Merge branch 'main' into fix/session-resume-new-agent

feat: GCU browser cleanup, draft loading state, and inner_turn message fix

.agent/mcp_config.json

@@ -1,9 +0,0 @@
-{
-  "mcpServers": {
-    "agent-builder": {
-      "command": "uv",
-      "args": ["run", "--directory", "core", "-m", "framework.mcp.agent_builder_server"],
-      "disabled": false
-    }
-  }
-}

.agent/skills/hive

@@ -1 +0,0 @@
-../../.claude/skills/hive

.agent/skills/hive-concepts

@@ -1 +0,0 @@
-../../.claude/skills/hive-concepts

.agent/skills/hive-create

@@ -1 +0,0 @@
-../../.claude/skills/hive-create

.agent/skills/hive-credentials

@@ -1 +0,0 @@
-../../.claude/skills/hive-credentials

.agent/skills/hive-patterns

@@ -1 +0,0 @@
-../../.claude/skills/hive-patterns

.agent/skills/hive-test

@@ -1 +0,0 @@
-../../.claude/skills/hive-test

.agent/workflows/hive-concepts.md

@@ -1,5 +0,0 @@
----
-description: hive-concepts
----
-
-use hive-concepts skill

.agent/workflows/hive-create.md

@@ -1,5 +0,0 @@
----
-description: hive-create
----
-
-use hive-create skill

.agent/workflows/hive-credentials.md

@@ -1,5 +0,0 @@
----
-description: hive-credentials
----
-
-use hive-credentials skill

.agent/workflows/hive-patterns.md

@@ -1,5 +0,0 @@
----
-description: hive-patterns
----
-
-use hive-patterns skill

.agent/workflows/hive-test.md

@@ -1,5 +0,0 @@
----
-description: hive-test
----
-
-use hive-test skill

.agent/workflows/hive.md

@@ -1,5 +0,0 @@
----
-description: hive
----
-
-use hive skill

.agents/skills/hive

@@ -1 +0,0 @@
-../../.claude/skills/hive

.agents/skills/hive-concepts

@@ -1 +0,0 @@
-../../.claude/skills/hive-concepts

.agents/skills/hive-create

@@ -1 +0,0 @@
-../../.claude/skills/hive-create

.agents/skills/hive-credentials

@@ -1 +0,0 @@
-../../.claude/skills/hive-credentials

.agents/skills/hive-patterns

@@ -1 +0,0 @@
-../../.claude/skills/hive-patterns

.agents/skills/hive-test

@@ -1 +0,0 @@
-../../.claude/skills/hive-test

.claude/settings.local.json.example

@@ -1,34 +1,16 @@
 {
   "permissions": {
     "allow": [
-      "mcp__agent-builder__create_session",
-      "mcp__agent-builder__set_goal",
-      "mcp__agent-builder__add_node",
-      "mcp__agent-builder__add_edge",
-      "mcp__agent-builder__configure_loop",
-      "mcp__agent-builder__add_mcp_server",
-      "mcp__agent-builder__validate_graph",
-      "mcp__agent-builder__export_graph",
-      "mcp__agent-builder__load_session_by_id",
       "Bash(git status:*)",
       "Bash(gh run view:*)",
       "Bash(uv run:*)",
       "Bash(env:*)",
-      "mcp__agent-builder__test_node",
-      "mcp__agent-builder__list_mcp_tools",
       "Bash(python -m py_compile:*)",
       "Bash(python -m pytest:*)",
       "Bash(source:*)",
-      "mcp__agent-builder__update_node",
-      "mcp__agent-builder__check_missing_credentials",
-      "mcp__agent-builder__list_stored_credentials",
       "Bash(find:*)",
-      "mcp__agent-builder__run_tests",
-      "Bash(PYTHONPATH=core:exports:tools/src uv run pytest:*)",
-      "mcp__agent-builder__list_agent_sessions",
-      "mcp__agent-builder__generate_constraint_tests",
-      "mcp__agent-builder__generate_success_tests"
+      "Bash(PYTHONPATH=core:exports:tools/src uv run pytest:*)"
     ]
   },
-  "enabledMcpjsonServers": ["agent-builder", "tools"]
+  "enabledMcpjsonServers": ["tools"]
 }

.claude/skills/hive-concepts/SKILL.md

@@ -1,399 +0,0 @@
----
-name: hive-concepts
-description: Core concepts for goal-driven agents - architecture, node types (event_loop, function), tool discovery, and workflow overview. Use when starting agent development or need to understand agent fundamentals.
-license: Apache-2.0
-metadata:
-  author: hive
-  version: "2.0"
-  type: foundational
-  part_of: hive
----
-
-# Building Agents - Core Concepts
-
-Foundational knowledge for building goal-driven agents as Python packages.
-
-## Architecture: Python Services (Not JSON Configs)
-
-Agents are built as Python packages:
-
-```
-exports/my_agent/
-├── __init__.py          # Package exports
-├── __main__.py          # CLI (run, info, validate, shell)
-├── agent.py             # Graph construction (goal, edges, agent class)
-├── nodes/__init__.py    # Node definitions (NodeSpec)
-├── config.py            # Runtime config
-└── README.md            # Documentation
-```
-
-**Key Principle: Agent is visible and editable during build**
-
-- Files created immediately as components are approved
-- User can watch files grow in their editor
-- No session state - just direct file writes
-- No "export" step - agent is ready when build completes
-
-## Core Concepts
-
-### Goal
-
-Success criteria and constraints (written to agent.py)
-
-```python
-goal = Goal(
-    id="research-goal",
-    name="Technical Research Agent",
-    description="Research technical topics thoroughly",
-    success_criteria=[
-        SuccessCriterion(
-            id="completeness",
-            description="Cover all aspects of topic",
-            metric="coverage_score",
-            target=">=0.9",
-            weight=0.4,
-        ),
-        # 3-5 success criteria total
-    ],
-    constraints=[
-        Constraint(
-            id="accuracy",
-            description="All information must be verified",
-            constraint_type="hard",
-            category="quality",
-        ),
-        # 1-5 constraints total
-    ],
-)
-```
-
-### Node
-
-Unit of work (written to nodes/__init__.py)
-
-**Node Types:**
-
-- `event_loop` — Multi-turn streaming loop with tool execution and judge-based evaluation. Works with or without tools.
-- `function` — Deterministic Python operations. No LLM involved.
-
-```python
-search_node = NodeSpec(
-    id="search-web",
-    name="Search Web",
-    description="Search for information and extract results",
-    node_type="event_loop",
-    input_keys=["query"],
-    output_keys=["search_results"],
-    system_prompt="Search the web for: {query}. Use the web_search tool to find results, then call set_output to store them.",
-    tools=["web_search"],
-)
-```
-
-**NodeSpec Fields for Event Loop Nodes:**
-
-| Field | Default | Description |
-|-------|---------|-------------|
-| `client_facing` | `False` | If True, streams output to user and blocks for input between turns |
-| `nullable_output_keys` | `[]` | Output keys that may remain unset (for mutually exclusive outputs) |
-| `max_node_visits` | `1` | Max times this node executes per run. Set >1 for feedback loop targets |
-
-### Edge
-
-Connection between nodes (written to agent.py)
-
-**Edge Conditions:**
-
-- `on_success` — Proceed if node succeeds (most common)
-- `on_failure` — Handle errors
-- `always` — Always proceed
-- `conditional` — Based on expression evaluating node output
-
-**Edge Priority:**
-
-Priority controls evaluation order when multiple edges leave the same node. Higher priority edges are evaluated first. Use negative priority for feedback edges (edges that loop back to earlier nodes).
-
-```python
-# Forward edge (evaluated first)
-EdgeSpec(
-    id="review-to-campaign",
-    source="review",
-    target="campaign-builder",
-    condition=EdgeCondition.CONDITIONAL,
-    condition_expr="output.get('approved_contacts') is not None",
-    priority=1,
-)
-
-# Feedback edge (evaluated after forward edges)
-EdgeSpec(
-    id="review-feedback",
-    source="review",
-    target="extractor",
-    condition=EdgeCondition.CONDITIONAL,
-    condition_expr="output.get('redo_extraction') is not None",
-    priority=-1,
-)
-```
-
-### Client-Facing Nodes
-
-For multi-turn conversations with the user, set `client_facing=True` on a node. The node will:
-- Stream its LLM output directly to the end user
-- Block for user input between conversational turns
-- Resume when new input is injected via `inject_event()`
-
-```python
-intake_node = NodeSpec(
-    id="intake",
-    name="Intake",
-    description="Gather requirements from the user",
-    node_type="event_loop",
-    client_facing=True,
-    input_keys=[],
-    output_keys=["repo_url", "project_url"],
-    system_prompt="You are the intake agent. Ask the user for the repo URL and project URL.",
-)
-```
-
-> **Legacy Note:** The old `pause_nodes` / `entry_points` pattern still works but `client_facing=True` is preferred for new agents.
-
-**STEP 1 / STEP 2 Prompt Pattern:** For client-facing nodes, structure the system prompt with two explicit phases:
-
-```python
-system_prompt="""\
-**STEP 1 — Respond to the user (text only, NO tool calls):**
-[Present information, ask questions, etc.]
-
-**STEP 2 — After the user responds, call set_output:**
-[Call set_output with the structured outputs]
-"""
-```
-
-This prevents the LLM from calling `set_output` prematurely before the user has had a chance to respond.
-
-### Node Design: Fewer, Richer Nodes
-
-Prefer fewer nodes that do more work over many thin single-purpose nodes:
-
-- **Bad**: 8 thin nodes (parse query → search → fetch → evaluate → synthesize → write → check → save)
-- **Good**: 4 rich nodes (intake → research → review → report)
-
-Why: Each node boundary requires serializing outputs and passing context. Fewer nodes means the LLM retains full context of its work within the node. A research node that searches, fetches, and analyzes keeps all the source material in its conversation history.
-
-### nullable_output_keys for Cross-Edge Inputs
-
-When a node receives inputs that only arrive on certain edges (e.g., `feedback` only comes from a review → research feedback loop, not from intake → research), mark those keys as `nullable_output_keys`:
-
-```python
-research_node = NodeSpec(
-    id="research",
-    input_keys=["research_brief", "feedback"],
-    nullable_output_keys=["feedback"],  # Not present on first visit
-    max_node_visits=3,
-    ...
-)
-```
-
-## Event Loop Architecture Concepts
-
-### How EventLoopNode Works
-
-An event loop node runs a multi-turn loop:
-1. LLM receives system prompt + conversation history
-2. LLM responds (text and/or tool calls)
-3. Tool calls are executed, results added to conversation
-4. Judge evaluates: ACCEPT (exit loop), RETRY (loop again), or ESCALATE
-5. Repeat until judge ACCEPTs or max_iterations reached
-
-### EventLoopNode Runtime
-
-EventLoopNodes are **auto-created** by `GraphExecutor` at runtime. You do NOT need to manually register them. Both `GraphExecutor` (direct) and `AgentRuntime` / `create_agent_runtime()` handle event_loop nodes automatically.
-
-```python
-# Direct execution — executor auto-creates EventLoopNodes
-from framework.graph.executor import GraphExecutor
-from framework.runtime.core import Runtime
-
-runtime = Runtime(storage_path)
-executor = GraphExecutor(
-    runtime=runtime,
-    llm=llm,
-    tools=tools,
-    tool_executor=tool_executor,
-    storage_path=storage_path,
-)
-result = await executor.execute(graph=graph, goal=goal, input_data=input_data)
-
-# TUI execution — AgentRuntime also works
-from framework.runtime.agent_runtime import create_agent_runtime
-runtime = create_agent_runtime(
-    graph=graph, goal=goal, storage_path=storage_path,
-    entry_points=[...], llm=llm, tools=tools, tool_executor=tool_executor,
-)
-```
-
-### set_output
-
-Nodes produce structured outputs by calling `set_output(key, value)` — a synthetic tool injected by the framework. When the LLM calls `set_output`, the value is stored in the output accumulator and made available to downstream nodes via shared memory.
-
-`set_output` is NOT a real tool — it is excluded from `real_tool_results`. For client-facing nodes, this means a turn where the LLM only calls `set_output` (no other tools) is treated as a conversational boundary and will block for user input.
-
-### JudgeProtocol
-
-**The judge is the SOLE mechanism for acceptance decisions.** Do not add ad-hoc framework gating, output rollback, or premature rejection logic. If the LLM calls `set_output` too early, fix it with better prompts or a custom judge — not framework-level guards.
-
-The judge controls when a node's loop exits:
-- **Implicit judge** (default, no judge configured): ACCEPTs when the LLM finishes with no tool calls and all required output keys are set
-- **SchemaJudge**: Validates outputs against a Pydantic model
-- **Custom judges**: Implement `evaluate(context) -> JudgeVerdict`
-
-### LoopConfig
-
-Controls loop behavior:
-- `max_iterations` (default 50) — prevents infinite loops
-- `max_tool_calls_per_turn` (default 10) — limits tool calls per LLM response
-- `tool_call_overflow_margin` (default 0.5) — wiggle room before discarding extra tool calls (50% means hard cutoff at 150% of limit)
-- `stall_detection_threshold` (default 3) — detects repeated identical responses
-- `max_history_tokens` (default 32000) — triggers conversation compaction
-
-### Data Tools (Spillover Management)
-
-When tool results exceed the context window, the framework automatically saves them to a spillover directory and truncates with a hint. Nodes that produce or consume large data should include the data tools:
-
-- `save_data(filename, data)` — Write data to a file in the data directory
-- `load_data(filename, offset=0, limit=50)` — Read data with line-based pagination
-- `list_data_files()` — List available data files
-- `serve_file_to_user(filename, label="")` — Get a clickable file:// URI for the user
-
-Note: `data_dir` is a framework-injected context parameter — the LLM never sees or passes it. `GraphExecutor.execute()` sets it per-execution via `contextvars`, so data tools and spillover always share the same session-scoped directory.
-
-These are real MCP tools (not synthetic). Add them to nodes that handle large tool results:
-
-```python
-research_node = NodeSpec(
-    ...
-    tools=["web_search", "web_scrape", "load_data", "save_data", "list_data_files"],
-)
-```
-
-### Fan-Out / Fan-In
-
-Multiple ON_SUCCESS edges from the same source create parallel execution. All branches run concurrently via `asyncio.gather()`. Parallel event_loop nodes must have disjoint `output_keys`.
-
-### max_node_visits
-
-Controls how many times a node can execute in one graph run. Default is 1. Set higher for nodes that are targets of feedback edges (review-reject loops). Set 0 for unlimited (guarded by max_steps).
-
-## Tool Discovery & Validation
-
-**CRITICAL:** Before adding a node with tools, you MUST verify the tools exist.
-
-Tools are provided by MCP servers. Never assume a tool exists - always discover dynamically.
-
-### Step 1: Register MCP Server (if not already done)
-
-```python
-mcp__agent-builder__add_mcp_server(
-    name="tools",
-    transport="stdio",
-    command="python",
-    args='["mcp_server.py", "--stdio"]',
-    cwd="../tools"
-)
-```
-
-### Step 2: Discover Available Tools
-
-```python
-# List all tools from all registered servers
-mcp__agent-builder__list_mcp_tools()
-
-# Or list tools from a specific server
-mcp__agent-builder__list_mcp_tools(server_name="tools")
-```
-
-### Step 3: Validate Before Adding Nodes
-
-Before writing a node with `tools=[...]`:
-
-1. Call `list_mcp_tools()` to get available tools
-2. Check each tool in your node exists in the response
-3. If a tool doesn't exist:
-   - **DO NOT proceed** with the node
-   - Inform the user: "The tool 'X' is not available. Available tools are: ..."
-   - Ask if they want to use an alternative or proceed without the tool
-
-### Tool Validation Anti-Patterns
-
-- **Never assume a tool exists** - always call `list_mcp_tools()` first
-- **Never write a node with unverified tools** - validate before writing
-- **Never silently drop tools** - if a tool doesn't exist, inform the user
-- **Never guess tool names** - use exact names from discovery response
-
-## Workflow Overview: Incremental File Construction
-
-```
-1. CREATE PACKAGE → mkdir + write skeletons
-2. DEFINE GOAL → Write to agent.py + config.py
-3. FOR EACH NODE:
-   - Propose design (event_loop for LLM work, function for deterministic)
-   - User approves
-   - Write to nodes/__init__.py IMMEDIATELY
-   - (Optional) Validate with test_node
-4. CONNECT EDGES → Update agent.py
-   - Use priority for feedback edges (negative priority)
-   - (Optional) Validate with validate_graph
-5. FINALIZE → Write agent class to agent.py
-6. DONE - Agent ready at exports/my_agent/
-```
-
-**Files written immediately. MCP tools optional for validation/testing bookkeeping.**
-
-## When to Use This Skill
-
-Use hive-concepts when:
-- Starting a new agent project and need to understand fundamentals
-- Need to understand agent architecture before building
-- Want to validate tool availability before proceeding
-- Learning about node types, edges, and graph execution
-
-**Next Steps:**
-- Ready to build? → Use `hive-create` skill
-- Need patterns and examples? → Use `hive-patterns` skill
-
-## MCP Tools for Validation
-
-After writing files, optionally use MCP tools for validation:
-
-**test_node** - Validate node configuration with mock inputs
-```python
-mcp__agent-builder__test_node(
-    node_id="search-web",
-    test_input='{"query": "test query"}',
-    mock_llm_response='{"results": "mock output"}'
-)
-```
-
-**validate_graph** - Check graph structure
-```python
-mcp__agent-builder__validate_graph()
-# Returns: unreachable nodes, missing connections, event_loop validation, etc.
-```
-
-**configure_loop** - Set event loop parameters
-```python
-mcp__agent-builder__configure_loop(
-    max_iterations=50,
-    max_tool_calls_per_turn=10,
-    stall_detection_threshold=3,
-    max_history_tokens=32000
-)
-```
-
-**Key Point:** Files are written FIRST. MCP tools are for validation only.
-
-## Related Skills
-
-- **hive-create** - Step-by-step building process
-- **hive-patterns** - Best practices: judges, feedback edges, fan-out, context management
-- **hive** - Complete workflow orchestrator
-- **hive-test** - Test and validate completed agents

.claude/skills/hive-create/examples/deep_research_agent/__init__.py

@@ -1,24 +0,0 @@
-"""
-Deep Research Agent - Interactive, rigorous research with TUI conversation.
-
-Research any topic through multi-source web search, quality evaluation,
-and synthesis. Features client-facing TUI interaction at key checkpoints
-for user guidance and iterative deepening.
-"""
-
-from .agent import DeepResearchAgent, default_agent, goal, nodes, edges
-from .config import RuntimeConfig, AgentMetadata, default_config, metadata
-
-__version__ = "1.0.0"
-
-__all__ = [
-    "DeepResearchAgent",
-    "default_agent",
-    "goal",
-    "nodes",
-    "edges",
-    "RuntimeConfig",
-    "AgentMetadata",
-    "default_config",
-    "metadata",
-]

.claude/skills/hive-create/examples/deep_research_agent/__main__.py

@@ -1,241 +0,0 @@
-"""
-CLI entry point for Deep Research Agent.
-
-Uses AgentRuntime for multi-entrypoint support with HITL pause/resume.
-"""
-
-import asyncio
-import json
-import logging
-import sys
-import click
-
-from .agent import default_agent, DeepResearchAgent
-
-
-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():
-    """Deep Research Agent - Interactive, rigorous research with TUI conversation."""
-    pass
-
-
-@cli.command()
-@click.option("--topic", "-t", type=str, required=True, help="Research topic")
-@click.option("--mock", is_flag=True, help="Run in mock mode")
-@click.option("--quiet", "-q", is_flag=True, help="Only output result JSON")
-@click.option("--verbose", "-v", is_flag=True, help="Show execution details")
-@click.option("--debug", is_flag=True, help="Show debug logging")
-def run(topic, mock, quiet, verbose, debug):
-    """Execute research on a topic."""
-    if not quiet:
-        setup_logging(verbose=verbose, debug=debug)
-
-    context = {"topic": topic}
-
-    result = asyncio.run(default_agent.run(context, mock_mode=mock))
-
-    output_data = {
-        "success": result.success,
-        "steps_executed": result.steps_executed,
-        "output": result.output,
-    }
-    if result.error:
-        output_data["error"] = result.error
-
-    click.echo(json.dumps(output_data, indent=2, default=str))
-    sys.exit(0 if result.success else 1)
-
-
-@cli.command()
-@click.option("--mock", is_flag=True, help="Run in mock mode")
-@click.option("--verbose", "-v", is_flag=True, help="Show execution details")
-@click.option("--debug", is_flag=True, help="Show debug logging")
-def tui(mock, verbose, debug):
-    """Launch the TUI dashboard for interactive research."""
-    setup_logging(verbose=verbose, debug=debug)
-
-    try:
-        from framework.tui.app import AdenTUI
-    except ImportError:
-        click.echo(
-            "TUI requires the 'textual' package. Install with: pip install textual"
-        )
-        sys.exit(1)
-
-    from pathlib import Path
-
-    from framework.llm import LiteLLMProvider
-    from framework.runner.tool_registry import ToolRegistry
-    from framework.runtime.agent_runtime import create_agent_runtime
-    from framework.runtime.event_bus import EventBus
-    from framework.runtime.execution_stream import EntryPointSpec
-
-    async def run_with_tui():
-        agent = DeepResearchAgent()
-
-        # Build graph and tools
-        agent._event_bus = EventBus()
-        agent._tool_registry = ToolRegistry()
-
-        storage_path = Path.home() / ".hive" / "agents" / "deep_research_agent"
-        storage_path.mkdir(parents=True, exist_ok=True)
-
-        mcp_config_path = Path(__file__).parent / "mcp_servers.json"
-        if mcp_config_path.exists():
-            agent._tool_registry.load_mcp_config(mcp_config_path)
-
-        llm = None
-        if not mock:
-            llm = LiteLLMProvider(
-                model=agent.config.model,
-                api_key=agent.config.api_key,
-                api_base=agent.config.api_base,
-            )
-
-        tools = list(agent._tool_registry.get_tools().values())
-        tool_executor = agent._tool_registry.get_executor()
-        graph = agent._build_graph()
-
-        runtime = create_agent_runtime(
-            graph=graph,
-            goal=agent.goal,
-            storage_path=storage_path,
-            entry_points=[
-                EntryPointSpec(
-                    id="start",
-                    name="Start Research",
-                    entry_node="intake",
-                    trigger_type="manual",
-                    isolation_level="isolated",
-                ),
-            ],
-            llm=llm,
-            tools=tools,
-            tool_executor=tool_executor,
-        )
-
-        await runtime.start()
-
-        try:
-            app = AdenTUI(runtime)
-            await app.run_async()
-        finally:
-            await runtime.stop()
-
-    asyncio.run(run_with_tui())
-
-
-@cli.command()
-@click.option("--json", "output_json", is_flag=True)
-def info(output_json):
-    """Show agent information."""
-    info_data = default_agent.info()
-    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']}")
-        click.echo(f"Terminal: {', '.join(info_data['terminal_nodes'])}")
-
-
-@cli.command()
-def validate():
-    """Validate agent structure."""
-    validation = default_agent.validate()
-    if validation["valid"]:
-        click.echo("Agent is valid")
-        if validation["warnings"]:
-            for warning in validation["warnings"]:
-                click.echo(f"  WARNING: {warning}")
-    else:
-        click.echo("Agent has errors:")
-        for error in validation["errors"]:
-            click.echo(f"  ERROR: {error}")
-    sys.exit(0 if validation["valid"] else 1)
-
-
-@cli.command()
-@click.option("--verbose", "-v", is_flag=True)
-def shell(verbose):
-    """Interactive research session (CLI, no TUI)."""
-    asyncio.run(_interactive_shell(verbose))
-
-
-async def _interactive_shell(verbose=False):
-    """Async interactive shell."""
-    setup_logging(verbose=verbose)
-
-    click.echo("=== Deep Research Agent ===")
-    click.echo("Enter a topic to research (or 'quit' to exit):\n")
-
-    agent = DeepResearchAgent()
-    await agent.start()
-
-    try:
-        while True:
-            try:
-                topic = await asyncio.get_event_loop().run_in_executor(
-                    None, input, "Topic> "
-                )
-                if topic.lower() in ["quit", "exit", "q"]:
-                    click.echo("Goodbye!")
-                    break
-
-                if not topic.strip():
-                    continue
-
-                click.echo("\nResearching...\n")
-
-                result = await agent.trigger_and_wait("start", {"topic": topic})
-
-                if result is None:
-                    click.echo("\n[Execution timed out]\n")
-                    continue
-
-                if result.success:
-                    output = result.output
-                    if "report_content" in output:
-                        click.echo("\n--- Report ---\n")
-                        click.echo(output["report_content"])
-                        click.echo("\n")
-                    if "references" in output:
-                        click.echo("--- References ---\n")
-                        for ref in output.get("references", []):
-                            click.echo(
-                                f"  [{ref.get('number', '?')}] {ref.get('title', '')} - {ref.get('url', '')}"
-                            )
-                        click.echo("\n")
-                else:
-                    click.echo(f"\nResearch failed: {result.error}\n")
-
-            except KeyboardInterrupt:
-                click.echo("\nGoodbye!")
-                break
-            except Exception as e:
-                click.echo(f"Error: {e}", err=True)
-                import traceback
-
-                traceback.print_exc()
-    finally:
-        await agent.stop()
-
-
-if __name__ == "__main__":
-    cli()

