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Memory Consolidation

Master how AI agents consolidate short-term memories into efficient long-term knowledge bases

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Clustering & Organization

After filtering important memories, we group semantically similar ones together. Clustering uses embedding similarity to discover natural categories—like grouping all technical discussions or personal facts—making consolidation more efficient.

Interactive: Memory Clustering Visualizer

2D semantic space (simplified visualization):
User prefers Python
Discussed ML models
Uses TensorFlow
Lives in San Francisco
ML engineer at Google
Enjoys hiking
Asked about transformers
Studying attention mechanisms
Working on NLP project

🔬 Clustering Algorithms

🎯

K-Means

Assigns memories to K centroids iteratively.

Fast, requires K upfront, spherical clusters
🌳

Hierarchical

Builds tree of clusters (dendrogram).

No K needed, flexible, slower for large data
🔍

DBSCAN

Density-based, finds arbitrary shapes.

Handles noise, discovers outliers automatically

📊 Organization Best Practices

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Semantic Embeddings: Convert memories to vectors using sentence transformers (e.g., all-MiniLM-L6-v2). Similarity = cosine(vec1, vec2). Group memories with similarity > 0.8.
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Optimal K Selection: Use elbow method (plot inertia vs K) or silhouette score (measures cluster quality, range -1 to 1, higher is better). Typically 3-10 clusters for most agents.
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Cluster Labeling: After clustering, use LLM to generate descriptive labels. Prompt: "Summarize these memories in 2-3 words: [cluster memories]". Example: "Technical Preferences", "Career Facts", "Learning Goals".

💡 Why Clustering Matters

Clustering transforms scattered memories into organized knowledge categories. Instead of summarizing 100 random memories into one blob, you get 5-10 topical summaries (user facts, preferences, learning topics, etc.). This preserves structure and makes retrieval more precise—agents can fetch "technical preferences" without sifting through unrelated personal information.

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