Deterministic consolidation, a typed graph in SQLite, and an honest look at what agent-memory benchmarks can and can't measure.
You're building an agent. It answers questions across many sessions, and by session three it has forgotten what it learned in session one.
The reflex is to put a vector database in front of it - and now it remembers a blurry average of everything, ranked by cosine distance, contradicting itself and unable to tell a three-week-old preference from a stale throwaway. A graph database gives you structure, at the cost of a second persistence model with its own query language and deployment. A managed memory service starts you in a few lines, and moves the decision of what your agent remembers onto someone else's infrastructure.
Underneath all of them is one problem: if every plausible fact is written the moment it appears, memory turns into an accumulation layer instead of a judgement layer. Engrava is our answer to that - local, structured, and deliberate about what it keeps in reach. Here is how it is built, and why.
We started with a question, not a schema
