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cua

Active·★ 29.0k·MIT·Updated 2026-10-08
★ Most Popular★ Trending★ Essential

Cua gives AI agents computers they can use. Rather than being an agent itself, it provides the environment and controls: Cua Spaces gives agents full desktops (local macOS VMs on a Mac, or Linux images) on machines you own; Cua Driver lets agents inspect and operate native apps and browsers on macOS, Windows and Linux through a CLI, MCP or typed SDKs; Lume creates macOS and Linux VMs on Apple Silicon; and Cua Bench creates simulated tasks to evaluate agents and export trajectories.

It suits teams building or evaluating computer-use agents who need isolated, reproducible machines, and developers who want their coding agent (Claude Code, Codex, Cursor or OpenClaw) to operate desktop apps safely. Sandboxes can be created from the `cua` CLI or from SDKs in Python, TypeScript, Swift and Kotlin, and Cua Driver can work in the background without taking over your pointer. Spaces adds features such as moving a signed-in app into a sandbox and sharing a desktop between you and an agent.

Getting started: run `curl -fsSL https://cua.ai/install.sh | sh` on macOS or Linux (PowerShell on Windows). The installer sets up the `cua` CLI, the Spaces app on macOS 26+ and the Cua Driver MCP and skill; then `cua sb create ubuntu --name dev` creates a sandbox.

#AI Agents#Virtualization#UI Automation#Benchmarking#Code Execution#Coding#Data Analysis
$ Install
$ curl -fsSL https://cua.ai/install.sh | sh
↗ Visit site★ GitHub
01

Features

01Build AI agents for desktop UI automation and interaction.
02Provide isolated code execution environments (sandboxes).
03Benchmark computer-use models with standardized tasks.
04Train agents using reinforcement learning environments.
05Manage high-performance macOS/Linux VMs on Apple Silicon.
02

Why choose it

+Isolated desktops, VMs and containers keep computer-use agents away from your main system.
+Works with any agent and model through CLI, MCP and SDKs in four languages.
+Includes benchmarking tools for evaluating agents and generating training data.
+Core SDK, CLI, Driver and Lume are MIT licensed.
03

Trade-offs

!Licensing is mixed: Cua Spaces is source-available under FSL-1.1-MIT rather than open source, and some optional components are AGPL.
!The Spaces app requires macOS 26 or later; local macOS VMs require Apple Silicon.
!The installer signs you in to a Cua account by default, and some features target paid plans that are still being introduced.
04

Compatibility

Docker
Native Sandbox
Verified via docs
QEMU
Native Sandbox
Verified via docs
Apple Virtualization.Framework
Native Sandbox
Verified via docs
Linux
Target OS
Verified via docs
macOS
Target OS
Verified via docs
Apple Silicon
Optimized Host
Verified via docs
05

Quick start

1
$ curl -fsSL https://cua.ai/install.sh | sh
06

Use cases

↳Developing and deploying AI agents for autonomous desktop interaction and task completion.
↳Creating secure, isolated code execution environments for AI coding assistants and development workflows.
↳Benchmarking and training computer-use agents using standardized tasks and reinforcement learning.
07

How it compares

≈vs UI-TARS Desktop: UI-TARS is a vision agent that operates a computer; Cua supplies sandboxed computers and drivers that any agent, including vision agents, can use.
≈vs E2B: E2B provides secure cloud sandboxes for agents to run code and tools; Cua focuses on full desktops and native-app automation.
≈vs Windows MCP: Windows MCP controls the Windows machine it runs on; Cua can create isolated machines across operating systems.
08

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On this page
01Features02Why choose it03Trade-offs04Compatibility05Quick start06Use cases07How it compares08Alternatives
Stats
GitHub Stars★ 29.0k
Last commit1d ago
StatusActive
LicenseMIT
CategoryLLM Infra
Trend (30d)
+1.1k↑ 4.8%
Links
Documentation↗Discussion↗Issues↗Releases↗

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