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langgraph-agent-system

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A production-ready, scalable multi-agent system built with LangGraph Platform, featuring specialized agents, multi-provider LLM support, and complete LangGraph Studio compatibility.

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# 🚀 LangGraph Agent System

A production-ready, scalable multi-agent system built with LangGraph Platform, featuring specialized agents, multi-provider LLM support, and complete LangGraph Studio compatibility.

## ✨ Key Features

### 🎯 Multi-Agent Architecture
- **Orchestrator Agent**: Intelligent routing to specialized sub-agents
- **Multipurpose Bot**: Math calculations, chitchat, and headphones expertise
- **Hungry Services**: Food search, recipes, and restaurant recommendations
- **Embedding Service**: Vector storage and knowledge management

### 🔧 Multi-Provider LLM Support
- OpenAI (GPT-4.1, GPT-5)
- Anthropic (Claude 4)
- Google (Gemini)
- Ollama (Local models)
- Groq, Cohere, Mistral

### 🏗️ Production Features
- LangGraph Studio compatibility
- Persistent checkpointing (Memory, SQLite, PostgreSQL)
- Vector storage (Chroma, Pinecone, Weaviate)
- Streaming support
- Human-in-the-loop capabilities
- Time-travel debugging
- Docker deployment ready

## 🚀 Quick Start

### Prerequisites
- Python 3.11+
- LangSmith API key (free at [smith.langchain.com](https://smith.langchain.com))
- At least one LLM provider API key

### 1. Clone & Setup

```bash
# Clone the repository
git clone https://github.com/shamspias/langgraph-agent-system.git
cd langgraph-agent-system

# Create virtual environment
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

# Install dependencies
pip install -e .
```

### 2. Configure Environment

```bash
# Copy example environment
cp .env.example .env

# Edit .env with your API keys
nano .env  # or use your preferred editor
```

Required settings:
```env
LANGSMITH_API_KEY=your-langsmith-key-here
PRIMARY_PROVIDER=openai  # or anthropic, ollama, etc.
OPENAI_API_KEY=your-openai-key-here  # or other provider key
```

### 3. Run with LangGraph Studio

```bash
# Run with LangGraph Studio (recommended)
langgraph dev --tunnel

# This will output:
# Ready!
# - API: http://localhost:2024
# - Docs: http://localhost:2024/docs
# - LangGraph Studio Web UI: https://smith.langchain.com/studio/?baseUrl=...
```

Click the Studio URL to open the visual interface!

## 🎮 Usage Modes

### 1. LangGraph Studio (Visual Interface)
```bash
langgraph dev --tunnel
```
Then open the provided Studio URL in your browser.

### 2. CLI Mode (Interactive Terminal)
```bash
python -m src.main cli
```

Example session:
```
[orchestrator]> What's 234 * 567?
[Multipurpose Response]
Let me calculate that for you:
Expression: 234*567
Result: **132678**

[orchestrator]> /switch hungry
✓ Switched to hungry agent

[hungry]> Find me Italian recipes
[Hungry Response]
Here are some delicious Italian recipes...
```

### 3. API Server Mode
```bash
python -m src.main api
# Or with uvicorn
uvicorn src.api.server:create_app --reload --host 0.0.0.0 --port 8000
```

API endpoints:
- `POST /chat` - Chat with agents
- `POST /embed` - Embed content
- `POST /search` - Search vector store
- `GET /health` - Health check
- `GET /agents` - List available agents

### 4. Docker Deployment
```bash
# Build and run with Docker Compose
docker-compose up -d

# Run specific services
docker-compose up -d langgraph-app postgres redis

# With Ollama for local models
docker-compose --profile ollama up -d
```

## 🏗️ Architecture

```
┌─────────────────────┐
│   LangGraph Studio  │
│    (Visual UI)      │
└──────────┬──────────┘
           │
┌──────────▼──────────┐
│   Orchestrator      │
│   (Main Router)     │
└──────────┬──────────┘
           │
    ┌──────┴──────┬──────────┬────────────┐
    │             │          │            │
┌───▼──┐    ┌────▼───┐  ┌───▼───┐  ┌─────▼────┐
│Multi │    │ Hungry │  │Embed  │  │ General  │
│purpose│   │Services│  │Service│  │Assistant │
└───┬──┘    └────────┘  └───────┘  └──────────┘
    │
┌───┴───┬─────────┬──────────┐
│ Math  │Chitchat │Headphones│
│Agent  │ Agent   │  Agent   │
└───────┴─────────┴──────────┘
```

## 🔧 Configuration

### LLM Providers

Configure your preferred provider in `.env`:

#### OpenAI
```env
PRIMARY_PROVIDER=openai
OPENAI_API_KEY=sk-...
DEFAULT_MODEL=gpt-4o-mini
```

#### Anthropic
```env
PRIMARY_PROVIDER=anthropic
ANTHROPIC_API_KEY=sk-ant-...
DEFAULT_MODEL=claude-3-5-sonnet-latest
```

#### Ollama (Local)
```env
PRIMARY_PROVIDER=ollama
OLLAMA_BASE_URL=http://localhost:11434
OLLAMA_MODEL=llama3.2
```

### Memory & Storage

#### PostgreSQL (Production)
```env
MEMORY_TYPE=postgres
POSTGRES_HOST=localhost
POSTGRES_PORT=5432
POSTGRES_DB=langgraph_memory
POSTGRES_USER=postgres
POSTGRES_PASSWORD=secure-password
```

