- One engine for storage and memory: Combine durable storage and fast, agent-friendly memory in a single system, providing all the data your agent needs and removing the need to sync multiple systems.
- One-hop memory for agents: Run vector search, graph traversal, semantic joins, and transactional writes in a single query, giving LLM agents fast, consistent memory access without stitching relational, graph and vector databases together.
- In-place inference and real-time updates: SurrealDB enables agents to run inference next to data and receive millisecond-fresh updates, critical for real-time reasoning and collaboration.
- Versioned, durable context: SurrealDB supports time-travel queries and versioned records, letting agents audit or “replay” past states for consistent, explainable reasoning.
- Plug-and-play agent memory: Expose AI memory as a native concept, making it easy to use SurrealDB as a drop-in backend for AI frameworks.
SurrealDB
SurrealDB is a unified, multi-model database purpose-built for AI systems. It combines structured and unstructured data (including vector search, graph traversal, relational queries, full-text search, document storage, and time-series data) into a single ACID-compliant engine, scaling from a 3 MB edge binary to petabyte-scale clusters in the cloud. By eliminating the need for multiple specialized stores, SurrealDB simplifies architectures, reduces latency, and ensures consistency for AI workloads.
Why SurrealDB Matters for GenAI Systems