Primer

What is Jev — for agent builders?

Jev is TypeSafe AI’s first System One model. Instead of generating prose, it is designed to take application state plus bounded questions and return typed decisions with probabilities and confidence.

The useful mental model

Do not think “another chatbot.” Think fast probabilistic decision function inside code. Your agent or application owns the workflow; Jev answers narrow questions such as which route to take, which action to choose, whether a condition is true, or how strongly something should be scored.

Why this matters for agents

Agent systems repeatedly need small judgments between expensive generative steps: choose a tool, screen context, verify a claim, judge completion, route a task, or decide whether to escalate. Those are the patterns this site tracks.

Official primitives

TypeSafe’s workflow materials use three decision shapes: Noul for yes/no probability, Choice for one option from a defined set, and Score for ratings on a defined scale.

Jev operates as a specialized fast decision co-processor alongside frontier foundation models like OpenAI GPT-4o, Anthropic Claude 3.5 Sonnet, and Google Gemini. While foundation models handle creative synthesis and reasoning, Jev makes sub-50ms deterministic decisions—choosing tools, scoring context, routing models, and judging task completion.

Underlying Foundation Models & Architecture

The builds, coding agents, and workflows showcased across JevForAgents rely on industry-standard foundation models from mainstream frontier providers:

  • OpenAI: GPT-4o, GPT-4 Turbo, and OpenAI Codex for generative code synthesis, complex reasoning, and automated safety screening via the OpenAI Moderation API.
  • Anthropic: Claude 3.5 Sonnet and Claude Code for long-context tool invocation and multistep agent workflows.
  • Google: Gemini 1.5 Pro and Gemini Flash for multimodal context processing and visual retrieval.

Within these multi-model agent pipelines, Jev operates as a specialized, deterministic decision and routing co-processor (sub-50ms) to select tools, gate outputs, and route execution to the appropriate mainstream foundation model.

Read the architecture, typed-output and fit guide →

Evidence policy

[OFFICIAL] — Published by TypeSafe AI or an official TypeSafe repository/site.

[FIRST-HAND TEST] — Tested directly by JevForAgents with method and conditions documented.

[COMMUNITY OBSERVED] — Observed in a public project, repository, demo, or author report.

[SECONDARY REPORT] — Reported by a third-party source that is not the original implementation.

[HYPOTHESIS] — A working interpretation or opportunity that still needs verification.

About this site: JevForAgents is an independent technical database and directory. Its primary paid offering is a verified sponsor listing on our leaderboard. All directory listings and submissions are moderated in accordance with our Acceptable Use Policy (AUP) and Sponsor Pricing. Vendor speed, cost, accuracy, or “zero hallucination” claims are labeled as vendor claims unless reproduced directly.
FROM THE SOURCE

Meet Jev in the founder’s introduction

The original launch post and video from Diogo Almeida. Speed and cost comparisons in this post are author claims, not JevForAgents tests.

Try Jev with the setup guide →
FROM THE CO-INVENTOR OF CHATGPT38,000,000+ VIEWS ON 𝕏
Diogo Almeida
Diogo Almeida@CompleteSkeptic · Sep 15

After co-inventing ChatGPT, I kept asking myself: why have superhuman chat models not led to AGI?

I spent the last 2 years building a new way to train models (RLCD) and a new type of frontier AI model we are releasing today: Jev

  • 20–200× faster than conventional generative models
  • 40–400× cheaper (with output tokens free)
  • Composable intelligence optimized for agent decisions
💬 3.9K🔁 8.2K❤️ 74.5K📊 38.4M Views
WHAT THIS REPRESENTSJev makes the bounded decision. Code, tools, and agents execute.