penguin-harness

Why PenguinHarness

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

PenguinHarness runs on your computer or server and automates the agent app lifecycle from creation and evaluation to optimization and deployment. Three reasons define the platform:

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 generates a runnable agent app

Describe what you need in one sentence. PenguinHarness builds the complete agent application — 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. 🧬 Native agent self-evolution engine

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 plugins

Three plugin categories ship in the box (docs) — skills, plus the session hooks that drive goal mode and continual learning; agents can also write and optimize their own skills:

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

Start with the desktop app, or install the command line on a workstation or server. Both use the same ~/.penguin/data root and 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 server prints a first-login link as a framed notice on every start until a password is set — open it to claim the built-in admin account and choose one. 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).

Download the desktop app 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.

The macOS builds are Develop