Agent Memory Staleness: How Recency-Aware Ranking Fixes Retrieval Drift

Mem0 Memory Decay is an opt-in, per-project feature for v3 search that addresses retrieval staleness in long-running AI agents by boosting recently retrieved memories by up to 1.5 times and reducing the influence of idle memories toward a 0.3-times floor, without deleting records or changing public API calls. It is intended for applications where current context usually matters more than similarly relevant historical context, including coding assistants that switch projects, personal assistants with evolving routines, and support bots managing lengthy ticket histories. In an A/B test using identical memory stores, decay moved current memories to the top rank in several cases and created substantially wider score gaps than semantic similarity alone, which often produced near-tied results between fresh and stale information. However, the feature can also suppress older but safety-critical evergreen facts, as illustrated by an allergy memory falling outside the top results after recently accessed but less important penicillin-related content was favored. Memory Decay is therefore presented as a ranking aid for low-relevance staleness rather than a solution for contradictions or facts that have become false, which instead require timestamp-aware application-level resolution; users are encouraged to compare decay-enabled and disabled projects on real queries before deployment.

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