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

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Prerequisites

Before you begin, ensure you have the following:

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1. Install the LangGraph CLI

# Python >= 3.11 is required.

pip install --upgrade "langgraph-cli[inmem]"

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2. Create a LangGraph app 🌱

Create a new app from the new-langgraph-project-python template or new-langgraph-project-js 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.

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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 .

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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...

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5. Launch LangGraph Server 🚀

Start the LangGraph API server locally: 
langgraph dev
Sample output: 
>    Ready!
>
>    - API: [http://localhost:2024](http://localhost:2024/)
>
>    - Docs: http://localhost:2024/docs
>
>    - LangGraph Studio Web UI: https://smith.langchain.com/studio/?baseUrl=http://127.0.0.1:2024
The langgraph dev command starts LangGraph Server in an in-memory mode. This mode is suitable for development and testing purposes. For production use, deploy LangGraph Server with access to a persistent storage backend. For more information, see Deployment options.

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6. Test your application in LangGraph Studio

LangGraph 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 LangGraph 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 a LangGraph Server running on a custom host/port, update the baseURL parameter.
Use the --tunnel flag with your command to create a secure tunnel, as Safari has limitations when connecting to localhost servers:
langgraph dev --tunnel

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7. Test the API

  1. Install the LangGraph Python SDK:
pip install langgraph-sdk
  1. 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())

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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: