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

# Quickstart

> Sign up, authenticate, and run your first serverless function on Beam

Run your first function on Beam in a few minutes. Follow [For Humans](#for-humans) if you're setting things up by hand, or [For Agents](#for-agents) if you're a coding agent working on a user's behalf.

## For Humans

<Steps>
  <Step title="Set up">
    Create a free account at [platform.beam.cloud](https://platform.beam.cloud) — you'll get \$30 in free credit. Copy your token from [Settings → API Keys](https://platform.beam.cloud/settings/api-keys), then install the SDK and authenticate:

    ```bash theme={null}
    uv tool install beam-client

    beam configure default --token YOUR_TOKEN
    ```

    This saves your credentials to `~/.beam/config.ini` — without them, the next command will stop and prompt you instead of running your code. See [Installation](/v2/getting-started/installation) for Homebrew, Windows, and the [TypeScript SDK](/v2/reference/ts-sdk).
  </Step>

  <Step title="Run a function in the cloud">
    The simplest way to run code on Beam is to add the `@function` decorator to any Python function. Save this to `app.py`:

    ```python app.py theme={null}
    from beam import function


    @function(cpu=1, memory="1Gi")
    def square(x: int):
        return {"result": x**2}


    if __name__ == "__main__":
        print(square.remote(x=12))
    ```

    Run it like any other Python file:

    ```sh theme={null}
    python app.py
    ```

    Beam syncs your code, launches a container, runs the function, and streams the result back to your shell:

    ```
    => Building image
    => Using cached image
    => Syncing files
    => Files synced
    => Running function: <app:square>
    {'result': 144}
    => Function complete
    ```

    The container spins up in seconds, runs your code, and shuts itself down. No idle costs, no infrastructure to clean up.
  </Step>

  <Step title="Deploy a web endpoint">
    To turn your code into a live web API, swap `@function` for `@endpoint`. We'll include `numpy` in the image to show how easily you can add Python packages.

    * `Image()` defines your container environment. You can add Python packages, system dependencies, or even custom Dockerfiles.
    * `@endpoint` turns your function into a real, live web API that runs in the cloud.

    ```python app.py theme={null}
    from beam import endpoint, Image


    @endpoint(
        name="quickstart",
        cpu=1,
        memory="1Gi",
        image=Image().add_python_packages(["numpy"]),
    )
    def predict(**inputs):
        x = inputs.get("x", 256)
        return {"result": x**2}
    ```

    Deploy it to the cloud:

    ```sh theme={null}
    beam deploy app.py:predict
    ```
  </Step>

  <Step title="Call the API">
    When the deploy finishes, Beam prints your endpoint URL along with a ready-to-run `curl` command. Replace `YOUR_TOKEN` with your token and use the URL from your deploy output:

    <CodeGroup>
      ```sh curl theme={null}
      curl -X POST 'https://app.beam.cloud/endpoint/quickstart' \
        -H 'Authorization: Bearer YOUR_TOKEN' \
        -H 'Content-Type: application/json' \
        -d '{"x": 12}'
      ```

      ```typescript TypeScript theme={null}
      import { beamOpts, Deployments } from "@beamcloud/beam-js";

      beamOpts.token = process.env.BEAM_TOKEN!;
      beamOpts.workspaceId = process.env.BEAM_WORKSPACE_ID!;

      const deployment = await Deployments.get({
        name: "quickstart",
        stubType: "endpoint/deployment",
      });

      const response = await deployment.call({ x: 12 });
      console.log(response);
      ```
    </CodeGroup>

    Either way, you'll get back:

    ```json theme={null}
    { "result": 144 }
    ```
  </Step>
</Steps>

## For Agents

For coding agents setting up Beam on a user's behalf. There's no programmatic sign-up: ask the user for a token from [Settings → API Keys](https://platform.beam.cloud/settings/api-keys). Everything else is non-interactive — using the same `app.py` examples above:

```bash theme={null}
uv tool install beam-client

# saves credentials to ~/.beam/config.ini; in ephemeral environments and CI,
# exporting BEAM_TOKEN works without any config file
beam configure default --token "$BEAM_TOKEN"

python app.py                # run a @function remotely; prints the result
beam deploy app.py:predict   # deploy an @endpoint; prints the endpoint URL
beam deployment list         # confirm the deployment is live
```

<Tip>
  The docs are machine-readable: fetch [llms.txt](https://docs.beam.cloud/llms.txt) for an index of every page, append `.md` to any docs URL for raw Markdown, or connect the docs MCP server at `https://docs.beam.cloud/mcp`. See [Using Beam Docs with AI Tools](/v2/resources/ai-tools).
</Tip>

## What Next?

Here are some other things you can try:

* [Customize your container image](/v2/environment/custom-images)
* [Add a GPU to your app](/v2/environment/gpu)
* [Run a scheduled job](/v2/function/scheduled-job)
* [Parallelize a function across 10 containers](/v2/scaling/parallelizing-functions)