penguin-harness

Why PenguinHarness

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

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

1. 🏆 Outstanding results at tens of times less cost

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:

Best accuracy on data analysis — at 1/70 of Claude Code's cost.

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:

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:

https://github.com/user-attachments/assets/9b7033e8-f08a-4c3f-bd33-547896664e6e

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: 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.

https://github.com/user-attachments/assets/922d13a6-5ffc-4685-9a39-352f02f9afc0

Built-in Skills

Four Skill groups ship in the box (docs); agents can also write and optimize their own:

Supported Models

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.

Requirements

Installation

Two ways in — both work on the same ~/.penguin/data root, so a desktop install and a CLI install can be mixed freely:

• 🖥️ Desktop app — a double-click install: it embeds the server and opens already signed in, no terminal involved.
• ⌨️ CLI — a one-line installer (or npm / offline package) puts the penguin command on the machine; penguin web then serves the full Web experience in your browser at http://127.0.0.1:7364 (multi-session chat, agent / skill / model management, usage stats, Trace observability, evaluation center). The online installers bundle their own Node runtime — unpack and run; upgrades and reinstalls never touch your data.

[!NOTE] On a CLI install, the first Web login is admin, with the initial password (of the form penguin-1234) printed as a framed notice on every server start until it is changed — change it right after. Models are configured on the in-app Models page.

🖥️ Desktop app

The full Web experience as a standalone application: it embeds the server and opens already signed in — no terminal, no login page, no initial password to copy. It works on the same ~/.penguin/data root as a CLI install, so the two can be used interchangeably (a data root only ever runs one server; if a CLI-started instance is already up, the app attaches to it).

⬇️ Get it from the download page — the page serves the OSS-accelerated mirror when it is reachable, and every installer is also attached to each GitHub Release.

Current builds are unsigned, so the system may block the very first launch. Expand your platform for the one-time fix:

macOS quarantines files downloaded from the internet, and the missing signature makes that flag surface as a false “damaged” alert. Deleting the flag clears it:

1. Open the downloaded dmg and drag PenguinHarness.app into the Applications folder.
2. Open Terminal (Launchpad → Other → Terminal).
3. Paste this command into Terminal and press Enter, then type you