Sleep-Inspired Memory Consolidation Cuts AI Agent Memory Bloat by 58%

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AI agents running over extended periods accumulate tens of thousands of memory items, causing slower retrieval and reduced accuracy as irrelevant data drowns out useful context. Drawing inspiration from how the human brain consolidates memories during sleep, a proposed system runs offline consolidation cycles during idle periods of 30 or more minutes. The process merges near-duplicate memories, scores and prunes the least important entries, and summarizes low-value memory clusters by topic, while permanently protecting recent, procedural, and credential-based memories. In testing, the approach reduced memory store size from over 12,000 items to roughly 5,000, cut retrieval latency by 40%, and improved recall accuracy by 15%. The consolidation can also be triggered when the store exceeds 8,000 items, on owner command, or during scheduled maintenance windows.

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