#### SQLite (Development)
```env
MEMORY_TYPE=sqlite
MEMORY_DB_PATH=./data/memory.db
```

#### Vector Storage
```env
VECTOR_STORE_TYPE=chroma
VECTOR_DB_PATH=./data/vector_store
CHUNK_SIZE=500
CHUNK_OVERLAP=50
```

## 📚 API Examples

### Python Client
```python
import httpx

async with httpx.AsyncClient() as client:
    # Chat with orchestrator
    response = await client.post(
        "http://localhost:8000/chat",
        json={
            "message": "What's the best Italian restaurant nearby?",
            "agent": "orchestrator"
        }
    )
    print(response.json())
    
    # Embed content
    response = await client.post(
        "http://localhost:8000/embed",
        json={
            "content": "The Sony WH-1000XM5 are premium headphones...",
            "collection_name": "headphones_knowledge"
        }
    )
```

### JavaScript/TypeScript
```typescript
// Chat with agent
const response = await fetch('http://localhost:8000/chat', {
    method: 'POST',
    headers: { 'Content-Type': 'application/json' },
    body: JSON.stringify({
        message: 'Calculate 15% tip on $85',
        agent: 'multipurpose'
    })
});

const data = await response.json();
console.log(data.response);
```

### cURL
```bash
# Chat request
curl -X POST http://localhost:8000/chat \
  -H "Content-Type: application/json" \
  -d '{"message": "Find me a pasta recipe", "agent": "hungry"}'

# Embed content
curl -X POST http://localhost:8000/embed \
  -H "Content-Type: application/json" \
  -d '{"content": "https://example.com/article", "collection_name": "articles"}'
```

## 🧪 Testing

```bash
# Test all graphs
python -m src.main test

# Run unit tests
pytest tests/

# Test with coverage
pytest --cov=src tests/
```

## 🔍 Debugging with LangGraph Studio

1. **Visual Graph Inspection**: See your agent's decision flow in real-time
2. **Time Travel**: Step through past executions and modify state
3. **State Inspection**: View complete state at any point
4. **Tool Visualization**: See tool calls and results
5. **Human-in-the-Loop**: Pause and modify execution

## 📊 Monitoring

### LangSmith Integration
```env
LANGSMITH_TRACING=true
LANGSMITH_PROJECT=my-agent-project
```

View traces at [smith.langchain.com](https://smith.langchain.com)

### Structured Logging
```python
from src.utils.logging import get_logger

logger = get_logger("my_module", user_id="123")
logger.info("Processing request", agent="multipurpose", action="calculate")
```

## 🚢 Production Deployment

### 1. LangGraph Platform (Recommended)
```bash
# Deploy to LangGraph Platform
# 1. Push to GitHub
# 2. Connect repo in LangSmith Deployments
# 3. Click Deploy
```

### 2. Docker Deployment
```bash
# Production build
docker build -t langgraph-agent-system:prod .

# Run with environment
docker run -d \
  --name langgraph-prod \
  -p 8000:8000 \
  -p 2024:2024 \
  --env-file .env.prod \
  langgraph-agent-system:prod
```

### 3. Kubernetes
```yaml
apiVersion: apps/v1
kind: Deployment
metadata:
  name: langgraph-agents
spec:
  replicas: 3
  selector:
    matchLabels:
      app: langgraph
  template:
    metadata:
      labels:
        app: langgraph
    spec:
      containers:
      - name: agent-system
        image: langgraph-agent-system:prod
        ports:
        - containerPort: 8000
        - containerPort: 2024
        envFrom:
        - configMapRef:
            name: langgraph-config
```

## 📝 Adding Custom Agents

1. Create agent graph in `src/graphs/`:
```python
# src/graphs/custom_agent.py
from langgraph.graph import StateGraph, START, END
from src.core.providers import get_model, get_checkpointer

def build_custom_graph() -> StateGraph:
    workflow = StateGraph(YourState)
    # Add nodes and edges
    return workflow

# Required for langgraph.json
graph = build_custom_graph().compile(checkpointer=get_checkpointer())
```

2. Register in `langgraph.json`:
```json
{
  "graphs": {
    "custom": "./src/graphs/custom_agent:graph"
  }
}
```

## 🤝 Contributing

1. Fork the repository
2. Create a feature branch (`git checkout -b feature/amazing-feature`)
3. Commit changes (`git commit -m 'Add amazing feature'`)
4. Push to branch (`git push origin feature/amazing-feature`)
5. Open a Pull Request

## 📄 License

MIT License - see [LICENSE](LICENSE) file for details.

## 🙏 Acknowledgments

- Built with [LangGraph](https://github.com/langchain-ai/langgraph)
- Powered by [LangChain](https://github.com/langchain-ai/langchain)
- UI by [LangGraph Studio](https://smith.langchain.com/studio)

## 📧 Support

- Issues: [GitHub Issues](https://github.com/shamspias/langgraph-agent-system/issues)

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