Darwinian Evolver

Darwinian Evolver

Run Imbue's darwinian_evolver — an LLM-driven evolutionary search loop — to optimize a prompt, regex, SQL query, or small code snippet against a fitness function.

Status: thin wrapper around the upstream tool. The skill installs it, walks the agent through writing a Problem definition (organism + evaluator + mutator), and drives the loop via the upstream CLI or a small custom Python driver.

License: the upstream tool is AGPL-3.0. The skill ONLY ever invokes it via the upstream CLI or a subprocess/uv run call (mere aggregation). Do NOT import upstream classes into Hermes itself.

When to Use

• User says "optimize this prompt", "evolve a regex for X", "auto-improve this code/SQL", "search for a better instruction".
• You have a scorer (exact match, regex pass-rate, unit test, LLM-judge, runtime metric) AND a starting candidate (organism). If you don't have a scorer, stop and define one first — that's the hard part.
• Cost is OK: a typical run is 50–500 LLM calls. On gpt-4o-mini that's pennies; on Claude Sonnet it can be a few dollars.

Do not use this when:

• The optimization target is differentiable (use gradient descent / DSPy).
• You only need to try 2–3 variants — just write them by hand.
• The fitness signal is purely subjective with no measurable criterion.

Prerequisites

• Python ≥3.11
• git, uv (or pip)
• One of: OPENROUTER_API_KEY, ANTHROPIC_API_KEY, or OPENAI_API_KEY

The skill ships a small parrot_openrouter.py driver that uses OPENROUTER_API_KEY via the OpenAI SDK, so any model on OpenRouter works. The upstream CLI itself hardcodes Anthropic and needs ANTHROPIC_API_KEY.

Install (One-Time)

Run via the terminal tool:

mkdir -p ~/.hermes/cache/darwinian-evolver && cd ~/.hermes/cache/darwinian-evolver
[ -d darwinian_evolver ] || git clone --depth 1 https://github.com/imbue-ai/darwinian_evolver.git
cd darwinian_evolver && uv sync

Verify:

cd ~/.hermes/cache/darwinian-evolver/darwinian_evolver \
  && uv run darwinian_evolver --help | head -5

Quick Start — The Built-In Parrot Example

Tiny smoke test (requires ANTHROPIC_API_KEY):

cd ~/.hermes/cache/darwinian-evolver/darwinian_evolver
uv run darwinian_evolver parrot \
  --num_iterations 2 \
  --num_parents_per_iteration 2 \
  --mutator_concurrency 2 --evaluator_concurrency 2 \
  --output_dir /tmp/parrot_demo

Outputs:

• /tmp/parrot_demo/snapshots/iteration_N.pkl — pickled population per iteration
• /tmp/parrot_demo/<jsonl> — per-iteration JSON log (path printed at end)

Open ~/.hermes/cache/darwinian-evolver/darwinian_evolver/darwinian_evolver/lineage_visualizer.html in a browser and load the JSON log to see the evolutionary tree.

Quick Start — OpenRouter Driver (No Anthropic Key)

The skill ships scripts/parrot_openrouter.py — same parrot problem, but the LLM call goes through OpenRouter so any provider works.

# From wherever the skill is installed:
SKILL_DIR=~/.hermes/skills/research/darwinian-evolver
DE_DIR=~/.hermes/cache/darwinian-evolver/darwinian_evolver

cd "$DE_DIR" && \
  EVOLVER_MODEL='openai/gpt-4o-mini' \
  uv run --with openai python "$SKILL_DIR/scripts/parrot_openrouter.py" \
    --num_iterations 3 --num_parents_per_iteration 2 \
    --output_dir /tmp/parrot_or

Inspect the result with scripts/show_snapshot.py:

uv run --with openai python "$SKILL_DIR/scripts/show_snapshot.py" \
  /tmp/parrot_or/snapshots/iteration_3.pkl

Expected output: 7 evolved prompt templates ranked by score, with the best landing around 0.6–0.8 (the seed Say {{ phrase }} scored 0.000).

Defining a Custom Problem

The skill ships templates/custom_problem_template.py — copy, edit, run. Three things you must define:

1. Organism — a Pydantic BaseModel subclass holding the artifact being evolved (prompt_template: str, regex_pattern: str, sql_query: str, code_block: str, etc.). Add a run(*args) method that exercises it.

2. Evaluator — .evaluate(organism) -> EvaluationResult(score=..., trainable_failure_cases=[...], holdout_failure_cases=[...], is_viable=True).

   • score is in [0, 1]. Higher is better.
   • trainable_failure_cases — what the mutator sees. Include enough context (input, expected, actual) for the LLM to diagnose.
   • holdout_failure_cases — kept out of the mutator's view. Use these to detect overfitting.
   • is_viable=True unless the organism is completely broken (raises, returns None, etc.). A 0-score viable organism is fine — it just gets down-weighted in parent selection.

3. Mutator — .mutate(organism, failure_cases, learning_log_entries) -> list[Organism]. Typically: build an LLM prompt that includes the current organism + a failure case + an ask to propose a fix; parse the LLM's response; return a new Organism. Return [] on parse failure — the loop handles it.

Then write a driver script that wires Problem(initial_organism, evaluator, [mutators]) into EvolveProblemLoop and iterates over loop.run(num_iterations=N) — the shipped scripts/parrot_openrouter.py is the reference.