Everyone is building AI memory systems.
But how do we know which ones actually work?
As AI agents move from one-off interactions toward long-term collaboration, memory is becoming a core capability. Yet evaluating memory systems fairly is surprisingly difficult.
Different systems often use different datasets, answer models, prompts, and evaluation methods. When the final score changes, it can be hard to tell whether the difference comes from the memory system itself or from the evaluation setup.
That's why we built Agent Memory Leaderboard (AML).
