Dagestan
Temporal graph memory layer for LLMs — time-aware knowledge graphs replacing flat vector embeddings.
Dagestan stores LLM memory as a typed temporal knowledge graph instead of flat vector embeddings. It tracks entities, concepts, events, preferences, and goals — with time-aware confidence decay, contradiction detection, and relationship-based retrieval.
Why Dagestan?
Current LLM memory solutions (vector DBs) lack:
- Time awareness — old and new information treated identically
- Relationships — no structure between memories
- Contradiction detection — conflicting facts coexist silently
- Decay — nothing fades; nothing is curated
Features (v0.1)
- Typed temporal graph (Entity, Concept, Event, Preference, Goal nodes)
- LLM-based knowledge extraction from conversations
- Contradiction detection between conflicting preferences/goals
- Exponential temporal decay on confidence scores
- Gap detection and bridge node detection (cross-cluster connections)
- Centrality-based importance scoring
- Query-driven graph retrieval (keyword + structure, no embeddings)
Links: GitHub