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Quick Start: Launch Local LangGraph Server

This is a quick start guide to help you get a LangGraph app up and running locally.

Requirements

Install the LangGraph CLI

pip install "langgraph-cli[inmem]==0.1.58" python-dot-env

🌱 Create a LangGraph App

Create a new app from the react-agent template. This template is a simple agent that can be flexibly extended to many tools.

langgraph new path/to/your/app --template react-agent-python 

langgraph new path/to/your/app --template react-agent-js

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.

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...
TAVILY_API_KEY=tvly-...
ANTHROPIC_API_KEY=sk-
OPENAI_API_KEY=sk-...
Get API Keys

🚀 Launch LangGraph Server

langgraph dev

This will start up the LangGraph API server locally. If this runs successfully, you should see something like:

Ready!

In-Memory Mode

The langgraph dev command starts LangGraph Server in an in-memory mode. This mode is suitable for development and testing purposes. For production use, you should deploy LangGraph Server with access to a persistent storage backend.

If you want to test your application with a persistent storage backend, you can use the langgraph up command instead of langgraph dev. You will need to have docker installed on your machine to use this command.

LangGraph Studio Web UI

Test your graph in the LangGraph Studio Web UI by visiting the URL provided in the output of the langgraph up command.

Safari Compatibility

Currently, LangGraph Studio Web does not support Safari when running a server locally.

Test the API

Install the LangGraph Python SDK

pip install langgraph-sdk

Send a message to the assistant (threadless run)

from langgraph_sdk import get_client

client = get_client(url="http://localhost:8123")

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?",
        }],
    },
    stream_mode="updates",
):
    print(f"Receiving new event of type: {chunk.event}...")
    print(chunk.data)
    print("\n\n")

Install the LangGraph Python SDK

pip install langgraph-sdk

Send a message to the assistant (threadless run)

from langgraph_sdk import get_sync_client

client = get_sync_client(url="http://localhost:8123")

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="updates",
):
    print(f"Receiving new event of type: {chunk.event}...")
    print(chunk.data)
    print("\n\n")

Install the LangGraph JS SDK

npm install @langchain/langgraph-sdk

Send a message to the assistant (threadless run)

const { Client } = await import("@langchain/langgraph-sdk");

// only set the apiUrl if you changed the default port when calling langgraph up
const client = new Client({ apiUrl: "http://localhost:8123"});

const streamResponse = client.runs.stream(
    null, // Threadless run
    "agent", // Assistant ID
    {
        input: {
            "messages": [
                { "role": "user", "content": "What is LangGraph?"}
            ]
        },
        streamMode: "messages",
    }
);

for await (const chunk of streamResponse) {
    console.log(`Receiving new event of type: ${chunk.event}...`);
    console.log(JSON.stringify(chunk.data));
    console.log("\n\n");
}

curl -s --request POST \
    --url "http://localhost:8123/runs/stream" \
    --header 'Content-Type: application/json' \
    --data "{
        \"assistant_id\": \"agent\",
        \"input\": {
            \"messages\": [
                {
                    \"role\": \"human\",
                    \"content\": \"What is LangGraph?\"
                }
            ]
        },
        \"stream_mode\": \"updates\"
    }"

Auth

If you're connecting to a remote server, you will need to provide a LangSmith API Key for authorization. Please see the API Reference for the clients for more information.

Next Steps

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

🌐 Deploy to LangGraph Cloud

📚 Learn More about LangGraph Platform

Expand your knowledge with these resources:

🛠️ Developer References

Access detailed documentation for development and API usage:

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