Every guide to giving an AI agent memory starts the same way. Pick an embedding model, stand up a vector database, chunk your data, tune your retrieval. I assumed I would do exactly that. Then, before I wrote a single line of it, I looked hard at the problem I was actually trying to solve, and I could not find the part that needed any of it.
This is the reasoning that led me to build a memory API for agents with no vector database, no embeddings, and no model in the loop. I am also going to tell you exactly when that decision is wrong, because it often is, and you should know which side of the line you are on before you copy anyone's architecture, including mine.
The question nobody was asking
Here is what agents actually kept failing at. Not "find me something similar in meaning to this." They failed at "remember the specific thing I was told last session."
Take a coding agent, the case most people reading this have felt. The agent that helped you on Monday has no idea on Tuesday that you already decided to use Postgres, that the deploy step runs through a specific script, that the client's name is spelled a particular way. Every session starts from zero. You re-explain the same context every morning.
