gepa-optimize-anything

Optimize any text parameter — prompts, code, agent architectures, configurations — using LLM-based reflection and Pareto-efficient evolutionary search.

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What is GEPA?

GEPA (Genetic-Pareto) is a framework for optimizing any system with textual parameters against any evaluation metric. Unlike RL or gradient-based methods that collapse execution traces into a single scalar reward, GEPA uses LLMs to read full execution traces — error messages, profiling data, reasoning logs — to diagnose why a candidate failed and propose targeted fixes. Through iterative reflection, mutation, and Pareto-aware selection, GEPA evolves high-performing variants with minimal evaluations.

If you can measure it, you can optimize it: prompts, code, agent architectures, scheduling policies, vector graphics, and more.

Key Results

90x cheaper	Open-source models + GEPA beat Claude Opus 4.1 at Databricks
35x faster than RL	100–500 evaluations vs. 5,000–25,000+ for GRPO (paper)
32% → 89%	ARC-AGI agent accuracy via architecture discovery
40.2% cost savings	Cloud scheduling policy discovered by GEPA, beating expert heuristics
55% → 82%	Coding agent resolve rate on Jinja via auto-learned skills
50+ production uses	Across Shopify, Databricks, Dropbox, OpenAI, Pydantic, MLflow, Comet ML, and more

"Both DSPy and (especially) GEPA are currently severely under hyped in the AI context engineering world" — Tobi Lutke, CEO, Shopify

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Installation

pip install gepa

To install the latest from main:

pip install git+https://github.com/gepa-ai/gepa.git

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Quick Start

Simple Prompt Optimization

Optimize a system prompt for math problems from the AIME benchmark in a few lines of code (full tutorial):

import gepa

trainset, valset, _ = gepa.examples.aime.init_dataset()

seed_prompt = {
    "system_prompt": "You are a helpful assistant. Answer the question. "
                     "Put your final answer in the format '### <answer>'"
}

result = gepa.optimize(
    seed_candidate=seed_prompt,
    trainset=trainset,
    valset=valset,
    task_lm="openai/gpt-4.1-mini",
    max_metric_calls=150,
    reflection_lm="openai/gpt-5",
)

print("Optimized prompt:", result.best_candidate['system_prompt'])

Result: GPT-4.1 Mini goes from 46.6% → 56.6% on AIME 2025 (+10 percentage points).

With DSPy (Recommended for AI Pipelines)

The most powerful way to use GEPA for prompt optimization is within DSPy, where it's available as dspy.GEPA. See dspy.GEPA tutorials for executable notebooks.

import dspy