langchain
langchain-ai/langchain
Open-source framework for building AI agents and LLM-powered applications
Overview
LangChain is a Python framework that enables developers to create sophisticated AI agents and applications by chaining together interoperable components and integrations. It simplifies the development of generative AI solutions across various use cases.
Categories
Tags
Similar tools
dify
langgenius/dify
Production-ready platform for agentic workflow development.
firecrawl
firecrawl/firecrawl
The API to search, scrape, and interact with the web at scale. π₯
browser-use
browser-use/browser-use
π Make websites accessible for AI agents. Automate tasks online with ease.
OpenHands
OpenHands/OpenHands
π OpenHands: AI-Driven Development
graphify
Graphify-Labs/graphify
AI coding assistant skill (Claude Code, Codex, OpenCode, Cursor, Gemini CLI, and more). Turn any folder of code, SQL schemas, R scripts, she
rtk
rtk-ai/rtk
CLI proxy that reduces LLM token consumption by 60-90% on common dev commands. Single Rust binary, zero dependencies
Install
pip install langchainREADME
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 β all while future-proofing decisions as the underlying technology evolves.
[!TIP] Just getting started? Check out Deep Agents β a higher-level package built on LangChain for agents that have built-in capabilites for common usage patterns such as planning, subagents, file system usage, and more.
Quickstart
uv add langchain
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 β Build agents that can plan, use subagents, and leverage file systems for complex tasks
- LangGraph β Build agents that can reliably handle complex tasks with our low-level agent orchestration framework
- Integrations β Chat & embedding models, tools & toolkits, and more
- LangSmith β Agent evals, observability, and debugging for LLM apps
- LangSmith Deployment β 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.
- Real-time data augmentation β Easily connect LLMs to diverse data sources and external/internal systems, drawing from LangChain's vast library of integrations with model providers, tools, vector stores, retrievers, and more
- Model interoperability β Swap models in and out as your engineering team experiments to find the best choice for your application's needs. As the industry frontier evolves, adapt quickly β LangChain's abstractions keep you moving without losing momentum
- Rapid prototyping β