qt-ai-dev-tools 0.6.1

AI agent infrastructure for Qt/PySide apps — inspect, interact, screenshot via AT-SPI

qt-ai-dev-tools

Chrome Dev Tools but for Linux Qt desktop apps — give your AI agent eyes and hands to inspect, click, type, and screenshot any Qt/PySide application on Linux (X11).

The problem

AI coding agents can build Qt apps, but they can't see or interact with them. There's no equivalent of Chrome DevTools for desktop applications. When your agent writes UI code, it's flying blind — no way to verify layouts, click buttons, fill forms, or confirm that changes actually work.

What this gives your agent

qt-ai-dev-tools bridges that gap. Your AI agent can:

Core interaction — see and control the app:

• Full widget tree — every button, label, text field, menu, and dialog, with roles, names, and coordinates, via AT-SPI. Filter with --visible, --exact, --index, and --app flags for precise targeting
• Click, type, press keys — real X11 input events via xdotool. Compound commands like fill (focus + clear + type) and do (click + verify/screenshot) for common workflows
• Screenshots — visual verification after any interaction (~14-22 KB PNG, cheap to send to an LLM). Snapshot save/diff for comparison

Forms and data — handle user input:

• Fill forms — focus fields, clear existing text, type new values in one command
• Automate file dialogs — detect, fill, accept, and cancel native Qt file dialogs via AT-SPI
• Clipboard — read and write the system clipboard for copy/paste workflows

Desktop integration — interact beyond the app window:

• System tray — list tray icons, click them, read context menus, select items via D-Bus SNI
• Notifications — listen for desktop notifications, dismiss them, invoke actions via D-Bus
• Audio — create PipeWire virtual microphones, play audio into apps, record output, verify non-silence

Runtime eval — reach inside the process:

• Execute code inside the app — run arbitrary Python via a Unix socket bridge, accessing widgets, properties, and Qt internals directly

VM environment — isolated and reproducible:

• Vagrant VM — Ubuntu 24.04 with Xvfb, openbox, AT-SPI, and D-Bus pre-configured. No host contamination. Templated with Jinja2, multi-provider support

The agent doesn't modify or instrument the target app. It uses the same accessibility tree that screen readers use, from the outside.

How it works

  AI Agent (Claude Code, etc.)
       |
       |  shell commands
       v
  qt-ai-dev-tools CLI
       |
       |  auto-detects host vs VM
       |  (proxies through SSH when on host)
       |
       +---> AT-SPI (widget tree: roles, names, coords, text)
       +---> xdotool (clicks, keystrokes, text input)
       +---> scrot (screenshots)
       +---> subsystems (clipboard, file dialogs, tray, notifications, audio)
       |
  [ Vagrant VM: Ubuntu 24.04 + Xvfb + openbox + D-Bus ]
       |
       v
  Target Qt/PySide App (unmodified)

Most CLI commands work identically from the host or inside the VM — no SSH wrapping needed. Use vm run only for arbitrary commands (pytest, systemctl, etc.).

Note: This toolkit targets X11 applications. Wayland is not supported. All interaction happens inside the VM where Xvfb provides the X11 display server, so the host's display server doesn't matter.

Host requirements

• Linux (Fedora, Ubuntu, Arch, etc.)
• Vagrant with the libvirt provider (vagrant-libvirt plugin + QEMU/KVM), virtualbox is supported but not tested
• Python 3.12+ and uv

Getting started

1. Install the agent skills

npx -y skills add quick-brown-foxxx/qt-ai-dev-tools

This gives your agent five skills:

• qt-dev-tools-setup — install and configure the environment
• qt-app-interaction — inspect, click, type, verify (core workflow)
• qt-form-and-input — fill forms, handle file dialogs, clipboard
• qt-desktop-integration — system tray, notifications, audio
• qt-runtime-eval — execute Python inside running apps

2. Ask your agent to set up the toolkit

Run /qt-dev-tools-setup skill as command or ask agent to load and execute it:

• Copy the toolkit into your project
• Initialize a Vagrant workspace
• Boot the VM and verify the environment

3. Start interacting

Once set up, the agent uses the appropriate skill for each task. qt-app-interaction covers the core workflow: inspect the widget tree → interact with widgets → verify results. qt-form-and-input handles form filling, file dialogs, and clipboard. qt-desktop-integration covers system tray, notifications, and audio. qt-runtime-eval enables executing Python inside running apps for deeper inspection.

Manual installation

Recommended — use via uvx (no installation needed):

uvx qt-ai-dev-tools workspace init

Advanced — local copy (you own and maintain the code):

uvx qt-ai-dev-tools install-and-own ./qt-ai-dev-tools --yes-I-will-maintain-it

Project status

Working now (beta):

• CLI with one-liner commands — tree, click, type, screenshot, fill, do, snapshot save/diff, and more
• Widget addressing — --visible, --exact, --index flags for precise targeting
• Multi-app support — --app flag to target a specific application
• Python library (QtPilot) with strict typing (basedpyright strict, typed AT-SPI wrapper)
• Vagrant VM environment — Xvfb + openbox + AT-SPI, templated with Jinja2, multi-provider support
• Compound commands — fill (focus + clear + type), do (click + verify/screenshot)
• Bridge — execute arbitrary Python inside running Qt apps via Unix socket
• Five Linux subsystem modules:
  • Clipboard (xsel/xclip read/write)
  • File dialogs (AT-SPI detect, fill, accept, cancel)
  • System tray (D-Bus SNI list, click, menu, select)
  • Notifications (D-Bus listen, dismiss, action)
  • Audio (PipeWire virtual mic, recording, verification)
• Distribution — uvx qt-ai-dev-tools <any-command> without installation, install-and-own for local copies, five AI skills

Subsystem commands (clipboard, file dialog, tray, notifications, audio) are alpha — functional but less battle-tested.

Next up:

• Architecture rewrite — backend abstraction for multiple environments
• Docker environment — lighter-weight alternative to VM

See ROADMAP.md for the full plan and phase details.

Debugging

Use -v to see shell commands being executed, -vv for full output, and --dry-run to preview without executing:

qt-ai-dev-tools -v tree              # show commands on stderr
qt-ai-dev-tools -vv vm up            # show commands + full output
qt-ai-dev-tools --dry-run vm up      # preview without executing

Logs are always written to ~/.local/state/qt-ai-dev-tools/logs/qt-ai-dev-tools.log.

Development

See DEVELOPMENT.md for setup, make targets, and contribution guidance.