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: * https://docs.langchain.com/oss/python/langgraph/overview[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. * https://docs.langchain.com/oss/python/integrations/providers/overview[Integrations] -- List of LangChain integrations, including chat & embedding models, tools & toolkits, and more * https://www.langchain.com/langsmith[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. * https://docs.langchain.com/langsmith/deployments[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 https://docs.langchain.com/langsmith/studio[LangSmith Studio]. * https://github.com/langchain-ai/deepagents[Deep Agents] _(new!)_ -- Build agents that can plan, use subagents, and leverage file systems for complex tasks

* https://reference.langchain.com/python[API Reference] -- Detailed reference on navigating base packages and integrations for LangChain. * https://docs.langchain.com/oss/python/contributing/overview[Contributing Guide] -- Learn how to contribute to LangChain projects and find good first issues. * https://github.com/langchain-ai/langchain/blob/master/.github/CODE_OF_CONDUCT.md[Code of Conduct] -- Our community guidelines and standards for participation.