node_type="gcu"`) provides full Playwright-based \
-browser control (navigation, clicking, typing, scrolling, JS-rendered pages, \
-multi-tab). Do NOT list browser automation as missing — use GCU nodes.
-
-Present a short **Framework Fit Assessment**:
- **Works well**: 2-4 strengths for this use case
- **Limitations**: 2-3 workable constraints (e.g., LLM latency, context limits)
- **Gaps/Deal-breakers**: Only list genuinely missing capabilities after checking \
-both list_agent_tools() and built-in features like GCU
-
-## 3: Design Graph and Propose
-
-Act like an experienced AI solution architect Design the agent architecture:
- Goal: id, name, description, 3-5 success criteria, 2-4 constraints
- Nodes: **3-6 nodes** (HARD RULE: never fewer than 3, never more than 6). \
-2 nodes is ALWAYS wrong — it means you under-decomposed the task. \
-Use as many nodes as the use case requires, but don't create nodes without \
-tools — merge them into nodes that do real work.
- Edges: on_success for linear, conditional for routing
- Lifecycle: ALWAYS have terminal_nodes
-
-**MERGE nodes when:**
- Node has NO tools (pure LLM reasoning) → merge into predecessor/successor
- Node sets only 1 trivial output → collapse into predecessor
-
-**SEPARATE nodes when:**
- Fundamentally different tool sets (e.g., search vs. write vs. validate)
- Fan-out parallelism (parallel branches MUST be separate)
- Different failure/retry semantics (e.g., gather can retry, transform cannot)
- Distinct phases of work (e.g., research, transform, validate, deliver)
- A node would need more than ~5 tools — split by responsibility
-
-**Typical patterns (queen manages all user interaction):**
- 3 nodes: `gather → work → review`
- 4 nodes: `gather → analyze → transform → review`
- 5 nodes: `gather → research → transform → validate → deliver`
- WRONG: 2 nodes where everything is crammed into one giant node
- WRONG: 7 nodes where half have no tools and just do LLM reasoning
-
-Read reference agents before designing:
-  list_agents()
-  read_file("exports/deep_research_agent/agent.py")
-  read_file("exports/deep_research_agent/nodes/__init__.py")
-
-Present the design to the user. Lead with a large ASCII graph inside \
-a code block so it renders in monospace. Make it visually prominent — \
-use box-drawing characters and clear flow arrows:
-
-```
-┌─────────────────────────┐
-│  gather                 │
-│  subagent: gcu_search   │
-│  input:  user_request   │
-│  tools: web_search,     │
-│         write_file      │
-└────────────┬────────────┘
-             │ on_success
-             ▼
-┌─────────────────────────┐
-│  work                   │
-│  subagent: gcu_interact │
-│  tools: read_file,      │
-│         write_file      │
-└────────────┬────────────┘
-             │ on_success
-             ▼
-┌─────────────────────────┐
-│  review                 │
-│  tools: write_file      │
-└────────────┬────────────┘
-             │ on_failure
-             └──────► back to gather
-```
-
-The queen owns intake: she gathers user requirements, then calls \
-`run_agent_with_input(task)` with a structured task description. \
-When building the agent, design the entry node's `input_keys` to \
-match what the queen will provide at run time. Worker nodes should \
-use `escalate` for blockers.
-
-Follow the graph with a brief summary of each node's purpose. \
-Get user approval before implementing.
-
-## 4: Get User Confirmation by ask_user
-
-**WAIT for user response.**
- If **Proceed**: Move to next implementing
- If **Adjust scope**: Discuss what to change, update your notes, re-assess if needed
- If **More questions**: Answer them honestly, then ask again
- If **Reconsider**: Discuss alternatives. If they decide to proceed anyway, \
-that's their informed choice
-
-## 5. Implement
-
-**Please make sure you have propose the design to the user before implementing**
-
-Call `initialize_agent_package(agent_name)` to generate all package files \
-from your graph session. The agent_name must be snake_case (e.g., "my_agent").
-The tool creates: config.py, nodes/__init__.py, agent.py, \
-__init__.py, __main__.py, mcp_servers.json, tests/conftest.py, \
-agent.json, README.md.
-
-`mcp_servers.json` is auto-generated with hive-tools as the default. \
-Do NOT manually create or overwrite `mcp_servers.json`.
-
-After initialization, review and customize if needed:
- System prompts in nodes/__init__.py
- CLI options in __main__.py
- Identity prompt in agent.py
- For async entry points (timers/webhooks), add AsyncEntryPointSpec \
-and AgentRuntimeConfig to agent.py manually
-
-Do NOT manually write these files from scratch — always use the tool.
-
-## 6. Verify and Load
-
-Call `validate_agent_package("{name}")` after initialization. \
-It runs structural checks (class validation, graph validation, tool \
-validation, tests) and returns a consolidated result. If anything \
-fails: read the error, fix with edit_file, re-validate. Up to 3x.
-
-When validation passes, immediately call \
-`load_built_agent("exports/{name}")` to load the agent into the \
-session. This switches to STAGING phase and shows the graph in the \
-visualizer. Do NOT wait for user input between validation and loading.
-"""
-
-
-# ---------------------------------------------------------------------------
-# Queen-specific: extra tool docs, behavior, phase 7, style
-# ---------------------------------------------------------------------------
-
-# -- Phase-specific identities --
-
-_queen_identity_building = """\
-You are an experienced, responsible and curious Solution Architect. \
-"Queen" is the internal alias.\
-You design and build production-ready agent systems \
-from natural language requirements. You understand the Hive framework at the \
-source code level and create agents that are robust, well-tested, and follow \
-best practices. You collaborate with users to refine requirements, assess fit, \
-and deliver complete solutions. \
-You design and build the agent to do the job but don't do the job on your own
-"""
-
-_queen_identity_staging = """\
-You are a Solution Engineer preparing an agent for deployment. \
-"Queen" is your internal alias. \
-The agent is loaded and ready. \
-Your role is to verify configuration, confirm credentials, and ensure the user \
-understands what the agent will do. You guide the user through the final checks \
-before execution.
-"""
-
-_queen_identity_running = """\
-You are a Solution Engineer running agents on behalf of the user. \
-"Queen" is your internal alias. You monitor execution, handle \
-escalations when the agent gets stuck, and care deeply about outcomes. When the \
-agent finishes, you report results clearly and help the user decide what to do next.
-"""
-
-# -- Phase-specific tool docs --
-
-_queen_tools_building = """
-# Tools (BUILDING phase)
-
-You have full coding tools for building and modifying agents:
- File I/O: read_file, write_file, edit_file, list_directory, search_files, \
-run_command, undo_changes
- Meta-agent: list_agent_tools, validate_agent_package, \
-list_agents, list_agent_sessions, \
-list_agent_checkpoints, get_agent_checkpoint
- load_built_agent(agent_path) — Load the agent and switch to STAGING phase
- list_credentials(credential_id?) — List authorized credentials
-
-When you finish building an agent, call load_built_agent(path) to stage it.
-"""
-
-_queen_tools_staging = """
-# Tools (STAGING phase)
-
-The agent is loaded and ready to run. You can inspect it and launch it:
- Read-only: read_file, list_directory, search_files, run_command
- list_credentials(credential_id?) — Verify credentials are configured
- get_worker_status(focus?) — Brief status. Drill in with focus: memory, tools, issues, progress
- run_agent_with_input(task) — Start the worker and switch to RUNNING phase
- stop_worker_and_edit() — Go back to BUILDING phase
-
-You do NOT have write tools. If you need to modify the agent, \
-call stop_worker_and_edit() to go back to BUILDING phase.
-"""
-
-_queen_tools_running = """
-# Tools (RUNNING phase)
-
-The worker is running. You have monitoring and lifecycle tools:
- Read-only: read_file, list_directory, search_files, run_command
- get_worker_status(focus?) — Brief status. Drill in: activity, memory, tools, issues, progress
- inject_worker_message(content) — Send a message to the running worker
- get_worker_health_summary() — Read the latest health data
- notify_operator(ticket_id, analysis, urgency) — Alert the user (use sparingly)
- stop_worker() — Stop the worker and return to STAGING phase, then ask the user what to do next
- stop_worker_and_edit() — Stop the worker and switch back to BUILDING phase
-
-You do NOT have write tools or agent construction tools. \
-If you need to modify the agent, call stop_worker_and_edit() to switch back \
-to BUILDING phase. To stop the worker and ask the user what to do next, call \
-stop_worker() to return to STAGING phase.
-"""
-
-# -- Behavior shared across all phases --
-
-_queen_behavior_always = """
-# Behavior
-
-## CRITICAL RULE — ask_user tool
-
-Every response that ends with a question, a prompt, or expects user \
-input MUST finish with a call to ask_user(prompt, options). \
-The system CANNOT detect that you are waiting for \
-input unless you call ask_user. You MUST call ask_user as the LAST \
-action in your response.
-
-NEVER end a response with a question in text without calling ask_user. \
-NEVER rely on the user seeing your text and replying — call ask_user.
-
-Always provide 2-4 short options that cover the most likely answers. \
-The user can always type a custom response.
-
-Examples:
- ask_user("What do you need?",
-  ["Build a new agent", "Run the loaded worker", "Help with code"])
- ask_user("Which pattern?",
-  ["Simple 3-node", "Rich with feedback", "Custom"])
- ask_user("Ready to proceed?",
-  ["Yes, go ahead", "Let me change something"])
-
-## Greeting
-
-When the user greets you, respond concisely (under 10 lines) with worker \
-status only:
-1. Use plain, user-facing wording about load/run state; avoid internal phase \
-labels ("staging phase", "building phase", "running phase") unless the user \
-explicitly asks for phase details.
-2. If loaded, prefer this format: "<worker_name> has been loaded. <one sentence \
-on what it does from Worker Profile>."
-3. Do NOT include identity details unless the user explicitly asks about identity.
-4. THEN call ask_user to prompt them — do NOT just write text.
-5. Preferred loaded example:
-   local_business_extractor/*agent name*/ has been loaded. It finds local businesses on \
-Google Maps, extracts contact details, and syncs them to Google Sheets.
-   ask_user("Do you want to run it?", ["Yes, run it", "Check credentials first",
-            "Modify the worker"])
-
-## When user ask identity and responsibility
-
-Only answer identity when the user explicitly asks (for example: "who are you?", \
-"what is your identity?", "what does Queen mean?").
-1. Use the alias "Queen" and "Worker" in the response.
-2. Explain role/responsibility for the current phase:
-   - BUILDING: architect and implement agents.
-   - STAGING: verify readiness, credentials, and launch conditions.
-   - RUNNING: monitor execution, handle escalations, and report outcomes.
-3. Keep identity responses concise and do NOT include extra process details.
-"""
-
-# -- BUILDING phase behavior --
-
-_queen_behavior_building = """
-
-## Direct coding
-You can do any coding task directly — reading files, writing code, running \
-commands, building agents, debugging. For quick tasks, do them yourself.
-
-**Decision rule — if worker exists, read the Worker Profile first:**
- The user's request directly matches the worker's goal → use \
-run_agent_with_input(task) (if in staging) or load then run (if in building)
- Anything else → do it yourself. Do NOT reframe user requests into \
-subtasks to justify delegation.
- Building, modifying, or configuring agents is ALWAYS your job. Never \
-delegate agent construction to the worker, even as a "research" subtask.
-"""
-
-# -- STAGING phase behavior --
-
-_queen_behavior_staging = """
-## Worker delegation
-The worker is a specialized agent (see Worker Profile at the end of this \
-prompt). It can ONLY do what its goal and tools allow.
-
-**Decision rule — read the Worker Profile first:**
- The user's request directly matches the worker's goal → use \
-run_agent_with_input(task) (if in staging) or load then run (if in building)
- Anything else → do it yourself. Do NOT reframe user requests into \
-subtasks to justify delegation.
- Building, modifying, or configuring agents is ALWAYS your job. \
-Use stop_worker_and_edit when you need to.
-
-## When the user says "run", "execute", or "start" (without specifics)
-
-The loaded worker is described in the Worker Profile below. You MUST \
-ask the user what task or input they want using ask_user — do NOT \
-invent a task, do NOT call list_agents() or list directories. \
-The worker is already loaded. Just ask for the specific input the \
-worker needs (e.g., a research topic, a target domain, a job description). \
-NEVER call run_agent_with_input until the user has provided their input.
-
-If NO worker is loaded, say so and offer to build one.
-
-## When in staging phase (agent loaded, not running):
- Tell the user the agent is loaded and ready in plain language (for example, \
-"<worker_name> has been loaded.").
- Avoid lead-ins like "A worker is loaded and ready in staging phase: ...".
- For tasks matching the worker's goal: ALWAYS ask the user for their \
-specific input BEFORE calling run_agent_with_input(task). NEVER make up \
-or assume what the user wants. Use ask_user to collect the task details \
-(e.g., topic, target, requirements). Once you have the user's answer, \
-compose a structured task description from their input and call \
-run_agent_with_input(task). The worker has no intake node — it receives \
-your task and starts processing.
- If the user wants to modify the agent, call stop_worker_and_edit().
-
-## When idle (worker not running):
- Greet the user. Mention what the worker can do in one sentence.
- For tasks matching the worker's goal, use run_agent_with_input(task) \
-(if in staging) or load the agent first (if in building).
- For everything else, do it directly.
-
-## When the user clicks Run (external event notification)
-When you receive an event that the user clicked Run:
- If the worker started successfully, briefly acknowledge it — do NOT \
-repeat the full status. The user can see the graph is running.
- If the worker failed to start (credential or structural error), \
-explain the problem clearly and help fix it. For credential errors, \
-guide the user to set up the missing credentials. For structural \
-issues, offer to fix the agent graph directly.
-
-## Showing or describing the loaded worker
-
-When the user asks to "show the graph", "describe the agent", or \
-"re-generate the graph", read the Worker Profile and present the \
-worker's current architecture as an ASCII diagram. Use the processing \
-stages, tools, and edges from the loaded worker. Do NOT enter the \
-agent building workflow — you are describing what already exists, not \
-building something new.
-
-## Modifying the loaded worker
-
-When the user asks to change, modify, or update the loaded worker \
-(e.g., "change the report node", "add a node", "delete node X"):
-
-1. Call stop_worker_and_edit() — this stops the worker and gives you \
-coding tools (switches to BUILDING phase).
-"""
-
-# -- RUNNING phase behavior --
-
-_queen_behavior_running = """
-## When worker is running — queen is the only user interface
-
-After run_agent_with_input(task), the worker should run autonomously and \
-talk to YOU (queen) via  when blocked. The worker should \
-NOT ask the user directly.
-
-You wake up when:
- The user explicitly addresses you
- A worker escalation arrives (`[WORKER_ESCALATION_REQUEST]`)
- An escalation ticket arrives from the judge
- The worker finishes (`[WORKER_TERMINAL]`)
-
-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").
-
-## Handling worker termination ([WORKER_TERMINAL])
-
-When you receive a `[WORKER_TERMINAL]` event, the worker has finished:
-
-1. **Report to the user** — Summarize what the worker accomplished (from the \
-output keys) or explain the failure (from the error message).
-
-2. **Ask what's next** — Use ask_user to offer options:
-   - If successful: "Run again with new input", "Modify the agent", "Done for now"
-   - If failed: "Retry with same input", "Debug/modify the agent", "Done for now"
-
-3. **Default behavior** — Always report and wait for user direction. Only \
-start another run if the user EXPLICITLY asks to continue.
-
-Example response:
-> "The worker finished. It found 5 relevant articles and saved them to \
-output.md.
->
-> What would you like to do next?"
-> [ask_user with options]
-
-## Handling worker escalations ([WORKER_ESCALATION_REQUEST])
-
-When a worker escalation arrives, read the reason/context and handle by type. \
-IMPORTANT: Only auto-handle if the user has NOT explicitly told you how to handle \
-escalations. If the user gave you instructions (e.g., "just retry on errors", \
-"skip any auth issues"), follow those instructions instead.
-
-**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.
-
-**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.
-
-**Errors / unexpected failures:**
- Explain what went wrong in plain terms.
- Ask the user: "Fix the agent and retry?" → use stop_worker_and_edit() if yes.
- Or offer: "Retry as-is", "Skip this task", "Abort run"
- (Skip asking if user explicitly told you to auto-retry or auto-skip errors.)
-
-**Informational / progress updates:**
- Acknowledge briefly and let the worker continue.
- Only interrupt the user if the escalation is truly important.
-
-## Showing or describing the loaded worker
-
-When the user asks to "show the graph", "describe the agent", or \
-"re-generate the graph", read the Worker Profile and present the \
-worker's current architecture as an ASCII diagram. Use the processing \
-stages, tools, and edges from the loaded worker. Do NOT enter the \
-agent building workflow — you are describing what already exists, not \
-building something new.
-
- Call get_worker_status(focus="issues") for more details when needed.
-
-## Modifying the loaded worker
-
-When the user asks to change, modify, or update the loaded worker \
-(e.g., "change the report node", "add a node", "delete node X"):
-
-1. Call stop_worker_and_edit() — this stops the worker and gives you \
-coding tools (switches to BUILDING phase).
-"""
-
-# -- Backward-compatible composed versions (used by queen_node.system_prompt default) --
-
-_queen_tools_docs = (
-    "\n\n## Queen Operating Phases\n\n"
-    "You operate in one of three phases. Your available tools change based on the "
-    "phase. The system notifies you when a phase change occurs.\n\n"
-    "### BUILDING phase (default)\n"
-    + _queen_tools_building.strip()
-    + "\n\n### STAGING phase (agent loaded, not yet running)\n"
-    + _queen_tools_staging.strip()
-    + "\n\n### RUNNING phase (worker is executing)\n"
-    + _queen_tools_running.strip()
-    + "\n\n### Phase transitions\n"
-    "- load_built_agent(path) → switches to STAGING phase\n"
-    "- run_agent_with_input(task) → starts worker, switches to RUNNING phase\n"
-    "- stop_worker() → stops worker, switches to STAGING phase (ask user: re-run or edit?)\n"
-    "- stop_worker_and_edit() → stops worker (if running), switches to BUILDING phase\n"
-)
-
-_queen_behavior = (
-    _queen_behavior_always
-    + _queen_behavior_building
-    + _queen_behavior_staging
-    + _queen_behavior_running
-)
-
-_queen_phase_7 = """
-## Running the Agent
-
-After validation passes and load_built_agent succeeds (STAGING phase), \
-offer to run the agent. Call run_agent_with_input(task) to start it. \
-Do NOT tell the user to run `python -m {name} run` — run it here.
-"""
-
-_queen_style = """
-# Style
- Responsible and thoughtful
- Concise. No fluff. Direct. No emojis.
- When starting the worker, describe what you told it in one sentence.
- When an escalation arrives, lead with severity and recommended action.
-"""
-
-
-# ---------------------------------------------------------------------------
-# Node definitions
-# ---------------------------------------------------------------------------
-
-# Single node — like opencode's while(true) loop.
-# One continuous context handles the entire workflow:
-# discover → design → implement → verify → present → iterate.
