macOS-use: AI Agent for MacBook Interaction
What is the project about?
macOS-use is a project that enables AI agents to interact with a MacBook, allowing users to control their computer through natural language instructions across any application. It's essentially a way to "tell your MacBook what to do, and it's done."
What problem does it solve?
The project aims to bridge the gap between human intention and computer action. Instead of manually navigating menus, clicking buttons, and typing commands, users can simply describe what they want to achieve, and the AI agent handles the execution. This simplifies complex tasks, automates workflows, and makes technology more accessible. It removes the need to know how to do something on the computer, only what you want to do.
What are the features of the project?
- Natural Language Control: Users interact with their MacBook using natural language prompts.
- Cross-Application Functionality: The agent can interact with any application installed on the MacBook.
- AI-Powered Interaction: Leverages AI models (like those from OpenAI, Anthropic, and Gemini) to understand user requests and translate them into actions.
- Visual Interaction: The agent can "see" the screen (through screenshots) and interact with UI elements.
- Action Execution: The agent performs actions like clicking, typing, navigating menus, and opening applications.
- Example Scripts: Provides demonstration scripts for tasks like calculations, website logins, and online information retrieval.
- App Discovery: The agent can check which applications are installed on the machine.
- Self-Correction (Goal): The project aims to improve the agent's ability to correct its own mistakes.
- Local Inference (Future Goal): Plans to support local model inference using MLX and MLX-VLM for privacy and cost-effectiveness.
- User Input (Future Goal): Add action for the agent to ask input from the user.
What are the technologies used in the project?
- Python: The primary programming language.
- pip/uv: Package management.
- API Providers:
- OpenAI (OAI): For accessing GPT models.
- Anthropic: For accessing Claude models.
- Gemini (Google): For accessing Gemini models.
- MLX (Future): Apple's machine learning framework for local inference.
- MLX-VLM (Future): For local visual language model inference.
- Environment management: uv
What are the benefits of the project?
- Increased Productivity: Automates tasks and simplifies workflows.
- Improved Accessibility: Makes technology easier to use for everyone, regardless of technical expertise.
- Simplified Complex Tasks: Allows users to perform complex actions with simple instructions.
- Hands-Free Control: Enables users to control their computer without needing to physically interact with it.
- Open Source: Allows for community contributions and customization.
- Privacy and Cost Savings (Future): Local inference will enhance privacy and reduce reliance on paid API services.
What are the use cases of the project?
- Automating repetitive tasks: Filling out forms, data entry, scheduling appointments.
- Controlling applications: Opening apps, navigating menus, performing actions within apps.
- Retrieving information: Searching the web, finding files, checking the weather.
- Managing system settings: Adjusting volume, connecting to Wi-Fi, changing display settings.
- Creating complex workflows: Combining multiple actions across different applications.
- Accessibility aid: Assisting users with disabilities in interacting with their computer.
- Testing and QA: Automating UI testing.
- Anything a user can do on a mac: The ultimate goal is to be able to perform any task a user could manually perform.
Important Note: The project is still under development and comes with a strong warning about potential risks, as the agent can access private credentials and interact with any application. User discretion is strongly advised.
