⚡ Build context-aware reasoning applications ⚡

Looking for the JS/TS library? Check out LangChain.js.

To help you ship LangChain apps to production faster, check out LangSmith. LangSmith is a unified developer platform for building, testing, and monitoring LLM applications. Fill out this form to speak with our sales team.

With pip:

pip install langchain

With conda:

conda install langchain -c conda-forge

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. Use LangGraph to build stateful agents with first-class streaming and human-in-the-loop support.
• Productionization: Inspect, monitor, and evaluate your apps with LangSmith so that you can constantly optimize and deploy with confidence.
• Deployment: Turn your LangGraph applications into production-ready APIs and Assistants with LangGraph Cloud.

• langchain-core: Base abstractions and LangChain Expression Language.
• langchain-community: Third party integrations.
  • Some integrations have been further split into partner packages that only rely on langchain-core. Examples include langchain_openai and langchain_anthropic.
• langchain: Chains, agents, and retrieval strategies that make up an application's cognitive architecture.
• LangGraph: A library for building robust and stateful multi-actor applications with LLMs by modeling steps as edges and nodes in a graph. Integrates smoothly with LangChain, but can be used without it. To learn more about LangGraph, check out our first LangChain Academy course, Introduction to LangGraph, available here.

• LangSmith: A developer platform that lets you debug, test, evaluate, and monitor chains built on any LLM framework and seamlessly integrates with LangChain.

• LangGraph Cloud: Turn your LangGraph applications into production-ready APIs and Assistants.

❓ Question answering with RAG

• Documentation
• End-to-end Example: Chat LangChain and repo

🧱 Extracting structured output

• Documentation
• End-to-end Example: SQL Llama2 Template

🤖 Chatbots

• Documentation
• End-to-end Example: Web LangChain (web researcher chatbot) and repo

And much more! Head to the Tutorials section of the docs for more.

The main value props of the LangChain libraries are:

1. Components: composable building blocks, tools and integrations for working with language models. Components are modular and easy-to-use, whether you are using the rest of the LangChain framework or not
2. Off-the-shelf chains: built-in assemblages of components for accomplishing higher-level tasks

Off-the-shelf chains make it easy to get started. Components make it easy to customize existing chains and build new ones.

LCEL is a key part of LangChain, allowing you to build and organize chains of processes in a straightforward, declarative manner. It was designed to support taking prototypes directly into production without needing to alter any code. This means you can use LCEL to set up everything from basic "prompt + LLM" setups to intricate, multi-step workflows.

• Overview: LCEL and its benefits
• Interface: The standard Runnable interface for LCEL objects
• Primitives: More on the primitives LCEL includes
• Cheatsheet: Quick overview of the most common usage patterns

Components fall into the following modules:

📃 Model I/O

This includes prompt management, prompt optimization, a generic interface for chat models and LLMs, and common utilities for working with model outputs.

📚 Retrieval

Retrieval Augmented Generation involves loading data from a variety of sources, preparing it, then searching over (a.k.a. retrieving from) it for use in the generation step.

🤖 Agents

Agents allow an LLM autonomy over how a task is accomplished. Agents make decisions about which Actions to take, then take that Action, observe the result, and repeat until the task is complete. LangChain provides a standard interface for agents, along with LangGraph for building custom agents.

Please see here for full documentation, which includes:

• Introduction: Overview of the framework and the structure of the docs.
• Tutorials: If you're looking to build something specific or are more of a hands-on learner, check out our tutorials. This is the best place to get started.
• How-to guides: Answers to “How do I….?” type questions. These guides are goal-oriented and concrete; they're meant to help you complete a specific task.
• Conceptual guide: Conceptual explanations of the key parts of the framework.
• API Reference: Thorough documentation of every class and method.

• 🦜🛠️ LangSmith: Trace and evaluate your language model applications and intelligent agents to help you move from prototype to production.
• 🦜🕸️ LangGraph: Create stateful, multi-actor applications with LLMs. Integrates smoothly with LangChain, but can be used without it.
• 🦜🕸️ LangGraph Platform: Deploy LLM applications built with LangGraph into production.

As an open-source project in a rapidly developing field, we are extremely open to contributions, whether it be in the form of a new feature, improved infrastructure, or better documentation.

For detailed information on how to contribute, see here.