ReasoningBank Intelligence

It tracks task outcomes with metrics, recognizes patterns across similar contexts, and recommends approaches based on what's worked before. You'd use it when building agents that need to improve through experience, like a code reviewer that learns which analysis methods catch the most bugs in different languages, or a deployment bot that adapts its rollback triggers based on past incidents. It integrates with AgentDB for persistence and includes meta-learning capabilities, so the system can learn about its own learning process. The examples show meaningful stuff like comparing debugging strategies and transferring knowledge between similar domains. Requires at least 100 experiences per task type to give decent recommendations, which means this is for production systems with real volume, not one-off scripts.