QApybara: Autonomous Open-Source AI QA Engineer for Web & Real Android Devices — Channeling Sloth Energy to Never Test a PR Manually Again
This is a submission for the Hacktoberfest Open-Source AI Challenge Week 3: Slow and Steady What I Built AI coding assistants have made developers write twice as much code in half the time. But nobody doubled the size of testing teams. Over 60% of startups ship without dedicated QA. Review bots lik
Hacktoberfest Open-Source AI Challenge Week 3: Slow and Steady Submission 🦥
This is a submission for the Hacktoberfest Open-Source AI Challenge Week 3: Slow and Steady
AI coding assistants have made developers write twice as much code in half the time. But nobody doubled the size of testing teams. Over 60% of startups ship without dedicated QA. Review bots like CodeRabbit and Greptile read your pull request diffs, but research shows that only around 15% of review comments catch real defects. The bugs that actually ruin a user's day — a dead checkout button, a broken dropdown, an Android ANR ("Application Not Responding"), or a silent 500 error — only reveal themselves when someone actually runs and interacts with the app.
Manually clicking through staging apps, filling out test forms, and inspecting devtools on every PR is tedious, dread-inducing work.
This week’s theme celebrates International Sloth Day: channeling your inner sloth, moving at a relaxed pace, and letting open-source AI do the work so you don't have to.
Enter QApybara (inspired by the capybara, the calmest, most unbothered creature in nature).
QApybara is an autonomous, open-source AI QA Engineer that doesn't just read your code — it runs it on a real Chromium browser and a physical Android phone plugged in over USB.
Instead of doing manual regression testing, you embrace pure sloth mode:
Push your code. Slap a qa label (or comment /qa) on the GitHub pull request. Sit back, relax, and take a nap. QApybara spins up the app or installs the build APK on a physical phone, maps user journeys, exercises the app like a human user, catches crashes and console errors, captures screenshots and screen recordings, and posts an evidence-backed verdict directly back to your PR and team Slack thread.
Review bots read your code. QApybara runs it — on a real browser and a physical Android phone — and shows proof.
QApybara is an autonomous, open-source AI QA Engineer that tests pull requests by actually running and interacting with the application like a human user. When a developer labels a PR with qa or comments /qa, QApybara spins up the web preview or installs the APK on a USB-connected Android phone, maps user journeys, exercises the app, detects runtime errors, captures video and screenshot proof, and exports passing journeys as reusable Playwright or Maestro regression tests.
Built with open-weight models, the Eve agent framework, and the Model Context Protocol (MCP).