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LangChain.js

A TypeScript framework for building apps and agents on large language models.

GitHub Download @langchain/core@1.3.2
Stars
18.3k
Forks
3.5k
Licence
MIT
Language
TypeScript
Last update
4 days ago
Type
Library

What it is

LangChain.js is a TypeScript framework for building applications powered by large language models (LLMs). It offers a standard interface for models, embeddings, vector stores, and tools, so developers can connect an AI model to their own data and systems and swap providers without rewriting the app. It is the JavaScript counterpart of the Python LangChain library.

  • Connect an LLM to your own documents or databases so it can answer questions about them
  • Build an agent that calls external tools or APIs
  • Prototype and compare different models or workflows by swapping components
  • Add monitoring and evaluation to an LLM app through LangSmith integration

Great for

  • JavaScript or TypeScript developers building LLM-powered features
  • Teams that want to switch between model providers without rewriting code
  • Developers connecting AI models to external data sources and APIs
  • Projects that need to run in Node.js, browsers, or edge runtimes

Not ideal for

  • People who are not comfortable with JavaScript or TypeScript, since the README covers only the JS version (use the Python library instead)
  • Anyone wanting a no-code tool; this is a developer library that requires writing code
  • Projects needing only a single direct call to one model API, where a framework adds unnecessary layers
Step-by-step guide: how to use LangChain.jsInstall, first run, examples, tips and FAQs
LangChain.js vs VoltAgent

How to get it

  1. 1

    Install with a package manager

    You'll need: Node.js 20.x, 22.x, or 24.x (per README)

    Install with npm

    npm install -S langchain

    Install with pnpm

    pnpm install langchain

    Install with yarn

    yarn add langchain
  2. 2

    Run it

    • Install the langchain package with npm, pnpm, or yarn.
    • Follow the Getting started guide in the LangChain docs to set up your environment.
    • Run one of the simple examples from the docs to confirm your setup works.
  3. 3

    Get the most out of it

    • Use Deep Agents if you want higher-level agent features like planning and subagents, as the README suggests
    • Add LangGraph.js for more advanced agent orchestration and controllable workflows
    • Connect LangSmith to monitor, debug, and evaluate your LLM app runs
    • Browse the integrations catalog to find model providers, tools, and vector stores

Downloads

Source code (.zip)main branch · straight from GitHubRelease @langchain/core@1.3.2Release notes and sourceOfficial website & docsdocs.langchain.com/langchain/

Strengths

  • Standard interfaces let you swap models and providers with less rework
  • Large library of integrations for model providers, tools, and vector stores
  • Runs in many JavaScript environments, including Node.js, Deno, Bun, and browsers

Trade-offs

  • The README describes many layers and related packages, which can be confusing for newcomers
  • Some ecosystem features (LangSmith, LangGraph) are separate products that may require additional setup or accounts
  • The README is truncated and does not detail specific APIs, so evaluating fit requires reading the docs

Stats synced from GitHub. The assessment above was drafted with AI from the project's README on 2026-10-29 and may miss recent changes — check the README before relying on a command. MIT licence: free to use, modify and distribute, including commercially, as long as you keep the copyright notice.