chore: ruff lint

Refactor session lifecycle with flowchart planning and triggers

.github/ISSUE_TEMPLATE/link-discord.yml

@@ -1,31 +0,0 @@
-name: Link Discord Account
-description: Connect your GitHub and Discord for the bounty program
-title: "link: @{{ github.actor }}"
-labels: ["link-discord"]
-body:
-  - type: markdown
-    attributes:
-      value: |
-        Link your Discord account to receive XP and role rewards when your bounty PRs are merged.
-
-        **How to find your Discord ID:**
-        1. Open Discord Settings > Advanced > Enable **Developer Mode**
-        2. Right-click your username > **Copy User ID**
-
-  - type: input
-    id: discord_id
-    attributes:
-      label: Discord User ID
-      description: "Your numeric Discord ID (not your username). Example: 123456789012345678"
-      placeholder: "123456789012345678"
-    validations:
-      required: true
-
-  - type: input
-    id: display_name
-    attributes:
-      label: Display Name (optional)
-      description: How you'd like to be credited
-      placeholder: "Jane Doe"
-    validations:
-      required: false

AGENTS.md

@@ -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-- **TUI is deprecated.** The terminal UI (`hive tui`) is no longer maintained.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
 
 - 

Makefile

@@ -5,20 +5,20 @@ help: ## Show this help
 		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

README.md

@@ -111,7 +111,7 @@ This sets up:
 - **LLM provider** - Interactive default model configuration
 - 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.
 
@@ -125,18 +125,18 @@ Type the agent you want to build in the home input box
 
 ### 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" />
 
 ## 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

SECURITY.md

@@ -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
 

core/MCP_SERVER_GUIDE.md

@@ -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.
 

core/README.md

@@ -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.
 

core/demos/event_loop_wss_demo.py

@@ -601,7 +601,7 @@ async def handle_ws(websocket):
         )
         node = EventLoopNode(
             event_bus=bus,
-            config=LoopConfig(max_iterations=10_000, max_history_tokens=32_000),
+            config=LoopConfig(max_iterations=10_000, max_context_tokens=32_000),
             conversation_store=STORE,
             tool_executor=tool_executor,
         )

core/demos/github_outreach_demo.py

@@ -1769,7 +1769,7 @@ async def _run_pipeline(websocket, initial_message: str):
             config=LoopConfig(
                 max_iterations=30,
                 max_tool_calls_per_turn=30,
-                max_history_tokens=64000,
+                max_context_tokens=64000,
                 max_tool_result_chars=8_000,
                 spillover_dir=str(_DATA_DIR),
             ),

core/demos/handoff_demo.py

@@ -752,7 +752,7 @@ async def _run_pipeline(websocket, topic: str):
         config=LoopConfig(
             max_iterations=20,
             max_tool_calls_per_turn=30,
-            max_history_tokens=32_000,
+            max_context_tokens=32_000,
         ),
         conversation_store=store_a,
         tool_executor=tool_executor,
@@ -850,7 +850,7 @@ async def _run_pipeline(websocket, topic: str):
         config=LoopConfig(
             max_iterations=10,
             max_tool_calls_per_turn=30,
-            max_history_tokens=32_000,
+            max_context_tokens=32_000,
         ),
         conversation_store=store_b,
     )

core/demos/org_demo.py

@@ -1258,7 +1258,7 @@ async def _run_org_pipeline(websocket, topic: str):
             config=LoopConfig(
                 max_iterations=30,
                 max_tool_calls_per_turn=30,
-                max_history_tokens=32_000,
+                max_context_tokens=32_000,
             ),
             conversation_store=store,
             tool_executor=executor,

core/framework/__init__.py

@@ -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",

core/framework/agents/credential_tester/__main__.py

@@ -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)

core/framework/agents/credential_tester/agent.py

@@ -19,6 +19,7 @@ from __future__ import annotations
 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
@@ -455,7 +456,6 @@ identity_prompt = (
 loop_config = {
     "max_iterations": 50,
     "max_tool_calls_per_turn": 30,
-    "max_history_tokens": 32000,
 }
 
 # ---------------------------------------------------------------------------
@@ -541,7 +541,7 @@ 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=(

core/framework/agents/discovery.py

@@ -0,0 +1,178 @@
+"""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_name: str) -> str | None:
+    """Return the most recent updated_at timestamp across all sessions."""
+    sessions_dir = Path.home() / ".hive" / "agents" / agent_name / "sessions"
+    if not sessions_dir.exists():
+        return None
+    latest: str | None = None
+    for session_dir in sessions_dir.iterdir():
+        if not session_dir.is_dir() or not session_dir.name.startswith("session_"):
+            continue
+        state_file = session_dir / "state.json"
+        if not state_file.exists():
+            continue
+        try:
+            data = json.loads(state_file.read_text(encoding="utf-8"))
+            ts = data.get("timestamps", {}).get("updated_at")
+            if ts and (latest is None or ts > latest):
+                latest = ts
+        except Exception:
+            continue
+    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.name),
+                )
+            )
+        if entries:
+            groups[category] = entries
+
+    return groups

core/framework/agents/hive_coder/__init__.py

@@ -1,40 +0,0 @@
-"""
-Hive Coder — Native coding agent that builds Hive agent packages.
-
-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 (
-    conversation_mode,
-    edges,
-    entry_node,
-    entry_points,
-    goal,
-    identity_prompt,
-    loop_config,
-    nodes,
-    pause_nodes,
-    terminal_nodes,
-)
-from .config import AgentMetadata, RuntimeConfig, default_config, metadata
-
-__version__ = "1.0.0"
-
-__all__ = [
-    "goal",
-    "nodes",
-    "edges",
-    "entry_node",
-    "entry_points",
-    "pause_nodes",
-    "terminal_nodes",
-    "conversation_mode",
-    "identity_prompt",
-    "loop_config",
-    "RuntimeConfig",
-    "AgentMetadata",
-    "default_config",
-    "metadata",
-]

core/framework/agents/hive_coder/__main__.py

@@ -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()

core/framework/agents/hive_coder/agent.py

@@ -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,
-    },
-)

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

@@ -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",
-]

core/framework/agents/queen/__init__.py

@@ -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",
+]

core/framework/agents/queen/agent.py

@@ -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,
+    },
+)

core/framework/agents/queen/config.py

@@ -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."
     )
 

core/framework/agents/queen/queen_memory.py

@@ -0,0 +1,371 @@
+"""Queen global cross-session memory.
+
+Three-tier memory architecture:
+  ~/.hive/queen/MEMORY.md                            — semantic (who, what, why)
+  ~/.hive/queen/memories/MEMORY-YYYY-MM-DD.md        — episodic (daily journals)
+  ~/.hive/queen/session/{id}/data/adapt.md           — working (session-scoped)
+
+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 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 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()
+    episodic = read_episodic_memory()
+
+    # 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 episodic:
+        today_str = date.today().strftime("%B %-d, %Y")
+        parts.append(f"## Today — {today_str}\n\n{episodic}")
+
+    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_str = date.today().strftime("%B %-d, %Y")
+    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 + adapt.md.
+
+    Reads the last ``max_messages`` conversation parts and the session's
+    adapt.md (working memory). Tool results are omitted — only user and
+    assistant turns (with tool-call names noted) are included.
+    """
+    parts: list[str] = []
+
+    # Working notes
+    adapt_path = session_dir / "data" / "adapt.md"
+    if adapt_path.exists():
+        text = adapt_path.read_text(encoding="utf-8").strip()
+        if text:
+            parts.append(f"## Session Working Notes (adapt.md)\n\n{text}")
+
+    # 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 Exception:
+                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 and adapt.md 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 (used for the adapt.md path reference).
+        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_str = date.today().strftime("%B %-d, %Y")
+        adapt_path = session_dir / "data" / "adapt.md"
+
+        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 path: {adapt_path}\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 Exception:
+            pass

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

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

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

@@ -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

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

@@ -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
 

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

@@ -109,6 +109,45 @@ 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`)

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

@@ -0,0 +1,63 @@
+# 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/
+│               ├── adapt.md              ← Working memory (session-scoped)
+│               ├── web_search_1.txt      ← Spillover: large tool results
+│               ├── web_search_2.txt
+│               └── ...
+```
+
+---
+
+## The three 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) |
+| `data/adapt.md` | Working | Queen via `update_session_notes` tool | Every turn (inlined in system prompt) |
+
+---
+
+## 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)
+2. `data/adapt.md` — current working notes
+
+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`).

core/framework/agents/queen/tests/conftest.py

@@ -1,4 +1,4 @@
-"""Test fixtures for Hive Coder agent."""
+"""Test fixtures for Queen agent."""
 
 import sys
 from pathlib import Path

core/framework/agents/queen/ticket_receiver.py

@@ -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.

core/framework/builder/__init__.py

@@ -1,7 +0,0 @@
-"""Builder interface for analyzing and building agents."""
-
-from framework.builder.query import BuilderQuery
-
-__all__ = [
-    "BuilderQuery",
-]

core/framework/builder/query.py

@@ -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

core/framework/config.py

@@ -56,6 +56,14 @@ def get_max_tokens() -> int:
     return get_hive_config().get("llm", {}).get("max_tokens", DEFAULT_MAX_TOKENS)
 
 
+DEFAULT_MAX_CONTEXT_TOKENS = 32_000
+
+
+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 +98,17 @@ 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
+
     # 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:
@@ -102,12 +121,23 @@ def get_gcu_enabled() -> bool:
     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"
+    if llm.get("use_kimi_code_subscription"):
+        # Kimi Code uses an Anthropic-compatible endpoint (no /v1 suffix).
+        return "https://api.kimi.com/coding"
     return llm.get("api_base")
 
 
@@ -164,6 +194,7 @@ 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)

core/framework/credentials/__init__.py

@@ -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 (

core/framework/credentials/key_storage.py

@@ -149,8 +149,14 @@ def delete_aden_api_key() -> None:
 
         storage = EncryptedFileStorage()
         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)
 
@@ -167,8 +173,10 @@ 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 +204,12 @@ 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

core/framework/credentials/vault/__init__.py

@@ -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"]

core/framework/credentials/vault/hashicorp.py

@@ -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

core/framework/graph/conversation.py

@@ -307,13 +307,13 @@ 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
@@ -525,16 +525,16 @@ 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 ---------------------------------------------
 
@@ -1029,7 +1029,7 @@ 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 +1062,7 @@ 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,

core/framework/graph/conversation_judge.py

@@ -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():

core/framework/graph/edge.py

@@ -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")
 

core/framework/graph/executor.py

@@ -34,6 +34,16 @@ from framework.schemas.checkpoint import Checkpoint
 from framework.storage.checkpoint_store import CheckpointStore
 
 
+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
 class ExecutionResult:
     """Result of executing a graph."""
@@ -138,6 +148,7 @@ 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,
     ):
         """
         Initialize the executor.
@@ -183,6 +194,7 @@ 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
 
         # Parallel execution settings
         self.enable_parallel_execution = enable_parallel_execution
@@ -330,7 +342,7 @@ 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
 
@@ -1604,7 +1616,7 @@ 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,
@@ -1799,6 +1811,7 @@ 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,
         )
 
     VALID_NODE_TYPES = {
@@ -1872,7 +1885,7 @@ 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", {}),

core/framework/graph/gcu.py

@@ -37,24 +37,42 @@ 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.
+- 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` for reading text content
   — it produces huge base64 images with no searchable text.
 - 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 +83,33 @@ Follow these rules for reliable, efficient browser interaction.
   then `browser_start`, then retry.
 
 ## Tab Management
-- Use `browser_tabs` to list open tabs when managing multiple pages.
-- Pass `target_id` to tools when operating on a specific tab.
-- Open background tabs with `browser_open(url=..., background=true)`
-  to avoid losing your current context.
-- Close tabs you no longer need with `browser_close` to free resources.
+
+**Close tabs as soon as you are done with them** — not only at the end of the task.
+After reading or extracting data from a tab, close it immediately.
+
+**Decision rules:**
+- Finished reading/extracting from a tab? → `browser_close(target_id=...)`
+- Completed a multi-tab workflow? → `browser_close_finished()` to clean up all your tabs
+- More than 3 tabs open? → stop and close finished ones before opening more
+- Popup appeared that you didn't need? → close it immediately
+
+**Origin awareness:** `browser_tabs` returns an `origin` field for each tab:
+- `"agent"` — you opened it; you own it; close it when done
+- `"popup"` — opened by a link or script; close after extracting what you need
+- `"startup"` or `"user"` — leave these alone unless the task requires it
+
+**Cleanup tools:**
+- `browser_close(target_id=...)` — close one specific tab
+- `browser_close_finished()` — close all your agent/popup tabs (safe: leaves startup/user tabs)
+- `browser_close_all()` — close everything except the active tab (use only for full reset)
+
+**Multi-tab workflow pattern:**
+1. Open background tabs with `browser_open(url=..., background=true)` to stay on current tab
+2. Process each tab and close it with `browser_close` when done
+3. When the full workflow completes, call `browser_close_finished()` to confirm cleanup
+4. Check `browser_tabs` at any point — it shows `origin` and `age_seconds` per tab
+
+Never accumulate tabs. Treat every tab you open as a resource you must free.
 
 ## Login & Auth Walls
 - If you see a "Log in" or "Sign up" prompt instead of expected

core/framework/graph/hitl.py

@@ -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)

core/framework/graph/node.py

@@ -565,6 +565,11 @@ class NodeContext:
     # staging / running) without restarting the conversation.
     dynamic_prompt_provider: Any = None  # Callable[[], str] | 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:

core/framework/llm/litellm.py

@@ -119,6 +119,29 @@ RATE_LIMIT_BACKOFF_BASE = 2  # seconds
 RATE_LIMIT_MAX_DELAY = 120  # seconds - cap to prevent absurd waits
 MINIMAX_API_BASE = "https://api.minimax.io/v1"
 
+# Providers that accept cache_control on message content blocks.
+# Anthropic: native ephemeral caching. MiniMax & Z-AI/GLM: pass-through to their APIs.
+# (OpenAI caches automatically server-side; Groq/Gemini/etc. strip the header.)
+_CACHE_CONTROL_PREFIXES = (
+    "anthropic/",
+    "claude-",
+    "minimax/",
+    "minimax-",
+    "MiniMax-",
+    "zai-glm",
+    "glm-",
+)
+
+
+def _model_supports_cache_control(model: str) -> bool:
+    return any(model.startswith(p) for p in _CACHE_CONTROL_PREFIXES)
+
+
+# Kimi For Coding uses an Anthropic-compatible endpoint (no /v1 suffix).
+# Claude Code integration uses this format; the /v1 OpenAI-compatible endpoint
+# enforces a coding-agent whitelist that blocks unknown User-Agents.
+KIMI_API_BASE = "https://api.kimi.com/coding"
+
 # Empty-stream retries use a short fixed delay, not the rate-limit backoff.
 # Conversation-structure issues are deterministic — long waits don't help.
 EMPTY_STREAM_MAX_RETRIES = 3
@@ -323,9 +346,21 @@ class LiteLLMProvider(LLMProvider):
             api_base: Custom API base URL (for proxies or local deployments)
             **kwargs: Additional arguments passed to litellm.completion()
         """
+        # Kimi For Coding exposes an Anthropic-compatible endpoint at
+        # https://api.kimi.com/coding (the same format Claude Code uses natively).
+        # Translate kimi/ prefix to anthropic/ so litellm uses the Anthropic
+        # Messages API handler and routes to that endpoint — no special headers needed.
+        _original_model = model
+        if model.lower().startswith("kimi/"):
+            model = "anthropic/" + model[len("kimi/") :]
+            # Normalise api_base: litellm's Anthropic handler appends /v1/messages,
+            # so the base must be https://api.kimi.com/coding (no /v1 suffix).
+            # Strip a trailing /v1 in case the user's saved config has the old value.
+            if api_base and api_base.rstrip("/").endswith("/v1"):
+                api_base = api_base.rstrip("/")[:-3]
         self.model = model
         self.api_key = api_key
-        self.api_base = api_base or self._default_api_base_for_model(model)
+        self.api_base = api_base or self._default_api_base_for_model(_original_model)
         self.extra_kwargs = kwargs
         # The Codex ChatGPT backend (chatgpt.com/backend-api/codex) rejects
         # several standard OpenAI params: max_output_tokens, stream_options.
@@ -350,6 +385,8 @@ class LiteLLMProvider(LLMProvider):
         model_lower = model.lower()
         if model_lower.startswith("minimax/") or model_lower.startswith("minimax-"):
             return MINIMAX_API_BASE
+        if model_lower.startswith("kimi/"):
+            return KIMI_API_BASE
         return None
 
     def _completion_with_rate_limit_retry(
@@ -689,7 +726,10 @@ class LiteLLMProvider(LLMProvider):
 
         full_messages: list[dict[str, Any]] = []
         if system:
-            full_messages.append({"role": "system", "content": system})
+            sys_msg: dict[str, Any] = {"role": "system", "content": system}
+            if _model_supports_cache_control(self.model):
+                sys_msg["cache_control"] = {"type": "ephemeral"}
+            full_messages.append(sys_msg)
         full_messages.extend(messages)
 
         if json_mode:
@@ -860,7 +900,10 @@ class LiteLLMProvider(LLMProvider):
 
         full_messages: list[dict[str, Any]] = []
         if system:
-            full_messages.append({"role": "system", "content": system})
+            sys_msg: dict[str, Any] = {"role": "system", "content": system}
+            if _model_supports_cache_control(self.model):
+                sys_msg["cache_control"] = {"type": "ephemeral"}
+            full_messages.append(sys_msg)
         full_messages.extend(messages)
 
         # Codex Responses API requires an `instructions` field (system prompt).
@@ -925,9 +968,26 @@ class LiteLLMProvider(LLMProvider):
                 response = await litellm.acompletion(**kwargs)  # type: ignore[union-attr]
 
                 async for chunk in response:
-                    choice = chunk.choices[0] if chunk.choices else None
-                    if not choice:
+                    # Capture usage from the trailing usage-only chunk that
+                    # stream_options={"include_usage": True} sends with empty choices.
+                    if not chunk.choices:
+                        usage = getattr(chunk, "usage", None)
+                        if usage:
+                            input_tokens = getattr(usage, "prompt_tokens", 0) or 0
+                            output_tokens = getattr(usage, "completion_tokens", 0) or 0
+                            logger.debug(
+                                "[tokens] trailing usage chunk: input=%d output=%d model=%s",
+                                input_tokens,
+                                output_tokens,
+                                self.model,
+                            )
+                        else:
+                            logger.debug(
+                                "[tokens] empty-choices chunk with no usage (model=%s)",
+                                self.model,
+                            )
                         continue
+                    choice = chunk.choices[0]
 
                     delta = choice.delta
 
@@ -1000,19 +1060,91 @@ class LiteLLMProvider(LLMProvider):
                             tail_events.append(TextEndEvent(full_text=accumulated_text))
 
                         usage = getattr(chunk, "usage", None)
+                        logger.debug(
+                            "[tokens] finish-chunk raw usage: %r (type=%s)",
+                            usage,
+                            type(usage).__name__,
+                        )
+                        cached_tokens = 0
                         if usage:
                             input_tokens = getattr(usage, "prompt_tokens", 0) or 0
                             output_tokens = getattr(usage, "completion_tokens", 0) or 0
+                            _details = getattr(usage, "prompt_tokens_details", None)
+                            cached_tokens = (
+                                getattr(_details, "cached_tokens", 0) or 0
+                                if _details is not None
+                                else getattr(usage, "cache_read_input_tokens", 0) or 0
+                            )
+                            logger.debug(
+                                "[tokens] finish-chunk usage: "
+                                "input=%d output=%d cached=%d model=%s",
+                                input_tokens,
+                                output_tokens,
+                                cached_tokens,
+                                self.model,
+                            )
 
+                        logger.debug(
+                            "[tokens] finish event: input=%d output=%d cached=%d stop=%s model=%s",
+                            input_tokens,
+                            output_tokens,
+                            cached_tokens,
+                            choice.finish_reason,
+                            self.model,
+                        )
                         tail_events.append(
                             FinishEvent(
                                 stop_reason=choice.finish_reason,
                                 input_tokens=input_tokens,
                                 output_tokens=output_tokens,
+                                cached_tokens=cached_tokens,
                                 model=self.model,
                             )
                         )
 
+                # Fallback: LiteLLM strips usage from yielded chunks before
+                # returning them to us, but appends the original chunk (with
+                # usage intact) to response.chunks first.  Use LiteLLM's own
+                # calculate_total_usage() on that accumulated list.
+                if input_tokens == 0 and output_tokens == 0:
+                    try:
+                        from litellm.litellm_core_utils.streaming_handler import (
+                            calculate_total_usage,
+                        )
+
+                        _chunks = getattr(response, "chunks", None)
+                        if _chunks:
+                            _usage = calculate_total_usage(chunks=_chunks)
+                            input_tokens = _usage.prompt_tokens or 0
+                            output_tokens = _usage.completion_tokens or 0
+                            _details = getattr(_usage, "prompt_tokens_details", None)
+                            cached_tokens = (
+                                getattr(_details, "cached_tokens", 0) or 0
+                                if _details is not None
+                                else getattr(_usage, "cache_read_input_tokens", 0) or 0
+                            )
+                            logger.debug(
+                                "[tokens] post-loop chunks fallback:"
+                                " input=%d output=%d cached=%d model=%s",
+                                input_tokens,
+                                output_tokens,
+                                cached_tokens,
+                                self.model,
+                            )
+                            # Patch the FinishEvent already queued with 0 tokens
+                            for _i, _ev in enumerate(tail_events):
+                                if isinstance(_ev, FinishEvent) and _ev.input_tokens == 0:
+                                    tail_events[_i] = FinishEvent(
+                                        stop_reason=_ev.stop_reason,
+                                        input_tokens=input_tokens,
+                                        output_tokens=output_tokens,
+                                        cached_tokens=cached_tokens,
+                                        model=_ev.model,
+                                    )
+                                    break
+                    except Exception as _e:
+                        logger.debug("[tokens] chunks fallback failed: %s", _e)
+
                 # Check whether the stream produced any real content.
                 # (If text deltas were yielded above, has_content is True
                 # and we skip the retry path — nothing was yielded in vain.)

core/framework/llm/stream_events.py

@@ -71,6 +71,7 @@ class FinishEvent:
     stop_reason: str = ""
     input_tokens: int = 0
     output_tokens: int = 0
+    cached_tokens: int = 0
     model: str = ""
 
 

core/framework/mcp/__init__.py

@@ -1,4 +0,0 @@
-"""MCP servers for worker-bee."""
-
-# Don't auto-import servers to avoid double-import issues when running with -m
-__all__ = []

core/framework/monitoring/__init__.py

@@ -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."""

core/framework/monitoring/judge.py

@@ -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
-    },
-)

core/framework/observability/logging.py

@@ -148,8 +148,9 @@ 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(

core/framework/runner/cli.py

@@ -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
@@ -2029,18 +1625,18 @@ def _build_frontend() -> bool:
 
 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)

core/framework/runner/mcp_client.py

@@ -68,6 +68,7 @@ class MCPClient:
         self._read_stream = None
         self._write_stream = None
         self._stdio_context = None  # Context manager for stdio_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
@@ -200,7 +201,8 @@ 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,
@@ -475,6 +477,15 @@ class MCPClient:
         finally:
             self._stdio_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
@@ -545,6 +556,7 @@ class MCPClient:
             self._write_stream = None
             self._loop = None
             self._loop_thread = None
+            self._errlog_handle = None
 
         # Clean up HTTP client
         if self._http_client:

core/framework/runner/runner.py

@@ -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,
@@ -517,6 +516,41 @@ 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
+
+
 @dataclass
 class AgentInfo:
     """Information about an exported agent."""
@@ -535,9 +569,6 @@ 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 +626,6 @@ 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 +633,6 @@ 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 +784,6 @@ 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(
@@ -891,10 +903,32 @@ 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 +942,12 @@ 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)
@@ -1104,6 +1137,7 @@ 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)
             api_base = llm_config.get("api_base")
 
             api_key = None
@@ -1119,6 +1153,12 @@ class AgentRunner:
                 if not api_key:
                     print("Warning: Codex subscription configured but no token found.")
                     print("Run 'codex' to authenticate, then try again.")
+            elif use_kimi_code:
+                # Get API key from Kimi Code CLI config (~/.kimi/config.toml)
+                api_key = get_kimi_code_token()
+                if not api_key:
+                    print("Warning: Kimi Code subscription configured but no key found.")
+                    print("Run 'kimi /login' to authenticate, then try again.")
 
             if api_key and use_claude_code:
                 # Use litellm's built-in Anthropic OAuth support.
@@ -1149,6 +1189,14 @@ 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,
+                )
             else:
                 # Local models (e.g. Ollama) don't need an API key
                 if self._is_local_model(self.model):
@@ -1314,6 +1362,8 @@ 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"
         else:
             # Default: assume OpenAI-compatible
             return "OPENAI_API_KEY"
@@ -1334,6 +1384,8 @@ 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"
         # Add more mappings as providers are added to LLM_CREDENTIALS
 
         if cred_id is None:
@@ -1375,21 +1427,7 @@ class AgentRunner:
         event_bus=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.
@@ -1696,19 +1734,6 @@ 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 +1760,6 @@ 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 +2074,6 @@ 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,

core/framework/runtime/EVENT_TYPES.md

@@ -454,11 +454,11 @@ An agent has requested handoff to the Hive Coder (via the `escalate` synthetic t
 
 ## Worker Health Monitoring
 
-These events form the **judge → queen → operator** escalation pipeline.
+These events form the **queen → operator** escalation pipeline.
 
 ### `worker_escalation_ticket`
 
-The Worker Health Judge has detected a degradation pattern and is escalating to the Queen.
+A worker degradation pattern has been detected and is being escalated to the Queen.
 
 | Data Field | Type   | Description                          |
 | ---------- | ------ | ------------------------------------ |

core/framework/runtime/agent_runtime.py

@@ -8,6 +8,7 @@ while preserving the goal-driven approach.
 import asyncio
 import logging
 import time
+import uuid
 from collections.abc import Callable
 from dataclasses import dataclass, field
 from datetime import datetime
@@ -349,7 +350,7 @@ class AgentRuntime:
                             return
                         # Skip events originating from this graph's own
                         # executions (e.g. guardian should not fire on
-                        # hive_coder failures — only secondary graphs).
+                        # queen failures — only secondary graphs).
                         if _exclude_own and event.graph_id == self._graph_id:
                             return
                         ep_spec = self._entry_points.get(entry_point_id)
@@ -822,7 +823,8 @@ class AgentRuntime:
         if stream is None:
             raise ValueError(f"Entry point '{entry_point_id}' not found")
 
-        return await stream.execute(input_data, correlation_id, session_state)
+        run_id = uuid.uuid4().hex[:12]
+        return await stream.execute(input_data, correlation_id, session_state, run_id=run_id)
 
     async def trigger_and_wait(
         self,
@@ -1359,8 +1361,8 @@ class AgentRuntime:
                 allowed_keys = set(entry_node.input_keys)
 
         # Search primary graph's streams for an active session.
-        # Skip isolated streams (e.g. health judge) — they have their own
-        # session directories and must never be used as a shared session.
+        # Skip isolated streams — they have their own session directories
+        # and must never be used as a shared session.
         all_streams: list[tuple[str, ExecutionStream]] = []
         for _gid, reg in self._graphs.items():
             for ep_id, stream in reg.streams.items():
@@ -1531,6 +1533,11 @@ class AgentRuntime:
                 for executor in stream._active_executors.values():
                     for node_id, node in executor.node_registry.items():
                         if getattr(node, "_awaiting_input", False):
+                            # Skip escalation receivers — those are handled
+                            # by the queen via inject_worker_message(), not
+                            # by the user directly.
+                            if ":escalation:" in node_id:
+                                continue
                             return node_id, graph_id
         return None, None
 

core/framework/runtime/escalation_ticket.py

@@ -1,4 +1,4 @@
-"""EscalationTicket — structured schema for worker health judge escalations."""
+"""EscalationTicket — structured schema for worker health escalations."""
 
 from __future__ import annotations
 
@@ -10,10 +10,10 @@ from pydantic import BaseModel, Field
 
 
 class EscalationTicket(BaseModel):
-    """Structured escalation report emitted by the Worker Health Judge.
+    """Structured escalation report for worker health monitoring.
 
