Most retrieval-augmented systems stop at search. But agents and copilots need structured, persistent memory to reason, adapt, and evolve over time.
In this session, we’ll show how to turn SurrealDB into a long-term memory layer for your LLM apps, combining graph and vector data to power richer context, better decisions.
We’ll walk through practical patterns and show how SurrealDB collapses graph, vector, and relational data into a single memory substrate for next-gen AI.
