langchain-ai

Langgraphjs — Testing skill for Claude Code

Testing community intermediate

by langchain-ai - Offers comprehensive build and test commands with detailed TypeScript style guidelines, layered library architecture, and monorepo structure using yarn workspaces.

How to install Langgraphjs

This entry records only its repository, not the path inside it, so there is no exact command to give. Open langchain-ai/langgraphjs and copy the folder into ~/.claude/skills/, or the file into ~/.claude/agents/.

What Langgraphjs does

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

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README

🦜🕸️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",
    },
  ],
});