Deploy LangGraph Server with Authentication

This repository contains the code for a tutorial on how to deploy LangGraph agents in a production-ready environment with authentication. The tutorial demonstrates how to create a secure proxy server that adds authentication to your LangGraph API endpoints.

🎯 What You'll Learn

• Setting up a production-ready LangGraph server
• Implementing API key authentication
• Using Docker for containerization
• Creating a proxy server for additional security
• Handling environment variables and configuration
• Making your LangGraph agents production-ready

🏗️ Project Structure

├── docker-compose.yml        # Docker compose configuration
├── Dockerfile               # Docker build instructions
├── server/                  # Auth proxy server implementation
│   ├── middleware/         # Authentication and CORS middleware
│   ├── app.py             # Server application factory
│   └── config.py          # Server configuration
├── frontend/               # Client examples
│   ├── chat_local.py      # Local testing client
│   └── chat_remote_auth.py # Remote client with auth
└── src/                    # LangGraph agent implementation
    └── rocket/            # Example agent code

🚀 Getting Started

Prerequisites

• Python 3.11 or higher
• Docker and Docker Compose
• An OpenAI API key
• A YouTube Data API key

YouTube API Setup

1. Go to Google Cloud Console
2. Create a new project or select an existing one
3. Enable the YouTube Data API v3:
   • Navigate to "APIs & Services" > "Library"
   • Search for "YouTube Data API v3"
   • Click "Enable"
4. Create API credentials:
   • Go to "APIs & Services" > "Credentials"
   • Click "Create Credentials" > "API key"
   • Your new API key will be displayed
5. (Optional but recommended) Restrict the API key:
   • In the credentials page, click on the newly created API key
   • Under "API restrictions," choose "Restrict key"
   • Select "YouTube Data API v3" from the dropdown
   • Click "Save"

Setup

1. Clone this repository:

   git clone https://github.com/yourusername/deploy-langgraph-server-auth.git
   cd deploy-langgraph-server-auth

2. Create a .env file with your configuration:

   OPENAI_API_KEY=your_openai_api_key
   API_KEY=your_custom_api_key  # For authentication
   YOUTUBE_API_KEY=your_youtube_api_key  # From Google Cloud Console

3. Build and start the server:

   docker-compose up --build

The server will be available at http://localhost:8000.

🔒 Authentication

The project implements API key authentication using a custom middleware. Clients need to include the API key in their requests using either:

• Header: x-api-key: your_api_key
• Query parameter: ?api_key=your_api_key

📝 API Usage

Example of calling the API from Python:

async with httpx.AsyncClient() as client:
   response = await client.post(
         url=f"{LANGGRAPH_SERVER_URL}/threads/search",
         headers={
            "x-api-key": "your_api_key"
         },
         json={
            "metadata": {
               "user_id": str(user_id)
            },
         },
   )

🛠️ Development

Local Development

1. Install UV (if not already installed):

   curl -LsSf https://astral.sh/uv/install.sh | sh

2. Install dependencies:

   uv sync
   uv pip install -e .

3. Run the local development server:

   docker compose up --build

Running Tests

pytest

📚 Additional Resources

• Server Architecture Documentation
• LangGraph Documentation
• Docker Documentation

🤝 Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

🎥 Tutorial Video

Watch the full tutorial on YouTube: [Link to your YouTube video]

📧 Contact

For questions or feedback, please open an issue in this repository or reach out through YouTube comments.