§ 4Architecture Comparator
How do different memory architectures handle the same conversation? This module places six strategies side by side (sliding window, summarization, RAG, hierarchical tiers, infinite context, and graph memory) and feeds them identical input. Watch tokens flow, get compressed, retrieved, or discarded in real time. The synchronized playback reveals each architecture's trade-offs: latency vs. recall, compression vs. fidelity, simplicity vs. capacity. No single winner emerges; the optimal choice depends on your dialogue's structure.
4 / 6 selected
Figure 7
Conversation
View:
Sliding WindowTurn 0
Token Usage20 / 4096
Window: 1/8 slots used
Memory Contents
SummarizationTurn 0
Token Usage20 / 4096
Buffer: 1 of 5 before next compression
Memory Contents
RAGTurn 0
Token Usage20 / 4096
Retrieved top-3 relevant turns from vector store
Memory Contents
HierarchicalTurn 0
Token Usage20 / 4096
Tier 1: 1 verbatim | Tier 2: 0 summarized
Memory Contents
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019
Speed:
Figure 8
§ 4.5Validate Live: How Would Each Architecture Handle Your Conversation?
Provide a multi-turn conversation and the LLM will simulate what each architecture retains vs. loses. See the concrete tradeoffs of sliding window, summarization, RAG, and graph memory on your data.