Prism-Shadow / penguin-harness

PenguinHarness 
 Unified and Stable RSI Framework Fully automatic optimization: any model, any algorithm, any benchmark 
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## Why PenguinHarness
PenguinHarness is an open-source framework for agent self-evolution (recursive self-improvement, RSI). The model stays fixed; the harness evolves: prompts, Skills, tools and hooks. It runs on your computer or server as a desktop app, a Web App, a CLI and an SDK.
- **Every RSI algorithm in one framework.** Default, OPRO, APE, ACE and AWM ship as plugins and run on the same built-in Benchmarks, scoreboard and snapshots.
- **One sentence builds an agent app.** Describe what you need, and an agent writes the app, its page and its run instructions.
- **Cheap to run.** A deliberately small toolset, tuned for open models: on our data-analysis suite, the best accuracy at 1/70 of Claude Code's cost.
## Self-evolution
Pick an algorithm, pick a Benchmark, and an agent improves another agent round by round. Every candidate is a new version, snapshotted before it changes, and every score lands in the Evaluation Center.
| Toolkit | Algorithm |
| --- | --- |
| `default-rsi-toolkit` | Trial, reflect, improve: one hypothesis and one change per round |
| `opro-rsi-toolkit` | OPRO: new instructions from the history of scored ones |
| `ape-rsi-toolkit` | APE: candidate instructions induced from examples |
| `ace-rsi-toolkit` | ACE: lessons from Traces curated into a playbook |
| `awm-rsi-toolkit` | AWM: workflows induced from successful runs |
Every new Project includes seven Benchmarks, run as Harbor tasks in Docker: PenguinHarness Benchmark Sec A to Sec E (from rag-bench-essential, DeepSWE, AutomationBench, Terminal-Bench-Science and Terminal-Bench) and GDPevo Train and Test. More are in penguin-harness-benchmark.
**Demo:** a decision agent is evaluated, revises its working method once, and is retested on the same model. Its score goes from 84 to 100, and the cost of every run is on screen.
https://github.com/user-attachments/assets/7925b4b5-db78-4e00-b209-89deca394e34
## One sentence, one app
 [code] 
The result: a legal review app. Users upload contracts, follow each file's review, and download a report of risky clauses with explanations and suggested revisions. Five sample reviews cost $0.0423 in model calls.
https://github.com/user-attachments/assets/28c0ef95-1dfa-47be-bad0-7326ccdf4e7c
## What agents can do
 Browser automation 
 Agents drive your own Chrome through the PenguinHarness Browser extension, or the desktop app's built-in browser. They read pages as text and act through short scripts, so a step costs few tokens. Tutorial 
 Workbenches 
 Every agent can have its own hot-reloaded workbench: ask for one and the agent writes the page and its server code. The workbench appears as a tab beside the agent's chat. Tutorial 
 Remote machines 
 Add a machine the server reaches over ssh. Conversations in a Workspace on that machine run on it, in the same window. Tutorial 
 Agent API 
 One unifi