Enables AI agents to automate real desktop applications across Windows, Linux, and macOS using incremental screen perception, accessibility trees, OCR, and window management, dramatically reducing token usage compared to screenshot-per-step approaches.
A lightweight server that enables AI agents to interact natively with the Windows operating system for tasks like UI automation and application control. It allows LLMs to perform file navigation, simulate user input, and manage windows without requiring specialized computer vision models.
Enables AI agents to interact with Windows operating systems through native UI automation, file navigation, application control, and system commands. Provides seamless integration between LLMs and Windows environments for tasks like clicking, typing, launching apps, and capturing desktop state.
Enables AI agents to control Windows systems using AutoHotkey v2 and the UI Automation accessibility tree for efficient, text-based computer interaction. It provides tools for window management, keystrokes, and UI inspection while significantly reducing token costs compared to screenshot-based approaches.
Enables AI agents to interact with the Windows operating system, performing tasks such as file navigation, application control, UI interaction, and QA testing.
Enables reading and interacting with native Windows application UI through the UI Automation API, allowing listing windows, extracting structured content, and performing clicks/typing without screenshot-based vision.