.claude/skills/hive-create/examples/deep_research_agent/agent.py

@@ -1,358 +0,0 @@
-"""Agent graph construction for Deep Research Agent."""
-
-from pathlib import Path
-
-from framework.graph import EdgeSpec, EdgeCondition, Goal, SuccessCriterion, Constraint
-from framework.graph.edge import GraphSpec
-from framework.graph.executor import ExecutionResult
-from framework.graph.checkpoint_config import CheckpointConfig
-from framework.llm import LiteLLMProvider
-from framework.runner.tool_registry import ToolRegistry
-from framework.runtime.agent_runtime import AgentRuntime, create_agent_runtime
-from framework.runtime.execution_stream import EntryPointSpec
-
-from .config import default_config, metadata
-from .nodes import (
-    intake_node,
-    research_node,
-    review_node,
-    report_node,
-)
-
-# Goal definition
-goal = Goal(
-    id="rigorous-interactive-research",
-    name="Rigorous Interactive Research",
-    description=(
-        "Research any topic by searching diverse sources, analyzing findings, "
-        "and producing a cited report — with user checkpoints to guide direction."
-    ),
-    success_criteria=[
-        SuccessCriterion(
-            id="source-diversity",
-            description="Use multiple diverse, authoritative sources",
-            metric="source_count",
-            target=">=5",
-            weight=0.25,
-        ),
-        SuccessCriterion(
-            id="citation-coverage",
-            description="Every factual claim in the report cites its source",
-            metric="citation_coverage",
-            target="100%",
-            weight=0.25,
-        ),
-        SuccessCriterion(
-            id="user-satisfaction",
-            description="User reviews findings before report generation",
-            metric="user_approval",
-            target="true",
-            weight=0.25,
-        ),
-        SuccessCriterion(
-            id="report-completeness",
-            description="Final report answers the original research questions",
-            metric="question_coverage",
-            target="90%",
-            weight=0.25,
-        ),
-    ],
-    constraints=[
-        Constraint(
-            id="no-hallucination",
-            description="Only include information found in fetched sources",
-            constraint_type="quality",
-            category="accuracy",
-        ),
-        Constraint(
-            id="source-attribution",
-            description="Every claim must cite its source with a numbered reference",
-            constraint_type="quality",
-            category="accuracy",
-        ),
-        Constraint(
-            id="user-checkpoint",
-            description="Present findings to the user before writing the final report",
-            constraint_type="functional",
-            category="interaction",
-        ),
-    ],
-)
-
-# Node list
-nodes = [
-    intake_node,
-    research_node,
-    review_node,
-    report_node,
-]
-
-# Edge definitions
-edges = [
-    # intake -> research
-    EdgeSpec(
-        id="intake-to-research",
-        source="intake",
-        target="research",
-        condition=EdgeCondition.ON_SUCCESS,
-        priority=1,
-    ),
-    # research -> review
-    EdgeSpec(
-        id="research-to-review",
-        source="research",
-        target="review",
-        condition=EdgeCondition.ON_SUCCESS,
-        priority=1,
-    ),
-    # review -> research (feedback loop)
-    EdgeSpec(
-        id="review-to-research-feedback",
-        source="review",
-        target="research",
-        condition=EdgeCondition.CONDITIONAL,
-        condition_expr="needs_more_research == True",
-        priority=1,
-    ),
-    # review -> report (user satisfied)
-    EdgeSpec(
-        id="review-to-report",
-        source="review",
-        target="report",
-        condition=EdgeCondition.CONDITIONAL,
-        condition_expr="needs_more_research == False",
-        priority=2,
-    ),
-    # report -> research (user wants deeper research on current topic)
-    EdgeSpec(
-        id="report-to-research",
-        source="report",
-        target="research",
-        condition=EdgeCondition.CONDITIONAL,
-        condition_expr="str(next_action).lower() == 'more_research'",
-        priority=2,
-    ),
-    # report -> intake (user wants a new topic — default when not more_research)
-    EdgeSpec(
-        id="report-to-intake",
-        source="report",
-        target="intake",
-        condition=EdgeCondition.CONDITIONAL,
-        condition_expr="str(next_action).lower() != 'more_research'",
-        priority=1,
-    ),
-]
-
-# Graph configuration
-entry_node = "intake"
-entry_points = {"start": "intake"}
-pause_nodes = []
-terminal_nodes = []
-
-
-class DeepResearchAgent:
-    """
-    Deep Research Agent — 4-node pipeline with user checkpoints.
-
-    Flow: intake -> research -> review -> report
-                      ^           |
-                      +-- feedback loop (if user wants more)
-
-    Uses AgentRuntime for proper session management:
-    - Session-scoped storage (sessions/{session_id}/)
-    - Checkpointing for resume capability
-    - Runtime logging
-    - Data folder for save_data/load_data
-    """
-
-    def __init__(self, config=None):
-        self.config = config or default_config
-        self.goal = goal
-        self.nodes = nodes
-        self.edges = edges
-        self.entry_node = entry_node
-        self.entry_points = entry_points
-        self.pause_nodes = pause_nodes
-        self.terminal_nodes = terminal_nodes
-        self._graph: GraphSpec | None = None
-        self._agent_runtime: AgentRuntime | None = None
-        self._tool_registry: ToolRegistry | None = None
-        self._storage_path: Path | None = None
-
-    def _build_graph(self) -> GraphSpec:
-        """Build the GraphSpec."""
-        return GraphSpec(
-            id="deep-research-agent-graph",
-            goal_id=self.goal.id,
-            version="1.0.0",
-            entry_node=self.entry_node,
-            entry_points=self.entry_points,
-            terminal_nodes=self.terminal_nodes,
-            pause_nodes=self.pause_nodes,
-            nodes=self.nodes,
-            edges=self.edges,
-            default_model=self.config.model,
-            max_tokens=self.config.max_tokens,
-            loop_config={
-                "max_iterations": 100,
-                "max_tool_calls_per_turn": 30,
-                "max_history_tokens": 32000,
-            },
-            conversation_mode="continuous",
-            identity_prompt=(
-                "You are a rigorous research agent. You search for information "
-                "from diverse, authoritative sources, analyze findings critically, "
-                "and produce well-cited reports. You never fabricate information — "
-                "every claim must trace back to a source you actually retrieved."
-            ),
-        )
-
-    def _setup(self, mock_mode=False) -> None:
-        """Set up the agent runtime with sessions, checkpoints, and logging."""
-        self._storage_path = Path.home() / ".hive" / "agents" / "deep_research_agent"
-        self._storage_path.mkdir(parents=True, exist_ok=True)
-
-        self._tool_registry = ToolRegistry()
-
-        mcp_config_path = Path(__file__).parent / "mcp_servers.json"
-        if mcp_config_path.exists():
-            self._tool_registry.load_mcp_config(mcp_config_path)
-
-        llm = None
-        if not mock_mode:
-            llm = LiteLLMProvider(
-                model=self.config.model,
-                api_key=self.config.api_key,
-                api_base=self.config.api_base,
-            )
-
-        tool_executor = self._tool_registry.get_executor()
-        tools = list(self._tool_registry.get_tools().values())
-
-        self._graph = self._build_graph()
-
-        checkpoint_config = CheckpointConfig(
-            enabled=True,
-            checkpoint_on_node_start=False,
-            checkpoint_on_node_complete=True,
-            checkpoint_max_age_days=7,
-            async_checkpoint=True,
-        )
-
-        entry_point_specs = [
-            EntryPointSpec(
-                id="default",
-                name="Default",
-                entry_node=self.entry_node,
-                trigger_type="manual",
-                isolation_level="shared",
-            )
-        ]
-
-        self._agent_runtime = create_agent_runtime(
-            graph=self._graph,
-            goal=self.goal,
-            storage_path=self._storage_path,
-            entry_points=entry_point_specs,
-            llm=llm,
-            tools=tools,
-            tool_executor=tool_executor,
-            checkpoint_config=checkpoint_config,
-        )
-
-    async def start(self, mock_mode=False) -> None:
-        """Set up and start the agent runtime."""
-        if self._agent_runtime is None:
-            self._setup(mock_mode=mock_mode)
-        if not self._agent_runtime.is_running:
-            await self._agent_runtime.start()
-
-    async def stop(self) -> None:
-        """Stop the agent runtime and clean up."""
-        if self._agent_runtime and self._agent_runtime.is_running:
-            await self._agent_runtime.stop()
-        self._agent_runtime = None
-
-    async def trigger_and_wait(
-        self,
-        entry_point: str = "default",
-        input_data: dict | None = None,
-        timeout: float | None = None,
-        session_state: dict | None = None,
-    ) -> ExecutionResult | None:
-        """Execute the graph and wait for completion."""
-        if self._agent_runtime is None:
-            raise RuntimeError("Agent not started. Call start() first.")
-
-        return await self._agent_runtime.trigger_and_wait(
-            entry_point_id=entry_point,
-            input_data=input_data or {},
-            session_state=session_state,
-        )
-
-    async def run(
-        self, context: dict, mock_mode=False, session_state=None
-    ) -> ExecutionResult:
-        """Run the agent (convenience method for single execution)."""
-        await self.start(mock_mode=mock_mode)
-        try:
-            result = await self.trigger_and_wait(
-                "default", context, session_state=session_state
-            )
-            return result or ExecutionResult(success=False, error="Execution timeout")
-        finally:
-            await self.stop()
-
-    def info(self):
-        """Get agent information."""
-        return {
-            "name": metadata.name,
-            "version": metadata.version,
-            "description": metadata.description,
-            "goal": {
-                "name": self.goal.name,
-                "description": self.goal.description,
-            },
-            "nodes": [n.id for n in self.nodes],
-            "edges": [e.id for e in self.edges],
-            "entry_node": self.entry_node,
-            "entry_points": self.entry_points,
-            "pause_nodes": self.pause_nodes,
-            "terminal_nodes": self.terminal_nodes,
-            "client_facing_nodes": [n.id for n in self.nodes if n.client_facing],
-        }
-
-    def validate(self):
-        """Validate agent structure."""
-        errors = []
-        warnings = []
-
-        node_ids = {node.id for node in self.nodes}
-        for edge in self.edges:
-            if edge.source not in node_ids:
-                errors.append(f"Edge {edge.id}: source '{edge.source}' not found")
-            if edge.target not in node_ids:
-                errors.append(f"Edge {edge.id}: target '{edge.target}' not found")
-
-        if self.entry_node not in node_ids:
-            errors.append(f"Entry node '{self.entry_node}' not found")
-
-        for terminal in self.terminal_nodes:
-            if terminal not in node_ids:
-                errors.append(f"Terminal node '{terminal}' not found")
-
-        for ep_id, node_id in self.entry_points.items():
-            if node_id not in node_ids:
-                errors.append(
-                    f"Entry point '{ep_id}' references unknown node '{node_id}'"
-                )
-
-        return {
-            "valid": len(errors) == 0,
-            "errors": errors,
-            "warnings": warnings,
-        }
-
-
-# Create default instance
-default_agent = DeepResearchAgent()

.claude/skills/hive-create/examples/deep_research_agent/config.py

@@ -1,26 +0,0 @@
-"""Runtime configuration."""
-
-from dataclasses import dataclass
-
-from framework.config import RuntimeConfig
-
-default_config = RuntimeConfig()
-
-
-@dataclass
-class AgentMetadata:
-    name: str = "Deep Research Agent"
-    version: str = "1.0.0"
-    description: str = (
-        "Interactive research agent that rigorously investigates topics through "
-        "multi-source search, quality evaluation, and synthesis - with TUI conversation "
-        "at key checkpoints for user guidance and feedback."
-    )
-    intro_message: str = (
-        "Hi! I'm your deep research assistant. Tell me a topic and I'll investigate it "
-        "thoroughly — searching multiple sources, evaluating quality, and synthesizing "
-        "a comprehensive report. What would you like me to research?"
-    )
-
-
-metadata = AgentMetadata()

.claude/skills/hive-create/examples/deep_research_agent/nodes/__init__.py

@@ -1,213 +0,0 @@
-"""Node definitions for Deep Research Agent."""
-
-from framework.graph import NodeSpec
-
-# Node 1: Intake (client-facing)
-# Brief conversation to clarify what the user wants researched.
-intake_node = NodeSpec(
-    id="intake",
-    name="Research Intake",
-    description="Discuss the research topic with the user, clarify scope, and confirm direction",
-    node_type="event_loop",
-    client_facing=True,
-    max_node_visits=0,
-    input_keys=["topic"],
-    output_keys=["research_brief"],
-    success_criteria=(
-        "The research brief is specific and actionable: it states the topic, "
-        "the key questions to answer, the desired scope, and depth."
-    ),
-    system_prompt="""\
-You are a research intake specialist. The user wants to research a topic.
-Have a brief conversation to clarify what they need.
-
-**STEP 1 — Read and respond (text only, NO tool calls):**
-1. Read the topic provided
-2. If it's vague, ask 1-2 clarifying questions (scope, angle, depth)
-3. If it's already clear, confirm your understanding and ask the user to confirm
-
-Keep it short. Don't over-ask.
-
-**STEP 2 — After the user confirms, call set_output:**
-- set_output("research_brief", "A clear paragraph describing exactly what to research, \
-what questions to answer, what scope to cover, and how deep to go.")
-""",
-    tools=[],
-)
-
-# Node 2: Research
-# The workhorse — searches the web, fetches content, analyzes sources.
-# One node with both tools avoids the context-passing overhead of 5 separate nodes.
-research_node = NodeSpec(
-    id="research",
-    name="Research",
-    description="Search the web, fetch source content, and compile findings",
-    node_type="event_loop",
-    max_node_visits=0,
-    input_keys=["research_brief", "feedback"],
-    output_keys=["findings", "sources", "gaps"],
-    nullable_output_keys=["feedback"],
-    success_criteria=(
-        "Findings reference at least 3 distinct sources with URLs. "
-        "Key claims are substantiated by fetched content, not generated."
-    ),
-    system_prompt="""\
-You are a research agent. Given a research brief, find and analyze sources.
-
-If feedback is provided, this is a follow-up round — focus on the gaps identified.
-
-Work in phases:
-1. **Search**: Use web_search with 3-5 diverse queries covering different angles.
-   Prioritize authoritative sources (.edu, .gov, established publications).
-2. **Fetch**: Use web_scrape on the most promising URLs (aim for 5-8 sources).
-   Skip URLs that fail. Extract the substantive content.
-3. **Analyze**: Review what you've collected. Identify key findings, themes,
-   and any contradictions between sources.
-
-Important:
-- Work in batches of 3-4 tool calls at a time — never more than 10 per turn
-- After each batch, assess whether you have enough material
-- Prefer quality over quantity — 5 good sources beat 15 thin ones
-- Track which URL each finding comes from (you'll need citations later)
-- Call set_output for each key in a SEPARATE turn (not in the same turn as other tool calls)
-
-Context management:
-- Your tool results are automatically saved to files. After compaction, the file \
-references remain in the conversation — use load_data() to recover any content you need.
-- Use append_data('research_notes.md', ...) to maintain a running log of key findings \
-as you go. This survives compaction and helps the report node produce a detailed report.
-
-When done, use set_output (one key at a time, separate turns):
-- set_output("findings", "Structured summary: key findings with source URLs for each claim. \
-Include themes, contradictions, and confidence levels.")
-- set_output("sources", [{"url": "...", "title": "...", "summary": "..."}])
-- set_output("gaps", "What aspects of the research brief are NOT well-covered yet, if any.")
-""",
-    tools=[
-        "web_search",
-        "web_scrape",
-        "load_data",
-        "save_data",
-        "append_data",
-        "list_data_files",
-    ],
-)
-
-# Node 3: Review (client-facing)
-# Shows the user what was found and asks whether to dig deeper or proceed.
-review_node = NodeSpec(
-    id="review",
-    name="Review Findings",
-    description="Present findings to user and decide whether to research more or write the report",
-    node_type="event_loop",
-    client_facing=True,
-    max_node_visits=0,
-    input_keys=["findings", "sources", "gaps", "research_brief"],
-    output_keys=["needs_more_research", "feedback"],
-    success_criteria=(
-        "The user has been presented with findings and has explicitly indicated "
-        "whether they want more research or are ready for the report."
-    ),
-    system_prompt="""\
-Present the research findings to the user clearly and concisely.
-
-**STEP 1 — Present (your first message, text only, NO tool calls):**
-1. **Summary** (2-3 sentences of what was found)
-2. **Key Findings** (bulleted, with confidence levels)
-3. **Sources Used** (count and quality assessment)
-4. **Gaps** (what's still unclear or under-covered)
-
-End by asking: Are they satisfied, or do they want deeper research? \
-Should we proceed to writing the final report?
-
-**STEP 2 — After the user responds, call set_output:**
-- set_output("needs_more_research", "true")  — if they want more
-- set_output("needs_more_research", "false") — if they're satisfied
-- set_output("feedback", "What the user wants explored further, or empty string")
-""",
-    tools=[],
-)
-
-# Node 4: Report (client-facing)
-# Writes an HTML report, serves the link to the user, and answers follow-ups.
-report_node = NodeSpec(
-    id="report",
-    name="Write & Deliver Report",
-    description="Write a cited HTML report from the findings and present it to the user",
-    node_type="event_loop",
-    client_facing=True,
-    max_node_visits=0,
-    input_keys=["findings", "sources", "research_brief"],
-    output_keys=["delivery_status", "next_action"],
-    success_criteria=(
-        "An HTML report has been saved, the file link has been presented to the user, "
-        "and the user has indicated what they want to do next."
-    ),
-    system_prompt="""\
-Write a research report as an HTML file and present it to the user.
-
-IMPORTANT: save_data requires TWO separate arguments: filename and data.
-Call it like: save_data(filename="report.html", data="<html>...</html>")
-Do NOT use _raw, do NOT nest arguments inside a JSON string.
-
-**STEP 1 — Write and save the HTML report (tool calls, NO text to user yet):**
-
-Build a clean HTML document. Keep the HTML concise — aim for clarity over length.
-Use minimal embedded CSS (a few lines of style, not a full framework).
-
-Report structure:
-- Title & date
-- Executive Summary (2-3 paragraphs)
-- Key Findings (organized by theme, with [n] citation links)
-- Analysis (synthesis, implications)
-- Conclusion (key takeaways)
-- References (numbered list with clickable URLs)
-
-Requirements:
-- Every factual claim must cite its source with [n] notation
-- Be objective — present multiple viewpoints where sources disagree
-- Answer the original research questions from the brief
-- If findings appear incomplete or summarized, call list_data_files() and load_data() \
-to access the detailed source material from the research phase. The research node's \
-tool results and research_notes.md contain the full data.
-
-Save the HTML:
-  save_data(filename="report.html", data="<html>...</html>")
-
-Then get the clickable link:
-  serve_file_to_user(filename="report.html", label="Research Report")
-
-If save_data fails, simplify and shorten the HTML, then retry.
-
-**STEP 2 — Present the link to the user (text only, NO tool calls):**
-
-Tell the user the report is ready and include the file:// URI from
-serve_file_to_user so they can click it to open. Give a brief summary
-of what the report covers. Ask if they have questions or want to continue.
-
-**STEP 3 — After the user responds:**
-- Answer any follow-up questions from the research material
-- When the user is ready to move on, ask what they'd like to do next:
-  - Research a new topic?
-  - Dig deeper into the current topic?
-- Then call set_output:
-  - set_output("delivery_status", "completed")
-  - set_output("next_action", "new_topic")       — if they want a new topic
-  - set_output("next_action", "more_research")   — if they want deeper research
-""",
-    tools=[
-        "save_data",
-        "append_data",
-        "edit_data",
-        "serve_file_to_user",
-        "load_data",
-        "list_data_files",
-    ],
-)
-
-__all__ = [
-    "intake_node",
-    "research_node",
-    "review_node",
-    "report_node",
-]

.claude/skills/hive-credentials/SKILL.md

@@ -1,640 +0,0 @@
----
-name: hive-credentials
-description: Set up and install credentials for an agent. Detects missing credentials from agent config, collects them from the user, and stores them securely in the local encrypted store at ~/.hive/credentials.
-license: Apache-2.0
-metadata:
-  author: hive
-  version: "2.3"
-  type: utility
----
-
-# Setup Credentials
-
-Interactive credential setup for agents with multiple authentication options. Detects what's missing, offers auth method choices, validates with health checks, and stores credentials securely.
-
-## When to Use
-
-- Before running or testing an agent for the first time
-- When `AgentRunner.run()` fails with "missing required credentials"
-- When a user asks to configure credentials for an agent
-- After building a new agent that uses tools requiring API keys
-
-## Workflow
-
-### Step 1: Identify the Agent
-
-Determine which agent needs credentials. The user will either:
-
-- Name the agent directly (e.g., "set up credentials for hubspot-agent")
-- Have an agent directory open (check `exports/` for agent dirs)
-- Be working on an agent in the current session
-
-Locate the agent's directory under `exports/{agent_name}/`.
-
-### Step 2: Detect Missing Credentials
-
-Use the `check_missing_credentials` MCP tool to detect what the agent needs and what's already configured. This tool loads the agent, inspects its required tools and node types, maps them to credentials via `CREDENTIAL_SPECS`, and checks both the encrypted store and environment variables.
-
-```
-check_missing_credentials(agent_path="exports/{agent_name}")
-```
-
-The tool returns a JSON response:
-
-```json
-{
-  "agent": "exports/{agent_name}",
-  "missing": [
-    {
-      "credential_name": "brave_search",
-      "env_var": "BRAVE_SEARCH_API_KEY",
-      "description": "Brave Search API key for web search",
-      "help_url": "https://brave.com/search/api/",
-      "tools": ["web_search"]
-    }
-  ],
-  "available": [
-    {
-      "credential_name": "anthropic",
-      "env_var": "ANTHROPIC_API_KEY",
-      "source": "encrypted_store"
-    }
-  ],
-  "total_missing": 1,
-  "ready": false
-}
-```
-
-**If `ready` is true (nothing missing):** Report all credentials as configured and skip Steps 3-5. Example:
-
-```
-All required credentials are already configured:
-  ✓ anthropic (ANTHROPIC_API_KEY)
-  ✓ brave_search (BRAVE_SEARCH_API_KEY)
-Your agent is ready to run!
-```
-
-**If credentials are missing:** Continue to Step 3 with the `missing` list.
-
-### Step 3: Present Auth Options for Each Missing Credential
-
-For each missing credential, check what authentication methods are available:
-
-```python
-from aden_tools.credentials import CREDENTIAL_SPECS
-
-spec = CREDENTIAL_SPECS.get("hubspot")
-if spec:
-    # Determine available auth options
-    auth_options = []
-    if spec.aden_supported:
-        auth_options.append("aden")
-    if spec.direct_api_key_supported:
-        auth_options.append("direct")
-    auth_options.append("custom")  # Always available
-
-    # Get setup info
-    setup_info = {
-        "env_var": spec.env_var,
-        "description": spec.description,
-        "help_url": spec.help_url,
-        "api_key_instructions": spec.api_key_instructions,
-    }
-```
-
-Present the available options using AskUserQuestion:
-
-```
-Choose how to configure HUBSPOT_ACCESS_TOKEN:
-
-  1) Aden Platform (OAuth) (Recommended)
-     Secure OAuth2 flow via hive.adenhq.com
-     - Quick setup with automatic token refresh
-     - No need to manage API keys manually
-
-  2) Direct API Key
-     Enter your own API key manually
-     - Requires creating a HubSpot Private App
-     - Full control over scopes and permissions
-
-  3) Local Credential Setup (Advanced)
-     Programmatic configuration for CI/CD
-     - For automated deployments
-     - Requires manual API calls
-```
-
-### Step 4: Execute Auth Flow Based on User Choice
-
-#### Prerequisite: Ensure HIVE_CREDENTIAL_KEY Is Available
-
-Before storing any credentials, verify `HIVE_CREDENTIAL_KEY` is set (needed to encrypt/decrypt the local store). Check both the current session and shell config:
-
-```bash
-# Check current session
-printenv HIVE_CREDENTIAL_KEY > /dev/null 2>&1 && echo "session: set" || echo "session: not set"
-
-# Check shell config files
-for f in ~/.zshrc ~/.bashrc ~/.profile; do [ -f "$f" ] && grep -q 'HIVE_CREDENTIAL_KEY' "$f" && echo "$f"; done
-```
-
-- **In current session** — proceed to store credentials
-- **In shell config but NOT in current session** — run `source ~/.zshrc` (or `~/.bashrc`) first, then proceed
-- **Not set anywhere** — `EncryptedFileStorage` will auto-generate one. After storing, tell the user to persist it: `export HIVE_CREDENTIAL_KEY="{generated_key}"` in their shell profile
-
-> **⚠️ IMPORTANT: After adding `HIVE_CREDENTIAL_KEY` to the user's shell config, always display:**
-> ```
-> ⚠️  Environment variables were added to your shell config.
->     Open a NEW TERMINAL for them to take effect outside this session.
-> ```
-
-#### Option 1: Aden Platform (OAuth)
-
-This is the recommended flow for supported integrations (HubSpot, etc.).
-
-**How Aden OAuth Works:**
-
-The ADEN_API_KEY represents a user who has already completed OAuth authorization on Aden's platform. When users sign up and connect integrations on Aden, those OAuth tokens are stored server-side. Having an ADEN_API_KEY means:
-
-1. User has an Aden account
-2. User has already authorized integrations (HubSpot, etc.) via OAuth on Aden
-3. We just need to sync those credentials down to the local credential store
-
-**4.1a. Check for ADEN_API_KEY**
-
-```python
-import os
-aden_key = os.environ.get("ADEN_API_KEY")
-```
-
-If not set, guide user to get one from Aden (this is where they do OAuth):
-
-```python
-from aden_tools.credentials import open_browser, get_aden_setup_url
-
-# Open browser to Aden - user will sign up and connect integrations there
-url = get_aden_setup_url()  # https://hive.adenhq.com
-success, msg = open_browser(url)
-
-print("Please sign in to Aden and connect your integrations (HubSpot, etc.).")
-print("Once done, copy your API key and return here.")
-```
-
-Ask user to provide the ADEN_API_KEY they received.
-
-**4.1b. Save ADEN_API_KEY to Shell Config**
-
-With user approval, persist ADEN_API_KEY to their shell config:
-
-```python
-from aden_tools.credentials import (
-    detect_shell,
-    add_env_var_to_shell_config,
-    get_shell_source_command,
-)
-
-shell_type = detect_shell()  # 'bash', 'zsh', or 'unknown'
-
-# Ask user for approval before modifying shell config
-# If approved:
-success, config_path = add_env_var_to_shell_config(
-    "ADEN_API_KEY",
-    user_provided_key,
-    comment="Aden Platform (OAuth) API key"
-)
-
-if success:
-    source_cmd = get_shell_source_command()
-    print(f"Saved to {config_path}")
-    print(f"Run: {source_cmd}")
-```
-
-> **⚠️ IMPORTANT: After adding `ADEN_API_KEY` to the user's shell config, always display:**
-> ```
-> ⚠️  Environment variables were added to your shell config.
->     Open a NEW TERMINAL for them to take effect outside this session.
-> ```
-
-Also save to `~/.hive/configuration.json` for the framework:
-
-```python
-import json
-from pathlib import Path
-
-config_path = Path.home() / ".hive" / "configuration.json"
-config = json.loads(config_path.read_text()) if config_path.exists() else {}
-
-config["aden"] = {
-    "api_key_configured": True,
-    "api_url": "https://api.adenhq.com"
-}
-
-config_path.parent.mkdir(parents=True, exist_ok=True)
-config_path.write_text(json.dumps(config, indent=2))
-```
-
-**4.1c. Sync Credentials from Aden Server**
-
-Since the user has already authorized integrations on Aden, use the one-liner factory method:
-
-```python
-from core.framework.credentials import CredentialStore
-
-# This single call handles everything:
-# - Creates encrypted local storage at ~/.hive/credentials
-# - Configures Aden client from ADEN_API_KEY env var
-# - Syncs all credentials from Aden server automatically
-store = CredentialStore.with_aden_sync(
-    base_url="https://api.adenhq.com",
-    auto_sync=True,  # Syncs on creation
-)
-
-# Check what was synced
-synced = store.list_credentials()
-print(f"Synced credentials: {synced}")
-
-# If the required credential wasn't synced, the user hasn't authorized it on Aden yet
-if "hubspot" not in synced:
-    print("HubSpot not found in your Aden account.")
-    print("Please visit https://hive.adenhq.com to connect HubSpot, then try again.")
-```
-
-For more control over the sync process:
-
-```python
-from core.framework.credentials import CredentialStore
-from core.framework.credentials.aden import (
-    AdenCredentialClient,
-    AdenClientConfig,
-    AdenSyncProvider,
-)
-
-# Create client (API key loaded from ADEN_API_KEY env var)
-client = AdenCredentialClient(AdenClientConfig(
-    base_url="https://api.adenhq.com",
-))
-
-# Create provider and store
-provider = AdenSyncProvider(client=client)
-store = CredentialStore.with_encrypted_storage()
-
-# Manual sync
-synced_count = provider.sync_all(store)
-print(f"Synced {synced_count} credentials from Aden")
-```
-
-**4.1d. Run Health Check**
-
-```python
-from aden_tools.credentials import check_credential_health
-
-# Get the token from the store
-cred = store.get_credential("hubspot")
-token = cred.keys["access_token"].value.get_secret_value()
-
-result = check_credential_health("hubspot", token)
-if result.valid:
-    print("HubSpot credentials validated successfully!")
-else:
-    print(f"Validation failed: {result.message}")
-    # Offer to retry the OAuth flow
-```
-
-#### Option 2: Direct API Key
-
-For users who prefer manual API key management.
-
-**4.2a. Show Setup Instructions**
-
-```python
-from aden_tools.credentials import CREDENTIAL_SPECS
-
-spec = CREDENTIAL_SPECS.get("hubspot")
-if spec and spec.api_key_instructions:
-    print(spec.api_key_instructions)
-# Output:
-# To get a HubSpot Private App token:
-# 1. Go to HubSpot Settings > Integrations > Private Apps
-# 2. Click "Create a private app"
-# 3. Name your app (e.g., "Hive Agent")
-# ...
-
-if spec and spec.help_url:
-    print(f"More info: {spec.help_url}")
-```
-
-**4.2b. Collect API Key from User**
-
-Use AskUserQuestion to securely collect the API key:
-
-```
-Please provide your HubSpot access token:
-(This will be stored securely in ~/.hive/credentials)
-```
-
-**4.2c. Run Health Check Before Storing**
-
-```python
-from aden_tools.credentials import check_credential_health
-
-result = check_credential_health("hubspot", user_provided_token)
-if not result.valid:
-    print(f"Warning: {result.message}")
-    # Ask user if they want to:
-    # 1. Try a different token
-    # 2. Continue anyway (not recommended)
-```
-
-**4.2d. Store in Local Encrypted Store**
-
-```python
-from core.framework.credentials import CredentialStore, CredentialObject, CredentialKey
-from pydantic import SecretStr
-
-store = CredentialStore.with_encrypted_storage()
-
-cred = CredentialObject(
-    id="hubspot",
-    name="HubSpot Access Token",
-    keys={
-        "access_token": CredentialKey(
-            name="access_token",
-            value=SecretStr(user_provided_token),
-        )
-    },
-)
-store.save_credential(cred)
-```
-
-**4.2e. Export to Current Session**
-
-```bash
-export HUBSPOT_ACCESS_TOKEN="the-value"
-```
-
-#### Option 3: Local Credential Setup (Advanced)
-
-For programmatic/CI/CD setups.
-
-**4.3a. Show Documentation**
-
-```
-For advanced credential management, you can use the CredentialStore API directly:
-
-  from core.framework.credentials import CredentialStore, CredentialObject, CredentialKey
-  from pydantic import SecretStr
-
-  store = CredentialStore.with_encrypted_storage()
-
-  cred = CredentialObject(
-      id="hubspot",
-      name="HubSpot Access Token",
-      keys={"access_token": CredentialKey(name="access_token", value=SecretStr("..."))}
-  )
-  store.save_credential(cred)
-
-For CI/CD environments:
-  - Set HIVE_CREDENTIAL_KEY for encryption
-  - Pre-populate ~/.hive/credentials programmatically
-  - Or use environment variables directly (HUBSPOT_ACCESS_TOKEN)
-
-Documentation: See core/framework/credentials/README.md
-```
-
-### Step 5: Record Configuration Method
-
-Track which auth method was used for each credential in `~/.hive/configuration.json`:
-
-```python
-import json
-from pathlib import Path
-from datetime import datetime
-
-config_path = Path.home() / ".hive" / "configuration.json"
-config = json.loads(config_path.read_text()) if config_path.exists() else {}
-
-if "credential_methods" not in config:
-    config["credential_methods"] = {}
-
-config["credential_methods"]["hubspot"] = {
-    "method": "aden",  # or "direct" or "custom"
-    "configured_at": datetime.now().isoformat(),
-}
-
-config_path.write_text(json.dumps(config, indent=2))
-```
-
-### Step 6: Verify All Credentials
-
-Use the `verify_credentials` MCP tool to confirm everything is properly configured:
-
-```
-verify_credentials(agent_path="exports/{agent_name}")
-```
-
-The tool returns:
-
-```json
-{
-  "agent": "exports/{agent_name}",
-  "ready": true,
-  "missing_credentials": [],
-  "warnings": [],
-  "errors": []
-}
-```
-
-If `ready` is true, report success. If `missing_credentials` is non-empty, identify what failed and loop back to Step 3 for the remaining credentials.
-
-## Health Check Reference
-
-Health checks validate credentials by making lightweight API calls:
-
-| Credential      | Endpoint                                | What It Checks                    |
-| --------------- | --------------------------------------- | --------------------------------- |
-| `anthropic`     | `POST /v1/messages`                     | API key validity                  |
-| `brave_search`  | `GET /res/v1/web/search?q=test&count=1` | API key validity                  |
-| `google_search` | `GET /customsearch/v1?q=test&num=1`     | API key + CSE ID validity         |
-| `github`        | `GET /user`                             | Token validity, user identity     |
-| `hubspot`       | `GET /crm/v3/objects/contacts?limit=1`  | Bearer token validity, CRM scopes |
-| `resend`        | `GET /domains`                          | API key validity                  |
-
-```python
-from aden_tools.credentials import check_credential_health, HealthCheckResult
-
-result: HealthCheckResult = check_credential_health("hubspot", token_value)
-# result.valid: bool
-# result.message: str
-# result.details: dict (status_code, rate_limited, etc.)
-```
-
-## Encryption Key (HIVE_CREDENTIAL_KEY)
-
-The local encrypted store requires `HIVE_CREDENTIAL_KEY` to encrypt/decrypt credentials.
-
-- If the user doesn't have one, `EncryptedFileStorage` will auto-generate one and log it
-- The user MUST persist this key (e.g., in `~/.bashrc`/`~/.zshrc` or a secrets manager)
-- Without this key, stored credentials cannot be decrypted
-
-**Shell config rule:** Only TWO keys belong in shell config (`~/.zshrc`/`~/.bashrc`):
-- `HIVE_CREDENTIAL_KEY` — encryption key for the credential store
-- `ADEN_API_KEY` — Aden platform auth key (needed before the store can sync)
-
-All other API keys (Brave, Google, HubSpot, etc.) must go in the encrypted store only. **Never offer to add them to shell config.**
-
-If `HIVE_CREDENTIAL_KEY` is not set:
-
-1. Let the store generate one
-2. Tell the user to save it: `export HIVE_CREDENTIAL_KEY="{generated_key}"`
-3. Recommend adding it to `~/.bashrc` or their shell profile
-
-## Security Rules
-
-- **NEVER** log, print, or echo credential values in tool output
-- **NEVER** store credentials in plaintext files, git-tracked files, or agent configs
-- **NEVER** hardcode credentials in source code
-- **NEVER** offer to save API keys to shell config (`~/.zshrc`/`~/.bashrc`) — the **only** keys that belong in shell config are `HIVE_CREDENTIAL_KEY` and `ADEN_API_KEY`. All other credentials (Brave, Google, HubSpot, GitHub, Resend, etc.) go in the encrypted store only.
-- **ALWAYS** use `SecretStr` from Pydantic when handling credential values in Python
-- **ALWAYS** use the local encrypted store (`~/.hive/credentials`) for persistence
-- **ALWAYS** run health checks before storing credentials (when possible)
-- **ALWAYS** verify credentials were stored by re-running validation, not by reading them back
-- When modifying `~/.bashrc` or `~/.zshrc`, confirm with the user first
-
-## Credential Sources Reference
-
-All credential specs are defined in `tools/src/aden_tools/credentials/`:
-
-| File              | Category      | Credentials                                   | Aden Supported |
-| ----------------- | ------------- | --------------------------------------------- | -------------- |
-| `llm.py`          | LLM Providers | `anthropic`                                   | No             |
-| `search.py`       | Search Tools  | `brave_search`, `google_search`, `google_cse` | No             |
-| `email.py`        | Email         | `resend`                                      | No             |
-| `integrations.py` | Integrations  | `github`, `hubspot`, `google_calendar_oauth`  | No / Yes       |
-
-**Note:** Additional LLM providers (Cerebras, Groq, OpenAI) are handled by LiteLLM via environment
-variables (`CEREBRAS_API_KEY`, `GROQ_API_KEY`, `OPENAI_API_KEY`) but are not yet in CREDENTIAL_SPECS.
-Add them to `llm.py` as needed.
-
-To check what's registered:
-
-```python
-from aden_tools.credentials import CREDENTIAL_SPECS
-for name, spec in CREDENTIAL_SPECS.items():
-    print(f"{name}: aden={spec.aden_supported}, direct={spec.direct_api_key_supported}")
-```
-
-## Migration: CredentialManager → CredentialStore
-
-**CredentialManager is deprecated.** Use CredentialStore instead.
-
-| Old (Deprecated)                          | New (Recommended)                                                    |
-| ----------------------------------------- | -------------------------------------------------------------------- |
-| `CredentialManager()`                     | `CredentialStore.with_encrypted_storage()`                           |
-| `creds.get("hubspot")`                    | `store.get("hubspot")` or `store.get_key("hubspot", "access_token")` |
-| `creds.validate_for_tools(tools)`         | Use `store.is_available(cred_id)` per credential                     |
-| `creds.get_auth_options("hubspot")`       | Check `CREDENTIAL_SPECS["hubspot"].aden_supported`                   |
-| `creds.get_setup_instructions("hubspot")` | Access `CREDENTIAL_SPECS["hubspot"]` directly                        |
-
-**Why migrate?**
-
-- **CredentialStore** supports encrypted storage, multi-key credentials, template resolution, and automatic token refresh
-- **CredentialManager** only reads from environment variables and .env files (no encryption, no refresh)
-- **CredentialStoreAdapter** exists for backward compatibility during migration
-
-```python
-# Old way (deprecated)
-from aden_tools.credentials import CredentialManager
-creds = CredentialManager()
-token = creds.get("hubspot")
-
-# New way (recommended)
-from core.framework.credentials import CredentialStore
-store = CredentialStore.with_encrypted_storage()
-token = store.get("hubspot")
-
-# With Aden sync (recommended for OAuth integrations)
-store = CredentialStore.with_aden_sync()
-token = store.get_key("hubspot", "access_token")
-```
-
-## Example Session
-
-```
-User: /hive-credentials for my research-agent
-
-Agent: Let me check what credentials your research-agent needs.
-
-[Calls check_missing_credentials(agent_path="exports/research-agent")]
-→ Returns:
-  available: anthropic (encrypted_store), brave_search (encrypted_store)
-  missing: google_search (GOOGLE_API_KEY), google_cse (GOOGLE_CSE_ID)
-  ready: false
-
-Agent: 2 of 4 required credentials are already configured. Only Google Custom
-Search needs setup (2 values).
-
---- Setting up Google Custom Search (google_search + google_cse) ---
-
-This requires two values that work together.
-
-[Checks HIVE_CREDENTIAL_KEY before storing]
-$ printenv HIVE_CREDENTIAL_KEY > /dev/null 2>&1 && echo "set" || echo "not set"
-set
-
-First, the Google API Key:
-1. Go to https://console.cloud.google.com/apis/credentials
-2. Create a new project (or select an existing one)
-3. Enable the "Custom Search API" from the API Library
-4. Go to Credentials > Create Credentials > API Key
-5. Copy the generated API key
-
-[AskUserQuestion: "Please provide your Google API key:"]
-[User provides key]
-
-Now, the Custom Search Engine ID:
-1. Go to https://programmablesearchengine.google.com/controlpanel/all
-2. Click "Add" to create a new search engine
-3. Under "What to search", select "Search the entire web"
-4. Give your search engine a name
-5. Click "Create"
-6. Copy the Search Engine ID (cx value)
-
-[AskUserQuestion: "Please provide your Google CSE ID:"]
-[User provides ID]
-
-[Runs health check with both values - GET /customsearch/v1?q=test&num=1 → 200 OK]
-[Stores both in local encrypted store, exports to env]
-
-✓ Google Custom Search credentials valid
-
-[Calls verify_credentials(agent_path="exports/research-agent")]
-→ Returns: ready: true, missing_credentials: []
-
-All credentials are now configured:
-  ✓ anthropic (ANTHROPIC_API_KEY) — already in encrypted store
-  ✓ brave_search (BRAVE_SEARCH_API_KEY) — already in encrypted store
-  ✓ google_search (GOOGLE_API_KEY) — stored in encrypted store
-  ✓ google_cse (GOOGLE_CSE_ID) — stored in encrypted store
-
-┌─────────────────────────────────────────────────────────────────────────────┐
-│                      ✅ CREDENTIALS CONFIGURED                              │
-├─────────────────────────────────────────────────────────────────────────────┤
-│                                                                             │
-│     OPEN A NEW TERMINAL before running commands below.                      │
-│     Environment variables were saved to your shell config but               │
-│     only take effect in new terminal sessions.                              │
-│                                                                             │
-│  NEXT STEPS:                                                                │
-│                                                                             │
-│  1. RUN YOUR AGENT:                                                         │
-│                                                                             │
-│     hive tui                                                                │
-│                                                                             │
-│  2. IF YOU ENCOUNTER ISSUES, USE THE DEBUGGER:                              │
-│                                                                             │
-│     /hive-debugger                                                          │
-│                                                                             │
-│     The debugger analyzes runtime logs, identifies retry loops, tool        │
-│     failures, stalled execution, and provides actionable fix suggestions.   │
-│                                                                             │
-└─────────────────────────────────────────────────────────────────────────────┘
-```

