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

# Run Muse Code in a CoreWeave sandbox

> Run Muse Code CLI on a repository in a sandbox, retrieve the result, and stop compute.

Run Muse Code inside a CoreWeave sandbox to edit files and execute commands in a remote workspace. This guide uses the Sandbox software development kit (SDK) to install the command-line interface (CLI) and clone a repository. You then run a task without an interactive terminal and retrieve the result before you stop the sandbox. The agent process and workspace run on CoreWeave, and model requests go to Meta.

<Note>
  CoreWeave Serverless sandboxes are in public preview.
</Note>

## Prerequisites

Before you begin, you need the following:

* A [W\&B API key](https://wandb.ai/authorize). These examples select W\&B authentication. For other credentials, see [Choose a credential](/products/sandboxes/get-started#choose-a-credential).
* A [Meta API key](https://dev.meta.ai/docs/muse-code/auth) with access to Muse Code.
* A public repository URL the sandbox can clone. Private repositories require Git credentials inside the sandbox.
* Python 3.11 or later with `uv`, or Node.js 22 or later with `npm`.

Load your credentials from your secret manager into your local terminal environment. Replace `[WANDB-API-KEY]` with your W\&B API key and `[META-API-KEY]` with your Meta API key:

```bash theme={"system"}
export WANDB_API_KEY="[WANDB-API-KEY]"
export META_API_KEY="[META-API-KEY]"
```

The script passes the Meta key over standard input so it doesn't appear in command arguments or sandbox configuration. For a shared team credential injected as an environment variable at sandbox creation, see [W\&B secrets](/products/sandboxes/secrets).

## Install the Sandbox client

Choose a language and prepare a local project:

<Tabs>
  <Tab title="Python">
    ```bash theme={"system"}
    mkdir muse-sandbox
    cd muse-sandbox
    uv init --bare --python 3.11
    uv venv --python 3.11
    source .venv/bin/activate
    uv pip install 'cwsandbox[wandb]>=1.14.2'
    ```
  </Tab>

  <Tab title="TypeScript">
    ```bash theme={"system"}
    mkdir muse-sandbox
    cd muse-sandbox
    npm init -y
    npm install @coreweave/cwsandbox@0.5.0-beta.0 tsx
    ```
  </Tab>
</Tabs>

## Run a task in the sandbox

Save the script for your language using the filename shown. Each script creates a serverless sandbox with a 30-minute maximum lifetime and performs these steps:

1. Installs Muse Code with Meta's installer.
2. Clones your repository into the `/workspace/project` directory.
3. Runs `muse exec` to build and test a sparkline CLI that renders numbers as a small chart.
4. Runs the generated CLI independently, checks its output, and saves the `sparkline.mjs` file locally.
5. Stops the sandbox in a `finally` block, including when setup or execution fails.

These examples were tested with Muse Code 1.3.0. The installer can install a newer version, so each script runs `muse --version` after installation.

The example uses `--yolo` to disable Muse Code's approval prompts and operating system (OS) sandbox and trust the workspace for this run. CoreWeave supplies the outer sandbox. Muse can access files, credentials, and network destinations available inside that sandbox. Use a repository you trust and provide only the credentials the task needs. For more information, see [Muse Code permissions](https://dev.meta.ai/docs/muse-code/permissions).

<Tabs>
  <Tab title="Python">
    ```python title="run_muse.py" theme={"system"}
    import os
    import sys
    from pathlib import Path

    from cwsandbox import AuthStrategy, Sandbox

    if len(sys.argv) != 2:
        raise SystemExit("Usage: python run_muse.py [REPOSITORY-URL]")
    meta_api_key = os.environ["META_API_KEY"]

