# 📝 Project Summary: Multi-Agent AI Backend

## ✅ Status: Production Ready

### System Overview
Production-ready Python backend with 4 intelligent agents orchestrated by LangGraph:

1. **Weather Agent**: OpenWeatherMap API integration
2. **Document/Web Agent**: Docling + DuckDuckGo search, RAG with ChromaDB
3. **Meeting Agent**: Weather reasoning, scheduling, database operations
4. **NL-to-SQL Agent**: Natural language queries to SQLite

### Key Features
- Multi-provider LLM support (OpenAI, Google GenAI, Ollama)
- SQLite database (SQLModel ORM)
- DuckDuckGo search (no API key required)
- FastAPI REST endpoints
- LangGraph state management
- ChromaDB vector store for semantic search

### Testing Results
- Weather queries: ✅ Working
- Meeting scheduling: ✅ Functional
- SQL generation: ✅ SQLite-specific syntax
- Tool calling/routing: ✅ Successful

### Critical Fixes
1. LangChain compatibility: pinned to 0.3.x
2. DuckDB → SQLite: improved stability
3. Custom SQL prompt for correct date handling
4. Ollama integration: cost-free local LLM
5. LLM fallback logic: smart API key detection

### Main Files
- main.py: FastAPI application
- agents.py: LangGraph workflow (4 agents)
- tools.py: Weather, search, document tools
- models.py: SQLModel meeting schema
- database.py: SQLite connection
- seed_data.py: Sample data generator
- test_agents.py: Automated test suite
- OLLAMA_SETUP.md: Ollama configuration guide

### Production Readiness
- Clean, modular architecture
- Comprehensive error handling
- Deterministic tool orchestration
- One-command startup
- Full documentation and setup guides
- Environment-based configuration
- Extensible agent framework
- Local LLM support for cost savings

### Next Steps for User
1. Configure API keys in `.env`
2. Pull desired Ollama model: `ollama pull qwen3:0.6b`
3. Seed database: `uv run python seed_data.py`
4. Test: `uv run test_agents.py`
5. Deploy: `uv run python main.py`

**Status**: 🎉 Fully functional and verified!