The platform for reliable agents.

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.

pip install langchain

If you're looking for more advanced customization or agent orchestration, check out LangGraph, our framework for building controllable agent workflows.

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Documentation: To learn more about LangChain, check out the docs.

Discussions: Visit the LangChain Forum 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.

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.

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:

• LangGraph - 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.
• 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 - 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.

Additional resources

• API Reference: Detailed reference on navigating base packages and integrations for LangChain.
• Integrations: List of LangChain integrations, including chat & embedding models, tools & toolkits, and more
• Contributing Guide: Learn how to contribute to LangChain and find good first issues.