The in-process answer to 'my agent repeats the same mistakes': wrap your agent, feed it corrections, and ACE extracts reusable strategies it injects on later runs — no fine-tuning, no reward signals, and the numbers are concrete (2x pass^4 on Tau2, ~$1.50 to learn its way through a 14k-line translation). Pick it over a memory *service* when you want the learning inside your Python process rather than behind an HTTP API. NOT magic memory: strategies come from explicit feedback loops you wire up, quality follows the judge model, and the open-source engine is the on-ramp to the hosted Kayba product — check where the managed line lands before betting infra on it.