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overlord

by hanzoai

AutomationmacOSautomationGUI

AI Overlord manages disparate agents through local computer use, allowing AI to control macOS natively. It provides direct system control through native macOS commands and utilities.

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Last updated: N/A

overlord

AI Overlord, managing your disparate agents through local computer use. This project allows AI to control macOS natively, providing direct system control through native macOS commands and utilities.

[!CAUTION] This comes with obvious risks. Overlord can control everything on your Mac. Please be careful.

Features

  • Native macOS GUI interaction (no Docker required)
  • Screen capture using native macOS commands
  • Keyboard and mouse control through cliclick
  • Multiple LLM provider support (Anthropic, Bedrock, Vertex)
  • Streamlit-based interface
  • Automatic screen resolution scaling
  • File system interaction and editing capabilities

Prerequisites

  • macOS Sonoma 15.7 or later
  • Python 3.12+
  • Homebrew (for installing additional dependencies)
  • cliclick (brew install cliclick) - Required for mouse and keyboard control

Setup Instructions

  1. Clone the repository and navigate to it:
git clone https://github.com/hanzoai/overlord.git
cd overlord
  1. Create and activate a virtual environment:
python3.12 -m venv venv
source venv/bin/activate
  1. Run the setup script:
chmod +x setup.sh
./setup.sh
  1. Install Python requirements:
pip install -r requirements.txt

Running the Demo

Set up your environment and Anthropic API key

  1. In a .env file add:
API_PROVIDER=anthropic
ANTHROPIC_API_KEY=<key>
WIDTH=800
HEIGHT=600
DISPLAY_NUM=1

Set the screen dimensions (recommended: stay within XGA/WXGA resolution), and put in your key from Anthropic Console.

  1. Start the Streamlit app:
streamlit run streamlit.py

The interface will be available at http://localhost:8501

Screen Size Considerations

We recommend using one of these resolutions for optimal performance:

  • XGA: 1024x768 (4:3)
  • WXGA: 1280x800 (16:10)
  • FWXGA: 1366x768 (~16:9)

Higher resolutions will be automatically scaled down to these targets to optimize model performance. You can set the resolution using environment variables:

export WIDTH=1024
export HEIGHT=768
streamlit run streamlit.py

[!IMPORTANT] The Beta API used in this reference implementation is subject to change. Please refer to the API release notes for the most up-to-date information.