Skip to content
JevDirectory.org
CommunityTools & Integrations17.5k starsVerified 2026-09-22

Jev Ultrafast

Browser Use's ultrafast agent: Jev picks an operation and an element from an indexed action space in one request, and a small LLM writes text only when the action is TYPE_TEXT.

Category
Tools & Integrations
Published by
Community
Author
browser-use
Added
2026-09-22
Tagscommunitypythonbrowseragentsdemo

Highlights

  • One TypeSafe request returns the operation plus every target head, and only the target matching that operation executes.
  • The measured demo runs a Zürich to London Google Flights search in 7.1 seconds, including text generation and loading waits.
  • No screenshots in the default loop: Jev consumes an element table read atomically from the DOM.
  • Wait budgets are explicit: up to 200 ms for combobox suggestions, otherwise two animation frames or 50 ms.

Quickstart

python
from jev_ultrafast import Agent

with Agent(
    "https://www.google.com/travel/flights?hl=en",
    "Find one-way flights from Zurich to London on September 20, 2026.",
) as agent:
    for state in agent.run():
        print(state["elapsed_ms"], state["status"])

Watch out

MIT-licensed. Needs a TypeSafe key plus a separate text-model key (OpenRouter in the example); tabs, nested scrolling, and arbitrary keyboard widgets are outside this MVP.

More like this

2.6kGitHub stars
A cross-platform memory layer for AI coding agents: conversations from Claude Code, Codex, DeepSeek Harness, OpenClaw, and OpenCode become Markdown memories indexed in Milvus, with optional Jev reranking through the TypeSafe API.
Tools & Integrations#community#python#agents
Communitymemsearch
799GitHub stars
A macOS computer-use loop that OCRs the screen, asks Jev for the next action with typed Choices, and clicks, at about $0.0002 per decision without sending screenshots to a frontier model.
Tools & Integrations#community#python#agents
425GitHub stars
A Python toolkit that installs nine plain SKILL.md files for Hermes, Claude Code, and Codex, letting Jev route models, filter retrieved passages, select skills, and choose bounded computer or browser actions.
Tools & Integrations#community#python#agents
Back to all resources

From the community

Posts from builders shipping with Jev right now.

Follow @typesafeai

A playable 16-judgment demo

AI multiple choice, not essay writing

Screening agent actions with Jev

Tested TypeSafe’s Jev (no-text, probability-only model) as an AI agent safety monitor. Checking each action first worked well caught most attacks with almost no false blocks, and much faster than Gemini.

Image
Image
Image
Diogo Almeida
Diogo Almeida
TypeSafe AI
@CompleteSkeptic

After co-inventing ChatGPT, I kept asking myself: why have superhuman chat models not led to AGI? I’ve spent the last 2 years in stealth building a new way to train models (RLCD), and a new type of frontier AI model that we are releasing today: Jev • 20-200x faster • 40-400x

Reply

Cua's small System One models

A 706K-parameter form filler

cua open sourced a 706k param model that fills a whole form in one 50ms pass the llm agent doing the same form took 23 turns and 39.6 seconds the specialists are going to eat the generalists from the bottom

Cua
Cua
@trycua

1/ Introducing CUA-S1: a family of System One Models, small, specialized, and built for computer use. Today we're open-sourcing CUA-S1-FORMS, the first in the family: github.com/trycua/cua

Image
Reply

Navigating Neo4j with Jev

Jev 这个 waitlist 还是很给力的,昨天申请,今天就能用上。 给已经拿到 API、但还不知道怎么玩的人整理了一份 Awesome Jev,目前我能确认到的 Jev 项目基本都在这里: 1. jev-ultrafast Browser Use 做的高速浏览器 Agent。Jev Show more

Image
思维怪怪
思维怪怪
@0xLogicrw

前 OpenAI 研究员 Diogo Almeida 创办的 TypeSafe AI 推出新模型 Jev。它有点像一个能读懂自然语言的超级分类器,不生成文本,只返回选项、分数和概率,专门给软件做判断。 普通大模型需要一个 token 一个 token 往外生成,Jev 则可以并行给出多个结果。TypeSafe 还用新的 RLCD

Reply