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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 outLangGraph, our framework for building controllable agent workflows.

Documentation:

• docs.langchain.com – Comprehensive documentation, including conceptual overviews and guides
• reference.langchain.com/python – API reference docs for LangChain packages

Discussions: Visit theLangChain Forum to connect with the community and share all of your technical questions, ideas, and feedback.

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.

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.
• Integrations – List of LangChain integrations, including chat & embedding models, tools & toolkits, and more
• 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 inLangSmith Studio.
• Deep Agents(new!) – Build agents that can plan, use subagents, and leverage file systems for complex tasks

• API Reference – Detailed reference on navigating base packages and integrations for LangChain.
• Contributing Guide – Learn how to contribute to LangChain projects and find good first issues.
• Code of Conduct – Our community guidelines and standards for participation.