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Agent Memory at Meta Scale: One Query Language for Graph, Vector, and Memory#

@Scale: AI & Data 2026 digest Program Adjacent pick 17 June 2026

Abstract

Abdullah Ozturk, Yiting Li, Vishal Gandhi, Ming Chen, Junjie Qi (Meta)

  • Why: Agent memory is becoming a real infrastructure layer, and today's stacks stitch vector indexes, graph services, and RAG frameworks together on the critical path of every agent turn. This proposes treating memory as a query-surface problem — a genuinely architectural move.
  • What: The Cypher Query Layer (CQL): a declarative query surface over agent memory built on the openCypher/GQL ISO standard, collapsing hybrid retrieval (vector + text + graph + scope + freshness + provenance) into one optimizable query, with identity-aware ACLs as first-class primitives (from abstract only).
  • How: A pluggable backend routes each query to a right-fit substrate — in-process Velox for sub-50 ms agent-loop reads, reusing Meta's existing graph/vector/hybrid stores — with bi-temporal memory on the roadmap to enable agent-debugging replay, and procedure packs for distillation/consolidation/governance.
  • Where applicable: The standard-based query surface is broadly interesting; the substrate choices are Meta's. The in-process vs. distributed boundary discussion is exactly the kind of design decision that transfers. Early-stage — production patterns come from one early internal consumer.