cua README

The project's README from github.com/trycua/cua. As one markdown file: README.md.

Cua logo

Give AI agents computers they can use.
Cua provides open-source desktop automation, isolated cloud desktops, local macOS VMs, specialist decision models, and benchmarks for evaluating computer-use agents.

Try Cua Fleets now at run.cua.ai

cua.ai Discord Twitter Documentation
trycua%2Fcua | Trendshift

Choose your path

Cua Fleets: isolated cloud desktops for your agents CUA-S1: small, specialized models for computer use.
Cua Driver: inspect and operate apps on macOS, Windows, and Linux Lume: local macOS and Linux VMs on Apple Silicon Cua Bench: create tasks, evaluate agents, and export trajectories

Bring your own agent and model, or explore CUA-S1 for specialized decisions. Cua provides the computer and automation tools. Computer-Use 2.0 describes an agent moving between code, APIs, and graphical interfaces within the same task.

See Cua Driver in action

Two Cua Driver sessions select cells in LibreOffice Calc and objects in Inkscape on an Omarchy desktop while a terminal stays in the foreground. Watch the 50-second demo, then explore Omarchy on Fleet.

https://github.com/user-attachments/assets/b4e5517c-d2db-4758-b4cf-07131b0753b2

Cua Fleets

Provision isolated cloud desktops at run.cua.ai. A Fleet maintains sandbox capacity; your code claims a desktop from a pool and uses the Sandbox SDK to run commands, capture screenshots, and interact with apps inside it.

Your first result: provision a Linux desktop, run uname -a, save a screenshot, and delete the cloud resources. The tutorial covers Fleet credentials, dependencies, and cleanup. Pools can retain paid capacity after a claim ends, so follow its cleanup steps.

Local sandboxes and Fleets share the Sandbox SDK, but credentials, images, operations, and runtime requirements differ. Use the runtime support reference to choose an environment. For your own hardware, see Manage local sandbox lifecycle.

Your first Cloud Fleet | Fleet overview | Sandbox SDK reference

Cua Driver

Give your agent tools to inspect and operate native desktop apps and browsers on macOS, Windows, and Linux. Connect through the CLI, MCP, or typed SDKs. Background delivery lets agents work without moving your pointer or taking focus when the app and platform support it; see platform support for the boundaries.

macOS / Linux

/bin/bash -c "$(curl -fsSL https://cua.ai/driver/install.sh)"

Windows (PowerShell)

irm https://cua.ai/driver/install.ps1 | iex

Your first result: connect your agent, ask it to compute 6 × 7 in Calculator, and have it verify that the app displays 42. The tutorial covers platform setup, permissions, and agent connection.

Drive your first app | Installation | CLI Reference

Using Claude Code, Codex, Cursor, OpenClaw, or another agent? Find your integration. Source documentation and architecture notes live in libs/cua-driver/README.md.

CUA-S1

CUA-S1 is our family of small, specialized System 1 models for computer use. We use "System 1" as an engineering analogy for fast, bounded decisions, such as choosing which value belongs in a field or whether to leave an element alone. It is not a strict classification of model architectures or a replacement for a general-purpose agent's planning and reasoning.

The first research profile focuses on forms: scoring decisions from structured interface elements and document values rather than generating a response token by token. Application code orders the actions, and the optional Cua Driver integration handles execution with explicit action boundaries.

The project includes Python model code, synthetic-data generation, training, and evaluation. The GitHub component is an early, source-only research release; model weights are hosted separately on Hugging Face. The source is MIT-licensed. Check each model and dataset card for its scope, limitations, and artifact-specific license.

Explore CUA-S1 | Model card | Safety and deployment guidance

CUA-S1-FORMS on Hugging Face: Model weights | Dataset

Lume

Create and manage local macOS and Linux VMs on Apple Silicon using Apple's Virtualization.Framework.

/bin/bash -c "$(curl -fsSL https://cua.ai/lume/install.sh)"

Your first result: create a vanilla macOS Tahoe VM from an Apple restore image, start it, and connect over SSH. The tutorial uses the Lume CLI directly and explains the unattended setup defaults.

Create your first Lume VM | Installation | CLI reference

Cua Bench

Build computer-use tasks, evaluate agents, and export trajectories for training. Start with a simulated task that requires no VM, Docker, or model API key.

With Python 3.12 or 3.13 and uv installed:

uv tool install 'cua-bench[browser]'
uv tool run --from 'cua-bench[browser]' playwright install chromium

Your first result: create a small task, run its reference solution, and verify that its evaluator reports a reward of 1.0. Then try the same task yourself.

