pi-evolve

Evolutionary self-improvement for Pi. Inspired by AlphaEvolve (DeepMind), Sakana AI's ShinkaEvolve, and the Darwin Gödel Machine.

Mutate → Evaluate → Select → Archive → Repeat.

Install

pi install npm:@artale/pi-evolve

What it does

Applies evolutionary algorithms to anything the agent works with:

• Skills (SKILL.md files) — compress, restructure, improve
• Prompts — evolve system prompts for better output
• Extensions — optimize code for size + quality
• Any text file — evolutionary improvement of anything

Commands

/evolve <file>                    — quick evolution (3 variants)
/evolve status                    — show evolution state
/evolve archive                   — list all variants with scores
/evolve restore <id>              — restore a variant
/evolve compare <a> <b>           — diff two variants
/mutate <file> [goal]             — single mutation pass (3 strategies)
/darwin <file> [gens] [goal]      — full evolutionary loop

Mutation strategies

Strategy	What it does
compress	Reduce size 30-50% while preserving all functionality
quality	Improve effectiveness at similar length
hybrid	Both shorter AND better (Pareto optimization)
radical	Completely reimagine structure (divergent exploration)

Darwin mode

Full evolutionary loop with multiple generations:

/darwin SKILL.md 10 optimize for token efficiency

This runs 10 generations, each producing 3 variants (compress, quality, radical), evaluating fitness, selecting the best, and using it as the parent for the next generation.

Results (from our GEPA predecessor)

Tested on real Pi skills:

• canvas-design: −69% tokens, same quality
• pptx: −60% tokens, same quality
• xlsx: −63% tokens, same quality

Tools

• evolve_archive — save a variant with fitness score
• evolve_status — show archive stats
• evolve_list — list all variants
• evolve_restore — restore a variant to its file

Architecture

/darwin SKILL.md 5
  │
  ├─ Gen 0: Archive original (baseline fitness 50)
  │
  ├─ Gen 1: Mutate × 3 strategies → Evaluate → Select best
  │           compress: 65/100 (−40% tokens)
  │           quality:  58/100 (better structure)
  │           radical:  42/100 (too divergent)
  │           → Winner: compress (65)
  │
  ├─ Gen 2: Mutate winner × 3 → Evaluate → Select
  │           → Winner: hybrid (72)
  │
  └─ Gen 5: Converged at 78/100, −55% tokens

All variants archived with fitness scores, token counts, and lineage tracking.

Inspired by

• AlphaEvolve — DeepMind's Gemini-powered evolutionary coding agent
• ShinkaEvolve — Sakana AI's sample-efficient program evolution
• Darwin Gödel Machine — Self-improving coding agent via evolution
• OpenEvolve — Open-source AlphaEvolve reimplementation

Zero dependencies

Node.js built-ins only. The LLM itself is the mutation operator.

License

MIT