vercel-sandbox-bundle

This is a version of vercel-sandbox with third-party dependencies bundled. For normal use, install the unbundled vercel-sandbox package instead: https://pypi.org/project/vercel-sandbox/

Vercel Sandbox Python SDK

Create and manage Vercel Sandboxes with synchronous and asynchronous APIs.

from vercel import sandbox
from vercel.api import session

async with session():
    async with sandbox.create_sandbox() as instance:
        process = await instance.run_process("echo", ["hello"], capture_output=True)
        print(process.stdout)

The package can be installed independently with pip install vercel-sandbox.

When no image is provided, the Sandbox API uses vercel/sandbox/universal:latest.

The same promoted API is available synchronously:

from vercel.api import session
from vercel.sandbox import sync as sandbox

with session():
    with sandbox.create_sandbox() as instance:
        process = instance.run_process("echo", ["hello"], capture_output=True)
        print(process.stdout)

Custom images

Create a sandbox from a Vercel Container Registry (VCR) image with the image keyword. The image reference is sent to the Sandbox API unchanged; the backend validates access, resolves the image, and waits for it to be ready.

from vercel import sandbox
from vercel.api import session

async with session():
    async with sandbox.create_sandbox(image="my-repository:latest") as instance:
        result = await instance.run_process("my-command", capture_output=True)
        print(result.stdout)
        print(instance.image)  # The resolved digest-pinned image reference

The same option is available synchronously:

from vercel.api import session
from vercel.sandbox import sync as sandbox

with session():
    with sandbox.create_sandbox(image="my-repository:latest") as instance:
        result = instance.run_process("my-command", capture_output=True)
        print(result.stdout)
        print(instance.image)  # The resolved digest-pinned image reference

Image references may be a bare repository (my-repository), a tagged image (my-repository:latest), a digest-pinned image (my-repository@sha256:<digest>), or a fully qualified VCR reference such as vcr.vercel.com/team-slug/project-slug/my-repository:latest. The backend resolves the selected image, and Sandbox.image contains the resolved image reference.

Installing this package also provides the vercel-sandbox and sandbox console commands. Both are aliases that delegate all arguments to npx sandbox; they require Node.js with npm and npx installed. Node.js is not required when using the Python API directly.

Session lifecycles

Sandbox-level process and filesystem operations resume a stopped sandbox lazily. The original sandbox handle adopts the replacement current session:

box = await sandbox.get_sandbox(name="workspace")
result = await box.run_process("python", ["script.py"])

Use box.session() when the session boundary should be explicit. Direct acquisition leaves the acquired session running, while managed acquisition stops exactly the session it yielded:

active = await box.session()

async with box.session() as exact_session:
    await exact_session.run_process("python", ["script.py"])

The synchronous forms are active = box.session() and with box.session() as exact_session:. Operations through an explicit session remain pinned to its identity and never auto-resume. Operations through box may adopt a replacement; that replacement is not stopped by an older managed session scope.

Managed sandbox and session exit does not wait for concurrent operations. Callers must join sandbox work before leaving a context when deterministic cleanup is required.