Mac Computer Use
Run computer-use models natively on macOS for direct screen, keyboard, mouse, and file interaction.
Per-dimension scores and reasoning
Evidence: The repository explicitly warns about risks, but provides no implementation of least privilege, user confirmation, data flow transparency, sensitive data handling, dependency security, external effects, rollback, or source attribution. Deductions: These aspects are absent from the files, hence 0.
Evidence: README and requirements.txt are largely consistent, but no error handling or failure messages are provided. Deductions: Self-consistency is only partially met, dependency availability is only listed, failure messages are missing.
Evidence: README describes target users (Mac users) and scenarios (GUI automation), and lists capability boundaries (e.g., screen resolution limits). Deductions: Trigger precision (how users precisely control actions) is not specified, environment fit is only partially covered.
Evidence: README provides installation steps and known limitations, but lacks license, versioning, changelog, and maintenance responsibility. Deductions: Information architecture is reasonable, but naming stability, examples/FAQ, license, versioning, and maintenance are missing.
Evidence: README describes output (Streamlit interface) and marginal value (native Mac control), but no cost-benefit analysis. Deductions: Output usability is partially met, marginal value is described, cost-benefit is not quantified.
Evidence: Claims in README (e.g., multi-LLM support) are not traceable to verification, no cross-source corroboration, and no separation of facts and inferences. Deductions: All verification criteria are unmet.
- This repository grants AI models full control over the Mac, posing significant security risks, and provides no safety mechanisms (e.g., user confirmation, least privilege).
- No license information is provided; legal compliance must be verified before use.
- Dependencies (e.g., pyautogui, keyboard) could be misused, and no dependency security audit is provided.
What does this agent do, and when should you use it?
Mac Computer Use is a macOS-native fork of Anthropic Computer Use for giving models control of a local Mac. Instead of the upstream Ubuntu Docker image, it uses native macOS commands and utilities for direct system control. The repository exposes a Streamlit interface and names Anthropic, Bedrock, and Vertex as supported LLM providers. It captures the screen, drives keyboard and mouse input through cliclick, and supports filesystem interaction and file editing. It is aimed at macOS users who can accept the permissions and safety implications of autonomous local-computer control.
After configuring API_PROVIDER, ANTHROPIC_API_KEY, WIDTH, HEIGHT, and DISPLAY_NUM in .env, the user starts the interface with streamlit run streamlit.py. The project captures the screen with native macOS commands and sends mouse and keyboard actions through cliclick; it can also interact with and edit files. Higher display resolutions are automatically scaled to recommended targets for model performance. The documented providers are Anthropic, Bedrock, and Vertex, with an Anthropic API-key configuration example.
- A macOS developer who wants to operate local GUI workflows through an Anthropic model in a Streamlit interface.
- An individual automating a desktop task where the model must inspect screenshots, click controls, and type input.
- A Computer Use evaluator who wants native Mac system control instead of the upstream Ubuntu Docker environment.
- An experimental team using a controlled Mac where a model needs to interact with the filesystem and edit files.
What are this agent's strengths and limitations?
- Uses native macOS commands and utilities, eliminating the documented Docker requirement of the upstream approach.
- Provides a concrete keyboard and mouse control mechanism through cliclick.
- Includes a Streamlit interface available locally after startup.
- Documents support for Anthropic, Bedrock, and Vertex providers.
- Automatically scales higher screen resolutions toward documented performance targets.
- The README warns that the agent can control everything on the Mac, creating substantial safety exposure.
- Only macOS Sonoma 15.7 or later is documented; support for other operating systems is not evidenced.
- Adoption requires local Python 3.12+, Homebrew, and cliclick dependencies.
- The shown setup requires an Anthropic API key, and the referenced Beta API may change.
- The available documentation does not describe permission setup, recovery behavior, or action auditing.
How do you install or deploy this agent?
Prerequisites: macOS Sonoma 15.7 or later, Python 3.12+, Homebrew, and cliclick. Run:
git clone https://github.com/deedy/mac_computer_use.git
cd mac_computer_use
python3.12 -m venv venv
source venv/bin/activate
brew install cliclick
chmod +x setup.sh
./setup.sh
pip install -r requirements.txtHow do you use this agent?
Create .env in the repository root with at least:
API_PROVIDER=anthropic
ANTHROPIC_API_KEY=<key>
WIDTH=800
HEIGHT=600
DISPLAY_NUM=1Then run streamlit run streamlit.py. The interface is available at http://localhost:8501. Recommended display sizes include 1024x768, 1280x800, and 1366x768.
How does this agent compare with similar options?
Compared with Anthropic Computer Use as described in the upstream link, which runs an Ubuntu Docker image, this fork runs natively on macOS and uses native commands and utilities for direct system control.
FAQ
Does it require Docker?
What does it need on the machine?
How do I connect it to Anthropic?
API_PROVIDER=anthropic and ANTHROPIC_API_KEY=<key> in .env, then launch the Streamlit app.