-    The judge must fill every field before calling emit_escalation_ticket.
-    Pydantic validation rejects partial tickets, preventing impulsive escalation.
+    All fields must be filled before calling emit_escalation_ticket.
+    Pydantic validation rejects partial tickets.
     """
 
     ticket_id: str = Field(default_factory=lambda: str(uuid4()))
@@ -25,7 +25,7 @@ class EscalationTicket(BaseModel):
     worker_node_id: str
     worker_graph_id: str
 
-    # Problem characterization (filled by judge via LLM deliberation)
+    # Problem characterization
     severity: Literal["low", "medium", "high", "critical"]
     cause: str  # Human-readable: "Node has produced 18 RETRY verdicts..."
     judge_reasoning: str  # Judge's own deliberation chain

core/framework/runtime/event_bus.py

@@ -97,6 +97,7 @@ class EventType(StrEnum):
     # Client I/O (client_facing=True nodes only)
     CLIENT_OUTPUT_DELTA = "client_output_delta"
     CLIENT_INPUT_REQUESTED = "client_input_requested"
+    CLIENT_INPUT_RECEIVED = "client_input_received"
 
     # Internal node observability (client_facing=False nodes)
     NODE_INTERNAL_OUTPUT = "node_internal_output"
@@ -104,7 +105,7 @@ class EventType(StrEnum):
     NODE_STALLED = "node_stalled"
     NODE_TOOL_DOOM_LOOP = "node_tool_doom_loop"
 
-    # Judge decisions
+    # Judge decisions (implicit judge in event loop nodes)
     JUDGE_VERDICT = "judge_verdict"
 
     # Output tracking
@@ -123,10 +124,10 @@ class EventType(StrEnum):
     # Custom events
     CUSTOM = "custom"
 
-    # Escalation (agent requests handoff to hive_coder)
+    # Escalation (agent requests handoff to queen)
     ESCALATION_REQUESTED = "escalation_requested"
 
-    # Worker health monitoring (judge → queen → operator)
+    # Worker health monitoring
     WORKER_ESCALATION_TICKET = "worker_escalation_ticket"
     QUEEN_INTERVENTION_REQUESTED = "queen_intervention_requested"
 
@@ -137,6 +138,12 @@ class EventType(StrEnum):
     WORKER_LOADED = "worker_loaded"
     CREDENTIALS_REQUIRED = "credentials_required"
 
+    # Draft graph (planning phase — lightweight graph preview)
+    DRAFT_GRAPH_UPDATED = "draft_graph_updated"
+
+    # Flowchart map updated (after reconciliation with runtime graph)
+    FLOWCHART_MAP_UPDATED = "flowchart_map_updated"
+
     # Queen phase changes (building <-> staging <-> running)
     QUEEN_PHASE_CHANGED = "queen_phase_changed"
 
@@ -146,6 +153,13 @@ class EventType(StrEnum):
     # Subagent reports (one-way progress updates from sub-agents)
     SUBAGENT_REPORT = "subagent_report"
 
+    # Trigger lifecycle (queen-level triggers / heartbeats)
+    TRIGGER_AVAILABLE = "trigger_available"
+    TRIGGER_ACTIVATED = "trigger_activated"
+    TRIGGER_DEACTIVATED = "trigger_deactivated"
+    TRIGGER_FIRED = "trigger_fired"
+    TRIGGER_REMOVED = "trigger_removed"
+
 
 @dataclass
 class AgentEvent:
@@ -159,10 +173,11 @@ class AgentEvent:
     timestamp: datetime = field(default_factory=datetime.now)
     correlation_id: str | None = None  # For tracking related events
     graph_id: str | None = None  # Which graph emitted this event (multi-graph sessions)
+    run_id: str | None = None  # Unique ID per trigger() invocation — used for run dividers
 
     def to_dict(self) -> dict:
         """Convert to dictionary for serialization."""
-        return {
+        d = {
             "type": self.type.value,
             "stream_id": self.stream_id,
             "node_id": self.node_id,
@@ -172,6 +187,9 @@ class AgentEvent:
             "correlation_id": self.correlation_id,
             "graph_id": self.graph_id,
         }
+        if self.run_id is not None:
+            d["run_id"] = self.run_id
+        return d
 
 
 # Type for event handlers
@@ -240,6 +258,127 @@ class EventBus:
         self._semaphore = asyncio.Semaphore(max_concurrent_handlers)
         self._subscription_counter = 0
         self._lock = asyncio.Lock()
+        # Per-session persistent event log (always-on, survives restarts)
+        self._session_log: IO[str] | None = None
+        self._session_log_iteration_offset: int = 0
+        # Accumulator for client_output_delta snapshots — flushed on llm_turn_complete.
+        # Key: (stream_id, node_id, execution_id, iteration) → latest AgentEvent
+        self._pending_output_snapshots: dict[tuple, AgentEvent] = {}
+
+    def set_session_log(self, path: Path, *, iteration_offset: int = 0) -> None:
+        """Enable per-session event persistence to a JSONL file.
+
+        Called once when the queen starts so that all events survive server
+        restarts and can be replayed to reconstruct the frontend state.
+
+        ``iteration_offset`` is added to the ``iteration`` field in logged
+        events so that cold-resumed sessions produce monotonically increasing
+        iteration values — preventing frontend message ID collisions between
+        the original run and resumed runs.
+        """
+        if self._session_log is not None:
+            try:
+                self._session_log.close()
+            except Exception:
+                pass
+        path.parent.mkdir(parents=True, exist_ok=True)
+        self._session_log = open(path, "a", encoding="utf-8")  # noqa: SIM115
+        self._session_log_iteration_offset = iteration_offset
+        logger.info("Session event log → %s (iteration_offset=%d)", path, iteration_offset)
+
+    def close_session_log(self) -> None:
+        """Close the per-session event log file."""
+        # Flush any pending output snapshots before closing
+        self._flush_pending_snapshots()
+        if self._session_log is not None:
+            try:
+                self._session_log.close()
+            except Exception:
+                pass
+            self._session_log = None
+
+    # Event types that are high-frequency streaming deltas — accumulated rather
+    # than written individually to the session log.
+    _STREAMING_DELTA_TYPES = frozenset(
+        {
+            EventType.CLIENT_OUTPUT_DELTA,
+            EventType.LLM_TEXT_DELTA,
+            EventType.LLM_REASONING_DELTA,
+        }
+    )
+
+    def _write_session_log_event(self, event: AgentEvent) -> None:
+        """Write an event to the per-session log with streaming coalescing.
+
+        Streaming deltas (client_output_delta, llm_text_delta) are accumulated
+        in memory.  When llm_turn_complete fires, any pending snapshots for that
+        (stream_id, node_id, execution_id) are flushed as single consolidated
+        events before the turn-complete event itself is written.
+
+        Note: iteration offset is already applied in publish() before this is
+        called, so events here already have correct iteration values.
+        """
+        if self._session_log is None:
+            return
+
+        if event.type in self._STREAMING_DELTA_TYPES:
+            # Accumulate — keep only the latest event (which carries the full snapshot)
+            key = (
+                event.stream_id,
+                event.node_id,
+                event.execution_id,
+                event.data.get("iteration"),
+            )
+            self._pending_output_snapshots[key] = event
+            return
+
+        # On turn-complete, flush accumulated snapshots for this stream first
+        if event.type == EventType.LLM_TURN_COMPLETE:
+            self._flush_pending_snapshots(
+                stream_id=event.stream_id,
+                node_id=event.node_id,
+                execution_id=event.execution_id,
+            )
+
+        line = json.dumps(event.to_dict(), default=str)
+        self._session_log.write(line + "\n")
+        self._session_log.flush()
+
+    def _flush_pending_snapshots(
+        self,
+        stream_id: str | None = None,
+        node_id: str | None = None,
+        execution_id: str | None = None,
+    ) -> None:
+        """Flush accumulated streaming snapshots to the session log.
+
+        When called with filters, only matching entries are flushed.
+        When called without filters (e.g. on close), everything is flushed.
+        """
+        if self._session_log is None or not self._pending_output_snapshots:
+            return
+
+        to_flush: list[tuple] = []
+        for key, _evt in self._pending_output_snapshots.items():
+            if stream_id is not None:
+                k_stream, k_node, k_exec, _ = key
+                if k_stream != stream_id or k_node != node_id or k_exec != execution_id:
+                    continue
+            to_flush.append(key)
+
+        for key in to_flush:
+            evt = self._pending_output_snapshots.pop(key)
+            try:
+                line = json.dumps(evt.to_dict(), default=str)
+                self._session_log.write(line + "\n")
+            except Exception:
+                pass
+
+        if to_flush:
+            try:
+                self._session_log.flush()
+            except Exception:
+                pass
 
     def subscribe(
         self,
@@ -305,6 +444,19 @@ class EventBus:
         Args:
             event: Event to publish
         """
+        # Apply iteration offset at the source so ALL consumers (SSE subscribers,
+        # event history, session log) see the same monotonically increasing
+        # iteration values.  Without this, live SSE would use raw iterations
+        # while events.jsonl would use offset iterations, causing ID collisions
+        # on the frontend when replaying after cold resume.
+        if (
+            self._session_log_iteration_offset
+            and isinstance(event.data, dict)
+            and "iteration" in event.data
+        ):
+            offset = self._session_log_iteration_offset
+            event.data = {**event.data, "iteration": event.data["iteration"] + offset}
+
         # Add to history
         async with self._lock:
             self._event_history.append(event)
@@ -325,6 +477,15 @@ class EventBus:
                 except Exception:
                     pass  # never break event delivery
 
+        # Per-session persistent log (always-on when set_session_log was called).
+        # Streaming deltas are coalesced: client_output_delta and llm_text_delta
+        # are accumulated and flushed as a single snapshot event on llm_turn_complete.
+        if self._session_log is not None:
+            try:
+                self._write_session_log_event(event)
+            except Exception:
+                pass  # never break event delivery
+
         # Find matching subscriptions
         matching_handlers: list[EventHandler] = []
 
@@ -385,6 +546,7 @@ class EventBus:
         execution_id: str,
         input_data: dict[str, Any] | None = None,
         correlation_id: str | None = None,
+        run_id: str | None = None,
     ) -> None:
         """Emit execution started event."""
         await self.publish(
@@ -394,6 +556,7 @@ class EventBus:
                 execution_id=execution_id,
                 data={"input": input_data or {}},
                 correlation_id=correlation_id,
+                run_id=run_id,
             )
         )
 
@@ -403,6 +566,7 @@ class EventBus:
         execution_id: str,
         output: dict[str, Any] | None = None,
         correlation_id: str | None = None,
+        run_id: str | None = None,
     ) -> None:
         """Emit execution completed event."""
         await self.publish(
@@ -412,6 +576,7 @@ class EventBus:
                 execution_id=execution_id,
                 data={"output": output or {}},
                 correlation_id=correlation_id,
+                run_id=run_id,
             )
         )
 
@@ -421,6 +586,7 @@ class EventBus:
         execution_id: str,
         error: str,
         correlation_id: str | None = None,
+        run_id: str | None = None,
     ) -> None:
         """Emit execution failed event."""
         await self.publish(
@@ -430,6 +596,7 @@ class EventBus:
                 execution_id=execution_id,
                 data={"error": error},
                 correlation_id=correlation_id,
+                run_id=run_id,
             )
         )
 
@@ -521,15 +688,19 @@ class EventBus:
         node_id: str,
         iteration: int,
         execution_id: str | None = None,
+        extra_data: dict[str, Any] | None = None,
     ) -> None:
         """Emit node loop iteration event."""
+        data: dict[str, Any] = {"iteration": iteration}
+        if extra_data:
+            data.update(extra_data)
         await self.publish(
             AgentEvent(
                 type=EventType.NODE_LOOP_ITERATION,
                 stream_id=stream_id,
                 node_id=node_id,
                 execution_id=execution_id,
-                data={"iteration": iteration},
+                data=data,
             )
         )
 
@@ -616,6 +787,7 @@ class EventBus:
         model: str,
         input_tokens: int,
         output_tokens: int,
+        cached_tokens: int = 0,
         execution_id: str | None = None,
         iteration: int | None = None,
     ) -> None:
@@ -625,6 +797,7 @@ class EventBus:
             "model": model,
             "input_tokens": input_tokens,
             "output_tokens": output_tokens,
+            "cached_tokens": cached_tokens,
         }
         if iteration is not None:
             data["iteration"] = iteration
@@ -722,16 +895,23 @@ class EventBus:
         prompt: str = "",
         execution_id: str | None = None,
         options: list[str] | None = None,
+        questions: list[dict] | None = None,
     ) -> None:
         """Emit client input requested event (client_facing=True nodes).
 
         Args:
             options: Optional predefined choices for the user (1-3 items).
-                     The frontend appends an "Other" free-text option automatically.
+                     The frontend appends an "Other" free-text option
+                     automatically.
+            questions: Optional list of question dicts for multi-question
+                       batches (from ask_user_multiple). Each dict has id,
+                       prompt, and optional options.
         """
         data: dict[str, Any] = {"prompt": prompt}
         if options:
             data["options"] = options
+        if questions:
+            data["questions"] = questions
         await self.publish(
             AgentEvent(
                 type=EventType.CLIENT_INPUT_REQUESTED,
@@ -976,7 +1156,7 @@ class EventBus:
         context: str = "",
         execution_id: str | None = None,
     ) -> None:
-        """Emit escalation requested event (agent wants hive_coder)."""
+        """Emit escalation requested event (agent wants queen)."""
         await self.publish(
             AgentEvent(
                 type=EventType.ESCALATION_REQUESTED,
@@ -994,7 +1174,7 @@ class EventBus:
         ticket: dict,
         execution_id: str | None = None,
     ) -> None:
-        """Emitted by health judge when worker shows a degradation pattern."""
+        """Emitted when worker shows a degradation pattern."""
         await self.publish(
             AgentEvent(
                 type=EventType.WORKER_ESCALATION_TICKET,

core/framework/runtime/execution_stream.py

@@ -9,6 +9,7 @@ Each stream has:
 
 import asyncio
 import logging
+import os
 import time
 import uuid
 from collections import OrderedDict
@@ -126,6 +127,7 @@ class ExecutionContext:
     input_data: dict[str, Any]
     isolation_level: IsolationLevel
     session_state: dict[str, Any] | None = None  # For resuming from pause
+    run_id: str | None = None  # Unique ID per trigger() invocation
     started_at: datetime = field(default_factory=datetime.now)
     completed_at: datetime | None = None
     status: str = "pending"  # pending, running, completed, failed, paused
@@ -240,6 +242,7 @@ class ExecutionStream:
         self._active_executions: dict[str, ExecutionContext] = {}
         self._execution_tasks: dict[str, asyncio.Task] = {}
         self._active_executors: dict[str, GraphExecutor] = {}
+        self._cancel_reasons: dict[str, str] = {}
         self._execution_results: OrderedDict[str, ExecutionResult] = OrderedDict()
         self._execution_result_times: dict[str, float] = {}
         self._completion_events: dict[str, asyncio.Event] = {}
@@ -423,11 +426,36 @@ class ExecutionStream:
                 return True
         return False
 
+    async def inject_trigger(
+        self,
+        node_id: str,
+        trigger: Any,
+    ) -> bool:
+        """Inject a trigger event into a running queen EventLoopNode.
+
+        Searches active executors for a node matching ``node_id`` and calls
+        its ``inject_trigger()`` method to wake the queen.
+
+        Args:
+            node_id: The queen EventLoopNode ID.
+            trigger: A ``TriggerEvent`` instance (typed as Any to avoid
+                circular imports with graph layer).
+
+        Returns True if the trigger was delivered, False otherwise.
+        """
+        for executor in self._active_executors.values():
+            node = executor.node_registry.get(node_id)
+            if node is not None and hasattr(node, "inject_trigger"):
+                await node.inject_trigger(trigger)
+                return True
+        return False
+
     async def execute(
         self,
         input_data: dict[str, Any],
         correlation_id: str | None = None,
         session_state: dict[str, Any] | None = None,
+        run_id: str | None = None,
     ) -> str:
         """
         Queue an execution and return its ID.
@@ -438,6 +466,7 @@ class ExecutionStream:
             input_data: Input data for this execution
             correlation_id: Optional ID to correlate related executions
             session_state: Optional session state to resume from (with paused_at, memory)
+            run_id: Unique ID for this trigger invocation (for run dividers)
 
         Returns:
             Execution ID for tracking
@@ -464,7 +493,7 @@ class ExecutionStream:
                         node.signal_shutdown()
                     if hasattr(node, "cancel_current_turn"):
                         node.cancel_current_turn()
-            await self.cancel_execution(eid)
+            await self.cancel_execution(eid, reason="Restarted with new execution")
 
         # When resuming, reuse the original session ID so the execution
         # continues in the same session directory instead of creating a new one.
@@ -498,6 +527,7 @@ class ExecutionStream:
             input_data=input_data,
             isolation_level=self.entry_spec.get_isolation_level(),
             session_state=session_state,
+            run_id=run_id,
         )
 
         async with self._lock:
@@ -573,7 +603,9 @@ class ExecutionStream:
                         execution_id=execution_id,
                         input_data=ctx.input_data,
                         correlation_id=ctx.correlation_id,
+                        run_id=ctx.run_id,
                     )
+                self._write_run_event(execution_id, ctx.run_id, "run_started")
 
                 # Create execution-scoped memory
                 self._state_manager.create_memory(
@@ -738,6 +770,7 @@ class ExecutionStream:
                             execution_id=execution_id,
                             output=result.output,
                             correlation_id=ctx.correlation_id,
+                            run_id=ctx.run_id,
                         )
                     elif result.paused_at:
                         # The executor returns paused_at on CancelledError but
@@ -755,8 +788,22 @@ class ExecutionStream:
                             execution_id=execution_id,
                             error=result.error or "Unknown error",
                             correlation_id=ctx.correlation_id,
+                            run_id=ctx.run_id,
                         )
 
+                # Write run event for historical restoration
+                if result.success:
+                    self._write_run_event(execution_id, ctx.run_id, "run_completed")
+                elif result.paused_at:
+                    self._write_run_event(execution_id, ctx.run_id, "run_paused")
+                else:
+                    self._write_run_event(
+                        execution_id,
+                        ctx.run_id,
+                        "run_failed",
+                        {"error": result.error or "Unknown error"},
+                    )
+
                 logger.debug(f"Execution {execution_id} completed: success={result.success}")
 
             except asyncio.CancelledError:
@@ -801,22 +848,25 @@ class ExecutionStream:
                 # Emit SSE event so the frontend knows the execution stopped.
                 # The executor does NOT emit on CancelledError, so there is no
                 # risk of double-emitting.
+                cancel_reason = self._cancel_reasons.pop(execution_id, "Execution cancelled")
                 if self._scoped_event_bus:
                     if has_result and result.paused_at:
                         await self._scoped_event_bus.emit_execution_paused(
                             stream_id=self.stream_id,
                             node_id=result.paused_at,
-                            reason="Execution cancelled",
+                            reason=cancel_reason,
                             execution_id=execution_id,
                         )
                     else:
                         await self._scoped_event_bus.emit_execution_failed(
                             stream_id=self.stream_id,
                             execution_id=execution_id,
-                            error="Execution cancelled",
+                            error=cancel_reason,
                             correlation_id=ctx.correlation_id,
+                            run_id=ctx.run_id,
                         )
 
+                self._write_run_event(execution_id, ctx.run_id, "run_cancelled")
                 # Don't re-raise - we've handled it and saved state
 
             except Exception as e:
@@ -853,7 +903,9 @@ class ExecutionStream:
                         execution_id=execution_id,
                         error=str(e),
                         correlation_id=ctx.correlation_id,
+                        run_id=ctx.run_id,
                     )
+                self._write_run_event(execution_id, ctx.run_id, "run_failed", {"error": str(e)})
 
             finally:
                 # Clean up state
@@ -869,6 +921,36 @@ class ExecutionStream:
                     self._completion_events.pop(execution_id, None)
                     self._execution_tasks.pop(execution_id, None)
 
+    def _write_run_event(
+        self,
+        execution_id: str,
+        run_id: str | None,
+        event: str,
+        extra: dict[str, Any] | None = None,
+    ) -> None:
+        """Append a run lifecycle event to runs.jsonl for historical restoration."""
+        if not self._session_store or not run_id:
+            return
+        import json as _json
+
+        session_dir = self._session_store.get_session_path(execution_id)
+        runs_file = session_dir / "runs.jsonl"
+        now = datetime.now()
+        record = {
+            "run_id": run_id,
+            "event": event,
+            "timestamp": now.isoformat(),
+            "created_at": now.timestamp(),
+        }
+        if extra:
+            record.update(extra)
+        try:
+            runs_file.parent.mkdir(parents=True, exist_ok=True)
+            with open(runs_file, "a", encoding="utf-8") as f:
+                f.write(_json.dumps(record) + "\n")
+        except OSError:
+            pass  # Non-critical — don't break execution
+
     async def _write_session_state(
         self,
         execution_id: str,
@@ -961,6 +1043,9 @@ class ExecutionStream:
             if error:
                 state.result.error = error
 
+            # Stamp the owning process ID for cross-process stale detection
+            state.pid = os.getpid()
+
             # Write state.json
             await self._session_store.write_state(execution_id, state)
             logger.debug(f"Wrote state.json for session {execution_id} (status={status})")
@@ -972,8 +1057,8 @@ class ExecutionStream:
     def _create_modified_graph(self) -> "GraphSpec":
         """Create a graph with the entry point overridden.
 
-        Preserves the original graph's entry_points and async_entry_points
-        so that validation correctly considers ALL entry nodes reachable.
+        Preserves the original graph's entry_points so that validation
+        correctly considers ALL entry nodes reachable.
         Each stream only executes from its own entry_node, but the full
         graph must validate with all entry points accounted for.
         """
@@ -998,7 +1083,6 @@ class ExecutionStream:
             version=self.graph.version,
             entry_node=self.entry_spec.entry_node,  # Use our entry point
             entry_points=merged_entry_points,
-            async_entry_points=self.graph.async_entry_points,
             terminal_nodes=self.graph.terminal_nodes,
             pause_nodes=self.graph.pause_nodes,
             nodes=self.graph.nodes,
@@ -1054,18 +1138,24 @@ class ExecutionStream:
         """Get execution context."""
         return self._active_executions.get(execution_id)
 
-    async def cancel_execution(self, execution_id: str) -> bool:
+    async def cancel_execution(self, execution_id: str, *, reason: str | None = None) -> bool:
         """
         Cancel a running execution.
 
         Args:
             execution_id: Execution to cancel
+            reason: Human-readable reason for the cancellation (e.g.
+                "Stopped by queen", "User requested pause"). If not
+                provided, defaults to "Execution cancelled".
 
         Returns:
             True if cancelled, False if not found
         """
         task = self._execution_tasks.get(execution_id)
         if task and not task.done():
+            # Store the reason so the CancelledError handler can use it
+            # when emitting the pause/fail event.
+            self._cancel_reasons[execution_id] = reason or "Execution cancelled"
             task.cancel()
             # Wait briefly for the task to finish. Don't block indefinitely —
             # the task may be stuck in a long LLM API call that doesn't

core/framework/runtime/tests/test_agent_runtime.py

@@ -17,7 +17,7 @@ from pathlib import Path
 import pytest
 
 from framework.graph import Goal
-from framework.graph.edge import AsyncEntryPointSpec, EdgeCondition, EdgeSpec, GraphSpec
+from framework.graph.edge import EdgeCondition, EdgeSpec, GraphSpec
 from framework.graph.goal import Constraint, SuccessCriterion
 from framework.graph.node import NodeSpec
 from framework.runtime.agent_runtime import AgentRuntime, create_agent_runtime
@@ -101,30 +101,12 @@ def sample_graph():
         ),
     ]
 
-    async_entry_points = [
-        AsyncEntryPointSpec(
-            id="webhook",
-            name="Webhook Handler",
-            entry_node="process-webhook",
-            trigger_type="webhook",
-            isolation_level="shared",
-        ),
-        AsyncEntryPointSpec(
-            id="api",
-            name="API Handler",
-            entry_node="process-api",
-            trigger_type="api",
-            isolation_level="shared",
-        ),
-    ]
-
     return GraphSpec(
         id="test-graph",
         goal_id="test-goal",
         version="1.0.0",
         entry_node="process-webhook",
         entry_points={"start": "process-webhook"},
-        async_entry_points=async_entry_points,
         terminal_nodes=["complete"],
         pause_nodes=[],
         nodes=nodes,
@@ -504,108 +486,6 @@ class TestAgentRuntime:
 # === GraphSpec Validation Tests ===
 
 
-class TestGraphSpecValidation:
-    """Tests for GraphSpec with async_entry_points."""
-
-    def test_has_async_entry_points(self, sample_graph):
-        """Test checking for async entry points."""
-        assert sample_graph.has_async_entry_points() is True
-
-        # Graph without async entry points
-        simple_graph = GraphSpec(
-            id="simple",
-            goal_id="goal",
-            entry_node="start",
-            nodes=[],
-            edges=[],
-        )
-        assert simple_graph.has_async_entry_points() is False
-
-    def test_get_async_entry_point(self, sample_graph):
-        """Test getting async entry point by ID."""
-        ep = sample_graph.get_async_entry_point("webhook")
-        assert ep is not None
-        assert ep.id == "webhook"
-        assert ep.entry_node == "process-webhook"
-
-        ep_not_found = sample_graph.get_async_entry_point("nonexistent")
-        assert ep_not_found is None
-
-    def test_validate_async_entry_points(self):
-        """Test validation catches async entry point errors."""
-        nodes = [
-            NodeSpec(
-                id="valid-node",
-                name="Valid Node",
-                description="A valid node",
-                node_type="event_loop",
-                input_keys=[],
-                output_keys=[],
-            ),
-        ]
-
-        # Invalid entry node
-        graph = GraphSpec(
-            id="test",
-            goal_id="goal",
-            entry_node="valid-node",
-            async_entry_points=[
-                AsyncEntryPointSpec(
-                    id="invalid",
-                    name="Invalid",
-                    entry_node="nonexistent-node",
-                    trigger_type="webhook",
-                ),
-            ],
-            nodes=nodes,
-            edges=[],
-        )
-
-        errors = graph.validate()["errors"]
-        assert any("nonexistent-node" in e for e in errors)
-
-        # Invalid isolation level
-        graph2 = GraphSpec(
-            id="test",
-            goal_id="goal",
-            entry_node="valid-node",
-            async_entry_points=[
-                AsyncEntryPointSpec(
-                    id="bad-isolation",
-                    name="Bad Isolation",
-                    entry_node="valid-node",
-                    trigger_type="webhook",
-                    isolation_level="invalid",
-                ),
-            ],
-            nodes=nodes,
-            edges=[],
-        )
-
-        errors2 = graph2.validate()["errors"]
-        assert any("isolation_level" in e for e in errors2)
-
-        # Invalid trigger type
-        graph3 = GraphSpec(
-            id="test",
-            goal_id="goal",
-            entry_node="valid-node",
-            async_entry_points=[
-                AsyncEntryPointSpec(
-                    id="bad-trigger",
-                    name="Bad Trigger",
-                    entry_node="valid-node",
-                    trigger_type="invalid_trigger",
-                ),
-            ],
-            nodes=nodes,
-            edges=[],
-        )
-
-        errors3 = graph3.validate()["errors"]
-        assert any("trigger_type" in e for e in errors3)
-
-
 # === Integration Tests ===
 
 

core/framework/runtime/tests/test_webhook_server.py

@@ -483,7 +483,6 @@ class TestEventDrivenEntryPoints:
             version="1.0.0",
             entry_node="process-event",
             entry_points={"start": "process-event"},
-            async_entry_points=[],
             terminal_nodes=[],
             pause_nodes=[],
             nodes=nodes,

core/framework/runtime/triggers.py

@@ -0,0 +1,22 @@
+"""Trigger definitions for queen-level heartbeats (timers, webhooks)."""
+
+from __future__ import annotations
+
+from dataclasses import dataclass, field
+from typing import Any
+
+
+@dataclass
+class TriggerDefinition:
+    """A registered trigger that can be activated on the queen runtime.
+
+    Trigger *definitions* come from the worker's ``triggers.json``.
+    Activation state is per-session (persisted in ``SessionState.active_triggers``).
+    """
+
+    id: str
+    trigger_type: str  # "timer" | "webhook"
+    trigger_config: dict[str, Any] = field(default_factory=dict)
+    description: str = ""
+    task: str = ""
+    active: bool = False

core/framework/schemas/session_state.py

@@ -134,6 +134,9 @@ class SessionState(BaseModel):
     # Input data (for debugging/replay)
     input_data: dict[str, Any] = Field(default_factory=dict)
 
+    # Process ID of the owning process (for cross-process stale session detection)
+    pid: int | None = None
+
     # Isolation level (from ExecutionContext)
     isolation_level: str = "shared"
 
@@ -141,6 +144,13 @@ class SessionState(BaseModel):
     checkpoint_enabled: bool = False
     latest_checkpoint_id: str | None = None
 
+    # Trigger activation state (IDs of triggers the queen/user turned on)
+    active_triggers: list[str] = Field(default_factory=list)
+    # Per-trigger task strings (user overrides, keyed by trigger ID)
+    trigger_tasks: dict[str, str] = Field(default_factory=dict)
+    # True after first successful worker execution (gates trigger delivery on restart)
+    worker_configured: bool = Field(default=False)
+
     model_config = {"extra": "allow"}
 
     @computed_field

core/framework/server/agent_manager.py

@@ -1,36 +0,0 @@
-"""Backward-compatibility shim.
-
-The primary implementation is now in ``session_manager.py``.
-This module re-exports ``SessionManager`` as ``AgentManager`` and
-keeps ``AgentSlot`` for test compatibility.
-"""
-
-import asyncio
-from dataclasses import dataclass
-from pathlib import Path
-from typing import Any
-
-from framework.server.session_manager import Session, SessionManager  # noqa: F401
-
-
-@dataclass
-class AgentSlot:
-    """Legacy data class — kept for test compatibility only.
-
-    New code should use ``Session`` from ``session_manager``.
-    """
-
-    id: str
-    agent_path: Path
-    runner: Any
-    runtime: Any
-    info: Any
-    loaded_at: float
-    queen_executor: Any = None
-    queen_task: asyncio.Task | None = None
-    judge_task: asyncio.Task | None = None
-    escalation_sub: str | None = None
-
-
-# Backward compat alias
-AgentManager = SessionManager

core/framework/server/app.py

@@ -94,6 +94,29 @@ def sessions_dir(session: Session) -> Path:
     return Path.home() / ".hive" / "agents" / agent_name / "sessions"
 
 
+def cold_sessions_dir(session_id: str) -> Path | None:
+    """Resolve the worker sessions directory from disk for a cold/stopped session.
+
+    Reads agent_path from the queen session's meta.json to find the agent name,
+    then returns ~/.hive/agents/{agent_name}/sessions/.
+    Returns None if meta.json is missing or has no agent_path.
+    """
+    import json
+
+    meta_path = Path.home() / ".hive" / "queen" / "session" / session_id / "meta.json"
+    if not meta_path.exists():
+        return None
+    try:
+        meta = json.loads(meta_path.read_text(encoding="utf-8"))
+        agent_path = meta.get("agent_path")
+        if not agent_path:
+            return None
+        agent_name = Path(agent_path).name
+        return Path.home() / ".hive" / "agents" / agent_name / "sessions"
+    except (json.JSONDecodeError, OSError):
+        return None
+
+
 # Allowed CORS origins (localhost on any port)
 _CORS_ORIGINS = {"http://localhost", "http://127.0.0.1"}
 

