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.

uv add langchain

from langchain.chat_models import init_chat_model

model = init_chat_model("openai:gpt-5.5")
result = model.invoke("Hello, world!")

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

For an equivalent JS/TS library, check out LangChain.js.

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.

• Deep Agents — Build agents that can plan, use subagents, and leverage file systems for complex tasks
• LangGraph — Build agents that can reliably handle complex tasks with our low-level agent orchestration framework
• Integrations — Chat & embedding models, tools & toolkits, and more
• LangSmith — Agent evals, observability, and debugging for LLM apps
• LangSmith Deployment — Deploy and scale agents with a purpose-built platform for long-running, stateful workflows

LangChain helps developers build applications powered by LLMs through a standard interface for models, embeddings, vector stores, and more.

• 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

• Documentation — conceptual overviews and guides
• LangChain ecosystem overview — how LangChain, LangGraph, and Deep Agents fit together
• API reference — complete reference for all public classes, functions, and types
• Discussions — community forum for technical questions, ideas, and feedback
• LangChain Academy — comprehensive, free courses on LangChain libraries and products, made by the LangChain team
• Contributing Guide — how to contribute and find good first issues
• Code of Conduct — community guidelines and standards