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.
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.
LangChain provides standard, extendable interfaces and external integrations for the following modules, listed from least to most complex:
Interface with language models
Interface with application-specific data
Construct sequences of calls
Let chains choose which tools to use given high-level directives
Persist application state between runs of a chain
Log and stream intermediate steps of any chain
Examples, ecosystem, and resources
Walkthroughs and best-practices for common end-to-end use cases, like:
Learn best practices for developing with LangChain.
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.
Head to the Community navigator to find places to ask questions, share feedback, meet other developers, and dream about the future of LLM’s.
Head to the reference section for full documentation of all classes and methods in the LangChain Python package.