Langgraphjs

🦜🕸️LangGraph.js

[](https://langchain-ai.github.io/langgraphjs/)

[](https://www.npmjs.com/package/@langchain/langgraph) [](https://github.com/langchain-ai/langgraphjs/issues)

[!NOTE] Looking for the Python version? See the [Python repo](https://github.com/langchain-ai/langgraph) and the [Python docs](https://docs.langchain.com/oss/python/langgraph/overview).

LangGraph — used by Replit, Uber, LinkedIn, GitLab and more — is a low-level orchestration framework for building controllable agents. While langchain provides integrations and composable components to streamline LLM application development, the LangGraph library enables agent orchestration — offering customizable architectures, long-term memory, and human-in-the-loop to reliably handle complex tasks.

npm install @langchain/langgraph @langchain/core

To learn more about how to use LangGraph, check out [the docs](https://langchain-ai.github.io/langgraphjs/). We show a simple example below of how to create a ReAct agent.

// npm install @langchain-anthropic
import { createReactAgent, tool } from "langchain";
import { ChatAnthropic } from "@langchain/anthropic";

import { z } from "zod";

const search = tool(
  async ({ query }) => {
    if (
      query.toLowerCase().includes("sf") ||
      query.toLowerCase().includes("san francisco")
    ) {
      return "It's 60 degrees and foggy.";
    }
    return "It's 90 degrees and sunny.";
  },
  {
    name: "search",
    description: "Call to surf the web.",
    schema: z.object({
      query: z.string().describe("The query to use in your search."),
    }),
  }
);

const model = new ChatAnthropic({
  model: "claude-3-7-sonnet-latest",
});

const agent = createReactAgent({
  llm: model,
  tools: [search],
});

const result = await agent.invoke({
  messages: [
    {
      role: "user",
      content: "what is the weather in sf",
    },
  ],
});

Full-stack Quickstart

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