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Run a local server

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

Prerequisites

Before you begin, ensure you have the following:

1. Install the LangGraph CLI

pipuv

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

2. Create a LangGraph agent

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

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

Tip

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:

pipuv

cd path/to/your/app
pip install -e .

4. Create a .env file

Note

In an agent-based workspace, create an agent once before sending traces. Use agent addressing to select its identifier and environment. If an integration requires project_name, use the selected environment's tracer_session_name as a compatibility value.

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...
LANGSMITH_AGENT_ID="<your-agent-id>"
LANGSMITH_AGENT_ENVIRONMENT="production"

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 agent in Studio

Studio is a specialized UI that you can connect to LangGraph API server to visualize, interact with, and debug your agent 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

7. Test the API

Python SDK (async)Python SDK (sync)Rest 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())

Next steps

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