Memory Consolidation
Master how AI agents consolidate short-term memories into efficient long-term knowledge bases
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0%Memory consolidation transforms raw short-term memories into organized long-term knowledge
Consolidation reduces storage by 60-80% while improving search quality and retrieval speed
Importance scoring filters trivial memories from meaningful ones using weighted factors
Common importance factors: content quality, user signals, context relevance, recency
Threshold filtering (e.g., score > 0.5) determines which memories deserve long-term storage
Clustering groups semantically similar memories using embedding-based similarity
K-means, hierarchical, and DBSCAN are popular clustering algorithms for memory organization
Optimal cluster count (K) is typically 3-10, determined by elbow method or silhouette score
Summarization combines related memories into concise, dense knowledge entries
Extractive summarization selects key sentences; abstractive uses LLMs to generate new text
Typical compression ratios: 5:1 to 10:1 (5-10 memories consolidated into 1 summary)
Maintain traceability by storing source memory IDs with each consolidated summary
Consolidation pipeline: Score → Cluster → Summarize → Store with new embeddings
Run consolidation periodically (daily/weekly batch) rather than in real-time
Agents with consolidated memory are faster, cheaper, and more intelligent
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