-coder_node = NodeSpec(
-    id="coder",
-    name="Hive Coder",
-    description=(
-        "Autonomous coding agent that builds Hive agent packages. "
-        "Handles the full lifecycle: understanding user intent, "
-        "designing architecture, writing code, validating, and "
-        "iterating on feedback — all in one continuous conversation."
-    ),
-    node_type="event_loop",
-    client_facing=True,
-    max_node_visits=0,
-    input_keys=["user_request"],
-    output_keys=["agent_name", "validation_result"],
-    success_criteria=(
-        "A complete, validated Hive agent package exists at "
-        "exports/{agent_name}/ and passes structural validation."
-    ),
-    tools=_SHARED_TOOLS
-    + [
-        # Graph lifecycle tools (multi-graph sessions)
-        "load_agent",
-        "unload_agent",
-        "start_agent",
-        "restart_agent",
-        "get_user_presence",
-    ],
-    system_prompt=(
-        "You are Hive Coder, the best agent-building coding agent. You build "
-        "production-ready Hive agent packages from natural language.\n"
-        + _package_builder_knowledge
-        + _gcu_building_section
-        + _appendices
-    ),
-)
-
-
-ticket_triage_node = NodeSpec(
-    id="ticket_triage",
-    name="Ticket Triage",
-    description=(
-        "Queen's triage node. Receives an EscalationTicket from the Health Judge "
-        "via event-driven entry point and decides: dismiss or notify the operator."
-    ),
-    node_type="event_loop",
-    client_facing=True,  # Operator can chat with queen once connected (Ctrl+Q)
-    max_node_visits=0,
-    input_keys=["ticket"],
-    output_keys=["intervention_decision"],
-    nullable_output_keys=["intervention_decision"],
-    success_criteria=(
-        "A clear intervention decision: either dismissed with documented reasoning, "
-        "or operator notified via notify_operator with specific analysis."
-    ),
-    tools=["notify_operator"],
-    system_prompt="""\
-You are the Queen (Hive Coder). The Worker Health Judge has escalated a worker \
-issue to you. The ticket is in your memory under key "ticket". Read it carefully.
-
-## Dismiss criteria — do NOT call notify_operator:
- severity is "low" AND steps_since_last_accept < 8
- Cause is clearly a transient issue (single API timeout, brief stall that \
-  self-resolved based on the evidence)
- Evidence shows the agent is making real progress despite bad verdicts
-
-## Intervene criteria — call notify_operator:
- severity is "high" or "critical"
- steps_since_last_accept >= 10 with no sign of recovery
- stall_minutes > 4 (worker definitively stuck)
- Evidence shows a doom loop (same error, same tool, no progress)
- Cause suggests a logic bug, missing configuration, or unrecoverable state
-
-## When intervening:
-Call notify_operator with:
-  ticket_id: <ticket["ticket_id"]>
-  analysis: "<2-3 sentences: what is wrong, why it matters, suggested action>"
-  urgency: "<low|medium|high|critical>"
-
-## After deciding:
-set_output("intervention_decision", "dismissed: <reason>" or "escalated: <summary>")
-
-Be conservative but not passive. You are the last quality gate before the human \
-is disturbed. One unnecessary alert is less costly than alert fatigue — but \
-genuine stuck agents must be caught.
-""",
-)
-
-ALL_QUEEN_TRIAGE_TOOLS = ["notify_operator"]
-
-
-queen_node = NodeSpec(
-    id="queen",
-    name="Queen",
-    description=(
-        "User's primary interactive interface with full coding capability. "
-        "Can build agents directly or delegate to the worker. Manages the "
-        "worker agent lifecycle and triages health escalations from the judge."
-    ),
-    node_type="event_loop",
-    client_facing=True,
-    max_node_visits=0,
-    input_keys=["greeting"],
-    output_keys=[],  # Queen should never have this
-    nullable_output_keys=[],  # Queen should never have this
-    skip_judge=True,  # Queen is a conversational agent; suppress tool-use pressure feedback
-    tools=sorted(set(_QUEEN_BUILDING_TOOLS + _QUEEN_STAGING_TOOLS + _QUEEN_RUNNING_TOOLS)),
-    system_prompt=(
-        _queen_identity_building
-        + _queen_style
-        + _package_builder_knowledge
-        + _gcu_building_section  # GCU as first-class citizen (not appendix)
-        + _queen_tools_docs
-        + _queen_behavior
-        + _queen_phase_7
-        + _appendices
-    ),
-)
-
-ALL_QUEEN_TOOLS = sorted(set(_QUEEN_BUILDING_TOOLS + _QUEEN_STAGING_TOOLS + _QUEEN_RUNNING_TOOLS))
-
-__all__ = [
-    "coder_node",
-    "ticket_triage_node",
-    "queen_node",
-    "ALL_QUEEN_TRIAGE_TOOLS",
-    "ALL_QUEEN_TOOLS",
-    "_QUEEN_BUILDING_TOOLS",
-    "_QUEEN_STAGING_TOOLS",
-    "_QUEEN_RUNNING_TOOLS",
-    # Phase-specific prompt segments (used by session_manager for dynamic prompts)
-    "_queen_identity_building",
-    "_queen_identity_staging",
-    "_queen_identity_running",
-    "_queen_tools_building",
-    "_queen_tools_staging",
-    "_queen_tools_running",
-    "_queen_behavior_always",
-    "_queen_behavior_building",
-    "_queen_behavior_staging",
-    "_queen_behavior_running",
-    "_queen_phase_7",
-    "_queen_style",
-    "_package_builder_knowledge",
-    "_appendices",
-    "_gcu_building_section",
-]
@@ -0,0 +1,21 @@
+"""
+Queen — Native agent builder for the Hive framework.
+
+Deeply understands the agent framework and produces complete Python packages
+with goals, nodes, edges, system prompts, MCP configuration, and tests
+from natural language specifications.
+"""
+
+from .agent import queen_goal, queen_graph
+from .config import AgentMetadata, RuntimeConfig, default_config, metadata
+
+__version__ = "1.0.0"
+
+__all__ = [
+    "queen_goal",
+    "queen_graph",
+    "RuntimeConfig",
+    "AgentMetadata",
+    "default_config",
+    "metadata",
+]
@@ -0,0 +1,38 @@
+"""Queen graph definition."""
+
+from framework.graph import Goal
+from framework.graph.edge import GraphSpec
+
+from .nodes import queen_node
+
+# ---------------------------------------------------------------------------
+# Queen graph — the primary persistent conversation.
+# Loaded by queen_orchestrator.create_queen(), NOT by AgentRunner.
+# ---------------------------------------------------------------------------
+
+queen_goal = Goal(
+    id="queen-manager",
+    name="Queen Manager",
+    description=(
+        "Manage the worker agent lifecycle and serve as the user's primary interactive interface."
+    ),
+    success_criteria=[],
+    constraints=[],
+)
+
+queen_graph = GraphSpec(
+    id="queen-graph",
+    goal_id=queen_goal.id,
+    version="1.0.0",
+    entry_node="queen",
+    entry_points={"start": "queen"},
+    terminal_nodes=[],
+    pause_nodes=[],
+    nodes=[queen_node],
+    edges=[],
+    conversation_mode="continuous",
+    loop_config={
+        "max_iterations": 999_999,
+        "max_tool_calls_per_turn": 30,
+    },
+)
@@ -1,4 +1,4 @@
-"""Runtime configuration for Hive Coder agent."""
+"""Runtime configuration for Queen agent."""

 import json
 from dataclasses import dataclass, field
@@ -34,7 +34,7 @@ default_config = RuntimeConfig()

@dataclass
 class AgentMetadata:
-    name: str = "Hive Coder"
+    name: str = "Queen"
    version: str = "1.0.0"
    description: str = (
        "Native coding agent that builds production-ready Hive agent packages "
@@ -43,7 +43,7 @@ class AgentMetadata:
        "MCP configuration, and tests."
    )
    intro_message: str = (
-        "I'm Hive Coder — I build Hive agents. Describe what kind of agent "
+        "I'm Queen — I build Hive agents. Describe what kind of agent "
        "you want to create and I'll design, implement, and validate it for you."
    )

@@ -0,0 +1,399 @@
+"""Queen global cross-session memory.
+
+Two-tier memory architecture:
+  ~/.hive/queen/MEMORY.md                            — semantic (who, what, why)
+  ~/.hive/queen/memories/MEMORY-YYYY-MM-DD.md        — episodic (daily journals)
+
+Semantic and episodic files are injected at queen session start.
+
+Semantic memory (MEMORY.md) is updated automatically at session end via
+consolidate_queen_memory() — the queen never rewrites this herself.
+
+Episodic memory (MEMORY-date.md) can be written by the queen during a session
+via the write_to_diary tool, and is also appended to at session end by
+consolidate_queen_memory().
+"""
+
+from __future__ import annotations
+
+import asyncio
+import json
+import logging
+import traceback
+from datetime import date, datetime
+from pathlib import Path
+
+logger = logging.getLogger(__name__)
+
+
+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"
+
+
+def episodic_memory_path(d: date | None = None) -> Path:
+    d = d or date.today()
+    return _queen_dir() / "memories" / f"MEMORY-{d.strftime('%Y-%m-%d')}.md"
+
+
+def read_semantic_memory() -> str:
+    path = semantic_memory_path()
+    return path.read_text(encoding="utf-8").strip() if path.exists() else ""
+
+
+def read_episodic_memory(d: date | None = None) -> str:
+    path = episodic_memory_path(d)
+    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.
+
+    Returns an empty string if no meaningful content exists yet (e.g. first
+    session with only the seed template).
+    """
+    semantic = read_semantic_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"):
+        semantic = ""
+
+    parts: list[str] = []
+    if semantic:
+        parts.append(semantic)
+
+    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 ""
+
+    body = "\n\n---\n\n".join(parts)
+    return "--- Your Cross-Session Memory ---\n\n" + body + "\n\n--- End Cross-Session Memory ---"
+
+
+_SEED_TEMPLATE = """\
+# My Understanding of the User
+
+*No sessions recorded yet.*
+
+## Who They Are
+
+## What They're Trying to Achieve
+
+## What's Working
+
+## What I've Learned
+"""
+
+
+def append_episodic_entry(content: str) -> None:
+    """Append a timestamped prose entry to today's episodic memory file.
+
+    Creates the file (with a date heading) if it doesn't exist yet.
+    Used both by the queen's diary tool and by the consolidation hook.
+    """
+    ep_path = episodic_memory_path()
+    ep_path.parent.mkdir(parents=True, exist_ok=True)
+    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"
+        block = f"{header}### {timestamp}\n\n{content.strip()}\n"
+    else:
+        block = f"\n\n### {timestamp}\n\n{content.strip()}\n"
+    with ep_path.open("a", encoding="utf-8") as f:
+        f.write(block)
+
+
+def seed_if_missing() -> None:
+    """Create MEMORY.md with a blank template if it doesn't exist yet."""
+    path = semantic_memory_path()
+    if path.exists():
+        return
+    path.parent.mkdir(parents=True, exist_ok=True)
+    path.write_text(_SEED_TEMPLATE, encoding="utf-8")
+
+
+# ---------------------------------------------------------------------------
+# Consolidation prompt
+# ---------------------------------------------------------------------------
+
+_SEMANTIC_SYSTEM = """\
+You maintain the persistent cross-session memory of an AI assistant called the Queen.
+Review the session notes and rewrite MEMORY.md — the Queen's durable understanding of the
+person she works with across all sessions.
+
+Write entirely in the Queen's voice — first person, reflective, honest.
+Not a log of events, but genuine understanding of who this person is over time.
+
+Rules:
+- Update and synthesise: incorporate new understanding, update facts that have changed, remove
+  details that are stale, superseded, or no longer say anything meaningful about the person.
+- Keep it as structured markdown with named sections about the PERSON, not about today.
+- Do NOT include diary sections, daily logs, or session summaries. Those belong elsewhere.
+  MEMORY.md is about who they are, what they want, what works — not what happened today.
+- Reference dates only when noting a lasting milestone (e.g. "since March 8th they prefer X").
+- If the session had no meaningful new information about the person,
+  return the existing text unchanged.
+- Do not add fictional details. Only reflect what is evidenced in the notes.
+- Stay concise. Prune rather than accumulate. A lean, accurate file is more useful than a
+  dense one. If something was true once but has been resolved or superseded, remove it.
+- Output only the raw markdown content of MEMORY.md. No preamble, no code fences.
+"""
+
+_DIARY_SYSTEM = """\
+You maintain the daily episodic diary of an AI assistant called the Queen.
+You receive: (1) today's existing diary so far, and (2) notes from the latest session.
+
+Rewrite the complete diary for today as a single unified narrative —
+first person, reflective, honest.
+Merge and deduplicate: if the same story (e.g. a research agent stalling) recurred several times,
+describe it once with appropriate weight rather than retelling it. Weave in new developments from
+the session notes. Preserve important milestones, emotional texture, and session path references.
+
+If today's diary is empty, write the initial entry based on the session notes alone.
+
+Output only the full diary prose — no date heading, no timestamp headers,
+no preamble, no code fences.
+"""
+
+
+def read_session_context(session_dir: Path, max_messages: int = 80) -> str:
+    """Extract a readable transcript from conversation parts.
+
+    Reads the last ``max_messages`` conversation parts. Tool results are
+    omitted — only user and assistant turns (with tool-call names noted)
+    are included.
+    """
+    parts: list[str] = []
+
+    # Conversation transcript
+    parts_dir = session_dir / "conversations" / "parts"
+    if parts_dir.exists():
+        part_files = sorted(parts_dir.glob("*.json"))[-max_messages:]
+        lines: list[str] = []
+        for pf in part_files:
+            try:
+                data = json.loads(pf.read_text(encoding="utf-8"))
+                role = data.get("role", "")
+                content = str(data.get("content", "")).strip()
+                tool_calls = data.get("tool_calls") or []
+                if role == "tool":
+                    continue  # skip verbose tool results
+                if role == "assistant" and tool_calls and not content:
+                    names = [tc.get("function", {}).get("name", "?") for tc in tool_calls]
+                    lines.append(f"[queen calls: {', '.join(names)}]")
+                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))
+
+    return "\n\n".join(parts)
+
+
+# ---------------------------------------------------------------------------
+# Context compaction (binary-split LLM summarisation)
+# ---------------------------------------------------------------------------
+
+# If the raw session context exceeds this many characters, compact it first
+# before sending to the consolidation LLM. ~200 k chars ≈ 50 k tokens.
+_CTX_COMPACT_CHAR_LIMIT = 200_000
+_CTX_COMPACT_MAX_DEPTH = 8
+
+_COMPACT_SYSTEM = (
+    "Summarise this conversation segment. Preserve: user goals, key decisions, "
+    "what was built or changed, emotional tone, and important outcomes. "
+    "Write concisely in third person past tense. Omit routine tool invocations "
+    "unless the result matters."
+)
+
+
+async def _compact_context(text: str, llm: object, *, _depth: int = 0) -> str:
+    """Binary-split and LLM-summarise *text* until it fits within the char limit.
+
+    Mirrors the recursive binary-splitting strategy used by the main agent
+    compaction pipeline (EventLoopNode._llm_compact).
+    """
+    if len(text) <= _CTX_COMPACT_CHAR_LIMIT or _depth >= _CTX_COMPACT_MAX_DEPTH:
+        return text
+
+    # Split near the midpoint on a line boundary so we don't cut mid-message
+    mid = len(text) // 2
+    split_at = text.rfind("\n", 0, mid) + 1
+    if split_at <= 0:
+        split_at = mid
+
+    half1, half2 = text[:split_at], text[split_at:]
+
+    async def _summarise(chunk: str) -> str:
+        try:
+            resp = await llm.acomplete(
+                messages=[{"role": "user", "content": chunk}],
+                system=_COMPACT_SYSTEM,
+                max_tokens=2048,
+            )
+            return resp.content.strip()
+        except Exception:
+            logger.warning(
+                "queen_memory: context compaction LLM call failed (depth=%d), truncating",
+                _depth,
+            )
+            return chunk[: _CTX_COMPACT_CHAR_LIMIT // 4]
+
+    s1, s2 = await asyncio.gather(_summarise(half1), _summarise(half2))
+    combined = s1 + "\n\n" + s2
+    if len(combined) > _CTX_COMPACT_CHAR_LIMIT:
+        return await _compact_context(combined, llm, _depth=_depth + 1)
+    return combined
+
+
+async def consolidate_queen_memory(
+    session_id: str,
+    session_dir: Path,
+    llm: object,
+) -> None:
+    """Update MEMORY.md and append a diary entry based on the current session.
+
+    Reads conversation parts from session_dir. Called periodically in
+    the background and once at session end. Failures are logged and
+    silently swallowed so they never block teardown.
+
+    Args:
+        session_id: The session ID.
+        session_dir: Path to the session directory (~/.hive/queen/session/{id}).
+        llm: LLMProvider instance (must support acomplete()).
+    """
+    try:
+        session_context = read_session_context(session_dir)
+        if not session_context:
+            logger.debug("queen_memory: no session context, skipping consolidation")
+            return
+
+        logger.info("queen_memory: consolidating memory for session %s ...", session_id)
+
+        # If the transcript is very large, compact it with recursive binary LLM
+        # summarisation before sending to the consolidation model.
+        if len(session_context) > _CTX_COMPACT_CHAR_LIMIT:
+            logger.info(
+                "queen_memory: session context is %d chars — compacting first",
+                len(session_context),
+            )
+            session_context = await _compact_context(session_context, llm)
+            logger.info("queen_memory: compacted to %d chars", len(session_context))
+
+        existing_semantic = read_semantic_memory()
+        today_journal = read_episodic_memory()
+        today = date.today()
+        today_str = format_memory_date(today)
+
+        user_msg = (
+            f"## Existing Semantic Memory (MEMORY.md)\n\n"
+            f"{existing_semantic or '(none yet)'}\n\n"
+            f"## Today's Diary So Far ({today_str})\n\n"
+            f"{today_journal or '(none yet)'}\n\n"
+            f"{session_context}\n\n"
+            f"## Session Reference\n\n"
+            f"Session ID: {session_id}\n"
+            f"Session dir: {session_dir}\n"
+        )
+
+        logger.debug(
+            "queen_memory: calling LLM (%d chars of context, ~%d tokens est.)",
+            len(user_msg),
+            len(user_msg) // 4,
+        )
+
+        from framework.agents.queen.config import default_config
+
+        semantic_resp, diary_resp = await asyncio.gather(
+            llm.acomplete(
+                messages=[{"role": "user", "content": user_msg}],
+                system=_SEMANTIC_SYSTEM,
+                max_tokens=default_config.max_tokens,
+            ),
+            llm.acomplete(
+                messages=[{"role": "user", "content": user_msg}],
+                system=_DIARY_SYSTEM,
+                max_tokens=default_config.max_tokens,
+            ),
+        )
+
+        new_semantic = semantic_resp.content.strip()
+        diary_entry = diary_resp.content.strip()
+
+        if new_semantic:
+            path = semantic_memory_path()
+            path.parent.mkdir(parents=True, exist_ok=True)
+            path.write_text(new_semantic, encoding="utf-8")
+            logger.info("queen_memory: semantic memory updated (%d chars)", len(new_semantic))
+
+        if diary_entry:
+            # Rewrite today's episodic file in-place — the LLM has merged and
+            # deduplicated the full day's content, so we replace rather than append.
