Jev Ultrafast

Jev Ultrafast is an open-source Python browser agent published by the Browser Use organization on GitHub under the MIT License. It gives a browser agent one goal, then uses TypeSafe's Jev to pick an operation and a target element from a numbered table of page controls. The README says a Google Flights search from Zürich to London ran in 7.1 seconds, including text generation and loading waits. The Browser Use Cloud waitlist for ultrafast browser agents is open, according to the README banner.

What It Is

Jev Ultrafast is a web agent library and local demo that drives Chrome through Browser Harness. On each observation it builds a fresh element table of visible controls, such as buttons, comboboxes and textboxes. The model chooses among the operations CLICK, TYPE_TEXT, SELECT, SCROLL_UP, SCROLL_DOWN, WAIT, DONE and BLOCKED, and only supported operations and targets are offered. A small LLM writes text only when the operation is TYPE_TEXT.

How the Action Loop Works

The README describes target questions as speculative: operation and target heads share the same observed state, so two decisions are made in one network round trip. Each target head contains only compatible elements, and native dropdown choices carry an observed element and option index. The README says there are no site-specific action scripts or prepared field strings in the policy. Model output never becomes selectors, coordinates, shell commands or executable JavaScript, and text-helper output must parse as a small JSON object before typing.

Why It Moves Fast

The README lists several design choices behind its speed:

• One request per decision cycle.
• No screenshots in the default agent loop; it consumes structured state.
• One browser call per snapshot, reading visible controls atomically.
• Short waits after interactions, capped at 200 ms for combobox suggestions.
• Focus emulation to keep hidden tabs from being throttled.
• Only visible text is sent to the model.

The executor also rechecks page freshness and click occlusion before input.

Setup Path

Users clone the repository, run uv sync, copy the example environment file and add a TYPESAFE_API_KEY and a TEXT_MODEL_API_KEY. Running uv run jev opens a local inspector at 127.0.0.1:8766 that shows numbered elements, operation and target probabilities and executed actions. The example configuration uses an OpenRouter key and the inception/mercury-2.5 model, and the README says Gemini, GLM and DeepSeek can also be used through the OpenAI-compatible text helper. The library can also be used from Python through an Agent class that yields run states.

Evidence and Limits

The README reports a 7,073 ms Google Flights run and a median task time drop from 9.450 s to 7.092 s across six alternating runs, with both versions passing 3 of 3. It states this is three repeats of one task on one browser profile, not a general reliability benchmark. It also lists limits: a DONE choice still needs independent outcome verification, and shadow roots, frames, canvas, uploads, pop-up tabs, nested scrolling and arbitrary keyboard widgets are outside this MVP.