Knowledge graphs, natively

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Ontology. Schema as ontology, with rich edges between entities.

Vectors. Semantic search beside structured data, in the same store.

Traversal. Graph queries in SurrealQL, meaning and structure together.

01 |HYBRID RETRIEVAL

Vector search beside structured data

Vectors

Co-located embeddings

Vectors live beside the entities they represent, in the same record.

Explore vector search

Queries

Hybrid queries

Cosine similarity, graph traversal, full-text search and structured filters in one statement.

Explore SurrealQL

Enrichment

Knowledge enrichment

Discover connections by similarity, then materialise them as edges for later traversal.

How RELATE works

Compared

Beside Neo4j

Native graph beside documents, vectors, full-text and time-series in one engine. A dedicated graph database leans on other systems for the rest.

SurrealDB vs. Neo4j

Ontologies

Existing ontologies import

The ontology is defined in SurrealQL schema, so an existing one maps onto table and relation definitions.

Define the schema

02 |OPERATIONAL KNOWLEDGE GRAPHS

Digital twins as living knowledge graphs. Assets, dependencies and telemetry in one engine.

Assets

Assets as nodes

Machines, sites, suppliers and people as records, with their dependencies as typed edges between them.

Explore digital twins

Telemetry

Telemetry in time

Readings as time-indexed values on the same records, so the graph carries its own history.

Explore time-series

Recall

Recall over documents

Embeddings for the manuals, tickets and notes around each asset, queried with the graph.

Explore vector search

03 |ONTOLOGICAL MODELLING

Your schema is your ontology. Tables are entity types, relations are relationship types, and edges carry their own properties.

SCHEMA

Define the ontology

-- Define an ontology directly in the schema
DEFINE TABLE person SCHEMAFULL;
DEFINE FIELD name ON person TYPE string;
DEFINE FIELD role ON person TYPE string;

DEFINE TABLE knows SCHEMAFULL TYPE RELATION FROM person TO person;
DEFINE FIELD since ON knows TYPE datetime;
DEFINE FIELD confidence ON knows TYPE float;
DEFINE FIELD context ON knows TYPE string;

RELATIONS

Relationships as documents

-- Create a relationship with rich metadata
RELATE person:jaime->knows->person:sarah SET
    since = d"2020-03-15",
    confidence = 0.95,
    context = "worked together at Acme Corp";

-- Traverse with filters on edge properties
SELECT ->knows[WHERE confidence > 0.9]->person.name AS trusted
FROM person:jaime;

THE PLATFORM

Everything an application and its agents know. Five surfaces, one engine.

Database

Document, graph, vector, time-series and relational in one engine.

Five data models as dotted tiles: documents, graph, vector, time-series and relational

Agent Memory

What an agent learns, with its source and its time, in the same engine.

A timeline of remembered facts, each with its source

Cloud

Managed clusters in the regions you choose, scaled on demand.

Clusters in three regions on a world map, each running or scaling

Studio

Query, explore and design the schema from the browser.

MCP

Every model that speaks MCP reaches the database and the memory directly.

Three models connected through MCP to the database and Agent Memory

IN PRODUCTION

Trusted at scale. Samsung, Nvidia, Verizon, Tencent, and Walmart run on SurrealDB.

14,000+

Developers building on SurrealDB Cloud

4M+

Developers building on SurrealDB worldwide

FROM THE TEAMS

SurrealDB gives us a foundation where we can unify semantic search, knowledge graphs, and AI-driven decision making without stitching together multiple systems. Collapsing responsibility into SurrealDB has become our default engineering posture.

Justin Foley

VP of Engineering, Later

GET STARTED

Build knowledge graphs with SurrealDB. Ontology, rich edges and vector search, in one database.

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