Build Scalable Memory for LLMs with Graphs and Vectors

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.

You’ll learn how to:

• Store persistent memories with graph-linked facts
• Perform similarity search and structured reasoning in one query
• Use vector embeddings and graph hops inside SurrealDB

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.

Agenda

• Welcome & introductions – 10 minutes
• Building Memory with Graphs + Vectors in SurrealDB – 40 minutes
• Q&A – 10 minutes

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FAQs

Is this event for me?
SurrealDB events are for software engineers, developers, architects, data scientists, data engineers, or any tech professionals keen to discover more about SurrealDB: a scalable multi-model database that allows users and developers to focus on building their applications with ease and speed.

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