browser-use vs jev-ultrafast
The standard library for letting AI agents drive a real browser — click, type, fill forms and complete tasks from a natural-language goal. 100k+ stars, Python. — versus — Browser Use's speed experiment: Jev picks the operation and the target element from an indexed action space in one request, and a small LLM writes text only when typing is needed.
Same team, opposite trade-off: browser-use is the general library where an LLM reasons over each step, jev-ultrafast fixes the action space to eight operations so a decision model can pick operation and element in one request - far faster, far less flexible.
| browser-use | jev-ultrafast | |
|---|---|---|
| Stars | 115k | 8.3k |
| Forks | 13k | 518 |
| Language | Python | Python |
| License | MIT | MIT |
| Last activity | yesterday | yesterday |
| Topics | agents, web | web, agents |
| Curated connections | 13 | 4 |
browser-use — the curator's take
When the task lives behind login walls, forms and JavaScript — 'book this', 'apply to that', 'put these in my cart' — this is the default tool: it feeds the agent a cleaned DOM, executes its clicks/typing, and recovers from the endless weirdness of real websites. Install-as-skill support means Claude Code/Cursor agents pick it up in one prompt. NOT for bulk data extraction — an LLM driving a browser is the slowest, most expensive way to scrape a thousand pages (use a crawler); and treat any agent-with-a-browser as having the keys to whatever it's logged into — sandbox accordingly.
jev-ultrafast — the curator's take
Read this one for the architecture even if you never run it: the page becomes a numbered element table, Jev answers operation and target as speculative parallel heads in a single round trip, and a small LLM is called only for TYPE_TEXT. That is why Zurich-to-London on Google Flights lands in ~7s with no screenshots in the loop. The safety properties are the interesting part - model output never becomes selectors, coordinates, shell or JS; every target resolves to an observed DOM node and is rechecked for occlusion before input. Use it when latency is the product. Don't reach for it as a general agent: the action space is eight fixed operations, it needs a hosted TypeSafe key plus a text-model key, and browser-use proper is what you want for long, tool-rich, recoverable workflows.