cua-sandbox

Sandboxed VM environments with a unified Python API. Cloud by default.

pip install cua-sandbox

Fleet support is provided by the published cua-fleet wheel. It bundles the platform-specific fleet_sdk native binding. Install from the Cua wheel index when resolving dependencies with pip:

pip install --extra-index-url https://wheels.cua.ai/simple cua-sandbox

Ephemeral sandbox

Created on enter, destroyed on exit.

from cua_sandbox import Sandbox, Image

async with Sandbox.ephemeral(
    Image.from_registry("registry.example/desktop-workspace@sha256:...")
) as sb:
    await sb.shell.run("uname -a")
    await sb.screenshot()

Persistent sandbox

Provision a new sandbox that stays alive after your script exits.

from cua_sandbox import Sandbox, Image

sb = await Sandbox.create(
    Image.from_registry("registry.example/desktop-workspace@sha256:...")
)
await sb.shell.run("uname -a")
print(sb.claim_name)  # Fleet lifecycle identifier; save this to reconnect later
await sb.disconnect()

Connect to existing sandbox

Attach to a sandbox that's already running. Works as a plain await or context manager.

from cua_sandbox import Sandbox

# plain await
sb = await Sandbox.connect("my-sandbox")
await sb.shell.run("whoami")
await sb.disconnect()

# context manager — disconnects on exit, sandbox keeps running
async with Sandbox.connect("my-sandbox") as sb:
    await sb.shell.run("whoami")

Destroy a sandbox

await sb.destroy()  # disconnect + permanently delete

Local VM

Spins up a local VM using QEMU or Lume, destroyed on exit.

from cua_sandbox import Sandbox, Image
from cua_sandbox.runtime import QEMURuntime

async with Sandbox.ephemeral(Image.linux(), local=True, runtime=QEMURuntime()) as sb:
    await sb.shell.run("uname -a")

Localhost (unsandboxed)

Direct host control — not sandboxed, use with caution.

from cua_sandbox import Localhost

async with Localhost.connect() as host:
    await host.shell.run("echo hello")
    await host.screenshot()

Cloud sandbox

Fleet is the OAuth cloud backend. Configure OAuth credentials once; Fleet uses https://run.cua.ai by default and can be overridden with configure(fleet_base_url=...) or CUA_FLEET_BASE_URL. The legacy API-key VM API continues to use https://api.cua.ai. Cloud images must use a registry reference; expose() declares additional Fleet services.

Fleet does not support snapshots or custom disks, and currently supports only us-east-1. await sb.tunnel.forward(3000) returns the authenticated Fleet service URL for an exposed port; it does not open a local SSH tunnel.

Fleet pools and durable claims

For production workloads, claim from an existing pool. Supplying pool= never changes its configuration; name= names the claim, while sb.name is the separately bound sandbox resource.

from cua_sandbox import Sandbox

sb = await Sandbox.create(
    pool="workspace",
    name="workflow-123",
    service="mcp",
    keep_alive_minutes=30,
)

reference = sb.to_dict()
await sb.disconnect()  # claim remains held

# A later process or Temporal activity re-resolves the live claim.
sb = await Sandbox.from_dict(reference)
await sb.keep_alive(minutes=30)
await sb.close()  # idempotently releases the claim

If pool= is omitted, a registry image is required. Sandbox.create(image) applies a deterministic reusable pool and claims from it. Sandbox.ephemeral(image) instead creates an isolated temporary pool and deletes it after releasing the claim, preserving teardown-by-default semantics.

from cua_sandbox import Image, Sandbox

image = Image.from_registry("registry.example/desktop-workspace@sha256:...")

async with Sandbox.ephemeral(
    image,
    name="job-123",
    cpu=4,
    memory_mb=4096,
    server_port=5000,
) as sb:
    await sb.shell.run("uname -a")

To deliberately retain deterministic warm capacity for later calls, opt in with keep_pool=True:

async with Sandbox.ephemeral(image, keep_pool=True) as sb:
    await sb.shell.run("uname -a")

The equivalent lower-level reusable-pool API is:

from cua_sandbox import Image, Pool

pool = await Pool.apply(
    Image.from_registry("registry.example/desktop-workspace@sha256:..."),
    replicas=1,
    cpu=4,
    memory_mb=4096,
    services={"server": 8000, "mcp": 3000},
)

sb = await pool.claim(name="job-123", service="mcp")
await sb.close()

Pool.claim() is both awaitable and an async context manager, so existing scoped usage remains valid:

async with pool.claim(name="job-123") as sb:
    await sb.shell.run("echo hello")

Instead of a static replicas count, a pool can scale with claim demand by passing autoscaling=. The pool then grows toward max_pool_size while claims are pending and shrinks back to min_pool_size as they are released; initial_pool_size seeds a one-time warm head start at creation:

from cua_sandbox import Image, Pool, WarmPoolAutoscaling

pool = await Pool.apply(
    Image.from_registry("registry.example/desktop-workspace@sha256:..."),
    cpu=4,
    memory_mb=4096,
    autoscaling=WarmPoolAutoscaling(
        min_pool_size=0,
        initial_pool_size=2,
        max_pool_size=10,
    ),
)

Pool.reconcile(CreatePoolRequest(...)) and Template.reconcile(CreateTemplateRequest(...)) remain available for advanced generated-schema configuration. The public generated builders should be used instead of constructing builder-enabled Fleet records directly.

The image must run the CUA computer-server /cmd API on the configured server_port. Windows computer-server images continue to use the default port 8000.

Fleet currently supports registry images, CPU, memory, replica count, claim-demand autoscaling, and named TCP services. Local image builds, layers, injected files or environment, snapshots, custom disks, unsupported regions, and provider-crossing serialization raise NotImplementedError.