docs: change docs tune

model selection + max_tokens in quickstart

.claude/settings.local.json

@@ -1,46 +0,0 @@
-{
-  "permissions": {
-    "allow": [
-      "Bash(npm install:*)",
-      "Bash(npm test:*)",
-      "Skill(building-agents-construction)",
-      "Skill(building-agents-construction:*)",
-      "Bash(PYTHONPATH=core:exports pytest:*)",
-      "mcp__agent-builder__create_session",
-      "mcp__agent-builder__get_session_status",
-      "mcp__agent-builder__set_goal",
-      "mcp__agent-builder__list_mcp_servers",
-      "mcp__agent-builder__test_node",
-      "mcp__agent-builder__add_node",
-      "mcp__agent-builder__add_edge",
-      "mcp__agent-builder__validate_graph",
-      "Bash(ruff check:*)",
-      "Bash(PYTHONPATH=core:exports python:*)",
-      "mcp__agent-builder__list_tests",
-      "mcp__agent-builder__generate_constraint_tests",
-      "Bash(python -m agent:*)",
-      "Bash(python agent.py:*)",
-      "Bash(python -c:*)",
-      "Bash(done)",
-      "Bash(xargs cat:*)",
-      "mcp__agent-builder__list_mcp_tools",
-      "mcp__agent-builder__add_mcp_server",
-      "Bash(gh issue list:*)",
-      "WebFetch(domain:github.com)",
-      "Bash(pip install:*)",
-      "Bash(python -m pytest:*)",
-      "Bash(git checkout:*)",
-      "Bash(git add:*)",
-      "Bash(git commit -m \"$\\(cat <<''EOF''\nfeat\\(tools\\): Add Excel tool for spreadsheet operations\n\nAdds a new Excel tool for reading and manipulating .xlsx/.xlsm files:\n- excel_read: Read Excel files with pagination and sheet selection\n- excel_write: Create new Excel files with data\n- excel_append: Append rows to existing files\n- excel_info: Get metadata about Excel files \\(sheets, columns, row counts\\)\n- excel_sheet_list: List all sheets in a workbook\n\nIncludes comprehensive test coverage \\(37 tests\\) and documentation.\n\nReferences #2805\n\nCo-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>\nEOF\n\\)\")",
-      "Bash(git push:*)",
-      "Bash(git pull:*)",
-      "Bash(git stash:*)",
-      "Bash(git merge:*)"
-    ]
-  },
-  "enableAllProjectMcpServers": true,
-  "enabledMcpjsonServers": [
-    "agent-builder",
-    "tools"
-  ]
-}

.claude/skills/building-agents-construction/SKILL.md

@@ -1,361 +0,0 @@
----
-name: building-agents-construction
-description: Step-by-step guide for building goal-driven agents. Creates package structure, defines goals, adds nodes, connects edges, and finalizes agent class. Use when actively building an agent.
-license: Apache-2.0
-metadata:
-  author: hive
-  version: "2.0"
-  type: procedural
-  part_of: building-agents
-  requires: building-agents-core
----
-
-# Agent Construction - EXECUTE THESE STEPS
-
-**THIS IS AN EXECUTABLE WORKFLOW. DO NOT DISPLAY THIS FILE. EXECUTE THE STEPS BELOW.**
-
-When this skill is loaded, IMMEDIATELY begin executing Step 1. Do not explain what you will do - just do it.
-
----
-
-## STEP 1: Initialize Build Environment
-
-**EXECUTE THESE TOOL CALLS NOW:**
-
-1. Register the hive-tools MCP server:
-
-```
-mcp__agent-builder__add_mcp_server(
-    name="hive-tools",
-    transport="stdio",
-    command="python",
-    args='["mcp_server.py", "--stdio"]',
-    cwd="tools",
-    description="Hive tools MCP server"
-)
-```
-
-2. Create a build session (replace AGENT_NAME with the user's requested agent name in snake_case):
-
-```
-mcp__agent-builder__create_session(name="AGENT_NAME")
-```
-
-3. Discover available tools:
-
-```
-mcp__agent-builder__list_mcp_tools()
-```
-
-4. Create the package directory:
-
-```
-mkdir -p exports/AGENT_NAME/nodes
-```
-
-**AFTER completing these calls**, tell the user:
-
-> ✅ Build environment initialized
->
-> - Session created
-> - Available tools: [list the tools from step 3]
->
-> Proceeding to define the agent goal...
-
-**THEN immediately proceed to STEP 2.**
-
----
-
-## STEP 2: Define and Approve Goal
-
-**PROPOSE a goal to the user.** Based on what they asked for, propose:
-
-- Goal ID (kebab-case)
-- Goal name
-- Goal description
-- 3-5 success criteria (each with: id, description, metric, target, weight)
-- 2-4 constraints (each with: id, description, constraint_type, category)
-
-**FORMAT your proposal as a clear summary, then ask for approval:**
-
-> **Proposed Goal: [Name]**
->
-> [Description]
->
-> **Success Criteria:**
->
-> 1. [criterion 1]
-> 2. [criterion 2]
->    ...
->
-> **Constraints:**
->
-> 1. [constraint 1]
-> 2. [constraint 2]
->    ...
-
-**THEN call AskUserQuestion:**
-
-```
-AskUserQuestion(questions=[{
-    "question": "Do you approve this goal definition?",
-    "header": "Goal",
-    "options": [
-        {"label": "Approve", "description": "Goal looks good, proceed"},
-        {"label": "Modify", "description": "I want to change something"}
-    ],
-    "multiSelect": false
-}])
-```
-
-**WAIT for user response.**
-
-- If **Approve**: Call `mcp__agent-builder__set_goal(...)` with the goal details, then proceed to STEP 3
-- If **Modify**: Ask what they want to change, update proposal, ask again
-
----
-
-## STEP 3: Design Node Workflow
-
-**BEFORE designing nodes**, review the available tools from Step 1. Nodes can ONLY use tools that exist.
-
-**DESIGN the workflow** as a series of nodes. For each node, determine:
-
-- node_id (kebab-case)
-- name
-- description
-- node_type: `"llm_generate"` (no tools) or `"llm_tool_use"` (uses tools)
-- input_keys (what data this node receives)
-- output_keys (what data this node produces)
-- tools (ONLY tools that exist - empty list for llm_generate)
-- system_prompt
-
-**PRESENT the workflow to the user:**
-
-> **Proposed Workflow: [N] nodes**
->
-> 1. **[node-id]** - [description]
->
->    - Type: [llm_generate/llm_tool_use]
->    - Input: [keys]
->    - Output: [keys]
->    - Tools: [tools or "none"]
->
-> 2. **[node-id]** - [description]
->    ...
->
-> **Flow:** node1 → node2 → node3 → ...
-
-**THEN call AskUserQuestion:**
-
-```
-AskUserQuestion(questions=[{
-    "question": "Do you approve this workflow design?",
-    "header": "Workflow",
-    "options": [
-        {"label": "Approve", "description": "Workflow looks good, proceed to build nodes"},
-        {"label": "Modify", "description": "I want to change the workflow"}
-    ],
-    "multiSelect": false
-}])
-```
-
-**WAIT for user response.**
-
-- If **Approve**: Proceed to STEP 4
-- If **Modify**: Ask what they want to change, update design, ask again
-
----
-
-## STEP 4: Build Nodes One by One
-
-**FOR EACH node in the approved workflow:**
-
-1. **Call** `mcp__agent-builder__add_node(...)` with the node details
-
-   - input_keys and output_keys must be JSON strings: `'["key1", "key2"]'`
-   - tools must be a JSON string: `'["tool1"]'` or `'[]'`
-
-2. **Call** `mcp__agent-builder__test_node(...)` to validate:
-
-```
-mcp__agent-builder__test_node(
-    node_id="the-node-id",
-    test_input='{"key": "test value"}',
-    mock_llm_response='{"output_key": "test output"}'
-)
-```
-
-3. **Check result:**
-
-   - If valid: Tell user "✅ Node [id] validated" and continue to next node
-   - If invalid: Show errors, fix the node, re-validate
-
-4. **Show progress** after each node:
-
-```
-mcp__agent-builder__get_session_status()
-```
-
-> ✅ Node [X] of [Y] complete: [node-id]
-
-**AFTER all nodes are added and validated**, proceed to STEP 5.
-
----
-
-## STEP 5: Connect Edges
-
-**DETERMINE the edges** based on the workflow flow. For each connection:
-
-- edge_id (kebab-case)
-- source (node that outputs)
-- target (node that receives)
-- condition: `"on_success"`, `"always"`, `"on_failure"`, or `"conditional"`
-- condition_expr (Python expression, only if conditional)
-- priority (integer, lower = higher priority)
-
-**FOR EACH edge, call:**
-
-```
-mcp__agent-builder__add_edge(
-    edge_id="source-to-target",
-    source="source-node-id",
-    target="target-node-id",
-    condition="on_success",
-    condition_expr="",
-    priority=1
-)
-```
-
-**AFTER all edges are added, validate the graph:**
-
-```
-mcp__agent-builder__validate_graph()
-```
-
-- If valid: Tell user "✅ Graph structure validated" and proceed to STEP 6
-- If invalid: Show errors, fix edges, re-validate
-
----
-
-## STEP 6: Generate Agent Package
-
-**EXPORT the graph data:**
-
-```
-mcp__agent-builder__export_graph()
-```
-
-This returns JSON with all the goal, nodes, edges, and MCP server configurations.
-
-**THEN write the Python package files** using the exported data. Create these files in `exports/AGENT_NAME/`:
-
-1. `config.py` - Runtime configuration with model settings
-2. `nodes/__init__.py` - All NodeSpec definitions
-3. `agent.py` - Goal, edges, graph config, and agent class
-4. `__init__.py` - Package exports
-5. `__main__.py` - CLI interface
-6. `mcp_servers.json` - MCP server configurations
-7. `README.md` - Usage documentation
-
-**IMPORTANT entry_points format:**
-
-- MUST be: `{"start": "first-node-id"}`
-- NOT: `{"first-node-id": ["input_keys"]}` (WRONG)
-- NOT: `{"first-node-id"}` (WRONG - this is a set)
-
-**Use the example agent** at `.claude/skills/building-agents-construction/examples/online_research_agent/` as a template for file structure and patterns.
-
-**AFTER writing all files, tell the user:**
-
-> ✅ Agent package created: `exports/AGENT_NAME/`
->
-> **Files generated:**
->
-> - `__init__.py` - Package exports
-> - `agent.py` - Goal, nodes, edges, agent class
-> - `config.py` - Runtime configuration
-> - `__main__.py` - CLI interface
-> - `nodes/__init__.py` - Node definitions
-> - `mcp_servers.json` - MCP server config
-> - `README.md` - Usage documentation
->
-> **Test your agent:**
->
-> ```bash
-> cd /home/timothy/oss/hive
-> PYTHONPATH=core:exports python -m AGENT_NAME validate
-> PYTHONPATH=core:exports python -m AGENT_NAME info
-> ```
-
----
-
-## STEP 7: Verify and Test
-
-**RUN validation:**
-
-```bash
-cd /home/timothy/oss/hive && PYTHONPATH=core:exports python -m AGENT_NAME validate
-```
-
-- If valid: Agent is complete!
-- If errors: Fix the issues and re-run
-
-**SHOW final session summary:**
-
-```
-mcp__agent-builder__get_session_status()
-```
-
-**TELL the user the agent is ready** and suggest next steps:
-
-- Run with mock mode to test without API calls
-- Use `/testing-agent` skill for comprehensive testing
-- Use `/setup-credentials` if the agent needs API keys
-
----
-
-## REFERENCE: Node Types
-
-| Type           | tools param            | Use when                                       |
-| -------------- | ---------------------- | ---------------------------------------------- |
-| `llm_generate` | `'[]'`                 | Pure reasoning, JSON output, no external calls |
-| `llm_tool_use` | `'["tool1", "tool2"]'` | Needs to call MCP tools                        |
-
----
-
-## REFERENCE: Edge Conditions
-
-| Condition     | When edge is followed                 |
-| ------------- | ------------------------------------- |
-| `on_success`  | Source node completed successfully    |
-| `on_failure`  | Source node failed                    |
-| `always`      | Always, regardless of success/failure |
-| `conditional` | When condition_expr evaluates to True |
-
----
-
-## REFERENCE: System Prompt Best Practice
-
-For nodes with JSON output, include this in the system_prompt:
-
-```
-CRITICAL: Return ONLY raw JSON. NO markdown, NO code blocks.
-Just the JSON object starting with { and ending with }.
-
-Return this exact structure:
-{
-  "key1": "...",
-  "key2": "..."
-}
-```
-
----
-
-## COMMON MISTAKES TO AVOID
-
-1. **Using tools that don't exist** - Always check `mcp__agent-builder__list_mcp_tools()` first
-2. **Wrong entry_points format** - Must be `{"start": "node-id"}`, NOT a set or list
-3. **Skipping validation** - Always validate nodes and graph before proceeding
-4. **Not waiting for approval** - Always ask user before major steps
-5. **Displaying this file** - Execute the steps, don't show documentation

.claude/skills/building-agents-construction/examples/online_research_agent/README.md

@@ -1,80 +0,0 @@
-# Online Research Agent
-
-Deep-dive research agent that searches 10+ sources and produces comprehensive narrative reports with citations.
-
-## Features
-
-- Generates multiple search queries from a topic
-- Searches and fetches 15+ web sources
-- Evaluates and ranks sources by relevance
-- Synthesizes findings into themes
-- Writes narrative report with numbered citations
-- Quality checks for uncited claims
-- Saves report to local markdown file
-
-## Usage
-
-### CLI
-
-```bash
-# Show agent info
-python -m online_research_agent info
-
-# Validate structure
-python -m online_research_agent validate
-
-# Run research on a topic
-python -m online_research_agent run --topic "impact of AI on healthcare"
-
-# Interactive shell
-python -m online_research_agent shell
-```
-
-### Python API
-
-```python
-from online_research_agent import default_agent
-
-# Simple usage
-result = await default_agent.run({"topic": "climate change solutions"})
-
-# Check output
-if result.success:
-    print(f"Report saved to: {result.output['file_path']}")
-    print(result.output['final_report'])
-```
-
-## Workflow
-
-```
-parse-query → search-sources → fetch-content → evaluate-sources
-                                                      ↓
-                                write-report ← synthesize-findings
-                                      ↓
-                               quality-check → save-report
-```
-
-## Output
-
-Reports are saved to `./research_reports/` as markdown files with:
-
-1. Executive Summary
-2. Introduction
-3. Key Findings (by theme)
-4. Analysis
-5. Conclusion
-6. References
-
-## Requirements
-
-- Python 3.11+
-- LLM provider API key (Groq, Cerebras, etc.)
-- Internet access for web search/fetch
-
-## Configuration
-
-Edit `config.py` to change:
-
-- `model`: LLM model (default: groq/moonshotai/kimi-k2-instruct-0905)
-- `temperature`: Generation temperature (default: 0.7)
-- `max_tokens`: Max tokens per response (default: 16384)

.claude/skills/building-agents-construction/examples/online_research_agent/__init__.py

@@ -1,23 +0,0 @@
-"""
-Online Research Agent - Deep-dive research with narrative reports.
-
-Research any topic by searching multiple sources, synthesizing information,
-and producing a well-structured narrative report with citations.
-"""
-
-from .agent import OnlineResearchAgent, default_agent, goal, nodes, edges
-from .config import RuntimeConfig, AgentMetadata, default_config, metadata
-
-__version__ = "1.0.0"
-
-__all__ = [
-    "OnlineResearchAgent",
-    "default_agent",
-    "goal",
-    "nodes",
-    "edges",
-    "RuntimeConfig",
-    "AgentMetadata",
-    "default_config",
-    "metadata",
-]

.claude/skills/building-agents-construction/examples/online_research_agent/agent.py

@@ -1,429 +0,0 @@
-"""Agent graph construction for Online Research Agent."""
-
-from framework.graph import EdgeSpec, EdgeCondition, Goal, SuccessCriterion, Constraint
-from framework.graph.edge import GraphSpec
-from framework.graph.executor import ExecutionResult
-from framework.runtime.agent_runtime import AgentRuntime, create_agent_runtime
-from framework.runtime.execution_stream import EntryPointSpec
-from framework.llm import LiteLLMProvider
-from framework.runner.tool_registry import ToolRegistry
-
-from .config import default_config, metadata
-from .nodes import (
-    parse_query_node,
-    search_sources_node,
-    fetch_content_node,
-    evaluate_sources_node,
-    synthesize_findings_node,
-    write_report_node,
-    quality_check_node,
-    save_report_node,
-)
-
-# Goal definition
-goal = Goal(
-    id="comprehensive-online-research",
-    name="Comprehensive Online Research",
-    description="Research any topic by searching multiple sources, synthesizing information, and producing a well-structured narrative report with citations.",
-    success_criteria=[
-        SuccessCriterion(
-            id="source-coverage",
-            description="Query 10+ diverse sources",
-            metric="source_count",
-            target=">=10",
-            weight=0.20,
-        ),
-        SuccessCriterion(
-            id="relevance",
-            description="All sources directly address the query",
-            metric="relevance_score",
-            target="90%",
-            weight=0.25,
-        ),
-        SuccessCriterion(
-            id="synthesis",
-            description="Synthesize findings into coherent narrative",
-            metric="coherence_score",
-            target="85%",
-            weight=0.25,
-        ),
-        SuccessCriterion(
-            id="citations",
-            description="Include citations for all claims",
-            metric="citation_coverage",
-            target="100%",
-            weight=0.15,
-        ),
-        SuccessCriterion(
-            id="actionable",
-            description="Report answers the user's question",
-            metric="answer_completeness",
-            target="90%",
-            weight=0.15,
-        ),
-    ],
-    constraints=[
-        Constraint(
-            id="no-hallucination",
-            description="Only include information found in sources",
-            constraint_type="quality",
-            category="accuracy",
-        ),
-        Constraint(
-            id="source-attribution",
-            description="Every factual claim must cite its source",
-            constraint_type="quality",
-            category="accuracy",
-        ),
-        Constraint(
-            id="recency-preference",
-            description="Prefer recent sources when relevant",
-            constraint_type="quality",
-            category="relevance",
-        ),
-        Constraint(
-            id="no-paywalled",
-            description="Avoid sources that require payment to access",
-            constraint_type="functional",
-            category="accessibility",
-        ),
-    ],
-)
-
-# Node list
-nodes = [
-    parse_query_node,
-    search_sources_node,
-    fetch_content_node,
-    evaluate_sources_node,
-    synthesize_findings_node,
-    write_report_node,
-    quality_check_node,
-    save_report_node,
-]
-
-# Edge definitions
-edges = [
-    EdgeSpec(
-        id="parse-to-search",
-        source="parse-query",
-        target="search-sources",
-        condition=EdgeCondition.ON_SUCCESS,
-        priority=1,
-    ),
-    EdgeSpec(
-        id="search-to-fetch",
-        source="search-sources",
-        target="fetch-content",
-        condition=EdgeCondition.ON_SUCCESS,
-        priority=1,
-    ),
-    EdgeSpec(
-        id="fetch-to-evaluate",
-        source="fetch-content",
-        target="evaluate-sources",
-        condition=EdgeCondition.ON_SUCCESS,
-        priority=1,
-    ),
-    EdgeSpec(
-        id="evaluate-to-synthesize",
-        source="evaluate-sources",
-        target="synthesize-findings",
-        condition=EdgeCondition.ON_SUCCESS,
-        priority=1,
-    ),
-    EdgeSpec(
-        id="synthesize-to-write",
-        source="synthesize-findings",
-        target="write-report",
-        condition=EdgeCondition.ON_SUCCESS,
-        priority=1,
-    ),
-    EdgeSpec(
-        id="write-to-quality",
-        source="write-report",
-        target="quality-check",
-        condition=EdgeCondition.ON_SUCCESS,
-        priority=1,
-    ),
-    EdgeSpec(
-        id="quality-to-save",
-        source="quality-check",
-        target="save-report",
-        condition=EdgeCondition.ON_SUCCESS,
-        priority=1,
-    ),
-]
-
-# Graph configuration
-entry_node = "parse-query"
-entry_points = {"start": "parse-query"}
-pause_nodes = []
-terminal_nodes = ["save-report"]
-
-
-class OnlineResearchAgent:
-    """
-    Online Research Agent - Deep-dive research with narrative reports.
-
-    Uses AgentRuntime for multi-entrypoint support with HITL pause/resume.
-    """
-
-    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._runtime: AgentRuntime | None = None
-        self._graph: GraphSpec | None = None
-
-    def _build_entry_point_specs(self) -> list[EntryPointSpec]:
-        """Convert entry_points dict to EntryPointSpec list."""
-        specs = []
-        for ep_id, node_id in self.entry_points.items():
-            if ep_id == "start":
-                trigger_type = "manual"
-                name = "Start"
-            elif "_resume" in ep_id:
-                trigger_type = "resume"
-                name = f"Resume from {ep_id.replace('_resume', '')}"
-            else:
-                trigger_type = "manual"
-                name = ep_id.replace("-", " ").title()
-
-            specs.append(
-                EntryPointSpec(
-                    id=ep_id,
-                    name=name,
-                    entry_node=node_id,
-                    trigger_type=trigger_type,
-                    isolation_level="shared",
-                )
-            )
-        return specs
-
-    def _create_runtime(self, mock_mode=False) -> AgentRuntime:
-        """Create AgentRuntime instance."""
-        import json
-        from pathlib import Path
-
-        # Persistent storage in ~/.hive for telemetry and run history
-        storage_path = Path.home() / ".hive" / "online_research_agent"
-        storage_path.mkdir(parents=True, exist_ok=True)
-
-        tool_registry = ToolRegistry()
-
-        # Load MCP servers (always load, needed for tool validation)
-        agent_dir = Path(__file__).parent
-        mcp_config_path = agent_dir / "mcp_servers.json"
-
-        if mcp_config_path.exists():
-            with open(mcp_config_path) as f:
-                mcp_servers = json.load(f)
-
-            for server_config in mcp_servers.get("servers", []):
-                # Resolve relative cwd paths
-                cwd = server_config.get("cwd")
-                if cwd and not Path(cwd).is_absolute():
-                    server_config["cwd"] = str(agent_dir / cwd)
-                tool_registry.register_mcp_server(server_config)
-
-        llm = None
-        if not mock_mode:
-            # LiteLLMProvider uses environment variables for API keys
-            llm = LiteLLMProvider(
-                model=self.config.model,
-                api_key=self.config.api_key,
-                api_base=self.config.api_base,
-            )
-
-        self._graph = GraphSpec(
-            id="online-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,
-        )
-
-        # Create AgentRuntime with all entry points
-        self._runtime = create_agent_runtime(
-            graph=self._graph,
-            goal=self.goal,
-            storage_path=storage_path,
-            entry_points=self._build_entry_point_specs(),
-            llm=llm,
-            tools=list(tool_registry.get_tools().values()),
-            tool_executor=tool_registry.get_executor(),
-        )
-
-        return self._runtime
-
-    async def start(self, mock_mode=False) -> None:
-        """Start the agent runtime."""
-        if self._runtime is None:
-            self._create_runtime(mock_mode=mock_mode)
-        await self._runtime.start()
-
-    async def stop(self) -> None:
-        """Stop the agent runtime."""
-        if self._runtime is not None:
-            await self._runtime.stop()
-
-    async def trigger(
-        self,
-        entry_point: str,
-        input_data: dict,
-        correlation_id: str | None = None,
-        session_state: dict | None = None,
-    ) -> str:
-        """
-        Trigger execution at a specific entry point (non-blocking).
-
-        Args:
-            entry_point: Entry point ID (e.g., "start", "pause-node_resume")
-            input_data: Input data for the execution
-            correlation_id: Optional ID to correlate related executions
-            session_state: Optional session state to resume from (with paused_at, memory)
-
-        Returns:
-            Execution ID for tracking
-        """
-        if self._runtime is None or not self._runtime.is_running:
-            raise RuntimeError("Agent runtime not started. Call start() first.")
-        return await self._runtime.trigger(
-            entry_point, input_data, correlation_id, session_state=session_state
-        )
-
-    async def trigger_and_wait(
-        self,
-        entry_point: str,
-        input_data: dict,
-        timeout: float | None = None,
-        session_state: dict | None = None,
-    ) -> ExecutionResult | None:
-        """
-        Trigger execution and wait for completion.
-
-        Args:
-            entry_point: Entry point ID
-            input_data: Input data for the execution
-            timeout: Maximum time to wait (seconds)
-            session_state: Optional session state to resume from (with paused_at, memory)
-
-        Returns:
-            ExecutionResult or None if timeout
-        """
-        if self._runtime is None or not self._runtime.is_running:
-            raise RuntimeError("Agent runtime not started. Call start() first.")
-        return await self._runtime.trigger_and_wait(
-            entry_point, input_data, timeout, session_state=session_state
-        )
-
-    async def run(
-        self, context: dict, mock_mode=False, session_state=None
-    ) -> ExecutionResult:
-        """
-        Run the agent (convenience method for simple single execution).
-
-        For more control, use start() + trigger_and_wait() + stop().
-        """
-        await self.start(mock_mode=mock_mode)
-        try:
-            # Determine entry point based on session_state
-            if session_state and "paused_at" in session_state:
-                paused_node = session_state["paused_at"]
-                resume_key = f"{paused_node}_resume"
-                if resume_key in self.entry_points:
-                    entry_point = resume_key
-                else:
-                    entry_point = "start"
-            else:
-                entry_point = "start"
-
-            result = await self.trigger_and_wait(
-                entry_point, context, session_state=session_state
-            )
-            return result or ExecutionResult(success=False, error="Execution timeout")
-        finally:
-            await self.stop()
-
-    async def get_goal_progress(self) -> dict:
-        """Get goal progress across all executions."""
-        if self._runtime is None:
-            raise RuntimeError("Agent runtime not started")
-        return await self._runtime.get_goal_progress()
-
-    def get_stats(self) -> dict:
-        """Get runtime statistics."""
-        if self._runtime is None:
-            return {"running": False}
-        return self._runtime.get_stats()
-
-    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,
-            "multi_entrypoint": True,
-        }
-
-    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 pause in self.pause_nodes:
-            if pause not in node_ids:
-                errors.append(f"Pause node '{pause}' not found")
-
-        # Validate entry points
-        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 = OnlineResearchAgent()

.claude/skills/building-agents-construction/examples/online_research_agent/config.py

@@ -1,43 +0,0 @@
-"""Runtime configuration."""
-
-import json
-from dataclasses import dataclass, field
-from pathlib import Path
-
-
-def _load_preferred_model() -> str:
-    """Load preferred model from ~/.hive/configuration.json."""
-    config_path = Path.home() / ".hive" / "configuration.json"
-    if config_path.exists():
-        try:
-            with open(config_path) as f:
-                config = json.load(f)
-            llm = config.get("llm", {})
-            if llm.get("provider") and llm.get("model"):
-                return f"{llm['provider']}/{llm['model']}"
-        except Exception:
-            pass
-    return "anthropic/claude-sonnet-4-20250514"
-
-
-@dataclass
-class RuntimeConfig:
-    model: str = field(default_factory=_load_preferred_model)
-    temperature: float = 0.7
-    max_tokens: int = 8192
-    api_key: str | None = None
-    api_base: str | None = None
-
-
-default_config = RuntimeConfig()
-
-
-# Agent metadata
-@dataclass
-class AgentMetadata:
-    name: str = "Online Research Agent"
-    version: str = "1.0.0"
-    description: str = "Research any topic by searching multiple sources, synthesizing information, and producing a well-structured narrative report with citations."
-
-
-metadata = AgentMetadata()

.claude/skills/building-agents-construction/examples/online_research_agent/nodes/__init__.py

@@ -1,396 +0,0 @@
-"""Node definitions for Online Research Agent."""
-
-from framework.graph import NodeSpec
-
-# Node 1: Parse Query
-parse_query_node = NodeSpec(
-    id="parse-query",
-    name="Parse Query",
-    description="Analyze the research topic and generate 3-5 diverse search queries to cover different aspects",
-    node_type="llm_generate",
-    input_keys=["topic"],
-    output_keys=["search_queries", "research_focus", "key_aspects"],
-    output_schema={
-        "research_focus": {
-            "type": "string",
-            "required": True,
-            "description": "Brief statement of what we're researching",
-        },
-        "key_aspects": {
-            "type": "array",
-            "required": True,
-            "description": "List of 3-5 key aspects to investigate",
-        },
-        "search_queries": {
-            "type": "array",
-            "required": True,
-            "description": "List of 3-5 search queries",
-        },
-    },
-    system_prompt="""\
-You are a research query strategist. Given a research topic, analyze it and generate search queries.
-
-Your task:
-1. Understand the core research question
-2. Identify 3-5 key aspects to investigate
-3. Generate 3-5 diverse search queries that will find comprehensive information
-
-CRITICAL: Return ONLY raw JSON. NO markdown, NO code blocks.
-
-Return this JSON structure:
-{
-  "research_focus": "Brief statement of what we're researching",
-  "key_aspects": ["aspect1", "aspect2", "aspect3"],
-  "search_queries": [
-    "query 1 - broad overview",
-    "query 2 - specific angle",
-    "query 3 - recent developments",
-    "query 4 - expert opinions",
-    "query 5 - data/statistics"
-  ]
-}
-""",
-    tools=[],
-    max_retries=3,
-)
-
-# Node 2: Search Sources
-search_sources_node = NodeSpec(
-    id="search-sources",
-    name="Search Sources",
-    description="Execute web searches using the generated queries to find 15+ source URLs",
-    node_type="llm_tool_use",
-    input_keys=["search_queries", "research_focus"],
-    output_keys=["source_urls", "search_results_summary"],
-    output_schema={
-        "source_urls": {
-            "type": "array",
-            "required": True,
-            "description": "List of source URLs found",
-        },
-        "search_results_summary": {
-            "type": "string",
-            "required": True,
-            "description": "Brief summary of what was found",
-        },
-    },
-    system_prompt="""\
-You are a research assistant executing web searches. Use the web_search tool to find sources.
-
-Your task:
-1. Execute each search query using web_search tool
-2. Collect URLs from search results
-3. Aim for 15+ diverse sources
-
-After searching, return JSON with found sources:
-{
-  "source_urls": ["url1", "url2", ...],
-  "search_results_summary": "Brief summary of what was found"
-}
-""",
-    tools=["web_search"],
-    max_retries=3,
-)
-
-# Node 3: Fetch Content
-fetch_content_node = NodeSpec(
-    id="fetch-content",
-    name="Fetch Content",
-    description="Fetch and extract content from the discovered source URLs",
-    node_type="llm_tool_use",
-    input_keys=["source_urls", "research_focus"],
-    output_keys=["fetched_sources", "fetch_errors"],
-    output_schema={
-        "fetched_sources": {
-            "type": "array",
-            "required": True,
-            "description": "List of fetched source objects with url, title, content",
-        },
-        "fetch_errors": {
-            "type": "array",
-            "required": True,
-            "description": "List of URLs that failed to fetch",
-        },
-    },
-    system_prompt="""\
-You are a content fetcher. Use web_scrape tool to retrieve content from URLs.
-
-Your task:
-1. Fetch content from each source URL using web_scrape tool
-2. Extract the main content relevant to the research focus
-3. Track any URLs that failed to fetch
-
-After fetching, return JSON:
-{
-  "fetched_sources": [
-    {"url": "...", "title": "...", "content": "extracted text..."},
-    ...
-  ],
-  "fetch_errors": ["url that failed", ...]
-}
-""",
-    tools=["web_scrape"],
-    max_retries=3,
-)
-
-# Node 4: Evaluate Sources
-evaluate_sources_node = NodeSpec(
-    id="evaluate-sources",
-    name="Evaluate Sources",
-    description="Score sources for relevance and quality, filter to top 10",
-    node_type="llm_generate",
-    input_keys=["fetched_sources", "research_focus", "key_aspects"],
-    output_keys=["ranked_sources", "source_analysis"],
-    output_schema={
-        "ranked_sources": {
-            "type": "array",
-            "required": True,
-            "description": "List of ranked sources with scores",
-        },
-        "source_analysis": {
-            "type": "string",
-            "required": True,
-            "description": "Overview of source quality and coverage",
-        },
-    },
-    system_prompt="""\
-You are a source evaluator. Assess each source for quality and relevance.
-
-Scoring criteria:
-- Relevance to research focus (1-10)
-- Source credibility (1-10)
-- Information depth (1-10)
-- Recency if relevant (1-10)
-
-Your task:
-1. Score each source
-2. Rank by combined score
-3. Select top 10 sources
-4. Note what each source uniquely contributes
-
-Return JSON:
-{
-  "ranked_sources": [
-    {"url": "...", "title": "...", "content": "...", "score": 8.5, "unique_value": "..."},
-    ...
-  ],
-  "source_analysis": "Overview of source quality and coverage"
-}
-""",
-    tools=[],
-    max_retries=3,
-)
-
-# Node 5: Synthesize Findings
-synthesize_findings_node = NodeSpec(
-    id="synthesize-findings",
-    name="Synthesize Findings",
-    description="Extract key facts from sources and identify common themes",
-    node_type="llm_generate",
-    input_keys=["ranked_sources", "research_focus", "key_aspects"],
-    output_keys=["key_findings", "themes", "source_citations"],
-    output_schema={
-        "key_findings": {
-            "type": "array",
-            "required": True,
-            "description": "List of key findings with sources and confidence",
-        },
-        "themes": {
-            "type": "array",
-            "required": True,
-            "description": "List of themes with descriptions and supporting sources",
-        },
-        "source_citations": {
-            "type": "object",
-            "required": True,
-            "description": "Map of facts to supporting URLs",
-        },
-    },
-    system_prompt="""\
-You are a research synthesizer. Analyze multiple sources to extract insights.
-
-Your task:
-1. Identify key facts from each source
-2. Find common themes across sources
-3. Note contradictions or debates
-4. Build a citation map (fact -> source URL)
-
-Return JSON:
-{
-  "key_findings": [
-    {"finding": "...", "sources": ["url1", "url2"], "confidence": "high/medium/low"},
-    ...
-  ],
-  "themes": [
-    {"theme": "...", "description": "...", "supporting_sources": ["url1", ...]},
-    ...
-  ],
-  "source_citations": {
-    "fact or claim": ["supporting url1", "url2"],
-    ...
-  }
-}
-""",
-    tools=[],
-    max_retries=3,
-)
-
-# Node 6: Write Report
-write_report_node = NodeSpec(
-    id="write-report",
-    name="Write Report",
-    description="Generate a narrative report with proper citations",
-    node_type="llm_generate",
-    input_keys=[
-        "key_findings",
-        "themes",
-        "source_citations",
-        "research_focus",
-        "ranked_sources",
-    ],
-    output_keys=["report_content", "references"],
-    output_schema={
-        "report_content": {
-            "type": "string",
-            "required": True,
-            "description": "Full markdown report text with citations",
-        },
-        "references": {
-            "type": "array",
-            "required": True,
-            "description": "List of reference objects with number, url, title",
-        },
-    },
-    system_prompt="""\
-You are a research report writer. Create a well-structured narrative report.
-
-Report structure:
-1. Executive Summary (2-3 paragraphs)
-2. Introduction (context and scope)
-3. Key Findings (organized by theme)
-4. Analysis (synthesis and implications)
-5. Conclusion
-6. References (numbered list of all sources)
-
-Citation format: Use numbered citations like [1], [2] that correspond to the References section.
-
-IMPORTANT:
-- Every factual claim MUST have a citation
-- Write in clear, professional prose
-- Be objective and balanced
-- Highlight areas of consensus and debate
-
-Return JSON:
-{
-  "report_content": "Full markdown report text with citations...",
-  "references": [
-    {"number": 1, "url": "...", "title": "..."},
-    ...
-  ]
-}
-""",
-    tools=[],
-    max_retries=3,
-)
-
-# Node 7: Quality Check
-quality_check_node = NodeSpec(
-    id="quality-check",
-    name="Quality Check",
-    description="Verify all claims have citations and report is coherent",
-    node_type="llm_generate",
-    input_keys=["report_content", "references", "source_citations"],
-    output_keys=["quality_score", "issues", "final_report"],
-    output_schema={
-        "quality_score": {
-            "type": "number",
-            "required": True,
-            "description": "Quality score 0-1",
-        },
-        "issues": {
-            "type": "array",
-            "required": True,
-            "description": "List of issues found and fixed",
-        },
-        "final_report": {
-            "type": "string",
-            "required": True,
-            "description": "Corrected full report",
-        },
-    },
-    system_prompt="""\
-You are a quality assurance reviewer. Check the research report for issues.
-
-Check for:
-1. Uncited claims (factual statements without [n] citation)
-2. Broken citations (references to non-existent numbers)
-3. Coherence (logical flow between sections)
-4. Completeness (all key aspects covered)
-5. Accuracy (claims match source content)
-
-If issues found, fix them in the final report.
-
-Return JSON:
-{
-  "quality_score": 0.95,
-  "issues": [
-    {"type": "uncited_claim", "location": "paragraph 3", "fixed": true},
-    ...
-  ],
-  "final_report": "Corrected full report with all issues fixed..."
-}
-""",
-    tools=[],
-    max_retries=3,
-)
-
-# Node 8: Save Report
-save_report_node = NodeSpec(
-    id="save-report",
-    name="Save Report",
-    description="Write the final report to a local markdown file",
-    node_type="llm_tool_use",
-    input_keys=["final_report", "references", "research_focus"],
-    output_keys=["file_path", "save_status"],
-    output_schema={
-        "file_path": {
-            "type": "string",
-            "required": True,
-            "description": "Path where report was saved",
-        },
-        "save_status": {
-            "type": "string",
-            "required": True,
-            "description": "Status of save operation",
-        },
-    },
-    system_prompt="""\
-You are a file manager. Save the research report to disk.
-
-Your task:
-1. Generate a filename from the research focus (slugified, with date)
-2. Use the write_to_file tool to save the report as markdown
-3. Save to the ./research_reports/ directory
-
-Filename format: research_YYYY-MM-DD_topic-slug.md
-
-Return JSON:
-{
-  "file_path": "research_reports/research_2026-01-23_topic-name.md",
-  "save_status": "success"
-}
-""",
-    tools=["write_to_file"],
-    max_retries=3,
-)
-
-__all__ = [
-    "parse_query_node",
-    "search_sources_node",
-    "fetch_content_node",
-    "evaluate_sources_node",
-    "synthesize_findings_node",
-    "write_report_node",
-    "quality_check_node",
-    "save_report_node",
-]

.claude/skills/building-agents-core/SKILL.md

@@ -1,303 +0,0 @@
----
-name: building-agents-core
-description: Core concepts for goal-driven agents - architecture, node types, tool discovery, and workflow overview. Use when starting agent development or need to understand agent fundamentals.
-license: Apache-2.0
-metadata:
-  author: hive
-  version: "1.0"
-  type: foundational
-  part_of: building-agents
----
-
-# 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:**
-
-- `llm_generate` - Text generation, parsing
-- `llm_tool_use` - Actions requiring tools
-- `router` - Conditional branching
-- `function` - Deterministic operations
-
-```python
-search_node = NodeSpec(
-    id="search-web",
-    name="Search Web",
-    description="Search for information online",
-    node_type="llm_tool_use",
-    input_keys=["query"],
-    output_keys=["search_results"],
-    system_prompt="Search the web for: {query}",
-    tools=["web_search"],
-    max_retries=3,
-)
-```
-
-### Edge
-
-Connection between nodes (written to agent.py)
-
-**Edge Conditions:**
-
-- `on_success` - Proceed if node succeeds
-- `on_failure` - Handle errors
-- `always` - Always proceed
-- `conditional` - Based on expression
-
-```python
-EdgeSpec(
-    id="search-to-analyze",
-    source="search-web",
-    target="analyze-results",
-    condition=EdgeCondition.ON_SUCCESS,
-    priority=1,
-)
-```
-
-### Pause/Resume
-
-Multi-turn conversations
-
-- **Pause nodes** - Stop execution, wait for user input
-- **Resume entry points** - Continue from pause with user's response
-
-```python
-# Example pause/resume configuration
-pause_nodes = ["request-clarification"]
-entry_points = {
-    "start": "analyze-request",
-    "request-clarification_resume": "process-clarification"
-}
-```
-
-## 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")
-```
-
-This returns available tools with their descriptions and parameters:
-
-```json
-{
-  "success": true,
-  "tools_by_server": {
-    "tools": [
-      {
-        "name": "web_search",
-        "description": "Search the web...",
-        "parameters": ["query"]
-      },
-      {
-        "name": "web_scrape",
-        "description": "Scrape a URL...",
-        "parameters": ["url"]
-      }
-    ]
-  },
-  "total_tools": 14
-}
-```
-
-### 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
-
-### Example Validation Flow
-
-```python
-# 1. User requests: "Add a node that searches the web"
-# 2. Discover available tools
-tools_response = mcp__agent-builder__list_mcp_tools()
-
-# 3. Check if web_search exists
-available = [t["name"] for tools in tools_response["tools_by_server"].values() for t in tools]
-if "web_search" not in available:
-    # Inform user and ask how to proceed
-    print("❌ 'web_search' not available. Available tools:", available)
-else:
-    # Proceed with node creation
-    # ...
-```
-
-## 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
-   - User approves
-   - Write to nodes/__init__.py IMMEDIATELY ← FILE WRITTEN
-   - (Optional) Validate with test_node ← MCP VALIDATION
-   - User can open file and see it
-4. CONNECT EDGES → Update agent.py ← FILE WRITTEN
-   - (Optional) Validate with validate_graph ← MCP VALIDATION
-5. FINALIZE → Write agent class to agent.py ← FILE WRITTEN
-6. DONE - Agent ready at exports/my_agent/
-```
-
-**Files written immediately. MCP tools optional for validation/testing bookkeeping.**
-
-### The Key Difference
-
-**OLD (Bad):**
-
-```
-MCP add_node → Session State → MCP add_node → Session State → ...
-                                                                ↓
-                                                     MCP export_graph
-                                                                ↓
-                                                       Files appear
-```
-
-**NEW (Good):**
-
-```
-Write node to file → (Optional: MCP test_node) → Write node to file → ...
-       ↓                                               ↓
-  File visible                                    File visible
-  immediately                                     immediately
-```
-
-**Bottom line:** Use Write/Edit for construction, MCP for validation if needed.
-
-## When to Use This Skill
-
-Use building-agents-core 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 `building-agents-construction` skill
-- Need patterns and examples? → Use `building-agents-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, etc.
-```
-
-**create_session** - Track session state for bookkeeping
-```python
-mcp__agent-builder__create_session(session_name="my-build")
-```
-
-**Key Point:** Files are written FIRST. MCP tools are for validation only.
-
-## Related Skills
-
-- **building-agents-construction** - Step-by-step building process
-- **building-agents-patterns** - Best practices and examples
-- **agent-workflow** - Complete workflow orchestrator
-- **testing-agent** - Test and validate completed agents

.claude/skills/building-agents-patterns/SKILL.md

@@ -1,497 +0,0 @@
----
-name: building-agents-patterns
-description: Best practices, patterns, and examples for building goal-driven agents. Includes pause/resume architecture, hybrid workflows, anti-patterns, and handoff to testing. Use when optimizing agent design.
-license: Apache-2.0
-metadata:
-  author: hive
-  version: "1.0"
-  type: reference
-  part_of: building-agents
----
-
-# Building Agents - Patterns & Best Practices
-
-Design patterns, examples, and best practices for building robust goal-driven agents.
-
-**Prerequisites:** Complete agent structure using `building-agents-construction`.
-
-## 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="llm_tool_use",
-    input_keys=["query"],
-    output_keys=["search_results"],
-    system_prompt="Search the web for: {query}",
-    tools=["web_search"],
-)
-'''
-
-Edit(
-    file_path="exports/research_agent/nodes/__init__.py",
-    old_string="# Nodes will be added here",
-    new_string=node_code
-)
-
-print("✅ Added search_node to nodes/__init__.py")
-print("📁 Open exports/research_agent/nodes/__init__.py to see it!")
-
-# 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...]}'
-)
-
-print(f"✓ Validation: {validation['success']}")
-```
-
-**User experience:**
-
-- Immediately sees node in their editor (from step 1)
-- Gets validation feedback (from step 2)
-- Can edit the file directly if needed
-
-This combines visibility (files) with validation (MCP tools).
-
-## Pause/Resume Architecture
-
-For agents needing multi-turn conversations with user interaction:
-
-### Basic Pause/Resume Flow
-
-```python
-# Define pause nodes - execution stops at these nodes
-pause_nodes = ["request-clarification", "await-approval"]
-
-# Define entry points - where to resume from each pause
-entry_points = {
-    "start": "analyze-request",  # Initial entry
-    "request-clarification_resume": "process-clarification",  # Resume from clarification
-    "await-approval_resume": "execute-action",  # Resume from approval
-}
-```
-
-### Example: Multi-Turn Research Agent
-
-```python
-# Nodes
-nodes = [
-    NodeSpec(id="analyze-request", ...),
-    NodeSpec(id="request-clarification", ...),  # PAUSE NODE
-    NodeSpec(id="process-clarification", ...),
-    NodeSpec(id="generate-results", ...),
-    NodeSpec(id="await-approval", ...),  # PAUSE NODE
-    NodeSpec(id="execute-action", ...),
-]
-
-# Edges with resume flows
-edges = [
-    EdgeSpec(
-        id="analyze-to-clarify",
-        source="analyze-request",
-        target="request-clarification",
-        condition=EdgeCondition.CONDITIONAL,
-        condition_expr="needs_clarification == true",
-    ),
-    # When resumed, goes to process-clarification
-    EdgeSpec(
-        id="clarify-to-process",
-        source="request-clarification",
-        target="process-clarification",
-        condition=EdgeCondition.ALWAYS,
-    ),
-    EdgeSpec(
-        id="results-to-approval",
-        source="generate-results",
-        target="await-approval",
-        condition=EdgeCondition.ALWAYS,
-    ),
-    # When resumed, goes to execute-action
-    EdgeSpec(
-        id="approval-to-execute",
-        source="await-approval",
-        target="execute-action",
-        condition=EdgeCondition.ALWAYS,
-    ),
-]
-
-# Configuration
-pause_nodes = ["request-clarification", "await-approval"]
-entry_points = {
-    "start": "analyze-request",
-    "request-clarification_resume": "process-clarification",
-    "await-approval_resume": "execute-action",
-}
-```
-
-### Running Pause/Resume Agents
-
-```python
-# Initial run - will pause at first pause node
-result1 = await agent.run(
-    context={"query": "research topic"},
-    session_state=None
-)
-
-# Check if paused
-if result1.paused_at:
-    print(f"Paused at: {result1.paused_at}")
-
-    # Resume with user input
-    result2 = await agent.run(
-        context={"user_response": "clarification details"},
-        session_state=result1.session_state  # Pass previous state
-    )
-```
-
-## Anti-Patterns
-
-### What NOT to Do
-
-❌ **Don't rely on `export_graph`** - Write files immediately, not at end
-```python
-# BAD: Building in session state, exporting at end
-mcp__agent-builder__add_node(...)
-mcp__agent-builder__add_node(...)
-mcp__agent-builder__export_graph()  # Files appear only now
-
-# GOOD: Writing files immediately
-Write(file_path="...", content=node_code)  # File visible now
-Write(file_path="...", content=node_code)  # File visible now
-```
-
-❌ **Don't hide code in session** - Write to files as components approved
-```python
-# BAD: Accumulating changes invisibly
-session.add_component(component1)
-session.add_component(component2)
-# User can't see anything yet
-
-# GOOD: Incremental visibility
-Edit(file_path="...", ...)  # User sees change 1
-Edit(file_path="...", ...)  # User sees change 2
-```
-
-❌ **Don't wait to write files** - Agent visible from first step
-```python
-# BAD: Building everything before writing
-design_all_nodes()
-design_all_edges()
-write_everything_at_once()
-
-# GOOD: Write as you go
-write_package_structure()  # Visible
-write_goal()  # Visible
-write_node_1()  # Visible
-write_node_2()  # Visible
-```
-
-❌ **Don't batch everything** - Write incrementally
-```python
-# BAD: Batching all nodes
-nodes = [design_node_1(), design_node_2(), ...]
-write_all_nodes(nodes)
-
-# GOOD: One at a time with user feedback
-write_node_1()  # User approves
-write_node_2()  # User approves
-write_node_3()  # User approves
-```
-
-### MCP Tools - Correct Usage
-
-**MCP tools OK for:**
-✅ `test_node` - Validate node configuration with mock inputs
-✅ `validate_graph` - Check graph structure
-✅ `create_session` - Track session state for bookkeeping
-✅ Other validation tools
-
-**Just don't:** Use MCP as the primary construction method or rely on export_graph
-
-## Best Practices
-
-### 1. Show Progress After Each Write
-
-```python
-# After writing a node
-print("✅ Added analyze_request_node to nodes/__init__.py")
-print("📊 Progress: 1/6 nodes added")
-print("📁 Open exports/my_agent/nodes/__init__.py to see it!")
-```
-
-### 2. Let User Open Files During Build
-
-```python
-# Encourage file inspection
-print("✅ Goal written to agent.py")
-print("")
-print("💡 Tip: Open exports/my_agent/agent.py in your editor to see the goal!")
-```
-
-### 3. Write Incrementally - One Component at a Time
-
-```python
-# Good flow
-write_package_structure()
-show_user("Package created")
-
-write_goal()
-show_user("Goal written")
-
-for node in nodes:
-    get_approval(node)
-    write_node(node)
-    show_user(f"Node {node.id} written")
-```
-
-### 4. Test As You Build
-
-```python
-# After adding several nodes
-print("💡 You can test current state with:")
-print("  PYTHONPATH=core:exports python -m my_agent validate")
-print("  PYTHONPATH=core:exports python -m my_agent info")
-```
-
-### 5. Keep User Informed
-
-```python
-# Clear status updates
-print("🔨 Creating package structure...")
-print("✅ Package created: exports/my_agent/")
-print("")
-print("📝 Next: Define agent goal")
-```
-
-## Continuous Monitoring Agents
-
-For agents that run continuously without terminal nodes:
-
-```python
-# No terminal nodes - loops forever
-terminal_nodes = []
-
-# Workflow loops back to start
-edges = [
-    EdgeSpec(id="monitor-to-check", source="monitor", target="check-condition"),
-    EdgeSpec(id="check-to-wait", source="check-condition", target="wait"),
-    EdgeSpec(id="wait-to-monitor", source="wait", target="monitor"),  # Loop
-]
-
-# Entry node only
-entry_node = "monitor"
-entry_points = {"start": "monitor"}
-pause_nodes = []
-```
-
-**Example: File Monitor**
-
-```python
-nodes = [
-    NodeSpec(id="list-files", ...),
-    NodeSpec(id="check-new-files", node_type="router", ...),
-    NodeSpec(id="process-files", ...),
-    NodeSpec(id="wait-interval", node_type="function", ...),
-]
-
-edges = [
-    EdgeSpec(id="list-to-check", source="list-files", target="check-new-files"),
-    EdgeSpec(
-        id="check-to-process",
-        source="check-new-files",
-        target="process-files",
-        condition=EdgeCondition.CONDITIONAL,
-        condition_expr="new_files_count > 0",
-    ),
-    EdgeSpec(
-        id="check-to-wait",
-        source="check-new-files",
-        target="wait-interval",
-        condition=EdgeCondition.CONDITIONAL,
-        condition_expr="new_files_count == 0",
-    ),
-    EdgeSpec(id="process-to-wait", source="process-files", target="wait-interval"),
-    EdgeSpec(id="wait-to-list", source="wait-interval", target="list-files"),  # Loop back
-]
-
-terminal_nodes = []  # No terminal - runs forever
-```
-
-## Complex Routing Patterns
-
-### Multi-Condition Router
-
-```python
-router_node = NodeSpec(
-    id="decision-router",
-    node_type="router",
-    input_keys=["analysis_result"],
-    output_keys=["decision"],
-    system_prompt="""
-    Based on the analysis result, decide the next action:
-    - If confidence > 0.9: route to "execute"
-    - If 0.5 <= confidence <= 0.9: route to "review"
-    - If confidence < 0.5: route to "clarify"
-
-    Return: {"decision": "execute|review|clarify"}
-    """,
-)
-
-# Edges for each route
-edges = [
-    EdgeSpec(
-        id="router-to-execute",
-        source="decision-router",
-        target="execute-action",
-        condition=EdgeCondition.CONDITIONAL,
-        condition_expr="decision == 'execute'",
-        priority=1,
-    ),
-    EdgeSpec(
-        id="router-to-review",
-        source="decision-router",
-        target="human-review",
-        condition=EdgeCondition.CONDITIONAL,
-        condition_expr="decision == 'review'",
-        priority=2,
-    ),
-    EdgeSpec(
-        id="router-to-clarify",
-        source="decision-router",
-        target="request-clarification",
-        condition=EdgeCondition.CONDITIONAL,
-        condition_expr="decision == 'clarify'",
-        priority=3,
-    ),
-]
-```
-
-## Error Handling Patterns
-
-### Graceful Failure with Fallback
-
-```python
-# Primary node with error handling
-nodes = [
-    NodeSpec(id="api-call", max_retries=3, ...),
-    NodeSpec(id="fallback-cache", ...),
-    NodeSpec(id="report-error", ...),
-]
-
-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,
-    ),
-]
-```
-
-## Performance Optimization
-
-### Parallel Node Execution
-
-```python
-# Use multiple edges from same source for parallel execution
-edges = [
-    EdgeSpec(
-        id="start-to-search1",
-        source="start",
-        target="search-source-1",
-        condition=EdgeCondition.ALWAYS,
-    ),
-    EdgeSpec(
-        id="start-to-search2",
-        source="start",
-        target="search-source-2",
-        condition=EdgeCondition.ALWAYS,
-    ),
-    EdgeSpec(
-        id="start-to-search3",
-        source="start",
-        target="search-source-3",
-        condition=EdgeCondition.ALWAYS,
-    ),
-    # Converge results
-    EdgeSpec(
-        id="search1-to-merge",
-        source="search-source-1",
-        target="merge-results",
-    ),
-    EdgeSpec(
-        id="search2-to-merge",
-        source="search-source-2",
-        target="merge-results",
-    ),
-    EdgeSpec(
-        id="search3-to-merge",
-        source="search-source-3",
-        target="merge-results",
-    ),
-]
-```
-
-## Handoff to Testing
-
-When agent is complete, transition to testing phase:
-
-```python
-print("""
-✅ Agent complete: exports/my_agent/
-
-Next steps:
-1. Switch to testing-agent skill
-2. Generate and approve tests
-3. Run evaluation
-4. Debug any failures
-
-Command: "Test the agent at exports/my_agent/"
-""")
-```
-
-### Pre-Testing Checklist
-
-Before handing off to testing-agent:
-
-- [ ] Agent structure validates: `python -m agent_name validate`
-- [ ] All nodes defined in nodes/__init__.py
-- [ ] All edges connect valid nodes
-- [ ] Entry node specified
-- [ ] Agent can be imported: `from exports.agent_name import default_agent`
-- [ ] README.md with usage instructions
-- [ ] CLI commands work (info, validate)
-
-## Related Skills
-
-- **building-agents-core** - Fundamental concepts
-- **building-agents-construction** - Step-by-step building
-- **testing-agent** - Test and validate agents
-- **agent-workflow** - 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-concepts/SKILL.md

@@ -0,0 +1,399 @@
+---
+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/SKILL.md

