Laodouuu
84949882ab
Co-authored-by: Yaowei Zheng <hiyouga@buaa.edu.cn> Co-authored-by: Claude Fable 5 <noreply@anthropic.com>
244 lines
12 KiB
Markdown
244 lines
12 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>Your Automated Agent Builder, Right on Your Desktop / Server</b><br />Create Self-Evolving Agents in One Click</p>
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<p align="center">
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<a href="https://www.producthunt.com/products/penguinharness?embed=true&utm_source=badge-featured&utm_medium=badge&utm_campaign=badge-penguinharness" target="_blank" rel="noopener noreferrer"><img alt="PenguinHarness - Let Agents Autonomously Build Better Agents for $0.02 | Product Hunt" width="250" height="54" src="https://api.producthunt.com/widgets/embed-image/v1/featured.svg?post_id=1202577&theme=light&t=1784804711946" /></a>
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</p>
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<p align="center">
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<a href="https://www.npmjs.com/package/@prismshadow/penguin-core"><img src="https://img.shields.io/npm/v/@prismshadow/penguin-core" alt="npm version" /></a>
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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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> With LangChain, you build agents by hand — at 1× speed.<br />With PenguinHarness, agents build agents — at 100×.
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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. 🏆 Outstanding results at tens of times less cost
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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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## Built-in Skills
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Four Skill groups ship in the box ([docs](https://penguin.ooo/docs/skills)); agents can also write and optimize their own:
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| Group | Skills |
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| -------------------- | --------------------------------------------------------------------------------- |
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| Office Productivity | `data-analysis`, `firecrawl` |
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| Software Development | `web-design`, `software-engineering` |
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| AI App Development | `penguin-sdk`, `penguin-cli`, `agenthub-models`, `vllm`, `ollama`, `llamafactory` |
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| Agent Tuning | `agent-creation`, `benchmark-design`, `agent-evaluation`, `agent-optimization` |
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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 | Moonshot AI, OpenRouter, Qwen Pay-As-You-Go |
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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 5.6 | OpenRouter |
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| Gemini 3.6 Flash | Google Gemini, OpenRouter |
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| Claude 5 | Anthropic, OpenRouter |
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Each family's latest generation only — the app's **Models** page lists every built-in preset, and any OpenAI-protocol endpoint works too: pick a preset, 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, Windows 10+ |
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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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Every route installs the same `penguin` command: `penguin web` launches the full Web experience (multi-session chat, agent/skill/model management, usage stats, Trace observability, evaluation center; first login admin / penguin-2026 — change the password right after), and models are configured on the in-app Models page. The online installers bundle their own Node runtime — unpack and run; upgrades and reinstalls never touch your data.
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### 🐧 Linux (online install)
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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
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```
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### 🍎 macOS (online install)
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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
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```
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### 🪟 Windows (online install, PowerShell)
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```powershell
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irm https://penguin.ooo/install.ps1 | iex
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penguin web # start the service and open http://127.0.0.1:7364
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```
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### 📦 npm (any platform, Node >= 24)
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```bash
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npm install -g @prismshadow/penguin-cli
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penguin web # start the service and open http://127.0.0.1:7364
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```
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<details>
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<summary><b>📴 Offline install (air-gapped machines)</b></summary>
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Every <a href="https://github.com/Prism-Shadow/penguin-harness/releases">GitHub Release</a> attaches exactly one package per target — Linux and macOS in x64 / arm64, Windows in x64, plus a runtime-less universal package — and the same file serves online and offline installation. Each package seals the program payload, its SHA256 checksum and the platform's installer: download the one file on a networked machine, copy it to the target, extract once and run the bundled installer — no network, no separate checksum file to carry (the sealed SHA256 is always verified).
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**Linux (on arm64, use `penguin-linux-arm64.tar.gz`):**
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```bash
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mkdir penguin-install
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tar -xzf penguin-linux-x64.tar.gz -C penguin-install
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./penguin-install/install.sh
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```
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**macOS (Apple silicon shown; on Intel, use `penguin-darwin-x64.tar.gz`):**
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```bash
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mkdir penguin-install
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tar -xzf penguin-darwin-arm64.tar.gz -C penguin-install
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./penguin-install/install.sh
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```
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**Windows (unzip, then double-click `install.cmd` — or run it in PowerShell):**
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```powershell
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Expand-Archive penguin-win32-x64.zip -DestinationPath penguin-install
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cd penguin-install
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.\install.cmd
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```
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</details>
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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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- [x] Windows support
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- [ ] Agent company and templates
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- [ ] Company-level self evolving
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- [ ] OpenShell integration (permission-governed shell)
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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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## Contributors
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Thanks to everyone who has contributed to PenguinHarness!
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<p align="center">
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<a href="https://github.com/Prism-Shadow/penguin-harness/graphs/contributors"><img src="https://contrib.rocks/image?repo=Prism-Shadow/penguin-harness" alt="PenguinHarness contributors" /></a>
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</p>
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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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