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

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Prerequisites

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

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1. Install the LangGraph CLI

  • Python server
  • Node server
# Python >= 3.11 is required.

pip install -U "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.
  • Python server
  • Node server
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:
  • Python server
  • Node 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:
  • Python server
  • Node server
langgraph dev
Sample output: 
>    Ready!
>
>    - API: [http://localhost:2024](http://localhost:2024/)
>
>    - Docs: http://localhost:2024/docs
>
>    - 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 a persistent storage backend. For more information, refer to the LangSmith hosting options.

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6. Test the API

  • Python SDK (async)
  • Python SDK (sync)
  • Javascript SDK
  • Rest 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, you’re ready to deploy it: Choose a hosting option for LangSmith:
  • Cloud: Fastest setup, fully managed (recommended).
  • Hybrid: in your cloud, managed by LangChain.
  • Self-hosted: Full control in your infrastructure.
For more details, refer to the hosting comparison. Then deploy your app: Explore features:
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