Browse builds

Building a Harness with Jev Learn all about Jev, a new System One model from TypeSafe AI, and how you can use it in your agent harness. Jev is up to 200x ...

How To Build A Harness With Jev | A LangChain x TypeSafe Conversation Hear from Sydney Runkle, Product Manager, OSS at LangChain, Allie Laabs, Founding Developer Advocate at TypeSafe AI, and ...

Jev-native agent design with top-k proposals, fallback decisions, memory, and replanning.

MCP proxy that uses Jev to screen agent tool calls and tool results.

Open-weight Jev-inspired decision model fine-tuned from Qwen3.5 4B.

Jev-kit: all the Jev stuff I've wired into Claude Code, now in one repo (guard hook, sub-agent sizing, file search, browser agent) I've been using TypeSafe's Jev with Claude Code for a couple of days and kept adding more use cases/ideas. So I've cleaned it up and put it in one repo. For anyone who doesn't know Jev; Jev is a small fast model that answers pick-one and yes/no questions (and more) in about 0.3s, which makes it cheap so you can put it inside your agents loop and make calls Claude would otherwise spend a turn on (eating away our already limited weekly usage). What's in it: A PreToolUse guard. Plain code checks let about 93% of tool calls straight through in ~33ms, the grey ones get one question to Jev. Catches stuff like cat on an env file or a find across the whole disk Sub-ag

jev-browser: give it a task and a URL, Jev picks one action per step Fast and cheap browser use: Jev chooses one action per step from the clickable, typeable and selectable elements of the page, and scores how likely the goal is met or the run is stuck. Budgets, retries and stopping stay in code. Ships as an MCP server, CLI or library. Around 178 stars.

Tried using Jev to check what an agent should remember We've been trying Jev for agent memory, mostly to catch cases where the saved note says more than the original conversation did. Think "we might use Postgres" turning into "we chose Postgres." Once that gets saved, the next conversation starts from a decision nobody actually made. We gave Jev the original text and a proposed memory, then used its judgments to decide whether to save it, skip it, or leave it undecided. We ran the same setup with Luna as a comparison. On 100 synthetic cases, using a .40 cutoff, Jev kept 39 of the 50 memories labeled worth keeping. Luna kept 41. Neither saved a candidate labeled skip or defer. Median gate latency was 250 ms for Jev and 1,593 ms for Luna, including network time. Then we checked what happen

Jev judges yes-or-no assertions about an agent transcript, and the test passes when its probability reaches the configured threshold.

Jev-native AI security harness for autonomous research, multi-agent swarms, persistent hunt boards, and long-running agent workflows. CLI-first, open source, and built for authorized security research.

Command Code mod: nudges the agent to keep going when it stops with work left, judged by Jev.

The Jev integration routes agent memory queries between compact Markdown notes and searchable raw history.

Jev-powered plugins for coding agents.

The agent loop where decisions don't cost a large language model call. Zero deps, runs offline, no API key needed.

Jev-powered search infrastructure for AI agents: zero API keys, MCP-ready, LLM-context aware, with local neural evidence verification.

A Jev powered LinkedIn recruiting agent. Watch it browse relevant profiles, save links, and review evidence against your hiring brief.

Four agent skills use Jev Score and Noul judgments to assess subjective criteria with calibrated probabilities.

I just built a Jev X Swarm Controller and put 300 agents on autopilot for 6 minutes. 300 K3 workers on autopilot. 6 minutes. 12,480 turns. Jev made a decision on every one of them. 91 reached me. A swarm knows how to execute. It does not know when to stop. every turn gets 6 typed questions: is this result good enough, is there new information, is this the same state as last turn. then one action: continue / retry / stop / merge / spawn / escalate. what the loop actually did: 1,642 STOP - same state twice, the loop was going nowhere 806 MERGE - two workers on the identical finding 91 ESCALATE -

Been building a little browser agent with Jev. Give it a goal. Jev figures out what to do. Playwright does it. Look → Decide → Act → Repeat. No screenshots. No hardcoded clicks. Just an AI figuring out the website as it goes. Solves problems. Fast. Cheap. Simple.

OODA AI + OpenClaw + VM + Jev = 7,1 sec for a Google Flight Search with Agents running full Computer Use. Uses Sol as primary Model, Luna as Light Model and Jev as classifier. But pick your flavor! We also have Laya and Von and any of our over 80+ AI models wrapped in an /v1/systemone endpoint. OODA AI is an All-in-One AI platform with over +150 AI models across Text, Image, Video, Audio, Avatars. Check the comment for a short video of how we use Jev and System1 models to decrease 50% context use, increase speed on Agent decisions in OpenClaw, handle better intent and tool decisions, Guardrail

Over the weekend, our team built a novel agentic document processing system using Jev. The initial results are very promising. Our system completed the same editing task 97% faster than ChatGPT's Word plugin. https://t.co/sZFU4qcgUJ

Portable System-1 decision layer for agent harnesses with host-owned routing, receipts, replay, and fail-open integrations.

