Gepa
Optimize any text parameter, prompts, code, agent architectures, configurations, using LLM-based reflection and Pareto-efficient evolutionary search. 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.
View Gepa on GitHub
Optimize any text parameter, prompts, code, agent architectures, configurations, using LLM-based reflection and Pareto-efficient evolutionary search.
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.
Gepa at a glance
| Stars | 6.8k |
|---|---|
| Forks | 548 |
| Language | Jupyter Notebook |
| License | MIT |
| Last update | 2026-09-26 |
| Contributors | 48 |
How to install Gepa
bash pip install gepa
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