    sandbox = Sandbox.run(
        auth=AuthStrategy.WANDB,
        container_image="node:22-bookworm",
        resources={"cpu": "2", "memory": "4Gi"},
        max_lifetime_seconds=1800,
        tags=["muse-code"],
    )
    try:
        sandbox.wait()
        commands = [
            ["bash", "-o", "pipefail", "-ec", "curl -fsSL https://dev.meta.ai/install.sh | bash"],
            ["bash", "-ec", '"$HOME/.local/bin/muse" --version'],
            ["git", "clone", "--", sys.argv[1], "/workspace/project"],
        ]
        for command in commands:
            result = sandbox.exec(command, timeout_seconds=600, check=True).result()
            print(result.stdout, end="")

        agent = sandbox.exec(
            [
                "bash", "-ec",
                'IFS= read -r META_API_KEY; export META_API_KEY; '
                'exec "$HOME/.local/bin/muse" exec --yolo "$1"',
                "bash",
                "Create sparkline.mjs, a dependency-free Node.js CLI that converts its "
                "numeric arguments to a sparkline using ▁▂▃▄▅▆▇█. Scale from the minimum "
                "to the maximum and round to the nearest bar index. "
                "Running node sparkline.mjs 2 4 8 4 2 must print ▁▃█▃▁ followed by "
                "a newline. Run that command to test it.",
            ],
            cwd="/workspace/project",
            stdin=True,
            timeout_seconds=600,
        )
        # Keep stdin open until Muse finishes to avoid aborting a quiet request.
        agent.stdin.writeline(meta_api_key).result()
        result = agent.result()
        print(result.stdout, end="")
        print(result.stderr, end="", file=sys.stderr)
        if result.returncode != 0:
            raise SystemExit(result.returncode)

        chart = sandbox.exec(
            ["node", "sparkline.mjs", "2", "4", "8", "4", "2"],
            cwd="/workspace/project", check=True,
        ).result()
        if chart.stdout != "▁▃█▃▁\n":
            raise RuntimeError(f"Unexpected sparkline: {chart.stdout!r}")
        print(chart.stdout, end="")
        source = sandbox.exec(
            ["cat", "/workspace/project/sparkline.mjs"], check=True
        ).result()
        Path("sparkline.mjs").write_text(source.stdout, encoding="utf-8")
    finally:
        sandbox.stop().result()
    ```
  </Tab>

  <Tab title="TypeScript">
    ```typescript title="run_muse.mts" theme={"system"}
    import { writeFile } from "node:fs/promises";
    import { createSandboxClientFromEnv } from "@coreweave/cwsandbox/wandb";

    const repository = process.argv[2];
    if (!repository) throw new Error("Pass a repository URL.");
    const metaApiKey = process.env.META_API_KEY;
    if (!metaApiKey) throw new Error("Set META_API_KEY.");

    const client = createSandboxClientFromEnv();
    const sandbox = await client.create({
      containerImage: "node:22-bookworm",
      resources: { cpu: "2", memory: "4Gi" },
      maxLifetimeSeconds: 1800,
      waitUntilRunning: false,
      tags: ["muse-code"],
    });
    try {
      await sandbox.wait();
      const commands = [
        ["bash", "-o", "pipefail", "-ec", "curl -fsSL https://dev.meta.ai/install.sh | bash"],
        ["bash", "-ec", '"$HOME/.local/bin/muse" --version'],
        ["git", "clone", "--", repository, "/workspace/project"],
      ];
      for (const command of commands) {
        const result = await sandbox.commands.run(command, { timeoutMs: 600_000, check: true });
        process.stdout.write(result.stdout);
      }