.claude/skills/hive-patterns/SKILL.md

@@ -1,385 +0,0 @@
----
-name: hive-patterns
-description: Best practices, patterns, and examples for building goal-driven agents. Includes client-facing interaction, feedback edges, judge patterns, fan-out/fan-in, context management, and anti-patterns.
-license: Apache-2.0
-metadata:
-  author: hive
-  version: "2.0"
-  type: reference
-  part_of: hive
----
-
-# Building Agents - Patterns & Best Practices
-
-Design patterns, examples, and best practices for building robust goal-driven agents.
-
-**Prerequisites:** Complete agent structure using `hive-create`.
-
-## Practical Example: Hybrid Workflow
-
-How to build a node using both direct file writes and optional MCP validation:
-
-```python
-# 1. WRITE TO FILE FIRST (Primary - makes it visible)
-node_code = '''
-search_node = NodeSpec(
-    id="search-web",
-    node_type="event_loop",
-    input_keys=["query"],
-    output_keys=["search_results"],
-    system_prompt="Search the web for: {query}. Use web_search, then call set_output to store results.",
-    tools=["web_search"],
-)
-'''
-
-Edit(
-    file_path="exports/research_agent/nodes/__init__.py",
-    old_string="# Nodes will be added here",
-    new_string=node_code
-)
-
-# 2. OPTIONALLY VALIDATE WITH MCP (Secondary - bookkeeping)
-validation = mcp__agent-builder__test_node(
-    node_id="search-web",
-    test_input='{"query": "python tutorials"}',
-    mock_llm_response='{"search_results": [...mock results...]}'
-)
-```
-
-**User experience:**
-
-- Immediately sees node in their editor (from step 1)
-- Gets validation feedback (from step 2)
-- Can edit the file directly if needed
-
-## Multi-Turn Interaction Patterns
-
-For agents needing multi-turn conversations with users, use `client_facing=True` on event_loop nodes.
-
-### Client-Facing Nodes
-
-A client-facing node streams LLM output to the user and blocks for user input between conversational turns. This replaces the old pause/resume pattern.
-
-```python
-# Client-facing node with STEP 1/STEP 2 prompt pattern
-intake_node = NodeSpec(
-    id="intake",
-    name="Intake",
-    description="Gather requirements from the user",
-    node_type="event_loop",
-    client_facing=True,
-    input_keys=["topic"],
-    output_keys=["research_brief"],
-    system_prompt="""\
-You are an intake specialist.
-
-**STEP 1 — Read and respond (text only, NO tool calls):**
-1. Read the topic provided
-2. If it's vague, ask 1-2 clarifying questions
-3. If it's clear, confirm your understanding
-
-**STEP 2 — After the user confirms, call set_output:**
-- set_output("research_brief", "Clear description of what to research")
-""",
-)
-
-# Internal node runs without user interaction
-research_node = NodeSpec(
-    id="research",
-    name="Research",
-    description="Search and analyze sources",
-    node_type="event_loop",
-    input_keys=["research_brief"],
-    output_keys=["findings", "sources"],
-    system_prompt="Research the topic using web_search and web_scrape...",
-    tools=["web_search", "web_scrape", "load_data", "save_data"],
-)
-```
-
-**How it works:**
-
-- Client-facing nodes stream LLM text to the user and block for input after each response
-- User input is injected via `node.inject_event(text)`
-- When the LLM calls `set_output` to produce structured outputs, the judge evaluates and ACCEPTs
-- Internal nodes (non-client-facing) run their entire loop without blocking
-- `set_output` is a synthetic tool — a turn with only `set_output` calls (no real tools) triggers user input blocking
-
-**STEP 1/STEP 2 pattern:** Always structure client-facing prompts with explicit phases. STEP 1 is text-only conversation. STEP 2 calls `set_output` after user confirmation. This prevents the LLM from calling `set_output` prematurely before the user responds.
-
-### When to Use client_facing
-
-| Scenario                            | client_facing | Why                    |
-| ----------------------------------- | :-----------: | ---------------------- |
-| Gathering user requirements         |      Yes      | Need user input        |
-| Human review/approval checkpoint    |      Yes      | Need human decision    |
-| Data processing (scanning, scoring) |      No       | Runs autonomously      |
-| Report generation                   |      No       | No user input needed   |
-| Final confirmation before action    |      Yes      | Need explicit approval |
-
-> **Legacy Note:** The `pause_nodes` / `entry_points` pattern still works for backward compatibility but `client_facing=True` is preferred for new agents.
-
-## Edge-Based Routing and Feedback Loops
-
-### Conditional Edge Routing
-
-Multiple conditional edges from the same source replace the old `router` node type. Each edge checks a condition on the node's output.
-
-```python
-# Node with mutually exclusive outputs
-review_node = NodeSpec(
-    id="review",
-    name="Review",
-    node_type="event_loop",
-    client_facing=True,
-    output_keys=["approved_contacts", "redo_extraction"],
-    nullable_output_keys=["approved_contacts", "redo_extraction"],
-    max_node_visits=3,
-    system_prompt="Present the contact list to the operator. If they approve, call set_output('approved_contacts', ...). If they want changes, call set_output('redo_extraction', 'true').",
-)
-
-# Forward edge (positive priority, evaluated first)
-EdgeSpec(
-    id="review-to-campaign",
-    source="review",
-    target="campaign-builder",
-    condition=EdgeCondition.CONDITIONAL,
-    condition_expr="output.get('approved_contacts') is not None",
-    priority=1,
-)
-
-# Feedback edge (negative priority, evaluated after forward edges)
-EdgeSpec(
-    id="review-feedback",
-    source="review",
-    target="extractor",
-    condition=EdgeCondition.CONDITIONAL,
-    condition_expr="output.get('redo_extraction') is not None",
-    priority=-1,
-)
-```
-
-**Key concepts:**
-
-- `nullable_output_keys`: Lists output keys that may remain unset. The node sets exactly one of the mutually exclusive keys per execution.
-- `max_node_visits`: Must be >1 on the feedback target (extractor) so it can re-execute. Default is 1.
-- `priority`: Positive = forward edge (evaluated first). Negative = feedback edge. The executor tries forward edges first; if none match, falls back to feedback edges.
-
-### Routing Decision Table
-
-| Pattern                | Old Approach            | New Approach                                  |
-| ---------------------- | ----------------------- | --------------------------------------------- |
-| Conditional branching  | `router` node           | Conditional edges with `condition_expr`       |
-| Binary approve/reject  | `pause_nodes` + resume  | `client_facing=True` + `nullable_output_keys` |
-| Loop-back on rejection | Manual entry_points     | Feedback edge with `priority=-1`              |
-| Multi-way routing      | Router with routes dict | Multiple conditional edges with priorities    |
-
-## Judge Patterns
-
-**Core Principle: The judge is the SOLE mechanism for acceptance decisions.** Never add ad-hoc framework gating to compensate for LLM behavior. If the LLM calls `set_output` prematurely, fix the system prompt or use a custom judge. Anti-patterns to avoid:
-
-- Output rollback logic
-- `_user_has_responded` flags
-- Premature set_output rejection
-- Interaction protocol injection into system prompts
-
-Judges control when an event_loop node's loop exits. Choose based on validation needs.
-
-### Implicit Judge (Default)
-
-When no judge is configured, the implicit judge ACCEPTs when:
-
-- The LLM finishes its response with no tool calls
-- All required output keys have been set via `set_output`
-
-Best for simple nodes where "all outputs set" is sufficient validation.
-
-### SchemaJudge
-
-Validates outputs against a Pydantic model. Use when you need structural validation.
-
-```python
-from pydantic import BaseModel
-
-class ScannerOutput(BaseModel):
-    github_users: list[dict]  # Must be a list of user objects
-
-class SchemaJudge:
-    def __init__(self, output_model: type[BaseModel]):
-        self._model = output_model
-
-    async def evaluate(self, context: dict) -> JudgeVerdict:
-        missing = context.get("missing_keys", [])
-        if missing:
-            return JudgeVerdict(
-                action="RETRY",
-                feedback=f"Missing output keys: {missing}. Use set_output to provide them.",
-            )
-        try:
-            self._model.model_validate(context["output_accumulator"])
-            return JudgeVerdict(action="ACCEPT")
-        except ValidationError as e:
-            return JudgeVerdict(action="RETRY", feedback=str(e))
-```
-
-### When to Use Which Judge
-
-| Judge           | Use When                              | Example                |
-| --------------- | ------------------------------------- | ---------------------- |
-| Implicit (None) | Output keys are sufficient validation | Simple data extraction |
-| SchemaJudge     | Need structural validation of outputs | API response parsing   |
-| Custom          | Domain-specific validation logic      | Score must be 0.0-1.0  |
-
-## Fan-Out / Fan-In (Parallel Execution)
-
-Multiple ON_SUCCESS edges from the same source trigger parallel execution. All branches run concurrently via `asyncio.gather()`.
-
-```python
-# Scanner fans out to Profiler and Scorer in parallel
-EdgeSpec(id="scanner-to-profiler", source="scanner", target="profiler",
-         condition=EdgeCondition.ON_SUCCESS)
-EdgeSpec(id="scanner-to-scorer", source="scanner", target="scorer",
-         condition=EdgeCondition.ON_SUCCESS)
-
-# Both fan in to Extractor
-EdgeSpec(id="profiler-to-extractor", source="profiler", target="extractor",
-         condition=EdgeCondition.ON_SUCCESS)
-EdgeSpec(id="scorer-to-extractor", source="scorer", target="extractor",
-         condition=EdgeCondition.ON_SUCCESS)
-```
-
-**Requirements:**
-
-- Parallel event_loop nodes must have **disjoint output_keys** (no key written by both)
-- Only one parallel branch may contain a `client_facing` node
-- Fan-in node receives outputs from all completed branches in shared memory
-
-## Context Management Patterns
-
-### Tiered Compaction
-
-EventLoopNode automatically manages context window usage with tiered compaction:
-
-1. **Pruning** — Old tool results replaced with compact placeholders (zero-cost, no LLM call)
-2. **Normal compaction** — LLM summarizes older messages
-3. **Aggressive compaction** — Keeps only recent messages + summary
-4. **Emergency** — Hard reset with tool history preservation
-
-### Spillover Pattern
-
-The framework automatically truncates large tool results and saves full content to a spillover directory. The LLM receives a truncation message with instructions to use `load_data` to read the full result.
-
-For explicit data management, use the data tools (real MCP tools, not synthetic):
-
-```python
-# save_data, load_data, list_data_files, serve_file_to_user are real MCP tools
-# data_dir is auto-injected by the framework — the LLM never sees it
-
-# Saving large results
-save_data(filename="sources.json", data=large_json_string)
-
-# Reading with pagination (line-based offset/limit)
-load_data(filename="sources.json", offset=0, limit=50)
-
-# Listing available files
-list_data_files()
-
-# Serving a file to the user as a clickable link
-serve_file_to_user(filename="report.html", label="Research Report")
-```
-
-Add data tools to nodes that handle large tool results:
-
-```python
-research_node = NodeSpec(
-    ...
-    tools=["web_search", "web_scrape", "load_data", "save_data", "list_data_files"],
-)
-```
-
-`data_dir` is a framework context parameter — auto-injected at call time. `GraphExecutor.execute()` sets it per-execution via `ToolRegistry.set_execution_context(data_dir=...)` (using `contextvars` for concurrency safety), ensuring it matches the session-scoped spillover directory.
-
-## Anti-Patterns
-
-### What NOT to Do
-
-- **Don't rely on `export_graph`** — Write files immediately, not at end
-- **Don't hide code in session** — Write to files as components are approved
-- **Don't wait to write files** — Agent visible from first step
-- **Don't batch everything** — Write incrementally, one component at a time
-- **Don't create too many thin nodes** — Prefer fewer, richer nodes (see below)
-- **Don't add framework gating for LLM behavior** — Fix prompts or use judges instead
-
-### Fewer, Richer Nodes
-
-A common mistake is splitting work into too many small single-purpose nodes. Each node boundary requires serializing outputs, losing in-context information, and adding edge complexity.
-
-| Bad (8 thin nodes)  | Good (4 rich nodes)                 |
-| ------------------- | ----------------------------------- |
-| parse-query         | intake (client-facing)              |
-| search-sources      | research (search + fetch + analyze) |
-| fetch-content       | review (client-facing)              |
-| evaluate-sources    | report (write + deliver)            |
-| synthesize-findings |                                     |
-| write-report        |                                     |
-| quality-check       |                                     |
-| save-report         |                                     |
-
-**Why fewer nodes are better:**
-
-- The LLM retains full context of its work within a single node
-- A research node that searches, fetches, and analyzes keeps all source material in its conversation history
-- Fewer edges means simpler graph and fewer failure points
-- Data tools (`save_data`/`load_data`) handle context window limits within a single node
-
-### MCP Tools - Correct Usage
-
-**MCP tools OK for:**
-
-- `test_node` — Validate node configuration with mock inputs
-- `validate_graph` — Check graph structure
-- `configure_loop` — Set event loop parameters
-- `create_session` — Track session state for bookkeeping
-
-**Just don't:** Use MCP as the primary construction method or rely on export_graph
-
-## Error Handling Patterns
-
-### Graceful Failure with Fallback
-
-```python
-edges = [
-    # Success path
-    EdgeSpec(id="api-success", source="api-call", target="process-results",
-             condition=EdgeCondition.ON_SUCCESS),
-    # Fallback on failure
-    EdgeSpec(id="api-to-fallback", source="api-call", target="fallback-cache",
-             condition=EdgeCondition.ON_FAILURE, priority=1),
-    # Report if fallback also fails
-    EdgeSpec(id="fallback-to-error", source="fallback-cache", target="report-error",
-             condition=EdgeCondition.ON_FAILURE, priority=1),
-]
-```
-
-## Handoff to Testing
-
-When agent is complete, transition to testing phase:
-
-### Pre-Testing Checklist
-
-- [ ] Agent structure validates: `uv run python -m agent_name validate`
-- [ ] All nodes defined in nodes/**init**.py
-- [ ] All edges connect valid nodes with correct priorities
-- [ ] Feedback edge targets have `max_node_visits > 1`
-- [ ] Client-facing nodes have meaningful system prompts
-- [ ] Agent can be imported: `from exports.agent_name import default_agent`
-
-## Related Skills
-
-- **hive-concepts** — Fundamental concepts (node types, edges, event loop architecture)
-- **hive-create** — Step-by-step building process
-- **hive-test** — Test and validate agents
-- **hive** — Complete workflow orchestrator
-
----
-
-**Remember: Agent is actively constructed, visible the whole time. No hidden state. No surprise exports. Just transparent, incremental file building.**

