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langchain

langchain-ai/langchain · Updated

The agent engineering platform.

langchain-ai/langchain is a Python repository in the AI Agents category with 148,683 stars and 24,965 forks on GitHub, released under the MIT license, last updated today.

#agents#ai#ai-agents#anthropic#chatgpt#deepagents#enterprise#framework
View on GitHub 149kGitHub stars

How to install langchain in your AI client

Setup for Claude Code, Claude Desktop, Cursor, Codex, Windsurf, Cline and VS Code — copy-paste ready.

How to install langchain in Claude CodeAnthropic's agentic CLI

  1. Clone & open

    bash
    git clone https://github.com/langchain-ai/langchain.git
cd langchain
claude

  2. Let Claude read it

    Ask “/init” to generate a CLAUDE.md, then have Claude explain, run, or extend the codebase. It reads files on demand.

Documentation

Full README

The agent engineering platform.

LangChain is a framework for building agents and LLM-powered applications. It helps you chain together interoperable components and third-party integrations to simplify AI application development — all while future-proofing decisions as the underlying technology evolves.

[!TIP] Just getting started? Check out [Deep Agents](https://docs.langchain.com/oss/python/deepagents/) — a higher-level package built on LangChain for agents that have built-in capabilities for common usage patterns such as planning, subagents, file system usage, and more.

Quickstart

bash
uv add langchain
python
from langchain.chat_models import init_chat_model

model = init_chat_model("openai:gpt-5.5")
result = model.invoke("Hello, world!")

If you're looking for more advanced customization or agent orchestration, check out LangGraph, our framework for building controllable agent workflows.

For an equivalent JS/TS library, check out LangChain.js.

[!TIP] For developing, debugging, and deploying AI agents and LLM applications, see LangSmith.

LangChain ecosystem

While the LangChain framework can be used standalone, it also integrates seamlessly with any LangChain product, giving developers a full suite of tools when building LLM applications.

  • [Deep Agents](https://docs.langchain.com/oss/python/deepagents/) — Build agents that can plan, use subagents, and leverage file systems for complex tasks
  • [LangGraph](https://docs.langchain.com/oss/python/langgraph/overview) — Build agents that can reliably handle complex tasks with our low-level agent orchestration framework
  • [Integrations](https://docs.langchain.com/oss/python/integrations/providers/overview) — Chat & embedding models, tools & toolkits, and more
  • [LangSmith](https://www.langchain.com/langsmith) — Agent evals, observability, and debugging for LLM apps
  • [LangSmith Deployment](https://docs.langchain.com/langsmith/deployments) — Deploy and scale agents with a purpose-built platform for long-running, stateful workflows

Why use LangChain?

LangChain helps developers build applications powered by LLMs through a standard interface for models, embeddings, vector stores, and more.

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