Run a local server ↗

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#

# Python >= 3.11 is required.
pip install -U "langgraph-cli[inmem]"

# Python >= 3.11 is required.
uv add "langgraph-cli[inmem]"

2. Create a LangGraph app#

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

langgraph new path/to/your/app --template new-langgraph-project-python

Additional templates If you use langgraph new without specifying a template, you will be presented with an interactive menu that will allow you to choose from a list of available templates.

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
pip install -e .

cd path/to/your/app
uv sync

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:

langgraph 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

langgraph dev --tunnel

7. Test the API#

1. Install the LangGraph Python SDK:

       pip install langgraph-sdk
       ```

2. Send a message to the assistant (threadless run):

       from langgraph_sdk import get_client
       import asyncio
   
       client = get_client(url="http://localhost:2024")
   
       async def main():
           async for chunk in client.runs.stream(
               None,  # Threadless run
               "agent", # Name of assistant. Defined in langgraph.json.
               input={
               "messages": [{
                   "role": "human",
                   "content": "What is LangGraph?",
                   }],
               },
           ):
               print(f"Receiving new event of type: {chunk.event}...")
               print(chunk.data)
               print("\n\n")
   
       asyncio.run(main())
       ```

1. Install the LangGraph Python SDK:

       pip install langgraph-sdk
       ```

2. Send a message to the assistant (threadless run):

       from langgraph_sdk import get_sync_client
   
       client = get_sync_client(url="http://localhost:2024")
   
       for chunk in client.runs.stream(
           None,  # Threadless run
           "agent", # Name of assistant. Defined in langgraph.json.
           input={
               "messages": [{
                   "role": "human",
                   "content": "What is LangGraph?",
               }],
           },
           stream_mode="messages-tuple",
       ):
           print(f"Receiving new event of type: {chunk.event}...")
           print(chunk.data)
           print("\n\n")
       ```

    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\"
        }"
    ```
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## 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/python/langsmith/deployment/sdk/): Explore the SDK API Reference.

***

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  [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).
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