Store Your AI Agent's Memory and Context in a Knowledge Graph

Most AI agents today suffer from digital amnesia: they forget conversations, lose context between sessions, and treat every interaction as if meeting you for the first time. We explore how knowledge graphs can serve as persistent, temporally-aware memory systems for AI agents, moving beyond static vector embeddings to dynamic, relationship-rich storage that evolves with each interaction. This talk covers practical implementation patterns for building graph-based agent memory, comparing direct database integration with tool-based approaches through frameworks like Graphiti and Zep. You'll learn how to extract entities from conversations, model temporal relationships, and implement cross-session continuity that makes agents truly conversational partners rather than stateless responders.