A Graph-Native Bitemporal Memory Store for Conversational AI Agents

• URL: http://arxiv.org/abs/2607.26520v1
• Date: Wed, 29 Jul 2026 06:40:52 GMT
• Title: A Graph-Native Bitemporal Memory Store for Conversational AI Agents
• Abstract summary: We describe an agent-local Neo4j property graph augmented with HNSW vector indexes and a full bitemporal data model.<n>Each memory is stored as an immutable identity node linked to versioned content nodes carrying two closed-open time intervals.<n>We evaluate the system on LongMemEval, a 500-question benchmark spanning six question types designed to stress long-term memory.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Conversational AI agents commonly lack persistent memory across sessions. The obvious fixes like injecting full chat histories into the context window, or delegating to a third-party memory service, either exhaust the model's context budget or send personal data through infrastructure the user does not control. We describe a memory store that avoids both problems: an agent-local Neo4j property graph augmented with HNSW vector indexes and a full bitemporal data model. Each memory is stored as an immutable identity node linked to versioned content nodes carrying two closed-open time intervals: valid time (when the fact was true in the world) and transaction time (when the database recorded it). This design supports point-in-time semantic retrieval without physically overwriting history. Semantic edges between related memories are maintained automatically at write time using cosine similarity over 1024-dimensional embeddings. We evaluate the system on LongMemEval, a 500-question benchmark spanning six question types designed to stress long-term memory. Across 60 sampled questions, the current-state semantic search path achieves 46.7% R@10 overall, rising to 80% on knowledge-update questions. The time-travel path yields 80% R@10 on knowledge-update but decreases recall on temporal-reasoning questions (50% to 37.5%), a consequence of post-filter dilution that points directly to a concrete design improvement. We discuss what these results reveal about the limits of pure retrieval for different question types and what each failure mode suggests for future work.

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