> ## Documentation Index
> Fetch the complete documentation index at: https://langchain-5e9cc07a-preview-cbfron-1772840960-d2a2597.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

# Deploy your app to Cloud

This is a quickstart guide for deploying your first application to LangSmith Cloud.

<Tip>
  For a comprehensive Cloud deployment guide with all configuration options, refer to the [Cloud deployment setup guide](/langsmith/deploy-to-cloud).
</Tip>

## Prerequisites

Before you begin, ensure you have the following:

* A [GitHub account](https://github.com/)
* A [LangSmith account](https://smith.langchain.com/) (free to sign up)

## 1. Create a repository on GitHub

To deploy an application to **LangSmith**, your application code must reside in a GitHub repository. Both public and private repositories are supported. For this quickstart, use the [`new-langgraph-project` template](https://github.com/langchain-ai/react-agent) for your application:

1. Go to the [`new-langgraph-project` repository](https://github.com/langchain-ai/new-langgraph-project) or [`new-langgraphjs-project` template](https://github.com/langchain-ai/new-langgraphjs-project).
2. Click the `Fork` button in the top right corner to fork the repository to your GitHub account.
3. Click **Create fork**.

## 2. Deploy to LangSmith

1. Log in to [LangSmith](https://smith.langchain.com/).
2. In the left sidebar, select **Deployments**.
3. Click the **+ New Deployment** button. A pane will open where you can fill in the required fields.
4. If you are a first time user or adding a private repository that has not been previously connected, click the **Import from GitHub** button and follow the instructions to connect your GitHub account.
5. Select your New LangGraph Project repository.
6. Click **Submit** to deploy.
   This may take about 15 minutes to complete. You can check the status in the **Deployment details** view.

## 3. Test your application in Studio

Once your application is deployed:

1. Select the deployment you just created to view more details.
2. Click the **Studio** button in the top right corner. [Studio](/langsmith/studio) will open to display your graph.

## 4. Get the API URL for your deployment

1. In the **Deployment details** view, click the **API URL** to copy it to your clipboard.
2. Click the `URL` to copy it to the clipboard.

## 5. Test the API

You can now test the API:

<Tabs>
  <Tab title="Python SDK (Async)">
    1. Install the LangGraph Python SDK:

    ```shell theme={null}
    pip install langgraph-sdk
    ```

    2. Send a message to the assistant (threadless run):

    ```python theme={null}
    from langgraph_sdk import get_client

    client = get_client(url="your-deployment-url", api_key="your-langsmith-api-key")

    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")
    ```
  </Tab>

  <Tab title="Python SDK (Sync)">
    1. Install the LangGraph Python SDK:

    ```shell theme={null}
    pip install langgraph-sdk
    ```

    2. Send a message to the assistant (threadless run):

    ```python theme={null}
    from langgraph_sdk import get_sync_client

    client = get_sync_client(url="your-deployment-url", api_key="your-langsmith-api-key")

    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")
    ```
  </Tab>

  <Tab title="JavaScript SDK">
    1. Install the LangGraph JS SDK

    ```shell theme={null}
    npm install @langchain/langgraph-sdk
    ```

    2. Send a message to the assistant (threadless run):

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

    const client = new Client({ apiUrl: "your-deployment-url", apiKey: "your-langsmith-api-key" });

    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");
    }
    ```
  </Tab>

  <Tab title="Rest API">
    ```bash theme={null}
    curl -s --request POST \
        --url <DEPLOYMENT_URL>/runs/stream \
        --header 'Content-Type: application/json' \
        --header "X-Api-Key: <LANGSMITH API KEY> \
        --data "{
            \"assistant_id\": \"agent\",
            \"input\": {
                \"messages\": [
                    {
                        \"role\": \"human\",
                        \"content\": \"What is LangGraph?\"
                    }
                ]
            },
            \"stream_mode\": \"updates\"
        }"
    ```
  </Tab>
</Tabs>

## Next steps

You've successfully deployed your application to LangSmith Cloud. Here are some next steps:

* **Explore Studio**: Use [Studio](/langsmith/studio) to visualize and debug your graph interactively.
* **Monitor your app**: Set up [observability](/langsmith/observability) with traces, dashboards, and alerts.
* **Learn more about Cloud**: See the [complete Cloud setup guide](/langsmith/deploy-to-cloud) for all configuration options.

***

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