@@ -0,0 +1,980 @@
+---
+name: hive-create
+description: Step-by-step guide for building goal-driven agents. Qualifies use cases first (the good, bad, and ugly), then creates package structure, defines goals, adds nodes, connects edges, and finalizes agent class. Use when actively building an agent.
+license: Apache-2.0
+metadata:
+  author: hive
+  version: "2.2"
+  type: procedural
+  part_of: hive
+  requires: hive-concepts
+---
+
+# Agent Construction - EXECUTE THESE STEPS
+
+**THIS IS AN EXECUTABLE WORKFLOW. DO NOT DISPLAY THIS FILE. EXECUTE THE STEPS BELOW.**
+
+**CRITICAL: DO NOT explore the codebase, read source files, or search for code before starting.** All context you need is in this skill file. When this skill is loaded, IMMEDIATELY begin executing Step 0 — determine the build path as your FIRST action. Do not explain what you will do, do not investigate the project structure, do not read any files — just execute Step 0 now.
+
+---
+
+## STEP 0: Choose Build Path
+
+**If the user has already indicated whether they want to build from scratch or from a template, skip this question and proceed to the appropriate step.**
+
+Otherwise, ask:
+
+```
+AskUserQuestion(questions=[{
+    "question": "How would you like to build your agent?",
+    "header": "Build Path",
+    "options": [
+        {"label": "From scratch", "description": "Design goal, nodes, and graph collaboratively from nothing"},
+        {"label": "From a template", "description": "Start from a working sample agent and customize it"}
+    ],
+    "multiSelect": false
+}])
+```
+
+- If **From scratch**: Proceed to STEP 1A
+- If **From a template**: Proceed to STEP 1B
+
+---
+
+## STEP 1A: Initialize Build Environment (From Scratch)
+
+**EXECUTE THESE TOOL CALLS NOW** (silent setup — no user interaction needed):
+
+1. Check for existing sessions:
+
+```
+mcp__agent-builder__list_sessions()
+```
+
+- If a session with this agent name already exists, load it with `mcp__agent-builder__load_session_by_id(session_id="...")` and skip to step 3.
+- If no matching session exists, proceed to step 2.
+
+2. Create a build session (replace AGENT_NAME with the user's requested agent name in snake_case):
+
+```
+mcp__agent-builder__create_session(name="AGENT_NAME")
+```
+
+3. Register the hive-tools MCP server:
+
+```
+mcp__agent-builder__add_mcp_server(
+    name="hive-tools",
+    transport="stdio",
+    command="uv",
+    args='["run", "python", "mcp_server.py", "--stdio"]',
+    cwd="tools",
+    description="Hive tools MCP server"
+)
+```
+
+4. Discover available tools:
+
+```
+mcp__agent-builder__list_mcp_tools()
+```
+
+5. Create the package directory:
+
+```bash
+mkdir -p exports/AGENT_NAME/nodes
+```
+
+**Save the tool list for STEP 4** — you will need it for node design.
+
+**THEN immediately proceed to STEP 2** (do NOT display setup results to the user — just move on).
+
+---
+
+## STEP 1B: Initialize Build Environment (From Template)
+
+**EXECUTE THESE STEPS NOW:**
+
+### 1B.1: Discover available templates
+
+List the template directories and read each template's `agent.json` to get its name and description:
+
+```bash
+ls examples/templates/
+```
+
+For each directory found, read `examples/templates/TEMPLATE_DIR/agent.json` with the Read tool and extract:
+- `agent.name` — the template's display name
+- `agent.description` — what the template does
+
+### 1B.2: Present templates to user
+
+Show the user a table of available templates:
+
+> **Available Templates:**
+>
+> | # | Template | Description |
+> |---|----------|-------------|
+> | 1 | [name from agent.json] | [description from agent.json] |
+> | 2 | ... | ... |
+
+Then ask the user to pick a template and provide a name for their new agent:
+
+```
+AskUserQuestion(questions=[{
+    "question": "Which template would you like to start from?",
+    "header": "Template",
+    "options": [
+        {"label": "[template 1 name]", "description": "[template 1 description]"},
+        {"label": "[template 2 name]", "description": "[template 2 description]"},
+        ...
+    ],
+    "multiSelect": false
+}, {
+    "question": "What should the new agent be named? (snake_case)",
+    "header": "Agent Name",
+    "options": [
+        {"label": "Use template name", "description": "Keep the original template name as-is"},
+        {"label": "Custom name", "description": "I'll provide a new snake_case name"}
+    ],
+    "multiSelect": false
+}])
+```
+
+### 1B.3: Copy template to exports
+
+```bash
+cp -r examples/templates/TEMPLATE_DIR exports/NEW_AGENT_NAME
+```
+
+### 1B.4: Create session and register MCP (same logic as STEP 1A)
+
+First, check for existing sessions:
+
+```
+mcp__agent-builder__list_sessions()
+```
+
+- If a session with this agent name already exists, load it with `mcp__agent-builder__load_session_by_id(session_id="...")` and skip to `list_mcp_tools`.
+- If no matching session exists, create one:
+
+```
+mcp__agent-builder__create_session(name="NEW_AGENT_NAME")
+```
+
+Then register MCP and discover tools:
+
+```
+mcp__agent-builder__add_mcp_server(
+    name="hive-tools",
+    transport="stdio",
+    command="uv",
+    args='["run", "python", "mcp_server.py", "--stdio"]',
+    cwd="tools",
+    description="Hive tools MCP server"
+)
+```
+
+```
+mcp__agent-builder__list_mcp_tools()
+```
+
+### 1B.5: Load template into builder session
+
+Import the entire agent definition in one call:
+
+```
+mcp__agent-builder__import_from_export(agent_json_path="exports/NEW_AGENT_NAME/agent.json")
+```
+
+This reads the agent.json and populates the builder session with the goal, all nodes, and all edges.
+
+**THEN immediately proceed to STEP 2.**
+
+---
+
+## STEP 2: Define Goal Together with User
+**A responsible engineer doesn't jump into building. First, understand the problem and be transparent about what the framework can and cannot do.**
+
+**If starting from a template**, the goal is already loaded in the builder session. Present the existing goal to the user using the format below and ask for approval. Skip the collaborative drafting questions — go straight to presenting and asking "Do you approve this goal, or would you like to modify it?"
+
+**If the user has NOT already described what they want to build**, start by asking what kind of agent they have in mind:
+
+```
+AskUserQuestion(questions=[{
+    "question": "What kind of agent do you want to build? Select an option below, or choose 'Other' to describe your own.",
+    "header": "Agent type",
+    "options": [
+        {"label": "Data collection", "description": "Gathers information from the web, analyzes it, and produces a report or sends outreach (e.g. market research, news digest, email campaigns, competitive analysis)"},
+        {"label": "Workflow automation", "description": "Automates a multi-step business process end-to-end (e.g. lead qualification, content publishing pipeline, data entry)"},
+        {"label": "Personal assistant", "description": "Handles recurring tasks or monitors for events and acts on them (e.g. daily briefings, meeting prep, file organization)"}
+    ],
+    "multiSelect": false
+}])
+```
+
+Use the user's selection (or their custom description if they chose "Other") as context when shaping the goal below. If the user already described what they want before this step, skip the question and proceed directly.
+
+**DO NOT propose a complete goal on your own.** Instead, collaborate with the user to define it.
+
+### 2a: Fast Discovery (3-8 Turns)
+
+**The core principle**: Discovery should feel like progress, not paperwork. The stakeholder should walk away feeling like you understood them faster than anyone else would have.
+
+**Communication sytle**: Be concise. Say less. Mean more. Impatient stakeholders don't want a wall of text — they want to know you get it. Every sentence you say should either move the conversation forward or prove you understood something. If it does neither, cut it.
+
+**Ask Question Rules: Respect Their Time.** Every question must earn its place by:
+1. **Preventing a costly wrong turn** — you're about to build the wrong thing
+2. **Unlocking a shortcut** — their answer lets you simplify the design
+3. **Surfacing a dealbreaker** — there's a constraint that changes everything
+4. **Provide Options** - Provide options to your questions if possible, but also always allow the user to type something beyong the options.
+
+If a question doesn't do one of these, don't ask it. Make an assumption, state it, and move on.
+
+---
+
+#### 2a.1: Let Them Talk, But Listen Like an Architect
+
+When the stakeholder describes what they want, don't just hear the words — listen for the architecture underneath. While they talk, mentally construct:
+
+- **The actors**: Who are the people/systems involved?
+- **The trigger**: What kicks off the workflow?
+- **The core loop**: What's the main thing that happens repeatedly?
+- **The output**: What's the valuable thing produced at the end?
+- **The pain**: What about today's situation is broken, slow, or missing?
+
+You are extracting a **domain model** from natural language in real time. Most stakeholders won't give you this structure explicitly — they'll give you a story. Your job is to hear the structure inside the story.
+
+| 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 |
+| People they name | Your user types |
+
+---
+
+#### 2a.2: Use Domain Knowledge to Fill In the Blanks
+
+You have broad knowledge of how systems work. Use it aggressively.
+
+If they say "I need a research agent," you already know it probably involves: search, summarization, source tracking, and iteration. Don't ask about each — use them as your starting mental model and let their specifics override your defaults.
+
+If they say "I need to monitor files and alert me," you know this probably involves: watch patterns, triggers, notifications, and state tracking.
+
+**The key move**: Take your general knowledge of the domain and merge it with the specifics they've given you. The result is a draft understanding that's 60-80% right before you've asked a single question. Your questions close the remaining 20-40%.
+
+---
+
+#### 2a.3: Play Back a Proposed Model (Not a List of Questions)
+
+After listening, present a **concrete picture** of what you think they need. Make it specific enough that they can spot what's wrong.
+
+**Pattern: "Here's what I heard — tell me where I'm off"**
+
+> "OK here's how I'm picturing this: [User type] needs to [core action]. Right now they're [current painful workflow]. What you want is [proposed solution that replaces the pain].
+>
+> The way I'd structure this: [key entities] connected by [key relationships], with the main flow being [trigger → steps → outcome].
+>
+> For the MVP, I'd focus on [the one thing that delivers the most value] and hold off on [things that can wait].
+>
+> Before I start — [1-2 specific questions you genuinely can't infer]."
+
+Why this works:
+- **Proves you were listening** — they don't feel like they have to repeat themselves
+- **Shows competence** — you're already thinking in systems
+- **Fast to correct** — "no, it's more like X" takes 10 seconds vs. answering 15 questions
+- **Creates momentum** — heading toward building, not more talking
+
+---
+
+#### 2a.4: Ask Only What You Cannot Infer
+
+Your questions should be **narrow, specific, and consequential**. Never ask what you could answer yourself.
+
+**Good questions** (high-stakes, can't infer):
+- "Who's the primary user — you or your end customers?"
+- "Is this replacing a spreadsheet, or is there literally nothing today?"
+- "Does this need to integrate with anything, or standalone?"
+- "Is there existing data to migrate, or starting fresh?"
+
+**Bad questions** (low-stakes, inferable):
+- "What should happen if there's an error?" *(handle gracefully, obviously)*
+- "Should it have search?" *(if there's a list, yes)*
+- "How should we handle permissions?" *(follow standard patterns)*
+- "What tools should I use?" *(your call, not theirs)*
+
+---
+
+#### Conversation Flow (3-5 Turns)
+
+| Turn | Who | What |
+|------|-----|------|
+| 1 | User | Describes what they need |
+| 2 | Agent | Plays back understanding as a proposed model. Asks 1-2 critical questions max. |
+| 3 | User | Corrects, confirms, or adds detail |
+| 4 | Agent | Adjusts model, confirms MVP scope, states assumptions, declares starting point |
+| *(5)* | *(Only if Turn 3 revealed something that fundamentally changes the approach)* |
+
+**AFTER the conversation, IMMEDIATELY proceed to 2b. DO NOT skip to building.**
+
+---
+
+#### Anti-Patterns
+
+| Don't | Do Instead |
+|-------|------------|
+| Open with a list of questions | Open with what you understood from their request |
+| "What are your requirements?" | "Here's what I think you need — am I right?" |
+| Ask about every edge case | Handle with smart defaults, flag in summary |
+| 10+ turn discovery conversation | 3-8 turns. Start building, iterate with real software. |
+| Being lazy nd not understand what user want to achieve | Understand "what" and "why |
+| Ask for permission to start | State your plan and start |
+| Wait for certainty | Start at 80% confidence, iterate the rest |
+| Ask what tech/tools to use | That's your job. Decide, disclose, move on. |
+
+---
+
+
+
+### 2b: Capability Assessment
+
+**After the user responds, analyze the fit.** Present this assessment honestly:
+
+> **Framework Fit Assessment**
+>
+> Based on what you've described, here's my honest assessment of how well this framework fits your use case:
+>
+> **What Works Well (The Good):**
+> - [List 2-4 things the framework handles well for this use case]
+> - Examples: multi-turn conversations, human-in-the-loop review, tool orchestration, structured outputs
+>
+> **Limitations to Be Aware Of (The Bad):**
+> - [List 2-3 limitations that apply but are workable]
+> - Examples: LLM latency means not suitable for sub-second responses, context window limits for very large documents, cost per run for heavy tool usage
+>
+> **Potential Deal-Breakers (The Ugly):**
+> - [List any significant challenges or missing capabilities — be honest]
+> - Examples: no tool available for X, would require custom MCP server, framework not designed for Y
+
+**Be specific.** Reference the actual tools discovered in Step 1. If the user needs `send_email` but it's not available, say so. If they need real-time streaming from a database, explain that's not how the framework works.
+
+### 2c: Gap Analysis
+
+**Identify specific gaps** between what the user wants and what you can deliver:
+
+| Requirement | Framework Support | Gap/Workaround |
+|-------------|-------------------|----------------|
+| [User need] | [✅ Supported / ⚠️ Partial / ❌ Not supported] | [How to handle or why it's a problem] |
+
+**Examples of gaps to identify:**
+- Missing tools (user needs X, but only Y and Z are available)
+- Scope issues (user wants to process 10,000 items, but LLM rate limits apply)
+- Interaction mismatches (user wants CLI-only, but agent is designed for TUI)
+- Data flow issues (user needs to persist state across runs, but sessions are isolated)
+- Latency requirements (user needs instant responses, but LLM calls take seconds)
+
+### 2d: Recommendation
+
+**Give a clear recommendation:**
+
+> **My Recommendation:**
+>
+> [One of these three:]
+>
+> **✅ PROCEED** — This is a good fit. The framework handles your core needs well. [List any minor caveats.]
+>
+> **⚠️ PROCEED WITH SCOPE ADJUSTMENT** — This can work, but we should adjust: [specific changes]. Without these adjustments, you'll hit [specific problems].
+>
+> **🛑 RECONSIDER** — This framework may not be the right tool for this job because [specific reasons]. Consider instead: [alternatives — simpler script, different framework, custom solution].
+
+### 2e: Get Explicit Acknowledgment
+
+**CALL AskUserQuestion:**
+
+```
+AskUserQuestion(questions=[{
+    "question": "Based on this assessment, how would you like to proceed?",
+    "header": "Proceed",
+    "options": [
+        {"label": "Proceed as described", "description": "I understand the limitations, let's build it"},
+        {"label": "Adjust scope", "description": "Let's modify the requirements to fit better"},
+        {"label": "More questions", "description": "I have questions about the assessment"},
+        {"label": "Reconsider", "description": "Maybe this isn't the right approach"}
+    ],
+    "multiSelect": false
+}])
+```
+
+**WAIT for user response.**
+
+- If **Proceed**: Move to STEP 3
+- If **Adjust scope**: Discuss what to change, update your notes, re-assess if needed
+- If **More questions**: Answer them honestly, then ask again
+- If **Reconsider**: Discuss alternatives. If they decide to proceed anyway, that's their informed choice
+
+---
+
+## STEP 3: Define Goal Together with User
+
+**Now that the use case is qualified, collaborate on the goal definition.**
+
+**START by synthesizing what you learned:**
+
+> Based on our discussion, here's my understanding of the goal:
+>
+> **Core purpose:** [what you understood from 2a]
+> **Success looks like:** [what you inferred]
+> **Key constraints:** [what you inferred]
+>
+> Let me refine this with you:
+>
+> 1. **What should this agent accomplish?** (confirm or correct my understanding)
+> 2. **How will we know it succeeded?** (what specific outcomes matter)
+> 3. **Are there any hard constraints?** (things it must never do, quality bars)
+
+**WAIT for the user to respond.** Use their input (and the agent type they selected) to draft:
+
+- Goal ID (kebab-case)
+- Goal name
+- Goal description
+- 3-5 success criteria (each with: id, description, metric, target, weight)
+- 2-4 constraints (each with: id, description, constraint_type, category)
+
+**PRESENT the draft goal for approval:**
+
+> **Proposed Goal: [Name]**
+>
+> [Description]
+>
+> **Success Criteria:**
+>
+> 1. [criterion 1]
+> 2. [criterion 2]
+>    ...
+>
+> **Constraints:**
+>
+> 1. [constraint 1]
+> 2. [constraint 2]
+>    ...
+
+**THEN call AskUserQuestion:**
+
+```
+AskUserQuestion(questions=[{
+    "question": "Do you approve this goal definition?",
+    "header": "Goal",
+    "options": [
+        {"label": "Approve", "description": "Goal looks good, proceed to workflow design"},
+        {"label": "Modify", "description": "I want to change something"}
+    ],
+    "multiSelect": false
+}])
+```
+
+**WAIT for user response.**
+
+- If **Approve**: Call `mcp__agent-builder__set_goal(...)` with the goal details, then proceed to STEP 4
+- If **Modify**: Ask what they want to change, update the draft, ask again
+
+---
+
+## STEP 4: Design Conceptual Nodes
+
+**If starting from a template**, the nodes are already loaded in the builder session. Present the existing nodes using the table format below and ask for approval. Skip the design phase.
+
+**BEFORE designing nodes**, review the available tools from Step 1. Nodes can ONLY use tools that exist.
+
+**DESIGN the workflow** as a series of nodes. For each node, determine:
+
+- node_id (kebab-case)
+- name
+- description
+- node_type: `"event_loop"` (recommended for all LLM work) or `"function"` (deterministic, no LLM)
+- input_keys (what data this node receives)
+- output_keys (what data this node produces)
+- tools (ONLY tools that exist from Step 1 — empty list if no tools needed)
+- client_facing: True if this node interacts with the user
+- nullable_output_keys (for mutually exclusive outputs or feedback-only inputs)
+- max_node_visits (>1 if this node is a feedback loop target)
+
+**Prefer fewer, richer nodes** (4 nodes > 8 thin nodes). Each node boundary requires serializing outputs. A research node that searches, fetches, and analyzes keeps all source material in its conversation history.
+
+**PRESENT the nodes to the user for review:**
+
+> **Proposed Nodes ([N] total):**
+>
+> | #   | Node ID    | Type       | Description                   | Tools                  | Client-Facing |
+> | --- | ---------- | ---------- | ----------------------------- | ---------------------- | :-----------: |
+> | 1   | `intake`   | event_loop | Gather requirements from user | —                      |      Yes      |
+> | 2   | `research` | event_loop | Search and analyze sources    | web_search, web_scrape |      No       |
+> | 3   | `review`   | event_loop | Present findings for approval | —                      |      Yes      |
+> | 4   | `report`   | event_loop | Generate final report         | save_data              |      No       |
+>
+> **Data Flow:**
+>
+> - `intake` produces: `research_brief`
+> - `research` receives: `research_brief` → produces: `findings`, `sources`
+> - `review` receives: `findings`, `sources` → produces: `approved_findings` or `feedback`
+> - `report` receives: `approved_findings` → produces: `final_report`
+
+**THEN call AskUserQuestion:**
+
+```
+AskUserQuestion(questions=[{
+    "question": "Do you approve these nodes?",
+    "header": "Nodes",
+    "options": [
+        {"label": "Approve", "description": "Nodes look good, proceed to graph design"},
+        {"label": "Modify", "description": "I want to change the nodes"}
+    ],
+    "multiSelect": false
+}])
+```
+
+**WAIT for user response.**
+
+- If **Approve**: Proceed to STEP 5
+- If **Modify**: Ask what they want to change, update design, ask again
+
+---
+
+## STEP 5: Design Full Graph and Review
+
+**If starting from a template**, the edges are already loaded in the builder session. Render the existing graph as ASCII art and present it to the user for approval. Skip the edge design phase.
+
+**DETERMINE the edges** connecting the approved nodes. For each edge:
+
+- edge_id (kebab-case)
+- source → target
+- condition: `on_success`, `on_failure`, `always`, or `conditional`
+- condition_expr (Python expression, only if conditional)
+- priority (positive = forward, negative = feedback/loop-back)
+
+**RENDER the complete graph as ASCII art.** Make it large and clear — the user needs to see and understand the full workflow at a glance.
+
+**IMPORTANT: Make the ASCII art BIG and READABLE.** Use a box-and-arrow style with generous spacing. Do NOT make it tiny or compressed. Example format:
+
+```
+┌─────────────────────────────────────────────────────────────────────────────┐
+│                           AGENT: Research Agent                            │
+│                                                                            │
+│  Goal: Thoroughly research technical topics and produce verified reports   │
+└─────────────────────────────────────────────────────────────────────────────┘
+
+    ┌───────────────────────┐
+    │       INTAKE          │
+    │  (client-facing)      │
+    │                       │
+    │  in:  topic           │
+    │  out: research_brief  │
+    └───────────┬───────────┘
+                │ on_success
+                ▼
+    ┌───────────────────────┐
+    │      RESEARCH         │
+    │                       │
+    │  tools: web_search,   │
+    │         web_scrape    │
+    │                       │
+    │  in:  research_brief  │
+    │       [feedback]      │
+    │  out: findings,       │
+    │       sources         │
+    └───────────┬───────────┘
+                │ on_success
+                ▼
+    ┌───────────────────────┐
+    │       REVIEW          │
+    │  (client-facing)      │
+    │                       │
+    │  in:  findings,       │
+    │       sources         │
+    │  out: approved_findings│
+    │       OR feedback     │
+    └───────┬───────┬───────┘
+            │       │
+   approved │       │ feedback (priority: -1)
+            │       │
+            ▼       └──────────────────┐
+    ┌───────────────────────┐          │
+    │       REPORT          │          │
+    │                       │          │
+    │  tools: save_data     │          │
+    │                       │          │
+    │  in:  approved_       │          │
+    │       findings        │          │
+    │  out: final_report    │          │
+    └───────────────────────┘          │
+                                       │
+            ┌──────────────────────────┘
+            │ loops back to RESEARCH
+            ▼ (max_node_visits: 3)
+
+
+    EDGES:
+    ──────
+    1. intake → research         [on_success, priority: 1]
+    2. research → review         [on_success, priority: 1]
+    3. review → report           [conditional: approved_findings is not None, priority: 1]
+    4. review → research         [conditional: feedback is not None, priority: -1]
+```
+
+**PRESENT the graph and edges to the user:**
+
+> Here is the complete workflow graph:
+>
+> [ASCII art above]
+>
+> **Edge Summary:**
+>
+> | #   | Edge              | Condition                                    | Priority |
+> | --- | ----------------- | -------------------------------------------- | -------- |
+> | 1   | intake → research | on_success                                   | 1        |
+> | 2   | research → review | on_success                                   | 1        |
+> | 3   | review → report   | conditional: `approved_findings is not None` | 1        |
+> | 4   | review → research | conditional: `feedback is not None`          | -1       |
+
+**THEN call AskUserQuestion:**
+
+```
+AskUserQuestion(questions=[{
+    "question": "Do you approve this workflow graph?",
+    "header": "Graph",
+    "options": [
+        {"label": "Approve", "description": "Graph looks good, proceed to build the agent"},
+        {"label": "Modify", "description": "I want to change the graph"}
+    ],
+    "multiSelect": false
+}])
+```
+
+**WAIT for user response.**
+
+- If **Approve**: Proceed to STEP 6
+- If **Modify**: Ask what they want to change, update the graph, re-render, ask again
+
+---
+
+## STEP 6: Build the Agent
+
+**NOW — and only now — write the actual code.** The user has approved the goal, nodes, and graph.
+
+### 6a: Register nodes and edges with MCP
+**If starting from a template**, the copied files will be overwritten with the approved design. You MUST replace every occurrence of the old template name with the new agent name. Here is the complete checklist — miss NONE of these:
+
+| File | What to rename |
+|------|---------------|
+| `config.py` | `AgentMetadata.name` — the display name shown in TUI agent selection |
+| `config.py` | `AgentMetadata.description` — agent description |
+| `agent.py` | Module docstring (line 1) |
+| `agent.py` | `class OldNameAgent:` → `class NewNameAgent:` |
+| `agent.py` | `GraphSpec(id="old-name-graph")` → `GraphSpec(id="new-name-graph")` — shown in TUI status bar |
+| `agent.py` | Storage path: `Path.home() / ".hive" / "agents" / "old_name"` → `"new_name"` |
+| `__main__.py` | Module docstring (line 1) |
+| `__main__.py` | `from .agent import ... OldNameAgent` → `NewNameAgent` |
+| `__main__.py` | CLI help string in `def cli()` docstring |
+| `__main__.py` | All `OldNameAgent()` instantiations |
+| `__main__.py` | Storage path (duplicated from agent.py) |
+| `__main__.py` | Shell banner string (e.g. `"=== Old Name Agent ==="`) |
+| `__init__.py` | Package docstring |
+| `__init__.py` | `from .agent import OldNameAgent` import |
+| `__init__.py` | `__all__` list entry |
+
+**If starting from a template and no modifications were made in Steps 2-5**, the nodes and edges are already registered. Skip to validation (`mcp__agent-builder__validate_graph()`). If modifications were made, re-register the changed nodes/edges (the MCP tools handle duplicates by overwriting).
+
+**FOR EACH approved node**, call:
+
+```
+mcp__agent-builder__add_node(
+    node_id="...",
+    name="...",
+    description="...",
+    node_type="event_loop",
+    input_keys='["key1", "key2"]',
+    output_keys='["key1"]',
+    tools='["tool1"]',
+    system_prompt="...",
+    client_facing=True/False,
+    nullable_output_keys='["key"]',
+    max_node_visits=1
+)
+```
+
+**FOR EACH approved edge**, call:
+
+```
+mcp__agent-builder__add_edge(
+    edge_id="source-to-target",
+    source="source-node-id",
+    target="target-node-id",
+    condition="on_success",
+    condition_expr="",
+    priority=1
+)
+```
+
+**VALIDATE the graph:**
+
+```
+mcp__agent-builder__validate_graph()
+```
+
+- If invalid: Fix the issues and re-validate
+- If valid: Continue to 6b
+
+### 6b: Write Python package files
+
+**EXPORT the graph data:**
+
+```
+mcp__agent-builder__export_graph()
+```
+
+**THEN write the Python package files** using the exported data. Create these files in `exports/AGENT_NAME/`:
+
+1. `config.py` - Runtime configuration with model settings
+2. `nodes/__init__.py` - All NodeSpec definitions
+3. `agent.py` - Goal, edges, graph config, and agent class
+4. `__init__.py` - Package exports
+5. `__main__.py` - CLI interface
+6. `mcp_servers.json` - MCP server configurations
+7. `README.md` - Usage documentation
+
+**IMPORTANT entry_points format:**
+
+- MUST be: `{"start": "first-node-id"}`
+- NOT: `{"first-node-id": ["input_keys"]}` (WRONG)
+- NOT: `{"first-node-id"}` (WRONG - this is a set)
+
+**IMPORTANT mcp_servers.json format:**
+
+```json
+{
+  "hive-tools": {
+    "transport": "stdio",
+    "command": "uv",
+    "args": ["run", "python", "mcp_server.py", "--stdio"],
+    "cwd": "../../tools",
+    "description": "Hive tools MCP server"
+  }
+}
+```
+
+- NO `"mcpServers"` wrapper (that's Claude Desktop format, NOT hive format)
+- `cwd` MUST be `"../../tools"` (relative from `exports/AGENT_NAME/` to `tools/`)
+- `command` MUST be `"uv"` with `"args": ["run", "python", ...]` (NOT bare `"python"` which fails on Mac)
+
+**Use the example agent** at `.claude/skills/hive-create/examples/deep_research_agent/` as a template for file structure and patterns. It demonstrates: STEP 1/STEP 2 prompts, client-facing nodes, feedback loops, nullable_output_keys, and data tools.
+
+**AFTER writing all files, tell the user:**
+
+> Agent package created: `exports/AGENT_NAME/`
+>
+> **Files generated:**
+>
+> - `__init__.py` - Package exports
+> - `agent.py` - Goal, nodes, edges, agent class
+> - `config.py` - Runtime configuration
+> - `__main__.py` - CLI interface
+> - `nodes/__init__.py` - Node definitions
+> - `mcp_servers.json` - MCP server config
+> - `README.md` - Usage documentation
+
+---
+
+## STEP 7: Verify and Test
+
+**RUN validation:**
+
+```bash
+cd /home/timothy/oss/hive && PYTHONPATH=exports uv run python -m AGENT_NAME validate
+```
+
+- If valid: Agent is complete!
+- If errors: Fix the issues and re-run
+
+**TELL the user the agent is ready** and display the next steps box:
+
+```
+┌─────────────────────────────────────────────────────────────────────────────┐
+│                         ✅ AGENT BUILD COMPLETE                             │
+├─────────────────────────────────────────────────────────────────────────────┤
+│                                                                             │
+│  NEXT STEPS:                                                                │
+│                                                                             │
+│  1. SET UP CREDENTIALS (if agent uses tools like web_search, send_email):  │
+│                                                                             │
+│     /hive-credentials --agent AGENT_NAME                                    │
+│                                                                             │
+│  2. RUN YOUR AGENT:                                                         │
+│                                                                             │
+│     hive tui                                                                │
+│                                                                             │
+│     Then select your agent from the list and press Enter.                   │
+│                                                                             │
+│  3. DEBUG ANY ISSUES:                                                       │
+│                                                                             │
+│     /hive-debugger                                                          │
+│                                                                             │
+│     The debugger monitors runtime logs, identifies retry loops,             │
+│     tool failures, and missing outputs, and provides fix recommendations.  │
+│                                                                             │
+└─────────────────────────────────────────────────────────────────────────────┘
+```
+
+---
+
+## REFERENCE: Node Types
+
+| Type         | tools param             | Use when                                |
+| ------------ | ----------------------- | --------------------------------------- |
+| `event_loop` | `'["tool1"]'` or `'[]'` | LLM-powered work with or without tools  |
+| `function`   | N/A                     | Deterministic Python operations, no LLM |
+
+---
+
+## REFERENCE: NodeSpec Fields
+
+| Field                  | Default | Description                                                           |
+| ---------------------- | ------- | --------------------------------------------------------------------- |
+| `client_facing`        | `False` | Streams output to user, blocks for input between turns                |
+| `nullable_output_keys` | `[]`    | Output keys that may remain unset (mutually exclusive outputs)        |
+| `max_node_visits`      | `1`     | Max executions per run. Set >1 for feedback loop targets. 0=unlimited |
+
+---
+
+## REFERENCE: Edge Conditions & Priority
+
+| Condition     | When edge is followed                 |
+| ------------- | ------------------------------------- |
+| `on_success`  | Source node completed successfully    |
+| `on_failure`  | Source node failed                    |
+| `always`      | Always, regardless of success/failure |
+| `conditional` | When condition_expr evaluates to True |
+
+**Priority:** Positive = forward edge (evaluated first). Negative = feedback edge (loops back to earlier node). Multiple ON_SUCCESS edges from same source = parallel execution (fan-out).
+
+---
+
+## REFERENCE: System Prompt Best Practice
+
+For **internal** event_loop nodes (not client-facing), instruct the LLM to use `set_output`:
+
+```
+Use set_output(key, value) to store your results. For example:
+- set_output("search_results", <your results as a JSON string>)
+
+Do NOT return raw JSON. Use the set_output tool to produce outputs.
+```
+
+For **client-facing** event_loop nodes, use the STEP 1/STEP 2 pattern:
+
+```
+**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:**
+- set_output("key", "value based on user's response")
+```
+
+This prevents the LLM from calling `set_output` before the user has had a chance to respond. The "NO tool calls" instruction in STEP 1 ensures the node blocks for user input before proceeding.
+
+---
+
+## EventLoopNode Runtime
+
+EventLoopNodes are **auto-created** by `GraphExecutor` at runtime. Both direct `GraphExecutor` and `AgentRuntime` / `create_agent_runtime()` handle event_loop nodes automatically. No manual `node_registry` setup is needed.
+
+```python
+# Direct execution
+from framework.graph.executor import GraphExecutor
+from framework.runtime.core import Runtime
+
+storage_path = Path.home() / ".hive" / "agents" / "my_agent"
+storage_path.mkdir(parents=True, exist_ok=True)
+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)
+```
+
+**DO NOT pass `runtime=None` to `GraphExecutor`** — it will crash with `'NoneType' object has no attribute 'start_run'`.
+
+---
+
+## REFERENCE: Framework Capabilities for Qualification
+
+Use this reference during STEP 2 to give accurate, honest assessments.
+
+### What the Framework Does Well (The Good)
+
+| Capability | Description |
+|------------|-------------|
+| Multi-turn conversations | Client-facing nodes stream to users and block for input |
+| Human-in-the-loop review | Approval checkpoints with feedback loops back to earlier nodes |
+| Tool orchestration | LLM can call multiple tools, framework handles execution |
+| Structured outputs | `set_output` produces validated, typed outputs |
+| Parallel execution | Fan-out/fan-in for concurrent node execution |
+| Context management | Automatic compaction and spillover for large data |
+| Error recovery | Retry logic, judges, and feedback edges for self-correction |
+| Session persistence | State saved to disk, resumable sessions |
+
+### Framework Limitations (The Bad)
+
+| Limitation | Impact | Workaround |
+|------------|--------|------------|
+| LLM latency | 2-10+ seconds per turn | Not suitable for real-time/low-latency needs |
+| Context window limits | ~128K tokens max | Use data tools for spillover, design for chunking |
+| Cost per run | LLM API calls cost money | Budget planning, caching where possible |
+| Rate limits | API throttling on heavy usage | Backoff, queue management |
+| Node boundaries lose context | Outputs must be serialized | Prefer fewer, richer nodes |
+| Single-threaded within node | One LLM call at a time per node | Use fan-out for parallelism |
+
+### Not Designed For (The Ugly)
+
+| Use Case | Why It's Problematic | Alternative |
+|----------|---------------------|-------------|
+| Long-running daemons | Framework is request-response, not persistent | External scheduler + agent |
+| Sub-second responses | LLM latency is inherent | Traditional code, no LLM |
+| Processing millions of items | Context windows and rate limits | Batch processing + sampling |
+| Real-time streaming data | No built-in pub/sub or streaming input | Custom MCP server + agent |
+| Guaranteed determinism | LLM outputs vary | Function nodes for deterministic parts |
+| Offline/air-gapped | Requires LLM API access | Local models (not currently supported) |
+| Multi-user concurrency | Single-user session model | Separate agent instances per user |
+
+### Tool Availability Reality Check
+
+**Before promising any capability, check `list_mcp_tools()`.** Common gaps:
+
+- **Email**: May not have `send_email` — check before promising email automation
+- **Calendar**: May not have calendar APIs — check before promising scheduling
+- **Database**: May not have SQL tools — check before promising data queries
+- **File system**: Has data tools but not arbitrary filesystem access
+- **External APIs**: Depends entirely on what MCP servers are registered
+
+---
+
+## COMMON MISTAKES TO AVOID
+
+1. **Skipping use case qualification** - A responsible engineer qualifies the use case BEFORE building. Be transparent about what works, what doesn't, and what's problematic
+2. **Hiding limitations** - Don't oversell the framework. If a tool doesn't exist or a capability is missing, say so upfront
+3. **Using tools that don't exist** - Always check `mcp__agent-builder__list_mcp_tools()` first
+4. **Wrong entry_points format** - Must be `{"start": "node-id"}`, NOT a set or list
+5. **Skipping validation** - Always validate nodes and graph before proceeding
+6. **Not waiting for approval** - Always ask user before major steps
+7. **Displaying this file** - Execute the steps, don't show documentation
+8. **Too many thin nodes** - Prefer fewer, richer nodes (4 nodes > 8 nodes)
+9. **Missing STEP 1/STEP 2 in client-facing prompts** - Client-facing nodes need explicit phases to prevent premature set_output
+10. **Forgetting nullable_output_keys** - Mark input_keys that only arrive on certain edges (e.g., feedback) as nullable on the receiving node
+11. **Adding framework gating for LLM behavior** - Fix prompts or use judges, not ad-hoc code
+12. **Writing code before user approves the graph** - Always get approval on goal, nodes, and graph BEFORE writing any agent code
+13. **Wrong mcp_servers.json format** - Use flat format (no `"mcpServers"` wrapper), `cwd` must be `"../../tools"`, and `command` must be `"uv"` with args `["run", "python", ...]`

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

@@ -0,0 +1,24 @@
+"""
+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

@@ -0,0 +1,241 @@
+"""
+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

@@ -0,0 +1,311 @@
+"""Agent graph construction for Deep Research Agent."""
+
+from framework.graph import EdgeSpec, EdgeCondition, Goal, SuccessCriterion, Constraint
+from framework.graph.edge import GraphSpec
+from framework.graph.executor import ExecutionResult, GraphExecutor
+from framework.runtime.event_bus import EventBus
+from framework.runtime.core import Runtime
+from framework.llm import LiteLLMProvider
+from framework.runner.tool_registry import ToolRegistry
+
+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,
+    ),
+]
+
+# Graph configuration
+entry_node = "intake"
+entry_points = {"start": "intake"}
+pause_nodes = []
+terminal_nodes = ["report"]
+
+
+class DeepResearchAgent:
+    """
+    Deep Research Agent — 4-node pipeline with user checkpoints.
+
+    Flow: intake -> research -> review -> report
+                      ^           |
+                      +-- feedback loop (if user wants more)
+    """
+
+    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._executor: GraphExecutor | None = None
+        self._graph: GraphSpec | None = None
+        self._event_bus: EventBus | None = None
+        self._tool_registry: ToolRegistry | 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": 20,
+                "max_history_tokens": 32000,
+            },
+        )
+
+    def _setup(self, mock_mode=False) -> GraphExecutor:
+        """Set up the executor with all components."""
+        from pathlib import Path
+
+        storage_path = Path.home() / ".hive" / "agents" / "deep_research_agent"
+        storage_path.mkdir(parents=True, exist_ok=True)
+
+        self._event_bus = EventBus()
+        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()
+        runtime = Runtime(storage_path)
+
+        self._executor = GraphExecutor(
+            runtime=runtime,
+            llm=llm,
+            tools=tools,
+            tool_executor=tool_executor,
+            event_bus=self._event_bus,
+            storage_path=storage_path,
+            loop_config=self._graph.loop_config,
+        )
+
+        return self._executor
+
+    async def start(self, mock_mode=False) -> None:
+        """Set up the agent (initialize executor and tools)."""
+        if self._executor is None:
+            self._setup(mock_mode=mock_mode)
+
+    async def stop(self) -> None:
+        """Clean up resources."""
+        self._executor = None
+        self._event_bus = None
+
+    async def trigger_and_wait(
+        self,
+        entry_point: str,
+        input_data: dict,
+        timeout: float | None = None,
+        session_state: dict | None = None,
+    ) -> ExecutionResult | None:
+        """Execute the graph and wait for completion."""
+        if self._executor is None:
+            raise RuntimeError("Agent not started. Call start() first.")
+        if self._graph is None:
+            raise RuntimeError("Graph not built. Call start() first.")
+
+        return await self._executor.execute(
+            graph=self._graph,
+            goal=self.goal,
+            input_data=input_data,
+            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(
+                "start", 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

@@ -0,0 +1,21 @@
+"""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."
+    )
+
+
+metadata = AgentMetadata()

.claude/skills/hive-create/examples/deep_research_agent/mcp_servers.json

@@ -1,8 +1,8 @@
 {
   "hive-tools": {
     "transport": "stdio",
-    "command": "python",
-    "args": ["mcp_server.py", "--stdio"],
+    "command": "uv",
+    "args": ["run", "python", "mcp_server.py", "--stdio"],
     "cwd": "../../tools",
     "description": "Hive tools MCP server providing web_search, web_scrape, and write_to_file"
   }

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

@@ -0,0 +1,162 @@
+"""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,
+    input_keys=["topic"],
+    output_keys=["research_brief"],
+    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=3,
+    input_keys=["research_brief", "feedback"],
+    output_keys=["findings", "sources", "gaps"],
+    nullable_output_keys=["feedback"],
+    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 to manage context
+- 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)
+
+When done, use set_output:
+- 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", "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=3,
+    input_keys=["findings", "sources", "gaps", "research_brief"],
+    output_keys=["needs_more_research", "feedback"],
+    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,
+    input_keys=["findings", "sources", "research_brief"],
+    output_keys=["delivery_status"],
+    system_prompt="""\
+Write a comprehensive research report as an HTML file and present it to the user.
+
+**STEP 1 — Write the HTML report (tool calls, NO text to user yet):**
+
+1. Compose a complete, self-contained HTML document with embedded CSS styling.
+   Use a clean, readable design: max-width container, pleasant typography,
+   numbered citation links, a table of contents, and a references section.
+
+   Report structure inside the HTML:
+   - Title & date
+   - Executive Summary (2-3 paragraphs)
+   - Table of Contents
+   - Findings (organized by theme, with [n] citation links)
+   - Analysis (synthesis, implications, areas of debate)
+   - Conclusion (key takeaways, confidence assessment)
+   - 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
+   - Distinguish well-supported conclusions from speculation
+   - Answer the original research questions from the brief
+
+2. Save the HTML file:
+   save_data(filename="report.html", data=<your_html>)
+
+3. Get the clickable link:
+   serve_file_to_user(filename="report.html", label="Research Report")
+
+**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.
+
+**STEP 3 — After the user responds:**
+- Answer follow-up questions from the research material
+- When the user is satisfied: set_output("delivery_status", "completed")
+""",
+    tools=["save_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

@@ -1,10 +1,10 @@
 ---
-name: setup-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 encrypted credential store at ~/.hive/credentials.
+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: "2.1"
+  version: "2.3"
   type: utility
 ---
 
@@ -31,47 +31,50 @@ Determine which agent needs credentials. The user will either:
 
 Locate the agent's directory under `exports/{agent_name}/`.
 
-### Step 2: Detect Required Credentials
+### Step 2: Detect Missing Credentials
 
-Read the agent's configuration to determine which tools and node types it uses:
+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.
 
-```python
-from core.framework.runner import AgentRunner
-
-runner = AgentRunner.load("exports/{agent_name}")
-validation = runner.validate()
-
-# validation.missing_credentials contains env var names
-# validation.warnings contains detailed messages with help URLs
+```
+check_missing_credentials(agent_path="exports/{agent_name}")
 ```
 
-Alternatively, check the credential store directly:
+The tool returns a JSON response:
 
-```python
-from core.framework.credentials import CredentialStore
-
-# Use encrypted storage (default: ~/.hive/credentials)
-store = CredentialStore.with_encrypted_storage()
-
-# Check what's available
-available = store.list_credentials()
-print(f"Available credentials: {available}")
-
-# Check if specific credential exists
-if store.is_available("hubspot"):
-    print("HubSpot credential found")
-else:
-    print("HubSpot credential missing")
+```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
+}
 ```
 
-To see all known credential specs (for help URLs and setup instructions):
+**If `ready` is true (nothing missing):** Report all credentials as configured and skip Steps 3-5. Example:
 
-```python
-from aden_tools.credentials import CREDENTIAL_SPECS
-
-for name, spec in CREDENTIAL_SPECS.items():
-    print(f"{name}: env_var={spec.env_var}, aden={spec.aden_supported}")
 ```
+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
 
@@ -104,8 +107,8 @@ Present the available options using AskUserQuestion:
 ```
 Choose how to configure HUBSPOT_ACCESS_TOKEN:
 
-  1) Aden Authorization Server (Recommended)
-     Secure OAuth2 flow via integration.adenhq.com
+  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
 
@@ -114,7 +117,7 @@ Choose how to configure HUBSPOT_ACCESS_TOKEN:
      - Requires creating a HubSpot Private App
      - Full control over scopes and permissions
 
-  3) Custom Credential Store (Advanced)
+  3) Local Credential Setup (Advanced)
      Programmatic configuration for CI/CD
      - For automated deployments
      - Requires manual API calls
@@ -122,7 +125,23 @@ Choose how to configure HUBSPOT_ACCESS_TOKEN:
 
 ### Step 4: Execute Auth Flow Based on User Choice
 
-#### Option 1: Aden Authorization Server
+#### 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
+
+#### Option 1: Aden Platform (OAuth)
 
 This is the recommended flow for supported integrations (HubSpot, etc.).
 
@@ -147,7 +166,7 @@ If not set, guide user to get one from Aden (this is where they do OAuth):
 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://integration.adenhq.com/setup
+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.).")
@@ -174,7 +193,7 @@ shell_type = detect_shell()  # 'bash', 'zsh', or 'unknown'
 success, config_path = add_env_var_to_shell_config(
     "ADEN_API_KEY",
     user_provided_key,
-    comment="Aden authorization server API key"
+    comment="Aden Platform (OAuth) API key"
 )
 
 if success:
@@ -224,7 +243,7 @@ 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://integration.adenhq.com to connect HubSpot, then try again.")
+    print("Please visit https://hive.adenhq.com to connect HubSpot, then try again.")
 ```
 
 For more control over the sync process:
@@ -313,7 +332,7 @@ if not result.valid:
     # 2. Continue anyway (not recommended)
 ```
 
-**4.2d. Store in Encrypted Credential Store**
+**4.2d. Store in Local Encrypted Store**
 
 ```python
 from core.framework.credentials import CredentialStore, CredentialObject, CredentialKey
@@ -340,7 +359,7 @@ store.save_credential(cred)
 export HUBSPOT_ACCESS_TOKEN="the-value"
 ```
 
-#### Option 3: Custom Credential Store (Advanced)
+#### Option 3: Local Credential Setup (Advanced)
 
 For programmatic/CI/CD setups.
 
@@ -394,24 +413,38 @@ config_path.write_text(json.dumps(config, indent=2))
 
 ### Step 6: Verify All Credentials
 
-Run validation again to confirm everything is set:
+Use the `verify_credentials` MCP tool to confirm everything is properly configured:
 
-```python
-runner = AgentRunner.load("exports/{agent_name}")
-validation = runner.validate()
-assert not validation.missing_credentials, "Still missing credentials!"
+```
+verify_credentials(agent_path="exports/{agent_name}")
 ```
 
-Report the result to the user.
+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                    |
-| -------------- | --------------------------------------- | --------------------------------- |
-| `hubspot`      | `GET /crm/v3/objects/contacts?limit=1`  | Bearer token validity, CRM scopes |
-| `brave_search` | `GET /res/v1/web/search?q=test&count=1` | API key validity                  |
+| 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
@@ -424,7 +457,7 @@ result: HealthCheckResult = check_credential_health("hubspot", token_value)
 
 ## Encryption Key (HIVE_CREDENTIAL_KEY)
 
-The encrypted credential store requires `HIVE_CREDENTIAL_KEY` to encrypt/decrypt credentials.
+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` or a secrets manager)
@@ -443,7 +476,7 @@ If `HIVE_CREDENTIAL_KEY` is not set:
 - **NEVER** store credentials in plaintext files, git-tracked files, or agent configs
 - **NEVER** hardcode credentials in source code
 - **ALWAYS** use `SecretStr` from Pydantic when handling credential values in Python
-- **ALWAYS** use the encrypted credential store (`~/.hive/credentials`) for persistence
+- **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
@@ -456,7 +489,8 @@ All credential specs are defined in `tools/src/aden_tools/credentials/`:
 | ----------------- | ------------- | --------------------------------------------- | -------------- |
 | `llm.py`          | LLM Providers | `anthropic`                                   | No             |
 | `search.py`       | Search Tools  | `brave_search`, `google_search`, `google_cse` | No             |
-| `integrations.py` | Integrations  | `hubspot`                                     | Yes            |
+| `email.py`        | Email         | `resend`                                      | No             |
+| `integrations.py` | Integrations  | `github`, `hubspot`                           | 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.
@@ -507,66 +541,78 @@ token = store.get_key("hubspot", "access_token")
 ## Example Session
 
 ```
-User: /setup-credentials for my hubspot-agent
+User: /hive-credentials for my research-agent
 
-Agent: Let me check what credentials your hubspot-agent needs.
+Agent: Let me check what credentials your research-agent needs.
 
-[Runs validation, finds ANTHROPIC_API_KEY and HUBSPOT_ACCESS_TOKEN missing]
+[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: Your hubspot-agent requires 2 credentials:
+Agent: 2 of 4 required credentials are already configured. Only Google Custom
+Search needs setup (2 values).
 
-  1. ANTHROPIC_API_KEY - Anthropic API key for LLM calls
-  2. HUBSPOT_ACCESS_TOKEN - HubSpot access token
+--- Setting up Google Custom Search (google_search + google_cse) ---
 
-Let's set these up one at a time.
+This requires two values that work together.
 
---- Setting up ANTHROPIC_API_KEY ---
+[Checks HIVE_CREDENTIAL_KEY before storing]
+$ printenv HIVE_CREDENTIAL_KEY > /dev/null 2>&1 && echo "set" || echo "not set"
+set
 
-This credential only supports direct API key entry.
+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
 
-[Shows instructions for getting Anthropic API key]
-[AskUserQuestion: "Please provide your Anthropic API key:"]
-[User provides key]
-[Stores in credential store, exports to env]
-
---- Setting up HUBSPOT_ACCESS_TOKEN ---
-
-[AskUserQuestion with 3 options]
-
-Choose how to configure HUBSPOT_ACCESS_TOKEN:
-  1) Aden Authorization Server (Recommended)
-  2) Direct API Key
-  3) Custom Credential Store (Advanced)
-
-User: 1 (Aden)
-
-Agent: Great! Let me check if you have an Aden API key configured...
-
-[Checks for ADEN_API_KEY, not found]
-[Opens browser to integration.adenhq.com/setup]
-
-Agent: I've opened Aden's setup page. Please:
-  1. Sign in or create an account
-  2. Connect your HubSpot account (OAuth happens on Aden's site)
-  3. Copy the API key shown after setup
-
-[AskUserQuestion: "Please provide your Aden API key:"]
+[AskUserQuestion: "Please provide your Google API key:"]
 [User provides key]
 
-Agent: Would you like me to save this to your shell config (~/.zshrc) for future sessions?
+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)
 
-[User approves]
-[Saves to ~/.zshrc and ~/.hive/configuration.json]
+[AskUserQuestion: "Please provide your Google CSE ID:"]
+[User provides ID]
 
-Agent: Let me sync your credentials from Aden...
+[Runs health check with both values - GET /customsearch/v1?q=test&num=1 → 200 OK]
+[Stores both in local encrypted store, exports to env]
 
-[Syncs credentials from Aden server - OAuth already done on Aden's side]
-[Runs health check]
+✓ Google Custom Search credentials valid
 
-Agent: HubSpot credentials validated successfully!
+[Calls verify_credentials(agent_path="exports/research-agent")]
+→ Returns: ready: true, missing_credentials: []
 
 All credentials are now configured:
-  - ANTHROPIC_API_KEY: Stored in encrypted credential store
-  - HUBSPOT_ACCESS_TOKEN: Synced from Aden (OAuth completed on Aden)
-  - Validation passed - your agent is ready to run!
+  ✓ 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                              │
+├─────────────────────────────────────────────────────────────────────────────┤
+│                                                                             │
+│  NEXT STEPS:                                                                │
+│                                                                             │
+│  1. RUN YOUR AGENT:                                                         │
+│                                                                             │
+│     PYTHONPATH=core:exports python -m research-agent 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-debugger/SKILL.md

