macOS-use: The Revolutionary Tool That Lets AI Control Your MacBook

“Tell your MacBook what to do, and it’s done—across ANY app.” This bold promise defines macOS-use, the groundbreaking open-source framework that transforms how we interact with Apple devices.

What Exactly Is macOS-use?

macOS-use is a pioneering tool that enables AI agents to directly control your MacBook. Through simple natural language commands, it can:

  • Launch applications
  • Navigate user interfaces
  • Complete web forms
  • Extract information
  • Automate complex workflows

Created by Ofir Ozeri with collaborative development from Magnus and Gregor, this project represents a significant leap in human-computer interaction. The ultimate vision? “Tell every Apple device what to do, and see it done. On EVERY APP.”

Real-World Demonstrations

1. Automated Calculator Operations

python examples/calculate.py

What happens:

  1. AI launches Calculator app
  2. Inputs “5 × 4” calculation
  3. Retrieves and returns the result
  4. Automatically terminates after completion

View full demonstration video

2. Website Authentication

python examples/login_to_auth0.py

Workflow:

  1. Opens browser to auth0.com
  2. Selects Google authentication
  3. Chooses specified Gmail account
  4. Completes login sequence

View full demonstration video

3. Real-Time Information Retrieval

python examples/check_time_online.py

Process:

  1. Searches for “Shabbat times in Israel today”
  2. Extracts relevant information
  3. Returns verified results

View full demonstration video

Technical Implementation Guide

Prerequisites

Installation Methods

Option 1: Quick Install via pip

pip install mlx-use

Option 2: Source Installation (Recommended)

# Clone repository  
git clone https://github.com/browser-use/macOS-use.git  
cd macOS-use  

# Configure environment  
cp .env.example .env  
open ./.env  # Add your API key  

# Create virtual environment  
brew install uv  # Install UV package manager  
uv venv  
source .venv/bin/activate  

# Install dependencies  
uv pip install --editable .

Verification Test

Create test.py:

from macos_use import Agent  
agent = Agent()  
agent.run("open the calculator app")

Execute:

python test.py

Successful execution opens the Calculator application.

Technical Architecture Breakdown

Core Interaction Flow

graph TD  
A[User Command] --> B(Natural Language Processing)  
B --> C{Command Interpretation}  
C --> D[Application Control]  
C --> E[Browser Automation]  
C --> F[System Operations]  
D --> G[Result Compilation]  
E --> G  
F --> G  
G --> H[Output Delivery]

Key Technical Innovations

  1. Application Agnosticism: Operates across any installed software
  2. Self-Correction Mechanisms: Automatically adjusts failed actions
  3. Dynamic Environment Detection: Identifies available applications
  4. Multi-Provider API Support: Works with leading AI services

Development Roadmap

Phase 1: MacBook Optimization

Feature Status Impact
Agent prompt refinement In progress Improved command accuracy
Self-correction enhancement Planned Reduced manual intervention
Application compatibility detection Implemented ✅ Automatic software recognition
User input capability Development Task-time interaction support
Local inference integration Testing Reduced API dependency

Phase 2: Local Processing Advancement

  1. MLX Framework Integration: Apple’s machine learning library
  2. MLX-VLM Compatibility: Vision-language model support
  3. Specialized Model Training: Custom fine-tuned solutions
  4. Offline Operation: Full local execution capability

Phase 3: Apple Ecosystem Expansion

  1. iPhone support implementation
  2. iPad optimization
  3. Cross-device task coordination
  4. Unified control interface

Critical Security Considerations

Essential Precautions: This development-stage tool requires cautious implementation

  1. Credential Exposure Risks:

    • Accesses stored passwords
    • Operates authentication flows
    • Avoid use with sensitive accounts

  2. System-Level Access:

    • Controls all installed applications
    • Bypasses sandbox restrictions
    • Accesses all UI components

  3. No Protective Mechanisms:

    • Doesn’t recognize CAPTCHAs
    • Can trigger security alerts
    • No bot-detection avoidance

Recommended Safeguards:

  • Use in isolated test environments
  • Employ temporary user accounts
  • Avoid administrative privileges
  • Maintain active supervision during operation

Community Participation

Contribution Process

  1. Fork project repository
  2. Create feature branch (feature/your-contribution)
  3. Submit pull request
  4. Pass automated testing

Priority Development Areas

  • Error handling enhancement
  • Expanded test coverage
  • Documentation improvement
  • Local model integration

Support Channels

  1. GitHub Issues
  2. Discord Community
  3. Developer Contact

Technical Q&A Section

How much technical expertise is required?

Basic command-line proficiency suffices for standard operations. Custom task development requires Python knowledge.

Which AI providers are compatible?

Currently supported:

  • OpenAI
  • Anthropic
  • Gemini
    DeepSeek R1 support coming soon

Does usage incur API costs?

Yes. Each operation consumes provider credits. Gemini’s free tier is recommended for experimentation.

Is Windows or Linux supported?

Currently exclusive to macOS, as indicated by the project name.

When will iPhone support be available?

Roadmap includes iOS integration, but timeline depends on MLX framework adaptation progress.

How can accidental actions be prevented?

Recommended precautions:

  1. Test in virtual machines
  2. Use non-administrator accounts
  3. Restrict sensitive data access
  4. Monitor all operations

The Future Vision

Ultimate Objective: Create the first open-source AI agent framework for all Apple devices featuring:

pie  
    title Device Support Vision  
    “MacBook” : 45  
    “iPhone” : 30  
    “iPad” : 25

MLX Framework Integration Will Enable:

  • Local model processing
  • Zero-cost private deployment
  • End-to-end data encryption
  • Cloud-independent operation

Acknowledgments and Resources

Special recognition to:

  • Gregor for foundational Browser Use architecture
  • Magnus for migration collaboration

Project Resources:

This transformative technology thrives on community involvement. Whether you’re a developer, tester, or technology advocate, your participation helps redefine human-device interaction.