Coding agent with a TypeSafe judgment provider that uses Jev for typed checks, routing, and workflow decisions.

The open, local take on Jev: typed decisions from an LLM, without generating a single token

so uh i've been using my computer while driving lately, and it's in the best interest of the world that i stop doing that. so this weekend I used @typesafeai jev + @kwindla's @pipecat_ai to make a live voice agent for managing multiple tmux coding agents (claude code, codex, opencode, etc) fully hands-free I can talk to one voice agent, who gives me updates and let's me send messages to my team of coding agents working on different projects. something i usually hate about live voice agents is that they always interrupt! so I tried using jev for classifying whether I am speaking directly t

tried using jev to flag browser agents this weekend. it runs through the activity logs live every 3 seconds and builds an average score over the session sure you could do this without jev, but also it took a few hours and cost less than $0.01 per session https://t.co/9XUQIeSqWL

Hermes plugin and skill suite using Jev for model routing, skill selection, memory filtering, compaction, and GUI actions.

ACP and MCP adapter that exposes Jev typed decisions and computer-use actions to Codex, Claude, OpenCode, and other agents.

A tiny open source browser agent using Jev instead of an LLM for every click. Found a flight search in 7 SECONDS. Total cost: $0.0039. Here's why that's not a typo. A normal browser agent asks a chat model "what should I click" on every single step. That's a full generation call, just to pick a button. Mine doesn't. The DOM state at each step becomes the input. Jev gets the available actions as a typed choice question. It picks the action, not by generating text, by classifying against what's actually on the page. The only place a language model still runs is typing free text into a fie

Decision plugin for DeepSeek Harness and iPolloWork that lets OpenCode, Codex, and other agents use Jev to select tools, skills, and task owners and evaluate outputs.

noticed below cumulative impact of using Jev + Jevcache + Fable: - Per-decision: ~220–780ms Jev vs 20–40s claude before - Loop's decisions are ~50× cheaper; Fable was consulted only on the 3 low-confidence steps - jevcache: earlier identical-phrasing re-run showed cached (0ms replay) live.. repeat run was 26% faster (7.2→5.3 min)

No, 'caliente, caliente no más! We shipped JarvisCore 1.12 and it comes with Jev by @typesafeai ! Come and learn about Jev next week on Wednesday. We're bringing @askmuyukani founder at @prescottdata for a live discussion on how JarvisCore is using Jev to build faster and cheaper agents, and what opportunities exist to extend Jev in agent harnesses.

Holy shit. Using Jev in my JARVIS just made it like actual super intelligence. what the fuck. Will release the update asap. https://t.co/LJZA4z7fSf

Humble beginnings using Jev. Trying to integrate it into Codex and help with speeding up browser use. Been working on it all day and running hundreds of trails. So far I've spent... $0.00725 https://t.co/yX3mPB41pL

I made an AI agent using Jev to reply to my wife We’re getting a divorce now and she wants to know who Jason is

Using Jev to catch Codex workers ignoring AGENTS.md. Combine live Codex output, Git changes, and AGENTS.md into one observation Use Jev to score the probability that the worker is drifting from AGENTS.md Steer the active Codex turn when drift crosses a threshold, stopping it if the drift continues Added to Foreman, which automatically watches and steers workers on the software factory floor. https://t.co/g9prp3tsy8

Episode 285: Bendcoder We build an experimental coding agent from scratch using Jev from @typesafeai and new programming language Bend2 from @VictorTaelin. ...while playing WoW Forever on CoderOS; demoing the CoderOS GPU-accelerated multiplex panes and hand tracking (goodbye Hyprland, hello Jarvis); and introducing CoderQuest, a forthcoming game for commanding and upgrading coding agents with real-world prizes. Meanwhile in Azeroth, Eetum arrives in Thunder Bluff and purchases a guild charter. Bendcoder repo: https://t.co/UjtjgTEAZS

Runtime constraints for the pi coding agent: checks every side-effecting tool call against what you said, before it runs. Powered by TypeSafe Jev.

Minimal agent loop where Jev directs control flow and a LangChain chat model writes argument values and the final response.

Agent Skill: send closed coding-agent judgments to TypeSafe Jev.