@@ -0,0 +1,933 @@
+---
+name: hive-debugger
+type: utility
+description: Interactive debugging companion for Hive agents - identifies runtime issues and proposes solutions
+version: 1.0.0
+requires:
+  - hive-concepts
+tags:
+  - debugging
+  - runtime-logs
+  - agent-development
+---
+
+# Hive Debugger
+
+An interactive debugging companion that helps developers identify and fix runtime issues in Hive agents. The debugger analyzes runtime logs at three levels (L1/L2/L3), categorizes issues, and provides actionable fix recommendations.
+
+## When to Use This Skill
+
+Use `/hive-debugger` when:
+- Your agent is failing or producing unexpected results
+- You need to understand why a specific node is retrying repeatedly
+- Tool calls are failing and you need to identify the root cause
+- Agent execution is stalled or taking too long
+- You want to monitor agent behavior in real-time during development
+
+This skill works alongside agents running in TUI mode and provides supervisor-level insights into execution behavior.
+
+---
+
+## Prerequisites
+
+Before using this skill, ensure:
+1. You have an exported agent in `exports/{agent_name}/`
+2. The agent has been run at least once (logs exist)
+3. Runtime logging is enabled (default in Hive framework)
+4. You have access to the agent's working directory at `~/.hive/agents/{agent_name}/`
+
+---
+
+## Workflow
+
+### Stage 1: Setup & Context Gathering
+
+**Objective:** Understand the agent being debugged
+
+**What to do:**
+
+1. **Ask the developer which agent needs debugging:**
+   - Get agent name (e.g., "twitter_outreach", "deep_research_agent")
+   - Confirm the agent exists in `exports/{agent_name}/`
+
+2. **Determine agent working directory:**
+   - Calculate: `~/.hive/agents/{agent_name}/`
+   - Verify this directory exists and contains session logs
+
+3. **Read agent configuration:**
+   - Read file: `exports/{agent_name}/agent.json`
+   - Extract goal information from the JSON:
+     - `goal.id` - The goal identifier
+     - `goal.success_criteria` - What success looks like
+     - `goal.constraints` - Rules the agent must follow
+   - Extract graph information:
+     - List of node IDs from `graph.nodes`
+     - List of edges from `graph.edges`
+
+4. **Store context for the debugging session:**
+   - agent_name
+   - agent_work_dir (e.g., `/home/user/.hive/twitter_outreach`)
+   - goal_id
+   - success_criteria
+   - constraints
+   - node_ids
+
+**Example:**
+```
+Developer: "My twitter_outreach agent keeps failing"
+
+You: "I'll help debug the twitter_outreach agent. Let me gather context..."
+
+[Read exports/twitter_outreach/agent.json]
+
+Context gathered:
+- Agent: twitter_outreach
+- Goal: twitter-outreach-multi-loop
+- Working Directory: /home/user/.hive/twitter_outreach
+- Success Criteria: ["Successfully send 5 personalized outreach messages"]
+- Constraints: ["Must verify handle exists", "Must personalize message"]
+- Nodes: ["intake-collector", "profile-analyzer", "message-composer", "outreach-sender"]
+```
+
+---
+
+### Stage 2: Mode Selection
+
+**Objective:** Choose the debugging approach that best fits the situation
+
+**What to do:**
+
+Ask the developer which debugging mode they want to use. Use AskUserQuestion with these options:
+
+1. **Real-time Monitoring Mode**
+   - Description: Monitor active TUI session continuously, poll logs every 5-10 seconds, alert on new issues immediately
+   - Best for: Live debugging sessions where you want to catch issues as they happen
+   - Note: Requires agent to be currently running
+
+2. **Post-Mortem Analysis Mode**
+   - Description: Analyze completed or failed runs in detail, deep dive into specific session
+   - Best for: Understanding why a past execution failed
+   - Note: Most common mode for debugging
+
+3. **Historical Trends Mode**
+   - Description: Analyze patterns across multiple runs, identify recurring issues
+   - Best for: Finding systemic problems that happen repeatedly
+   - Note: Useful for agents that have run many times
+
+**Implementation:**
+```
+Use AskUserQuestion to present these options and let the developer choose.
+Store the selected mode for the session.
+```
+
+---
+
+### Stage 3: Triage (L1 Analysis)
+
+**Objective:** Identify which sessions need attention
+
+**What to do:**
+
+1. **Query high-level run summaries** using the MCP tool:
+   ```
+   query_runtime_logs(
+       agent_work_dir="{agent_work_dir}",
+       status="needs_attention",
+       limit=20
+   )
+   ```
+
+2. **Analyze the results:**
+   - Look for runs with `needs_attention: true`
+   - Check `attention_summary.categories` for issue types
+   - Note the `run_id` of problematic sessions
+   - Check `status` field: "degraded", "failure", "in_progress"
+
+3. **Attention flag triggers to understand:**
+   From runtime_logger.py, runs are flagged when:
+   - retry_count > 3
+   - escalate_count > 2
+   - latency_ms > 60000
+   - tokens_used > 100000
+   - total_steps > 20
+
+4. **Present findings to developer:**
+   - Summarize how many runs need attention
+   - List the most recent problematic runs
+   - Show attention categories for each
+   - Ask which run they want to investigate (if multiple)
+
+**Example Output:**
+```
+Found 2 runs needing attention:
+
+1. session_20260206_115718_e22339c5 (30 minutes ago)
+   Status: degraded
+   Categories: missing_outputs, retry_loops
+
+2. session_20260206_103422_9f8d1b2a (2 hours ago)
+   Status: failure
+   Categories: tool_failures, high_latency
+
+Which run would you like to investigate?
+```
+
+---
+
+### Stage 4: Diagnosis (L2 Analysis)
+
+**Objective:** Identify which nodes failed and what patterns exist
+
+**What to do:**
+
+1. **Query per-node details** using the MCP tool:
+   ```
+   query_runtime_log_details(
+       agent_work_dir="{agent_work_dir}",
+       run_id="{selected_run_id}",
+       needs_attention_only=True
+   )
+   ```
+
+2. **Categorize issues** using the Issue Taxonomy:
+
+   **10 Issue Categories:**
+
+   | Category | Detection Pattern | Meaning |
+   |----------|------------------|---------|
+   | **Missing Outputs** | `exit_status != "success"`, `attention_reasons` contains "missing_outputs" | Node didn't call set_output with required keys |
+   | **Tool Errors** | `tool_error_count > 0`, `attention_reasons` contains "tool_failures" | Tool calls failed (API errors, timeouts, auth issues) |
+   | **Retry Loops** | `retry_count > 3`, `verdict_counts.RETRY > 5` | Judge repeatedly rejecting outputs |
+   | **Guard Failures** | `guard_reject_count > 0` | Output validation failed (wrong types, missing keys) |
+   | **Stalled Execution** | `total_steps > 20`, `verdict_counts.CONTINUE > 10` | EventLoopNode not making progress |
+   | **High Latency** | `latency_ms > 60000`, `avg_step_latency > 5000` | Slow tool calls or LLM responses |
+   | **Client-Facing Issues** | `client_input_requested` but no `user_input_received` | Premature set_output before user input |
+   | **Edge Routing Errors** | `exit_status == "no_valid_edge"`, `attention_reasons` contains "routing_issue" | No edges match current state |
+   | **Memory/Context Issues** | `tokens_used > 100000`, `context_overflow_count > 0` | Conversation history too long |
+   | **Constraint Violations** | Compare output against goal constraints | Agent violated goal-level rules |
+
+3. **Analyze each flagged node:**
+   - Node ID and name
+   - Exit status
+   - Retry count
+   - Verdict distribution (ACCEPT/RETRY/ESCALATE/CONTINUE)
+   - Attention reasons
+   - Total steps executed
+
+4. **Present diagnosis to developer:**
+   - List problematic nodes
+   - Categorize each issue
+   - Highlight the most severe problems
+   - Show evidence (retry counts, error types)
+
+**Example Output:**
+```
+Diagnosis for session_20260206_115718_e22339c5:
+
+Problem Node: intake-collector
+├─ Exit Status: escalate
+├─ Retry Count: 5 (HIGH)
+├─ Verdict Counts: {RETRY: 5, ESCALATE: 1}
+├─ Attention Reasons: ["high_retry_count", "missing_outputs"]
+├─ Total Steps: 8
+└─ Categories: Missing Outputs + Retry Loops
+
+Root Issue: The intake-collector node is stuck in a retry loop because it's not setting required outputs.
+```
+
+---
+
+### Stage 5: Root Cause Analysis (L3 Analysis)
+
+**Objective:** Understand exactly what went wrong by examining detailed logs
+
+**What to do:**
+
+1. **Query detailed tool/LLM logs** using the MCP tool:
+   ```
+   query_runtime_log_raw(
+       agent_work_dir="{agent_work_dir}",
+       run_id="{run_id}",
+       node_id="{problem_node_id}"
+   )
+   ```
+
+2. **Analyze based on issue category:**
+
+   **For Missing Outputs:**
+   - Check `step.tool_calls` for set_output usage
+   - Look for conditional logic that skipped set_output
+   - Check if LLM is calling other tools instead
+
+   **For Tool Errors:**
+   - Check `step.tool_results` for error messages
+   - Identify error types: rate limits, auth failures, timeouts, network errors
+   - Note which specific tool is failing
+
+   **For Retry Loops:**
+   - Check `step.verdict_feedback` from judge
+   - Look for repeated failure reasons
+   - Identify if it's the same issue every time
+
+   **For Guard Failures:**
+   - Check `step.guard_results` for validation errors
+   - Identify missing keys or type mismatches
+   - Compare actual output to expected schema
+
+   **For Stalled Execution:**
+   - Check `step.llm_response_text` for repetition
+   - Look for LLM stuck in same action loop
+   - Check if tool calls are succeeding but not progressing
+
+3. **Extract evidence:**
+   - Specific error messages
+   - Tool call arguments and results
+   - LLM response text
+   - Judge feedback
+   - Step-by-step progression
+
+4. **Formulate root cause explanation:**
+   - Clearly state what is happening
+   - Explain why it's happening
+   - Show evidence from logs
+
+**Example Output:**
+```
+Root Cause Analysis for intake-collector:
+
+Step-by-step breakdown:
+
+Step 3:
+- Tool Call: web_search(query="@RomuloNevesOf")
+- Result: Found Twitter profile information
+- Verdict: RETRY
+- Feedback: "Missing required output 'twitter_handles'. You found the handle but didn't call set_output."
+
+Step 4:
+- Tool Call: web_search(query="@RomuloNevesOf twitter")
+- Result: Found additional Twitter information
+- Verdict: RETRY
+- Feedback: "Still missing 'twitter_handles'. Use set_output to save your findings."
+
+Steps 5-7: Similar pattern continues...
+
+ROOT CAUSE: The node is successfully finding Twitter handles via web_search, but the LLM is not calling set_output to save the results. It keeps searching for more information instead of completing the task.
+```
+
+---
+
+### Stage 6: Fix Recommendations
+
+**Objective:** Provide actionable solutions the developer can implement
+
+**What to do:**
+
+Based on the issue category identified, provide specific fix recommendations using these templates:
+
+#### Template 1: Missing Outputs (Client-Facing Nodes)
+
+```markdown
+## Issue: Premature set_output in Client-Facing Node
+
+**Root Cause:** Node called set_output before receiving user input
+
+**Fix:** Use STEP 1/STEP 2 prompt pattern
+
+**File to edit:** `exports/{agent_name}/nodes/{node_name}.py`
+
+**Changes:**
+1. Update the system_prompt to include explicit step guidance:
+   ```python
+   system_prompt = """
+   STEP 1: Analyze the user input and decide what action to take.
+   DO NOT call set_output in this step.
+
+   STEP 2: After receiving feedback or completing analysis,
+   ONLY THEN call set_output with your results.
+   """
+   ```
+
+2. If some inputs are optional (like feedback on retry edges), add nullable_output_keys:
+   ```python
+   nullable_output_keys=["feedback"]
+   ```
+
+**Verification:**
+- Run the agent with test input
+- Verify the client-facing node waits for user input before calling set_output
+```
+
+#### Template 2: Retry Loops
+
+```markdown
+## Issue: Judge Repeatedly Rejecting Outputs
+
+**Root Cause:** {Insert specific reason from verdict_feedback}
+
+**Fix Options:**
+
+**Option A - If outputs are actually correct:** Adjust judge evaluation rules
+- File: `exports/{agent_name}/agent.json`
+- Update `evaluation_rules` section to accept the current output format
+- Example: If judge expects list but gets string, update rule to accept both
+
+**Option B - If prompt is ambiguous:** Clarify node instructions
+- File: `exports/{agent_name}/nodes/{node_name}.py`
+- Make system_prompt more explicit about output format and requirements
+- Add examples of correct outputs
+
+**Option C - If tool is unreliable:** Add retry logic with fallback
+- Consider using alternative tools
+- Add manual fallback option
+- Update prompt to handle tool failures gracefully
+
+**Verification:**
+- Run the node with test input
+- Confirm judge accepts output on first try
+- Check that retry_count stays at 0
+```
+
+#### Template 3: Tool Errors
+
+```markdown
+## Issue: {tool_name} Failing with {error_type}
+
+**Root Cause:** {Insert specific error message from logs}
+
+**Fix Strategy:**
+
+**If API rate limit:**
+1. Add exponential backoff in tool retry logic
+2. Reduce API call frequency
+3. Consider caching results
+
+**If auth failure:**
+1. Check credentials using:
+   ```bash
+   /hive-credentials --agent {agent_name}
+   ```
+2. Verify API key environment variables
+3. Update `mcp_servers.json` if needed
+
+**If timeout:**
+1. Increase timeout in `mcp_servers.json`:
+   ```json
+   {
+     "timeout_ms": 60000
+   }
+   ```
+2. Consider using faster alternative tools
+3. Break large requests into smaller chunks
+
+**Verification:**
+- Test tool call manually
+- Confirm successful response
+- Monitor for recurring errors
+```
+
+#### Template 4: Edge Routing Errors
+
+```markdown
+## Issue: No Valid Edge from Node {node_id}
+
+**Root Cause:** No edge condition matched the current state
+
+**File to edit:** `exports/{agent_name}/agent.json`
+
+**Analysis:**
+- Current node output: {show actual output keys}
+- Existing edge conditions: {list edge conditions}
+- Why no match: {explain the mismatch}
+
+**Fix:**
+Add the missing edge to the graph:
+```json
+{
+  "edge_id": "{node_id}_to_{target_node}",
+  "source": "{node_id}",
+  "target": "{target_node}",
+  "condition": "on_success"
+}
+```
+
+**Alternative:** Update existing edge condition to cover this case
+
+**Verification:**
+- Run agent with same input
+- Verify edge is traversed successfully
+- Check that execution continues to next node
+```
+
+#### Template 5: Stalled Execution
+
+```markdown
+## Issue: EventLoopNode Not Making Progress
+
+**Root Cause:** {Insert analysis - e.g., "LLM repeating same failed action"}
+
+**File to edit:** `exports/{agent_name}/nodes/{node_name}.py`
+
+**Fix:** Update system_prompt to guide LLM out of loops
+
+**Add this guidance:**
+```python
+system_prompt = """
+{existing prompt}
+
+IMPORTANT: If a tool call fails multiple times:
+1. Try an alternative approach or different tool
+2. If no alternatives work, call set_output with partial results
+3. DO NOT retry the same failed action more than 3 times
+
+Progress is more important than perfection. Move forward even with incomplete data.
+"""
+```
+
+**Additional fix:** Lower max_iterations to prevent infinite loops
+```python
+# In node configuration
+max_node_visits=3  # Prevent getting stuck
+```
+
+**Verification:**
+- Run node with same input that caused stall
+- Verify it exits after reasonable attempts (< 10 steps)
+- Confirm it calls set_output eventually
+```
+
+#### Template 6: Checkpoint Recovery (Post-Fix Resume)
+
+```markdown
+## Recovery Strategy: Resume from Last Clean Checkpoint
+
+**Situation:** You've fixed the issue, but the failed session is stuck mid-execution
+
+**Solution:** Resume execution from a checkpoint before the failure
+
+### Option A: Auto-Resume from Latest Checkpoint (Recommended)
+
+Use CLI arguments to auto-resume when launching TUI:
+
+```bash
+PYTHONPATH=core:exports python -m {agent_name} --tui \
+    --resume-session {session_id}
+```
+
+This will:
+- Load session state from `state.json`
+- Continue from where it paused/failed
+- Apply your fixes immediately
+
+### Option B: Resume from Specific Checkpoint (Time-Travel)
+
+If you need to go back to an earlier point:
+
+```bash
+PYTHONPATH=core:exports python -m {agent_name} --tui \
+    --resume-session {session_id} \
+    --checkpoint {checkpoint_id}
+```
+
+Example:
+```bash
+PYTHONPATH=core:exports python -m deep_research_agent --tui \
+    --resume-session session_20260208_143022_abc12345 \
+    --checkpoint cp_node_complete_intake_143030
+```
+
+### Option C: Use TUI Commands
+
+Alternatively, launch TUI normally and use commands:
+
+```bash
+# Launch TUI
+PYTHONPATH=core:exports python -m {agent_name} --tui
+
+# In TUI, use commands:
+/resume {session_id}                    # Resume from session state
+/recover {session_id} {checkpoint_id}   # Recover from specific checkpoint
+```
+
+### When to Use Each Option:
+
+**Use `/resume` (or --resume-session) when:**
+- You fixed credentials and want to retry
+- Agent paused and you want to continue
+- Agent failed and you want to retry from last state
+
+**Use `/recover` (or --resume-session + --checkpoint) when:**
+- You need to go back to an earlier checkpoint
+- You want to try a different path from a specific point
+- Debugging requires time-travel to earlier state
+
+### Find Available Checkpoints:
+
+```bash
+# In TUI:
+/sessions {session_id}
+
+# This shows all checkpoints with timestamps:
+Available Checkpoints: (3)
+  1. cp_node_complete_intake_143030
+  2. cp_node_complete_research_143115
+  3. cp_pause_research_143130
+```
+
+**Verification:**
+- Use `--resume-session` to test your fix immediately
+- No need to re-run from the beginning
+- Session continues with your code changes applied
+```
+
+**Selecting the right template:**
+- Match the issue category from Stage 4
+- Customize with specific details from Stage 5
+- Include actual error messages and code snippets
+- Provide file paths and line numbers when possible
+- **Always include recovery commands** (Template 6) after providing fix recommendations
+
+---
+
+### Stage 7: Verification Support
+
+**Objective:** Help the developer confirm their fixes work
+
+**What to do:**
+
+1. **Suggest appropriate tests based on fix type:**
+
+   **For node-level fixes:**
+   ```bash
+   # Use hive-test to run goal-based tests
+   /hive-test --agent {agent_name} --goal {goal_id}
+
+   # Or run specific test scenarios
+   /hive-test --agent {agent_name} --scenario {specific_input}
+   ```
+
+   **For quick manual tests:**
+   ```bash
+   # Launch the interactive TUI dashboard
+   hive tui
+   ```
+   Then use arrow keys to select the agent from the list and press Enter to run it.
+
+2. **Provide MCP tool queries to validate the fix:**
+
+   **Check if issue is resolved:**
+   ```
+   query_runtime_logs(
+       agent_work_dir="~/.hive/agents/{agent_name}",
+       status="needs_attention",
+       limit=5
+   )
+   # Should show 0 results if fully fixed
+   ```
+
+   **Verify specific node behavior:**
+   ```
+   query_runtime_log_details(
+       agent_work_dir="~/.hive/agents/{agent_name}",
+       run_id="{new_run_id}",
+       node_id="{fixed_node_id}"
+   )
+   # Should show exit_status="success", retry_count=0
+   ```
+
+3. **Monitor for regression:**
+   - Run the agent multiple times
+   - Check for similar issues reappearing
+   - Verify fix works across different inputs
+
+4. **Provide verification checklist:**
+   ```
+   Verification Checklist:
+   □ Applied recommended fix to code
+   □ Ran agent with test input
+   □ Checked runtime logs show no attention flags
+   □ Verified specific node completes successfully
+   □ Tested with multiple inputs
+   □ No regression of original issue
+   □ Agent meets success criteria
+   ```
+
+**Example interaction:**
+```
+Developer: "I applied the fix to intake-collector. How do I verify it works?"
+
+You: "Great! Let's verify the fix with these steps:
+
+1. Launch the TUI dashboard:
+   hive tui
+   Then select your agent from the list and press Enter to run it.
+
+2. After it completes, check the logs:
+   [Use query_runtime_logs to check for attention flags]
+
+3. Verify the specific node:
+   [Use query_runtime_log_details for intake-collector]
+
+Expected results:
+- No 'needs_attention' flags
+- intake-collector shows exit_status='success'
+- retry_count should be 0
+
+Let me know when you've run it and I'll help check the logs!"
+```
+
+---
+
+## MCP Tool Usage Guide
+
+### Three Levels of Observability
+
+**L1: query_runtime_logs** - Session-level summaries
+- **When to use:** Initial triage, identifying problematic runs, monitoring trends
+- **Returns:** List of runs with status, attention flags, timestamps
+- **Example:**
+  ```
+  query_runtime_logs(
+      agent_work_dir="/home/user/.hive/twitter_outreach",
+      status="needs_attention",
+      limit=20
+  )
+  ```
+
+**L2: query_runtime_log_details** - Node-level details
+- **When to use:** Diagnosing which nodes failed, understanding retry patterns
+- **Returns:** Per-node completion details, retry counts, verdicts
+- **Example:**
+  ```
+  query_runtime_log_details(
+      agent_work_dir="/home/user/.hive/twitter_outreach",
+      run_id="session_20260206_115718_e22339c5",
+      needs_attention_only=True
+  )
+  ```
+
+**L3: query_runtime_log_raw** - Step-level details
+- **When to use:** Root cause analysis, understanding exact failures
+- **Returns:** Full tool calls, LLM responses, judge feedback
+- **Example:**
+  ```
+  query_runtime_log_raw(
+      agent_work_dir="/home/user/.hive/twitter_outreach",
+      run_id="session_20260206_115718_e22339c5",
+      node_id="intake-collector"
+  )
+  ```
+
+### Query Patterns
+
+**Pattern 1: Top-Down Investigation** (Most common)
+```
+1. L1: Find problematic runs
+2. L2: Identify failing nodes
+3. L3: Analyze specific failures
+```
+
+**Pattern 2: Node-Specific Debugging**
+```
+1. L2: Get details for specific node across all runs
+2. L3: Deep dive into worst failures
+```
+
+**Pattern 3: Real-time Monitoring**
+```
+Loop every 10 seconds:
+  1. L1: Check for new needs_attention runs
+  2. If found: Alert and drill into L2
+```
+
+---
+
+## Complete Example Walkthrough
+
+### Scenario: twitter_outreach Agent Failing
+
+**Developer says:** "My twitter_outreach agent keeps failing. It's not finding Twitter handles correctly."
+
+#### Stage 1: Setup
+```
+You: "I'll help debug the twitter_outreach agent. Let me gather context..."
+
+[Read exports/twitter_outreach/agent.json]
+
+Context:
+- Agent: twitter_outreach
+- Goal: twitter-outreach-multi-loop
+- Working Dir: ~/.hive/agents/twitter_outreach
+- Success Criteria: ["Successfully send 5 personalized outreach messages"]
+- Constraints: ["Must verify handle exists", "Must personalize message"]
+- Nodes: intake-collector, profile-analyzer, message-composer, outreach-sender
+```
+
+#### Stage 2: Mode Selection
+```
+You: "Which debugging approach would you prefer?"
+
+[Present 3 mode options using AskUserQuestion]
+
+Developer selects: "Post-Mortem Analysis Mode"
+```
+
+#### Stage 3: Triage (L1)
+```
+You: "Querying runtime logs for problematic runs..."
+
+[Call query_runtime_logs with status="needs_attention"]
+
+Results:
+- Found 1 run needing attention
+- session_20260206_115718_e22339c5 (30 min ago)
+- Status: degraded
+- Categories: missing_outputs, retry_loops
+```
+
+#### Stage 4: Diagnosis (L2)
+```
+You: "Analyzing node-level details..."
+
+[Call query_runtime_log_details for the session]
+
+Results:
+- Problem Node: intake-collector
+- Exit Status: escalate
+- Retry Count: 5
+- Verdict Counts: {RETRY: 5, ESCALATE: 1}
+- Attention Reasons: ["high_retry_count", "missing_outputs"]
+- Total Steps: 8
+
+Diagnosis: intake-collector has retry loop + missing outputs issue
+Category: Missing Outputs + Retry Loops
+```
+
+#### Stage 5: Root Cause (L3)
+```
+You: "Drilling into detailed logs for intake-collector..."
+
+[Call query_runtime_log_raw for node intake-collector]
+
+Evidence:
+Step 3: web_search("@RomuloNevesOf") → Found profile
+  Verdict: RETRY
+  Feedback: "Missing required output 'twitter_handles'. You found the handle but didn't call set_output."
+
+Step 4: web_search("@RomuloNevesOf twitter") → Found more info
+  Verdict: RETRY
+  Feedback: "Still missing 'twitter_handles'. Use set_output to save your findings."
+
+Steps 5-7: Same pattern repeats...
+
+ROOT CAUSE: Node successfully finds handles but LLM doesn't call set_output. It keeps searching instead of completing.
+```
+
+#### Stage 6: Fix Recommendation
+```
+You: "Here's the fix for this issue:
+
+## Issue: intake-collector Not Setting Outputs
+
+**Root Cause:** Node successfully finds Twitter handles but doesn't call set_output
+
+**Category:** Missing Outputs
+
+**File to edit:** exports/twitter_outreach/nodes/intake_collector.py
+
+**Changes:**
+Update the system_prompt to emphasize the output requirement:
+
+```python
+system_prompt = """
+You are the Intake Collector for the Twitter outreach agent.
+
+Your task:
+1. Extract Twitter handle from user input (URL or @mention)
+2. Verify it's a valid Twitter handle format
+3. **CRITICAL: Call set_output(twitter_handles=[handle]) immediately after finding valid handle**
+
+IMPORTANT: You MUST call set_output even if you found the handle on the first try.
+Do not continue searching once you have a valid handle - set output and finish.
+
+Output format:
+{
+  "twitter_handles": ["@handle1", "@handle2", ...]
+}
+"""
+```
+
+**Why this works:** The explicit instruction to call set_output immediately prevents the LLM from continuing to search after finding the handle.
+```
+
+#### Stage 7: Verification
+```
+Developer: "I updated the prompt. How do I test it?"
+
+You: "Let's verify the fix:
+
+1. Launch the TUI dashboard:
+   hive tui
+   Then select your agent and press Enter to run it.
+
+2. I'll check the logs after it runs..."
+
+[Developer runs agent]
+
+Developer: "It completed! Can you check if it worked?"
+
+You: "Checking now..."
+
+[Call query_runtime_log_details for the new run]
+
+Results:
+- Node: intake-collector
+- Exit Status: success ✓
+- Retry Count: 0 ✓
+- Total Steps: 2 ✓
+- No attention flags ✓
+
+"Perfect! The fix worked. The intake-collector now:
+- Completes successfully on first try
+- No retries needed
+- Calls set_output properly
+
+Your agent should now work correctly!"
+```
+
+---
+
+## Tips for Effective Debugging
+
+1. **Always start with L1 logs** - Don't jump straight to detailed logs
+2. **Focus on attention flags** - They highlight the real issues
+3. **Compare verdict_feedback across steps** - Patterns reveal root causes
+4. **Check tool error messages carefully** - They often contain the exact problem
+5. **Consider the agent's goal** - Fixes should align with success criteria
+6. **Test fixes immediately** - Quick verification prevents wasted effort
+7. **Look for patterns across multiple runs** - One-time failures might be transient
+
+## Common Pitfalls to Avoid
+
+1. **Don't recommend code you haven't verified exists** - Always read files first
+2. **Don't assume tool capabilities** - Check MCP server configs
+3. **Don't ignore edge conditions** - Missing edges cause routing failures
+4. **Don't overlook judge configuration** - Mismatched expectations cause retry loops
+5. **Don't forget nullable_output_keys** - Optional inputs need explicit marking
+
+---
+
+## Storage Locations Reference
+
+**New unified storage (default):**
+- Logs: `~/.hive/agents/{agent_name}/sessions/session_YYYYMMDD_HHMMSS_{uuid}/logs/`
+- State: `~/.hive/agents/{agent_name}/sessions/{session_id}/state.json`
+- Conversations: `~/.hive/agents/{agent_name}/sessions/{session_id}/conversations/`
+
+**Old storage (deprecated, still supported):**
+- Logs: `~/.hive/agents/{agent_name}/runtime_logs/runs/{run_id}/`
+
+The MCP tools automatically check both locations.
+
+---
+
+**Remember:** Your role is to be a debugging companion and thought partner. Guide the developer through the investigation, explain what you find, and provide actionable fixes. Don't just report errors - help understand and solve them.

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

@@ -0,0 +1,385 @@
+---
+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

@@ -1,11 +1,11 @@
 ---
-name: testing-agent
+name: hive-test
 description: Run goal-based evaluation tests for agents. Use when you need to verify an agent meets its goals, debug failing tests, or iterate on agent improvements based on test results.
 ---
 
 # Testing Workflow
 
-This skill provides tools for testing agents built with the building-agents skill.
+This skill provides tools for testing agents built with the hive-create skill.
 
 ## Workflow Overview
 
@@ -61,7 +61,7 @@ mcp__agent-builder__debug_test(
 
 # Testing Agents with MCP Tools
 
-Run goal-based evaluation tests for agents built with the building-agents skill.
+Run goal-based evaluation tests for agents built with the hive-create skill.
 
 **Key Principle: MCP tools provide guidelines, Claude writes tests directly**
 - ✅ Get guidelines: `generate_constraint_tests`, `generate_success_tests` → returns templates and guidelines
@@ -279,7 +279,7 @@ if missing_creds:
 ```
 ┌─────────────────────────────────────────────────────────────────────────┐
 │                           GOAL STAGE                                     │
-│  (building-agents skill)                                                 │
+│  (hive-create skill)                                                 │
 │                                                                          │
 │  1. User defines goal with success_criteria and constraints             │
 │  2. Goal written to agent.py immediately                                │
@@ -289,7 +289,7 @@ if missing_creds:
                                    ↓
 ┌─────────────────────────────────────────────────────────────────────────┐
 │                          AGENT STAGE                                     │
-│  (building-agents skill)                                                 │
+│  (hive-create skill)                                                 │
 │                                                                          │
 │  Build nodes + edges, written immediately to files                      │
 │  Constraint tests can run during development:                           │
@@ -608,7 +608,7 @@ Edit(
 )
 
 # 4. May need to regenerate agent nodes if goal changed significantly
-# This requires going back to building-agents skill
+# This requires going back to hive-create skill
 ```
 
 #### EDGE_CASE → Add Test and Fix
@@ -930,9 +930,10 @@ assert approval == "APPROVED", f"Expected APPROVED, got {approval}"
 - `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
+- `path: list[str]` - Node IDs traversed (may contain repeated IDs from feedback loops)
 - `paused_at: str | None` - Node ID if HITL pause occurred
 - `session_state: dict` - State for resuming
+- `node_visit_counts: dict[str, int]` - How many times each node executed (useful for feedback loop testing)
 
 ### Happy Path Test
 ```python
@@ -975,17 +976,68 @@ async def test_performance_latency(mock_mode):
     assert duration < 5.0, f"Took {{duration}}s, expected <5s"
 ```
 
-## Integration with building-agents
+### Testing Event Loop Nodes
+
+Event loop nodes run multi-turn loops internally. Tests should verify:
+
+**Output Keys Test** — All required keys are set via `set_output`:
+```python
+@pytest.mark.asyncio
+async def test_all_output_keys_set(mock_mode):
+    """Test that event_loop nodes set all required output keys."""
+    result = await default_agent.run({{"query": "test"}}, mock_mode=mock_mode)
+    assert result.success, f"Agent failed: {{result.error}}"
+    output = result.output or {{}}
+    for key in ["expected_key_1", "expected_key_2"]:
+        assert key in output, f"Output key '{{key}}' not set by event_loop node"
+```
+
+**Feedback Loop Test** — Verify feedback loops terminate:
+```python
+@pytest.mark.asyncio
+async def test_feedback_loop_respects_max_visits(mock_mode):
+    """Test that feedback loops terminate at max_node_visits."""
+    result = await default_agent.run({{"input": "trigger_rejection"}}, mock_mode=mock_mode)
+    assert result.success or result.error is not None
+    visits = getattr(result, "node_visit_counts", {{}}) or {{}}
+    for node_id, count in visits.items():
+        assert count <= 5, f"Node {{node_id}} visited {{count}} times"
+```
+
+**Fan-Out Test** — Verify parallel branches both complete:
+```python
+@pytest.mark.asyncio
+async def test_parallel_branches_complete(mock_mode):
+    """Test that fan-out branches all complete and produce outputs."""
+    result = await default_agent.run({{"query": "test"}}, mock_mode=mock_mode)
+    assert result.success
+    output = result.output or {{}}
+    # Check outputs from both parallel branches
+    assert "branch_a_output" in output, "Branch A output missing"
+    assert "branch_b_output" in output, "Branch B output missing"
+```
+
+**Client-Facing Node Test** — In mock mode, client-facing nodes may not block:
+```python
+@pytest.mark.asyncio
+async def test_client_facing_node(mock_mode):
+    """Test that client-facing nodes produce output."""
+    result = await default_agent.run({{"query": "test"}}, mock_mode=mock_mode)
+    # In mock mode, client-facing blocking is typically bypassed
+    assert result.success or result.paused_at is not None
+```
+
+## Integration with hive-create
 
 ### Handoff Points
 
 | Scenario | From | To | Action |
 |----------|------|-----|--------|
-| Agent built, ready to test | building-agents | testing-agent | Generate success tests |
-| LOGIC_ERROR found | testing-agent | building-agents | Update goal, rebuild |
-| IMPLEMENTATION_ERROR found | testing-agent | Direct fix | Edit agent files, re-run tests |
-| EDGE_CASE found | testing-agent | testing-agent | Add edge case test |
-| All tests pass | testing-agent | Done | Agent validated ✅ |
+| Agent built, ready to test | hive-create | hive-test | Generate success tests |
+| LOGIC_ERROR found | hive-test | hive-create | Update goal, rebuild |
+| IMPLEMENTATION_ERROR found | hive-test | Direct fix | Edit agent files, re-run tests |
+| EDGE_CASE found | hive-test | hive-test | Add edge case test |
+| All tests pass | hive-test | Done | Agent validated ✅ |
 
 ### Iteration Speed Comparison
 

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

@@ -4,7 +4,7 @@ This example walks through testing a YouTube research agent that finds relevant
 
 ## Prerequisites
 
-- Agent built with building-agents skill at `exports/youtube-research/`
+- Agent built with hive-create skill at `exports/youtube-research/`
 - Goal defined with success criteria and constraints
 
 ## Step 1: Load the Goal
@@ -283,11 +283,11 @@ result = debug_test(
 Since this is an **IMPLEMENTATION_ERROR**, we:
 
 1. **Don't restart** the Goal → Agent → Eval flow
-2. **Fix the agent** using building-agents skill:
+2. **Fix the agent** using hive-create skill:
    - Modify `filter_node` to handle null results
 3. **Re-run Eval** (tests only)
 
-### Fix in building-agents:
+### Fix in hive-create:
 
 ```python
 # Update the filter_node to handle null

.claude/skills/hive/SKILL.md

@@ -1,32 +1,53 @@
 ---
-name: agent-workflow
-description: Complete workflow for building, implementing, and testing goal-driven agents. Orchestrates building-agents-* and testing-agent skills. Use when starting a new agent project, unsure which skill to use, or need end-to-end guidance.
+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:
-    - building-agents-core
-    - building-agents-construction
-    - building-agents-patterns
-    - testing-agent
-    - setup-credentials
+    - 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** → `/building-agents-core` (optional)
-2. **Build Structure** → `/building-agents-construction`
-3. **Optimize Design** → `/building-agents-patterns` (optional)
-4. **Setup Credentials** → `/setup-credentials` (if agent uses tools requiring API keys)
-5. **Test & Validate** → `/testing-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
 
@@ -37,25 +58,26 @@ Use this meta-skill when:
 - Want consistent, repeatable agent builds
 
 **Skip this workflow** if:
-- You only need to test an existing agent → use `/testing-agent` directly
+- 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" → building-agents-core
-"Build a new agent" → building-agents-construction
-"Optimize my agent design" → building-agents-patterns
-"Set up API keys for my agent" → setup-credentials
-"Test my agent" → testing-agent
+"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)
 
-**Duration**: 5-10 minutes
-**Skill**: `/building-agents-core`
+**Skill**: `/hive-concepts`
 **Input**: Questions about agent architecture
 
 ### When to Use
@@ -63,12 +85,12 @@ Use this meta-skill when:
 - First time building an agent
 - Need to understand node types, edges, goals
 - Want to validate tool availability
-- Learning about pause/resume architecture
+- Learning about event loop architecture and client-facing nodes
 
 ### What This Phase Provides
 
 - Architecture overview (Python packages, not JSON)
-- Core concepts (Goal, Node, Edge, Pause/Resume)
+- Core concepts (Goal, Node, Edge, Event Loop, Judges)
 - Tool discovery and validation procedures
 - Workflow overview
 
@@ -76,9 +98,8 @@ Use this meta-skill when:
 
 ## Phase 1: Build Agent Structure
 
-**Duration**: 15-30 minutes
-**Skill**: `/building-agents-construction`
-**Input**: User requirements ("Build an agent that...")
+**Skill**: `/hive-create`
+**Input**: User requirements ("Build an agent that...") or a template to start from
 
 ### What This Phase Does
 
@@ -106,7 +127,7 @@ Creates the complete agent architecture:
 - ✅ 1-5 constraints defined
 - ✅ 5-10 nodes specified in nodes/__init__.py
 - ✅ 8-15 edges connecting workflow
-- ✅ Validated structure (passes `python -m agent_name validate`)
+- ✅ Validated structure (passes `uv run python -m agent_name validate`)
 - ✅ README.md with usage instructions
 - ✅ CLI commands (info, validate, run, shell)
 
@@ -120,7 +141,7 @@ You're ready for Phase 2 when:
 
 ### Common Outputs
 
-The building-agents-construction skill produces:
+The hive-create skill produces:
 ```
 exports/agent_name/
 ├── __init__.py          (package exports)
@@ -140,53 +161,52 @@ exports/agent_name/
 → 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 (building-agents-patterns)
+→ Proceed to Phase 1.5 (hive-patterns)
 
 **If ready to test:**
 → Proceed to Phase 2
 
 ## Phase 1.5: Optimize Design (Optional)
 
-**Duration**: 10-15 minutes
-**Skill**: `/building-agents-patterns`
+**Skill**: `/hive-patterns`
 **Input**: Completed agent structure
 
 ### When to Use
 
-- Want to add pause/resume functionality
+- 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 to optimize performance
-- Need examples of complex routing
 - Want best practices guidance
 
 ### What This Phase Provides
 
-- Practical examples and patterns
-- Pause/resume architecture
-- Error handling strategies
+- 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
-- Performance optimization techniques
 
 **Skip this phase** if your agent design is straightforward.
 
 ## Phase 2: Test & Validate
 
-**Duration**: 20-40 minutes
-**Skill**: `/testing-agent`
+**Skill**: `/hive-test`
 **Input**: Working agent from Phase 1
 
 ### What This Phase Does
 
-Creates comprehensive test suite:
-- Constraint tests (verify hard requirements)
-- Success criteria tests (measure goal achievement)
-- Edge case tests (handle failures gracefully)
-- Integration tests (end-to-end workflows)
+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** - Create pytest files in `exports/agent_name/tests/`
+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
@@ -249,9 +269,9 @@ You're done when:
 
 ```
 User: "Build an agent that monitors files"
-→ Use /building-agents-construction
+→ Use /hive-create
 → Agent structure created
-→ Use /testing-agent
+→ Use /hive-test
 → Tests created and passing
 → Done: Production-ready agent
 ```
@@ -260,19 +280,32 @@ User: "Build an agent that monitors files"
 
 ```
 User: "Build an agent (first time)"
-→ Use /building-agents-core (understand concepts)
-→ Use /building-agents-construction (build structure)
-→ Use /building-agents-patterns (optimize design)
-→ Use /testing-agent (validate)
+→ 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 /testing-agent directly
+→ Use /hive-test directly
 → Tests created
 → Done: Validated agent
 ```
@@ -281,58 +314,71 @@ User: "Test my agent at exports/my_agent"
 
 ```
 User: "Build an agent"
-→ Use /building-agents-construction (Phase 1)
+→ Use /hive-create (Phase 1)
 → Implementation needed (see STATUS.md)
 → [User implements functions]
-→ Use /testing-agent (Phase 2)
+→ Use /hive-test (Phase 2)
 → Tests reveal bugs
 → [Fix bugs manually]
 → Re-run tests
 → Done: Working agent
 ```
 
-### Pattern 4: Complex Agent with Patterns
+### Pattern 4: Agent with Review Loops and HITL Checkpoints
 
 ```
-User: "Build an agent with multi-turn conversations"
-→ Use /building-agents-core (learn pause/resume)
-→ Use /building-agents-construction (build structure)
-→ Use /building-agents-patterns (implement pause/resume pattern)
-→ Use /testing-agent (validate conversation flows)
-→ Done: Complex conversational agent
+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
 
 ```
-agent-workflow (meta-skill)
+hive (meta-skill)
     │
-    ├── building-agents-core (foundational)
-    │   ├── Architecture concepts
-    │   ├── Node/Edge/Goal definitions
+    ├── hive-concepts (foundational)
+    │   ├── Architecture concepts (event loop, judges)
+    │   ├── Node types (event_loop, function)
+    │   ├── Edge routing and priority
     │   ├── Tool discovery procedures
     │   └── Workflow overview
     │
-    ├── building-agents-construction (procedural)
+    ├── hive-create (procedural)
     │   ├── Creates package structure
     │   ├── Defines goal
-    │   ├── Adds nodes incrementally
-    │   ├── Connects edges
+    │   ├── Adds nodes (event_loop, function)
+    │   ├── Connects edges with priority routing
     │   ├── Finalizes agent class
-    │   └── Requires: building-agents-core
+    │   └── Requires: hive-concepts
     │
-    ├── building-agents-patterns (reference)
-    │   ├── Best practices
-    │   ├── Pause/resume patterns
-    │   ├── Error handling
-    │   ├── Anti-patterns
-    │   └── Performance optimization
+    ├── 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
     │
-    └── testing-agent
-        ├── Reads agent goal
-        ├── Generates tests
-        ├── Runs evaluation
-        └── Reports results
+    ├── 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
@@ -342,13 +388,13 @@ agent-workflow (meta-skill)
 - 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=core:exports python -m agent_name validate`
+- 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 - building-agents-construction creates structure, not implementation
+- This is expected - hive-create creates structure, not implementation
 - See implementation guide for completion options
 
 ### "Tests are failing"
@@ -356,9 +402,16 @@ agent-workflow (meta-skill)
 - Review test output for specific failures
 - Check agent goal and success criteria
 - Verify constraints are met
-- Use `/testing-agent` to debug and iterate
+- 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:
@@ -368,7 +421,7 @@ Run these checks:
 ls exports/my_agent/agent.py
 
 # Check if it validates
-PYTHONPATH=core:exports python -m my_agent validate
+PYTHONPATH=exports uv run python -m my_agent validate
 
 # Check if tests exist
 ls exports/my_agent/tests/
@@ -417,10 +470,10 @@ You're done with the workflow when:
 
 ## Additional Resources
 
-- **building-agents-core**: See `.claude/skills/building-agents-core/SKILL.md`
-- **building-agents-construction**: See `.claude/skills/building-agents-construction/SKILL.md`
-- **building-agents-patterns**: See `.claude/skills/building-agents-patterns/SKILL.md`
-- **testing-agent**: See `.claude/skills/testing-agent/SKILL.md`
+- **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
 
@@ -428,36 +481,46 @@ You're done with the workflow when:
 
 This workflow provides a proven path from concept to production-ready agent:
 
-1. **Learn** with `/building-agents-core` → Understand fundamentals (optional)
-2. **Build** with `/building-agents-construction` → Get validated structure
-3. **Optimize** with `/building-agents-patterns` → Apply best practices (optional)
-4. **Test** with `/testing-agent` → Get verified functionality
+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 building-agents-core when:**
+**Choose hive-concepts when:**
 - First time building agents
-- Need to understand architecture
+- Need to understand event loop architecture
 - Validating tool availability
-- Learning about node types and edges
+- Learning about node types, edges, and judges
 
-**Choose building-agents-construction when:**
+**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 building-agents-patterns when:**
+**Choose hive-patterns when:**
 - Agent structure complete
-- Need advanced patterns
-- Implementing pause/resume
-- Optimizing performance
+- 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 testing-agent when:**
+**Choose hive-test when:**
 - Agent structure complete
 - Ready to validate functionality
 - Need comprehensive test coverage
-- Debugging agent behavior
+- 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

@@ -1,6 +1,6 @@
 # Example: File Monitor Agent
 
-This example shows the complete agent-workflow in action for building a file monitoring agent.
+This example shows the complete /hive workflow in action for building a file monitoring agent.
 
 ## Initial Request
 
@@ -12,7 +12,7 @@ User: "Build an agent that monitors ~/Downloads and copies new files to ~/Docume
 
 ### Step 1: Create Structure
 
-Agent invokes `/building-agents` skill and:
+Agent invokes `/hive-create` skill and:
 
 1. Creates `exports/file_monitor_agent/` package
 2. Writes skeleton files (__init__.py, __main__.py, agent.py, etc.)
@@ -75,10 +75,10 @@ initialize → list → identify → check
 ### Step 5: Finalize
 
 ```bash
-$ PYTHONPATH=core:exports python -m file_monitor_agent validate
+$ PYTHONPATH=exports uv run python -m file_monitor_agent validate
 ✓ Agent is valid
 
-$ PYTHONPATH=core:exports python -m file_monitor_agent info
+$ PYTHONPATH=exports uv run python -m file_monitor_agent info
 Agent: File Monitor & Copy Agent
 Nodes: 7
 Edges: 8
@@ -107,7 +107,7 @@ exports/file_monitor_agent/
 
 ### Step 1: Analyze Agent
 
-Agent invokes `/testing-agent` skill and:
+Agent invokes `/hive-test` skill and:
 
 1. Reads goal from `exports/file_monitor_agent/agent.py`
 2. Identifies 4 success criteria to test
@@ -131,7 +131,7 @@ Tests approved incrementally by user.
 ### Step 3: Run Tests
 
 ```bash
-$ PYTHONPATH=core:exports pytest exports/file_monitor_agent/tests/
+$ PYTHONPATH=exports uv run pytest exports/file_monitor_agent/tests/
 
 test_constraints.py::test_preserves_originals     PASSED
 test_constraints.py::test_handles_errors          PASSED
@@ -162,7 +162,7 @@ test_edge_cases.py::test_large_files              PASSED
 ./RUN_AGENT.sh
 
 # Or manually
-PYTHONPATH=core:exports:tools/src python -m file_monitor_agent run
+PYTHONPATH=exports uv run python -m file_monitor_agent run
 ```
 
 **Capabilities:**

.cursor/skills/agent-workflow

@@ -1 +0,0 @@
-../../.claude/skills/agent-workflow

.cursor/skills/building-agents-construction

@@ -1 +0,0 @@
-../../.claude/skills/building-agents-construction

.cursor/skills/building-agents-core

@@ -1 +0,0 @@
-../../.claude/skills/building-agents-core

.cursor/skills/building-agents-patterns

@@ -1 +0,0 @@
-../../.claude/skills/building-agents-patterns

.cursor/skills/hive

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

.cursor/skills/hive-concepts

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

.cursor/skills/hive-create

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

.cursor/skills/hive-credentials

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

.cursor/skills/hive-patterns

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

.cursor/skills/hive-test

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

.cursor/skills/testing-agent

@@ -1 +0,0 @@
-../../.claude/skills/testing-agent

.github/workflows/ci.yml

@@ -55,14 +55,10 @@ jobs:
       - name: Install uv
         uses: astral-sh/setup-uv@v4
 
-      - name: Install dependencies
+      - name: Install dependencies and run tests
         run: |
           cd core
           uv sync
-
-      - name: Run tests
-        run: |
-          cd core
           uv run pytest tests/ -v
 
   test-tools:
@@ -83,8 +79,7 @@ jobs:
         run: |
           cd tools
           uv sync --extra dev
-          uv pip install --python .venv/bin/python -e ../core
-          uv run --extra dev pytest tests/ -v
+          uv run pytest tests/ -v
 
   validate:
     name: Validate Agent Exports
@@ -127,7 +122,7 @@ jobs:
           for agent_dir in "${agent_dirs[@]}"; do
             if [ -f "$agent_dir/agent.json" ]; then
               echo "Validating $agent_dir"
-              python -c "import json; json.load(open('$agent_dir/agent.json'))"
+              uv run python -c "import json; json.load(open('$agent_dir/agent.json'))"
               validated=$((validated + 1))
             fi
           done

.gitignore

@@ -54,7 +54,6 @@ __pycache__/
 *.egg-info/
 .eggs/
 *.egg
-uv.lock
 
 # Generated runtime data
 core/data/
@@ -69,4 +68,10 @@ exports/*
 
 .agent-builder-sessions/*
 
-.venv
+.claude/settings.local.json
+
+.venv
+
+docs/github-issues/*
+core/tests/*dumps/*
+screenshots/*

.mcp.json

@@ -1,20 +1,14 @@
 {
   "mcpServers": {
     "agent-builder": {
-      "command": ".venv/bin/python",
-      "args": ["-m", "framework.mcp.agent_builder_server"],
-      "cwd": "core",
-      "env": {
-        "PYTHONPATH": "../tools/src"
-      }
+      "command": "uv",
+      "args": ["run", "-m", "framework.mcp.agent_builder_server"],
+      "cwd": "core"
     },
     "tools": {
-      "command": ".venv/bin/python",
-      "args": ["mcp_server.py", "--stdio"],
-      "cwd": "tools",
-      "env": {
-        "PYTHONPATH": "src:../core"
-      }
+      "command": "uv",
+      "args": ["run", "mcp_server.py", "--stdio"],
+      "cwd": "tools"
     }
   }
 }

.pre-commit-config.yaml

@@ -1,6 +1,6 @@
 repos:
   - repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: v0.8.6
+    rev: v0.15.0
     hooks:
       - id: ruff
         name: ruff lint (core)

CHANGELOG.md

@@ -1,41 +0,0 @@
-# Changelog
-
-All notable changes to this project will be documented in this file.
-
-The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.1.0/),
-and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html).
-
-## [Unreleased]
-
-### Added
-- Initial project structure
-- React frontend (honeycomb) with Vite and TypeScript
-- Node.js backend (hive) with Express and TypeScript
-- Docker Compose configuration for local development
-- Configuration system via `config.yaml`
-- GitHub Actions CI/CD workflows
-- Comprehensive documentation
-
-### Changed
-- N/A
-
-### Deprecated
-- N/A
-
-### Removed
-- N/A
-
-
-### Fixed
-- tools: Fixed web_scrape tool attempting to parse non-HTML content (PDF, JSON) as HTML (#487)
-
-### Security
-- N/A
-
-## [0.1.0] - 2025-01-13
-
-### Added
-- Initial release
-
-[Unreleased]: https://github.com/adenhq/hive/compare/v0.1.0...HEAD
-[0.1.0]: https://github.com/adenhq/hive/releases/tag/v0.1.0

CONTRIBUTING.md

@@ -1,10 +1,10 @@
 # Contributing to Aden Agent Framework
 
-Thank you for your interest in contributing to the Aden Agent Framework! This document provides guidelines and information for contributors. We’re especially looking for help building tools, integrations([check #2805](https://github.com/adenhq/hive/issues/2805)), and example agents for the framework. If you’re interested in extending its functionality, this is the perfect place to start. 
+Thank you for your interest in contributing to the Aden Agent Framework! This document provides guidelines and information for contributors. We’re especially looking for help building tools, integrations ([check #2805](https://github.com/adenhq/hive/issues/2805)), and example agents for the framework. If you’re interested in extending its functionality, this is the perfect place to start. 
 
 ## Code of Conduct
 
-By participating in this project, you agree to abide by our [Code of Conduct](CODE_OF_CONDUCT.md).
+By participating in this project, you agree to abide by our [Code of Conduct](docs/CODE_OF_CONDUCT.md).
 
 ## Issue Assignment Policy
 
@@ -35,9 +35,16 @@ You may submit PRs without prior assignment for:
 
 1. Fork the repository
 2. Clone your fork: `git clone https://github.com/YOUR_USERNAME/hive.git`
-3. Create a feature branch: `git checkout -b feature/your-feature-name`
-4. Make your changes
-5. Run checks and tests:
+3. Add the upstream repository: `git remote add upstream https://github.com/adenhq/hive.git`
+4. Sync with upstream to ensure you're starting from the latest code:
+   ```bash
+   git fetch upstream
+   git checkout main
+   git merge upstream/main
+   ```
+5. Create a feature branch: `git checkout -b feature/your-feature-name`
+6. Make your changes
+7. Run checks and tests:
    ```bash
    make check    # Lint and format checks (ruff check + ruff format --check on core/ and tools/)
    make test     # Core tests (cd core && pytest tests/ -v)
@@ -125,7 +132,7 @@ feat(component): add new feature description
 > **Note:** When testing agents in `exports/`, always set PYTHONPATH:
 >
 > ```bash
-> PYTHONPATH=core:exports python -m agent_name test
+> PYTHONPATH=exports uv run python -m agent_name test
 > ```
 
 ```bash
@@ -139,7 +146,7 @@ make test
 cd core && pytest tests/ -v
 
 # Run tests for a specific agent
-PYTHONPATH=core:exports python -m agent_name test
+PYTHONPATH=exports uv run python -m agent_name test
 ```
 
 > **CI also validates** that all exported agent JSON files (`exports/*/agent.json`) are well-formed JSON. Ensure your agent exports are valid before submitting.
@@ -152,4 +159,4 @@ By submitting a Pull Request, you agree that your contributions will be licensed
 
 Feel free to open an issue for questions or join our [Discord community](https://discord.com/invite/MXE49hrKDk).
 
-Thank you for contributing!
+Thank you for contributing!

Makefile

@@ -4,9 +4,11 @@ help: ## Show this help
 	@grep -E '^[a-zA-Z_-]+:.*?## .*$$' $(MAKEFILE_LIST) | \
 		awk 'BEGIN {FS = ":.*?## "}; {printf "  \033[36m%-15s\033[0m %s\n", $$1, $$2}'
 
-lint: ## Run ruff linter (with auto-fix)
+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 .
 
 format: ## Run ruff formatter
 	cd core && ruff format .
@@ -19,8 +21,8 @@ check: ## Run all checks without modifying files (CI-safe)
 	cd tools && ruff format --check .
 
 test: ## Run all tests
-	cd core && python -m pytest tests/ -v
+	cd core && uv run python -m pytest tests/ -v
 
 install-hooks: ## Install pre-commit hooks
-	pip install pre-commit
+	uv pip install pre-commit
 	pre-commit install

PR.md

@@ -1,51 +0,0 @@
-## Summary
-- **Added HubSpot integration** — new HubSpot MCP tool with search, get, create, and update operations for contacts, companies, and deals. Includes OAuth2 provider for HubSpot credentials and credential store adapter for the tools layer.
-- **Replaced web_scrape tool with Playwright + stealth** — swapped httpx/BeautifulSoup for a headless Chromium browser using `playwright` (async API) and `playwright-stealth`, enabling JS-rendered page scraping and bot detection evasion
-- **Added empty response retry logic** — LLM provider now detects empty responses (e.g. Gemini returning 200 with no content on rate limit) and retries with exponential backoff, preventing hallucinated output from the cleanup LLM
-- **Added context-aware input compaction** — LLM nodes now estimate input token count before calling the model and progressively truncate the largest values if they exceed the context window budget
-- **Increased rate limit retries to 10** with verbose `[retry]` and `[compaction]` logging that includes model name, finish reason, and attempt count
-- **Updated setup scripts** — `scripts/setup-python.sh` now installs Playwright Chromium browser automatically for web scraping support
-- **Interactive quickstart onboarding** — `quickstart.sh` rewritten as bee-themed interactive wizard that detects existing API keys (including Claude Code subscription), lets user pick ONE default LLM provider, and saves configuration to `~/.hive/configuration.json`
-- **Fixed lint errors** across `hubspot_tool.py` (line length) and `agent_builder_server.py` (unused variable)
-
-## Changed files
-
-### HubSpot Integration
-- `tools/src/aden_tools/tools/hubspot_tool/` — New MCP tool: contacts, companies, and deals CRUD
-- `tools/src/aden_tools/tools/__init__.py` — Registered HubSpot tools
-- `tools/src/aden_tools/credentials/integrations.py` — HubSpot credential integration
-- `tools/src/aden_tools/credentials/__init__.py` — Updated credential exports
-- `core/framework/credentials/oauth2/hubspot_provider.py` — HubSpot OAuth2 provider
-- `core/framework/credentials/oauth2/__init__.py` — Registered HubSpot OAuth2 provider
-- `core/framework/runner/runner.py` — Updated runner for credential support
-
-### Web Scrape Rewrite
-- `tools/src/aden_tools/tools/web_scrape_tool/web_scrape_tool.py` — Playwright async rewrite
-- `tools/src/aden_tools/tools/web_scrape_tool/README.md` — Updated docs
-- `tools/pyproject.toml` — Added `playwright`, `playwright-stealth` deps
-- `tools/Dockerfile` — Added `playwright install chromium --with-deps`
-- `scripts/setup-python.sh` — Added Playwright Chromium browser install step
-
-### LLM Reliability
-- `core/framework/llm/litellm.py` — Empty response retry + max retries 10 + verbose logging
-- `core/framework/graph/node.py` — Input compaction via `_compact_inputs()`, `_estimate_tokens()`, `_get_context_limit()`
-
-### Quickstart & Setup
-- `quickstart.sh` — Interactive bee-themed onboarding wizard with single provider selection
-- `~/.hive/configuration.json` — New user config file for default LLM provider/model
-
-### Fixes
-- `core/framework/mcp/agent_builder_server.py` — Removed unused variable
-- `tools/src/aden_tools/tools/hubspot_tool/hubspot_tool.py` — Fixed E501 line length violations
-
-## Test plan
-- [ ] Run `make lint` — passes clean
-- [ ] Run `./quickstart.sh` and verify interactive flow works, config saved to `~/.hive/configuration.json`
-- [ ] Run `./scripts/setup-python.sh` and verify Playwright Chromium installs
-- [ ] Run `pytest tests/tools/test_web_scrape_tool.py -v`
-- [ ] Run agent against a JS-heavy site and verify `web_scrape` returns rendered content
-- [ ] Set `HUBSPOT_ACCESS_TOKEN` and verify HubSpot tool CRUD operations work
-- [ ] Trigger rate limit and verify `[retry]` logs appear with correct attempt counts
-- [ ] Run agent with large inputs and verify `[compaction]` logs show truncation
-
-🤖 Generated with [Claude Code](https://claude.com/claude-code)

README.md

@@ -1,5 +1,5 @@
 <p align="center">
-  <img width="100%" alt="Hive Banner" src="https://storage.googleapis.com/aden-prod-assets/website/aden-title-card.png" />
+  <img width="100%" alt="Hive Banner" src="https://github.com/user-attachments/assets/a027429b-5d3c-4d34-88e4-0feaeaabbab3" />
 </p>
 
 <p align="center">
@@ -13,16 +13,20 @@
   <a href="docs/i18n/ko.md">한국어</a>
 </p>
 
-[![Apache 2.0 License](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](https://github.com/adenhq/hive/blob/main/LICENSE)
-[![Y Combinator](https://img.shields.io/badge/Y%20Combinator-Aden-orange)](https://www.ycombinator.com/companies/aden)
-[![Discord](https://img.shields.io/discord/1172610340073242735?logo=discord&labelColor=%235462eb&logoColor=%23f5f5f5&color=%235462eb)](https://discord.com/invite/MXE49hrKDk)
-[![Twitter Follow](https://img.shields.io/twitter/follow/teamaden?logo=X&color=%23f5f5f5)](https://x.com/aden_hq)
-[![LinkedIn](https://custom-icon-badges.demolab.com/badge/LinkedIn-0A66C2?logo=linkedin-white&logoColor=fff)](https://www.linkedin.com/company/teamaden/)
+<p align="center">
+  <a href="https://github.com/adenhq/hive/blob/main/LICENSE"><img src="https://img.shields.io/badge/License-Apache%202.0-blue.svg" alt="Apache 2.0 License" /></a>
+  <a href="https://www.ycombinator.com/companies/aden"><img src="https://img.shields.io/badge/Y%20Combinator-Aden-orange" alt="Y Combinator" /></a>
+  <a href="https://discord.com/invite/MXE49hrKDk"><img src="https://img.shields.io/discord/1172610340073242735?logo=discord&labelColor=%235462eb&logoColor=%23f5f5f5&color=%235462eb" alt="Discord" /></a>
+  <a href="https://x.com/aden_hq"><img src="https://img.shields.io/twitter/follow/teamaden?logo=X&color=%23f5f5f5" alt="Twitter Follow" /></a>
+  <a href="https://www.linkedin.com/company/teamaden/"><img src="https://custom-icon-badges.demolab.com/badge/LinkedIn-0A66C2?logo=linkedin-white&logoColor=fff" alt="LinkedIn" /></a>
+  <img src="https://img.shields.io/badge/MCP-102_Tools-00ADD8?style=flat-square" alt="MCP" />
+</p>
+
 
 <p align="center">
   <img src="https://img.shields.io/badge/AI_Agents-Self--Improving-brightgreen?style=flat-square" alt="AI Agents" />
   <img src="https://img.shields.io/badge/Multi--Agent-Systems-blue?style=flat-square" alt="Multi-Agent" />
-  <img src="https://img.shields.io/badge/Goal--Driven-Development-purple?style=flat-square" alt="Goal-Driven" />
+  <img src="https://img.shields.io/badge/Headless-Development-purple?style=flat-square" alt="Headless" />
   <img src="https://img.shields.io/badge/Human--in--the--Loop-orange?style=flat-square" alt="HITL" />
   <img src="https://img.shields.io/badge/Production--Ready-red?style=flat-square" alt="Production" />
 </p>
@@ -30,15 +34,16 @@
   <img src="https://img.shields.io/badge/OpenAI-supported-412991?style=flat-square&logo=openai" alt="OpenAI" />
   <img src="https://img.shields.io/badge/Anthropic-supported-d4a574?style=flat-square" alt="Anthropic" />
   <img src="https://img.shields.io/badge/Google_Gemini-supported-4285F4?style=flat-square&logo=google" alt="Gemini" />
-  <img src="https://img.shields.io/badge/MCP-19_Tools-00ADD8?style=flat-square" alt="MCP" />
 </p>
 
 ## Overview
 
-Build reliable, self-improving AI agents without hardcoding workflows. Define your goal through conversation with a coding agent, and the framework generates a node graph with dynamically created connection code. When things break, the framework captures failure data, evolves the agent through the coding agent, and redeploys. Built-in human-in-the-loop nodes, credential management, and real-time monitoring give you control without sacrificing adaptability.
+Build autonomous, reliable, self-improving AI agents without hardcoding workflows. Define your goal through conversation with a coding agent, and the framework generates a node graph with dynamically created connection code. When things break, the framework captures failure data, evolves the agent through the coding agent, and redeploys. Built-in human-in-the-loop nodes, credential management, and real-time monitoring give you control without sacrificing adaptability.
 
 Visit [adenhq.com](https://adenhq.com) for complete documentation, examples, and guides.
 
+https://github.com/user-attachments/assets/846c0cc7-ffd6-47fa-b4b7-495494857a55
+
 ## Who Is Hive For?
 
 Hive is designed for developers and teams who want to build **production-grade AI agents** without manually wiring complex workflows.
@@ -58,41 +63,29 @@ Hive may not be the best fit if you’re only experimenting with simple agent ch
 Use Hive when you need:
 
 - Long-running, autonomous agents
-- Multi-agent coordination
+- Strong guardrails, process, and controls
 - Continuous improvement based on failures
-- Strong monitoring, safety, and budget controls
+- Multi-agent coordination
 - A framework that evolves with your goals
 
-
-## What is Aden
-
-<p align="center">
-  <img width="100%" alt="Aden Architecture" src="docs/assets/aden-architecture-diagram.jpg" />
-</p>
-
-Aden is a platform for building, deploying, operating, and adapting AI agents:
-
-- **Build** - A Coding Agent generates specialized Worker Agents (Sales, Marketing, Ops) from natural language goals
-- **Deploy** - Headless deployment with CI/CD integration and full API lifecycle management
-- **Operate** - Real-time monitoring, observability, and runtime guardrails keep agents reliable
-- **Adapt** - Continuous evaluation, supervision, and adaptation ensure agents improve over time
-- **Infra** - Shared memory, LLM integrations, tools, and skills power every agent
-
 ## Quick Links
 
 - **[Documentation](https://docs.adenhq.com/)** - Complete guides and API reference
 - **[Self-Hosting Guide](https://docs.adenhq.com/getting-started/quickstart)** - Deploy Hive on your infrastructure
 - **[Changelog](https://github.com/adenhq/hive/releases)** - Latest updates and releases
-<!-- - **[Roadmap](https://adenhq.com/roadmap)** - Upcoming features and plans -->
+- **[Roadmap](docs/roadmap.md)** - Upcoming features and plans
 - **[Report Issues](https://github.com/adenhq/hive/issues)** - Bug reports and feature requests
+- **[Contributing](CONTRIBUTING.md)** - How to contribute and submit PRs
 
 ## Quick Start
 
 ### Prerequisites
 
-- [Python 3.11+](https://www.python.org/downloads/) for agent development
+- Python 3.11+ for agent development
 - Claude Code or Cursor for utilizing agent skills
 
+> **Note for Windows Users:** It is strongly recommended to use **WSL (Windows Subsystem for Linux)** or **Git Bash** to run this framework. Some core automation scripts may not execute correctly in standard Command Prompt or PowerShell.
+
 ### Installation
 
 ```bash
@@ -105,45 +98,53 @@ cd hive
 ```
 
 This sets up:
+
 - **framework** - Core agent runtime and graph executor (in `core/.venv`)
 - **aden_tools** - MCP tools for agent capabilities (in `tools/.venv`)
-- All required Python dependencies
+- **credential store** - Encrypted API key storage (`~/.hive/credentials`)
+- **LLM provider** - Interactive default model configuration
+- All required Python dependencies with `uv`
 
 ### Build Your First Agent
 
 ```bash
 # Build an agent using Claude Code
-claude> /building-agents-construction
+claude> /hive
 
 # Test your agent
-claude> /testing-agent
+claude> /hive-debugger
 
-# Run your agent
-PYTHONPATH=core:exports python -m your_agent_name run --input '{...}'
+# (at separate terminal) Launch the interactive dashboard
+hive tui
+
+# Or run directly
+hive run exports/your_agent_name --input '{"key": "value"}'
 ```
 
-**[📖 Complete Setup Guide](ENVIRONMENT_SETUP.md)** - Detailed instructions for agent development
-
-### Cursor IDE Support
-
-Skills are also available in Cursor. To enable:
-
-1. Open Command Palette (`Cmd+Shift+P` / `Ctrl+Shift+P`)
-2. Run `MCP: Enable` to enable MCP servers
-3. Restart Cursor to load the MCP servers from `.cursor/mcp.json`
-4. Type `/` in Agent chat and search for skills (e.g., `/building-agents-construction`)
+**[📖 Complete Setup Guide](docs/environment-setup.md)** - Detailed instructions for agent development
 
 ## Features
 
-- **Goal-Driven Development** - Define objectives in natural language; the coding agent generates the agent graph and connection code to achieve them
-- **Adaptiveness** - Framework captures failures, calibrates according to the objectives, and evolves the agent graph
-- **Dynamic Node Connections** - No predefined edges; connection code is generated by any capable LLM based on your goals
+- **[Goal-Driven Development](docs/key_concepts/goals_outcome.md)** - Define objectives in natural language; the coding agent generates the agent graph and connection code to achieve them
+- **[Adaptiveness](docs/key_concepts/evolution.md)** - Framework captures failures, calibrates according to the objectives, and evolves the agent graph
+- **[Dynamic Node Connections](docs/key_concepts/graph.md)** - No predefined edges; connection code is generated by any capable LLM based on your goals
 - **SDK-Wrapped Nodes** - Every node gets shared memory, local RLM memory, monitoring, tools, and LLM access out of the box
-- **Human-in-the-Loop** - Intervention nodes that pause execution for human input with configurable timeouts and escalation
+- **[Human-in-the-Loop](docs/key_concepts/graph.md#human-in-the-loop)** - Intervention nodes that pause execution for human input with configurable timeouts and escalation
 - **Real-time Observability** - WebSocket streaming for live monitoring of agent execution, decisions, and node-to-node communication
+- **Interactive TUI Dashboard** - Terminal-based dashboard with live graph view, event log, and chat interface for agent interaction
 - **Cost & Budget Control** - Set spending limits, throttles, and automatic model degradation policies
 - **Production-Ready** - Self-hostable, built for scale and reliability
 
+## Integration
+
+<a href="https://github.com/adenhq/hive/tree/main/tools/src/aden_tools/tools"><img width="100%" alt="Integration" src="https://github.com/user-attachments/assets/a1573f93-cf02-4bb8-b3d5-b305b05b1e51" /></a>
+
+Hive is built to be model-agnostic and system-agnostic.
+
+- **LLM flexibility** - Hive Framework is designed to support various types of LLMs, including hosted and local models through LiteLLM-compatible providers.
+- **Business system connectivity** - Hive Framework is designed to connect to all kinds of business systems as tools, such as CRM, support, messaging, data, file, and internal APIs via MCP.
+
+
 ## Why Aden
 
 Hive focuses on generating agents that run real business processes rather than generic agents. Instead of requiring you to manually design workflows, define agent interactions, and handle failures reactively, Hive flips the paradigm: **you describe outcomes, and the system builds itself**—delivering an outcome-driven, adaptive experience with an easy-to-use set of tools and integrations.
@@ -180,67 +181,60 @@ flowchart LR
     style V6 fill:#fff,stroke:#ed8c00,stroke-width:1px,color:#cc5d00
 ```
 
-### The Aden Advantage
+### The Hive Advantage
 
-| Traditional Frameworks     | Aden                                   |
+| Traditional Frameworks     | Hive                                   |
 | -------------------------- | -------------------------------------- |
 | Hardcode agent workflows   | Describe goals in natural language     |
 | Manual graph definition    | Auto-generated agent graphs            |
-| Reactive error handling    | Outcome-evaluation and adaptiveness               |
+| Reactive error handling    | Outcome-evaluation and adaptiveness    |
 | Static tool configurations | Dynamic SDK-wrapped nodes              |
 | Separate monitoring setup  | Built-in real-time observability       |
 | DIY budget management      | Integrated cost controls & degradation |
 
 ### How It Works
 
-1. **Define Your Goal** → Describe what you want to achieve in plain English
-2. **Coding Agent Generates** → Creates the agent graph, connection code, and test cases
-3. **Workers Execute** → SDK-wrapped nodes run with full observability and tool access
+1. **[Define Your Goal](docs/key_concepts/goals_outcome.md)** → Describe what you want to achieve in plain English
+2. **Coding Agent Generates** → Creates the [agent graph](docs/key_concepts/graph.md), connection code, and test cases
+3. **[Workers Execute](docs/key_concepts/worker_agent.md)** → SDK-wrapped nodes run with full observability and tool access
 4. **Control Plane Monitors** → Real-time metrics, budget enforcement, policy management
-5. **Adaptiveness** → On failure, the system evolves the graph and redeploys automatically
+5. **[Adaptiveness](docs/key_concepts/evolution.md)** → On failure, the system evolves the graph and redeploys automatically
 
-## Run pre-built Agents (Coming Soon)
+## Run Agents
 
-### Run a sample agent
-Aden Hive provides a list of featured agents that you can use and build on top of.
-
-### Run an agent shared by others
-Put the agent in `exports/` and run `PYTHONPATH=core:exports python -m your_agent_name run --input '{...}'`
-
-
-For building and running goal-driven agents with the framework:
+The `hive` CLI is the primary interface for running agents.
 
 ```bash
-# One-time setup
-./quickstart.sh
+# Browse and run agents interactively (Recommended)
+hive tui
 
-# This sets up:
-# - framework package (core runtime)
-# - aden_tools package (MCP tools)
-# - All Python dependencies
+# Run a specific agent directly
+hive run exports/my_agent --input '{"task": "Your input here"}'
 
-# Build new agents using Claude Code skills
-claude> /building-agents-construction
+# Run a specific agent with the TUI dashboard
+hive run exports/my_agent --tui
 
-# Test agents
-claude> /testing-agent
-
-# Run agents
-PYTHONPATH=core:exports python -m agent_name run --input '{...}'
+# Interactive REPL
+hive shell
 ```
 
-See [ENVIRONMENT_SETUP.md](ENVIRONMENT_SETUP.md) for complete setup instructions.
+The TUI scans both `exports/` and `examples/templates/` for available agents.
+
+> **Using Python directly (alternative):** You can also run agents with `PYTHONPATH=exports uv run python -m agent_name run --input '{...}'`
+
+See [environment-setup.md](docs/environment-setup.md) for complete setup instructions.
 