.claude/skills/hive-test/SKILL.md

@@ -1,940 +0,0 @@
----
-name: hive-test
-description: Iterative agent testing with session recovery. Execute, analyze, fix, resume from checkpoints. Use when testing an agent, debugging test failures, or verifying fixes without re-running from scratch.
----
-
-# Agent Testing
-
-Test agents iteratively: execute, analyze failures, fix, resume from checkpoint, repeat.
-
-## When to Use
-
-- Testing a newly built agent against its goal
-- Debugging a failing agent iteratively
-- Verifying fixes without re-running expensive early nodes
-- Running final regression tests before deployment
-
-## Prerequisites
-
-1. Agent package at `exports/{agent_name}/` (built with `/hive-create`)
-2. Credentials configured (`/hive-credentials`)
-3. `ANTHROPIC_API_KEY` set (or appropriate LLM provider key)
-
-**Path distinction** (critical — don't confuse these):
-- `exports/{agent_name}/` — agent source code (edit here)
-- `~/.hive/agents/{agent_name}/` — runtime data: sessions, checkpoints, logs (read here)
-
----
-
-## The Iterative Test Loop
-
-This is the core workflow. Don't re-run the entire agent when a late node fails — analyze, fix, and resume from the last clean checkpoint.
-
-```
-┌──────────────────────────────────────┐
-│ PHASE 1: Generate Test Scenarios     │
-│ Goal → synthetic test inputs + tests │
-└──────────────┬───────────────────────┘
-               ↓
-┌──────────────────────────────────────┐
-│ PHASE 2: Execute                     │◄────────────────┐
-│ Run agent (CLI or pytest)            │                 │
-└──────────────┬───────────────────────┘                 │
-               ↓                                         │
-          Pass? ──yes──► PHASE 6: Final Verification     │
-               │                                         │
-               no                                        │
-               ↓                                         │
-┌──────────────────────────────────────┐                 │
-│ PHASE 3: Analyze                     │                 │
-│ Session + runtime logs + checkpoints │                 │
-└──────────────┬───────────────────────┘                 │
-               ↓                                         │
-┌──────────────────────────────────────┐                 │
-│ PHASE 4: Fix                         │                 │
-│ Prompt / code / graph / goal         │                 │
-└──────────────┬───────────────────────┘                 │
-               ↓                                         │
-┌──────────────────────────────────────┐                 │
-│ PHASE 5: Recover & Resume            │─────────────────┘
-│ Checkpoint resume OR fresh re-run    │
-└──────────────────────────────────────┘
-```
-
----
-
-### Phase 1: Generate Test Scenarios
-
-Create synthetic tests from the agent's goal, constraints, and success criteria.
-
-#### Step 1a: Read the goal
-
-```python
-# Read goal from agent.py
-Read(file_path="exports/{agent_name}/agent.py")
-# Extract the Goal definition and convert to JSON string
-```
-
-#### Step 1b: Get test guidelines
-
-```python
-# Get constraint test guidelines
-generate_constraint_tests(
-    goal_id="your-goal-id",
-    goal_json='{"id": "...", "constraints": [...]}',
-    agent_path="exports/{agent_name}"
-)
-
-# Get success criteria test guidelines
-generate_success_tests(
-    goal_id="your-goal-id",
-    goal_json='{"id": "...", "success_criteria": [...]}',
-    node_names="intake,research,review,report",
-    tool_names="web_search,web_scrape",
-    agent_path="exports/{agent_name}"
-)
-```
-
-These return `file_header`, `test_template`, `constraints_formatted`/`success_criteria_formatted`, and `test_guidelines`. They do NOT generate test code — you write the tests.
-
-#### Step 1c: Write tests
-
-```python
-Write(
-    file_path=result["output_file"],
-    content=result["file_header"] + "\n\n" + your_test_code
-)
-```
-
-#### Test writing rules
-
-- Every test MUST be `async` with `@pytest.mark.asyncio`
-- Every test MUST accept `runner, auto_responder, mock_mode` fixtures
-- Use `await auto_responder.start()` before running, `await auto_responder.stop()` in `finally`
-- Use `await runner.run(input_dict)` — this goes through AgentRunner → AgentRuntime → ExecutionStream
-- Access output via `result.output.get("key")` — NEVER `result.output["key"]`
-- `result.success=True` means no exception, NOT goal achieved — always check output
-- Write 8-15 tests total, not 30+
-- Each real test costs ~3 seconds + LLM tokens
-- NEVER use `default_agent.run()` — it bypasses the runtime (no sessions, no logs, client-facing nodes hang)
-
-#### Step 1d: Check existing tests
-
-Before generating, check if tests already exist:
-
-```python
-list_tests(
-    goal_id="your-goal-id",
-    agent_path="exports/{agent_name}"
-)
-```
-
----
-
-### Phase 2: Execute
-
-Two execution paths, use the right one for your situation.
-
-#### Iterative debugging (for complex agents)
-
-Run the agent via CLI. This creates sessions with checkpoints at `~/.hive/agents/{agent_name}/sessions/`:
-
-```bash
-uv run hive run exports/{agent_name} --input '{"query": "test topic"}'
-```
-
-Sessions and checkpoints are saved automatically.
-
-**Client-facing nodes**: Agents with `client_facing=True` nodes (interactive conversation) work in headless mode when run from a real terminal — the agent streams output to stdout and reads user input from stdin via a `>>> ` prompt. In non-interactive shells (like Claude Code's Bash tool), client-facing nodes will hang because there is no stdin. For testing interactive agents from Claude Code, use `run_tests` with mock mode or have the user run the agent manually in their terminal.
-
-#### Automated regression (for CI or final verification)
-
-Use the `run_tests` MCP tool to run all pytest tests:
-
-```python
-run_tests(
-    goal_id="your-goal-id",
-    agent_path="exports/{agent_name}"
-)
-```
-
-Returns structured results:
-```json
-{
-  "overall_passed": false,
-  "summary": {"total": 12, "passed": 10, "failed": 2, "pass_rate": "83.3%"},
-  "test_results": [{"test_name": "test_success_source_diversity", "status": "failed"}],
-  "failures": [{"test_name": "test_success_source_diversity", "details": "..."}]
-}
-```
-
-**Options:**
-```python
-# Run only constraint tests
-run_tests(goal_id, agent_path, test_types='["constraint"]')
-
-# Stop on first failure
-run_tests(goal_id, agent_path, fail_fast=True)
-
-# Parallel execution
-run_tests(goal_id, agent_path, parallel=4)
-```
-
-**Note:** `run_tests` uses `AgentRunner` with `tmp_path` storage, so sessions are isolated per test run. For checkpoint-based recovery with persistent sessions, use CLI execution. Use `run_tests` for quick regression checks and final verification.
-
----
-
-### Phase 3: Analyze Failures
-
-When a test fails, drill down systematically. Don't guess — use the tools.
-
-#### Step 3a: Get error category
-
-```python
-debug_test(
-    goal_id="your-goal-id",
-    test_name="test_success_source_diversity",
-    agent_path="exports/{agent_name}"
-)
-```
-
-Returns error category (`IMPLEMENTATION_ERROR`, `ASSERTION_FAILURE`, `TIMEOUT`, `IMPORT_ERROR`, `API_ERROR`) plus full traceback and suggestions.
-
-#### Step 3b: Find the failed session
-
-```python
-list_agent_sessions(
-    agent_work_dir="~/.hive/agents/{agent_name}",
-    status="failed",
-    limit=5
-)
-```
-
-Returns session list with IDs, timestamps, current_node (where it failed), execution_quality.
-
-#### Step 3c: Inspect session state
-
-```python
-get_agent_session_state(
-    agent_work_dir="~/.hive/agents/{agent_name}",
-    session_id="session_20260209_143022_abc12345"
-)
-```
-
-Returns execution path, which node was current, step count, timestamps — but excludes memory values (to avoid context bloat). Shows `memory_keys` and `memory_size` instead.
-
-#### Step 3d: Examine runtime logs (L2/L3)
-
-```python
-# L2: Per-node success/failure, retry counts
-query_runtime_log_details(
-    agent_work_dir="~/.hive/agents/{agent_name}",
-    run_id="session_20260209_143022_abc12345",
-    needs_attention_only=True
-)
-
-# L3: Exact LLM responses, tool call inputs/outputs
-query_runtime_log_raw(
-    agent_work_dir="~/.hive/agents/{agent_name}",
-    run_id="session_20260209_143022_abc12345",
-    node_id="research"
-)
-```
-
-#### Step 3e: Inspect memory data
-
-```python
-# See what data a node actually produced
-get_agent_session_memory(
-    agent_work_dir="~/.hive/agents/{agent_name}",
-    session_id="session_20260209_143022_abc12345",
-    key="research_results"
-)
-```
-
-#### Step 3f: Find recovery points
-
-```python
-list_agent_checkpoints(
-    agent_work_dir="~/.hive/agents/{agent_name}",
-    session_id="session_20260209_143022_abc12345",
-    is_clean="true"
-)
-```
-
-Returns checkpoint summaries with IDs, types (`node_start`, `node_complete`), which node, and `is_clean` flag. Clean checkpoints are safe resume points.
-
-#### Step 3g: Compare checkpoints (optional)
-
-To understand what changed between two points in execution:
-
-```python
-compare_agent_checkpoints(
-    agent_work_dir="~/.hive/agents/{agent_name}",
-    session_id="session_20260209_143022_abc12345",
-    checkpoint_id_before="cp_node_complete_research_143030",
-    checkpoint_id_after="cp_node_complete_review_143115"
-)
-```
-
-Returns memory diff (added/removed/changed keys) and execution path diff.
-
----
-
-### Phase 4: Fix Based on Root Cause
-
-Use the analysis from Phase 3 to determine what to fix and where.
-
-| Root Cause | What to Fix | Where to Edit |
-|------------|------------|---------------|
-| **Prompt issue** — LLM produces wrong output format, misses instructions | Node `system_prompt` | `exports/{agent}/nodes/__init__.py` |
-| **Code bug** — TypeError, KeyError, logic error in Python | Agent code | `exports/{agent}/agent.py`, `nodes/__init__.py` |
-| **Graph issue** — wrong routing, missing edge, bad condition_expr | Edges, node config | `exports/{agent}/agent.py` |
-| **Tool issue** — MCP tool fails, wrong config, missing credential | Tool config | `exports/{agent}/mcp_servers.json`, `/hive-credentials` |
-| **Goal issue** — success criteria too strict/vague, wrong constraints | Goal definition | `exports/{agent}/agent.py` (goal section) |
-| **Test issue** — test expectations don't match actual agent behavior | Test code | `exports/{agent}/tests/test_*.py` |
-
-#### Fix strategies by error category
-
-**IMPLEMENTATION_ERROR** (TypeError, AttributeError, KeyError):
-```python
-# Read the failing code
-Read(file_path="exports/{agent_name}/nodes/__init__.py")
-
-# Fix the bug
-Edit(
-    file_path="exports/{agent_name}/nodes/__init__.py",
-    old_string="results.get('videos')",
-    new_string="(results or {}).get('videos', [])"
-)
-```
-
-**ASSERTION_FAILURE** (test assertions fail but agent ran successfully):
-- Check if the agent's output is actually wrong → fix the prompt
-- Check if the test's expectations are unrealistic → fix the test
-- Use `get_agent_session_memory` to see what the agent actually produced
-
-**TIMEOUT / STALL** (agent runs too long):
-- Check `node_visit_counts` for feedback loops hitting max_node_visits
-- Check L3 logs for tool calls that hang
-- Reduce `max_iterations` in loop_config or fix the prompt to converge faster
-
-**API_ERROR** (connection, rate limit, auth):
-- Verify credentials with `/hive-credentials`
-- Check MCP server configuration
-
----
-
-### Phase 5: Recover & Resume
-
-After fixing the agent, decide whether to resume or re-run.
-
-#### When to resume from checkpoint
-
-Resume when ALL of these are true:
-- The fix is to a node that comes AFTER existing clean checkpoints
-- Clean checkpoints exist (from a CLI execution with checkpointing)
-- The early nodes are expensive (web scraping, API calls, long LLM chains)
-
-```bash
-# Resume from the last clean checkpoint before the failing node
-uv run hive run exports/{agent_name} \
-  --resume-session session_20260209_143022_abc12345 \
-  --checkpoint cp_node_complete_research_143030
-```
-
-This skips all nodes before the checkpoint and only re-runs the fixed node onward.
-
-#### When to re-run from scratch
-
-Re-run when ANY of these are true:
-- The fix is to the entry node or an early node
-- No checkpoints exist (e.g., agent was run via `run_tests`)
-- The agent is fast (2-3 nodes, completes in seconds)
-- You changed the graph structure (added/removed nodes/edges)
-
-```bash
-uv run hive run exports/{agent_name} --input '{"query": "test topic"}'
-```
-
-#### Inspecting a checkpoint before resuming
-
-```python
-get_agent_checkpoint(
-    agent_work_dir="~/.hive/agents/{agent_name}",
-    session_id="session_20260209_143022_abc12345",
-    checkpoint_id="cp_node_complete_research_143030"
-)
-```
-
-Returns the full checkpoint: shared_memory snapshot, execution_path, current_node, next_node, is_clean.
-
-#### Loop back to Phase 2
-
-After resuming or re-running, check if the fix worked. If not, go back to Phase 3.
-
----
-
-### Phase 6: Final Verification
-
-Once the iterative fix loop converges (the agent produces correct output), run the full automated test suite:
-
-```python
-run_tests(
-    goal_id="your-goal-id",
-    agent_path="exports/{agent_name}"
-)
-```
-
-All tests should pass. If not, repeat the loop for remaining failures.
-
----
-
-## Credential Requirements
-
-**CRITICAL: Testing requires ALL credentials the agent depends on.** This includes both the LLM API key AND any tool-specific credentials (HubSpot, Brave Search, etc.).
-
-### Prerequisites
-
-Before running agent tests, you MUST collect ALL required credentials from the user.
-
-**Step 1: LLM API Key (always required)**
-```bash
-export ANTHROPIC_API_KEY="your-key-here"
-```
-
-**Step 2: Tool-specific credentials (depends on agent's tools)**
-
-Inspect the agent's `mcp_servers.json` and tool configuration to determine which tools the agent uses, then check for all required credentials:
-
-```python
-from aden_tools.credentials import CredentialManager, CREDENTIAL_SPECS
-
-creds = CredentialManager()
-
-# Determine which tools the agent uses (from agent.json or mcp_servers.json)
-agent_tools = [...]  # e.g., ["hubspot_search_contacts", "web_search", ...]
-
-# Find all missing credentials for those tools
-missing = creds.get_missing_for_tools(agent_tools)
-```
-
-Common tool credentials:
-| Tool | Env Var | Help URL |
-|------|---------|----------|
-| HubSpot CRM | `HUBSPOT_ACCESS_TOKEN` | https://developers.hubspot.com/docs/api/private-apps |
-| Brave Search | `BRAVE_SEARCH_API_KEY` | https://brave.com/search/api/ |
-| Google Search | `GOOGLE_SEARCH_API_KEY` + `GOOGLE_SEARCH_CX` | https://developers.google.com/custom-search |
-
-**Why ALL credentials are required:**
-- Tests need to execute the agent's LLM nodes to validate behavior
-- Tools with missing credentials will return error dicts instead of real data
-- Mock mode bypasses everything, providing no confidence in real-world performance
-
-### Mock Mode Limitations
-
-Mock mode (`--mock` flag or `MOCK_MODE=1`) is **ONLY for structure validation**:
-
-- Validates graph structure (nodes, edges, connections)
-- Validates that `AgentRunner.load()` succeeds and the agent is importable
-- Does NOT execute event_loop agents — MockLLMProvider never calls `set_output`, so event_loop nodes loop forever
-- Does NOT test LLM reasoning, content quality, or constraint validation
-- Does NOT test real API integrations or tool use
-
-**Bottom line:** If you're testing whether an agent achieves its goal, you MUST use real credentials.
-
-### Enforcing Credentials in Tests
-
-When writing tests, **ALWAYS include credential checks**:
-
-```python
-import os
-import pytest
-from aden_tools.credentials import CredentialManager
-
-pytestmark = pytest.mark.skipif(
-    not CredentialManager().is_available("anthropic") and not os.environ.get("MOCK_MODE"),
-    reason="API key required for real testing. Set ANTHROPIC_API_KEY or use MOCK_MODE=1."
-)
-
-
-@pytest.fixture(scope="session", autouse=True)
-def check_credentials():
-    """Ensure ALL required credentials are set for real testing."""
-    creds = CredentialManager()
-    mock_mode = os.environ.get("MOCK_MODE")
-
-    if not creds.is_available("anthropic"):
-        if mock_mode:
-            print("\nRunning in MOCK MODE - structure validation only")
-        else:
-            pytest.fail(
-                "\nANTHROPIC_API_KEY not set!\n"
-                "Set API key: export ANTHROPIC_API_KEY='your-key-here'\n"
-                "Or run structure validation: MOCK_MODE=1 pytest exports/{agent}/tests/"
-            )
-
-    if not mock_mode:
-        agent_tools = []  # Update per agent
-        missing = creds.get_missing_for_tools(agent_tools)
-        if missing:
-            lines = ["\nMissing tool credentials!"]
-            for name in missing:
-                spec = creds.specs.get(name)
-                if spec:
-                    lines.append(f"  {spec.env_var} - {spec.description}")
-            pytest.fail("\n".join(lines))
-```
-
-### User Communication
-
-When the user asks to test an agent, **ALWAYS check for ALL credentials first**:
-
-1. **Identify the agent's tools** from `mcp_servers.json`
-2. **Check ALL required credentials** using `CredentialManager`
-3. **Ask the user to provide any missing credentials** before proceeding
-4. Collect ALL missing credentials in a single prompt — not one at a time
-
----
-
-## Safe Test Patterns
-
-### OutputCleaner
-
-The framework automatically validates and cleans node outputs using a fast LLM at edge traversal time. Tests should still use safe patterns because OutputCleaner may not catch all issues.
-
-### Safe Access (REQUIRED)
-
-```python
-# UNSAFE - will crash on missing keys
-approval = result.output["approval_decision"]
-category = result.output["analysis"]["category"]
-
-# SAFE - use .get() with defaults
-output = result.output or {}
-approval = output.get("approval_decision", "UNKNOWN")
-
-# SAFE - type check before operations
-analysis = output.get("analysis", {})
-if isinstance(analysis, dict):
-    category = analysis.get("category", "unknown")
-
-# SAFE - handle JSON parsing trap (LLM response as string)
-import json
-recommendation = output.get("recommendation", "{}")
-if isinstance(recommendation, str):
-    try:
-        parsed = json.loads(recommendation)
-        if isinstance(parsed, dict):
-            approval = parsed.get("approval_decision", "UNKNOWN")
-    except json.JSONDecodeError:
-        approval = "UNKNOWN"
-elif isinstance(recommendation, dict):
-    approval = recommendation.get("approval_decision", "UNKNOWN")
-
-# SAFE - type check before iteration
-items = output.get("items", [])
-if isinstance(items, list):
-    for item in items:
-        ...
-```
-
-### Helper Functions for conftest.py
-
-```python
-import json
-import re
-
-def _parse_json_from_output(result, key):
-    """Parse JSON from agent output (framework may store full LLM response as string)."""
-    response_text = result.output.get(key, "")
-    json_text = re.sub(r'```json\s*|\s*```', '', response_text).strip()
-    try:
-        return json.loads(json_text)
-    except (json.JSONDecodeError, AttributeError, TypeError):
-        return result.output.get(key)
-
-def safe_get_nested(result, key_path, default=None):
-    """Safely get nested value from result.output."""
-    output = result.output or {}
-    current = output
-    for key in key_path:
-        if isinstance(current, dict):
-            current = current.get(key)
-        elif isinstance(current, str):
-            try:
-                json_text = re.sub(r'```json\s*|\s*```', '', current).strip()
-                parsed = json.loads(json_text)
-                if isinstance(parsed, dict):
-                    current = parsed.get(key)
-                else:
-                    return default
-            except json.JSONDecodeError:
-                return default
-        else:
-            return default
-    return current if current is not None else default
-
-# Make available in tests
-pytest.parse_json_from_output = _parse_json_from_output
-pytest.safe_get_nested = safe_get_nested
-```
-
-### ExecutionResult Fields
-
-**`result.success=True` means NO exception, NOT goal achieved**
-
-```python
-# WRONG
-assert result.success
-
-# RIGHT
-assert result.success, f"Agent failed: {result.error}"
-output = result.output or {}
-approval = output.get("approval_decision")
-assert approval == "APPROVED", f"Expected APPROVED, got {approval}"
-```
-
-All fields:
-- `success: bool` — Completed without exception (NOT goal achieved!)
-- `output: dict` — Complete memory snapshot (may contain raw strings)
-- `error: str | None` — Error message if failed
-- `steps_executed: int` — Number of nodes executed
-- `total_tokens: int` — Cumulative token usage
-- `total_latency_ms: int` — Total execution time
-- `path: list[str]` — Node IDs traversed (may repeat in feedback loops)
-- `paused_at: str | None` — Node ID if paused
-- `session_state: dict` — State for resuming
-- `node_visit_counts: dict[str, int]` — Visit counts per node (feedback loop testing)
-- `execution_quality: str` — "clean", "degraded", or "failed"
-
-### Test Count Guidance
-
-**Write 8-15 tests, not 30+**
-
-- 2-3 tests per success criterion
-- 1 happy path test
-- 1 boundary/edge case test
-- 1 error handling test (optional)
-
-Each real test costs ~3 seconds + LLM tokens. 12 tests = ~36 seconds, $0.12.
-
----
-
-## Test Patterns
-
-### Happy Path
-```python
-@pytest.mark.asyncio
-async def test_happy_path(runner, auto_responder, mock_mode):
-    """Test normal successful execution."""
-    await auto_responder.start()
-    try:
-        result = await runner.run({"query": "python tutorials"})
-    finally:
-        await auto_responder.stop()
-    assert result.success, f"Agent failed: {result.error}"
-    output = result.output or {}
-    assert output.get("report"), "No report produced"
-```
-
-### Boundary Condition
-```python
-@pytest.mark.asyncio
-async def test_minimum_sources(runner, auto_responder, mock_mode):
-    """Test at minimum source threshold."""
-    await auto_responder.start()
-    try:
-        result = await runner.run({"query": "niche topic"})
-    finally:
-        await auto_responder.stop()
-    assert result.success, f"Agent failed: {result.error}"
-    output = result.output or {}
-    sources = output.get("sources", [])
-    if isinstance(sources, list):
-        assert len(sources) >= 3, f"Expected >= 3 sources, got {len(sources)}"
-```
-
-### Error Handling
-```python
-@pytest.mark.asyncio
-async def test_empty_input(runner, auto_responder, mock_mode):
-    """Test graceful handling of empty input."""
-    await auto_responder.start()
-    try:
-        result = await runner.run({"query": ""})
-    finally:
-        await auto_responder.stop()
-    # Agent should either fail gracefully or produce an error message
-    output = result.output or {}
-    assert not result.success or output.get("error"), "Should handle empty input"
-```
-
-### Feedback Loop
-```python
-@pytest.mark.asyncio
-async def test_feedback_loop_terminates(runner, auto_responder, mock_mode):
-    """Test that feedback loops don't run forever."""
-    await auto_responder.start()
-    try:
-        result = await runner.run({"query": "test"})
-    finally:
-        await auto_responder.stop()
-    visits = result.node_visit_counts or {}
-    for node_id, count in visits.items():
-        assert count <= 5, f"Node {node_id} visited {count} times — possible infinite loop"
-```
-
----
-
-## MCP Tool Reference
-
-### Phase 1: Test Generation
-
-```python
-# Check existing tests
-list_tests(goal_id, agent_path)
-
-# Get constraint test guidelines (returns templates, NOT generated tests)
-generate_constraint_tests(goal_id, goal_json, agent_path)
-# Returns: output_file, file_header, test_template, constraints_formatted, test_guidelines
-
-# Get success criteria test guidelines
-generate_success_tests(goal_id, goal_json, node_names, tool_names, agent_path)
-# Returns: output_file, file_header, test_template, success_criteria_formatted, test_guidelines
-```
-
-### Phase 2: Execution
-
-```python
-# Automated regression (no checkpoints, fresh runs)
-run_tests(goal_id, agent_path, test_types='["all"]', parallel=-1, fail_fast=False)
-
-# Run only specific test types
-run_tests(goal_id, agent_path, test_types='["constraint"]')
-run_tests(goal_id, agent_path, test_types='["success"]')
-```
-
-```bash
-# Iterative debugging with checkpoints (via CLI)
-uv run hive run exports/{agent_name} --input '{"query": "test"}'
-```
-
-### Phase 3: Analysis
-
-```python
-# Debug a specific failed test
-debug_test(goal_id, test_name, agent_path)
-
-# Find failed sessions
-list_agent_sessions(agent_work_dir, status="failed", limit=5)
-
-# Inspect session state (excludes memory values)
-get_agent_session_state(agent_work_dir, session_id)
-
-# Inspect memory data
-get_agent_session_memory(agent_work_dir, session_id, key="research_results")
-
-# Runtime logs: L1 summaries
-query_runtime_logs(agent_work_dir, status="needs_attention")
-
-# Runtime logs: L2 per-node details
-query_runtime_log_details(agent_work_dir, run_id, needs_attention_only=True)
-
-# Runtime logs: L3 tool/LLM raw data
-query_runtime_log_raw(agent_work_dir, run_id, node_id="research")
-
-# Find clean checkpoints
-list_agent_checkpoints(agent_work_dir, session_id, is_clean="true")
-
-# Compare checkpoints (memory diff)
-compare_agent_checkpoints(agent_work_dir, session_id, cp_before, cp_after)
-```
-
-### Phase 5: Recovery
-
-```python
-# Inspect checkpoint before resuming
-get_agent_checkpoint(agent_work_dir, session_id, checkpoint_id)
-# Empty checkpoint_id = latest checkpoint
-```
-
-```bash
-# Resume from checkpoint via CLI (headless)
-uv run hive run exports/{agent_name} \
-  --resume-session {session_id} --checkpoint {checkpoint_id}
-```
-
----
-
-## Anti-Patterns
-
-| Don't | Do Instead |
-|-------|-----------|
-| Use `default_agent.run()` in tests | Use `runner.run()` with `auto_responder` fixtures (goes through AgentRuntime) |
-| Re-run entire agent when a late node fails | Resume from last clean checkpoint |
-| Treat `result.success` as goal achieved | Check `result.output` for actual criteria |
-| Access `result.output["key"]` directly | Use `result.output.get("key")` |
-| Fix random things hoping tests pass | Analyze L2/L3 logs to find root cause first |
-| Write 30+ tests | Write 8-15 focused tests |
-| Skip credential check | Use `/hive-credentials` before testing |
-| Confuse `exports/` with `~/.hive/agents/` | Code in `exports/`, runtime data in `~/.hive/` |
-| Use `run_tests` for iterative debugging | Use headless CLI with checkpoints for iterative debugging |
-| Use headless CLI for final regression | Use `run_tests` for automated regression |
-| Use `--tui` from Claude Code | Use headless `run` command — TUI hangs in non-interactive shells |
-| Test client-facing nodes from Claude Code | Use mock mode, or have the user run the agent in their terminal |
-| Run tests without reading goal first | Always understand the goal before writing tests |
-| Skip Phase 3 analysis and guess | Use session + log tools to identify root cause |
-
----
-
-## Example Walkthrough: Deep Research Agent
-
-A complete iteration showing the test loop for an agent with nodes: `intake → research → review → report`.
-
-### Phase 1: Generate tests
-
-```python
-# Read the goal
-Read(file_path="exports/deep_research_agent/agent.py")
-
-# Get success criteria test guidelines
-result = generate_success_tests(
-    goal_id="rigorous-interactive-research",
-    goal_json='{"id": "rigorous-interactive-research", "success_criteria": [{"id": "source-diversity", "target": ">=5"}, {"id": "citation-coverage", "target": "100%"}, {"id": "report-completeness", "target": "90%"}]}',
-    node_names="intake,research,review,report",
-    tool_names="web_search,web_scrape",
-    agent_path="exports/deep_research_agent"
-)
-
-# Write tests
-Write(
-    file_path=result["output_file"],
-    content=result["file_header"] + "\n\n" + test_code
-)
-```
-
-### Phase 2: First execution
-
-```python
-run_tests(
-    goal_id="rigorous-interactive-research",
-    agent_path="exports/deep_research_agent",
-    fail_fast=True
-)
-```
-
-Result: `test_success_source_diversity` fails — agent only found 2 sources instead of 5.
-
-### Phase 3: Analyze
-
-```python
-# Debug the failing test
-debug_test(
-    goal_id="rigorous-interactive-research",
-    test_name="test_success_source_diversity",
-    agent_path="exports/deep_research_agent"
-)
-# → ASSERTION_FAILURE: Expected >= 5 sources, got 2
-
-# Find the session
-list_agent_sessions(
-    agent_work_dir="~/.hive/agents/deep_research_agent",
-    status="completed",
-    limit=1
-)
-# → session_20260209_150000_abc12345
-
-# See what the research node produced
-get_agent_session_memory(
-    agent_work_dir="~/.hive/agents/deep_research_agent",
-    session_id="session_20260209_150000_abc12345",
-    key="research_results"
-)
-# → Only 2 web_search calls made, each returned 1 source
-
-# Check the LLM's behavior in the research node
-query_runtime_log_raw(
-    agent_work_dir="~/.hive/agents/deep_research_agent",
-    run_id="session_20260209_150000_abc12345",
-    node_id="research"
-)
-# → LLM called web_search only twice, then called set_output
-```
-
-Root cause: The research node's prompt doesn't tell the LLM to search for at least 5 diverse sources. It stops after the first couple of searches.
-
-### Phase 4: Fix the prompt
-
-```python
-Read(file_path="exports/deep_research_agent/nodes/__init__.py")
-
-Edit(
-    file_path="exports/deep_research_agent/nodes/__init__.py",
-    old_string='system_prompt="Search for information on the user\'s topic."',
-    new_string='system_prompt="Search for information on the user\'s topic. You MUST find at least 5 diverse, authoritative sources. Use multiple different search queries to ensure source diversity. Do not stop searching until you have at least 5 distinct sources."'
-)
-```
-
-### Phase 5: Resume from checkpoint
-
-For this example, the fix is to the `research` node. If we had run via CLI with checkpointing, we could resume from the checkpoint after `intake` to skip re-running intake:
-
-```bash
-# Check if clean checkpoint exists after intake
-list_agent_checkpoints(
-    agent_work_dir="~/.hive/agents/deep_research_agent",
-    session_id="session_20260209_150000_abc12345",
-    is_clean="true"
-)
-# → cp_node_complete_intake_150005
-
-# Resume from after intake, re-run research with fixed prompt
-uv run hive run exports/deep_research_agent \
-  --resume-session session_20260209_150000_abc12345 \
-  --checkpoint cp_node_complete_intake_150005
-```
-
-Or for this simple case (intake is fast), just re-run:
-
-```bash
-uv run hive run exports/deep_research_agent --input '{"topic": "test"}'
-```
-
-### Phase 6: Final verification
-
-```python
-run_tests(
-    goal_id="rigorous-interactive-research",
-    agent_path="exports/deep_research_agent"
-)
-# → All 12 tests pass
-```
-
----
-
-## Test File Structure
-
-```
-exports/{agent_name}/
-├── agent.py              ← Agent to test (goal, nodes, edges)
-├── nodes/__init__.py     ← Node implementations (prompts, config)
-├── config.py             ← Agent configuration
-├── mcp_servers.json      ← Tool server config
-└── tests/
-    ├── conftest.py           ← Shared fixtures + safe access helpers
-    ├── test_constraints.py   ← Constraint tests
-    ├── test_success_criteria.py  ← Success criteria tests
-    └── test_edge_cases.py    ← Edge case tests
-```
-
-## Integration with Other Skills
-
-| Scenario | From | To | Action |
-|----------|------|----|--------|
-| Agent built, ready to test | `/hive-create` | `/hive-test` | Generate tests, start loop |
-| Prompt fix needed | `/hive-test` Phase 4 | Direct edit | Edit `nodes/__init__.py`, resume |
-| Goal definition wrong | `/hive-test` Phase 4 | `/hive-create` | Update goal, may need rebuild |
-| Missing credentials | `/hive-test` Phase 3 | `/hive-credentials` | Set up credentials |
-| Complex runtime failure | `/hive-test` Phase 3 | `/hive-debugger` | Deep L1/L2/L3 analysis |
-| All tests pass | `/hive-test` Phase 6 | Done | Agent validated |

