DesktopCtl vs Screenbox

DesktopCtl is a macOS launcher and command-line interface for AI agents that observe and control a computer through its screen, mouse, and keyboard. It works with any desktop application without app integrations or APIs, and accepts any CLI-capable agent and AI model, including local LLMs. Local GPU-accelerated text recognition and computer vision capture interface content and extract structured information. Deterministic CLI actions include clicking, typing, and waiting. The keyboard-first launcher shows active-window context and can run or resume sessions with multiple local agents. The maker says recognition runs locally, screenshots remain local by default, and a visual indicator appears during screen, mouse, or keyboard access. Operating-system permissions are granted only to DesktopCtl. It is open source under the MIT License, deployed locally, free to use, and currently supports macOS on Apple Silicon only. Release builds use an ad-hoc signature and are not Developer ID signed or notarized.

Screenbox gives AI agents isolated virtual desktops with real browsers and desktop applications. Each desktop includes Chromium with DevTools and extensions, plus tools such as file managers, terminals, and office applications. Agents can use 21 MCP tools across more than 100 actions, including screenshots, OCR, clicks, typing, shell access, file operations, and clipboard control. It connects to MCP clients including Claude Desktop, Claude Code, and Cursor through stdio, streamable HTTP, or SSE. A web dashboard supports live viewing, agent management, snapshots, session logs, and a knowledge browser. Users can save and restore desktop states, including files and browser sessions, and clone desktops from templates. Activity can be viewed through RDP or noVNC, with mouse and keyboard control available. Desktops run in Docker containers with memory limits, network isolation, and no bind mounts; the maker cautions that they are not hardened sandboxes. The software is self-hosted and open source under AGPL-3.0. The maker says deployment data stays on the user's infrastructure and the software sends no telemetry to Screenbox or third parties. It requires about 2 GB of RAM per desktop and no GPU; idle desktops pause automatically.