797f02c74d
Sets up the full backend foundation for CouncilOS:
- CouncilState TypedDict with all required fields and LangGraph reducers
- Three agent nodes: master_agent (drafts), critic_agent (scores + routes),
writer_agent (final polish)
- LangGraph graph with cyclic rework loop: Master → Critic → (score < 8:
back to Master | score ≥ 8: Writer → END)
- Safety valve: MAX_ITERATIONS=5 prevents infinite loops
- FastAPI app with REST endpoints (POST /api/councils/run, GET /api/councils/run/{id})
and WebSocket endpoint (/ws/council/{run_id}) for real-time agent status events
- In-memory RunStore for Phase 1 (PostgreSQL-backed in Phase 3)
- pytest test suite: state, routing logic, critic parser, agent nodes, API endpoints
- .env.example, .gitignore, docker-compose.yml, Dockerfile
https://claude.ai/code/session_01RfMpt3TbMjZEtK3CAyP5iQ
75 lines
2.5 KiB
Python
75 lines
2.5 KiB
Python
"""
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Master Agent Node — creates and refines drafts based on critic feedback.
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This agent is the primary content creator. On the first iteration it produces
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an initial draft. On subsequent iterations it incorporates all feedback from
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the feedback_history to improve the draft.
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"""
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import os
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from langchain_anthropic import ChatAnthropic
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from langchain_core.messages import HumanMessage, SystemMessage
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from state import CouncilState
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_SYSTEM_PROMPT = """You are the Master AI in a council of expert AIs.
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Your job is to write high-quality content on the given topic.
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When you receive critic feedback, carefully incorporate ALL feedback points
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and produce an improved draft. Be thorough and precise."""
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def _build_master_prompt(state: CouncilState) -> str:
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"""Build the user-facing prompt for the master agent based on current state."""
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if not state["feedback_history"]:
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return (
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f"Please write a comprehensive, well-structured document on the following topic:\n\n"
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f"{state['input_topic']}"
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)
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feedback_block = "\n\n---\n".join(
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f"Feedback round {i + 1}:\n{fb}"
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for i, fb in enumerate(state["feedback_history"])
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)
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return (
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f"Topic: {state['input_topic']}\n\n"
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f"Your current draft:\n{state['current_draft']}\n\n"
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f"The critic has provided the following feedback across {len(state['feedback_history'])} round(s):\n\n"
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f"{feedback_block}\n\n"
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f"Please produce an improved draft that fully addresses ALL feedback points above."
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)
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def master_agent_node(state: CouncilState) -> dict:
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"""
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LangGraph node function for the Master Agent.
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Reads input_topic and feedback_history from state, calls the LLM,
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and returns an updated current_draft.
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Args:
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state: The current CouncilState.
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Returns:
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A dict with updated state fields: current_draft, messages, active_node.
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"""
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llm = ChatAnthropic(
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model="claude-3-5-sonnet-20241022",
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api_key=os.environ.get("ANTHROPIC_API_KEY"),
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temperature=0.7,
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max_tokens=2048,
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)
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system_msg = SystemMessage(content=_SYSTEM_PROMPT)
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user_msg = HumanMessage(content=_build_master_prompt(state))
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response = llm.invoke([system_msg, user_msg])
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draft = response.content
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return {
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"current_draft": draft,
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"messages": [system_msg, user_msg, response],
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"active_node": "master_agent",
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"iteration_count": state.get("iteration_count", 0) + 1,
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}
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