Implement Phase 1: LangGraph backend MVP

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

backend/api/routes.py

@@ -0,0 +1,135 @@
+"""
+REST API routes for CouncilOS.
+
+Endpoints:
+    POST /api/councils/run    — Start a new council run (async, returns run_id)
+    GET  /api/councils/run/{run_id}  — Poll the status/result of a run
+    GET  /api/health          — Health check
+"""
+
+import uuid
+from typing import Optional
+from fastapi import APIRouter, HTTPException, BackgroundTasks
+from pydantic import BaseModel, Field
+
+from services.graph_builder import run_council_async
+from api.run_store import run_store
+
+
+router = APIRouter()
+
+
+# ---------------------------------------------------------------------------
+# Request / Response Models
+# ---------------------------------------------------------------------------
+
+class CouncilRunRequest(BaseModel):
+    input_topic: str = Field(
+        ...,
+        min_length=1,
+        max_length=10_000,
+        description="The user's prompt or document content for the council to work on.",
+        examples=["Erkläre die wichtigsten Konzepte des maschinellen Lernens für Einsteiger."],
+    )
+
+
+class CouncilRunResponse(BaseModel):
+    run_id: str
+    status: str  # "pending" | "running" | "completed" | "failed"
+    message: str
+
+
+class CouncilResultResponse(BaseModel):
+    run_id: str
+    status: str
+    final_draft: Optional[str] = None
+    critic_score: Optional[float] = None
+    iteration_count: Optional[int] = None
+    error: Optional[str] = None
+
+
+# ---------------------------------------------------------------------------
+# Endpoints
+# ---------------------------------------------------------------------------
+
+@router.get("/health")
+async def health_check():
+    """Health check endpoint."""
+    return {"status": "ok", "service": "CouncilOS API"}
+
+
+@router.post("/councils/run", response_model=CouncilRunResponse, status_code=202)
+async def start_council_run(
+    request: CouncilRunRequest,
+    background_tasks: BackgroundTasks,
+):
+    """
+    Start a new council run.
+
+    The run executes asynchronously in the background. Poll
+    GET /api/councils/run/{run_id} for the result, or connect to the
+    WebSocket at /ws/council/{run_id} for real-time updates.
+    """
+    run_id = str(uuid.uuid4())
+
+    # Register the run as pending in the in-memory store
+    run_store.create(run_id, request.input_topic)
+
+    # Schedule the graph execution as a background task
+    background_tasks.add_task(_execute_run, run_id, request.input_topic)
+
+    return CouncilRunResponse(
+        run_id=run_id,
+        status="pending",
+        message=f"Council run started. Connect to /ws/council/{run_id} for live updates.",
+    )
+
+
+@router.get("/councils/run/{run_id}", response_model=CouncilResultResponse)
+async def get_council_result(run_id: str):
+    """
+    Retrieve the current status or final result of a council run.
+    """
+    run = run_store.get(run_id)
+    if run is None:
+        raise HTTPException(status_code=404, detail=f"Run '{run_id}' not found.")
+
+    return CouncilResultResponse(
+        run_id=run_id,
+        status=run["status"],
+        final_draft=run.get("final_draft"),
+        critic_score=run.get("critic_score"),
+        iteration_count=run.get("iteration_count"),
+        error=run.get("error"),
+    )
+
+
+# ---------------------------------------------------------------------------
+# Internal helpers
+# ---------------------------------------------------------------------------
+
+async def _execute_run(run_id: str, input_topic: str) -> None:
+    """
+    Background task that runs the LangGraph council and updates the run store.
+    """
+    run_store.update(run_id, {"status": "running"})
+    try:
+        final_state = await run_council_async(
+            input_topic=input_topic,
+            run_id=run_id,
+            on_node_event=lambda nid, node: run_store.update(
+                nid, {"active_node": node}
+            ),
+        )
+        run_store.update(
+            run_id,
+            {
+                "status": "completed",
+                "final_draft": final_state.get("current_draft"),
+                "critic_score": final_state.get("critic_score"),
+                "iteration_count": final_state.get("iteration_count"),
+                "active_node": "done",
+            },
+        )
+    except Exception as exc:  # noqa: BLE001
+        run_store.update(run_id, {"status": "failed", "error": str(exc)})