Introduction

LangChain is a framework for developing applications powered by language models. It enables applications that:

• Are context-aware: connect a language model to sources of context (prompt instructions, few shot examples, content to ground its response in, etc.)
• Reason: rely on a language model to reason (about how to answer based on provided context, what actions to take, etc.)

The main value props of LangChain are:

Components: abstractions for working with language models, along with a collection of implementations for each abstraction. Components are modular and easy-to-use, whether you are using the rest of the LangChain framework or not Off-the-shelf chains: a structured assembly of components for accomplishing specific higher-level tasks

Off-the-shelf chains make it easy to get started. For complex applications, components make it easy to customize existing chains and build new ones.

Get started​

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.

Note: These docs are for the LangChain Python package. For documentation on LangChain.js, the JS/TS version, head here.

Modules​

LangChain provides standard, extendable interfaces and external integrations for the following modules, listed from least to most complex:

Model I/O​

Interface with language models

Retrieval​

Interface with application-specific data

Chains​

Construct sequences of calls

Agents​

Let chains choose which tools to use given high-level directives

Memory​

Persist application state between runs of a chain

Callbacks​

Log and stream intermediate steps of any chain

Examples, ecosystem, and resources​

Use cases​

Walkthroughs and best-practices for common end-to-end use cases, like:

• Document question answering
• Chatbots
• Analyzing structured data
• and much more…

Guides​

Learn best practices for developing with LangChain.

Ecosystem​

LangChain is part of a rich ecosystem of tools that integrate with our framework and build on top of it. Check out our growing list of integrations and dependent repos.

Additional resources​

Our community is full of prolific developers, creative builders, and fantastic teachers. Check out YouTube tutorials for great tutorials from folks in the community, and Gallery for a list of awesome LangChain projects, compiled by the folks at KyroLabs.

Community​

Head to the Community navigator to find places to ask questions, share feedback, meet other developers, and dream about the future of LLM’s.

API reference​

Head to the reference section for full documentation of all classes and methods in the LangChain Python package.