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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30K ·GitHub
Cua
What is this?An open-source stack for computer-use AI agents: desktop automation drivers, isolated cloud desktops, local macOS VMs, small specialist decision models, and benchmarks for training, evaluation, and data generation.
Is it one product or a bundle of separate tools?A suite of five parts you pick from: Cua Fleets (cloud desktops), Cua Driver (desktop automation), CUA-S1 (decision models), Lume (local VMs), and Cua Bench (task building and evaluation).
How do I get started with it?Each path has its own tutorial: Fleets are provisioned at run.cua.ai, and the Cua Driver connects through the CLI, MCP, or typed SDKs after a per-OS install.
Do I need a specific AI agent or an API key to use it?No — you bring your own agent and model. Ready integrations exist for Claude Code, Codex, Cursor, and OpenClaw.
Does the agent take over my mouse and keyboard while it works?Not necessarily: background delivery lets agents work without moving your pointer or taking focus, but only where the app and platform support it — the docs' platform support page lists the boundaries.
Which operating systems can it control?The Cua Driver inspects and operates native desktop apps and browsers on macOS, Windows, and Linux; Lume's local VMs are Apple Silicon only.
Can it run fully locally, or do I have to use their cloud?Both options exist: Lume creates and manages local macOS and Linux VMs on Apple Silicon via Apple's Virtualization.Framework, and local sandboxes use the same Sandbox SDK as cloud Fleets.
Is everything MIT-licensed, or are there catches?The core is MIT, but the optional cua-agent[omni] extra pulls in ultralytics under AGPL-3.0, OmniParser is CC-BY-4.0, and CUA-S1 model weights and datasets on Hugging Face carry their own licenses.
What does Computer-Use 2.0 mean?It describes an agent moving between code, APIs, and graphical interfaces within the same task, instead of only clicking through a GUI. Cua provides the computer and the automation tools for that.
What is CUA-S1?A family of small, specialized System 1 models for fast, bounded computer-use decisions — the first research profile focuses on forms, scoring choices from structured interface elements and document values. It is an early, source-only research release: MIT-licensed code on GitHub, weights on Hugging Face.
Is there something I can try without a VM or API keys?Yes — Cua Bench's first simulated task requires no VM, Docker, or model API key: with Python 3.12 or 3.13 and uv installed, you create a small task, run its reference solution, and verify the evaluator reports a reward of 1.0.
Does the cloud side cost money?The code is open source, but Fleets involve paid capacity — pools can retain paid capacity after a claim ends, so the tutorial walks you through cleanup of cloud resources.
What are similar projects?Agent-S, midscene, fara, agent-desktop, bytebot, vphone-cli, tart, magentic-ui
Which topics is it in?cua, manus, gui-agent, containerization, computer-use-agent, desktop-automation, vm, computer-use, virtualization, apple, apple-silicon, swift
Creator
trycua
Size
595.2 MB
Stars
30K
Updated
CommitOct 16Releasecua-spaces-v0.9.3Oct 16
Age
1y
Commits · 90 Days