hwchase17/langchain

The LangChain repository describes an agent engineering platform for building agent based software.

github.com/langchain-ai/langchain
hwchase17/langchain homepage screenshot

About hwchase17/langchain

LangChain is an open-source framework for building agents and LLM-powered applications. It provides interoperable components and third-party integrations for chat models, embedding models, vector stores, retrievers, and tools, with a standard interface for connecting LLMs to data sources and swapping models.

It can be used standalone or with related products including Deep Agents for planning and subagents, LangGraph for agent orchestration, and LangSmith for evaluation, observability, and debugging. A separate equivalent library is available for JavaScript/TypeScript.

Key features

  • Build agents and LLM-powered applications from interoperable components
  • Connect LLMs to data sources and external systems
  • Standard interface for models, embeddings, vector stores, retrievers
  • Swap models as requirements or available models change
  • Integrates with LangGraph for controllable agent workflows
  • Integrates with LangSmith for evals, observability, and debugging
Best for: Developers building agents and LLM-powered applications that use multiple models and third-party integrations.
Good for
  • Open-source framework with community-contributed integrations and templates
  • Can be used standalone or with Deep Agents, LangGraph, and LangSmith
  • Provides Python package with an equivalent JavaScript/TypeScript library

hwchase17/langchain at a glance

Type
Open-source project
Pricing
Not confirmed. Check the site
Open source
Yes · Source code
Language
English
Last checked
Oct 6, 2026 · Online when checked
Listed
Oct 5, 2026

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hwchase17/langchain FAQ

How do I start using LangChain?
Install with `uv add langchain`, then initialize a chat model with `init_chat_model` and call `invoke` as shown in the quickstart.
Is there a JavaScript/TypeScript version?
Yes, an equivalent JS/TS library is available as LangChain.js.
When should I use Deep Agents or LangGraph?
Use Deep Agents for built-in planning, subagents, and file system usage, and LangGraph for advanced customization and controllable agent workflows.
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