Enables AI agents to see, locate UI elements, and operate any Windows desktop app through natural language, using accessibility-tree matching with optional vision-model fallback, plus an autonomous visual loop with introspection and meta-learning.
Enables low-cost agent models to control Windows applications through a compact, state-safe proxy over Open Computer Use, reducing model-visible context by up to 99.8% with support for record/replay and reusable UI component memory.
Lets AI agents see and control desktop applications through the accessibility layer, enabling clicking, typing, scrolling, dragging, and window/app management across macOS, Windows, and Linux entirely on the local machine.
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 query Windows process, window, and console information via structured JSON instead of screenshots, reducing token usage by 94-98%.
AutoFlow enables AI agents to automate Windows desktop tasks by visually recognizing screen elements and simulating keyboard and mouse actions, with 18 MCP tools for workflow control.