core/framework/server/queen_orchestrator.py

@@ -0,0 +1,358 @@
+"""Queen orchestrator — builds and runs the queen executor.
+
+Extracted from SessionManager._start_queen() to keep session management
+and queen orchestration concerns separate.
+"""
+
+from __future__ import annotations
+
+import asyncio
+import logging
+from pathlib import Path
+from typing import TYPE_CHECKING, Any
+
+if TYPE_CHECKING:
+    from framework.server.session_manager import Session
+
+logger = logging.getLogger(__name__)
+
+
+async def create_queen(
+    session: Session,
+    session_manager: Any,
+    worker_identity: str | None,
+    queen_dir: Path,
+    initial_prompt: str | None = None,
+) -> asyncio.Task:
+    """Build the queen executor and return the running asyncio task.
+
+    Handles tool registration, phase-state initialization, prompt
+    composition, persona hook setup, graph preparation, and the queen
+    event loop.
+    """
+    from framework.agents.queen.agent import (
+        queen_goal,
+        queen_graph as _queen_graph,
+    )
+    from framework.agents.queen.nodes import (
+        _QUEEN_BUILDING_TOOLS,
+        _QUEEN_PLANNING_TOOLS,
+        _QUEEN_RUNNING_TOOLS,
+        _QUEEN_STAGING_TOOLS,
+        _appendices,
+        _building_knowledge,
+        _planning_knowledge,
+        _queen_behavior_always,
+        _queen_behavior_building,
+        _queen_behavior_planning,
+        _queen_behavior_running,
+        _queen_behavior_staging,
+        _queen_identity_building,
+        _queen_identity_planning,
+        _queen_identity_running,
+        _queen_identity_staging,
+        _queen_phase_7,
+        _queen_style,
+        _queen_tools_building,
+        _queen_tools_planning,
+        _queen_tools_running,
+        _queen_tools_staging,
+        _shared_building_knowledge,
+    )
+    from framework.agents.queen.nodes.thinking_hook import select_expert_persona
+    from framework.graph.event_loop_node import HookContext, HookResult
+    from framework.graph.executor import GraphExecutor
+    from framework.runner.tool_registry import ToolRegistry
+    from framework.runtime.core import Runtime
+    from framework.runtime.event_bus import AgentEvent, EventType
+    from framework.tools.queen_lifecycle_tools import (
+        QueenPhaseState,
+        register_queen_lifecycle_tools,
+    )
+
+    hive_home = Path.home() / ".hive"
+
+    # ---- Tool registry ------------------------------------------------
+    queen_registry = ToolRegistry()
+    import framework.agents.queen as _queen_pkg
+
+    queen_pkg_dir = Path(_queen_pkg.__file__).parent
+    mcp_config = queen_pkg_dir / "mcp_servers.json"
+    if mcp_config.exists():
+        try:
+            queen_registry.load_mcp_config(mcp_config)
+            logger.info("Queen: loaded MCP tools from %s", mcp_config)
+        except Exception:
+            logger.warning("Queen: MCP config failed to load", exc_info=True)
+
+    # ---- Phase state --------------------------------------------------
+    initial_phase = "staging" if worker_identity else "planning"
+    phase_state = QueenPhaseState(phase=initial_phase, event_bus=session.event_bus)
+    session.phase_state = phase_state
+
+    # ---- Track ask rounds during planning ----------------------------
+    # Increment planning_ask_rounds each time the queen requests user
+    # input (ask_user or ask_user_multiple) while in the planning phase.
+    async def _track_planning_asks(event: AgentEvent) -> None:
+        if phase_state.phase != "planning":
+            return
+        # Only count explicit ask_user / ask_user_multiple calls, not
+        # auto-block (text-only turns emit CLIENT_INPUT_REQUESTED with
+        # an empty prompt and no options/questions).
+        data = event.data or {}
+        has_prompt = bool(data.get("prompt"))
+        has_questions = bool(data.get("questions"))
+        has_options = bool(data.get("options"))
+        if has_prompt or has_questions or has_options:
+            phase_state.planning_ask_rounds += 1
+
+    session.event_bus.subscribe(
+        [EventType.CLIENT_INPUT_REQUESTED],
+        _track_planning_asks,
+        filter_stream="queen",
+    )
+
+    # ---- Lifecycle tools (always registered) --------------------------
+    register_queen_lifecycle_tools(
+        queen_registry,
+        session=session,
+        session_id=session.id,
+        session_manager=session_manager,
+        manager_session_id=session.id,
+        phase_state=phase_state,
+    )
+
+    # ---- Monitoring tools (only when worker is loaded) ----------------
+    if session.worker_runtime:
+        from framework.tools.worker_monitoring_tools import register_worker_monitoring_tools
+
+        register_worker_monitoring_tools(
+            queen_registry,
+            session.event_bus,
+            session.worker_path,
+            stream_id="queen",
+            worker_graph_id=session.worker_runtime._graph_id,
+            default_session_id=session.id,
+        )
+
+    queen_tools = list(queen_registry.get_tools().values())
+    queen_tool_executor = queen_registry.get_executor()
+
+    # ---- Partition tools by phase ------------------------------------
+    planning_names = set(_QUEEN_PLANNING_TOOLS)
+    building_names = set(_QUEEN_BUILDING_TOOLS)
+    staging_names = set(_QUEEN_STAGING_TOOLS)
+    running_names = set(_QUEEN_RUNNING_TOOLS)
+
+    registered_names = {t.name for t in queen_tools}
+    missing_building = building_names - registered_names
+    if missing_building:
+        logger.warning(
+            "Queen: %d/%d building tools NOT registered: %s",
+            len(missing_building),
+            len(building_names),
+            sorted(missing_building),
+        )
+    logger.info("Queen: registered tools: %s", sorted(registered_names))
+
+    phase_state.planning_tools = [t for t in queen_tools if t.name in planning_names]
+    phase_state.building_tools = [t for t in queen_tools if t.name in building_names]
+    phase_state.staging_tools = [t for t in queen_tools if t.name in staging_names]
+    phase_state.running_tools = [t for t in queen_tools if t.name in running_names]
+
+    # ---- Cross-session memory ----------------------------------------
+    from framework.agents.queen.queen_memory import seed_if_missing
+
+    seed_if_missing()
+
+    # ---- Compose phase-specific prompts ------------------------------
+    _orig_node = _queen_graph.nodes[0]
+
+    if worker_identity is None:
+        worker_identity = (
+            "\n\n# Worker Profile\n"
+            "No worker agent loaded. You are operating independently.\n"
+            "Design or build the agent to solve the user's problem "
+            "according to your current phase."
+        )
+
+    _planning_body = (
+        _queen_style
+        + _shared_building_knowledge
+        + _queen_tools_planning
+        + _queen_behavior_always
+        + _queen_behavior_planning
+        + _planning_knowledge
+        + worker_identity
+    )
+    phase_state.prompt_planning = _queen_identity_planning + _planning_body
+
+    _building_body = (
+        _queen_style
+        + _shared_building_knowledge
+        + _queen_tools_building
+        + _queen_behavior_always
+        + _queen_behavior_building
+        + _building_knowledge
+        + _queen_phase_7
+        + _appendices
+        + worker_identity
+    )
+    phase_state.prompt_building = _queen_identity_building + _building_body
+    phase_state.prompt_staging = (
+        _queen_identity_staging
+        + _queen_style
+        + _queen_tools_staging
+        + _queen_behavior_always
+        + _queen_behavior_staging
+        + worker_identity
+    )
+    phase_state.prompt_running = (
+        _queen_identity_running
+        + _queen_style
+        + _queen_tools_running
+        + _queen_behavior_always
+        + _queen_behavior_running
+        + worker_identity
+    )
+
+    # ---- Persona hook ------------------------------------------------
+    _session_llm = session.llm
+    _session_event_bus = session.event_bus
+
+    async def _persona_hook(ctx: HookContext) -> HookResult | None:
+        persona = await select_expert_persona(ctx.trigger or "", _session_llm)
+        if not persona:
+            return None
+        if _session_event_bus is not None:
+            await _session_event_bus.publish(
+                AgentEvent(
+                    type=EventType.QUEEN_PERSONA_SELECTED,
+                    stream_id="queen",
+                    data={"persona": persona},
+                )
+            )
+        return HookResult(system_prompt=persona + "\n\n" + phase_state.get_current_prompt())
+
+    # ---- Graph preparation -------------------------------------------
+    initial_prompt_text = phase_state.get_current_prompt()
+
+    registered_tool_names = set(queen_registry.get_tools().keys())
+    declared_tools = _orig_node.tools or []
+    available_tools = [t for t in declared_tools if t in registered_tool_names]
+
+    node_updates: dict = {
+        "system_prompt": initial_prompt_text,
+    }
+    if set(available_tools) != set(declared_tools):
+        missing = sorted(set(declared_tools) - registered_tool_names)
+        if missing:
+            logger.warning("Queen: tools not available: %s", missing)
+        node_updates["tools"] = available_tools
+
+    adjusted_node = _orig_node.model_copy(update=node_updates)
+    _queen_loop_config = {
+        **(_queen_graph.loop_config or {}),
+        "hooks": {"session_start": [_persona_hook]},
+    }
+    queen_graph = _queen_graph.model_copy(
+        update={"nodes": [adjusted_node], "loop_config": _queen_loop_config}
+    )
+
+    # ---- Queen event loop --------------------------------------------
+    queen_runtime = Runtime(hive_home / "queen")
+
+    async def _queen_loop():
+        try:
+            executor = GraphExecutor(
+                runtime=queen_runtime,
+                llm=session.llm,
+                tools=queen_tools,
+                tool_executor=queen_tool_executor,
+                event_bus=session.event_bus,
+                stream_id="queen",
+                storage_path=queen_dir,
+                loop_config=_queen_loop_config,
+                execution_id=session.id,
+                dynamic_tools_provider=phase_state.get_current_tools,
+                dynamic_prompt_provider=phase_state.get_current_prompt,
+                iteration_metadata_provider=lambda: {"phase": phase_state.phase},
+            )
+            session.queen_executor = executor
+
+            # Wire inject_notification so phase switches notify the queen LLM
+            async def _inject_phase_notification(content: str) -> None:
+                node = executor.node_registry.get("queen")
+                if node is not None and hasattr(node, "inject_event"):
+                    await node.inject_event(content)
+
+            phase_state.inject_notification = _inject_phase_notification
+
+            # Auto-switch to staging when worker execution finishes
+            async def _on_worker_done(event):
+                if event.stream_id == "queen":
+                    return
+                if phase_state.phase == "running":
+                    if event.type == EventType.EXECUTION_COMPLETED:
+                        # Mark worker as configured after first successful run
+                        session.worker_configured = True
+                        output = event.data.get("output", {})
+                        output_summary = ""
+                        if output:
+                            for key, value in output.items():
+                                val_str = str(value)
+                                if len(val_str) > 200:
+                                    val_str = val_str[:200] + "..."
+                                output_summary += f"\n  {key}: {val_str}"
+                        _out = output_summary or " (no output keys set)"
+                        notification = (
+                            "[WORKER_TERMINAL] Worker finished successfully.\n"
+                            f"Output:{_out}\n"
+                            "Report this to the user. "
+                            "Ask if they want to continue with another run."
+                        )
+                    else:  # EXECUTION_FAILED
+                        error = event.data.get("error", "Unknown error")
+                        notification = (
+                            "[WORKER_TERMINAL] Worker failed.\n"
+                            f"Error: {error}\n"
+                            "Report this to the user and help them troubleshoot."
+                        )
+
+                    node = executor.node_registry.get("queen")
+                    if node is not None and hasattr(node, "inject_event"):
+                        await node.inject_event(notification)
+
+                    await phase_state.switch_to_staging(source="auto")
+
+            session.event_bus.subscribe(
+                event_types=[EventType.EXECUTION_COMPLETED, EventType.EXECUTION_FAILED],
+                handler=_on_worker_done,
+            )
+            session_manager._subscribe_worker_handoffs(session, executor)
+
+            logger.info(
+                "Queen starting in %s phase with %d tools: %s",
+                phase_state.phase,
+                len(phase_state.get_current_tools()),
+                [t.name for t in phase_state.get_current_tools()],
+            )
+            result = await executor.execute(
+                graph=queen_graph,
+                goal=queen_goal,
+                input_data={"greeting": initial_prompt or "Session started."},
+                session_state={"resume_session_id": session.id},
+            )
+            if result.success:
+                logger.warning("Queen executor returned (should be forever-alive)")
+            else:
+                logger.error(
+                    "Queen executor failed: %s",
+                    result.error or "(no error message)",
+                )
+        except Exception:
+            logger.error("Queen conversation crashed", exc_info=True)
+        finally:
+            session.queen_executor = None
+
+    return asyncio.create_task(_queen_loop())

core/framework/server/routes_events.py

@@ -15,6 +15,7 @@ logger = logging.getLogger(__name__)
 DEFAULT_EVENT_TYPES = [
     EventType.CLIENT_OUTPUT_DELTA,
     EventType.CLIENT_INPUT_REQUESTED,
+    EventType.CLIENT_INPUT_RECEIVED,
     EventType.LLM_TEXT_DELTA,
     EventType.TOOL_CALL_STARTED,
     EventType.TOOL_CALL_COMPLETED,
@@ -40,6 +41,12 @@ DEFAULT_EVENT_TYPES = [
     EventType.CREDENTIALS_REQUIRED,
     EventType.SUBAGENT_REPORT,
     EventType.QUEEN_PHASE_CHANGED,
+    EventType.TRIGGER_AVAILABLE,
+    EventType.TRIGGER_ACTIVATED,
+    EventType.TRIGGER_DEACTIVATED,
+    EventType.TRIGGER_FIRED,
+    EventType.TRIGGER_REMOVED,
+    EventType.DRAFT_GRAPH_UPDATED,
 ]
 
 # Keepalive interval in seconds
@@ -89,6 +96,7 @@ async def handle_events(request: web.Request) -> web.StreamResponse:
         "execution_failed",
         "execution_paused",
         "client_input_requested",
+        "client_input_received",
         "node_loop_iteration",
         "node_loop_started",
         "credentials_required",
@@ -142,6 +150,7 @@ async def handle_events(request: web.Request) -> web.StreamResponse:
         EventType.CLIENT_OUTPUT_DELTA.value,
         EventType.EXECUTION_STARTED.value,
         EventType.CLIENT_INPUT_REQUESTED.value,
+        EventType.CLIENT_INPUT_RECEIVED.value,
     }
     event_type_values = {et.value for et in event_types}
     replay_types = _REPLAY_TYPES & event_type_values

core/framework/server/routes_execution.py

@@ -125,6 +125,18 @@ async def handle_chat(request: web.Request) -> web.Response:
         node = queen_executor.node_registry.get("queen")
         if node is not None and hasattr(node, "inject_event"):
             await node.inject_event(message, is_client_input=True)
+            # Publish to EventBus so the session event log captures user messages
+            from framework.runtime.event_bus import AgentEvent, EventType
+
+            await session.event_bus.publish(
+                AgentEvent(
+                    type=EventType.CLIENT_INPUT_RECEIVED,
+                    stream_id="queen",
+                    node_id="queen",
+                    execution_id=session.id,
+                    data={"content": message},
+                )
+            )
             return web.json_response(
                 {
                     "status": "queen",
@@ -347,7 +359,7 @@ async def handle_pause(request: web.Request) -> web.Response:
 
             for exec_id in list(stream.active_execution_ids):
                 try:
-                    ok = await stream.cancel_execution(exec_id)
+                    ok = await stream.cancel_execution(exec_id, reason="Execution paused by user")
                     if ok:
                         cancelled.append(exec_id)
                 except Exception:
@@ -357,8 +369,8 @@ async def handle_pause(request: web.Request) -> web.Response:
     runtime.pause_timers()
 
     # Switch to staging (agent still loaded, ready to re-run)
-    if session.mode_state is not None:
-        await session.mode_state.switch_to_staging(source="frontend")
+    if session.phase_state is not None:
+        await session.phase_state.switch_to_staging(source="frontend")
 
     return web.json_response(
         {
@@ -400,7 +412,9 @@ async def handle_stop(request: web.Request) -> web.Response:
                     if hasattr(node, "cancel_current_turn"):
                         node.cancel_current_turn()
 
-            cancelled = await stream.cancel_execution(execution_id)
+            cancelled = await stream.cancel_execution(
+                execution_id, reason="Execution stopped by user"
+            )
             if cancelled:
                 # Cancel queen's in-progress LLM turn
                 if session.queen_executor:

core/framework/server/routes_graphs.py

@@ -2,6 +2,7 @@
 
 import json
 import logging
+import time
 
 from aiohttp import web
 
@@ -116,6 +117,20 @@ async def handle_list_nodes(request: web.Request) -> web.Response:
         }
         for ep in reg.entry_points.values()
     ]
+    # Append triggers from triggers.json (stored on session)
+    for t in getattr(session, "available_triggers", {}).values():
+        entry = {
+            "id": t.id,
+            "name": t.description or t.id,
+            "entry_node": graph.entry_node,
+            "trigger_type": t.trigger_type,
+            "trigger_config": t.trigger_config,
+            "task": t.task,
+        }
+        mono = getattr(session, "trigger_next_fire", {}).get(t.id)
+        if mono is not None:
+            entry["next_fire_in"] = max(0.0, mono - time.monotonic())
+        entry_points.append(entry)
     return web.json_response(
         {
             "nodes": nodes,
@@ -234,8 +249,73 @@ async def handle_node_tools(request: web.Request) -> web.Response:
     return web.json_response({"tools": tools_out})
 
 
+async def handle_draft_graph(request: web.Request) -> web.Response:
+    """Return the current draft graph from planning phase (if any)."""
+    session, err = resolve_session(request)
+    if err:
+        return err
+
+    phase_state = getattr(session, "phase_state", None)
+    if phase_state is None or phase_state.draft_graph is None:
+        return web.json_response({"draft": None})
+
+    return web.json_response({"draft": phase_state.draft_graph})
+
+
+async def handle_flowchart_map(request: web.Request) -> web.Response:
+    """Return the flowchart→runtime node mapping and the original (pre-dissolution) draft.
+
+    Available after confirm_and_build() dissolves decision nodes, or loaded
+    from the agent's flowchart.json file, or synthesized from the runtime graph.
+    """
+    session, err = resolve_session(request)
+    if err:
+        return err
+
+    phase_state = getattr(session, "phase_state", None)
+
+    # Fast path: already in memory
+    if phase_state is not None and phase_state.original_draft_graph is not None:
+        return web.json_response(
+            {
+                "map": phase_state.flowchart_map,
+                "original_draft": phase_state.original_draft_graph,
+            }
+        )
+
+    # Try loading from flowchart.json in the agent folder
+    worker_path = getattr(session, "worker_path", None)
+    if worker_path is not None:
+        from pathlib import Path
+
+        target = Path(worker_path) / "flowchart.json"
+        if target.is_file():
+            try:
+                data = json.loads(target.read_text(encoding="utf-8"))
+                original_draft = data.get("original_draft")
+                fmap = data.get("flowchart_map")
+                # Cache in phase_state for future requests
+                if phase_state is not None and original_draft:
+                    phase_state.original_draft_graph = original_draft
+                    phase_state.flowchart_map = fmap
+                return web.json_response(
+                    {
+                        "map": fmap,
+                        "original_draft": original_draft,
+                    }
+                )
+            except Exception:
+                logger.warning("Failed to read flowchart.json from %s", worker_path)
+
+    return web.json_response({"map": None, "original_draft": None})
+
+
 def register_routes(app: web.Application) -> None:
     """Register graph/node inspection routes."""
+    # Draft graph (planning phase — visual only, no loaded worker required)
+    app.router.add_get("/api/sessions/{session_id}/draft-graph", handle_draft_graph)
+    # Flowchart map (post-dissolution — maps runtime nodes to original draft nodes)
+    app.router.add_get("/api/sessions/{session_id}/flowchart-map", handle_flowchart_map)
     # Session-primary routes
     app.router.add_get("/api/sessions/{session_id}/graphs/{graph_id}/nodes", handle_list_nodes)
     app.router.add_get(

core/framework/server/routes_sessions.py

@@ -9,8 +9,10 @@ Session-primary routes:
 - DELETE /api/sessions/{session_id}/worker           — unload worker from session
 - GET    /api/sessions/{session_id}/stats            — runtime statistics
 - GET    /api/sessions/{session_id}/entry-points     — list entry points
+- PATCH  /api/sessions/{session_id}/triggers/{id}   — update trigger task
 - GET    /api/sessions/{session_id}/graphs           — list graph IDs
 - GET    /api/sessions/{session_id}/queen-messages   — queen conversation history
+- GET    /api/sessions/{session_id}/events/history  — persisted eventbus log (for replay)
 
 Worker session browsing (persisted execution runs on disk):
 - GET    /api/sessions/{session_id}/worker-sessions                             — list
@@ -31,6 +33,7 @@ from pathlib import Path
 from aiohttp import web
 
 from framework.server.app import (
+    cold_sessions_dir,
     resolve_session,
     safe_path_segment,
     sessions_dir,
@@ -61,7 +64,7 @@ def _session_to_live_dict(session) -> dict:
         "loaded_at": session.loaded_at,
         "uptime_seconds": round(time.time() - session.loaded_at, 1),
         "intro_message": getattr(session.runner, "intro_message", "") or "",
-        "queen_phase": phase_state.phase if phase_state else "building",
+        "queen_phase": phase_state.phase if phase_state else "planning",
     }
 
 
@@ -140,6 +143,7 @@ async def handle_create_session(request: web.Request) -> web.Response:
             session = await manager.create_session_with_worker(
                 agent_path,
                 agent_id=agent_id,
+                session_id=session_id,
                 model=model,
                 initial_prompt=initial_prompt,
                 queen_resume_from=queen_resume_from,
@@ -228,6 +232,22 @@ async def handle_get_live_session(request: web.Request) -> web.Response:
             }
             for ep in rt.get_entry_points()
         ]
+        # Append triggers from triggers.json (stored on session)
+        runner = getattr(session, "runner", None)
+        graph_entry = runner.graph.entry_node if runner else ""
+        for t in getattr(session, "available_triggers", {}).values():
+            entry = {
+                "id": t.id,
+                "name": t.description or t.id,
+                "entry_node": graph_entry,
+                "trigger_type": t.trigger_type,
+                "trigger_config": t.trigger_config,
+                "task": t.task,
+            }
+            mono = getattr(session, "trigger_next_fire", {}).get(t.id)
+            if mono is not None:
+                entry["next_fire_in"] = max(0.0, mono - time.monotonic())
+            data["entry_points"].append(entry)
         data["graphs"] = session.worker_runtime.list_graphs()
 
     return web.json_response(data)
@@ -351,23 +371,84 @@ async def handle_session_entry_points(request: web.Request) -> web.Response:
 
     rt = session.worker_runtime
     eps = rt.get_entry_points() if rt else []
+    entry_points = [
+        {
+            "id": ep.id,
+            "name": ep.name,
+            "entry_node": ep.entry_node,
+            "trigger_type": ep.trigger_type,
+            "trigger_config": ep.trigger_config,
+            **(
+                {"next_fire_in": nf}
+                if rt and (nf := rt.get_timer_next_fire_in(ep.id)) is not None
+                else {}
+            ),
+        }
+        for ep in eps
+    ]
+    # Append triggers from triggers.json (stored on session)
+    runner = getattr(session, "runner", None)
+    graph_entry = runner.graph.entry_node if runner else ""
+    for t in getattr(session, "available_triggers", {}).values():
+        entry = {
+            "id": t.id,
+            "name": t.description or t.id,
+            "entry_node": graph_entry,
+            "trigger_type": t.trigger_type,
+            "trigger_config": t.trigger_config,
+            "task": t.task,
+        }
+        mono = getattr(session, "trigger_next_fire", {}).get(t.id)
+        if mono is not None:
+            entry["next_fire_in"] = max(0.0, mono - time.monotonic())
+        entry_points.append(entry)
+    return web.json_response({"entry_points": entry_points})
+
+
+async def handle_update_trigger_task(request: web.Request) -> web.Response:
+    """PATCH /api/sessions/{session_id}/triggers/{trigger_id} — update trigger task."""
+    session, err = resolve_session(request)
+    if err:
+        return err
+
+    trigger_id = request.match_info["trigger_id"]
+    available = getattr(session, "available_triggers", {})
+    tdef = available.get(trigger_id)
+    if tdef is None:
+        return web.json_response(
+            {"error": f"Trigger '{trigger_id}' not found"},
+            status=404,
+        )
+
+    try:
+        body = await request.json()
+    except Exception:
+        return web.json_response({"error": "Invalid JSON body"}, status=400)
+
+    task = body.get("task")
+    if task is None:
+        return web.json_response({"error": "Missing 'task' field"}, status=400)
+    if not isinstance(task, str):
+        return web.json_response({"error": "'task' must be a string"}, status=400)
+
+    tdef.task = task
+
+    # Persist to session state and agent definition
+    from framework.tools.queen_lifecycle_tools import (
+        _persist_active_triggers,
+        _save_trigger_to_agent,
+    )
+
+    if trigger_id in getattr(session, "active_trigger_ids", set()):
+        session_id = request.match_info["session_id"]
+        await _persist_active_triggers(session, session_id)
+
+    _save_trigger_to_agent(session, trigger_id, tdef)
+
     return web.json_response(
         {
-            "entry_points": [
-                {
-                    "id": ep.id,
-                    "name": ep.name,
-                    "entry_node": ep.entry_node,
-                    "trigger_type": ep.trigger_type,
-                    "trigger_config": ep.trigger_config,
-                    **(
-                        {"next_fire_in": nf}
-                        if rt and (nf := rt.get_timer_next_fire_in(ep.id)) is not None
-                        else {}
-                    ),
-                }
-                for ep in eps
-            ]
+            "trigger_id": trigger_id,
+            "task": tdef.task,
         }
     )
 
@@ -397,12 +478,15 @@ async def handle_list_worker_sessions(request: web.Request) -> web.Response:
     """List worker sessions on disk."""
     session, err = resolve_session(request)
     if err:
-        return err
-
-    if not session.worker_path:
-        return web.json_response({"sessions": []})
-
-    sess_dir = sessions_dir(session)
+        # Fall back to cold session lookup from disk
+        sid = request.match_info["session_id"]
+        sess_dir = cold_sessions_dir(sid)
+        if sess_dir is None:
+            return err
+    else:
+        if not session.worker_path:
+            return web.json_response({"sessions": []})
+        sess_dir = sessions_dir(session)
     if not sess_dir.exists():
         return web.json_response({"sessions": []})
 
@@ -564,48 +648,85 @@ async def handle_messages(request: web.Request) -> web.Response:
     """Get messages for a worker session."""
     session, err = resolve_session(request)
     if err:
-        return err
-
-    if not session.worker_path:
-        return web.json_response({"error": "No worker loaded"}, status=503)
+        # Fall back to cold session lookup from disk
+        sid = request.match_info["session_id"]
+        sess_dir = cold_sessions_dir(sid)
+        if sess_dir is None:
+            return err
+    else:
+        if not session.worker_path:
+            return web.json_response({"error": "No worker loaded"}, status=503)
+        sess_dir = sessions_dir(session)
 
     ws_id = request.match_info.get("ws_id") or request.match_info.get("session_id", "")
     ws_id = safe_path_segment(ws_id)
 
-    convs_dir = sessions_dir(session) / ws_id / "conversations"
+    convs_dir = sess_dir / ws_id / "conversations"
     if not convs_dir.exists():
         return web.json_response({"messages": []})
 
     filter_node = request.query.get("node_id")
     all_messages = []
 
-    for node_dir in convs_dir.iterdir():
-        if not node_dir.is_dir():
-            continue
-        if filter_node and node_dir.name != filter_node:
-            continue
-
-        parts_dir = node_dir / "parts"
+    def _collect_msg_parts(parts_dir: Path, node_id: str) -> None:
         if not parts_dir.exists():
-            continue
-
+            return
         for part_file in sorted(parts_dir.iterdir()):
             if part_file.suffix != ".json":
                 continue
             try:
                 part = json.loads(part_file.read_text(encoding="utf-8"))
-                part["_node_id"] = node_dir.name
+                part["_node_id"] = node_id
                 part.setdefault("created_at", part_file.stat().st_mtime)
                 all_messages.append(part)
             except (json.JSONDecodeError, OSError):
                 continue
 
+    # Flat layout: conversations/parts/*.json
+    if not filter_node:
+        _collect_msg_parts(convs_dir / "parts", "worker")
+
+    # Node-based layout: conversations/<node_id>/parts/*.json
+    for node_dir in convs_dir.iterdir():
+        if not node_dir.is_dir() or node_dir.name == "parts":
+            continue
+        if filter_node and node_dir.name != filter_node:
+            continue
+        _collect_msg_parts(node_dir / "parts", node_dir.name)
+
+    # Merge run lifecycle markers from runs.jsonl (for historical dividers)
+    runs_file = sess_dir / ws_id / "runs.jsonl"
+    if runs_file.exists():
+        try:
+            for line in runs_file.read_text(encoding="utf-8").splitlines():
+                line = line.strip()
+                if not line:
+                    continue
+                try:
+                    record = json.loads(line)
+                    all_messages.append(
+                        {
+                            "seq": -1,
+                            "role": "system",
+                            "content": "",
+                            "_node_id": "_run_marker",
+                            "is_run_marker": True,
+                            "run_id": record.get("run_id"),
+                            "run_event": record.get("event"),
+                            "created_at": record.get("created_at", 0),
+                        }
+                    )
+                except json.JSONDecodeError:
+                    continue
+        except OSError:
+            pass
+
     all_messages.sort(key=lambda m: m.get("created_at", m.get("seq", 0)))
 
     client_only = request.query.get("client_only", "").lower() in ("true", "1")
     if client_only:
         client_facing_nodes: set[str] = set()
-        if session.runner and hasattr(session.runner, "graph"):
+        if session and session.runner and hasattr(session.runner, "graph"):
             for node in session.runner.graph.nodes:
                 if node.client_facing:
                     client_facing_nodes.add(node.id)
@@ -614,12 +735,15 @@ async def handle_messages(request: web.Request) -> web.Response:
             all_messages = [
                 m
                 for m in all_messages
-                if not m.get("is_transition_marker")
-                and m["role"] != "tool"
-                and not (m["role"] == "assistant" and m.get("tool_calls"))
-                and (
-                    (m["role"] == "user" and m.get("is_client_input"))
-                    or (m["role"] == "assistant" and m.get("_node_id") in client_facing_nodes)
+                if m.get("is_run_marker")
+                or (
+                    not m.get("is_transition_marker")
+                    and m["role"] != "tool"
+                    and not (m["role"] == "assistant" and m.get("tool_calls"))
+                    and (
+                        (m["role"] == "user" and m.get("is_client_input"))
+                        or (m["role"] == "assistant" and m.get("_node_id") in client_facing_nodes)
+                    )
                 )
             ]
 
@@ -640,18 +764,16 @@ async def handle_queen_messages(request: web.Request) -> web.Response:
         return web.json_response({"messages": [], "session_id": session_id})
 
     all_messages: list[dict] = []
-    for node_dir in convs_dir.iterdir():
-        if not node_dir.is_dir():
-            continue
-        parts_dir = node_dir / "parts"
+
+    def _read_parts(parts_dir: Path, node_id: str) -> None:
         if not parts_dir.exists():
-            continue
+            return
         for part_file in sorted(parts_dir.iterdir()):
             if part_file.suffix != ".json":
                 continue
             try:
                 part = json.loads(part_file.read_text(encoding="utf-8"))
-                part["_node_id"] = node_dir.name
+                part["_node_id"] = node_id
                 # Use file mtime as created_at so frontend can order
                 # queen and worker messages chronologically.
                 part.setdefault("created_at", part_file.stat().st_mtime)
@@ -659,6 +781,15 @@ async def handle_queen_messages(request: web.Request) -> web.Response:
             except (json.JSONDecodeError, OSError):
                 continue
 
+    # Flat layout: conversations/parts/*.json
+    _read_parts(convs_dir / "parts", "queen")
+
+    # Node-based layout: conversations/<node_id>/parts/*.json
+    for node_dir in convs_dir.iterdir():
+        if not node_dir.is_dir() or node_dir.name == "parts":
+            continue
+        _read_parts(node_dir / "parts", node_dir.name)
+
     all_messages.sort(key=lambda m: m.get("created_at", m.get("seq", 0)))
 
     # Filter to client-facing messages only
@@ -673,6 +804,38 @@ async def handle_queen_messages(request: web.Request) -> web.Response:
     return web.json_response({"messages": all_messages, "session_id": session_id})
 
 
+async def handle_session_events_history(request: web.Request) -> web.Response:
+    """GET /api/sessions/{session_id}/events/history — persisted eventbus log.
+
+    Reads ``events.jsonl`` from the session directory on disk so it works for
+    both live sessions and cold (post-server-restart) sessions.  The frontend
+    replays these events through ``sseEventToChatMessage`` to fully reconstruct
+    the UI state on resume.
+    """
+    session_id = request.match_info["session_id"]
+
+    queen_dir = Path.home() / ".hive" / "queen" / "session" / session_id
+    events_path = queen_dir / "events.jsonl"
+    if not events_path.exists():
+        return web.json_response({"events": [], "session_id": session_id})
+
+    events: list[dict] = []
+    try:
+        with open(events_path, encoding="utf-8") as f:
+            for line in f:
+                line = line.strip()
+                if not line:
+                    continue
+                try:
+                    events.append(json.loads(line))
+                except json.JSONDecodeError:
+                    continue
+    except OSError:
+        return web.json_response({"events": [], "session_id": session_id})
+
+    return web.json_response({"events": events, "session_id": session_id})
+
+
 async def handle_session_history(request: web.Request) -> web.Response:
     """GET /api/sessions/history — all queen sessions on disk (live + cold).
 
@@ -731,7 +894,7 @@ async def handle_delete_history_session(request: web.Request) -> web.Response:
 
 async def handle_discover(request: web.Request) -> web.Response:
     """GET /api/discover — discover agents from filesystem."""
-    from framework.tui.screens.agent_picker import discover_agents
+    from framework.agents.discovery import discover_agents
 
     manager = _get_manager(request)
     loaded_paths = {str(s.worker_path) for s in manager.list_sessions() if s.worker_path}
@@ -746,6 +909,7 @@ async def handle_discover(request: web.Request) -> web.Response:
                 "description": entry.description,
                 "category": entry.category,
                 "session_count": entry.session_count,
+                "run_count": entry.run_count,
                 "node_count": entry.node_count,
                 "tool_count": entry.tool_count,
                 "tags": entry.tags,
@@ -783,8 +947,12 @@ def register_routes(app: web.Application) -> None:
     # Session info
     app.router.add_get("/api/sessions/{session_id}/stats", handle_session_stats)
     app.router.add_get("/api/sessions/{session_id}/entry-points", handle_session_entry_points)
+    app.router.add_patch(
+        "/api/sessions/{session_id}/triggers/{trigger_id}", handle_update_trigger_task
+    )
     app.router.add_get("/api/sessions/{session_id}/graphs", handle_session_graphs)
     app.router.add_get("/api/sessions/{session_id}/queen-messages", handle_queen_messages)
+    app.router.add_get("/api/sessions/{session_id}/events/history", handle_session_events_history)
 
     # Worker session browsing (session-primary)
     app.router.add_get("/api/sessions/{session_id}/worker-sessions", handle_list_worker_sessions)

core/framework/server/session_manager.py

@@ -7,7 +7,6 @@ Architecture:
 - Session owns EventBus + LLM, shared with queen and worker
 - Queen is always present once a session starts
 - Worker is optional — loaded into an existing session
-- Judge is active only when a worker is loaded
 """
 
 import asyncio
@@ -15,11 +14,13 @@ import json
 import logging
 import time
 import uuid
-from dataclasses import dataclass
+from dataclasses import dataclass, field
 from datetime import datetime
 from pathlib import Path
 from typing import Any
 
+from framework.runtime.triggers import TriggerDefinition
+
 logger = logging.getLogger(__name__)
 
 
@@ -42,10 +43,23 @@ class Session:
     worker_info: Any | None = None  # AgentInfo
     # Queen phase state (building/staging/running)
     phase_state: Any = None  # QueenPhaseState
-    # Judge (active when worker is loaded)
-    judge_task: asyncio.Task | None = None
-    escalation_sub: str | None = None
+    # Worker handoff subscription
     worker_handoff_sub: str | None = None
+    # Memory consolidation subscription (fires on CONTEXT_COMPACTED)
+    memory_consolidation_sub: str | None = None
+    # Trigger definitions loaded from agent's triggers.json (available but inactive)
+    available_triggers: dict[str, TriggerDefinition] = field(default_factory=dict)
+    # Active trigger tracking (IDs currently firing + their asyncio tasks)
+    active_trigger_ids: set[str] = field(default_factory=set)
+    active_timer_tasks: dict[str, asyncio.Task] = field(default_factory=dict)
+    # Queen-owned webhook server (lazy singleton, created on first webhook trigger activation)
+    queen_webhook_server: Any = None
+    # EventBus subscription IDs for active webhook triggers (trigger_id -> sub_id)
+    active_webhook_subs: dict[str, str] = field(default_factory=dict)
+    # True after first successful worker execution (gates trigger delivery)
+    worker_configured: bool = False
+    # Monotonic timestamps for next trigger fire (mirrors AgentRuntime._timer_next_fire)
+    trigger_next_fire: dict[str, float] = field(default_factory=dict)
     # Session directory resumption:
     # When set, _start_queen writes queen conversations to this existing session's
     # directory instead of creating a new one.  This lets cold-restores accumulate
@@ -128,7 +142,9 @@ class SessionManager:
         to that existing session's directory instead of creating a new one.
         This preserves full conversation history across server restarts.
         """
-        session = await self._create_session_core(session_id=session_id, model=model)
+        # Reuse the original session ID when cold-restoring
+        resolved_session_id = queen_resume_from or session_id
+        session = await self._create_session_core(session_id=resolved_session_id, model=model)
         session.queen_resume_from = queen_resume_from
 
         # Start queen immediately (queen-only, no worker tools yet)
@@ -145,22 +161,28 @@ class SessionManager:
         self,
         agent_path: str | Path,
         agent_id: str | None = None,
+        session_id: str | None = None,
         model: str | None = None,
         initial_prompt: str | None = None,
         queen_resume_from: str | None = None,
     ) -> Session:
         """Create a session and load a worker in one step.
 
