langgraph-agent-toolkit


Namelanggraph-agent-toolkit JSON
Version 0.7.7 PyPI version JSON
download
home_pageNone
SummaryFull toolkit for running an AI agent service built with LangGraph, FastAPI and Streamlit
upload_time2025-07-11 13:42:33
maintainerNone
docs_urlNone
authorNone
requires_python<3.14,>=3.11
licenseNone
keywords agents blueprint fastapi langgraph litellm streamlit
VCS
bugtrack_url
requirements No requirements were recorded.
Travis-CI No Travis.
coveralls test coverage 
            <div align="center">
   <img alt="LangGraph Agent Toolkit Logo" src="https://raw.githubusercontent.com/kryvokhyzha/langgraph-agent-toolkit/main/docs/media/logo.svg" width="300">
</div>

---

# 🧰 LangGraph Agent Toolkit

|            |                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            |
| ---------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| CI/Testing | [![build status](https://github.com/kryvokhyzha/langgraph-agent-toolkit/actions/workflows/test.yml/badge.svg)](https://github.com/kryvokhyzha/langgraph-agent-toolkit/actions/workflows/test.yml) [![docs status](https://github.com/kryvokhyzha/langgraph-agent-toolkit/actions/workflows/sphinx.yml/badge.svg)](https://github.com/kryvokhyzha/langgraph-agent-toolkit/actions/workflows/sphinx.yml) [![codecov](https://codecov.io/gh/kryvokhyzha/langgraph-agent-toolkit/graph/badge.svg?token=OHSACTNSWZ)](https://codecov.io/gh/kryvokhyzha/langgraph-agent-toolkit) |
| Package    | [![PyPI version](https://img.shields.io/pypi/v/langgraph-agent-toolkit.svg)](https://pypi.org/project/langgraph-agent-toolkit/) [![PyPI Downloads](https://img.shields.io/pypi/dm/langgraph-agent-toolkit.svg)](https://pypi.org/project/langgraph-agent-toolkit/) [![Python Version](https://img.shields.io/python/required-version-toml?tomlFilePath=https%3A%2F%2Fraw.githubusercontent.com%2Fkryvokhyzha%2Flanggraph-agent-toolkit%2Fmain%2Fpyproject.toml)](https://github.com/kryvokhyzha/langgraph-agent-toolkit/blob/main/pyproject.toml)                          |
| Meta       | [![Code style: ruff](https://img.shields.io/badge/code%20style-ruff-000000.svg)](https://github.com/astral-sh/ruff) [![GitHub License](https://img.shields.io/github/license/kryvokhyzha/langgraph-agent-toolkit)](https://github.com/kryvokhyzha/langgraph-agent-toolkit/blob/main/LICENSE)                                                                                                                                                                                                                                                                               |

<!-- [![Streamlit App](https://static.streamlit.io/badges/streamlit_badge_black_red.svg)](https://langgraph-agent-toolkit.streamlit.app/) -->

## 📋 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_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**

   ```sh
   pip install langgraph-agent-toolkit
   ```

   ℹī¸ For more details on installation options, see the
   [Installation Documentation](docs/installation.rst).

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

   ```sh
   docker compose watch
   ```

<a name="installation-options"></a>

## đŸ“Ļ 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).

<a name="architecture"></a>

## 🏗ī¸ Architecture

<img src="https://raw.githubusercontent.com/kryvokhyzha/langgraph-agent-toolkit/main/docs/media/agent_architecture.png" width="800">

<a name="key-features"></a>

## ✨ 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).

<a name="environment-setup"></a>

## ⚙ī¸ Environment Setup

For detailed environment setup instructions, including creating your `.env` file
and configuring LiteLLM, see the
[Environment Setup Documentation](docs/environment_setup.rst).

