This quickstart shows you how to set up a LangGraph application locally for testing and development.

​
Prerequisites

Before you begin, ensure you have an API key for LangSmith (free to sign up).

​
1. Install the LangGraph CLI

# Python >= 3.11 is required.

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

​
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.

​
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 .

​
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 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, refer to the Deployment options.

​
6. 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())

​
Next steps

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