Iteratively optimize prompts and text-based agent configs against scored eval sets with GEPA
Use reflective search to improve prompts or text-configured agent components against a real eval set instead of manual prompt tweaking.
Prerequisites
Python environment, GEPA package, train and validation examples with a scoring function, model provider credentials or local models, target prompt or text configuration to optimize
Installation
Use the upstream install or setup path that matches your environment:
- pip install gepa
- pip install git+https://github.com/gepa-ai/gepa.git
Requirements and caveats from upstream:
Basic usage or getting-started notes:
Quick Start |
bash
To install the latest from main:
Source: https://github.com/gepa-ai/gepa
Extracted from upstream docs: https://raw.githubusercontent.com/gepa-ai/gepa/HEAD/README.md