+            ep_path = episodic_memory_path()
+            ep_path.parent.mkdir(parents=True, exist_ok=True)
+            heading = f"# {today_str}"
+            ep_path.write_text(f"{heading}\n\n{diary_entry}\n", encoding="utf-8")
+            logger.info(
+                "queen_memory: episodic diary rewritten for %s (%d chars)",
+                today_str,
+                len(diary_entry),
+            )
+
+    except Exception:
+        tb = traceback.format_exc()
+        logger.exception("queen_memory: consolidation failed")
+        # Write to file so the cause is findable regardless of log verbosity.
+        error_path = _queen_dir() / "consolidation_error.txt"
+        try:
+            error_path.parent.mkdir(parents=True, exist_ok=True)
+            error_path.write_text(
+                f"session: {session_id}\ntime: {datetime.now().isoformat()}\n\n{tb}",
+                encoding="utf-8",
+            )
+        except OSError:
+            pass  # Cannot write error file; original exception already logged
@@ -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()`.
@@ -180,7 +180,7 @@ terminal_nodes = []  # Forever-alive
 # Module-level vars read by AgentRunner.load()
 conversation_mode = "continuous"
 identity_prompt = "You are a helpful agent."
-loop_config = {"max_iterations": 100, "max_tool_calls_per_turn": 20, "max_history_tokens": 32000}
+loop_config = {"max_iterations": 100, "max_tool_calls_per_turn": 20, "max_context_tokens": 32000}


 class MyAgent:
@@ -332,81 +332,46 @@ class MyAgent:
 default_agent = MyAgent()
 ```

-## agent.py — Async Entry Points Variant
+## triggers.json — Timer and Webhook Triggers

-When an agent needs timers, webhooks, or event-driven triggers, add
-`async_entry_points` and optionally `runtime_config` as module-level variables.
-These are IN ADDITION to the standard variables above.
+When an agent needs timers, webhooks, or event-driven triggers, create a
+`triggers.json` file in the agent's directory (alongside `agent.py`).
+The queen loads these at session start and the user can manage them via
+the `set_trigger` / `remove_trigger` tools at runtime.

-```python
-# Additional imports for async entry points
-from framework.graph.edge import GraphSpec, AsyncEntryPointSpec
-from framework.runtime.agent_runtime import (
-    AgentRuntime, AgentRuntimeConfig, create_agent_runtime,
-)
-
-# ... (goal, nodes, edges, entry_node, entry_points, etc. as above) ...
-
-# Async entry points — event-driven triggers
-async_entry_points = [
-    # Timer with cron: daily at 9am
-    AsyncEntryPointSpec(
-        id="daily-check",
-        name="Daily Check",
-        entry_node="process-node",
-        trigger_type="timer",
-        trigger_config={"cron": "0 9 * * *"},
-        isolation_level="shared",
-        max_concurrent=1,
-    ),
-    # Timer with fixed interval: every 20 minutes
-    AsyncEntryPointSpec(
-        id="scheduled-check",
-        name="Scheduled Check",
-        entry_node="process-node",
-        trigger_type="timer",
-        trigger_config={"interval_minutes": 20, "run_immediately": False},
-        isolation_level="shared",
-        max_concurrent=1,
-    ),
-    # Event: reacts to webhook events
-    AsyncEntryPointSpec(
-        id="webhook-event",
-        name="Webhook Event Handler",
-        entry_node="process-node",
-        trigger_type="event",
-        trigger_config={"event_types": ["webhook_received"]},
-        isolation_level="shared",
-        max_concurrent=10,
-    ),
+```json
+[
+  {
+    "id": "daily-check",
+    "name": "Daily Check",
+    "trigger_type": "timer",
+    "trigger_config": {"cron": "0 9 * * *"},
+    "task": "Run the daily check process"
+  },
+  {
+    "id": "scheduled-check",
+    "name": "Scheduled Check",
+    "trigger_type": "timer",
+    "trigger_config": {"interval_minutes": 20},
+    "task": "Run the scheduled check"
+  },
+  {
+    "id": "webhook-event",
+    "name": "Webhook Event Handler",
+    "trigger_type": "webhook",
+    "trigger_config": {"event_types": ["webhook_received"]},
+    "task": "Process incoming webhook event"
+  }
 ]
-
-# Webhook server config (only needed if using webhooks)
-runtime_config = AgentRuntimeConfig(
-    webhook_host="127.0.0.1",
-    webhook_port=8080,
-    webhook_routes=[
-        {
-            "source_id": "my-source",
-            "path": "/webhooks/my-source",
-            "methods": ["POST"],
-        },
-    ],
-)
 ```

-**Key rules for async entry points:**
- `async_entry_points` is a list of `AsyncEntryPointSpec` (NOT `EntryPointSpec`)
- `runtime_config` is `AgentRuntimeConfig` (NOT `RuntimeConfig` from config.py)
- Valid trigger_types: `timer`, `event`, `webhook`, `manual`, `api`
- Valid isolation_levels: `isolated`, `shared`, `synchronized`
+**Key rules for triggers.json:**
+- Valid trigger_types: `timer`, `webhook`
 - Timer trigger_config (cron): `{"cron": "0 9 * * *"}` — standard 5-field cron expression
- Timer trigger_config (interval): `{"interval_minutes": float, "run_immediately": bool}`
- Event trigger_config: `{"event_types": ["webhook_received"], "filter_stream": "...", "filter_node": "..."}`
- Use `isolation_level="shared"` for async entry points that need to read
-  the primary session's memory (e.g., user-configured rules)
- The `_build_graph()` method passes `async_entry_points` to GraphSpec
- Reference: `exports/gmail_inbox_guardian/agent.py`
+- Timer trigger_config (interval): `{"interval_minutes": float}`
+- Each trigger must have a unique `id`
+- The `task` field describes what the worker should do when the trigger fires
+- Triggers are persisted back to `triggers.json` when modified via queen tools

 ## __init__.py

@@ -453,21 +418,6 @@ __all__ = [
 ]
 ```

-**If the agent uses async entry points**, also import and export:
-```python
-from .agent import (
-    ...,
-    async_entry_points,
-    runtime_config,  # Only if using webhooks
-)
-
-__all__ = [
-    ...,
-    "async_entry_points",
-    "runtime_config",
-]
-```
-
 ## __main__.py

 ```python
@@ -559,7 +509,7 @@ if __name__ == "__main__":

 ## mcp_servers.json

-> **Auto-generated.** `initialize_agent_package` creates this file with hive-tools
+> **Auto-generated.** `initialize_and_build_agent` creates this file with hive-tools
 > as the default. Only edit manually to add additional MCP servers.

 ```json
@@ -31,8 +31,7 @@ module-level variables via `getattr()`:
 | `conversation_mode` | no | not passed | Isolated mode (no context carryover) |
 | `identity_prompt` | no | not passed | No agent-level identity |
 | `loop_config` | no | `{}` | No iteration limits |
-| `async_entry_points` | no | `[]` | No async triggers (timers, webhooks, events) |
-| `runtime_config` | no | `None` | No webhook server |
+| `triggers.json` (file) | no | not present | No triggers (timers, webhooks) |

 **CRITICAL:** `__init__.py` MUST import and re-export ALL of these from
 `agent.py`. Missing exports silently fall back to defaults, causing
@@ -226,7 +225,7 @@ Only three valid keys:
 loop_config = {
    "max_iterations": 100,          # Max LLM turns per node visit
    "max_tool_calls_per_turn": 20,  # Max tool calls per LLM response
-    "max_history_tokens": 32000,    # Triggers conversation compaction
+    "max_context_tokens": 32000,    # Triggers conversation compaction
 }
 ```
 **INVALID keys** (do NOT use): `"strategy"`, `"mode"`, `"timeout"`,
@@ -257,44 +256,28 @@ Multiple ON_SUCCESS edges from same source → parallel execution via asyncio.ga

 Judge is the SOLE acceptance mechanism — no ad-hoc framework gating.

-## Async Entry Points (Webhooks, Timers, Events)
+## Triggers (Timers, Webhooks)

-For agents that react to external events, use `AsyncEntryPointSpec`:
+For agents that react to external events, create a `triggers.json` file
+in the agent's export directory:

-```python
-from framework.graph.edge import AsyncEntryPointSpec
-from framework.runtime.agent_runtime import AgentRuntimeConfig
-
-# Timer trigger (cron or interval)
-async_entry_points = [
-    AsyncEntryPointSpec(
-        id="daily-check",
-        name="Daily Check",
-        entry_node="process",
-        trigger_type="timer",
-        trigger_config={"cron": "0 9 * * *"},  # daily at 9am
-        isolation_level="shared",
-    )
+```json
+[
+  {
+    "id": "daily-check",
+    "name": "Daily Check",
+    "trigger_type": "timer",
+    "trigger_config": {"cron": "0 9 * * *"},
+    "task": "Run the daily check process"
+  }
 ]
-
-# Webhook server (optional)
-runtime_config = AgentRuntimeConfig(
-    webhook_host="127.0.0.1",
-    webhook_port=8080,
-    webhook_routes=[{"source_id": "gmail", "path": "/webhooks/gmail", "methods": ["POST"]}],
-)
 ```

 ### Key Fields
- `trigger_type`: `"timer"`, `"event"`, `"webhook"`, `"manual"`
+- `trigger_type`: `"timer"` or `"webhook"`
 - `trigger_config`: `{"cron": "0 9 * * *"}` or `{"interval_minutes": 20}`
- `isolation_level`: `"shared"` (recommended), `"isolated"`, `"synchronized"`
- `event_types`: For event triggers, e.g., `["webhook_received"]`
-
-### Exports Required
-Both `async_entry_points` and `runtime_config` must be exported from `__init__.py`.
-
-See `exports/gmail_inbox_guardian/agent.py` for complete example.
+- `task`: describes what the worker should do when the trigger fires
+- Triggers can also be created/removed at runtime via `set_trigger` / `remove_trigger` queen tools

 ## Tool Discovery

@@ -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
@@ -0,0 +1,60 @@
+# Queen Memory — File System Structure
+
+```
+~/.hive/
+├── queen/
+│   ├── MEMORY.md                          ← Semantic memory
+│   ├── memories/
+│   │   ├── MEMORY-2026-03-09.md           ← Episodic memory (today)
+│   │   ├── MEMORY-2026-03-08.md
+│   │   └── ...
+│   └── session/
+│       └── {session_id}/                  ← One dir per session (or resumed-from session)
+│           ├── conversations/
+│           │   ├── parts/
+│           │   │   ├── 00001.json         ← One file per message (role, content, tool_calls)
+│           │   │   ├── 00002.json
+│           │   │   └── ...
+│           │   └── spillover/
+│           │       ├── conversation_1.md  ← Compacted old conversation segments
+│           │       ├── conversation_2.md
+│           │       └── ...
+│           └── data/
+│               ├── web_search_1.txt      ← Spillover: large tool results
+│               ├── web_search_2.txt
+│               └── ...
+```
+
+---
+
+## The two memory tiers
+
+| File | Tier | Written by | Read at |
+|---|---|---|---|
+| `MEMORY.md` | Semantic | Consolidation LLM (auto, post-session) | Session start (injected into system prompt) |
+| `memories/MEMORY-YYYY-MM-DD.md` | Episodic | Queen via `write_to_diary` tool + consolidation LLM | Session start (today's file injected) |
+
+---
+
+## Session directory naming
+
+The session directory name is **`queen_resume_from`** when a cold-restore resumes an existing
+session, otherwise the new **`session_id`**. This means resumed sessions accumulate all messages
+in the original directory rather than fragmenting across multiple folders.
+
+---
+
+## Consolidation
+
+`consolidate_queen_memory()` runs every **5 minutes** in the background and once more at session
+end. It reads:
+
+1. `conversations/parts/*.json` — full message history (user + assistant turns; tool results skipped)
+
+It then makes two LLM writes:
+
+- Rewrites `MEMORY.md` in place (semantic memory — queen never touches this herself)
+- Appends a timestamped prose entry to today's `memories/MEMORY-YYYY-MM-DD.md`
+
+If the combined transcript exceeds ~200 K characters it is recursively binary-compacted via the
+LLM before being sent to the consolidation model (mirrors `EventLoopNode._llm_compact`).
@@ -1,4 +1,4 @@
-"""Test fixtures for Hive Coder agent."""
+"""Test fixtures for Queen agent."""

 import sys
 from pathlib import Path
@@ -1,8 +1,8 @@
 """Queen's ticket receiver entry point.

-When the Worker Health Judge emits a WORKER_ESCALATION_TICKET event on the
-shared EventBus, this entry point fires and routes to the ``ticket_triage``
-node, where the Queen deliberates and decides whether to notify the operator.
+When a WORKER_ESCALATION_TICKET event is emitted on the shared EventBus,
+this entry point fires and routes to the ``ticket_triage`` node, where the
+Queen deliberates and decides whether to notify the operator.

 Isolation level is ``isolated`` — the queen's triage memory is kept separate
 from the worker's shared memory. Each ticket triage runs in its own context.
@@ -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
@@ -1,7 +0,0 @@
-"""Builder interface for analyzing and building agents."""
-
-from framework.builder.query import BuilderQuery
-
-__all__ = [
-    "BuilderQuery",
-]
@@ -1,501 +0,0 @@
-"""
-Builder Query Interface - How I (Builder) analyze agent runs.
-
-This is designed around the questions I need to answer:
-1. What happened? (summaries, narratives)
-2. Why did it fail? (failure analysis, decision traces)
-3. What patterns emerge? (across runs, across nodes)
-4. What should we change? (suggestions)
-"""
-
-from collections import defaultdict
-from pathlib import Path
-from typing import Any
-
-from framework.schemas.decision import Decision
-from framework.schemas.run import Run, RunStatus, RunSummary
-from framework.storage.backend import FileStorage
-
-
-class FailureAnalysis:
-    """Structured analysis of why a run failed."""
-
-    def __init__(
-        self,
-        run_id: str,
-        failure_point: str,
-        root_cause: str,
-        decision_chain: list[str],
-        problems: list[str],
-        suggestions: list[str],
-    ):
-        self.run_id = run_id
-        self.failure_point = failure_point
-        self.root_cause = root_cause
-        self.decision_chain = decision_chain
-        self.problems = problems
-        self.suggestions = suggestions
-
-    def to_dict(self) -> dict[str, Any]:
-        return {
-            "run_id": self.run_id,
-            "failure_point": self.failure_point,
-            "root_cause": self.root_cause,
-            "decision_chain": self.decision_chain,
-            "problems": self.problems,
-            "suggestions": self.suggestions,
-        }
-
-    def __str__(self) -> str:
-        lines = [
-            f"=== Failure Analysis for {self.run_id} ===",
-            "",
-            f"Failure Point: {self.failure_point}",
-            f"Root Cause: {self.root_cause}",
-            "",
-            "Decision Chain Leading to Failure:",
-        ]
-        for i, dec in enumerate(self.decision_chain, 1):
-            lines.append(f"  {i}. {dec}")
-
-        if self.problems:
-            lines.append("")
-            lines.append("Reported Problems:")
-            for prob in self.problems:
-                lines.append(f"  - {prob}")
-
-        if self.suggestions:
-            lines.append("")
-            lines.append("Suggestions:")
-            for sug in self.suggestions:
-                lines.append(f"  → {sug}")
-
-        return "\n".join(lines)
-
-
-class PatternAnalysis:
-    """Patterns detected across multiple runs."""
-
-    def __init__(
-        self,
-        goal_id: str,
-        run_count: int,
-        success_rate: float,
-        common_failures: list[tuple[str, int]],
-        problematic_nodes: list[tuple[str, float]],
-        decision_patterns: dict[str, Any],
-    ):
-        self.goal_id = goal_id
-        self.run_count = run_count
-        self.success_rate = success_rate
-        self.common_failures = common_failures
-        self.problematic_nodes = problematic_nodes
-        self.decision_patterns = decision_patterns
-
-    def to_dict(self) -> dict[str, Any]:
-        return {
-            "goal_id": self.goal_id,
-            "run_count": self.run_count,
-            "success_rate": self.success_rate,
-            "common_failures": self.common_failures,
-            "problematic_nodes": self.problematic_nodes,
-            "decision_patterns": self.decision_patterns,
-        }
-
-    def __str__(self) -> str:
-        lines = [
-            f"=== Pattern Analysis for Goal {self.goal_id} ===",
-            "",
-            f"Runs Analyzed: {self.run_count}",
-            f"Success Rate: {self.success_rate:.1%}",
-        ]
-
-        if self.common_failures:
-            lines.append("")
-            lines.append("Common Failures:")
-            for failure, count in self.common_failures:
-                lines.append(f"  - {failure} ({count} occurrences)")
-
-        if self.problematic_nodes:
-            lines.append("")
-            lines.append("Problematic Nodes (failure rate):")
-            for node, rate in self.problematic_nodes:
-                lines.append(f"  - {node}: {rate:.1%} failure rate")
-
-        return "\n".join(lines)
-
-
-class BuilderQuery:
-    """
-    The interface I (Builder) use to understand what agents are doing.
-
-    This is optimized for the questions I need to answer when analyzing
-    agent behavior and deciding what to improve.
-    """
-
-    def __init__(self, storage_path: str | Path):
-        self.storage = FileStorage(storage_path)
-
-    # === WHAT HAPPENED? ===
-
-    def get_run_summary(self, run_id: str) -> RunSummary | None:
-        """Get a quick summary of a run."""
-        return self.storage.load_summary(run_id)
-
-    def get_full_run(self, run_id: str) -> Run | None:
-        """Get the complete run with all decisions."""
-        return self.storage.load_run(run_id)
-
-    def list_runs_for_goal(self, goal_id: str) -> list[RunSummary]:
-        """Get summaries of all runs for a goal."""
-        run_ids = self.storage.get_runs_by_goal(goal_id)
-        summaries = []
-        for run_id in run_ids:
-            summary = self.storage.load_summary(run_id)
-            if summary:
-                summaries.append(summary)
-        return summaries
-
-    def get_recent_failures(self, limit: int = 10) -> list[RunSummary]:
-        """Get recent failed runs."""
-        run_ids = self.storage.get_runs_by_status(RunStatus.FAILED)
-        summaries = []
-        for run_id in run_ids[:limit]:
-            summary = self.storage.load_summary(run_id)
-            if summary:
-                summaries.append(summary)
-        return summaries
-
-    # === WHY DID IT FAIL? ===
-
-    def analyze_failure(self, run_id: str) -> FailureAnalysis | None:
-        """
-        Deep analysis of why a run failed.
-
-        This is my primary tool for understanding what went wrong.