-        When ``queen_resume_from`` is set the queen writes conversation messages
-        to that existing session's directory instead of creating a new one.
+        When ``queen_resume_from`` is set the session reuses the original session
+        ID so the frontend sees a single continuous session.  The queen writes
+        conversation messages to that existing directory, preserving full history.
         """
         from framework.tools.queen_lifecycle_tools import build_worker_profile
 
         agent_path = Path(agent_path)
         resolved_worker_id = agent_id or agent_path.name
 
-        # Auto-generate session ID (not the agent name)
-        session = await self._create_session_core(model=model)
+        # Reuse the original session ID when cold-restoring so the frontend
+        # sees one continuous session instead of a new one each time.
+        session = await self._create_session_core(
+            session_id=queen_resume_from,
+            model=model,
+        )
         session.queen_resume_from = queen_resume_from
         try:
             # Load worker FIRST (before queen) so queen gets full tools
@@ -200,8 +222,8 @@ class SessionManager:
     ) -> None:
         """Load a worker agent into a session (core logic).
 
-        Sets up the runner, runtime, and session fields. Does NOT start the
-        judge or notify the queen — callers handle those steps.
+        Sets up the runner, runtime, and session fields. Does NOT notify
+        the queen — callers handle that step.
         """
         from framework.runner import AgentRunner
 
@@ -240,6 +262,25 @@ class SessionManager:
 
             runtime = runner._agent_runtime
 
+            # Load triggers from the agent's triggers.json definition file.
+            from framework.tools.queen_lifecycle_tools import _read_agent_triggers_json
+
+            for tdata in _read_agent_triggers_json(agent_path):
+                tid = tdata.get("id", "")
+                ttype = tdata.get("trigger_type", "")
+                if tid and ttype in ("timer", "webhook"):
+                    session.available_triggers[tid] = TriggerDefinition(
+                        id=tid,
+                        trigger_type=ttype,
+                        trigger_config=tdata.get("trigger_config", {}),
+                        description=tdata.get("name", tid),
+                        task=tdata.get("task", ""),
+                    )
+                    logger.info("Loaded trigger '%s' (%s) from triggers.json", tid, ttype)
+
+            if session.available_triggers:
+                await self._emit_trigger_events(session, "available", session.available_triggers)
+
             # Start runtime on event loop
             if runtime and not runtime.is_running:
                 await runtime.start()
@@ -276,11 +317,20 @@ class SessionManager:
         When a new runtime starts, any on-disk session still marked 'active'
         is from a process that no longer exists. 'Paused' sessions are left
         intact so they remain resumable.
+
+        Two-layer protection against corrupting live sessions:
+        1. In-memory: skip any session ID currently tracked in self._sessions
+           (guaranteed alive in this process).
+        2. PID validation: if state.json contains a ``pid`` field, check whether
+           that process is still running on the host. If it is, the session is
+           owned by another healthy worker process, so leave it alone.
         """
         sessions_path = Path.home() / ".hive" / "agents" / agent_path.name / "sessions"
         if not sessions_path.exists():
             return
 
+        live_session_ids = set(self._sessions.keys())
+
         for d in sessions_path.iterdir():
             if not d.is_dir() or not d.name.startswith("session_"):
                 continue
@@ -291,6 +341,26 @@ class SessionManager:
                 state = json.loads(state_path.read_text(encoding="utf-8"))
                 if state.get("status") != "active":
                     continue
+
+                # Layer 1: skip sessions that are alive in this process
+                session_id = state.get("session_id", d.name)
+                if session_id in live_session_ids or d.name in live_session_ids:
+                    logger.debug(
+                        "Skipping live in-memory session '%s' during stale cleanup",
+                        d.name,
+                    )
+                    continue
+
+                # Layer 2: skip sessions whose owning process is still alive
+                recorded_pid = state.get("pid")
+                if recorded_pid is not None and self._is_pid_alive(recorded_pid):
+                    logger.debug(
+                        "Skipping session '%s' — owning process %d is still running",
+                        d.name,
+                        recorded_pid,
+                    )
+                    continue
+
                 state["status"] = "cancelled"
                 state.setdefault("result", {})["error"] = "Stale session: runtime restarted"
                 state.setdefault("timestamps", {})["updated_at"] = datetime.now().isoformat()
@@ -301,6 +371,34 @@ class SessionManager:
             except (json.JSONDecodeError, OSError) as e:
                 logger.warning("Failed to clean up stale session %s: %s", d.name, e)
 
+    @staticmethod
+    def _is_pid_alive(pid: int) -> bool:
+        """Check whether a process with the given PID is still running."""
+        import os
+        import platform
+
+        if platform.system() == "Windows":
+            import ctypes
+
+            # PROCESS_QUERY_LIMITED_INFORMATION = 0x1000
+            kernel32 = ctypes.windll.kernel32
+            handle = kernel32.OpenProcess(0x1000, False, pid)
+            if not handle:
+                # 5 is ERROR_ACCESS_DENIED, meaning the process exists but is protected
+                return kernel32.GetLastError() == 5
+
+            exit_code = ctypes.c_ulong()
+            kernel32.GetExitCodeProcess(handle, ctypes.byref(exit_code))
+            kernel32.CloseHandle(handle)
+            # 259 is STILL_ACTIVE
+            return exit_code.value == 259
+        else:
+            try:
+                os.kill(pid, 0)
+            except OSError:
+                return False
+            return True
+
     async def load_worker(
         self,
         session_id: str,
@@ -310,7 +408,7 @@ class SessionManager:
     ) -> Session:
         """Load a worker agent into an existing session (with running queen).
 
-        Starts the worker runtime, health judge, and notifies the queen.
+        Starts the worker runtime and notifies the queen.
         """
         agent_path = Path(agent_path)
 
@@ -325,12 +423,69 @@ class SessionManager:
             model=model,
         )
 
-        # Notify queen about the loaded worker (skip for hive_coder itself).
-        # Health judge disabled for simplicity.
-        if agent_path.name != "hive_coder" and session.worker_runtime:
-            # await self._start_judge(session, session.runner._storage_path)
+        # Notify queen about the loaded worker (skip for queen itself).
+        if agent_path.name != "queen" and session.worker_runtime:
             await self._notify_queen_worker_loaded(session)
 
+        # Update meta.json so cold-restore can discover this session by agent_path
+        storage_session_id = session.queen_resume_from or session.id
+        meta_path = Path.home() / ".hive" / "queen" / "session" / storage_session_id / "meta.json"
+        try:
+            _agent_name = (
+                session.worker_info.name
+                if session.worker_info
+                else str(agent_path.name).replace("_", " ").title()
+            )
+            existing_meta = {}
+            if meta_path.exists():
+                existing_meta = json.loads(meta_path.read_text(encoding="utf-8"))
+            existing_meta["agent_name"] = _agent_name
+            existing_meta["agent_path"] = (
+                str(session.worker_path) if session.worker_path else str(agent_path)
+            )
+            meta_path.write_text(json.dumps(existing_meta), encoding="utf-8")
+        except OSError:
+            pass
+
+        # Restore previously active triggers from persisted session state
+        if session.available_triggers and session.worker_runtime:
+            try:
+                store = session.worker_runtime._session_store
+                state = await store.read_state(session_id)
+                if state and state.active_triggers:
+                    from framework.tools.queen_lifecycle_tools import (
+                        _start_trigger_timer,
+                        _start_trigger_webhook,
+                    )
+
+                    saved_tasks = getattr(state, "trigger_tasks", {}) or {}
+                    for tid in state.active_triggers:
+                        tdef = session.available_triggers.get(tid)
+                        if tdef:
+                            # Restore user-configured task override
+                            saved_task = saved_tasks.get(tid, "")
+                            if saved_task:
+                                tdef.task = saved_task
+                            tdef.active = True
+                            session.active_trigger_ids.add(tid)
+                            if tdef.trigger_type == "timer":
+                                await _start_trigger_timer(session, tid, tdef)
+                                logger.info("Restored trigger timer '%s'", tid)
+                            elif tdef.trigger_type == "webhook":
+                                await _start_trigger_webhook(session, tid, tdef)
+                                logger.info("Restored webhook trigger '%s'", tid)
+                        else:
+                            logger.warning(
+                                "Saved trigger '%s' not found in worker entry points, skipping",
+                                tid,
+                            )
+
+                # Restore worker_configured flag
+                if state and getattr(state, "worker_configured", False):
+                    session.worker_configured = True
+            except Exception as e:
+                logger.warning("Failed to restore active triggers: %s", e)
+
         # Emit SSE event so the frontend can update UI
         await self._emit_worker_loaded(session)
 
@@ -344,9 +499,6 @@ class SessionManager:
         if session.worker_runtime is None:
             return False
 
-        # Stop judge + escalation
-        self._stop_judge(session)
-
         # Cleanup worker
         if session.runner:
             try:
@@ -354,6 +506,26 @@ class SessionManager:
             except Exception as e:
                 logger.error("Error cleaning up worker '%s': %s", session.worker_id, e)
 
+        # Cancel active trigger timers
+        for tid, task in session.active_timer_tasks.items():
+            task.cancel()
+            logger.info("Cancelled trigger timer '%s' on unload", tid)
+        session.active_timer_tasks.clear()
+
+        # Unsubscribe webhook handlers (server stays alive — queen-owned)
+        for sub_id in session.active_webhook_subs.values():
+            try:
+                session.event_bus.unsubscribe(sub_id)
+            except Exception:
+                pass
+        session.active_webhook_subs.clear()
+        session.active_trigger_ids.clear()
+
+        # Clean up triggers
+        if session.available_triggers:
+            await self._emit_trigger_events(session, "removed", session.available_triggers)
+            session.available_triggers.clear()
+
         worker_id = session.worker_id
         session.worker_id = None
         session.worker_path = None
@@ -379,8 +551,11 @@ class SessionManager:
         if session is None:
             return False
 
-        # Stop judge
-        self._stop_judge(session)
+        # Capture session data for memory consolidation before teardown
+        _llm = getattr(session, "llm", None)
+        _storage_id = getattr(session, "queen_resume_from", None) or session_id
+        _session_dir = Path.home() / ".hive" / "queen" / "session" / _storage_id
+
         if session.worker_handoff_sub is not None:
             try:
                 session.event_bus.unsubscribe(session.worker_handoff_sub)
@@ -388,12 +563,37 @@ class SessionManager:
                 pass
             session.worker_handoff_sub = None
 
-        # Stop queen
+        # Stop queen and memory consolidation subscription
+        if session.memory_consolidation_sub is not None:
+            try:
+                session.event_bus.unsubscribe(session.memory_consolidation_sub)
+            except Exception:
+                pass
+            session.memory_consolidation_sub = None
         if session.queen_task is not None:
             session.queen_task.cancel()
             session.queen_task = None
         session.queen_executor = None
 
+        # Cancel active trigger timers
+        for task in session.active_timer_tasks.values():
+            task.cancel()
+        session.active_timer_tasks.clear()
+
+        # Unsubscribe webhook handlers and stop queen webhook server
+        for sub_id in session.active_webhook_subs.values():
+            try:
+                session.event_bus.unsubscribe(sub_id)
+            except Exception:
+                pass
+        session.active_webhook_subs.clear()
+        if session.queen_webhook_server is not None:
+            try:
+                await session.queen_webhook_server.stop()
+            except Exception:
+                logger.error("Error stopping queen webhook server", exc_info=True)
+            session.queen_webhook_server = None
+
         # Cleanup worker
         if session.runner:
             try:
@@ -401,6 +601,20 @@ class SessionManager:
             except Exception as e:
                 logger.error("Error cleaning up worker: %s", e)
 
+        # Final memory consolidation — fire-and-forget so teardown isn't blocked.
+        if _llm is not None and _session_dir.exists():
+            import asyncio
+
+            from framework.agents.queen.queen_memory import consolidate_queen_memory
+
+            asyncio.create_task(
+                consolidate_queen_memory(session_id, _session_dir, _llm),
+                name=f"queen-memory-consolidation-{session_id}",
+            )
+
+        # Close per-session event log
+        session.event_bus.close_session_log()
+
         logger.info("Session '%s' stopped", session_id)
         return True
 
@@ -410,7 +624,7 @@ class SessionManager:
 
     async def _handle_worker_handoff(self, session: Session, executor: Any, event: Any) -> None:
         """Route worker escalation events into the queen conversation."""
-        if event.stream_id in ("queen", "judge"):
+        if event.stream_id == "queen":
             return
 
         reason = str(event.data.get("reason", "")).strip()
@@ -461,13 +675,7 @@ class SessionManager:
         are written to the ORIGINAL session's directory so the full conversation
         history accumulates in one place across server restarts.
         """
-        from framework.agents.hive_coder.agent import (
-            queen_goal,
-            queen_graph as _queen_graph,
-        )
-        from framework.graph.executor import GraphExecutor
-        from framework.runner.tool_registry import ToolRegistry
-        from framework.runtime.core import Runtime
+        from framework.server.queen_orchestrator import create_queen
 
         hive_home = Path.home() / ".hive"
 
@@ -505,393 +713,81 @@ class SessionManager:
         except OSError:
             pass
 
-        # Register MCP coding tools
-        queen_registry = ToolRegistry()
-        import framework.agents.hive_coder as _hive_coder_pkg
-
-        hive_coder_dir = Path(_hive_coder_pkg.__file__).parent
-        mcp_config = hive_coder_dir / "mcp_servers.json"
-        if mcp_config.exists():
-            try:
-                queen_registry.load_mcp_config(mcp_config)
-                logger.info("Queen: loaded MCP tools from %s", mcp_config)
-            except Exception:
-                logger.warning("Queen: MCP config failed to load", exc_info=True)
-
-        # Phase state for building/running phase switching
-        from framework.tools.queen_lifecycle_tools import (
-            QueenPhaseState,
-            register_queen_lifecycle_tools,
-        )
-
-        # Start in staging when the caller provided an agent, building otherwise.
-        initial_phase = "staging" if worker_identity else "building"
-        phase_state = QueenPhaseState(phase=initial_phase, event_bus=session.event_bus)
-        session.phase_state = phase_state
-
-        # Always register lifecycle tools — they check session.worker_runtime
-        # at call time, so they work even if no worker is loaded yet.
-        register_queen_lifecycle_tools(
-            queen_registry,
-            session=session,
-            session_id=session.id,
-            session_manager=self,
-            manager_session_id=session.id,
-            phase_state=phase_state,
-        )
-
-        # Monitoring tools need concrete worker paths — only register when present
-        if session.worker_runtime:
-            from framework.tools.worker_monitoring_tools import register_worker_monitoring_tools
-
-            register_worker_monitoring_tools(
-                queen_registry,
-                session.event_bus,
-                session.worker_path,
-                stream_id="queen",
-                worker_graph_id=session.worker_runtime._graph_id,
-            )
-
-        queen_tools = list(queen_registry.get_tools().values())
-        queen_tool_executor = queen_registry.get_executor()
-
-        # Partition tools into phase-specific sets and import prompt segments
-        from framework.agents.hive_coder.nodes import (
-            _QUEEN_BUILDING_TOOLS,
-            _QUEEN_RUNNING_TOOLS,
-            _QUEEN_STAGING_TOOLS,
-            _appendices,
-            _gcu_building_section,
-            _package_builder_knowledge,
-            _queen_behavior_always,
-            _queen_behavior_building,
-            _queen_behavior_running,
-            _queen_behavior_staging,
-            _queen_identity_building,
-            _queen_identity_running,
-            _queen_identity_staging,
-            _queen_phase_7,
-            _queen_style,
-            _queen_tools_building,
-            _queen_tools_running,
-            _queen_tools_staging,
-        )
-
-        building_names = set(_QUEEN_BUILDING_TOOLS)
-        staging_names = set(_QUEEN_STAGING_TOOLS)
-        running_names = set(_QUEEN_RUNNING_TOOLS)
-
-        registered_names = {t.name for t in queen_tools}
-        missing_building = building_names - registered_names
-        if missing_building:
-            logger.warning(
-                "Queen: %d/%d building tools NOT registered: %s",
-                len(missing_building),
-                len(building_names),
-                sorted(missing_building),
-            )
-        logger.info("Queen: registered tools: %s", sorted(registered_names))
-
-        phase_state.building_tools = [t for t in queen_tools if t.name in building_names]
-        phase_state.staging_tools = [t for t in queen_tools if t.name in staging_names]
-        phase_state.running_tools = [t for t in queen_tools if t.name in running_names]
-
-        # Build queen graph with adjusted prompt + tools
-        _orig_node = _queen_graph.nodes[0]
-
-        if worker_identity is None:
-            worker_identity = (
-                "\n\n# Worker Profile\n"
-                "No worker agent loaded. You are operating independently.\n"
-                "Handle all tasks directly using your coding tools."
-            )
-
-        # Compose phase-specific prompts.
-        _building_body = (
-            _queen_style
-            + _queen_tools_building
-            + _queen_behavior_always
-            + _queen_behavior_building
-            + _package_builder_knowledge
-            + _gcu_building_section
-            + _queen_phase_7
-            + _appendices
-            + worker_identity
-        )
-        phase_state.prompt_building = _queen_identity_building + _building_body
-        phase_state.prompt_staging = (
-            _queen_identity_staging
-            + _queen_style
-            + _queen_tools_staging
-            + _queen_behavior_always
-            + _queen_behavior_staging
-            + worker_identity
-        )
-        phase_state.prompt_running = (
-            _queen_identity_running
-            + _queen_style
-            + _queen_tools_running
-            + _queen_behavior_always
-            + _queen_behavior_running
-            + worker_identity
-        )
-
-        # Build the session_start hook: selects the best-fit expert persona
-        # from the user's opening message and replaces the identity prefix.
-        from framework.agents.hive_coder.nodes.thinking_hook import select_expert_persona
-        from framework.graph.event_loop_node import HookContext, HookResult
-        from framework.runtime.event_bus import AgentEvent, EventType
-
-        _session_llm = session.llm
-        _session_event_bus = session.event_bus
-
-        async def _persona_hook(ctx: HookContext) -> HookResult | None:
-            persona = await select_expert_persona(ctx.trigger or "", _session_llm)
-            if not persona:
-                return None
-            if _session_event_bus is not None:
-                await _session_event_bus.publish(
-                    AgentEvent(
-                        type=EventType.QUEEN_PERSONA_SELECTED,
-                        stream_id="queen",
-                        data={"persona": persona},
-                    )
-                )
-            return HookResult(system_prompt=persona + "\n\n" + _building_body)
-
-        initial_prompt_text = phase_state.get_current_prompt()
-
-        registered_tool_names = set(queen_registry.get_tools().keys())
-        declared_tools = _orig_node.tools or []
-        available_tools = [t for t in declared_tools if t in registered_tool_names]
-
-        node_updates: dict = {
-            "system_prompt": initial_prompt_text,
-        }
-        if set(available_tools) != set(declared_tools):
-            missing = sorted(set(declared_tools) - registered_tool_names)
-            if missing:
-                logger.warning("Queen: tools not available: %s", missing)
-            node_updates["tools"] = available_tools
-
-        adjusted_node = _orig_node.model_copy(update=node_updates)
-        _queen_loop_config = {
-            **(_queen_graph.loop_config or {}),
-            "hooks": {"session_start": [_persona_hook]},
-        }
-        queen_graph = _queen_graph.model_copy(
-            update={"nodes": [adjusted_node], "loop_config": _queen_loop_config}
-        )
-
-        queen_runtime = Runtime(hive_home / "queen")
-
-        async def _queen_loop():
-            try:
-                executor = GraphExecutor(
-                    runtime=queen_runtime,
-                    llm=session.llm,
-                    tools=queen_tools,
-                    tool_executor=queen_tool_executor,
-                    event_bus=session.event_bus,
-                    stream_id="queen",
-                    storage_path=queen_dir,
-                    loop_config=_queen_loop_config,
-                    execution_id=session.id,
-                    dynamic_tools_provider=phase_state.get_current_tools,
-                    dynamic_prompt_provider=phase_state.get_current_prompt,
-                )
-                session.queen_executor = executor
-
-                # Wire inject_notification so phase switches notify the queen LLM
-                async def _inject_phase_notification(content: str) -> None:
-                    node = executor.node_registry.get("queen")
-                    if node is not None and hasattr(node, "inject_event"):
-                        await node.inject_event(content)
-
-                phase_state.inject_notification = _inject_phase_notification
-
-                # Auto-switch to staging when worker execution finishes naturally
-                # and notify the queen about the termination
-                from framework.runtime.event_bus import EventType as _ET
-
-                async def _on_worker_done(event):
-                    if event.stream_id == "queen":
-                        return
-                    if phase_state.phase == "running":
-                        # Build termination notification for the queen
-                        if event.type == _ET.EXECUTION_COMPLETED:
-                            output = event.data.get("output", {})
-                            output_summary = ""
-                            if output:
-                                # Summarize key outputs for the queen
-                                for key, value in output.items():
-                                    val_str = str(value)
-                                    if len(val_str) > 200:
-                                        val_str = val_str[:200] + "..."
-                                    output_summary += f"\n  {key}: {val_str}"
-                            _out = output_summary or " (no output keys set)"
-                            notification = (
-                                "[WORKER_TERMINAL] Worker finished successfully.\n"
-                                f"Output:{_out}\n"
-                                "Report this to the user. "
-                                "Ask if they want to continue with another run."
-                            )
-                        else:  # EXECUTION_FAILED
-                            error = event.data.get("error", "Unknown error")
-                            notification = (
-                                "[WORKER_TERMINAL] Worker failed.\n"
-                                f"Error: {error}\n"
-                                "Report this to the user and help them troubleshoot."
-                            )
-
-                        # Inject notification to queen before phase switch
-                        node = executor.node_registry.get("queen")
-                        if node is not None and hasattr(node, "inject_event"):
-                            await node.inject_event(notification)
-
-                        await phase_state.switch_to_staging(source="auto")
-
-                session.event_bus.subscribe(
-                    event_types=[_ET.EXECUTION_COMPLETED, _ET.EXECUTION_FAILED],
-                    handler=_on_worker_done,
-                )
-                self._subscribe_worker_handoffs(session, executor)
-
-                logger.info(
-                    "Queen starting in %s phase with %d tools: %s",
-                    phase_state.phase,
-                    len(phase_state.get_current_tools()),
-                    [t.name for t in phase_state.get_current_tools()],
-                )
-                result = await executor.execute(
-                    graph=queen_graph,
-                    goal=queen_goal,
-                    input_data={"greeting": initial_prompt or "Session started."},
-                    session_state={"resume_session_id": session.id},
-                )
-                if result.success:
-                    logger.warning("Queen executor returned (should be forever-alive)")
-                else:
-                    logger.error(
-                        "Queen executor failed: %s",
-                        result.error or "(no error message)",
-                    )
-            except Exception:
-                logger.error("Queen conversation crashed", exc_info=True)
-            finally:
-                session.queen_executor = None
-
-        session.queen_task = asyncio.create_task(_queen_loop())
-
-    # ------------------------------------------------------------------
-    # Judge startup / teardown
-    # ------------------------------------------------------------------
-
-    async def _start_judge(
-        self,
-        session: Session,
-        worker_storage_path: str | Path,
-    ) -> None:
-        """Start the health judge for a session's worker."""
-        from framework.graph.executor import GraphExecutor
-        from framework.monitoring import judge_goal, judge_graph
-        from framework.runner.tool_registry import ToolRegistry
-        from framework.runtime.core import Runtime
-        from framework.runtime.event_bus import EventType as _ET
-        from framework.tools.worker_monitoring_tools import register_worker_monitoring_tools
-
-        worker_storage_path = Path(worker_storage_path)
-
+        # Enable per-session event persistence so that all eventbus events
+        # survive server restarts and can be replayed on cold-session resume.
+        # Scan the existing event log to find the max iteration ever written,
+        # then use max+1 as offset so resumed sessions produce monotonically
+        # increasing iteration values — preventing frontend message ID collisions.
+        iteration_offset = 0
+        events_path = queen_dir / "events.jsonl"
         try:
-            # Monitoring tools
-            monitoring_registry = ToolRegistry()
-            register_worker_monitoring_tools(
-                monitoring_registry,
-                session.event_bus,
-                worker_storage_path,
-                worker_graph_id=session.worker_runtime._graph_id,
-            )
-
-            hive_home = Path.home() / ".hive"
-            judge_dir = hive_home / "judge" / "session" / session.id
-            judge_dir.mkdir(parents=True, exist_ok=True)
-
-            judge_runtime = Runtime(hive_home / "judge")
-            monitoring_tools = list(monitoring_registry.get_tools().values())
-            monitoring_executor = monitoring_registry.get_executor()
-
-            async def _judge_loop():
-                interval = 300  # 5 minutes between checks
-                # Wait before the first check — let the worker actually do something
-                await asyncio.sleep(interval)
-                while True:
-                    try:
-                        executor = GraphExecutor(
-                            runtime=judge_runtime,
-                            llm=session.llm,
-                            tools=monitoring_tools,
-                            tool_executor=monitoring_executor,
-                            event_bus=session.event_bus,
-                            stream_id="judge",
-                            storage_path=judge_dir,
-                            loop_config=judge_graph.loop_config,
-                        )
-                        await executor.execute(
-                            graph=judge_graph,
-                            goal=judge_goal,
-                            input_data={
-                                "event": {"source": "timer", "reason": "scheduled"},
-                            },
-                            session_state={"resume_session_id": session.id},
-                        )
-                    except Exception:
-                        logger.error("Health judge tick failed", exc_info=True)
-                    await asyncio.sleep(interval)
-
-            session.judge_task = asyncio.create_task(_judge_loop())
-
-            # Escalation: judge → queen
-            async def _on_escalation(event):
-                ticket = event.data.get("ticket", {})
-                executor = session.queen_executor
-                if executor is None:
-                    logger.warning("Escalation received but queen executor is None")
-                    return
-                node = executor.node_registry.get("queen")
-                if node is not None and hasattr(node, "inject_event"):
-                    msg = "[ESCALATION TICKET from Health Judge]\n" + json.dumps(
-                        ticket, indent=2, ensure_ascii=False
+            if events_path.exists():
+                max_iter = -1
+                with open(events_path, encoding="utf-8") as f:
+                    for line in f:
+                        line = line.strip()
+                        if not line:
+                            continue
+                        try:
+                            evt = json.loads(line)
+                            it = evt.get("data", {}).get("iteration")
+                            if isinstance(it, int) and it > max_iter:
+                                max_iter = it
+                        except (json.JSONDecodeError, TypeError):
+                            continue
+                if max_iter >= 0:
+                    iteration_offset = max_iter + 1
+                    logger.info(
+                        "Session '%s' resuming with iteration_offset=%d (from events.jsonl max)",
+                        session.id,
+                        iteration_offset,
                     )
-                    await node.inject_event(msg)
-                else:
-                    logger.warning("Escalation received but queen node not ready")
+        except OSError:
+            pass
+        session.event_bus.set_session_log(events_path, iteration_offset=iteration_offset)
 
-            session.escalation_sub = session.event_bus.subscribe(
-                event_types=[_ET.WORKER_ESCALATION_TICKET],
-                handler=_on_escalation,
+        session.queen_task = await create_queen(
+            session=session,
+            session_manager=self,
+            worker_identity=worker_identity,
+            queen_dir=queen_dir,
+            initial_prompt=initial_prompt,
+        )
+
+        # Auto-load worker on cold restore — the queen's conversation expects
+        # the agent to be loaded, but the new session has no worker.
+        if session.queen_resume_from and not session.worker_runtime:
+            meta_path = queen_dir / "meta.json"
+            if meta_path.exists():
+                try:
+                    _meta = json.loads(meta_path.read_text(encoding="utf-8"))
+                    _agent_path = _meta.get("agent_path")
+                    if _agent_path and Path(_agent_path).exists():
+                        await self.load_worker(session.id, _agent_path)
+                        if session.phase_state:
+                            await session.phase_state.switch_to_staging(source="auto")
+                        logger.info("Cold restore: auto-loaded worker from %s", _agent_path)
+                except Exception:
+                    logger.warning("Cold restore: failed to auto-load worker", exc_info=True)
+
+        # Memory consolidation — triggered by context compaction events.
+        # Compaction is a natural signal that "enough has happened to be worth remembering".
+        _consolidation_llm = session.llm
+        _consolidation_session_dir = queen_dir
+
+        async def _on_compaction(_event) -> None:
+            from framework.agents.queen.queen_memory import consolidate_queen_memory
+
+            await consolidate_queen_memory(
+                session.id, _consolidation_session_dir, _consolidation_llm
             )
 
-            logger.info("Judge started for session '%s'", session.id)
+        from framework.runtime.event_bus import EventType as _ET
 
-        except Exception as e:
-            logger.error(
-                "Failed to start judge for session '%s': %s",
-                session.id,
-                e,
-                exc_info=True,
-            )
-
-    def _stop_judge(self, session: Session) -> None:
-        """Cancel judge task and unsubscribe escalation events."""
-        if session.judge_task is not None:
-            session.judge_task.cancel()
-            session.judge_task = None
-        if session.escalation_sub is not None:
-            try:
-                session.event_bus.unsubscribe(session.escalation_sub)
-            except Exception:
-                pass
-            session.escalation_sub = None
+        session.memory_consolidation_sub = session.event_bus.subscribe(
+            event_types=[_ET.CONTEXT_COMPACTED],
+            handler=_on_compaction,
+        )
 
     # ------------------------------------------------------------------
     # Queen notifications
@@ -909,7 +805,22 @@ class SessionManager:
             return
 
         profile = build_worker_profile(session.worker_runtime, agent_path=session.worker_path)
-        await node.inject_event(f"[SYSTEM] Worker loaded.{profile}")
+
+        # Append available trigger info so the queen knows what's schedulable
+        trigger_lines = ""
+        if session.available_triggers:
+            parts = []
+            for t in session.available_triggers.values():
+                cfg = t.trigger_config
+                detail = cfg.get("cron") or f"every {cfg.get('interval_minutes', '?')} min"
+                task_info = f' -> task: "{t.task}"' if t.task else " (no task configured)"
+                parts.append(f"  - {t.id} ({t.trigger_type}: {detail}){task_info}")
+            trigger_lines = (
+                "\n\nAvailable triggers (inactive — use set_trigger to activate):\n"
+                + "\n".join(parts)
+            )
+
+        await node.inject_event(f"[SYSTEM] Worker loaded.{profile}{trigger_lines}")
 
     async def _emit_worker_loaded(self, session: Session) -> None:
         """Publish a WORKER_LOADED event so the frontend can update."""
@@ -941,9 +852,35 @@ class SessionManager:
 
         await node.inject_event(
             "[SYSTEM] Worker unloaded. You are now operating independently. "
-            "Handle all tasks directly using your coding tools."
+            "Design or build the agent to solve the user's problem "
+            "according to your current phase."
         )
 
+    async def _emit_trigger_events(
+        self,
+        session: Session,
+        kind: str,
+        triggers: dict[str, TriggerDefinition],
+    ) -> None:
+        """Emit TRIGGER_AVAILABLE or TRIGGER_REMOVED events for each trigger."""
+        from framework.runtime.event_bus import AgentEvent, EventType
+
+        event_type = (
+            EventType.TRIGGER_AVAILABLE if kind == "available" else EventType.TRIGGER_REMOVED
+        )
+        for t in triggers.values():
+            await session.event_bus.publish(
+                AgentEvent(
+                    type=event_type,
+                    stream_id="queen",
+                    data={
+                        "trigger_id": t.id,
+                        "trigger_type": t.trigger_type,
+                        "trigger_config": t.trigger_config,
+                    },
+                )
+            )
+
     async def revive_queen(self, session: Session, initial_prompt: str | None = None) -> None:
         """Revive a dead queen executor on an existing session.
 
