I built a scikit-learn for RSI [P]

After reading a bunch of RSI papers, I keep coming back to familiar problems from RL and evolutionary search: Sampling: How do we find promising changes efficiently? Evaluation: How do we know a new variant actually improves the agent? Diversity: How do we avoid converging too early? That motivated us to build Gear , an open-source, meta-learning-inspired framework. A task agent produces execution traces; a meta-agent uses those traces and evaluation feedback to improve prompts, tools, workflows, and context management. Gear separates search decisions from execution and evaluation. Our goal is