gepa-ai/gepa

Health v2 · maintenance only

Adoption not part of the score

6254 stars · 523 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

GEPA is a Python framework that optimizes textual system parameters such as prompts, code, and agent configurations using LLM-based reflection combined with Pareto-efficient evolutionary search. It reads full execution traces to diagnose failures and evolve high-performing candidates with far fewer evaluations than reinforcement learning.

Use cases

• optimize llm prompts automatically
• improve agent accuracy with prompt tuning
• cheaper alternative to RL fine-tuning for prompts
• evolve agent architectures with AI feedback
• tune system prompts against a custom evaluation metric
• optimize code snippets with reflective mutation

When to choose

• you have a measurable evaluation metric and want to improve prompts or agent designs
• you want prompt optimization that is cheaper and faster than RL or gradient methods
• you use DSPy or custom LLM pipelines and need automated prompt tuning

When to avoid

• you need traditional weight-based model fine-tuning rather than textual parameter optimization
• you have no evaluation metric or dataset to score candidates against
• you want a zero-configuration one-click optimizer without wiring an evaluation loop

Facets

library · maturity active

prompt-engineering llm-training machine-learning agent-framework large-language-models machine-learning developer-tools python prompt-optimization evolutionary-search pareto-optimization llm-reflection dspy-integration ai-agents

Member repositories

For agents

markdown · JSON · MCP: product_card(name="gepa-ai/gepa")