<a name="project-structure"></a>

## 📂 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/streamlit_app.py`: Chat interface
- `docker/`: Docker configurations
- `tests/`: Test suite

<a name="setup-and-usage"></a>

## 🛠ī¸ 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).

<a name="documentation"></a>

## 📚 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)

<a name="useful-resources"></a>

## 📚 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/)

<a name="development-and-contributing"></a>

## đŸ‘Ĩ 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).

<a name="license"></a>

## 📄 License

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

Raw data

            {
    "_id": null,
    "home_page": null,
    "name": "langgraph-agent-toolkit",
    "maintainer": null,
    "docs_url": null,
    "requires_python": "<3.14,>=3.11",
    "maintainer_email": null,
    "keywords": "agents, blueprint, fastapi, langgraph, litellm, streamlit",
    "author": null,
    "author_email": "Roman Kryvokhyzha <kriwohizha@gmail.com>, Joshua Carroll <carroll.joshk@gmail.com>",
    "download_url": "https://files.pythonhosted.org/packages/68/0b/4c9c19a2b4362e77f4135d898ee5a78fd02c8d8c93731fd3b232c9d02324/langgraph_agent_toolkit-0.7.7.tar.gz",
    "platform": null,
    "description": "<div align=\"center\">\n   <img alt=\"LangGraph Agent Toolkit Logo\" src=\"https://raw.githubusercontent.com/kryvokhyzha/langgraph-agent-toolkit/main/docs/media/logo.svg\" width=\"300\">\n</div>\n\n---\n\n# \ud83e\uddf0 LangGraph Agent Toolkit\n\n|            |                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            |\n| ---------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |\n| CI/Testing | [![build status](https://github.com/kryvokhyzha/langgraph-agent-toolkit/actions/workflows/test.yml/badge.svg)](https://github.com/kryvokhyzha/langgraph-agent-toolkit/actions/workflows/test.yml) [![docs status](https://github.com/kryvokhyzha/langgraph-agent-toolkit/actions/workflows/sphinx.yml/badge.svg)](https://github.com/kryvokhyzha/langgraph-agent-toolkit/actions/workflows/sphinx.yml) [![codecov](https://codecov.io/gh/kryvokhyzha/langgraph-agent-toolkit/graph/badge.svg?token=OHSACTNSWZ)](https://codecov.io/gh/kryvokhyzha/langgraph-agent-toolkit) |\n| Package    | [![PyPI version](https://img.shields.io/pypi/v/langgraph-agent-toolkit.svg)](https://pypi.org/project/langgraph-agent-toolkit/) [![PyPI Downloads](https://img.shields.io/pypi/dm/langgraph-agent-toolkit.svg)](https://pypi.org/project/langgraph-agent-toolkit/) [![Python Version](https://img.shields.io/python/required-version-toml?tomlFilePath=https%3A%2F%2Fraw.githubusercontent.com%2Fkryvokhyzha%2Flanggraph-agent-toolkit%2Fmain%2Fpyproject.toml)](https://github.com/kryvokhyzha/langgraph-agent-toolkit/blob/main/pyproject.toml)                          |\n| Meta       | [![Code style: ruff](https://img.shields.io/badge/code%20style-ruff-000000.svg)](https://github.com/astral-sh/ruff) [![GitHub License](https://img.shields.io/github/license/kryvokhyzha/langgraph-agent-toolkit)](https://github.com/kryvokhyzha/langgraph-agent-toolkit/blob/main/LICENSE)                                                                                                                                                                                                                                                                               |\n\n<!