memory-systems

Cross-session memory persistence is achieved by storing structured memories that support vector-based semantic search and graph-based entity relationships. This enables continuity for long-running agents by loading context just-in-time across conversations.

Hybrid retrieval combines vector-based semantic search with graph-based entity relationships to provide comprehensive context. This approach allows agents to query both unstructured semantic similarities and structured temporal knowledge graph connections simultaneously.

Numpy is the required dependency for managing temporal knowledge graphs and entity tracking. It handles the numerical operations underlying the vector-based semantic search and structured memory consolidation processes across sessions.

Answering time-bound queries requires a temporal knowledge graph that tracks entity validity over time. This enables agents to retrieve historically accurate context and consolidate memories based on temporal provenance tracking.

Memory consolidation and provenance tracking integrate directly with property graphs by storing structured memories with graph-based entity relationships. This integration supports efficient retrieval and temporal reasoning across persistent sessions.

Just-in-time loading is needed when long-running agents require efficient retrieval of persistent semantic memory without loading entire histories. It optimizes cross-session continuity by fetching specific vector and graph contexts on demand.