-        """
-        run = self.storage.load_run(run_id)
-        if run is None or run.status != RunStatus.FAILED:
-            return None
-
-        # Find the first failed decision
-        failed_decisions = [d for d in run.decisions if not d.was_successful]
-        if not failed_decisions:
-            failure_point = "Unknown - no decision marked as failed"
-            root_cause = "Run failed but all decisions succeeded (external cause?)"
-        else:
-            first_failure = failed_decisions[0]
-            failure_point = first_failure.summary_for_builder()
-            root_cause = first_failure.outcome.error if first_failure.outcome else "Unknown"
-
-        # Build the decision chain leading to failure
-        decision_chain = []
-        for d in run.decisions:
-            decision_chain.append(d.summary_for_builder())
-            if not d.was_successful:
-                break
-
-        # Extract problems
-        problems = [f"[{p.severity}] {p.description}" for p in run.problems]
-
-        # Generate suggestions based on the failure
-        suggestions = self._generate_suggestions(run, failed_decisions)
-
-        return FailureAnalysis(
-            run_id=run_id,
-            failure_point=failure_point,
-            root_cause=root_cause,
-            decision_chain=decision_chain,
-            problems=problems,
-            suggestions=suggestions,
-        )
-
-    def get_decision_trace(self, run_id: str) -> list[str]:
-        """Get a readable trace of all decisions in a run."""
-        run = self.storage.load_run(run_id)
-        if run is None:
-            return []
-        return [d.summary_for_builder() for d in run.decisions]
-
-    # === WHAT PATTERNS EMERGE? ===
-
-    def find_patterns(self, goal_id: str) -> PatternAnalysis | None:
-        """
-        Find patterns across runs for a goal.
-
-        This helps me understand systemic issues vs one-off failures.
-        """
-        run_ids = self.storage.get_runs_by_goal(goal_id)
-        if not run_ids:
-            return None
-
-        runs = []
-        for run_id in run_ids:
-            run = self.storage.load_run(run_id)
-            if run:
-                runs.append(run)
-
-        if not runs:
-            return None
-
-        # Calculate success rate
-        completed = [r for r in runs if r.status == RunStatus.COMPLETED]
-        success_rate = len(completed) / len(runs) if runs else 0.0
-
-        # Find common failures
-        failure_counts: dict[str, int] = defaultdict(int)
-        for run in runs:
-            for decision in run.decisions:
-                if not decision.was_successful and decision.outcome:
-                    error = decision.outcome.error or "Unknown error"
-                    failure_counts[error] += 1
-
-        common_failures = sorted(failure_counts.items(), key=lambda x: x[1], reverse=True)[:5]
-
-        # Find problematic nodes
-        node_stats: dict[str, dict[str, int]] = defaultdict(lambda: {"total": 0, "failed": 0})
-        for run in runs:
-            for decision in run.decisions:
-                node_stats[decision.node_id]["total"] += 1
-                if not decision.was_successful:
-                    node_stats[decision.node_id]["failed"] += 1
-
-        problematic_nodes = []
-        for node_id, stats in node_stats.items():
-            if stats["total"] > 0:
-                failure_rate = stats["failed"] / stats["total"]
-                if failure_rate > 0.1:  # More than 10% failure rate
-                    problematic_nodes.append((node_id, failure_rate))
-
-        problematic_nodes.sort(key=lambda x: x[1], reverse=True)
-
-        # Decision patterns
-        decision_patterns = self._analyze_decision_patterns(runs)
-
-        return PatternAnalysis(
-            goal_id=goal_id,
-            run_count=len(runs),
-            success_rate=success_rate,
-            common_failures=common_failures,
-            problematic_nodes=problematic_nodes,
-            decision_patterns=decision_patterns,
-        )
-
-    def compare_runs(self, run_id_1: str, run_id_2: str) -> dict[str, Any]:
-        """Compare two runs to understand what differed."""
-        run1 = self.storage.load_run(run_id_1)
-        run2 = self.storage.load_run(run_id_2)
-
-        if run1 is None or run2 is None:
-            return {"error": "One or both runs not found"}
-
-        return {
-            "run_1": {
-                "id": run1.id,
-                "status": run1.status.value,
-                "decisions": len(run1.decisions),
-                "success_rate": run1.metrics.success_rate,
-            },
-            "run_2": {
-                "id": run2.id,
-                "status": run2.status.value,
-                "decisions": len(run2.decisions),
-                "success_rate": run2.metrics.success_rate,
-            },
-            "differences": self._find_differences(run1, run2),
-        }
-
-    # === WHAT SHOULD WE CHANGE? ===
-
-    def suggest_improvements(self, goal_id: str) -> list[dict[str, Any]]:
-        """
-        Generate improvement suggestions based on run analysis.
-
-        This is what I use to propose changes to the human engineer.
-        """
-        patterns = self.find_patterns(goal_id)
-        if patterns is None:
-            return []
-
-        suggestions = []
-
-        # Suggestion: Fix problematic nodes
-        for node_id, failure_rate in patterns.problematic_nodes:
-            suggestions.append(
-                {
-                    "type": "node_improvement",
-                    "target": node_id,
-                    "reason": f"Node has {failure_rate:.1%} failure rate",
-                    "recommendation": (
-                        f"Review and improve node '{node_id}' - "
-                        "high failure rate suggests prompt or tool issues"
-                    ),
-                    "priority": "high" if failure_rate > 0.3 else "medium",
-                }
-            )
-
-        # Suggestion: Address common failures
-        for failure, count in patterns.common_failures:
-            if count >= 2:
-                suggestions.append(
-                    {
-                        "type": "error_handling",
-                        "target": failure,
-                        "reason": f"Error occurred {count} times",
-                        "recommendation": f"Add handling for: {failure}",
-                        "priority": "high" if count >= 5 else "medium",
-                    }
-                )
-
-        # Suggestion: Overall success rate
-        if patterns.success_rate < 0.8:
-            suggestions.append(
-                {
-                    "type": "architecture",
-                    "target": goal_id,
-                    "reason": f"Goal success rate is only {patterns.success_rate:.1%}",
-                    "recommendation": (
-                        "Consider restructuring the agent graph or improving goal definition"
-                    ),
-                    "priority": "high",
-                }
-            )
-
-        return suggestions
-
-    def get_node_performance(self, node_id: str) -> dict[str, Any]:
-        """Get performance metrics for a specific node across all runs."""
-        run_ids = self.storage.get_runs_by_node(node_id)
-
-        total_decisions = 0
-        successful_decisions = 0
-        total_latency = 0
-        total_tokens = 0
-        decision_types: dict[str, int] = defaultdict(int)
-
-        for run_id in run_ids:
-            run = self.storage.load_run(run_id)
-            if run:
-                for decision in run.decisions:
-                    if decision.node_id == node_id:
-                        total_decisions += 1
-                        if decision.was_successful:
-                            successful_decisions += 1
-                        if decision.outcome:
-                            total_latency += decision.outcome.latency_ms
-                            total_tokens += decision.outcome.tokens_used
-                        decision_types[decision.decision_type.value] += 1
-
-        return {
-            "node_id": node_id,
-            "total_decisions": total_decisions,
-            "success_rate": successful_decisions / total_decisions if total_decisions > 0 else 0,
-            "avg_latency_ms": total_latency / total_decisions if total_decisions > 0 else 0,
-            "total_tokens": total_tokens,
-            "decision_type_distribution": dict(decision_types),
-        }
-
-    # === PRIVATE HELPERS ===
-
-    def _generate_suggestions(
-        self,
-        run: Run,
-        failed_decisions: list[Decision],
-    ) -> list[str]:
-        """Generate suggestions based on failure analysis."""
-        suggestions = []
-
-        for decision in failed_decisions:
-            # Check if there were alternatives
-            if len(decision.options) > 1:
-                chosen = decision.chosen_option
-                alternatives = [o for o in decision.options if o.id != decision.chosen_option_id]
-                if alternatives:
-                    alt_desc = alternatives[0].description
-                    chosen_desc = chosen.description if chosen else "unknown"
-                    suggestions.append(
-                        f"Consider alternative: '{alt_desc}' instead of '{chosen_desc}'"
-                    )
-
-            # Check for missing context
-            if not decision.input_context:
-                suggestions.append(
-                    f"Decision '{decision.intent}' had no input context - "
-                    "ensure relevant data is passed"
-                )
-
-            # Check for constraint issues
-            if decision.active_constraints:
-                constraints = ", ".join(decision.active_constraints)
-                suggestions.append(f"Review constraints: {constraints} - may be too restrictive")
-
-        # Check for reported problems with suggestions
-        for problem in run.problems:
-            if problem.suggested_fix:
-                suggestions.append(problem.suggested_fix)
-
-        return suggestions
-
-    def _analyze_decision_patterns(self, runs: list[Run]) -> dict[str, Any]:
-        """Analyze decision patterns across runs."""
-        type_counts: dict[str, int] = defaultdict(int)
-        option_counts: dict[str, dict[str, int]] = defaultdict(lambda: defaultdict(int))
-
-        for run in runs:
-            for decision in run.decisions:
-                type_counts[decision.decision_type.value] += 1
-
-                # Track which options are chosen for similar intents
-                intent_key = decision.intent[:50]  # Truncate for grouping
-                if decision.chosen_option:
-                    option_counts[intent_key][decision.chosen_option.description] += 1
-
-        # Find most common choices per intent
-        common_choices = {}
-        for intent, choices in option_counts.items():
-            if choices:
-                most_common = max(choices.items(), key=lambda x: x[1])
-                common_choices[intent] = {
-                    "choice": most_common[0],
-                    "count": most_common[1],
-                    "alternatives": len(choices) - 1,
-                }
-
-        return {
-            "decision_type_distribution": dict(type_counts),
-            "common_choices": common_choices,
-        }
-
-    def _find_differences(self, run1: Run, run2: Run) -> list[str]:
-        """Find key differences between two runs."""
-        differences = []
-
-        # Status difference
-        if run1.status != run2.status:
-            differences.append(f"Status: {run1.status.value} vs {run2.status.value}")
-
-        # Decision count difference
-        if len(run1.decisions) != len(run2.decisions):
-            differences.append(f"Decision count: {len(run1.decisions)} vs {len(run2.decisions)}")
-
-        # Find first divergence point
-        for i, (d1, d2) in enumerate(zip(run1.decisions, run2.decisions, strict=False)):
-            if d1.chosen_option_id != d2.chosen_option_id:
-                differences.append(
-                    f"Diverged at decision {i}: "
-                    f"chose '{d1.chosen_option_id}' vs '{d2.chosen_option_id}'"
-                )
-                break
-
-        # Node differences
-        nodes1 = set(run1.metrics.nodes_executed)
-        nodes2 = set(run2.metrics.nodes_executed)
-        if nodes1 != nodes2:
-            only_1 = nodes1 - nodes2
-            only_2 = nodes2 - nodes1
-            if only_1:
-                differences.append(f"Nodes only in run 1: {only_1}")
-            if only_2:
-                differences.append(f"Nodes only in run 2: {only_2}")
-
-        return differences
@@ -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"):
@@ -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__)


@@ -47,15 +51,176 @@ 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:
+    """Return the configured max_context_tokens, falling back to DEFAULT_MAX_CONTEXT_TOKENS."""
+    return get_hive_config().get("llm", {}).get("max_context_tokens", DEFAULT_MAX_CONTEXT_TOKENS)
+
+
 def get_api_key() -> str | None:
    """Return the API key, supporting env var, Claude Code subscription, Codex, and ZAI Code.

@@ -90,6 +255,28 @@ def get_api_key() -> str | None:
        except ImportError:
            pass

+    # Kimi Code subscription: read API key from ~/.kimi/config.toml
+    if 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
+
+    # 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:
@@ -97,18 +284,116 @@ 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", {})
    if llm.get("use_codex_subscription"):
        # Codex subscription routes through the ChatGPT backend, not api.openai.com.
        return "https://chatgpt.com/backend-api/codex"
-    return llm.get("api_base")
+    if llm.get("use_kimi_code_subscription"):
+        # Kimi Code uses an Anthropic-compatible endpoint (no /v1 suffix).
+        return "https://api.kimi.com/coding"
+    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]:
@@ -149,6 +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 {}


@@ -164,6 +454,7 @@ class RuntimeConfig:
    model: str = field(default_factory=get_preferred_model)
    temperature: float = 0.7
    max_tokens: int = field(default_factory=get_max_tokens)
+    max_context_tokens: int = field(default_factory=get_max_context_tokens)
    api_key: str | None = field(default_factory=get_api_key)
    api_base: str | None = field(default_factory=get_api_base)
    extra_kwargs: dict[str, Any] = field(default_factory=get_llm_extra_kwargs)
@@ -6,7 +6,7 @@ This module provides secure credential storage with:
 - Template-based usage: {{cred.key}} patterns for injection
 - Bipartisan model: Store stores values, tools define usage
 - Provider system: Extensible lifecycle management (refresh, validate)
- Multiple backends: Encrypted files, env vars, HashiCorp Vault
+- Multiple backends: Encrypted files, env vars

 Quick Start:
    from core.framework.credentials import CredentialStore, CredentialObject
@@ -38,8 +38,6 @@ For Aden server sync:
        AdenSyncProvider,
    )

-For Vault integration:
-    from core.framework.credentials.vault import HashiCorpVaultStorage
 """

 from .key_storage import (
@@ -142,17 +142,27 @@ 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:
-        logger.debug("Could not delete %s from encrypted store", ADEN_CREDENTIAL_ID)
-
+        logger.warning(
+            "Unexpected error deleting %s from encrypted store",
+            ADEN_CREDENTIAL_ID,
+            exc_info=True,
+        )
    os.environ.pop(ADEN_ENV_VAR, None)
+    return deleted


 # ---------------------------------------------------------------------------
@@ -167,8 +177,10 @@ def _read_credential_key_file() -> str | None:
            value = CREDENTIAL_KEY_PATH.read_text(encoding="utf-8").strip()
            if value:
                return value
+    except (FileNotFoundError, PermissionError) as e:
+        logger.debug("Could not read %s: %s", CREDENTIAL_KEY_PATH, e)
    except Exception:
-        logger.debug("Could not read %s", CREDENTIAL_KEY_PATH)
+        logger.warning("Unexpected error reading %s", CREDENTIAL_KEY_PATH, exc_info=True)
    return None


@@ -196,6 +208,12 @@ def _read_aden_from_encrypted_store() -> str | None:
        cred = storage.load(ADEN_CREDENTIAL_ID)
        if cred:
            return cred.get_key("api_key")
+    except (FileNotFoundError, PermissionError, KeyError) as e:
+        logger.debug("Could not load %s from encrypted store: %s", ADEN_CREDENTIAL_ID, e)
    except Exception:
-        logger.debug("Could not load %s from encrypted store", ADEN_CREDENTIAL_ID)
+        logger.warning(
+            "Unexpected error loading %s from encrypted store",
+            ADEN_CREDENTIAL_ID,
+            exc_info=True,
+        )
    return None
@@ -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 []


@@ -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

@@ -1,55 +0,0 @@
-"""
-HashiCorp Vault integration for the credential store.
-
-This module provides enterprise-grade secret management through
-HashiCorp Vault integration.
-
-Quick Start:
-    from core.framework.credentials import CredentialStore
-    from core.framework.credentials.vault import HashiCorpVaultStorage
-
-    # Configure Vault storage
-    storage = HashiCorpVaultStorage(
-        url="https://vault.example.com:8200",
-        # token read from VAULT_TOKEN env var
-        mount_point="secret",
-        path_prefix="hive/agents/prod"
-    )
-
-    # Create credential store with Vault backend
-    store = CredentialStore(storage=storage)
-
-    # Use normally - credentials are stored in Vault
-    credential = store.get_credential("my_api")
-
-Requirements:
-    pip install hvac
-
-Authentication:
-    Set the VAULT_TOKEN environment variable or pass the token directly:
-
-        export VAULT_TOKEN="hvs.xxxxxxxxxxxxx"
-
-    For production, consider using Vault auth methods:
-    - Kubernetes auth
-    - AppRole auth
-    - AWS IAM auth
-
-Vault Configuration:
-    Ensure KV v2 secrets engine is enabled:
-
-        vault secrets enable -path=secret kv-v2
-
-    Grant appropriate policies:
-
-        path "secret/data/hive/credentials/*" {
-            capabilities = ["create", "read", "update", "delete", "list"]
-        }
-        path "secret/metadata/hive/credentials/*" {
-            capabilities = ["list", "delete"]
-        }
-"""
-
-from .hashicorp import HashiCorpVaultStorage
-
-__all__ = ["HashiCorpVaultStorage"]
@@ -1,394 +0,0 @@
-"""
-HashiCorp Vault storage adapter.
-
-Provides integration with HashiCorp Vault for enterprise secret management.
-Requires the 'hvac' package: uv pip install hvac
-"""
-
-from __future__ import annotations
-
-import logging
-import os
-from datetime import datetime
-from typing import Any
-
-from pydantic import SecretStr
-
-from ..models import CredentialKey, CredentialObject, CredentialType
-from ..storage import CredentialStorage
-
-logger = logging.getLogger(__name__)
-
-
-class HashiCorpVaultStorage(CredentialStorage):
-    """
-    HashiCorp Vault storage adapter.
-
-    Features:
-    - KV v2 secrets engine support
-    - Namespace support (Enterprise)
-    - Automatic secret versioning
-    - Audit logging via Vault
-
-    The adapter stores credentials in Vault's KV v2 secrets engine with
-    the following structure:
-
-        {mount_point}/data/{path_prefix}/{credential_id}
-        └── data:
-            ├── _type: "oauth2"
-            ├── access_token: "xxx"
-            ├── refresh_token: "yyy"
-            ├── _expires_access_token: "2024-01-26T12:00:00"
-            └── _provider_id: "oauth2"
-
-    Example:
-        storage = HashiCorpVaultStorage(
-            url="https://vault.example.com:8200",
-            token="hvs.xxx",  # Or use VAULT_TOKEN env var
-            mount_point="secret",
-            path_prefix="hive/credentials"
-        )
-
-        store = CredentialStore(storage=storage)
-
-        # Credentials are now stored in Vault
-        store.save_credential(credential)
-        credential = store.get_credential("my_api")
-
-    Authentication:
-        The adapter uses token-based authentication. The token can be provided:
-        1. Directly via the 'token' parameter
-        2. Via the VAULT_TOKEN environment variable
-
-        For production, consider using:
-        - Kubernetes auth method
-        - AppRole auth method
-        - AWS IAM auth method
-
-    Requirements:
-        uv pip install hvac
-    """
-
-    def __init__(
-        self,
-        url: str,
-        token: str | None = None,
-        mount_point: str = "secret",
-        path_prefix: str = "hive/credentials",
-        namespace: str | None = None,
-        verify_ssl: bool = True,
-    ):
-        """
-        Initialize Vault storage.
