langchain-ai/langchain
The agent engineering platform.
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Use Cases & Benefits
- Provides a framework to build applications powered by large language models by chaining interoperable components and integrations.
- Enables seamless model interoperability and easy connection to diverse data sources, future-proofing AI application development.
- Use for real-time data augmentation by integrating LLMs with external and internal systems through a vast library of connectors.
- Use for experimenting with and swapping different LLM providers and models to optimize application performance and capabilities.
- Use for building complex, controllable agent workflows with customizable architecture and long-term memory using LangGraph integration.
About langchain
The platform for reliable agents.
LangChain is a framework for building 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
Documentation: To learn more about LangChain, check out the docs.
If you're looking for more advanced customization or agent orchestration, check out LangGraph, our framework for building controllable agent workflows.
[!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’s 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:
- 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.
- 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.
- LangGraph Platform - 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 LangGraph Studio.
Additional resources
- Learn: Use cases, conceptual overviews, and more.
- API Reference: Detailed reference on navigating base packages and integrations for LangChain.
- Contributing Guide: Learn how to contribute to LangChain and find good first issues.
- LangChain Forum: Connect with the community and share all of your technical questions, ideas, and feedback.
- Chat LangChain: Ask questions & chat with our documentation.
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