Yaowei Zheng
d4faee3a1e
Branch-length batch covering tooling, the model layer, the Web App and the public surfaces. Highlights: - Changelog: a per-release `changelog/<version>/` tree, grouped by the surface each change touches, with a root CHANGELOG.md holding one line per release. - Dev startup: `scripts/dev-prebuild.mjs` serializes the skills+core prebuild behind a lock and keeps `pnpm install` current; `pnpm dev` runs server+web together. - AgentHub 0.3.3 -> 0.4.0: OmniMessage complete payloads carry one opaque `fidelity` object in place of item-level `signature`/`phase`, threaded verbatim through Trace, replay and resume; malformed classification adapted to the new error types. - Model layer: a model is always referenced by an explicit `(provider, model_id)` pair. The provider is never inferred, guessed or defaulted -- both the catalog inference and the unique-match config resolution are gone, and CLI, SDK, server routes and run_subagent all require the complete pair. Catalog gains the Qwen Token Plan, Qwen Pay-As-You-Go and Fireworks AI gateways, plus an expanded OpenRouter group. - Web App: catalog preset sync and per-group speed test on the Models page, positional slash commands, a markdown renderer, skill-library update reminders, and a vertically centred draft page whose upward menus size themselves to the room available. - Public surfaces: restructured READMEs, the penguin.ooo landing site and blog, refreshed benchmark results for both suites, and the demo videos playing on the landing page. Includes the fixes from a full review of the branch: 23 confirmed findings, among them a provider-inference bug that could send one vendor's API key to another vendor's endpoint, and an Escape handler that destroyed the composer's contents unrecoverably. Verified on the branch head: pnpm test (1127 passing, 7 packages), pnpm typecheck and pnpm format:check clean, Playwright e2e 14/14.
168 lines
8.2 KiB
Markdown
168 lines
8.2 KiB
Markdown
<p align="center">
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<img src="packages/landing/public/penguin-logo.svg" alt="PenguinHarness logo" width="88" />
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</p>
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<h1 align="center">PenguinHarness</h1>
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<p align="center"><b>使用 LangChain，以 1 倍速度人工构建 Agent；<br />使用 PenguinHarness，以 100 倍速度用 Agent 构建 Agent。</b></p>
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<p align="center">零代码 Harness CLI 与 Web UI，连接 1000+ 模型。</p>
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<p align="center">
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<a href="https://github.com/Prism-Shadow/penguin-harness/actions/workflows/ci.yml"><img src="https://github.com/Prism-Shadow/penguin-harness/actions/workflows/ci.yml/badge.svg" alt="CI" /></a>
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<a href="https://github.com/Prism-Shadow/penguin-harness/actions/workflows/pages.yml"><img src="https://github.com/Prism-Shadow/penguin-harness/actions/workflows/pages.yml/badge.svg" alt="Deploy Site" /></a>
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<a href="LICENSE"><img src="https://img.shields.io/badge/license-Apache--2.0-blue" alt="License: Apache-2.0" /></a>
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<img src="https://img.shields.io/badge/node-%E2%89%A5%2024-brightgreen" alt="Node >= 24" />
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</p>
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<p align="center">
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<a href="https://penguin.ooo/"><img src="https://img.shields.io/badge/%E5%AE%98%E7%BD%91-penguin.ooo-1f6feb?logo=googlechrome&logoColor=white" alt="官网" /></a>
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<a href="https://penguin.ooo/docs/"><img src="https://img.shields.io/badge/%E6%96%87%E6%A1%A3-penguin.ooo%2Fdocs-1f6feb?logo=readthedocs&logoColor=white" alt="文档" /></a>
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<a href="https://penguin.ooo/blog"><img src="https://img.shields.io/badge/%E5%8D%9A%E5%AE%A2-penguin.ooo%2Fblog-1f6feb?logo=rss&logoColor=white" alt="博客" /></a>
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</p>
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<p align="center">
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<a href="https://discord.gg/eFHKqqcU3D"><img src="https://img.shields.io/badge/Discord-%E5%8A%A0%E5%85%A5%E8%AE%A8%E8%AE%BA-5865F2?logo=discord&logoColor=white" alt="Discord" /></a>
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<a href="https://x.com/code_hiyouga"><img src="https://img.shields.io/badge/X-code%5Fhiyouga-000000?logo=x&logoColor=white" alt="X（Twitter）" /></a>
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<a href="https://github.com/Prism-Shadow/penguin-harness-community/blob/main/wechat/group.jpg"><img src="https://img.shields.io/badge/%E5%BE%AE%E4%BF%A1-%E4%BA%A4%E6%B5%81%E7%BE%A4-07C160?logo=wechat&logoColor=white" alt="微信群" /></a>
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</p>
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<p align="center"><a href="README.md">English</a> | 简体中文</p>
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## 为什么选择 PenguinHarness
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三个递进的理由——从任务效果，到构建方式，再到进化能力。
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### 1. 🏆 效果同级，成本低一到两个数量级
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刻意精简的工具集配合干净的底层接口：更少的工具调用、更少的 Token，对 DeepSeek 等开放模型深度适配。各自搭配常用模型、同一批任务，正面对比：
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<p align="center">
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<picture>
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<source media="(prefers-color-scheme: dark)" srcset="assets/readme/benchmark-dark.svg" />
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<img src="assets/readme/benchmark-light.svg" alt="Benchmark：PenguinHarness 在数据分析题库准确率最高、编程题库与 OpenAI Codex 持平，成本仅为两者的零头" width="920" />
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</picture>
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</p>
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**数据分析准确率最高——成本只有 Claude Code 的 1/70。**
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### 2. ⚡ 一句话，让 Agent 构建 Agent 应用
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输入一句话，Agent 为你构建完整的 Agent 应用——脚手架、代码、运行说明，一步到位：
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```text
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收集 https://github.com/ericbuess/claude-code-docs 的文档，做一个化身 Claude Code 配置专家、回答带来源引用的 RAG 问答应用。
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```
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这是做出来的成品——一个文档专家：检索增强、引用可点击直达原文、内置示例问题：
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https://github.com/user-attachments/assets/604eb626-0a5d-4a62-87e3-14ebade1cd5f
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**而生成整个 RAG 应用，仅消耗了 0.2 元（$0.02）的 token——使用 DeepSeek V4 Pro 模型。**
