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Vercel Sandbox

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Run untrusted code safely in ephemeral microVMs.

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Free · Opens the source repo

What Vercel Sandbox does

Vercel Sandbox provides a secure environment for executing untrusted code using ephemeral Firecracker microVMs. This skill is particularly useful for developers and designers who need to run user-generated or AI-generated code in isolation, ensuring that the execution does not affect the host system. By leveraging Vercel's infrastructure, the sandbox allows for the execution of browser automation tasks using headless Chrome, making it suitable for a variety of web-related workflows.

The sandbox operates by spinning up a Linux VM on demand, executing browser commands, and shutting down once the task is complete. This ensures that each execution is contained and does not leave residual effects on the system. The skill supports any Vercel-deployed framework, such as Next.js, SvelteKit, and others, allowing for seamless integration into existing projects. Developers can utilize the provided functions to automate browser interactions, take screenshots, and perform multi-step workflows without the risk of running malicious code on their local environment.

To get started, users need to install the @vercel/sandbox package and set up the necessary system dependencies for Chromium. The skill also supports the use of sandbox snapshots, which can significantly reduce startup time by pre-installing required libraries and tools. This feature is akin to using Docker images, allowing for sub-second startup times when executing repetitive tasks.

Overall, Vercel Sandbox is an essential tool for those looking to safely experiment with code execution and browser automation, providing a robust solution for handling untrusted code in a controlled manner.

When to use it

Use Vercel Sandbox when you need to execute user-generated or AI-generated code in a secure and isolated environment.

When not to use it

This skill may not be suitable for applications requiring persistent state or long-running processes, as the sandbox is ephemeral.

What you can build with it

Automating Form Submission

Use Vercel Sandbox to automate the process of filling out and submitting web forms, ensuring that the execution is isolated and secure.

Taking Screenshots of Web Pages

Leverage Vercel Sandbox to capture screenshots of web pages in a controlled environment, useful for testing and documentation.

Running User-Generated Code

Safely execute user-generated scripts in Vercel Sandbox to prevent any potential harm to your local development environment.

How to install Vercel Sandbox

View source

1. Install with the skills CLI

npx skills add vercel/vercel-plugin/vercel-sandbox --agent claude-code

2. Or install it manually

Download the skill folder and drop it into ~/.claude/skills/ for all projects, or .claude/skills/ to scope it to one repo. Restart Claude Code so it picks up the new skill.

Anthropic's agentic coding CLI, and the reference implementation of Agent Skills. Drop a skill folder into ~/.claude/skills and Claude Code loads it automatically whenever a task matches the skill's description. Claude Code docs

Inside SKILL.md

Written by vercel

Browser Automation with Vercel Sandbox

Run agent-browser + headless Chrome inside ephemeral Vercel Sandbox microVMs. A Linux VM spins up on demand, executes browser commands, and shuts down. Works with any Vercel-deployed framework (Next.js, SvelteKit, Nuxt, Remix, Astro, etc.).

Dependencies

pnpm add @vercel/sandbox

The sandbox VM needs system dependencies for Chromium plus agent-browser itself. Use sandbox snapshots (below) to pre-install everything for sub-second startup.

Core Pattern

import { Sandbox } from "@vercel/sandbox";

// System libraries required by Chromium on the sandbox VM (Amazon Linux / dnf)
const CHROMIUM_SYSTEM_DEPS = [
  "nss", "nspr", "libxkbcommon", "atk", "at-spi2-atk", "at-spi2-core",
  "libXcomposite", "libXdamage", "libXrandr", "libXfixes", "libXcursor",
  "libXi", "libXtst", "libXScrnSaver", "libXext", "mesa-libgbm", "libdrm",
  "mesa-libGL", "mesa-libEGL", "cups-libs", "alsa-lib", "pango", "cairo",
  "gtk3", "dbus-libs",
];

function getSandboxCredentials() {
  if (
    process.env.VERCEL_TOKEN &&
    process.env.VERCEL_TEAM_ID &&
    process.env.VERCEL_PROJECT_ID
  ) {
    return {
      token: process.env.VERCEL_TOKEN,
      teamId: process.env.VERCEL_TEAM_ID,
      projectId: process.env.VERCEL_PROJECT_ID,
    };
  }
  return {};
}

