Scalable, high performance knowledge graph memory system with semantic retrieval, contextual recall, and temporal awareness. Provides any LLM client that supports the model context protocol (e.g., Claude Desktop, Cursor, Github Copilot) with resilient, adaptive, and persistent long-term ontological memory.
Entities are the primary nodes in the knowledge graph. Each entity has:
- A unique name (identifier)
- An entity type (e.g., "person", "organization", "event")
- A list of observations
- Vector embeddings (for semantic search)
- Complete version history
Example:
{
"name": "John_Smith",
"entityType": "person",
"observations": ["Speaks fluent Spanish"]
}Relations define directed connections between entities with enhanced properties:
- Strength indicators (0.0-1.0)
- Confidence levels (0.0-1.0)
- Rich metadata (source, timestamps, tags)
- Temporal awareness with version history
- Time-based confidence decay
Example:
{
"from": "John_Smith",
"to": "Anthropic",
"relationType": "works_at",
"strength": 0.9,
"confidence": 0.95,
"metadata": {
"source": "linkedin_profile",
"last_verified": "2025-03-21"
}
}