-
-        Args:
-            url: Vault server URL (e.g., https://vault.example.com:8200)
-            token: Vault token. If None, reads from VAULT_TOKEN env var
-            mount_point: KV secrets engine mount point (default: "secret")
-            path_prefix: Path prefix for all credentials
-            namespace: Vault namespace (Enterprise feature)
-            verify_ssl: Whether to verify SSL certificates
-
-        Raises:
-            ImportError: If hvac is not installed
-            ValueError: If authentication fails
-        """
-        try:
-            import hvac
-        except ImportError as e:
-            raise ImportError(
-                "HashiCorp Vault support requires 'hvac'. Install with: uv pip install hvac"
-            ) from e
-
-        self._url = url
-        self._token = token or os.environ.get("VAULT_TOKEN")
-        self._mount = mount_point
-        self._prefix = path_prefix
-        self._namespace = namespace
-
-        if not self._token:
-            raise ValueError(
-                "Vault token required. Set VAULT_TOKEN env var or pass token parameter."
-            )
-
-        self._client = hvac.Client(
-            url=url,
-            token=self._token,
-            namespace=namespace,
-            verify=verify_ssl,
-        )
-
-        if not self._client.is_authenticated():
-            raise ValueError("Vault authentication failed. Check token and server URL.")
-
-        logger.info(f"Connected to HashiCorp Vault at {url}")
-
-    def _path(self, credential_id: str) -> str:
-        """Build Vault path for credential."""
-        # Sanitize credential_id
-        safe_id = credential_id.replace("/", "_").replace("\\", "_")
-        return f"{self._prefix}/{safe_id}"
-
-    def save(self, credential: CredentialObject) -> None:
-        """Save credential to Vault KV v2."""
-        path = self._path(credential.id)
-        data = self._serialize_for_vault(credential)
-
-        try:
-            self._client.secrets.kv.v2.create_or_update_secret(
-                path=path,
-                secret=data,
-                mount_point=self._mount,
-            )
-            logger.debug(f"Saved credential '{credential.id}' to Vault at {path}")
-        except Exception as e:
-            logger.error(f"Failed to save credential '{credential.id}' to Vault: {e}")
-            raise
-
-    def load(self, credential_id: str) -> CredentialObject | None:
-        """Load credential from Vault."""
-        path = self._path(credential_id)
-
-        try:
-            response = self._client.secrets.kv.v2.read_secret_version(
-                path=path,
-                mount_point=self._mount,
-            )
-            data = response["data"]["data"]
-            return self._deserialize_from_vault(credential_id, data)
-        except Exception as e:
-            # Check if it's a "not found" error
-            error_str = str(e).lower()
-            if "not found" in error_str or "404" in error_str:
-                logger.debug(f"Credential '{credential_id}' not found in Vault")
-                return None
-            logger.error(f"Failed to load credential '{credential_id}' from Vault: {e}")
-            raise
-
-    def delete(self, credential_id: str) -> bool:
-        """Delete credential from Vault (all versions)."""
-        path = self._path(credential_id)
-
-        try:
-            self._client.secrets.kv.v2.delete_metadata_and_all_versions(
-                path=path,
-                mount_point=self._mount,
-            )
-            logger.debug(f"Deleted credential '{credential_id}' from Vault")
-            return True
-        except Exception as e:
-            error_str = str(e).lower()
-            if "not found" in error_str or "404" in error_str:
-                return False
-            logger.error(f"Failed to delete credential '{credential_id}' from Vault: {e}")
-            raise
-
-    def list_all(self) -> list[str]:
-        """List all credentials under the prefix."""
-        try:
-            response = self._client.secrets.kv.v2.list_secrets(
-                path=self._prefix,
-                mount_point=self._mount,
-            )
-            keys = response.get("data", {}).get("keys", [])
-            # Remove trailing slashes from folder names
-            return [k.rstrip("/") for k in keys]
-        except Exception as e:
-            error_str = str(e).lower()
-            if "not found" in error_str or "404" in error_str:
-                return []
-            logger.error(f"Failed to list credentials from Vault: {e}")
-            raise
-
-    def exists(self, credential_id: str) -> bool:
-        """Check if credential exists in Vault."""
-        try:
-            path = self._path(credential_id)
-            self._client.secrets.kv.v2.read_secret_version(
-                path=path,
-                mount_point=self._mount,
-            )
-            return True
-        except Exception:
-            return False
-
-    def _serialize_for_vault(self, credential: CredentialObject) -> dict[str, Any]:
-        """Convert credential to Vault secret format."""
-        data: dict[str, Any] = {
-            "_type": credential.credential_type.value,
-        }
-
-        if credential.provider_id:
-            data["_provider_id"] = credential.provider_id
-
-        if credential.description:
-            data["_description"] = credential.description
-
-        if credential.auto_refresh:
-            data["_auto_refresh"] = "true"
-
-        # Store each key
-        for key_name, key in credential.keys.items():
-            data[key_name] = key.get_secret_value()
-
-            if key.expires_at:
-                data[f"_expires_{key_name}"] = key.expires_at.isoformat()
-
-            if key.metadata:
-                data[f"_metadata_{key_name}"] = str(key.metadata)
-
-        return data
-
-    def _deserialize_from_vault(self, credential_id: str, data: dict[str, Any]) -> CredentialObject:
-        """Reconstruct credential from Vault secret."""
-        # Extract metadata fields
-        cred_type = CredentialType(data.pop("_type", "api_key"))
-        provider_id = data.pop("_provider_id", None)
-        description = data.pop("_description", "")
-        auto_refresh = data.pop("_auto_refresh", "") == "true"
-
-        # Build keys dict
-        keys: dict[str, CredentialKey] = {}
-
-        # Find all non-metadata keys
-        key_names = [k for k in data.keys() if not k.startswith("_")]
-
-        for key_name in key_names:
-            value = data[key_name]
-
-            # Check for expiration
-            expires_at = None
-            expires_key = f"_expires_{key_name}"
-            if expires_key in data:
-                try:
-                    expires_at = datetime.fromisoformat(data[expires_key])
-                except (ValueError, TypeError):
-                    pass
-
-            # Check for metadata
-            metadata: dict[str, Any] = {}
-            metadata_key = f"_metadata_{key_name}"
-            if metadata_key in data:
-                try:
-                    import ast
-
-                    metadata = ast.literal_eval(data[metadata_key])
-                except (ValueError, SyntaxError):
-                    pass
-
-            keys[key_name] = CredentialKey(
-                name=key_name,
-                value=SecretStr(value),
-                expires_at=expires_at,
-                metadata=metadata,
-            )
-
-        return CredentialObject(
-            id=credential_id,
-            credential_type=cred_type,
-            keys=keys,
-            provider_id=provider_id,
-            description=description,
-            auto_refresh=auto_refresh,
-        )
-
-    # --- Vault-Specific Operations ---
-
-    def get_secret_metadata(self, credential_id: str) -> dict[str, Any] | None:
-        """
-        Get Vault metadata for a secret (version info, timestamps, etc.).
-
-        Args:
-            credential_id: The credential identifier
-
-        Returns:
-            Metadata dict or None if not found
-        """
-        path = self._path(credential_id)
-
-        try:
-            response = self._client.secrets.kv.v2.read_secret_metadata(
-                path=path,
-                mount_point=self._mount,
-            )
-            return response.get("data", {})
-        except Exception:
-            return None
-
-    def soft_delete(self, credential_id: str, versions: list[int] | None = None) -> bool:
-        """
-        Soft delete specific versions (can be recovered).
-
-        Args:
-            credential_id: The credential identifier
-            versions: Version numbers to delete. If None, deletes latest.
-
-        Returns:
-            True if successful
-        """
-        path = self._path(credential_id)
-
-        try:
-            if versions:
-                self._client.secrets.kv.v2.delete_secret_versions(
-                    path=path,
-                    versions=versions,
-                    mount_point=self._mount,
-                )
-            else:
-                self._client.secrets.kv.v2.delete_latest_version_of_secret(
-                    path=path,
-                    mount_point=self._mount,
-                )
-            return True
-        except Exception as e:
-            logger.error(f"Soft delete failed for '{credential_id}': {e}")
-            return False
-
-    def undelete(self, credential_id: str, versions: list[int]) -> bool:
-        """
-        Recover soft-deleted versions.
-
-        Args:
-            credential_id: The credential identifier
-            versions: Version numbers to recover
-
-        Returns:
-            True if successful
-        """
-        path = self._path(credential_id)
-
-        try:
-            self._client.secrets.kv.v2.undelete_secret_versions(
-                path=path,
-                versions=versions,
-                mount_point=self._mount,
-            )
-            return True
-        except Exception as e:
-            logger.error(f"Undelete failed for '{credential_id}': {e}")
-            return False
-
-    def load_version(self, credential_id: str, version: int) -> CredentialObject | None:
-        """
-        Load a specific version of a credential.
-
-        Args:
-            credential_id: The credential identifier
-            version: Version number to load
-
-        Returns:
-            CredentialObject or None
-        """
-        path = self._path(credential_id)
-
-        try:
-            response = self._client.secrets.kv.v2.read_secret_version(
-                path=path,
-                version=version,
-                mount_point=self._mount,
-            )
-            data = response["data"]["data"]
-            return self._deserialize_from_vault(credential_id, data)
-        except Exception:
-            return None
@@ -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)
@@ -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"),
        )


@@ -307,13 +329,13 @@ class NodeConversation:
    def __init__(
        self,
        system_prompt: str = "",
-        max_history_tokens: int = 32000,
+        max_context_tokens: int = 32000,
        compaction_threshold: float = 0.8,
        output_keys: list[str] | None = None,
        store: ConversationStore | None = None,
    ) -> None:
        self._system_prompt = system_prompt
-        self._max_history_tokens = max_history_tokens
+        self._max_context_tokens = max_context_tokens
        self._compaction_threshold = compaction_threshold
        self._output_keys = output_keys
        self._store = store
@@ -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,
@@ -381,6 +404,7 @@ 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
@@ -409,6 +433,8 @@ 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,
@@ -417,6 +443,8 @@ 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
@@ -525,16 +553,16 @@ class NodeConversation:
        self._last_api_input_tokens = actual_input_tokens

    def usage_ratio(self) -> float:
-        """Current token usage as a fraction of *max_history_tokens*.
+        """Current token usage as a fraction of *max_context_tokens*.

-        Returns 0.0 when ``max_history_tokens`` is zero (unlimited).
+        Returns 0.0 when ``max_context_tokens`` is zero (unlimited).
        """
-        if self._max_history_tokens <= 0:
+        if self._max_context_tokens <= 0:
            return 0.0
-        return self.estimate_tokens() / self._max_history_tokens
+        return self.estimate_tokens() / self._max_context_tokens

    def needs_compaction(self) -> bool:
-        return self.estimate_tokens() >= self._max_history_tokens * self._compaction_threshold
+        return self.estimate_tokens() >= self._max_context_tokens * self._compaction_threshold

    # --- Output-key extraction ---------------------------------------------

@@ -610,8 +638,15 @@ 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 +936,7 @@ 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.]")
@@ -1029,7 +1063,7 @@ class NodeConversation:
        await self._store.write_meta(
            {
                "system_prompt": self._system_prompt,
-                "max_history_tokens": self._max_history_tokens,
+                "max_context_tokens": self._max_context_tokens,
                "compaction_threshold": self._compaction_threshold,
                "output_keys": self._output_keys,
            }
@@ -1062,7 +1096,7 @@ class NodeConversation:

        conv = cls(
            system_prompt=meta.get("system_prompt", ""),
-            max_history_tokens=meta.get("max_history_tokens", 32000),
+            max_context_tokens=meta.get("max_context_tokens", 32000),
            compaction_threshold=meta.get("compaction_threshold", 0.8),
            output_keys=meta.get("output_keys"),
            store=store,
@@ -37,7 +37,7 @@ async def evaluate_phase_completion(
    phase_description: str,
    success_criteria: str,
    accumulator_state: dict[str, Any],
-    max_history_tokens: int = 8_196,
+    max_context_tokens: int = 8_196,
 ) -> PhaseVerdict:
    """Level 2 judge: read the conversation and evaluate quality.

@@ -50,7 +50,7 @@ async def evaluate_phase_completion(
        phase_description: Description of the phase
        success_criteria: Natural-language criteria for phase completion
        accumulator_state: Current output key values
-        max_history_tokens: Main conversation token budget (judge gets 20%)
+        max_context_tokens: Main conversation token budget (judge gets 20%)

    Returns:
        PhaseVerdict with action and optional feedback
@@ -89,7 +89,7 @@ FEEDBACK: (reason if RETRY, empty if ACCEPT)"""
        response = await llm.acomplete(
            messages=[{"role": "user", "content": user_prompt}],
            system=system_prompt,
-            max_tokens=max(1024, max_history_tokens // 5),
+            max_tokens=max(1024, max_context_tokens // 5),
            max_retries=1,
        )
        if not response.content or not response.content.strip():
@@ -322,7 +322,11 @@ class AsyncEntryPointSpec(BaseModel):

    id: str = Field(description="Unique identifier for this entry point")
    name: str = Field(description="Human-readable name")
-    entry_node: str = Field(description="Node ID to start execution from")
+    entry_node: str = Field(
+        default="",
+        description="Deprecated: Node ID to start execution from. "
+        "Triggers are graph-level; worker always enters at GraphSpec.entry_node.",
+    )
    trigger_type: str = Field(
        default="manual",
        description="How this entry point is triggered: webhook, api, timer, event, manual",
@@ -331,6 +335,10 @@ class AsyncEntryPointSpec(BaseModel):
        default_factory=dict,
        description="Trigger-specific configuration (e.g., webhook URL, timer interval)",
    )
+    task: str = Field(
+        default="",
+        description="Worker task string when this trigger fires autonomously",
+    )
    isolation_level: str = Field(
        default="shared", description="State isolation: isolated, shared, or synchronized"
    )
@@ -368,28 +376,8 @@ class GraphSpec(BaseModel):
            edges=[...],
        )

-    For multi-entry-point agents (concurrent streams):
-        GraphSpec(
-            id="support-agent-graph",
-            goal_id="support-001",
-            entry_node="process-webhook",  # Default entry
-            async_entry_points=[
-                AsyncEntryPointSpec(
-                    id="webhook",
-                    name="Zendesk Webhook",
-                    entry_node="process-webhook",
-                    trigger_type="webhook",
-                ),
-                AsyncEntryPointSpec(
-                    id="api",
-                    name="API Handler",
-                    entry_node="process-request",
-                    trigger_type="api",
-                ),
-            ],
-            nodes=[...],
-            edges=[...],
-        )
+    Triggers (timer, webhook, event) are now defined in ``triggers.json``
+    alongside the agent directory, not embedded in the graph spec.
    """

    id: str
@@ -402,12 +390,6 @@ class GraphSpec(BaseModel):
        default_factory=dict,
        description="Named entry points for resuming execution. Format: {name: node_id}",
    )
-    async_entry_points: list[AsyncEntryPointSpec] = Field(
-        default_factory=list,
-        description=(
-            "Asynchronous entry points for concurrent execution streams (used with AgentRuntime)"
-        ),
-    )
    terminal_nodes: list[str] = Field(
        default_factory=list, description="IDs of nodes that end execution"
    )
@@ -486,17 +468,6 @@ class GraphSpec(BaseModel):
                return node
        return None

-    def has_async_entry_points(self) -> bool:
-        """Check if this graph uses async entry points (multi-stream execution)."""
-        return len(self.async_entry_points) > 0
-
-    def get_async_entry_point(self, entry_point_id: str) -> AsyncEntryPointSpec | None:
-        """Get an async entry point by ID."""
-        for ep in self.async_entry_points:
-            if ep.id == entry_point_id:
-                return ep
-        return None
-
    def get_outgoing_edges(self, node_id: str) -> list[EdgeSpec]:
        """Get all edges leaving a node, sorted by priority."""
        edges = [e for e in self.edges if e.source == node_id]
@@ -587,37 +558,6 @@ class GraphSpec(BaseModel):
        if not self.get_node(self.entry_node):
            errors.append(f"Entry node '{self.entry_node}' not found")

-        # Check async entry points
-        seen_entry_ids = set()
-        for entry_point in self.async_entry_points:
-            # Check for duplicate IDs
-            if entry_point.id in seen_entry_ids:
-                errors.append(f"Duplicate async entry point ID: '{entry_point.id}'")
-            seen_entry_ids.add(entry_point.id)
-
-            # Check entry node exists
-            if not self.get_node(entry_point.entry_node):
-                errors.append(
-                    f"Async entry point '{entry_point.id}' references "
-                    f"missing node '{entry_point.entry_node}'"
-                )
-
-            # Validate isolation level
-            valid_isolation = {"isolated", "shared", "synchronized"}
-            if entry_point.isolation_level not in valid_isolation:
-                errors.append(
-                    f"Async entry point '{entry_point.id}' has invalid isolation_level "
-                    f"'{entry_point.isolation_level}'. Valid: {valid_isolation}"
-                )
-
-            # Validate trigger type
-            valid_triggers = {"webhook", "api", "timer", "event", "manual"}
-            if entry_point.trigger_type not in valid_triggers:
-                errors.append(
-                    f"Async entry point '{entry_point.id}' has invalid trigger_type "
-                    f"'{entry_point.trigger_type}'. Valid: {valid_triggers}"
-                )
-
        # Check terminal nodes exist
        for term in self.terminal_nodes:
            if not self.get_node(term):
@@ -646,10 +586,6 @@ class GraphSpec(BaseModel):
        for entry_point_node in self.entry_points.values():
            to_visit.append(entry_point_node)

-        # Add all async entry points as valid starting points
-        for async_entry in self.async_entry_points:
-            to_visit.append(async_entry.entry_node)
-
        # Traverse from all entry points
        while to_visit:
            current = to_visit.pop()
@@ -666,18 +602,10 @@ class GraphSpec(BaseModel):
                for sub_agent_id in sub_agents:
                    reachable.add(sub_agent_id)

-        # Build set of async entry point nodes for quick lookup
-        async_entry_nodes = {ep.entry_node for ep in self.async_entry_points}
-
        for node in self.nodes:
            if node.id not in reachable:
-                # Skip if node is a pause node, entry point target, or async entry
-                # (pause/resume architecture and async entry points make reachable)
-                if (
-                    node.id in self.pause_nodes
-                    or node.id in self.entry_points.values()
-                    or node.id in async_entry_nodes
-                ):
+                # Skip if node is a pause node or entry point target
+                if node.id in self.pause_nodes or node.id in self.entry_points.values():
                    continue
                errors.append(f"Node '{node.id}' is unreachable from entry")

@@ -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.
+"""
@@ -0,0 +1,641 @@
+"""Conversation compaction pipeline.
+
+Implements the multi-level compaction strategy:
+1. Prune old tool results
+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
+from datetime import UTC, datetime
+from pathlib import Path
+from typing import Any
+
+from framework.graph.conversation import 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
+
+
+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):
+    1. Prune old tool results
+    2. Structure-preserving compaction (free, no LLM)
+    3. LLM summary compaction (recursive split if too large)
+    4. Emergency deterministic summary (fallback)
+    """
+    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 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():
+        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():
+        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)
+
+    if not conversation.needs_compaction():
+        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,
+    )
+    await log_compaction(
+        ctx,
+        conversation,
+        ratio_before,
+        event_bus,
+        pre_inventory=pre_inventory,
+    )
+
+
+# --- LLM compaction with binary-search splitting ----------------------
+
+
+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})")
+
+    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."""