-- [![Streamlit App](https://static.streamlit.io/badges/streamlit_badge_black_red.svg)](https://langgraph-agent-toolkit.streamlit.app/) -->\n\n## \ud83d\udccb Introduction\n\nA comprehensive toolkit for building, deploying, and managing AI agents using\nLangGraph, FastAPI, and Streamlit. It provides a production-ready framework for\ncreating conversational AI agents with features like multi-provider LLM support,\nstreaming responses, observability, memory and prompt management.\n\n### What is langGraph-agent-toolkit?\n\nThe langgraph-agent-toolkit is a full-featured framework for developing and\ndeploying AI agent services. Built on the foundation of:\n\n- **[LangGraph](https://langchain-ai.github.io/langgraph/)** for agent creation\n  with advanced flows and human-in-the-loop capabilities\n- **[FastAPI](https://fastapi.tiangolo.com/)** for robust, high-performance API\n  services with streaming support\n- **[Streamlit](https://streamlit.io/)** for intuitive user interfaces\n\nKey components include:\n\n- Data structures and settings built with\n  **[Pydantic](https://github.com/pydantic/pydantic)**\n- **[LiteLLM](https://github.com/BerriAI/litellm)** proxy for universal\n  multi-provider LLM support\n- Comprehensive memory management and persistence using PostgreSQL/SQLite\n- Advanced observability tooling via Langfuse and Langsmith\n- Modular architecture allowing customization while maintaining a consistent\n  application structure\n\nWhether you're building a simple chatbot or complex multi-agent system, this\ntoolkit provides the infrastructure to develop, test, and deploy your\nLangGraph-based agents with confidence.\n\nYou can use [DeepWiki](https://deepwiki.com/kryvokhyzha/langgraph-agent-toolkit)\nto learn more about this repository.\n\n## \ud83d\udcd1 Contents\n\n- [Introduction](#-introduction)\n- [Quickstart](#-quickstart)\n- [Installation Options](#-installation-options)\n- [Architecture](#architecture)\n- [Key Features](#-key-features)\n- [Environment Setup](#environment-setup)\n- [Project Structure](#-project-structure)\n- [Setup and Usage](#setup-and-usage)\n- [Documentation](#-documentation)\n- [Useful Resources](#-useful-resources)\n- [Development and Contributing](#-development-and-contributing)\n- [License](#-license)\n\n## \ud83d\ude80 Quickstart\n\n1. Create a `.env` file based on [`.env.example`](./.env.example)\n\n2. **Option 1: Run with Python from source**\n\n   ```sh\n   # Install dependencies\n   pip install uv\n   uv sync --frozen\n   source .venv/bin/activate\n\n   # Start the service\n   python langgraph_agent_toolkit/run_service.py\n\n   # In another terminal\n   source .venv/bin/activate\n   streamlit run langgraph_agent_toolkit/streamlit_app.py\n   ```\n\n3. **Option 2: Run with Python from PyPi repository**\n\n   ```sh\n   pip install langgraph-agent-toolkit\n   ```\n\n   \u2139\ufe0f For more details on installation options, see the\n   [Installation Documentation](docs/installation.rst).\n\n4. **Option 3: Run with Docker**\n\n   ```sh\n   docker compose watch\n   ```\n\n<a name=\"installation-options\"></a>\n\n## \ud83d\udce6 Installation Options\n\nThe toolkit supports multiple installation options using \"extras\" to include\njust the dependencies you need.\n\nFor detailed installation instructions and available extras, see the\n[Installation Documentation](docs/installation.rst).