SurrealDB is a multi-model database designed to replace the common practice of stitching together separate document stores, graph databases, search engines, and vector databases for modern applications. Its unified engine handles relational tables, schemaless documents, graph edges, time-series data, and vector embeddings through a single query language called SurrealQL. This consolidation is particularly valuable for AI agent architectures where persistent memory requires both structured relationships and semantic similarity search.
The database is written entirely in Rust, delivering the memory safety and concurrency guarantees that production workloads demand. Deployment modes span from an embedded library for edge applications to a distributed cluster for horizontally scaled services. The real-time subscription system pushes live query results to connected clients, enabling reactive interfaces without polling. Row-level security and fine-grained permissions are defined declaratively in the schema, making multi-tenant and agent-facing access patterns straightforward to implement.
SurrealDB has raised over $33 million in funding and cultivated a large open-source community with over 26,000 GitHub stars. The managed cloud offering, Surreal Cloud, handles provisioning and scaling for teams that prefer not to self-host. The query language supports native graph traversals, computed fields, changefeeds, and built-in ML model execution, positioning the database as infrastructure specifically suited for applications where AI agents need to store, relate, and retrieve heterogeneous data at scale.