@@ -1015,13 +952,19 @@ class SessionManager:
         # Check whether any message part files are actually present
         has_messages = False
         try:
-            for node_dir in convs_dir.iterdir():
-                if not node_dir.is_dir():
-                    continue
-                parts_dir = node_dir / "parts"
-                if parts_dir.exists() and any(f.suffix == ".json" for f in parts_dir.iterdir()):
-                    has_messages = True
-                    break
+            # Flat layout: conversations/parts/*.json
+            flat_parts = convs_dir / "parts"
+            if flat_parts.exists() and any(f.suffix == ".json" for f in flat_parts.iterdir()):
+                has_messages = True
+            else:
+                # Node-based layout: conversations/<node_id>/parts/*.json
+                for node_dir in convs_dir.iterdir():
+                    if not node_dir.is_dir() or node_dir.name == "parts":
+                        continue
+                    parts_dir = node_dir / "parts"
+                    if parts_dir.exists() and any(f.suffix == ".json" for f in parts_dir.iterdir()):
+                        has_messages = True
+                        break
         except OSError:
             pass
 
@@ -1098,21 +1041,27 @@ class SessionManager:
             if convs_dir.exists():
                 try:
                     all_parts: list[dict] = []
-                    for node_dir in convs_dir.iterdir():
-                        if not node_dir.is_dir():
-                            continue
-                        parts_dir = node_dir / "parts"
+
+                    def _collect_parts(parts_dir: Path, _dest: list[dict] = all_parts) -> None:
                         if not parts_dir.exists():
-                            continue
+                            return
                         for part_file in sorted(parts_dir.iterdir()):
                             if part_file.suffix != ".json":
                                 continue
                             try:
                                 part = json.loads(part_file.read_text(encoding="utf-8"))
                                 part.setdefault("created_at", part_file.stat().st_mtime)
-                                all_parts.append(part)
+                                _dest.append(part)
                             except (json.JSONDecodeError, OSError):
                                 continue
+
+                    # Flat layout: conversations/parts/*.json
+                    _collect_parts(convs_dir / "parts")
+                    # Node-based layout: conversations/<node_id>/parts/*.json
+                    for node_dir in convs_dir.iterdir():
+                        if not node_dir.is_dir() or node_dir.name == "parts":
+                            continue
+                        _collect_parts(node_dir / "parts")
                     # Filter to client-facing messages only
                     client_msgs = [
                         p

core/framework/server/tests/test_api.py

@@ -16,6 +16,9 @@ from aiohttp.test_utils import TestClient, TestServer
 from framework.server.app import create_app
 from framework.server.session_manager import Session
 
+REPO_ROOT = Path(__file__).resolve().parents[4]
+EXAMPLE_AGENT_PATH = REPO_ROOT / "examples" / "templates" / "deep_research_agent"
+
 # ---------------------------------------------------------------------------
 # Mock helpers
 # ---------------------------------------------------------------------------
@@ -37,6 +40,7 @@ class MockNodeSpec:
     client_facing: bool = False
     success_criteria: str | None = None
     system_prompt: str | None = None
+    sub_agents: list = field(default_factory=list)
 
 
 @dataclass
@@ -67,6 +71,7 @@ class MockEntryPoint:
     name: str = "Default"
     entry_node: str = "start"
     trigger_type: str = "manual"
+    trigger_config: dict = field(default_factory=dict)
 
 
 @dataclass
@@ -130,6 +135,9 @@ class MockRuntime:
     def get_stats(self):
         return {"running": True, "executions": 1}
 
+    def get_timer_next_fire_in(self, ep_id):
+        return None
+
 
 class MockAgentInfo:
     name: str = "test_agent"
@@ -342,6 +350,35 @@ class TestHealth:
 
 
 class TestSessionCRUD:
+    @pytest.mark.asyncio
+    async def test_create_session_with_worker_forwards_session_id(self):
+        app = create_app()
+        manager = app["manager"]
+        manager.create_session_with_worker = AsyncMock(
+            return_value=_make_session(agent_id="my-custom-session")
+        )
+
+        async with TestClient(TestServer(app)) as client:
+            resp = await client.post(
+                "/api/sessions",
+                json={
+                    "session_id": "my-custom-session",
+                    "agent_path": str(EXAMPLE_AGENT_PATH),
+                },
+            )
+            data = await resp.json()
+
+        assert resp.status == 201
+        assert data["session_id"] == "my-custom-session"
+        manager.create_session_with_worker.assert_awaited_once_with(
+            str(EXAMPLE_AGENT_PATH.resolve()),
+            agent_id=None,
+            session_id="my-custom-session",
+            model=None,
+            initial_prompt=None,
+            queen_resume_from=None,
+        )
+
     @pytest.mark.asyncio
     async def test_list_sessions_empty(self):
         app = create_app()
@@ -1556,3 +1593,106 @@ class TestErrorMiddleware:
         async with TestClient(TestServer(app)) as client:
             resp = await client.get("/api/nonexistent")
             assert resp.status == 404
+
+
+class TestCleanupStaleActiveSessions:
+    """Tests for _cleanup_stale_active_sessions with two-layer protection."""
+
+    def _make_manager(self):
+        from framework.server.session_manager import SessionManager
+
+        return SessionManager()
+
+    def _write_state(self, session_dir: Path, status: str, pid: int | None = None) -> None:
+        session_dir.mkdir(parents=True, exist_ok=True)
+        state: dict = {"status": status, "session_id": session_dir.name}
+        if pid is not None:
+            state["pid"] = pid
+        (session_dir / "state.json").write_text(json.dumps(state))
+
+    def _read_state(self, session_dir: Path) -> dict:
+        return json.loads((session_dir / "state.json").read_text())
+
+    def test_stale_session_is_cancelled(self, tmp_path, monkeypatch):
+        """Truly stale active sessions (no live tracking, no PID) get cancelled."""
+        monkeypatch.setattr(Path, "home", lambda: tmp_path)
+        agent_path = Path("my_agent")
+        sessions_dir = tmp_path / ".hive" / "agents" / "my_agent" / "sessions"
+        session_dir = sessions_dir / "session_stale_001"
+
+        self._write_state(session_dir, "active")
+
+        mgr = self._make_manager()
+        mgr._cleanup_stale_active_sessions(agent_path)
+
+        state = self._read_state(session_dir)
+        assert state["status"] == "cancelled"
+        assert "Stale session" in state["result"]["error"]
+
+    def test_live_in_memory_session_is_skipped(self, tmp_path, monkeypatch):
+        """Sessions tracked in self._sessions must NOT be cancelled (Layer 1)."""
+        monkeypatch.setattr(Path, "home", lambda: tmp_path)
+        agent_path = Path("my_agent")
+        sessions_dir = tmp_path / ".hive" / "agents" / "my_agent" / "sessions"
+        session_dir = sessions_dir / "session_live_002"
+
+        self._write_state(session_dir, "active")
+
+        mgr = self._make_manager()
+        # Simulate a live session in the manager's in-memory map
+        mgr._sessions["session_live_002"] = MagicMock()
+
+        mgr._cleanup_stale_active_sessions(agent_path)
+
+        state = self._read_state(session_dir)
+        assert state["status"] == "active", "Live in-memory session should NOT be cancelled"
+
+    def test_session_with_live_pid_is_skipped(self, tmp_path, monkeypatch):
+        """Sessions whose owning PID is still alive must NOT be cancelled (Layer 2)."""
+        import os
+
+        monkeypatch.setattr(Path, "home", lambda: tmp_path)
+        agent_path = Path("my_agent")
+        sessions_dir = tmp_path / ".hive" / "agents" / "my_agent" / "sessions"
+        session_dir = sessions_dir / "session_pid_003"
+
+        # Use the current process PID — guaranteed to be alive
+        self._write_state(session_dir, "active", pid=os.getpid())
+
+        mgr = self._make_manager()
+        mgr._cleanup_stale_active_sessions(agent_path)
+
+        state = self._read_state(session_dir)
+        assert state["status"] == "active", "Session with live PID should NOT be cancelled"
+
+    def test_session_with_dead_pid_is_cancelled(self, tmp_path, monkeypatch):
+        """Sessions whose owning PID is dead should be cancelled."""
+        monkeypatch.setattr(Path, "home", lambda: tmp_path)
+        agent_path = Path("my_agent")
+        sessions_dir = tmp_path / ".hive" / "agents" / "my_agent" / "sessions"
+        session_dir = sessions_dir / "session_dead_004"
+
+        # Use a PID that is almost certainly not running
+        self._write_state(session_dir, "active", pid=999999999)
+
+        mgr = self._make_manager()
+        mgr._cleanup_stale_active_sessions(agent_path)
+
+        state = self._read_state(session_dir)
+        assert state["status"] == "cancelled"
+        assert "Stale session" in state["result"]["error"]
+
+    def test_paused_session_is_never_touched(self, tmp_path, monkeypatch):
+        """Paused sessions should remain intact regardless of PID or tracking."""
+        monkeypatch.setattr(Path, "home", lambda: tmp_path)
+        agent_path = Path("my_agent")
+        sessions_dir = tmp_path / ".hive" / "agents" / "my_agent" / "sessions"
+        session_dir = sessions_dir / "session_paused_005"
+
+        self._write_state(session_dir, "paused")
+
+        mgr = self._make_manager()
+        mgr._cleanup_stale_active_sessions(agent_path)
+
+        state = self._read_state(session_dir)
+        assert state["status"] == "paused", "Paused sessions must remain untouched"

core/framework/storage/state_writer.py

@@ -1,179 +0,0 @@
-"""
-State Writer - Dual-write adapter for migration period.
-
-Writes execution state to both old (Run/RunSummary) and new (state.json) formats
-to maintain backward compatibility during the transition period.
-"""
-
-import logging
-import os
-from datetime import datetime
-
-from framework.schemas.run import Problem, Run, RunMetrics, RunStatus
-from framework.schemas.session_state import SessionState, SessionStatus
-from framework.storage.concurrent import ConcurrentStorage
-from framework.storage.session_store import SessionStore
-
-logger = logging.getLogger(__name__)
-
-
-class StateWriter:
-    """
-    Writes execution state to both old and new formats during migration.
-
-    During the dual-write phase:
-    - New format (state.json) is written when USE_UNIFIED_SESSIONS=true
-    - Old format (Run/RunSummary) is always written for backward compatibility
-    """
-
-    def __init__(self, old_storage: ConcurrentStorage, session_store: SessionStore):
-        """
-        Initialize state writer.
-
-        Args:
-            old_storage: ConcurrentStorage for old format (runs/, summaries/)
-            session_store: SessionStore for new format (sessions/*/state.json)
-        """
-        self.old = old_storage
-        self.new = session_store
-        self.dual_write_enabled = os.getenv("USE_UNIFIED_SESSIONS", "false").lower() == "true"
-
-    async def write_execution_state(
-        self,
-        session_id: str,
-        state: SessionState,
-    ) -> None:
-        """
-        Write execution state to both old and new formats.
-
-        Args:
-            session_id: Session ID
-            state: SessionState to write
-        """
-        # Write to new format if enabled
-        if self.dual_write_enabled:
-            try:
-                await self.new.write_state(session_id, state)
-                logger.debug(f"Wrote state.json for session {session_id}")
-            except Exception as e:
-                logger.error(f"Failed to write state.json for {session_id}: {e}")
-                # Don't fail - old format is still written
-
-        # Always write to old format for backward compatibility
-        try:
-            run = self._convert_to_run(state)
-            await self.old.save_run(run)
-            logger.debug(f"Wrote Run object for session {session_id}")
-        except Exception as e:
-            logger.error(f"Failed to write Run object for {session_id}: {e}")
-            # This is more critical - reraise if old format fails
-            raise
-
-    def _convert_to_run(self, state: SessionState) -> Run:
-        """
-        Convert SessionState to legacy Run object.
-
-        Args:
-            state: SessionState to convert
-
-        Returns:
-            Run object
-        """
-        # Map SessionStatus to RunStatus
-        status_mapping = {
-            SessionStatus.ACTIVE: RunStatus.RUNNING,
-            SessionStatus.PAUSED: RunStatus.RUNNING,  # Paused is still "running" in old format
-            SessionStatus.COMPLETED: RunStatus.COMPLETED,
-            SessionStatus.FAILED: RunStatus.FAILED,
-            SessionStatus.CANCELLED: RunStatus.CANCELLED,
-        }
-        run_status = status_mapping.get(state.status, RunStatus.FAILED)
-
-        # Convert timestamps
-        started_at = datetime.fromisoformat(state.timestamps.started_at)
-        completed_at = (
-            datetime.fromisoformat(state.timestamps.completed_at)
-            if state.timestamps.completed_at
-            else None
-        )
-
-        # Build RunMetrics
-        metrics = RunMetrics(
-            total_decisions=state.metrics.decision_count,
-            successful_decisions=state.metrics.decision_count
-            - len(state.progress.nodes_with_failures),  # Approximate
-            failed_decisions=len(state.progress.nodes_with_failures),
-            total_tokens=state.metrics.total_input_tokens + state.metrics.total_output_tokens,
-            total_latency_ms=state.progress.total_latency_ms,
-            nodes_executed=state.metrics.nodes_executed,
-            edges_traversed=state.metrics.edges_traversed,
-        )
-
-        # Convert problems (SessionState stores as dicts, Run expects Problem objects)
-        problems = []
-        for p_dict in state.problems:
-            # Handle both old Problem objects and new dict format
-            if isinstance(p_dict, dict):
-                problems.append(Problem(**p_dict))
-            else:
-                problems.append(p_dict)
-
-        # Convert decisions (SessionState stores as dicts, Run expects Decision objects)
-        from framework.schemas.decision import Decision
-
-        decisions = []
-        for d_dict in state.decisions:
-            # Handle both old Decision objects and new dict format
-            if isinstance(d_dict, dict):
-                try:
-                    decisions.append(Decision(**d_dict))
-                except Exception:
-                    # Skip invalid decisions
-                    continue
-            else:
-                decisions.append(d_dict)
-
-        # Create Run object
-        run = Run(
-            id=state.session_id,  # Use session_id as run_id
-            goal_id=state.goal_id,
-            started_at=started_at,
-            status=run_status,
-            completed_at=completed_at,
-            decisions=decisions,
-            problems=problems,
-            metrics=metrics,
-            goal_description="",  # Not stored in SessionState
-            input_data=state.input_data,
-            output_data=state.result.output,
-        )
-
-        return run
-
-    async def read_state(
-        self,
-        session_id: str,
-        prefer_new: bool = True,
-    ) -> SessionState | None:
-        """
-        Read execution state from either format.
-
-        Args:
-            session_id: Session ID
-            prefer_new: If True, try new format first (default)
-
-        Returns:
-            SessionState or None if not found
-        """
-        if prefer_new:
-            # Try new format first
-            state = await self.new.read_state(session_id)
-            if state:
-                return state
-
-        # Fall back to old format
-        run = await self.old.load_run(session_id)
-        if run:
-            return SessionState.from_legacy_run(run, session_id)
-
-        return None

core/framework/tools/queen_memory_tools.py

@@ -0,0 +1,38 @@
+"""Tool for the queen to write to her episodic memory.
+
+The queen can consciously record significant moments during a session — like
+writing in a diary. Semantic memory (MEMORY.md) is updated automatically at
+session end and is never written by the queen directly.
+"""
+
+from __future__ import annotations
+
+from typing import TYPE_CHECKING
+
+if TYPE_CHECKING:
+    from framework.runner.tool_registry import ToolRegistry
+
+
+def write_to_diary(entry: str) -> str:
+    """Write a prose entry to today's episodic memory.
+
+    Use this when something significant just happened: a pipeline went live, the
+    user shared an important preference, a goal was achieved or abandoned, or
+    you want to record something that should be remembered across sessions.
+
+    Write in first person, as you would in a private diary. Be specific — what
+    happened, how the user responded, what it means going forward. One or two
+    paragraphs is enough.
+
+    You do not need to include a timestamp or date heading; those are added
+    automatically.
+    """
+    from framework.agents.queen.queen_memory import append_episodic_entry
+
+    append_episodic_entry(entry)
+    return "Diary entry recorded."
+
+
+def register_queen_memory_tools(registry: ToolRegistry) -> None:
+    """Register the episodic memory tool into the queen's tool registry."""
+    registry.register_function(write_to_diary)

core/framework/tools/session_graph_tools.py

@@ -1,6 +1,6 @@
 """Graph lifecycle tools for multi-graph sessions.
 
-These tools allow an agent (e.g. hive_coder) to load, unload, start,
+These tools allow an agent (e.g. queen) to load, unload, start,
 restart, and query other agent graphs within the same runtime session.
 
 Usage::
@@ -78,19 +78,6 @@ def register_graph_tools(registry: ToolRegistry, runtime: AgentRuntime) -> int:
                 isolation_level="shared",
             )
 
-        # Async entry points
-        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=graph_id,
             graph=runner.graph,

core/framework/tools/worker_monitoring_tools.py

@@ -1,20 +1,17 @@
-"""Worker monitoring tools for the Health Judge and Queen triage agents.
+"""Worker monitoring tools for Queen triage agents.
 
 Three tools are registered by ``register_worker_monitoring_tools()``:
 
 - ``get_worker_health_summary`` — reads the worker's session log files and
   returns a compact health snapshot (recent verdicts, step count, timing).
   session_id is optional: if omitted, the most recent active session is
-  auto-discovered from storage. No agent-side configuration required.
-  Used by the Health Judge on every timer tick.
+  auto-discovered from storage.
 
 - ``emit_escalation_ticket`` — validates and publishes an EscalationTicket
   to the shared EventBus as a WORKER_ESCALATION_TICKET event.
-  Used by the Health Judge when it decides to escalate.
 
 - ``notify_operator`` — emits a QUEEN_INTERVENTION_REQUESTED event so the TUI
   can surface a non-disruptive operator notification.
-  Used by the Queen's ticket_triage_node when it decides to intervene.
 
 Usage::
 
@@ -45,8 +42,9 @@ def register_worker_monitoring_tools(
     registry: ToolRegistry,
     event_bus: EventBus,
     storage_path: Path,
-    stream_id: str = "judge",
+    stream_id: str = "monitoring",
     worker_graph_id: str | None = None,
+    default_session_id: str | None = None,
 ) -> int:
     """Register worker monitoring tools bound to *event_bus* and *storage_path*.
 
@@ -55,9 +53,15 @@ def register_worker_monitoring_tools(
         event_bus: The shared EventBus for the worker runtime.
         storage_path: Root storage path of the worker runtime
                       (e.g. ``~/.hive/agents/{name}``).
-        stream_id: Stream ID used when emitting events; defaults to judge's stream.
+        stream_id: Stream ID used when emitting events.
         worker_graph_id: The primary worker graph's ID. Included in health summary
                          so the judge can populate ticket identity fields accurately.
+        default_session_id: When set, ``get_worker_health_summary`` uses this
+                            session ID as the default instead of auto-discovering
+                            the most-recent-by-mtime session. Callers should pass
+                            the queen's own session ID so that after a cold-restore
+                            the monitoring tool reads the correct worker session
+                            rather than a stale orphaned one.
 
     Returns:
         Number of tools registered.
@@ -65,7 +69,7 @@ def register_worker_monitoring_tools(
     from framework.llm.provider import Tool
 
     storage_path = Path(storage_path)
-    # Derive agent identity from storage path so the judge can fill ticket fields.
+    # Derive agent identity from storage path for ticket fields.
     # storage_path is ~/.hive/agents/{agent_name} — the name is the last component.
     _worker_agent_id: str = storage_path.name
     _worker_graph_id: str = worker_graph_id or storage_path.name
@@ -100,23 +104,29 @@ def register_worker_monitoring_tools(
             if not sessions_dir.exists():
                 return json.dumps({"error": "No sessions found — worker has not started yet"})
 
-            candidates = [
-                d for d in sessions_dir.iterdir() if d.is_dir() and (d / "state.json").exists()
-            ]
-            if not candidates:
-                return json.dumps({"error": "No sessions found — worker has not started yet"})
+            # Prefer the queen's own session ID (set at registration time) over
+            # mtime-based discovery, which can pick a stale orphaned session after
+            # a cold-restore when a newer-but-empty session directory exists.
+            if default_session_id and (sessions_dir / default_session_id).is_dir():
+                session_id = default_session_id
+            else:
+                candidates = [
+                    d for d in sessions_dir.iterdir() if d.is_dir() and (d / "state.json").exists()
+                ]
+                if not candidates:
+                    return json.dumps({"error": "No sessions found — worker has not started yet"})
 
-            def _sort_key(d: Path):
-                try:
-                    state = json.loads((d / "state.json").read_text(encoding="utf-8"))
-                    # in_progress/running sorts before completed/failed
-                    priority = 0 if state.get("status", "") in ("in_progress", "running") else 1
-                    return (priority, -d.stat().st_mtime)
-                except Exception:
-                    return (2, 0)
+                def _sort_key(d: Path):
+                    try:
+                        state = json.loads((d / "state.json").read_text(encoding="utf-8"))
+                        # in_progress/running sorts before completed/failed
+                        priority = 0 if state.get("status", "") in ("in_progress", "running") else 1
+                        return (priority, -d.stat().st_mtime)
+                    except Exception:
+                        return (2, 0)
 
-            candidates.sort(key=_sort_key)
-            session_id = candidates[0].name
+                candidates.sort(key=_sort_key)
+                session_id = candidates[0].name
 
         # Resolve log paths
         session_dir = storage_path / "sessions" / session_id
@@ -201,10 +211,9 @@ def register_worker_monitoring_tools(
         description=(
             "Read the worker agent's execution logs and return a compact health snapshot. "
             "Returns worker_agent_id and worker_graph_id (use these for ticket identity fields), "
-            "recent judge verdicts, step count, time since last step, and "
+            "recent verdicts, step count, time since last step, and "
             "a snippet of the most recent LLM output. "
-            "session_id is optional — omit it to auto-discover the most recent active session. "
-            "Use this on every health check to observe trends."
+            "session_id is optional — omit it to auto-discover the most recent active session."
         ),
         parameters={
             "type": "object",
@@ -241,8 +250,7 @@ def register_worker_monitoring_tools(
         """Validate and publish an EscalationTicket to the shared EventBus.
 
         ticket_json must be a JSON string containing all required EscalationTicket
-        fields. The ticket is validated before publishing — this ensures the judge
-        has genuinely filled out all required evidence fields.
+        fields. The ticket is validated before publishing.
 
         Returns a confirmation JSON with the ticket_id on success, or an error.
         """
@@ -257,7 +265,7 @@ def register_worker_monitoring_tools(
         try:
             await event_bus.emit_worker_escalation_ticket(
                 stream_id=stream_id,
-                node_id="judge",
+                node_id="monitoring",
                 ticket=ticket.model_dump(),
             )
             logger.info(
@@ -280,7 +288,6 @@ def register_worker_monitoring_tools(
         name="emit_escalation_ticket",
         description=(
             "Validate and publish a structured EscalationTicket to the shared EventBus. "
-            "The Queen's ticket_receiver entry point will fire and triage the ticket. "
             "ticket_json must be a JSON string with all required EscalationTicket fields: "
             "worker_agent_id, worker_session_id, worker_node_id, worker_graph_id, "
             "severity (low/medium/high/critical), cause, judge_reasoning, suggested_action, "

core/framework/tui/screens/__init__.py

@@ -1,13 +0,0 @@
-"""TUI screens package."""
-
-from .account_selection import AccountSelectionScreen
-from .add_local_credential import AddLocalCredentialScreen
-from .agent_picker import AgentPickerScreen
-from .credential_setup import CredentialSetupScreen
-
-__all__ = [
-    "AccountSelectionScreen",
-    "AddLocalCredentialScreen",
-    "AgentPickerScreen",
-    "CredentialSetupScreen",
-]

core/framework/tui/screens/account_selection.py

@@ -1,111 +0,0 @@
-"""Account selection ModalScreen for picking a connected account before agent start."""
-
-from __future__ import annotations
-
-from rich.text import Text
-from textual.app import ComposeResult
-from textual.binding import Binding
-from textual.containers import Vertical
-from textual.screen import ModalScreen
-from textual.widgets import Label, OptionList
-from textual.widgets._option_list import Option
-
-
-class AccountSelectionScreen(ModalScreen[dict | None]):
-    """Modal screen showing connected accounts for pre-run selection.
-
-    Returns the selected account dict, or None if dismissed.
-    """
-
-    SCOPED_CSS = False
-
-    BINDINGS = [
-        Binding("escape", "dismiss_picker", "Cancel"),
-    ]
-
-    DEFAULT_CSS = """
-    AccountSelectionScreen {
-        align: center middle;
-    }
-    #acct-container {
-        width: 70%;
-        max-width: 80;
-        height: 60%;
-        background: $surface;
-        border: heavy $primary;
-        padding: 1 2;
-    }
-    #acct-title {
-        text-align: center;
-        text-style: bold;
-        width: 100%;
-        color: $text;
-    }
-    #acct-subtitle {
-        text-align: center;
-        width: 100%;
-        margin-bottom: 1;
-    }
-    #acct-footer {
-        text-align: center;
-        width: 100%;
-        margin-top: 1;
-    }
-    """
-
-    def __init__(self, accounts: list[dict]) -> None:
-        super().__init__()
-        self._accounts = accounts
-
-    def compose(self) -> ComposeResult:
-        n = len(self._accounts)
-        with Vertical(id="acct-container"):
-            yield Label("Select Account to Test", id="acct-title")
-            yield Label(
-                f"[dim]{n} connected account{'s' if n != 1 else ''}[/dim]",
-                id="acct-subtitle",
-            )
-            option_list = OptionList(id="acct-list")
-            # Group: Aden accounts first, then local
-            aden = [a for a in self._accounts if a.get("source") != "local"]
-            local = [a for a in self._accounts if a.get("source") == "local"]
-            ordered = aden + local
-            for i, acct in enumerate(ordered):
-                provider = acct.get("provider", "unknown")
-                alias = acct.get("alias", "unknown")
-                identity = acct.get("identity", {})
-                source = acct.get("source", "aden")
-                # Build identity label: prefer email, then username/workspace
-                identity_label = (
-                    identity.get("email")
-                    or identity.get("username")
-                    or identity.get("workspace")
-                    or ""
-                )
-                label = Text()
-                label.append(f"{provider}/", style="bold")
-                label.append(alias, style="bold cyan")
-                if source == "local":
-                    label.append("  [local]", style="dim yellow")
-                if identity_label:
-                    label.append(f"  ({identity_label})", style="dim")
-                option_list.add_option(Option(label, id=f"acct-{i}"))
-            # Keep ordered list for index lookups
-            self._accounts = ordered
-            yield option_list
-            yield Label(
-                "[dim]Enter[/dim] Select  [dim]Esc[/dim] Cancel",
-                id="acct-footer",
-            )
-
-    def on_mount(self) -> None:
-        ol = self.query_one("#acct-list", OptionList)
-        ol.styles.height = "1fr"
-
-    def on_option_list_option_selected(self, event: OptionList.OptionSelected) -> None:
-        idx = event.option_index
-        if 0 <= idx < len(self._accounts):
-            self.dismiss(self._accounts[idx])
-
-    def action_dismiss_picker(self) -> None:
-        self.dismiss(None)

core/framework/tui/screens/add_local_credential.py

@@ -1,244 +0,0 @@
-"""Add Local Credential ModalScreen for storing named local API key accounts."""
-
-from __future__ import annotations
-
-from textual.app import ComposeResult
-from textual.binding import Binding
-from textual.containers import Vertical, VerticalScroll
-from textual.screen import ModalScreen
-from textual.widgets import Button, Input, Label, OptionList
-from textual.widgets._option_list import Option
-
-
-class AddLocalCredentialScreen(ModalScreen[dict | None]):
-    """Modal screen for adding a named local API key credential.
-
-    Phase 1: Pick credential type from list.
-    Phase 2: Enter alias + API key, run health check, save.
-
-    Returns a dict with credential_id, alias, and identity on success, or None on cancel.
-    """
-
-    BINDINGS = [
-        Binding("escape", "dismiss_screen", "Cancel"),
-    ]
-
-    DEFAULT_CSS = """
-    AddLocalCredentialScreen {
-        align: center middle;
-    }
-    #alc-container {
-        width: 80%;
-        max-width: 90;
-        height: 80%;
-        background: $surface;
-        border: heavy $primary;
-        padding: 1 2;
-    }
-    #alc-title {
-        text-align: center;
-        text-style: bold;
-        width: 100%;
-        color: $text;
-    }
-    #alc-subtitle {
-        text-align: center;
-        width: 100%;
-        margin-bottom: 1;
-    }
-    #alc-type-list {
-        height: 1fr;
-    }
-    #alc-form {
-        height: 1fr;
-    }
-    .alc-field {
-        margin-bottom: 1;
-        height: auto;
-    }
-    .alc-field Label {
-        margin-bottom: 0;
-    }
-    #alc-status {
-        width: 100%;
-        height: auto;
-        margin-top: 1;
-        padding: 1;
-        background: $panel;
-    }
-    .alc-buttons {
-        height: auto;
-        margin-top: 1;
-        align: center middle;
-    }
-    .alc-buttons Button {
-        margin: 0 1;
-    }
-    #alc-footer {
-        text-align: center;
-        width: 100%;
-        margin-top: 1;
-    }
-    """
-
-    def __init__(self) -> None:
-        super().__init__()
-        # Load credential specs that support direct API keys
-        self._specs: list[tuple[str, object]] = self._load_specs()
-        # Selected credential spec (set in phase 2)
-        self._selected_id: str = ""
-        self._selected_spec: object = None
-        self._phase: int = 1  # 1 = type selection, 2 = form
-
-    @staticmethod
-    def _load_specs() -> list[tuple[str, object]]:
-        """Return (credential_id, spec) pairs for direct-API-key credentials."""
-        try:
-            from aden_tools.credentials import CREDENTIAL_SPECS
-
-            return [
-                (cid, spec)
-                for cid, spec in CREDENTIAL_SPECS.items()
-                if getattr(spec, "direct_api_key_supported", False)
-            ]
-        except Exception:
-            return []
-
-    # ------------------------------------------------------------------
-    # Compose
-    # ------------------------------------------------------------------
-
-    def compose(self) -> ComposeResult:
-        with Vertical(id="alc-container"):
-            yield Label("Add Local Credential", id="alc-title")
-            yield Label("[dim]Store a named API key account[/dim]", id="alc-subtitle")
-            # Phase 1: type selection
-            option_list = OptionList(id="alc-type-list")
-            for cid, spec in self._specs:
-                description = getattr(spec, "description", cid)
-                option_list.add_option(Option(f"{cid}  [dim]{description}[/dim]", id=f"type-{cid}"))
-            yield option_list
-            # Phase 2: form (hidden initially)
-            with VerticalScroll(id="alc-form"):
-                with Vertical(classes="alc-field"):
-                    yield Label("[bold]Alias[/bold]  [dim](e.g. work, personal)[/dim]")
-                    yield Input(value="default", id="alc-alias")
-                with Vertical(classes="alc-field"):
-                    yield Label("[bold]API Key[/bold]")
-                    yield Input(placeholder="Paste API key...", password=True, id="alc-key")
-                yield Label("", id="alc-status")
-                with Vertical(classes="alc-buttons"):
-                    yield Button("Test & Save", variant="primary", id="btn-save")
-                    yield Button("Back", variant="default", id="btn-back")
-            yield Label(
-                "[dim]Enter[/dim] Select  [dim]Esc[/dim] Cancel",
-                id="alc-footer",
-            )
-
-    def on_mount(self) -> None:
-        self._show_phase(1)
-
-    # ------------------------------------------------------------------
-    # Phase switching
-    # ------------------------------------------------------------------
-
-    def _show_phase(self, phase: int) -> None:
-        self._phase = phase
-        type_list = self.query_one("#alc-type-list", OptionList)
-        form = self.query_one("#alc-form", VerticalScroll)
-        if phase == 1:
-            type_list.display = True
-            form.display = False
-            subtitle = self.query_one("#alc-subtitle", Label)
-            subtitle.update("[dim]Select the credential type to add[/dim]")
-        else:
-            type_list.display = False
-            form.display = True
-            spec = self._selected_spec
-            description = (
-                getattr(spec, "description", self._selected_id) if spec else self._selected_id
-            )
-            subtitle = self.query_one("#alc-subtitle", Label)
-            subtitle.update(f"[dim]{self._selected_id}[/dim]  {description}")
-            self._clear_status()
-            # Focus the alias input
-            self.query_one("#alc-alias", Input).focus()
-
-    # ------------------------------------------------------------------
-    # Event handlers
-    # ------------------------------------------------------------------
-
-    def on_option_list_option_selected(self, event: OptionList.OptionSelected) -> None:
-        if self._phase != 1:
-            return
-        option_id = event.option.id or ""
-        if option_id.startswith("type-"):
-            cid = option_id[5:]  # strip "type-" prefix
-            self._selected_id = cid
-            self._selected_spec = next(
-                (spec for spec_id, spec in self._specs if spec_id == cid), None
-            )
-            self._show_phase(2)
-
-    def on_button_pressed(self, event: Button.Pressed) -> None:
-        if event.button.id == "btn-save":
-            self._do_save()
-        elif event.button.id == "btn-back":
-            self._show_phase(1)
-
-    # ------------------------------------------------------------------
-    # Save logic
-    # ------------------------------------------------------------------
-
-    def _do_save(self) -> None:
-        alias = self.query_one("#alc-alias", Input).value.strip() or "default"
-        api_key = self.query_one("#alc-key", Input).value.strip()
-
-        if not api_key:
-            self._set_status("[red]API key cannot be empty.[/red]")
-            return
-
-        self._set_status("[dim]Running health check...[/dim]")
-        # Disable save button while running
-        btn = self.query_one("#btn-save", Button)
-        btn.disabled = True
-
-        try:
-            from framework.credentials.local.registry import LocalCredentialRegistry
-
-            registry = LocalCredentialRegistry.default()
-            info, health_result = registry.save_account(
-                credential_id=self._selected_id,
-                alias=alias,
-                api_key=api_key,
-                run_health_check=True,
-            )
-
-            if health_result is not None and not health_result.valid:
-                self._set_status(
-                    f"[yellow]Saved with failed health check:[/yellow] {health_result.message}\n"
-                    "[dim]You can re-validate later via validate_credential().[/dim]"
-                )
-            else:
-                identity = info.identity.to_dict()
-                identity_str = ""
-                if identity:
-                    parts = [f"{k}: {v}" for k, v in identity.items() if v]
-                    identity_str = "  " + ", ".join(parts) if parts else ""
-                self._set_status(f"[green]Saved:[/green] {info.storage_id}{identity_str}")
-                # Dismiss with result so callers can react
-                self.set_timer(1.0, lambda: self.dismiss(info.to_account_dict()))
-                return
-        except Exception as e:
-            self._set_status(f"[red]Error:[/red] {e}")
-        finally:
-            btn.disabled = False
-
-    def _set_status(self, markup: str) -> None:
-        self.query_one("#alc-status", Label).update(markup)
-
-    def _clear_status(self) -> None:
-        self.query_one("#alc-status", Label).update("")
-
-    def action_dismiss_screen(self) -> None:
-        self.dismiss(None)