\n\n<a name=\"architecture\"></a>\n\n## \ud83c\udfd7\ufe0f Architecture\n\n<img src=\"https://raw.githubusercontent.com/kryvokhyzha/langgraph-agent-toolkit/main/docs/media/agent_architecture.png\" width=\"800\">\n\n<a name=\"key-features\"></a>\n\n## \u2728 Key Features\n\n1. **LangGraph Integration**\n\n   - Latest LangGraph v0.3 features\n   - Human-in-the-loop with `interrupt()`\n   - Flow control with `Command` and `langgraph-supervisor`\n\n2. **API Service**\n\n   - FastAPI with streaming and non-streaming endpoints\n   - Support for both token-based and message-based streaming\n   - Multiple agent support with URL path routing\n   - Available agents and models listed at `/info` endpoint\n   - Supports different runners (unicorn, gunicorn, mangum, azure functions)\n\n3. **Developer Experience**\n\n   - Asynchronous design with async/await\n   - Docker configuration with live reloading\n   - Comprehensive testing suite\n\n4. **Enterprise Components**\n   - Configurable PostgreSQL/SQLite connection pools\n   - Observability via Langfuse and Langsmith\n   - User feedback system\n   - Prompt management system\n   - LiteLLM proxy integration\n\nFor more details on features, see the [Usage Documentation](docs/usage.rst).\n\n<a name=\"environment-setup\"></a>\n\n## \u2699\ufe0f Environment Setup\n\nFor detailed environment setup instructions, including creating your `.env` file\nand configuring LiteLLM, see the\n[Environment Setup Documentation](docs/environment_setup.rst).\n\n<a name=\"project-structure\"></a>\n\n## \ud83d\udcc2 Project Structure\n\nThe repository contains:\n\n- `langgraph_agent_toolkit/agents/blueprints/`: Agent definitions\n- `langgraph_agent_toolkit/agents/agent_executor.py`: Agent execution control\n- `langgraph_agent_toolkit/schema/`: Protocol schema definitions\n- `langgraph_agent_toolkit/core/`: Core modules (LLM, memory, settings)\n- `langgraph_agent_toolkit/service/service.py`: FastAPI service\n- `langgraph_agent_toolkit/client/client.py`: Service client\n- `langgraph_agent_toolkit/streamlit_app.py`: Chat interface\n- `docker/`: Docker configurations\n- `tests/`: Test suite\n\n<a name=\"setup-and-usage\"></a>\n\n## \ud83d\udee0\ufe0f Setup and Usage\n\nFor detailed setup and usage instructions, including building your own agent,\nDocker setup, using the AgentClient, and local development, see the\n[Usage Documentation](docs/usage.rst).\n\n<a name=\"documentation\"></a>\n\n## \ud83d\udcda Documentation\n\nFull documentation is available at\n[GitHub repository](https://github.com/kryvokhyzha/langgraph-agent-toolkit/tree/main/docs/)\nand includes:\n\n- [Installation Guide](https://github.com/kryvokhyzha/langgraph-agent-toolkit/blob/main/docs/installation.rst)\n- [Environment Setup](https://github.com/kryvokhyzha/langgraph-agent-toolkit/blob/main/docs/environment_setup.rst)\n- [Usage Guide](https://github.com/kryvokhyzha/langgraph-agent-toolkit/blob/main/docs/usage.rst)\n\n<a name=\"useful-resources\"></a>\n\n## \ud83d\udcda Useful Resources\n\n- [LangGraph documentation](https://langchain-ai.github.io/langgraph/concepts/low_level/#multiple-schemas)\n- [LangGraph Memory Concept](https://langchain-ai.github.io/langgraph/concepts/memory/)\n- [LangGraph Memory Persistence](https://langchain-ai.github.io/langgraph/concepts/persistence/#memory)\n- [LangGraph Memory Template](https://github.com/langchain-ai/memory-template)\n- [LangGraph Human in the Loop](https://langchain-ai.github.io/langgraph/how-tos/human_in_the_loop/wait-user-input/)\n- [LangGraph 101 - blueprints](https://github.com/langchain-ai/langgraph-101)\n- [LangGraph - Examples](https://github.com/langchain-ai/langgraph/tree/main/examples)\n- [Complex data extraction with function calling](https://langchain-ai.github.io/langgraph/tutorials/extraction/retries/)\n- [How to edit graph state](https://langchain-ai.github.io/langgraph/how-tos/human_in_the_loop/edit-graph-state/)\n- [Memory in the background](https://www.youtube.com/watch?v=R1jKQ1Jn5T4&ab_channel=LangChain)\n- [Building an agent with LangGraph](https://www.kaggle.com/code/markishere/day-3-building-an-agent-with-langgraph/)\n- [How to create tools in Langchain](https://python.langchain.com/docs/how_to/custom_tools/)\n- [Simple Serverless FastAPI with AWS Lambda](https://www.deadbear.io/simple-serverless-fastapi-with-aws-lambda/)\n\n<a name=\"development-and-contributing\"></a>\n\n## \ud83d\udc65 Development and Contributing\n\nThank you for considering contributing to `Langgraph Agent Toolkit`! We\nencourage the community to post Issues and Pull Requests.