Associative key-graph memory for LLM agents — recall facts by association (recall → read_key → read_memory) instead of vector similarity alone, with persistent cross-session memory and cross-lingual keys.
Enables persistent, graph-based memory for AI agents, allowing them to store, traverse, and recall relationships between facts, decisions, and context across sessions for efficient reasoning and reduced token usage.
Provides persistent long-term memory for AI agents through semantic search and automated knowledge graph extraction. It enables agents to store, recall, and reason over facts, preferences, and relationships across multiple conversations and sessions.
Enables AI agents to store, retrieve, and connect information in a Neo4j graph database as persistent memory, with semantic relationships, natural language search, and temporal tracking across conversations.
Provides persistent knowledge graph memory for AI agents, enabling them to store, recall, and query facts about people, projects, and relationships across sessions.
Enables AI assistants to have persistent associative memory across sessions by storing memories, discovering associations, and retrieving them via spreading activation.