Build your first task | What is Cua-Bench? | CLI reference | Partner with us

Resources

Citation

If Cua supports your research, please cite the software:

@software{cua2025,
  author  = {{Cua AI, Inc.}},
  title   = {Cua},
  year    = {2025},
  url     = {https://github.com/trycua/cua},
  license = {MIT}
}

For reproducibility, include the Cua release or commit used in your experiments. Citation metadata is also available in CITATION.cff.

Contributing

We welcome contributions! See our Contributing Guidelines for details.

License

MIT License — see LICENSE for details.

Third-party components have their own licenses:

Trademarks

Apple, macOS, Ubuntu, Canonical, and Microsoft are trademarks of their respective owners. This project is not affiliated with or endorsed by these companies.

Sponsors

Thank you to all our GitHub Sponsors!

Adam Cohen Hillel CodeRabbit Zephyr Cloud IO
Adam Cohen Hillel CodeRabbit Zephyr Cloud IO

cua (trycua/cua)

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30.2K ·GitHub
Cua
这是什么?一套开源的计算机操作 AI 智能体技术栈:桌面自动化驱动、隔离的云端桌面、本地 macOS 虚拟机、小型专用决策模型,以及用于训练、评估和数据生成的基准测试。
它是单一产品,还是一组各自独立的工具?它是一个包含五个部分的套件,可按需选用:Cua Fleets(云端桌面)、Cua Driver(桌面自动化)、CUA-S1(决策模型)、Lume(本地虚拟机)和 Cua Bench(任务构建与评估)。
我该怎么上手?每种路径都有各自的教程:Fleets 在 run.cua.ai 上开通,Cua Driver 则是按操作系统分别安装,之后通过 CLI、MCP 或类型化 SDK 连接。
使用它需要特定的 AI 智能体或 API 密钥吗?不需要——你可以自带智能体和模型。目前已有 Claude Code、Codex、Cursor 和 OpenClaw 的现成集成。
智能体工作时会不会接管我的鼠标和键盘?不一定:通过后台交付方式,智能体可以在不移动你的指针、不抢占焦点的情况下工作,但前提是应用和平台支持——文档的平台支持页面列出了具体边界。
它能控制哪些操作系统?Cua Driver 可以检查和操作 macOS、Windows 和 Linux 上的原生桌面应用及浏览器;Lume 的本地虚拟机仅支持 Apple Silicon。
它能完全在本地运行,还是必须用他们的云?两种方式都有:Lume 借助 Apple 的 Virtualization.Framework 在 Apple Silicon 上创建和管理本地 macOS 与 Linux 虚拟机,本地沙箱与云端 Fleets 使用相同的 Sandbox SDK。
所有内容都是 MIT 许可吗,有没有什么限制?核心部分采用 MIT 许可,但可选的 cua-agent[omni] 扩展会引入 AGPL-3.0 许可的 ultralytics,OmniParser 采用 CC-BY-4.0,而 Hugging Face 上的 CUA-S1 模型权重和数据集则各自带有自己的许可证。
Computer-Use 2.0 是什么意思?它指的是智能体在同一个任务中可以在代码、API 和图形界面之间切换,而不只是点击图形界面。Cua 为此提供了计算机和自动化工具。
CUA-S1 是什么?这是一系列小型专用的 System 1 模型,用于快速、有边界的计算机操作决策——首个研究方向聚焦表单,根据结构化的界面元素和文档内容对选项打分。它目前还是早期的纯源码研究版:GitHub 上的代码采用 MIT 许可,权重托管在 Hugging Face 上。
有没有不用虚拟机或 API 密钥就能试的东西?有——Cua Bench 的第一个模拟任务不需要虚拟机、Docker 或模型 API 密钥:只要装好 Python 3.12 或 3.13 和 uv,你就可以创建一个小任务、运行它的参考解法,并验证评估器给出的奖励为 1.0。
云端部分要花钱吗?代码是开源的,但 Fleets 涉及付费容量——任务领取结束后,资源池可能会继续保留已付费的容量,所以教程会带你清理云端资源。
What are similar projects?Agent-S, midscene, fara, agent-desktop, bytebot, tart, vphone-cli, typesafe-computer-use
Which topics is it in?manus, cua, gui-agent, containerization, computer-use-agent, desktop-automation, vm, computer-use, virtualization, apple, apple-silicon, swift
Creator
trycua
Size
617.1 MB
Stars
30.2K
Updated
CommitOct 20Releaseagent-v0.9.1Oct 19
Age
1y
Commits · 90 Days