async function withBrowser<T>(
  fn: (sandbox: InstanceType<typeof Sandbox>) => Promise<T>,
): Promise<T> {
  const snapshotId = process.env.AGENT_BROWSER_SNAPSHOT_ID;
  const credentials = getSandboxCredentials();

  const sandbox = snapshotId
    ? await Sandbox.create({
        ...credentials,
        source: { type: "snapshot", snapshotId },
        timeout: 120_000,
      })
    : await Sandbox.create({ ...credentials, runtime: "node24", timeout: 120_000 });

  if (!snapshotId) {
    await sandbox.runCommand("sh", [
      "-c",
      `sudo dnf clean all 2>&1 && sudo dnf install -y --skip-broken ${CHROMIUM_SYSTEM_DEPS.join(" ")} 2>&1 && sudo ldconfig 2>&1`,
    ]);
    await sandbox.runCommand("npm", ["install", "-g", "agent-browser"]);
    await sandbox.runCommand("npx", ["agent-browser", "install"]);
  }

  try {
    return await fn(sandbox);
  } finally {
    await sandbox.stop();
  }
}

Screenshot

The screenshot --json command saves to a file and returns the path. Read the file back as base64:

export async function screenshotUrl(url: string) {
  return withBrowser(async (sandbox) => {
    await sandbox.runCommand("agent-browser", ["open", url]);

    const titleResult = await sandbox.runCommand("agent-browser", [
      "get", "title", "--json",
    ]);
    const title = JSON.parse(await titleResult.stdout())?.data?.title || url;

    const ssResult = await sandbox.runCommand("agent-browser", [
      "screenshot", "--json",
    ]);
    const ssPath = JSON.parse(await ssResult.stdout())?.data?.path;
    const b64Result = await sandbox.runCommand("base64", ["-w", "0", ssPath]);
    const screenshot = (await b64Result.stdout()).trim();

    await sandbox.runCommand("agent-browser", ["close"]);

    return { title, screenshot };
  });
}

Accessibility Snapshot

export async function snapshotUrl(url: string) {
  return withBrowser(async (sandbox) => {
    await sandbox.runCommand("agent-browser", ["open", url]);

    const titleResult = await sandbox.runCommand("agent-browser", [
      "get", "title", "--json",
    ]);
    const title = JSON.parse(await titleResult.stdout())?.data?.title || url;

    const snapResult = await sandbox.runCommand("agent-browser", [
      "snapshot", "-i", "-c",
    ]);
    const snapshot = await snapResult.stdout();

    await sandbox.runCommand("agent-browser", ["close"]);

    return { title, snapshot };
  });
}

Multi-Step Workflows

The sandbox persists between commands, so you can run full automation sequences:

export async function fillAndSubmitForm(url: string, data: Record<string, string>) {
  return withBrowser(async (sandbox) => {
    await sandbox.runCommand("agent-browser", ["open", url]);

    const snapResult = await sandbox.runCommand("agent-browser", [
      "snapshot", "-i",
    ]);
    const snapshot = await snapResult.stdout();
    // Parse snapshot to find element refs...

    for (const [ref, value] of Object.entries(data)) {
      await sandbox.runCommand("agent-browser", ["fill", ref, value]);
    }

    await sandbox.runCommand("agent-browser", ["click", "@e5"]);
    await sandbox.runCommand("agent-browser", ["wait", "--load", "networkidle"]);

    const ssResult = await sandbox.runCommand("agent-browser", [
      "screenshot", "--json",
    ]);
    const ssPath = JSON.parse(await ssResult.stdout())?.data?.path;
    const b64Result = await sandbox.runCommand("base64", ["-w", "0", ssPath]);
    const screenshot = (await b64Result.stdout()).trim();

    await sandbox.runCommand("agent-browser", ["close"]);

    return { screenshot };
  });
}

Sandbox Snapshots (Fast Startup)

A sandbox snapshot is a saved VM image of a Vercel Sandbox with system dependencies + agent-browser + Chromium already installed. Think of it like a Docker image -- instead of installing dependencies from scratch every time, the sandbox boots from the pre-built image.

