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Welcome to LangChain
 Contents 
Getting Started
Modules
Use Cases
Reference Docs
LangChain Ecosystem
Additional Resources
Welcome to LangChain#
LangChain is a framework for developing applications powered by language models. We believe that the most powerful and differentiated applications will not only call out to a language model via an API, but will also:
Be data-aware: connect a language model to other sources of data
Be agentic: allow a language model to interact with its environment
The LangChain framework is designed with the above principles in mind.
This is the Python specific portion of the documentation. For a purely conceptual guide to LangChain, see here. For the JavaScript documentation, see here.
Getting Started#
Checkout the below guide for a walkthrough of how to get started using LangChain to create an Language Model application.
Getting Started Documentation
Modules#
There are several main modules that LangChain provides support for.
For each module we provide some examples to get started, how-to guides, reference docs, and conceptual guides.
These modules are, in increasing order of complexity:
Models: The various model types and model integrations LangChain supports.
Prompts: This includes prompt management, prompt optimization, and prompt serialization.
Memory: Memory is the concept of persisting state between calls of a chain/agent. LangChain provides a standard interface for memory, a collection of memory implementations, and examples of chains/agents that use memory.
Indexes: Language models are often more powerful when combined with your own text data - this module covers best practices for doing exactly that.
Chains: Chains go beyond just a single LLM call, and are sequences of calls (whether to an LLM or a different utility). LangChain provides a standard interface for chains, lots of integrations with other tools, and end-to-end chains for common applications.