      const agent = await sandbox.commands.start(
        [
          "bash", "-ec",
          'IFS= read -r META_API_KEY; export META_API_KEY; ' +
            'exec "$HOME/.local/bin/muse" exec --yolo "$1"',
          "bash",
          "Create sparkline.mjs, a dependency-free Node.js CLI that converts its " +
            "numeric arguments to a sparkline using ▁▂▃▄▅▆▇█. Scale from the minimum " +
            "to the maximum and round to the nearest bar index. " +
            "Running node sparkline.mjs 2 4 8 4 2 must print ▁▃█▃▁ followed by " +
            "a newline. Run that command to test it.",
        ],
        {
          cwd: "/workspace/project",
          stdin: true,
          timeoutMs: 600_000,
        },
      );
      // Keep stdin open until Muse finishes to avoid aborting a quiet request.
      await agent.stdin.write(metaApiKey + "\n");
      const result = await agent.wait();
      process.stdout.write(result.stdout);
      process.stderr.write(result.stderr);
      if (!result.ok) throw new Error(`Muse Code exited with code ${result.exitCode}.`);

      const chart = await sandbox.commands.run(
        ["node", "sparkline.mjs", "2", "4", "8", "4", "2"],
        { cwd: "/workspace/project", check: true },
      );
      if (chart.stdout !== "▁▃█▃▁\n") {
        throw new Error(`Unexpected sparkline: ${JSON.stringify(chart.stdout)}`);
      }
      process.stdout.write(chart.stdout);
      const source = await sandbox.commands.run(
        ["cat", "/workspace/project/sparkline.mjs"], { check: true },
      );
      await writeFile("sparkline.mjs", source.stdout, "utf8");
    } finally {
      await sandbox.stop();
    }
    ```
  </Tab>
</Tabs>

In your local project directory, replace `[REPOSITORY-URL]` with your public repository URL, then run the script:

<Tabs>
  <Tab title="Python">
    ```bash theme={"system"}
    python run_muse.py [REPOSITORY-URL]
    ```
  </Tab>

  <Tab title="TypeScript">
    ```bash theme={"system"}
    npx tsx run_muse.mts [REPOSITORY-URL]
    ```
  </Tab>
</Tabs>

After the installation and agent output, the script checks and prints the sparkline:

```text theme={"system"}
▁▃█▃▁
```

The script saves the generated `sparkline.mjs` file in your local project directory, then stops the sandbox. If the file already exists locally, the script overwrites it. The output check runs independently of Muse's own test and verifies this example's input. For a larger task, replace it with tests for your requirements.

These examples don't configure persistent storage. Copy the files you need out of the sandbox before the `finally` block runs. See [file operations](/products/sandboxes/client/guides/file-operations) and [file system snapshots](/products/sandboxes/file-system-snapshots).

## Troubleshoot

Use these checks to resolve common issues:

* If sandbox creation fails, check your W\&B key and authentication settings.
* If Muse Code reports an authentication error, check `META_API_KEY` and your Meta account setup. The sandbox credential doesn't authenticate model requests.
* If installation, cloning, or model requests fail, check outbound access to Meta and your Git host. These examples use serverless placement. To use your own CoreWeave Kubernetes Service (CKS) cluster, switch to a CoreWeave API access token and the client settings in [Configure CKS placement and policy](/products/sandboxes/agents#configure-cks-placement-and-policy). Confirm that the runner's policy grants egress to those destinations.
* If a larger task times out, adjust the command timeout and sandbox lifetime before running it. The 30-minute lifetime includes startup and installation. See [command timeouts](/products/sandboxes/client/guides/execution#set-a-timeout) and [sandbox lifecycle](/products/sandboxes/client/guides/sandbox-lifecycle).

## Next steps

For more information, see these guides:

* [Interactive shells and TTY](/products/sandboxes/client/guides/interactive-shells) covers terminal access for interactive agents.
* [Run agents on CoreWeave sandboxes](/products/sandboxes/agents) compares agent integrations and workspace options.
* [Meta's sandboxed execution cookbook](https://dev.meta.ai/docs/cookbook/sandboxed-execution) demonstrates a reproduce, fix, and verify workflow with a custom tool loop in Docker.
* [Muse Code documentation](https://dev.meta.ai/docs/muse-code) covers agent configuration and other CLI workflows.


## Related topics

- [About CoreWeave sandboxes](/products/sandboxes.md)