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.

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://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://langchain-ai.github.io/langgraph/[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/langgraph-platform[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 https://langchain-ai.github.io/langgraph/concepts/langgraph_studio/[LangGraph Studio].

* https://python.langchain.com/docs/tutorials/[Tutorials]: Simple walkthroughs with guided examples on getting started with LangChain. * https://python.langchain.com/docs/how_to/[How-to Guides]: Quick, actionable code snippets for topics such as tool calling, RAG use cases, and more. * https://python.langchain.com/docs/concepts/[Conceptual Guides]: Explanations of key concepts behind the LangChain framework. * https://forum.langchain.com/[LangChain Forum]: Connect with the community and share all of your technical questions, ideas, and feedback. * https://python.langchain.com/api_reference/[API Reference]: Detailed reference on navigating base packages and integrations for LangChain. * https://chat.langchain.com/[Chat LangChain]: Ask questions & chat with our documentation.