I built memory decay for AI agents using the Ebbinghaus forgetting curve

Most AI memory systems treat every fact equally, forever. That felt wrong to me. If you tell Claude you use React, then six weeks later say you switched to Vue both facts exist in memory with the same weight. The system has no way to know which one is current. You either manually delete the old one or the model gets confused. That's the problem I was trying to solve. What I built YourMemory is an MCP memory server that applies the Ebbinghaus forgetting curve to retrieval. Memories decay over time based on importance and how often they're recalled. Frequently accessed memories stay strong. Memories you never revisit fade out and get pruned automatically. The retrieval score is: score = cosine_similarity × Ebbinghaus_strength strength = importance × e^(−λ_eff × days) × (1 + recall_count × 0.2) λ_eff = 0.16 × (1 − importance × 0.8) So results rank by both relevance and recency not just one. Benchmark results I ran it against Mem0 on the LoCoMo dataset from Snap Research 200 QA pairs acros