---
title: "Introducing PenguinHarness: agents that build agents"
date: 2026-07-17
category: news
pinned: true
excerpt: We proved agents can self-evolve in our GDPevo Benchmark — now we are bringing that capability to everyone. The first open-source harness with recursive self-improvement covers everything from one-sentence agent construction to continuous self-evolution.
---

Today we are releasing **PenguinHarness** — an open-source harness built for constructing and evolving agents: a zero-code Harness CLI and Web UI, connected to 1000+ models. The story it tells fits in one line:

> With LangChain, you build agents by hand — at 1× speed. With PenguinHarness, agents build agents — at 100×.

## From GDPevo to PenguinHarness: why we built this

Before PenguinHarness, our team published the [GDPevo Benchmark](https://prism-shadow.github.io/GDPevo/). In GDPevo we systematically verified one thing: **agents can self-evolve** — an Agent can score its own performance, find where the points were lost, rewrite its own prompts and Skills, and climb version after version.

With the capability proven, the question became: how does everyone get to use it? Self-evolution should not stay a curve in a paper — it should be infrastructure that works out of the box on every developer's desk. **Bringing an efficient self-improving harness to everyone is why we built PenguinHarness** — and it is right there in the name: Efficient Self-Improving Harness for Everyone.

## Why PenguinHarness

Three reasons, in deliberate order — from task quality, to how agents get built, to how they keep improving.

### 1. Better on complex tasks, at lower cost

A deliberately minimal toolset over clean low-level interfaces: fewer tool calls, fewer Tokens, deeply tuned for open models like DeepSeek. Each harness runs the model it is normally paired with — the comparison is between the products as people actually use them — head-to-head on two suites:

![Benchmark: PenguinHarness leads the data-analysis suite and ties OpenAI Codex on coding, at a small fraction of both rivals' cost](/blog-assets/benchmark-light.svg)

Complex data analysis (15 tasks, single run; PenguinHarness and Codex at thinking xhigh, Claude Code at max):

| Framework      | Model           | Accuracy (%) | Tokens (M) | Cost ($) |
| -------------- | --------------- | -----------: | ---------: | -------: |
| PenguinHarness | DeepSeek V4 Pro |        66.67 |      18.04 |     0.55 |
| Claude Code    | Claude Opus 4.8 |        53.33 |      22.20 |    38.48 |
| OpenAI Codex   | GPT-5.5         |        53.33 |      13.72 |    19.41 |

Coding tasks (40 tasks × 2 runs; accuracy is over all 80 outcomes):

| Framework      | Model           | Accuracy (%) | Tokens (M) | Cost ($) |
| -------------- | --------------- | -----------: | ---------: | -------: |
| PenguinHarness | DeepSeek V4 Pro |        71.25 |     200.00 |     3.81 |
| Claude Code    | Claude Opus 4.8 |        86.25 |     151.61 |   146.97 |
| OpenAI Codex   | GPT-5.5         |        71.25 |     251.20 |   220.08 |

Tokens and cost are suite totals, not per-run means. On data analysis we take the highest accuracy of the three — 66.67% against 53.33% for both — while spending 1/35 of what Codex spent and 1/70 of Claude Code's. On coding we tie Codex at 71.25% and trail Claude Code's 86.25%, but the whole suite cost us $3.81 against their $220.08 and $146.97: comparable work, one to two orders of magnitude apart on the bill.

### 2. One sentence, and an Agent builds your Agent app

Type one sentence, and an Agent builds the complete Agent application for you — scaffold, code, and run instructions, end to end:

```text
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.
```

And this is the finished product — a docs expert with retrieval, cited sources that link to the original files, and example questions built in:

![The generated RAG app: a Claude Code docs expert answering with cited, clickable sources and example questions](/blog-assets/rag-app-en-light.webp)

**And generating this entire RAG app burned just $0.02 (¥0.2) of tokens — on DeepSeek V4 Pro.**

### 3. Self-evolution: it gets stronger with use

With PenguinHarness Skills, an Agent evaluates and optimizes itself: the Optimizer orchestrates multiple Evaluators to score in parallel, uses the scores and run traces to find where points were lost, and upgrades the Agent from version N to N+1 — with a snapshot before every round, and every request replayable in the Trace view. A self-evolution demo video is coming soon.

## Evolution within bounds, security first

The biggest worry about self-evolution is losing control. PenguinHarness answers with a contract (CONTRACT.md):

- Evolution is strictly confined to Workspace and Skills — the harness core security boundary is never modified;
- Tool calls require approval first, and every approval leaves an audit record;
- Risky changes are preceded by version snapshots, so any round of evolution can be rolled back;
- Fully open source and locally deployed — data never leaves your machine, meeting enterprise data-security requirements.

## Supported models

| Model            | Providers                                                                        |
| ---------------- | -------------------------------------------------------------------------------- |
| DeepSeek V4      | DeepSeek, OpenRouter, Fireworks AI, SiliconFlow, Qwen Token Plan                 |
| Kimi K3          | Moonshot AI, OpenRouter, Qwen Pay-As-You-Go                                      |
| GLM 5.2          | Z.AI, OpenRouter, Fireworks AI, SiliconFlow, Qwen Token Plan, Qwen Pay-As-You-Go |
| Hunyuan 3        | OpenRouter                                                                       |
| Qwen 3.8 Max     | Qwen Token Plan (preview)                                                        |
| GPT 5.5          | OpenAI, OpenRouter                                                               |
| Gemini 3.5 Flash | Google Gemini, OpenRouter                                                        |
| Claude Opus 4.8  | Anthropic, OpenRouter                                                            |

Any OpenAI-protocol endpoint is supported: pick a preset above, or point a custom endpoint at any of the 1000+ online and local models.

## How to use it

Install with one command (Linux / macOS, x64 / arm64, bundled Node runtime), then launch the Web UI:

```bash
curl -fsSL https://penguin.ooo/install.sh | sh
penguin web        # opens http://127.0.0.1:7364 (first login: admin, initial password printed on first start)
```

Open the Models page, paste an API key under the DeepSeek or OpenRouter group and set it as default; then head back to Chat and hand the Agent its first task — e.g. "Analyze data.csv and summarize quarterly sales".

## What's next

- Public release of the benchmark suite;
- A desktop app;
- Windows support;
- More to come.

## Join the community and build with us

A self-improving harness needs a community that improves with it. Come discuss, request, and contribute — your first Issue is the best way to start:

- [Discord](https://discord.gg/eFHKqqcU3D): chat with us and other developers in real time;
- [X (Twitter)](https://x.com/code_hiyouga): follow the latest updates;
- [WeChat group](https://github.com/Prism-Shadow/penguin-harness-community/blob/main/wechat/group.jpg): Chinese community discussions;
- [GitHub](https://github.com/Prism-Shadow/penguin-harness): stars, Issues, and PRs all welcome.

Self-evolving agent infrastructure, for everyone — starting today.
