Hybrid Memory: Vector + Graph + KV
Hybrid memory runs three stores in parallel — vector for semantic similarity, KV for fast fact lookup, graph for entity-relationship reasoning — with a scoring layer that fuses them on retrieval. This is a widely used production pattern for external memory; Mem0 (Chhikara et al., 2025) is one reference implementation. Explain why a single store (vector only, graph only, KV only) is insufficient for agent memory. Name Mem0's three parallel stores and what each one optimizes for. Describe Mem0's fusion scoring — relevance, importance, recency — and why it is a weighted sum, not a hierarchy. Implement a toy three-store memory in stdlib with an add() that writes to all three and a search() that fuses results. One store is wrong for one of three query classes: Semantic similarity — "what did we discuss about agent drift last week?" Vector wins; KV and graph miss. Fact lookup — "what is the user's phone number?" KV wins; vector is wasteful, graph is overkill. Relationship reasoning — "which customers share the same billing entity?" Graph wins; vector and KV cannot answer. Production agents issue all three in one session. A single-store memory is always wrong for two of them. Mem0's contribution is wiring all three behind a single add/search surface with a scoring function that fuses them. Mem0 (arXiv:2504.19413, April 2025) on…
Hybrid Memory: Vector + Graph + KV: Hybrid memory runs three stores in parallel — vector for semantic similarity, KV for fast fact lookup, graph for…
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