Meaning Memory is a significance-weighted curation layer for AI agent memory. It learns what's important, compiles it under budget, and shows you why. Field-tested. Stage 1 commercial beta in 2026.
Existing memory systems are excellent at storage and retrieval. But they treat all memories equally. We studied what happens when you add a significance layer on top. Here's what the data shows.
When context budgets are tight (100 lines), significance-weighted compilation retains more critical facts than relevance ranking alone (54.8%). The curation layer matters most when space is scarce.
Every observation stays in the ledger with full provenance. The compiler decides what's top-of-mind, but nothing is permanently discarded. You can always trace back.
Graph-enriched entity awareness during ingestion improved fact coverage by 17%. The value isn't in graph queries at search time; it's in knowing what to extract.
Run with zero dependencies (SQLite, 30 seconds). Or add significance on top of Mem0, Qdrant, or pgvector. Your existing memory backend stays; we add the curation layer.
Existing memory systems handle storage and retrieval well. Meaning Memory is the curation layer that sits on top.
| Layer | Your Existing Stack | + Meaning Memory |
|---|---|---|
| What to keep | Everything (or recency-based) | Significance-weighted curation |
| Context assembly | Best-effort top-K | Deterministic budget compilation |
| Audit trail | Metadata timestamps | Full provenance (what changed, when, why) |
| Multi-agent memory | Shared storage (same view for all) | Per-agent significance (same facts, different priorities) |
| Human feedback | Binary remember/forget | Gradient scoring that trains the system |
| Deployment | Vendor-specific backend | Standalone (SQLite) or on top of Mem0 / Qdrant / pgvector |
Stage 1 NDA design partners now. Stage 2 commercial beta planned for late 2026. Get notified and shape the roadmap.