Claude Code / Codex / pi plugin that hands agent steps needing no text output to Jev (TypeSafe's judgment model) — measured p50 ~230 ms and ~$0.02 per 1,000 judgments, with typed escalation back to the LLM
- ✓Open-source license (MIT)
- ✓Actively maintained (<30d)
- ✓Clear description
- ✓Topics declared
- ✓Documented (README)
claude mcp add jev-use -- npx -y jev-use{
"mcpServers": {
"jev-use": {
"command": "npx",
"args": ["-y", "jev-use"]
}
}
}MCP Servers overview
# jev-use
**English** | [简体中文](README.zh-CN.md)
The best way for Claude Code, Codex, and [pi](https://github.com/badlogic/pi-mono)
to work with [Jev](https://typesafe.ai/blog/introducing-system-one-models-and-jev):
hand the tasks that need no text output to Jev — faster steps, fewer
tokens, tasks done sooner and better.
It makes the LLM and Jev true collaborators: when content needs to be
written, the LLM takes over; when a step just needs a fast decision, Jev
executes it.
## Demos — real runs, 1× speed
<table>
<tr>
<td width="50%" valign="top"><b>Directions task: Jev clicks, the LLM types</b> — 10 decisions (p50 274 ms) · 4 writes; Jev rejects a wrong route, the LLM rewrites<br><img src="assets/collab.gif" alt="OpenStreetMap directions: Jev picks controls in green, the LLM types the locations in blue; a wrong 1809km geocode is rejected by Jev and repaired by the LLM, ending on the real 3.7km walking route" width="100%"></td>
<td width="50%" valign="top"><b>Context compaction</b> — 200 messages judged in 7 calls, one LLM paragraph replaces the dropped pile; recall 3/3<br><img src="assets/compact.gif" alt="A real transcript fills the context window to 94%; Jev tints each message keep or drop, the LLM's summary paragraph replaces the dropped block, the window falls to 44% and three recall checks pass" width="100%"></td>
</tr>
<tr>
<td width="50%" valign="top"><b>Pong: ball speed = decision latency</b> — 86 Jev decisions in 20 s vs 6 (haiku) and 3 (gemini) called the usual way; enum-constrain both and the gap is 3×<br><img src="assets/pong.gif" alt="Three Pong lanes replaying a live run at 1x: the Jev ball sweeps the field at ~224ms per decision while the LLM balls crawl" width="100%"></td>
<td width="50%" valign="top"><b>Gate every shell command</b> — dangerous ones denied in ~230 ms with a reason, zero LLM tokens<br><img src="assets/gate.gif" alt="A 24-command dev session gated at 1x: dangerous commands denied at confidence 1.00, benign ones allowed" width="100%"></td>
</tr>
</table>
Every demo is a rerunnable script in [bench/examples/](bench/examples);
all numbers, methodology, variance and caveats:
[bench/RESULTS.md](bench/RESULTS.md) · third-party measurements:
[docs/evidence.md](docs/evidence.md).
## Install
```bash
npx -y jev-use install # wires Claude Code, Codex, and pi — whichever it finds
```
Set one key in the environment your agent runs in (`JEV_BACKEND=mock` for
a keyless dry run):
| Provider | Env var |
| --- | --- |
| [TypeSafe direct](https://typesafe.ai/) | `TYPESAFE_API_KEY` |
| [OpenRouter](https://openrouter.ai/typesafe/jev-1.13) | `OPENROUTER_API_KEY` |
| [Vercel AI Gateway](https://vercel.com/ai-gateway/models/jev) | `AI_GATEWAY_API_KEY` |
`npx -y jev-use doctor` checks the wiring. Judged state goes to the
provider you configure; `JEV_BACKEND=mock` stays local. Plugin form with
the routing skill and the PreToolUse gate:
[harness/claude-code](harness/claude-code/README.md) ·
[harness/codex](harness/codex/README.md).