.claude/skills/hive-test/examples/testing-youtube-agent.md

@@ -1,333 +0,0 @@
-# Example: Iterative Testing of a Research Agent
-
-This example walks through the full iterative test loop for a research agent that searches the web, reviews findings, and produces a cited report.
-
-## Agent Structure
-
-```
-exports/deep_research_agent/
-├── agent.py          # Goal + graph: intake → research → review → report
-├── nodes/__init__.py # Node definitions (system_prompt, input/output keys)
-├── config.py         # Model config
-├── mcp_servers.json  # Tools: web_search, web_scrape
-└── tests/            # Test files (we'll create these)
-```
-
-**Goal:** "Rigorous Interactive Research" — find 5+ diverse sources, cite every claim, produce a complete report.
-
----
-
-## Phase 1: Generate Tests
-
-### Read the goal
-
-```python
-Read(file_path="exports/deep_research_agent/agent.py")
-# Extract: goal_id="rigorous-interactive-research"
-# success_criteria: source-diversity (>=5), citation-coverage (100%), report-completeness (90%)
-# constraints: no-hallucination, source-attribution
-```
-
-### Get test guidelines
-
-```python
-result = generate_success_tests(
-    goal_id="rigorous-interactive-research",
-    goal_json='{"id": "rigorous-interactive-research", "success_criteria": [{"id": "source-diversity", "description": "Use multiple diverse sources", "target": ">=5"}, {"id": "citation-coverage", "description": "Every claim cites its source", "target": "100%"}, {"id": "report-completeness", "description": "Report answers the research questions", "target": "90%"}]}',
-    node_names="intake,research,review,report",
-    tool_names="web_search,web_scrape",
-    agent_path="exports/deep_research_agent"
-)
-```
-
-### Write tests
-
-```python
-Write(
-    file_path="exports/deep_research_agent/tests/test_success_criteria.py",
-    content=result["file_header"] + '''
-
-@pytest.mark.asyncio
-async def test_success_source_diversity(runner, auto_responder, mock_mode):
-    """At least 5 diverse sources are found."""
-    await auto_responder.start()
-    try:
-        result = await runner.run({"query": "impact of remote work on productivity"})
-    finally:
-        await auto_responder.stop()
-    assert result.success, f"Agent failed: {result.error}"
-    output = result.output or {}
-    sources = output.get("sources", [])
-    if isinstance(sources, list):
-        assert len(sources) >= 5, f"Expected >= 5 sources, got {len(sources)}"
-
-@pytest.mark.asyncio
-async def test_success_citation_coverage(runner, auto_responder, mock_mode):
-    """Every factual claim in the report cites its source."""
-    await auto_responder.start()
-    try:
-        result = await runner.run({"query": "climate change effects on agriculture"})
-    finally:
-        await auto_responder.stop()
-    assert result.success, f"Agent failed: {result.error}"
-    output = result.output or {}
-    report = output.get("report", "")
-    # Check that report contains numbered references
-    assert "[1]" in str(report) or "[source" in str(report).lower(), "Report lacks citations"
-
-@pytest.mark.asyncio
-async def test_success_report_completeness(runner, auto_responder, mock_mode):
-    """Report addresses the original research question."""
-    query = "pros and cons of nuclear energy"
-    await auto_responder.start()
-    try:
-        result = await runner.run({"query": query})
-    finally:
-        await auto_responder.stop()
-    assert result.success, f"Agent failed: {result.error}"
-    output = result.output or {}
-    report = output.get("report", "")
-    assert len(str(report)) > 200, f"Report too short: {len(str(report))} chars"
-
-@pytest.mark.asyncio
-async def test_empty_query_handling(runner, auto_responder, mock_mode):
-    """Agent handles empty input gracefully."""
-    await auto_responder.start()
-    try:
-        result = await runner.run({"query": ""})
-    finally:
-        await auto_responder.stop()
-    output = result.output or {}
-    assert not result.success or output.get("error"), "Should handle empty query"
-
-@pytest.mark.asyncio
-async def test_feedback_loop_terminates(runner, auto_responder, mock_mode):
-    """Feedback loop between review and research terminates."""
-    await auto_responder.start()
-    try:
-        result = await runner.run({"query": "quantum computing basics"})
-    finally:
-        await auto_responder.stop()
-    visits = result.node_visit_counts or {}
-    for node_id, count in visits.items():
-        assert count <= 5, f"Node {node_id} visited {count} times"
-'''
-)
-```
-
----
-
-## Phase 2: First Execution
-
-```python
-run_tests(
-    goal_id="rigorous-interactive-research",
-    agent_path="exports/deep_research_agent",
-    fail_fast=True
-)
-```
-
-**Result:**
-```json
-{
-  "overall_passed": false,
-  "summary": {"total": 5, "passed": 3, "failed": 2, "pass_rate": "60.0%"},
-  "failures": [
-    {"test_name": "test_success_source_diversity", "details": "AssertionError: Expected >= 5 sources, got 2"},
-    {"test_name": "test_success_citation_coverage", "details": "AssertionError: Report lacks citations"}
-  ]
-}
-```
-
----
-
-## Phase 3: Analyze (Iteration 1)
-
-### Debug the first failure
-
-```python
-debug_test(
-    goal_id="rigorous-interactive-research",
-    test_name="test_success_source_diversity",
-    agent_path="exports/deep_research_agent"
-)
-# Category: ASSERTION_FAILURE — Expected >= 5 sources, got 2
-```
-
-### Find the session and inspect memory
-
-```python
-list_agent_sessions(
-    agent_work_dir="~/.hive/agents/deep_research_agent",
-    status="completed",
-    limit=1
-)
-# → session_20260209_150000_abc12345
-
-get_agent_session_memory(
-    agent_work_dir="~/.hive/agents/deep_research_agent",
-    session_id="session_20260209_150000_abc12345",
-    key="research_results"
-)
-# → Only 2 sources found. LLM stopped searching after 2 queries.
-```
-
-### Check LLM behavior in the research node
-
-```python
-query_runtime_log_raw(
-    agent_work_dir="~/.hive/agents/deep_research_agent",
-    run_id="session_20260209_150000_abc12345",
-    node_id="research"
-)
-# → LLM called web_search twice, got results, immediately called set_output.
-# → Prompt doesn't instruct it to find at least 5 sources.
-```
-
-**Root cause:** The research node's system_prompt doesn't specify minimum source requirements.
-
----
-
-## Phase 4: Fix (Iteration 1)
-
-```python
-Read(file_path="exports/deep_research_agent/nodes/__init__.py")
-
-# Fix the research node prompt
-Edit(
-    file_path="exports/deep_research_agent/nodes/__init__.py",
-    old_string='system_prompt="Search for information on the user\'s topic using web search."',
-    new_string='system_prompt="Search for information on the user\'s topic using web search. You MUST find at least 5 diverse, authoritative sources. Use multiple different search queries with varied keywords. Do NOT call set_output until you have gathered at least 5 distinct sources from different domains."'
-)
-```
-
----
-
-## Phase 5: Recover & Resume (Iteration 1)
-
-The fix is to the `research` node. Since this was a `run_tests` execution (no checkpoints), we re-run from scratch:
-
-```python
-run_tests(
-    goal_id="rigorous-interactive-research",
-    agent_path="exports/deep_research_agent",
-    fail_fast=True
-)
-```
-
-**Result:**
-```json
-{
-  "overall_passed": false,
-  "summary": {"total": 5, "passed": 4, "failed": 1, "pass_rate": "80.0%"},
-  "failures": [
-    {"test_name": "test_success_citation_coverage", "details": "AssertionError: Report lacks citations"}
-  ]
-}
-```
-
-Source diversity now passes. Citation coverage still fails.
-
----
-
-## Phase 3: Analyze (Iteration 2)
-
-```python
-debug_test(
-    goal_id="rigorous-interactive-research",
-    test_name="test_success_citation_coverage",
-    agent_path="exports/deep_research_agent"
-)
-# Category: ASSERTION_FAILURE — Report lacks citations
-
-# Check what the report node produced
-list_agent_sessions(
-    agent_work_dir="~/.hive/agents/deep_research_agent",
-    status="completed",
-    limit=1
-)
-# → session_20260209_151500_def67890
-
-get_agent_session_memory(
-    agent_work_dir="~/.hive/agents/deep_research_agent",
-    session_id="session_20260209_151500_def67890",
-    key="report"
-)
-# → Report text exists but uses no numbered references.
-# → Sources are in memory but report node doesn't cite them.
-```
-
-**Root cause:** The report node's prompt doesn't instruct the LLM to include numbered citations.
-
----
-
-## Phase 4: Fix (Iteration 2)
-
-```python
-Edit(
-    file_path="exports/deep_research_agent/nodes/__init__.py",
-    old_string='system_prompt="Write a comprehensive report based on the research findings."',
-    new_string='system_prompt="Write a comprehensive report based on the research findings. You MUST include numbered citations [1], [2], etc. for every factual claim. At the end, include a References section listing all sources with their URLs. Every claim must be traceable to a specific source."'
-)
-```
-
----
-
-## Phase 5: Resume (Iteration 2)
-
-The fix is to the `report` node (the last node). To demonstrate checkpoint recovery, run via CLI:
-
-```bash
-# Run via CLI to get checkpoints
-uv run hive run exports/deep_research_agent --input '{"topic": "climate change effects"}'
-
-# After it runs, find the clean checkpoint before report
-list_agent_checkpoints(
-    agent_work_dir="~/.hive/agents/deep_research_agent",
-    session_id="session_20260209_152000_ghi34567",
-    is_clean="true"
-)
-# → cp_node_complete_review_152100 (after review, before report)
-
-# Resume — skips intake, research, review entirely
-uv run hive run exports/deep_research_agent \
-  --resume-session session_20260209_152000_ghi34567 \
-  --checkpoint cp_node_complete_review_152100
-```
-
-Only the `report` node re-runs with the fixed prompt, using research data from the checkpoint.
-
----
-
-## Phase 6: Final Verification
-
-```python
-run_tests(
-    goal_id="rigorous-interactive-research",
-    agent_path="exports/deep_research_agent"
-)
-```
-
-**Result:**
-```json
-{
-  "overall_passed": true,
-  "summary": {"total": 5, "passed": 5, "failed": 0, "pass_rate": "100.0%"}
-}
-```
-
-All tests pass.
-
----
-
-## Summary
-
-| Iteration | Failure | Root Cause | Fix | Recovery |
-|-----------|---------|------------|-----|----------|
-| 1 | Source diversity (2 < 5) | Research prompt too vague | Added "at least 5 sources" to prompt | Re-run (no checkpoints) |
-| 2 | No citations in report | Report prompt lacks citation instructions | Added citation requirements | Checkpoint resume (skipped 3 nodes) |
-
-**Key takeaways:**
-- Phase 3 analysis (session memory + L3 logs) identified root causes without guessing
-- Checkpoint recovery in iteration 2 saved time by skipping 3 expensive nodes
-- Final `run_tests` confirms all scenarios pass end-to-end

.claude/skills/hive/SKILL.md

@@ -1,526 +0,0 @@
----
-name: hive
-description: Complete workflow for building, implementing, and testing goal-driven agents. Orchestrates hive-* skills. Use when starting a new agent project, unsure which skill to use, or need end-to-end guidance.
-license: Apache-2.0
-metadata:
-  author: hive
-  version: "2.0"
-  type: workflow-orchestrator
-  orchestrates:
-    - hive-concepts
-    - hive-create
-    - hive-patterns
-    - hive-test
-    - hive-credentials
-    - hive-debugger
----
-
-# Agent Development Workflow
-
-**THIS IS AN EXECUTABLE WORKFLOW. DO NOT explore the codebase or read source files. ROUTE to the correct skill IMMEDIATELY.**
-
-When this skill is loaded, **ALWAYS use the AskUserQuestion tool** to present options:
-
-```
-Use AskUserQuestion with these options:
-- "Build a new agent" → Then invoke /hive-create
-- "Test an existing agent" → Then invoke /hive-test
-- "Learn agent concepts" → Then invoke /hive-concepts
-- "Optimize agent design" → Then invoke /hive-patterns
-- "Set up credentials" → Then invoke /hive-credentials
-- "Debug a failing agent" → Then invoke /hive-debugger
-- "Other" (please describe what you want to achieve)
-```
-
-**DO NOT:** Read source files, explore the codebase, search for code, or do any investigation before routing. The sub-skills handle all of that.
-
----
-
-Complete Standard Operating Procedure (SOP) for building production-ready goal-driven agents.
-
-## Overview
-
-This workflow orchestrates specialized skills to take you from initial concept to production-ready agent:
-
-1. **Understand Concepts** → `/hive-concepts` (optional)
-2. **Build Structure** → `/hive-create`
-3. **Optimize Design** → `/hive-patterns` (optional)
-4. **Setup Credentials** → `/hive-credentials` (if agent uses tools requiring API keys)
-5. **Test & Validate** → `/hive-test`
-6. **Debug Issues** → `/hive-debugger` (if agent fails at runtime)
-
-## When to Use This Workflow
-
-Use this meta-skill when:
-- Starting a new agent from scratch
-- Unclear which skill to use first
-- Need end-to-end guidance for agent development
-- Want consistent, repeatable agent builds
-
-**Skip this workflow** if:
-- You only need to test an existing agent → use `/hive-test` directly
-- You know exactly which phase you're in → use specific skill directly
-
-## Quick Decision Tree
-
-```
-"Need to understand agent concepts" → hive-concepts
-"Build a new agent" → hive-create
-"Optimize my agent design" → hive-patterns
-"Need client-facing nodes or feedback loops" → hive-patterns
-"Set up API keys for my agent" → hive-credentials
-"Test my agent" → hive-test
-"My agent is failing/stuck/has errors" → hive-debugger
-"Not sure what I need" → Read phases below, then decide
-"Agent has structure but needs implementation" → See agent directory STATUS.md
-```
-
-## Phase 0: Understand Concepts (Optional)
-
-**Skill**: `/hive-concepts`
-**Input**: Questions about agent architecture
-
-### When to Use
-
-- First time building an agent
-- Need to understand node types, edges, goals
-- Want to validate tool availability
-- Learning about event loop architecture and client-facing nodes
-
-### What This Phase Provides
-
-- Architecture overview (Python packages, not JSON)
-- Core concepts (Goal, Node, Edge, Event Loop, Judges)
-- Tool discovery and validation procedures
-- Workflow overview
-
-**Skip this phase** if you already understand agent fundamentals.
-
-## Phase 1: Build Agent Structure
-
-**Skill**: `/hive-create`
-**Input**: User requirements ("Build an agent that...") or a template to start from
-
-### What This Phase Does
-
-Creates the complete agent architecture:
-- Package structure (`exports/agent_name/`)
-- Goal with success criteria and constraints
-- Workflow graph (nodes and edges)
-- Node specifications
-- CLI interface
-- Documentation
-
-### Process
-
-1. **Create package** - Directory structure with skeleton files
-2. **Define goal** - Success criteria and constraints written to agent.py
-3. **Design nodes** - Each node approved and written incrementally
-4. **Connect edges** - Workflow graph with conditional routing
-5. **Finalize** - Agent class, exports, and documentation
-
-### Outputs
-
-- ✅ `exports/agent_name/` package created
-- ✅ Goal defined in agent.py
-- ✅ 3-5 success criteria defined
-- ✅ 1-5 constraints defined
-- ✅ 5-10 nodes specified in nodes/__init__.py
-- ✅ 8-15 edges connecting workflow
-- ✅ Validated structure (passes `uv run python -m agent_name validate`)
-- ✅ README.md with usage instructions
-- ✅ CLI commands (info, validate, run, shell)
-
-### Success Criteria
-
-You're ready for Phase 2 when:
-- Agent structure validates without errors
-- All nodes and edges are defined
-- CLI commands work (info, validate)
-- You see: "Agent complete: exports/agent_name/"
-
-### Common Outputs
-
-The hive-create skill produces:
-```
-exports/agent_name/
-├── __init__.py          (package exports)
-├── __main__.py          (CLI interface)
-├── agent.py             (goal, graph, agent class)
-├── nodes/__init__.py    (node specifications)
-├── config.py            (configuration)
-├── implementations.py   (may be created for Python functions)
-└── README.md            (documentation)
-```
-
-### Next Steps
-
-**If structure complete and validated:**
-→ Check `exports/agent_name/STATUS.md` or `IMPLEMENTATION_GUIDE.md`
-→ These files explain implementation options
-→ You may need to add Python functions or MCP tools (not covered by current skills)
-
-**If want to optimize design:**
-→ Proceed to Phase 1.5 (hive-patterns)
-
-**If ready to test:**
-→ Proceed to Phase 2
-
-## Phase 1.5: Optimize Design (Optional)
-
-**Skill**: `/hive-patterns`
-**Input**: Completed agent structure
-
-### When to Use
-
-- Want to add client-facing blocking or feedback edges
-- Need judge patterns for output validation
-- Want fan-out/fan-in (parallel execution)
-- Need error handling patterns
-- Want best practices guidance
-
-### What This Phase Provides
-
-- Client-facing interaction patterns
-- Feedback edge routing with nullable output keys
-- Judge patterns (implicit, SchemaJudge)
-- Fan-out/fan-in parallel execution
-- Context management and spillover patterns
-- Anti-patterns to avoid
-
-**Skip this phase** if your agent design is straightforward.
-
-## Phase 2: Test & Validate
-
-**Skill**: `/hive-test`
-**Input**: Working agent from Phase 1
-
-### What This Phase Does
-
-Guides the creation and execution of a comprehensive test suite:
-- Constraint tests
-- Success criteria tests
-- Edge case tests
-- Integration tests
-
-### Process
-
-1. **Analyze agent** - Read goal, constraints, success criteria
-2. **Generate tests** - The calling agent writes pytest files in `exports/agent_name/tests/` using hive-test guidelines and templates
-3. **User approval** - Review and approve each test
-4. **Run evaluation** - Execute tests and collect results
-5. **Debug failures** - Identify and fix issues
-6. **Iterate** - Repeat until all tests pass
-
-### Outputs
-
-- ✅ Test files in `exports/agent_name/tests/`
-- ✅ Test report with pass/fail metrics
-- ✅ Coverage of all success criteria
-- ✅ Coverage of all constraints
-- ✅ Edge case handling verified
-
-### Success Criteria
-
-You're done when:
-- All tests pass
-- All success criteria validated
-- All constraints verified
-- Agent handles edge cases
-- Test coverage is comprehensive
-
-### Next Steps
-
-**Agent ready for:**
-- Production deployment
-- Integration into larger systems
-- Documentation and handoff
-- Continuous monitoring
-
-## Phase Transitions
-
-### From Phase 1 to Phase 2
-
-**Trigger signals:**
-- "Agent complete: exports/..."
-- Structure validation passes
-- README indicates implementation complete
-
-**Before proceeding:**
-- Verify agent can be imported: `from exports.agent_name import default_agent`
-- Check if implementation is needed (see STATUS.md or IMPLEMENTATION_GUIDE.md)
-- Confirm agent executes without import errors
-
-### Skipping Phases
-
-**When to skip Phase 1:**
-- Agent structure already exists
-- Only need to add tests
-- Modifying existing agent
-
-**When to skip Phase 2:**
-- Prototyping or exploring
-- Agent not production-bound
-- Manual testing sufficient
-
-## Common Patterns
-
-### Pattern 1: Complete New Build (Simple)
-
-```
-User: "Build an agent that monitors files"
-→ Use /hive-create
-→ Agent structure created
-→ Use /hive-test
-→ Tests created and passing
-→ Done: Production-ready agent
-```
-
-### Pattern 1b: Complete New Build (With Learning)
-
-```
-User: "Build an agent (first time)"
-→ Use /hive-concepts (understand concepts)
-→ Use /hive-create (build structure)
-→ Use /hive-patterns (optimize design)
-→ Use /hive-test (validate)
-→ Done: Production-ready agent
-```
-
-### Pattern 1c: Build from Template
-
-```
-User: "Build an agent based on the deep research template"
-→ Use /hive-create
-→ Select "From a template" path
-→ Pick template, name new agent
-→ Review/modify goal, nodes, graph
-→ Agent exported with customizations
-→ Use /hive-test
-→ Done: Customized agent
-```
-
-### Pattern 2: Test Existing Agent
-
-```
-User: "Test my agent at exports/my_agent"
-→ Skip Phase 1
-→ Use /hive-test directly
-→ Tests created
-→ Done: Validated agent
-```
-
-### Pattern 3: Iterative Development
-
-```
-User: "Build an agent"
-→ Use /hive-create (Phase 1)
-→ Implementation needed (see STATUS.md)
-→ [User implements functions]
-→ Use /hive-test (Phase 2)
-→ Tests reveal bugs
-→ [Fix bugs manually]
-→ Re-run tests
-→ Done: Working agent
-```
-
-### Pattern 4: Agent with Review Loops and HITL Checkpoints
-
-```
-User: "Build an agent with human review and feedback loops"
-→ Use /hive-concepts (learn event loop, client-facing nodes)
-→ Use /hive-create (build structure with feedback edges)
-→ Use /hive-patterns (implement client-facing + feedback patterns)
-→ Use /hive-test (validate review flows and edge routing)
-→ Done: Agent with HITL checkpoints and review loops
-```
-
-## Skill Dependencies
-
-```
-hive (meta-skill)
-    │
-    ├── hive-concepts (foundational)
-    │   ├── Architecture concepts (event loop, judges)
-    │   ├── Node types (event_loop, function)
-    │   ├── Edge routing and priority
-    │   ├── Tool discovery procedures
-    │   └── Workflow overview
-    │
-    ├── hive-create (procedural)
-    │   ├── Creates package structure
-    │   ├── Defines goal
-    │   ├── Adds nodes (event_loop, function)
-    │   ├── Connects edges with priority routing
-    │   ├── Finalizes agent class
-    │   └── Requires: hive-concepts
-    │
-    ├── hive-patterns (reference)
-    │   ├── Client-facing interaction patterns
-    │   ├── Feedback edges and review loops
-    │   ├── Judge patterns (implicit, SchemaJudge)
-    │   ├── Fan-out/fan-in parallel execution
-    │   └── Context management and anti-patterns
-    │
-    ├── hive-credentials (utility)
-    │   ├── Detects missing credentials
-    │   ├── Offers auth method choices (Aden OAuth, direct API key)
-    │   ├── Stores securely in ~/.hive/credentials
-    │   └── Validates with health checks
-    │
-    ├── hive-test (validation)
-    │   ├── Reads agent goal
-    │   ├── Generates tests
-    │   ├── Runs evaluation
-    │   └── Reports results
-    │
-    └── hive-debugger (troubleshooting)
-        ├── Monitors runtime logs (L1/L2/L3)
-        ├── Identifies retry loops, tool failures
-        ├── Categorizes issues (10 categories)
-        └── Provides fix recommendations
-```
-
-## Troubleshooting
-
-### "Agent structure won't validate"
-
-- Check node IDs match between nodes/__init__.py and agent.py
-- Verify all edges reference valid node IDs
-- Ensure entry_node exists in nodes list
-- Run: `PYTHONPATH=exports uv run python -m agent_name validate`
-
-### "Agent has structure but won't run"
-
-- Check for STATUS.md or IMPLEMENTATION_GUIDE.md in agent directory
-- Implementation may be needed (Python functions or MCP tools)
-- This is expected - hive-create creates structure, not implementation
-- See implementation guide for completion options
-
-### "Tests are failing"
-
-- Review test output for specific failures
-- Check agent goal and success criteria
-- Verify constraints are met
-- Use `/hive-test` to debug and iterate
-- Fix agent code and re-run tests
-
-### "Agent is failing at runtime"
-
-- Use `/hive-debugger` to analyze runtime logs
-- The debugger identifies retry loops, tool failures, and stalled execution
-- Get actionable fix recommendations with code changes
-- Monitor the agent in real-time during TUI sessions
-
-### "Not sure which phase I'm in"
-
-Run these checks:
-
-```bash
-# Check if agent structure exists
-ls exports/my_agent/agent.py
-
-# Check if it validates
-PYTHONPATH=exports uv run python -m my_agent validate
-
-# Check if tests exist
-ls exports/my_agent/tests/
-
-# If structure exists and validates → Phase 2 (testing)
-# If structure doesn't exist → Phase 1 (building)
-# If tests exist but failing → Debug phase
-```
-
-## Best Practices
-
-### For Phase 1 (Building)
-
-1. **Start with clear requirements** - Know what the agent should do
-2. **Define success criteria early** - Measurable goals drive design
-3. **Keep nodes focused** - One responsibility per node
-4. **Use descriptive names** - Node IDs should explain purpose
-5. **Validate incrementally** - Check structure after each major addition
-
-### For Phase 2 (Testing)
-
-1. **Test constraints first** - Hard requirements must pass
-2. **Mock external dependencies** - Use mock mode for LLMs/APIs
-3. **Cover edge cases** - Test failures, not just success paths
-4. **Iterate quickly** - Fix one test at a time
-5. **Document test patterns** - Future tests follow same structure
-
-### General Workflow
-
-1. **Use version control** - Git commit after each phase
-2. **Document decisions** - Update README with changes
-3. **Keep iterations small** - Build → Test → Fix → Repeat
-4. **Preserve working states** - Tag successful iterations
-5. **Learn from failures** - Failed tests reveal design issues
-
-## Exit Criteria
-
-You're done with the workflow when:
-
-✅ Agent structure validates
-✅ All tests pass
-✅ Success criteria met
-✅ Constraints verified
-✅ Documentation complete
-✅ Agent ready for deployment
-
-## Additional Resources
-
-- **hive-concepts**: See `.claude/skills/hive-concepts/SKILL.md`
-- **hive-create**: See `.claude/skills/hive-create/SKILL.md`
-- **hive-patterns**: See `.claude/skills/hive-patterns/SKILL.md`
-- **hive-test**: See `.claude/skills/hive-test/SKILL.md`
-- **Agent framework docs**: See `core/README.md`
-- **Example agents**: See `exports/` directory
-
-## Summary
-
-This workflow provides a proven path from concept to production-ready agent:
-
-1. **Learn** with `/hive-concepts` → Understand fundamentals (optional)
-2. **Build** with `/hive-create` → Get validated structure
-3. **Optimize** with `/hive-patterns` → Apply best practices (optional)
-4. **Configure** with `/hive-credentials` → Set up API keys (if needed)
-5. **Test** with `/hive-test` → Get verified functionality
-6. **Debug** with `/hive-debugger` → Fix runtime issues (if needed)
-
-The workflow is **flexible** - skip phases as needed, iterate freely, and adapt to your specific requirements. The goal is **production-ready agents** built with **consistent, repeatable processes**.
-
-## Skill Selection Guide
-
-**Choose hive-concepts when:**
-- First time building agents
-- Need to understand event loop architecture
-- Validating tool availability
-- Learning about node types, edges, and judges
-
-**Choose hive-create when:**
-- Actually building an agent
-- Have clear requirements
-- Ready to write code
-- Want step-by-step guidance
-- Want to start from an existing template and customize it
-
-**Choose hive-patterns when:**
-- Agent structure complete
-- Need client-facing nodes or feedback edges
-- Implementing review loops or fan-out/fan-in
-- Want judge patterns or context management
-- Want best practices
-
-**Choose hive-test when:**
-- Agent structure complete
-- Ready to validate functionality
-- Need comprehensive test coverage
-- Testing feedback loops, output keys, or fan-out
-
-**Choose hive-debugger when:**
-- Agent is failing or stuck at runtime
-- Seeing retry loops or escalations
-- Tool calls are failing
-- Need to understand why a node isn't completing
-- Want real-time monitoring of agent execution

.claude/skills/hive/examples/file-monitor-example.md

@@ -1,199 +0,0 @@
-# Example: File Monitor Agent
-
-This example shows the complete /hive workflow in action for building a file monitoring agent.
-
-## Initial Request
-
-```
-User: "Build an agent that monitors ~/Downloads and copies new files to ~/Documents"
-```
-
-## Phase 1: Building (20 minutes)
-
-### Step 1: Create Structure
-
-Agent invokes `/hive-create` skill and:
-
-1. Creates `exports/file_monitor_agent/` package
-2. Writes skeleton files (__init__.py, __main__.py, agent.py, etc.)
-
-**Output**: Package structure visible immediately
-
-### Step 2: Define Goal
-
-```python
-goal = Goal(
-    id="file-monitor-copy",
-    name="Automated File Monitor & Copy",
-    success_criteria=[
-        # 100% detection rate
-        # 100% copy success
-        # 100% conflict resolution
-        # >99% uptime
-    ],
-    constraints=[
-        # Preserve originals
-        # Handle errors gracefully
-        # Track state
-        # Respect permissions
-    ]
-)
-```
-
-**Output**: Goal written to agent.py
-
-### Step 3: Design Nodes
-
-7 nodes approved and written incrementally:
-
-1. `initialize-state` - Set up tracking
-2. `list-downloads` - Scan directory
-3. `identify-new-files` - Find new files
-4. `check-for-new-files` - Router
-5. `copy-files` - Copy with conflict resolution
-6. `update-state` - Mark as processed
-7. `wait-interval` - Sleep between cycles
-
-**Output**: All nodes in nodes/__init__.py
-
-### Step 4: Connect Edges
-
-8 edges connecting the workflow loop:
-
-```
-initialize → list → identify → check
-                                ↓  ↓
-                              copy  wait
-                                ↓    ↑
-                              update ↓
-                                ↓    ↓
-                              wait → list (loop)
-```
-
-**Output**: Edges written to agent.py
-
-### Step 5: Finalize
-
-```bash
-$ PYTHONPATH=exports uv run python -m file_monitor_agent validate
-✓ Agent is valid
-
-$ PYTHONPATH=exports uv run python -m file_monitor_agent info
-Agent: File Monitor & Copy Agent
-Nodes: 7
-Edges: 8
-```
-
-**Phase 1 Complete**: Structure validated ✅
-
-### Status After Phase 1
-
-```
-exports/file_monitor_agent/
-├── __init__.py          ✅ (exports)
-├── __main__.py          ✅ (CLI)
-├── agent.py             ✅ (goal, graph, agent class)
-├── nodes/__init__.py    ✅ (7 nodes)
-├── config.py            ✅ (configuration)
-├── implementations.py   ✅ (Python functions)
-├── README.md            ✅ (documentation)
-├── IMPLEMENTATION_GUIDE.md ✅ (next steps)
-└── STATUS.md            ✅ (current state)
-```
-
-**Note**: Implementation gap exists - data flow needs connection (covered in STATUS.md)
-
-## Phase 2: Testing (25 minutes)
-
-### Step 1: Analyze Agent
-
-Agent invokes `/hive-test` skill and:
-
-1. Reads goal from `exports/file_monitor_agent/agent.py`
-2. Identifies 4 success criteria to test
-3. Identifies 4 constraints to verify
-4. Plans test coverage
-
-### Step 2: Generate Tests
-
-Creates test files:
-
-```
-exports/file_monitor_agent/tests/
-├── conftest.py              (fixtures)
-├── test_constraints.py      (4 constraint tests)
-├── test_success_criteria.py (4 success tests)
-└── test_edge_cases.py       (error handling)
-```
-
-Tests approved incrementally by user.
-
-### Step 3: Run Tests
-
-```bash
-$ PYTHONPATH=exports uv run pytest exports/file_monitor_agent/tests/
-
-test_constraints.py::test_preserves_originals     PASSED
-test_constraints.py::test_handles_errors          PASSED
-test_constraints.py::test_tracks_state            PASSED
-test_constraints.py::test_respects_permissions    PASSED
-
-test_success_criteria.py::test_detects_all_files  PASSED
-test_success_criteria.py::test_copies_all_files   PASSED
-test_success_criteria.py::test_resolves_conflicts PASSED
-test_success_criteria.py::test_continuous_run     PASSED
-
-test_edge_cases.py::test_empty_directory          PASSED
-test_edge_cases.py::test_permission_denied        PASSED
-test_edge_cases.py::test_disk_full                PASSED
-test_edge_cases.py::test_large_files              PASSED
-
-========================== 12 passed in 3.42s ==========================
-```
-
-**Phase 2 Complete**: All tests pass ✅
-
-## Final Output
-
-**Production-Ready Agent:**
-
-```bash
-# Run the agent
-./RUN_AGENT.sh
-
-# Or manually
-PYTHONPATH=exports uv run python -m file_monitor_agent run
-```
-
-**Capabilities:**
-- Monitors ~/Downloads continuously
-- Copies new files to ~/Documents
-- Resolves conflicts with timestamps
-- Handles errors gracefully
-- Tracks processed files
-- Runs as background service
-
-**Total Time**: ~45 minutes from concept to production
-
-## Key Learnings
-
-1. **Incremental building** - Files written immediately, visible throughout
-2. **Validation early** - Structure validated before moving to implementation
-3. **Test-driven** - Tests reveal real behavior
-4. **Documentation included** - README, STATUS, and guides auto-generated
-5. **Repeatable process** - Same workflow for any agent type
-
-## Variations
-
-**For simpler agents:**
-- Fewer nodes (3-5 instead of 7)
-- Simpler workflow (linear instead of looping)
-- Faster build time (10-15 minutes)
-
-**For complex agents:**
-- More nodes (10-15+)
-- Multiple subgraphs
-- Pause/resume points for human-in-the-loop
-- Longer build time (45-60 minutes)
-
-The workflow scales to your needs!