 ## Documentation
 
-- **[Developer Guide](DEVELOPER.md)** - Comprehensive guide for developers
+- **[Developer Guide](docs/developer-guide.md)** - Comprehensive guide for developers
 - [Getting Started](docs/getting-started.md) - Quick setup instructions
+- [TUI Guide](docs/tui-selection-guide.md) - Interactive dashboard usage
 - [Configuration Guide](docs/configuration.md) - All configuration options
 - [Architecture Overview](docs/architecture/README.md) - System design and structure
 
 ## Roadmap
 
-Aden Hive Agent Framework aims to help developers build outcome-oriented, self-adaptive agents. See [ROADMAP.md](ROADMAP.md) for details.
+Aden Hive Agent Framework aims to help developers build outcome-oriented, self-adaptive agents. See [roadmap.md](docs/roadmap.md) for details.
 
 ```mermaid
 flowchart TD
@@ -330,11 +324,12 @@ end
 
 classDef done fill:#9e9e9e,color:#fff,stroke:#757575
 ```
+
 ## Contributing
 
 We welcome contributions from the community! We’re especially looking for help building tools, integrations, and example agents for the framework ([check #2805](https://github.com/adenhq/hive/issues/2805)). If you’re interested in extending its functionality, this is the perfect place to start. Please see [CONTRIBUTING.md](CONTRIBUTING.md) for guidelines.
 
-**Important:** Please get assigned to an issue before submitting a PR. Comment on an issue to claim it, and a maintainer will assign you. Issues with reproducible steps and proposals are prioritized. This helps prevent duplicate work. 
+**Important:** Please get assigned to an issue before submitting a PR. Comment on an issue to claim it, and a maintainer will assign you. Issues with reproducible steps and proposals are prioritized. This helps prevent duplicate work.
 
 1. Find or create an issue and get assigned
 2. Fork the repository
@@ -367,10 +362,6 @@ This project is licensed under the Apache License 2.0 - see the [LICENSE](LICENS
 
 ## Frequently Asked Questions (FAQ)
 
-**Q: Does Hive depend on LangChain or other agent frameworks?**
-
-No. Hive is built from the ground up with no dependencies on LangChain, CrewAI, or other agent frameworks. The framework is designed to be lean and flexible, generating agent graphs dynamically rather than relying on predefined components.
-
 **Q: What LLM providers does Hive support?**
 
 Hive supports 100+ LLM providers through LiteLLM integration, including OpenAI (GPT-4, GPT-4o), Anthropic (Claude models), Google Gemini, DeepSeek, Mistral, Groq, and many more. Simply set the appropriate API key environment variable and specify the model name.
@@ -381,37 +372,25 @@ Yes! Hive supports local models through LiteLLM. Simply use the model name forma
 
 **Q: What makes Hive different from other agent frameworks?**
 
-Hive generates your entire agent system from natural language goals using a coding agent—you don't hardcode workflows or manually define graphs. When agents fail, the framework automatically captures failure data, evolves the agent graph, and redeploys. This self-improving loop is unique to Aden.
+Hive generates your entire agent system from natural language goals using a coding agent—you don't hardcode workflows or manually define graphs. When agents fail, the framework automatically captures failure data, [evolves the agent graph](docs/key_concepts/evolution.md), and redeploys. This self-improving loop is unique to Aden.
 
 **Q: Is Hive open-source?**
 
 Yes, Hive is fully open-source under the Apache License 2.0. We actively encourage community contributions and collaboration.
 
-**Q: Does Hive collect data from users?**
-
-Hive collects telemetry data for monitoring and observability purposes, including token usage, latency metrics, and cost tracking. Content capture (prompts and responses) is configurable and stored with team-scoped data isolation. All data stays within your infrastructure when self-hosted.
-
-**Q: What deployment options does Hive support?**
-
-Hive supports self-hosted deployments via Python packages. See the [Environment Setup Guide](ENVIRONMENT_SETUP.md) for installation instructions. Cloud deployment options and Kubernetes-ready configurations are on the roadmap.
-
 **Q: Can Hive handle complex, production-scale use cases?**
 
 Yes. Hive is explicitly designed for production environments with features like automatic failure recovery, real-time observability, cost controls, and horizontal scaling support. The framework handles both simple automations and complex multi-agent workflows.
 
 **Q: Does Hive support human-in-the-loop workflows?**
 
-Yes, Hive fully supports human-in-the-loop workflows through intervention nodes that pause execution for human input. These include configurable timeouts and escalation policies, allowing seamless collaboration between human experts and AI agents.
-
-**Q: What monitoring and debugging tools does Hive provide?**
-
-Hive includes comprehensive observability features: real-time WebSocket streaming for live agent execution monitoring, TimescaleDB-powered analytics for cost and performance metrics, health check endpoints for Kubernetes integration, and MCP tools for agent execution, including file operations, web search, data processing, and more.
+Yes, Hive fully supports [human-in-the-loop](docs/key_concepts/graph.md#human-in-the-loop) workflows through intervention nodes that pause execution for human input. These include configurable timeouts and escalation policies, allowing seamless collaboration between human experts and AI agents.
 
 **Q: What programming languages does Hive support?**
 
 The Hive framework is built in Python. A JavaScript/TypeScript SDK is on the roadmap.
 
-**Q: Can Aden agents interact with external tools and APIs?**
+**Q: Can Hive agents interact with external tools and APIs?**
 
 Yes. Aden's SDK-wrapped nodes provide built-in tool access, and the framework supports flexible tool ecosystems. Agents can integrate with external APIs, databases, and services through the node architecture.
 
@@ -421,7 +400,7 @@ Hive provides granular budget controls including spending limits, throttles, and
 
 **Q: Where can I find examples and documentation?**
 
-Visit [docs.adenhq.com](https://docs.adenhq.com/) for complete guides, API reference, and getting started tutorials. The repository also includes documentation in the `docs/` folder and a comprehensive [DEVELOPER.md](DEVELOPER.md) guide.
+Visit [docs.adenhq.com](https://docs.adenhq.com/) for complete guides, API reference, and getting started tutorials. The repository also includes documentation in the `docs/` folder and a comprehensive [developer guide](docs/developer-guide.md).
 
 **Q: How can I contribute to Aden?**
 
@@ -435,10 +414,6 @@ Aden's adaptation loop begins working from the first execution. When an agent fa
 
 Hive focuses on generating agents that run real business processes, rather than generic agents. This vision emphasizes outcome-driven design, adaptability, and an easy-to-use set of tools and integrations.
 
-**Q: Does Aden offer enterprise support?**
-
-For enterprise inquiries, contact the Aden team through [adenhq.com](https://adenhq.com) or join our [Discord community](https://discord.com/invite/MXE49hrKDk) for support and discussions.
-
 ---
 
 <p align="center">

core/.gitignore

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

core/README.md

@@ -14,7 +14,7 @@ Framework provides a runtime framework that captures **decisions**, not just act
 ## Installation
 
 ```bash
-pip install -e .
+uv pip install -e .
 ```
 
 ## MCP Server Setup
@@ -45,13 +45,13 @@ If you prefer manual setup:
 
 ```bash
 # Install framework
-pip install -e .
+uv pip install -e .
 
 # Install MCP dependencies
-pip install mcp fastmcp
+uv pip install mcp fastmcp
 
 # Test the server
-python -m framework.mcp.agent_builder_server
+uv run python -m framework.mcp.agent_builder_server
 ```
 
 ### Using with MCP Clients
@@ -86,13 +86,13 @@ Run an LLM-powered calculator:
 
 ```bash
 # Single calculation
-python -m framework calculate "2 + 3 * 4"
+uv run python -m framework calculate "2 + 3 * 4"
 
 # Interactive mode
-python -m framework interactive
+uv run python -m framework interactive
 
 # Analyze runs with Builder
-python -m framework analyze calculator
+uv run python -m framework analyze calculator
 ```
 
 ### Using the Runtime
@@ -136,16 +136,16 @@ Tests are generated using MCP tools (`generate_constraint_tests`, `generate_succ
 
 ```bash
 # Run tests against an agent
-python -m framework test-run <agent_path> --goal <goal_id> --parallel 4
+uv run python -m framework test-run <agent_path> --goal <goal_id> --parallel 4
 
 # Debug failed tests
-python -m framework test-debug <agent_path> <test_name>
+uv run python -m framework test-debug <agent_path> <test_name>
 
 # List tests for a goal
-python -m framework test-list <goal_id>
+uv run python -m framework test-list <goal_id>
 ```
 
-For detailed testing workflows, see the [testing-agent skill](../.claude/skills/testing-agent/SKILL.md).
+For detailed testing workflows, see the [hive-test skill](../.claude/skills/hive-test/SKILL.md).
 
 ### Analyzing Agent Behavior with Builder
 

core/demos/event_loop_wss_demo.py

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

core/demos/handoff_demo.py

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

core/examples/manual_agent.py

@@ -9,7 +9,7 @@ for understanding the core runtime loop:
 Setup -> Graph definition -> Execution -> Result
 
 Run with:
-    PYTHONPATH=core python core/examples/manual_agent.py
+    uv run python core/examples/manual_agent.py
 """
 
 import asyncio

core/examples/mcp_servers.json

@@ -4,8 +4,8 @@
       "name": "tools",
       "description": "Aden tools including web search, file operations, and PDF reading",
       "transport": "stdio",
-      "command": "python",
-      "args": ["mcp_server.py", "--stdio"],
+      "command": "uv",
+      "args": ["run", "python", "mcp_server.py", "--stdio"],
       "cwd": "../tools",
       "env": {
         "BRAVE_SEARCH_API_KEY": "${BRAVE_SEARCH_API_KEY}"

core/framework/builder/workflow.py

@@ -15,7 +15,7 @@ You cannot skip steps or bypass validation.
 
 from collections.abc import Callable
 from datetime import datetime
-from enum import Enum
+from enum import StrEnum
 from pathlib import Path
 from typing import Any
 
@@ -26,7 +26,7 @@ from framework.graph.goal import Goal
 from framework.graph.node import NodeSpec
 
 
-class BuildPhase(str, Enum):
+class BuildPhase(StrEnum):
     """Current phase of the build process."""
 
     INIT = "init"  # Just started

core/framework/cli.py

@@ -44,6 +44,13 @@ def _configure_paths():
         if exports_str not in sys.path:
             sys.path.insert(0, exports_str)
 
+    # Add examples/templates/ to sys.path so template agents are importable
+    templates_dir = project_root / "examples" / "templates"
+    if templates_dir.is_dir():
+        templates_str = str(templates_dir)
+        if templates_str not in sys.path:
+            sys.path.insert(0, templates_str)
+
     # Ensure core/ is also in sys.path (for non-editable-install scenarios)
     core_str = str(project_root / "core")
     if (project_root / "core").is_dir() and core_str not in sys.path:

core/framework/config.py

@@ -0,0 +1,64 @@
+"""Shared Hive configuration utilities.
+
+Centralises reading of ~/.hive/configuration.json so that the runner
+and every agent template share one implementation instead of copy-pasting
+helper functions.
+"""
+
+import json
+from dataclasses import dataclass, field
+from pathlib import Path
+from typing import Any
+
+from framework.graph.edge import DEFAULT_MAX_TOKENS
+
+# ---------------------------------------------------------------------------
+# Low-level config file access
+# ---------------------------------------------------------------------------
+
+HIVE_CONFIG_FILE = Path.home() / ".hive" / "configuration.json"
+
+
+def get_hive_config() -> dict[str, Any]:
+    """Load hive configuration from ~/.hive/configuration.json."""
+    if not HIVE_CONFIG_FILE.exists():
+        return {}
+    try:
+        with open(HIVE_CONFIG_FILE) as f:
+            return json.load(f)
+    except (json.JSONDecodeError, OSError):
+        return {}
+
+
+# ---------------------------------------------------------------------------
+# Derived helpers
+# ---------------------------------------------------------------------------
+
+
+def get_preferred_model() -> str:
+    """Return the user's preferred LLM model string (e.g. 'anthropic/claude-sonnet-4-20250514')."""
+    llm = get_hive_config().get("llm", {})
+    if llm.get("provider") and llm.get("model"):
+        return f"{llm['provider']}/{llm['model']}"
+    return "anthropic/claude-sonnet-4-20250514"
+
+
+def get_max_tokens() -> int:
+    """Return the configured max_tokens, falling back to DEFAULT_MAX_TOKENS."""
+    return get_hive_config().get("llm", {}).get("max_tokens", DEFAULT_MAX_TOKENS)
+
+
+# ---------------------------------------------------------------------------
+# RuntimeConfig – shared across agent templates
+# ---------------------------------------------------------------------------
+
+
+@dataclass
+class RuntimeConfig:
+    """Agent runtime configuration loaded from ~/.hive/configuration.json."""
+
+    model: str = field(default_factory=get_preferred_model)
+    temperature: float = 0.7
+    max_tokens: int = field(default_factory=get_max_tokens)
+    api_key: str | None = None
+    api_base: str | None = None

core/framework/credentials/aden/client.py

@@ -143,19 +143,34 @@ class AdenCredentialResponse:
     def from_dict(
         cls, data: dict[str, Any], integration_id: str | None = None
     ) -> AdenCredentialResponse:
-        """Create from API response dictionary."""
+        """Create from API response dictionary or normalized credential dict."""
+
         expires_at = None
         if data.get("expires_at"):
             expires_at = datetime.fromisoformat(data["expires_at"].replace("Z", "+00:00"))
 
+        resolved_integration_id = (
+            integration_id
+            or data.get("integration_id")
+            or data.get("alias")
+            or data.get("provider", "")
+        )
+
+        resolved_integration_type = data.get("integration_type") or data.get("provider", "")
+        metadata = data.get("metadata")
+        if metadata is None and data.get("email"):
+            metadata = {"email": data.get("email")}
+        if metadata is None:
+            metadata = {}
+
         return cls(
-            integration_id=integration_id or data.get("alias", data.get("provider", "")),
-            integration_type=data.get("provider", ""),
+            integration_id=resolved_integration_id,
+            integration_type=resolved_integration_type,
             access_token=data["access_token"],
             token_type=data.get("token_type", "Bearer"),
             expires_at=expires_at,
             scopes=data.get("scopes", []),
-            metadata={"email": data.get("email")} if data.get("email") else {},
+            metadata=metadata,
         )
 
 

core/framework/credentials/aden/storage.py

@@ -64,6 +64,8 @@ class AdenCachedStorage(CredentialStorage):
     - **Reads**: Try local cache first, fallback to Aden if stale/missing
     - **Writes**: Always write to local cache
     - **Offline resilience**: Uses cached credentials when Aden is unreachable
+    - **Provider-based lookup**: Match credentials by provider name (e.g., "hubspot")
+      when direct ID lookup fails, since Aden uses hash-based IDs internally.
 
     The cache TTL determines how long to trust local credentials before
     checking with the Aden server for updates. This balances:
@@ -85,6 +87,7 @@ class AdenCachedStorage(CredentialStorage):
 
         # First access fetches from Aden
         # Subsequent accesses use cache until TTL expires
+        # Can look up by provider name OR credential ID
         token = store.get_key("hubspot", "access_token")
     """
 
@@ -111,21 +114,24 @@ class AdenCachedStorage(CredentialStorage):
         self._cache_ttl = timedelta(seconds=cache_ttl_seconds)
         self._prefer_local = prefer_local
         self._cache_timestamps: dict[str, datetime] = {}
+        # Index: provider name (e.g., "hubspot") -> credential hash ID
+        self._provider_index: dict[str, str] = {}
 
     def save(self, credential: CredentialObject) -> None:
         """
-        Save credential to local cache.
+        Save credential to local cache and update provider index.
 
         Args:
             credential: The credential to save.
         """
         self._local.save(credential)
         self._cache_timestamps[credential.id] = datetime.now(UTC)
+        self._index_provider(credential)
         logger.debug(f"Cached credential '{credential.id}'")
 
     def load(self, credential_id: str) -> CredentialObject | None:
         """
-        Load credential from cache, with Aden fallback.
+        Load credential from cache, with Aden fallback and provider-based lookup.
 
         The loading strategy depends on the `prefer_local` setting:
 
@@ -141,8 +147,37 @@ class AdenCachedStorage(CredentialStorage):
         2. Update local cache with response
         3. Fall back to local cache only if Aden fails
 
+        Provider-based lookup:
+        When a provider index mapping exists for the credential_id (e.g.,
+        "hubspot" → hash ID), the Aden-synced credential is loaded first.
+        This ensures fresh OAuth tokens from Aden take priority over stale
+        local credentials (env vars, old encrypted files).
+
         Args:
-            credential_id: The credential identifier.
+            credential_id: The credential identifier or provider name.
+
+        Returns:
+            CredentialObject if found, None otherwise.
+        """
+        # Check provider index first — Aden-synced credentials take priority
+        resolved_id = self._provider_index.get(credential_id)
+        if resolved_id and resolved_id != credential_id:
+            result = self._load_by_id(resolved_id)
+            if result is not None:
+                logger.info(
+                    f"Loaded credential '{credential_id}' via provider index (id='{resolved_id}')"
+                )
+                return result
+
+        # Direct lookup (exact credential_id match)
+        return self._load_by_id(credential_id)
+
+    def _load_by_id(self, credential_id: str) -> CredentialObject | None:
+        """
+        Load credential by exact ID from cache, with Aden fallback.
+
+        Args:
+            credential_id: The exact credential identifier.
 
         Returns:
             CredentialObject if found, None otherwise.
@@ -200,15 +235,21 @@ class AdenCachedStorage(CredentialStorage):
 
     def exists(self, credential_id: str) -> bool:
         """
-        Check if credential exists in local cache.
+        Check if credential exists in local cache (by ID or provider name).
 
         Args:
-            credential_id: The credential identifier.
+            credential_id: The credential identifier or provider name.
 
         Returns:
             True if credential exists locally.
         """
-        return self._local.exists(credential_id)
+        if self._local.exists(credential_id):
+            return True
+        # Check provider index
+        resolved_id = self._provider_index.get(credential_id)
+        if resolved_id and resolved_id != credential_id:
+            return self._local.exists(resolved_id)
+        return False
 
     def _is_cache_fresh(self, credential_id: str) -> bool:
         """
@@ -242,6 +283,47 @@ class AdenCachedStorage(CredentialStorage):
         self._cache_timestamps.clear()
         logger.debug("Invalidated all cache entries")
 
+    def _index_provider(self, credential: CredentialObject) -> None:
+        """
+        Index a credential by its provider/integration type.
+
+        Aden credentials carry an ``_integration_type`` key whose value is
+        the provider name (e.g., ``hubspot``).  This method maps that
+        provider name to the credential's hash ID so that subsequent
+        ``load("hubspot")`` calls resolve to the correct credential.
+
+        Args:
+            credential: The credential to index.
+        """
+        integration_type_key = credential.keys.get("_integration_type")
+        if integration_type_key is None:
+            return
+        provider_name = integration_type_key.value.get_secret_value()
+        if provider_name:
+            self._provider_index[provider_name] = credential.id
+            logger.debug(f"Indexed provider '{provider_name}' -> '{credential.id}'")
+
+    def rebuild_provider_index(self) -> int:
+        """
+        Rebuild the provider index from all locally cached credentials.
+
+        Useful after loading from disk when the in-memory index is empty.
+
+        Returns:
+            Number of provider mappings indexed.
+        """
+        self._provider_index.clear()
+        indexed = 0
+        for cred_id in self._local.list_all():
+            cred = self._local.load(cred_id)
+            if cred:
+                before = len(self._provider_index)
+                self._index_provider(cred)
+                if len(self._provider_index) > before:
+                    indexed += 1
+        logger.debug(f"Rebuilt provider index with {indexed} mappings")
+        return indexed
+
     def sync_all_from_aden(self) -> int:
         """
         Sync all credentials from Aden server to local cache.

core/framework/credentials/aden/tests/test_aden_sync.py

@@ -589,6 +589,149 @@ class TestAdenCachedStorage:
         assert info["stale"]["is_fresh"] is False
         assert info["stale"]["ttl_remaining_seconds"] == 0
 
+    def test_save_indexes_provider(self, cached_storage):
+        """Test save builds the provider index from _integration_type key."""
+        cred = CredentialObject(
+            id="aHVic3BvdDp0ZXN0OjEzNjExOjExNTI1",
+            credential_type=CredentialType.OAUTH2,
+            keys={
+                "access_token": CredentialKey(
+                    name="access_token",
+                    value=SecretStr("token-value"),
+                ),
+                "_integration_type": CredentialKey(
+                    name="_integration_type",
+                    value=SecretStr("hubspot"),
+                ),
+            },
+        )
+
+        cached_storage.save(cred)
+
+        assert cached_storage._provider_index["hubspot"] == "aHVic3BvdDp0ZXN0OjEzNjExOjExNTI1"
+
+    def test_load_by_provider_name(self, cached_storage):
+        """Test load resolves provider name to hash-based credential ID."""
+        hash_id = "aHVic3BvdDp0ZXN0OjEzNjExOjExNTI1"
+        cred = CredentialObject(
+            id=hash_id,
+            credential_type=CredentialType.OAUTH2,
+            keys={
+                "access_token": CredentialKey(
+                    name="access_token",
+                    value=SecretStr("hubspot-token"),
+                ),
+                "_integration_type": CredentialKey(
+                    name="_integration_type",
+                    value=SecretStr("hubspot"),
+                ),
+            },
+        )
+
+        # Save builds the index
+        cached_storage.save(cred)
+
+        # Load by provider name should resolve to the hash ID
+        loaded = cached_storage.load("hubspot")
+
+        assert loaded is not None
+        assert loaded.id == hash_id
+        assert loaded.keys["access_token"].value.get_secret_value() == "hubspot-token"
+
+    def test_load_by_direct_id_still_works(self, cached_storage):
+        """Test load by direct hash ID still works as before."""
+        hash_id = "aHVic3BvdDp0ZXN0OjEzNjExOjExNTI1"
+        cred = CredentialObject(
+            id=hash_id,
+            credential_type=CredentialType.OAUTH2,
+            keys={
+                "access_token": CredentialKey(
+                    name="access_token",
+                    value=SecretStr("token"),
+                ),
+                "_integration_type": CredentialKey(
+                    name="_integration_type",
+                    value=SecretStr("hubspot"),
+                ),
+            },
+        )
+
+        cached_storage.save(cred)
+
+        # Direct ID lookup should still work
+        loaded = cached_storage.load(hash_id)
+
+        assert loaded is not None
+        assert loaded.id == hash_id
+
+    def test_exists_by_provider_name(self, cached_storage):
+        """Test exists resolves provider name to hash-based credential ID."""
+        hash_id = "c2xhY2s6dGVzdDo5OTk="
+        cred = CredentialObject(
+            id=hash_id,
+            credential_type=CredentialType.OAUTH2,
+            keys={
+                "access_token": CredentialKey(
+                    name="access_token",
+                    value=SecretStr("slack-token"),
+                ),
+                "_integration_type": CredentialKey(
+                    name="_integration_type",
+                    value=SecretStr("slack"),
+                ),
+            },
+        )
+
+        cached_storage.save(cred)
+
+        assert cached_storage.exists("slack") is True
+        assert cached_storage.exists(hash_id) is True
+        assert cached_storage.exists("nonexistent") is False
+
+    def test_rebuild_provider_index(self, cached_storage, local_storage):
+        """Test rebuild_provider_index reconstructs from local storage."""
+        # Manually save credentials to local storage (bypassing cached_storage.save)
+        for provider_name, hash_id in [("hubspot", "hash_hub"), ("slack", "hash_slack")]:
+            cred = CredentialObject(
+                id=hash_id,
+                credential_type=CredentialType.OAUTH2,
+                keys={
+                    "_integration_type": CredentialKey(
+                        name="_integration_type",
+                        value=SecretStr(provider_name),
+                    ),
+                },
+            )
+            local_storage.save(cred)
+
+        # Index should be empty (we bypassed save)
+        assert len(cached_storage._provider_index) == 0
+
+        # Rebuild
+        indexed = cached_storage.rebuild_provider_index()
+
+        assert indexed == 2
+        assert cached_storage._provider_index["hubspot"] == "hash_hub"
+        assert cached_storage._provider_index["slack"] == "hash_slack"
+
+    def test_save_without_integration_type_no_index(self, cached_storage):
+        """Test save does not index credentials without _integration_type key."""
+        cred = CredentialObject(
+            id="plain-cred",
+            credential_type=CredentialType.API_KEY,
+            keys={
+                "api_key": CredentialKey(
+                    name="api_key",
+                    value=SecretStr("key-value"),
+                ),
+            },
+        )
+
+        cached_storage.save(cred)
+
+        assert "plain-cred" not in cached_storage._provider_index
+        assert len(cached_storage._provider_index) == 0
+
 
 # =============================================================================
 # Integration Tests

core/framework/credentials/models.py

@@ -8,7 +8,7 @@ containing one or more keys (e.g., api_key, access_token, refresh_token).
 from __future__ import annotations
 
 from datetime import UTC, datetime
-from enum import Enum
+from enum import StrEnum
 from typing import Any
 
 from pydantic import BaseModel, Field, SecretStr
@@ -19,7 +19,7 @@ def _utc_now() -> datetime:
     return datetime.now(UTC)
 
 
-class CredentialType(str, Enum):
+class CredentialType(StrEnum):
     """Types of credentials the store can manage."""
 
     API_KEY = "api_key"

core/framework/credentials/oauth2/base_provider.py

@@ -96,7 +96,7 @@ class BaseOAuth2Provider(CredentialProvider):
                 self._client = httpx.Client(timeout=self.config.request_timeout)
             except ImportError as e:
                 raise ImportError(
-                    "OAuth2 provider requires 'httpx'. Install with: pip install httpx"
+                    "OAuth2 provider requires 'httpx'. Install with: uv pip install httpx"
                 ) from e
         return self._client
 

core/framework/credentials/oauth2/provider.py

@@ -11,11 +11,11 @@ from __future__ import annotations
 
 from dataclasses import dataclass, field
 from datetime import UTC, datetime, timedelta
-from enum import Enum
+from enum import StrEnum
 from typing import Any
 
 
-class TokenPlacement(str, Enum):
+class TokenPlacement(StrEnum):
     """Where to place the access token in HTTP requests."""
 
     HEADER_BEARER = "header_bearer"

core/framework/credentials/storage.py

@@ -136,7 +136,8 @@ class EncryptedFileStorage(CredentialStorage):
             from cryptography.fernet import Fernet
         except ImportError as e:
             raise ImportError(
-                "Encrypted storage requires 'cryptography'. Install with: pip install cryptography"
+                "Encrypted storage requires 'cryptography'. "
+                "Install with: uv pip install cryptography"
             ) from e
 
         self.base_path = Path(base_path or self.DEFAULT_PATH).expanduser()

core/framework/credentials/vault/hashicorp.py

@@ -2,7 +2,7 @@
 HashiCorp Vault storage adapter.
 
 Provides integration with HashiCorp Vault for enterprise secret management.
-Requires the 'hvac' package: pip install hvac
+Requires the 'hvac' package: uv pip install hvac
 """
 
 from __future__ import annotations
@@ -66,7 +66,7 @@ class HashiCorpVaultStorage(CredentialStorage):
         - AWS IAM auth method
 
     Requirements:
-        pip install hvac
+        uv pip install hvac
     """
 
     def __init__(
@@ -97,7 +97,7 @@ class HashiCorpVaultStorage(CredentialStorage):
             import hvac
         except ImportError as e:
             raise ImportError(
-                "HashiCorp Vault support requires 'hvac'. Install with: pip install hvac"
+                "HashiCorp Vault support requires 'hvac'. Install with: uv pip install hvac"
             ) from e
 
         self._url = url

core/framework/graph/__init__.py

@@ -1,8 +1,22 @@
 """Graph structures: Goals, Nodes, Edges, and Flexible Execution."""
 
+from framework.graph.client_io import (
+    ActiveNodeClientIO,
+    ClientIOGateway,
+    InertNodeClientIO,
+    NodeClientIO,
+)
 from framework.graph.code_sandbox import CodeSandbox, safe_eval, safe_exec
+from framework.graph.context_handoff import ContextHandoff, HandoffContext
 from framework.graph.conversation import ConversationStore, Message, NodeConversation
-from framework.graph.edge import EdgeCondition, EdgeSpec, GraphSpec
+from framework.graph.edge import DEFAULT_MAX_TOKENS, EdgeCondition, EdgeSpec, GraphSpec
+from framework.graph.event_loop_node import (
+    EventLoopNode,
+    JudgeProtocol,
+    JudgeVerdict,
+    LoopConfig,
+    OutputAccumulator,
+)
 from framework.graph.executor import GraphExecutor
 from framework.graph.flexible_executor import ExecutorConfig, FlexibleGraphExecutor
 from framework.graph.goal import Constraint, Goal, GoalStatus, SuccessCriterion
@@ -44,6 +58,7 @@ __all__ = [
     "EdgeSpec",
     "EdgeCondition",
     "GraphSpec",
+    "DEFAULT_MAX_TOKENS",
     # Executor (fixed graph)
     "GraphExecutor",
     # Plan (flexible execution)
@@ -77,4 +92,18 @@ __all__ = [
     "NodeConversation",
     "ConversationStore",
     "Message",
+    # Event Loop
+    "EventLoopNode",
+    "LoopConfig",
+    "OutputAccumulator",
+    "JudgeProtocol",
+    "JudgeVerdict",
+    # Context Handoff
+    "ContextHandoff",
+    "HandoffContext",
+    # Client I/O
+    "NodeClientIO",
+    "ActiveNodeClientIO",
+    "InertNodeClientIO",
+    "ClientIOGateway",
 ]

core/framework/graph/checkpoint_config.py

@@ -0,0 +1,85 @@
+"""
+Checkpoint Configuration - Controls checkpoint behavior during execution.
+"""
+
+from dataclasses import dataclass
+
+
+@dataclass
+class CheckpointConfig:
+    """
+    Configuration for checkpoint behavior during graph execution.
+
+    Controls when checkpoints are created, how they're stored,
+    and when they're pruned.
+    """
+
+    # Enable/disable checkpointing
+    enabled: bool = True
+
+    # When to checkpoint
+    checkpoint_on_node_start: bool = True
+    checkpoint_on_node_complete: bool = True
+
+    # Pruning (time-based)
+    checkpoint_max_age_days: int = 7  # Prune checkpoints older than 1 week
+    prune_every_n_nodes: int = 10  # Check for pruning every N nodes
+
+    # Performance
+    async_checkpoint: bool = True  # Don't block execution on checkpoint writes
+
+    # What to include in checkpoints
+    include_full_memory: bool = True
+    include_metrics: bool = True
+
+    def should_checkpoint_node_start(self) -> bool:
+        """Check if should checkpoint before node execution."""
+        return self.enabled and self.checkpoint_on_node_start
+
+    def should_checkpoint_node_complete(self) -> bool:
+        """Check if should checkpoint after node execution."""
+        return self.enabled and self.checkpoint_on_node_complete
+
+    def should_prune_checkpoints(self, nodes_executed: int) -> bool:
+        """
+        Check if should prune checkpoints based on execution progress.
+
+        Args:
+            nodes_executed: Number of nodes executed so far
+
+        Returns:
+            True if should check for old checkpoints and prune them
+        """
+        return (
+            self.enabled
+            and self.prune_every_n_nodes > 0
+            and nodes_executed % self.prune_every_n_nodes == 0
+        )
+
+
+# Default configuration for most agents
+DEFAULT_CHECKPOINT_CONFIG = CheckpointConfig(
+    enabled=True,
+    checkpoint_on_node_start=True,
+    checkpoint_on_node_complete=True,
+    checkpoint_max_age_days=7,
+    prune_every_n_nodes=10,
+    async_checkpoint=True,
+)
+
+
+# Minimal configuration (only checkpoint at node completion)
+MINIMAL_CHECKPOINT_CONFIG = CheckpointConfig(
+    enabled=True,
+    checkpoint_on_node_start=False,
+    checkpoint_on_node_complete=True,
+    checkpoint_max_age_days=7,
+    prune_every_n_nodes=20,
+    async_checkpoint=True,
+)
+
+
+# Disabled configuration (no checkpointing)
+DISABLED_CHECKPOINT_CONFIG = CheckpointConfig(
+    enabled=False,
+)

core/framework/graph/client_io.py

@@ -0,0 +1,170 @@
+"""
+Client I/O gateway for graph nodes.
+
+Provides the bridge between node code and external clients:
+- ActiveNodeClientIO: for client_facing=True nodes (streams output, accepts input)
+- InertNodeClientIO: for client_facing=False nodes (logs internally, redirects input)
+- ClientIOGateway: factory that creates the right variant per node
+"""
+
+from __future__ import annotations
+
+import asyncio
+import logging
+from abc import ABC, abstractmethod
+from collections.abc import AsyncIterator
+from typing import TYPE_CHECKING
+
+if TYPE_CHECKING:
+    from framework.runtime.event_bus import EventBus
+
+logger = logging.getLogger(__name__)
+
+
+class NodeClientIO(ABC):
+    """Abstract base for node client I/O."""
+
+    @abstractmethod
+    async def emit_output(self, content: str, is_final: bool = False) -> None:
+        """Emit output content. If is_final=True, signal end of stream."""
+
+    @abstractmethod
+    async def request_input(self, prompt: str = "", timeout: float | None = None) -> str:
+        """Request input. Behavior depends on whether the node is client-facing."""
+
+
+class ActiveNodeClientIO(NodeClientIO):
+    """
+    Client I/O for client_facing=True nodes.
+
+    - emit_output() queues content and publishes CLIENT_OUTPUT_DELTA.
+    - request_input() publishes CLIENT_INPUT_REQUESTED, then awaits provide_input().
+    - output_stream() yields queued content until the final sentinel.
+    """
+
+    def __init__(
+        self,
+        node_id: str,
+        event_bus: EventBus | None = None,
+    ) -> None:
+        self.node_id = node_id
+        self._event_bus = event_bus
+
+        self._output_queue: asyncio.Queue[str | None] = asyncio.Queue()
+        self._output_snapshot = ""
+
+        self._input_event: asyncio.Event | None = None
+        self._input_result: str | None = None
+
+    async def emit_output(self, content: str, is_final: bool = False) -> None:
+        self._output_snapshot += content
+        await self._output_queue.put(content)
+
+        if self._event_bus is not None:
+            await self._event_bus.emit_client_output_delta(
+                stream_id=self.node_id,
+                node_id=self.node_id,
+                content=content,
+                snapshot=self._output_snapshot,
+            )
+
+        if is_final:
+            await self._output_queue.put(None)
+
+    async def request_input(self, prompt: str = "", timeout: float | None = None) -> str:
+        if self._input_event is not None:
+            raise RuntimeError("request_input already pending for this node")
+
+        self._input_event = asyncio.Event()
+        self._input_result = None
+
+        if self._event_bus is not None:
+            await self._event_bus.emit_client_input_requested(
+                stream_id=self.node_id,
+                node_id=self.node_id,
+                prompt=prompt,
+            )
+
+        try:
+            if timeout is not None:
+                await asyncio.wait_for(self._input_event.wait(), timeout=timeout)
+            else:
+                await self._input_event.wait()
+        finally:
+            self._input_event = None
+
+        if self._input_result is None:
+            raise RuntimeError("input event was set but no input was provided")
+        result = self._input_result
+        self._input_result = None
+        return result
+
+    async def provide_input(self, content: str) -> None:
+        """Called externally to fulfill a pending request_input()."""
+        if self._input_event is None:
+            raise RuntimeError("no pending request_input to fulfill")
+        self._input_result = content
+        self._input_event.set()
+
+    async def output_stream(self) -> AsyncIterator[str]:
+        """Async iterator that yields output chunks until the final sentinel."""
+        while True:
+            chunk = await self._output_queue.get()
+            if chunk is None:
+                break
+            yield chunk
+
+
+class InertNodeClientIO(NodeClientIO):
+    """
+    Client I/O for client_facing=False nodes.
+
+    - emit_output() publishes NODE_INTERNAL_OUTPUT (content is not discarded).
+    - request_input() publishes NODE_INPUT_BLOCKED and returns a redirect string.
+    """
+
+    def __init__(
+        self,
+        node_id: str,
+        event_bus: EventBus | None = None,
+    ) -> None:
+        self.node_id = node_id
+        self._event_bus = event_bus
+
+    async def emit_output(self, content: str, is_final: bool = False) -> None:
+        if self._event_bus is not None:
+            await self._event_bus.emit_node_internal_output(
+                stream_id=self.node_id,
+                node_id=self.node_id,
+                content=content,
+            )
+
+    async def request_input(self, prompt: str = "", timeout: float | None = None) -> str:
+        if self._event_bus is not None:
+            await self._event_bus.emit_node_input_blocked(
+                stream_id=self.node_id,
+                node_id=self.node_id,
+                prompt=prompt,
+            )
+        return (
+            "You are an internal processing node. There is no user to interact with."
+            " Work with the data provided in your inputs to complete your task."
+        )
+
+
+class ClientIOGateway:
+    """Factory that creates the appropriate NodeClientIO for a node."""
+
+    def __init__(self, event_bus: EventBus | None = None) -> None:
+        self._event_bus = event_bus
+
+    def create_io(self, node_id: str, client_facing: bool) -> NodeClientIO:
+        if client_facing:
+            return ActiveNodeClientIO(
+                node_id=node_id,
+                event_bus=self._event_bus,
+            )
+        return InertNodeClientIO(
+            node_id=node_id,
+            event_bus=self._event_bus,
+        )

core/framework/graph/context_handoff.py

@@ -0,0 +1,191 @@
+"""Context handoff: summarize a completed NodeConversation for the next graph node."""
+
+from __future__ import annotations
+
+import logging
+from dataclasses import dataclass
+from typing import TYPE_CHECKING, Any
+
+from framework.graph.conversation import _try_extract_key
+
+if TYPE_CHECKING:
+    from framework.graph.conversation import NodeConversation
+    from framework.llm.provider import LLMProvider
+
+logger = logging.getLogger(__name__)
+
+_TRUNCATE_CHARS = 500
+
+
+# ---------------------------------------------------------------------------
+# Data
+# ---------------------------------------------------------------------------
+
+
+@dataclass
+class HandoffContext:
+    """Structured summary of a completed node conversation."""
+
+    source_node_id: str
+    summary: str
+    key_outputs: dict[str, Any]
+    turn_count: int
+    total_tokens_used: int
+
+
+# ---------------------------------------------------------------------------
+# ContextHandoff
+# ---------------------------------------------------------------------------
+
+
+class ContextHandoff:
+    """Summarize a completed NodeConversation into a HandoffContext.
+
+    Parameters
+    ----------
+    llm : LLMProvider | None
+        Optional LLM provider for abstractive summarization.
+        When *None*, all summarization uses the extractive fallback.
+    """
+
+    def __init__(self, llm: LLMProvider | None = None) -> None:
+        self.llm = llm
+
+    # ------------------------------------------------------------------
+    # Public API
+    # ------------------------------------------------------------------
+
+    def summarize_conversation(
+        self,
+        conversation: NodeConversation,
+        node_id: str,
+        output_keys: list[str] | None = None,
+    ) -> HandoffContext:
+        """Produce a HandoffContext from *conversation*.
+
+        1. Extracts turn_count & total_tokens_used (sync properties).
+        2. Extracts key_outputs by scanning assistant messages most-recent-first.
+        3. Builds a summary via the LLM (if available) or extractive fallback.
+        """
+        turn_count = conversation.turn_count
+        total_tokens_used = conversation.estimate_tokens()
+        messages = conversation.messages  # defensive copy
+
+        # --- key outputs ---------------------------------------------------
+        key_outputs: dict[str, Any] = {}
+        if output_keys:
+            remaining = set(output_keys)
+            for msg in reversed(messages):
+                if msg.role != "assistant" or not remaining:
+                    continue
+                for key in list(remaining):
+                    value = _try_extract_key(msg.content, key)
+                    if value is not None:
+                        key_outputs[key] = value
+                        remaining.discard(key)
+
+        # --- summary -------------------------------------------------------
+        if self.llm is not None:
+            try:
+                summary = self._llm_summary(messages, output_keys or [])
+            except Exception:
+                logger.warning(
+                    "LLM summarization failed; falling back to extractive.",
+                    exc_info=True,
+                )
+                summary = self._extractive_summary(messages)
+        else:
+            summary = self._extractive_summary(messages)
+
+        return HandoffContext(
+            source_node_id=node_id,
+            summary=summary,
+            key_outputs=key_outputs,
+            turn_count=turn_count,
+            total_tokens_used=total_tokens_used,
+        )
+
+    @staticmethod
+    def format_as_input(handoff: HandoffContext) -> str:
+        """Render *handoff* as structured plain text for the next node's input."""
+        header = (
+            f"--- CONTEXT FROM: {handoff.source_node_id} "
+            f"({handoff.turn_count} turns, ~{handoff.total_tokens_used} tokens) ---"
+        )
+
+        sections: list[str] = [header, ""]
+
+        if handoff.key_outputs:
+            sections.append("KEY OUTPUTS:")
+            for k, v in handoff.key_outputs.items():
+                sections.append(f"- {k}: {v}")
+            sections.append("")
+
+        summary_text = handoff.summary or "No summary available."
+        sections.append("SUMMARY:")
+        sections.append(summary_text)
+        sections.append("")
+        sections.append("--- END CONTEXT ---")
+
+        return "\n".join(sections)
+
+    # ------------------------------------------------------------------
+    # Private helpers
+    # ------------------------------------------------------------------
+
+    @staticmethod
+    def _extractive_summary(messages: list) -> str:
+        """Build a summary from key assistant messages without an LLM.
+
+        Strategy:
+        - Include the first assistant message (initial assessment).
+        - Include the last assistant message (final conclusion).
+        - Truncate each to ~500 chars.
+        """
+        if not messages:
+            return "Empty conversation."
+
+        assistant_msgs = [m for m in messages if m.role == "assistant"]
+        if not assistant_msgs:
+            return "No assistant responses."
+
+        parts: list[str] = []
+
+        first = assistant_msgs[0].content
+        parts.append(first[:_TRUNCATE_CHARS])
+
+        if len(assistant_msgs) > 1:
+            last = assistant_msgs[-1].content
+            parts.append(last[:_TRUNCATE_CHARS])
+
+        return "\n\n".join(parts)
+
+    def _llm_summary(self, messages: list, output_keys: list[str]) -> str:
+        """Produce a summary by calling the LLM provider."""
+        if self.llm is None:
+            raise ValueError("_llm_summary called without an LLM provider")
+
+        conversation_text = "\n".join(f"[{m.role}]: {m.content}" for m in messages)
+
+        key_hint = ""
+        if output_keys:
+            key_hint = (
+                "\nThe following output keys are especially important: "
+                + ", ".join(output_keys)
+                + ".\n"
+            )
+
+        system_prompt = (
+            "You are a concise summarizer. Given the conversation below, "
+            "produce a brief summary (at most ~500 tokens) that captures the "
+            "key decisions, findings, and outcomes. Focus on what was concluded "
+            "rather than the back-and-forth process." + key_hint
+        )
+
+        response = self.llm.complete(
+            messages=[{"role": "user", "content": conversation_text}],
+            system=system_prompt,
+            max_tokens=500,
+        )
+
+        return response.content.strip()

core/framework/graph/conversation.py

@@ -75,6 +75,16 @@ class Message:
         )
 
 
+def _extract_spillover_filename(content: str) -> str | None:
+    """Extract spillover filename from a truncated tool result.
+
+    Matches the pattern produced by EventLoopNode._truncate_tool_result():
+        "saved to 'tool_github_list_stargazers_abc123.txt'"
+    """
+    match = re.search(r"saved to '([^']+)'", content)
+    return match.group(1) if match else None
+
+
 # ---------------------------------------------------------------------------
 # ConversationStore protocol (Phase 2)
 # ---------------------------------------------------------------------------
@@ -108,6 +118,50 @@ class ConversationStore(Protocol):
 # ---------------------------------------------------------------------------
 
 
+def _try_extract_key(content: str, key: str) -> str | None:
+    """Try 4 strategies to extract a *key*'s value from message content.
+
+    Strategies (in order):
+    1. Whole message is JSON — ``json.loads``, check for key.
+    2. Embedded JSON via ``find_json_object`` helper.
+    3. Colon format: ``key: value``.
+    4. Equals format: ``key = value``.
+    """
+    from framework.graph.node import find_json_object
+
+    # 1. Whole message is JSON
+    try:
+        parsed = json.loads(content)
+        if isinstance(parsed, dict) and key in parsed:
+            val = parsed[key]
+            return json.dumps(val) if not isinstance(val, str) else val
+    except (json.JSONDecodeError, TypeError):
+        pass
+
+    # 2. Embedded JSON via find_json_object
+    json_str = find_json_object(content)
+    if json_str:
+        try:
+            parsed = json.loads(json_str)
+            if isinstance(parsed, dict) and key in parsed:
+                val = parsed[key]
+                return json.dumps(val) if not isinstance(val, str) else val
+        except (json.JSONDecodeError, TypeError):
+            pass
+
+    # 3. Colon format: key: value
+    match = re.search(rf"\b{re.escape(key)}\s*:\s*(.+)", content)
+    if match:
+        return match.group(1).strip()
+
+    # 4. Equals format: key = value
+    match = re.search(rf"\b{re.escape(key)}\s*=\s*(.+)", content)
+    if match:
+        return match.group(1).strip()
+
+    return None
+
+
 class NodeConversation:
     """Message history for a graph node with optional write-through persistence.
 
@@ -133,6 +187,7 @@ class NodeConversation:
         self._messages: list[Message] = []
         self._next_seq: int = 0
         self._meta_persisted: bool = False
+        self._last_api_input_tokens: int | None = None
 
     # --- Properties --------------------------------------------------------
 
@@ -205,14 +260,78 @@ class NodeConversation:
     # --- Query -------------------------------------------------------------
 
     def to_llm_messages(self) -> list[dict[str, Any]]:
-        """Return messages as OpenAI-format dicts (system prompt excluded)."""
-        return [m.to_llm_dict() for m in self._messages]
+        """Return messages as OpenAI-format dicts (system prompt excluded).
+
+        Automatically repairs orphaned tool_use blocks (assistant messages
+        with tool_calls that lack corresponding tool-result messages).  This
+        can happen when a loop is cancelled mid-tool-execution.
+        """
+        msgs = [m.to_llm_dict() for m in self._messages]
+        return self._repair_orphaned_tool_calls(msgs)
+
+    @staticmethod
+    def _repair_orphaned_tool_calls(
+        msgs: list[dict[str, Any]],
+    ) -> list[dict[str, Any]]:
+        """Ensure every tool_call has a matching tool-result message."""
+        repaired: list[dict[str, Any]] = []
+        for i, m in enumerate(msgs):
+            repaired.append(m)
+            tool_calls = m.get("tool_calls")
+            if m.get("role") != "assistant" or not tool_calls:
+                continue
+            # Collect IDs of tool results that follow this assistant message
+            answered: set[str] = set()
+            for j in range(i + 1, len(msgs)):
+                if msgs[j].get("role") == "tool":
+                    tid = msgs[j].get("tool_call_id")
+                    if tid:
+                        answered.add(tid)
+                else:
+                    break  # stop at first non-tool message
+            # Patch any missing results
+            for tc in tool_calls:
+                tc_id = tc.get("id")
+                if tc_id and tc_id not in answered:
+                    repaired.append(
+                        {
+                            "role": "tool",
+                            "tool_call_id": tc_id,
+                            "content": "ERROR: Tool execution was interrupted.",
+                        }
+                    )
+        return repaired
 
     def estimate_tokens(self) -> int:
-        """Rough token estimate: total characters / 4."""
+        """Best available token estimate.
+
+        Uses actual API input token count when available (set via
+        :meth:`update_token_count`), otherwise falls back to the rough
+        ``total_chars / 4`` heuristic.
+        """
+        if self._last_api_input_tokens is not None:
+            return self._last_api_input_tokens
         total_chars = sum(len(m.content) for m in self._messages)
         return total_chars // 4
 
+    def update_token_count(self, actual_input_tokens: int) -> None:
+        """Store actual API input token count for more accurate compaction.
+
+        Called by EventLoopNode after each LLM call with the ``input_tokens``
+        value from the API response.  This value includes system prompt and
+        tool definitions, so it may be higher than a message-only estimate.
+        """
+        self._last_api_input_tokens = actual_input_tokens
+
+    def usage_ratio(self) -> float:
+        """Current token usage as a fraction of *max_history_tokens*.
+
+        Returns 0.0 when ``max_history_tokens`` is zero (unlimited).
+        """
+        if self._max_history_tokens <= 0:
+            return 0.0
+        return self.estimate_tokens() / self._max_history_tokens
+
     def needs_compaction(self) -> bool:
         return self.estimate_tokens() >= self._max_history_tokens * self._compaction_threshold
 
@@ -244,42 +363,89 @@ class NodeConversation:
 
     def _try_extract_key(self, content: str, key: str) -> str | None:
         """Try 4 strategies to extract a key's value from message content."""
-        from framework.graph.node import find_json_object
-
-        # 1. Whole message is JSON
-        try:
-            parsed = json.loads(content)
-            if isinstance(parsed, dict) and key in parsed:
-                val = parsed[key]
-                return json.dumps(val) if not isinstance(val, str) else val
-        except (json.JSONDecodeError, TypeError):
-            pass
-
-        # 2. Embedded JSON via find_json_object
-        json_str = find_json_object(content)
-        if json_str:
-            try:
-                parsed = json.loads(json_str)
-                if isinstance(parsed, dict) and key in parsed:
-                    val = parsed[key]
-                    return json.dumps(val) if not isinstance(val, str) else val
-            except (json.JSONDecodeError, TypeError):
-                pass
-
-        # 3. Colon format: key: value
-        match = re.search(rf"\b{re.escape(key)}\s*:\s*(.+)", content)
-        if match:
-            return match.group(1).strip()
-
-        # 4. Equals format: key = value
-        match = re.search(rf"\b{re.escape(key)}\s*=\s*(.+)", content)
-        if match:
-            return match.group(1).strip()
-
-        return None
+        return _try_extract_key(content, key)
 
     # --- Lifecycle ---------------------------------------------------------
 
+    async def prune_old_tool_results(
+        self,
+        protect_tokens: int = 5000,
+        min_prune_tokens: int = 2000,
+    ) -> int:
+        """Replace old tool result content with compact placeholders.
+
+        Walks backward through messages. Recent tool results (within
+        *protect_tokens*) are kept intact. Older tool results have their
+        content replaced with a ~100-char placeholder that preserves the
+        spillover filename reference (if any). Message structure (role,
+        seq, tool_use_id) stays valid for the LLM API.
+
+        Error tool results are never pruned — they prevent re-calling
+        failing tools.
+
+        Returns the number of messages pruned (0 if nothing was pruned).
+        """
+        if not self._messages:
+            return 0
+
+        # Phase 1: Walk backward, classify tool results as protected vs pruneable
+        protected_tokens = 0
+        pruneable: list[int] = []  # indices into self._messages
+        pruneable_tokens = 0
+
+        for i in range(len(self._messages) - 1, -1, -1):
+            msg = self._messages[i]
+            if msg.role != "tool":
+                continue
+            if msg.is_error:
+                continue  # never prune errors
+            if msg.content.startswith("[Pruned tool result"):
+                continue  # already pruned
+
+            est = len(msg.content) // 4
+            if protected_tokens < protect_tokens:
+                protected_tokens += est
+            else:
+                pruneable.append(i)
+                pruneable_tokens += est
+
+        # Phase 2: Only prune if enough to be worthwhile
+        if pruneable_tokens < min_prune_tokens:
+            return 0
+
+        # Phase 3: Replace content with compact placeholder
+        count = 0
+        for i in pruneable:
+            msg = self._messages[i]
+            orig_len = len(msg.content)
+            spillover = _extract_spillover_filename(msg.content)
+
+            if spillover:
+                placeholder = (
+                    f"[Pruned tool result: {orig_len} chars. "
+                    f"Full data in '{spillover}'. "
+                    f"Use load_data('{spillover}') to retrieve.]"
+                )
+            else:
+                placeholder = f"[Pruned tool result: {orig_len} chars cleared from context.]"
+
+            self._messages[i] = Message(
+                seq=msg.seq,
+                role=msg.role,
+                content=placeholder,
+                tool_use_id=msg.tool_use_id,
+                tool_calls=msg.tool_calls,
+                is_error=msg.is_error,
+            )
+            count += 1
+
+            if self._store:
+                await self._store.write_part(msg.seq, self._messages[i].to_storage_dict())
+
+        # Reset token estimate — content lengths changed
+        self._last_api_input_tokens = None
+        return count
+
     async def compact(self, summary: str, keep_recent: int = 2) -> None:
         """Replace old messages with a summary, optionally keeping recent ones.
 
@@ -294,12 +460,18 @@ class NodeConversation:
         # Clamp: must discard at least 1 message
         keep_recent = max(0, min(keep_recent, len(self._messages) - 1))
 
-        if keep_recent > 0:
-            old_messages = self._messages[:-keep_recent]
-            recent_messages = self._messages[-keep_recent:]
-        else:
-            old_messages = self._messages
-            recent_messages = []
+        total = len(self._messages)
+        split = total - keep_recent if keep_recent > 0 else total
+
+        # Advance split past orphaned tool results at the boundary.
+        # Tool-role messages reference a tool_use from the preceding
+        # assistant message; if that assistant message falls into the
+        # compacted (old) portion the tool_result becomes invalid.
+        while split < total and self._messages[split].role == "tool":
+            split += 1
+
+        old_messages = list(self._messages[:split])
+        recent_messages = list(self._messages[split:])
 
         # Extract protected values from messages being discarded
         if self._output_keys:
@@ -330,6 +502,7 @@ class NodeConversation:
             await self._store.write_cursor({"next_seq": self._next_seq})
 
         self._messages = [summary_msg] + recent_messages
+        self._last_api_input_tokens = None  # reset; next LLM call will recalibrate
 
     async def clear(self) -> None:
         """Remove all messages, keep system prompt, preserve ``_next_seq``."""
@@ -337,6 +510,7 @@ class NodeConversation:
             await self._store.delete_parts_before(self._next_seq)
             await self._store.write_cursor({"next_seq": self._next_seq})
         self._messages.clear()
+        self._last_api_input_tokens = None
 
     def export_summary(self) -> str:
         """Structured summary with [STATS], [CONFIG], [RECENT_MESSAGES] sections."""

core/framework/graph/edge.py

@@ -21,15 +21,17 @@ allowing the LLM to evaluate whether proceeding along an edge makes sense
 given the current goal, context, and execution state.
 """
 
-from enum import Enum
+from enum import StrEnum
 from typing import Any
 
-from pydantic import BaseModel, Field
+from pydantic import BaseModel, Field, model_validator
 
 from framework.graph.safe_eval import safe_eval
 
+DEFAULT_MAX_TOKENS = 8192
 
-class EdgeCondition(str, Enum):
+
+class EdgeCondition(StrEnum):
     """When an edge should be traversed."""
 