This is unrelated to agent-browser's accessibility snapshot feature (agent-browser snapshot), which dumps a page's accessibility tree. A sandbox snapshot is a Vercel infrastructure concept for fast VM startup.

Without a sandbox snapshot, each run installs system deps + agent-browser + Chromium (~30s). With one, startup is sub-second.

Creating a sandbox snapshot

The snapshot must include system dependencies (via dnf), agent-browser, and Chromium:

import { Sandbox } from "@vercel/sandbox";

const CHROMIUM_SYSTEM_DEPS = [
  "nss", "nspr", "libxkbcommon", "atk", "at-spi2-atk", "at-spi2-core",
  "libXcomposite", "libXdamage", "libXrandr", "libXfixes", "libXcursor",
  "libXi", "libXtst", "libXScrnSaver", "libXext", "mesa-libgbm", "libdrm",
  "mesa-libGL", "mesa-libEGL", "cups-libs", "alsa-lib", "pango", "cairo",
  "gtk3", "dbus-libs",
];

async function createSnapshot(): Promise<string> {
  const sandbox = await Sandbox.create({
    runtime: "node24",
    timeout: 300_000,
  });

  await sandbox.runCommand("sh", [
    "-c",
    `sudo dnf clean all 2>&1 && sudo dnf install -y --skip-broken ${CHROMIUM_SYSTEM_DEPS.join(" ")} 2>&1 && sudo ldconfig 2>&1`,
  ]);
  await sandbox.runCommand("npm", ["install", "-g", "agent-browser"]);
  await sandbox.runCommand("npx", ["agent-browser", "install"]);

  const snapshot = await sandbox.snapshot();
  return snapshot.snapshotId;
}

Run this once, then set the environment variable:

AGENT_BROWSER_SNAPSHOT_ID=snap_xxxxxxxxxxxx

A helper script is available in the demo app:

npx tsx examples/environments/scripts/create-snapshot.ts

Recommended for any production deployment using the Sandbox pattern.

Authentication

On Vercel deployments, the Sandbox SDK authenticates automatically via OIDC. For local development or explicit control, set:

VERCEL_TOKEN=<personal-access-token>
VERCEL_TEAM_ID=<team-id>
VERCEL_PROJECT_ID=<project-id>

These are spread into Sandbox.create() calls. When absent, the SDK falls back to VERCEL_OIDC_TOKEN (automatic on Vercel).

Scheduled Workflows (Cron)

Combine with Vercel Cron Jobs for recurring browser tasks:

// app/api/cron/route.ts  (or equivalent in your framework)
export async function GET() {
  const result = await withBrowser(async (sandbox) => {
    await sandbox.runCommand("agent-browser", ["open", "https://example.com/pricing"]);
    const snap = await sandbox.runCommand("agent-browser", ["snapshot", "-i", "-c"]);
    await sandbox.runCommand("agent-browser", ["close"]);
    return await snap.stdout();
  });

  // Process results, send alerts, store data...
  return Response.json({ ok: true, snapshot: result });
}
// vercel.json
{ "crons": [{ "path": "/api/cron", "schedule": "0 9 * * *" }] }

Environment Variables

VariableRequiredDescription
AGENT_BROWSER_SNAPSHOT_IDNo (but recommended)Pre-built sandbox snapshot ID for sub-second startup (see above)
VERCEL_TOKENNoVercel personal access token (for local dev; OIDC is automatic on Vercel)
VERCEL_TEAM_IDNoVercel team ID (for local dev)
VERCEL_PROJECT_IDNoVercel project ID (for local dev)

Framework Examples

The pattern works identically across frameworks. The only difference is where you put the server-side code:

FrameworkServer code location
Next.jsServer actions, API routes, route handlers
SvelteKit+page.server.ts, +server.ts
Nuxtserver/api/, server/routes/
Remixloader, action functions
Astro.astro frontmatter, API routes

Example

See examples/environments/ in the agent-browser repo for a working app with the Vercel Sandbox pattern, including a sandbox snapshot creation script, streaming progress UI, and rate limiting.

Frequently asked questions about Vercel Sandbox

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