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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
Navid Moazzezby Navid Moazzez·Updated Sept 30, 2026·1 min read
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.

Gepa at a glance

Stars6.8k
Forks548
LanguageJupyter Notebook
LicenseMIT
Last update2026-09-26
Contributors48

How to install Gepa

bash pip install gepa 

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