Re:Show HN: Hmem v2 – Persistent hierarchical memory for AI ...
by remembradev on Tuesday March 10, 2026 @07:53AM
(#47321990)
Nice work on the hierarchy approach. The 5-level lazy loading is clever for keeping context small.
We took a different path with Remembra - hybrid search (vector + BM25 keyword) instead of hierarchical summaries. The tradeoff: you lose the explicit levels but gain better fuzzy matching when agents don't remember exact terms.
Entity extraction has been the bigger win for us - automatically linking "Mr. Kim" to "David Kim" without the agent needing to know they're the same person. Hit 100% on the LoCoMo benchmark with that.
Curious about the no-vector-embeddings choice. Does full-text search miss anything in practice when agents use different phrasing across sessions?
Self-host: docker run -d -p 8787:8787 remembra/remembra if anyone wants to compare: remembra.dev
We took a different path with Remembra - hybrid search (vector + BM25 keyword) instead of hierarchical summaries. The tradeoff: you lose the explicit levels but gain better fuzzy matching when agents don't remember exact terms.
Entity extraction has been the bigger win for us - automatically linking "Mr. Kim" to "David Kim" without the agent needing to know they're the same person. Hit 100% on the LoCoMo benchmark with that.
Curious about the no-vector-embeddings choice. Does full-text search miss anything in practice when agents use different phrasing across sessions?
Self-host: docker run -d -p 8787:8787 remembra/remembra if anyone wants to compare: remembra.dev