browser-use's Jev Ultrafast racks up 16,500 stars — but 'fastest' is still just a vibe
A new open-source web agent promises to click through websites in seconds flat, and developers are piling in — though the eye-catching benchmark comes entirely from its own demo.
The claim
A repository called jev-ultrafast, published by the team behind browser-use, has picked up more than 16,500 GitHub stars and over 1,000 forks in under a fortnight — a growth curve that usually only accompanies something either genuinely useful or extremely well marketed. The pitch is bold: the “fastest and cheapest web agent” around, capable of completing a flight search on Google Flights, from typing “Zürich” to landing on results for London, in 7.1 seconds.
What the tool actually does
Strip away the branding and this is a browser-automation agent with a twist. Rather than getting a large language model to reason over screenshots or full page HTML on every step — the slow, expensive way most AI browser agents currently work — Jev builds a fresh table of numbered, clickable elements each time the page changes (buttons, text boxes, dropdowns), and asks a model to pick a number and an action. A separate, smaller model is only invoked to actually generate text, and only when the chosen action is “type”. In principle that’s a sensible way to cut down on the number of expensive model calls and the amount of data being shovelled at an LLM for every click.
The 7.1-second flight-search figure comes from a video the project itself published, covering one goal on one website. There is no independent benchmark, no comparison against rival agents such as browser-use’s own earlier releases or competitors, and no published methodology for how “fastest and cheapest” was measured. It may well be quick — the underlying approach (indexed elements over screenshots) is a reasonable engineering shortcut — but right now the headline number is a demo reel, not a peer-reviewed result.
So who is actually behind this, and is the growth organic?
browser-use is a known name in the AI-agent space, already selling browser-automation tools to developers, and this release sits alongside a prominent call-out to join the waitlist for “Browser Use Cloud” — a paid, hosted version of the same idea. That context matters: a splashy open-source repo that drives sign-ups to a commercial product is a well-worn playbook, and it doesn’t automatically mean the stars are fake, but it does mean the surge deserves the same scepticism you’d apply to any other launch stunt. GitHub star counts can be inflated by bot activity or coordinated campaigns, and 16,000-plus stars in under two weeks is unusually fast even for a hot open-source project — worth watching to see whether real usage (issues, pull requests, actual production adoption) keeps pace, rather than taking the number at face value.
Does it work, and who should care
If you’re a developer building or evaluating AI agents that navigate websites — for testing, scraping, or automation — this is worth a look purely on the technical merits of the indexed-action approach, which is a genuinely interesting way to reduce LLM overhead. If you’re not writing code that touches browser automation, none of this changes anything about your day-to-day use of the web or AI tools; there’s no consumer product here yet, just a developer library and a waitlist.
The takeaway
An open-source agent gaining traction fast is not inherently suspicious, and the underlying technique is sound engineering. But “fastest and cheapest” remains a marketing line backed by one video, not a benchmark, and a rapid star count attached to a commercial waitlist is exactly the kind of claim that deserves a second look before repeating it as fact.