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qpilot
Run your manual test cases in a real browser — no code
qpilot is an open-source AI agent that runs your plain-text QA test cases in a real Chrome browser. Paste your steps → hit Run → watch pass/fail/warn per step stream live. If it hits a captcha or OTP, it pauses and asks you directly, then continues. No Playwright scripts. No selectors to maintain. Works with Claude or any OpenAI-compatible model.
I built qpilot because I was tired of watching QA teams (and myself) click through the same test cases release after release — while "proper" automation kept breaking on every UI change and needed a developer to maintain.
qpilot takes a different path: you paste your manual test case as plain text, and an AI agent opens a real Chrome browser and walks through the steps itself. You watch pass / fail / warn appear live for each step. When it hits a captcha or OTP, it doesn't die — it pauses and asks you, then continues.
What makes it different:
🧠 Reads the page like a human — via ARIA semantics, not CSS selectors, so it survives UI changes
📝 Anyone can write tests — if you can write a step-by-step description, you can automate it
⏸️ Human-in-the-loop — captchas and 2FA pause the run instead of killing it
📂 Batch mode — point it at a folder of .md test cases and run them all
🔓 Open source (MIT) — bring your own model: Claude or any OpenAI-compatible endpoint (Qwen, Ollama, vLLM)
Try it in one command:
npx qpilot
I'd love your honest feedback — especially: where would this break in YOUR testing workflow? What's the one feature that would make you actually adopt it?
“Run your manual test cases in a real browser — no code”
qpilot was submitted on Product Hunt and earned 8 upvotes and 13 comments, placing #72 on the daily leaderboard. qpilot is an open-source AI agent that runs your plain-text QA test cases in a real Chrome browser. Paste your steps → hit Run → watch pass/fail/warn per step stream live. If it hits a captcha or OTP, it pauses and asks you directly, then continues. No Playwright scripts. No selectors to maintain. Works with Claude or any OpenAI-compatible model.
On the analytics side, qpilot competes within Open Source, Developer Tools, Artificial Intelligence and GitHub — topics that collectively have 1.1M followers on Product Hunt. The dashboard above tracks how qpilot performed against the three products that launched closest to it on the same day.
Who hunted qpilot?
qpilot was hunted by Muhammad Mahmudov. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
For a complete overview of qpilot including community comment highlights and product details, visit the product overview.
Muhammad Mahmudov
Hey Product Hunt! 👋
I built qpilot because I was tired of watching QA teams (and myself) click through the same test cases release after release — while "proper" automation kept breaking on every UI change and needed a developer to maintain.
qpilot takes a different path: you paste your manual test case as plain text, and an AI agent opens a real Chrome browser and walks through the steps itself. You watch pass / fail / warn appear live for each step. When it hits a captcha or OTP, it doesn't die — it pauses and asks you, then continues.
What makes it different:
🧠 Reads the page like a human — via ARIA semantics, not CSS selectors, so it survives UI changes
📝 Anyone can write tests — if you can write a step-by-step description, you can automate it
⏸️ Human-in-the-loop — captchas and 2FA pause the run instead of killing it
📂 Batch mode — point it at a folder of .md test cases and run them all
🔓 Open source (MIT) — bring your own model: Claude or any OpenAI-compatible endpoint (Qwen, Ollama, vLLM)
Try it in one command:
npx qpilot
I'd love your honest feedback — especially: where would this break in YOUR testing workflow? What's the one feature that would make you actually adopt it?
GitHub: https://github.com/broxhq/qpilot