Enables AI agents to control Windows GUI applications like a human using screen capture, OCR, mouse and keyboard input, and window management, with safety levels and memory.
Enables AI agents to query Windows process, window, and console information via structured JSON instead of screenshots, reducing token usage by 94-98%.
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 coding agents to automate Windows desktop applications through semantic UI Automation instead of brittle coordinate clicks, with tools for discovering windows, finding controls by stable identifiers, and verifying actions.
Enables AI agents to interact with the Windows operating system, performing tasks such as file navigation, application control, UI interaction, and QA testing.
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.