langchain

<div align="center"> <a href="https://www.langchain.com/"> <picture> <source media="(prefers-color-scheme: light)" srcset=".github/images/logo-dark.svg"> <source media="(prefers-color-scheme: dark)" srcset=".github/images/logo-light.svg"> <img alt="LangChain Logo" src=".github/images/logo-dark.svg" width="80%"> </picture> </a> </div> <div align="center"> <h3>The platform for reliable agents.</h3> </div> <div align="center"> <a href="https://opensource.org/licenses/MIT" target="_blank"><img src="https://img.shields.io/pypi/l/langchain" alt="PyPI - License"></a> <a href="https://pypistats.org/packages/langchain" target="_blank"><img src="https://img.shields.io/pepy/dt/langchain" alt="PyPI - Downloads"></a> <a href="https://pypi.org/project/langchain/#history" target="_blank"><img src="https://img.shields.io/pypi/v/langchain?label=%20" alt="Version"></a> <a href="https://vscode.dev/redirect?url=vscode://ms-vscode-remote.remote-containers/cloneInVolume?url=https://github.com/langchain-ai/langchain" target="_blank"><img src="https://img.shields.io/static/v1?label=Dev%20Containers&message=Open&color=blue&logo=visualstudiocode" alt="Open in Dev Containers"></a> <a href="https://codespaces.new/langchain-ai/langchain" target="_blank"><img src="https://github.com/codespaces/badge.svg" alt="Open in Github Codespace" title="Open in Github Codespace" width="150" height="20"></a> <a href="https://codspeed.io/langchain-ai/langchain" target="_blank"><img src="https://img.shields.io/endpoint?url=https://codspeed.io/badge.json" alt="CodSpeed Badge"></a> <a href="https://x.com/langchain" target="_blank"><img src="https://img.shields.io/twitter/url/https/twitter.com/langchain.svg?style=social&label=Follow%20%40LangChain" alt="Twitter / X"></a> </div> LangChain is a framework for building agents and LLM-powered applications. It helps you chain together interoperable components and third-party integrations to simplify AI application development – all while future-proofing decisions as the underlying technology evolves. ```bash pip install langchain ``` If you're looking for more advanced customization or agent orchestration, check out [LangGraph](https://docs.langchain.com/oss/python/langgraph/overview), our framework for building controllable agent workflows. --- **Documentation**: - [docs.langchain.com](https://docs.langchain.com/oss/python/langchain/overview) – Comprehensive documentation, including conceptual overviews and guides - [reference.langchain.com/python](https://reference.langchain.com/python) – API reference docs for LangChain packages - [Chat LangChain](https://chat.langchain.com/) – Chat with the LangChain documentation and get answers to your questions **Discussions**: Visit the [LangChain Forum](https://forum.langchain.com) to connect with the community and share all of your technical questions, ideas, and feedback. > [!NOTE] > Looking for the JS/TS library? Check out [LangChain.js](https://github.com/langchain-ai/langchainjs). ## Why use LangChain? LangChain helps developers build applications powered by LLMs through a standard interface for models, embeddings, vector stores, and more. Use LangChain for: - **Real-time data augmentation**. Easily connect LLMs to diverse data sources and external/internal systems, drawing from LangChain's vast library of integrations with model providers, tools, vector stores, retrievers, and more. - **Model interoperability**. Swap models in and out as your engineering team experiments to find the best choice for your application's needs. As the industry frontier evolves, adapt quickly – LangChain's abstractions keep you moving without losing momentum. - **Rapid prototyping**. Quickly build and iterate on LLM applications with LangChain's modular, component-based architecture. Test different approaches and workflows without rebuilding from scratch, accelerating your development cycle. - **Production-ready features**. Deploy reliable applications with built-in support for monitoring, evaluation, and debugging through integrations like LangSmith. Scale with confidence using battle-tested patterns and best practices. - **Vibrant community and ecosystem**. Leverage a rich ecosystem of integrations, templates, and community-contributed components. Benefit from continuous improvements and stay up-to-date with the latest AI developments through an active open-source community. - **Flexible abstraction layers**. Work at the level of abstraction that suits your needs - from high-level chains for quick starts to low-level components for fine-grained control. LangChain grows with your application's complexity. ## LangChain ecosystem While the LangChain framework can be used standalone, it also integrates seamlessly with any LangChain product, giving developers a full suite of tools when building LLM applications. To improve your LLM application development, pair LangChain with: - [Deep Agents](https://github.com/langchain-ai/deepagents) *(new!)* – Build agents that can plan, use subagents, and leverage file systems for complex tasks - [LangGraph](https://docs.langchain.com/oss/python/langgraph/overview) – Build agents that can reliably handle complex tasks with LangGraph, our low-level agent orchestration framework. LangGraph offers customizable architecture, long-term memory, and human-in-the-loop workflows – and is trusted in production by companies like LinkedIn, Uber, Klarna, and GitLab. - [Integrations](https://docs.langchain.com/oss/python/integrations/providers/overview) – List of LangChain integrations, including chat & embedding models, tools & toolkits, and more - [LangSmith](https://www.langchain.com/langsmith) – Helpful for agent evals and observability. Debug poor-performing LLM app runs, evaluate agent trajectories, gain visibility in production, and improve performance over time. - [LangSmith Deployment](https://docs.langchain.com/langsmith/deployments) – Deploy and scale agents effortlessly with a purpose-built deployment platform for long-running, stateful workflows. Discover, reuse, configure, and share agents across teams – and iterate quickly with visual prototyping in [LangSmith Studio](https://docs.langchain.com/langsmith/studio). ## Additional resources - [API Reference](https://reference.langchain.com/python) – Detailed reference on navigating base packages and integrations for LangChain. - [Contributing Guide](https://docs.langchain.com/oss/python/contributing/overview) – Learn how to contribute to LangChain projects and find good first issues. - [Code of Conduct](https://github.com/langchain-ai/langchain/?tab=coc-ov-file) – Our community guidelines and standards for participation. - [LangChain Academy](https://academy.langchain.com/) – Comprehensive, free courses on LangChain libraries and products, made by the LangChain team.