fuzzyevolve 0.2.1

AlphaEvolve with fuzzy evaluation. Evolve anything, not just code.

fuzzyevolve

Evolve text with LLM mutation + LLM judging, using TrueSkill for noisy multi-metric feedback and a fixed-size, crowding-based population for diversity.

Inspired by AlphaEvolve, but designed for “fuzzy” criteria like prose, coherence, originality, funny, interesting, etc.

Quick start

export GOOGLE_API_KEY=... # default config uses google-gla:* models
uv sync

# Uses ./config.toml if present (or defaults)
uv run fuzzyevolve "This is my starting prompt."

Input can be a string, a file path, or stdin:

uv run fuzzyevolve seed.txt
cat seed.txt | uv run fuzzyevolve

Output goes to best.md by default (override with --output). By default it includes the top 20 individuals by fitness (override with --top).

By default, each run is recorded under .fuzzyevolve/runs/<run_id>/ (checkpoints, events, and raw LLM prompts/outputs). Resume with:

uv run fuzzyevolve --resume .fuzzyevolve/runs/<run_id> --iterations 100

Browse runs in the TUI:

uv run fuzzyevolve tui
# or open a specific run/checkpoint:
uv run fuzzyevolve tui --run .fuzzyevolve/runs/<run_id>

Disable recording with --no-store.

Note: semantic embeddings require sentence-transformers. Install the extra or use the built-in hash embedding:

uv sync --extra semantic

What it does

• Critiques the selected parent once per iteration (structured: preserve / issues / rewrite routes).
• Generates children via a set of LLM-backed mutation operators (e.g. “exploit” vs “explore” full rewrites).
• Judges parent/children by ranking them per metric (tiered rankings, ties allowed).
• Updates per-metric TrueSkill ratings (μ/σ) from those rankings (with uncertainty-aware scoring).
• Keeps diversity with a fixed-size pool + crowding: repeatedly remove the weaker of the closest pair in embedding space.

Mental model

• A text is a “player” with a TrueSkill rating per metric (e.g. one rating for prose, one for coherence).
• The judge doesn’t assign absolute scores; it ranks candidates relative to each other for each metric.
• The population is a fixed-size “portfolio” of texts in embedding space.
• Each iteration is: pick a parent → critique → propose children → rank a battle → update ratings → insert children → apply crowding elimination.

How it works (core loop)

1. Embed: compute embedding = embed(text) for parent/children (hash or semantic).
2. Select parent: mixture policy: uniform sampling, or an optimistic tournament (μ + β·σ).
3. Critique (optional): ask an LLM for actionable guidance (issues + distinct rewrite routes).
4. Mutate: allocate a per-iteration job budget across operators; each job proposes one rewritten child.
5. Assemble battle: parent + children + frozen anchors + an opponent (defaults to farthest-from-parent in the pool).
6. Judge: ask an LLM to return tiered rankings for each metric (with validation + optional repair retries).
7. Update ratings: apply per-metric TrueSkill updates; score uses a conservative LCB (mu - c*sigma) averaged across metrics.
8. Crowding: add children to the pool; while size > N, remove the weaker of the closest pair in embedding space.

Configuration

Config is a single TOML/JSON file. If config.toml or config.json exists in the current directory it’s auto-detected; pass an explicit file with --config.

See config.toml for a complete example. The structure is intentionally nested:

• [task] and [metrics] define what “good” means (goal + metric names/descriptions).
• [mutation] defines the operator set, job budget, and per-operator uncertainty.
• [judging] controls judge retries + optional opponents.
• [rating] controls TrueSkill parameters and the score’s LCB constant.
• [embeddings] defines the embedding model (hash or a sentence-transformers model name).
• [population] defines the fixed pool size.
• [selection] configures the parent-selection mixture policy.
• [anchors] optionally injects frozen reference anchors (seed + periodic “ghosts”) into battles.

CLI

run is the default command, so these are equivalent:

uv run fuzzyevolve "Seed text..."
uv run fuzzyevolve run "Seed text..."

To open the run browser:

uv run fuzzyevolve tui

run options

• --config / -c: Path to TOML/JSON config
• --output / -o: Output path (default best.md)
• --top: How many top individuals to include (default 20; 0 = all)
• --iterations / -i: Override run.iterations
• --goal / -g: Override task.goal
• --metric / -m: Override metrics.names (repeatable)
• --resume: Resume from a previous run directory (or checkpoint file)
• --store/--no-store: Enable/disable recording under .fuzzyevolve/
• --log-level / -l: Logging level (debug|info|warning|error|critical or a number)
• --log-file: Write logs to a specific file
• --quiet / -q: Hide the progress bar and non-essential logging

Requirements

• Python 3.10+
• uv (recommended)
• Any model supported by pydantic-ai (configure via [llm].judge_model and [[llm.ensemble]].model)
• An API key for the provider you choose

export GOOGLE_API_KEY=...     # e.g. google-gla:*
export OPENAI_API_KEY=...     # e.g. openai:*
export ANTHROPIC_API_KEY=...  # e.g. anthropic:*

Semantic embeddings require:

uv sync --extra semantic

Development

uv sync --extra dev
uv run ruff format .
uv run ruff check .
uv run pytest -q

License

Apache 2.0 — see LICENSE.