> ## Documentation Index
> Fetch the complete documentation index at: https://docs.langchain.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Deploy your app to cloud

> Deploy your first application to LangSmith Cloud (AWS and GCP) using the LangGraph CLI.

This quickstart shows you how to deploy an application to LangSmith Cloud (AWS and GCP) using the [`langgraph deploy`](/langsmith/cli#deploy) command.

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

<Note>
  The `langgraph deploy` command is in **beta**.
</Note>

## Prerequisites

Before you begin, ensure you have:

* A [LangSmith account](https://smith.langchain.com?utm_source=docs\&utm_medium=cta\&utm_campaign=langsmith-signup\&utm_content=langsmith-deployment-quickstart) on the [Plus plan or above](https://www.langchain.com/pricing) and an [API key](/langsmith/create-account-api-key).
* [Docker](https://docs.docker.com/get-docker/) installed and running. Verify with `docker ps`.
* On Apple Silicon (M1/M2/M3): [Docker Buildx](https://docs.docker.com/build/install-buildx/) for cross-compiling to `linux/amd64`.
* The [LangGraph CLI](/langsmith/cli):

  ```shell theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
  uv tool install langgraph-cli
  ```

## 1. Create a LangGraph app

Create a new app from the [`new-langgraph-project-python` template](https://github.com/langchain-ai/new-langgraph-project):

```shell theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
langgraph new path/to/your/app --template new-langgraph-project-python
cd path/to/your/app
```

<Tip>
  Run `langgraph new` without `--template` for an interactive menu of available templates.
</Tip>

## 2. Set your API key

Add your LangSmith API key to a `.env` file in your project root:

```shell theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
LANGSMITH_API_KEY=lsv2_...
```

The `langgraph deploy` command reads this automatically. Alternatively, pass it inline:

```shell theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
LANGSMITH_API_KEY=lsv2_... langgraph deploy
```

## 3. Deploy

Run the deploy command from your project directory:

```shell theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
langgraph deploy
```

This creates a `dev` deployment named after your project directory by default. Use `--name` or `--deployment-type prod` to override.

<Tip>
  To update an existing deployment after making code changes, re-run `langgraph deploy`. It finds the existing deployment by name and updates it in place.
</Tip>

You can also use `langgraph deploy list` to see all deployments, `langgraph deploy logs` to tail runtime logs, and `langgraph deploy delete <ID>` to remove a deployment. For details, refer to the [CLI reference](/langsmith/cli#deploy).

## 4. Test in Studio

[Studio](/langsmith/studio) is an interactive agent IDE connected directly to your deployment. Use it to send messages, inspect intermediate state at each node, edit state mid-run, and replay from any prior checkpoint without writing code.

Once the deployment is ready:

1. Go to [LangSmith](https://smith.langchain.com?utm_source=docs\&utm_medium=cta\&utm_campaign=langsmith-signup\&utm_content=langsmith-deployment-quickstart) and select **Deployments** in the left sidebar.
2. Select your deployment to view its details.
3. Click **Studio** in the top right corner to open [Studio](/langsmith/studio).

## 5. Test the API

Copy the **API URL** from the deployment details view, then use it to call your application:

<Tabs>
  <Tab title="Python SDK (Async)">
    1. Install the LangGraph Python SDK:
       ```shell theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
       pip install langgraph-sdk
       ```
    2. Send a message to the assistant (stateless run):
       ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
       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={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
       pip install langgraph-sdk
       ```
    2. Send a message to the assistant (threadless run):
       ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
       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={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
       npm install @langchain/langgraph-sdk
       ```
    2. Send a message to the assistant (threadless run):
       ```js theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
       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={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
    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

<CardGroup cols={3}>
  <Card title="Assistants" icon="robot" href="/langsmith/assistants">
    Deploy the same graph with different models, prompts, or tools per assistant.
  </Card>

  <Card title="Threads" icon="messages" href="/langsmith/use-threads">
    Persist state across multiple runs so your agent remembers context between interactions.
  </Card>

  <Card title="Runs" icon="player-play" href="/langsmith/background-run">
    Kick off background runs for long-running jobs and stream results back to your client.
  </Card>
</CardGroup>

***

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  </Callout>

  <Callout icon="edit">
    [Edit this page on GitHub](https://github.com/langchain-ai/docs/edit/main/src/langsmith/deployment-quickstart.mdx) or [file an issue](https://github.com/langchain-ai/docs/issues/new/choose).
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