Learn how to turn SurrealDB into a long-term memory layer for your LLM apps by combining graph data and vector embeddings to power richer context and better decisions. Store persistent memories with graph-linked facts; perform similarity search and structured reasoning in one query; use vector embeddings and graph hops inside SurrealDB. This session walks through practical patterns and demonstrates how SurrealDB collapses graph, vector, and relational data into a single memory substrate for next-gen AI.