REPS

A self-improving evolutionary code search agent that reflects, diversifies, and steers.

REPS evolves programs with an LLM-driven loop that reflects between batches, balances explorer/exploiter workers, detects convergence, and steers compute by distance to a known target.

Result: Circle Packing n=26

Verified against DeepMind's official validator.

What REPS does

• Adaptive selection — selection_strategy="map_elites" | "pareto" | "mixed" with pareto_fraction for blending MAP-Elites bins and per-instance Pareto fronts. (reps/api/optimizer.py:64, GEPA Phase 2)
• Trace reflection — trace_reflection=True: the reflection LLM sees per-instance scores + feedback from the parent's failures, not just aggregate scores. (reps/api/optimizer.py:66, GEPA Phase 3)
• Ancestry-aware reflection — lineage_depth=N: extends reflection with the last N parents in a candidate's chain. (reps/api/optimizer.py:67, GEPA Phase 5)
• System-aware merge — merge=True: candidates from different islands recombine via an LLM-driven merge prompt that targets disjoint instance dimensions. (reps/api/optimizer.py:68, GEPA Phase 4)
• Convergence + SOTA steering — built-in convergence monitor (edit entropy + strategy divergence) and gap-aware compute steering when a target score is set. On by default.

Status: pre-1.0

REPS is pre-1.0. The Python API (docs/python_api_spec.md) shipped recently and may still evolve. Per docs/release_spec.md, minor version bumps (0.1 → 0.2) may include breaking changes during the pre-1.0 era. Pin to a specific minor version (e.g. reps-py==0.1.*) if you need stability across upgrades. Strict semver applies once REPS reaches 1.0.0.

Install

Requires Python 3.12+ and uv.

git clone https://github.com/zkhorozianbc/reps.git
cd reps
uv venv .venv --python 3.12
uv pip install -e .

Install from PyPI with pip install reps-py. Optional extras: [dspy] (the dspy_react worker), [benchmarks] (scipy + matplotlib for the bundled circle-packing benchmark).

Set the API key matching your model's provider:

export ANTHROPIC_API_KEY=sk-ant-...      # provider: anthropic
export OPENROUTER_API_KEY=sk-or-...      # provider: openrouter
export OPENAI_API_KEY=sk-...             # provider: openai

A sibling .env file is auto-loaded.

Quick start (Python)

REPS is a Python library. Pass a seed program string and an evaluator callable; get back the best evolved program.

import reps

def evaluate(code: str) -> float:
    # Run the candidate, return a score. Higher is better.
    namespace = {}
    exec(code, namespace)
    return float(namespace["solve"]())

result = reps.Optimizer(
    model="anthropic/claude-sonnet-4.6",   # api_key from $ANTHROPIC_API_KEY
    max_iterations=20,
).optimize(
    initial=open("seed.py").read(),
    evaluate=evaluate,
)

print(result.best_score)
print(result.best_code)

What's an evaluator?

An evaluator is any Callable[[str], float | dict | reps.EvaluationResult]. REPS calls it with the candidate program text and uses the returned score to drive selection. Return a float for a quick start, a dict with combined_score and optional per_instance_scores / feedback for richer signal, or a reps.EvaluationResult to unlock the per-objective Pareto + trace-reflection paths described in docs/python_api_spec.md.

def eval_simple(code: str) -> float:    return 1.0
def eval_dict(code: str) -> dict:       return {"combined_score": 0.9, "feedback": "..."}
def eval_full(code: str) -> reps.EvaluationResult: ...

GEPA-style features (constructor knobs)

Full surface (escape hatches, model knobs, deferred kwargs) in docs/python_api_spec.md.

Reusing a Model

Most users pass a model-name string to Optimizer(model=...). Build a reps.Model directly when you want to call the model outside the optimizer or share one configured client across multiple runs.

import reps

model = reps.Model("anthropic/claude-sonnet-4.6", temperature=0.7)
print(model("hello"))                                    # standalone use

# Share one Model across multiple optimizers
o1 = reps.Optimizer(model=model, max_iterations=20)
o2 = reps.Optimizer(model=model, max_iterations=50, merge=True)

Power-user: CLI / YAML

For batch experiments, reproducible sweeps, or YAML-driven configuration, REPS ships a CLI: reps-run --config <yaml>. The Python API above is built on the same engine, so anything achievable via YAML is achievable via Optimizer(...) plus Optimizer.from_config(cfg).

Run

Everything lives in the YAML — point reps-run at a config and go:

reps-run --config experiment/configs/circle_sonnet_reps.yaml

Results land in experiment/results/<config-stem>/run_NNN/ (auto-versioned). The best program is saved as best_program.py; per-iteration metrics under metrics/.

Common overrides:

reps-run --config <yaml> --iterations 50 --output my_runs/
reps-run --config <yaml> -o llm.temperature=0.9 -o reps.batch_size=10

The config decides everything else — model, workers, harness (reps or openevolve), and which benchmark to evolve (via task:).

Add a benchmark

Drop two files into experiment/benchmarks/<name>/:

experiment/benchmarks/<name>/
├── initial_program.py    # seed code (wrap evolvable region in EVOLVE-BLOCK markers)
└── evaluator.py          # defines evaluate(program_path) -> {"combined_score": float, ...}

initial_program.py:

# EVOLVE-BLOCK-START
def solve():
    return naive_result
# EVOLVE-BLOCK-END

evaluator.py:

def evaluate(program_path):
    # import program_path, run it, score it
    return {"combined_score": score}

Optional files in the same directory:

• system_prompt.md — task-specific system prompt (auto-loaded)
• visualize.py — visualize_from_program(path, save_path) for best-program plots

Then point a config at it:

task: ../benchmarks/<name>     # resolved relative to this YAML
max_iterations: 100
provider: anthropic
# ... see experiment/configs/circle_sonnet_reps.yaml for a full example

Run it: reps-run --config experiment/configs/<your_config>.yaml.

For cascade evaluation, also define evaluate_stage1 / evaluate_stage2. If the primary objective metric isn't combined_score, set reps.sota.target_metric: so SOTA steering compares the right value.

Configs

Reference configs in experiment/configs/:

• circle_sonnet_reps.yaml, circle_opus47_anthropic.yaml, reps_full.yaml — full REPS runs
• verify_*.yaml — minimal smoke tests, one per worker impl
• circle_base.yaml, circle_sonnet_base.yaml — harness: openevolve baselines (uv pip install openevolve)

reps/config.py is the source of truth for every field and default.

Tests

uv run python -m pytest tests/

Design docs

• docs/python_api_spec.md — the v1 Python API contract: every public class, kwarg, return shape, with file:line references into the implementation.
• docs/gepa_implementation_plan.md — phase-by-phase rollout plan for the GEPA-inspired features (Pareto selection, trace reflection, merge, ancestry-aware reflection).
• docs/optimizer_engine_separation_spec.md — internal refactor splitting the public Optimizer facade from the runtime engine.

Acknowledgements

Forked from OpenEvolve; now self-contained.