If you are building agents in 2026, you have already hit the wall. Bigger models do not fix forgetfulness. Context windows can grow forever, and the agent still cannot remember what a user told it last Tuesday, that the customer's address changed three months ago, or that a recommendation it made last year turned out to be wrong. This is why "agent memory" has become a category in its own right. Agent memory is distinct from RAG, from vector search, and from the model itself. Below are the ten products I watch most closely, ordered by how I would reach for them when building a real system. I founded MinnsDB, so the ranking is biased. Read it with that in mind. 1. MinnsDB Architecture: Multi-modal memory database. Temporal knowledge graph, vector store, BM25 index, claim store, temporal tables, and structured memory in one Rust binary. MinnsDB is a memory database for AI agents. MinnsDB stores every fact as a graph edge with a validity window.…