Agent Memory Is a Search Problem, Not a Storage Problem

A study of long-term conversational memory argues that AI assistants may not need elaborate preprocessing pipelines such as fact extraction, session summaries, entity graphs, or custom memory managers if they can autonomously search raw conversation turns through well-designed tools. The system stores each turn in a single Postgres table with vector, BM25, regex, hierarchical, and temporal indexes, then lets Claude Sonnet or Haiku choose how to query the data through three MCP tools; it reported raw LoCoMo benchmark F1 scores of 0.665 for Sonnet and 0.638 for Haiku, exceeding the cited best Omni-SimpleMem result of 0.613. Experiments suggested that agent-directed search, simple tool schemas, unrestricted tool calls, temporal filtering, and searchable image captions can improve retrieval, while LLM-generated atomic facts increased ingestion cost without measurable benefit. The work also emphasizes a recurring tradeoff between stronger answering performance and correctly rejecting adversarial, unsupported questions, arguing that evaluations excluding adversarial cases can obscure hallucination risks. However, the authors note important limitations, including model-confounded comparisons with GPT-based baselines, tuning on one conversation, bundled changes that prevent precise attribution, incomplete cost data, and LoCoMo’s limited scale and benchmark errors, concluding that the approach is promising but not yet a definitive replacement for production memory architectures.

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