AI Engineering/Phase 14/Lesson 09

Hybrid Memory: Vector + Graph + KV (Mem0)

Mem0 (Chhikara et al., 2025) treats memory as three stores in parallel — vector for semantic similarity, KV for fast fact lookup, graph for entity-relationship reasoning. A scoring layer fuses the three on retrieval. This is the 2026 production standard for external memory.

Build 1.3 hr Python (stdlib)

Prerequisites: Phase 14 · 07 (MemGPT), Phase 14 · 08 (Letta Blocks)

#Learning Objectives

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.

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The text and code of this lesson are based on AI Engineering from Scratch by Rohit Ghumare, MIT licensed.