Search

More Filters

Show More
Add Listing
Go Back

macOS use

0
New
Multi-Agent Platforms & Frameworks
Report

Report Abuse

Basic Information

macOS-use is an open source project that enables AI agents to control and interact with a MacBook across any app and UI component. It provides tooling, examples, and a Python package to let developers run agent-driven actions on macOS via natural language prompts. The repo demonstrates integration with large model providers and local inference plans, and it aims to become a component for the MLX ecosystem to run private on-device models. The README includes quick start instructions for installing the package, configuring API keys and environment files, running example scripts, and warnings about security and supervised use. The project is intended for developers and researchers who want to build or experiment with agents that can automate tasks on Apple devices.

Links

Stars
1524
App URL
https://github.com/browser-use/macOS-use
Github Repository
https://github.com/browser-use/macOS-use/blob/main/README.md

App Details

Features
Provides a Python package installable via pip and instructions for cloning and running examples. Includes ready examples demonstrating tasks such as opening apps, performing calculations, logging into websites, and checking online information. Supports major LLM providers (OpenAI, Anthropic, and experimental Gemini) configurable via environment files and API keys. Contains utilities for detecting installed apps and mapping app names, plus a roadmap toward local inference using mlx and mlx-vlm. Documentation offers quick start steps, demo GIFs, and recommended use of a virtual environment. The README highlights safety warnings about credential use and automated interactions with UI elements.
Use Cases
macOS-use helps developers prototype and test AI agents that perform real interactions on macOS without building the full automation stack from scratch. It lowers friction by supplying examples, installation steps, and provider configuration so teams can focus on agent prompting and task design. The agent can inspect installed applications and adapt to app name differences, enabling more reliable cross-app automation. The project documents risks and current limitations so users can evaluate trust and supervision needs. Roadmap items promise improved self-correction, cheaper tasks, and support for local private inference, which could make on-device agent automation more practical and privacy-preserving.

Categories

Content Creation (5042)
Monetization & Business (2246)
Development Tools (1407)
Marketing & Social Media (1403)
Education & Career (1380)
Multi-Agent Platforms & Frameworks (1009)
Health & Life (957)
AI Resources (920)
Agents & Automation (778)
Data & Analytics (764)
Productivity Tools (679)
Writing & Editing (666)

Similar Listings

listing image

virtual lab

0 (0 review)
listing image

mnemo

0 (0 review)
listing image

EdgeChains

0 (0 review)
listing image

RAG Agents Accelerator

0 (0 review)
listing image

LLM Zero to Hundred

0 (0 review)

Featured Listings

Tags

Please fill the required fields*