
Open-source computer-use tooling for desktop automation, cloud desktops, local macOS VMs, decision models, and agent benchmarks.
Cua is an open-source computer-use project for giving agents usable desktop environments and automation tools. The GitHub project describes support for desktop automation, isolated cloud desktops, local macOS VMs, specialist decision models, and benchmarks for evaluating computer-use agents. It is aimed at builders who want to bring their own agent and model, operate graphical applications, provision Linux desktops, run local macOS VMs, and evaluate agent behavior on simulated computer-use tasks.
Product overview
Cua is distinctive as an open-source computer-use stack focused on giving agents desktop environments they can operate, rather than only exposing APIs or text tools. The repository positions it around open-source drivers, isolated cloud desktops, local macOS VMs, specialist decision models, and benchmarks for training, evaluation, and data generation.
Information as of September 22, 2026
The project describes Cua as providing open-source desktop automation and computer-use tooling that lets agents use graphical desktop environments.[1]
Cua includes Cua Fleets, described in the repository excerpt as a way to provision a Linux desktop, run a command, and save a screenshot.[1]
The README excerpt lists local macOS VMs and includes Lume as a path for creating a Tahoe VM and connecting over SSH.[1]
The project references CUA-S1 as small, specialized models for computer-use decisions.[1]
Cua Bench is described as supporting simulated task creation and verification for evaluating computer-use agents.[1]
The official excerpt states that users can bring their own agent and model, with Cua providing the computer and automation tools.[1]
The supplied official context shows a public GitHub repository described as open-source and displaying an MIT license. No separate hosted pricing details are provided in the supplied context.[1]
Platforms: GitHub, Linux desktop, macOS VM[1]
Access: open-source repository, local VM, cloud desktop[1]
79/100
Our subjective rating of how independently the product carries out its documented tasks, considering the human setup, input, approval and review it needs. This is not a measured success rate.
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