we want some kind of fractal knowledge graph. It starts coarse at some zoom level and you can move in and out. You can insert knowledge at any level and it's out/in levels adjust accordingly. The search semantics at every level are the same, but whats in the visible area changes based on what your focus is.
One way I've toyed with a graph outline with it using "whitening" (https://arxiv.org/pdf/2104.01767v3) for embeddings rather than just text, so you add things like file path, nearest title/method/const etc. You have to have dummy text though because it fails with "null"; all embeddings need to carry some kind of text and of the same size.
so when the agent rembembers something, the memory would be an embedding that includes where they found the file, what method or const or whatever they're in, what the task they're working on is, etc. That all becomes a single embedding. You could imagine a metadata tag that also describes the tools they're using etc.
Map out a complete space of tags for whitening an embedding and there's surely a proper mix. Then when you're searching for things in the embedding, you also store some of the other metadata as plane strings & edges, which gets you some useful granularity.