## Use as a library
`npm i jev-use` — zero runtime dependencies on the judgment path:
```js
import { Jev, check, pick, rate } from "jev-use";
const jev = new Jev();
const { answers } = await jev.judge(state, {
next: pick("Next action?", { merge: "all green", rerun: "looks flaky", hold: "needs attention" }),
risk: rate("How risky?", ["routine", "worth a look", "incident"]),
passed: check("Did the run fully succeed?"),
});
// answers.next → { answer: "merge", confidence: 0.93, confidenceFrom: "reported", escalate: false }
```
Anything Jev can't or shouldn't decide comes back with `escalate: true`
and a typed reason. Tools, verdict shape, escalation contract, CLI:
[docs/reference.md](docs/reference.md).
## Small enough to read
| File | Job |
| --- | --- |
| [src/protocol.ts](src/protocol.ts) | Questions (`check`/`pick`/`rate`), verdicts, escalation reasons |
| [src/dispatch.ts](src/dispatch.ts) | Pre-call routing: what never reaches Jev |
| [src/judge.ts](src/judge.ts) | screen → backend → hand back what is unsure; `gate` |
| [src/jev.ts](src/jev.ts) | The `Jev` client over that engine |
| [src/backends/](src/backends) | TypeSafe, OpenRouter, Vercel, mock adapters |
| [src/server.ts](src/server.ts) | The two MCP tools |
| [src/cli.ts](src/cli.ts) | `install`, `serve`, `hook gate`, `doctor` |
| [skills/jev-use/SKILL.md](skills/jev-use/SKILL.md) | The routing rules the agent follows |
## Development
```console
$ npm run typecheck && npm test # unit tests incl. per-provider wire fixtures
$ npm run smoke # real MCP client ↔ built CLI over stdio
$ node bench/run.mjs # micro-benchmarks, your key and region
```
Substantially written with Claude Code (AI-assisted).
MIT © [shitianfang](https://github.com/shitianfang)
What people ask about jev-use
What is shitianfang/jev-use?
+
shitianfang/jev-use is mcp servers for the Claude AI ecosystem. Claude Code / Codex / pi plugin that hands agent steps needing no text output to Jev (TypeSafe's judgment model) — measured p50 ~230 ms and ~$0.02 per 1,000 judgments, with typed escalation back to the LLM It has 10 GitHub stars and its last recorded update is dated 2026-09-20.
How do I install jev-use?
+
You can install jev-use by cloning the repository (https://github.com/shitianfang/jev-use) or following the README instructions on GitHub. ClaudeWave also provides quick install blocks on this page.
Is shitianfang/jev-use safe to use?
+
Our security agent has analyzed shitianfang/jev-use and assigned a Trust Score of 95/100 (tier: Verified). See the full breakdown of passed checks and flags on this page.
Who maintains shitianfang/jev-use?
+
shitianfang/jev-use is maintained by shitianfang. The last recorded GitHub activity is dated 2026-09-20, with 0 open issues.
Are there alternatives to jev-use?
+
Yes. On ClaudeWave you can browse similar mcp servers at /categories/mcp, sorted by popularity or recent activity.
Deploy jev-use to your cloud
Ship this repo to production in minutes. Each platform spins up its own environment with editable env vars.
Maintain this repo? Add a badge to your README
Drop the badge into your GitHub README to show it's tracked on ClaudeWave. Each badge links back to this page and reflects the live Trust Score.
[](https://claudewave.com/repo/shitianfang-jev-use)<a href="https://claudewave.com/repo/shitianfang-jev-use"><img src="https://claudewave.com/api/badge/shitianfang-jev-use" alt="Featured on ClaudeWave: shitianfang/jev-use" width="320" height="64" /></a>More MCP Servers
Fair-code workflow automation platform with native AI capabilities. Combine visual building with custom code, self-host or cloud, 400+ integrations.
User-friendly AI Interface (Supports Ollama, OpenAI API, ...)
An open-source AI agent that brings the power of Gemini directly into your terminal.
Real-time global intelligence dashboard. AI-powered news aggregation, geopolitical monitoring, and infrastructure tracking in a unified situational awareness interface
🕷️ An adaptive Web Scraping framework that handles everything from a single request to a full-scale crawl! Don't be shy, join here: https://discord.gg/EMgGbDceNQ
The fastest path to AI-powered full stack observability, even for lean teams.