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vercel-sandbox — Codex skill: what it does, how to install

This skill runs user-generated and AI-generated code inside an ephemeral Vercel Sandbox. Use it for browser automation and code checks that should stay outside the main environment.

Install into Codex · Skills CLI (Vercel) All methods and platforms ↓
npx skills add openai/plugins --skill vercel-sandbox -a codex
Open on GitHub

What vercel-sandbox does

vercel-sandbox starts a temporary Firecracker microVM, runs commands inside it, then stops the environment. Its browser workflow uses agent-browser with headless Chrome: an agent can open a page, read its title, capture a screenshot, or collect an accessibility snapshot.

The Sandbox remains available between commands, so the skill supports multi-step flows. An agent can inspect a form snapshot, fill fields, click a control, wait for network idle, and capture the result. A run may use VERCEL_TOKEN, VERCEL_TEAM_ID, and VERCEL_PROJECT_ID for Vercel credentials. Without a snapshot, the VM needs Chromium system packages, agent-browser, and the browser installed; a snapshot keeps those dependencies ready.

Install it when an agent must execute untrusted code or browser commands in a separate VM. It is unnecessary for ordinary commands that already run safely in the project environment.

What it helps with

  • run untrusted code inside a separate VM
  • open a page and capture its screenshot
  • collect an accessibility snapshot
  • fill and submit a web form
  • wait for network idle after a click

Example prompts for the agent

  • Run this URL in Vercel Sandbox with `agent-browser` and return its title and screenshot.
  • Open the page, collect an accessibility snapshot, fill the form fields, and submit it.
  • Execute this generated code in an isolated Sandbox, then stop the VM.

How to install vercel-sandbox

1Skills CLI (Vercel)

npx skills add openai/plugins --skill vercel-sandbox -a codex

2Manually: put the files in place

.agents/skills/vercel-sandbox/SKILL.md

For all projects use ~/.agents/skills/

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Questions about vercel-sandbox

How do I install vercel-sandbox in Codex?

Run npx skills add openai/plugins --skill vercel-sandbox. The skill file is placed at .agents/skills/vercel-sandbox/SKILL.md.

What does vercel-sandbox do?

It runs commands, user-generated code, and AI-generated code inside a temporary Firecracker microVM. Its included workflows use agent-browser and headless Chrome for browser automation.

What is required to run a browser in Vercel Sandbox?

Without a snapshot, the VM needs Chromium system dependencies, agent-browser, and the browser installed. Vercel credentials may use VERCEL_TOKEN, VERCEL_TEAM_ID, and VERCEL_PROJECT_ID.

Source text

SKILL.md · Apache-2.0
---
name: vercel-sandbox
description: Vercel Sandbox guidance — ephemeral Firecracker microVMs for running untrusted code safely. Supports AI agents, code generation, and experimentation. Use when executing user-generated or AI-generated code in isolation.
metadata:
  priority: 4
  docs:
    - "https://vercel.com/docs/sandbox"
  sitemap: "https://vercel.com/sitemap/docs.xml"
  pathPatterns: []
  importPatterns:
    - '@vercel/sandbox'
  bashPatterns:
    - '\bnpm\s+(install|i|add)\s+[^\n]*@vercel/sandbox\b'
    - '\bpnpm\s+(install|i|add)\s+[^\n]*@vercel/sandbox\b'
    - '\bbun\s+(install|i|add)\s+[^\n]*@vercel/sandbox\b'
    - '\byarn\s+add\s+[^\n]*@vercel/sandbox\b'
  promptSignals:
    phrases:
      - "@vercel/sandbox"
      - "sandbox"
      - "code sandbox"
      - "vercel sandbox"
      - "isolated environment"
      - "sandboxed execution"
    allOf:
      - [sandbox, code]
      - [sandbox, execute]
      - [sandbox, run]
      - [sandbox, isolated]
      - [sandbox, safe]
      - [sandbox, environment]
      - [isolated, execute]
      - [isolated, code]
      - [isolated, environment]
      - [isolated, run]
      - [safe, execute]
      - [safe, code]
      - [untrusted, code]
      - [untrusted, execute]
      - [code, runner]
      - [code, playground]
      - [execute, safely]
      - [run, safely]
      - [run, isolation]
      - [execute, isolation]
      - [ffmpeg, process]
      - [ffmpeg, convert]
      - [ffmpeg, compress]
      - [student, code]
      - [student, execute]
      - [student, run]
    anyOf:
      - "sandbox"
      - "isolated"
      - "isolation"
      - "untrusted"
      - "safely"
      - "microvm"
      - "ffmpeg"
      - "playground"
    noneOf:
      - "iframe sandbox"
      - "sandbox attribute"
      - "codesandbox.io"
      - "stackblitz"
    minScore: 4
---

# 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

```bash
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

```ts
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:

```ts
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

```ts
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:

```ts
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:

```ts
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:

```bash
AGENT_BROWSER_SNAPSHOT_ID=snap_xxxxxxxxxxxx
```

A helper script is available in the demo app:

```bash
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:

```bash
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:

```ts
// 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 });
}
```

```json
// vercel.json
{ "crons": [{ "path": "/api/cron", "schedule": "0 9 * * *" }] }
```

## Environment Variables

| Variable | Required | Description |
|---|---|---|
| `AGENT_BROWSER_SNAPSHOT_ID` | No (but recommended) | Pre-built sandbox snapshot ID for sub-second startup (see above) |
| `VERCEL_TOKEN` | No | Vercel personal access token (for local dev; OIDC is automatic on Vercel) |
| `VERCEL_TEAM_ID` | No | Vercel team ID (for local dev) |
| `VERCEL_PROJECT_ID` | No | Vercel project ID (for local dev) |

## Framework Examples

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

| Framework | Server code location |
|---|---|
| Next.js | Server actions, API routes, route handlers |
| SvelteKit | `+page.server.ts`, `+server.ts` |
| Nuxt | `server/api/`, `server/routes/` |
| Remix | `loader`, `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.

© Vercel Labs · Apache-2.0 · openai/plugins

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