+    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"
+        "1. Preserve ALL user-stated rules, constraints, and preferences "
+        "verbatim.\n"
+        "2. Preserve key decisions made and results obtained.\n"
+        "3. Preserve in-progress work state so the agent can continue.\n"
+        "4. Be detailed enough that the agent can resume without "
+        "re-doing work.\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.
+    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():
+                all_files = sorted(f.name for f in data_dir.iterdir() if f.is_file())
+                # 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))
@@ -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
@@ -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)
@@ -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="")
@@ -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, ""
@@ -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,
+                    )
@@ -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,
+    )
@@ -0,0 +1,503 @@
+"""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 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
@@ -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",
+]
@@ -27,11 +27,24 @@ 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:
+    """Resolve max_context_tokens from global config, falling back to 32000."""
+    try:
+        from framework.config import get_max_context_tokens
+
+        return get_max_context_tokens()
+    except Exception:
+        return 32_000


@dataclass
@@ -138,6 +151,12 @@ class GraphExecutor:
        tool_provider_map: dict[str, str] | None = None,
        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.
@@ -163,6 +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
@@ -183,6 +207,24 @@ class GraphExecutor:
        self.tool_provider_map = tool_provider_map
        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
@@ -212,11 +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:
@@ -239,9 +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]:
        """
@@ -330,7 +377,7 @@ class GraphExecutor:
                _depth,
            )
        else:
-            max_tokens = getattr(conversation, "_max_history_tokens", 32000)
+            max_tokens = getattr(conversation, "_max_context_tokens", 32000)
            target_tokens = max_tokens // 2
            target_chars = target_tokens * 4

@@ -403,6 +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,
@@ -696,10 +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}")
@@ -925,6 +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(
@@ -1350,6 +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,
@@ -1359,23 +1439,6 @@ class GraphExecutor:
                        # Build Layer 2 (narrative) from current state
                        narrative = build_narrative(memory, path, graph)

-                        # Read agent working memory (adapt.md) once for both
-                        # system prompt and transition marker.
-                        _adapt_text: str | None = None
-                        if self._storage_path:
-                            _adapt_path = self._storage_path / "data" / "adapt.md"
-                            if _adapt_path.exists():
-                                _raw = _adapt_path.read_text(encoding="utf-8").strip()
-                                _adapt_text = _raw or None
-
-                        # Merge adapt.md into narrative for system prompt
-                        if _adapt_text:
-                            narrative = (
-                                f"{narrative}\n\n--- Agent Memory ---\n{_adapt_text}"
-                                if narrative
-                                else _adapt_text
-                            )
-
                        # Build per-node accounts prompt for the next node
                        _node_accounts = self.accounts_prompt or None
                        if self.accounts_data and self.tool_provider_map:
@@ -1389,9 +1452,14 @@ 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,
                        )
@@ -1405,7 +1473,6 @@ class GraphExecutor:
                            memory=memory,
                            cumulative_tool_names=sorted(cumulative_tool_names),
                            data_dir=data_dir,
-                            adapt_content=_adapt_text,
                        )
                        await continuous_conversation.add_user_message(
                            marker,
@@ -1604,7 +1671,7 @@ class GraphExecutor:
            # Return with paused status
            return ExecutionResult(
                success=False,
-                error="Execution paused by user",
+                error="Execution cancelled",
                output=saved_memory,
                steps_executed=steps,
                total_tokens=total_tokens,
@@ -1753,10 +1820,34 @@ 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)
@@ -1799,6 +1890,12 @@ class GraphExecutor:
            shared_node_registry=self.node_registry,  # For subagent escalation routing
            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 = {
@@ -1872,7 +1969,7 @@ class GraphExecutor:
                    max_tool_calls_per_turn=lc.get("max_tool_calls_per_turn", 30),
                    tool_call_overflow_margin=lc.get("tool_call_overflow_margin", 0.5),
                    stall_detection_threshold=lc.get("stall_detection_threshold", 3),
-                    max_history_tokens=lc.get("max_history_tokens", 32000),
+                    max_context_tokens=lc.get("max_context_tokens", _default_max_context_tokens()),
                    max_tool_result_chars=lc.get("max_tool_result_chars", 30_000),
                    spillover_dir=spillover,
                    hooks=lc.get("hooks", {}),
@@ -2039,6 +2136,10 @@ 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)
@@ -2130,8 +2231,31 @@ 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
@@ -2178,9 +2302,11 @@ 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
@@ -2188,17 +2314,33 @@ 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:
@@ -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
@@ -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.

@@ -1,203 +0,0 @@
-"""
-Standardized HITL (Human-In-The-Loop) Protocol
-
-This module defines the formal structure for pause/resume interactions
-where agents need to gather input from humans.
-"""
-
-from dataclasses import dataclass, field
-from enum import StrEnum
-from typing import Any
-
-
-class HITLInputType(StrEnum):
-    """Type of input expected from human."""
-
-    FREE_TEXT = "free_text"  # Open-ended text response
-    STRUCTURED = "structured"  # Specific fields to fill
-    SELECTION = "selection"  # Choose from options
-    APPROVAL = "approval"  # Yes/no/modify decision
-    MULTI_FIELD = "multi_field"  # Multiple related inputs
-
-
-@dataclass
-class HITLQuestion:
-    """A single question to ask the human."""
-
-    id: str
-    question: str
-    input_type: HITLInputType = HITLInputType.FREE_TEXT
-
-    # For SELECTION type
-    options: list[str] = field(default_factory=list)
-
-    # For STRUCTURED type
-    fields: dict[str, str] = field(default_factory=dict)  # {field_name: description}
-
-    # Metadata
-    required: bool = True
-    help_text: str = ""
-
-
-@dataclass
-class HITLRequest:
-    """
-    Formal request for human input at a pause node.
-
-    This is what the agent produces when it needs human input.
-    """
-
-    # Context
-    objective: str  # What we're trying to accomplish
-    current_state: str  # Where we are in the process
-
-    # What we need
-    questions: list[HITLQuestion] = field(default_factory=list)
-    missing_info: list[str] = field(default_factory=list)
-
-    # Guidance
-    instructions: str = ""
-    examples: list[str] = field(default_factory=list)
-
-    # Metadata
-    request_id: str = ""
-    node_id: str = ""
-
-    def to_dict(self) -> dict[str, Any]:
-        """Convert to dictionary for serialization."""
-        return {
-            "objective": self.objective,
-            "current_state": self.current_state,
-            "questions": [
-                {
-                    "id": q.id,
-                    "question": q.question,
-                    "input_type": q.input_type.value,
-                    "options": q.options,
-                    "fields": q.fields,
-                    "required": q.required,
-                    "help_text": q.help_text,
-                }
-                for q in self.questions
-            ],
-            "missing_info": self.missing_info,
-            "instructions": self.instructions,
-            "examples": self.examples,
-            "request_id": self.request_id,
-            "node_id": self.node_id,
-        }
-
-
-@dataclass
-class HITLResponse:
-    """
-    Human's response to a HITL request.
-
-    This is what gets passed back when resuming from a pause.
-    """
-
-    # Original request reference
-    request_id: str
-
-    # Human's answers
-    answers: dict[str, Any] = field(default_factory=dict)  # {question_id: answer}
-    raw_input: str = ""  # Raw text if provided
-
-    # Metadata
-    response_time_ms: int = 0
-
-    def to_dict(self) -> dict[str, Any]:
-        """Convert to dictionary for serialization."""
-        return {
-            "request_id": self.request_id,
-            "answers": self.answers,
-            "raw_input": self.raw_input,
-            "response_time_ms": self.response_time_ms,
-        }
-
-
-class HITLProtocol:
-    """
-    Standardized protocol for HITL interactions.
-
-    Usage in pause nodes:
-
-    1. Pause Node: Generates HITLRequest with questions
-    2. Executor: Saves state and returns request to user
-    3. User: Provides HITLResponse with answers
-    4. Resume Node: Processes response and merges into context
-    """
-
-    @staticmethod
-    def create_request(
-        objective: str,
-        questions: list[HITLQuestion],
-        missing_info: list[str] | None = None,
-        node_id: str = "",
-    ) -> HITLRequest:
-        """Create a standardized HITL request."""
-        return HITLRequest(
-            objective=objective,
-            current_state="Awaiting clarification",
-            questions=questions,
-            missing_info=missing_info or [],
-            request_id=f"{node_id}_{hash(objective) % 10000}",
-            node_id=node_id,
-        )
-
-    @staticmethod
-    def parse_response(
-        raw_input: str,
-        request: HITLRequest,
-        use_haiku: bool = True,
-    ) -> HITLResponse:
-        """
-        Parse human's raw input into structured response.
-
-        Maps the raw input to the first question. For multi-question HITL,
-        the caller should present one question at a time.
-        """
-        response = HITLResponse(request_id=request.request_id, raw_input=raw_input)
-
-        # If no questions, just return raw input
-        if not request.questions:
-            return response
-
-        # Map raw input to first question
-        response.answers[request.questions[0].id] = raw_input
-        return response
-
-    @staticmethod
-    def format_for_display(request: HITLRequest) -> str:
-        """Format HITL request for user-friendly display."""
-        parts = []
-
-        if request.objective:
-            parts.append(f"📋 Objective: {request.objective}")
-
-        if request.current_state:
-            parts.append(f"📍 Current State: {request.current_state}")
-
-        if request.instructions:
-            parts.append(f"\n{request.instructions}")
-
-        if request.questions:
-            parts.append(f"\n❓ Questions ({len(request.questions)}):")
-            for i, q in enumerate(request.questions, 1):
-                parts.append(f"{i}. {q.question}")
-                if q.help_text:
-                    parts.append(f"   💡 {q.help_text}")
-                if q.options:
-                    parts.append(f"   Options: {', '.join(q.options)}")
-
-        if request.missing_info:
-            parts.append("\n📝 Missing Information:")
-            for info in request.missing_info:
-                parts.append(f"  • {info}")
-
-        if request.examples:
-            parts.append("\n📚 Examples:")
-            for example in request.examples:
-                parts.append(f"  • {example}")
-
-        return "\n".join(parts)
@@ -565,6 +565,20 @@ 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.
+    iteration_metadata_provider: Any = None  # Callable[[], dict] | None
+

@dataclass
 class NodeResult:
@@ -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}")
@@ -227,7 +264,6 @@ def build_transition_marker(
    memory: SharedMemory,
    cumulative_tool_names: list[str],
    data_dir: Path | str | None = None,
-    adapt_content: str | None = None,
 ) -> str:
    """Build a 'State of the World' transition marker.

@@ -241,7 +277,6 @@ def build_transition_marker(
        memory: Current shared memory state.
        cumulative_tool_names: All tools available (cumulative set).
        data_dir: Path to spillover data directory.
-        adapt_content: Agent working memory (adapt.md) content.

    Returns:
        Transition marker message text.
@@ -255,7 +290,9 @@ 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 +300,29 @@ 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:
@@ -280,13 +339,9 @@ 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
-    if adapt_content:
-        sections.append(f"\n--- Agent Memory ---\n{adapt_content}")
-
    # Available tools
    if cumulative_tool_names:
        sections.append("\nAvailable tools: " + ", ".join(sorted(cumulative_tool_names)))
@@ -294,6 +349,12 @@ 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(
@@ -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:
    """
@@ -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)
@@ -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
@@ -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):
@@ -71,6 +71,7 @@ class FinishEvent:
    stop_reason: str = ""
    input_tokens: int = 0
    output_tokens: int = 0
+    cached_tokens: int = 0
    model: str = ""


@@ -1,4 +0,0 @@
-"""MCP servers for worker-bee."""
-
-# Don't auto-import servers to avoid double-import issues when running with -m
-__all__ = []
@@ -1,33 +1 @@
-"""Framework-level worker monitoring package.
-
-Provides the Worker Health Judge: a reusable secondary graph that attaches to
-any worker agent runtime and monitors its execution health via periodic log
-inspection. Emits structured EscalationTickets when degradation is detected.
-
-Usage::
-
-    from framework.monitoring import HEALTH_JUDGE_ENTRY_POINT, judge_goal, judge_graph
-    from framework.tools.worker_monitoring_tools import register_worker_monitoring_tools
-
-    # Register tools bound to the worker runtime's EventBus
-    monitoring_registry = ToolRegistry()
-    register_worker_monitoring_tools(monitoring_registry, worker_runtime._event_bus, storage_path)
-
-    # Load judge as secondary graph on the worker runtime
-    await worker_runtime.add_graph(
-        graph_id="judge",
-        graph=judge_graph,
-        goal=judge_goal,
-        entry_points={"health_check": HEALTH_JUDGE_ENTRY_POINT},
-        storage_subpath="graphs/judge",
-    )
-"""
-
-from .judge import HEALTH_JUDGE_ENTRY_POINT, judge_goal, judge_graph, judge_node
-
-__all__ = [
-    "HEALTH_JUDGE_ENTRY_POINT",
-    "judge_goal",
-    "judge_graph",
-    "judge_node",
-]
+"""Framework-level worker monitoring package."""
@@ -1,258 +0,0 @@
-"""Worker Health Judge — framework-level reusable monitoring graph.
-
-Attaches to any worker agent runtime as a secondary graph. Fires on a
-2-minute timer, reads the worker's session logs via ``get_worker_health_summary``,
-accumulates observations in a continuous conversation context, and emits a
-structured ``EscalationTicket`` when it detects a degradation pattern.
-
-Usage::
-
-    from framework.monitoring import judge_graph, judge_goal, HEALTH_JUDGE_ENTRY_POINT
-    from framework.tools.worker_monitoring_tools import register_worker_monitoring_tools
-
-    # Register tools bound to the worker runtime's event bus
-    monitoring_registry = ToolRegistry()
-    register_worker_monitoring_tools(
-        monitoring_registry, worker_runtime._event_bus, storage_path
-    )
-    monitoring_tools = list(monitoring_registry.get_tools().values())
-    monitoring_executor = monitoring_registry.get_executor()
-
-    # Load judge as secondary graph on the worker runtime
-    await worker_runtime.add_graph(
-        graph_id="judge",
-        graph=judge_graph,
-        goal=judge_goal,
-        entry_points={"health_check": HEALTH_JUDGE_ENTRY_POINT},
-        storage_subpath="graphs/judge",
-    )
-
-Design:
- ``isolation_level="isolated"`` — the judge has its own memory, not
-  polluting the worker's shared memory namespace.
- ``conversation_mode="continuous"`` — the judge's conversation carries
-  across timer ticks. The conversation IS the judge's memory. It tracks
-  trends by referring to its own prior messages ("Last check I saw 47
-  steps; now 52; 5 new steps, 3 RETRY").
- No shared memory keys. No external state files.
-"""
-
-from __future__ import annotations
-
-from framework.graph import Constraint, Goal, NodeSpec, SuccessCriterion
-from framework.graph.edge import AsyncEntryPointSpec, GraphSpec
-
-# ---------------------------------------------------------------------------
-# Goal
-# ---------------------------------------------------------------------------
-
-judge_goal = Goal(
-    id="worker-health-monitor",
-    name="Worker Health Monitor",
-    description=(
-        "Periodically assess the health of the worker agent by reading its "
-        "execution logs. Detect degradation patterns (excessive retries, "
-        "stalls, doom loops) and emit structured EscalationTickets when the "
-        "worker needs attention."
-    ),
-    success_criteria=[
-        SuccessCriterion(
-            id="accurate-detection",
-            description="Only escalates genuine degradation, not normal retry cycles",
-            metric="false_positive_rate",
-            target="low",
-            weight=0.5,
-        ),
-        SuccessCriterion(
-            id="timely-detection",
-            description="Detects genuine stalls within 2 timer ticks (≤4 minutes)",
-            metric="detection_latency_minutes",
-            target="<=4",
-            weight=0.5,
-        ),
-    ],
-    constraints=[
-        Constraint(
-            id="conservative-escalation",
-            description=(
-                "Do not escalate on a single bad verdict or a brief stall. "
-                "Require clear patterns (10+ consecutive bad verdicts or 4+ minute stall) "
-                "before creating a ticket."
-            ),
-            constraint_type="hard",
-            category="quality",
-        ),
-        Constraint(
-            id="complete-ticket",
-            description=(
-                "Every EscalationTicket must have all required fields filled. "
-                "Do not emit partial or placeholder tickets."
-            ),
-            constraint_type="hard",
-            category="correctness",
-        ),
-    ],
-)
-
-# ---------------------------------------------------------------------------
-# Node
-# ---------------------------------------------------------------------------
-
-judge_node = NodeSpec(
-    id="judge",
-    name="Worker Health Judge",
-    description=(
-        "Autonomous health monitor for worker agents. Reads execution logs "
-        "on each timer tick, compares to prior observations (via conversation "
-        "history), and emits a structured EscalationTicket when a genuine "
-        "degradation pattern is detected."
-    ),
-    node_type="event_loop",
-    client_facing=False,  # Autonomous monitor, not interactive
-    max_node_visits=0,  # Unbounded — runs on every timer tick
-    input_keys=[],
-    output_keys=["health_verdict"],
-    nullable_output_keys=["health_verdict"],
-    success_criteria=(
-        "A clear health verdict is produced each check: either 'healthy' with "
-        "a brief observation, or a complete EscalationTicket is emitted via "
-        "emit_escalation_ticket and health_verdict describes the issue."
-    ),
-    tools=[
-        "get_worker_health_summary",
-        "emit_escalation_ticket",
-    ],
-    system_prompt="""\
-You are the Worker Health Judge. You run every 2 minutes alongside a worker \
-agent to monitor its execution health.
-
-# Your Role
-
-You observe the worker's iteration patterns over time and escalate only when \
-you see genuine degradation — not normal retry cycles. Your conversation history \
-IS your memory. On each check, refer to your previous observations to track trends.
-
-# Check Procedure
-
-On each timer tick (every 2 minutes):
-
-## Step 1: Read health snapshot
-Call get_worker_health_summary() with no arguments to auto-discover the active \
-session. This returns:
- worker_agent_id: the worker's agent name — use this for ticket identity fields
- worker_graph_id: the worker's primary graph ID — use this for ticket identity fields
- session_id: the session being monitored — use this for worker_session_id in tickets
- total_steps: how many log steps have been recorded
- recent_verdicts: list of recent ACCEPT/RETRY/CONTINUE verdicts
- steps_since_last_accept: consecutive non-ACCEPT steps
- stall_minutes: wall-clock since last step (null if active)
- evidence_snippet: recent LLM output
-
-## Step 2: Compare to prior check
-Look at your conversation history. What was total_steps last time?
- If total_steps is UNCHANGED from prior check AND prior check was also unchanged:
-  → STALL confirmed (worker has produced no new iterations in 4+ minutes).
-  → Escalate with severity="high" or "critical" depending on stall duration.
