Hermes Agent Self-Evolution: DSPy + GEPA Evolutionary Optimization of Skills and Prompts Without GPU Training — ICLR 2026 Oral

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NousResearch released hermes-agent-self-evolution, using DSPy + GEPA (Genetic-Pareto Prompt Evolution) to automatically evolve agent skills, tool descriptions, and system prompts. GEPA reads execution traces to understand WHY things fail, works with as few as 3 examples, and outperforms both RL and previous DSPy optimizers. Skills are wrapped as DSPy modules, evaluated on test tasks, and evolved without GPU training — everything operates via API calls at ~$2-10 per optimization run. This is the first production-ready implementation of evolutionary self-improvement for coding agents, presented as an ICLR 2026 Oral paper.