Feature
Online Learning
MetaMemory doesn't just store memories — it learns from them. The system continuously adapts its retrieval models, relevance scoring, and strategy selection based on real usage patterns, getting sharper with every interaction.
Real-Time Adaptation
Retrieval models update incrementally after every interaction. No batch retraining, no scheduled jobs — the system improves in real time as it learns which memories are actually useful.
Drift Detection
Statistical monitors track model performance over time. If usage patterns shift (new topics, different user behavior), the system detects the drift and accelerates adaptation to the new distribution.
Automatic Rollback
If an adaptation degrades performance, the system automatically rolls back to the last known-good state. This provides a safety net for continuous learning without manual intervention.
Bayesian Optimization
Hyperparameters across the entire pipeline — embedding weights, retrieval thresholds, consolidation triggers — are continuously tuned using Bayesian optimization to maximize end-to-end quality.
Real-time
Adaptation Speed
<50 queries
Drift Detection
+22% / month
Quality Improvement
<1s
Rollback Time