\n\nBefore you get started, please see our [Contribution Guide](CONTRIBUTING.md).\n\n<a name=\"license\"></a>\n\n## \ud83d\udcc4 License\n\nThis project is licensed under the MIT License - see the LICENSE file for\ndetails.\n",
    "bugtrack_url": null,
    "license": null,
    "summary": "Full toolkit for running an AI agent service built with LangGraph, FastAPI and Streamlit",
    "version": "0.7.7",
    "project_urls": {
        "PyPI": "https://pypi.org/project/langgraph-agent-toolkit",
        "documentation": "https://kryvokhyzha.github.io/langgraph-agent-toolkit",
        "repository": "https://github.com/kryvokhyzha/langgraph-agent-toolkit"
    },
    "split_keywords": [
        "agents",
        " blueprint",
        " fastapi",
        " langgraph",
        " litellm",
        " streamlit"
    ],
    "urls": [
        {
            "comment_text": null,
            "digests": {
                "blake2b_256": "f5595ec3e5c51bf371c20edfc100644e744c238b3940bcb8ba806d06ddd2a530",
                "md5": "bc21c33b153f358567e15c92f035d19a",
                "sha256": "665e2561d65d6251a7d72d6324181f3761579ede1da1707a26f88eeb4ab3c0f2"
            },
            "downloads": -1,
            "filename": "langgraph_agent_toolkit-0.7.7-py3-none-any.whl",
            "has_sig": false,
            "md5_digest": "bc21c33b153f358567e15c92f035d19a",
            "packagetype": "bdist_wheel",
            "python_version": "py3",
            "requires_python": "<3.14,>=3.11",
            "size": 87934,
            "upload_time": "2025-07-11T13:42:32",
            "upload_time_iso_8601": "2025-07-11T13:42:32.483164Z",
            "url": "https://files.pythonhosted.org/packages/f5/59/5ec3e5c51bf371c20edfc100644e744c238b3940bcb8ba806d06ddd2a530/langgraph_agent_toolkit-0.7.7-py3-none-any.whl",
            "yanked": false,
            "yanked_reason": null
        },
        {
            "comment_text": null,
            "digests": {
                "blake2b_256": "680b4c9c19a2b4362e77f4135d898ee5a78fd02c8d8c93731fd3b232c9d02324",
                "md5": "7f4b25cd05c8f208ccafeaccd52580ba",
                "sha256": "f4faab977dc72fe44586fc9eddd0721416e0c45219270a675017c3d4e7cecd3f"
            },
            "downloads": -1,
            "filename": "langgraph_agent_toolkit-0.7.7.tar.gz",
            "has_sig": false,
            "md5_digest": "7f4b25cd05c8f208ccafeaccd52580ba",
            "packagetype": "sdist",
            "python_version": "source",
            "requires_python": "<3.14,>=3.11",
            "size": 1500645,
            "upload_time": "2025-07-11T13:42:33",
            "upload_time_iso_8601": "2025-07-11T13:42:33.992734Z",
            "url": "https://files.pythonhosted.org/packages/68/0b/4c9c19a2b4362e77f4135d898ee5a78fd02c8d8c93731fd3b232c9d02324/langgraph_agent_toolkit-0.7.7.tar.gz",
            "yanked": false,
            "yanked_reason": null
        }
    ],
    "upload_time": "2025-07-11 13:42:33",
    "github": true,
    "gitlab": false,
    "bitbucket": false,
    "codeberg": false,
    "github_user": "kryvokhyzha",
    "github_project": "langgraph-agent-toolkit",
    "travis_ci": false,
    "coveralls": true,
    "github_actions": true,
    "lcname": "langgraph-agent-toolkit"
}
None
Elapsed time: 1.25463s