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LangChain is a framework for developing applications powered by large language models (LLMs). LangChain simplifies every stage of the LLM application lifecycle: Development: Build your applications using LangChain's open-source building blocks, components, and third-party integrations.
Basics. Build a Simple LLM Application with LCEL. Build a Chatbot. Build vector stores and retrievers. Build an Agent. Working with external knowledge. Build a Retrieval Augmented Generation (RAG) Application. Build a Conversational RAG Application. Build a Question/Answering system over SQL data. Build a Query Analysis System.
See the following migration guides for replacements based on chain_type: chains.structured_output.base.create_openai_fn_runnable (...) Deprecated since version 0.1.14: LangChain has introduced a method called with_structured_output that is available on ChatModels capable of tool calling.
These guides are goal-oriented and concrete; they're meant to help you complete a specific task. For conceptual explanations see the Conceptual guide. For end-to-end walkthroughs see Tutorials. For comprehensive descriptions of every class and function see the API Reference.
LangChain is a framework for developing applications powered by large language models (LLMs). For these applications, LangChain simplifies the entire application lifecycle: Open-source libraries: Build your applications using LangChain's open-source building blocks, components, and third-party integrations.
All of LangChain’s reference documentation, in one place. Full documentation on all methods, classes, installation methods, and integration setups for LangChain. Reference Documentation
Here's how to install LangChain, set up your environment, and start building. We recommend following our Quickstart guide to familiarize yourself with the framework by building your first LangChain application. Read up on our Security best practices to make sure you're developing safely with LangChain.
Agents use language models to choose a sequence of actions to take. A basic agent works in the following manner: Given a prompt an agent uses an LLM to request an action to take (e.g., a tool to run). The agent executes the action (e.g., runs the tool), and receives an observation.
LangChain is a framework for developing applications powered by large language models (LLMs). LangChain simplifies every stage of the LLM application lifecycle: Development: Build your applications using LangChain's open-source building blocks, components, and third-party integrations.
Read the docs. See a tutorial. Build a RAG app. Smart connections to any source of data or knowledge. See all our integrations. Why choose LangChain? LangChain is easy to get started with and gives you choice, flexibility, and power as you scale. Our Methods. Retrieval. Agents. Evaluation. LangChain FAQs.
