KI-Agenten
osaurus
Osaurus — own your AI: a native macOS agent harness with any model, memory and fully offline execution.
★ 8,0 Tsd. SterneMIT
Anleitung ansehen →macos-harness ist ein Open-Source-Projekt aus der Kategorie KI-Agenten von browser-use (browser-use/macos-harness auf GitHub). Es hat 902 Sterne auf GitHub und 61 Forks, wird unter der Lizenz MIT veröffentlicht und wurde zuletzt am 17. August 2026 auf GitHub aktualisiert.
A minimal harness that lets an LLM fully control a Mac.
Decide what you want the project to do before installing anything.
Grab the source and skim the README for setup requirements.
Install dependencies and add any API keys or settings it needs.
Launch it and follow the example workflow from the docs.
Accessibility APIs drive real apps.
Thin on purpose - easy to understand.
Bring your own model.
OS-level, not just browser-level.
Free to hack on.
Built by agent-infra veterans.
Download the source and move into the project folder.
git clone https://github.com/browser-use/macos-harness.git
cd macos-harnessCreate a virtual environment and install the requirements.
python -m venv .venv && source .venv/bin/activate
pip install -r requirements.txtTreat these as starting points - the README documents the exact commands.
python examples/quickstart.pyIt is an open-source project at browser-use/macos-harness with 61 forks. Check the repo's contributors and recent commits to judge how active maintenance is.
Yes - it is MIT licensed, so you can use, modify, and distribute it freely, including in commercial projects.
The harness is free; the model driving it is usually a paid API or a local model.
It gives an LLM full control of your Mac - run it in a VM or a spare machine, never on your daily driver with sensitive data. Treat it as research tooling.
Clone the repo, read the README's quickstart, and run the provided example - most projects get you to a first result in minutes.
Osaurus — own your AI: a native macOS agent harness with any model, memory and fully offline execution.
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Kostenlos und offen — hol dir den Quellcode, gib dem Repository einen Stern und leg los.