A fast open-source browser agent. It reads every button, link and field on the page and has a System One model (ex: jev) pick the next step from that list. If it's unsure, it falls back to a standard model model. Fast, cheap, and every answer provides a citation.
Hey Product Hunt, I'm Furqan, one of the makers of fastbrowse.
Browser agents have become exceptionally popular, but there's some huge problems. They're slow, expensive and miss easy tasks.
A lot of them take a screenshot and have a slow model decide every step, so each task takes a while and the bill adds up.
Fastbrowse reads the elements on the page instead. Every button, link and field is handed to a System One model (like Jev) with a list of choices. This model decies what to do next, when it isn't sure, fastbrowse falls back to a LLM to get it right. And because it only picks from what's on the page, it can't click something that isn't there.
We tested this against the latest open-source browser agents agent: same sign-in and add-to-cart task on a demo shop, three runs each.
fastbrowse took 25.7 seconds and $0.020 a run. Browser Use took 42.2 seconds and $0.045. Both got it right every time. On a wider 46-task benchmark against Browser Use's hosted agent running Claude Opus, our median task cost $0.026 vs $0.284. 99.2% of tasks passed.
Fastbrowse produces citations for everything it referenced to get answers, so you can trust and verify the results.
falling back to a screenshot only when the element list is unsure is a smart split. how often does the fallback actually fire on the 46-task benchmark?
Congrats on the launch! The quoted evidence sounds pretty neat, but just for observability purposes, if a page changes after the run, can someone still open a snapshot/screenshot and see what the browser saw at that moment?
Solid open source approach. Curious about the citation mechanism - how does it handle dynamic content and SPA updates? Also, what's the error recovery strategy when the model picks wrong paths? Comparing speed vs accuracy tradeoff vs proprietary agents would be interesting. Local deployment story is a big plus for privacy-focused teams.
Reading the elements instead of taking a screenshot for every step is the choice I'd want to understand first — especially with the 25.7-second, $0.020 run against Browser Use's 42.2 seconds and $0.045. When the small model is unsure and it falls back, does the citation trail still show which elements it was choosing between?
"Can't click something that isn't there" is an underrated feature. Half of browser-agent debugging is discovering the button the model invented. Reading the DOM first is the right kind of boring. Congrats on the launch!
Seeing the list of element choices alongside the fallback would be useful when debugging a run. It gives you a way to check what the agent actually had available at that step.
Congrats on the launch, Furqan! Sharing the benchmark harness and raw results on GitHub is the kind of transparency that makes me trust a tool right away.
Question from the design side: since fastbrowse reads the elements instead of screenshots, how does it handle icon-only buttons with no label, or canvas-heavy UIs where there's barely any DOM to read? Feels like this approach quietly rewards apps with good accessibility, which I love.
We're launching on Product Hunt today too, good luck! 🚀
About fastbrowse on Product Hunt
“Fastest open source browser agent”
fastbrowse launched on Product Hunt on October 30th, 2026 and earned 138 upvotes and 14 comments, placing #6 on the daily leaderboard. A fast open-source browser agent. It reads every button, link and field on the page and has a System One model (ex: jev) pick the next step from that list. If it's unsure, it falls back to a standard model model. Fast, cheap, and every answer provides a citation.
fastbrowse was featured in Open Source (69k followers), Developer Tools (522.3k followers) and Artificial Intelligence (482.7k followers) on Product Hunt. Together, these topics include over 243.9k products, making this a competitive space to launch in.
Who hunted fastbrowse?
fastbrowse was hunted by Furqan Rydhan. 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.
Want to see how fastbrowse stacked up against nearby launches in real time? Check out the live launch dashboard for upvote speed charts, proximity comparisons, and more analytics.
Furqan Rydhan
Hey Product Hunt, I'm Furqan, one of the makers of fastbrowse.
Browser agents have become exceptionally popular, but there's some huge problems. They're slow, expensive and miss easy tasks.
A lot of them take a screenshot and have a slow model decide every step, so each task takes a while and the bill adds up.
Fastbrowse reads the elements on the page instead. Every button, link and field is handed to a System One model (like Jev) with a list of choices. This model decies what to do next, when it isn't sure, fastbrowse falls back to a LLM to get it right. And because it only picks from what's on the page, it can't click something that isn't there.
We tested this against the latest open-source browser agents agent: same sign-in and add-to-cart task on a demo shop, three runs each.
fastbrowse took 25.7 seconds and $0.020 a run. Browser Use took 42.2 seconds and $0.045. Both got it right every time. On a wider 46-task benchmark against Browser Use's hosted agent running Claude Opus, our median task cost $0.026 vs $0.284. 99.2% of tasks passed.
Fastbrowse produces citations for everything it referenced to get answers, so you can trust and verify the results.
It's MIT licensed, and the code, the benchmark harness and the raw results are all on GitHub: https://github.com/agent-labs-dev/fastbrowse