Langgraph Multiagent Boilerplate

LangGraph Multi-Agent Boilerplate

 

A robust boilerplate for building AI agent clusters efficiently using LangGraph with supervisor architecture, Model Context Protocol (MCP) integration, and comprehensive API.

🌟 Features

• Multi-Agent Architecture: Build AI agent clusters with supervisor coordination
• LangGraph Integration: Leverage LangGraph's powerful state management for agent workflows
• MCP Support: Integrate tools via Model Context Protocol servers
• Streaming API: Real-time streaming responses for interactive conversations
• Database Persistence: Store conversations, agent states, and activity logs in PostgreSQL
• Cloud Storage: File management with Cloudflare R2
• Comprehensive API: RESTful endpoints with FastAPI, including Swagger documentation
• Security: Authentication middleware, error handling, and security best practices

🚀 Getting Started

Prerequisites

• Python 3.10+
• PostgreSQL
• Cloudflare R2 account (optional, for cloud storage)
• OpenRouter AI API key (or other compatible AI provider)

Installation

1. Clone the repository

git clone https://github.com/yourusername/langgraph-multiagent-boilerplate.git
cd langgraph-multiagent-boilerplate

1. Set up a Python virtual environment

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

1. Install dependencies

pip install -r requirements.txt

1. Configure environment variables

cp .env.example .env
# Edit .env with your settings (database, API keys, etc.)

1. Set up the database

# Create a PostgreSQL database
# Then run migrations (once implemented)

1. Run the server

uvicorn app.main:app --reload

1. Access the API documentation

• Swagger UI: http://localhost:8000/api/docs
• ReDoc: