Run a local server ↗

Original Documentation

Documentation Index#
Fetch the complete documentation index at: https://docs.langchain.com/llms.txt Use this file to discover all available pages before exploring further.

This guide shows you how to run a LangGraph application locally.

Prerequisites#

Before you begin, ensure you have the following:

An API key for LangSmith - free to sign up

1. Install the LangGraph CLI#

npm install --save-dev @langchain/langgraph-cli

2. Create a LangGraph app#

Create a new app from the new-langgraph-project-js template. This template demonstrates a single-node application you can extend with your own logic.

npm create langgraph

If you have an existing project with LangGraph agents, you can automatically generate a `langgraph.json` configuration file using the `config` command:

npm create langgraph config

This command scans your project for LangGraph agents (such as createAgent(), StateGraph.compile(), or workflow.compile() patterns) and generates a configuration file with all exported agents.

Example output:

{
  "node_version": "24",
  "graphs": {
    "agent": "./src/agent.ts:agent",
    "searchAgent": "./src/search.ts:searchAgent"
  },
  "env": ".env"
}

Only exported agents are included in the configuration. If an agent is not exported, the command will warn you so you can add the export keyword.

3. Install dependencies#

In the root of your new LangGraph app, install the dependencies in edit mode so your local changes are used by the server:

cd path/to/your/app
npm install

4. Create a `.env` file#

You will find a .env.example in the root of your new LangGraph app. Create a .env file in the root of your new LangGraph app and copy the contents of the .env.example file into it, filling in the necessary API keys:

LANGSMITH_API_KEY=lsv2...

5. Launch Agent server#

Start the LangGraph API server locally:

npx @langchain/langgraph-cli dev

Sample output:

INFO:langgraph_api.cli:

        Welcome to

╦  ┌─┐┌┐┌┌─┐╔═╗┬─┐┌─┐┌─┐┬ ┬
║  ├─┤││││ ┬║ ╦├┬┘├─┤├─┘├─┤
╩═╝┴ ┴┘└┘└─┘╚═╝┴└─┴ ┴┴  ┴ ┴

- 🚀 API: http://127.0.0.1:2024
- 🎨 Studio UI: https://smith.langchain.com/studio/?baseUrl=http://127.0.0.1:2024
- 📚 API Docs: http://127.0.0.1:2024/docs

This in-memory server is designed for development and testing.
For production use, please use LangSmith Deployment.

The langgraph dev command starts Agent Server in an in-memory mode. This mode is suitable for development and testing purposes. For production use, deploy Agent Server with access to a persistent storage backend. For more information, see the Platform setup overview.

6. Test your application in Studio#

Studio is a specialized UI that you can connect to LangGraph API server to visualize, interact with, and debug your application locally. Test your graph in Studio by visiting the URL provided in the output of the langgraph dev command:

>    - LangGraph Studio Web UI: https://smith.langchain.com/studio/?baseUrl=http://127.0.0.1:2024

For an Agent Server running on a custom host/port, update the baseUrl query parameter in the URL. For example, if your server is running on http://myhost:3000:

https://smith.langchain.com/studio/?baseUrl=http://myhost:3000

Use the `--tunnel` flag with your command to create a secure tunnel, as Safari has limitations when connecting to localhost servers:

langgraph dev --tunnel

7. Test the API#

Install the LangGraph JS SDK:

    npm install @langchain/langgraph-sdk
    ```

Send a message to the assistant (threadless run):

    import { Client } from "@langchain/langgraph-sdk";

    // only set the apiUrl if you changed the default port when calling langgraph dev
    const client = new Client({ apiUrl: "http://localhost:2024"});

    const streamResponse = client.runs.stream(
      null, // Threadless run
      "agent", // Assistant ID
      {
        input: {
          "messages": [
            { "role": "user", "content": "What is LangGraph?"}
          ]
        },
        streamMode: "messages-tuple",
      }
    );

    for await (const chunk of streamResponse) {
      console.log(`Receiving new event of type: ${chunk.event}...`);
      console.log(JSON.stringify(chunk.data));
      console.log("\n\n");
    }
    ```
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  <span class="tab-start" data-tab-title="Rest API"></span>
```bash
    curl -s --request POST \
        --url "http://localhost:2024/runs/stream" \
        --header 'Content-Type: application/json' \
        --data "{
            \"assistant_id\": \"agent\",
            \"input\": {
                \"messages\": [
                    {
                        \"role\": \"human\",
                        \"content\": \"What is LangGraph?\"
                    }
                ]
            },
            \"stream_mode\": \"messages-tuple\"
        }"
    ```
  <span class="tab-end"></span>
<span class="tab-group-end"></span>

## Next steps

Now that you have a LangGraph app running locally, take your journey further by exploring deployment and advanced features:

* [Deployment quickstart](/langsmith/deployment-quickstart): Deploy your LangGraph app using LangSmith.

* [LangSmith](/langsmith/home): Learn about foundational LangSmith concepts.

* [SDK Reference](https://reference.langchain.com/javascript/modules/_langchain_langgraph-sdk.html): Explore the SDK API Reference.

***


  <span class="callout-start" data-callout-type="note"></span>
[Edit this page on GitHub](https://github.com/langchain-ai/docs/edit/main/src/oss/langgraph/local-server.mdx) or [file an issue](https://github.com/langchain-ai/docs/issues/new/choose).
  <span class="callout-end"></span>

  <span class="callout-start" data-callout-type="note"></span>
[Connect these docs](/use-these-docs) to Claude, VSCode, and more via MCP for real-time answers.
  <span class="callout-end"></span>

Link last verified June 7, 2026. View original ↗

Source: LangChain Docs

Link last verified: 2026-03-04