.codex/config.toml

@@ -1,7 +0,0 @@
-# Project-level Codex config for Hive.
-# Keep this file minimal: MCP connectivity + skill discovery.
-
-[mcp_servers.agent-builder]
-command = "uv"
-args = ["run", "--directory", "core", "-m", "framework.mcp.agent_builder_server"]
-cwd = "."

.cursor/mcp.json

@@ -1,20 +0,0 @@
-{
-  "mcpServers": {
-    "agent-builder": {
-      "command": "python",
-      "args": ["-m", "framework.mcp.agent_builder_server"],
-      "cwd": "core",
-      "env": {
-        "PYTHONPATH": "../tools/src"
-      }
-    },
-    "tools": {
-      "command": "python",
-      "args": ["mcp_server.py", "--stdio"],
-      "cwd": "tools",
-      "env": {
-        "PYTHONPATH": "src"
-      }
-    }
-  }
-}

.cursor/skills/hive

@@ -1 +0,0 @@
-../../.claude/skills/hive

.cursor/skills/hive-concepts

@@ -1 +0,0 @@
-../../.claude/skills/hive-concepts

.cursor/skills/hive-create

@@ -1 +0,0 @@
-../../.claude/skills/hive-create

.cursor/skills/hive-credentials

@@ -1 +0,0 @@
-../../.claude/skills/hive-credentials

.cursor/skills/hive-patterns

@@ -1 +0,0 @@
-../../.claude/skills/hive-patterns

.cursor/skills/hive-test

@@ -1 +0,0 @@
-../../.claude/skills/hive-test

.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

.github/ISSUE_TEMPLATE/standard-bounty.yml

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

.github/workflows/ci.yml

@@ -5,7 +5,7 @@ on:
     branches: [main]
   pull_request:
     branches: [main]
-
+    
 concurrency:
   group: ${{ github.workflow }}-${{ github.ref }}
   cancel-in-progress: true
@@ -24,6 +24,8 @@ jobs:
 
       - name: Install uv
         uses: astral-sh/setup-uv@v4
+        with:
+          enable-cache: true
 
       - name: Install dependencies
         run: uv sync --project core --group dev
@@ -54,10 +56,12 @@ jobs:
 
       - name: Install uv
         uses: astral-sh/setup-uv@v4
+        with:
+          enable-cache: true
 
       - name: Install dependencies and run tests
+        working-directory: core
         run: |
-          cd core
           uv sync
           uv run pytest tests/ -v
 
@@ -77,10 +81,12 @@ jobs:
 
       - name: Install uv
         uses: astral-sh/setup-uv@v4
+        with:
+          enable-cache: true
 
       - name: Install dependencies and run tests
+        working-directory: tools
         run: |
-          cd tools
           uv sync --extra dev
           uv run pytest tests/ -v
 
@@ -98,10 +104,12 @@ jobs:
 
       - name: Install uv
         uses: astral-sh/setup-uv@v4
-
+        with:
+          enable-cache: true
+            
       - name: Install dependencies
+        working-directory: core
         run: |
-          cd core
           uv sync
 
       - name: Validate exported agents

.github/workflows/pr-requirements-enforce.yml

@@ -0,0 +1,54 @@
+# Closes PRs that still have the `pr-requirements-warning` label
+# after contributors were warned in pr-requirements.yml.
+name: PR Requirements Enforcement
+on:
+  schedule:
+    - cron: "0 0 * * *"   # runs every day once at midnight 
+jobs:
+  enforce:
+    name: Close PRs still failing contribution requirements
+    runs-on: ubuntu-latest
+    permissions:
+      pull-requests: write
+      issues: write
+    steps:
+      - name: Close PRs still failing requirements
+        uses: actions/github-script@v7
+        with:
+          script: |
+            const { owner, repo } = context.repo;
+            const prs = await github.paginate(github.rest.pulls.list, {
+              owner,
+              repo,
+              state: "open",
+              per_page: 100
+            });
+            for (const pr of prs) {
+              // Skip draft PRs — author may still be actively working toward compliance
+              if (pr.draft) continue;
+              const labels = pr.labels.map(l => l.name);
+              if (!labels.includes("pr-requirements-warning")) continue;
+              const gracePeriod = 24 * 60 * 60 * 1000;
+              const lastUpdated = new Date(pr.created_at);
+              const now = new Date();
+              if (now - lastUpdated < gracePeriod) {
+                console.log(`Skipping PR #${pr.number} — still within grace period`);
+                continue;
+              }
+              const prNumber = pr.number;
+              const prAuthor = pr.user.login;
+              await github.rest.issues.createComment({
+                owner,
+                repo,
+                issue_number: prNumber,
+                body: `Closing PR because the contribution requirements were not resolved within the 24-hour grace period.
+                If this was closed in error, feel free to reopen the PR after fixing the requirements.`
+              });
+              await github.rest.pulls.update({
+                owner,
+                repo,
+                pull_number: prNumber,
+                state: "closed"
+              });
+              console.log(`Closed PR #${prNumber} by ${prAuthor} (PR requirements were not met)`);
+            }

.github/workflows/pr-requirements.yml

@@ -43,9 +43,10 @@ jobs:
             console.log(`  Found issue references: ${issueNumbers.length > 0 ? issueNumbers.join(', ') : 'none'}`);
 
             if (issueNumbers.length === 0) {
-              const message = `## PR Closed - Requirements Not Met
+              const message = `## PR Requirements Warning
 
-            This PR has been automatically closed because it doesn't meet the requirements.
+            This PR does not meet the contribution requirements.
+            If the issue is not fixed within ~24 hours, it may be automatically closed.
 
             **Missing:** No linked issue found.
 
@@ -67,14 +68,15 @@ jobs:
 
             **Why is this required?** See #472 for details.`;
 
-              const comments = await github.rest.issues.listComments({
+              const comments = await github.paginate(github.rest.issues.listComments, {
                 owner: context.repo.owner,
                 repo: context.repo.repo,
                 issue_number: prNumber,
+                per_page: 100,
               });
 
-              const botComment = comments.data.find(
-                (c) => c.user.type === 'Bot' && c.body.includes('PR Closed - Requirements Not Met')
+              const botComment = comments.find(
+                (c) => c.user.type === 'Bot' && c.body.includes('PR Requirements Warning')
               );
 
               if (!botComment) {
@@ -86,11 +88,11 @@ jobs:
                 });
               }
 
-              await github.rest.pulls.update({
+              await github.rest.issues.addLabels({
                 owner: context.repo.owner,
                 repo: context.repo.repo,
-                pull_number: prNumber,
-                state: 'closed',
+                issue_number: prNumber,
+                labels: ['pr-requirements-warning'],
               });
 
               core.setFailed('PR must reference an issue');
@@ -132,9 +134,10 @@ jobs:
                 `#${i.number} (assignees: ${i.assignees.length > 0 ? i.assignees.join(', ') : 'none'})`
               ).join(', ');
 
-              const message = `## PR Closed - Requirements Not Met
+              const message = `## PR Requirements Warning
 
-            This PR has been automatically closed because it doesn't meet the requirements.
+            This PR does not meet the contribution requirements.
+            If the issue is not fixed within ~24 hours, it may be automatically closed.
 
             **PR Author:** @${prAuthor}
             **Found issues:** ${issueList}
@@ -157,14 +160,15 @@ jobs:
 
             **Why is this required?** See #472 for details.`;
 
-              const comments = await github.rest.issues.listComments({
+              const comments = await github.paginate(github.rest.issues.listComments, {
                 owner: context.repo.owner,
                 repo: context.repo.repo,
                 issue_number: prNumber,
+                per_page: 100,
               });
 
-              const botComment = comments.data.find(
-                (c) => c.user.type === 'Bot' && c.body.includes('PR Closed - Requirements Not Met')
+              const botComment = comments.find(
+                (c) => c.user.type === 'Bot' && c.body.includes('PR Requirements Warning')
               );
 
               if (!botComment) {
@@ -176,14 +180,24 @@ jobs:
                 });
               }
 
-              await github.rest.pulls.update({
+              await github.rest.issues.addLabels({
                 owner: context.repo.owner,
                 repo: context.repo.repo,
-                pull_number: prNumber,
-                state: 'closed',
+                issue_number: prNumber,
+                labels: ['pr-requirements-warning'],
               });
 
               core.setFailed('PR author must be assigned to the linked issue');
             } else {
               console.log(`PR requirements met! Issue #${issueWithAuthorAssigned} has ${prAuthor} as assignee.`);
-            }
+              try {
+                await github.rest.issues.removeLabel({
+                  owner: context.repo.owner,
+                  repo: context.repo.repo,
+                  issue_number: prNumber,
+                  name: "pr-requirements-warning"
+                });
+              }catch (error){
+                //ignore if label doesn't exist
+              }
+            }

.gitignore

@@ -67,8 +67,6 @@ temp/
 
 exports/*
 
-.agent-builder-sessions/*
-
 .claude/settings.local.json
 .claude/skills/ship-it/
 

.mcp.json

@@ -1,9 +1,3 @@
 {
-  "mcpServers": {
-    "agent-builder": {
-      "command": "uv",
-      "args": ["run", "-m", "framework.mcp.agent_builder_server"],
-      "cwd": "core"
-    }
-  }
+  "mcpServers": {}
 }

.opencode/mcp.json

@@ -1,30 +0,0 @@
-{
-  "mcpServers": {
-    "agent-builder": {
-      "command": "uv",
-      "args": [
-        "run",
-        "python",
-        "-m",
-        "framework.mcp.agent_builder_server"
-      ],
-      "cwd": "core",
-      "env": {
-        "PYTHONPATH": "../tools/src"
-      }
-    },
-    "tools": {
-      "command": "uv",
-      "args": [
-        "run",
-        "python",
-        "mcp_server.py",
-        "--stdio"
-      ],
-      "cwd": "tools",
-      "env": {
-        "PYTHONPATH": "src"
-      }
-    }
-  }
-}

.opencode/skills/hive

@@ -1 +0,0 @@
-../../.claude/skills/hive

.opencode/skills/hive-concepts

@@ -1 +0,0 @@
-../../.claude/skills/hive-concepts

.opencode/skills/hive-create

@@ -1 +0,0 @@
-../../.claude/skills/hive-create

.opencode/skills/hive-credentials

@@ -1 +0,0 @@
-../../.claude/skills/hive-credentials

.opencode/skills/hive-debugger

@@ -1 +0,0 @@
-../../.claude/skills/hive-debugger

.opencode/skills/hive-patterns

@@ -1 +0,0 @@
-../../.claude/skills/hive-patterns

.opencode/skills/hive-test

@@ -1 +0,0 @@
-../../.claude/skills/hive-test

.opencode/skills/triage-issue

@@ -1 +0,0 @@
-../../.claude/skills/triage-issue

.vscode/extensions.json

@@ -1,7 +0,0 @@
-{
-  "recommendations": [
-    "charliermarsh.ruff",
-    "editorconfig.editorconfig",
-    "ms-python.python"
-  ]
-}

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
@@ -420,6 +420,16 @@ Visit [docs.adenhq.com](https://docs.adenhq.com/) for complete guides, API refer
 
 Contributions are welcome! Fork the repository, create your feature branch, implement your changes, and submit a pull request. See [CONTRIBUTING.md](CONTRIBUTING.md) for detailed guidelines.
 
+## Star History
+
+<a href="https://star-history.com/#aden-hive/hive&Date">
+ <picture>
+   <source media="(prefers-color-scheme: dark)" srcset="https://api.star-history.com/svg?repos=aden-hive/hive&type=Date&theme=dark" />
+   <source media="(prefers-color-scheme: light)" srcset="https://api.star-history.com/svg?repos=aden-hive/hive&type=Date" />
+   <img alt="Star History Chart" src="https://api.star-history.com/svg?repos=aden-hive/hive&type=Date" />
+ </picture>
+</a>
+
 ---
 
 <p align="center">

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
 

contributors.yml

@@ -22,3 +22,6 @@ contributors:
   # - github: example-user
   #   discord: "123456789012345678"
   #   name: Example User
+  - github: TimothyZhang7
+    discord: "408460790061072384"
+    name: Timothy@Aden

core/.gitignore

@@ -1,5 +1,4 @@
 exports/
 docs/
-.agent-builder-sessions/
 .pytest_cache/
 **/__pycache__/

core/.mcp.json

@@ -1,10 +1,5 @@
 {
   "mcpServers": {
-    "agent-builder": {
-      "command": "python",
-      "args": ["-m", "framework.mcp.agent_builder_server"],
-      "cwd": "core"
-    },
     "tools": {
       "command": "python",
       "args": ["-m", "aden_tools.mcp_server", "--stdio"],

core/MCP_SERVER_GUIDE.md

@@ -1,17 +1,16 @@
-# MCP Server Guide - Agent Builder
+# MCP Server Guide - Agent Building Tools
 
-This guide covers the MCP (Model Context Protocol) server for building goal-driven agents.
+> **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.
 
 ## Setup
 
 ### Quick Setup
 
 ```bash
-# Using the setup script (recommended)
-python setup_mcp.py
-
-# Or using bash
-./setup_mcp.sh
+# Run the quickstart script (recommended)
+./quickstart.sh
 ```
 
 ### Manual Configuration
@@ -21,10 +20,10 @@ Add to your MCP client configuration (e.g., Claude Desktop):
 ```json
 {
   "mcpServers": {
-    "agent-builder": {
-      "command": "python",
-      "args": ["-m", "framework.mcp.agent_builder_server"],
-      "cwd": "/path/to/goal-agent"
+    "coder-tools": {
+      "command": "uv",
+      "args": ["run", "coder_tools_server.py", "--stdio"],
+      "cwd": "/path/to/hive/tools"
     }
   }
 }

core/README.md

@@ -17,66 +17,11 @@ Framework provides a runtime framework that captures **decisions**, not just act
 uv pip install -e .
 ```
 
-## MCP Server Setup
+## Agent Building
 
-The framework includes an MCP (Model Context Protocol) server for building agents. To set up the MCP server:
+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`.
 
-### Automated Setup
-
-**Using bash (Linux/macOS):**
-```bash
-./setup_mcp.sh
-```
-
-**Using Python (cross-platform):**
-```bash
-python setup_mcp.py
-```
-
-The setup script will:
-1. Install the framework package
-2. Install MCP dependencies (mcp, fastmcp)
-3. Create/verify `.mcp.json` configuration
-4. Test the MCP server module
-
-### Manual Setup
-
-If you prefer manual setup:
-
-```bash
-# Install framework
-uv pip install -e .
-
-# Install MCP dependencies
-uv pip install mcp fastmcp
-
-# Test the server
-uv run python -m framework.mcp.agent_builder_server
-```
-
-### Using with MCP Clients
-
-To use the agent builder with Claude Desktop or other MCP clients, add this to your MCP client configuration:
-
-```json
-{
-  "mcpServers": {
-    "agent-builder": {
-      "command": "python",
-      "args": ["-m", "framework.mcp.agent_builder_server"],
-      "cwd": "/path/to/hive/core"
-    }
-  }
-}
-```
-
-The MCP server provides tools for:
-- Creating agent building sessions
-- Defining goals with success criteria
-- Adding nodes (event_loop only)
-- Connecting nodes with edges
-- Validating and exporting agent graphs
-- Testing nodes and full agent graphs
+See the [Getting Started Guide](../docs/getting-started.md) for building agents.
 
