Vector databases are almost always talked about in the context of RAG. Store your documents, embed them, retrieve the relevant chunks at inference time. That's the default pattern and it works — until it doesn't.
I've been working on Actian VectorAI DB and started wondering: what if the vector DB isn't a document store at all? What if it's a memory layer for an agent?
So I built it to find out.
The Idea
The distinction sounds subtle but it matters. In a classic RAG setup, you pre-load a vector store with documents. The corpus is static. The agent queries it but never changes it.
