LangGraph Agent Toolkit Documentation

A comprehensive toolkit for building, deploying, and managing AI agents using LangGraph, FastAPI, and Streamlit. It provides a production-ready framework for creating conversational AI agents with features like multi-provider LLM support, streaming responses, observability, and memory management.

What is langgraph-agent-toolkit?

The langgraph-agent-toolkit is a full-featured framework for developing and deploying AI agent services. Built on the foundation of:

  • LangGraph for agent creation with advanced flows and human-in-the-loop capabilities

  • FastAPI for robust, high-performance API services with streaming support

  • Streamlit for intuitive user interfaces

Key components include:

  • Data structures and settings built with Pydantic

  • Multi-provider LLM support

  • Comprehensive memory management and persistence using PostgreSQL/SQLite

  • Advanced observability tooling via Langfuse and Langsmith

  • Modular architecture allowing customization while maintaining a consistent application structure

Whether you’re building a simple chatbot or complex multi-agent system, this toolkit provides the infrastructure to develop, test, and deploy your LangGraph-based agents with confidence.

Architecture

Architecture Diagram

Quickstart

  1. Create a .env file based on .env.example

  2. Option 1: Run with Python from source

    # Install dependencies
pip install uv
uv sync --frozen
source .venv/bin/activate

# Start the service
python langgraph_agent_toolkit/run_service.py

# In another terminal
source .venv/bin/activate
streamlit run langgraph_agent_toolkit/streamlit_app.py

  3. Option 2: Run with Python from PyPi repository

    pip install langgraph-agent-toolkit

  4. Option 3: Run with Docker

    docker compose watch

Content

User Guide:

Development:

Indices and tables