     ALWAYS = "always"  # Always after source completes
@@ -156,6 +158,10 @@ class EdgeSpec(BaseModel):
         memory: dict[str, Any],
     ) -> bool:
         """Evaluate a conditional expression."""
+        import logging
+
+        logger = logging.getLogger(__name__)
+
         if not self.condition_expr:
             return True
 
@@ -172,12 +178,24 @@ class EdgeSpec(BaseModel):
 
         try:
             # Safe evaluation using AST-based whitelist
-            return bool(safe_eval(self.condition_expr, context))
+            result = bool(safe_eval(self.condition_expr, context))
+            # Log the evaluation for visibility
+            # Extract the variable names used in the expression for debugging
+            expr_vars = {
+                k: repr(context[k])
+                for k in context
+                if k not in ("output", "memory", "result", "true", "false")
+                and k in self.condition_expr
+            }
+            logger.info(
+                "  Edge %s: condition '%s' → %s  (vars: %s)",
+                self.id,
+                self.condition_expr,
+                result,
+                expr_vars or "none matched",
+            )
+            return result
         except Exception as e:
-            # Log the error for debugging
-            import logging
-
-            logger = logging.getLogger(__name__)
             logger.warning(f"      ⚠ Condition evaluation failed: {self.condition_expr}")
             logger.warning(f"         Error: {e}")
             logger.warning(f"         Available context keys: {list(context.keys())}")
@@ -408,7 +426,7 @@ class GraphSpec(BaseModel):
 
     # Default LLM settings
     default_model: str = "claude-haiku-4-5-20251001"
-    max_tokens: int = 1024
+    max_tokens: int = Field(default=None)  # resolved by _resolve_max_tokens validator
 
     # Cleanup LLM for JSON extraction fallback (fast/cheap model preferred)
     # If not set, uses CEREBRAS_API_KEY -> cerebras/llama-3.3-70b or
@@ -419,12 +437,28 @@ class GraphSpec(BaseModel):
     max_steps: int = Field(default=100, description="Maximum node executions before timeout")
     max_retries_per_node: int = 3
 
+    # EventLoopNode configuration (from configure_loop)
+    loop_config: dict[str, Any] = Field(
+        default_factory=dict,
+        description="EventLoopNode configuration (max_iterations, max_tool_calls_per_turn, etc.)",
+    )
+
     # Metadata
     description: str = ""
     created_by: str = ""  # "human" or "builder_agent"
 
     model_config = {"extra": "allow"}
 
+    @model_validator(mode="before")
+    @classmethod
+    def _resolve_max_tokens(cls, values: Any) -> Any:
+        """Resolve max_tokens from the global config store when not explicitly set."""
+        if isinstance(values, dict) and values.get("max_tokens") is None:
+            from framework.config import get_max_tokens
+
+            values["max_tokens"] = get_max_tokens()
+        return values
+
     def get_node(self, node_id: str) -> Any | None:
         """Get a node by ID."""
         for node in self.nodes:
@@ -608,4 +642,40 @@ class GraphSpec(BaseModel):
                     continue
                 errors.append(f"Node '{node.id}' is unreachable from entry")
 
+        # Client-facing fan-out validation
+        fan_outs = self.detect_fan_out_nodes()
+        for source_id, targets in fan_outs.items():
+            client_facing_targets = [
+                t
+                for t in targets
+                if self.get_node(t) and getattr(self.get_node(t), "client_facing", False)
+            ]
+            if len(client_facing_targets) > 1:
+                errors.append(
+                    f"Fan-out from '{source_id}' has multiple client-facing nodes: "
+                    f"{client_facing_targets}. Only one branch may be client-facing."
+                )
+
+        # Output key overlap on parallel event_loop nodes
+        for source_id, targets in fan_outs.items():
+            event_loop_targets = [
+                t
+                for t in targets
+                if self.get_node(t) and getattr(self.get_node(t), "node_type", "") == "event_loop"
+            ]
+            if len(event_loop_targets) > 1:
+                seen_keys: dict[str, str] = {}
+                for node_id in event_loop_targets:
+                    node = self.get_node(node_id)
+                    for key in getattr(node, "output_keys", []):
+                        if key in seen_keys:
+                            errors.append(
+                                f"Fan-out from '{source_id}': event_loop nodes "
+                                f"'{seen_keys[key]}' and '{node_id}' both write to "
+                                f"output_key '{key}'. Parallel event_loop nodes must "
+                                f"have disjoint output_keys to prevent last-wins data loss."
+                            )
+                        else:
+                            seen_keys[key] = node_id
+
         return errors

core/framework/graph/goal.py

@@ -12,13 +12,13 @@ Goals are:
 """
 
 from datetime import datetime
-from enum import Enum
+from enum import StrEnum
 from typing import Any
 
 from pydantic import BaseModel, Field
 
 
-class GoalStatus(str, Enum):
+class GoalStatus(StrEnum):
     """Lifecycle status of a goal."""
 
     DRAFT = "draft"  # Being defined

core/framework/graph/hitl.py

@@ -6,11 +6,11 @@ where agents need to gather input from humans.
 """
 
 from dataclasses import dataclass, field
-from enum import Enum
+from enum import StrEnum
 from typing import Any
 
 
-class HITLInputType(str, Enum):
+class HITLInputType(StrEnum):
     """Type of input expected from human."""
 
     FREE_TEXT = "free_text"  # Open-ended text response

core/framework/graph/node.py

@@ -16,6 +16,7 @@ Protocol:
 """
 
 import asyncio
+import inspect
 import logging
 from abc import ABC, abstractmethod
 from collections.abc import Callable
@@ -154,7 +155,10 @@ class NodeSpec(BaseModel):
     # Node behavior type
     node_type: str = Field(
         default="llm_tool_use",
-        description="Type: 'llm_tool_use', 'llm_generate', 'function', 'router', 'human_input'",
+        description=(
+            "Type: 'event_loop', 'function', 'router', 'human_input'. "
+            "Deprecated: 'llm_tool_use', 'llm_generate' (use 'event_loop' instead)."
+        ),
     )
 
     # Data flow
@@ -206,6 +210,15 @@ class NodeSpec(BaseModel):
     max_retries: int = Field(default=3)
     retry_on: list[str] = Field(default_factory=list, description="Error types to retry on")
 
+    # Visit limits (for feedback/callback edges)
+    max_node_visits: int = Field(
+        default=1,
+        description=(
+            "Max times this node executes in one graph run. "
+            "Set >1 for feedback loops. 0 = unlimited (max_steps guards)."
+        ),
+    )
+
     # Pydantic model for output validation
     output_model: type[BaseModel] | None = Field(
         default=None,
@@ -219,6 +232,12 @@ class NodeSpec(BaseModel):
         description="Maximum retries when Pydantic validation fails (with feedback to LLM)",
     )
 
+    # Client-facing behavior
+    client_facing: bool = Field(
+        default=False,
+        description="If True, this node streams output to the end user and can request input.",
+    )
+
     model_config = {"extra": "allow", "arbitrary_types_allowed": True}
 
 
@@ -458,6 +477,12 @@ class NodeContext:
     attempt: int = 1
     max_attempts: int = 3
 
+    # Runtime logging (optional)
+    runtime_logger: Any = None  # RuntimeLogger | None — uses Any to avoid import
+
+    # Pause control (optional) - asyncio.Event for pause requests
+    pause_event: Any = None  # asyncio.Event | None
+
 
 @dataclass
 class NodeResult:
@@ -835,6 +860,8 @@ Keep the same JSON structure but with shorter content values.
         )
 
         start = time.time()
+        _step_index = 0
+        _captured_tool_calls: list[dict] = []
 
         try:
             # Build messages
@@ -874,6 +901,16 @@ Keep the same JSON structure but with shorter content values.
                     if len(str(result.content)) > 150:
                         result_str += "..."
                     logger.info(f"         ✓ Tool result: {result_str}")
+                    # Capture for runtime logging
+                    _captured_tool_calls.append(
+                        {
+                            "tool_use_id": tool_use.id,
+                            "tool_name": tool_use.name,
+                            "tool_input": tool_use.input,
+                            "content": result.content,
+                            "is_error": result.is_error,
+                        }
+                    )
                     return result
 
                 response = ctx.llm.complete_with_tools(
@@ -1053,6 +1090,29 @@ Keep the same JSON structure but with shorter content values.
                                     f"Pydantic validation failed after "
                                     f"{max_validation_retries} retries: {err}"
                                 )
+                                if ctx.runtime_logger:
+                                    ctx.runtime_logger.log_step(
+                                        node_id=ctx.node_id,
+                                        node_type=ctx.node_spec.node_type,
+                                        step_index=_step_index,
+                                        llm_text=response.content,
+                                        tool_calls=_captured_tool_calls,
+                                        input_tokens=total_input_tokens,
+                                        output_tokens=total_output_tokens,
+                                        latency_ms=latency_ms,
+                                    )
+                                    ctx.runtime_logger.log_node_complete(
+                                        node_id=ctx.node_id,
+                                        node_name=ctx.node_spec.name,
+                                        node_type=ctx.node_spec.node_type,
+                                        success=False,
+                                        error=error_msg,
+                                        total_steps=_step_index + 1,
+                                        tokens_used=total_input_tokens + total_output_tokens,
+                                        input_tokens=total_input_tokens,
+                                        output_tokens=total_output_tokens,
+                                        latency_ms=latency_ms,
+                                    )
                                 return NodeResult(
                                     success=False,
                                     error=error_msg,
@@ -1142,12 +1202,36 @@ Keep the same JSON structure but with shorter content values.
                     )
 
                     # Return failure instead of writing garbage to all keys
+                    _extraction_error = (
+                        f"Output extraction failed: {e}. LLM returned non-JSON response. "
+                        f"Expected keys: {ctx.node_spec.output_keys}"
+                    )
+                    if ctx.runtime_logger:
+                        ctx.runtime_logger.log_step(
+                            node_id=ctx.node_id,
+                            node_type=ctx.node_spec.node_type,
+                            step_index=_step_index,
+                            llm_text=response.content,
+                            tool_calls=_captured_tool_calls,
+                            input_tokens=response.input_tokens,
+                            output_tokens=response.output_tokens,
+                            latency_ms=latency_ms,
+                        )
+                        ctx.runtime_logger.log_node_complete(
+                            node_id=ctx.node_id,
+                            node_name=ctx.node_spec.name,
+                            node_type=ctx.node_spec.node_type,
+                            success=False,
+                            error=_extraction_error,
+                            total_steps=_step_index + 1,
+                            tokens_used=response.input_tokens + response.output_tokens,
+                            input_tokens=response.input_tokens,
+                            output_tokens=response.output_tokens,
+                            latency_ms=latency_ms,
+                        )
                     return NodeResult(
                         success=False,
-                        error=(
-                            f"Output extraction failed: {e}. LLM returned non-JSON response. "
-                            f"Expected keys: {ctx.node_spec.output_keys}"
-                        ),
+                        error=_extraction_error,
                         output={},
                         tokens_used=response.input_tokens + response.output_tokens,
                         latency_ms=latency_ms,
@@ -1165,6 +1249,29 @@ Keep the same JSON structure but with shorter content values.
                     ctx.memory.write(key, stripped_content, validate=False)
                     output[key] = stripped_content
 
+            if ctx.runtime_logger:
+                ctx.runtime_logger.log_step(
+                    node_id=ctx.node_id,
+                    node_type=ctx.node_spec.node_type,
+                    step_index=_step_index,
+                    llm_text=response.content,
+                    tool_calls=_captured_tool_calls,
+                    input_tokens=response.input_tokens,
+                    output_tokens=response.output_tokens,
+                    latency_ms=latency_ms,
+                )
+                ctx.runtime_logger.log_node_complete(
+                    node_id=ctx.node_id,
+                    node_name=ctx.node_spec.name,
+                    node_type=ctx.node_spec.node_type,
+                    success=True,
+                    total_steps=_step_index + 1,
+                    tokens_used=response.input_tokens + response.output_tokens,
+                    input_tokens=response.input_tokens,
+                    output_tokens=response.output_tokens,
+                    latency_ms=latency_ms,
+                )
+
             return NodeResult(
                 success=True,
                 output=output,
@@ -1180,6 +1287,15 @@ Keep the same JSON structure but with shorter content values.
                 error=str(e),
                 latency_ms=latency_ms,
             )
+            if ctx.runtime_logger:
+                ctx.runtime_logger.log_node_complete(
+                    node_id=ctx.node_id,
+                    node_name=ctx.node_spec.name,
+                    node_type=ctx.node_spec.node_type,
+                    success=False,
+                    error=str(e),
+                    latency_ms=latency_ms,
+                )
             return NodeResult(success=False, error=str(e), latency_ms=latency_ms)
 
     def _parse_output(self, content: str, node_spec: NodeSpec) -> dict[str, Any]:
@@ -1572,6 +1688,9 @@ class RouterNode(NodeProtocol):
 
     async def execute(self, ctx: NodeContext) -> NodeResult:
         """Execute routing logic."""
+        import time as _time
+
+        start = _time.time()
         ctx.runtime.set_node(ctx.node_id)
 
         # Build options from routes
@@ -1616,10 +1735,30 @@ class RouterNode(NodeProtocol):
             summary=f"Routing to {chosen_route[1]}",
         )
 
+        latency_ms = int((_time.time() - start) * 1000)
+
+        if ctx.runtime_logger:
+            ctx.runtime_logger.log_step(
+                node_id=ctx.node_id,
+                node_type="router",
+                step_index=0,
+                llm_text=f"Route: {chosen_route[0]} -> {chosen_route[1]}",
+                latency_ms=latency_ms,
+            )
+            ctx.runtime_logger.log_node_complete(
+                node_id=ctx.node_id,
+                node_name=ctx.node_spec.name,
+                node_type="router",
+                success=True,
+                total_steps=1,
+                latency_ms=latency_ms,
+            )
+
         return NodeResult(
             success=True,
             next_node=chosen_route[1],
             route_reason=f"Chose route: {chosen_route[0]}",
+            latency_ms=latency_ms,
         )
 
     async def _llm_route(
@@ -1749,8 +1888,19 @@ class FunctionNode(NodeProtocol):
         start = time.time()
 
         try:
-            # Call the function
-            result = self.func(**ctx.input_data)
+            # Filter input_data to only declared input_keys to prevent
+            # leaking extra memory keys from upstream nodes.
+            if ctx.node_spec.input_keys:
+                filtered = {
+                    k: v for k, v in ctx.input_data.items() if k in ctx.node_spec.input_keys
+                }
+            else:
+                filtered = ctx.input_data
+
+            # Call the function (supports both sync and async)
+            result = self.func(**filtered)
+            if inspect.isawaitable(result):
+                result = await result
 
             latency_ms = int((time.time() - start) * 1000)
 
@@ -1770,6 +1920,22 @@ class FunctionNode(NodeProtocol):
             else:
                 output = {"result": result}
 
+            if ctx.runtime_logger:
+                ctx.runtime_logger.log_step(
+                    node_id=ctx.node_id,
+                    node_type="function",
+                    step_index=0,
+                    latency_ms=latency_ms,
+                )
+                ctx.runtime_logger.log_node_complete(
+                    node_id=ctx.node_id,
+                    node_name=ctx.node_spec.name,
+                    node_type="function",
+                    success=True,
+                    total_steps=1,
+                    latency_ms=latency_ms,
+                )
+
             return NodeResult(success=True, output=output, latency_ms=latency_ms)
 
         except Exception as e:
@@ -1780,4 +1946,22 @@ class FunctionNode(NodeProtocol):
                 error=str(e),
                 latency_ms=latency_ms,
             )
+
+            if ctx.runtime_logger:
+                ctx.runtime_logger.log_step(
+                    node_id=ctx.node_id,
+                    node_type="function",
+                    step_index=0,
+                    latency_ms=latency_ms,
+                )
+                ctx.runtime_logger.log_node_complete(
+                    node_id=ctx.node_id,
+                    node_name=ctx.node_spec.name,
+                    node_type="function",
+                    success=False,
+                    error=str(e),
+                    total_steps=1,
+                    latency_ms=latency_ms,
+                )
+
             return NodeResult(success=False, error=str(e), latency_ms=latency_ms)

core/framework/graph/output_cleaner.py

@@ -144,8 +144,11 @@ class OutputCleaner:
         errors = []
         warnings = []
 
-        # Check 1: Required input keys present
+        # Check 1: Required input keys present (skip nullable keys)
+        nullable = set(getattr(target_node_spec, "nullable_output_keys", None) or [])
         for key in target_node_spec.input_keys:
+            if key in nullable:
+                continue
             if key not in output:
                 errors.append(f"Missing required key: '{key}'")
                 continue

core/framework/graph/plan.py

@@ -11,13 +11,13 @@ The Plan is the contract between the external planner and the executor:
 """
 
 from datetime import datetime
-from enum import Enum
+from enum import StrEnum
 from typing import Any
 
 from pydantic import BaseModel, Field
 
 
-class ActionType(str, Enum):
+class ActionType(StrEnum):
     """Types of actions a PlanStep can perform."""
 
     LLM_CALL = "llm_call"  # Call LLM for generation
@@ -27,7 +27,7 @@ class ActionType(str, Enum):
     CODE_EXECUTION = "code_execution"  # Execute dynamic code (sandboxed)
 
 
-class StepStatus(str, Enum):
+class StepStatus(StrEnum):
     """Status of a plan step."""
 
     PENDING = "pending"
@@ -56,7 +56,7 @@ class StepStatus(str, Enum):
         return self == StepStatus.COMPLETED
 
 
-class ApprovalDecision(str, Enum):
+class ApprovalDecision(StrEnum):
     """Human decision on a step requiring approval."""
 
     APPROVE = "approve"  # Execute as planned
@@ -91,7 +91,7 @@ class ApprovalResult(BaseModel):
     model_config = {"extra": "allow"}
 
 
-class JudgmentAction(str, Enum):
+class JudgmentAction(StrEnum):
     """Actions the judge can take after evaluating a step."""
 
     ACCEPT = "accept"  # Step completed successfully, continue
@@ -423,7 +423,7 @@ class Plan(BaseModel):
         }
 
 
-class ExecutionStatus(str, Enum):
+class ExecutionStatus(StrEnum):
     """Status of plan execution."""
 
     COMPLETED = "completed"

core/framework/graph/validator.py

@@ -126,14 +126,16 @@ class OutputValidator:
 
         for key in expected_keys:
             if key not in output:
-                errors.append(f"Missing required output key: '{key}'")
+                if key not in nullable_keys:
+                    errors.append(f"Missing required output key: '{key}'")
             elif not allow_empty:
                 value = output[key]
                 if value is None:
                     if key not in nullable_keys:
                         errors.append(f"Output key '{key}' is None")
                 elif isinstance(value, str) and len(value.strip()) == 0:
-                    errors.append(f"Output key '{key}' is empty string")
+                    if key not in nullable_keys:
+                        errors.append(f"Output key '{key}' is empty string")
 
         return ValidationResult(success=len(errors) == 0, errors=errors)
 
@@ -205,7 +207,7 @@ class OutputValidator:
     def validate_no_hallucination(
         self,
         output: dict[str, Any],
-        max_length: int = 10000,
+        max_length: int = 50000,
     ) -> ValidationResult:
         """
         Check for signs of LLM hallucination in output values.

core/framework/llm/__init__.py

@@ -1,8 +1,31 @@
 """LLM provider abstraction."""
 
 from framework.llm.provider import LLMProvider, LLMResponse
+from framework.llm.stream_events import (
+    FinishEvent,
+    ReasoningDeltaEvent,
+    ReasoningStartEvent,
+    StreamErrorEvent,
+    StreamEvent,
+    TextDeltaEvent,
+    TextEndEvent,
+    ToolCallEvent,
+    ToolResultEvent,
+)
 
-__all__ = ["LLMProvider", "LLMResponse"]
+__all__ = [
+    "LLMProvider",
+    "LLMResponse",
+    "StreamEvent",
+    "TextDeltaEvent",
+    "TextEndEvent",
+    "ToolCallEvent",
+    "ToolResultEvent",
+    "ReasoningStartEvent",
+    "ReasoningDeltaEvent",
+    "FinishEvent",
+    "StreamErrorEvent",
+]
 
 try:
     from framework.llm.anthropic import AnthropicProvider  # noqa: F401

core/framework/llm/anthropic.py

@@ -18,7 +18,7 @@ def _get_api_key_from_credential_store() -> str | None:
     try:
         from aden_tools.credentials import CredentialStoreAdapter
 
-        creds = CredentialStoreAdapter.with_env_storage()
+        creds = CredentialStoreAdapter.default()
         if creds.is_available("anthropic"):
             return creds.get("anthropic")
     except ImportError:

core/framework/llm/litellm.py

@@ -7,10 +7,11 @@ Groq, and local models.
 See: https://docs.litellm.ai/docs/providers
 """
 
+import asyncio
 import json
 import logging
 import time
-from collections.abc import Callable
+from collections.abc import AsyncIterator, Callable
 from datetime import datetime
 from pathlib import Path
 from typing import Any
@@ -23,6 +24,7 @@ except ImportError:
     RateLimitError = Exception  # type: ignore[assignment, misc]
 
 from framework.llm.provider import LLMProvider, LLMResponse, Tool, ToolResult, ToolUse
+from framework.llm.stream_events import StreamEvent
 
 logger = logging.getLogger(__name__)
 
@@ -145,7 +147,7 @@ class LiteLLMProvider(LLMProvider):
 
         if litellm is None:
             raise ImportError(
-                "LiteLLM is not installed. Please install it with: pip install litellm"
+                "LiteLLM is not installed. Please install it with: uv pip install litellm"
             )
 
     def _completion_with_rate_limit_retry(self, **kwargs: Any) -> Any:
@@ -161,11 +163,24 @@ class LiteLLMProvider(LLMProvider):
                 content = response.choices[0].message.content if response.choices else None
                 has_tool_calls = bool(response.choices and response.choices[0].message.tool_calls)
                 if not content and not has_tool_calls:
+                    # If the conversation ends with an assistant message,
+                    # an empty response is expected — don't retry.
+                    messages = kwargs.get("messages", [])
+                    last_role = next(
+                        (m["role"] for m in reversed(messages) if m.get("role") != "system"),
+                        None,
+                    )
+                    if last_role == "assistant":
+                        logger.debug(
+                            "[retry] Empty response after assistant message — "
+                            "expected, not retrying."
+                        )
+                        return response
+
                     finish_reason = (
                         response.choices[0].finish_reason if response.choices else "unknown"
                     )
                     # Dump full request to file for debugging
-                    messages = kwargs.get("messages", [])
                     token_count, token_method = _estimate_tokens(model, messages)
                     dump_path = _dump_failed_request(
                         model=model,
@@ -432,3 +447,189 @@ class LiteLLMProvider(LLMProvider):
                 },
             },
         }
+
+    async def stream(
+        self,
+        messages: list[dict[str, Any]],
+        system: str = "",
+        tools: list[Tool] | None = None,
+        max_tokens: int = 4096,
+    ) -> AsyncIterator[StreamEvent]:
+        """Stream a completion via litellm.acompletion(stream=True).
+
+        Yields StreamEvent objects as chunks arrive from the provider.
+        Tool call arguments are accumulated across chunks and yielded as
+        a single ToolCallEvent with fully parsed JSON when complete.
+
+        Empty responses (e.g. Gemini stealth rate-limits that return 200
+        with no content) are retried with exponential backoff, mirroring
+        the retry behaviour of ``_completion_with_rate_limit_retry``.
+        """
+        from framework.llm.stream_events import (
+            FinishEvent,
+            StreamErrorEvent,
+            TextDeltaEvent,
+            TextEndEvent,
+            ToolCallEvent,
+        )
+
+        full_messages: list[dict[str, Any]] = []
+        if system:
+            full_messages.append({"role": "system", "content": system})
+        full_messages.extend(messages)
+
+        kwargs: dict[str, Any] = {
+            "model": self.model,
+            "messages": full_messages,
+            "max_tokens": max_tokens,
+            "stream": True,
+            "stream_options": {"include_usage": True},
+            **self.extra_kwargs,
+        }
+        if self.api_key:
+            kwargs["api_key"] = self.api_key
+        if self.api_base:
+            kwargs["api_base"] = self.api_base
+        if tools:
+            kwargs["tools"] = [self._tool_to_openai_format(t) for t in tools]
+
+        for attempt in range(RATE_LIMIT_MAX_RETRIES + 1):
+            # Post-stream events (ToolCall, TextEnd, Finish) are buffered
+            # because they depend on the full stream.  TextDeltaEvents are
+            # yielded immediately so callers see tokens in real time.
+            tail_events: list[StreamEvent] = []
+            accumulated_text = ""
+            tool_calls_acc: dict[int, dict[str, str]] = {}
+            input_tokens = 0
+            output_tokens = 0
+
+            try:
+                response = await litellm.acompletion(**kwargs)  # type: ignore[union-attr]
+
+                async for chunk in response:
+                    choice = chunk.choices[0] if chunk.choices else None
+                    if not choice:
+                        continue
+
+                    delta = choice.delta
+
+                    # --- Text content — yield immediately for real-time streaming ---
+                    if delta and delta.content:
+                        accumulated_text += delta.content
+                        yield TextDeltaEvent(
+                            content=delta.content,
+                            snapshot=accumulated_text,
+                        )
+
+                    # --- Tool calls (accumulate across chunks) ---
+                    if delta and delta.tool_calls:
+                        for tc in delta.tool_calls:
+                            idx = tc.index if hasattr(tc, "index") and tc.index is not None else 0
+                            if idx not in tool_calls_acc:
+                                tool_calls_acc[idx] = {"id": "", "name": "", "arguments": ""}
+                            if tc.id:
+                                tool_calls_acc[idx]["id"] = tc.id
+                            if tc.function:
+                                if tc.function.name:
+                                    tool_calls_acc[idx]["name"] = tc.function.name
+                                if tc.function.arguments:
+                                    tool_calls_acc[idx]["arguments"] += tc.function.arguments
+
+                    # --- Finish ---
+                    if choice.finish_reason:
+                        for _idx, tc_data in sorted(tool_calls_acc.items()):
+                            try:
+                                parsed_args = json.loads(tc_data["arguments"])
+                            except (json.JSONDecodeError, KeyError):
+                                parsed_args = {"_raw": tc_data.get("arguments", "")}
+                            tail_events.append(
+                                ToolCallEvent(
+                                    tool_use_id=tc_data["id"],
+                                    tool_name=tc_data["name"],
+                                    tool_input=parsed_args,
+                                )
+                            )
+
+                        if accumulated_text:
+                            tail_events.append(TextEndEvent(full_text=accumulated_text))
+
+                        usage = getattr(chunk, "usage", None)
+                        if usage:
+                            input_tokens = getattr(usage, "prompt_tokens", 0) or 0
+                            output_tokens = getattr(usage, "completion_tokens", 0) or 0
+
+                        tail_events.append(
+                            FinishEvent(
+                                stop_reason=choice.finish_reason,
+                                input_tokens=input_tokens,
+                                output_tokens=output_tokens,
+                                model=self.model,
+                            )
+                        )
+
+                # Check whether the stream produced any real content.
+                # (If text deltas were yielded above, has_content is True
+                # and we skip the retry path — nothing was yielded in vain.)
+                has_content = accumulated_text or tool_calls_acc
+                if not has_content and attempt < RATE_LIMIT_MAX_RETRIES:
+                    # If the conversation ends with an assistant or tool
+                    # message, an empty stream is expected — the LLM has
+                    # nothing new to say.  Don't burn retries on this;
+                    # let the caller (EventLoopNode) decide what to do.
+                    # Typical case: client_facing node where the LLM set
+                    # all outputs via set_output tool calls, and the tool
+                    # results are the last messages.
+                    last_role = next(
+                        (m["role"] for m in reversed(full_messages) if m.get("role") != "system"),
+                        None,
+                    )
+                    if last_role in ("assistant", "tool"):
+                        logger.debug(
+                            "[stream] Empty response after %s message — expected, not retrying.",
+                            last_role,
+                        )
+                        for event in tail_events:
+                            yield event
+                        return
+                    wait = RATE_LIMIT_BACKOFF_BASE * (2**attempt)
+                    token_count, token_method = _estimate_tokens(
+                        self.model,
+                        full_messages,
+                    )
+                    dump_path = _dump_failed_request(
+                        model=self.model,
+                        kwargs=kwargs,
+                        error_type="empty_stream",
+                        attempt=attempt,
+                    )
+                    logger.warning(
+                        f"[stream-retry] {self.model} returned empty stream — "
+                        f"~{token_count} tokens ({token_method}). "
+                        f"Request dumped to: {dump_path}. "
+                        f"Retrying in {wait}s "
+                        f"(attempt {attempt + 1}/{RATE_LIMIT_MAX_RETRIES})"
+                    )
+                    await asyncio.sleep(wait)
+                    continue
+
+                # Success (or final attempt) — flush remaining events.
+                for event in tail_events:
+                    yield event
+                return
+
+            except RateLimitError as e:
+                if attempt < RATE_LIMIT_MAX_RETRIES:
+                    wait = RATE_LIMIT_BACKOFF_BASE * (2**attempt)
+                    logger.warning(
+                        f"[stream-retry] {self.model} rate limited (429): {e!s}. "
+                        f"Retrying in {wait}s "
+                        f"(attempt {attempt + 1}/{RATE_LIMIT_MAX_RETRIES})"
+                    )
+                    await asyncio.sleep(wait)
+                    continue
+                yield StreamErrorEvent(error=str(e), recoverable=False)
+                return
+
+            except Exception as e:
+                yield StreamErrorEvent(error=str(e), recoverable=False)
+                return

core/framework/llm/mock.py

@@ -2,10 +2,16 @@
 
 import json
 import re
-from collections.abc import Callable
+from collections.abc import AsyncIterator, Callable
 from typing import Any
 
 from framework.llm.provider import LLMProvider, LLMResponse, Tool, ToolResult, ToolUse
+from framework.llm.stream_events import (
+    FinishEvent,
+    StreamEvent,
+    TextDeltaEvent,
+    TextEndEvent,
+)
 
 
 class MockLLMProvider(LLMProvider):
@@ -175,3 +181,28 @@ class MockLLMProvider(LLMProvider):
             output_tokens=0,
             stop_reason="mock_complete",
         )
+
+    async def stream(
+        self,
+        messages: list[dict[str, Any]],
+        system: str = "",
+        tools: list[Tool] | None = None,
+        max_tokens: int = 4096,
+    ) -> AsyncIterator[StreamEvent]:
+        """Stream a mock completion as word-level TextDeltaEvents.
+
+        Splits the mock response into words and yields each as a separate
+        TextDeltaEvent with an accumulating snapshot, exercising the full
+        streaming pipeline without any API calls.
+        """
+        content = self._generate_mock_response(system=system, json_mode=False)
+        words = content.split(" ")
+        accumulated = ""
+
+        for i, word in enumerate(words):
+            chunk = word if i == 0 else " " + word
+            accumulated += chunk
+            yield TextDeltaEvent(content=chunk, snapshot=accumulated)
+
+        yield TextEndEvent(full_text=accumulated)
+        yield FinishEvent(stop_reason="mock_complete", model=self.model)

core/framework/llm/provider.py

@@ -1,7 +1,7 @@
 """LLM Provider abstraction for pluggable LLM backends."""
 
 from abc import ABC, abstractmethod
-from collections.abc import Callable
+from collections.abc import AsyncIterator, Callable
 from dataclasses import dataclass, field
 from typing import Any
 
@@ -108,3 +108,45 @@ class LLMProvider(ABC):
             Final LLMResponse after tool use completes
         """
         pass
+
+    async def stream(
+        self,
+        messages: list[dict[str, Any]],
+        system: str = "",
+        tools: list[Tool] | None = None,
+        max_tokens: int = 4096,
+    ) -> AsyncIterator["StreamEvent"]:
+        """
+        Stream a completion as an async iterator of StreamEvents.
+
+        Default implementation wraps complete() with synthetic events.
+        Subclasses SHOULD override for true streaming.
+
+        Tool orchestration is the CALLER's responsibility:
+        - Caller detects ToolCallEvent, executes tool, adds result
+          to messages, calls stream() again.
+        """
+        from framework.llm.stream_events import (
+            FinishEvent,
+            TextDeltaEvent,
+            TextEndEvent,
+        )
+
+        response = self.complete(
+            messages=messages,
+            system=system,
+            tools=tools,
+            max_tokens=max_tokens,
+        )
+        yield TextDeltaEvent(content=response.content, snapshot=response.content)
+        yield TextEndEvent(full_text=response.content)
+        yield FinishEvent(
+            stop_reason=response.stop_reason,
+            input_tokens=response.input_tokens,
+            output_tokens=response.output_tokens,
+            model=response.model,
+        )
+
+
+# Deferred import target for type annotation
+from framework.llm.stream_events import StreamEvent as StreamEvent  # noqa: E402, F401

core/framework/llm/stream_events.py

@@ -0,0 +1,96 @@
+"""Stream event types for LLM streaming responses.
+
+Defines a discriminated union of frozen dataclasses representing every event
+a streaming LLM call can produce. These types form the contract between the
+LLM provider layer, EventLoopNode, event bus, persistence, and monitoring.
+"""
+
+from __future__ import annotations
+
+from dataclasses import dataclass, field
+from typing import Any, Literal
+
+
+@dataclass(frozen=True)
+class TextDeltaEvent:
+    """A chunk of text produced by the LLM."""
+
+    type: Literal["text_delta"] = "text_delta"
+    content: str = ""  # this chunk's text
+    snapshot: str = ""  # accumulated text so far
+
+
+@dataclass(frozen=True)
+class TextEndEvent:
+    """Signals that text generation is complete."""
+
+    type: Literal["text_end"] = "text_end"
+    full_text: str = ""
+
+
+@dataclass(frozen=True)
+class ToolCallEvent:
+    """The LLM has requested a tool call."""
+
+    type: Literal["tool_call"] = "tool_call"
+    tool_use_id: str = ""
+    tool_name: str = ""
+    tool_input: dict[str, Any] = field(default_factory=dict)
+
+
+@dataclass(frozen=True)
+class ToolResultEvent:
+    """Result of executing a tool call."""
+
+    type: Literal["tool_result"] = "tool_result"
+    tool_use_id: str = ""
+    content: str = ""
+    is_error: bool = False
+
+
+@dataclass(frozen=True)
+class ReasoningStartEvent:
+    """The LLM has started a reasoning/thinking block."""
+
+    type: Literal["reasoning_start"] = "reasoning_start"
+
+
+@dataclass(frozen=True)
+class ReasoningDeltaEvent:
+    """A chunk of reasoning/thinking content."""
+
+    type: Literal["reasoning_delta"] = "reasoning_delta"
+    content: str = ""
+
+
+@dataclass(frozen=True)
+class FinishEvent:
+    """The LLM has finished generating."""
+
+    type: Literal["finish"] = "finish"
+    stop_reason: str = ""
+    input_tokens: int = 0
+    output_tokens: int = 0
+    model: str = ""
+
+
+@dataclass(frozen=True)
+class StreamErrorEvent:
+    """An error occurred during streaming."""
+
+    type: Literal["error"] = "error"
+    error: str = ""
+    recoverable: bool = False
+
+
+# Discriminated union of all stream event types
+StreamEvent = (
+    TextDeltaEvent
+    | TextEndEvent
+    | ToolCallEvent
+    | ToolResultEvent
+    | ReasoningStartEvent
+    | ReasoningDeltaEvent
+    | FinishEvent
+    | StreamErrorEvent
+)

core/framework/mcp/agent_builder_server.py

@@ -4,25 +4,42 @@ MCP Server for Agent Building Tools
 Exposes tools for building goal-driven agents via the Model Context Protocol.
 
 Usage:
-    python -m framework.mcp.agent_builder_server
+    uv run python -m framework.mcp.agent_builder_server
 """
 
 import json
 import os
+import sys
 from datetime import datetime
 from pathlib import Path
 from typing import Annotated
 
-from mcp.server import FastMCP
+# Ensure exports/ is on sys.path so AgentRunner can import agent modules.
+_framework_dir = Path(__file__).resolve().parent.parent  # core/framework/ -> core/
+_project_root = _framework_dir.parent  # core/ -> project root
+_exports_dir = _project_root / "exports"
+if _exports_dir.is_dir() and str(_exports_dir) not in sys.path:
+    sys.path.insert(0, str(_exports_dir))
+del _framework_dir, _project_root, _exports_dir
 
-from framework.graph import Constraint, EdgeCondition, EdgeSpec, Goal, NodeSpec, SuccessCriterion
-from framework.graph.plan import Plan
+from mcp.server import FastMCP  # noqa: E402
+from pydantic import ValidationError  # noqa: E402
+
+from framework.graph import (  # noqa: E402
+    Constraint,
+    EdgeCondition,
+    EdgeSpec,
+    Goal,
+    NodeSpec,
+    SuccessCriterion,
+)
+from framework.graph.plan import Plan  # noqa: E402
 
 # Testing framework imports
-from framework.testing.prompts import (
+from framework.testing.prompts import (  # noqa: E402
     PYTEST_TEST_FILE_HEADER,
 )
-from framework.utils.io import atomic_write
+from framework.utils.io import atomic_write  # noqa: E402
 
 # Initialize MCP server
 mcp = FastMCP("agent-builder")
@@ -44,6 +61,7 @@ class BuildSession:
         self.nodes: list[NodeSpec] = []
         self.edges: list[EdgeSpec] = []
         self.mcp_servers: list[dict] = []  # MCP server configurations
+        self.loop_config: dict = {}  # LoopConfig parameters for EventLoopNodes
         self.created_at = datetime.now().isoformat()
         self.last_modified = datetime.now().isoformat()
 
@@ -56,6 +74,7 @@ class BuildSession:
             "nodes": [n.model_dump() for n in self.nodes],
             "edges": [e.model_dump() for e in self.edges],
             "mcp_servers": self.mcp_servers,
+            "loop_config": self.loop_config,
             "created_at": self.created_at,
             "last_modified": self.last_modified,
         }
@@ -102,6 +121,9 @@ class BuildSession:
         # Restore MCP servers
         session.mcp_servers = data.get("mcp_servers", [])
 
+        # Restore loop config
+        session.loop_config = data.get("loop_config", {})
+
         return session
 
 
@@ -551,14 +573,32 @@ def add_node(
     node_id: Annotated[str, "Unique identifier for the node"],
     name: Annotated[str, "Human-readable name"],
     description: Annotated[str, "What this node does"],
-    node_type: Annotated[str, "Type: llm_generate, llm_tool_use, router, or function"],
+    node_type: Annotated[
+        str,
+        "Type: event_loop (recommended), function, router. "
+        "Deprecated: llm_generate, llm_tool_use (use event_loop instead)",
+    ],
     input_keys: Annotated[str, "JSON array of keys this node reads from shared memory"],
     output_keys: Annotated[str, "JSON array of keys this node writes to shared memory"],
     system_prompt: Annotated[str, "Instructions for LLM nodes"] = "",
-    tools: Annotated[str, "JSON array of tool names for llm_tool_use nodes"] = "[]",
+    tools: Annotated[str, "JSON array of tool names for event_loop or llm_tool_use nodes"] = "[]",
     routes: Annotated[
         str, "JSON object mapping conditions to target node IDs for router nodes"
     ] = "{}",
+    client_facing: Annotated[
+        bool,
+        "If True, an ask_user() tool is injected so the LLM can explicitly request user input. "
+        "The node blocks ONLY when ask_user() is called — text-only turns stream freely. "
+        "Set True for nodes that interact with users (intake, review, approval). "
+        "Nodes that do autonomous work (research, data processing, API calls) MUST be False.",
+    ] = False,
+    nullable_output_keys: Annotated[
+        str, "JSON array of output keys that may remain unset (for mutually exclusive outputs)"
+    ] = "[]",
+    max_node_visits: Annotated[
+        int,
+        "Max times this node executes per graph run. Set >1 for feedback loop targets. 0=unlimited",
+    ] = 1,
 ) -> str:
     """Add a node to the agent graph. Nodes process inputs and produce outputs."""
     session = get_session()
@@ -569,6 +609,7 @@ def add_node(
         output_keys_list = json.loads(output_keys)
         tools_list = json.loads(tools)
         routes_dict = json.loads(routes)
+        nullable_output_keys_list = json.loads(nullable_output_keys)
     except json.JSONDecodeError as e:
         return json.dumps(
             {
@@ -597,6 +638,9 @@ def add_node(
         system_prompt=system_prompt or None,
         tools=tools_list,
         routes=routes_dict,
+        client_facing=client_facing,
+        nullable_output_keys=nullable_output_keys_list,
+        max_node_visits=max_node_visits,
     )
 
     session.nodes.append(node)
@@ -616,6 +660,34 @@ def add_node(
     if node_type in ("llm_generate", "llm_tool_use") and not system_prompt:
         warnings.append(f"LLM node '{node_id}' should have a system_prompt")
 
+    # EventLoopNode validation
+    if node_type == "event_loop" and not system_prompt:
+        warnings.append(f"Event loop node '{node_id}' should have a system_prompt")
+
+    # Deprecated type warnings
+    if node_type in ("llm_generate", "llm_tool_use"):
+        warnings.append(
+            f"Node type '{node_type}' is deprecated. Use 'event_loop' instead. "
+            "EventLoopNode supports tool use, streaming, and judge-based evaluation."
+        )
+
+    # Warn about client_facing on nodes with tools (likely autonomous work)
+    if node_type == "event_loop" and client_facing and tools_list:
+        warnings.append(
+            f"Node '{node_id}' is client_facing=True but has tools {tools_list}. "
+            "Nodes with tools typically do autonomous work and should be "
+            "client_facing=False. Only set True if this node needs user approval."
+        )
+
+    # nullable_output_keys must be a subset of output_keys
+    if nullable_output_keys_list:
+        invalid_nullable = [k for k in nullable_output_keys_list if k not in output_keys_list]
+        if invalid_nullable:
+            errors.append(
+                f"nullable_output_keys {invalid_nullable} must be a subset of "
+                f"output_keys {output_keys_list}"
+            )
+
     _save_session(session)  # Auto-save
 
     return json.dumps(
@@ -692,6 +764,7 @@ def add_edge(
 
     # Validate
     errors = []
+    warnings = []
 
     if not any(n.id == source for n in session.nodes):
         errors.append(f"Source node '{source}' not found")
@@ -700,12 +773,24 @@ def add_edge(
     if edge_condition == EdgeCondition.CONDITIONAL and not condition_expr:
         errors.append(f"Conditional edge '{edge_id}' needs condition_expr")
 
+    # Feedback edge validation
+    if priority < 0:
+        target_node = next((n for n in session.nodes if n.id == target), None)
+        if target_node and target_node.max_node_visits <= 1:
+            warnings.append(
+                f"Edge '{edge_id}' has negative priority (feedback edge) "
+                f"targeting '{target}', but node '{target}' has "
+                f"max_node_visits={target_node.max_node_visits}. "
+                "Consider increasing max_node_visits on the target node."
+            )
+
     _save_session(session)  # Auto-save
 
     return json.dumps(
         {
             "valid": len(errors) == 0,
             "errors": errors,
+            "warnings": warnings,
             "edge": edge.model_dump(),
             "total_edges": len(session.edges),
             "approval_required": True,
@@ -739,12 +824,23 @@ def update_node(
     node_id: Annotated[str, "ID of the node to update"],
     name: Annotated[str, "Updated human-readable name"] = "",
     description: Annotated[str, "Updated description"] = "",
-    node_type: Annotated[str, "Updated type: llm_generate, llm_tool_use, router, or function"] = "",
+    node_type: Annotated[
+        str,
+        "Updated type: event_loop (recommended), function, router. "
+        "Deprecated: llm_generate, llm_tool_use",
+    ] = "",
     input_keys: Annotated[str, "Updated JSON array of input keys"] = "",
     output_keys: Annotated[str, "Updated JSON array of output keys"] = "",
     system_prompt: Annotated[str, "Updated instructions for LLM nodes"] = "",
     tools: Annotated[str, "Updated JSON array of tool names"] = "",
     routes: Annotated[str, "Updated JSON object mapping conditions to target node IDs"] = "",
+    client_facing: Annotated[
+        str, "Updated client-facing flag ('true'/'false', empty=no change)"
+    ] = "",
+    nullable_output_keys: Annotated[
+        str, "Updated JSON array of nullable output keys (empty=no change)"
+    ] = "",
+    max_node_visits: Annotated[int, "Updated max node visits per graph run. 0=no change"] = 0,
 ) -> str:
     """Update an existing node in the agent graph. Only provided fields will be updated."""
     session = get_session()
@@ -765,6 +861,9 @@ def update_node(
         output_keys_list = json.loads(output_keys) if output_keys else None
         tools_list = json.loads(tools) if tools else None
         routes_dict = json.loads(routes) if routes else None
+        nullable_output_keys_list = (
+            json.loads(nullable_output_keys) if nullable_output_keys else None
+        )
     except json.JSONDecodeError as e:
         return json.dumps(
             {
@@ -797,6 +896,12 @@ def update_node(
         node.tools = tools_list
     if routes_dict is not None:
         node.routes = routes_dict
+    if client_facing:
+        node.client_facing = client_facing.lower() == "true"
+    if nullable_output_keys_list is not None:
+        node.nullable_output_keys = nullable_output_keys_list
+    if max_node_visits > 0:
+        node.max_node_visits = max_node_visits
 
     # Validate
     errors = []
@@ -809,6 +914,26 @@ def update_node(
     if node.node_type in ("llm_generate", "llm_tool_use") and not node.system_prompt:
         warnings.append(f"LLM node '{node_id}' should have a system_prompt")
 
+    # EventLoopNode validation
+    if node.node_type == "event_loop" and not node.system_prompt:
+        warnings.append(f"Event loop node '{node_id}' should have a system_prompt")
+
+    # Deprecated type warnings
+    if node.node_type in ("llm_generate", "llm_tool_use"):
+        warnings.append(
+            f"Node type '{node.node_type}' is deprecated. Use 'event_loop' instead. "
+            "EventLoopNode supports tool use, streaming, and judge-based evaluation."
+        )
+
+    # nullable_output_keys must be a subset of output_keys
+    if node.nullable_output_keys:
+        invalid_nullable = [k for k in node.nullable_output_keys if k not in node.output_keys]
+        if invalid_nullable:
+            errors.append(
+                f"nullable_output_keys {invalid_nullable} must be a subset of "
+                f"output_keys {node.output_keys}"
+            )
+
     _save_session(session)  # Auto-save
 
     return json.dumps(
@@ -1009,17 +1134,30 @@ def validate_graph() -> str:
                 errors.append(f"Unreachable nodes: {unreachable}")
 
     # === CONTEXT FLOW VALIDATION ===
-    # Build dependency map (node_id -> list of nodes it depends on)
+    # Build dependency maps — separate forward edges from feedback edges.
+    # Feedback edges (priority < 0) create cycles; they must not block the
+    # topological sort.  Context they carry arrives on *revisits*, not on
+    # the first execution of a node.
+    feedback_edge_ids = {e.id for e in session.edges if e.priority < 0}
+    forward_dependencies: dict[str, list[str]] = {node.id: [] for node in session.nodes}
+    feedback_sources: dict[str, list[str]] = {node.id: [] for node in session.nodes}
+    # Combined map kept for error-message generation (all deps)
     dependencies: dict[str, list[str]] = {node.id: [] for node in session.nodes}
+
     for edge in session.edges:
-        if edge.target in dependencies:
-            dependencies[edge.target].append(edge.source)
+        if edge.target not in forward_dependencies:
+            continue
+        dependencies[edge.target].append(edge.source)
+        if edge.id in feedback_edge_ids:
+            feedback_sources[edge.target].append(edge.source)
+        else:
+            forward_dependencies[edge.target].append(edge.source)
 
     # Build output map (node_id -> keys it produces)
     node_outputs: dict[str, set[str]] = {node.id: set(node.output_keys) for node in session.nodes}
 
     # Compute available context for each node (what keys it can read)
-    # Using topological order
+    # Using topological order on the forward-edge DAG
     available_context: dict[str, set[str]] = {}
     computed = set()
     nodes_by_id = {n.id: n for n in session.nodes}
@@ -1029,7 +1167,8 @@ def validate_graph() -> str:
     # Entry nodes can only read from initial context
     initial_context_keys: set[str] = set()
 
-    # Compute in topological order
+    # Compute in topological order (forward edges only — feedback edges
+    # don't block, since their context arrives on revisits)
     remaining = {n.id for n in session.nodes}
     max_iterations = len(session.nodes) * 2
 
@@ -1038,18 +1177,23 @@ def validate_graph() -> str:
             break
 
         for node_id in list(remaining):
-            deps = dependencies.get(node_id, [])
+            fwd_deps = forward_dependencies.get(node_id, [])
 
-            # Can compute if all dependencies are computed (or no dependencies)
-            if all(d in computed for d in deps):
-                # Collect outputs from all dependencies
+            # Can compute if all FORWARD dependencies are computed
+            if all(d in computed for d in fwd_deps):
+                # Collect outputs from all forward dependencies
                 available = set(initial_context_keys)
-                for dep_id in deps:
-                    # Add outputs from dependency
+                for dep_id in fwd_deps:
                     available.update(node_outputs.get(dep_id, set()))
-                    # Also add what was available to the dependency (transitive)
                     available.update(available_context.get(dep_id, set()))
 
+                # Also include context from already-computed feedback
+                # sources (bonus, not blocking)
+                for fb_src in feedback_sources.get(node_id, []):
+                    if fb_src in computed:
+                        available.update(node_outputs.get(fb_src, set()))
+                        available.update(available_context.get(fb_src, set()))
+
                 available_context[node_id] = available
                 computed.add(node_id)
                 remaining.remove(node_id)
@@ -1059,15 +1203,37 @@ def validate_graph() -> str:
     context_errors = []
     context_warnings = []
     missing_inputs: dict[str, list[str]] = {}
+    feedback_only_inputs: dict[str, list[str]] = {}
 
     for node in session.nodes:
         available = available_context.get(node.id, set())
 
         for input_key in node.input_keys:
             if input_key not in available:
-                if node.id not in missing_inputs:
-                    missing_inputs[node.id] = []
-                missing_inputs[node.id].append(input_key)
+                # Check if this input is provided by a feedback source
+                fb_provides = set()
+                for fb_src in feedback_sources.get(node.id, []):
+                    fb_provides.update(node_outputs.get(fb_src, set()))
+                    fb_provides.update(available_context.get(fb_src, set()))
+
+                if input_key in fb_provides:
+                    # Input arrives via feedback edge — warn, don't error
+                    if node.id not in feedback_only_inputs:
+                        feedback_only_inputs[node.id] = []
+                    feedback_only_inputs[node.id].append(input_key)
+                else:
+                    if node.id not in missing_inputs:
+                        missing_inputs[node.id] = []
+                    missing_inputs[node.id].append(input_key)
+
+    # Warn about feedback-only inputs (available on revisits, not first run)
+    for node_id, fb_keys in feedback_only_inputs.items():
+        fb_srcs = feedback_sources.get(node_id, [])
+        context_warnings.append(
+            f"Node '{node_id}' input(s) {fb_keys} are only provided via "
+            f"feedback edge(s) from {fb_srcs}. These will be available on "
+            f"revisits but not on the first execution."
+        )
 
     # Generate helpful error messages
     for node_id, missing in missing_inputs.items():
@@ -1147,6 +1313,98 @@ def validate_graph() -> str:
     errors.extend(context_errors)
     warnings.extend(context_warnings)
 
+    # === EventLoopNode-specific validation ===
+    from collections import defaultdict
+
+    # Detect fan-out: multiple ON_SUCCESS edges from same source
+    outgoing_success: dict[str, list[str]] = defaultdict(list)
+    for edge in session.edges:
+        cond = edge.condition.value if hasattr(edge.condition, "value") else edge.condition
+        if cond == "on_success":
+            outgoing_success[edge.source].append(edge.target)
+
+    for source_id, targets in outgoing_success.items():
+        if len(targets) > 1:
+            # Client-facing fan-out: cannot target multiple client_facing nodes
+            cf_targets = [
+                t for t in targets if any(n.id == t and n.client_facing for n in session.nodes)
+            ]
+            if len(cf_targets) > 1:
+                errors.append(
+                    f"Fan-out from '{source_id}' targets multiple client_facing "
+                    f"nodes: {cf_targets}. Only one branch may be client-facing."
+                )
+
+            # Output key overlap on parallel event_loop nodes
+            el_targets = [
+                t
+                for t in targets
+                if any(n.id == t and n.node_type == "event_loop" for n in session.nodes)
+            ]
+            if len(el_targets) > 1:
+                seen_keys: dict[str, str] = {}
+                for nid in el_targets:
+                    node_obj = next((n for n in session.nodes if n.id == nid), None)
+                    if node_obj:
+                        for key in node_obj.output_keys:
+                            if key in seen_keys:
+                                errors.append(
+                                    f"Fan-out from '{source_id}': event_loop "
+                                    f"nodes '{seen_keys[key]}' and '{nid}' both "
+                                    f"write to output_key '{key}'. Parallel "
+                                    "nodes must have disjoint output_keys."
+                                )
+                            else:
+                                seen_keys[key] = nid
+
+    # Feedback loop validation: targets should allow re-visits
+    for edge in session.edges:
+        if edge.priority < 0:
+            target_node = next((n for n in session.nodes if n.id == edge.target), None)
+            if target_node and target_node.max_node_visits <= 1:
+                warnings.append(
+                    f"Feedback edge '{edge.id}' targets '{edge.target}' "
+                    f"which has max_node_visits={target_node.max_node_visits}. "
+                    "Consider setting max_node_visits > 1."
+                )
+
+    # nullable_output_keys must be subset of output_keys
+    for node in session.nodes:
+        if node.nullable_output_keys:
+            invalid = [k for k in node.nullable_output_keys if k not in node.output_keys]
+            if invalid:
+                errors.append(
+                    f"Node '{node.id}': nullable_output_keys {invalid} "
+                    f"must be a subset of output_keys {node.output_keys}"
+                )
+
+    # Deprecated node type warnings
+    deprecated_nodes = [
+        {"node_id": n.id, "type": n.node_type, "replacement": "event_loop"}
+        for n in session.nodes
+        if n.node_type in ("llm_generate", "llm_tool_use")
+    ]
+    for dn in deprecated_nodes:
+        warnings.append(
+            f"Node '{dn['node_id']}' uses deprecated type '{dn['type']}'. Use 'event_loop' instead."
+        )
+
+    # Warn if all event_loop nodes are client_facing (common misconfiguration)
+    el_nodes = [n for n in session.nodes if n.node_type == "event_loop"]
+    cf_el_nodes = [n for n in el_nodes if n.client_facing]
+    if len(el_nodes) > 1 and len(cf_el_nodes) == len(el_nodes):
+        warnings.append(
+            f"ALL {len(el_nodes)} event_loop nodes are client_facing=True. "
+            "This injects ask_user() on every node. Only nodes that need user "
+            "interaction (intake, review, approval) should be client_facing. Set "
+            "client_facing=False on autonomous processing nodes."
+        )
+
+    # Collect summary info
+    event_loop_nodes = [n.id for n in session.nodes if n.node_type == "event_loop"]
+    client_facing_nodes = [n.id for n in session.nodes if n.client_facing]
+    feedback_edges = [e.id for e in session.edges if e.priority < 0]
+
     return json.dumps(
         {
             "valid": len(errors) == 0,
@@ -1163,6 +1421,10 @@ def validate_graph() -> str:
             "context_flow": {node_id: list(keys) for node_id, keys in available_context.items()}
             if available_context
             else None,
+            "event_loop_nodes": event_loop_nodes,
+            "client_facing_nodes": client_facing_nodes,
+            "feedback_edges": feedback_edges,
+            "deprecated_node_types": deprecated_nodes,
         }
     )
 
@@ -1213,6 +1475,12 @@ def _generate_readme(session: BuildSession, export_data: dict, all_tools: set) -
         if node.routes:
             routes_str = ", ".join([f"{k}→{v}" for k, v in node.routes.items()])
             node_info.append(f"   - Routes: {routes_str}")
+        if node.client_facing:
+            node_info.append("   - Client-facing: Yes (blocks for user input)")
+        if node.nullable_output_keys:
+            node_info.append(f"   - Nullable outputs: `{', '.join(node.nullable_output_keys)}`")
+        if node.max_node_visits > 1:
+            node_info.append(f"   - Max visits: {node.max_node_visits}")
         nodes_section.append("\n".join(node_info))
 
     # Build success criteria section
@@ -1266,7 +1534,12 @@ def _generate_readme(session: BuildSession, export_data: dict, all_tools: set) -
 
     for edge in edges:
         cond = edge.condition.value if hasattr(edge.condition, "value") else edge.condition
-        readme += f"- `{edge.source}` → `{edge.target}` (condition: {cond})\n"
+        priority_note = f", priority={edge.priority}" if edge.priority != 0 else ""
+        feedback_note = " **[FEEDBACK]**" if edge.priority < 0 else ""
+        readme += (
+            f"- `{edge.source}` → `{edge.target}` "
+            f"(condition: {cond}{priority_note}){feedback_note}\n"
+        )
 
     readme += f"""
 
@@ -1481,6 +1754,10 @@ def export_graph() -> str:
         "created_at": datetime.now().isoformat(),
     }
 
+    # Include loop config if configured
+    if session.loop_config:
+        graph_spec["loop_config"] = session.loop_config
+
     # Collect all tools referenced by nodes
     all_tools = set()
     for node in session.nodes:
@@ -1580,6 +1857,85 @@ def export_graph() -> str:
     )
 
 
+@mcp.tool()
+def import_from_export(
+    agent_json_path: Annotated[str, "Path to the agent.json file to import"],
+) -> str:
+    """
+    Import an agent definition from an exported agent.json file into the current build session.
+
+    Reads the agent.json, parses goal/nodes/edges, and populates the current session.
+    This is the reverse of export_graph().
+
+    Args:
+        agent_json_path: Path to the agent.json file to import
+
+    Returns:
+        JSON summary of what was imported (goal name, node count, edge count)
+    """
+    session = get_session()
+
+    path = Path(agent_json_path)
+    if not path.exists():
+        return json.dumps({"success": False, "error": f"File not found: {agent_json_path}"})
+
+    try:
+        data = json.loads(path.read_text())
+    except json.JSONDecodeError as e:
+        return json.dumps({"success": False, "error": f"Invalid JSON: {e}"})
+
+    try:
+        # Parse goal (same pattern as BuildSession.from_dict lines 88-99)
+        goal_data = data.get("goal")
+        if goal_data:
+            session.goal = Goal(
+                id=goal_data["id"],
+                name=goal_data["name"],
+                description=goal_data["description"],
+                success_criteria=[
+                    SuccessCriterion(**sc) for sc in goal_data.get("success_criteria", [])
+                ],
+                constraints=[Constraint(**c) for c in goal_data.get("constraints", [])],
+            )
+
+        # Parse nodes (same pattern as BuildSession.from_dict line 102)
+        graph_data = data.get("graph", {})
+        nodes_data = graph_data.get("nodes", [])
+        session.nodes = [NodeSpec(**n) for n in nodes_data]
+
+        # Parse edges (same pattern as BuildSession.from_dict lines 105-118)
+        edges_data = graph_data.get("edges", [])
+        session.edges = []
+        for e in edges_data:
+            condition_str = e.get("condition")
+            if isinstance(condition_str, str):
+                condition_map = {
+                    "always": EdgeCondition.ALWAYS,
+                    "on_success": EdgeCondition.ON_SUCCESS,
+                    "on_failure": EdgeCondition.ON_FAILURE,
+                    "conditional": EdgeCondition.CONDITIONAL,
+                    "llm_decide": EdgeCondition.LLM_DECIDE,
+                }
+                e["condition"] = condition_map.get(condition_str, EdgeCondition.ON_SUCCESS)
+            session.edges.append(EdgeSpec(**e))
+    except (KeyError, TypeError, ValueError, ValidationError) as e:
+        return json.dumps({"success": False, "error": f"Malformed agent.json: {e}"})
+
+    # Persist updated session
+    _save_session(session)
+
+    return json.dumps(
+        {
+            "success": True,
+            "goal": session.goal.name if session.goal else None,
+            "nodes_count": len(session.nodes),
+            "edges_count": len(session.edges),
+            "node_ids": [n.id for n in session.nodes],
+            "edge_ids": [e.id for e in session.edges],
+        }
+    )
+
+
 @mcp.tool()
 def get_session_status() -> str:
     """Get the current status of the build session."""
@@ -1596,6 +1952,58 @@ def get_session_status() -> str:
             "nodes": [n.id for n in session.nodes],
             "edges": [(e.source, e.target) for e in session.edges],
             "mcp_servers": [s["name"] for s in session.mcp_servers],
+            "event_loop_nodes": [n.id for n in session.nodes if n.node_type == "event_loop"],
+            "client_facing_nodes": [n.id for n in session.nodes if n.client_facing],
+            "deprecated_nodes": [
+                n.id for n in session.nodes if n.node_type in ("llm_generate", "llm_tool_use")
+            ],
+            "feedback_edges": [e.id for e in session.edges if e.priority < 0],
+        }
+    )
+
+
+@mcp.tool()
+def configure_loop(
+    max_iterations: Annotated[int, "Maximum loop iterations per node execution (default 50)"] = 50,
+    max_tool_calls_per_turn: Annotated[int, "Maximum tool calls per LLM turn (default 10)"] = 10,
+    stall_detection_threshold: Annotated[
+        int, "Consecutive identical responses before stall detection triggers (default 3)"
+    ] = 3,
+    max_history_tokens: Annotated[
+        int, "Maximum conversation history tokens before compaction (default 32000)"
+    ] = 32000,
+    tool_call_overflow_margin: Annotated[
+        float,
+        "Overflow margin for max_tool_calls_per_turn. "
+        "Tool calls are only discarded when count exceeds "
+        "max_tool_calls_per_turn * (1 + margin). Default 0.5 (50% wiggle room)",
+    ] = 0.5,
+) -> str:
+    """Configure event loop parameters for EventLoopNode execution.
+
+    These settings control how EventLoopNodes behave at runtime:
+    - max_iterations: prevents infinite loops
+    - max_tool_calls_per_turn: limits tool calls per LLM response
+    - tool_call_overflow_margin: wiggle room before tool calls are discarded (default 50%)
+    - stall_detection_threshold: detects when LLM repeats itself
+    - max_history_tokens: triggers conversation compaction
+    """
+    session = get_session()
+
+    session.loop_config = {
+        "max_iterations": max_iterations,
+        "max_tool_calls_per_turn": max_tool_calls_per_turn,
+        "tool_call_overflow_margin": tool_call_overflow_margin,
+        "stall_detection_threshold": stall_detection_threshold,
+        "max_history_tokens": max_history_tokens,
+    }
+
+    _save_session(session)
+
+    return json.dumps(
+        {
+            "success": True,
+            "loop_config": session.loop_config,
         }
     )
 
@@ -1891,10 +2299,41 @@ def test_node(
         result["routing_options"] = node_spec.routes
         result["simulation"] = "Router would evaluate routes based on input and select target node"
 
-    elif node_spec.node_type in ("llm_generate", "llm_tool_use"):
-        # Show what prompt would be sent
+    elif node_spec.node_type == "event_loop":
+        # EventLoopNode simulation
         result["system_prompt"] = node_spec.system_prompt
         result["available_tools"] = node_spec.tools
+        result["client_facing"] = node_spec.client_facing
+        result["nullable_output_keys"] = node_spec.nullable_output_keys
+        result["max_node_visits"] = node_spec.max_node_visits
+
+        if mock_llm_response:
+            result["mock_response"] = mock_llm_response
+            result["simulation"] = (
+                "EventLoopNode would run a multi-turn streaming loop. "
+                "Each iteration: LLM call -> tool execution -> judge evaluation. "
+                "Loop continues until judge ACCEPTs or max_iterations reached."
+            )
+        else:
+            cf_note = (
+                "Node is client-facing: has ask_user() tool, blocks when LLM calls it. "
+                if node_spec.client_facing
+                else ""
+            )
+            result["simulation"] = (
+                "EventLoopNode would stream LLM responses, execute tool calls, "
+                "and use judge evaluation to decide when to stop. "
+                + cf_note
+                + f"Max visits per graph run: {node_spec.max_node_visits}."
+            )
+
+    elif node_spec.node_type in ("llm_generate", "llm_tool_use"):
+        # Legacy LLM node types
+        result["system_prompt"] = node_spec.system_prompt
+        result["available_tools"] = node_spec.tools
+        result["deprecation_warning"] = (
+            f"Node type '{node_spec.node_type}' is deprecated. Use 'event_loop' instead."
+        )
 
         if mock_llm_response:
             result["mock_response"] = mock_llm_response
@@ -1909,6 +2348,7 @@ def test_node(
     result["expected_memory_state"] = {
         "inputs_available": {k: input_data.get(k, "<not provided>") for k in node_spec.input_keys},
         "outputs_to_write": node_spec.output_keys,
+        "nullable_outputs": node_spec.nullable_output_keys or [],
     }
 
     return json.dumps(
@@ -1997,13 +2437,19 @@ def test_graph(
             "writes": current_node.output_keys,
         }
 
-        if current_node.node_type in ("llm_generate", "llm_tool_use"):
+        if current_node.node_type in ("llm_generate", "llm_tool_use", "event_loop"):
             step_info["prompt_preview"] = (
                 current_node.system_prompt[:200] + "..."
                 if current_node.system_prompt and len(current_node.system_prompt) > 200
                 else current_node.system_prompt
             )
             step_info["tools_available"] = current_node.tools
+            if current_node.node_type == "event_loop":
+                step_info["event_loop_config"] = {
+                    "client_facing": current_node.client_facing,
+                    "max_node_visits": current_node.max_node_visits,
+                    "nullable_output_keys": current_node.nullable_output_keys,
+                }
 
         execution_trace.append(step_info)
 
@@ -2012,16 +2458,32 @@ def test_graph(
             step_info["is_terminal"] = True
             break
 
-        # Find next node via edges
+        # Find next node via edges (sorted by priority, highest first)
+        outgoing = sorted(
+            [e for e in session.edges if e.source == current_node_id],
+            key=lambda e: -e.priority,
+        )
         next_node = None
-        for edge in session.edges:
-            if edge.source == current_node_id:
-                # In dry run, assume success path
-                if edge.condition.value in ("always", "on_success"):
-                    next_node = edge.target
-                    step_info["next_node"] = next_node
-                    step_info["edge_condition"] = edge.condition.value
-                    break
+        for edge in outgoing:
+            # In dry run, follow success/always edges (highest priority first)
+            if edge.condition.value in ("always", "on_success"):
+                next_node = edge.target
+                step_info["next_node"] = next_node
+                step_info["edge_condition"] = edge.condition.value
+                step_info["edge_priority"] = edge.priority
+                break
+
+        # Note any feedback edges from this node
+        feedback = [e for e in outgoing if e.priority < 0]
+        if feedback:
+            step_info["feedback_edges"] = [
+                {
+                    "target": e.target,
+                    "condition_expr": e.condition_expr,
+                    "priority": e.priority,
+                }
+                for e in feedback
+            ]
 
         if next_node is None:
             step_info["note"] = "No outgoing edge found (end of path)"
@@ -3417,7 +3879,7 @@ def store_credential(
     display_name: Annotated[str, "Human-readable name (e.g., 'HubSpot Access Token')"] = "",
 ) -> str:
     """
-    Store a credential securely in the encrypted credential store at ~/.hive/credentials.
+    Store a credential securely in the local encrypted store at ~/.hive/credentials.
 
     Uses Fernet encryption (AES-128-CBC + HMAC). Requires HIVE_CREDENTIAL_KEY env var.
     """
@@ -3459,7 +3921,7 @@ def store_credential(
 @mcp.tool()
 def list_stored_credentials() -> str:
     """
-    List all credentials currently stored in the encrypted credential store.
+    List all credentials currently stored in the local encrypted store.
 
     Returns credential IDs and metadata (never returns secret values).
     """
@@ -3499,7 +3961,7 @@ def delete_stored_credential(
     credential_name: Annotated[str, "Logical credential name to delete (e.g., 'hubspot')"],
 ) -> str:
     """
-    Delete a credential from the encrypted credential store.
+    Delete a credential from the local encrypted store.
     """
     try:
         store = _get_credential_store()

core/framework/observability/README.md

@@ -0,0 +1,236 @@
+# Observability - Structured Logging
+
+## Configuration via Environment Variables
+
+Control logging format using environment variables:
+
+```bash
+# JSON logging (production) - Machine-parseable, one line per log
+export LOG_FORMAT=json
+python -m my_agent run
+
+# Human-readable (development) - Color-coded, easy to read
+# Default if LOG_FORMAT is not set
+python -m my_agent run
+```
+
+**Alternative:** Set `ENV=production` to automatically use JSON format:
+
+```bash
+export ENV=production
+python -m my_agent run
+```
+
+---
+
+## Overview
+
+The Hive framework provides automatic structured logging with trace context propagation. Logs include correlation IDs (`trace_id`, `execution_id`) that automatically follow your agent execution flow.
+
+**Features:**
+- **Zero developer friction**: Standard `logger.info()` calls automatically get trace context
+- **ContextVar-based propagation**: Thread-safe and async-safe for concurrent executions
+- **Dual output modes**: JSON for production, human-readable for development
+- **Automatic correlation**: `trace_id` and `execution_id` propagate through all logs
+
+## Quick Start
+
+Logging is automatically configured when you use `AgentRunner`. No setup required:
+
+```python
+from framework.runner import AgentRunner
+
+runner = AgentRunner(graph=my_graph, goal=my_goal)
+result = await runner.run({"input": "data"})
+# Logs automatically include trace_id, execution_id, agent_id, etc.
+```
+
+## Programmatic Configuration
+
+Configure logging explicitly in your code:
+
+```python
+from framework.observability import configure_logging
+
+# Human-readable (development)
+configure_logging(level="DEBUG", format="human")
+
+# JSON (production)
+configure_logging(level="INFO", format="json")
+
+# Auto-detect from environment
+configure_logging(level="INFO", format="auto")
+```
+
+### Configuration Options
+
+- **level**: `"DEBUG"`, `"INFO"`, `"WARNING"`, `"ERROR"`, `"CRITICAL"`
+- **format**: 
+  - `"json"` - Machine-parseable JSON (one line per log entry)
+  - `"human"` - Human-readable with colors
+  - `"auto"` - Detects from `LOG_FORMAT` env var or `ENV=production`
+
+## Log Format Examples
+
+### JSON Format (Machine-parseable)
+
+```json
+{"timestamp": "2026-01-28T15:01:02.671126+00:00", "level": "info", "logger": "framework.runtime", "message": "Starting agent execution", "trace_id": "54e80d7b5bd6409dbc3217e5cd16a4fd", "execution_id": "b4c348ec54e80d7b5bd6409dbc3217e50", "agent_id": "sales-agent", "goal_id": "qualify-leads"}
+```
+
+**Features:**
+- `trace_id` and `execution_id` are 32 hex chars (W3C/OTel-aligned, no prefixes)
+- Compact single-line format (easy to stream/parse)
+- All trace context fields included automatically
+
+### Human-Readable Format (Development)
+
+```
+[INFO    ] [trace:12345678 | exec:a1b2c3d4 | agent:sales-agent] Starting agent execution
+[INFO    ] [trace:12345678 | exec:a1b2c3d4 | agent:sales-agent] Processing input data [node_id:input-processor]
+[INFO    ] [trace:12345678 | exec:a1b2c3d4 | agent:sales-agent] LLM call completed [latency_ms:1250] [tokens_used:450]
+```
+
+**Features:**
+- Color-coded log levels
+- Shortened IDs for readability (first 8 chars)
+- Context prefix shows trace correlation
+
+## Trace Context Fields
+
+When the framework sets trace context, these fields are included in all logs. IDs are 32 hex (W3C/OTel-aligned, no prefixes).
+
+- **trace_id**: Trace identifier
+- **execution_id**: Run/session correlation
+- **agent_id**: Agent/graph identifier
+- **goal_id**: Goal being pursued
+- **node_id**: Current node (when set)
+
+## Custom Log Fields
+
+Add custom fields using the `extra` parameter:
+
+```python
+import logging
+
+logger = logging.getLogger("my_module")
+
+# Add custom fields
+logger.info("LLM call completed", extra={
+    "latency_ms": 1250,
+    "tokens_used": 450,
+    "model": "claude-3-5-sonnet-20241022",
+    "node_id": "web-search"
+})
+```
+
+These fields appear in both JSON and human-readable formats.
+
+## Usage in Your Code
+
+### Standard Logging (Recommended)
+
+Just use Python's standard logging - context is automatic:
+
+```python
+import logging
+
+logger = logging.getLogger(__name__)
+
+def my_function():
+    # This log automatically includes trace_id, execution_id, etc.
+    logger.info("Processing data")
+    
+    try:
+        result = do_work()
+        logger.info("Work completed", extra={"result_count": len(result)})
+    except Exception as e:
+        logger.error("Work failed", exc_info=True)
+```
+
+### Framework-Managed Context
+
+The framework automatically sets trace context at key points:
+
+- **Runtime.start_run()**: Sets `trace_id`, `execution_id`, `goal_id`
+- **GraphExecutor.execute()**: Adds `agent_id`
+- **Node execution**: Adds `node_id`
+
+Propagation is automatic via ContextVar.
+
+## Advanced Usage
+
+### Manual Context Management
+
+If you need to set trace context manually (rare):
+
+```python
+from framework.observability import set_trace_context, get_trace_context
+
+# Set context (32-hex, no prefixes)
+set_trace_context(
+    trace_id="54e80d7b5bd6409dbc3217e5cd16a4fd",
+    execution_id="b4c348ec54e80d7b5bd6409dbc3217e50",
+    agent_id="my-agent"
+)
+
+# Get current context
+context = get_trace_context()
+print(context["execution_id"])
+
+# Clear context (usually not needed)
+from framework.observability import clear_trace_context
+clear_trace_context()
+```
+
+### Testing
+
+For tests, you may want to configure logging explicitly:
+
+```python
+import pytest
+from framework.observability import configure_logging
+
+@pytest.fixture(autouse=True)
+def setup_logging():
+    configure_logging(level="DEBUG", format="human")
+```
+
+## Best Practices
+
+1. **Production**: Use JSON format (`LOG_FORMAT=json` or `ENV=production`)
+2. **Development**: Use human-readable format (default)
+3. **Don't manually set context**: Let the framework manage it
+4. **Use standard logging**: No special APIs needed - just `logger.info()`
+5. **Add custom fields**: Use `extra` dict for additional metadata
+
+## Troubleshooting
+
+### Logs missing trace context
+
+Ensure `configure_logging()` has been called (usually automatic via `AgentRunner._setup()`).
+
+### JSON logs not appearing
+
+Check environment variables:
+```bash
+echo $LOG_FORMAT
+echo $ENV
+```
+
+Or explicitly set:
+```python
+configure_logging(format="json")
+```
+
+### Context not propagating
+
+ContextVar automatically propagates through async calls. If context seems lost, check:
+- Are you in the same async execution context?
+- Has `set_trace_context()` been called for this execution?
+
+## See Also
+
+- [Logging Implementation](../observability/logging.py) - Source code
+- [AgentRunner](../runner/runner.py) - Where logging is configured
+- [Runtime Core](../runtime/core.py) - Where trace context is set

core/framework/observability/__init__.py

@@ -0,0 +1,23 @@
+"""
+Observability module for automatic trace correlation and structured logging.
+
+This module provides zero-friction observability:
+- Automatic trace context propagation via ContextVar
+- Structured JSON logging for production
+- Human-readable logging for development
+- No manual ID passing required
+"""
+
+from framework.observability.logging import (
+    clear_trace_context,
+    configure_logging,
+    get_trace_context,
+    set_trace_context,
+)
+
+__all__ = [
+    "configure_logging",
+    "get_trace_context",
+    "set_trace_context",
+    "clear_trace_context",
+]

core/framework/observability/logging.py

@@ -0,0 +1,302 @@
+"""
+Structured logging with automatic trace context propagation.
+
+Key Features:
+- Zero developer friction: Standard logger.info() calls get automatic context
+- ContextVar-based propagation: Thread-safe and async-safe
+- Dual output modes: JSON for production, human-readable for development
+- Correlation IDs: trace_id follows entire request flow automatically
+
+Architecture:
+    Runtime.start_run() → Generates trace_id, sets context once
+        ↓ (automatic propagation via ContextVar)
+    GraphExecutor.execute() → Adds agent_id to context
+        ↓ (automatic propagation)
+    Node.execute() → Adds node_id to context
+        ↓ (automatic propagation)
+    User code → logger.info("message") → Gets ALL context automatically!
+"""
+
+import json
+import logging
+import os
+import re
+from contextvars import ContextVar
+from datetime import UTC, datetime
+from typing import Any
+
+# Context variable for trace propagation
+# ContextVar is thread-safe and async-safe - perfect for concurrent agent execution
+trace_context: ContextVar[dict[str, Any] | None] = ContextVar("trace_context", default=None)
+
+# ANSI escape code pattern (matches \033[...m or \x1b[...m)
+ANSI_ESCAPE_PATTERN = re.compile(r"\x1b\[[0-9;]*m|\033\[[0-9;]*m")
+
+
+def strip_ansi_codes(text: str) -> str:
+    """Remove ANSI escape codes from text for clean JSON logging."""
+    return ANSI_ESCAPE_PATTERN.sub("", text)
+
+
+class StructuredFormatter(logging.Formatter):
+    """
+    JSON formatter for structured logging.
+
+    Produces machine-parseable log entries with:
+    - Standard fields (timestamp, level, logger, message)
+    - Trace context (trace_id, execution_id, agent_id, etc.) - AUTOMATIC
+    - Custom fields from extra dict
+    """
+
+    def format(self, record: logging.LogRecord) -> str:
+        """Format log record as JSON."""
+        # Get trace context for correlation - AUTOMATIC!
+        context = trace_context.get() or {}
+
+        # Strip ANSI codes from message for clean JSON output
+        message = strip_ansi_codes(record.getMessage())
+
+        # Build base log entry
+        log_entry = {
+            "timestamp": datetime.now(UTC).isoformat(),
+            "level": record.levelname.lower(),
+            "logger": record.name,
+            "message": message,
+        }
+
+        # Add trace context (trace_id, execution_id, agent_id, etc.) - AUTOMATIC!
+        log_entry.update(context)
+
+        # Add custom fields from extra (optional)
+        event = getattr(record, "event", None)
+        if event is not None:
+            if isinstance(event, str):
+                log_entry["event"] = strip_ansi_codes(str(event))
+            else:
+                log_entry["event"] = event
+
+        latency_ms = getattr(record, "latency_ms", None)
+        if latency_ms is not None:
+            log_entry["latency_ms"] = latency_ms
+
+        tokens_used = getattr(record, "tokens_used", None)
+        if tokens_used is not None:
+            log_entry["tokens_used"] = tokens_used
+
+        node_id = getattr(record, "node_id", None)
+        if node_id is not None:
+            log_entry["node_id"] = node_id
+
+        model = getattr(record, "model", None)
+        if model is not None:
+            log_entry["model"] = model
+
+        # Add exception info if present (strip ANSI codes from exception text too)
+        if record.exc_info:
+            exception_text = self.formatException(record.exc_info)
+            log_entry["exception"] = strip_ansi_codes(exception_text)
+
+        return json.dumps(log_entry)
+
+
+class HumanReadableFormatter(logging.Formatter):
+    """
+    Human-readable formatter for development.
+
+    Provides colorized logs with trace context for local debugging.
+    Includes trace_id prefix for correlation - AUTOMATIC!
+    """
+
+    COLORS = {
+        "DEBUG": "\033[36m",  # Cyan
+        "INFO": "\033[32m",  # Green
+        "WARNING": "\033[33m",  # Yellow
+        "ERROR": "\033[31m",  # Red
+        "CRITICAL": "\033[35m",  # Magenta
+    }
+    RESET = "\033[0m"
+
+    def format(self, record: logging.LogRecord) -> str:
+        """Format log record as human-readable string."""
+        # Get trace context - AUTOMATIC!
+        context = trace_context.get() or {}
+        trace_id = context.get("trace_id", "")
+        execution_id = context.get("execution_id", "")
+        agent_id = context.get("agent_id", "")
+
+        # Build context prefix
+        prefix_parts = []
+        if trace_id:
+            prefix_parts.append(f"trace:{trace_id[:8]}")
+        if execution_id:
+            prefix_parts.append(f"exec:{execution_id[-8:]}")
+        if agent_id:
+            prefix_parts.append(f"agent:{agent_id}")
+
+        context_prefix = f"[{' | '.join(prefix_parts)}] " if prefix_parts else ""
+
+        # Get color
+        color = self.COLORS.get(record.levelname, "")
+        reset = self.RESET
+
+        # Format log level (5 chars wide for alignment)
+        level = f"{record.levelname:<8}"
+
+        # Add event if present
+        event = ""
+        record_event = getattr(record, "event", None)
+        if record_event is not None:
+            event = f" [{record_event}]"
+
+        # Format message: [LEVEL] [trace context] message
+        return f"{color}[{level}]{reset} {context_prefix}{record.getMessage()}{event}"
+
+
+def configure_logging(
+    level: str = "INFO",
+    format: str = "auto",  # "json", "human", or "auto"
+) -> None:
+    """
+    Configure structured logging for the application.
+
+    This should be called ONCE at application startup, typically in:
+    - AgentRunner._setup()
+    - Main entry point
+    - Test fixtures
+
+    Args:
+        level: Log level (DEBUG, INFO, WARNING, ERROR, CRITICAL)
+        format: Output format:
+            - "json": Machine-parseable JSON (for production)
+            - "human": Human-readable with colors (for development)
+            - "auto": JSON if LOG_FORMAT=json or ENV=production, else human
+
+    Examples:
+        # Development mode (human-readable)
+        configure_logging(level="DEBUG", format="human")
+
+        # Production mode (JSON)
+        configure_logging(level="INFO", format="json")
+
+        # Auto-detect from environment
+        configure_logging(level="INFO", format="auto")
+    """
+    # Auto-detect format
+    if format == "auto":
+        # Use JSON if LOG_FORMAT=json or ENV=production
+        log_format_env = os.getenv("LOG_FORMAT", "").lower()
+        env = os.getenv("ENV", "development").lower()
+
+        if log_format_env == "json" or env == "production":
+            format = "json"
+        else:
+            format = "human"
+
+    # Select formatter
+    if format == "json":
+        formatter = StructuredFormatter()
+        # Disable colors in third-party libraries when using JSON format
+        _disable_third_party_colors()
+    else:
+        formatter = HumanReadableFormatter()
+
+    # Configure handler
+    handler = logging.StreamHandler()
+    handler.setFormatter(formatter)
+
+    # Configure root logger
+    root_logger = logging.getLogger()
+    root_logger.handlers.clear()
+    root_logger.addHandler(handler)
+    root_logger.setLevel(level.upper())
+
+    # When in JSON mode, configure known third-party loggers to use JSON formatter
+    # This ensures libraries like LiteLLM, httpcore also output clean JSON
+    if format == "json":
+        third_party_loggers = [
+            "LiteLLM",
+            "httpcore",
+            "httpx",
+            "openai",
+        ]
+        for logger_name in third_party_loggers:
+            logger = logging.getLogger(logger_name)
+            # Clear existing handlers so records propagate to root and use our formatter there
+            logger.handlers.clear()
+            logger.propagate = True  # Still propagate to root for consistency
+
+
+def _disable_third_party_colors() -> None:
+    """Disable color output in third-party libraries for clean JSON logging."""
+    # Set NO_COLOR environment variable (common convention for disabling colors)
+    os.environ["NO_COLOR"] = "1"
+    os.environ["FORCE_COLOR"] = "0"
+
+    # Disable LiteLLM debug/verbose output colors if available
+    try:
+        import litellm
+
+        # LiteLLM respects NO_COLOR, but we can also suppress debug info
+        if hasattr(litellm, "suppress_debug_info"):
+            litellm.suppress_debug_info = True  # type: ignore[attr-defined]
+    except (ImportError, AttributeError):
+        pass
+
+
+def set_trace_context(**kwargs: Any) -> None:
+    """
+    Set trace context for current execution.
+
+    Context is stored in a ContextVar and AUTOMATICALLY propagates
+    through async calls within the same execution context.
+
+    This is called by the framework at key points:
+    - Runtime.start_run(): Sets trace_id, execution_id, goal_id
+    - GraphExecutor.execute(): Adds agent_id
+    - Node execution: Adds node_id
+
+    Developers/agents NEVER call this directly - it's framework-managed.
+
+    Args:
+        **kwargs: Context fields (trace_id, execution_id, agent_id, etc.)
+
+    Example (framework code):
+        # In Runtime.start_run()
+        trace_id = uuid.uuid4().hex  # 32 hex, W3C Trace Context compliant
+        execution_id = uuid.uuid4().hex  # 32 hex, OTel-aligned for correlation
+        set_trace_context(
+            trace_id=trace_id,
+            execution_id=execution_id,
+            goal_id=goal_id
+        )
+        # All subsequent logs in this execution get these fields automatically!
+    """
+    current = trace_context.get() or {}
+    trace_context.set({**current, **kwargs})
+
+
+def get_trace_context() -> dict:
+    """
+    Get current trace context.
+
+    Returns:
+        Dict with trace_id, execution_id, agent_id, etc.
+        Empty dict if no context set.
+    """
+    context = trace_context.get() or {}
+    return context.copy()
+
+
+def clear_trace_context() -> None:
+    """
+    Clear trace context.
+
+    Useful for:
+    - Cleanup between test runs
+    - Starting a completely new execution context
+    - Manual context management (rare)
+
+    Note: Framework typically doesn't need to call this - ContextVar
+    is execution-scoped and cleans itself up automatically.
+    """
+    trace_context.set(None)

core/framework/runner/cli.py

@@ -33,11 +33,6 @@ def register_commands(subparsers: argparse._SubParsersAction) -> None:
         type=str,
         help="Input context from JSON file",
     )
-    run_parser.add_argument(
-        "--mock",
-        action="store_true",
-        help="Run in mock mode (no real LLM calls)",
-    )
     run_parser.add_argument(
         "--output",
         "-o",
@@ -56,6 +51,30 @@ def register_commands(subparsers: argparse._SubParsersAction) -> None:
         action="store_true",
         help="Show detailed execution logs (steps, LLM calls, etc.)",
     )
+    run_parser.add_argument(
+        "--tui",
+        action="store_true",
+        help="Launch interactive terminal dashboard",
+    )
+    run_parser.add_argument(
+        "--model",
+        "-m",
+        type=str,
+        default=None,
+        help="LLM model to use (any LiteLLM-compatible name)",
+    )
+    run_parser.add_argument(
+        "--resume-session",
+        type=str,
+        default=None,
+        help="Resume from a specific session ID",
+    )
+    run_parser.add_argument(
+        "--checkpoint",
+        type=str,
+        default=None,
+        help="Resume from a specific checkpoint (requires --resume-session)",
+    )
     run_parser.set_defaults(func=cmd_run)
 
     # info command
@@ -174,6 +193,144 @@ def register_commands(subparsers: argparse._SubParsersAction) -> None:
     )
     shell_parser.set_defaults(func=cmd_shell)
 
+    # tui command (interactive agent dashboard)
+    tui_parser = subparsers.add_parser(
+        "tui",
+        help="Launch interactive TUI dashboard",
+        description="Browse available agents and launch the terminal dashboard.",
+    )
+    tui_parser.add_argument(
+        "--model",
+        "-m",
+        type=str,
+        default=None,
+        help="LLM model to use (any LiteLLM-compatible name)",
+    )
+    tui_parser.set_defaults(func=cmd_tui)
+
+    # sessions command group (checkpoint/resume management)
+    sessions_parser = subparsers.add_parser(
+        "sessions",
+        help="Manage agent sessions",
+        description="List, inspect, and manage agent execution sessions.",
+    )
+    sessions_subparsers = sessions_parser.add_subparsers(
+        dest="sessions_cmd",
+        help="Session management commands",
+    )
+
+    # sessions list
+    sessions_list_parser = sessions_subparsers.add_parser(
+        "list",
+        help="List agent sessions",
+        description="List all sessions for an agent.",
+    )
+    sessions_list_parser.add_argument(
+        "agent_path",
+        type=str,
+        help="Path to agent folder",
+    )
+    sessions_list_parser.add_argument(
+        "--status",
+        choices=["all", "active", "failed", "completed", "paused"],
+        default="all",
+        help="Filter by session status (default: all)",
+    )
+    sessions_list_parser.add_argument(
+        "--has-checkpoints",
+        action="store_true",
+        help="Show only sessions with checkpoints",
+    )
+    sessions_list_parser.set_defaults(func=cmd_sessions_list)
+
+    # sessions show
+    sessions_show_parser = sessions_subparsers.add_parser(
+        "show",
+        help="Show session details",
+        description="Display detailed information about a specific session.",
+    )
+    sessions_show_parser.add_argument(
+        "agent_path",
+        type=str,
+        help="Path to agent folder",
+    )
+    sessions_show_parser.add_argument(
+        "session_id",
+        type=str,
+        help="Session ID to inspect",
+    )
+    sessions_show_parser.add_argument(
+        "--json",
+        action="store_true",
+        help="Output as JSON",
+    )
+    sessions_show_parser.set_defaults(func=cmd_sessions_show)
+
+    # sessions checkpoints
+    sessions_checkpoints_parser = sessions_subparsers.add_parser(
+        "checkpoints",
+        help="List session checkpoints",
+        description="List all checkpoints for a session.",
+    )
+    sessions_checkpoints_parser.add_argument(
+        "agent_path",
+        type=str,
+        help="Path to agent folder",
+    )
+    sessions_checkpoints_parser.add_argument(
+        "session_id",
+        type=str,
+        help="Session ID",
+    )
+    sessions_checkpoints_parser.set_defaults(func=cmd_sessions_checkpoints)
+
+    # pause command
+    pause_parser = subparsers.add_parser(
+        "pause",
+        help="Pause running session",
+        description="Request graceful pause of a running agent session.",
+    )
+    pause_parser.add_argument(
+        "agent_path",
+        type=str,
+        help="Path to agent folder",
+    )
+    pause_parser.add_argument(
+        "session_id",
+        type=str,
+        help="Session ID to pause",
+    )
+    pause_parser.set_defaults(func=cmd_pause)
+
+    # resume command
+    resume_parser = subparsers.add_parser(
+        "resume",
+        help="Resume session from checkpoint",
+        description="Resume a paused or failed session from a checkpoint.",
+    )
+    resume_parser.add_argument(
+        "agent_path",
+        type=str,
+        help="Path to agent folder",
+    )
+    resume_parser.add_argument(
+        "session_id",
+        type=str,
+        help="Session ID to resume",
+    )
+    resume_parser.add_argument(
+        "--checkpoint",
+        "-c",
+        type=str,
+        help="Specific checkpoint ID to resume from (default: latest)",
+    )
+    resume_parser.add_argument(
+        "--tui",
+        action="store_true",
+        help="Resume in TUI dashboard mode",
+    )
+    resume_parser.set_defaults(func=cmd_resume)
+
 
 def cmd_run(args: argparse.Namespace) -> int:
     """Run an exported agent."""
@@ -205,38 +362,85 @@ def cmd_run(args: argparse.Namespace) -> int:
             print(f"Error reading input file: {e}", file=sys.stderr)
             return 1
 
-    # Load and run agent
-    try:
-        runner = AgentRunner.load(
-            args.agent_path,
-            mock_mode=args.mock,
-            model=getattr(args, "model", "claude-haiku-4-5-20251001"),
-        )
-    except FileNotFoundError as e:
-        print(f"Error: {e}", file=sys.stderr)
-        return 1
+    # Run the agent (with TUI or standard)
+    if getattr(args, "tui", False):
+        from framework.tui.app import AdenTUI
 
-    # Auto-inject user_id if the agent expects it but it's not provided
-    entry_input_keys = runner.graph.nodes[0].input_keys if runner.graph.nodes else []
-    if "user_id" in entry_input_keys and context.get("user_id") is None:
-        import os
+        async def run_with_tui():
+            try:
+                # Load runner inside the async loop to ensure strict loop affinity
+                # (only one load — avoids spawning duplicate MCP subprocesses)
+                try:
+                    runner = AgentRunner.load(
+                        args.agent_path,
+                        model=args.model,
+                        enable_tui=True,
+                    )
+                except Exception as e:
+                    print(f"Error loading agent: {e}")
+                    return
 
-        context["user_id"] = os.environ.get("USER", "default_user")
+                # Force setup inside the loop
+                if runner._agent_runtime is None:
+                    runner._setup()
 
-    if not args.quiet:
-        info = runner.info()
-        print(f"Agent: {info.name}")
-        print(f"Goal: {info.goal_name}")
-        print(f"Steps: {info.node_count}")
-        print(f"Input: {json.dumps(context)}")
-        print()
-        print("=" * 60)
-        print("Executing agent...")
-        print("=" * 60)
-        print()
+                # Start runtime before TUI so it's ready for user input
+                if runner._agent_runtime and not runner._agent_runtime.is_running:
+                    await runner._agent_runtime.start()
 
-    # Run the agent
-    result = asyncio.run(runner.run(context))
+                app = AdenTUI(
+                    runner._agent_runtime,
+                    resume_session=getattr(args, "resume_session", None),
+                    resume_checkpoint=getattr(args, "checkpoint", None),
+                )
+
+                # TUI handles execution via ChatRepl — user submits input,
+                # ChatRepl calls runtime.trigger_and_wait(). No auto-launch.
+                await app.run_async()
+            except Exception as e:
+                import traceback
+
+                traceback.print_exc()
+                print(f"TUI error: {e}")
+
+            await runner.cleanup_async()
+            return None
+
+        asyncio.run(run_with_tui())
+        print("TUI session ended.")
+        return 0
+    else:
+        # Standard execution — load runner here (not shared with TUI path)
+        try:
+            runner = AgentRunner.load(
+                args.agent_path,
+                model=args.model,
+                enable_tui=False,
+            )
+        except FileNotFoundError as e:
+            print(f"Error: {e}", file=sys.stderr)
+            return 1
+
+        # Auto-inject user_id if the agent expects it but it's not provided
+        entry_input_keys = runner.graph.nodes[0].input_keys if runner.graph.nodes else []
+        if "user_id" in entry_input_keys and context.get("user_id") is None:
+            import os
+
+            context["user_id"] = os.environ.get("USER", "default_user")
+
+        if not args.quiet:
+            info = runner.info()
+            print(f"Agent: {info.name}")
+            print(f"Goal: {info.goal_name}")
+            print(f"Steps: {info.node_count}")
+            print(f"Input: {json.dumps(context)}")
+            print()
+            print("=" * 60)
+            print("Executing agent...")
+            print("=" * 60)
+            print()
+
+        result = asyncio.run(runner.run(context))
 
     # Format output
     output = {
@@ -928,8 +1132,215 @@ def cmd_shell(args: argparse.Namespace) -> int:
     return 0
 
 
+def cmd_tui(args: argparse.Namespace) -> int:
+    """Browse agents and launch the interactive TUI dashboard."""
+    import logging
+
+    from framework.runner import AgentRunner
+    from framework.tui.app import AdenTUI
+
+    logging.basicConfig(level=logging.WARNING, format="%(message)s")
+
+    exports_dir = Path("exports")
+    examples_dir = Path("examples/templates")
+
+    has_exports = _has_agents(exports_dir)
+    has_examples = _has_agents(examples_dir)
+
+    if not has_exports and not has_examples:
+        print("No agents found in exports/ or examples/templates/", file=sys.stderr)
+        return 1
+
+    # Determine which directory to browse
+    if has_exports and has_examples:
+        print("\nAgent sources:\n")
+        print("  1. Your Agents (exports/)")
+        print("  2. Sample Agents (examples/templates/)")
+        print()
+        try:
+            choice = input("Select source (number): ").strip()
+            if choice == "1":
+                agents_dir = exports_dir
+            elif choice == "2":
+                agents_dir = examples_dir
+            else:
+                print("Invalid selection")
+                return 1
+        except (EOFError, KeyboardInterrupt):
+            print()
+            return 1
+    elif has_exports:
+        agents_dir = exports_dir
+    else:
+        agents_dir = examples_dir
+
+    # Let user pick an agent
+    agent_path = _select_agent(agents_dir)
+    if not agent_path:
+        return 1
+
+    # Launch TUI (same pattern as cmd_run --tui)
+    async def run_with_tui():
+        try:
+            runner = AgentRunner.load(
+                agent_path,
+                model=args.model,
+                enable_tui=True,
+            )
+        except Exception as e:
+            print(f"Error loading agent: {e}")
+            return
+
+        if runner._agent_runtime is None:
+            runner._setup()
+
+        if runner._agent_runtime and not runner._agent_runtime.is_running:
+            await runner._agent_runtime.start()
+
+        app = AdenTUI(runner._agent_runtime)
+        try:
+            await app.run_async()
+        except Exception as e:
+            import traceback
+
+            traceback.print_exc()
+            print(f"TUI error: {e}")
+
+        await runner.cleanup_async()
+
+    asyncio.run(run_with_tui())
+    print("TUI session ended.")
+    return 0
+
+
+def _extract_python_agent_metadata(agent_path: Path) -> tuple[str, str]:
+    """Extract name and description from a Python-based agent's config.py.
+
+    Uses AST parsing to safely extract values without executing code.
+    Returns (name, description) tuple, with fallbacks if parsing fails.
+    """
+    import ast
+
+    config_path = agent_path / "config.py"
+    fallback_name = agent_path.name.replace("_", " ").title()
+    fallback_desc = "(Python-based agent)"
+
+    if not config_path.exists():
+        return fallback_name, fallback_desc
+
+    try:
+        with open(config_path) as f:
+            tree = ast.parse(f.read())
+
+        # Find AgentMetadata class definition
+        for node in ast.walk(tree):
+            if isinstance(node, ast.ClassDef) and node.name == "AgentMetadata":
+                name = fallback_name
+                desc = fallback_desc
+
+                # Extract default values from class body
+                for item in node.body:
+                    if isinstance(item, ast.AnnAssign) and isinstance(item.target, ast.Name):
+                        field_name = item.target.id
+                        if item.value:
+                            # Handle simple string constants
+                            if isinstance(item.value, ast.Constant):
+                                if field_name == "name":
+                                    name = item.value.value
+                                elif field_name == "description":
+                                    desc = item.value.value
+                            # Handle parenthesized multi-line strings (concatenated)
+                            elif isinstance(item.value, ast.JoinedStr):
+                                # f-strings - skip, use fallback
+                                pass
+                            elif isinstance(item.value, ast.BinOp):
+                                # String concatenation with + - try to evaluate
+                                try:
+                                    result = _eval_string_binop(item.value)
+                                    if result and field_name == "name":
+                                        name = result
+                                    elif result and field_name == "description":
+                                        desc = result
+                                except Exception:
+                                    pass
+
+                return name, desc
+
+        return fallback_name, fallback_desc
+    except Exception:
+        return fallback_name, fallback_desc
+
+
+def _eval_string_binop(node) -> str | None:
+    """Recursively evaluate a BinOp of string constants."""
+    import ast
+
+    if isinstance(node, ast.Constant) and isinstance(node.value, str):
+        return node.value
+    elif isinstance(node, ast.BinOp) and isinstance(node.op, ast.Add):
+        left = _eval_string_binop(node.left)
+        right = _eval_string_binop(node.right)
+        if left is not None and right is not None:
+            return left + right
+    return None
+
+
+def _is_valid_agent_dir(path: Path) -> bool:
+    """Check if a directory contains a valid agent (agent.json or agent.py)."""
+    if not path.is_dir():
+        return False
+    return (path / "agent.json").exists() or (path / "agent.py").exists()
+
+
+def _has_agents(directory: Path) -> bool:
+    """Check if a directory contains any valid agents (folders with agent.json or agent.py)."""
+    if not directory.exists():
+        return False
+    return any(_is_valid_agent_dir(p) for p in directory.iterdir())
+
+
+def _getch() -> str:
+    """Read a single character from stdin without waiting for Enter."""
+    try:
+        if sys.platform == "win32":
+            import msvcrt
+
+            ch = msvcrt.getch()
+            return ch.decode("utf-8", errors="ignore")
+        else:
+            import termios
+            import tty
+
+            fd = sys.stdin.fileno()
+            old_settings = termios.tcgetattr(fd)
+            try:
+                tty.setraw(fd)
+                ch = sys.stdin.read(1)
+            finally:
+                termios.tcsetattr(fd, termios.TCSADRAIN, old_settings)
+            return ch
+    except Exception:
+        return ""
+
+
+def _read_key() -> str:
+    """Read a key, handling arrow key escape sequences."""
+    ch = _getch()
+    if ch == "\x1b":  # Escape sequence start
+        ch2 = _getch()
+        if ch2 == "[":
+            ch3 = _getch()
+            if ch3 == "C":  # Right arrow
+                return "RIGHT"
+            elif ch3 == "D":  # Left arrow
+                return "LEFT"
+    return ch
+
+
 def _select_agent(agents_dir: Path) -> str | None:
-    """Let user select an agent from available agents."""
+    """Let user select an agent from available agents with pagination."""
+    AGENTS_PER_PAGE = 10
+
     if not agents_dir.exists():
         print(f"Directory not found: {agents_dir}", file=sys.stderr)
         # fixes issue #696, creates an exports folder if it does not exist
@@ -939,37 +1350,126 @@ def _select_agent(agents_dir: Path) -> str | None:
 
     agents = []
     for path in agents_dir.iterdir():
-        if path.is_dir() and (path / "agent.json").exists():
+        if _is_valid_agent_dir(path):
             agents.append(path)
 
     if not agents:
         print(f"No agents found in {agents_dir}", file=sys.stderr)
         return None
 
-    print(f"\nAvailable agents in {agents_dir}:\n")
-    for i, agent_path in enumerate(agents, 1):
+    # Pagination setup
+    page = 0
+    total_pages = (len(agents) + AGENTS_PER_PAGE - 1) // AGENTS_PER_PAGE
+
+    while True:
+        start_idx = page * AGENTS_PER_PAGE
+        end_idx = min(start_idx + AGENTS_PER_PAGE, len(agents))
+        page_agents = agents[start_idx:end_idx]
+
+        # Show page header with indicator
+        if total_pages > 1:
+            print(f"\nAvailable agents in {agents_dir} (Page {page + 1}/{total_pages}):\n")
+        else:
+            print(f"\nAvailable agents in {agents_dir}:\n")
+
+        # Display agents for current page (with global numbering)
+        for i, agent_path in enumerate(page_agents, start_idx + 1):
+            try:
+                agent_json = agent_path / "agent.json"
+                if agent_json.exists():
+                    with open(agent_json) as f:
+                        data = json.load(f)
+                    agent_meta = data.get("agent", {})
+                    name = agent_meta.get("name", agent_path.name)
+                    desc = agent_meta.get("description", "")
+                else:
+                    # Python-based agent - extract from config.py
+                    name, desc = _extract_python_agent_metadata(agent_path)
+                desc = desc[:50] + "..." if len(desc) > 50 else desc
+                print(f"  {i}. {name}")
+                print(f"     {desc}")
+            except Exception as e:
+                print(f"  {i}. {agent_path.name} (error: {e})")
+
+        # Build navigation options
+        nav_options = []
+        if total_pages > 1:
+            nav_options.append("←/→ or p/n=navigate")
+        nav_options.append("q=quit")
+
+        print()
+        if total_pages > 1:
+            print(f"  [{', '.join(nav_options)}]")
+            print()
+
+        # Show prompt
+        print("Select agent (number), use arrows to navigate, or q to quit: ", end="", flush=True)
+
         try:
-            from framework.runner import AgentRunner
+            key = _read_key()
 
-            runner = AgentRunner.load(agent_path)
-            info = runner.info()
-            desc = info.description[:50] + "..." if len(info.description) > 50 else info.description
-            print(f"  {i}. {info.name}")
-            print(f"     {desc}")
-            runner.cleanup()
-        except Exception as e:
-            print(f"  {i}. {agent_path.name} (error: {e})")
+            if key == "RIGHT" and page < total_pages - 1:
+                page += 1
+                print()  # Newline before redrawing
+            elif key == "LEFT" and page > 0:
+                page -= 1
+                print()
+            elif key == "q":
+                print()
+                return None
+            elif key in ("n", ">") and page < total_pages - 1:
+                page += 1
+                print()
+            elif key in ("p", "<") and page > 0:
+                page -= 1
+                print()
+            elif key.isdigit():
+                # Build number with support for backspace
+                buffer = key
+                print(key, end="", flush=True)
 
-    print()
-    try:
-        choice = input("Select agent (number): ").strip()
-        idx = int(choice) - 1
-        if 0 <= idx < len(agents):
-            return str(agents[idx])
-        print("Invalid selection")
-        return None
-    except (ValueError, EOFError, KeyboardInterrupt):
-        return None
+                while True:
+                    ch = _getch()
+                    if ch in ("\r", "\n"):
+                        # Enter pressed - submit
+                        print()
+                        break
+                    elif ch in ("\x7f", "\x08"):
+                        # Backspace (DEL or BS)
+                        if buffer:
+                            buffer = buffer[:-1]
+                            # Erase character: move back, print space, move back
+                            print("\b \b", end="", flush=True)
+                    elif ch.isdigit():
+                        buffer += ch
+                        print(ch, end="", flush=True)
+                    elif ch == "\x1b":
+                        # Escape - cancel input
+                        print()
+                        buffer = ""
+                        break
+                    elif ch == "\x03":
+                        # Ctrl+C
+                        print()
+                        return None
+                    # Ignore other characters
+
+                if buffer:
+                    try:
+                        idx = int(buffer) - 1
+                        if 0 <= idx < len(agents):
+                            return str(agents[idx])
+                        print("Invalid selection")
+                    except ValueError:
+                        print("Invalid input")
+            elif key == "\r" or key == "\n":
+                print()  # Just pressed enter, redraw
+            else:
+                print()
+                print("Invalid input")
+        except (EOFError, KeyboardInterrupt):
+            print()
+            return None
 
 
 def _interactive_multi(agents_dir: Path) -> int:
@@ -985,7 +1485,7 @@ def _interactive_multi(agents_dir: Path) -> int:
 
     # Register all agents
     for path in agents_dir.iterdir():
-        if path.is_dir() and (path / "agent.json").exists():
+        if _is_valid_agent_dir(path):
             try:
                 orchestrator.register(path.name, path)
                 agent_count += 1
@@ -1071,3 +1571,53 @@ def _interactive_multi(agents_dir: Path) -> int:
 
     orchestrator.cleanup()
     return 0
+
+
+def cmd_sessions_list(args: argparse.Namespace) -> int:
+    """List agent sessions."""
+    print("⚠ Sessions list command not yet implemented")
+    print("This will be available once checkpoint infrastructure is complete.")
+    print(f"\nAgent: {args.agent_path}")
+    print(f"Status filter: {args.status}")
+    print(f"Has checkpoints: {args.has_checkpoints}")
+    return 1
+
+
+def cmd_sessions_show(args: argparse.Namespace) -> int:
+    """Show detailed session information."""
+    print("⚠ Session show command not yet implemented")
+    print("This will be available once checkpoint infrastructure is complete.")
+    print(f"\nAgent: {args.agent_path}")
+    print(f"Session: {args.session_id}")
+    return 1
+
+
+def cmd_sessions_checkpoints(args: argparse.Namespace) -> int:
+    """List checkpoints for a session."""
+    print("⚠ Session checkpoints command not yet implemented")
+    print("This will be available once checkpoint infrastructure is complete.")
+    print(f"\nAgent: {args.agent_path}")
+    print(f"Session: {args.session_id}")
+    return 1
+
+
+def cmd_pause(args: argparse.Namespace) -> int:
+    """Pause a running session."""
+    print("⚠ Pause command not yet implemented")
+    print("This will be available once executor pause integration is complete.")
+    print(f"\nAgent: {args.agent_path}")
+    print(f"Session: {args.session_id}")
+    return 1
+
+
+def cmd_resume(args: argparse.Namespace) -> int:
+    """Resume a session from checkpoint."""
+    print("⚠ Resume command not yet implemented")
+    print("This will be available once checkpoint resume integration is complete.")
+    print(f"\nAgent: {args.agent_path}")
+    print(f"Session: {args.session_id}")
+    if args.checkpoint:
+        print(f"Checkpoint: {args.checkpoint}")
+    if args.tui:
+        print("Mode: TUI")
+    return 1

core/framework/runner/mcp_client.py

@@ -362,6 +362,15 @@ class MCPClient:
         # Call tool using persistent session
         result = await self._session.call_tool(tool_name, arguments=arguments)
 
+        # Check for server-side errors (validation failures, tool exceptions, etc.)
+        if getattr(result, "isError", False):
+            error_text = ""
+            if result.content:
+                content_item = result.content[0]
+                if hasattr(content_item, "text"):
+                    error_text = content_item.text
+            raise RuntimeError(f"MCP tool '{tool_name}' failed: {error_text}")
+
         # Extract content
         if result.content:
             # MCP returns content as a list of content items

core/framework/runner/runner.py

@@ -8,8 +8,15 @@ from dataclasses import dataclass, field
 from pathlib import Path
 from typing import TYPE_CHECKING, Any
 
+from framework.config import get_hive_config, get_preferred_model
 from framework.graph import Goal
-from framework.graph.edge import AsyncEntryPointSpec, EdgeCondition, EdgeSpec, GraphSpec
+from framework.graph.edge import (
+    DEFAULT_MAX_TOKENS,
+    AsyncEntryPointSpec,
+    EdgeCondition,
+    EdgeSpec,
+    GraphSpec,
+)
 from framework.graph.executor import ExecutionResult, GraphExecutor
 from framework.graph.node import NodeSpec
 from framework.llm.provider import LLMProvider, Tool
@@ -19,6 +26,8 @@ from framework.runner.tool_registry import ToolRegistry
 from framework.runtime.agent_runtime import AgentRuntime, create_agent_runtime
 from framework.runtime.core import Runtime
 from framework.runtime.execution_stream import EntryPointSpec
+from framework.runtime.runtime_log_store import RuntimeLogStore
+from framework.runtime.runtime_logger import RuntimeLogger
 
 if TYPE_CHECKING:
     from framework.runner.protocol import AgentMessage, CapabilityResponse
@@ -26,20 +35,33 @@ if TYPE_CHECKING:
 
 logger = logging.getLogger(__name__)
 
-# Configuration paths
-HIVE_CONFIG_FILE = Path.home() / ".hive" / "configuration.json"
-CLAUDE_CREDENTIALS_FILE = Path.home() / ".claude" / ".credentials.json"
 
+def _ensure_credential_key_env() -> None:
+    """Load HIVE_CREDENTIAL_KEY from shell config if not already in environment.
+
+    The setup-credentials skill writes the encryption key to ~/.zshrc or ~/.bashrc.
+    If the user hasn't sourced their config in the current shell, this reads it
+    directly so the runner (and any MCP subprocesses it spawns) can unlock the
+    encrypted credential store.
+
+    Only HIVE_CREDENTIAL_KEY is loaded this way — all other secrets (API keys, etc.)
+    come from the credential store itself.
+    """
+    if os.environ.get("HIVE_CREDENTIAL_KEY"):
+        return
 
-def get_hive_config() -> dict[str, Any]:
-    """Load hive configuration from ~/.hive/configuration.json."""
-    if not HIVE_CONFIG_FILE.exists():
-        return {}
     try:
-        with open(HIVE_CONFIG_FILE) as f:
-            return json.load(f)
-    except (json.JSONDecodeError, OSError):
-        return {}
+        from aden_tools.credentials.shell_config import check_env_var_in_shell_config
+
+        found, value = check_env_var_in_shell_config("HIVE_CREDENTIAL_KEY")
+        if found and value:
+            os.environ["HIVE_CREDENTIAL_KEY"] = value
+            logger.debug("Loaded HIVE_CREDENTIAL_KEY from shell config")
+    except ImportError:
+        pass
+
+
+CLAUDE_CREDENTIALS_FILE = Path.home() / ".claude" / ".credentials.json"
 
 
 def get_claude_code_token() -> str | None:
@@ -236,6 +258,11 @@ class AgentRunner:
         result = await runner.run({"lead_id": "123"})
     """
 
+    @staticmethod
+    def _resolve_default_model() -> str:
+        """Resolve the default model from ~/.hive/configuration.json."""
+        return get_preferred_model()
+
     def __init__(
         self,
         agent_path: Path,
@@ -243,7 +270,8 @@ class AgentRunner:
         goal: Goal,
         mock_mode: bool = False,
         storage_path: Path | None = None,
-        model: str = "cerebras/zai-glm-4.7",
+        model: str | None = None,
+        enable_tui: bool = False,
     ):
         """
         Initialize the runner (use AgentRunner.load() instead).
@@ -254,27 +282,32 @@ class AgentRunner:
             goal: Loaded Goal object
             mock_mode: If True, use mock LLM responses
             storage_path: Path for runtime storage (defaults to temp)
-            model: Model to use - any LiteLLM-compatible model name
-                   (e.g., "claude-sonnet-4-20250514", "gpt-4o-mini", "gemini/gemini-pro")
+            model: Model to use (reads from agent config or ~/.hive/configuration.json if None)
+            enable_tui: If True, forces use of AgentRuntime with EventBus
         """
         self.agent_path = agent_path
         self.graph = graph
         self.goal = goal
         self.mock_mode = mock_mode
-        self.model = model
+        self.model = model or self._resolve_default_model()
+        self.enable_tui = enable_tui
 
         # Set up storage
         if storage_path:
             self._storage_path = storage_path
             self._temp_dir = None
         else:
-            # Use persistent storage in ~/.hive by default
+            # Use persistent storage in ~/.hive/agents/{agent_name}/ per RUNTIME_LOGGING.md spec
             home = Path.home()
-            default_storage = home / ".hive" / "storage" / agent_path.name
+            default_storage = home / ".hive" / "agents" / agent_path.name
             default_storage.mkdir(parents=True, exist_ok=True)
             self._storage_path = default_storage
             self._temp_dir = None
 
+        # Load HIVE_CREDENTIAL_KEY from shell config if not in env.
+        # Must happen before MCP subprocesses are spawned so they inherit it.
+        _ensure_credential_key_env()
+
         # Initialize components
         self._tool_registry = ToolRegistry()
         self._runtime: Runtime | None = None
@@ -296,32 +329,125 @@ class AgentRunner:
         if mcp_config_path.exists():
             self._load_mcp_servers_from_config(mcp_config_path)
 
+    @staticmethod
+    def _import_agent_module(agent_path: Path):
+        """Import an agent package from its directory path.
+
+        Tries package import first (works when exports/ is on sys.path,
+        which cli.py:_configure_paths() ensures). Falls back to direct
+        file import of agent.py via importlib.util.
+        """
+        import importlib
+
+        package_name = agent_path.name
+
+        # Try importing as a package (works when exports/ is on sys.path)
+        try:
+            return importlib.import_module(package_name)
+        except ImportError:
+            pass
+
+        # Fallback: import agent.py directly via file path
+        import importlib.util
+
+        agent_py = agent_path / "agent.py"
+        if not agent_py.exists():
+            raise FileNotFoundError(
+                f"No importable agent found at {agent_path}. "
+                f"Expected a Python package with agent.py."
+            )
+        spec = importlib.util.spec_from_file_location(
+            f"{package_name}.agent",
+            agent_py,
+            submodule_search_locations=[str(agent_path)],
+        )
+        module = importlib.util.module_from_spec(spec)
+        spec.loader.exec_module(module)
+        return module
+
     @classmethod
     def load(
         cls,
         agent_path: str | Path,
         mock_mode: bool = False,
         storage_path: Path | None = None,
-        model: str = "cerebras/zai-glm-4.7",
+        model: str | None = None,
+        enable_tui: bool = False,
     ) -> "AgentRunner":
         """
         Load an agent from an export folder.
 
