https://github.com/kryvokhyzha/langgraph-agent-toolkit

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, memory and prompt management.
https://github.com/kryvokhyzha/langgraph-agent-toolkit

fastapi langchain langgraph streamlit

Last synced: 2 months ago
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Host: GitHub
URL: https://github.com/kryvokhyzha/langgraph-agent-toolkit
Owner: kryvokhyzha
License: mit
Created: 2025-04-07T15:18:14.000Z (about 1 year ago)
Default Branch: main
Last Pushed: 2026-01-30T19:59:45.000Z (5 months ago)
Last Synced: 2026-01-31T12:06:09.648Z (5 months ago)
Topics: fastapi, langchain, langgraph, streamlit
Language: Python
Homepage: https://kryvokhyzha.github.io/langgraph-agent-toolkit
Size: 11.9 MB
Stars: 9
Watchers: 1
Forks: 3
Open Issues: 2
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- Contributing: CONTRIBUTING.md
- License: LICENSE

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---

# 🧰 LangGraph Agent Toolkit

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## 📋 Introduction

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, memory and prompt 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](https://langchain-ai.github.io/langgraph/)** for agent creation

  with advanced flows and human-in-the-loop capabilities

- **[FastAPI](https://fastapi.tiangolo.com/)** for robust, high-performance API

  services with streaming support

- **[Streamlit](https://streamlit.io/)** for intuitive user interfaces

Key components include:

- Data structures and settings built with

  **[Pydantic](https://github.com/pydantic/pydantic)**

- **[LiteLLM](https://github.com/BerriAI/litellm)** proxy for universal

  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.

You can use [DeepWiki](https://deepwiki.com/kryvokhyzha/langgraph-agent-toolkit)

to learn more about this repository.

## 📑 Contents

- [Introduction](#-introduction)

- [Quickstart](#-quickstart)

- [Installation Options](#-installation-options)

- [Architecture](#architecture)

- [Key Features](#-key-features)

- [Environment Setup](#environment-setup)

- [Project Structure](#-project-structure)

- [Setup and Usage](#setup-and-usage)

- [Documentation](#-documentation)

- [Useful Resources](#-useful-resources)

- [Development and Contributing](#-development-and-contributing)

- [License](#-license)

## 🚀 Quickstart

1. Create a `.env` file based on [`.env.example`](./.env.example)

2. **Option 1: Run with Python from source**

   ```sh

   # Install dependencies

   pip install uv

   uv sync --frozen

   source .venv/bin/activate

   # Start the service

   python langgraph_agent_toolkit/run_api.py

   # In another terminal

   source .venv/bin/activate

   streamlit run langgraph_agent_toolkit/run_app.py

   ```

3. **Option 2: Run with Python from PyPi repository**

   ```sh

   pip install langgraph-agent-toolkit

   ```

   ℹ️ For more details on installation options, see the

   [Installation Documentation](docs/installation.rst).

4. **Option 3: Run with Docker**

   ```sh

   docker compose watch

   ```



## 📦 Installation Options

The toolkit supports multiple installation options using "extras" to include

just the dependencies you need.

For detailed installation instructions and available extras, see the

[Installation Documentation](docs/installation.rst).



## 🏗️ Architecture





## ✨ Key Features

1. **LangGraph Integration**

   - Latest LangGraph v0.3 features

   - Human-in-the-loop with `interrupt()`

   - Flow control with `Command` and `langgraph-supervisor`

2. **API Service**

   - FastAPI with streaming and non-streaming endpoints

   - Support for both token-based and message-based streaming

   - Multiple agent support with URL path routing

   - Available agents and models listed at `/info` endpoint

   - Supports different runners (unicorn, gunicorn, mangum, azure functions)

3. **Developer Experience**

   - Asynchronous design with async/await

   - Docker configuration with live reloading

   - Comprehensive testing suite

4. **Enterprise Components**

   - Configurable PostgreSQL/SQLite connection pools

   - Observability via Langfuse and Langsmith

   - User feedback system

   - Prompt management system

   - LiteLLM proxy integration

For more details on features, see the [Usage Documentation](docs/usage.rst).



## ⚙️ Environment Setup

For detailed environment setup instructions, including creating your `.env` file

and configuring LiteLLM, see the

[Environment Setup Documentation](docs/environment_setup.rst).



## 📂 Project Structure

The repository contains:

- `langgraph_agent_toolkit/agents/blueprints/`: Agent definitions

- `langgraph_agent_toolkit/agents/agent_executor.py`: Agent execution control

- `langgraph_agent_toolkit/schema/`: Protocol schema definitions

- `langgraph_agent_toolkit/core/`: Core modules (LLM, memory, settings)

- `langgraph_agent_toolkit/service/service.py`: FastAPI service

- `langgraph_agent_toolkit/client/client.py`: Service client

- `langgraph_agent_toolkit/run_app.py`: Chat interface

- `docker/`: Docker configurations

- `tests/`: Test suite



## 🛠️ Setup and Usage

For detailed setup and usage instructions, including building your own agent,

Docker setup, using the AgentClient, and local development, see the

[Usage Documentation](docs/usage.rst).



## 📚 Documentation

Full documentation is available at

[GitHub repository](https://github.com/kryvokhyzha/langgraph-agent-toolkit/tree/main/docs/)

and includes:

- [Installation Guide](https://github.com/kryvokhyzha/langgraph-agent-toolkit/blob/main/docs/installation.rst)

- [Environment Setup](https://github.com/kryvokhyzha/langgraph-agent-toolkit/blob/main/docs/environment_setup.rst)

- [Usage Guide](https://github.com/kryvokhyzha/langgraph-agent-toolkit/blob/main/docs/usage.rst)



## 📚 Useful Resources

- [LangGraph documentation](https://langchain-ai.github.io/langgraph/concepts/low_level/#multiple-schemas)

- [LangGraph Memory Concept](https://langchain-ai.github.io/langgraph/concepts/memory/)

- [LangGraph Memory Persistence](https://langchain-ai.github.io/langgraph/concepts/persistence/#memory)

- [LangGraph Memory Template](https://github.com/langchain-ai/memory-template)

- [LangGraph Human in the Loop](https://langchain-ai.github.io/langgraph/how-tos/human_in_the_loop/wait-user-input/)

- [LangGraph 101 - blueprints](https://github.com/langchain-ai/langgraph-101)

- [LangGraph - Examples](https://github.com/langchain-ai/langgraph/tree/main/examples)

- [Complex data extraction with function calling](https://langchain-ai.github.io/langgraph/tutorials/extraction/retries/)

- [How to edit graph state](https://langchain-ai.github.io/langgraph/how-tos/human_in_the_loop/edit-graph-state/)

- [Memory in the background](https://www.youtube.com/watch?v=R1jKQ1Jn5T4&ab_channel=LangChain)

- [Building an agent with LangGraph](https://www.kaggle.com/code/markishere/day-3-building-an-agent-with-langgraph/)

- [How to create tools in Langchain](https://python.langchain.com/docs/how_to/custom_tools/)

- [Simple Serverless FastAPI with AWS Lambda](https://www.deadbear.io/simple-serverless-fastapi-with-aws-lambda/)

- [LangGraph Middleware](https://docs.langchain.com/oss/python/langchain/middleware)



## 👥 Development and Contributing

Thank you for considering contributing to `Langgraph Agent Toolkit`! We

encourage the community to post Issues and Pull Requests.

Before you get started, please see our [Contribution Guide](CONTRIBUTING.md).



## 📄 License

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

details.

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