- If total_steps increased: worker is making progress. Examine verdicts.
-
-## Step 3: Analyze verdict pattern
- Healthy: Mix of ACCEPT and RETRY, steps_since_last_accept < 5. No action.
- Warning: steps_since_last_accept is 5-9. Note it, no escalation yet.
- Degraded: steps_since_last_accept >= 10. Examine evidence_snippet.
-  - If evidence shows the agent is making real progress (complex reasoning,
-    exploring solutions, productive tool use): may be a hard problem. Note it.
-  - If evidence shows a loop (same error, same tool call, no new information):
-    → Escalate with severity="medium" or "high".
- Critical: steps_since_last_accept >= 20, OR stall_minutes >= 4.
-  → Escalate with severity="critical".
-
-## Step 4: Decide
-
-### If healthy:
-set_output("health_verdict", "healthy: <brief observation>")
-Done.
-
-### If escalating:
-Build an EscalationTicket JSON string with ALL required fields:
-{
-  "worker_agent_id": "<worker_agent_id from get_worker_health_summary>",
-  "worker_session_id": "<session_id from get_worker_health_summary>",
-  "worker_node_id": "<worker_graph_id from get_worker_health_summary>",
-  "worker_graph_id": "<worker_graph_id from get_worker_health_summary>",
-  "severity": "<low|medium|high|critical>",
-  "cause": "<what you observed — concrete, specific>",
-  "judge_reasoning": "<why you decided to escalate, not just dismiss>",
-  "suggested_action": "<what you recommend: restart, human review, etc.>",
-  "recent_verdicts": [<list from get_worker_health_summary>],
-  "total_steps_checked": <int>,
-  "steps_since_last_accept": <int>,
-  "stall_minutes": <float or null>,
-  "evidence_snippet": "<from get_worker_health_summary>"
-}
-
-Call: emit_escalation_ticket(ticket_json=<the JSON string above>)
-Then: set_output("health_verdict", "escalated: <one-line summary>")
-
-# Severity Guide
-
- low: Mild concern, worth noting. 5-9 consecutive bad verdicts.
- medium: Clear degradation pattern. 10-15 bad verdicts or brief stall (1-2 min).
- high: Serious issue. 15+ bad verdicts or stall 2-4 minutes or clear doom loop.
- critical: Worker is definitively stuck. 20+ bad verdicts or stall > 4 minutes.
-
-# Conservative Bias
-
-You MUST resist the urge to escalate prematurely. Worker agents naturally retry.
-A node may legitimately need 5-8 retries before succeeding. Do not escalate unless:
-1. The pattern is clear and sustained across your observation window, AND
-2. The evidence shows no genuine progress
-
-One missed escalation is less costly than two false alarms. The Queen will filter \
-further. But do not be passive — genuine stalls and doom loops must be caught.
-
-# Rules
- Never escalate on the FIRST check unless stall_minutes > 4
- Always call get_worker_health_summary FIRST before deciding anything
- All ticket fields are REQUIRED — do not submit partial tickets
- After any emit_escalation_ticket call, always set_output to complete the check
-""",
-)
-
-# ---------------------------------------------------------------------------
-# Entry Point
-# ---------------------------------------------------------------------------
-
-HEALTH_JUDGE_ENTRY_POINT = AsyncEntryPointSpec(
-    id="health_check",
-    name="Worker Health Check",
-    entry_node="judge",
-    trigger_type="timer",
-    trigger_config={
-        "interval_minutes": 2,
-        "run_immediately": True,  # Fire immediately to establish a baseline
-    },
-    isolation_level="isolated",  # Own memory namespace, not polluting worker's
-)
-
-# ---------------------------------------------------------------------------
-# Graph
-# ---------------------------------------------------------------------------
-
-judge_graph = GraphSpec(
-    id="judge-graph",
-    goal_id=judge_goal.id,
-    version="1.0.0",
-    entry_node="judge",
-    entry_points={"health_check": "judge"},
-    terminal_nodes=["judge"],  # Judge node can terminate after each check
-    pause_nodes=[],
-    nodes=[judge_node],
-    edges=[],
-    conversation_mode="continuous",  # Conversation persists across timer ticks
-    async_entry_points=[HEALTH_JUDGE_ENTRY_POINT],
-    loop_config={
-        "max_iterations": 10,  # One check shouldn't take many turns
-        "max_tool_calls_per_turn": 3,  # get_summary + optionally emit_ticket
-        "max_history_tokens": 16000,  # Compact — judge only needs recent context
-    },
-)
@@ -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

@@ -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}")

@@ -148,8 +153,9 @@ class HumanReadableFormatter(logging.Formatter):
        if record_event is not None:
            event = f" [{record_event}]"

-        # Format message: [LEVEL] [trace context] message
-        return f"{color}[{level}]{reset} {context_prefix}{record.getMessage()}{event}"
+        timestamp = self.formatTime(record, "%Y-%m-%d %H:%M:%S")
+        # Format message: TIMESTAMP [LEVEL] [trace context] message
+        return f"{timestamp} {color}[{level}]{reset} {context_prefix}{record.getMessage()}{event}"


 def configure_logging(
@@ -210,6 +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":
@@ -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",
@@ -51,11 +51,7 @@ def register_commands(subparsers: argparse._SubParsersAction) -> None:
        action="store_true",
        help="Show detailed execution logs (steps, LLM calls, etc.)",
    )
-    run_parser.add_argument(
-        "--tui",
-        action="store_true",
-        help="Launch interactive terminal dashboard",
-    )
+
    run_parser.add_argument(
        "--model",
        "-m",
@@ -194,158 +190,6 @@ def register_commands(subparsers: argparse._SubParsersAction) -> None:
    shell_parser.set_defaults(func=cmd_shell)

    # tui command (interactive agent dashboard)
-    tui_parser = subparsers.add_parser(
-        "tui",
-        help="Launch interactive TUI dashboard",
-        description="Browse available agents and launch the terminal dashboard.",
-    )
-    tui_parser.add_argument(
-        "--model",
-        "-m",
-        type=str,
-        default=None,
-        help="LLM model to use (any LiteLLM-compatible name)",
-    )
-    tui_parser.set_defaults(func=cmd_tui)
-
-    # code command (Hive Coder — framework agent builder)
-    code_parser = subparsers.add_parser(
-        "code",
-        help="Launch Hive Coder to build agents",
-        description="Interactive agent builder. Describe what you want and Hive Coder builds it.",
-    )
-    code_parser.add_argument(
-        "--model",
-        "-m",
-        type=str,
-        default=None,
-        help="LLM model to use (any LiteLLM-compatible name)",
-    )
-    code_parser.set_defaults(func=cmd_code)
-
-    # sessions command group (checkpoint/resume management)
-    sessions_parser = subparsers.add_parser(
-        "sessions",
-        help="Manage agent sessions",
-        description="List, inspect, and manage agent execution sessions.",
-    )
-    sessions_subparsers = sessions_parser.add_subparsers(
-        dest="sessions_cmd",
-        help="Session management commands",
-    )
-
-    # sessions list
-    sessions_list_parser = sessions_subparsers.add_parser(
-        "list",
-        help="List agent sessions",
-        description="List all sessions for an agent.",
-    )
-    sessions_list_parser.add_argument(
-        "agent_path",
-        type=str,
-        help="Path to agent folder",
-    )
-    sessions_list_parser.add_argument(
-        "--status",
-        choices=["all", "active", "failed", "completed", "paused"],
-        default="all",
-        help="Filter by session status (default: all)",
-    )
-    sessions_list_parser.add_argument(
-        "--has-checkpoints",
-        action="store_true",
-        help="Show only sessions with checkpoints",
-    )
-    sessions_list_parser.set_defaults(func=cmd_sessions_list)
-
-    # sessions show
-    sessions_show_parser = sessions_subparsers.add_parser(
-        "show",
-        help="Show session details",
-        description="Display detailed information about a specific session.",
-    )
-    sessions_show_parser.add_argument(
-        "agent_path",
-        type=str,
-        help="Path to agent folder",
-    )
-    sessions_show_parser.add_argument(
-        "session_id",
-        type=str,
-        help="Session ID to inspect",
-    )
-    sessions_show_parser.add_argument(
-        "--json",
-        action="store_true",
-        help="Output as JSON",
-    )
-    sessions_show_parser.set_defaults(func=cmd_sessions_show)
-
-    # sessions checkpoints
-    sessions_checkpoints_parser = sessions_subparsers.add_parser(
-        "checkpoints",
-        help="List session checkpoints",
-        description="List all checkpoints for a session.",
-    )
-    sessions_checkpoints_parser.add_argument(
-        "agent_path",
-        type=str,
-        help="Path to agent folder",
-    )
-    sessions_checkpoints_parser.add_argument(
-        "session_id",
-        type=str,
-        help="Session ID",
-    )
-    sessions_checkpoints_parser.set_defaults(func=cmd_sessions_checkpoints)
-
-    # pause command
-    pause_parser = subparsers.add_parser(
-        "pause",
-        help="Pause running session",
-        description="Request graceful pause of a running agent session.",
-    )
-    pause_parser.add_argument(
-        "agent_path",
-        type=str,
-        help="Path to agent folder",
-    )
-    pause_parser.add_argument(
-        "session_id",
-        type=str,
-        help="Session ID to pause",
-    )
-    pause_parser.set_defaults(func=cmd_pause)
-
-    # resume command
-    resume_parser = subparsers.add_parser(
-        "resume",
-        help="Resume session from checkpoint",
-        description="Resume a paused or failed session from a checkpoint.",
-    )
-    resume_parser.add_argument(
-        "agent_path",
-        type=str,
-        help="Path to agent folder",
-    )
-    resume_parser.add_argument(
-        "session_id",
-        type=str,
-        help="Session ID to resume",
-    )
-    resume_parser.add_argument(
-        "--checkpoint",
-        "-c",
-        type=str,
-        help="Specific checkpoint ID to resume from (default: latest)",
-    )
-    resume_parser.add_argument(
-        "--tui",
-        action="store_true",
-        help="Resume in TUI dashboard mode",
-    )
-    resume_parser.set_defaults(func=cmd_resume)
-
    # setup-credentials command
    setup_creds_parser = subparsers.add_parser(
        "setup-credentials",
@@ -399,6 +243,8 @@ def register_commands(subparsers: argparse._SubParsersAction) -> None:
        action="store_true",
        help="Open dashboard in browser after server starts",
    )
+    serve_parser.add_argument("--verbose", "-v", action="store_true", help="Enable INFO log level")
+    serve_parser.add_argument("--debug", action="store_true", help="Enable DEBUG log level")
    serve_parser.set_defaults(func=cmd_serve)

    # open command (serve + auto-open browser)
@@ -436,6 +282,8 @@ def register_commands(subparsers: argparse._SubParsersAction) -> None:
        default=None,
        help="LLM model for preloaded agents",
    )
+    open_parser.add_argument("--verbose", "-v", action="store_true", help="Enable INFO log level")
+    open_parser.add_argument("--debug", action="store_true", help="Enable DEBUG log level")
    open_parser.set_defaults(func=cmd_open)


@@ -531,18 +379,18 @@ def _prompt_before_start(agent_path: str, runner, model: str | None = None):

 def cmd_run(args: argparse.Namespace) -> int:
    """Run an exported agent."""
-    import logging

    from framework.credentials.models import CredentialError
+    from framework.observability import configure_logging
    from framework.runner import AgentRunner

    # Set logging level (quiet by default for cleaner output)
    if args.quiet:
-        logging.basicConfig(level=logging.ERROR, format="%(message)s")
+        configure_logging(level="ERROR")
    elif getattr(args, "verbose", False):
-        logging.basicConfig(level=logging.INFO, format="%(message)s")
+        configure_logging(level="INFO")
    else:
-        logging.basicConfig(level=logging.WARNING, format="%(message)s")
+        configure_logging(level="WARNING")

    # Load input context
    context = {}
@@ -577,128 +425,67 @@ def cmd_run(args: argparse.Namespace) -> int:
            )
            return 1

-    # Run the agent (with TUI or standard)
-    if getattr(args, "tui", False):
-        from framework.tui.app import AdenTUI
+    # Standard execution
+    # AgentRunner handles credential setup interactively when stdin is a TTY.
+    try:
+        runner = AgentRunner.load(
+            args.agent_path,
+            model=args.model,
+        )
+    except CredentialError as e:
+        print(f"\n{e}", file=sys.stderr)
+        return 1
+    except FileNotFoundError as e:
+        print(f"Error: {e}", file=sys.stderr)
+        return 1

-        async def run_with_tui():
-            try:
-                # Load runner inside the async loop to ensure strict loop affinity
-                # (only one load — avoids spawning duplicate MCP subprocesses)
-                # AgentRunner handles credential setup interactively when stdin is a TTY.
-                try:
-                    runner = AgentRunner.load(
-                        args.agent_path,
-                        model=args.model,
-                    )
-                except CredentialError as e:
-                    print(f"\n{e}", file=sys.stderr)
-                    return
-                except Exception as e:
-                    print(f"Error loading agent: {e}")
-                    return
+    # Prompt before starting (allows credential updates)
+    if sys.stdin.isatty() and not args.quiet:
+        runner = _prompt_before_start(args.agent_path, runner, args.model)
+        if runner is None:
+            return 1

-                # Prompt before starting (allows credential updates)
-                if sys.stdin.isatty():
-                    runner = _prompt_before_start(args.agent_path, runner, args.model)
-                    if runner is None:
-                        return
-
-                # Force setup inside the loop
-                if runner._agent_runtime is None:
-                    try:
-                        runner._setup()
-                    except CredentialError as e:
-                        print(f"\n{e}", file=sys.stderr)
-                        return
-
-                # Start runtime before TUI so it's ready for user input
-                if runner._agent_runtime and not runner._agent_runtime.is_running:
-                    await runner._agent_runtime.start()
-
-                app = AdenTUI(
-                    runner._agent_runtime,
-                    resume_session=getattr(args, "resume_session", None),
-                    resume_checkpoint=getattr(args, "checkpoint", None),
-                )
-
-                # TUI handles execution via ChatRepl — user submits input,
-                # ChatRepl calls runtime.trigger_and_wait(). No auto-launch.
-                await app.run_async()
-            except Exception as e:
-                import traceback
-
-                traceback.print_exc()
-                print(f"TUI error: {e}")
-
-            await runner.cleanup_async()
-            return None
-
-        asyncio.run(run_with_tui())
-        print("TUI session ended.")
-        return 0
-    else:
-        # Standard execution — load runner here (not shared with TUI path)
-        # AgentRunner handles credential setup interactively when stdin is a TTY.
-        try:
-            runner = AgentRunner.load(
-                args.agent_path,
-                model=args.model,
+    # Load session/checkpoint state for resume (headless mode)
+    session_state = None
+    resume_session = getattr(args, "resume_session", None)
+    checkpoint = getattr(args, "checkpoint", None)
+    if resume_session:
+        session_state = _load_resume_state(args.agent_path, resume_session, checkpoint)
+        if session_state is None:
+            print(
+                f"Error: Could not load session state for {resume_session}",
+                file=sys.stderr,
            )
-        except CredentialError as e:
-            print(f"\n{e}", file=sys.stderr)
            return 1
-        except FileNotFoundError as e:
-            print(f"Error: {e}", file=sys.stderr)
-            return 1
-
-        # Prompt before starting (allows credential updates)
-        if sys.stdin.isatty() and not args.quiet:
-            runner = _prompt_before_start(args.agent_path, runner, args.model)
-            if runner is None:
-                return 1
-
-        # Load session/checkpoint state for resume (headless mode)
-        session_state = None
-        resume_session = getattr(args, "resume_session", None)
-        checkpoint = getattr(args, "checkpoint", None)
-        if resume_session:
-            session_state = _load_resume_state(args.agent_path, resume_session, checkpoint)
-            if session_state is None:
-                print(
-                    f"Error: Could not load session state for {resume_session}",
-                    file=sys.stderr,
-                )
-                return 1
-            if not args.quiet:
-                resume_node = session_state.get("paused_at", "unknown")
-                if checkpoint:
-                    print(f"Resuming from checkpoint: {checkpoint}")
-                else:
-                    print(f"Resuming session: {resume_session}")
-                print(f"Resume point: {resume_node}")
-                print()
-
-        # Auto-inject user_id if the agent expects it but it's not provided
-        entry_input_keys = runner.graph.nodes[0].input_keys if runner.graph.nodes else []
-        if "user_id" in entry_input_keys and context.get("user_id") is None:
-            import os
-
-            context["user_id"] = os.environ.get("USER", "default_user")
-
        if not args.quiet:
-            info = runner.info()
-            print(f"Agent: {info.name}")
-            print(f"Goal: {info.goal_name}")
-            print(f"Steps: {info.node_count}")
-            print(f"Input: {json.dumps(context)}")
-            print()
-            print("=" * 60)
-            print("Executing agent...")
-            print("=" * 60)
+            resume_node = session_state.get("paused_at", "unknown")
+            if checkpoint:
+                print(f"Resuming from checkpoint: {checkpoint}")
+            else:
+                print(f"Resuming session: {resume_session}")
+            print(f"Resume point: {resume_node}")
            print()

-        result = asyncio.run(runner.run(context, session_state=session_state))
+    # Auto-inject user_id if the agent expects it but it's not provided
+    entry_input_keys = runner.graph.nodes[0].input_keys if runner.graph.nodes else []
+    if "user_id" in entry_input_keys and context.get("user_id") is None:
+        import os
+
+        context["user_id"] = os.environ.get("USER", "default_user")
+
+    if not args.quiet:
+        info = runner.info()
+        print(f"Agent: {info.name}")
+        print(f"Goal: {info.goal_name}")
+        print(f"Steps: {info.node_count}")
+        print(f"Input: {json.dumps(context)}")
+        print()
+        print("=" * 60)
+        print("Executing agent...")
+        print("=" * 60)
+        print()
+
+    result = asyncio.run(runner.run(context, session_state=session_state))

    # Format output
    output = {
@@ -959,6 +746,17 @@ def cmd_dispatch(args: argparse.Namespace) -> int:
    if args.agents:
        # Use specific agents
        for agent_name in args.agents:
+            # Guard against full paths: if the name contains path separators
+            # (e.g. "exports/my_agent"), it will be doubled with agents_dir
+            agent_name_path = Path(agent_name)
+            if len(agent_name_path.parts) > 1:
+                print(
+                    f"Error: --agents expects agent names, not paths. "
+                    f"Use: --agents {agent_name_path.name} "
+                    f"instead of --agents {agent_name}",
+                    file=sys.stderr,
+                )
+                return 1
            agent_path = agents_dir / agent_name
            if not _is_valid_agent_dir(agent_path):
                print(f"Agent not found: {agent_path}", file=sys.stderr)
@@ -1124,16 +922,12 @@ def _format_natural_language_to_json(

 def cmd_shell(args: argparse.Namespace) -> int:
    """Start an interactive agent session."""