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### 3. 🧬 自进化，越用越强
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借助 PenguinHarness 技能库，Agent 自己评估、自己优化：跑 Benchmark、找失分点、发布 N+1 版——每轮之前自动快照，每个请求都可在轨迹观测中回放。
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https://github.com/user-attachments/assets/aec49ae9-b743-467b-b247-37bedfeaa36e
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## 支持的模型
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| 模型 | 可用供应商 |
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| ---------------- | -------------------------------------------------------------------------------- |
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| DeepSeek V4 | DeepSeek, OpenRouter, Fireworks AI, SiliconFlow, Qwen Token Plan |
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| Kimi K3 | OpenRouter, Qwen Pay-As-You-Go |
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| Kimi K2.6 | Moonshot AI |
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| GLM 5.2 | Z.AI, OpenRouter, Fireworks AI, SiliconFlow, Qwen Token Plan, Qwen Pay-As-You-Go |
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| Hunyuan 3 | OpenRouter |
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| Qwen 3.8 Max | Qwen Token Plan（预览） |
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| GPT 5.5 | OpenAI, OpenRouter |
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| Gemini 3.5 Flash | Google Gemini, OpenRouter |
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| Claude Opus 4.8 | Anthropic, OpenRouter |
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只要是 OpenAI 协议的端点都可以接入：从上表选择预置，或用自定义端点连接 1000+ 在线与本地模型。
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## 系统需求
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| 需求项 | 支持情况 |
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| -------- | ------------------------------------------------- |
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| 操作系统 | Linux、macOS |
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| 架构 | x64、arm64 |
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| 运行时 | 一行安装器自带（经 npm 安装需 Node >= 24） |
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| 模型 | 至少一个模型的 API key |
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## 安装
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### 🌐 Web 应用——面向人
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🚀 一行安装，启动完整体验（多会话对话、Agent / 技能 / 模型管理、用量统计、轨迹观测、评估中心）：
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```bash
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curl -fsSL https://penguin.ooo/install.sh | sh
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penguin web # 启动服务并打开 http://127.0.0.1:7364（首次登录：admin / penguin-2026）
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```
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📦 或经 npm 安装：`npm install -g @prismshadow/penguin-cli`。在应用内模型页配置模型后即可对话。
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### 🤖 CLI 与 SDK——面向 Agent
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同一引擎、可脚本化——为被 Agent 驱动而生（以及让 Agent 构建 Agent）：
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```bash
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penguin config model add --provider deepseek --model-id deepseek-v4-pro --api-key sk-... --set-default
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penguin run -m "Create hello.txt containing Hello, Penguin" # 单次任务
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penguin chat # 交互式 REPL（/compact、/exit、Ctrl-C 中断）
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penguin server # 无界面服务（与 Web 应用同一套 API）
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```
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```ts
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import { createAgent, isCompleteModelMessage, userText } from "@prismshadow/penguin-core";
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const agent = await createAgent({ agentId: "default_agent" });
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const session = await agent.createSession({ workspaceDir: process.cwd() });
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for await (const output of session.run([userText("Create hello.txt containing hi")], {
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approve: async () => "allow", // 按工具调用逐个审批
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})) {
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if (isCompleteModelMessage(output) && output.payload.type === "text") {
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console.log(output.payload.text);
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}
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}
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```
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## 路线图
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- [ ] Benchmark 套件正式发布
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- [ ] 桌面端应用
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- [ ] Windows 系统支持
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- 更多规划，敬请期待……
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## 参与开发
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```bash
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pnpm install && pnpm build # 先构建：core 的导出指向 dist/
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pnpm dev # 服务端 + Web 一起启动（带前缀日志，依赖只构建一次）
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```
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完整工作区指南见 [CONTRIBUTING.md](CONTRIBUTING.md)：开发命令、质量门禁、仓库结构与 changelog 规则。
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## 引用
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如果 PenguinHarness 对你的研究有帮助，请引用：
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```bibtex
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@software{penguinharness2026,
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author = {{PrismShadow Team}},
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title = {PenguinHarness: Efficient Self-Improving Harness for Everyone},
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year = {2026},
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url = {https://github.com/Prism-Shadow/penguin-harness},
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license = {Apache-2.0}
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}
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```
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## 协议
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[Apache-2.0](LICENSE) © 2026 Prism Shadow
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由 [LlamaFactory](https://github.com/hiyouga/LlamaFactory) 作者 [Yaowei Zheng](https://github.com/hiyouga)、[PrismShadow AI Team](https://github.com/Prism-Shadow) 与 [Fable 5](https://www.anthropic.com/news/claude-fable-5-mythos-5) 共同用 ❤️ 构建。
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