Last week I needed to give my AI agent a memory that connects facts instead of just listing them. Not "the user likes Postgres," but "the user likes Postgres, worked with him at two companies, and every incident review he runs mentions connection pools." That is a graph. My first instinct was the one I always have: just use SQLite. And my second instinct, after two days of recursive CTEs, was to check what the HN front page was trying to tell me.
That same week, a Show HN called LatticeDB landed: an embedded, single-file property-graph database written in Zig, positioned as "like SQLite but for graph databases," with native HNSW vector search and BM25 full-text search in the same query layer. The marketing number going around is up to 2,819x faster graph traversal than SQLite. Numbers like that are usually a sign to keep scrolling. This time I did not. I read the benchmark methodology, then rebuilt the SQLite side myself and ran it on my own server.
What I found is more useful than either the hype or the dismissal: the gap is real, but it lives in one specific place. If your queries stay shallow, you will not see it. If they go deep, it is not a gap, it is a cliff.
What LatticeDB actually is
