Thanks for the kind words! The adaptive memory decay in Memoripy works by dynamically adjusting the relevance of stored memories based on time, frequency of access, and contextual importance. Over time, less frequently accessed memories decay, ensuring the system stays focused on what’s most relevant without overwhelming the AI with outdated information.

Unlike Mem0, which uses a hybrid database system and scoring layers to prioritize data, Memoripy applies decay and reinforcement directly within its memory layer, integrating semantic clustering to group related memories for more context-aware retrieval. This makes the approach more lightweight and adaptive, especially for applications that don’t require complex multi-database setups.

Hope that clears it up! Let me know if you'd like more details.