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
8.3 KiB
Markdown
168 lines
8.3 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>With LangChain, you build agents by hand — at 1× speed.<br />With PenguinHarness, agents build agents — at 100×.</b></p>
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<p align="center">A zero-code Harness CLI and Web UI, connected to 1000+ models.</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/Website-penguin.ooo-1f6feb?logo=googlechrome&logoColor=white" alt="Website" /></a>
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<a href="https://penguin.ooo/docs/"><img src="https://img.shields.io/badge/Docs-penguin.ooo%2Fdocs-1f6feb?logo=readthedocs&logoColor=white" alt="Docs" /></a>
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<a href="https://penguin.ooo/blog"><img src="https://img.shields.io/badge/Blog-penguin.ooo%2Fblog-1f6feb?logo=rss&logoColor=white" alt="Blog" /></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-join%20chat-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/WeChat-user%20group-07C160?logo=wechat&logoColor=white" alt="WeChat" /></a>
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</p>
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<p align="center">English | <a href="README.zh.md">简体中文</a></p>
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## Why PenguinHarness
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Three reasons, in deliberate order — from task quality, to how agents get built, to how they keep improving.
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### 1. 🏆 Comparable quality, one to two orders of magnitude cheaper
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A deliberately minimal toolset over clean low-level interfaces: fewer tool calls, fewer tokens — deeply tuned for open models like DeepSeek. Each harness on the model it is normally paired with, same tasks, head-to-head:
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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 leads the data-analysis suite and ties OpenAI Codex on coding, at a small fraction of both rivals' cost" width="920" />
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</picture>
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</p>
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**Best accuracy on data analysis — at 1/70 of Claude Code's cost.**
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### 2. ⚡ One sentence, and an Agent builds your Agent app
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Type one sentence, and an Agent builds the complete Agent application for you — scaffold, code, and run instructions, end to end:
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```text
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Collect the docs from https://github.com/ericbuess/claude-code-docs and build a RAG app that answers Claude Code questions as a configuration expert, citing its sources.
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```
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And this is the finished product — a docs expert with retrieval, cited sources that link to the original files, and example questions built in:
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https://github.com/user-attachments/assets/9b7033e8-f08a-4c3f-bd33-547896664e6e
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**And generating this entire RAG app burned just $0.02 (¥0.2) of tokens — on DeepSeek V4 Pro.**
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### 3. 🧬 Self-evolution: it gets stronger with use
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With PenguinHarness Skills, an Agent evaluates and optimizes itself: run the benchmark, find the lost points, ship version N+1 — with a snapshot before every round, and every request observable in the Trace view.
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https://github.com/user-attachments/assets/922d13a6-5ffc-4685-9a39-352f02f9afc0
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## Supported Models
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| Model | Providers |
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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 (preview) |
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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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Any OpenAI-protocol endpoint is supported: pick a preset above, or point a custom endpoint at any of the 1000+ online and local models.
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## Requirements
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| Requirement | Supported |
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| ------------ | -------------------------------------------------------------------------- |
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| OS | Linux, macOS |
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| Architecture | x64, arm64 |
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| Runtime | bundled by the one-line installer (npm installs need Node >= 24) |
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| Model | an API key for at least one model |
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## Installation
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### 🌐 Web App — for humans
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🚀 Install and launch the full experience (multi-session chat, Agent/skill/model management, usage stats, Trace observability, evaluation center):
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```bash
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curl -fsSL https://penguin.ooo/install.sh | sh
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penguin web # start the service and open http://127.0.0.1:7364 (first login: admin / penguin-2026)
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```
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📦 Or via npm: `npm install -g @prismshadow/penguin-cli`. Configure models on the in-app Models page, then chat.
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### 🤖 CLI & SDK — for agents
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The same engine, scriptable — made to be driven by agents (and agents building agents):
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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" # one-shot task
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penguin chat # interactive REPL (/compact, /exit, Ctrl-C to interrupt)
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penguin server # headless service (same API the Web App uses)
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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", // per-tool-call approval
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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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## Roadmap
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- [ ] Public release of the benchmark suite
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- [ ] Desktop app
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- [ ] Windows support
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- More to come…
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## Development
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```bash
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pnpm install && pnpm build # build first: core's exports point at dist/
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pnpm dev # backend + web app together (prefixed logs, deps built once)
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```
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See [CONTRIBUTING.md](CONTRIBUTING.md) for the full workspace guide: dev commands, quality gates, repo layout, and the changelog rule.
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## Citation
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If you use PenguinHarness in your research, please cite:
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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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## License
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[Apache-2.0](LICENSE) © 2026 Prism Shadow
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Built with ❤️ by [Yaowei Zheng](https://github.com/hiyouga) (author of [LlamaFactory](https://github.com/hiyouga/LlamaFactory)), the [PrismShadow AI Team](https://github.com/Prism-Shadow), and [Fable 5](https://www.anthropic.com/news/claude-fable-5-mythos-5).
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