Initial commit: Multi-service AI agent system

- Frontend: Vite + React + TypeScript chat interface
- Backend: FastAPI gateway with LangGraph routing
- Knowledge Service: ChromaDB RAG with Gitea scraper
- LangGraph Service: Multi-agent orchestration
- Airflow: Scheduled Gitea ingestion DAG
- Documentation: Complete plan and implementation guides

Architecture:
- Modular Docker Compose per service
- External ai-mesh network for communication
- Fast rebuilds with /app/packages pattern
- Intelligent agent routing (no hardcoded keywords)

Services:
- Frontend (5173): React chat UI
- Chat Gateway (8000): FastAPI entry point
- LangGraph (8090): Agent orchestration
- Knowledge (8080): ChromaDB RAG
- Airflow (8081): Scheduled ingestion
- PostgreSQL (5432): Chat history

Excludes: node_modules, .venv, chroma_db, logs, .env files
Includes: All source code, configs, docs, docker files

langgraph_service/main.py

@@ -0,0 +1,80 @@
+from fastapi import FastAPI
+from fastapi.middleware.cors import CORSMiddleware
+from pydantic import BaseModel
+from supervisor_agent import process_query
+import logging
+import sys
+
+logging.basicConfig(level=logging.INFO, stream=sys.stdout)
+logger = logging.getLogger(__name__)
+
+app = FastAPI(title="LangGraph Supervisor Service")
+
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+class QueryRequest(BaseModel):
+    query: str
+
+class QueryResponse(BaseModel):
+    response: str
+    agent_used: str
+    context: dict
+
+@app.get("/health")
+async def health():
+    return {"status": "healthy", "service": "langgraph-supervisor"}
+
+@app.post("/query", response_model=QueryResponse)
+async def query_supervisor(request: QueryRequest):
+    """Main entry point for agent orchestration."""
+    logger.info(f"Received query: {request.query}")
+    
+    try:
+        result = await process_query(request.query)
+        
+        return QueryResponse(
+            response=result["response"],
+            agent_used=result["context"].get("source", "unknown"),
+            context=result["context"]
+        )
+    except Exception as e:
+        logger.error(f"Error processing query: {e}")
+        return QueryResponse(
+            response="Error processing your request",
+            agent_used="error",
+            context={"error": str(e)}
+        )
+
+@app.get("/agents")
+async def list_agents():
+    """List available specialist agents."""
+    return {
+        "agents": [
+            {
+                "name": "librarian",
+                "description": "Queries the knowledge base for semantic information",
+                "triggers": ["repo", "code", "git", "hobby", "about", "skill"]
+            },
+            {
+                "name": "opencode",
+                "description": "Handles coding tasks and file modifications",
+                "triggers": ["write", "edit", "create", "fix", "implement"]
+            },
+            {
+                "name": "brain",
+                "description": "General LLM for reasoning and generation",
+                "triggers": ["default", "general questions"]
+            }
+        ]
+    }
+
+if __name__ == "__main__":
+    import uvicorn
+    uvicorn.run(app, host="0.0.0.0", port=8090)
+