 ## Quick Start
 
@@ -145,7 +90,7 @@ uv run python -m framework test-debug <agent_path> <test_name>
 uv run python -m framework test-list <agent_path>
 ```
 
-For detailed testing workflows, see the [hive-test skill](../.claude/skills/hive-test/SKILL.md).
+For detailed testing workflows, see [developer-guide.md](../docs/developer-guide.md).
 
 ### Analyzing Agent Behavior with Builder
 

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/examples/mcp_integration_example.py

@@ -95,81 +95,6 @@ async def example_3_config_file():
     (test_agent_path / "mcp_servers.json").unlink()
 
 
-async def example_4_custom_agent_with_mcp_tools():
-    """Example 4: Build custom agent that uses MCP tools"""
-    print("\n=== Example 4: Custom Agent with MCP Tools ===\n")
-
-    from framework.builder.workflow import GraphBuilder
-
-    # Create a workflow builder
-    builder = GraphBuilder()
-
-    # Define goal
-    builder.set_goal(
-        goal_id="web-researcher",
-        name="Web Research Agent",
-        description="Search the web and summarize findings",
-    )
-
-    # Add success criteria
-    builder.add_success_criterion(
-        "search-results", "Successfully retrieve at least 3 web search results"
-    )
-    builder.add_success_criterion("summary", "Provide a clear, concise summary of the findings")
-
-    # Add nodes that will use MCP tools
-    builder.add_node(
-        node_id="web-searcher",
-        name="Web Search",
-        description="Search the web for information",
-        node_type="event_loop",
-        system_prompt="Search for {query} and return the top results. Use the web_search tool.",
-        tools=["web_search"],  # This tool comes from tools MCP server
-        input_keys=["query"],
-        output_keys=["search_results"],
-    )
-
-    builder.add_node(
-        node_id="summarizer",
-        name="Summarize Results",
-        description="Summarize the search results",
-        node_type="event_loop",
-        system_prompt="Summarize the following search results in 2-3 sentences: {search_results}",
-        input_keys=["search_results"],
-        output_keys=["summary"],
-    )
-
-    # Connect nodes
-    builder.add_edge("web-searcher", "summarizer")
-
-    # Set entry point
-    builder.set_entry("web-searcher")
-    builder.set_terminal("summarizer")
-
-    # Export the agent
-    export_path = Path("exports/web-research-agent")
-    export_path.mkdir(parents=True, exist_ok=True)
-    builder.export(export_path)
-
-    # Load and register MCP server
-    runner = AgentRunner.load(export_path)
-    runner.register_mcp_server(
-        name="tools",
-        transport="stdio",
-        command="python",
-        args=["-m", "aden_tools.mcp_server", "--stdio"],
-        cwd="../tools",
-    )
-
-    # Run the agent
-    result = await runner.run({"query": "latest AI breakthroughs 2026"})
-
-    print(f"\nAgent completed with result:\n{result}")
-
-    # Cleanup
-    runner.cleanup()
-
-
 async def main():
     """Run all examples"""
     print("=" * 60)

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
@@ -406,7 +407,8 @@ nodes = [
         client_facing=True,
         max_node_visits=0,
         input_keys=[],
-        output_keys=[],
+        output_keys=["test_result"],
+        nullable_output_keys=["test_result"],
         tools=["get_account_info"],
         system_prompt="""\
 You are a credential tester. Your job is to help the user verify that their \
@@ -444,7 +446,7 @@ edges = []
 entry_node = "tester"
 entry_points = {"start": "tester"}
 pause_nodes = []
-terminal_nodes = []  # Forever-alive: loops until user exits
+terminal_nodes = ["tester"]  # Tester node can terminate
 
 conversation_mode = "continuous"
 identity_prompt = (
@@ -454,7 +456,6 @@ identity_prompt = (
 loop_config = {
     "max_iterations": 50,
     "max_tool_calls_per_turn": 30,
-    "max_history_tokens": 32000,
 }
 
 # ---------------------------------------------------------------------------
@@ -531,7 +532,7 @@ class CredentialTesterAgent:
             version="1.0.0",
             entry_node="tester",
             entry_points={"start": "tester"},
-            terminal_nodes=[],
+            terminal_nodes=["tester"],  # Tester node can terminate
             pause_nodes=[],
             nodes=[tester_node],
             edges=[],
@@ -540,7 +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/credential_tester/nodes/__init__.py

@@ -51,7 +51,8 @@ The key is pre-injected into the session environment and tools read it automatic
         client_facing=True,
         max_node_visits=0,
         input_keys=[],
-        output_keys=[],
+        output_keys=["test_result"],
+        nullable_output_keys=["test_result"],
         tools=tools,
         system_prompt=f"""\
 You are a credential tester for the {account_label}: {provider}/{alias}{detail}

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/__main__.py

@@ -1,223 +0,0 @@
-"""CLI entry point for Hive Coder agent."""
-
-import asyncio
-import json
-import logging
-import sys
-
-import click
-
-from .agent import HiveCoderAgent, default_agent
-
-
-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("--request", "-r", type=str, required=True, help="What agent to build")
-@click.option("--mock", is_flag=True, help="Run in mock mode")
-@click.option("--quiet", "-q", is_flag=True, help="Only output result JSON")
-@click.option("--verbose", "-v", is_flag=True, help="Show execution details")
-@click.option("--debug", is_flag=True, help="Show debug logging")
-def run(request, mock, quiet, verbose, debug):
-    """Execute agent building from a request."""
-    if not quiet:
-        setup_logging(verbose=verbose, debug=debug)
-
-    context = {"user_request": request}
-
-    result = asyncio.run(default_agent.run(context, mock_mode=mock))
-
-    output_data = {
-        "success": result.success,
-        "steps_executed": result.steps_executed,
-        "output": result.output,
-    }
-    if result.error:
-        output_data["error"] = result.error
-
-    click.echo(json.dumps(output_data, indent=2, default=str))
-    sys.exit(0 if result.success else 1)
-
-
-@cli.command()
-@click.option("--mock", is_flag=True, help="Run in mock mode")
-@click.option("--verbose", "-v", is_flag=True, help="Show execution details")
-@click.option("--debug", is_flag=True, help="Show debug logging")
-def tui(mock, verbose, debug):
-    """Launch the TUI dashboard for interactive agent building."""
-    setup_logging(verbose=verbose, debug=debug)
-
-    try:
-        from framework.tui.app import AdenTUI
-    except ImportError:
-        click.echo("TUI requires the 'textual' package. Install with: pip install textual")
-        sys.exit(1)
-
-    from pathlib import Path
-
-    from framework.llm import LiteLLMProvider
-    from framework.runner.tool_registry import ToolRegistry
-    from framework.runtime.agent_runtime import create_agent_runtime
-    from framework.runtime.execution_stream import EntryPointSpec
-
-    async def run_with_tui():
-        agent = HiveCoderAgent()
-
-        agent._tool_registry = ToolRegistry()
-
-        storage_path = Path.home() / ".hive" / "agents" / "hive_coder"
-        storage_path.mkdir(parents=True, exist_ok=True)
-
-        mcp_config_path = Path(__file__).parent / "mcp_servers.json"
-        if mcp_config_path.exists():
-            agent._tool_registry.load_mcp_config(mcp_config_path)
-
-        llm = None
-        if not mock:
-            llm = LiteLLMProvider(
-                model=agent.config.model,
-                api_key=agent.config.api_key,
-                api_base=agent.config.api_base,
-            )
-
-        tools = list(agent._tool_registry.get_tools().values())
-        tool_executor = agent._tool_registry.get_executor()
-        graph = agent._build_graph()
-
-        runtime = create_agent_runtime(
-            graph=graph,
-            goal=agent.goal,
-            storage_path=storage_path,
-            entry_points=[
-                EntryPointSpec(
-                    id="start",
-                    name="Build Agent",
-                    entry_node="coder",
-                    trigger_type="manual",
-                    isolation_level="isolated",
-                ),
-            ],
-            llm=llm,
-            tools=tools,
-            tool_executor=tool_executor,
-        )
-
-        await runtime.start()
-
-        try:
-            app = AdenTUI(runtime)
-            await app.run_async()
-        finally:
-            await runtime.stop()
-
-    asyncio.run(run_with_tui())
-
-
-@cli.command()
-@click.option("--json", "output_json", is_flag=True)
-def info(output_json):
-    """Show agent information."""
-    info_data = default_agent.info()
-    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']}")
-        click.echo(f"Terminal: {', '.join(info_data['terminal_nodes']) or '(forever-alive)'}")
-
-
-@cli.command()
-def validate():
-    """Validate agent structure."""
-    validation = default_agent.validate()
-    if validation["valid"]:
-        click.echo("Agent is valid")
-        if validation["warnings"]:
-            for warning in validation["warnings"]:
-                click.echo(f"  WARNING: {warning}")
-    else:
-        click.echo("Agent has errors:")
-        for error in validation["errors"]:
-            click.echo(f"  ERROR: {error}")
-    sys.exit(0 if validation["valid"] else 1)
-
-
-@cli.command()
-@click.option("--verbose", "-v", is_flag=True)
-def shell(verbose):
-    """Interactive agent building session (CLI, no TUI)."""
-    asyncio.run(_interactive_shell(verbose))
-
-
-async def _interactive_shell(verbose=False):
-    """Async interactive shell."""
-    setup_logging(verbose=verbose)
-
-    click.echo("=== Hive Coder ===")
-    click.echo("Describe the agent you want to build (or 'quit' to exit):\n")
-
-    agent = HiveCoderAgent()
-    await agent.start()
-
-    try:
-        while True:
-            try:
-                request = await asyncio.get_event_loop().run_in_executor(None, input, "Build> ")
-                if request.lower() in ["quit", "exit", "q"]:
-                    click.echo("Goodbye!")
-                    break
-
-                if not request.strip():
-                    continue
-
-                click.echo("\nBuilding agent...\n")
-
-                result = await agent.trigger_and_wait("default", {"user_request": request})
-
-                if result is None:
-                    click.echo("\n[Execution timed out]\n")
-                    continue
-
-                if result.success:
-                    output = result.output or {}
-                    agent_name = output.get("agent_name", "unknown")
-                    validation = output.get("validation_result", "unknown")
-                    click.echo(f"\nAgent '{agent_name}' built. Validation: {validation}\n")
-                else:
-                    click.echo(f"\nBuild failed: {result.error}\n")
-
-            except KeyboardInterrupt:
-                click.echo("\nGoodbye!")
-                break
-            except Exception as e:
-                click.echo(f"Error: {e}", err=True)
-                import traceback
-
-                traceback.print_exc()
-    finally:
-        await agent.stop()
-
-
-if __name__ == "__main__":
-    cli()

core/framework/agents/hive_coder/agent.py

@@ -1,357 +0,0 @@
-"""Agent graph construction for Hive Coder."""
-
-from pathlib import Path
-
-from framework.graph import Constraint, Goal, SuccessCriterion
-from framework.graph.checkpoint_config import CheckpointConfig
-from framework.graph.edge import GraphSpec
-from framework.graph.executor import ExecutionResult
-from framework.llm import LiteLLMProvider
-from framework.runner.tool_registry import ToolRegistry
-from framework.runtime.agent_runtime import AgentRuntime, create_agent_runtime
-from framework.runtime.execution_stream import EntryPointSpec
-
-from .config import default_config, metadata
-from .nodes import coder_node, queen_node
-
-# ticket_receiver is no longer needed — the queen runs as an independent
-# GraphExecutor and receives escalation tickets via inject_event().
-# Keeping the import commented for reference:
-# from .ticket_receiver import TICKET_RECEIVER_ENTRY_POINT
-
-# Goal definition
-goal = Goal(
-    id="agent-builder",
-    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, correct imports, entry_points format"
-            ),
-            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 forever-alive event_loop node
-edges = []
-
-# Graph configuration
-entry_node = "coder"
-entry_points = {"start": "coder"}
-pause_nodes = []
-terminal_nodes = []  # Forever-alive: loops until user exits
-
-# 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,
-    },
-)
-
-
-class HiveCoderAgent:
-    """
-    Hive Coder — builds Hive agent packages from natural language.
-
-    Single-node architecture: the coder runs in a continuous while(true) loop.
-    The queen runs as an independent GraphExecutor (loaded by the TUI via
-    _load_judge_and_queen), not as part of this graph.
-    """
-
-    def __init__(self, config=None):
-        self.config = config or default_config
-        self.goal = goal
-        self.nodes = nodes
-        self.edges = edges
-        self.entry_node = entry_node
-        self.entry_points = entry_points
-        self.pause_nodes = pause_nodes
-        self.terminal_nodes = terminal_nodes
-        self.async_entry_points = async_entry_points
-        self._graph: GraphSpec | None = None
-        self._agent_runtime: AgentRuntime | None = None
-        self._tool_registry: ToolRegistry | None = None
-        self._storage_path: Path | None = None
-
-    def _build_graph(self) -> GraphSpec:
-        """Build the GraphSpec."""
-        return GraphSpec(
-            id="hive-coder-graph",
-            goal_id=self.goal.id,
-            version="1.0.0",
-            entry_node=self.entry_node,
-            entry_points=self.entry_points,
-            terminal_nodes=self.terminal_nodes,
-            pause_nodes=self.pause_nodes,
-            nodes=self.nodes,
-            edges=self.edges,
-            default_model=self.config.model,
-            max_tokens=self.config.max_tokens,
-            loop_config=loop_config,
-            conversation_mode=conversation_mode,
-            identity_prompt=identity_prompt,
-            async_entry_points=self.async_entry_points,
-        )
-
-    def _setup(self, mock_mode=False) -> None:
-        """Set up the agent runtime."""
-        self._storage_path = Path.home() / ".hive" / "agents" / "hive_coder"
-        self._storage_path.mkdir(parents=True, exist_ok=True)
-
-        self._tool_registry = ToolRegistry()
-
-        mcp_config_path = Path(__file__).parent / "mcp_servers.json"
-        if mcp_config_path.exists():
-            self._tool_registry.load_mcp_config(mcp_config_path)
-
-        llm = None
-        if not mock_mode:
-            llm = LiteLLMProvider(
-                model=self.config.model,
-                api_key=self.config.api_key,
-                api_base=self.config.api_base,
-            )
-
-        tool_executor = self._tool_registry.get_executor()
-        tools = list(self._tool_registry.get_tools().values())
-
-        self._graph = self._build_graph()
-
-        checkpoint_config = CheckpointConfig(
-            enabled=True,
-            checkpoint_on_node_start=False,
-            checkpoint_on_node_complete=True,
-            checkpoint_max_age_days=7,
-            async_checkpoint=True,
-        )
-
-        entry_point_specs = [
-            EntryPointSpec(
-                id="default",
-                name="Default",
-                entry_node=self.entry_node,
-                trigger_type="manual",
-                isolation_level="shared",
-            ),
-        ]
-
-        self._agent_runtime = create_agent_runtime(
-            graph=self._graph,
-            goal=self.goal,
-            storage_path=self._storage_path,
-            entry_points=entry_point_specs,
-            llm=llm,
-            tools=tools,
-            tool_executor=tool_executor,
-            checkpoint_config=checkpoint_config,
-            graph_id="hive_coder",
-        )
-
-    async def start(self, mock_mode=False) -> None:
-        """Set up and start the agent runtime."""
-        if self._agent_runtime is None:
-            self._setup(mock_mode=mock_mode)
-        if not self._agent_runtime.is_running:
-            await self._agent_runtime.start()
-
-    async def stop(self) -> None:
-        """Stop the agent runtime and clean up."""
-        if self._agent_runtime and self._agent_runtime.is_running:
-            await self._agent_runtime.stop()
-        self._agent_runtime = None
-
-    async def trigger_and_wait(
-        self,
-        entry_point: str = "default",
-        input_data: dict | None = None,
-        timeout: float | None = None,
-        session_state: dict | None = None,
-    ) -> ExecutionResult | None:
-        """Execute the graph and wait for completion."""
-        if self._agent_runtime is None:
-            raise RuntimeError("Agent not started. Call start() first.")
-
-        return await self._agent_runtime.trigger_and_wait(
-            entry_point_id=entry_point,
-            input_data=input_data or {},
-            session_state=session_state,
-        )
-
-    async def run(self, context: dict, mock_mode=False, session_state=None) -> ExecutionResult:
-        """Run the agent (convenience method for single execution)."""
-        await self.start(mock_mode=mock_mode)
-        try:
-            result = await self.trigger_and_wait("default", context, session_state=session_state)
-            return result or ExecutionResult(success=False, error="Execution timeout")
-        finally:
-            await self.stop()
-
-    def info(self):
-        """Get agent information."""
-        return {
-            "name": metadata.name,
-            "version": metadata.version,
-            "description": metadata.description,
-            "goal": {
-                "name": self.goal.name,
-                "description": self.goal.description,
-            },
-            "nodes": [n.id for n in self.nodes],
-            "edges": [e.id for e in self.edges],
-            "entry_node": self.entry_node,
-            "entry_points": self.entry_points,
-            "pause_nodes": self.pause_nodes,
-            "terminal_nodes": self.terminal_nodes,
-            "client_facing_nodes": [n.id for n in self.nodes if n.client_facing],
-        }
-
-    def validate(self):
-        """Validate agent structure."""
-        errors = []
-        warnings = []
-
-        node_ids = {node.id for node in self.nodes}
-        for edge in self.edges:
-            if edge.source not in node_ids:
-                errors.append(f"Edge {edge.id}: source '{edge.source}' not found")
-            if edge.target not in node_ids:
-                errors.append(f"Edge {edge.id}: target '{edge.target}' not found")
-
-        if self.entry_node not in node_ids:
-            errors.append(f"Entry node '{self.entry_node}' not found")
-
-        for terminal in self.terminal_nodes:
-            if terminal not in node_ids:
-                errors.append(f"Terminal node '{terminal}' not found")
-
-        for ep_id, node_id in self.entry_points.items():
-            if node_id not in node_ids:
-                errors.append(f"Entry point '{ep_id}' references unknown node '{node_id}'")
-
-        return {
-            "valid": len(errors) == 0,
-            "errors": errors,
-            "warnings": warnings,
-        }
-
-
-# Create default instance
-default_agent = HiveCoderAgent()

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

@@ -1,968 +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")
-_file_templates = (_ref_dir / "file_templates.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: File Templates\n\n"
-        + _file_templates
-        + "\n\n# Appendix: Anti-Patterns\n\n"
-        + _anti_patterns
-    )
-    if _is_gcu_enabled() and _gcu_guide:
-        parts += "\n\n# Appendix: GCU Browser Automation Guide\n\n" + _gcu_guide
-    return parts
-
-
-# Shared appendices — appended to every coding node's system prompt.
-_appendices = _build_appendices()
-
-# 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_tools",
-    "list_agents",
-    "list_agent_sessions",
-    "list_agent_checkpoints",
-    "get_agent_checkpoint",
-    "run_agent_tests",
-]
-
-# Queen mode-specific tool sets.
-# Building mode: full coding + agent construction tools.
-_QUEEN_BUILDING_TOOLS = _SHARED_TOOLS + [
-    "load_built_agent",
-    "list_credentials",
-]
-
-# Staging mode: 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 mode: 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.
-# ---------------------------------------------------------------------------
-
-_agent_builder_knowledge = """\
-
-# Core Mandates
-
-- **Read before writing.** NEVER write code from assumptions. Read \
-reference agents and templates first. Read every file before editing.
-- **Conventions first.** Follow existing project patterns exactly. \
-Analyze imports, structure, and style in reference agents.
-- **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.
-- **Professional objectivity.** If a use case is a poor fit for the \
-framework, say so. Technical accuracy over validation.
-- **Concise.** No emojis. No preambles. No postambles. Substance only.
-- **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) or \
-"full" (includes input_schema). group: "all" (default) or a prefix like \
-"gmail". Call FIRST before designing.
-- validate_agent_tools(agent_path) — validate that all tools declared \
-in an agent's nodes actually exist. 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
-- run_agent_tests(agent_name, test_types?, fail_fast?) — run pytest with parsing
-
-# 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() to discover all \
-available tools. ONLY use tools from this list in your node definitions. \
-NEVER guess or fabricate tool names from memory.
-
-  list_agent_tools()                                    # names + descriptions
-  list_agent_tools(output_schema="full")                # include input_schema
-  list_agent_tools(group="gmail")                       # only gmail_* tools
-  list_agent_tools("exports/{agent_name}/mcp_servers.json")  # specific agent
-
-## Agent Awareness
-Run list_agents() to see what agents already exist. Read their code \
-for patterns:
-  read_file("exports/{name}/agent.py")
-  read_file("exports/{name}/nodes/__init__.py")
-
-## Post-Build Testing
-After writing agent code, validate structurally AND run tests:
-  run_command("python -c 'from {name} import default_agent; \\
-    print(default_agent.validate())'")
-  run_agent_tests("{name}")
-
-## 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
-4. 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. Understand & Qualify (3-5 turns)
-
-This is ONE conversation, not two phases. Discovery and qualification \
-happen together. Surface problems as you find them, not in a batch.
-
-**Before your first response**, silently run list_agent_tools() and \
-consult the **Framework Reference** appendix. Know what's possible \
-before you speak.
-
-### How to respond to the user's first message
-
-**Listen like an architect.** While they talk, hear the structure:
-- **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?
-- **The pain**: What about today is broken, slow, or missing?
-
-| They say... | You're hearing... |
-|-------------|-------------------|
-| Nouns they repeat | Your entities |
-| Verbs they emphasize | Your core operations |
-| Frustrations they mention | Your design constraints |
-| Workarounds they describe | What the system must replace |
-
-**Use domain knowledge aggressively.** If they say "research agent," \
-you already know it involves search, summarization, source tracking, \
-iteration. Don't ask about each — use them as defaults and let their \
-specifics override. Merge your general knowledge with their specifics: \
-60-80% right before you ask a single question.
-
-### Play back a model WITH qualification baked in
-
-Don't separate "here's what I understood" from "here's what might be \
-a problem." Weave them together. Your playback should sound like:
-
-"Here's how I'm picturing this: [concrete proposed solution]. \
-The framework handles [X and Y] well for this. [One concern: Z tool \
-doesn't exist, so we'd use W instead / Z would need real-time which \
-isn't a fit, but we could do polling]. For MVP I'd focus on \
-[highest-value thing]. Before I start — [1-2 questions]."
-
-If there's a deal-breaker, lead with it: "Before I go further — \
-this needs [X] which the framework can't do because [Y]. We could \
-[workaround] or reconsider the approach. What do you think?"
-
-**Surface problems immediately. Don't save them for a formal review.**
-
-### Ask only what you CANNOT infer
-
-Every question must earn its place by preventing a costly wrong turn, \
-unlocking a shortcut, or surfacing a dealbreaker.
-
-Good questions: "Who's the primary user?", "Is this replacing \
-something or net new?", "Does this integrate with anything?"
-
-Bad questions (DON'T ask): "What should happen on error?", "Should \
-it have search?", "What tools should I use?" — these are your job.
-
-### Conversation flow
-
-| Turn | Who | What |
-|------|-----|------|
-| 1 | User | Describes what they need |
-| 2 | You | Play back model with concerns baked in. 1-2 questions max. |
-| 3 | User | Corrects, confirms, or adds detail |
-| 4 | You | Adjust model, confirm scope, move to design |
-
-### Anti-patterns
-
-| Don't | Do instead |
-|-------|------------|
-| Open with a list of questions | Open with what you understood |
-| Separate "assessment" dump | Weave concerns into your playback |
-| Good/Bad/Ugly formal section | Mention issues naturally in context |
-| Ask about every edge case | Smart defaults, flag in summary |
-| 10+ turn discovery | 3-5 turns, then start building |
-| Wait for certainty | Start at 80% confidence, iterate |
-| Ask what tech/tools to use | Decide, disclose, move on |
-
-## 3. Design
-
-Design the agent architecture:
-- Goal: id, name, description, 3-5 success criteria, 2-4 constraints
-- Nodes: **2-4 nodes MAXIMUM** (see rules below)
-- Edges: on_success for linear, conditional for routing
-- Lifecycle: ALWAYS forever-alive (`terminal_nodes=[]`) unless the user \
-explicitly requests a one-shot/batch agent. Forever-alive agents loop \
-continuously — the user exits by closing the TUI. This is the standard \
-pattern for all interactive agents.
-
-### Node Design Rules
-
-Each node boundary serializes outputs to shared memory \
-and DESTROYS all in-context information (tool results, reasoning, history). \
-Use as many nodes as the use case requires, but don't create nodes without \
-tools — merge them into nodes that do real work.
-
-**MERGE nodes when:**
-- Node has NO tools (pure LLM reasoning) → merge into predecessor/successor
-- Node sets only 1 trivial output → collapse into predecessor
-- Multiple consecutive autonomous nodes → combine into one rich node
-- A "report" or "summary" node → merge into the client-facing node
-- A "confirm" or "schedule" node that calls no external service → remove
-
-**SEPARATE nodes only when:**
-- Client-facing vs autonomous (different interaction models)
-- Fundamentally different tool sets
-- Fan-out parallelism (parallel branches MUST be separate)
-
-**Typical patterns (queen manages intake — NO client-facing intake node):**
-- 2 nodes: `process (autonomous) → review (client-facing) → process`
-- 1 node: `process (autonomous)` — simplest; queen handles all interaction
-- WRONG: 7 nodes where half have no tools and just do LLM reasoning
-- WRONG: Intake node that asks the user for requirements — the queen does intake
-
-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:
-
-```
-┌─────────────────────────┐
-│  process (autonomous)    │
-│  in:  user_request       │
-│  tools: web_search,      │
-│         save_data        │
-└────────────┬────────────┘
-             │ on_success
-             ▼
-┌─────────────────────────┐
-│  review (client-facing)  │
-│  tools: set_output       │
-└────────────┬────────────┘
-             │ on_success
-             └──────► back to process
-```
-
-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. No client-facing \
-intake node in the worker.
-
-Follow the graph with a brief summary of each node's purpose. \
-Get user approval before implementing.
-
-## 4. Implement
-
-Consult the **File Templates** and **Anti-Patterns** appendices below.
-
-Write files in order:
-1. mkdir -p exports/{name}/nodes exports/{name}/tests
-2. config.py — RuntimeConfig + AgentMetadata
-3. nodes/__init__.py — NodeSpec definitions with system prompts
-4. agent.py — Goal, edges, graph, agent class
-5. __init__.py — package exports
-6. __main__.py — CLI with click
-7. mcp_servers.json — tool server config
-8. tests/ — fixtures
-
-### Critical Rules
-
-**Imports** (must match exactly — only import what you use):
-```python
-from framework.graph import (
-    NodeSpec, EdgeSpec, EdgeCondition,
-    Goal, SuccessCriterion, Constraint,
-)
-from framework.graph.edge import GraphSpec
-from framework.graph.executor import ExecutionResult
-from framework.graph.checkpoint_config import CheckpointConfig
-from framework.llm import LiteLLMProvider
-from framework.runner.tool_registry import ToolRegistry
-from framework.runtime.agent_runtime import (
-    AgentRuntime, create_agent_runtime,
-)
-from framework.runtime.execution_stream import EntryPointSpec
-```
-For agents with async entry points (timers, webhooks, events), also add:
-```python
-from framework.graph.edge import GraphSpec, AsyncEntryPointSpec
-from framework.runtime.agent_runtime import (
-    AgentRuntime, AgentRuntimeConfig, create_agent_runtime,
-)
-```
-NEVER `from core.framework...` — PYTHONPATH includes core/.
-
-**__init__.py MUST re-export ALL module-level variables** \
-(THIS IS THE #1 SOURCE OF AGENT LOAD FAILURES):
-The runner imports the package (__init__.py), NOT agent.py. It reads \
-goal, nodes, edges, entry_node, entry_points, pause_nodes, \
-terminal_nodes, conversation_mode, identity_prompt, loop_config via \
-getattr(). If ANY are missing from __init__.py, they silently default \
-to None or {} — causing "must define goal, nodes, edges" or "node X \
-is unreachable" errors. The __init__.py MUST import and re-export \
-ALL of these from .agent:
-```python
-from .agent import (
-    MyAgent, default_agent, goal, nodes, edges,
-    entry_node, entry_points, pause_nodes, terminal_nodes,
-    conversation_mode, identity_prompt, loop_config,
-)
-```
-
-**entry_points**: `{"start": "first-node-id"}`
-The first node should be an autonomous processing node (NOT a \
-client-facing intake). For agents with multiple entry points, \
-add them: `{"start": "process", "reminder": "check"}`
-
-**conversation_mode** — ONLY two valid values:
-- `"continuous"` — recommended for interactive agents (context carries \
-across node transitions)
-- Omit entirely — for isolated per-node conversations
-NEVER use: "client_facing", "interactive", "adaptive", or any other \
-value. These DO NOT EXIST.
-
-**loop_config** — ONLY three valid keys:
-```python
-loop_config = {
-    "max_iterations": 100,
-    "max_tool_calls_per_turn": 30,
-    "max_history_tokens": 32000,
-}
-```
-NEVER add: "strategy", "mode", "timeout", or other keys.
-
-**mcp_servers.json**:
-```json
-{
-  "hive-tools": {
-    "transport": "stdio",
-    "command": "uv",
-    "args": ["run", "python", "mcp_server.py", "--stdio"],
-    "cwd": "../../tools"
-  }
-}
-```
-NO "mcpServers" wrapper. cwd "../../tools". command "uv".
-
-**Storage**: `Path.home() / ".hive" / "agents" / "{name}"`
-
-**Client-facing system prompts** (review/approval nodes only, NOT intake) \
-— STEP 1/STEP 2 pattern:
-```
-STEP 1 — Present to user (text only, NO tool calls):
-[instructions]
-
-STEP 2 — After user responds, call set_output:
-[set_output calls]
-```
-The queen manages intake. Workers should NOT have a client-facing node \
-that asks for requirements. Use client_facing=True only for review or \
-approval checkpoints mid-execution.
-
-**Autonomous system prompts** — set_output in SEPARATE turn.
-
-**Tools** — NEVER fabricate tool names. Common hallucinations: \
-csv_read, csv_write, csv_append, file_upload, database_query. \
-If list_agent_tools() shows these don't exist, use alternatives \
-(e.g. save_data/load_data for data persistence).
-
-**Node rules**:
-- **NO intake nodes.** The queen owns intake. She defines the entry \
-node's input_keys at build time and fills them via \
-`run_agent_with_input(task)` at run time.
-- Don't abuse nodes without tools — merge them into a node that does work.
-- A node with 0 tools is NOT a real node — merge it.
-- node_type "event_loop" for all regular graph nodes. Use "gcu" ONLY for
-  browser automation subagents (see GCU appendix). GCU nodes MUST be in a
-  parent node's sub_agents list, NEVER connected via edges, and NEVER used
-  as entry/terminal nodes.
-- max_node_visits default is 0 (unbounded) — correct for forever-alive. \
-Only set >0 in one-shot agents with bounded feedback loops.
-- Feedback inputs: nullable_output_keys
-- terminal_nodes=[] for forever-alive (the default)
-- Every node MUST have at least one outgoing edge (no dead ends)
-- Agents are forever-alive unless user explicitly asks for one-shot
-
-**Agent class**: CamelCase name, default_agent at module level. \
-Constructor takes `config=None`. Follow the exact pattern in \
-file_templates.md — do NOT invent constructor params like \
-`llm_provider` or `tool_registry`.
-
-**Module-level variables** (read by AgentRunner.load()):
-goal, nodes, edges, entry_node, entry_points, pause_nodes,
-terminal_nodes, conversation_mode, identity_prompt, loop_config
-
-For agents with async triggers, also export:
-async_entry_points, runtime_config
-
-**Async entry points** (timers, webhooks, events):
-When an agent needs scheduled tasks, webhook reactions, or event-driven \
-triggers, use `AsyncEntryPointSpec` (from framework.graph.edge) and \
-`AgentRuntimeConfig` (from framework.runtime.agent_runtime):
-- Timer (cron): `trigger_type="timer"`, \
-`trigger_config={"cron": "0 9 * * *"}` — standard 5-field cron expression \
-(e.g. `"0 9 * * MON-FRI"` weekdays 9am, `"*/30 * * * *"` every 30 min)
-- Timer (interval): `trigger_type="timer"`, \
-`trigger_config={"interval_minutes": 20, "run_immediately": False}`
-- Event (for webhooks): `trigger_type="event"`, \
-`trigger_config={"event_types": ["webhook_received"]}`
-- `isolation_level="shared"` so async runs can read primary session memory
-- `runtime_config = AgentRuntimeConfig(webhook_routes=[...])` for HTTP webhooks
-- Reference: `exports/gmail_inbox_guardian/agent.py`
-- Full docs: see **Framework Reference** appendix (Async Entry Points section)
-
-## 5. Verify
-
-Run FOUR validation steps after writing. All must pass:
-
-**Step A — Class validation** (checks graph structure):
-```
-run_command("python -c 'from {name} import default_agent; \\
-  print(default_agent.validate())'")
-```
-
-**Step B — Runner load test** (checks package export contract — \
-THIS IS THE SAME PATH THE TUI USES):
-```
-run_command("python -c 'from framework.runner.runner import \\
-  AgentRunner; r = AgentRunner.load(\"exports/{name}\"); \\
-  print(\"AgentRunner.load: OK\")'")
-```
-This catches missing __init__.py exports, bad conversation_mode, \
-invalid loop_config, and unreachable nodes. If Step A passes but \
-Step B fails, the problem is in __init__.py exports.
-
-**Step C — Tool validation** (checks that declared tools actually exist \
-in the agent's MCP servers — catches hallucinated tool names):
-```
-validate_agent_tools("exports/{name}")
-```
-If any tools are missing: fix the node definitions to use only tools \
-that exist. Run list_agent_tools() to see what's available.
-
-**Step D — Run tests:**
-```
-run_agent_tests("{name}")
-```
-
-If anything fails: read error, fix with edit_file, re-validate. Up to 3x.
-
-**CRITICAL: Testing forever-alive agents**
-Most agents use `terminal_nodes=[]` (forever-alive). This means \
-`runner.run()` NEVER returns — it hangs forever waiting for a \
-terminal node that doesn't exist. Agent tests MUST be structural:
-- Validate graph, node specs, edges, tools, prompts
-- Check goal/constraints/success criteria definitions
-- Test `AgentRunner.load()` succeeds (structural, no API key needed)
-- NEVER call `runner.run()` or `trigger_and_wait()` in tests for \
-forever-alive agents — they will hang and time out.
-When you restructure an agent (change nodes/edges), always update \
-the tests to match. Stale tests referencing old node names will fail.
-
-## 6. Present
-
-Show the user what you built: agent name, goal summary, graph (same \
-ASCII style as Design), files created, validation status. Offer to \
-revise or build another.
-"""
-
-
-# ---------------------------------------------------------------------------
-# Coder-specific: set_output after presentation + standalone phase 7
-# ---------------------------------------------------------------------------
-
-_coder_completion = """
-After user confirms satisfaction:
-  set_output("agent_name", "the_agent_name")
-  set_output("validation_result", "valid")
-
-If building another agent, just start the loop again — no need to \
-set_output until the user is done.
-
-## 7. Live Test (optional)
-
-After the user approves, offer to load and run the agent in-session.
-
-If running with a queen (server/frontend):
-```
-load_built_agent("exports/{name}")  # loads as the session worker
-```
-The frontend updates automatically — the user sees the agent's graph, \
-the tab renames, and you can delegate via start_worker(task).
-
-If running standalone (TUI):
-```
-load_agent("exports/{name}")   # registers as secondary graph
-start_agent("{name}")           # triggers default entry point
-```
-"""
-
-
-# ---------------------------------------------------------------------------
-# Queen-specific: extra tool docs, behavior, phase 7, style
-# ---------------------------------------------------------------------------
-
-_queen_tools_docs = """
-
-## Operating Modes
-
-You operate in one of three modes. Your available tools change based on the \
-mode. The system notifies you when a mode change occurs.
-
-### BUILDING mode (default)
-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_tools, \
-list_agents, list_agent_sessions, \
-list_agent_checkpoints, get_agent_checkpoint, run_agent_tests
-- load_built_agent(agent_path) — Load the agent and switch to STAGING mode
-- list_credentials(credential_id?) — List authorized credentials
-
-When you finish building an agent, call load_built_agent(path) to stage it.
-
-### STAGING mode (agent loaded, not yet running)
-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() — Check the loaded worker
-- run_agent_with_input(task) — Start the worker and switch to RUNNING mode
-- stop_worker_and_edit() — Go back to BUILDING mode
-
-In STAGING mode you do NOT have write tools. If you need to modify the agent, \
-call stop_worker_and_edit() to go back to BUILDING mode.
-
-### RUNNING mode (worker is executing)
-The worker is running. You have monitoring and lifecycle tools:
-- Read-only: read_file, list_directory, search_files, run_command
-- get_worker_status() — Check worker status (idle, running, waiting)
-- 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 mode, then ask the user what to do next
-- stop_worker_and_edit() — Stop the worker and switch back to BUILDING mode
-
-In RUNNING mode 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 mode. To stop the worker and ask the user what to do next, call \
-stop_worker() to return to STAGING mode.
-
-### Mode transitions
-- load_built_agent(path) → switches to STAGING mode
-- run_agent_with_input(task) → starts worker, switches to RUNNING mode
-- stop_worker() → stops worker, switches to STAGING mode (ask user: re-run or edit?)
-- stop_worker_and_edit() → stops worker (if running), switches to BUILDING mode
-"""
-
-_queen_behavior = """
-# 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). This is \
-NON-NEGOTIABLE. 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 2-node", "Rich with feedback", "Custom"])
-- ask_user("Ready to proceed?",
-  ["Yes, go ahead", "Let me change something"])
-
-## Greeting and identity
-
-When the user greets you or asks what you can do, respond concisely \
-(under 10 lines). DO NOT list internal processes. Focus on:
-1. Direct capabilities: coding, agent building & debugging.
-2. What the loaded worker does (one sentence from Worker Profile). \
-If no worker is loaded, say so.
-3. THEN call ask_user to prompt them — do NOT just write text.
-
-## Direct coding
-You can do any coding task directly — reading files, writing code, running \
-commands, building agents, debugging. For quick tasks, do them yourself.
-
-## 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. Never \
-delegate agent construction to the worker, even as a "research" subtask.
-
-## 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 mode (agent loaded, not running):
-- Tell the user the agent is loaded and ready.
-- 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.
-
-## When worker is running — GO SILENT
-
-Once you call start_worker(), your job is DONE. Do NOT call ask_user, \
-do NOT call get_worker_status(), do NOT emit any text. Just stop. \
-The worker owns the conversation now — it has its own client-facing \
-nodes that talk to the user directly.
-
-**After start_worker, your ENTIRE response should be ONE short \
-confirmation sentence with NO tool calls.** Example: \
-"Started the vulnerability assessment." — that's it. No ask_user, \
-no get_worker_status, no follow-up questions.
-
-You only wake up again when:
-- The user explicitly addresses you (not answering a worker question)
-- A worker question is forwarded to you for relay
-- An escalation ticket arrives from the judge
-- The worker finishes
-
-If the user explicitly asks about progress, call get_worker_status() \
-ONCE and report. Do NOT poll or check proactively.
-
-For escalation tickets: low/transient → acknowledge silently. \
-High/critical → notify the user with a brief analysis.
-
-## When the worker asks the user a question:
-- The user's answer is routed to you with context: \
-[Worker asked: "...", Options: ...] User answered: "...".
-- If the user is answering the worker's question normally, relay it \
-using inject_worker_message(answer_text). Then go silent again.
-- If the user is rejecting the approach, asking to stop, or giving \
-you an instruction, handle it yourself — do NOT relay.
-
-## 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 mode).
-2. Use the **Path** from the Worker Profile to locate the agent files.
-3. Read the relevant files (nodes/__init__.py, agent.py, etc.).
-4. Make the requested changes using edit_file / write_file.
-5. Run validation (default_agent.validate(), AgentRunner.load(), \
-validate_agent_tools()).
-6. **Reload the modified worker**: call load_built_agent("{path}") \
-so the changes take effect immediately (switches to STAGING mode). \
-Then call run_agent_with_input(task) to restart execution.
-
-Do NOT skip step 6 — without reloading, the user will still be \
-interacting with the old version.
-"""
-
-_queen_phase_7 = """
-## 7. Load into Session
-
-After building and verifying, load the agent into the current session:
-  load_built_agent("exports/{name}")
-This switches to STAGING mode — the user sees the agent's graph and \
-the tab name updates. Then call run_agent_with_input(task) to start it. \
-Do NOT tell the user to run `python -m {name} run` — load and run it here.
-"""
-
-_queen_style = """
-# Style
-
-- Concise. No fluff. Direct. No emojis.
-- **One phase per response.** Stop after each phase and get user \
-confirmation before moving on. Never combine understand + design + \
-implement in one response.
-- 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"
-        + _agent_builder_knowledge
-        + _coder_completion
-        + _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=[],
-    nullable_output_keys=[],
-    success_criteria=(
-        "User's intent is understood, coding tasks are completed correctly, "
-        "and the worker is managed effectively when delegated to."
-    ),
-    tools=sorted(set(_QUEEN_BUILDING_TOOLS + _QUEEN_STAGING_TOOLS + _QUEEN_RUNNING_TOOLS)),
-    system_prompt=(
-        "You are the Queen — the user's primary interface. You are a coding agent "
-        "with the same capabilities as the Hive Coder worker, PLUS the ability to "
-        "manage the worker's lifecycle.\n"
-        + _agent_builder_knowledge
-        + _queen_tools_docs
-        + _queen_behavior
-        + _queen_phase_7
-        + _queen_style
-        + _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",
-]

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

@@ -1,113 +0,0 @@
-# Common Mistakes When Building Hive Agents
-
-## Critical Errors
-
-1. **Using tools that don't exist** — Always verify tools are available in the hive-tools MCP server before assigning them to nodes. Never guess tool names.
-
-2. **Wrong entry_points format** — MUST be `{"start": "first-node-id"}`. NOT a set, NOT `{node_id: [keys]}`.
-
-3. **Wrong mcp_servers.json format** — Flat dict (no `"mcpServers"` wrapper). `cwd` must be `"../../tools"`. `command` must be `"uv"` with args `["run", "python", ...]`.
-
-4. **Missing STEP 1/STEP 2 in client-facing prompts** — Without explicit phases, the LLM calls set_output before the user responds. Always use the pattern.
-
-5. **Forgetting nullable_output_keys** — When a node receives inputs from multiple edges and some inputs only arrive on certain edges (e.g., feedback), mark those as nullable. Without this, the executor blocks waiting for a value that will never arrive.
-
-6. **Creating dead-end nodes in forever-alive graphs** — Every node must have at least one outgoing edge. A node with no outgoing edges ends the execution, breaking the loop.
-
-7. **Setting max_node_visits to a non-zero value in forever-alive agents** — The framework default is `max_node_visits=0` (unbounded). Setting it to any positive value (e.g., 1) means the node stops executing after that many visits, silently breaking the forever-alive loop. Only set `max_node_visits > 0` in one-shot agents with feedback loops that need bounded retries.
-
-7. **Missing module-level exports in `__init__.py`** — The runner loads agents via `importlib.import_module(package_name)`, which imports `__init__.py`. It then reads `goal`, `nodes`, `edges`, `entry_node`, `entry_points`, `pause_nodes`, `terminal_nodes`, `conversation_mode`, `identity_prompt`, `loop_config` via `getattr()`. If ANY of these are missing from `__init__.py`, they default to `None` or `{}` — causing "must define goal, nodes, edges" errors or "node X is unreachable" validation failures. **ALL module-level variables from agent.py must be re-exported in `__init__.py`.**
-
-## Value Errors
-
-8. **Invalid `conversation_mode` value** — Only two valid values: `"continuous"` (recommended for interactive agents) or omit entirely (for isolated per-node conversations). Values like `"client_facing"`, `"interactive"`, `"adaptive"` do NOT exist and will cause runtime errors.
-
-9. **Invalid `loop_config` keys** — Only three valid keys: `max_iterations` (int), `max_tool_calls_per_turn` (int), `max_history_tokens` (int). Keys like `"strategy"`, `"mode"`, `"timeout"` are NOT valid and are silently ignored or cause errors.
-
-10. **Fabricating tools that don't exist** — Never guess tool names. Always verify via `list_agent_tools()` before designing and `validate_agent_tools()` after building. Common hallucinations: `csv_read`, `csv_write`, `csv_append`, `file_upload`, `database_query`, `bulk_fetch_emails`. If a required tool doesn't exist, redesign the agent to use tools that DO exist (e.g., `save_data`/`load_data` for data persistence).
-
-## Design Errors
-
-11. **Too many thin nodes** — Hard limit: **2-4 nodes** for most agents. Each node boundary serializes outputs to shared memory and loses all in-context information (tool results, intermediate reasoning, conversation history). A node with 0 tools that just does LLM reasoning is NOT a real node — merge it into its predecessor or successor.
-
-**Merge when:**
-- Node has NO tools — pure LLM reasoning belongs in the node that produces or consumes its data
-- Node sets only 1 trivial output (e.g., `set_output("done", "true")`) — collapse into predecessor
-- Multiple consecutive autonomous nodes with same/similar tools — combine into one
-- A "report" or "summary" node that just presents analysis — merge into the client-facing node
-- A "schedule" or "confirm" node that doesn't actually schedule anything — remove entirely
-
-**Keep separate when:**
-- Client-facing vs autonomous — different interaction models require separate nodes
-- Fundamentally different tool sets (e.g., web search vs file I/O)
-- Fan-out parallelism — parallel branches MUST be separate nodes
-
-**Bad example** (7 nodes — WAY too many):
-```
-profile_setup → daily_intake → update_tracker → analyze_progress → generate_plan → schedule_reminders → report
-```
-`analyze_progress` has no tools. `schedule_reminders` just sets one boolean. `report` just presents analysis. `update_tracker` and `generate_plan` are sequential autonomous work.
-
-**Good example** (2 nodes):
-```
-process (autonomous: track + analyze + plan) → review (client-facing) → process (loop back)
-```
-The queen handles intake (gathering requirements from the user) and passes the task via `run_agent_with_input(task)`. One autonomous node handles ALL backend work (CSV update, analysis, plan generation) with tools and context preserved. One client-facing node handles review/approval when needed.
-
-12. **Adding framework gating for LLM behavior** — Don't add output rollback, premature rejection, or interaction protocol injection. Fix with better prompts or custom judges.
-
-13. **Not using continuous conversation mode** — Interactive agents should use `conversation_mode="continuous"`. Without it, each node starts with blank context.
-
-14. **Adding terminal nodes by default** — ALL agents should use `terminal_nodes=[]` (forever-alive) unless the user explicitly requests a one-shot/batch agent. Forever-alive is the standard pattern. Every node must have at least one outgoing edge. Dead-end nodes break the loop.
-
-15. **Calling set_output in same turn as tool calls** — Instruct the LLM to call set_output in a SEPARATE turn from real tool calls.
-
-## File Template Errors
-
-16. **Wrong import paths** — Use `from framework.graph import ...`, NOT `from core.framework.graph import ...`. The PYTHONPATH includes `core/`.
-
-17. **Missing storage path** — Agent class must set `self._storage_path = Path.home() / ".hive" / "agents" / "agent_name"`.
-
-18. **Missing mcp_servers.json** — Without this, the agent has no tools at runtime.
-
-19. **Bare `python` command in mcp_servers.json** — Use `"command": "uv"` with args `["run", "python", ...]`.
-
-## Testing Errors
-
-20. **Using `runner.run()` on forever-alive agents** — `runner.run()` calls `trigger_and_wait()` which blocks until the graph reaches a terminal node. Forever-alive agents have `terminal_nodes=[]`, so **`runner.run()` hangs forever**. This is the #1 cause of stuck test suites.
-
-**For forever-alive agents, write structural tests instead:**
-- Validate graph structure (nodes, edges, entry points)
-- Verify node specs (tools, prompts, client-facing flag)
-- Check goal/constraints/success criteria definitions
-- Test that `AgentRunner.load()` succeeds (structural, no API key needed)
-
-**What NOT to do:**
-```python
-# WRONG — hangs forever on forever-alive agents
-result = await runner.run({"topic": "quantum computing"})
-```
-
-**Correct pattern for structure tests:**
-```python
-def test_research_has_web_tools(self):
-    assert "web_search" in research_node.tools
-
-def test_research_routes_back_to_interact(self):
-    edges_to_interact = [e for e in edges if e.source == "research" and e.target == "interact"]
-    assert edges_to_interact
-```
-
-21. **Stale tests after agent restructuring** — When you change an agent's node count or names (e.g., 4 nodes → 2 nodes), the tests MUST be updated too. Tests referencing old node names (e.g., `"review"`, `"report"`) will fail or hang. Always check that test assertions match the current `nodes/__init__.py`.
-
-22. **Running full integration tests without API keys** — Structural tests (validate, import) work without keys. Full integration tests need `ANTHROPIC_API_KEY`. Use `pytest.skip()` in the runner fixture when `_setup()` fails due to missing credentials.
-
-23. **Forgetting sys.path setup in conftest.py** — Tests need `exports/` and `core/` on sys.path.
-
-24. **Not using auto_responder for client-facing nodes** — Tests with client-facing nodes hang without an auto-responder that injects input. But note: even WITH auto_responder, forever-alive agents still hang because the graph never terminates. Auto-responder only helps for agents with terminal nodes.
-
-25. **Manually wiring browser tools on event_loop nodes** — If the agent needs browser automation, use `node_type="gcu"` which auto-includes all browser tools and prepends best-practices guidance. Do NOT manually list browser tool names on event_loop nodes — they may not exist in the MCP server or may be incomplete. See the GCU Guide appendix.
-
-26. **Using GCU nodes as regular graph nodes** — GCU nodes (`node_type="gcu"`) are exclusively subagents. They must ONLY appear in a parent node's `sub_agents=["gcu-node-id"]` list and be invoked via `delegate_to_sub_agent()`. They must NEVER be connected via edges, used as entry nodes, or used as terminal nodes. If a GCU node appears as an edge source or target, the graph will fail pre-load validation.
-
-27. **Adding a client-facing intake node to worker agents** — The queen owns intake. She defines the entry node's `input_keys` at build time and fills them via `run_agent_with_input(task)` at run time. Worker agents should start with an autonomous processing node, NOT a client-facing intake node that asks the user for requirements. Client-facing nodes in workers are for mid-execution review/approval only.

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/nodes/thinking_hook.py

@@ -0,0 +1,80 @@
+"""Queen thinking hook — HR persona classifier.
+
+Fires once when the queen enters building mode at session start.
+Makes a single non-streaming LLM call (acting as an HR Director) to select
+the best-fit expert persona for the user's request, then returns a persona
+prefix string that replaces the queen's default "Solution Architect" identity.
+
+This is designed to activate the model's latent domain expertise — a CFO
+persona on a financial question, a Lawyer on a legal question, etc.
+"""
+
+from __future__ import annotations
+
+import json
+import logging
+from typing import TYPE_CHECKING
+
+if TYPE_CHECKING:
+    from framework.llm.provider import LLMProvider
+
+logger = logging.getLogger(__name__)
+
+_HR_SYSTEM_PROMPT = """\
+You are an expert HR Director and talent consultant at a world-class firm.
+A new request has arrived and you must identify which professional's expertise
+would produce the highest-quality response.
+
+Reply with ONLY a valid JSON object — no markdown, no prose, no explanation:
+{"role": "<job title>", "persona": "<2-3 sentence first-person identity statement>"}
+
+Rules:
+- Choose from any real professional role: CFO, CEO, CTO, Lawyer, Data Scientist,
+  Product Manager, Security Engineer, DevOps Engineer, Software Architect,
+  HR Director, Marketing Director, Business Analyst, UX Designer,
+  Financial Analyst, Operations Director, Legal Counsel, etc.
+- The persona statement must be written in first person ("I am..." or "I have...").
+- Select the role whose domain knowledge most directly applies to solving the request.
+- If the request is clearly about coding or building software systems, pick Software Architect.
+- "Queen" is your internal alias — do not include it in the persona.
+"""
+
+
+async def select_expert_persona(user_message: str, llm: LLMProvider) -> str:
+    """Run the HR classifier and return a persona prefix string.
+
+    Makes a single non-streaming acomplete() call with the session LLM.
+    Returns an empty string on any failure so the queen falls back
+    gracefully to its default "Solution Architect" identity.
+
+    Args:
+        user_message: The user's opening message for the session.
+        llm: The session LLM provider.
+
+    Returns:
+        A persona prefix like "You are a CFO. I am a CFO with 20 years..."
+        or "" on failure.
+    """
+    if not user_message.strip():
+        return ""
+
+    try:
+        response = await llm.acomplete(
+            messages=[{"role": "user", "content": user_message}],
+            system=_HR_SYSTEM_PROMPT,
+            max_tokens=1024,
+            json_mode=True,
+        )
+        raw = response.content.strip()
+        parsed = json.loads(raw)
+        role = parsed.get("role", "").strip()
+        persona = parsed.get("persona", "").strip()
+        if not role or not persona:
+            logger.warning("Thinking hook: empty role/persona in response: %r", raw)
+            return ""
+        result = f"You are a {role}. {persona}"
+        logger.info("Thinking hook: selected persona — %s", role)
+        return result
+    except Exception:
+        logger.warning("Thinking hook: persona classification failed", exc_info=True)
+        return ""

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

@@ -0,0 +1,35 @@
+# Common Mistakes When Building Hive Agents
+
+## Critical Errors
+1. **Using tools that don't exist** — Always verify tools via `list_agent_tools()` before designing. Common hallucinations: `csv_read`, `csv_write`, `file_upload`, `database_query`, `bulk_fetch_emails`.
+2. **Wrong mcp_servers.json format** — Flat dict (no `"mcpServers"` wrapper). `cwd` must be `"../../tools"`. `command` must be `"uv"` with args `["run", "python", ...]`.
+3. **Missing module-level exports in `__init__.py`** — The runner reads `goal`, `nodes`, `edges`, `entry_node`, `entry_points`, `terminal_nodes`, `conversation_mode`, `identity_prompt`, `loop_config` via `getattr()`. ALL module-level variables from agent.py must be re-exported in `__init__.py`.
+
+## Value Errors
+4. **Fabricating tools** — Always verify via `list_agent_tools()` before designing and `validate_agent_package()` after building.
+
+## Design Errors
+5. **Adding framework gating for LLM behavior** — Don't add output rollback or premature rejection. Fix with better prompts or custom judges.
+6. **Calling set_output in same turn as tool calls** — Call set_output in a SEPARATE turn.
+
+## File Template Errors
+7. **Wrong import paths** — Use `from framework.graph import ...`, NOT `from core.framework.graph import ...`.
+8. **Missing storage path** — Agent class must set `self._storage_path = Path.home() / ".hive" / "agents" / "agent_name"`.
+9. **Missing mcp_servers.json** — Without this, the agent has no tools at runtime.
+10. **Bare `python` command** — Use `"command": "uv"` with args `["run", "python", ...]`.
+
+## Testing Errors
+11. **Using `runner.run()` on forever-alive agents** — `runner.run()` hangs forever because forever-alive agents have no terminal node. Write structural tests instead: validate graph structure, verify node specs, test `AgentRunner.load()` succeeds (no API key needed).
+12. **Stale tests after restructuring** — When changing nodes/edges, update tests to match. Tests referencing old node names will fail.
+13. **Running integration tests without API keys** — Use `pytest.skip()` when credentials are missing.
+14. **Forgetting sys.path setup in conftest.py** — Tests need `exports/` and `core/` on sys.path.
+
+## 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
+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

@@ -84,35 +84,36 @@ Work in phases:
     tools=["web_search", "web_scrape", "save_data", "load_data", "list_data_files"],
 )
 
-# Node 3: Review (client-facing)
-review_node = NodeSpec(
-    id="review",
-    name="Review",
-    description="Present results for user approval",
+# Node 2: Handoff (autonomous)
+handoff_node = NodeSpec(
+    id="handoff",
+    name="Handoff",
+    description="Prepare worker results for queen review",
     node_type="event_loop",
-    client_facing=True,
+    client_facing=False,
     max_node_visits=0,
     input_keys=["results", "user_request"],
-    output_keys=["next_action", "feedback"],
-    nullable_output_keys=["feedback"],
-    success_criteria="User has reviewed and decided next steps.",
+    output_keys=["next_action", "feedback", "worker_summary"],
+    nullable_output_keys=["feedback", "worker_summary"],
+    success_criteria="Results are packaged for queen decision-making.",
     system_prompt="""\
-Present the results to the user.
+Do NOT talk to the user directly. The queen is the only user interface.
 
-**STEP 1 — Present (text only, NO tool calls):**
-1. Summary of work done
-2. Key results
-3. Ask: satisfied, or want changes?
+If blocked by tool failures, missing credentials, or unclear constraints, call:
+- escalate(reason, context)
+Then set:
+- set_output("next_action", "escalated")
+- set_output("feedback", "what help is needed")
 
-**STEP 2 — After user responds, call set_output:**
-- set_output("next_action", "done")        — if satisfied
-- set_output("next_action", "revise")      — if changes needed
-- set_output("feedback", "what to change") — only if revising
+Otherwise summarize findings for queen and set:
+- set_output("worker_summary", "short summary for queen")
+- set_output("next_action", "done") or set_output("next_action", "revise")
+- set_output("feedback", "what to revise") only when revising
 """,
     tools=[],
 )
 
-__all__ = ["process_node", "review_node"]
+__all__ = ["process_node", "handoff_node"]
 ```
 
