Not all agent failures are equally useful for learning—curating which tasks inform prompt optimization, and providing successful examples as context, significantly improves agent performance in complex multi-turn tasks.
GEPA-Curator improves how LLM agents learn from their mistakes by being selective about which failed tasks to learn from. Instead of learning from all failures, it filters out noisy or unreliable learning signals and retrieves successful past examples to guide prompt optimization, achieving up to 20.7% performance gains over existing methods.