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, but will also be:

  1. Data-aware: connect a language model to other sources of data

  2. Agentic: allow a language model to interact with its environment

The LangChain framework is designed around these principles.
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#

How to get started using LangChain to create an Language Model application.
Concepts and terminology.
Tutorials created by community experts and presented on YouTube.

Modules#

These modules are the core abstractions which we view as the building blocks of any LLM-powered application.

For each module LangChain provides standard, extendable interfaces. LangChain also provides external integrations and even end-to-end implementations for off-the-shelf use.

The docs for each module contain quickstart examples, how-to guides, reference docs, and conceptual guides.
The modules are (from least to most complex):

  • Models: Supported model types and integrations.

  • Prompts: Prompt management, optimization, and serialization.

  • Memory: Memory refers to state that is persisted between calls of a chain/agent.

  • Indexes: Language models become much more powerful when combined with application-specific data - this module contains interfaces and integrations for loading, querying and updating external data.

  • Chains: Chains are structured sequences of calls (to an LLM or to a different utility).

  • Agents: An agent is a Chain in which an LLM, given a high-level directive and a set of tools, repeatedly decides an action, executes the action and observes the outcome until the high-level directive is complete.

  • Callbacks: Callbacks let you log and stream the intermediate steps of any chain, making it easy to observe, debug, and evaluate the internals of an application.

Use Cases#

Best practices and built-in implementations for common LangChain use cases:

  • Autonomous Agents: Autonomous agents are long-running agents that take many steps in an attempt to accomplish an objective. Examples include AutoGPT and BabyAGI.

  • Agent Simulations: Putting agents in a sandbox and observing how they interact with each other and react to events can be an effective way to evaluate their long-range reasoning and planning abilities.

  • Personal Assistants: One of the primary LangChain use cases. Personal assistants need to take actions, remember interactions, and have knowledge about your data.

  • Question Answering: Another common LangChain use case. Answering questions over specific documents, only utilizing the information in those documents to construct an answer.

  • Chatbots: Language models love to chat, making this a very natural use of them.

  • Querying Tabular Data: Recommended reading if you want to use language models to query structured data (CSVs, SQL, dataframes, etc).

  • Code Understanding: Recommended reading if you want to use language models to analyze code.

  • Interacting with APIs: Enabling language models to interact with APIs is extremely powerful. It gives them access to up-to-date information and allows them to take actions.

  • Extraction: Extract structured information from text.

  • Summarization: Compressing longer documents. A type of Data-Augmented Generation.

  • Evaluation: Generative models are hard to evaluate with traditional metrics. One promising approach is to use language models themselves to do the evaluation.

Reference Docs#

Full documentation on all methods, classes, installation methods, and integration setups for LangChain.

Ecosystem#

LangChain integrates a lot of different LLMs, systems, and products.
From the other side, many systems and products depend on LangChain.
It creates a vibrant and thriving ecosystem.

  • Integrations: Guides for how other products can be used with LangChain.

  • Dependents: List of repositories that use LangChain.

  • Deployments: A collection of instructions, code snippets, and template repositories for deploying LangChain apps.

Additional Resources#

Additional resources we think may be useful as you develop your application!

  • LangChainHub: The LangChainHub is a place to share and explore other prompts, chains, and agents.

  • Gallery: A collection of great projects that use Langchain, compiled by the folks at Kyrolabs. Useful for finding inspiration and example implementations.

  • Deploying LLMs in Production: A collection of best practices and tutorials for deploying LLMs in production.

  • Tracing: A guide on using tracing in LangChain to visualize the execution of chains and agents.

  • Model Laboratory: Experimenting with different prompts, models, and chains is a big part of developing the best possible application. The ModelLaboratory makes it easy to do so.

  • Discord: Join us on our Discord to discuss all things LangChain!

  • YouTube: A collection of the LangChain tutorials and videos.

  • Production Support: As you move your LangChains into production, we’d love to offer more comprehensive support. Please fill out this form and we’ll set up a dedicated support Slack channel.