§ 6Graph Memory Explorer
Graph-based memory frameworks like Mem0, Graphiti, and Microsoft's GraphRAG represent a paradigm shift from flat vector stores to structured knowledge graphs. Rather than embedding conversation fragments as opaque vectors, these systems extract entities and relationships, constructing a navigable web of knowledge. This module lets you watch a knowledge graph grow turn-by-turn, compare graph traversal against vector similarity retrieval, observe how temporal relationships evolve and contradict, and understand why graphs excel at multi-hop reasoning where vector retrieval falls short.
§ 6.1Knowledge Graph Construction
Watch entities and relationships emerge from conversation
Turn 1 / 20
120
[user] Alice from the London office is leading the Atlas project.
3
Total Entities2
Total Relationships2
Active Relationships0.67
Avg ConnectivityFigure 12
Person
Location
Event
Concept
Preference
Temporal
Entity Extraction, Turn 1
Alice from the London office is leading the Atlas project.
3 new entities2 triplets
AliceNEWLondonNEWAtlasNEW
Extracted Triplets
Alice→based in→London
Alice→leads→Atlas
§ 6.2Graph vs. Vector Retrieval
Why structured traversal outperforms similarity search for multi-hop queries
Figure 13
Graph Traversal Retrieval
Preset Queries
Select a query above to see graph traversal results.
Vector Similarity Retrieval
Same Queries
Select a query above to see vector retrieval results.
§ 6.3Temporal Evolution and Conflict Resolution
Tracking how relationships change, contradict, and resolve over time
Figure 14
Created
Invalidated
Updated
Conflict Detection & Resolution Log
No conflicts detected yet. Advance the conversation to see contradictions emerge.
§ 6.5Validate Live: Extract a Knowledge Graph from Your Text
Provide any conversation or text. The LLM extracts entities, relationships (as triples), and detects contradictions, the same process that powers Mem0, Graphiti, and GraphRAG.