+        Imports the agent's Python package and reads module-level variables
+        (goal, nodes, edges, etc.) to build a GraphSpec. Falls back to
+        agent.json if no Python module is found.
+
         Args:
-            agent_path: Path to agent folder (containing agent.json)
+            agent_path: Path to agent folder
             mock_mode: If True, use mock LLM responses
-            storage_path: Path for runtime storage (defaults to temp)
-            model: LLM model to use (any LiteLLM-compatible model name)
+            storage_path: Path for runtime storage (defaults to ~/.hive/agents/{name})
+            model: LLM model to use (reads from agent's default_config if None)
+            enable_tui: If True, forces use of AgentRuntime with EventBus
 
         Returns:
             AgentRunner instance ready to run
         """
         agent_path = Path(agent_path)
 
-        # Load agent.json
+        # Try loading from Python module first (code-based agents)
+        agent_py = agent_path / "agent.py"
+        if agent_py.exists():
+            agent_module = cls._import_agent_module(agent_path)
+
+            goal = getattr(agent_module, "goal", None)
+            nodes = getattr(agent_module, "nodes", None)
+            edges = getattr(agent_module, "edges", None)
+
+            if goal is None or nodes is None or edges is None:
+                raise ValueError(
+                    f"Agent at {agent_path} must define 'goal', 'nodes', and 'edges' "
+                    f"in agent.py (or __init__.py)"
+                )
+
+            # Read model and max_tokens from agent's config if not explicitly provided
+            agent_config = getattr(agent_module, "default_config", None)
+            if model is None:
+                if agent_config and hasattr(agent_config, "model"):
+                    model = agent_config.model
+
+            if agent_config and hasattr(agent_config, "max_tokens"):
+                max_tokens = agent_config.max_tokens
+            else:
+                hive_config = get_hive_config()
+                max_tokens = hive_config.get("llm", {}).get("max_tokens", DEFAULT_MAX_TOKENS)
+
+            # Build GraphSpec from module-level variables
+            graph = GraphSpec(
+                id=f"{agent_path.name}-graph",
+                goal_id=goal.id,
+                version="1.0.0",
+                entry_node=getattr(agent_module, "entry_node", nodes[0].id),
+                entry_points=getattr(agent_module, "entry_points", {}),
+                terminal_nodes=getattr(agent_module, "terminal_nodes", []),
+                pause_nodes=getattr(agent_module, "pause_nodes", []),
+                nodes=nodes,
+                edges=edges,
+                max_tokens=max_tokens,
+            )
+
+            return cls(
+                agent_path=agent_path,
+                graph=graph,
+                goal=goal,
+                mock_mode=mock_mode,
+                storage_path=storage_path,
+                model=model,
+                enable_tui=enable_tui,
+            )
+
+        # Fallback: load from agent.json (legacy JSON-based agents)
         agent_json_path = agent_path / "agent.json"
         if not agent_json_path.exists():
-            raise FileNotFoundError(f"agent.json not found in {agent_path}")
+            raise FileNotFoundError(f"No agent.py or agent.json found in {agent_path}")
 
         with open(agent_json_path) as f:
             graph, goal = load_agent_export(f.read())
@@ -333,6 +459,7 @@ class AgentRunner:
             mock_mode=mock_mode,
             storage_path=storage_path,
             model=model,
+            enable_tui=enable_tui,
         )
 
     def register_tool(
@@ -411,25 +538,8 @@ class AgentRunner:
         return self._tool_registry.register_mcp_server(server_config)
 
     def _load_mcp_servers_from_config(self, config_path: Path) -> None:
-        """
-        Load and register MCP servers from a configuration file.
-
-        Args:
-            config_path: Path to mcp_servers.json file
-        """
-        try:
-            with open(config_path) as f:
-                config = json.load(f)
-
-            servers = config.get("servers", [])
-            for server_config in servers:
-                try:
-                    self._tool_registry.register_mcp_server(server_config)
-                except Exception as e:
-                    server_name = server_config.get("name", "unknown")
-                    logger.warning(f"Failed to register MCP server '{server_name}': {e}")
-        except Exception as e:
-            logger.warning(f"Failed to load MCP servers config from {config_path}: {e}")
+        """Load and register MCP servers from a configuration file."""
+        self._tool_registry.load_mcp_config(config_path)
 
     def set_approval_callback(self, callback: Callable) -> None:
         """
@@ -445,6 +555,11 @@ class AgentRunner:
 
     def _setup(self) -> None:
         """Set up runtime, LLM, and executor."""
+        # Configure structured logging (auto-detects JSON vs human-readable)
+        from framework.observability import configure_logging
+
+        configure_logging(level="INFO", format="auto")
+
         # Set up session context for tools (workspace_id, agent_id, session_id)
         workspace_id = "default"  # Could be derived from storage path
         agent_id = self.graph.id or "unknown"
@@ -488,16 +603,25 @@ class AgentRunner:
                 api_key_env = self._get_api_key_env_var(self.model)
                 if api_key_env and os.environ.get(api_key_env):
                     self._llm = LiteLLMProvider(model=self.model)
-                elif api_key_env:
-                    print(f"Warning: {api_key_env} not set. LLM calls will fail.")
-                    print(f"Set it with: export {api_key_env}=your-api-key")
+                else:
+                    # Fall back to credential store
+                    api_key = self._get_api_key_from_credential_store()
+                    if api_key:
+                        self._llm = LiteLLMProvider(model=self.model, api_key=api_key)
+                        # Set env var so downstream code (e.g. cleanup LLM in
+                        # node._extract_json) can also find it
+                        if api_key_env:
+                            os.environ[api_key_env] = api_key
+                    elif api_key_env:
+                        print(f"Warning: {api_key_env} not set. LLM calls will fail.")
+                        print(f"Set it with: export {api_key_env}=your-api-key")
 
         # Get tools for executor/runtime
         tools = list(self._tool_registry.get_tools().values())
         tool_executor = self._tool_registry.get_executor()
 
-        if self._uses_async_entry_points:
-            # Multi-entry-point mode: use AgentRuntime
+        if self._uses_async_entry_points or self.enable_tui:
+            # Multi-entry-point mode or TUI mode: use AgentRuntime
             self._setup_agent_runtime(tools, tool_executor)
         else:
             # Single-entry-point mode: use legacy GraphExecutor
@@ -535,11 +659,42 @@ class AgentRunner:
             # Default: assume OpenAI-compatible
             return "OPENAI_API_KEY"
 
+    def _get_api_key_from_credential_store(self) -> str | None:
+        """Get the LLM API key from the encrypted credential store.
+
+        Maps model name to credential store ID (e.g. "anthropic/..." -> "anthropic")
+        and retrieves the key via CredentialStore.get().
+        """
+        if not os.environ.get("HIVE_CREDENTIAL_KEY"):
+            return None
+
+        # Map model prefix to credential store ID
+        model_lower = self.model.lower()
+        cred_id = None
+        if model_lower.startswith("anthropic/") or model_lower.startswith("claude"):
+            cred_id = "anthropic"
+        # Add more mappings as providers are added to LLM_CREDENTIALS
+
+        if cred_id is None:
+            return None
+
+        try:
+            from framework.credentials import CredentialStore
+
+            store = CredentialStore.with_encrypted_storage()
+            return store.get(cred_id)
+        except Exception:
+            return None
+
     def _setup_legacy_executor(self, tools: list, tool_executor: Callable | None) -> None:
         """Set up legacy single-entry-point execution using GraphExecutor."""
         # Create runtime
         self._runtime = Runtime(storage_path=self._storage_path)
 
+        # Create runtime logger
+        log_store = RuntimeLogStore(base_path=self._storage_path / "runtime_logs")
+        runtime_logger = RuntimeLogger(store=log_store, agent_id=self.graph.id)
+
         # Create executor
         self._executor = GraphExecutor(
             runtime=self._runtime,
@@ -547,6 +702,8 @@ class AgentRunner:
             tools=tools,
             tool_executor=tool_executor,
             approval_callback=self._approval_callback,
+            runtime_logger=runtime_logger,
+            loop_config=self.graph.loop_config,
         )
 
     def _setup_agent_runtime(self, tools: list, tool_executor: Callable | None) -> None:
@@ -566,7 +723,33 @@ class AgentRunner:
             )
             entry_points.append(ep)
 
+        # If TUI enabled but no entry points (single-entry agent), create default
+        if not entry_points and self.enable_tui and self.graph.entry_node:
+            logger.info("Creating default entry point for TUI")
+            entry_points.append(
+                EntryPointSpec(
+                    id="default",
+                    name="Default",
+                    entry_node=self.graph.entry_node,
+                    trigger_type="manual",
+                    isolation_level="shared",
+                )
+            )
+
         # Create AgentRuntime with all entry points
+        log_store = RuntimeLogStore(base_path=self._storage_path / "runtime_logs")
+
+        # Enable checkpointing by default for resumable sessions
+        from framework.graph.checkpoint_config import CheckpointConfig
+
+        checkpoint_config = CheckpointConfig(
+            enabled=True,
+            checkpoint_on_node_start=False,  # Only checkpoint after nodes complete
+            checkpoint_on_node_complete=True,
+            checkpoint_max_age_days=7,
+            async_checkpoint=True,  # Non-blocking
+        )
+
         self._agent_runtime = create_agent_runtime(
             graph=self.graph,
             goal=self.goal,
@@ -575,6 +758,8 @@ class AgentRunner:
             llm=self._llm,
             tools=tools,
             tool_executor=tool_executor,
+            runtime_log_store=log_store,
+            checkpoint_config=checkpoint_config,
         )
 
     async def run(
@@ -616,7 +801,7 @@ class AgentRunner:
                 error=error_msg,
             )
 
-        if self._uses_async_entry_points:
+        if self._uses_async_entry_points or self.enable_tui:
             # Multi-entry-point mode: use AgentRuntime
             return await self._run_with_agent_runtime(
                 input_data=input_data or {},
@@ -908,15 +1093,25 @@ class AgentRunner:
                 EnvVarStorage,
             )
 
-            # Build env mapping for fallback
+            # Build env mapping for credential lookup
             env_mapping = {
                 (spec.credential_id or name): spec.env_var
                 for name, spec in CREDENTIAL_SPECS.items()
             }
-            storage = CompositeStorage(
-                primary=EncryptedFileStorage(),
-                fallbacks=[EnvVarStorage(env_mapping=env_mapping)],
-            )
+
+            # Only use EncryptedFileStorage if the encryption key is configured;
+            # otherwise just check env vars (avoids generating a throwaway key)
+            storages: list = [EnvVarStorage(env_mapping=env_mapping)]
+            if os.environ.get("HIVE_CREDENTIAL_KEY"):
+                storages.insert(0, EncryptedFileStorage())
+
+            if len(storages) == 1:
+                storage = storages[0]
+            else:
+                storage = CompositeStorage(
+                    primary=storages[0],
+                    fallbacks=storages[1:],
+                )
             store = CredentialStore(storage=storage)
 
             # Build reverse mappings

core/framework/runner/tool_registry.py

@@ -1,5 +1,6 @@
 """Tool discovery and registration for agent runner."""
 
+import contextvars
 import importlib.util
 import inspect
 import json
@@ -13,6 +14,13 @@ from framework.llm.provider import Tool, ToolResult, ToolUse
 
 logger = logging.getLogger(__name__)
 
+# Per-execution context overrides.  Each asyncio task (and thus each
+# concurrent graph execution) gets its own copy, so there are no races
+# when multiple ExecutionStreams run in parallel.
+_execution_context: contextvars.ContextVar[dict[str, Any] | None] = contextvars.ContextVar(
+    "_execution_context", default=None
+)
+
 
 @dataclass
 class RegisteredTool:
@@ -33,6 +41,11 @@ class ToolRegistry:
     4. Manually registered tools
     """
 
+    # Framework-internal context keys injected into tool calls.
+    # Stripped from LLM-facing schemas (the LLM doesn't know these values)
+    # and auto-injected at call time for tools that accept them.
+    CONTEXT_PARAMS = frozenset({"workspace_id", "agent_id", "session_id", "data_dir"})
+
     def __init__(self):
         self._tools: dict[str, RegisteredTool] = {}
         self._mcp_clients: list[Any] = []  # List of MCPClient instances
@@ -257,6 +270,61 @@ class ToolRegistry:
         """
         self._session_context.update(context)
 
+    @staticmethod
+    def set_execution_context(**context) -> contextvars.Token:
+        """Set per-execution context overrides (concurrency-safe via contextvars).
+
+        Values set here take precedence over session context.  Each asyncio
+        task gets its own copy, so concurrent executions don't interfere.
+
+        Returns a token that must be passed to :meth:`reset_execution_context`
+        to restore the previous state.
+        """
+        current = _execution_context.get() or {}
+        return _execution_context.set({**current, **context})
+
+    @staticmethod
+    def reset_execution_context(token: contextvars.Token) -> None:
+        """Restore execution context to its previous state."""
+        _execution_context.reset(token)
+
+    def load_mcp_config(self, config_path: Path) -> None:
+        """
+        Load and register MCP servers from a config file.
+
+        Resolves relative ``cwd`` paths against the config file's parent
+        directory so callers never need to handle path resolution themselves.
+
+        Args:
+            config_path: Path to an ``mcp_servers.json`` file.
+        """
+        try:
+            with open(config_path) as f:
+                config = json.load(f)
+        except Exception as e:
+            logger.warning(f"Failed to load MCP config from {config_path}: {e}")
+            return
+
+        base_dir = config_path.parent
+
+        # Support both formats:
+        #   {"servers": [{"name": "x", ...}]}        (list format)
+        #   {"server-name": {"transport": ...}, ...}  (dict format)
+        server_list = config.get("servers", [])
+        if not server_list and "servers" not in config:
+            # Treat top-level keys as server names
+            server_list = [{"name": name, **cfg} for name, cfg in config.items()]
+
+        for server_config in server_list:
+            cwd = server_config.get("cwd")
+            if cwd and not Path(cwd).is_absolute():
+                server_config["cwd"] = str((base_dir / cwd).resolve())
+            try:
+                self.register_mcp_server(server_config)
+            except Exception as e:
+                name = server_config.get("name", "unknown")
+                logger.warning(f"Failed to register MCP server '{name}': {e}")
+
     def register_mcp_server(
         self,
         server_config: dict[str, Any],
@@ -305,15 +373,29 @@ class ToolRegistry:
             # Register each tool
             count = 0
             for mcp_tool in client.list_tools():
-                # Convert MCP tool to framework Tool
+                # Convert MCP tool to framework Tool (strips context params from LLM schema)
                 tool = self._convert_mcp_tool_to_framework_tool(mcp_tool)
 
                 # Create executor that calls the MCP server
-                def make_mcp_executor(client_ref: MCPClient, tool_name: str, registry_ref):
+                def make_mcp_executor(
+                    client_ref: MCPClient,
+                    tool_name: str,
+                    registry_ref,
+                    tool_params: set[str],
+                ):
                     def executor(inputs: dict) -> Any:
                         try:
-                            # Inject session context for tools that need it
-                            merged_inputs = {**registry_ref._session_context, **inputs}
+                            # Build base context: session < execution (execution wins)
+                            base_context = dict(registry_ref._session_context)
+                            exec_ctx = _execution_context.get()
+                            if exec_ctx:
+                                base_context.update(exec_ctx)
+
+                            # Only inject context params the tool accepts
+                            filtered_context = {
+                                k: v for k, v in base_context.items() if k in tool_params
+                            }
+                            merged_inputs = {**filtered_context, **inputs}
                             result = client_ref.call_tool(tool_name, merged_inputs)
                             # MCP tools return content array, extract the result
                             if isinstance(result, list) and len(result) > 0:
@@ -327,10 +409,11 @@ class ToolRegistry:
 
                     return executor
 
+                tool_params = set(mcp_tool.input_schema.get("properties", {}).keys())
                 self.register(
                     mcp_tool.name,
                     tool,
-                    make_mcp_executor(client, mcp_tool.name, self),
+                    make_mcp_executor(client, mcp_tool.name, self, tool_params),
                 )
                 count += 1
 
@@ -356,6 +439,11 @@ class ToolRegistry:
         properties = input_schema.get("properties", {})
         required = input_schema.get("required", [])
 
+        # Strip framework-internal context params from LLM-facing schema.
+        # The LLM can't know these values; they're auto-injected at call time.
+        properties = {k: v for k, v in properties.items() if k not in self.CONTEXT_PARAMS}
+        required = [r for r in required if r not in self.CONTEXT_PARAMS]
+
         # Convert to framework Tool format
         tool = Tool(
             name=mcp_tool.name,

core/framework/runtime/RESUMABLE_SESSIONS_DESIGN.md

@@ -0,0 +1,842 @@
+# Resumable Sessions Design
+
+## Problem Statement
+
+Currently, when an agent encounters a failure during execution (e.g., credential validation, API errors, tool failures), the entire session is lost. This creates a poor user experience, especially when:
+
+1. The agent has completed significant work before the failure
+2. The failure is recoverable (e.g., adding missing credentials)
+3. The user wants to retry from the exact failure point without redoing work
+
+## Design Goals
+
+1. **Crash Recovery**: Sessions can resume after process crashes or errors
+2. **Partial Completion**: Preserve work done by nodes that completed successfully
+3. **Flexible Resume Points**: Resume from exact failure point or previous checkpoints
+4. **State Consistency**: Guarantee consistent SharedMemory and conversation state
+5. **Minimal Overhead**: Checkpointing shouldn't significantly impact performance
+6. **User Control**: Users can inspect, modify, and resume sessions explicitly
+
+## Architecture
+
+### 1. Checkpoint System
+
+#### Checkpoint Types
+
+**Automatic Checkpoints** (saved automatically by framework):
+- `node_start`: Before each node begins execution
+- `node_complete`: After each node successfully completes
+- `edge_transition`: Before traversing to next node
+- `loop_iteration`: At each iteration in EventLoopNode (optional)
+
+**Manual Checkpoints** (triggered by agent designer):
+- `safe_point`: Explicitly marked safe points in graph
+- `user_checkpoint`: Before awaiting user input in client-facing nodes
+
+#### Checkpoint Data Structure
+
+```python
+@dataclass
+class Checkpoint:
+    """Single checkpoint in execution timeline."""
+
+    # Identity
+    checkpoint_id: str  # Format: checkpoint_{timestamp}_{uuid_short}
+    session_id: str
+    checkpoint_type: str  # "node_start", "node_complete", etc.
+
+    # Timestamps
+    created_at: str  # ISO 8601
+
+    # Execution state
+    current_node: str | None
+    next_node: str | None  # For edge_transition checkpoints
+    execution_path: list[str]  # Nodes executed so far
+
+    # Memory state (snapshot)
+    shared_memory: dict[str, Any]  # Full SharedMemory._data
+
+    # Per-node conversation state references
+    # (actual conversations stored separately, reference by node_id)
+    conversation_states: dict[str, str]  # {node_id: conversation_checkpoint_id}
+
+    # Output accumulator state
+    accumulated_outputs: dict[str, Any]
+
+    # Execution metrics (for resuming quality tracking)
+    metrics_snapshot: dict[str, Any]
+
+    # Metadata
+    is_clean: bool  # True if no failures/retries before this checkpoint
+    can_resume_from: bool  # False if checkpoint is in unstable state
+    description: str  # Human-readable checkpoint description
+```
+
+#### Storage Structure
+
+```
+~/.hive/agents/{agent_name}/
+└── sessions/
+    └── session_YYYYMMDD_HHMMSS_{uuid}/
+        ├── state.json                    # Session state (existing)
+        ├── checkpoints/
+        │   ├── index.json                # Checkpoint index/manifest
+        │   ├── checkpoint_1.json         # Individual checkpoints
+        │   ├── checkpoint_2.json
+        │   └── checkpoint_N.json
+        ├── conversations/                # Per-node conversation state (existing)
+        │   ├── node_id_1/
+        │   │   ├── parts/
+        │   │   ├── meta.json
+        │   │   └── cursor.json
+        │   └── node_id_2/...
+        ├── data/                         # Spillover artifacts (existing)
+        └── logs/                         # L1/L2/L3 logs (existing)
+```
+
+**Checkpoint Index Format** (`checkpoints/index.json`):
+```json
+{
+  "session_id": "session_20260208_143022_abc12345",
+  "checkpoints": [
+    {
+      "checkpoint_id": "checkpoint_20260208_143030_xyz123",
+      "type": "node_complete",
+      "created_at": "2026-02-08T14:30:30.123Z",
+      "current_node": "collector",
+      "is_clean": true,
+      "can_resume_from": true,
+      "description": "Completed collector node successfully"
+    },
+    {
+      "checkpoint_id": "checkpoint_20260208_143045_abc789",
+      "type": "node_start",
+      "created_at": "2026-02-08T14:30:45.456Z",
+      "current_node": "analyzer",
+      "is_clean": true,
+      "can_resume_from": true,
+      "description": "Starting analyzer node"
+    }
+  ],
+  "latest_checkpoint_id": "checkpoint_20260208_143045_abc789",
+  "total_checkpoints": 2
+}
+```
+
+### 2. Resume Mechanism
+
+#### Resume Flow
+
+```python
+# High-level resume flow
+async def resume_session(
+    session_id: str,
+    checkpoint_id: str | None = None,  # None = resume from latest
+    modifications: dict[str, Any] | None = None,  # Override memory values
+) -> ExecutionResult:
+    """
+    Resume a session from a checkpoint.
+
+    Args:
+        session_id: Session to resume
+        checkpoint_id: Specific checkpoint (None = latest)
+        modifications: Optional memory/state modifications before resume
+
+    Returns:
+        ExecutionResult with resumed execution
+    """
+    # 1. Load session state
+    session_state = await session_store.read_state(session_id)
+
+    # 2. Verify session is resumable
+    if not session_state.is_resumable:
+        raise ValueError(f"Session {session_id} is not resumable")
+
+    # 3. Load checkpoint
+    checkpoint = await checkpoint_store.load_checkpoint(
+        session_id,
+        checkpoint_id or session_state.progress.resume_from
+    )
+
+    # 4. Restore state
+    # - Restore SharedMemory from checkpoint.shared_memory
+    # - Restore per-node conversations from checkpoint.conversation_states
+    # - Restore output accumulator from checkpoint.accumulated_outputs
+    # - Apply modifications if provided
+
+    # 5. Resume execution from checkpoint.next_node or checkpoint.current_node
+    result = await executor.execute(
+        graph=graph,
+        goal=goal,
+        memory=restored_memory,
+        entry_point=checkpoint.next_node or checkpoint.current_node,
+        session_state=restored_session_state,
+    )
+
+    # 6. Update session state with resumed execution
+    await session_store.write_state(session_id, updated_state)
+
+    return result
+```
+
+#### Checkpoint Restoration
+
+```python
+@dataclass
+class CheckpointStore:
+    """Manages checkpoint storage and retrieval."""
+
+    async def save_checkpoint(
+        self,
+        session_id: str,
+        checkpoint: Checkpoint,
+    ) -> None:
+        """Save a checkpoint atomically."""
+        # 1. Write checkpoint file: checkpoints/checkpoint_{id}.json
+        # 2. Update index: checkpoints/index.json
+        # 3. Use atomic write for crash safety
+
+    async def load_checkpoint(
+        self,
+        session_id: str,
+        checkpoint_id: str | None = None,
+    ) -> Checkpoint | None:
+        """Load a checkpoint by ID or latest."""
+        # 1. Read checkpoint index
+        # 2. Find checkpoint by ID (or latest if None)
+        # 3. Load and deserialize checkpoint file
+
+    async def list_checkpoints(
+        self,
+        session_id: str,
+        checkpoint_type: str | None = None,
+        is_clean: bool | None = None,
+    ) -> list[Checkpoint]:
+        """List all checkpoints for a session with optional filters."""
+
+    async def delete_checkpoint(
+        self,
+        session_id: str,
+        checkpoint_id: str,
+    ) -> bool:
+        """Delete a specific checkpoint."""
+
+    async def prune_checkpoints(
+        self,
+        session_id: str,
+        keep_count: int = 10,
+        keep_clean_only: bool = False,
+    ) -> int:
+        """Prune old checkpoints, keeping most recent N."""
+```
+
+### 3. GraphExecutor Integration
+
+#### Modified Execution Loop
+
+```python
+# In GraphExecutor.execute()
+
+async def execute(
+    self,
+    graph: GraphSpec,
+    goal: Goal,
+    memory: SharedMemory | None = None,
+    entry_point: str = "start",
+    session_state: dict[str, Any] | None = None,
+    checkpoint_config: CheckpointConfig | None = None,
+) -> ExecutionResult:
+    """
+    Execute graph with checkpointing support.
+
+    New parameters:
+        checkpoint_config: Configuration for checkpointing behavior
+    """
+
+    # Initialize checkpoint store
+    checkpoint_store = CheckpointStore(storage_path / "checkpoints")
+
+    # Restore from checkpoint if session_state indicates resume
+    if session_state and session_state.get("resume_from"):
+        checkpoint = await checkpoint_store.load_checkpoint(
+            session_id,
+            session_state["resume_from"]
+        )
+        memory = self._restore_memory_from_checkpoint(checkpoint)
+        entry_point = checkpoint.next_node or checkpoint.current_node
+
+    current_node = entry_point
+
+    while current_node:
+        # CHECKPOINT: node_start
+        if checkpoint_config and checkpoint_config.checkpoint_on_node_start:
+            await self._save_checkpoint(
+                checkpoint_store,
+                checkpoint_type="node_start",
+                current_node=current_node,
+                memory=memory,
+                # ... other state
+            )
+
+        try:
+            # Execute node
+            result = await self._execute_node(current_node, memory, context)
+
+            # CHECKPOINT: node_complete
+            if checkpoint_config and checkpoint_config.checkpoint_on_node_complete:
+                await self._save_checkpoint(
+                    checkpoint_store,
+                    checkpoint_type="node_complete",
+                    current_node=current_node,
+                    memory=memory,
+                    # ... other state
+                )
+
+        except Exception as e:
+            # On failure, mark current checkpoint as resume point
+            await self._mark_failure_checkpoint(
+                checkpoint_store,
+                current_node=current_node,
+                error=str(e),
+            )
+            raise
+
+        # Find next edge
+        next_node = self._find_next_node(current_node, result, memory)
+
+        # CHECKPOINT: edge_transition
+        if next_node and checkpoint_config and checkpoint_config.checkpoint_on_edge:
+            await self._save_checkpoint(
+                checkpoint_store,
+                checkpoint_type="edge_transition",
+                current_node=current_node,
+                next_node=next_node,
+                memory=memory,
+                # ... other state
+            )
+
+        current_node = next_node
+```
+
+### 4. EventLoopNode Integration
+
+#### Conversation State Checkpointing
+
+EventLoopNode already has conversation persistence via `ConversationStore`. For resumability:
+
+```python
+class EventLoopNode:
+    async def execute(self, ctx: NodeContext) -> NodeResult:
+        """Execute with checkpoint support."""
+
+        # Try to restore from checkpoint
+        if ctx.checkpoint_id:
+            conversation = await self._restore_conversation(ctx.checkpoint_id)
+            output_accumulator = await OutputAccumulator.restore(self.store)
+        else:
+            # Fresh start
+            conversation = await self._initialize_conversation(ctx)
+            output_accumulator = OutputAccumulator(store=self.store)
+
+        # Event loop with periodic checkpointing
+        iteration = 0
+        while iteration < self.config.max_iterations:
+
+            # Optional: checkpoint every N iterations
+            if self.config.checkpoint_every_n_iterations:
+                if iteration % self.config.checkpoint_every_n_iterations == 0:
+                    await self._save_loop_checkpoint(
+                        conversation,
+                        output_accumulator,
+                        iteration,
+                    )
+
+            # ... rest of event loop
+
+            iteration += 1
+```
+
+**Note**: EventLoopNode conversation state is already persisted to disk after each turn via `ConversationStore`, so it's naturally resumable. We just need to:
+1. Track which conversation checkpoint to restore from
+2. Ensure output accumulator state is also restored
+
+### 5. User-Facing API
+
+#### MCP Tools for Resume
+
+```python
+# In tools/src/aden_tools/tools/session_management/
+
+@tool
+async def list_resumable_sessions(
+    agent_work_dir: str,
+    status: str = "failed",  # "failed", "paused", "cancelled"
+    limit: int = 20,
+) -> dict:
+    """
+    List sessions that can be resumed.
+
+    Returns:
+        {
+            "sessions": [
+                {
+                    "session_id": "session_20260208_143022_abc12345",
+                    "status": "failed",
+                    "error": "Missing API key: OPENAI_API_KEY",
+                    "failed_at_node": "analyzer",
+                    "last_checkpoint": "checkpoint_20260208_143045_abc789",
+                    "created_at": "2026-02-08T14:30:22Z",
+                    "updated_at": "2026-02-08T14:30:45Z"
+                }
+            ],
+            "total": 1
+        }
+    """
+
+@tool
+async def list_session_checkpoints(
+    agent_work_dir: str,
+    session_id: str,
+    checkpoint_type: str = "",  # Filter by type
+    clean_only: bool = False,  # Only show clean checkpoints
+) -> dict:
+    """
+    List all checkpoints for a session.
+
+    Returns:
+        {
+            "session_id": "session_20260208_143022_abc12345",
+            "checkpoints": [
+                {
+                    "checkpoint_id": "checkpoint_20260208_143030_xyz123",
+                    "type": "node_complete",
+                    "created_at": "2026-02-08T14:30:30Z",
+                    "current_node": "collector",
+                    "is_clean": true,
+                    "can_resume_from": true,
+                    "description": "Completed collector node successfully"
+                },
+                ...
+            ]
+        }
+    """
+
+@tool
+async def inspect_checkpoint(
+    agent_work_dir: str,
+    session_id: str,
+    checkpoint_id: str,
+    include_memory: bool = False,  # Include full memory state
+) -> dict:
+    """
+    Inspect a checkpoint's detailed state.
+
+    Returns:
+        {
+            "checkpoint_id": "checkpoint_20260208_143030_xyz123",
+            "type": "node_complete",
+            "current_node": "collector",
+            "execution_path": ["start", "collector"],
+            "accumulated_outputs": {
+                "twitter_handles": ["@user1", "@user2"]
+            },
+            "memory": {...},  # If include_memory=True
+            "metrics_snapshot": {
+                "total_retries": 2,
+                "nodes_with_failures": []
+            }
+        }
+    """
+
+@tool
+async def resume_session(
+    agent_work_dir: str,
+    session_id: str,
+    checkpoint_id: str = "",  # Empty = latest checkpoint
+    memory_modifications: str = "{}",  # JSON string of memory overrides
+) -> dict:
+    """
+    Resume a session from a checkpoint.
+
+    Args:
+        agent_work_dir: Path to agent workspace
+        session_id: Session to resume
+        checkpoint_id: Specific checkpoint (empty = latest)
+        memory_modifications: JSON object with memory key overrides
+
+    Returns:
+        {
+            "session_id": "session_20260208_143022_abc12345",
+            "resumed_from": "checkpoint_20260208_143045_abc789",
+            "status": "active",  # Now actively running
+            "message": "Session resumed successfully from checkpoint_20260208_143045_abc789"
+        }
+    """
+```
+
+#### CLI Commands
+
+```bash
+# List resumable sessions
+hive sessions list --agent twitter_outreach --status failed
+
+# Show checkpoints for a session
+hive sessions checkpoints session_20260208_143022_abc12345
+
+# Inspect a checkpoint
+hive sessions inspect session_20260208_143022_abc12345 checkpoint_20260208_143045_abc789
+
+# Resume a session
+hive sessions resume session_20260208_143022_abc12345
+
+# Resume from specific checkpoint
+hive sessions resume session_20260208_143022_abc12345 --checkpoint checkpoint_20260208_143030_xyz123
+
+# Resume with memory modifications (e.g., after adding credentials)
+hive sessions resume session_20260208_143022_abc12345 --set api_key=sk-...
+```
+
+### 6. Configuration
+
+#### CheckpointConfig
+
+```python
+@dataclass
+class CheckpointConfig:
+    """Configuration for checkpoint behavior."""
+
+    # When to checkpoint
+    checkpoint_on_node_start: bool = True
+    checkpoint_on_node_complete: bool = True
+    checkpoint_on_edge: bool = False  # Usually redundant with node_start
+    checkpoint_on_loop_iteration: bool = False  # Can be expensive
+    checkpoint_every_n_iterations: int = 0  # 0 = disabled
+
+    # Pruning
+    max_checkpoints_per_session: int = 100
+    prune_after_node_count: int = 10  # Prune every N nodes
+    keep_clean_checkpoints_only: bool = False
+
+    # Performance
+    async_checkpoint: bool = True  # Don't block execution on checkpoint writes
+
+    # What to include
+    include_conversation_snapshots: bool = True
+    include_full_memory: bool = True
+```
+
+#### Agent-Level Configuration
+
+```python
+# In agent.py or config.py
+
+class MyAgent(Agent):
+    def get_checkpoint_config(self) -> CheckpointConfig:
+        """Override to customize checkpoint behavior."""
+        return CheckpointConfig(
+            checkpoint_on_node_start=True,
+            checkpoint_on_node_complete=True,
+            checkpoint_every_n_iterations=5,  # Checkpoint every 5 iterations in loops
+            max_checkpoints_per_session=50,
+        )
+```
+
+## Implementation Plan
+
+### Phase 1: Core Checkpoint Infrastructure (Week 1)
+
+1. **Create checkpoint schemas**
+   - `Checkpoint` dataclass
+   - `CheckpointIndex` for manifest
+   - Serialization/deserialization
+
+2. **Implement CheckpointStore**
+   - `save_checkpoint()` with atomic writes
+   - `load_checkpoint()` with deserialization
+   - `list_checkpoints()` with filtering
+   - `prune_checkpoints()` for cleanup
+
+3. **Update SessionState schema**
+   - Add `resume_from_checkpoint_id` field
+   - Add `checkpoints_enabled` flag
+
+### Phase 2: GraphExecutor Integration (Week 2)
+
+1. **Modify GraphExecutor**
+   - Add `CheckpointConfig` parameter
+   - Implement checkpoint saving at node boundaries
+   - Implement checkpoint restoration logic
+   - Handle memory state snapshots
+
+2. **Update execution loop**
+   - Checkpoint before node execution
+   - Checkpoint after successful completion
+   - Mark failure checkpoints on errors
+
+### Phase 3: EventLoopNode Integration (Week 3)
+
+1. **Enhance conversation restoration**
+   - Link checkpoints to conversation states
+   - Ensure OutputAccumulator is checkpointed
+   - Test loop resumption from middle of execution
+
+2. **Add optional loop iteration checkpoints**
+   - Configurable iteration frequency
+   - Balance between granularity and performance
+
+### Phase 4: User-Facing Features (Week 4)
+
+1. **Implement MCP tools**
+   - `list_resumable_sessions`
+   - `list_session_checkpoints`
+   - `inspect_checkpoint`
+   - `resume_session`
+
+2. **Add CLI commands**
+   - `hive sessions list`
+   - `hive sessions checkpoints`
+   - `hive sessions inspect`
+   - `hive sessions resume`
+
+3. **Update TUI**
+   - Show resumable sessions in UI
+   - Allow resume from TUI interface
+
+### Phase 5: Testing & Documentation (Week 5)
+
+1. **Write comprehensive tests**
+   - Unit tests for CheckpointStore
+   - Integration tests for resume flow
+   - Edge case testing (concurrent checkpoints, corruption, etc.)
+
+2. **Performance testing**
+   - Measure checkpoint overhead
+   - Optimize async checkpoint writing
+   - Test with large memory states
+
+3. **Documentation**
+   - Update skills with resume patterns
+   - Document checkpoint configuration
+   - Add troubleshooting guide
+
+## Performance Considerations
+
+### Checkpoint Overhead
+
+**Estimated overhead per checkpoint**:
+- Memory serialization: ~5-10ms for typical state (< 1MB)
+- File I/O: ~10-20ms for atomic write
+- Total: ~15-30ms per checkpoint
+
+**Mitigation strategies**:
+1. **Async checkpointing**: Don't block execution on writes
+2. **Selective checkpointing**: Only checkpoint at important boundaries
+3. **Incremental checkpoints**: Store deltas instead of full state (future)
+4. **Compression**: Compress large memory states before writing
+
+### Storage Size
+
+**Typical checkpoint size**:
+- Small memory state (< 100KB): ~50-100KB per checkpoint
+- Medium memory state (< 1MB): ~500KB-1MB per checkpoint
+- Large memory state (> 1MB): ~1-5MB per checkpoint
+
+**Mitigation strategies**:
+1. **Pruning**: Keep only N most recent checkpoints
+2. **Clean-only retention**: Only keep checkpoints from clean execution
+3. **Compression**: Use gzip for checkpoint files
+4. **Archiving**: Move old checkpoints to archive storage
+
+## Error Handling
+
+### Checkpoint Save Failures
+
+**Scenarios**:
+- Disk full
+- Permission errors
+- Serialization failures
+- Concurrent writes
+
+**Handling**:
+```python
+try:
+    await checkpoint_store.save_checkpoint(session_id, checkpoint)
+except CheckpointSaveError as e:
+    # Log warning but don't fail execution
+    logger.warning(f"Failed to save checkpoint: {e}")
+    # Continue execution without checkpoint
+```
+
+### Checkpoint Load Failures
+
+**Scenarios**:
+- Checkpoint file corrupted
+- Checkpoint format incompatible
+- Referenced conversation state missing
+
+**Handling**:
+```python
+try:
+    checkpoint = await checkpoint_store.load_checkpoint(session_id, checkpoint_id)
+except CheckpointLoadError as e:
+    # Try to find previous valid checkpoint
+    checkpoints = await checkpoint_store.list_checkpoints(session_id)
+    for cp in reversed(checkpoints):
+        try:
+            checkpoint = await checkpoint_store.load_checkpoint(session_id, cp.checkpoint_id)
+            logger.info(f"Fell back to checkpoint {cp.checkpoint_id}")
+            break
+        except CheckpointLoadError:
+            continue
+    else:
+        raise ValueError(f"No valid checkpoints found for session {session_id}")
+```
+
+### Resume Failures
+
+**Scenarios**:
+- Checkpoint state inconsistent with current graph
+- Node no longer exists in updated agent code
+- Memory keys missing required values
+
+**Handling**:
+1. **Validation**: Verify checkpoint compatibility before resume
+2. **Graceful degradation**: Resume from earlier checkpoint if possible
+3. **User notification**: Clear error messages about why resume failed
+
+## Migration Path
+
+### Backward Compatibility
+
+**Existing sessions** (without checkpoints):
+- Can still be executed normally
+- Checkpoint system is opt-in per agent
+- No breaking changes to existing APIs
+
+**Enabling checkpoints**:
+```python
+# Option 1: Agent-level default
+class MyAgent(Agent):
+    checkpoint_config = CheckpointConfig(
+        checkpoint_on_node_complete=True,
+    )
+
+# Option 2: Runtime override
+runtime = create_agent_runtime(
+    agent=my_agent,
+    checkpoint_config=CheckpointConfig(...),
+)
+
+# Option 3: Per-execution
+result = await executor.execute(
+    graph=graph,
+    goal=goal,
+    checkpoint_config=CheckpointConfig(...),
+)
+```
+
+### Gradual Rollout
+
+1. **Phase 1**: Core infrastructure, no user-facing features
+2. **Phase 2**: Opt-in for specific agents via config
+3. **Phase 3**: User-facing MCP tools and CLI
+4. **Phase 4**: Enable by default for all new agents
+5. **Phase 5**: TUI integration
+
+## Future Enhancements
+
+### 1. Incremental Checkpoints
+
+Instead of full state snapshots, store only deltas:
+```python
+@dataclass
+class IncrementalCheckpoint:
+    """Checkpoint with only changed state."""
+    base_checkpoint_id: str  # Parent checkpoint
+    memory_delta: dict[str, Any]  # Only changed keys
+    added_outputs: dict[str, Any]  # Only new outputs
+```
+
+### 2. Distributed Checkpointing
+
+For long-running agents, checkpoint to cloud storage:
+```python
+checkpoint_config = CheckpointConfig(
+    storage_backend="s3",  # or "gcs", "azure"
+    storage_url="s3://my-bucket/checkpoints/",
+)
+```
+
+### 3. Checkpoint Compression
+
+Compress large memory states:
+```python
+checkpoint_config = CheckpointConfig(
+    compress=True,
+    compression_threshold_bytes=100_000,  # Compress if > 100KB
+)
+```
+
+### 4. Smart Checkpoint Selection
+
+Use heuristics to decide when to checkpoint:
+```python
+class SmartCheckpointStrategy:
+    def should_checkpoint(self, context: ExecutionContext) -> bool:
+        # Checkpoint after expensive nodes
+        if context.node_latency_ms > 30_000:
+            return True
+        # Checkpoint before risky operations
+        if context.node_id in ["api_call", "external_tool"]:
+            return True
+        # Checkpoint after significant memory changes
+        if context.memory_delta_size > 10:
+            return True
+        return False
+```
+
+## Security Considerations
+
+### 1. Sensitive Data in Checkpoints
+
+**Problem**: Checkpoints may contain sensitive data (API keys, credentials, PII)
+
+**Mitigation**:
+```python
+@dataclass
+class CheckpointConfig:
+    # Exclude sensitive keys from checkpoint
+    exclude_memory_keys: list[str] = field(default_factory=lambda: [
+        "api_key",
+        "credentials",
+        "access_token",
+    ])
+
+    # Encrypt checkpoint files
+    encrypt_checkpoints: bool = True
+    encryption_key_source: str = "keychain"  # or "env_var", "file"
+```
+
+### 2. Checkpoint Tampering
+
+**Problem**: Malicious modification of checkpoint files
+
+**Mitigation**:
+```python
+@dataclass
+class Checkpoint:
+    # Add cryptographic signature
+    signature: str  # HMAC of checkpoint content
+
+    def verify_signature(self, secret_key: str) -> bool:
+        """Verify checkpoint hasn't been tampered with."""
+        ...
+```
+
+## References
+
+- [RUNTIME_LOGGING.md](./RUNTIME_LOGGING.md) - Current logging system
+- [session_state.py](../schemas/session_state.py) - Session state schema
+- [session_store.py](../storage/session_store.py) - Session storage
+- [executor.py](../graph/executor.py) - Graph executor
+- [event_loop_node.py](../graph/event_loop_node.py) - EventLoop implementation

core/framework/runtime/RUNTIME_LOGGING.md

@@ -0,0 +1,698 @@
+# Runtime Logging System
+
+## Overview
+
+The Hive framework uses a **three-level observability system** for tracking agent execution at different granularities:
+
+- **L1 (Summary)**: High-level run outcomes - success/failure, execution quality, attention flags
+- **L2 (Details)**: Per-node completion details - retries, verdicts, latency, attention reasons
+- **L3 (Tool Logs)**: Step-by-step execution - tool calls, LLM responses, judge feedback
+
+This layered approach enables efficient debugging: start with L1 to identify problematic runs, drill into L2 to find failing nodes, and analyze L3 for root cause details.
+
+---
+
+## Storage Architecture
+
+### Current Structure (Unified Sessions)
+
+**Default since 2026-02-06**
+
+```
+~/.hive/agents/{agent_name}/
+└── sessions/
+    └── session_YYYYMMDD_HHMMSS_{uuid}/
+        ├── state.json           # Session state and metadata
+        ├── logs/                # Runtime logs (L1/L2/L3)
+        │   ├── summary.json     # L1: Run outcome
+        │   ├── details.jsonl    # L2: Per-node results
+        │   └── tool_logs.jsonl  # L3: Step-by-step execution
+        ├── conversations/       # Per-node EventLoop state
+        └── data/                # Spillover artifacts
+```
+
+**Key characteristics:**
+- All session data colocated in one directory
+- Consistent ID format: `session_YYYYMMDD_HHMMSS_{short_uuid}`
+- Logs written incrementally (JSONL for L2/L3)
+- Single source of truth: `state.json`
+
+### Legacy Structure (Deprecated)
+
+**Read-only for backward compatibility**
+
+```
+~/.hive/agents/{agent_name}/
+├── runtime_logs/
+│   └── runs/
+│       └── {run_id}/
+│           ├── summary.json     # L1
+│           ├── details.jsonl    # L2
+│           └── tool_logs.jsonl  # L3
+├── sessions/
+│   └── exec_{stream_id}_{uuid}/
+│       ├── conversations/
+│       └── data/
+├── runs/                        # Deprecated
+│   └── run_start_*.json
+└── summaries/                   # Deprecated
+    └── run_start_*.json
+```
+
+**Migration status:**
+- ✅ New sessions write to unified structure only
+- ✅ Old sessions remain readable
+- ❌ No new writes to `runs/`, `summaries/`, `runtime_logs/runs/`
+- ⚠️ Deprecation warnings emitted when reading old locations
+
+---
+
+## Components
+
+### RuntimeLogger
+
+**Location:** `core/framework/runtime/runtime_logger.py`
+
+**Responsibilities:**
+- Receives execution events from GraphExecutor
+- Tracks per-node execution details
+- Aggregates attention flags
+- Coordinates with RuntimeLogStore
+
+**Key methods:**
+```python
+def start_run(goal_id: str, session_id: str = "") -> str:
+    """Initialize a new run. Uses session_id as run_id if provided."""
+
+def log_step(node_id: str, step_index: int, tool_calls: list, ...):
+    """Record one LLM step (L3). Appends to tool_logs.jsonl immediately."""
+
+def log_node_complete(node_id: str, exit_status: str, ...):
+    """Record node completion (L2). Appends to details.jsonl immediately."""
+
+async def end_run(status: str):
+    """Finalize run, aggregate L2→L1, write summary.json."""
+```
+
+**Attention flag triggers:**
+```python
+# From runtime_logger.py:190-203
+needs_attention = any([
+    retry_count > 3,
+    escalate_count > 2,
+    latency_ms > 60000,
+    tokens_used > 100000,
+    total_steps > 20,
+])
+```
+
+### RuntimeLogStore
+
+**Location:** `core/framework/runtime/runtime_log_store.py`
+
+**Responsibilities:**
+- Manages log file I/O
+- Handles both old and new storage paths
+- Provides incremental append for L2/L3 (crash-safe)
+- Atomic writes for L1
+
+**Storage path resolution:**
+```python
+def _get_run_dir(run_id: str) -> Path:
+    """Determine log directory based on run_id format.
+
+    - session_* → {storage_root}/sessions/{run_id}/logs/
+    - Other     → {base_path}/runtime_logs/runs/{run_id}/ (deprecated)
+    """
+```
+
+**Key methods:**
+```python
+def ensure_run_dir(run_id: str):
+    """Create log directory immediately at start_run()."""
+
+def append_step(run_id: str, step: NodeStepLog):
+    """Append L3 entry to tool_logs.jsonl. Thread-safe sync write."""
+
+def append_node_detail(run_id: str, detail: NodeDetail):
+    """Append L2 entry to details.jsonl. Thread-safe sync write."""
+
+async def save_summary(run_id: str, summary: RunSummaryLog):
+    """Write L1 summary.json atomically at end_run()."""
+```
+
+**File format:**
+- **L1 (summary.json)**: Standard JSON, written once at end
+- **L2 (details.jsonl)**: JSONL (one object per line), appended per node
+- **L3 (tool_logs.jsonl)**: JSONL (one object per line), appended per step
+
+### Runtime Log Schemas
+
+**Location:** `core/framework/runtime/runtime_log_schemas.py`
+
+**L1: RunSummaryLog**
+```python
+@dataclass
+class RunSummaryLog:
+    run_id: str
+    goal_id: str
+    status: str  # "success", "failure", "degraded", "in_progress"
+    started_at: str  # ISO 8601
+    ended_at: str | None
+    needs_attention: bool
+    attention_summary: AttentionSummary
+    total_nodes_executed: int
+    nodes_with_failures: list[str]
+    execution_quality: str  # "clean", "degraded", "failed"
+    total_latency_ms: int
+    # ... additional metrics
+```
+
+**L2: NodeDetail**
+```python
+@dataclass
+class NodeDetail:
+    node_id: str
+    exit_status: str  # "success", "escalate", "no_valid_edge"
+    retry_count: int
+    verdict_counts: dict[str, int]  # {ACCEPT: 1, RETRY: 3, ...}
+    total_steps: int
+    latency_ms: int
+    needs_attention: bool
+    attention_reasons: list[str]
+    # ... tool error tracking, token counts
+```
+
+**L3: NodeStepLog**
+```python
+@dataclass
+class NodeStepLog:
+    node_id: str
+    step_index: int
+    tool_calls: list[dict]
+    tool_results: list[dict]
+    verdict: str  # "ACCEPT", "RETRY", "ESCALATE", "CONTINUE"
+    verdict_feedback: str
+    llm_response_text: str
+    tokens_used: int
+    latency_ms: int
+    # ... detailed execution state
+    # Trace context (OTel-aligned; empty if observability context not set):
+    trace_id: str   # From set_trace_context (OTel trace)
+    span_id: str    # 16 hex chars per step (OTel span)
+    parent_span_id: str  # Optional; for nested span hierarchy
+    execution_id: str    # Session/run correlation id
+```
+
+L3 entries include `trace_id`, `span_id`, and `execution_id` for correlation and **OpenTelemetry (OTel) compatibility**. When the framework sets trace context (e.g. via `Runtime.start_run()` or `StreamRuntime.start_run()`), these fields are populated automatically so L3 data can be exported to OTel backends without schema changes.
+
+**L2: NodeDetail** also includes `trace_id` and `span_id`; **L1: RunSummaryLog** includes `trace_id` and `execution_id` for the same correlation.
+
+---
+
+## Querying Logs (MCP Tools)
+
+### Tools Location
+
+**MCP Server:** `tools/src/aden_tools/tools/runtime_logs_tool/runtime_logs_tool.py`
+
+Three MCP tools provide access to the logging system:
+
+### L1: query_runtime_logs
+
+**Purpose:** Find problematic runs
+
+```python
+query_runtime_logs(
+    agent_work_dir: str,        # e.g., "~/.hive/agents/twitter_outreach"
+    status: str = "",           # "needs_attention", "success", "failure", "degraded"
+    limit: int = 20
+) -> dict  # {"runs": [...], "total": int}
+```
+
+**Returns:**
+```json
+{
+  "runs": [
+    {
+      "run_id": "session_20260206_115718_e22339c5",
+      "status": "degraded",
+      "needs_attention": true,
+      "attention_summary": {
+        "total_attention_flags": 3,
+        "categories": ["missing_outputs", "retry_loops"]
+      },
+      "started_at": "2026-02-06T11:57:18Z"
+    }
+  ],
+  "total": 1
+}
+```
+
+**Common queries:**
+```python
+# Find all problematic runs
+query_runtime_logs(agent_work_dir, status="needs_attention")
+
+# Get recent runs regardless of status
+query_runtime_logs(agent_work_dir, limit=10)
+
+# Check for failures
+query_runtime_logs(agent_work_dir, status="failure")
+```
+
+### L2: query_runtime_log_details
+
+**Purpose:** Identify which nodes failed
+
+```python
+query_runtime_log_details(
+    agent_work_dir: str,
+    run_id: str,                    # From L1 query
+    needs_attention_only: bool = False,
+    node_id: str = ""               # Filter to specific node
+) -> dict  # {"run_id": str, "nodes": [...]}
+```
+
+**Returns:**
+```json
+{
+  "run_id": "session_20260206_115718_e22339c5",
+  "nodes": [
+    {
+      "node_id": "intake-collector",
+      "exit_status": "escalate",
+      "retry_count": 5,
+      "verdict_counts": {"RETRY": 5, "ESCALATE": 1},
+      "attention_reasons": ["high_retry_count", "missing_outputs"],
+      "total_steps": 8,
+      "latency_ms": 12500,
+      "needs_attention": true
+    }
+  ]
+}
+```
+
+**Common queries:**
+```python
+# Get all problematic nodes
+query_runtime_log_details(agent_work_dir, run_id, needs_attention_only=True)
+
+# Analyze specific node across run
+query_runtime_log_details(agent_work_dir, run_id, node_id="intake-collector")
+
+# Full node breakdown
+query_runtime_log_details(agent_work_dir, run_id)
+```
+
+### L3: query_runtime_log_raw
+
+**Purpose:** Root cause analysis
+
+```python
+query_runtime_log_raw(
+    agent_work_dir: str,
+    run_id: str,
+    step_index: int = -1,           # Specific step or -1 for all
+    node_id: str = ""               # Filter to specific node
+) -> dict  # {"run_id": str, "steps": [...]}
+```
+
+**Returns:**
+```json
+{
+  "run_id": "session_20260206_115718_e22339c5",
+  "steps": [
+    {
+      "node_id": "intake-collector",
+      "step_index": 3,
+      "tool_calls": [
+        {
+          "tool": "web_search",
+          "args": {"query": "@RomuloNevesOf"}
+        }
+      ],
+      "tool_results": [
+        {
+          "status": "success",
+          "data": "..."
+        }
+      ],
+      "verdict": "RETRY",
+      "verdict_feedback": "Missing required output 'twitter_handles'. You found the handle but didn't call set_output.",
+      "llm_response_text": "I found the Twitter profile...",
+      "tokens_used": 1234,
+      "latency_ms": 2500
+    }
+  ]
+}
+```
+
+**Common queries:**
+```python
+# All steps for a problematic node
+query_runtime_log_raw(agent_work_dir, run_id, node_id="intake-collector")
+
+# Specific step analysis
+query_runtime_log_raw(agent_work_dir, run_id, step_index=5)
+
+# Full execution trace
+query_runtime_log_raw(agent_work_dir, run_id)
+```
+
+---
+
+## Usage Patterns
+
+### Pattern 1: Top-Down Investigation
+
+**Use case:** Debug a failing agent
+
+```python
+# 1. Find problematic runs (L1)
+result = query_runtime_logs(
+    agent_work_dir="~/.hive/agents/twitter_outreach",
+    status="needs_attention"
+)
+run_id = result["runs"][0]["run_id"]
+
+# 2. Identify failing nodes (L2)
+details = query_runtime_log_details(
+    agent_work_dir="~/.hive/agents/twitter_outreach",
+    run_id=run_id,
+    needs_attention_only=True
+)
+problem_node = details["nodes"][0]["node_id"]
+
+# 3. Analyze root cause (L3)
+raw = query_runtime_log_raw(
+    agent_work_dir="~/.hive/agents/twitter_outreach",
+    run_id=run_id,
+    node_id=problem_node
+)
+# Examine verdict_feedback, tool_results, etc.
+```
+
+### Pattern 2: Node-Specific Debugging
+
+**Use case:** Investigate why a specific node keeps failing
+
+```python
+# Get recent runs
+runs = query_runtime_logs("~/.hive/agents/my_agent", limit=10)
+
+# For each run, check specific node
+for run in runs["runs"]:
+    node_details = query_runtime_log_details(
+        "~/.hive/agents/my_agent",
+        run["run_id"],
+        node_id="problematic-node"
+    )
+    # Analyze retry patterns, error types
+```
+
+### Pattern 3: Real-Time Monitoring
+
+**Use case:** Watch for issues during development
+
+```python
+import time
+
+while True:
+    result = query_runtime_logs(
+        agent_work_dir="~/.hive/agents/my_agent",
+        status="needs_attention",
+        limit=1
+    )
+
+    if result["total"] > 0:
+        new_issue = result["runs"][0]
+        print(f"⚠️  New issue detected: {new_issue['run_id']}")
+        # Alert or drill into L2/L3
+
+    time.sleep(10)  # Poll every 10 seconds
+```
+
+---
+
+## Integration Points
+
+### GraphExecutor → RuntimeLogger
+
+**Location:** `core/framework/graph/executor.py`
+
+```python
+# Executor creates logger and passes session_id
+logger = RuntimeLogger(store, agent_id)
+run_id = logger.start_run(goal_id, session_id=execution_id)
+
+# During execution
+logger.log_step(node_id, step_index, tool_calls, ...)
+logger.log_node_complete(node_id, exit_status, ...)
+
+# At completion
+await logger.end_run(status="success")
+```
+
+### EventLoopNode → RuntimeLogger
+
+**Location:** `core/framework/graph/event_loop_node.py`
+
+```python
+# EventLoopNode logs each step
+self._logger.log_step(
+    node_id=self.id,
+    step_index=step_count,
+    tool_calls=current_tool_calls,
+    tool_results=current_tool_results,
+    verdict=verdict,
+    verdict_feedback=feedback,
+    ...
+)
+```
+
+### AgentRuntime → RuntimeLogger
+
+**Location:** `core/framework/runtime/agent_runtime.py`
+
+```python
+# Runtime initializes logger with storage path
+log_store = RuntimeLogStore(base_path / "runtime_logs")
+logger = RuntimeLogger(log_store, agent_id)
+
+# Passes session_id from ExecutionStream
+logger.start_run(goal_id, session_id=execution_id)
+```
+
+---
+
+## File Format Details
+
+### L1: summary.json
+
+**Written:** Once at end_run()
+**Format:** Standard JSON
+
+```json
+{
+  "run_id": "session_20260206_115718_e22339c5",
+  "goal_id": "twitter-outreach-multi-loop",
+  "status": "degraded",
+  "started_at": "2026-02-06T11:57:18.593081",
+  "ended_at": "2026-02-06T11:58:45.123456",
+  "needs_attention": true,
+  "attention_summary": {
+    "total_attention_flags": 3,
+    "categories": ["missing_outputs", "retry_loops"],
+    "nodes_with_attention": ["intake-collector"]
+  },
+  "total_nodes_executed": 4,
+  "nodes_with_failures": ["intake-collector"],
+  "execution_quality": "degraded",
+  "total_latency_ms": 86530,
+  "total_retries": 5
+}
+```
+
+### L2: details.jsonl
+
+**Written:** Incrementally (append per node completion)
+**Format:** JSONL (one JSON object per line)
+
+```jsonl
+{"node_id":"intake-collector","exit_status":"escalate","retry_count":5,"verdict_counts":{"RETRY":5,"ESCALATE":1},"total_steps":8,"latency_ms":12500,"needs_attention":true,"attention_reasons":["high_retry_count","missing_outputs"],"tool_error_count":0,"tokens_used":9876}
+{"node_id":"profile-analyzer","exit_status":"success","retry_count":0,"verdict_counts":{"ACCEPT":1},"total_steps":2,"latency_ms":5432,"needs_attention":false,"attention_reasons":[],"tool_error_count":0,"tokens_used":3456}
+```
+
+### L3: tool_logs.jsonl
+
+**Written:** Incrementally (append per step)
+**Format:** JSONL (one JSON object per line)
+
+Each line includes **trace context** when the framework has set it (via the observability module): `trace_id`, `span_id`, `parent_span_id` (optional), and `execution_id`. These align with OpenTelemetry/W3C TraceContext so L3 data can be exported to OTel backends without schema changes.
+
+```jsonl
+{"node_id":"intake-collector","step_index":3,"trace_id":"54e80d7b5bd6409dbc3217e5cd16a4fd","span_id":"a1b2c3d4e5f67890","execution_id":"b4c348ec54e80d7b5bd6409dbc3217e50","tool_calls":[...],"verdict":"RETRY",...}
+```
+
+**Why JSONL?**
+- Incremental append during execution (crash-safe)
+- No need to parse entire file to add one line
+- Data persisted immediately, not buffered
+- Easy to stream/process line-by-line
+
+---
+
+## Attention Flags System
+
+### Automatic Detection
+
+The runtime logger automatically flags issues based on execution metrics:
+
+| Trigger | Threshold | Attention Reason | Category |
+|---------|-----------|------------------|----------|
+| High retries | `retry_count > 3` | `high_retry_count` | Retry Loops |
+| Escalations | `escalate_count > 2` | `escalation_pattern` | Guard Failures |
+| High latency | `latency_ms > 60000` | `high_latency` | High Latency |
+| Token usage | `tokens_used > 100000` | `high_token_usage` | Memory/Context |
+| Stalled steps | `total_steps > 20` | `excessive_steps` | Stalled Execution |
+| Tool errors | `tool_error_count > 0` | `tool_failures` | Tool Errors |
+| Missing outputs | `exit_status != "success"` | `missing_outputs` | Missing Outputs |
+
+### Attention Categories
+
+Used by `/hive-debugger` skill for issue categorization:
+
+1. **Missing Outputs**: Node didn't set required output keys
+2. **Tool Errors**: Tool calls failed (API errors, timeouts)
+3. **Retry Loops**: Judge repeatedly rejecting outputs
+4. **Guard Failures**: Output validation failed
+5. **Stalled Execution**: EventLoopNode not making progress
+6. **High Latency**: Slow tool calls or LLM responses
+7. **Client-Facing Issues**: Premature set_output before user input
+8. **Edge Routing Errors**: No edges match current state
+9. **Memory/Context Issues**: Conversation history too long
+10. **Constraint Violations**: Agent violated goal-level rules
+
+---
+
+## Migration Guide
+
+### Reading Old Logs
+
+The system automatically handles both old and new formats:
+
+```python
+# MCP tools check both locations automatically
+result = query_runtime_logs("~/.hive/agents/old_agent")
+# Returns logs from both:
+# - ~/.hive/agents/old_agent/runtime_logs/runs/*/
+# - ~/.hive/agents/old_agent/sessions/session_*/logs/
+```
+
+### Deprecation Warnings
+
+When reading from old locations, deprecation warnings are emitted:
+
+```
+DeprecationWarning: Reading logs from deprecated location for run_id=20260101T120000_abc12345.
+New sessions use unified storage at sessions/session_*/logs/
+```
+
+### Migration Script (Optional)
+
+For migrating existing old logs to new format, see:
+- `EXECUTION_STORAGE_REDESIGN.md` - Migration strategy
+- Future: `scripts/migrate_to_unified_sessions.py`
+
+---
+
+## Performance Characteristics
+
+### Write Performance
+
+- **L3 append**: ~1-2ms per step (sync I/O, thread-safe)
+- **L2 append**: ~1-2ms per node (sync I/O, thread-safe)
+- **L1 write**: ~5-10ms at end_run (atomic, async)
+
+**Overhead:** < 5% of total execution time for typical agents
+
+### Read Performance
+
+- **L1 summary**: ~1-5ms (single JSON file)
+- **L2 details**: ~10-50ms (JSONL, depends on node count)
+- **L3 raw logs**: ~50-500ms (JSONL, depends on step count)
+
+**Optimization:** Use filters (node_id, step_index) to reduce data read
+
+### Storage Size
+
+Typical session with 5 nodes, 20 steps:
+
+- **L1 (summary.json)**: ~2-5 KB
+- **L2 (details.jsonl)**: ~5-10 KB (1-2 KB per node)
+- **L3 (tool_logs.jsonl)**: ~50-200 KB (2-10 KB per step)
+
+**Total per session:** ~60-215 KB
+
+**Compression:** Consider archiving old sessions after 90 days
+
+---
+
+## Troubleshooting
+
+### Issue: Logs not appearing
+
+**Symptom:** MCP tools return empty results
+
+**Check:**
+1. Verify storage path exists: `~/.hive/agents/{agent_name}/`
+2. Check session directories: `ls ~/.hive/agents/{agent_name}/sessions/`
+3. Verify logs directory exists: `ls ~/.hive/agents/{agent_name}/sessions/session_*/logs/`
+4. Check file permissions
+
+### Issue: Corrupt JSONL files
+
+**Symptom:** Partial data or JSON decode errors
+
+**Cause:** Process crash during write (rare, but possible)
+
+**Recovery:**
+```python
+# MCP tools skip corrupt lines automatically
+query_runtime_log_details(agent_work_dir, run_id)
+# Logs warning but continues with valid lines
+```
+
+### Issue: High disk usage
+
+**Symptom:** Storage growing too large
+
+**Solution:**
+```bash
+# Archive old sessions
+cd ~/.hive/agents/{agent_name}/sessions/
+find . -name "session_2025*" -type d -exec tar -czf archive.tar.gz {} +
+rm -rf session_2025*
+
+# Or set up automatic cleanup (future feature)
+```
+
+---
+
+## References
+
+**Implementation:**
+- `core/framework/runtime/runtime_logger.py` - Logger implementation
+- `core/framework/runtime/runtime_log_store.py` - Storage layer
+- `core/framework/runtime/runtime_log_schemas.py` - Data schemas
+- `tools/src/aden_tools/tools/runtime_logs_tool/runtime_logs_tool.py` - MCP query tools
+
+**Documentation:**
+- `EXECUTION_STORAGE_REDESIGN.md` - Unified session storage design
+- `/.claude/skills/hive-debugger/SKILL.md` - Interactive debugging skill
+
+**Related:**
+- `core/framework/schemas/session_state.py` - Session state schema
+- `core/framework/storage/session_store.py` - Session state storage
+- `core/framework/graph/executor.py` - GraphExecutor integration

core/framework/runtime/agent_runtime.py

@@ -12,12 +12,14 @@ from dataclasses import dataclass
 from pathlib import Path
 from typing import TYPE_CHECKING, Any
 
+from framework.graph.checkpoint_config import CheckpointConfig
 from framework.graph.executor import ExecutionResult
 from framework.runtime.event_bus import EventBus
 from framework.runtime.execution_stream import EntryPointSpec, ExecutionStream
 from framework.runtime.outcome_aggregator import OutcomeAggregator
 from framework.runtime.shared_state import SharedStateManager
 from framework.storage.concurrent import ConcurrentStorage
+from framework.storage.session_store import SessionStore
 
 if TYPE_CHECKING:
     from framework.graph.edge import GraphSpec
@@ -100,6 +102,8 @@ class AgentRuntime:
         tools: list["Tool"] | None = None,
         tool_executor: Callable | None = None,
         config: AgentRuntimeConfig | None = None,
+        runtime_log_store: Any = None,
+        checkpoint_config: CheckpointConfig | None = None,
     ):
         """
         Initialize agent runtime.
@@ -112,18 +116,26 @@ class AgentRuntime:
             tools: Available tools
             tool_executor: Function to execute tools
             config: Optional runtime configuration
+            runtime_log_store: Optional RuntimeLogStore for per-execution logging
+            checkpoint_config: Optional checkpoint configuration for resumable sessions
         """
         self.graph = graph
         self.goal = goal
         self._config = config or AgentRuntimeConfig()
+        self._runtime_log_store = runtime_log_store
+        self._checkpoint_config = checkpoint_config
 
         # Initialize storage
+        storage_path_obj = Path(storage_path) if isinstance(storage_path, str) else storage_path
         self._storage = ConcurrentStorage(
-            base_path=storage_path,
+            base_path=storage_path_obj,
             cache_ttl=self._config.cache_ttl,
             batch_interval=self._config.batch_interval,
         )
 
+        # Initialize SessionStore for unified sessions (always enabled)
+        self._session_store = SessionStore(storage_path_obj)
+
         # Initialize shared components
         self._state_manager = SharedStateManager()
         self._event_bus = EventBus(max_history=self._config.max_history)
@@ -212,6 +224,9 @@ class AgentRuntime:
                     tool_executor=self._tool_executor,
                     result_retention_max=self._config.execution_result_max,
                     result_retention_ttl_seconds=self._config.execution_result_ttl_seconds,
+                    runtime_log_store=self._runtime_log_store,
+                    session_store=self._session_store,
+                    checkpoint_config=self._checkpoint_config,
                 )
                 await stream.start()
                 self._streams[ep_id] = stream
@@ -296,6 +311,25 @@ class AgentRuntime:
             raise ValueError(f"Entry point '{entry_point_id}' not found")
         return await stream.wait_for_completion(exec_id, timeout)
 
+    async def inject_input(self, node_id: str, content: str) -> bool:
+        """Inject user input into a running client-facing node.
+
+        Routes input to the EventLoopNode identified by ``node_id``
+        across all active streams. Used by the TUI ChatRepl to deliver
+        user responses during client-facing node execution.
+
+        Args:
+            node_id: The node currently waiting for input
+            content: The user's input text
+
+        Returns:
+            True if input was delivered, False if no matching node found
+        """
+        for stream in self._streams.values():
+            if await stream.inject_input(node_id, content):
+                return True
+        return False
+
     async def get_goal_progress(self) -> dict[str, Any]:
         """
         Evaluate goal progress across all streams.
@@ -429,11 +463,15 @@ def create_agent_runtime(
     tools: list["Tool"] | None = None,
     tool_executor: Callable | None = None,
     config: AgentRuntimeConfig | None = None,
+    runtime_log_store: Any = None,
+    enable_logging: bool = True,
+    checkpoint_config: CheckpointConfig | None = None,
 ) -> AgentRuntime:
     """
     Create and configure an AgentRuntime with entry points.
 
