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.
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.
On the analytics side, fastbrowse competes within Open Source, Developer Tools and Artificial Intelligence — topics that collectively have 1.1M followers on Product Hunt. The dashboard above tracks how fastbrowse performed against the three products that launched closest to it on the same day.
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.
For a complete overview of fastbrowse including community comment highlights and product details, visit the product overview.
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