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
127 lines
4.1 KiB
Python
127 lines
4.1 KiB
Python
"""
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Critic Agent Node — evaluates the current draft and decides whether to approve or rework.
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The critic scores the draft from 0–10 and returns structured feedback.
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If the score meets APPROVAL_THRESHOLD, route_decision is set to "approve".
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Otherwise it is set to "rework" and the feedback is appended to feedback_history.
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"""
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import os
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import re
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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, APPROVAL_THRESHOLD, MAX_ITERATIONS
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_SYSTEM_PROMPT = """You are the Critic AI in a council of expert AIs.
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Your job is to rigorously evaluate the quality of a draft document.
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You must respond in EXACTLY this format — no deviations:
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SCORE: <integer 0-10>
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VERDICT: <"approve" if score >= 8, otherwise "rework">
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FEEDBACK:
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<detailed, actionable feedback explaining what must be improved>
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Scoring criteria:
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- 0–3: Poor structure, major factual gaps, incoherent
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- 4–6: Adequate but needs significant improvement
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- 7: Good but has notable weaknesses
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- 8–9: High quality, minor improvements possible
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- 10: Exceptional, publication-ready
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Be strict. Only award 8+ if the document genuinely meets high quality standards."""
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def _parse_critic_response(content: str) -> tuple[float, str, str]:
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"""
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Parse the structured critic response.
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Returns:
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(score, verdict, feedback) tuple.
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Falls back to ("rework", full content) on parse failure.
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"""
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score_match = re.search(r"SCORE:\s*(\d+(?:\.\d+)?)", content)
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verdict_match = re.search(r"VERDICT:\s*(approve|rework)", content, re.IGNORECASE)
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feedback_match = re.search(r"FEEDBACK:\s*(.*)", content, re.DOTALL)
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score = float(score_match.group(1)) if score_match else 0.0
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verdict = verdict_match.group(1).lower() if verdict_match else "rework"
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feedback = feedback_match.group(1).strip() if feedback_match else content.strip()
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# Clamp score to 0–10
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score = max(0.0, min(10.0, score))
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return score, verdict, feedback
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def critic_agent_node(state: CouncilState) -> dict:
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"""
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LangGraph node function for the Critic Agent.
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Reads current_draft from state, evaluates it, and returns:
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- route_decision: "approve" or "rework"
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- critic_score: numeric score
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- feedback_history: appended with new feedback (if rework)
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- active_node: "critic_agent"
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Safety valve: if iteration_count >= MAX_ITERATIONS, force approval
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to prevent infinite loops.
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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.
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"""
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# Safety valve: prevent infinite loops
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if state.get("iteration_count", 0) >= MAX_ITERATIONS:
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return {
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"route_decision": "approve",
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"critic_score": APPROVAL_THRESHOLD,
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"feedback_history": [
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f"[Auto-approved after {MAX_ITERATIONS} iterations]"
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],
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"messages": [],
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"active_node": "critic_agent",
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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=0.2, # Low temperature for consistent evaluation
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max_tokens=1024,
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)
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system_msg = SystemMessage(content=_SYSTEM_PROMPT)
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user_msg = HumanMessage(
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content=(
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f"Please evaluate this draft on the topic '{state['input_topic']}':\n\n"
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f"{state['current_draft']}"
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)
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)
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response = llm.invoke([system_msg, user_msg])
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score, verdict, feedback = _parse_critic_response(response.content)
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# Override verdict based on threshold to ensure consistency
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if score >= APPROVAL_THRESHOLD:
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route_decision = "approve"
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else:
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route_decision = "rework"
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result: dict = {
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"critic_score": score,
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"route_decision": route_decision,
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"messages": [system_msg, user_msg, response],
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"active_node": "critic_agent",
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}
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# Only append feedback if we're sending back for rework
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if route_decision == "rework":
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result["feedback_history"] = [
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f"Score: {score}/10\n{feedback}"
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]
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return result
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