MemoryStackMemoryStack/Documentation
    Back to Documentation

    Knowledge Graph

    Memories aren't isolated - they're connected. The Knowledge Graph reveals relationships between memories, enabling your AI to make connections and discover insights.

    Watch Knowledge Graph Form in Real-Time

    User:"Hi, my name is Alex"
    Alex
    Alex
    First memory created - identity node

    See how each conversation builds your knowledge network progressively

    Connections are automatically discovered as memories are created

    What is a Knowledge Graph?

    A knowledge graph is a network of connected memories. Instead of storing memories in isolation, Memory OS automatically discovers and maintains relationships between them - just like how your brain connects related concepts.

    This enables powerful features like discovering related information, finding patterns, and understanding context.

    Isolated Memories

    Memory A: "User likes Python"
    Memory B: "Working on API project"
    Memory C: "Prefers FastAPI framework"

    No connections - each memory stands alone.

    Connected Knowledge Graph

    Memory A: "User likes Python"
    ↓ related_to
    Memory B: "Working on API project"
    ↓ uses
    Memory C: "Prefers FastAPI framework"

    Connected - the AI understands relationships and context.

    Types of Relationships

    Memory OS automatically detects and maintains different types of relationships between memories:

    Semantic Similarity

    Memories about similar topics are automatically connected based on meaning.

    "Python programming" ←→ "FastAPI framework" ←→ "API development"

    Temporal Connections

    Memories created around the same time or in sequence are linked.

    "Started project" → "First milestone" → "Project completed"

    Causal Relationships

    Cause-and-effect connections between memories and events.

    "User reported bug" → "Fixed authentication" → "Bug resolved"

    Entity Relationships

    Connections through shared entities (people, places, projects).

    All memories mentioning "Project Alpha" are connected

    Pro & Enterprise Feature

    Knowledge Graph capabilities are available on Pro and Enterprise plans. The graph is automatically built and maintained as you create memories - no additional configuration required.

    View pricing plans

    Powerful Use Cases

    🔍Discovery & Recommendations

    "You asked about Python APIs. Based on your previous work with FastAPI and interest in authentication, you might want to explore OAuth2 implementation."

    How it works:
    The graph connects "Python" → "FastAPI" → "Authentication" → "OAuth2" to make intelligent suggestions.

    🧩Context Building

    When answering a question, the AI can pull in connected memories to provide richer, more contextual responses.

    Example:
    Question about "the project" automatically includes memories about Project Alpha, its timeline, team members, and current status.

    📊Pattern Recognition

    Identify recurring themes, common issues, or emerging trends across your memories.

    Insight:
    "You've asked about authentication 5 times this month, always related to API projects. Consider creating a reusable auth module."

    🎯Smart Navigation

    Navigate through your knowledge by following connections, discovering related information naturally.

    Flow:
    "Python" → "Web Frameworks" → "FastAPI" → "Documentation" → "OpenAPI Spec" - each step reveals related knowledge.

    Visualizing Your Knowledge

    Memory OS provides interactive graph visualizations in your dashboard. See your knowledge network, explore connections, and discover insights visually.

    Graph Features

    🎨 Visual Clustering
    Related memories are grouped by color and proximity
    🔍 Interactive Exploration
    Click nodes to explore connections and view details
    📊 Importance Sizing
    Node size reflects memory importance
    🎯 Filtered Views
    Filter by type, date, or custom criteria

    Best Practices

    ✅ Do

    • • Let the system auto-discover connections
    • • Use rich, descriptive memory content
    • • Explore related memories when searching
    • • Leverage graph insights for recommendations
    • • Visualize your knowledge periodically

    ❌ Don't

    • • Manually create all connections
    • • Ignore relationship suggestions
    • • Store isolated, context-free memories
    • • Overlook graph-based insights
    • • Forget to explore memory neighborhoods

    Next Steps