trycua/cua
cua
Open-source infrastructure for Computer-Use Agents. Sandboxes, SDKs, and benchmarks to train and evaluate AI agents that can control full desktops (macOS, Linux, Windows).
Usage guide
cua is an open-source project around agent, ai-agent, apple with 19,162 GitHub stars. This guide focuses on when to use it, how to install it, how to run the first example, and what to verify before adopting it.
Key features
- Implemented mainly in HTML, useful for judging integration effort in a similar stack.
- GitHub detected the MIT repository license, which generally permits commercial use. This signal only covers the repository license; review its obligations and any model weights, datasets, dependencies, or external services before commercial adoption.
- The project has a homepage, so cross-check docs, examples, and release information beyond GitHub.
Best for
- Evaluating cua for HTML AI workflows.
- Comparing a GitHub project with 19,162 stars and current repository activity.
Pros
- cua has visible GitHub traction with 19,162 stars. Topics: agent, ai-agent, apple.
- The project provides an external homepage for deeper evaluation.
Cons
- Production fit still depends on documentation depth, issue activity, and release cadence.
- License review should confirm the MIT terms fit your use case.
Production readiness
cua should be validated with its README, release history, open issues, and integration requirements before production use.
License risk
MIT is reported by GitHub; review the repository license before redistribution or commercial use.
cua architecture preview
cua's main path starts at the entry surface, runs through Agent orchestration runtime, combines LLM / model client, Runtime context, GitHub, and returns Assistant response / action result.
Entry
Web / product entry
Users start from a web UI, hosted product surface, or browser-based workflow.
https://cua.ai
Runtime
Agent orchestration runtime
The orchestration layer plans tasks, calls tools, manages context, and decides the next action.
agent workflow
Model
LLM / model client
The project connects its core runtime to local models or hosted AI APIs when model inference is required.
model signal
Context
Runtime context
Runtime state, user input, repository files, or configuration provide context for each task.
context signal
Tools
GitHub
Tool adapters let the runtime act outside the model through GitHub.
GitHub
Output
Assistant response / action result
The final result is a response, action, or task completion returned through the active channel.
assistant output
Install tutorial
Before you install
- A clean working directory for the first test run
Check the runtime environment
Confirm your system can run a HTML project before starting the installation steps.
Get the project files
Start from the official repository or package so the first run matches the documented behavior.
$ git clone https://github.com/trycua/cua.gitInstall or build dependencies
No extra setup command was detected. Check the README before adding custom configuration.
Adoption guidance and sources
Practical use cases
Agent workflow prototype
Use it to validate task decomposition, tool calling, memory, tool permissions, and result review loops.
Open-source infrastructure for Computer-Use Agents. Sandboxes, SDKs, a
This is one of the documented reasons to evaluate cua before choosing a stack.
Focus area: agent
This is one of the documented reasons to evaluate cua before choosing a stack.
AI Agents project comparison
Compare cua with similar projects before committing to a stack.
Before adopting
- Complete one clean-environment verification using the official cua setup path.
- Review repository license, model weights, external services, and dependency terms for your use case.
- Check recent commits, release cadence, issue response, and documentation depth.
- Evaluate output quality, latency, resource usage, and recovery behavior with a small dataset.
Configuration notes
- Review README configuration notes before using production data.
Sources checked
These links are used to verify repository, documentation, or tutorial details. Review the source pages before adopting the project.
Troubleshooting
- If installation fails, first confirm the command is being run from the README-specified directory.
- If dependencies conflict, retry in a fresh virtual environment, container, or working directory.
- If output looks wrong, return to the smallest documented cua example before adding complex data.
- For keys, model files, or external services, verify environment variables, local paths, and permissions one by one.
- Before production use, review recent updates, open issues, license terms, and safety boundaries.
What is cua?
cua is an open-source ai agents project. Open-source infrastructure for Computer-Use Agents. Sandboxes, SDKs, and benchmarks to train and evaluate AI agents that can control full desktops (macOS, Linux, Windows).
How do I install cua?
Start with the official README. The first detected setup step is: git clone https://github.com/trycua/cua.git.
Is cua beginner-friendly?
If you already know the HTML ecosystem, start with the smallest example. Otherwise test it in an isolated environment first.
Can cua be used commercially?
GitHub detected the MIT repository license, which generally permits commercial use. This signal only covers the repository license; review its obligations and any model weights, datasets, dependencies, or external services before commercial adoption.
Does cua need a GPU?
GPU requirements depend on the workload, model, and dataset size. Start with the smallest README example before scaling up.
How should I decide whether to adopt cua?
Evaluate setup cost, maintenance activity, issue health, license terms, and fit with your real workflow.