Storage Is Not Memory: A Retrieval-Centered Architecture for Agent Recall

📰 ArXiv cs.AI

Learn how True Memory, a retrieval-centered architecture, improves agent recall by preserving events verbatim and using a multi-stage retrieval pipeline

advanced Published 7 May 2026
Action Steps
  1. Design a six-layer architecture for agent recall
  2. Implement a multi-stage retrieval pipeline operating over preserved events
  3. Use a SQLite file to store events verbatim on commodity CPU
  4. Configure the system to run without external databases or vector indexes
  5. Test the True Memory architecture for improved recall performance
  6. Compare the results with traditional storage-based architectures
Who Needs to Know This

AI engineers and researchers working on agent memory and recall systems can benefit from this architecture, as it provides a new approach to handling agent memory

Key Insight

💡 Preserving events verbatim and using a multi-stage retrieval pipeline can improve agent recall performance

Share This
🚀 Introducing True Memory: a retrieval-centered architecture for agent recall #AI #Memory

Key Takeaways

Learn how True Memory, a retrieval-centered architecture, improves agent recall by preserving events verbatim and using a multi-stage retrieval pipeline

Full Article

Title: Storage Is Not Memory: A Retrieval-Centered Architecture for Agent Recall

Abstract:
arXiv:2605.04897v1 Announce Type: cross Abstract: Extraction at ingestion is the wrong primitive for agent memory: content discarded before the query is known cannot be recovered at retrieval time. We propose True Memory, a six-layer architecture that shifts the center of the system from a storage schema to a multi-stage retrieval pipeline operating over events preserved verbatim. The full system runs as a single SQLite file on commodity CPU with no external database, vector index, graph store,
Read full paper → ← Back to Reads

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