How are people handling long‑term memory for local agents without vector DBs?

I've been building a local agent stack and keep hitting the same wall: every session starts from zero. Vector search is the default answer, but it's heavy, fuzzy, and overkill for the kind of structured memory I actually need - project decisions, entity relationships, execution history. I ended up going down a rabbit hole and built something that uses **graph traversal instead of embeddings**. The core idea: turn conversations into a graph where concepts are nodes and relationships are edges.