core/framework/tui/screens/agent_picker.py

@@ -1,362 +0,0 @@
-"""Agent picker ModalScreen for selecting agents within the TUI."""
-
-from __future__ import annotations
-
-import json
-from dataclasses import dataclass, field
-from enum import Enum
-from pathlib import Path
-
-from rich.console import Group
-from rich.text import Text
-from textual.app import ComposeResult
-from textual.binding import Binding
-from textual.containers import Vertical
-from textual.screen import ModalScreen
-from textual.widgets import Label, OptionList, TabbedContent, TabPane
-from textual.widgets._option_list import Option
-
-
-class GetStartedAction(Enum):
-    """Actions available in the Get Started tab."""
-
-    RUN_EXAMPLES = "run_examples"
-    RUN_EXISTING = "run_existing"
-    BUILD_EDIT = "build_edit"
-
-
-@dataclass
-class AgentEntry:
-    """Lightweight agent metadata for the picker."""
-
-    path: Path
-    name: str
-    description: str
-    category: str
-    session_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_name: str) -> str | None:
-    """Return the most recent updated_at timestamp across all sessions."""
-    sessions_dir = Path.home() / ".hive" / "agents" / agent_name / "sessions"
-    if not sessions_dir.exists():
-        return None
-    latest: str | None = None
-    for session_dir in sessions_dir.iterdir():
-        if not session_dir.is_dir() or not session_dir.name.startswith("session_"):
-            continue
-        state_file = session_dir / "state.json"
-        if not state_file.exists():
-            continue
-        try:
-            data = json.loads(state_file.read_text(encoding="utf-8"))
-            ts = data.get("timestamps", {}).get("updated_at")
-            if ts and (latest is None or ts > latest):
-                latest = ts
-        except Exception:
-            continue
-    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 _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, []
-
-    # Try agent.py first — source of truth for nodes
-    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):
-                # Find `nodes = [...]` assignment
-                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
-
-    # Fall back to / supplement from agent.json
-    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("nodes", [])
-            if node_count == 0:
-                node_count = len(json_nodes)
-            # Tool count: use whichever source gave us nodes, but agent.json
-            # has the structured tool lists so prefer it for tool counting
-            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
-
-            # config.py is source of truth for name/description
-            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:
-                # config.py didn't provide values, fall back to agent.json
-                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),
-                    node_count=node_count,
-                    tool_count=tool_count,
-                    tags=tags,
-                    last_active=_get_last_active(path.name),
-                )
-            )
-        if entries:
-            groups[category] = entries
-
-    return groups
-
-
-def _render_agent_option(agent: AgentEntry) -> Group:
-    """Build a Rich renderable for a single agent option."""
-    # Line 1: name + session badge
-    line1 = Text()
-    line1.append(agent.name, style="bold")
-    if agent.session_count:
-        line1.append(f"  {agent.session_count} sessions", style="dim cyan")
-
-    # Line 2: description (word-wrapped by the widget)
-    desc = agent.description if agent.description else "No description"
-    line2 = Text(desc, style="dim")
-
-    # Line 3: stats chips
-    chips = Text()
-    if agent.node_count:
-        chips.append(f" {agent.node_count} nodes ", style="on dark_green white")
-        chips.append(" ")
-    if agent.tool_count:
-        chips.append(f" {agent.tool_count} tools ", style="on dark_blue white")
-        chips.append(" ")
-    for tag in agent.tags[:3]:
-        chips.append(f" {tag} ", style="on grey37 white")
-        chips.append(" ")
-
-    parts = [line1, line2]
-    if chips.plain.strip():
-        parts.append(chips)
-    return Group(*parts)
-
-
-def _render_get_started_option(title: str, description: str, icon: str = "→") -> Group:
-    """Build a Rich renderable for a Get Started option."""
-    line1 = Text()
-    line1.append(f"{icon} ", style="bold cyan")
-    line1.append(title, style="bold")
-    line2 = Text(description, style="dim")
-    return Group(line1, line2)
-
-
-class AgentPickerScreen(ModalScreen[str | None]):
-    """Modal screen showing available agents organized by tabbed categories.
-
-    Returns the selected agent path as a string, or None if dismissed.
-    For Get Started actions, returns a special prefix like "action:run_examples".
-    """
-
-    BINDINGS = [
-        Binding("escape", "dismiss_picker", "Cancel"),
-    ]
-
-    DEFAULT_CSS = """
-    AgentPickerScreen {
-        align: center middle;
-    }
-    #picker-container {
-        width: 90%;
-        max-width: 120;
-        height: 85%;
-        background: $surface;
-        border: heavy $primary;
-        padding: 1 2;
-    }
-    #picker-title {
-        text-align: center;
-        text-style: bold;
-        width: 100%;
-        color: $text;
-    }
-    #picker-subtitle {
-        text-align: center;
-        width: 100%;
-        margin-bottom: 1;
-    }
-    #picker-footer {
-        text-align: center;
-        width: 100%;
-        margin-top: 1;
-    }
-    TabPane {
-        padding: 0;
-    }
-    OptionList {
-        height: 1fr;
-    }
-    OptionList > .option-list--option {
-        padding: 1 2;
-    }
-    """
-
-    def __init__(
-        self,
-        agent_groups: dict[str, list[AgentEntry]],
-        show_get_started: bool = False,
-    ) -> None:
-        super().__init__()
-        self._groups = agent_groups
-        self._show_get_started = show_get_started
-        # Map (tab_id, option_index) -> AgentEntry
-        self._option_map: dict[str, dict[int, AgentEntry]] = {}
-
-    def compose(self) -> ComposeResult:
-        total = sum(len(v) for v in self._groups.values())
-        with Vertical(id="picker-container"):
-            yield Label("Hive Agent Launcher", id="picker-title")
-            yield Label(
-                f"[dim]{total} agents available[/dim]",
-                id="picker-subtitle",
-            )
-            with TabbedContent():
-                # Get Started tab (only on initial launch)
-                if self._show_get_started:
-                    with TabPane("Get Started", id="get-started"):
-                        option_list = OptionList(id="list-get-started")
-                        option_list.add_option(
-                            Option(
-                                _render_get_started_option(
-                                    "Test and run example agents",
-                                    "Try pre-built example agents to learn how Hive works",
-                                    "📚",
-                                ),
-                                id="action:run_examples",
-                            )
-                        )
-                        option_list.add_option(
-                            Option(
-                                _render_get_started_option(
-                                    "Test and run existing agent",
-                                    "Load and run an agent you've already built (from exports/)",
-                                    "🚀",
-                                ),
-                                id="action:run_existing",
-                            )
-                        )
-                        option_list.add_option(
-                            Option(
-                                _render_get_started_option(
-                                    "Build or edit agent",
-                                    "Create a new agent or modify an existing one",
-                                    "🛠️ ",
-                                ),
-                                id="action:build_edit",
-                            )
-                        )
-                        yield option_list
-
-                # Agent category tabs
-                for category, agents in self._groups.items():
-                    tab_id = category.lower().replace(" ", "-")
-                    with TabPane(f"{category} ({len(agents)})", id=tab_id):
-                        option_list = OptionList(id=f"list-{tab_id}")
-                        self._option_map[f"list-{tab_id}"] = {}
-                        for i, agent in enumerate(agents):
-                            option_list.add_option(
-                                Option(
-                                    _render_agent_option(agent),
-                                    id=str(agent.path),
-                                )
-                            )
-                            self._option_map[f"list-{tab_id}"][i] = agent
-                        yield option_list
-            yield Label(
-                "[dim]Enter[/dim] Select  [dim]Tab[/dim] Switch category  [dim]Esc[/dim] Cancel",
-                id="picker-footer",
-            )
-
-    def on_option_list_option_selected(self, event: OptionList.OptionSelected) -> None:
-        list_id = event.option_list.id or ""
-
-        # Handle Get Started tab options
-        if list_id == "list-get-started":
-            option = event.option
-            if option and option.id:
-                self.dismiss(option.id)  # Returns "action:run_examples", etc.
-            return
-
-        # Handle agent selection from other tabs
-        idx = event.option_index
-        agent_map = self._option_map.get(list_id, {})
-        agent = agent_map.get(idx)
-        if agent:
-            self.dismiss(str(agent.path))
-
-    def action_dismiss_picker(self) -> None:
-        self.dismiss(None)

core/framework/tui/screens/credential_setup.py

@@ -1,304 +0,0 @@
-"""Credential setup ModalScreen for configuring missing agent credentials."""
-
-from __future__ import annotations
-
-import os
-
-from textual.app import ComposeResult
-from textual.binding import Binding
-from textual.containers import Vertical, VerticalScroll
-from textual.screen import ModalScreen
-from textual.widgets import Button, Input, Label
-
-from framework.credentials.setup import CredentialSetupSession, MissingCredential
-
-
-class CredentialSetupScreen(ModalScreen[bool | None]):
-    """Modal screen for configuring missing agent credentials.
-
-    Shows a form with one password Input per missing credential.
-    For Aden-backed credentials (``aden_supported=True``), prompts for
-    ``ADEN_API_KEY`` and runs the Aden sync flow instead of storing a
-    raw value.
-
-    Returns True on successful save, or None on cancel/skip.
-    """
-
-    BINDINGS = [
-        Binding("escape", "dismiss_setup", "Cancel"),
-    ]
-
-    DEFAULT_CSS = """
-    CredentialSetupScreen {
-        align: center middle;
-    }
-    #cred-container {
-        width: 80%;
-        max-width: 100;
-        height: 80%;
-        background: $surface;
-        border: heavy $primary;
-        padding: 1 2;
-    }
-    #cred-title {
-        text-align: center;
-        text-style: bold;
-        width: 100%;
-        color: $text;
-    }
-    #cred-subtitle {
-        text-align: center;
-        width: 100%;
-        margin-bottom: 1;
-    }
-    #cred-scroll {
-        height: 1fr;
-    }
-    .cred-entry {
-        margin-bottom: 1;
-        padding: 1;
-        background: $panel;
-        height: auto;
-    }
-    .cred-entry Input {
-        margin-top: 1;
-    }
-    .cred-buttons {
-        height: auto;
-        margin-top: 1;
-        align: center middle;
-    }
-    .cred-buttons Button {
-        margin: 0 1;
-    }
-    #cred-footer {
-        text-align: center;
-        width: 100%;
-        margin-top: 1;
-    }
-    """
-
-    def __init__(self, session: CredentialSetupSession) -> None:
-        super().__init__()
-        self._session = session
-        self._missing: list[MissingCredential] = session.missing
-        # Track which credentials need Aden sync vs direct API key
-        self._aden_creds: set[int] = set()
-        self._needs_aden_key = False
-        for i, cred in enumerate(self._missing):
-            if cred.aden_supported and not cred.direct_api_key_supported:
-                self._aden_creds.add(i)
-                self._needs_aden_key = True
-
-    def compose(self) -> ComposeResult:
-        n = len(self._missing)
-        with Vertical(id="cred-container"):
-            yield Label("Credential Setup", id="cred-title")
-            yield Label(
-                f"[dim]{n} credential{'s' if n != 1 else ''} needed to run this agent[/dim]",
-                id="cred-subtitle",
-            )
-            with VerticalScroll(id="cred-scroll"):
-                # If any credential needs Aden, show ADEN_API_KEY input first
-                if self._needs_aden_key:
-                    aden_key = os.environ.get("ADEN_API_KEY", "")
-                    with Vertical(classes="cred-entry"):
-                        yield Label("[bold]ADEN_API_KEY[/bold]")
-                        aden_names = [
-                            self._missing[i].credential_name for i in sorted(self._aden_creds)
-                        ]
-                        yield Label(f"[dim]Required for OAuth sync: {', '.join(aden_names)}[/dim]")
-                        yield Label("[cyan]Get key:[/cyan] https://hive.adenhq.com")
-                        yield Input(
-                            placeholder="Paste ADEN_API_KEY..."
-                            if not aden_key
-                            else "Already set (leave blank to keep)",
-                            password=True,
-                            id="key-aden",
-                        )
-
-                # Show direct API key inputs for non-Aden credentials
-                for i, cred in enumerate(self._missing):
-                    if i in self._aden_creds:
-                        continue  # Handled via Aden sync above
-                    with Vertical(classes="cred-entry"):
-                        yield Label(f"[bold]{cred.env_var}[/bold]")
-                        affected = cred.tools or cred.node_types
-                        if affected:
-                            yield Label(f"[dim]Required by: {', '.join(affected)}[/dim]")
-                        if cred.description:
-                            yield Label(f"[dim]{cred.description}[/dim]")
-                        if cred.help_url:
-                            yield Label(f"[cyan]Get key:[/cyan] {cred.help_url}")
-                        yield Input(
-                            placeholder="Paste API key...",
-                            password=True,
-                            id=f"key-{i}",
-                        )
-            with Vertical(classes="cred-buttons"):
-                yield Button("Save & Continue", variant="primary", id="btn-save")
-                yield Button("Skip", variant="default", id="btn-skip")
-            yield Label(
-                "[dim]Enter[/dim] Submit  [dim]Esc[/dim] Cancel",
-                id="cred-footer",
-            )
-
-    def on_button_pressed(self, event: Button.Pressed) -> None:
-        if event.button.id == "btn-save":
-            self._save_credentials()
-        elif event.button.id == "btn-skip":
-            self.dismiss(None)
-
-    def _save_credentials(self) -> None:
-        """Collect inputs, store credentials, and dismiss."""
-        self._session._ensure_credential_key()
-
-        configured = 0
-
-        # Handle Aden-backed credentials
-        if self._needs_aden_key:
-            aden_input = self.query_one("#key-aden", Input)
-            aden_key = aden_input.value.strip()
-            if aden_key:
-                from framework.credentials.key_storage import save_aden_api_key
-
-                save_aden_api_key(aden_key)
-                configured += 1  # ADEN_API_KEY itself counts as configured
-
-            # Run Aden sync for all Aden-backed creds (best-effort)
-            if aden_key or os.environ.get("ADEN_API_KEY"):
-                self._sync_aden_credentials()
-
-        # Handle direct API key credentials
-        for i, cred in enumerate(self._missing):
-            if i in self._aden_creds:
-                continue
-            input_widget = self.query_one(f"#key-{i}", Input)
-            value = input_widget.value.strip()
-            if not value:
-                continue
-            try:
-                self._session._store_credential(cred, value)
-                configured += 1
-            except Exception as e:
-                self.notify(f"Error storing {cred.env_var}: {e}", severity="error")
-
-        if configured > 0:
-            self.dismiss(True)
-        else:
-            self.notify("No credentials configured", severity="warning", timeout=3)
-
-    def _sync_aden_credentials(self) -> int:
-        """Sync Aden-backed credentials and return count of successfully synced."""
-        # Build the Aden sync components directly so we get real errors
-        # instead of CredentialStore.with_aden_sync() silently falling back.
-        try:
-            from framework.credentials.aden import (
-                AdenCachedStorage,
-                AdenClientConfig,
-                AdenCredentialClient,
-                AdenSyncProvider,
-            )
-            from framework.credentials.storage import EncryptedFileStorage
-
-            client = AdenCredentialClient(AdenClientConfig(base_url="https://api.adenhq.com"))
-            provider = AdenSyncProvider(client=client)
-            local_storage = EncryptedFileStorage()
-            cached_storage = AdenCachedStorage(
-                local_storage=local_storage,
-                aden_provider=provider,
-            )
-        except Exception as e:
-            self.notify(
-                f"Aden setup error: {e}",
-                severity="error",
-                timeout=8,
-            )
-            return 0
-
-        # Sync all integrations from Aden to get the provider index populated
-        try:
-            from framework.credentials import CredentialStore
-
-            store = CredentialStore(
-                storage=cached_storage,
-                providers=[provider],
-                auto_refresh=True,
-            )
-            num_synced = provider.sync_all(store)
-            if num_synced == 0:
-                self.notify(
-                    "No active integrations found in Aden. "
-                    "Connect integrations at hive.adenhq.com.",
-                    severity="warning",
-                    timeout=8,
-                )
-        except Exception as e:
-            self.notify(
-                f"Aden sync error: {e}",
-                severity="error",
-                timeout=8,
-            )
-            return 0
-
-        synced = 0
-        for i in sorted(self._aden_creds):
-            cred = self._missing[i]
-            cred_id = cred.credential_id or cred.credential_name
-            if store.is_available(cred_id):
-                try:
-                    value = store.get_key(cred_id, cred.credential_key)
-                    if value:
-                        os.environ[cred.env_var] = value
-                        self._persist_to_local_store(cred_id, cred.credential_key, value)
-                        synced += 1
-                    else:
-                        self.notify(
-                            f"{cred.credential_name}: key "
-                            f"'{cred.credential_key}' not found "
-                            f"in credential '{cred_id}'",
-                            severity="warning",
-                            timeout=8,
-                        )
-                except Exception as e:
-                    self.notify(
-                        f"{cred.credential_name} extraction failed: {e}",
-                        severity="error",
-                        timeout=8,
-                    )
-            else:
-                self.notify(
-                    f"{cred.credential_name} (id='{cred_id}') "
-                    f"not found in Aden. Connect this "
-                    f"integration at hive.adenhq.com first.",
-                    severity="warning",
-                    timeout=8,
-                )
-        return synced
-
-    @staticmethod
-    def _persist_to_local_store(cred_id: str, key_name: str, value: str) -> None:
-        """Save a synced token to the local encrypted store under the canonical ID."""
-        try:
-            from pydantic import SecretStr
-
-            from framework.credentials.models import CredentialKey, CredentialObject, CredentialType
-            from framework.credentials.storage import EncryptedFileStorage
-
-            cred_obj = CredentialObject(
-                id=cred_id,
-                credential_type=CredentialType.OAUTH2,
-                keys={
-                    key_name: CredentialKey(
-                        name=key_name,
-                        value=SecretStr(value),
-                    ),
-                },
-                auto_refresh=True,
-            )
-            EncryptedFileStorage().save(cred_obj)
-        except Exception:
-            pass  # Best-effort; env var is the primary delivery mechanism
-
-    def action_dismiss_setup(self) -> None:
-        self.dismiss(None)

core/framework/tui/widgets/file_browser.py

@@ -1,139 +0,0 @@
-"""
-Native OS file dialog for PDF selection.
-
-Launches the platform's native file picker (macOS: NSOpenPanel via osascript,
-Linux: zenity/kdialog, Windows: PowerShell OpenFileDialog) in a background
-thread so Textual's event loop stays responsive.
-
-Falls back to None when no GUI is available (SSH, headless).
-"""
-
-import asyncio
-import os
-import subprocess
-import sys
-from pathlib import Path
-
-
-def _has_gui() -> bool:
-    """Detect whether a GUI display is available."""
-    if sys.platform == "darwin":
-        # macOS: GUI is available unless running over SSH without display forwarding.
-        return "SSH_CONNECTION" not in os.environ or "DISPLAY" in os.environ
-    elif sys.platform == "win32":
-        return True
-    else:
-        # Linux/BSD: Need X11 or Wayland.
-        return bool(os.environ.get("DISPLAY") or os.environ.get("WAYLAND_DISPLAY"))
-
-
-def _linux_file_dialog() -> subprocess.CompletedProcess | None:
-    """Try zenity, then kdialog, on Linux. Returns CompletedProcess or None."""
-    # Try zenity (GTK)
-    try:
-        return subprocess.run(
-            [
-                "zenity",
-                "--file-selection",
-                "--title=Select a PDF file",
-                "--file-filter=PDF files (*.pdf)|*.pdf",
-            ],
-            encoding="utf-8",
-            capture_output=True,
-            text=True,
-            timeout=300,
-        )
-    except FileNotFoundError:
-        pass
-
-    # Try kdialog (KDE)
-    try:
-        return subprocess.run(
-            [
-                "kdialog",
-                "--getopenfilename",
-                ".",
-                "PDF files (*.pdf)",
-            ],
-            encoding="utf-8",
-            capture_output=True,
-            text=True,
-            timeout=300,
-        )
-    except FileNotFoundError:
-        pass
-
-    return None
-
-
-def _pick_pdf_subprocess() -> Path | None:
-    """Run the native file dialog. BLOCKS until user picks or cancels.
-
-    Returns a Path on success, None on cancel or error.
-    Must be called from a non-main thread (via asyncio.to_thread).
-    """
-    try:
-        if sys.platform == "darwin":
-            result = subprocess.run(
-                [
-                    "osascript",
-                    "-e",
-                    'POSIX path of (choose file of type {"com.adobe.pdf"} '
-                    'with prompt "Select a PDF file")',
-                ],
-                encoding="utf-8",
-                capture_output=True,
-                text=True,
-                timeout=300,
-            )
-        elif sys.platform == "win32":
-            ps_script = (
-                "Add-Type -AssemblyName System.Windows.Forms; "
-                "$f = New-Object System.Windows.Forms.OpenFileDialog; "
-                "$f.Filter = 'PDF files (*.pdf)|*.pdf'; "
-                "$f.Title = 'Select a PDF file'; "
-                "if ($f.ShowDialog() -eq 'OK') { $f.FileName }"
-            )
-            result = subprocess.run(
-                ["powershell", "-NoProfile", "-Command", ps_script],
-                encoding="utf-8",
-                capture_output=True,
-                text=True,
-                timeout=300,
-            )
-        else:
-            result = _linux_file_dialog()
-            if result is None:
-                return None
-
-        if result.returncode != 0:
-            return None
-
-        path_str = result.stdout.strip()
-        if not path_str:
-            return None
-
-        path = Path(path_str)
-        if path.is_file() and path.suffix.lower() == ".pdf":
-            return path
-
-        return None
-
-    except (subprocess.TimeoutExpired, FileNotFoundError, OSError):
-        return None
-
-
-async def pick_pdf_file() -> Path | None:
-    """Open a native OS file dialog to pick a PDF file.
-
-    Non-blocking: runs the dialog subprocess in a background thread via
-    asyncio.to_thread(), so the calling event loop stays responsive.
-
-    Returns:
-        Path to the selected PDF, or None if the user cancelled,
-        no GUI is available, or the dialog command was not found.
-    """
-    if not _has_gui():
-        return None
-
-    return await asyncio.to_thread(_pick_pdf_subprocess)

