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Code / Codex / pi plugin that hands the 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.","maintainers":[{"name":"shitianfang","email":"jialany@foxmail.com"}],"readme":"# jev-use\n\n[English](README.md) | **简体中文**\n\nClaude Code / Codex / [pi](https://github.com/badlogic/pi-mono) 与\n[Jev](https://typesafe.ai/blog/introducing-system-one-models-and-jev)\n的最佳协作方式:把不需要输出内容的任务交给 Jev——加速任务、减少 token\n消耗,更快更好地完成任务。\n\n它让 LLM 与 Jev 真正协作:需要输出内容时,LLM 接管;不需要内容、只要快速执行时,交给\nJev。\n\n## 演示——真实运行,1× 速度\n\n<table>\n<tr>\n<td width=\"50%\" valign=\"top\"><b>路线查询:Jev 点击,LLM 打字</b>——10 次决策(p50 274 ms)、4 次写入;查错地点被 Jev 拒绝后 LLM 重写<br><img src=\"assets/collab.gif\" alt=\"OSM 路线查询:Jev 绿色选控件,LLM 蓝色打字输入地点;错误的 1809 公里地理编码被 Jev 拒绝、LLM 修正,最终得到真实的 3.7 公里步行路线\" width=\"100%\"></td>\n<td width=\"50%\" valign=\"top\"><b>上下文压缩</b>——200 条消息 7 次调用判完,LLM 一段摘要替换丢弃堆;召回 3/3<br><img src=\"assets/compact.gif\" alt=\"真实转录把上下文窗口填到 94%;Jev 逐条判留/丢,LLM 的摘要段替换灰色块,窗口降到 44%,三题召回全中\" width=\"100%\"></td>\n</tr>\n<tr>\n<td width=\"50%\" valign=\"top\"><b>Pong:球速=决策延迟</b>——按常规调用时 20 秒内 Jev 86 次决策,haiku 6 次,gemini 3 次;给两个基线都加上 enum 约束后差距是 3×<br><img src=\"assets/pong.gif\" alt=\"三条 Pong 通道 1× 回放真实运行:Jev 的球以每步约 224ms 来回穿场,LLM 的球缓慢爬行\" width=\"100%\"></td>\n<td width=\"50%\" valign=\"top\"><b>把关每条 shell 命令</b>——危险命令约 230 ms 内带理由拒绝,零 LLM token<br><img src=\"assets/gate.gif\" alt=\"24 条命令的开发会话 1× 实录:危险命令以置信度 1.00 被拒,正常命令放行\" width=\"100%\"></td>\n</tr>\n</table>\n\n每个演示都是可复跑的脚本,在 [bench/examples/](bench/examples);全部数字、方法、方差与告示:[bench/RESULTS.md](bench/RESULTS.md)\n· 第三方实测:[docs/evidence.md](docs/evidence.md)。\n\n## 安装\n\n```bash\nnpx -y jev-use install    # 自动配置 Claude Code / Codex / pi——检测到哪个装哪个\n```\n\n在 agent 运行的环境里配一个 key（`JEV_BACKEND=mock` 可无 key 干跑）：\n\n| 供应商 | 环境变量 |\n| --- | --- |\n| [TypeSafe 直连](https://typesafe.ai/) | `TYPESAFE_API_KEY` |\n| [OpenRouter](https://openrouter.ai/typesafe/jev-1.13) | `OPENROUTER_API_KEY` |\n| [Vercel AI Gateway](https://vercel.com/ai-gateway/models/jev) | `AI_GATEWAY_API_KEY` |\n\n`npx -y jev-use doctor` 检查链路。被判断的状态会发往你配置的供应商;`JEV_BACKEND=mock`\n全程本地。插件形态（含路由技能和 PreToolUse\n把关）：[harness/claude-code](harness/claude-code/README.md) ·\n[harness/codex](harness/codex/README.md)。\n\n## 作为库使用\n\n`npm i jev-use`——判断路径零运行时依赖：\n\n```js\nimport { Jev, check, pick, rate } from \"jev-use\";\n\nconst jev = new Jev();\n\nconst { answers } = await jev.judge(state, {\n  next: pick(\"Next action?\", { merge: \"all green\", rerun: \"looks flaky\", hold: \"needs attention\" }),\n  risk: rate(\"How risky?\", [\"routine\", \"worth a look\", \"incident\"]),\n  passed: check(\"Did the run fully succeed?\"),\n});\n// answers.next → { answer: \"merge\", confidence: 0.93, confidenceFrom: \"reported\", escalate: false }\n```\n\nJev 拍不了板的判决会带着 `escalate: true`\n和类型化原因交还。工具、判决结构、升级契约、CLI：[docs/reference.md](docs/reference.md)。\n\n## 小到能读完\n\n| 文件 | 职责 |\n| --- | --- |\n| [src/protocol.ts](src/protocol.ts) | 问题（`check`/`pick`/`rate`）、判决、升级原因的类型 |\n| [src/dispatch.ts](src/dispatch.ts) | 调用前分路：哪些步骤根本不进 Jev |\n| [src/judge.ts](src/judge.ts) | 筛查 → 后端 → 交还不确定的；`gate` |\n| [src/jev.ts](src/jev.ts) | 引擎之上的 `Jev` 客户端 |\n| [src/redact.ts](src/redact.ts) | 被门控的动作发出前，先抹掉其中的凭据 |\n| [src/backends/](src/backends) | TypeSafe、OpenRouter、Vercel、mock 适配器 |\n| [src/server.ts](src/server.ts) | 两个 MCP 工具 |\n| [src/cli.ts](src/cli.ts) | `install`、`serve`、`hook gate`、`doctor` |\n| [skills/jev-use/SKILL.md](skills/jev-use/SKILL.md) | agent 遵循的路由规则 |\n\n## 开发\n\n```console\n$ npm run typecheck && npm test    # 单元测试，含各供应商线上格式 fixture\n$ npm run smoke                    # 真实 MCP 客户端 ↔ 构建产物 CLI，走 stdio\n$ node bench/run.mjs               # 微基准，用你的 key 和网络\n```\n\n主体由 Claude Code（AI 辅助）编写。\n\nMIT © [shitianfang](https://github.com/shitianfang)\n","readmeFilename":"README.zh-CN.md"}