LangChain v1 is under active development and is not recommended for production use.
  • APIs may change without notice
  • The docs are incomplete and subject to change
For the latest stable version, see the v0 LangChain docs.
LangChain is the easiest way to start building with LLMs, letting you get started on building agents with OpenAI, Anthropic, Google, and more in under 10 lines of code. LangChain agents are built on top of LangGraph in order to provide durable execution, streaming, human-in-the-loop, persistence, and more. You do not need to know LangGraph for basic LangChain agent usage.

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Install

npm install langchain@next

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Create an agent

import { z } from "zod";
// npm install @langchain/anthropic to call the model
import { createAgent, tool } from "langchain";

const getWeather = tool(({ city }) => `It's always sunny in ${city}!`, {
    name: "get_weather",
    description: "Get the weather for a given city",
    schema: z.object({
        city: z.string(),
    }),
});

const agent = createAgent({
    model: "anthropic:claude-3-7-sonnet-latest",
    tools: [getWeather],
});

console.log(
    await agent.invoke({
        messages: [{ role: "user", content: "What's the weather in Tokyo?" }],
    })
);

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Core benefits

Standard model interface

Different providers have unique APIs for interacting with models, including the format of responses. LangChain standardizes how you interact with models so that you can seamlessly swap providers and avoid lock-in.

Easy to use, highly flexible agent

LangChain’s agent abstraction is designed to be easy to get started with, letting you build a simple agent in ~10 lines of code. But it also provides enough flexibility to allow you to do all the context engineering your heart desires.

Built on top of LangGraph

LangChain’s agents are built on top of LangGraph. This allows us to take advantage of LangGraph’s durable execution, human-in-the-loop support, persistence, and more.

Debug with LangSmith

Gain deep visibility into complex agent behavior with visualization tools that trace execution paths, capture state transitions, and provide detailed runtime metrics.