 ## agent.py
@@ -132,7 +133,7 @@ from framework.runtime.agent_runtime import AgentRuntime, create_agent_runtime
 from framework.runtime.execution_stream import EntryPointSpec
 
 from .config import default_config, metadata
-from .nodes import process_node, review_node
+from .nodes import process_node, handoff_node
 
 # Goal definition
 goal = Goal(
@@ -149,18 +150,22 @@ goal = Goal(
 )
 
 # Node list
-nodes = [process_node, review_node]
+nodes = [process_node, handoff_node]
 
 # Edge definitions
 edges = [
-    EdgeSpec(id="process-to-review", source="process", target="review",
+    EdgeSpec(id="process-to-handoff", source="process", target="handoff",
              condition=EdgeCondition.ON_SUCCESS, priority=1),
     # Feedback loop — revise results
-    EdgeSpec(id="review-to-process", source="review", target="process",
+    EdgeSpec(id="handoff-to-process", source="handoff", target="process",
              condition=EdgeCondition.CONDITIONAL,
              condition_expr="str(next_action).lower() == 'revise'", priority=2),
-    # Loop back for next task (queen sends new input)
-    EdgeSpec(id="review-done", source="review", target="process",
+    # Escalation loop — queen injects guidance and worker retries
+    EdgeSpec(id="handoff-escalated", source="handoff", target="process",
+             condition=EdgeCondition.CONDITIONAL,
+             condition_expr="str(next_action).lower() == 'escalated'", priority=3),
+    # Loop back for next task after queen decision
+    EdgeSpec(id="handoff-done", source="handoff", target="process",
              condition=EdgeCondition.CONDITIONAL,
              condition_expr="str(next_action).lower() == 'done'", priority=1),
 ]
@@ -175,7 +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:
@@ -267,97 +272,106 @@ class MyAgent:
         }
 
     def validate(self):
+        """Validate graph wiring and entry-point contract."""
         errors, warnings = [], []
         node_ids = {n.id for n in self.nodes}
         for e in self.edges:
-            if e.source not in node_ids: errors.append(f"Edge {e.id}: source '{e.source}' not found")
-            if e.target not in node_ids: errors.append(f"Edge {e.id}: target '{e.target}' not found")
-        if self.entry_node not in node_ids: errors.append(f"Entry node '{self.entry_node}' not found")
+            if e.source not in node_ids:
+                errors.append(f"Edge {e.id}: source '{e.source}' not found")
+            if e.target not in node_ids:
+                errors.append(f"Edge {e.id}: target '{e.target}' not found")
+        if self.entry_node not in node_ids:
+            errors.append(f"Entry node '{self.entry_node}' not found")
         for t in self.terminal_nodes:
-            if t not in node_ids: errors.append(f"Terminal node '{t}' not found")
-        for ep_id, nid in self.entry_points.items():
-            if nid not in node_ids: errors.append(f"Entry point '{ep_id}' references unknown node '{nid}'")
+            if t not in node_ids:
+                errors.append(f"Terminal node '{t}' not found")
+
+        if not isinstance(self.entry_points, dict):
+            errors.append(
+                "Invalid entry_points: expected dict[str, str] like "
+                "{'start': '<entry-node-id>'}. "
+                f"Got {type(self.entry_points).__name__}. "
+                "Fix agent.py: set entry_points = {'start': '<entry-node-id>'}."
+            )
+        else:
+            if "start" not in self.entry_points:
+                errors.append(
+                    "entry_points must include 'start' mapped to entry_node. "
+                    "Example: {'start': '<entry-node-id>'}."
+                )
+            else:
+                start_node = self.entry_points.get("start")
+                if start_node != self.entry_node:
+                    errors.append(
+                        f"entry_points['start'] points to '{start_node}' "
+                        f"but entry_node is '{self.entry_node}'. Keep these aligned."
+                    )
+
+            for ep_id, nid in self.entry_points.items():
+                if not isinstance(ep_id, str):
+                    errors.append(
+                        f"Invalid entry_points key {ep_id!r} "
+                        f"({type(ep_id).__name__}). Entry point names must be strings."
+                    )
+                    continue
+                if not isinstance(nid, str):
+                    errors.append(
+                        f"Invalid entry_points['{ep_id}']={nid!r} "
+                        f"({type(nid).__name__}). Node ids must be strings."
+                    )
+                    continue
+                if nid not in node_ids:
+                    errors.append(
+                        f"Entry point '{ep_id}' references unknown node '{nid}'. "
+                        f"Known nodes: {sorted(node_ids)}"
+                    )
+
         return {"valid": len(errors) == 0, "errors": errors, "warnings": warnings}
 
 
 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
 
@@ -404,21 +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
@@ -510,6 +509,9 @@ if __name__ == "__main__":
 
 ## mcp_servers.json
 
+> **Auto-generated.** `initialize_and_build_agent` creates this file with hive-tools
+> as the default. Only edit manually to add additional MCP servers.
+
 ```json
 {
   "hive-tools": {

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

@@ -26,13 +26,12 @@ module-level variables via `getattr()`:
 | `edges` | YES | `None` | **FATAL** — same error |
 | `entry_node` | no | `nodes[0].id` | Probably wrong node |
 | `entry_points` | no | `{}` | **Nodes unreachable** — validation fails |
-| `terminal_nodes` | no | `[]` | OK for forever-alive |
+| `terminal_nodes` | **YES** | `[]` | **FATAL** — graph must have at least one terminal node |
 | `pause_nodes` | no | `[]` | OK |
 | `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
@@ -108,7 +107,7 @@ This prevents premature set_output before user interaction.
 
 ### Fewer, Richer Nodes (CRITICAL)
 
-**Hard limit: 2-4 nodes for most agents.** Never exceed 5 unless the user
+**Hard limit: 3-6 nodes for most agents.** Never exceed 6 unless the user
 explicitly requests a complex multi-phase pipeline.
 
 Each node boundary serializes outputs to shared memory and **destroys** all
@@ -165,8 +164,9 @@ review_node = NodeSpec(
 )
 ```
 
-### Forever-Alive Pattern
-`terminal_nodes=[]` — every node has outgoing edges, graph loops until user exits.
+### Continuous Loop Pattern
+Mark the primary event_loop node as terminal: `terminal_nodes=["process"]`.
+The node has `output_keys` and can complete when the agent finishes its work.
 Use `conversation_mode="continuous"` to preserve context across transitions.
 
 ### set_output
@@ -192,16 +192,16 @@ condition_expr examples:
 
 | Pattern | terminal_nodes | When |
 |---------|---------------|------|
-| **Forever-alive** | `[]` | **DEFAULT for all agents** |
-| Linear | `["last-node"]` | Only if user explicitly requests one-shot/batch |
+| **Continuous loop** | `["node-with-output-keys"]` | **DEFAULT for all agents** |
+| Linear | `["last-node"]` | One-shot/batch agents |
 
-**Forever-alive is the default.** Always use `terminal_nodes=[]`.
-The framework default for `max_node_visits` is 0 (unbounded), so
-nodes work correctly in forever-alive loops without explicit override.
-Only set `max_node_visits > 0` in one-shot agents with feedback loops.
-Every node must have at least one outgoing edge — no dead ends. The
-user exits by closing the TUI. Only use terminal nodes if the user
-explicitly asks for a batch/one-shot agent that runs once and exits.
+**Every graph must have at least one terminal node.** Terminal nodes
+define where execution ends. For interactive agents that loop continuously,
+mark the primary event_loop node as terminal (it has `output_keys` and can
+complete at any point). The framework default for `max_node_visits` is 0
+(unbounded), so nodes work correctly in continuous loops without explicit
+override. Only set `max_node_visits > 0` in one-shot agents with feedback loops.
+Every node must have at least one outgoing edge — no dead ends.
 
 ## Continuous Conversation Mode
 
@@ -225,7 +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"`,
@@ -256,179 +256,43 @@ 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 need to react to external events (incoming emails, scheduled
-tasks, API calls), use `AsyncEntryPointSpec` and optionally `AgentRuntimeConfig`.
+For agents that react to external events, create a `triggers.json` file
+in the agent's export directory:
 
-### Imports
-```python
-from framework.graph.edge import GraphSpec, AsyncEntryPointSpec
-from framework.runtime.agent_runtime import AgentRuntime, AgentRuntimeConfig, create_agent_runtime
-```
-Note: `AsyncEntryPointSpec` is in `framework.graph.edge` (the graph/declarative layer).
-`AgentRuntimeConfig` is in `framework.runtime.agent_runtime` (the runtime layer).
-
-### AsyncEntryPointSpec Fields
-
-| Field | Type | Default | Description |
-|-------|------|---------|-------------|
-| id | str | required | Unique identifier |
-| name | str | required | Human-readable name |
-| entry_node | str | required | Node ID to start execution from |
-| trigger_type | str | `"manual"` | `webhook`, `api`, `timer`, `event`, `manual` |
-| trigger_config | dict | `{}` | Trigger-specific config (see below) |
-| isolation_level | str | `"shared"` | `isolated`, `shared`, `synchronized` |
-| priority | int | `0` | Execution priority (higher = more priority) |
-| max_concurrent | int | `10` | Max concurrent executions |
-
-### Trigger Types
-
-**timer** — Fires on a schedule. Two modes: cron expressions or fixed interval.
-
-Cron (preferred for precise scheduling):
-```python
-AsyncEntryPointSpec(
-    id="daily-digest",
-    name="Daily Digest",
-    entry_node="check-node",
-    trigger_type="timer",
-    trigger_config={"cron": "0 9 * * *"},  # daily at 9am
-    isolation_level="shared",
-    max_concurrent=1,
-)
-```
-- `cron` (str) — standard cron expression (5 fields: min hour dom month dow)
-- Examples: `"0 9 * * *"` (daily 9am), `"0 9 * * MON-FRI"` (weekdays 9am), `"*/30 * * * *"` (every 30 min)
-
-Fixed interval (simpler, for polling-style tasks):
-```python
-AsyncEntryPointSpec(
-    id="scheduled-check",
-    name="Scheduled Check",
-    entry_node="check-node",
-    trigger_type="timer",
-    trigger_config={"interval_minutes": 20, "run_immediately": False},
-    isolation_level="shared",
-    max_concurrent=1,
-)
-```
-- `interval_minutes` (float) — how often to fire
-- `run_immediately` (bool, default False) — fire once on startup
-
-**event** — Subscribes to EventBus (e.g., webhook events):
-```python
-AsyncEntryPointSpec(
-    id="email-event",
-    name="Email Event Handler",
-    entry_node="process-emails",
-    trigger_type="event",
-    trigger_config={"event_types": ["webhook_received"]},
-    isolation_level="shared",
-    max_concurrent=10,
-)
-```
-- `event_types` (list[str]) — EventType values to subscribe to
-- `filter_stream` (str, optional) — only receive from this stream
-- `filter_node` (str, optional) — only receive from this node
-
-**webhook** — HTTP endpoint (requires AgentRuntimeConfig):
-The webhook server publishes `WEBHOOK_RECEIVED` events on the EventBus.
-An `event` trigger type with `event_types: ["webhook_received"]` subscribes
-to those events. The flow is:
-```
-HTTP POST /webhooks/gmail → WebhookServer → EventBus (WEBHOOK_RECEIVED)
-  → event entry point → triggers graph execution from entry_node
+```json
+[
+  {
+    "id": "daily-check",
+    "name": "Daily Check",
+    "trigger_type": "timer",
+    "trigger_config": {"cron": "0 9 * * *"},
+    "task": "Run the daily check process"
+  }
+]
 ```
 
-**manual** — Triggered programmatically via `runtime.trigger()`.
-
-### Isolation Levels
-
-| Level | Meaning |
-|-------|---------|
-| `isolated` | Private state per execution |
-| `shared` | Eventual consistency — async executions can read primary session memory |
-| `synchronized` | Shared with write locks (use when ordering matters) |
-
-For most async patterns, use `shared` — the async execution reads the primary
-session's memory (e.g., user-configured rules) and runs its own workflow.
-
-### AgentRuntimeConfig (for webhook servers)
-
-```python
-from framework.runtime.agent_runtime import AgentRuntimeConfig
-
-runtime_config = AgentRuntimeConfig(
-    webhook_host="127.0.0.1",
-    webhook_port=8080,
-    webhook_routes=[
-        {
-            "source_id": "gmail",
-            "path": "/webhooks/gmail",
-            "methods": ["POST"],
-            "secret": None,  # Optional HMAC-SHA256 secret
-        },
-    ],
-)
-```
-`runtime_config` is a module-level variable read by `AgentRunner.load()`.
-The runner passes it to `create_agent_runtime()`. On `runtime.start()`,
-if webhook_routes is non-empty, an embedded HTTP server starts.
-
-### Session Sharing
-
-Timer and event triggers automatically call `_get_primary_session_state()`
-before execution. This finds the active user-facing session and provides
-its memory to the async execution, filtered to only the async entry node's
-`input_keys`. This means the async flow can read user-configured values
-(like rules, preferences) without needing separate configuration.
-
-### Module-Level Variables
-
-Agents with async entry points must export two additional variables:
-```python
-# In agent.py:
-async_entry_points = [AsyncEntryPointSpec(...), ...]
-runtime_config = AgentRuntimeConfig(...)  # Only if using webhooks
-```
-
-Both must be re-exported from `__init__.py`:
-```python
-from .agent import (
-    ..., async_entry_points, runtime_config,
-)
-```
-
-### Reference Agent
-
-See `exports/gmail_inbox_guardian/agent.py` for a complete example with:
-- Primary client-facing node (user configures rules)
-- Timer-based scheduled inbox checks (every 20 min)
-- Webhook-triggered email event handling
-- Shared isolation for memory access across streams
-
-## Framework Capabilities
-
-**Works well:** Multi-turn conversations, HITL review, tool orchestration, structured outputs, parallel execution, context management, error recovery, session persistence.
-
-**Limitations:** LLM latency (2-10s/turn), context window limits (~128K), cost per run, rate limits, node boundaries lose context.
-
-**Not designed for:** Sub-second responses, millions of items, real-time streaming, guaranteed determinism, offline/air-gapped.
+### Key Fields
+- `trigger_type`: `"timer"` or `"webhook"`
+- `trigger_config`: `{"cron": "0 9 * * *"}` or `{"interval_minutes": 20}`
+- `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
 
-Do NOT rely on a static tool list — it will be outdated. Always use
-`list_agent_tools()` to discover available tools, grouped by category.
+Do NOT rely on a static tool list — it will be outdated. Always call
+`list_agent_tools()` with NO arguments first to see ALL available tools.
+Only use `group=` or `output_schema=` as follow-up calls after seeing the
+full list.
 
 ```
-list_agent_tools()                            # names + descriptions, all groups
-list_agent_tools(output_schema="full")        # include input_schema
-list_agent_tools(group="gmail")               # only gmail_* tools
+list_agent_tools()                            # ALWAYS call this first
+list_agent_tools(group="gmail", output_schema="full")  # then drill into a category
 list_agent_tools("exports/my_agent/mcp_servers.json")  # specific agent's tools
 ```
 
-After building, validate tools exist: `validate_agent_tools("exports/{name}")`
+After building, run `validate_agent_package("{name}")` to check everything at once.
 
 Common tool categories (verify via list_agent_tools):
 - **Web**: search, scrape, PDF

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

@@ -1,21 +0,0 @@
-"""Builder interface for analyzing and building agents."""
-
-from framework.builder.query import BuilderQuery
-from framework.builder.workflow import (
-    BuildPhase,
-    BuildSession,
-    GraphBuilder,
-    TestCase,
-    TestResult,
-    ValidationResult,
-)
-
-__all__ = [
-    "BuilderQuery",
-    "GraphBuilder",
-    "BuildSession",
-    "BuildPhase",
-    "ValidationResult",
-    "TestCase",
-    "TestResult",
-]

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