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Memory Graph

The Memory Graph is an interactive network visualization that shows how your companion’s memories are semantically connected.

Accessing the Memory Graph

Click the brain icon in the top-left corner of the main screen to open the Memory Graph in a full-screen modal.

Understanding the Visualization

Nodes (Memories)

Each node represents a stored memory (fact) about you, your relationship, or shared experiences.

Node Colors:

  • Cyan — User facts (your preferences, background, attributes)
  • Pink — Relationship facts (dynamics between you and the companion)
  • Green — Shared experiences (events you’ve discussed together)

Connections

Lines between nodes indicate semantic similarity — memories that are related in meaning are connected. Animated particles flow along connections to visualize these relationships.

Statistics

The bottom-left corner shows total memory count and number of connections in the current view.

Interactions

Selecting a Memory

Click any node to select it:

  • The selected memory and its connections are highlighted
  • Unrelated memories fade
  • A detail panel appears on the right showing the full memory content, importance score, and reference count

Filtering Categories

Use the category toggles in the top-left control panel to show/hide specific memory types. This helps focus on particular aspects of what your companion knows.

Reset View

Click “Reset View” to zoom out and see the full graph, clearing any selection.

Technical Details

The Memory Graph uses 384-dimensional embeddings (via Transformers.js with the all-MiniLM-L6-v2 model) to compute semantic relationships between memories. Memories with a cosine similarity >= 0.5 are connected.

Reference Count tracks how many times a memory has been retrieved during conversations — higher counts indicate memories that frequently inform responses.

Importance Score (0-100) reflects how significant the memory is based on emotional content, personal details, and other heuristics.

Requirements

  • Memories must have embeddings to appear in the graph
  • The embedding model is loaded automatically on app startup
  • Existing memories without embeddings are backfilled automatically when the embedding model finishes loading