Interactive Memory Demo
Experience AI agent memory systems hands-on through interactive demonstrations and real-world scenarios
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0 / 5 completedConsolidation Workshop
Watch memories transform through the complete consolidation pipeline: from raw data to scored importance to clustered groups to final summaries.
Interactive: Consolidation Pipeline
8 raw memories (includes trivial data):
User said good morning
User is ML engineer at Google
Weather was sunny
Prefers Python over Java
Discussed transformer models
Clicked settings button
Working on NLP project
Session lasted 30 minutes
⚠️ Problem: 50% of memories are trivial noise (greetings, clicks, timestamps)
8
Raw Memories
4
After Scoring
2
Clusters
2
Final Summaries
🎯 Key Insights
•Importance scoring filters 50% of noise immediately
•Clustering groups related memories for better organization
•Summarization achieves 75% compression while preserving key facts
•Result: Dense, searchable knowledge instead of scattered raw data