gepa-ai / gepa

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
 [code] 
To install the latest from `main`:
 [code] 
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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):
 [code] 
**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.
 [code] 
### optimize_anything: Beyond Prompts
The `optimize_anything` API optimizes *any* text artifact — code, agent architectures, configurations, SVGs — not just prompts. You provide an evaluator; the system handles the search.
 [code] 
### Use GEPA as an Agent Skill
GEPA also ships as an **Agent Skill** so coding agents can drive `optimize_anything` for you. In a clone of this repo, Claude Code — and other agents that read `.claude/skills/` (Cursor, VS Code/Copilot, Codex, Gemini CLI) — auto-discover it at `.claude/skills/gepa-optimize-anything/`. To use it in any other project, install the plugin from this repo's marketplace:
 [code] 
See the Agent Skill guide for details.
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## How It Works
Traditional optimizers know *that* a candidate failed but not *why*. GEPA takes a different approach:
1. **Select** a candidate from the Pareto frontier (candidates excelling on