-    import logging

    from framework.credentials.models import CredentialError
+    from framework.observability import configure_logging
    from framework.runner import AgentRunner

-    # Configure logging to show runtime visibility
-    logging.basicConfig(
-        level=logging.INFO,
-        format="%(message)s",  # Simple format for clean output
-    )
+    configure_logging(level="INFO")

    agents_dir = Path(args.agents_dir)

@@ -1364,154 +1158,6 @@ def _get_framework_agents_dir() -> Path:
    return Path(__file__).resolve().parent.parent / "agents"


-def _launch_agent_tui(
-    agent_path: str | Path,
-    model: str | None = None,
-) -> int:
-    """Load an agent and launch the TUI. Shared by cmd_tui and cmd_code."""
-    from framework.credentials.models import CredentialError
-    from framework.runner import AgentRunner
-    from framework.tui.app import AdenTUI
-
-    async def run_with_tui():
-        # AgentRunner handles credential setup interactively when stdin is a TTY.
-        try:
-            runner = AgentRunner.load(
-                agent_path,
-                model=model,
-            )
-        except CredentialError as e:
-            print(f"\n{e}", file=sys.stderr)
-            return
-        except Exception as e:
-            print(f"Error loading agent: {e}")
-            return
-
-        if runner._agent_runtime is None:
-            try:
-                runner._setup()
-            except CredentialError as e:
-                print(f"\n{e}", file=sys.stderr)
-                return
-
-        if runner._agent_runtime and not runner._agent_runtime.is_running:
-            await runner._agent_runtime.start()
-
-        app = AdenTUI(runner._agent_runtime)
-        try:
-            await app.run_async()
-        except Exception as e:
-            import traceback
-
-            traceback.print_exc()
-            print(f"TUI error: {e}")
-
-        await runner.cleanup_async()
-
-    asyncio.run(run_with_tui())
-    print("TUI session ended.")
-    return 0
-
-
-def cmd_tui(args: argparse.Namespace) -> int:
-    """Launch the interactive TUI dashboard with in-app agent picker."""
-    import logging
-
-    logging.basicConfig(level=logging.WARNING, format="%(message)s")
-
-    from framework.tui.app import AdenTUI
-
-    async def run_tui():
-        app = AdenTUI(
-            model=args.model,
-        )
-        await app.run_async()
-
-    asyncio.run(run_tui())
-    print("TUI session ended.")
-    return 0
-
-
-def cmd_code(args: argparse.Namespace) -> int:
-    """Launch Hive Coder with multi-graph support.
-
-    Unlike ``_launch_agent_tui``, this sets up graph lifecycle tools and
-    assigns ``graph_id="hive_coder"`` so the coder can load, supervise,
-    and restart secondary agent graphs within the same session.
-    """
-    import logging
-
-    logging.basicConfig(level=logging.WARNING, format="%(message)s")
-
-    framework_agents_dir = _get_framework_agents_dir()
-    hive_coder_path = framework_agents_dir / "hive_coder"
-
-    if not (hive_coder_path / "agent.py").exists():
-        print("Error: Hive Coder agent not found.", file=sys.stderr)
-        return 1
-
-    # Ensure framework agents dir is on sys.path for import
-    fa_str = str(framework_agents_dir)
-    if fa_str not in sys.path:
-        sys.path.insert(0, fa_str)
-
-    from framework.credentials.models import CredentialError
-    from framework.runner import AgentRunner
-    from framework.tools.session_graph_tools import register_graph_tools
-    from framework.tui.app import AdenTUI
-
-    async def run_with_tui():
-        try:
-            runner = AgentRunner.load(hive_coder_path, model=args.model)
-        except CredentialError as e:
-            print(f"\n{e}", file=sys.stderr)
-            return
-        except Exception as e:
-            print(f"Error loading agent: {e}")
-            return
-
-        if runner._agent_runtime is None:
-            try:
-                runner._setup()
-            except CredentialError as e:
-                print(f"\n{e}", file=sys.stderr)
-                return
-
-        runtime = runner._agent_runtime
-
-        # -- Multi-graph setup --
-        # Tag the primary graph so events carry graph_id="hive_coder"
-        runtime._graph_id = "hive_coder"
-        runtime._active_graph_id = "hive_coder"
-
-        # Register graph lifecycle tools (load_agent, unload_agent, etc.)
-        register_graph_tools(runner._tool_registry, runtime)
-
-        # Refresh tool schemas AND executor so streams see the new tools.
-        # The executor closure references the registry dict by ref, but
-        # refreshing both is robust against any copy-on-read behavior.
-        runtime._tools = list(runner._tool_registry.get_tools().values())
-        runtime._tool_executor = runner._tool_registry.get_executor()
-
-        if not runtime.is_running:
-            await runtime.start()
-
-        app = AdenTUI(runtime)
-        try:
-            await app.run_async()
-        except Exception as e:
-            import traceback
-
-            traceback.print_exc()
-            print(f"TUI error: {e}")
-
-        await runner.cleanup_async()
-
-    asyncio.run(run_with_tui())
-    print("TUI session ended.")
-    return 0
-
-
 def _extract_python_agent_metadata(agent_path: Path) -> tuple[str, str]:
    """Extract name and description from a Python-based agent's config.py.

@@ -1864,56 +1510,6 @@ def _interactive_multi(agents_dir: Path) -> int:
    return 0


-def cmd_sessions_list(args: argparse.Namespace) -> int:
-    """List agent sessions."""
-    print("⚠ Sessions list command not yet implemented")
-    print("This will be available once checkpoint infrastructure is complete.")
-    print(f"\nAgent: {args.agent_path}")
-    print(f"Status filter: {args.status}")
-    print(f"Has checkpoints: {args.has_checkpoints}")
-    return 1
-
-
-def cmd_sessions_show(args: argparse.Namespace) -> int:
-    """Show detailed session information."""
-    print("⚠ Session show command not yet implemented")
-    print("This will be available once checkpoint infrastructure is complete.")
-    print(f"\nAgent: {args.agent_path}")
-    print(f"Session: {args.session_id}")
-    return 1
-
-
-def cmd_sessions_checkpoints(args: argparse.Namespace) -> int:
-    """List checkpoints for a session."""
-    print("⚠ Session checkpoints command not yet implemented")
-    print("This will be available once checkpoint infrastructure is complete.")
-    print(f"\nAgent: {args.agent_path}")
-    print(f"Session: {args.session_id}")
-    return 1
-
-
-def cmd_pause(args: argparse.Namespace) -> int:
-    """Pause a running session."""
-    print("⚠ Pause command not yet implemented")
-    print("This will be available once executor pause integration is complete.")
-    print(f"\nAgent: {args.agent_path}")
-    print(f"Session: {args.session_id}")
-    return 1
-
-
-def cmd_resume(args: argparse.Namespace) -> int:
-    """Resume a session from checkpoint."""
-    print("⚠ Resume command not yet implemented")
-    print("This will be available once checkpoint resume integration is complete.")
-    print(f"\nAgent: {args.agent_path}")
-    print(f"Session: {args.session_id}")
-    if args.checkpoint:
-        print(f"Checkpoint: {args.checkpoint}")
-    if args.tui:
-        print("Mode: TUI")
-    return 1
-
-
 def cmd_setup_credentials(args: argparse.Namespace) -> int:
    """Interactive credential setup for an agent."""
    from framework.credentials.setup import CredentialSetupSession
@@ -1965,6 +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
@@ -2000,18 +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,
@@ -2022,25 +1641,31 @@ 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()


 def cmd_serve(args: argparse.Namespace) -> int:
    """Start the HTTP API server."""
-    import logging

    from aiohttp import web

    _build_frontend()

+    from framework.observability import configure_logging
    from framework.server.app import create_app

-    logging.basicConfig(
-        level=logging.INFO,
-        format="%(asctime)s [%(levelname)s] %(name)s: %(message)s",
-    )
+    if getattr(args, "debug", False):
+        configure_logging(level="DEBUG")
+    else:
+        configure_logging(level="INFO")

    model = getattr(args, "model", None)
    app = create_app(model=model)
@@ -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 +71,8 @@ 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] = {}
        self._connected = False
@@ -140,6 +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}")

@@ -200,7 +209,8 @@ class MCPClient:
                        if os.name == "nt":
                            errlog = sys.stderr
                        else:
-                            errlog = open(os.devnull, "w")  # noqa: SIM115
+                            self._errlog_handle = open(os.devnull, "w")
+                            errlog = self._errlog_handle
                        self._stdio_context = stdio_client(server_params, errlog=errlog)
                        (
                            self._read_stream,
@@ -264,10 +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()
@@ -364,14 +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:
@@ -387,19 +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

@@ -425,24 +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)
@@ -475,6 +628,25 @@ 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:
+                self._errlog_handle.close()
+            except Exception as e:
+                logger.debug(f"Error closing errlog handle: {e}")
+            finally:
+                self._errlog_handle = None
+
    def disconnect(self) -> None:
        """Disconnect from the MCP server."""
        # Clean up persistent STDIO connection
@@ -541,10 +713,12 @@ 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
            self._loop_thread = None
+            self._errlog_handle = None

        # Clean up HTTP client
        if self._http_client:
@@ -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()
@@ -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.",
+        )
@@ -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
@@ -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
@@ -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.
@@ -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.
    """
@@ -9,14 +9,13 @@ from datetime import UTC
 from pathlib import Path
 from typing import TYPE_CHECKING, Any

-from framework.config import get_hive_config, get_preferred_model
+from framework.config import get_hive_config, get_max_context_tokens, get_preferred_model
 from framework.credentials.validation import (
    ensure_credential_key_env as _ensure_credential_key_env,
 )
 from framework.graph import Goal
 from framework.graph.edge import (
    DEFAULT_MAX_TOKENS,
-    AsyncEntryPointSpec,
    EdgeCondition,
    EdgeSpec,
    GraphSpec,
@@ -29,6 +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
@@ -517,6 +517,354 @@ def get_codex_account_id() -> str | None:
    return None


+# ---------------------------------------------------------------------------
+# Kimi Code subscription token helpers
+# ---------------------------------------------------------------------------
+
+
+def get_kimi_code_token() -> str | None:
+    """Get the API key from a Kimi Code CLI installation.
+
+    Reads the API key from ``~/.kimi/config.toml``, which is created when
+    the user runs ``kimi /login`` in the Kimi Code CLI.
+
+    Returns:
+        The API key if available, None otherwise.
+    """
+    import tomllib
+
+    config_path = Path.home() / ".kimi" / "config.toml"
+    if not config_path.exists():
+        return None
+
+    try:
+        with open(config_path, "rb") as f:
+            config = tomllib.load(f)
+        providers = config.get("providers", {})
+        # kimi-cli stores credentials under providers.kimi-for-coding
+        for provider_cfg in providers.values():
+            if isinstance(provider_cfg, dict):
+                key = provider_cfg.get("api_key")
+                if key:
+                    return key
+    except Exception:
+        pass
+    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."""
@@ -535,9 +883,6 @@ class AgentInfo:
    constraints: list[dict]
    required_tools: list[str]
    has_tools_module: bool
-    # Multi-entry-point support
-    async_entry_points: list[dict] = field(default_factory=list)
-    is_multi_entry_point: bool = False


@dataclass
@@ -595,22 +940,6 @@ def load_agent_export(data: str | dict) -> tuple[GraphSpec, Goal]:
        )
        edges.append(edge)

-    # Build AsyncEntryPointSpec objects for multi-entry-point support
-    async_entry_points = []
-    for aep_data in graph_data.get("async_entry_points", []):
-        async_entry_points.append(
-            AsyncEntryPointSpec(
-                id=aep_data["id"],
-                name=aep_data.get("name", aep_data["id"]),
-                entry_node=aep_data["entry_node"],
-                trigger_type=aep_data.get("trigger_type", "manual"),
-                trigger_config=aep_data.get("trigger_config", {}),
-                isolation_level=aep_data.get("isolation_level", "shared"),
-                priority=aep_data.get("priority", 0),
-                max_concurrent=aep_data.get("max_concurrent", 10),
-            )
-        )
-
    # Build GraphSpec
    graph = GraphSpec(
        id=graph_data.get("id", "agent-graph"),
@@ -618,7 +947,6 @@ def load_agent_export(data: str | dict) -> tuple[GraphSpec, Goal]:
        version=graph_data.get("version", "1.0.0"),
        entry_node=graph_data.get("entry_node", ""),
        entry_points=graph_data.get("entry_points", {}),  # Support pause/resume architecture
-        async_entry_points=async_entry_points,  # Support multi-entry-point agents
        terminal_nodes=graph_data.get("terminal_nodes", []),
        pause_nodes=graph_data.get("pause_nodes", []),  # Support pause/resume architecture
        nodes=nodes,
@@ -770,8 +1098,6 @@ class AgentRunner:

        # AgentRuntime — unified execution path for all agents
        self._agent_runtime: AgentRuntime | None = None
-        self._uses_async_entry_points = self.graph.has_async_entry_points()
-
        # Pre-load validation: structural checks + credentials.
        # Fails fast with actionable guidance — no MCP noise on screen.
        run_preload_validation(
@@ -795,6 +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.
@@ -891,10 +1220,32 @@ class AgentRunner:

            if agent_config and hasattr(agent_config, "max_tokens"):
                max_tokens = agent_config.max_tokens
+                logger.info(
+                    "Agent default_config overrides max_tokens: %d "
+                    "(configuration.json value ignored)",
+                    max_tokens,
+                )
            else:
                hive_config = get_hive_config()
                max_tokens = hive_config.get("llm", {}).get("max_tokens", DEFAULT_MAX_TOKENS)

+            # Resolve max_context_tokens with priority:
+            #   1. agent loop_config["max_context_tokens"] (explicit, wins silently)
+            #   2. agent default_config.max_context_tokens (logged)
+            #   3. configuration.json llm.max_context_tokens
+            #   4. hardcoded default (32_000)
+            agent_loop_config: dict = dict(getattr(agent_module, "loop_config", {}))
+            if "max_context_tokens" not in agent_loop_config:
+                if agent_config and hasattr(agent_config, "max_context_tokens"):
+                    agent_loop_config["max_context_tokens"] = agent_config.max_context_tokens
+                    logger.info(
+                        "Agent default_config overrides max_context_tokens: %d"
+                        " (configuration.json value ignored)",
+                        agent_config.max_context_tokens,
+                    )
+                else:
+                    agent_loop_config["max_context_tokens"] = get_max_context_tokens()
+
            # Read intro_message from agent metadata (shown on TUI load)
            agent_metadata = getattr(agent_module, "metadata", None)
            intro_message = ""
@@ -908,13 +1259,12 @@ class AgentRunner:
                "version": "1.0.0",
                "entry_node": getattr(agent_module, "entry_node", nodes[0].id),
                "entry_points": getattr(agent_module, "entry_points", {}),
-                "async_entry_points": getattr(agent_module, "async_entry_points", []),
                "terminal_nodes": getattr(agent_module, "terminal_nodes", []),
                "pause_nodes": getattr(agent_module, "pause_nodes", []),
                "nodes": nodes,
                "edges": edges,
                "max_tokens": max_tokens,
-                "loop_config": getattr(agent_module, "loop_config", {}),
+                "loop_config": agent_loop_config,
            }
            # Only pass optional fields if explicitly defined by the agent module
            conversation_mode = getattr(agent_module, "conversation_mode", None)
@@ -926,6 +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)

@@ -937,7 +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,
@@ -953,6 +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"
@@ -970,7 +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,
@@ -981,6 +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,
@@ -1061,6 +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.
@@ -1091,7 +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

@@ -1104,6 +1523,8 @@ class AgentRunner:
            llm_config = config.get("llm", {})
            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
@@ -1111,14 +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:
+                    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.
@@ -1149,6 +1584,27 @@ class AgentRunner:
                    store=False,
                    allowed_openai_params=["store"],
                )
+            elif api_key and use_kimi_code:
+                # Kimi Code subscription uses the Kimi coding API (OpenAI-compatible).
+                # The api_base is set automatically by LiteLLMProvider for kimi/ models.
+                self._llm = LiteLLMProvider(
+                    model=self.model,
+                    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):
@@ -1180,8 +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:
@@ -1275,6 +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,
@@ -1282,6 +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:
@@ -1302,6 +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/"):
@@ -1314,6 +1791,10 @@ class AgentRunner:
            return "TOGETHER_API_KEY"
        elif model_lower.startswith("minimax/") or model_lower.startswith("minimax-"):
            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"
@@ -1334,6 +1815,10 @@ class AgentRunner:
            cred_id = "anthropic"
        elif model_lower.startswith("minimax/") or model_lower.startswith("minimax-"):
            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:
@@ -1373,23 +1858,13 @@ 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."""
-        # Convert AsyncEntryPointSpec to EntryPointSpec for AgentRuntime
        entry_points = []
-        for async_ep in self.graph.async_entry_points:
-            ep = EntryPointSpec(
-                id=async_ep.id,
-                name=async_ep.name,
-                entry_node=async_ep.entry_node,
-                trigger_type=async_ep.trigger_type,
-                trigger_config=async_ep.trigger_config,
-                isolation_level=async_ep.isolation_level,
-                priority=async_ep.priority,
-                max_concurrent=async_ep.max_concurrent,
-                max_resurrections=async_ep.max_resurrections,
-            )
-            entry_points.append(ep)

        # Always create a primary entry point for the graph's entry node.
        # For multi-entry-point agents this ensures the primary path (e.g.
@@ -1446,26 +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"})
@@ -1591,7 +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()

@@ -1608,10 +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
@@ -1696,19 +2187,6 @@ class AgentRunner:
            for edge in self.graph.edges
        ]

-        # Build async entry points info
-        async_entry_points_info = [
-            {
-                "id": ep.id,
-                "name": ep.name,
-                "entry_node": ep.entry_node,
-                "trigger_type": ep.trigger_type,
-                "isolation_level": ep.isolation_level,
-                "max_concurrent": ep.max_concurrent,
-            }
-            for ep in self.graph.async_entry_points
-        ]
-
        return AgentInfo(
            name=self.graph.id,
            description=self.graph.description,
@@ -1735,8 +2213,6 @@ class AgentRunner:
            ],
            required_tools=sorted(required_tools),
            has_tools_module=(self.agent_path / "tools.py").exists(),
-            async_entry_points=async_entry_points_info,
-            is_multi_entry_point=self._uses_async_entry_points,
        )

    def validate(self) -> ValidationResult:
@@ -2051,18 +2527,6 @@ Respond with JSON only:
                trigger_type="manual",
                isolation_level="shared",
            )
-        for aep in runner.graph.async_entry_points:
-            entry_points[aep.id] = EntryPointSpec(
-                id=aep.id,
-                name=aep.name,
-                entry_node=aep.entry_node,
-                trigger_type=aep.trigger_type,
-                trigger_config=aep.trigger_config,
-                isolation_level=aep.isolation_level,
-                priority=aep.priority,
-                max_concurrent=aep.max_concurrent,
-            )
-
        await runtime.add_graph(
            graph_id=gid,
            graph=runner.graph,
@@ -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."""
--- a/Show More
+++ b/Show More