     Convenience factory that creates runtime and registers entry points.
+    Runtime logging is enabled by default for observability.
 
     Args:
         graph: Graph specification
@@ -444,10 +482,23 @@ def create_agent_runtime(
         tools: Available tools
         tool_executor: Tool executor function
         config: Runtime configuration
+        runtime_log_store: Optional RuntimeLogStore for per-execution logging.
+            If None and enable_logging=True, creates one automatically.
+        enable_logging: Whether to enable runtime logging (default: True).
+            Set to False to disable logging entirely.
+        checkpoint_config: Optional checkpoint configuration for resumable sessions.
+            If None, uses default checkpointing behavior.
 
     Returns:
         Configured AgentRuntime (not yet started)
     """
+    # Auto-create runtime log store if logging is enabled and not provided
+    if enable_logging and runtime_log_store is None:
+        from framework.runtime.runtime_log_store import RuntimeLogStore
+
+        storage_path_obj = Path(storage_path) if isinstance(storage_path, str) else storage_path
+        runtime_log_store = RuntimeLogStore(storage_path_obj / "runtime_logs")
+
     runtime = AgentRuntime(
         graph=graph,
         goal=goal,
@@ -456,6 +507,8 @@ def create_agent_runtime(
         tools=tools,
         tool_executor=tool_executor,
         config=config,
+        runtime_log_store=runtime_log_store,
+        checkpoint_config=checkpoint_config,
     )
 
     for spec in entry_points:

core/framework/runtime/core.py

@@ -13,6 +13,7 @@ from datetime import datetime
 from pathlib import Path
 from typing import Any
 
+from framework.observability import set_trace_context
 from framework.schemas.decision import Decision, DecisionType, Option, Outcome
 from framework.schemas.run import Run, RunStatus
 from framework.storage.backend import FileStorage
@@ -79,6 +80,14 @@ class Runtime:
             The run ID
         """
         run_id = f"run_{datetime.now().strftime('%Y%m%d_%H%M%S')}_{uuid.uuid4().hex[:8]}"
+        trace_id = uuid.uuid4().hex
+        execution_id = uuid.uuid4().hex  # 32 hex, OTel/W3C-aligned for logs
+
+        set_trace_context(
+            trace_id=trace_id,
+            execution_id=execution_id,
+            goal_id=goal_id,
+        )
 
         self._current_run = Run(
             id=run_id,

core/framework/runtime/event_bus.py

@@ -12,13 +12,13 @@ import logging
 from collections.abc import Awaitable, Callable
 from dataclasses import dataclass, field
 from datetime import datetime
-from enum import Enum
+from enum import StrEnum
 from typing import Any
 
 logger = logging.getLogger(__name__)
 
 
-class EventType(str, Enum):
+class EventType(StrEnum):
     """Types of events that can be published."""
 
     # Execution lifecycle
@@ -41,6 +41,28 @@ class EventType(str, Enum):
     STREAM_STARTED = "stream_started"
     STREAM_STOPPED = "stream_stopped"
 
+    # Node event-loop lifecycle
+    NODE_LOOP_STARTED = "node_loop_started"
+    NODE_LOOP_ITERATION = "node_loop_iteration"
+    NODE_LOOP_COMPLETED = "node_loop_completed"
+
+    # LLM streaming observability
+    LLM_TEXT_DELTA = "llm_text_delta"
+    LLM_REASONING_DELTA = "llm_reasoning_delta"
+
+    # Tool lifecycle
+    TOOL_CALL_STARTED = "tool_call_started"
+    TOOL_CALL_COMPLETED = "tool_call_completed"
+
+    # Client I/O (client_facing=True nodes only)
+    CLIENT_OUTPUT_DELTA = "client_output_delta"
+    CLIENT_INPUT_REQUESTED = "client_input_requested"
+
+    # Internal node observability (client_facing=False nodes)
+    NODE_INTERNAL_OUTPUT = "node_internal_output"
+    NODE_INPUT_BLOCKED = "node_input_blocked"
+    NODE_STALLED = "node_stalled"
+
     # Custom events
     CUSTOM = "custom"
 
@@ -51,6 +73,7 @@ class AgentEvent:
 
     type: EventType
     stream_id: str
+    node_id: str | None = None  # Which node emitted this event
     execution_id: str | None = None
     data: dict[str, Any] = field(default_factory=dict)
     timestamp: datetime = field(default_factory=datetime.now)
@@ -61,6 +84,7 @@ class AgentEvent:
         return {
             "type": self.type.value,
             "stream_id": self.stream_id,
+            "node_id": self.node_id,
             "execution_id": self.execution_id,
             "data": self.data,
             "timestamp": self.timestamp.isoformat(),
@@ -80,6 +104,7 @@ class Subscription:
     event_types: set[EventType]
     handler: EventHandler
     filter_stream: str | None = None  # Only receive events from this stream
+    filter_node: str | None = None  # Only receive events from this node
     filter_execution: str | None = None  # Only receive events from this execution
 
 
@@ -138,6 +163,7 @@ class EventBus:
         event_types: list[EventType],
         handler: EventHandler,
         filter_stream: str | None = None,
+        filter_node: str | None = None,
         filter_execution: str | None = None,
     ) -> str:
         """
@@ -147,6 +173,7 @@ class EventBus:
             event_types: Types of events to receive
             handler: Async function to call when event occurs
             filter_stream: Only receive events from this stream
+            filter_node: Only receive events from this node
             filter_execution: Only receive events from this execution
 
         Returns:
@@ -160,6 +187,7 @@ class EventBus:
             event_types=set(event_types),
             handler=handler,
             filter_stream=filter_stream,
+            filter_node=filter_node,
             filter_execution=filter_execution,
         )
 
@@ -218,6 +246,10 @@ class EventBus:
         if subscription.filter_stream and subscription.filter_stream != event.stream_id:
             return False
 
+        # Check node filter
+        if subscription.filter_node and subscription.filter_node != event.node_id:
+            return False
+
         # Check execution filter
         if subscription.filter_execution and subscription.filter_execution != event.execution_id:
             return False
@@ -359,6 +391,248 @@ class EventBus:
             )
         )
 
+    # === NODE EVENT-LOOP PUBLISHERS ===
+
+    async def emit_node_loop_started(
+        self,
+        stream_id: str,
+        node_id: str,
+        execution_id: str | None = None,
+        max_iterations: int | None = None,
+    ) -> None:
+        """Emit node loop started event."""
+        await self.publish(
+            AgentEvent(
+                type=EventType.NODE_LOOP_STARTED,
+                stream_id=stream_id,
+                node_id=node_id,
+                execution_id=execution_id,
+                data={"max_iterations": max_iterations},
+            )
+        )
+
+    async def emit_node_loop_iteration(
+        self,
+        stream_id: str,
+        node_id: str,
+        iteration: int,
+        execution_id: str | None = None,
+    ) -> None:
+        """Emit node loop iteration event."""
+        await self.publish(
+            AgentEvent(
+                type=EventType.NODE_LOOP_ITERATION,
+                stream_id=stream_id,
+                node_id=node_id,
+                execution_id=execution_id,
+                data={"iteration": iteration},
+            )
+        )
+
+    async def emit_node_loop_completed(
+        self,
+        stream_id: str,
+        node_id: str,
+        iterations: int,
+        execution_id: str | None = None,
+    ) -> None:
+        """Emit node loop completed event."""
+        await self.publish(
+            AgentEvent(
+                type=EventType.NODE_LOOP_COMPLETED,
+                stream_id=stream_id,
+                node_id=node_id,
+                execution_id=execution_id,
+                data={"iterations": iterations},
+            )
+        )
+
+    # === LLM STREAMING PUBLISHERS ===
+
+    async def emit_llm_text_delta(
+        self,
+        stream_id: str,
+        node_id: str,
+        content: str,
+        snapshot: str,
+        execution_id: str | None = None,
+    ) -> None:
+        """Emit LLM text delta event."""
+        await self.publish(
+            AgentEvent(
+                type=EventType.LLM_TEXT_DELTA,
+                stream_id=stream_id,
+                node_id=node_id,
+                execution_id=execution_id,
+                data={"content": content, "snapshot": snapshot},
+            )
+        )
+
+    async def emit_llm_reasoning_delta(
+        self,
+        stream_id: str,
+        node_id: str,
+        content: str,
+        execution_id: str | None = None,
+    ) -> None:
+        """Emit LLM reasoning delta event."""
+        await self.publish(
+            AgentEvent(
+                type=EventType.LLM_REASONING_DELTA,
+                stream_id=stream_id,
+                node_id=node_id,
+                execution_id=execution_id,
+                data={"content": content},
+            )
+        )
+
+    # === TOOL LIFECYCLE PUBLISHERS ===
+
+    async def emit_tool_call_started(
+        self,
+        stream_id: str,
+        node_id: str,
+        tool_use_id: str,
+        tool_name: str,
+        tool_input: dict[str, Any] | None = None,
+        execution_id: str | None = None,
+    ) -> None:
+        """Emit tool call started event."""
+        await self.publish(
+            AgentEvent(
+                type=EventType.TOOL_CALL_STARTED,
+                stream_id=stream_id,
+                node_id=node_id,
+                execution_id=execution_id,
+                data={
+                    "tool_use_id": tool_use_id,
+                    "tool_name": tool_name,
+                    "tool_input": tool_input or {},
+                },
+            )
+        )
+
+    async def emit_tool_call_completed(
+        self,
+        stream_id: str,
+        node_id: str,
+        tool_use_id: str,
+        tool_name: str,
+        result: str = "",
+        is_error: bool = False,
+        execution_id: str | None = None,
+    ) -> None:
+        """Emit tool call completed event."""
+        await self.publish(
+            AgentEvent(
+                type=EventType.TOOL_CALL_COMPLETED,
+                stream_id=stream_id,
+                node_id=node_id,
+                execution_id=execution_id,
+                data={
+                    "tool_use_id": tool_use_id,
+                    "tool_name": tool_name,
+                    "result": result,
+                    "is_error": is_error,
+                },
+            )
+        )
+
+    # === CLIENT I/O PUBLISHERS ===
+
+    async def emit_client_output_delta(
+        self,
+        stream_id: str,
+        node_id: str,
+        content: str,
+        snapshot: str,
+        execution_id: str | None = None,
+    ) -> None:
+        """Emit client output delta event (client_facing=True nodes)."""
+        await self.publish(
+            AgentEvent(
+                type=EventType.CLIENT_OUTPUT_DELTA,
+                stream_id=stream_id,
+                node_id=node_id,
+                execution_id=execution_id,
+                data={"content": content, "snapshot": snapshot},
+            )
+        )
+
+    async def emit_client_input_requested(
+        self,
+        stream_id: str,
+        node_id: str,
+        prompt: str = "",
+        execution_id: str | None = None,
+    ) -> None:
+        """Emit client input requested event (client_facing=True nodes)."""
+        await self.publish(
+            AgentEvent(
+                type=EventType.CLIENT_INPUT_REQUESTED,
+                stream_id=stream_id,
+                node_id=node_id,
+                execution_id=execution_id,
+                data={"prompt": prompt},
+            )
+        )
+
+    # === INTERNAL NODE PUBLISHERS ===
+
+    async def emit_node_internal_output(
+        self,
+        stream_id: str,
+        node_id: str,
+        content: str,
+        execution_id: str | None = None,
+    ) -> None:
+        """Emit node internal output event (client_facing=False nodes)."""
+        await self.publish(
+            AgentEvent(
+                type=EventType.NODE_INTERNAL_OUTPUT,
+                stream_id=stream_id,
+                node_id=node_id,
+                execution_id=execution_id,
+                data={"content": content},
+            )
+        )
+
+    async def emit_node_stalled(
+        self,
+        stream_id: str,
+        node_id: str,
+        reason: str = "",
+        execution_id: str | None = None,
+    ) -> None:
+        """Emit node stalled event."""
+        await self.publish(
+            AgentEvent(
+                type=EventType.NODE_STALLED,
+                stream_id=stream_id,
+                node_id=node_id,
+                execution_id=execution_id,
+                data={"reason": reason},
+            )
+        )
+
+    async def emit_node_input_blocked(
+        self,
+        stream_id: str,
+        node_id: str,
+        prompt: str = "",
+        execution_id: str | None = None,
+    ) -> None:
+        """Emit node input blocked event."""
+        await self.publish(
+            AgentEvent(
+                type=EventType.NODE_INPUT_BLOCKED,
+                stream_id=stream_id,
+                node_id=node_id,
+                execution_id=execution_id,
+                data={"prompt": prompt},
+            )
+        )
+
     # === QUERY OPERATIONS ===
 
     def get_history(
@@ -410,6 +684,7 @@ class EventBus:
         self,
         event_type: EventType,
         stream_id: str | None = None,
+        node_id: str | None = None,
         execution_id: str | None = None,
         timeout: float | None = None,
     ) -> AgentEvent | None:
@@ -419,6 +694,7 @@ class EventBus:
         Args:
             event_type: Type of event to wait for
             stream_id: Filter by stream
+            node_id: Filter by node
             execution_id: Filter by execution
             timeout: Maximum time to wait (seconds)
 
@@ -438,6 +714,7 @@ class EventBus:
             event_types=[event_type],
             handler=handler,
             filter_stream=stream_id,
+            filter_node=node_id,
             filter_execution=execution_id,
         )
 

core/framework/runtime/execution_stream.py

@@ -17,6 +17,7 @@ from dataclasses import dataclass, field
 from datetime import datetime
 from typing import TYPE_CHECKING, Any
 
+from framework.graph.checkpoint_config import CheckpointConfig
 from framework.graph.executor import ExecutionResult, GraphExecutor
 from framework.runtime.shared_state import IsolationLevel, SharedStateManager
 from framework.runtime.stream_runtime import StreamRuntime, StreamRuntimeAdapter
@@ -28,6 +29,7 @@ if TYPE_CHECKING:
     from framework.runtime.event_bus import EventBus
     from framework.runtime.outcome_aggregator import OutcomeAggregator
     from framework.storage.concurrent import ConcurrentStorage
+    from framework.storage.session_store import SessionStore
 
 logger = logging.getLogger(__name__)
 
@@ -112,6 +114,9 @@ class ExecutionStream:
         tool_executor: Callable | None = None,
         result_retention_max: int | None = 1000,
         result_retention_ttl_seconds: float | None = None,
+        runtime_log_store: Any = None,
+        session_store: "SessionStore | None" = None,
+        checkpoint_config: CheckpointConfig | None = None,
     ):
         """
         Initialize execution stream.
@@ -128,6 +133,9 @@ class ExecutionStream:
             llm: LLM provider for nodes
             tools: Available tools
             tool_executor: Function to execute tools
+            runtime_log_store: Optional RuntimeLogStore for per-execution logging
+            session_store: Optional SessionStore for unified session storage
+            checkpoint_config: Optional checkpoint configuration for resumable sessions
         """
         self.stream_id = stream_id
         self.entry_spec = entry_spec
@@ -142,6 +150,9 @@ class ExecutionStream:
         self._tool_executor = tool_executor
         self._result_retention_max = result_retention_max
         self._result_retention_ttl_seconds = result_retention_ttl_seconds
+        self._runtime_log_store = runtime_log_store
+        self._checkpoint_config = checkpoint_config
+        self._session_store = session_store
 
         # Create stream-scoped runtime
         self._runtime = StreamRuntime(
@@ -153,6 +164,7 @@ class ExecutionStream:
         # Execution tracking
         self._active_executions: dict[str, ExecutionContext] = {}
         self._execution_tasks: dict[str, asyncio.Task] = {}
+        self._active_executors: dict[str, GraphExecutor] = {}
         self._execution_results: OrderedDict[str, ExecutionResult] = OrderedDict()
         self._execution_result_times: dict[str, float] = {}
         self._completion_events: dict[str, asyncio.Event] = {}
@@ -220,6 +232,13 @@ class ExecutionStream:
                     await task
                 except asyncio.CancelledError:
                     pass
+                except RuntimeError as e:
+                    # Task may be attached to a different event loop (e.g., when TUI
+                    # uses a separate loop). Log and continue cleanup.
+                    if "attached to a different loop" in str(e):
+                        logger.warning(f"Task cleanup skipped (different event loop): {e}")
+                    else:
+                        raise
 
         self._execution_tasks.clear()
         self._active_executions.clear()
@@ -237,6 +256,21 @@ class ExecutionStream:
                 )
             )
 
+    async def inject_input(self, node_id: str, content: str) -> bool:
+        """Inject user input into a running client-facing EventLoopNode.
+
+        Searches active executors for a node matching ``node_id`` and calls
+        its ``inject_event()`` method to unblock ``_await_user_input()``.
+
+        Returns True if input was delivered, False otherwise.
+        """
+        for executor in self._active_executors.values():
+            node = executor.node_registry.get(node_id)
+            if node is not None and hasattr(node, "inject_event"):
+                await node.inject_event(content)
+                return True
+        return False
+
     async def execute(
         self,
         input_data: dict[str, Any],
@@ -259,8 +293,21 @@ class ExecutionStream:
         if not self._running:
             raise RuntimeError(f"ExecutionStream '{self.stream_id}' is not running")
 
-        # Generate execution ID
-        execution_id = f"exec_{self.stream_id}_{uuid.uuid4().hex[:8]}"
+        # Generate execution ID using unified session format
+        if self._session_store:
+            execution_id = self._session_store.generate_session_id()
+        else:
+            # Fallback to old format if SessionStore not available (shouldn't happen)
+            import warnings
+
+            warnings.warn(
+                "SessionStore not available, using deprecated exec_* ID format. "
+                "Please ensure AgentRuntime is properly initialized.",
+                DeprecationWarning,
+                stacklevel=2,
+            )
+            execution_id = f"exec_{self.stream_id}_{uuid.uuid4().hex[:8]}"
+
         if correlation_id is None:
             correlation_id = execution_id
 
@@ -314,13 +361,45 @@ class ExecutionStream:
                 # Create runtime adapter for this execution
                 runtime_adapter = StreamRuntimeAdapter(self._runtime, execution_id)
 
-                # Create executor for this execution
+                # Start run to set trace context (CRITICAL for observability)
+                runtime_adapter.start_run(
+                    goal_id=self.goal.id,
+                    goal_description=self.goal.description,
+                    input_data=ctx.input_data,
+                )
+
+                # Create per-execution runtime logger
+                runtime_logger = None
+                if self._runtime_log_store:
+                    from framework.runtime.runtime_logger import RuntimeLogger
+
+                    runtime_logger = RuntimeLogger(
+                        store=self._runtime_log_store, agent_id=self.graph.id
+                    )
+
+                # Create executor for this execution.
+                # Each execution gets its own storage under sessions/{exec_id}/
+                # so conversations, spillover, and data files are all scoped
+                # to this execution.  The executor sets data_dir via execution
+                # context (contextvars) so data tools and spillover share the
+                # same session-scoped directory.
+                exec_storage = self._storage.base_path / "sessions" / execution_id
                 executor = GraphExecutor(
                     runtime=runtime_adapter,
                     llm=self._llm,
                     tools=self._tools,
                     tool_executor=self._tool_executor,
+                    event_bus=self._event_bus,
+                    stream_id=self.stream_id,
+                    storage_path=exec_storage,
+                    runtime_logger=runtime_logger,
+                    loop_config=self.graph.loop_config,
                 )
+                # Track executor so inject_input() can reach EventLoopNode instances
+                self._active_executors[execution_id] = executor
+
+                # Write initial session state
+                await self._write_session_state(execution_id, ctx)
 
                 # Create modified graph with entry point
                 # We need to override the entry_node to use our entry point
@@ -332,17 +411,31 @@ class ExecutionStream:
                     goal=self.goal,
                     input_data=ctx.input_data,
                     session_state=ctx.session_state,
+                    checkpoint_config=self._checkpoint_config,
                 )
 
+                # Clean up executor reference
+                self._active_executors.pop(execution_id, None)
+
                 # Store result with retention
                 self._record_execution_result(execution_id, result)
 
+                # End run to complete trace (for observability)
+                runtime_adapter.end_run(
+                    success=result.success,
+                    narrative=f"Execution {'succeeded' if result.success else 'failed'}",
+                    output_data=result.output,
+                )
+
                 # Update context
                 ctx.completed_at = datetime.now()
                 ctx.status = "completed" if result.success else "failed"
                 if result.paused_at:
                     ctx.status = "paused"
 
+                # Write final session state
+                await self._write_session_state(execution_id, ctx, result=result)
+
                 # Emit completion/failure event
                 if self._event_bus:
                     if result.success:
@@ -363,8 +456,42 @@ class ExecutionStream:
                 logger.debug(f"Execution {execution_id} completed: success={result.success}")
 
             except asyncio.CancelledError:
-                ctx.status = "cancelled"
-                raise
+                # Execution was cancelled
+                # The executor catches CancelledError and returns a paused result,
+                # but if cancellation happened before executor started, we won't have a result
+                logger.info(f"Execution {execution_id} cancelled")
+
+                # Check if we have a result (executor completed and returned)
+                try:
+                    _ = result  # Check if result variable exists
+                    has_result = True
+                except NameError:
+                    has_result = False
+                    result = ExecutionResult(
+                        success=False,
+                        error="Execution cancelled",
+                    )
+
+                # Update context status based on result
+                if has_result and result.paused_at:
+                    ctx.status = "paused"
+                    ctx.completed_at = datetime.now()
+                else:
+                    ctx.status = "cancelled"
+
+                # Clean up executor reference
+                self._active_executors.pop(execution_id, None)
+
+                # Store result with retention
+                self._record_execution_result(execution_id, result)
+
+                # Write session state
+                if has_result and result.paused_at:
+                    await self._write_session_state(execution_id, ctx, result=result)
+                else:
+                    await self._write_session_state(execution_id, ctx, error="Execution cancelled")
+
+                # Don't re-raise - we've handled it and saved state
 
             except Exception as e:
                 ctx.status = "failed"
@@ -379,6 +506,19 @@ class ExecutionStream:
                     ),
                 )
 
+                # Write error session state
+                await self._write_session_state(execution_id, ctx, error=str(e))
+
+                # End run with failure (for observability)
+                try:
+                    runtime_adapter.end_run(
+                        success=False,
+                        narrative=f"Execution failed: {str(e)}",
+                        output_data={},
+                    )
+                except Exception:
+                    pass  # Don't let end_run errors mask the original error
+
                 # Emit failure event
                 if self._event_bus:
                     await self._event_bus.emit_execution_failed(
@@ -402,6 +542,92 @@ class ExecutionStream:
                     self._completion_events.pop(execution_id, None)
                     self._execution_tasks.pop(execution_id, None)
 
+    async def _write_session_state(
+        self,
+        execution_id: str,
+        ctx: ExecutionContext,
+        result: ExecutionResult | None = None,
+        error: str | None = None,
+    ) -> None:
+        """
+        Write state.json for a session.
+
+        Args:
+            execution_id: Session/execution ID
+            ctx: Execution context
+            result: Optional execution result (if completed)
+            error: Optional error message (if failed)
+        """
+        # Only write if session_store is available
+        if not self._session_store:
+            return
+
+        from framework.schemas.session_state import SessionState, SessionStatus
+
+        try:
+            # Determine status
+            if result:
+                if result.paused_at:
+                    status = SessionStatus.PAUSED
+                elif result.success:
+                    status = SessionStatus.COMPLETED
+                else:
+                    status = SessionStatus.FAILED
+            elif error:
+                # Check if this is a cancellation
+                if ctx.status == "cancelled" or "cancelled" in error.lower():
+                    status = SessionStatus.CANCELLED
+                else:
+                    status = SessionStatus.FAILED
+            else:
+                status = SessionStatus.ACTIVE
+
+            # Create SessionState
+            if result:
+                # Create from execution result
+                state = SessionState.from_execution_result(
+                    session_id=execution_id,
+                    goal_id=self.goal.id,
+                    result=result,
+                    stream_id=self.stream_id,
+                    correlation_id=ctx.correlation_id,
+                    started_at=ctx.started_at.isoformat(),
+                    input_data=ctx.input_data,
+                    agent_id=self.graph.id,
+                    entry_point=self.entry_spec.id,
+                )
+            else:
+                # Create initial state
+                from framework.schemas.session_state import SessionTimestamps
+
+                now = datetime.now().isoformat()
+                state = SessionState(
+                    session_id=execution_id,
+                    stream_id=self.stream_id,
+                    correlation_id=ctx.correlation_id,
+                    goal_id=self.goal.id,
+                    agent_id=self.graph.id,
+                    entry_point=self.entry_spec.id,
+                    status=status,
+                    timestamps=SessionTimestamps(
+                        started_at=ctx.started_at.isoformat(),
+                        updated_at=now,
+                    ),
+                    input_data=ctx.input_data,
+                )
+
+            # Handle error case
+            if error:
+                state.result.error = error
+
+            # Write state.json
+            await self._session_store.write_state(execution_id, state)
+            logger.debug(f"Wrote state.json for session {execution_id} (status={status})")
+
+        except Exception as e:
+            # Log but don't fail the execution
+            logger.error(f"Failed to write state.json for {execution_id}: {e}")
+
     def _create_modified_graph(self) -> "GraphSpec":
         """Create a graph with the entry point overridden."""
         # Use the existing graph but override entry_node

core/framework/runtime/runtime_log_schemas.py

@@ -0,0 +1,142 @@
+"""Pydantic models for the three-level runtime logging system.
+
+Level 1 - SUMMARY:    Per graph run pass/fail, token counts, timing
+Level 2 - DETAILS:    Per node completion results and attention flags
+Level 3 - TOOL LOGS:  Per step within any node (tool calls, LLM text, tokens)
+"""
+
+from __future__ import annotations
+
+from typing import Any
+
+from pydantic import BaseModel, Field
+
+# ---------------------------------------------------------------------------
+# Level 3: Tool logs (most granular) — per step within any node
+# ---------------------------------------------------------------------------
+
+
+class ToolCallLog(BaseModel):
+    """A single tool call within a step."""
+
+    tool_use_id: str
+    tool_name: str
+    tool_input: dict[str, Any] = Field(default_factory=dict)
+    result: str = ""
+    is_error: bool = False
+
+
+class NodeStepLog(BaseModel):
+    """Full tool and LLM details for one step within a node.
+
+    For EventLoopNode, each iteration is a step. For single-step nodes
+    (LLMNode, FunctionNode, RouterNode), step_index is 0.
+
+    OTel-aligned fields (trace_id, span_id, execution_id) enable correlation
+    and future OpenTelemetry export without schema changes.
+    """
+
+    node_id: str
+    node_type: str = ""  # "event_loop"|"llm_tool_use"|"llm_generate"|"function"|"router"
+    step_index: int = 0  # iteration number for event_loop, 0 for single-step nodes
+    llm_text: str = ""
+    tool_calls: list[ToolCallLog] = Field(default_factory=list)
+    input_tokens: int = 0
+    output_tokens: int = 0
+    latency_ms: int = 0
+    # EventLoopNode only:
+    verdict: str = ""  # "ACCEPT"|"RETRY"|"ESCALATE"|"CONTINUE"
+    verdict_feedback: str = ""
+    # Error tracking:
+    error: str = ""  # Error message if step failed
+    stacktrace: str = ""  # Full stack trace if exception occurred
+    is_partial: bool = False  # True if step didn't complete normally
+    # OTel / trace context (from observability; empty if not set):
+    trace_id: str = ""  # OTel trace id (e.g. from set_trace_context)
+    span_id: str = ""  # OTel span id (16 hex chars per step)
+    parent_span_id: str = ""  # Optional; for nested span hierarchy
+    execution_id: str = ""  # Session/run correlation id
+
+
+# ---------------------------------------------------------------------------
+# Level 2: Per-node completion details
+# ---------------------------------------------------------------------------
+
+
+class NodeDetail(BaseModel):
+    """Per-node completion result and attention flags.
+
+    OTel-aligned fields (trace_id, span_id) tie L2 to the same trace as L3.
+    """
+
+    node_id: str
+    node_name: str = ""
+    node_type: str = ""
+    success: bool = True
+    error: str | None = None
+    stacktrace: str = ""  # Full stack trace if exception occurred
+    total_steps: int = 0
+    tokens_used: int = 0  # combined input+output from NodeResult
+    input_tokens: int = 0
+    output_tokens: int = 0
+    latency_ms: int = 0
+    attempt: int = 1  # retry attempt number
+    # EventLoopNode-specific:
+    exit_status: str = ""  # "success"|"failure"|"stalled"|"escalated"|"paused"|"guard_failure"
+    accept_count: int = 0
+    retry_count: int = 0
+    escalate_count: int = 0
+    continue_count: int = 0
+    needs_attention: bool = False
+    attention_reasons: list[str] = Field(default_factory=list)
+    # OTel / trace context (from observability; empty if not set):
+    trace_id: str = ""
+    span_id: str = ""  # Optional node-level span for hierarchy
+
+
+# ---------------------------------------------------------------------------
+# Level 1: Run summary — one per full graph execution
+# ---------------------------------------------------------------------------
+
+
+class RunSummaryLog(BaseModel):
+    """Run-level summary for a full graph execution.
+
+    OTel-aligned fields (trace_id, execution_id) tie L1 to the same trace as L2/L3.
+    """
+
+    run_id: str
+    agent_id: str = ""
+    goal_id: str = ""
+    status: str = ""  # "success"|"failure"|"degraded"
+    total_nodes_executed: int = 0
+    node_path: list[str] = Field(default_factory=list)
+    total_input_tokens: int = 0
+    total_output_tokens: int = 0
+    needs_attention: bool = False
+    attention_reasons: list[str] = Field(default_factory=list)
+    started_at: str = ""  # ISO timestamp
+    duration_ms: int = 0
+    execution_quality: str = ""  # "clean"|"degraded"|"failed"
+    # OTel / trace context (from observability; empty if not set):
+    trace_id: str = ""
+    execution_id: str = ""
+
+
+# ---------------------------------------------------------------------------
+# Container models for file serialization
+# ---------------------------------------------------------------------------
+
+
+class RunDetailsLog(BaseModel):
+    """Level 2 container: all node details for a run."""
+
+    run_id: str
+    nodes: list[NodeDetail] = Field(default_factory=list)
+
+
+class RunToolLogs(BaseModel):
+    """Level 3 container: all step logs for a run."""
+
+    run_id: str
+    steps: list[NodeStepLog] = Field(default_factory=list)

core/framework/runtime/runtime_log_store.py

@@ -0,0 +1,297 @@
+"""File-based storage for runtime logs.
+
+Each run gets its own directory under ``runs/``. No shared mutable index —
+``list_runs()`` scans the directory and loads summary.json from each run.
+This eliminates concurrency issues when parallel EventLoopNodes write
+simultaneously.
+
+L2 (details) and L3 (tool logs) use JSONL (one JSON object per line) for
+incremental append-on-write. This provides crash resilience — data is on
+disk as soon as it's logged, not only at end_run(). L1 (summary) is still
+written once at end as a regular JSON file since it aggregates L2.
+
+Storage layout (current)::
+
+    {base_path}/
+      sessions/
+        {session_id}/
+          logs/
+            summary.json     # Level 1 — written once at end
+            details.jsonl    # Level 2 — appended per node completion
+            tool_logs.jsonl  # Level 3 — appended per step
+"""
+
+from __future__ import annotations
+
+import asyncio
+import json
+import logging
+from datetime import UTC, datetime
+from pathlib import Path
+
+from framework.runtime.runtime_log_schemas import (
+    NodeDetail,
+    NodeStepLog,
+    RunDetailsLog,
+    RunSummaryLog,
+    RunToolLogs,
+)
+
+logger = logging.getLogger(__name__)
+
+
+class RuntimeLogStore:
+    """Persists runtime logs at three levels. Thread-safe via per-run directories."""
+
+    def __init__(self, base_path: Path) -> None:
+        self._base_path = base_path
+        # Note: _runs_dir is determined per-run_id by _get_run_dir()
+
+    def _get_run_dir(self, run_id: str) -> Path:
+        """Determine run directory path based on run_id format.
+
+        - New format (session_*): {storage_root}/sessions/{run_id}/logs/
+        - Old format (anything else): {base_path}/runs/{run_id}/ (deprecated)
+        """
+        if run_id.startswith("session_"):
+            is_runtime_logs = self._base_path.name == "runtime_logs"
+            root = self._base_path.parent if is_runtime_logs else self._base_path
+            return root / "sessions" / run_id / "logs"
+        import warnings
+
+        warnings.warn(
+            f"Reading logs from deprecated location for run_id={run_id}. "
+            "New sessions use unified storage at sessions/session_*/logs/",
+            DeprecationWarning,
+            stacklevel=3,
+        )
+        return self._base_path / "runs" / run_id
+
+    # -------------------------------------------------------------------
+    # Incremental write (sync — called from locked sections)
+    # -------------------------------------------------------------------
+
+    def ensure_run_dir(self, run_id: str) -> None:
+        """Create the run directory immediately. Called by start_run()."""
+        run_dir = self._get_run_dir(run_id)
+        run_dir.mkdir(parents=True, exist_ok=True)
+
+    def append_step(self, run_id: str, step: NodeStepLog) -> None:
+        """Append one JSONL line to tool_logs.jsonl. Sync."""
+        path = self._get_run_dir(run_id) / "tool_logs.jsonl"
+        line = json.dumps(step.model_dump(), ensure_ascii=False) + "\n"
+        with open(path, "a", encoding="utf-8") as f:
+            f.write(line)
+
+    def append_node_detail(self, run_id: str, detail: NodeDetail) -> None:
+        """Append one JSONL line to details.jsonl. Sync."""
+        path = self._get_run_dir(run_id) / "details.jsonl"
+        line = json.dumps(detail.model_dump(), ensure_ascii=False) + "\n"
+        with open(path, "a", encoding="utf-8") as f:
+            f.write(line)
+
+    def read_node_details_sync(self, run_id: str) -> list[NodeDetail]:
+        """Read details.jsonl back into a list of NodeDetail. Sync.
+
+        Used by end_run() to aggregate L2 into L1. Skips corrupt lines.
+        """
+        path = self._get_run_dir(run_id) / "details.jsonl"
+        return _read_jsonl_as_models(path, NodeDetail)
+
+    # -------------------------------------------------------------------
+    # Summary write (async — called from end_run)
+    # -------------------------------------------------------------------
+
+    async def save_summary(self, run_id: str, summary: RunSummaryLog) -> None:
+        """Write summary.json atomically. Called once at end_run()."""
+        run_dir = self._get_run_dir(run_id)
+        await asyncio.to_thread(run_dir.mkdir, parents=True, exist_ok=True)
+        await self._write_json(run_dir / "summary.json", summary.model_dump())
+
+    # -------------------------------------------------------------------
+    # Read
+    # -------------------------------------------------------------------
+
+    async def load_summary(self, run_id: str) -> RunSummaryLog | None:
+        """Load Level 1 summary for a specific run."""
+        data = await self._read_json(self._get_run_dir(run_id) / "summary.json")
+        return RunSummaryLog(**data) if data is not None else None
+
+    async def load_details(self, run_id: str) -> RunDetailsLog | None:
+        """Load Level 2 details from details.jsonl for a specific run."""
+        path = self._get_run_dir(run_id) / "details.jsonl"
+
+        def _read() -> RunDetailsLog | None:
+            if not path.exists():
+                return None
+            nodes = _read_jsonl_as_models(path, NodeDetail)
+            return RunDetailsLog(run_id=run_id, nodes=nodes)
+
+        return await asyncio.to_thread(_read)
+
+    async def load_tool_logs(self, run_id: str) -> RunToolLogs | None:
+        """Load Level 3 tool logs from tool_logs.jsonl for a specific run."""
+        path = self._get_run_dir(run_id) / "tool_logs.jsonl"
+
+        def _read() -> RunToolLogs | None:
+            if not path.exists():
+                return None
+            steps = _read_jsonl_as_models(path, NodeStepLog)
+            return RunToolLogs(run_id=run_id, steps=steps)
+
+        return await asyncio.to_thread(_read)
+
+    async def list_runs(
+        self,
+        status: str = "",
+        needs_attention: bool | None = None,
+        limit: int = 20,
+    ) -> list[RunSummaryLog]:
+        """Scan both old and new directory structures, load summaries, filter, and sort.
+
+        Scans:
+        - Old: base_path/runs/{run_id}/
+        - New: base_path/sessions/{session_id}/logs/
+
+        Directories without summary.json are treated as in-progress runs and
+        get a synthetic summary with status="in_progress".
+        """
+        entries = await asyncio.to_thread(self._scan_run_dirs)
+        summaries: list[RunSummaryLog] = []
+
+        for run_id in entries:
+            summary = await self.load_summary(run_id)
+            if summary is None:
+                # In-progress run: no summary.json yet. Synthesize one.
+                run_dir = self._get_run_dir(run_id)
+                if not run_dir.is_dir():
+                    continue
+                summary = RunSummaryLog(
+                    run_id=run_id,
+                    status="in_progress",
+                    started_at=_infer_started_at(run_id),
+                )
+            if status and status != "needs_attention" and summary.status != status:
+                continue
+            if status == "needs_attention" and not summary.needs_attention:
+                continue
+            if needs_attention is not None and summary.needs_attention != needs_attention:
+                continue
+            summaries.append(summary)
+
+        # Sort by started_at descending (most recent first)
+        summaries.sort(key=lambda s: s.started_at, reverse=True)
+        return summaries[:limit]
+
+    # -------------------------------------------------------------------
+    # Internal helpers
+    # -------------------------------------------------------------------
+
+    def _scan_run_dirs(self) -> list[str]:
+        """Return list of run_id directory names from both old and new locations.
+
+        Scans:
+        - New: base_path/sessions/{session_id}/logs/ (preferred)
+        - Old: base_path/runs/{run_id}/ (deprecated, backward compatibility)
+
+        Returns run_ids/session_ids. Includes all directories, not just those
+        with summary.json, so in-progress runs are visible.
+        """
+        run_ids = []
+
+        # Scan new location: base_path/sessions/{session_id}/logs/
+        # Determine the correct base path for sessions
+        is_runtime_logs = self._base_path.name == "runtime_logs"
+        root = self._base_path.parent if is_runtime_logs else self._base_path
+        sessions_dir = root / "sessions"
+
+        if sessions_dir.exists():
+            for session_dir in sessions_dir.iterdir():
+                if session_dir.is_dir() and session_dir.name.startswith("session_"):
+                    logs_dir = session_dir / "logs"
+                    if logs_dir.exists() and logs_dir.is_dir():
+                        run_ids.append(session_dir.name)
+
+        # Scan old location: base_path/runs/ (deprecated)
+        old_runs_dir = self._base_path / "runs"
+        if old_runs_dir.exists():
+            old_ids = [d.name for d in old_runs_dir.iterdir() if d.is_dir()]
+            if old_ids:
+                import warnings
+
+                warnings.warn(
+                    f"Found {len(old_ids)} runs in deprecated location. "
+                    "Consider migrating to unified session storage.",
+                    DeprecationWarning,
+                    stacklevel=3,
+                )
+            run_ids.extend(old_ids)
+
+        return run_ids
+
+    @staticmethod
+    async def _write_json(path: Path, data: dict) -> None:
+        """Write JSON atomically: write to .tmp then rename."""
+        tmp = path.with_suffix(".tmp")
+        content = json.dumps(data, indent=2, ensure_ascii=False)
+
+        def _write() -> None:
+            tmp.write_text(content, encoding="utf-8")
+            tmp.rename(path)
+
+        await asyncio.to_thread(_write)
+
+    @staticmethod
+    async def _read_json(path: Path) -> dict | None:
+        """Read and parse a JSON file. Returns None if missing or corrupt."""
+
+        def _read() -> dict | None:
+            if not path.exists():
+                return None
+            try:
+                return json.loads(path.read_text(encoding="utf-8"))
+            except (json.JSONDecodeError, OSError) as e:
+                logger.warning("Failed to read %s: %s", path, e)
+                return None
+
+        return await asyncio.to_thread(_read)
+
+
+# -------------------------------------------------------------------
+# Module-level helpers
+# -------------------------------------------------------------------
+
+
+def _read_jsonl_as_models(path: Path, model_cls: type) -> list:
+    """Parse a JSONL file into a list of Pydantic model instances.
+
+    Skips blank lines and corrupt JSON lines (partial writes from crashes).
+    """
+    results = []
+    if not path.exists():
+        return results
+    try:
+        with open(path, encoding="utf-8") as f:
+            for line in f:
+                line = line.strip()
+                if not line:
+                    continue
+                try:
+                    data = json.loads(line)
+                    results.append(model_cls(**data))
+                except (json.JSONDecodeError, Exception) as e:
+                    logger.warning("Skipping corrupt JSONL line in %s: %s", path, e)
+                    continue
+    except OSError as e:
+        logger.warning("Failed to read %s: %s", path, e)
+    return results
+
+
+def _infer_started_at(run_id: str) -> str:
+    """Best-effort ISO timestamp from a run_id like '20250101T120000_abc12345'."""
+    try:
+        ts_part = run_id.split("_")[0]  # '20250101T120000'
+        dt = datetime.strptime(ts_part, "%Y%m%dT%H%M%S").replace(tzinfo=UTC)
+        return dt.isoformat()
+    except (ValueError, IndexError):
+        return ""

core/framework/runtime/runtime_logger.py

@@ -0,0 +1,326 @@
+"""RuntimeLogger: captures runtime data during graph execution.
+
+Injected into GraphExecutor as an optional parameter. Each log_step() and
+log_node_complete() call writes immediately to disk (JSONL append). Only
+the L1 summary is written at end_run() since it aggregates L2 data.
+
+This provides crash resilience — L2 and L3 data survives process death
+without needing end_run() to complete.
+
+Usage::
+
+    store = RuntimeLogStore(Path(work_dir) / "runtime_logs")
+    runtime_logger = RuntimeLogger(store=store, agent_id="my-agent")
+    executor = GraphExecutor(..., runtime_logger=runtime_logger)
+    # After execution, logger has persisted all data to store
+
+Safety: ``end_run()`` catches all exceptions internally and logs them via
+the Python logger. Logging failure must never kill a successful run.
+"""
+
+from __future__ import annotations
+
+import logging
+import threading
+import uuid
+from datetime import UTC, datetime
+from typing import Any
+
+from framework.observability import get_trace_context
+from framework.runtime.runtime_log_schemas import (
+    NodeDetail,
+    NodeStepLog,
+    RunSummaryLog,
+    ToolCallLog,
+)
+from framework.runtime.runtime_log_store import RuntimeLogStore
+
+logger = logging.getLogger(__name__)
+
+
+class RuntimeLogger:
+    """Captures runtime data during graph execution.
+
+    Thread-safe: uses a lock around file appends for parallel node safety.
+    """
+
+    def __init__(self, store: RuntimeLogStore, agent_id: str = "") -> None:
+        self._store = store
+        self._agent_id = agent_id
+        self._run_id = ""
+        self._goal_id = ""
+        self._started_at = ""
+        self._logged_node_ids: set[str] = set()
+        self._lock = threading.Lock()
+
+    def start_run(self, goal_id: str = "", session_id: str = "") -> str:
+        """Start a new run. Called by GraphExecutor at graph start. Returns run_id.
+
+        Args:
+            goal_id: Goal ID for this run
+            session_id: Optional session ID. If provided, uses it as run_id (for unified sessions).
+                       Otherwise generates a new run_id in old format.
+
+        Returns:
+            The run_id (same as session_id if provided)
+        """
+        if session_id:
+            self._run_id = session_id
+        else:
+            ts = datetime.now(UTC).strftime("%Y%m%dT%H%M%S")
+            short_uuid = uuid.uuid4().hex[:8]
+            self._run_id = f"{ts}_{short_uuid}"
+
+        self._goal_id = goal_id
+        self._started_at = datetime.now(UTC).isoformat()
+        self._logged_node_ids = set()
+        self._store.ensure_run_dir(self._run_id)
+        return self._run_id
+
+    def log_step(
+        self,
+        node_id: str,
+        node_type: str,
+        step_index: int,
+        llm_text: str = "",
+        tool_calls: list[dict[str, Any]] | None = None,
+        input_tokens: int = 0,
+        output_tokens: int = 0,
+        latency_ms: int = 0,
+        verdict: str = "",
+        verdict_feedback: str = "",
+        error: str = "",
+        stacktrace: str = "",
+        is_partial: bool = False,
+    ) -> None:
+        """Record data for one step within a node.
+
+        Called by any node during execution. Synchronous, appends to JSONL file.
+
+        Args:
+            error: Error message if step failed
+            stacktrace: Full stack trace if exception occurred
+            is_partial: True if step didn't complete normally (e.g., LLM call crashed)
+        """
+        if tool_calls is None:
+            tool_calls = []
+
+        call_logs = []
+        for tc in tool_calls:
+            call_logs.append(
+                ToolCallLog(
+                    tool_use_id=tc.get("tool_use_id", ""),
+                    tool_name=tc.get("tool_name", ""),
+                    tool_input=tc.get("tool_input", {}),
+                    result=tc.get("content", ""),
+                    is_error=tc.get("is_error", False),
+                )
+            )
+
+        # OTel / trace context: from observability ContextVar (empty if not set)
+        ctx = get_trace_context()
+        trace_id = ctx.get("trace_id", "")
+        execution_id = ctx.get("execution_id", "")
+        span_id = uuid.uuid4().hex[:16]  # OTel 16-hex span_id per step
+
+        step_log = NodeStepLog(
+            node_id=node_id,
+            node_type=node_type,
+            step_index=step_index,
+            llm_text=llm_text,
+            tool_calls=call_logs,
+            input_tokens=input_tokens,
+            output_tokens=output_tokens,
+            latency_ms=latency_ms,
+            verdict=verdict,
+            verdict_feedback=verdict_feedback,
+            error=error,
+            stacktrace=stacktrace,
+            is_partial=is_partial,
+            trace_id=trace_id,
+            span_id=span_id,
+            execution_id=execution_id,
+        )
+
+        with self._lock:
+            self._store.append_step(self._run_id, step_log)
+
+    def log_node_complete(
+        self,
+        node_id: str,
+        node_name: str,
+        node_type: str,
+        success: bool,
+        error: str | None = None,
+        stacktrace: str = "",
+        total_steps: int = 0,
+        tokens_used: int = 0,
+        input_tokens: int = 0,
+        output_tokens: int = 0,
+        latency_ms: int = 0,
+        attempt: int = 1,
+        # EventLoopNode-specific kwargs:
+        exit_status: str = "",
+        accept_count: int = 0,
+        retry_count: int = 0,
+        escalate_count: int = 0,
+        continue_count: int = 0,
+    ) -> None:
+        """Record completion of a node.
+
+        Called after each node completes. EventLoopNode calls this with
+        verdict counts and exit_status. Other nodes: executor calls this
+        from NodeResult data.
+        """
+        needs_attention = not success
+        attention_reasons: list[str] = []
+        if not success and error:
+            attention_reasons.append(f"Node {node_id} failed: {error}")
+
+        # Enhanced attention flags
+        if retry_count > 3:
+            needs_attention = True
+            attention_reasons.append(f"Excessive retries: {retry_count}")
+
+        if escalate_count > 2:
+            needs_attention = True
+            attention_reasons.append(f"Excessive escalations: {escalate_count}")
+
+        if latency_ms > 60000:  # > 1 minute
+            needs_attention = True
+            attention_reasons.append(f"High latency: {latency_ms}ms")
+
+        if tokens_used > 100000:  # High token usage
+            needs_attention = True
+            attention_reasons.append(f"High token usage: {tokens_used}")
+
+        if total_steps > 20:  # Many iterations
+            needs_attention = True
+            attention_reasons.append(f"Many iterations: {total_steps}")
+
+        # OTel / trace context for L2 correlation
+        ctx = get_trace_context()
+        trace_id = ctx.get("trace_id", "")
+        span_id = uuid.uuid4().hex[:16]  # Optional node-level span
+
+        detail = NodeDetail(
+            node_id=node_id,
+            node_name=node_name,
+            node_type=node_type,
+            success=success,
+            error=error,
+            stacktrace=stacktrace,
+            total_steps=total_steps,
+            tokens_used=tokens_used,
+            input_tokens=input_tokens,
+            output_tokens=output_tokens,
+            latency_ms=latency_ms,
+            attempt=attempt,
+            exit_status=exit_status,
+            accept_count=accept_count,
+            retry_count=retry_count,
+            escalate_count=escalate_count,
+            continue_count=continue_count,
+            needs_attention=needs_attention,
+            attention_reasons=attention_reasons,
+            trace_id=trace_id,
+            span_id=span_id,
+        )
+
+        with self._lock:
+            self._store.append_node_detail(self._run_id, detail)
+            self._logged_node_ids.add(node_id)
+
+    def ensure_node_logged(
+        self,
+        node_id: str,
+        node_name: str,
+        node_type: str,
+        success: bool,
+        error: str | None = None,
+        stacktrace: str = "",
+        tokens_used: int = 0,
+        latency_ms: int = 0,
+    ) -> None:
+        """Fallback: ensure a node has an L2 entry.
+
+        Called by executor after each node returns. If node_id already
+        appears in _logged_node_ids (because the node called log_node_complete
+        itself), this is a no-op. Otherwise appends a basic NodeDetail.
+        """
+        with self._lock:
+            if node_id in self._logged_node_ids:
+                return  # Already logged by the node itself
+
+        # Not yet logged — create a basic entry
+        self.log_node_complete(
+            node_id=node_id,
+            node_name=node_name,
+            node_type=node_type,
+            success=success,
+            error=error,
+            stacktrace=stacktrace,
+            tokens_used=tokens_used,
+            latency_ms=latency_ms,
+        )
+
+    async def end_run(
+        self,
+        status: str,
+        duration_ms: int,
+        node_path: list[str] | None = None,
+        execution_quality: str = "",
+    ) -> None:
+        """Read L2 from disk, aggregate into L1, write summary.json.
+
+        Called by GraphExecutor when graph finishes. Async, writes 1 file.
+        Catches all exceptions internally -- logging failure must not
+        propagate to the caller.
+        """
+        try:
+            # Read L2 back from disk to aggregate into L1
+            node_details = self._store.read_node_details_sync(self._run_id)
+
+            total_input = sum(nd.input_tokens for nd in node_details)
+            total_output = sum(nd.output_tokens for nd in node_details)
+
+            needs_attention = any(nd.needs_attention for nd in node_details)
+            attention_reasons: list[str] = []
+            for nd in node_details:
+                attention_reasons.extend(nd.attention_reasons)
+
+            # OTel / trace context for L1 correlation
+            ctx = get_trace_context()
+            trace_id = ctx.get("trace_id", "")
+            execution_id = ctx.get("execution_id", "")
+
+            summary = RunSummaryLog(
+                run_id=self._run_id,
+                agent_id=self._agent_id,
+                goal_id=self._goal_id,
+                status=status,
+                total_nodes_executed=len(node_details),
+                node_path=node_path or [],
+                total_input_tokens=total_input,
+                total_output_tokens=total_output,
+                needs_attention=needs_attention,
+                attention_reasons=attention_reasons,
+                started_at=self._started_at,
+                duration_ms=duration_ms,
+                execution_quality=execution_quality,
+                trace_id=trace_id,
+                execution_id=execution_id,
+            )
+
+            await self._store.save_summary(self._run_id, summary)
+            logger.info(
+                "Runtime logs saved: run_id=%s status=%s nodes=%d",
+                self._run_id,
+                status,
+                len(node_details),
+            )
+        except Exception:
+            logger.exception(
+                "Failed to save runtime logs for run_id=%s (non-fatal)",
+                self._run_id,
+            )