core/framework/tui/widgets/graph_view.py

@@ -1,585 +0,0 @@
-"""
-Graph/Tree Overview Widget - Displays real agent graph structure.
-
-Supports rendering loops (back-edges) via right-side return channels:
-arrows drawn on the right margin that visually point back up to earlier nodes.
-"""
-
-from __future__ import annotations
-
-import re
-import time
-
-from textual.app import ComposeResult
-from textual.containers import Vertical
-
-from framework.runtime.agent_runtime import AgentRuntime
-from framework.runtime.event_bus import EventType
-from framework.tui.widgets.selectable_rich_log import SelectableRichLog as RichLog
-
-# Width of each return-channel column (padding + │ + gap)
-_CHANNEL_WIDTH = 5
-
-# Regex to strip Rich markup tags for measuring visible width
-_MARKUP_RE = re.compile(r"\[/?[^\]]*\]")
-
-
-def _plain_len(s: str) -> int:
-    """Return the visible character length of a Rich-markup string."""
-    return len(_MARKUP_RE.sub("", s))
-
-
-class GraphOverview(Vertical):
-    """Widget to display Agent execution graph/tree with real data."""
-
-    DEFAULT_CSS = """
-    GraphOverview {
-        width: 100%;
-        height: 100%;
-        background: $panel;
-    }
-
-    GraphOverview > RichLog {
-        width: 100%;
-        height: 100%;
-        background: $panel;
-        border: none;
-        scrollbar-background: $surface;
-        scrollbar-color: $primary;
-    }
-    """
-
-    def __init__(self, runtime: AgentRuntime):
-        super().__init__()
-        self.runtime = runtime
-        self._override_graph = None  # Set by switch_graph() for secondary graphs
-        self.active_node: str | None = None
-        self.execution_path: list[str] = []
-        # Per-node status strings shown next to the node in the graph display.
-        # e.g. {"planner": "thinking...", "searcher": "web_search..."}
-        self._node_status: dict[str, str] = {}
-
-    @property
-    def _graph(self):
-        """The graph currently being displayed (may be a secondary graph)."""
-        return self._override_graph or self.runtime.graph
-
-    def switch_graph(self, graph) -> None:
-        """Switch to displaying a different graph and refresh."""
-        self._override_graph = graph
-        self.active_node = None
-        self.execution_path = []
-        self._node_status = {}
-        self._display_graph()
-
-    def compose(self) -> ComposeResult:
-        # Use RichLog for formatted output
-        yield RichLog(id="graph-display", highlight=True, markup=True)
-
-    def on_mount(self) -> None:
-        """Display initial graph structure."""
-        self._display_graph()
-        # Refresh every 1s so timer countdowns stay current
-        if self.runtime._timer_next_fire is not None:
-            self.set_interval(1.0, self._display_graph)
-
-    # ------------------------------------------------------------------
-    # Graph analysis helpers
-    # ------------------------------------------------------------------
-
-    def _topo_order(self) -> list[str]:
-        """BFS from entry_node following edges."""
-        graph = self._graph
-        visited: list[str] = []
-        seen: set[str] = set()
-        queue = [graph.entry_node]
-        while queue:
-            nid = queue.pop(0)
-            if nid in seen:
-                continue
-            seen.add(nid)
-            visited.append(nid)
-            for edge in graph.get_outgoing_edges(nid):
-                if edge.target not in seen:
-                    queue.append(edge.target)
-        # Append orphan nodes not reachable from entry
-        for node in graph.nodes:
-            if node.id not in seen:
-                visited.append(node.id)
-        return visited
-
-    def _detect_back_edges(self, ordered: list[str]) -> list[dict]:
-        """Find edges where target appears before (or equal to) source in topo order.
-
-        Returns a list of dicts with keys: edge, source, target, source_idx, target_idx.
-        """
-        order_idx = {nid: i for i, nid in enumerate(ordered)}
-        back_edges: list[dict] = []
-        for node_id in ordered:
-            for edge in self._graph.get_outgoing_edges(node_id):
-                target_idx = order_idx.get(edge.target, -1)
-                source_idx = order_idx.get(node_id, -1)
-                if target_idx != -1 and target_idx <= source_idx:
-                    back_edges.append(
-                        {
-                            "edge": edge,
-                            "source": node_id,
-                            "target": edge.target,
-                            "source_idx": source_idx,
-                            "target_idx": target_idx,
-                        }
-                    )
-        return back_edges
-
-    def _is_back_edge(self, source: str, target: str, order_idx: dict[str, int]) -> bool:
-        """Check whether an edge from *source* to *target* is a back-edge."""
-        si = order_idx.get(source, -1)
-        ti = order_idx.get(target, -1)
-        return ti != -1 and ti <= si
-
-    # ------------------------------------------------------------------
-    # Line rendering (Pass 1)
-    # ------------------------------------------------------------------
-
-    def _render_node_line(self, node_id: str) -> str:
-        """Render a single node with status symbol and optional status text."""
-        graph = self._graph
-        is_terminal = node_id in (graph.terminal_nodes or [])
-        is_active = node_id == self.active_node
-        is_done = node_id in self.execution_path and not is_active
-        status = self._node_status.get(node_id, "")
-
-        if is_active:
-            sym = "[bold green]●[/bold green]"
-        elif is_done:
-            sym = "[dim]✓[/dim]"
-        elif is_terminal:
-            sym = "[yellow]■[/yellow]"
-        else:
-            sym = "○"
-
-        if is_active:
-            name = f"[bold green]{node_id}[/bold green]"
-        elif is_done:
-            name = f"[dim]{node_id}[/dim]"
-        else:
-            name = node_id
-
-        suffix = f"  [italic]{status}[/italic]" if status else ""
-        return f"  {sym} {name}{suffix}"
-
-    def _render_edges(self, node_id: str, order_idx: dict[str, int]) -> list[str]:
-        """Render forward-edge connectors from *node_id*.
-
-        Back-edges are excluded here — they are drawn by the return-channel
-        overlay in Pass 2.
-        """
-        all_edges = self._graph.get_outgoing_edges(node_id)
-        if not all_edges:
-            return []
-
-        # Split into forward and back
-        forward = [e for e in all_edges if not self._is_back_edge(node_id, e.target, order_idx)]
-
-        if not forward:
-            # All edges are back-edges — nothing to render here
-            return []
-
-        if len(forward) == 1:
-            return ["  │", "  ▼"]
-
-        # Fan-out: show branches
-        lines: list[str] = []
-        for i, edge in enumerate(forward):
-            connector = "└" if i == len(forward) - 1 else "├"
-            cond = ""
-            if edge.condition.value not in ("always", "on_success"):
-                cond = f" [dim]({edge.condition.value})[/dim]"
-            lines.append(f"  {connector}──▶ {edge.target}{cond}")
-        return lines
-
-    # ------------------------------------------------------------------
-    # Return-channel overlay (Pass 2)
-    # ------------------------------------------------------------------
-
-    def _overlay_return_channels(
-        self,
-        lines: list[str],
-        node_line_map: dict[str, int],
-        back_edges: list[dict],
-        available_width: int,
-    ) -> list[str]:
-        """Overlay right-side return channels onto the line buffer.
-
-        Each back-edge gets a vertical channel on the right margin.  Channels
-        are allocated left-to-right by increasing span length so that shorter
-        (inner) loops are closer to the graph body and longer (outer) loops are
-        further right.
-
-        If the terminal is too narrow to fit even one channel, we fall back to
-        simple inline ``↺`` annotations instead.
-        """
-        if not back_edges:
-            return lines
-
-        num_channels = len(back_edges)
-
-        # Sort by span length ascending → inner loops get nearest channel
-        sorted_be = sorted(back_edges, key=lambda b: b["source_idx"] - b["target_idx"])
-
-        # --- Insert dedicated connector lines for back-edge sources ---
-        # Each back-edge source gets a blank line inserted after its node
-        # section (after any forward-edge lines).  We process insertions in
-        # reverse order so that earlier indices remain valid.
-        all_node_lines_set = set(node_line_map.values())
-
-        insertions: list[tuple[int, int]] = []  # (insert_after_line, be_index)
-        for be_idx, be in enumerate(sorted_be):
-            source_node_line = node_line_map.get(be["source"])
-            if source_node_line is None:
-                continue
-            # Walk forward to find the last line in this node's section
-            last_section_line = source_node_line
-            for li in range(source_node_line + 1, len(lines)):
-                if li in all_node_lines_set:
-                    break
-                last_section_line = li
-            insertions.append((last_section_line, be_idx))
-
-        source_line_for_be: dict[int, int] = {}
-        for insert_after, be_idx in sorted(insertions, reverse=True):
-            insert_at = insert_after + 1
-            lines.insert(insert_at, "")  # placeholder for connector
-            source_line_for_be[be_idx] = insert_at
-            # Shift node_line_map entries that come after the insertion point
-            for nid in node_line_map:
-                if node_line_map[nid] > insert_after:
-                    node_line_map[nid] += 1
-            # Also shift already-assigned source lines
-            for prev_idx in source_line_for_be:
-                if prev_idx != be_idx and source_line_for_be[prev_idx] > insert_after:
-                    source_line_for_be[prev_idx] += 1
-
-        # Recompute max content width after insertions
-        max_content_w = max(_plain_len(ln) for ln in lines) if lines else 0
-
-        # Check if we have room for channels
-        channels_total_w = num_channels * _CHANNEL_WIDTH
-        if max_content_w + channels_total_w + 2 > available_width:
-            return self._inline_back_edge_fallback(lines, node_line_map, back_edges)
-
-        content_pad = max_content_w + 3  # gap between content and first channel
-
-        # Build channel info with final line positions
-        channel_info: list[dict] = []
-        for ch_idx, be in enumerate(sorted_be):
-            target_line = node_line_map.get(be["target"])
-            source_line = source_line_for_be.get(ch_idx)
-            if target_line is None or source_line is None:
-                continue
-            col = content_pad + ch_idx * _CHANNEL_WIDTH
-            channel_info.append(
-                {
-                    "target_line": target_line,
-                    "source_line": source_line,
-                    "col": col,
-                }
-            )
-
-        if not channel_info:
-            return lines
-
-        # Build overlay grid — one row per line, columns for channel area
-        total_width = content_pad + num_channels * _CHANNEL_WIDTH + 1
-        overlay_width = total_width - max_content_w
-        overlays: list[list[str]] = [[" "] * overlay_width for _ in range(len(lines))]
-
-        for ci in channel_info:
-            tl = ci["target_line"]
-            sl = ci["source_line"]
-            col_offset = ci["col"] - max_content_w
-
-            if col_offset < 0 or col_offset >= overlay_width:
-                continue
-
-            # Target line: ◄──...──┐
-            if 0 <= tl < len(overlays):
-                for c in range(col_offset):
-                    if overlays[tl][c] == " ":
-                        overlays[tl][c] = "─"
-                overlays[tl][col_offset] = "┐"
-
-            # Source line: ──...──┘
-            if 0 <= sl < len(overlays):
-                for c in range(col_offset):
-                    if overlays[sl][c] == " ":
-                        overlays[sl][c] = "─"
-                overlays[sl][col_offset] = "┘"
-
-            # Vertical lines between target+1 and source-1
-            for li in range(tl + 1, sl):
-                if 0 <= li < len(overlays) and overlays[li][col_offset] == " ":
-                    overlays[li][col_offset] = "│"
-
-        # Merge overlays into the line strings
-        result: list[str] = []
-        for i, line in enumerate(lines):
-            pw = _plain_len(line)
-            pad = max_content_w - pw
-            overlay_chars = overlays[i] if i < len(overlays) else []
-            overlay_str = "".join(overlay_chars)
-            overlay_trimmed = overlay_str.rstrip()
-            if overlay_trimmed:
-                is_target_line = any(ci["target_line"] == i for ci in channel_info)
-                if is_target_line:
-                    overlay_trimmed = "◄" + overlay_trimmed[1:]
-
-                is_source_line = any(ci["source_line"] == i for ci in channel_info)
-                if is_source_line and not line.strip():
-                    # Inserted blank line → build └───┘ connector.
-                    # "  └" = 3 chars of content prefix, so remaining pad = max_content_w - 3
-                    remaining_pad = max_content_w - 3
-                    full = list(" " * remaining_pad + overlay_trimmed)
-                    # Find the ┘ corner for this source connector
-                    corner_pos = -1
-                    for ci_s in channel_info:
-                        if ci_s["source_line"] == i:
-                            corner_pos = remaining_pad + (ci_s["col"] - max_content_w)
-                            break
-                    # Fill everything up to the corner with ─
-                    if corner_pos >= 0:
-                        for c in range(corner_pos):
-                            if full[c] not in ("│", "┘", "┐"):
-                                full[c] = "─"
-                    connector = "  └" + "".join(full).rstrip()
-                    result.append(f"[dim]{connector}[/dim]")
-                    continue
-
-                colored_overlay = f"[dim]{' ' * pad}{overlay_trimmed}[/dim]"
-                result.append(f"{line}{colored_overlay}")
-            else:
-                result.append(line)
-
-        return result
-
-    def _inline_back_edge_fallback(
-        self,
-        lines: list[str],
-        node_line_map: dict[str, int],
-        back_edges: list[dict],
-    ) -> list[str]:
-        """Fallback: add inline ↺ annotations when terminal is too narrow for channels."""
-        # Group back-edges by source node
-        source_to_be: dict[str, list[dict]] = {}
-        for be in back_edges:
-            source_to_be.setdefault(be["source"], []).append(be)
-
-        result = list(lines)
-        # Insert annotation lines after each source node's section
-        offset = 0
-        all_node_lines = sorted(node_line_map.values())
-        for source, bes in source_to_be.items():
-            source_line = node_line_map.get(source)
-            if source_line is None:
-                continue
-            # Find end of source node section
-            end_line = source_line
-            for nl in all_node_lines:
-                if nl > source_line:
-                    end_line = nl - 1
-                    break
-            else:
-                end_line = len(lines) - 1
-            # Insert after last content line of this node's section
-            insert_at = end_line + offset + 1
-            for be in bes:
-                cond = ""
-                edge = be["edge"]
-                if edge.condition.value not in ("always", "on_success"):
-                    cond = f" [dim]({edge.condition.value})[/dim]"
-                annotation = f"  [yellow]↺[/yellow] {be['target']}{cond}"
-                result.insert(insert_at, annotation)
-                insert_at += 1
-                offset += 1
-
-        return result
-
-    # ------------------------------------------------------------------
-    # Main display
-    # ------------------------------------------------------------------
-
-    def _display_graph(self) -> None:
-        """Display the graph as an ASCII DAG with edge connectors and loop channels."""
-        display = self.query_one("#graph-display", RichLog)
-        display.clear()
-
-        graph = self._graph
-        display.write(f"[bold cyan]Agent Graph:[/bold cyan] {graph.id}\n")
-
-        ordered = self._topo_order()
-        order_idx = {nid: i for i, nid in enumerate(ordered)}
-
-        # --- Pass 1: Build line buffer ---
-        lines: list[str] = []
-        node_line_map: dict[str, int] = {}
-
-        for node_id in ordered:
-            node_line_map[node_id] = len(lines)
-            lines.append(self._render_node_line(node_id))
-            for edge_line in self._render_edges(node_id, order_idx):
-                lines.append(edge_line)
-
-        # --- Pass 2: Overlay return channels for back-edges ---
-        back_edges = self._detect_back_edges(ordered)
-        if back_edges:
-            # Try to get actual widget width; default to a reasonable value
-            try:
-                available_width = self.size.width or 60
-            except Exception:
-                available_width = 60
-            lines = self._overlay_return_channels(lines, node_line_map, back_edges, available_width)
-
-        # Write all lines
-        for line in lines:
-            display.write(line)
-
-        # Execution path footer
-        if self.execution_path:
-            display.write("")
-            display.write(f"[dim]Path:[/dim] {' → '.join(self.execution_path[-5:])}")
-
-        # Event sources section
-        self._render_event_sources(display)
-
-    # ------------------------------------------------------------------
-    # Event sources display
-    # ------------------------------------------------------------------
-
-    def _render_event_sources(self, display: RichLog) -> None:
-        """Render event source info (webhooks, timers) below the graph."""
-        entry_points = self.runtime.get_entry_points()
-
-        # Filter to non-manual entry points (webhooks, timers, events)
-        event_sources = [ep for ep in entry_points if ep.trigger_type not in ("manual",)]
-        if not event_sources:
-            return
-
-        display.write("")
-        display.write("[bold cyan]Event Sources[/bold cyan]")
-
-        config = self.runtime._config
-
-        for ep in event_sources:
-            if ep.trigger_type == "timer":
-                cron_expr = ep.trigger_config.get("cron")
-                interval = ep.trigger_config.get("interval_minutes", "?")
-                schedule_label = f"cron: {cron_expr}" if cron_expr else f"every {interval} min"
-                display.write(f"  [green]⏱[/green]  {ep.name} [dim]→ {ep.entry_node}[/dim]")
-                # Show schedule + next fire countdown
-                next_fire = self.runtime._timer_next_fire.get(ep.id)
-                if next_fire is not None:
-                    remaining = max(0, next_fire - time.monotonic())
-                    hours, rem = divmod(int(remaining), 3600)
-                    mins, secs = divmod(rem, 60)
-                    if hours > 0:
-                        countdown = f"{hours}h {mins:02d}m {secs:02d}s"
-                    else:
-                        countdown = f"{mins}m {secs:02d}s"
-                    display.write(f"     [dim]{schedule_label} — next in {countdown}[/dim]")
-                else:
-                    display.write(f"     [dim]{schedule_label}[/dim]")
-
-            elif ep.trigger_type in ("event", "webhook"):
-                display.write(f"  [yellow]⚡[/yellow] {ep.name} [dim]→ {ep.entry_node}[/dim]")
-                # Show webhook endpoint if configured
-                route = None
-                for r in config.webhook_routes:
-                    src = r.get("source_id", "")
-                    if src and src in ep.id:
-                        route = r
-                        break
-                if not route and config.webhook_routes:
-                    # Fall back to first route
-                    route = config.webhook_routes[0]
-
-                if route:
-                    host = config.webhook_host
-                    port = config.webhook_port
-                    path = route.get("path", "/webhook")
-                    display.write(f"     [dim]{host}:{port}{path}[/dim]")
-                else:
-                    event_types = ep.trigger_config.get("event_types", [])
-                    if event_types:
-                        display.write(f"     [dim]events: {', '.join(event_types)}[/dim]")
-
-    # ------------------------------------------------------------------
-    # Public API (called by app.py)
-    # ------------------------------------------------------------------
-
-    def update_active_node(self, node_id: str) -> None:
-        """Update the currently active node."""
-        self.active_node = node_id
-        if node_id not in self.execution_path:
-            self.execution_path.append(node_id)
-        self._display_graph()
-
-    def update_execution(self, event) -> None:
-        """Update the displayed node status based on execution lifecycle events."""
-        if event.type == EventType.EXECUTION_STARTED:
-            self._node_status.clear()
-            self.execution_path.clear()
-            entry_node = event.data.get("entry_node") or (
-                self._graph.entry_node if self.runtime else None
-            )
-            if entry_node:
-                self.update_active_node(entry_node)
-
-        elif event.type == EventType.EXECUTION_COMPLETED:
-            self.active_node = None
-            self._node_status.clear()
-            self._display_graph()
-
-        elif event.type == EventType.EXECUTION_FAILED:
-            error = event.data.get("error", "Unknown error")
-            if self.active_node:
-                self._node_status[self.active_node] = f"[red]FAILED: {error}[/red]"
-            self.active_node = None
-            self._display_graph()
-
-    # -- Event handlers called by app.py _handle_event --
-
-    def handle_node_loop_started(self, node_id: str) -> None:
-        """A node's event loop has started."""
-        self._node_status[node_id] = "thinking..."
-        self.update_active_node(node_id)
-
-    def handle_node_loop_iteration(self, node_id: str, iteration: int) -> None:
-        """A node advanced to a new loop iteration."""
-        self._node_status[node_id] = f"step {iteration}"
-        self._display_graph()
-
-    def handle_node_loop_completed(self, node_id: str) -> None:
-        """A node's event loop completed."""
-        self._node_status.pop(node_id, None)
-        if self.active_node == node_id:
-            self.active_node = None
-        self._display_graph()
-
-    def handle_tool_call(self, node_id: str, tool_name: str, *, started: bool) -> None:
-        """Show tool activity next to the active node."""
-        if started:
-            self._node_status[node_id] = f"{tool_name}..."
-        else:
-            # Restore to generic thinking status after tool completes
-            self._node_status[node_id] = "thinking..."
-        self._display_graph()
-
-    def handle_stalled(self, node_id: str, reason: str) -> None:
-        """Highlight a stalled node."""
-        self._node_status[node_id] = f"[red]stalled: {reason}[/red]"
-        self._display_graph()
-
-    def handle_edge_traversed(self, source_node: str, target_node: str) -> None:
-        """Highlight an edge being traversed."""
-        self._node_status[source_node] = f"[dim]→ {target_node}[/dim]"
-        self._display_graph()

core/framework/tui/widgets/log_pane.py

@@ -1,172 +0,0 @@
-"""
-Log formatting utilities and LogPane widget.
-
-The module-level functions (format_event, extract_event_text, format_python_log)
-can be used by any widget that needs to render log lines without instantiating LogPane.
-"""
-
-import logging
-from datetime import datetime
-
-from textual.app import ComposeResult
-from textual.containers import Container
-
-from framework.runtime.event_bus import AgentEvent, EventType
-from framework.tui.widgets.selectable_rich_log import SelectableRichLog as RichLog
-
-# --- Module-level formatting constants ---
-
-EVENT_FORMAT: dict[EventType, tuple[str, str]] = {
-    EventType.EXECUTION_STARTED: (">>", "bold cyan"),
-    EventType.EXECUTION_COMPLETED: ("<<", "bold green"),
-    EventType.EXECUTION_FAILED: ("!!", "bold red"),
-    EventType.TOOL_CALL_STARTED: ("->", "yellow"),
-    EventType.TOOL_CALL_COMPLETED: ("<-", "green"),
-    EventType.NODE_LOOP_STARTED: ("@@", "cyan"),
-    EventType.NODE_LOOP_ITERATION: ("..", "dim"),
-    EventType.NODE_LOOP_COMPLETED: ("@@", "dim"),
-    EventType.LLM_TURN_COMPLETE: ("◆", "green"),
-    EventType.NODE_STALLED: ("!!", "bold yellow"),
-    EventType.NODE_INPUT_BLOCKED: ("!!", "yellow"),
-    EventType.GOAL_PROGRESS: ("%%", "blue"),
-    EventType.GOAL_ACHIEVED: ("**", "bold green"),
-    EventType.CONSTRAINT_VIOLATION: ("!!", "bold red"),
-    EventType.STATE_CHANGED: ("~~", "dim"),
-    EventType.CLIENT_INPUT_REQUESTED: ("??", "magenta"),
-}
-
-LOG_LEVEL_COLORS: dict[int, str] = {
-    logging.DEBUG: "dim",
-    logging.INFO: "",
-    logging.WARNING: "yellow",
-    logging.ERROR: "red",
-    logging.CRITICAL: "bold red",
-}
-
-
-# --- Module-level formatting functions ---
-
-
-def extract_event_text(event: AgentEvent) -> str:
-    """Extract human-readable text from an event's data dict."""
-    et = event.type
-    data = event.data
-
-    if et == EventType.EXECUTION_STARTED:
-        return "Execution started"
-    elif et == EventType.EXECUTION_COMPLETED:
-        return "Execution completed"
-    elif et == EventType.EXECUTION_FAILED:
-        return f"Execution FAILED: {data.get('error', 'unknown')}"
-    elif et == EventType.TOOL_CALL_STARTED:
-        return f"Tool call: {data.get('tool_name', 'unknown')}"
-    elif et == EventType.TOOL_CALL_COMPLETED:
-        name = data.get("tool_name", "unknown")
-        if data.get("is_error"):
-            preview = str(data.get("result", ""))[:80]
-            return f"Tool error: {name} - {preview}"
-        return f"Tool done: {name}"
-    elif et == EventType.NODE_LOOP_STARTED:
-        return f"Node started: {event.node_id or 'unknown'}"
-    elif et == EventType.NODE_LOOP_ITERATION:
-        return f"{event.node_id or 'unknown'} iteration {data.get('iteration', '?')}"
-    elif et == EventType.NODE_LOOP_COMPLETED:
-        return f"Node done: {event.node_id or 'unknown'}"
-    elif et == EventType.NODE_STALLED:
-        reason = data.get("reason", "")
-        node = event.node_id or "unknown"
-        return f"Node stalled: {node} - {reason}" if reason else f"Node stalled: {node}"
-    elif et == EventType.NODE_INPUT_BLOCKED:
-        return f"Node input blocked: {event.node_id or 'unknown'}"
-    elif et == EventType.GOAL_PROGRESS:
-        return f"Goal progress: {data.get('progress', '?')}"
-    elif et == EventType.GOAL_ACHIEVED:
-        return "Goal achieved"
-    elif et == EventType.CONSTRAINT_VIOLATION:
-        return f"Constraint violated: {data.get('description', 'unknown')}"
-    elif et == EventType.STATE_CHANGED:
-        return f"State changed: {data.get('key', 'unknown')}"
-    elif et == EventType.CLIENT_INPUT_REQUESTED:
-        return "Waiting for user input"
-    elif et == EventType.LLM_TURN_COMPLETE:
-        stop = data.get("stop_reason", "?")
-        model = data.get("model", "?")
-        inp = data.get("input_tokens", 0)
-        out = data.get("output_tokens", 0)
-        return f"{model} → {stop} ({inp}+{out} tokens)"
-    else:
-        return f"{et.value}: {data}"
-
-
-def format_event(event: AgentEvent) -> str:
-    """Format an AgentEvent as a Rich markup string with timestamp + symbol."""
-    ts = event.timestamp.strftime("%H:%M:%S")
-    symbol, color = EVENT_FORMAT.get(event.type, ("--", "dim"))
-    text = extract_event_text(event)
-    return f"[dim]{ts}[/dim] [{color}]{symbol} {text}[/{color}]"
-
-
-def format_python_log(record: logging.LogRecord) -> str:
-    """Format a Python log record as a Rich markup string with timestamp and severity color."""
-    ts = datetime.fromtimestamp(record.created).strftime("%H:%M:%S")
-    color = LOG_LEVEL_COLORS.get(record.levelno, "")
-    msg = record.getMessage()
-    if color:
-        return f"[dim]{ts}[/dim] [{color}]{record.levelname}[/{color}] {msg}"
-    else:
-        return f"[dim]{ts}[/dim] {record.levelname} {msg}"
-
-
-# --- LogPane widget (kept for backward compatibility) ---
-
-
-class LogPane(Container):
-    """Widget to display logs with reliable rendering."""
-
-    DEFAULT_CSS = """
-    LogPane {
-        width: 100%;
-        height: 100%;
-    }
-
-    LogPane > RichLog {
-        width: 100%;
-        height: 100%;
-        background: $surface;
-        border: none;
-        scrollbar-background: $panel;
-        scrollbar-color: $primary;
-    }
-    """
-
-    def compose(self) -> ComposeResult:
-        yield RichLog(id="main-log", highlight=True, markup=True, auto_scroll=False)
-
-    def write_event(self, event: AgentEvent) -> None:
-        """Format an AgentEvent with timestamp + symbol and write to the log."""
-        self.write_log(format_event(event))
-
-    def write_python_log(self, record: logging.LogRecord) -> None:
-        """Format a Python log record with timestamp and severity color."""
-        self.write_log(format_python_log(record))
-
-    def write_log(self, message: str) -> None:
-        """Write a log message to the log pane."""
-        try:
-            if not self.is_mounted:
-                return
-
-            log = self.query_one("#main-log", RichLog)
-
-            if not log.is_mounted:
-                return
-
-            was_at_bottom = log.is_vertical_scroll_end
-
-            log.write(message)
-
-            if was_at_bottom:
-                log.scroll_end(animate=False)
-
-        except Exception:
-            pass

core/framework/tui/widgets/selectable_rich_log.py

@@ -1,229 +0,0 @@
-"""
-SelectableRichLog - RichLog with mouse-driven text selection and clipboard copy.
-
-Drop-in replacement for RichLog. Click-and-drag to select text, which is
-visually highlighted. Press Ctrl+C to copy selection to clipboard (handled
-by app.py). Press Escape or single-click to clear selection.
-"""
-
-from __future__ import annotations
-
-import subprocess
-import sys
-
-from rich.segment import Segment as RichSegment
-from rich.style import Style
-from textual.geometry import Offset
-from textual.selection import Selection
-from textual.strip import Strip
-from textual.widgets import RichLog
-
-# Highlight style for selected text
-_HIGHLIGHT_STYLE = Style(bgcolor="blue", color="white")
-
-
-class SelectableRichLog(RichLog):
-    """RichLog with mouse-driven text selection."""
-
-    DEFAULT_CSS = """
-    SelectableRichLog {
-        pointer: text;
-    }
-    """
-
-    def __init__(self, **kwargs) -> None:
-        super().__init__(**kwargs)
-        self._sel_anchor: Offset | None = None
-        self._sel_end: Offset | None = None
-        self._selecting: bool = False
-
-    # -- Internal helpers --
-
-    def _apply_highlight(self, strip: Strip) -> Strip:
-        """Apply highlight with correct precedence (highlight wins over base style)."""
-        segments = []
-        for text, style, control in strip._segments:
-            if control:
-                segments.append(RichSegment(text, style, control))
-            else:
-                new_style = (style + _HIGHLIGHT_STYLE) if style else _HIGHLIGHT_STYLE
-                segments.append(RichSegment(text, new_style, control))
-        return Strip(segments, strip.cell_length)
-
-    # -- Selection helpers --
-
-    @property
-    def selection(self) -> Selection | None:
-        """Build a Selection from current anchor/end, or None if no selection."""
-        if self._sel_anchor is None or self._sel_end is None:
-            return None
-        if self._sel_anchor == self._sel_end:
-            return None
-        return Selection.from_offsets(self._sel_anchor, self._sel_end)
-
-    def _mouse_to_content(self, event_x: int, event_y: int) -> Offset:
-        """Convert viewport mouse coords to content (line, col) coords."""
-        scroll_x, scroll_y = self.scroll_offset
-        return Offset(scroll_x + event_x, scroll_y + event_y)
-
-    def clear_selection(self) -> None:
-        """Clear any active selection."""
-        had_selection = self._sel_anchor is not None
-        self._sel_anchor = None
-        self._sel_end = None
-        self._selecting = False
-        if had_selection:
-            self.refresh()
-
-    # -- Mouse handlers (left button only) --
-
-    def on_mouse_down(self, event) -> None:
-        """Start selection on left mouse button."""
-        if event.button != 1:
-            return
-        self._sel_anchor = self._mouse_to_content(event.x, event.y)
-        self._sel_end = self._sel_anchor
-        self._selecting = True
-        self.capture_mouse()
-        self.refresh()
-
-    def on_mouse_move(self, event) -> None:
-        """Extend selection while dragging."""
-        if not self._selecting:
-            return
-        self._sel_end = self._mouse_to_content(event.x, event.y)
-        self.refresh()
-
-    def on_mouse_up(self, event) -> None:
-        """End selection on mouse release."""
-        if not self._selecting:
-            return
-        self._selecting = False
-        self.release_mouse()
-
-        # Single-click (no drag) clears selection
-        if self._sel_anchor == self._sel_end:
-            self.clear_selection()
-
-    # -- Keyboard handlers --
-
-    def on_key(self, event) -> None:
-        """Clear selection on Escape."""
-        if event.key == "escape":
-            self.clear_selection()
-
-    # -- Rendering with highlight --
-
-    def render_line(self, y: int) -> Strip:
-        """Override to apply selection highlight on top of the base strip."""
-        strip = super().render_line(y)
-
-        sel = self.selection
-        if sel is None:
-            return strip
-
-        # Determine which content line this viewport row corresponds to
-        _, scroll_y = self.scroll_offset
-        content_y = scroll_y + y
-
-        span = sel.get_span(content_y)
-        if span is None:
-            return strip
-
-        start_x, end_x = span
-        cell_len = strip.cell_length
-        if cell_len == 0:
-            return strip
-
-        scroll_x, _ = self.scroll_offset
-
-        # -1 means "to end of content line" — use viewport end
-        if end_x == -1:
-            end_x = cell_len
-        else:
-            # Convert content-space x to viewport-space x
-            end_x = end_x - scroll_x
-
-        # Convert content-space x to viewport-space x
-        start_x = start_x - scroll_x
-
-        # Clamp to viewport strip bounds
-        start_x = max(0, start_x)
-        end_x = min(end_x, cell_len)
-
-        if start_x >= end_x:
-            return strip
-
-        # Divide strip into [before, selected, after] and highlight the middle
-        parts = strip.divide([start_x, end_x])
-        if len(parts) < 2:
-            return strip
-
-        highlighted_parts: list[Strip] = []
-        for i, part in enumerate(parts):
-            if i == 1:
-                highlighted_parts.append(self._apply_highlight(part))
-            else:
-                highlighted_parts.append(part)
-
-        return Strip.join(highlighted_parts)
-
-    # -- Text extraction & clipboard --
-
-    def get_selected_text(self) -> str | None:
-        """Extract the plain text of the current selection, or None."""
-        sel = self.selection
-        if sel is None:
-            return None
-
-        # Build full text from all lines
-        all_text = "\n".join(strip.text for strip in self.lines)
-        try:
-            extracted = sel.extract(all_text)
-        except (IndexError, ValueError):
-            # Selection coordinates can exceed line count when the virtual
-            # canvas is larger than the actual content (e.g. after scroll).
-            return None
-        return extracted if extracted else None
-
-    def copy_selection(self) -> str | None:
-        """Copy selected text to system clipboard. Returns text or None."""
-        text = self.get_selected_text()
-        if not text:
-            return None
-        _copy_to_clipboard(text)
-        return text
-
-
-def _copy_to_clipboard(text: str) -> None:
-    """Copy text to system clipboard using platform-native tools."""
-    try:
-        if sys.platform == "darwin":
-            subprocess.run(["pbcopy"], encoding="utf-8", input=text.encode(), check=True, timeout=5)
-        elif sys.platform == "win32":
-            subprocess.run(
-                ["clip.exe"],
-                encoding="utf-8",
-                input=text.encode("utf-16le"),
-                check=True,
-                timeout=5,
-            )
-        elif sys.platform.startswith("linux"):
-            try:
-                subprocess.run(
-                    ["xclip", "-selection", "clipboard"],
-                    encoding="utf-8",
-                    input=text.encode(),
-                    check=True,
-                    timeout=5,
-                )
-            except (subprocess.SubprocessError, FileNotFoundError):
-                subprocess.run(
-                    ["xsel", "--clipboard", "--input"],
-                    encoding="utf-8",
-                    input=text.encode(),
-                    check=True,
-                    timeout=5,
-                )
-    except (subprocess.SubprocessError, FileNotFoundError):
-        pass

core/frontend/src/api/client.ts

@@ -38,4 +38,9 @@ export const api = {
       body: body ? JSON.stringify(body) : undefined,
     }),
   delete: <T>(path: string) => request<T>(path, { method: "DELETE" }),
+  patch: <T>(path: string, body?: unknown) =>
+    request<T>(path, {
+      method: "PATCH",
+      body: body ? JSON.stringify(body) : undefined,
+    }),
 };

core/frontend/src/api/graphs.ts

@@ -1,5 +1,5 @@
 import { api } from "./client";
-import type { GraphTopology, NodeDetail, NodeCriteria, ToolInfo } from "./types";
+import type { GraphTopology, NodeDetail, NodeCriteria, ToolInfo, DraftGraph, FlowchartMap } from "./types";
 
 export const graphsApi = {
   nodes: (sessionId: string, graphId: string, workerSessionId?: string) =>
@@ -26,4 +26,14 @@ export const graphsApi = {
     api.get<{ tools: ToolInfo[] }>(
       `/sessions/${sessionId}/graphs/${graphId}/nodes/${nodeId}/tools`,
     ),
+
+  draftGraph: (sessionId: string) =>
+    api.get<{ draft: DraftGraph | null }>(
+      `/sessions/${sessionId}/draft-graph`,
+    ),
+
+  flowchartMap: (sessionId: string) =>
+    api.get<FlowchartMap>(
+      `/sessions/${sessionId}/flowchart-map`,
+    ),
 };

core/frontend/src/api/sessions.ts

@@ -1,11 +1,11 @@
 import { api } from "./client";
 import type {
+  AgentEvent,
   LiveSession,
   LiveSessionDetail,
   SessionSummary,
   SessionDetail,
   Checkpoint,
-  Message,
   EntryPoint,
 } from "./types";
 
@@ -64,12 +64,18 @@ export const sessionsApi = {
       `/sessions/${sessionId}/entry-points`,
     ),
 
+  updateTriggerTask: (sessionId: string, triggerId: string, task: string) =>
+    api.patch<{ trigger_id: string; task: string }>(
+      `/sessions/${sessionId}/triggers/${triggerId}`,
+      { task },
+    ),
+
   graphs: (sessionId: string) =>
     api.get<{ graphs: string[] }>(`/sessions/${sessionId}/graphs`),
 
-  /** Get queen conversation history for a session (works for cold/post-restart sessions too). */
-  queenMessages: (sessionId: string) =>
-    api.get<{ messages: Message[]; session_id: string }>(`/sessions/${sessionId}/queen-messages`),
+  /** Get persisted eventbus log for a session (works for cold sessions — used for full UI replay). */
+  eventsHistory: (sessionId: string) =>
+    api.get<{ events: AgentEvent[]; session_id: string }>(`/sessions/${sessionId}/events/history`),
 
   /** List all queen sessions on disk — live + cold (post-restart). */
   history: () =>
@@ -105,12 +111,4 @@ export const sessionsApi = {
     api.post<{ execution_id: string }>(
       `/sessions/${sessionId}/worker-sessions/${wsId}/checkpoints/${checkpointId}/restore`,
     ),
-
-  messages: (sessionId: string, wsId: string, nodeId?: string) => {
-    const params = new URLSearchParams({ client_only: "true" });
-    if (nodeId) params.set("node_id", nodeId);
-    return api.get<{ messages: Message[] }>(
-      `/sessions/${sessionId}/worker-sessions/${wsId}/messages?${params}`,
-    );
-  },
 };

core/frontend/src/api/types.ts

@@ -12,8 +12,8 @@ export interface LiveSession {
   loaded_at: number;
   uptime_seconds: number;
   intro_message?: string;
-  /** Queen operating phase — "building", "staging", or "running" */
-  queen_phase?: "building" | "staging" | "running";
+  /** Queen operating phase — "planning", "building", "staging", or "running" */
+  queen_phase?: "planning" | "building" | "staging" | "running";
   /** Present in 409 conflict responses when worker is still loading */
   loading?: boolean;
 }
@@ -31,6 +31,8 @@ export interface EntryPoint {
   entry_node: string;
   trigger_type: string;
   trigger_config?: Record<string, unknown>;
+  /** Worker task string when this trigger fires autonomously. */
+  task?: string;
   /** Seconds until the next timer fire (only present for timer entry points). */
   next_fire_in?: number;
 }
@@ -41,6 +43,7 @@ export interface DiscoverEntry {
   description: string;
   category: string;
   session_count: number;
+  run_count: number;
   node_count: number;
   tool_count: number;
   tags: string[];
@@ -191,6 +194,56 @@ export interface GraphTopology {
   entry_points?: EntryPoint[];
 }
 
