recursive-intelligence 0.1.7

Recursive prompt improvement — VSR loop with linguistic gate (ri-engine CLI)

Recursive Intelligence Engine

Open-source recursive prompt improvement — Variation → Selection → Retention (VSR), with an experimental linguistic gate.

Give a seed prompt and a goal. The engine runs an offline VSR loop (no API key by default), resolves register leanings (plain, latinate, mixed, …), and returns an improved prompt.

pip install recursive-intelligence
ri-engine improve \
  --seed "You are a helper." \
  --goal "When this works, the AI will produce a structured answer with measurable success criteria"

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Overview

Package	recursive-intelligence
CLI	ri-engine
Python API	from ri_engine import improve
Default provider	Mock (deterministic, offline)
License	MIT

Research inspiration: Raymond Uzwyshyn Ph.D. — agentic AI, recursion, and selection environments. See CREDITS.md and docs/research_and_citations.md.

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Features

• VSR loop — generate variants, score fitness, retain winners, repeat until convergence
• Linguistic gate (experimental) — weighted register prior; ablation modes via CLI
• Baseline comparison — VSR output vs one-shot finalize; returns simpler result when VSR does not win
• Objective alignment scoring — rubric weights goal fit over structural bloat
• Template fixtures — optional benchmark configs; primary path is --seed + --goal
• Local ablation — experiments/run_gate_ablation.py (no API key)

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Installation

pip install recursive-intelligence

# Optional real LLM backends:
pip install "recursive-intelligence[all]"

From source:

git clone https://github.com/russfranky/recursive-intelligence.git
cd recursive-intelligence
pip install -e ".[all]"

Requirements: Python 3.10+

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Quick start

CLI

ri-engine improve \
  --seed "You are a helper." \
  --goal "When this works, the AI will resolve the task in one pass"

Python

from ri_engine import improve

result = improve(
    seed_prompt="You are a helper.",
    objective="When this works, the AI will produce a structured answer.",
)
print(result.improved_prompt)
print(result.fitness)

Research flags

ri-engine improve --seed "…" --goal "…" --linguistic-gate auto   # default
ri-engine improve --seed "…" --goal "…" --linguistic-gate off
ri-engine improve --seed "…" --goal "…" --leaning plain
ri-engine improve --seed "…" --goal "…" --diagnostics

Full walkthrough: docs/getting_started.md

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How it works

Linguistic Gate → [Macro Priors] → [Membrane] → Variation → Selection → Retention → repeat
                                                      ↓
                                            Baseline vs VSR pick

1. Linguistic gate — experimental prior; defaults to mixed when confidence is low
2. Variation — eight mutation strategies
3. Selection — objective alignment, clarity, utility, coherence, register fit, simplicity
4. Retention — lineage memory and convergence detection
5. Baseline check — compare against one-shot finalize_prompt()

Architecture details: docs/technical_reference.md

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Project structure

recursive-intelligence/
├── src/ri_engine/          # Core library and CLI
├── tests/                  # Pytest suite
├── docs/                   # Documentation index → docs/README.md
├── config/                 # Templates, use cases, registries → config/README.md
├── experiments/            # Local ablation scripts → experiments/README.md
├── examples/               # Sample scripts
├── prompts/                # VSR operator prompts (bundled into package)
├── CONTRIBUTING.md         # Contribution guidelines
├── CREDITS.md              # Attribution
├── CITATION.cff            # Machine-readable citation
└── CHANGELOG.md            # Release history

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Documentation

Document	Description
docs/README.md	Documentation hub
getting_started.md	Install and first run
technical_reference.md	Architecture and API
research_and_citations.md	Related work and BibTeX
publication.md	Scope, limitations, release notes
cloud_development.md	Codespaces and Colab

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Scope and limitations

Mock mode (default) is a deterministic offline test of the recursive improvement process. It measures structural prompt quality using local heuristics (prompt_rubric.py). It does not prove that the resulting prompt will improve downstream LLM task performance.

Use --provider openai or --provider anthropic with appropriate API keys for semantic rewriting. Evaluate task outcomes separately.

Persistent macro trait registry is off by default (--use-persistent-macro-registry to enable).

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Optional providers

export OPENAI_API_KEY=sk-...
ri-engine improve --seed prompt.txt --goal "…" --provider openai

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Contributing

Contributions welcome. Please read CONTRIBUTING.md and CODE_OF_CONDUCT.md.

pytest tests/ -q

Report security issues per SECURITY.md.

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Citation

See CITATION.cff and docs/research_and_citations.md.

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Changelog

See CHANGELOG.md.