Agents need true temporal memory. Well, they now have it.

Agent memory has gotten good at answering "what does the agent know?" Your agent can store that Alice lives in Berlin and retrieve it later. That part works. The harder question is when. When did Alice move to Berlin? Where did she live before? Did her job change at the same time? And if you had asked the system last Tuesday, before it learned about the move, what would it have told you? These are temporal questions. And they come up constantly in any agent that operates over more than a single session. Hu et al.'s 107-page survey on agent memory, StructMemEval's benchmarks, Memori's semantic triple work: the research keeps converging on the same finding. The interesting unsolved problems in agent memory are not about storage or retrieval. They are about how facts change over time, how experience consolidates into knowledge, and how an agent's understanding of the world evolves. We built MinnsDB to make those temporal problems first-class primitives rather than things you build on top