+// --- Draft graph types (planning phase) ---
+
+export interface DraftNode {
+  id: string;
+  name: string;
+  description: string;
+  node_type: string;
+  tools: string[];
+  input_keys: string[];
+  output_keys: string[];
+  success_criteria: string;
+  sub_agents: string[];
+  /** For decision nodes: the yes/no question evaluated during dissolution. */
+  decision_clause?: string;
+  flowchart_type: string;
+  flowchart_shape: string;
+  flowchart_color: string;
+}
+
+export interface DraftEdge {
+  id: string;
+  source: string;
+  target: string;
+  condition: string;
+  description: string;
+  /** Short label shown on the flowchart edge (e.g. "Yes", "No"). */
+  label?: string;
+}
+
+export interface DraftGraph {
+  agent_name: string;
+  goal: string;
+  description: string;
+  success_criteria: string[];
+  constraints: string[];
+  nodes: DraftNode[];
+  edges: DraftEdge[];
+  entry_node: string;
+  terminal_nodes: string[];
+  flowchart_legend: Record<string, { shape: string; color: string }>;
+}
+
+/** Mapping from runtime graph nodes → original flowchart draft nodes. */
+export interface FlowchartMap {
+  /** runtime_node_id → list of original draft node IDs it absorbed. */
+  map: Record<string, string[]> | null;
+  /** Original draft graph preserved before planning-node dissolution (decision + subagent). */
+  original_draft: DraftGraph | null;
+}
+
 export interface NodeCriteria {
   node_id: string;
   success_criteria: string | null;
@@ -261,6 +314,7 @@ export type EventTypeName =
   | "tool_call_completed"
   | "client_output_delta"
   | "client_input_requested"
+  | "client_input_received"
   | "node_internal_output"
   | "node_input_blocked"
   | "node_stalled"
@@ -276,7 +330,14 @@ export type EventTypeName =
   | "worker_loaded"
   | "credentials_required"
   | "queen_phase_changed"
-  | "subagent_report";
+  | "subagent_report"
+  | "draft_graph_updated"
+  | "flowchart_map_updated"
+  | "trigger_available"
+  | "trigger_activated"
+  | "trigger_deactivated"
+  | "trigger_fired"
+  | "trigger_removed";
 
 export interface AgentEvent {
   type: EventTypeName;
@@ -287,4 +348,5 @@ export interface AgentEvent {
   timestamp: string;
   correlation_id: string | null;
   graph_id: string | null;
+  run_id?: string | null;
 }

core/frontend/src/components/AgentGraph.tsx

@@ -1,4 +1,4 @@
-import { memo, useMemo, useState, useRef } from "react";
+import { memo, useMemo, useState, useRef, useEffect, useCallback } from "react";
 import { Play, Pause, Loader2, CheckCircle2 } from "lucide-react";
 
 export type NodeStatus = "running" | "complete" | "pending" | "error" | "looping";
@@ -20,7 +20,7 @@ export interface GraphNode {
   edgeLabels?: Record<string, string>;
 }
 
-type RunState = "idle" | "deploying" | "running";
+export type RunState = "idle" | "deploying" | "running";
 
 interface AgentGraphProps {
   nodes: GraphNode[];
@@ -31,11 +31,11 @@ interface AgentGraphProps {
   version?: string;
   runState?: RunState;
   building?: boolean;
-  queenPhase?: "building" | "staging" | "running";
+  queenPhase?: "planning" | "building" | "staging" | "running";
 }
 
 // --- Extracted RunButton so hover state survives parent re-renders ---
-interface RunButtonProps {
+export interface RunButtonProps {
   runState: RunState;
   disabled: boolean;
   onRun: () => void;
@@ -43,7 +43,7 @@ interface RunButtonProps {
   btnRef: React.Ref<HTMLButtonElement>;
 }
 
-const RunButton = memo(function RunButton({ runState, disabled, onRun, onPause, btnRef }: RunButtonProps) {
+export const RunButton = memo(function RunButton({ runState, disabled, onRun, onPause, btnRef }: RunButtonProps) {
   const [hovered, setHovered] = useState(false);
   const showPause = runState === "running" && hovered;
 
@@ -89,46 +89,94 @@ const MARGIN_RIGHT = 50; // space for back-edge arcs
 const SVG_BASE_W = 320;
 const GAP_X = 12;
 
-// Unified amber/gold palette
-const statusColors: Record<NodeStatus, { dot: string; bg: string; border: string; glow: string }> = {
-  running: {
-    dot: "hsl(45,95%,58%)",
-    bg: "hsl(45,95%,58%,0.08)",
-    border: "hsl(45,95%,58%,0.5)",
-    glow: "hsl(45,95%,58%,0.15)",
-  },
-  looping: {
-    dot: "hsl(38,90%,55%)",
-    bg: "hsl(38,90%,55%,0.08)",
-    border: "hsl(38,90%,55%,0.5)",
-    glow: "hsl(38,90%,55%,0.15)",
-  },
-  complete: {
-    dot: "hsl(43,70%,45%)",
-    bg: "hsl(43,70%,45%,0.05)",
-    border: "hsl(43,70%,45%,0.25)",
-    glow: "none",
-  },
-  pending: {
-    dot: "hsl(35,15%,28%)",
-    bg: "hsl(35,10%,12%)",
-    border: "hsl(35,10%,20%)",
-    glow: "none",
-  },
-  error: {
-    dot: "hsl(0,65%,55%)",
-    bg: "hsl(0,65%,55%,0.06)",
-    border: "hsl(0,65%,55%,0.3)",
-    glow: "hsl(0,65%,55%,0.1)",
-  },
-};
+// Read a CSS custom property value (space-separated HSL components)
+function cssVar(name: string): string {
+  return getComputedStyle(document.documentElement).getPropertyValue(name).trim();
+}
 
-// Trigger node palette — cool blue-gray, visually distinct from amber execution nodes
-const triggerColors = {
-  bg: "hsl(210,25%,14%)",
-  border: "hsl(210,30%,30%)",
-  text: "hsl(210,30%,65%)",
-  icon: "hsl(210,40%,55%)",
+type StatusColorSet = Record<NodeStatus, { dot: string; bg: string; border: string; glow: string }>;
+type TriggerColorSet = { bg: string; border: string; text: string; icon: string };
+
+function buildStatusColors(): StatusColorSet {
+  const running = cssVar("--node-running") || "45 95% 58%";
+  const looping = cssVar("--node-looping") || "38 90% 55%";
+  const complete = cssVar("--node-complete") || "43 70% 45%";
+  const pending = cssVar("--node-pending") || "35 15% 28%";
+  const pendingBg = cssVar("--node-pending-bg") || "35 10% 12%";
+  const pendingBorder = cssVar("--node-pending-border") || "35 10% 20%";
+  const error = cssVar("--node-error") || "0 65% 55%";
+
+  return {
+    running: {
+      dot: `hsl(${running})`,
+      bg: `hsl(${running} / 0.08)`,
+      border: `hsl(${running} / 0.5)`,
+      glow: `hsl(${running} / 0.15)`,
+    },
+    looping: {
+      dot: `hsl(${looping})`,
+      bg: `hsl(${looping} / 0.08)`,
+      border: `hsl(${looping} / 0.5)`,
+      glow: `hsl(${looping} / 0.15)`,
+    },
+    complete: {
+      dot: `hsl(${complete})`,
+      bg: `hsl(${complete} / 0.05)`,
+      border: `hsl(${complete} / 0.25)`,
+      glow: "none",
+    },
+    pending: {
+      dot: `hsl(${pending})`,
+      bg: `hsl(${pendingBg})`,
+      border: `hsl(${pendingBorder})`,
+      glow: "none",
+    },
+    error: {
+      dot: `hsl(${error})`,
+      bg: `hsl(${error} / 0.06)`,
+      border: `hsl(${error} / 0.3)`,
+      glow: `hsl(${error} / 0.1)`,
+    },
+  };
+}
+
+function buildTriggerColors(): TriggerColorSet {
+  const bg = cssVar("--trigger-bg") || "210 25% 14%";
+  const border = cssVar("--trigger-border") || "210 30% 30%";
+  const text = cssVar("--trigger-text") || "210 30% 65%";
+  const icon = cssVar("--trigger-icon") || "210 40% 55%";
+  return {
+    bg: `hsl(${bg})`,
+    border: `hsl(${border})`,
+    text: `hsl(${text})`,
+    icon: `hsl(${icon})`,
+  };
+}
+
+/** Hook that reads node/trigger colors from CSS vars and updates on theme changes. */
+function useThemeColors() {
+  const [statusColors, setStatusColors] = useState<StatusColorSet>(buildStatusColors);
+  const [triggerColors, setTriggerColors] = useState<TriggerColorSet>(buildTriggerColors);
+
+  useEffect(() => {
+    const rebuild = () => {
+      setStatusColors(buildStatusColors());
+      setTriggerColors(buildTriggerColors());
+    };
+    const obs = new MutationObserver(rebuild);
+    obs.observe(document.documentElement, { attributes: true, attributeFilter: ["class", "style"] });
+    return () => obs.disconnect();
+  }, []);
+
+  return { statusColors, triggerColors };
+}
+
+// Active trigger — brighter, more saturated blue
+const activeTriggerColors = {
+  bg: "hsl(210,30%,18%)",
+  border: "hsl(210,50%,50%)",
+  text: "hsl(210,40%,75%)",
+  icon: "hsl(210,60%,65%)",
 };
 
 const triggerIcons: Record<string, string> = {
@@ -146,10 +194,96 @@ function truncateLabel(label: string, availablePx: number, fontSize: number): st
   return label.slice(0, Math.max(maxChars - 1, 1)) + "\u2026";
 }
 
+// ─── Pan & Zoom wrapper ───
+function PanZoomSvg({ svgW, svgH, className, children }: { svgW: number; svgH: number; className?: string; children: React.ReactNode }) {
+  const [zoom, setZoom] = useState(1);
+  const [pan, setPan] = useState({ x: 0, y: 0 });
+  const [dragging, setDragging] = useState(false);
+  const dragStart = useRef({ x: 0, y: 0, panX: 0, panY: 0 });
+
+  const MIN_ZOOM = 0.4;
+  const MAX_ZOOM = 3;
+
+  const handleWheel = useCallback((e: React.WheelEvent) => {
+    e.preventDefault();
+    const delta = e.deltaY > 0 ? 0.9 : 1.1;
+    setZoom(z => Math.min(MAX_ZOOM, Math.max(MIN_ZOOM, z * delta)));
+  }, []);
+
+  const handleMouseDown = useCallback((e: React.MouseEvent) => {
+    if (e.button !== 0) return;
+    setDragging(true);
+    dragStart.current = { x: e.clientX, y: e.clientY, panX: pan.x, panY: pan.y };
+  }, [pan]);
+
+  const handleMouseMove = useCallback((e: React.MouseEvent) => {
+    if (!dragging) return;
+    setPan({
+      x: dragStart.current.panX + (e.clientX - dragStart.current.x),
+      y: dragStart.current.panY + (e.clientY - dragStart.current.y),
+    });
+  }, [dragging]);
+
+  const handleMouseUp = useCallback(() => setDragging(false), []);
+
+  const resetView = useCallback(() => {
+    setZoom(1);
+    setPan({ x: 0, y: 0 });
+  }, []);
+
+  return (
+    <div className="flex-1 relative overflow-hidden px-1 pb-5">
+      <div
+        onWheel={handleWheel}
+        onMouseDown={handleMouseDown}
+        onMouseMove={handleMouseMove}
+        onMouseUp={handleMouseUp}
+        onMouseLeave={handleMouseUp}
+        className="w-full h-full"
+        style={{ cursor: dragging ? "grabbing" : "grab" }}
+      >
+        <svg
+          width="100%"
+          viewBox={`0 0 ${svgW} ${svgH}`}
+          preserveAspectRatio="xMidYMin meet"
+          className={`select-none ${className || ""}`}
+          style={{
+            fontFamily: "'Inter', system-ui, sans-serif",
+            transform: `translate(${pan.x}px, ${pan.y}px) scale(${zoom})`,
+            transformOrigin: "center top",
+          }}
+        >
+          {children}
+        </svg>
+      </div>
+
+      {/* Zoom controls */}
+      <div className="absolute bottom-7 right-3 flex items-center gap-1 bg-card/80 backdrop-blur-sm border border-border/40 rounded-lg p-0.5 shadow-sm">
+        <button
+          onClick={() => setZoom(z => Math.min(MAX_ZOOM, z * 1.2))}
+          className="w-6 h-6 flex items-center justify-center rounded text-muted-foreground hover:text-foreground hover:bg-muted/60 transition-colors text-xs font-bold"
+          aria-label="Zoom in"
+        >+</button>
+        <button
+          onClick={resetView}
+          className="px-1.5 h-6 flex items-center justify-center rounded text-[10px] font-mono text-muted-foreground hover:text-foreground hover:bg-muted/60 transition-colors"
+          aria-label="Reset zoom"
+        >{Math.round(zoom * 100)}%</button>
+        <button
+          onClick={() => setZoom(z => Math.max(MIN_ZOOM, z * 0.8))}
+          className="w-6 h-6 flex items-center justify-center rounded text-muted-foreground hover:text-foreground hover:bg-muted/60 transition-colors text-xs font-bold"
+          aria-label="Zoom out"
+        >{"\u2212"}</button>
+      </div>
+    </div>
+  );
+}
+
 export default function AgentGraph({ nodes, title: _title, onNodeClick, onRun, onPause, version, runState: externalRunState, building, queenPhase }: AgentGraphProps) {
   const [localRunState, setLocalRunState] = useState<RunState>("idle");
   const runState = externalRunState ?? localRunState;
   const runBtnRef = useRef<HTMLButtonElement>(null);
+  const { statusColors, triggerColors } = useThemeColors();
 
   const handleRun = () => {
     if (runState !== "idle") return;
@@ -278,7 +412,7 @@ export default function AgentGraph({ nodes, title: _title, onNodeClick, onRun, o
               </span>
             )}
           </div>
-          <RunButton runState={runState} disabled={nodes.length === 0 || queenPhase === "building"} onRun={handleRun} onPause={onPause ?? (() => {})} btnRef={runBtnRef} />
+          <RunButton runState={runState} disabled={nodes.length === 0 || queenPhase === "building" || queenPhase === "planning"} onRun={handleRun} onPause={onPause ?? (() => {})} btnRef={runBtnRef} />
         </div>
         <div className="flex-1 flex items-center justify-center px-5">
           {building ? (
@@ -344,18 +478,21 @@ export default function AgentGraph({ nodes, title: _title, onNodeClick, onRun, o
 
     let d: string;
     if (skipsLayers && hasCollision(fromLayer, toLayer, from.x, to.x)) {
-      // Route around intermediate nodes: curve to the left
+      // Route around intermediate nodes: orthogonal detour to the left
       const detourX = Math.min(from.x, to.x) - nodeW * 0.4;
-      d = `M ${startX} ${y1} C ${startX} ${y1 + 20}, ${detourX} ${y1 + 20}, ${detourX} ${midY} S ${toCenterX} ${y2 - 20} ${toCenterX} ${y2}`;
+      d = `M ${startX} ${y1} L ${startX} ${midY} L ${detourX} ${midY} L ${detourX} ${y2 - 10} L ${toCenterX} ${y2 - 10} L ${toCenterX} ${y2}`;
+    } else if (Math.abs(startX - toCenterX) < 2) {
+      // Straight vertical line when aligned
+      d = `M ${startX} ${y1} L ${toCenterX} ${y2}`;
     } else {
-      // Standard bezier: from source bottom to target top
-      d = `M ${startX} ${y1} C ${startX} ${midY}, ${toCenterX} ${midY}, ${toCenterX} ${y2}`;
+      // Orthogonal: down, across, down
+      d = `M ${startX} ${y1} L ${startX} ${midY} L ${toCenterX} ${midY} L ${toCenterX} ${y2}`;
     }
 
     const fromNode = nodes[edge.fromIdx];
     const isActive = fromNode.status === "complete" || fromNode.status === "running" || fromNode.status === "looping";
-    const strokeColor = isActive ? "hsl(43,70%,45%,0.35)" : "hsl(35,10%,20%)";
-    const arrowColor = isActive ? "hsl(43,70%,45%,0.5)" : "hsl(35,10%,22%)";
+    const strokeColor = isActive ? statusColors.complete.border : statusColors.pending.border;
+    const arrowColor = isActive ? statusColors.complete.dot : statusColors.pending.border;
 
     return (
       <g key={`fwd-${i}`}>
@@ -368,7 +505,7 @@ export default function AgentGraph({ nodes, title: _title, onNodeClick, onRun, o
           <text
             x={(startX + toCenterX) / 2 + 8}
             y={midY - 2}
-            fill="hsl(35,15%,40%)"
+            fill={statusColors.pending.dot}
             fontSize={9}
             fontStyle="italic"
           >
@@ -394,9 +531,9 @@ export default function AgentGraph({ nodes, title: _title, onNodeClick, onRun, o
 
     const fromNode = nodes[edge.fromIdx];
     const isActive = fromNode.status === "complete" || fromNode.status === "running" || fromNode.status === "looping";
-    const color = isActive ? "hsl(38,80%,50%,0.3)" : "hsl(35,10%,20%)";
+    const color = isActive ? statusColors.looping.border : statusColors.pending.border;
 
-    // Bezier curve with rounded corners
+    // Bezier curve with rounded corners (kept as curves for back edges)
     const path = `M ${startX} ${startY} C ${startX + r} ${startY}, ${curveX} ${startY}, ${curveX} ${startY - r} L ${curveX} ${endY + r} C ${curveX} ${endY}, ${endX + r} ${endY}, ${endX + 6} ${endY}`;
 
     return (
@@ -404,7 +541,7 @@ export default function AgentGraph({ nodes, title: _title, onNodeClick, onRun, o
         <path d={path} fill="none" stroke={color} strokeWidth={1.5} strokeDasharray="4 3" />
         <polygon
           points={`${endX + 6},${endY - 3} ${endX + 6},${endY + 3} ${endX},${endY}`}
-          fill={isActive ? "hsl(38,80%,50%,0.45)" : "hsl(35,10%,22%)"}
+          fill={isActive ? statusColors.looping.dot : statusColors.pending.border}
         />
       </g>
     );
@@ -417,10 +554,12 @@ export default function AgentGraph({ nodes, title: _title, onNodeClick, onRun, o
     const triggerAvailW = nodeW - 38;
     const triggerDisplayLabel = truncateLabel(node.label, triggerAvailW, triggerFontSize);
     const nextFireIn = node.triggerConfig?.next_fire_in as number | undefined;
+    const isActive = node.status === "running" || node.status === "complete";
+    const colors = isActive ? activeTriggerColors : triggerColors;
 
     // Format countdown for display below node
     let countdownLabel: string | null = null;
-    if (nextFireIn != null && nextFireIn > 0) {
+    if (isActive && nextFireIn != null && nextFireIn > 0) {
       const h = Math.floor(nextFireIn / 3600);
       const m = Math.floor((nextFireIn % 3600) / 60);
       const s = Math.floor(nextFireIn % 60);
@@ -429,24 +568,28 @@ export default function AgentGraph({ nodes, title: _title, onNodeClick, onRun, o
         : `next in ${m}m ${String(s).padStart(2, "0")}s`;
     }
 
+    // Status label below countdown
+    const statusLabel = isActive ? "active" : "inactive";
+    const statusColor = isActive ? "hsl(140,40%,50%)" : "hsl(210,20%,40%)";
+
     return (
       <g key={node.id} onClick={() => onNodeClick?.(node)} style={{ cursor: onNodeClick ? "pointer" : "default" }}>
         <title>{node.label}</title>
-        {/* Pill-shaped background with dashed border */}
+        {/* Pill-shaped background — solid border when active, dashed when inactive */}
         <rect
           x={pos.x} y={pos.y}
           width={nodeW} height={NODE_H}
           rx={NODE_H / 2}
-          fill={triggerColors.bg}
-          stroke={triggerColors.border}
-          strokeWidth={1}
-          strokeDasharray="4 2"
+          fill={colors.bg}
+          stroke={colors.border}
+          strokeWidth={isActive ? 1.5 : 1}
+          strokeDasharray={isActive ? undefined : "4 2"}
         />
 
         {/* Trigger type icon */}
         <text
           x={pos.x + 18} y={pos.y + NODE_H / 2}
-          fill={triggerColors.icon} fontSize={13}
+          fill={colors.icon} fontSize={13}
           textAnchor="middle" dominantBaseline="middle"
         >
           {icon}
@@ -455,7 +598,7 @@ export default function AgentGraph({ nodes, title: _title, onNodeClick, onRun, o
         {/* Label */}
         <text
           x={pos.x + 32} y={pos.y + NODE_H / 2}
-          fill={triggerColors.text}
+          fill={colors.text}
           fontSize={triggerFontSize}
           fontWeight={500}
           dominantBaseline="middle"
@@ -468,12 +611,21 @@ export default function AgentGraph({ nodes, title: _title, onNodeClick, onRun, o
         {countdownLabel && (
           <text
             x={pos.x + nodeW / 2} y={pos.y + NODE_H + 13}
-            fill="hsl(210,30%,50%)" fontSize={9.5}
+            fill={triggerColors.text} fontSize={9.5}
             textAnchor="middle" fontStyle="italic" opacity={0.7}
           >
             {countdownLabel}
           </text>
         )}
+
+        {/* Status label */}
+        <text
+          x={pos.x + nodeW / 2} y={pos.y + NODE_H + (countdownLabel ? 25 : 13)}
+          fill={statusColor} fontSize={9}
+          textAnchor="middle" opacity={0.8}
+        >
+          {statusLabel}
+        </text>
       </g>
     );
   };
@@ -543,7 +695,7 @@ export default function AgentGraph({ nodes, title: _title, onNodeClick, onRun, o
         {/* Label -- truncated with ellipsis for narrow nodes */}
         <text
           x={pos.x + 32} y={pos.y + NODE_H / 2}
-          fill={isActive ? "hsl(45,90%,85%)" : isDone ? "hsl(40,20%,75%)" : "hsl(35,10%,45%)"}
+          fill={isActive ? statusColors.running.dot : isDone ? statusColors.complete.dot : statusColors.pending.dot}
           fontSize={fontSize}
           fontWeight={isActive ? 600 : isDone ? 500 : 400}
           dominantBaseline="middle"
@@ -556,7 +708,7 @@ export default function AgentGraph({ nodes, title: _title, onNodeClick, onRun, o
         {node.statusLabel && isActive && (
           <text
             x={pos.x + nodeW + 10} y={pos.y + NODE_H / 2}
-            fill="hsl(45,80%,60%)" fontSize={10.5} fontStyle="italic"
+            fill={statusColors.running.dot} fontSize={10.5} fontStyle="italic"
             dominantBaseline="middle" opacity={0.8}
           >
             {node.statusLabel}
@@ -600,27 +752,19 @@ export default function AgentGraph({ nodes, title: _title, onNodeClick, onRun, o
       </div>
 
       {/* Graph */}
-      <div className="flex-1 overflow-y-auto overflow-x-hidden px-3 pb-5 relative">
-        <svg
-          width={svgWidth}
-          height={svgHeight}
-          viewBox={`0 0 ${svgWidth} ${svgHeight}`}
-          className={`select-none${building ? " opacity-30" : ""}`}
-          style={{ fontFamily: "'Inter', system-ui, sans-serif" }}
-        >
-          {forwardEdges.map((e, i) => renderForwardEdge(e, i))}
-          {backEdges.map((e, i) => renderBackEdge(e, i))}
-          {nodes.map((n, i) => renderNode(n, i))}
-        </svg>
-        {building && (
-          <div className="absolute inset-0 flex items-center justify-center">
-            <div className="flex flex-col items-center gap-3">
-              <Loader2 className="w-6 h-6 animate-spin text-primary/60" />
-              <p className="text-xs text-muted-foreground/80">Rebuilding agent...</p>
-            </div>
+      <PanZoomSvg svgW={svgWidth} svgH={svgHeight} className={building ? "opacity-30" : ""}>
+        {forwardEdges.map((e, i) => renderForwardEdge(e, i))}
+        {backEdges.map((e, i) => renderBackEdge(e, i))}
+        {nodes.map((n, i) => renderNode(n, i))}
+      </PanZoomSvg>
+      {building && (
+        <div className="absolute inset-0 flex items-center justify-center">
+          <div className="flex flex-col items-center gap-3">
+            <Loader2 className="w-6 h-6 animate-spin text-primary/60" />
+            <p className="text-xs text-muted-foreground/80">Rebuilding agent...</p>
           </div>
-        )}
-      </div>
+        </div>
+      )}
     </div>
   );
 }

core/frontend/src/components/ChatPanel.tsx

@@ -2,6 +2,7 @@ import { memo, useState, useRef, useEffect } from "react";
 import { Send, Square, Crown, Cpu, Check, Loader2 } from "lucide-react";
 import MarkdownContent from "@/components/MarkdownContent";
 import QuestionWidget from "@/components/QuestionWidget";
+import MultiQuestionWidget from "@/components/MultiQuestionWidget";
 
 export interface ChatMessage {
   id: string;
@@ -9,12 +10,14 @@ export interface ChatMessage {
   agentColor: string;
   content: string;
   timestamp: string;
-  type?: "system" | "agent" | "user" | "tool_status" | "worker_input_request";
+  type?: "system" | "agent" | "user" | "tool_status" | "worker_input_request" | "run_divider";
   role?: "queen" | "worker";
   /** Which worker thread this message belongs to (worker agent name) */
   thread?: string;
   /** Epoch ms when this message was first created — used for ordering queen/worker interleaving */
   createdAt?: number;
+  /** Queen phase active when this message was created */
+  phase?: "planning" | "building" | "staging" | "running";
 }
 
 interface ChatPanelProps {
@@ -34,12 +37,16 @@ interface ChatPanelProps {
   pendingQuestion?: string | null;
   /** Options for the pending question */
   pendingOptions?: string[] | null;
+  /** Multiple questions from ask_user_multiple */
+  pendingQuestions?: { id: string; prompt: string; options?: string[] }[] | null;
   /** Called when user submits an answer to the pending question */
   onQuestionSubmit?: (answer: string, isOther: boolean) => void;
+  /** Called when user submits answers to multiple questions */
+  onMultiQuestionSubmit?: (answers: Record<string, string>) => void;
   /** Called when user dismisses the pending question without answering */
   onQuestionDismiss?: () => void;
   /** Queen operating phase — shown as a tag on queen messages */
-  queenPhase?: "building" | "staging" | "running";
+  queenPhase?: "planning" | "building" | "staging" | "running";
 }
 
 const queenColor = "hsl(45,95%,58%)";
@@ -144,11 +151,23 @@ function ToolActivityRow({ content }: { content: string }) {
   );
 }
 
-const MessageBubble = memo(function MessageBubble({ msg, queenPhase }: { msg: ChatMessage; queenPhase?: "building" | "staging" | "running" }) {
+const MessageBubble = memo(function MessageBubble({ msg, queenPhase }: { msg: ChatMessage; queenPhase?: "planning" | "building" | "staging" | "running" }) {
   const isUser = msg.type === "user";
   const isQueen = msg.role === "queen";
   const color = getColor(msg.agent, msg.role);
 
+  if (msg.type === "run_divider") {
+    return (
+      <div className="flex items-center gap-3 py-2 my-1">
+        <div className="flex-1 h-px bg-border/60" />
+        <span className="text-[10px] text-muted-foreground font-medium uppercase tracking-wider">
+          {msg.content}
+        </span>
+        <div className="flex-1 h-px bg-border/60" />
+      </div>
+    );
+  }
+
   if (msg.type === "system") {
     return (
       <div className="flex justify-center py-1">
@@ -200,11 +219,13 @@ const MessageBubble = memo(function MessageBubble({ msg, queenPhase }: { msg: Ch
             }`}
           >
             {isQueen
-              ? queenPhase === "running"
-                ? "running phase"
-                : queenPhase === "staging"
-                  ? "staging phase"
-                  : "building phase"
+              ? ((msg.phase ?? queenPhase) === "running"
+                ? "running"
+                : (msg.phase ?? queenPhase) === "staging"
+                  ? "staging"
+                  : (msg.phase ?? queenPhase) === "planning"
+                    ? "planning"
+                    : "building")
               : "Worker"}
           </span>
         </div>
@@ -218,9 +239,9 @@ const MessageBubble = memo(function MessageBubble({ msg, queenPhase }: { msg: Ch
       </div>
     </div>
   );
-}, (prev, next) => prev.msg.id === next.msg.id && prev.msg.content === next.msg.content && prev.queenPhase === next.queenPhase);
+}, (prev, next) => prev.msg.id === next.msg.id && prev.msg.content === next.msg.content && prev.msg.phase === next.msg.phase && prev.queenPhase === next.queenPhase);
 
-export default function ChatPanel({ messages, onSend, isWaiting, isWorkerWaiting, isBusy, activeThread, disabled, onCancel, pendingQuestion, pendingOptions, onQuestionSubmit, onQuestionDismiss, queenPhase }: ChatPanelProps) {
+export default function ChatPanel({ messages, onSend, isWaiting, isWorkerWaiting, isBusy, activeThread, disabled, onCancel, pendingQuestion, pendingOptions, pendingQuestions, onQuestionSubmit, onMultiQuestionSubmit, onQuestionDismiss, queenPhase }: ChatPanelProps) {
   const [input, setInput] = useState("");
   const [readMap, setReadMap] = useState<Record<string, number>>({});
   const bottomRef = useRef<HTMLDivElement>(null);
@@ -330,7 +351,13 @@ export default function ChatPanel({ messages, onSend, isWaiting, isWorkerWaiting
       </div>
 
       {/* Input area — question widget replaces textarea when a question is pending */}
-      {pendingQuestion && pendingOptions && onQuestionSubmit ? (
+      {pendingQuestions && pendingQuestions.length >= 2 && onMultiQuestionSubmit ? (
+        <MultiQuestionWidget
+          questions={pendingQuestions}
+          onSubmit={onMultiQuestionSubmit}
+          onDismiss={onQuestionDismiss}
+        />
+      ) : pendingQuestion && pendingOptions && onQuestionSubmit ? (
         <QuestionWidget
           question={pendingQuestion}
           options={pendingOptions}

core/frontend/src/components/CredentialsModal.tsx

@@ -126,8 +126,13 @@ export default function CredentialsModal({
         // No real path — no credentials to show
         setRows([]);
       }
-    } catch {
-      // Backend unavailable — fall back to legacy props or empty
+    } catch (err) {
+      // Surface the error so the modal shows a meaningful message
+      const message =
+        err instanceof Error ? err.message : "Failed to check credentials";
+      setError(message);
+
+      // Fall back to legacy props or empty rows
       if (legacyCredentials) {
         setRows(legacyCredentials.map(c => ({
           ...c,
@@ -289,11 +294,18 @@ export default function CredentialsModal({
           {/* Status banner */}
           {!loading && (
             <div className={`mx-5 mt-4 px-3 py-2.5 rounded-lg border text-xs font-medium flex items-center gap-2 ${
-              allRequiredMet
-                ? "bg-emerald-500/10 border-emerald-500/20 text-emerald-600"
-                : "bg-destructive/5 border-destructive/20 text-destructive"
+              error && rows.length === 0
+                ? "bg-destructive/5 border-destructive/20 text-destructive"
+                : allRequiredMet
+                  ? "bg-emerald-500/10 border-emerald-500/20 text-emerald-600"
+                  : "bg-destructive/5 border-destructive/20 text-destructive"
             }`}>
-              {allRequiredMet ? (
+              {error && rows.length === 0 ? (
+                <>
+                  <AlertCircle className="w-3.5 h-3.5 flex-shrink-0" />
+                  <span className="break-words">Failed to check credentials: {error}</span>
+                </>
+              ) : allRequiredMet ? (
                 <>
                   <Shield className="w-3.5 h-3.5" />
                   {rows.length === 0