Graph-native multi-model database enabling rich semantics, real-time traversal, and reasoning - turning knowledge graphs into a programmable foundation for intelligent applications and AI agents.
Why knowledge graphs matter
Knowledge graphs transform scattered data points into a connected, machine-understandable network that powers deeper insights, stronger reasoning, and more reliable AI.
Connected data
Knowledge graphs model information as nodes and edges, creating clear relationships that make data easier to navigate and understand.
AI context and memory
They give AI and LLMs a reliable semantic layer for context and memory, grounding outputs in connected information for more consistent reasoning.
Multi-hop reasoning
Graphs enable multi-step traversals and pattern detection, supporting insights and queries that simple keyword or vector search can't provide.
Unified context
They combine structured data, documents, events, and embeddings into one connected system, giving applications richer context to work with.
The challenges with traditional graph databases
Legacy limits
Legacy graph databases were built for large data analysis, not for real-time updates, multi-model data, and the demands of modern AI workloads.
Fragmented data
Data gets scattered across multiple systems, making it hard to keep a clear and connected picture of your domain.
Operational overhead
Each hop between systems adds latency, ops overhead, and failure modes that complicate your architecture.
Complex stacks
Building AI or knowledge graph workloads requires several databases and pipelines, increasing latency and operational burden.
The solution: a multi-model graph database for the AI era
Real-time graphs
SurrealDB supports continuous reads, writes, and relationship updates without cache fragility - ideal for fast-changing AI memory and agent workloads.
Native multi-model
Graphs, documents, vectors, events, and relational data work together seamlessly, enabling rich queries across all data types in one system.
Elastic scaling
A decoupled compute-and-storage design provides predictable horizontal scaling with no manual sharding or complex clustering.
Developer-friendly
SurrealQL combines familiar SQL-like syntax with graph and JSON querying, making graph and multi-model development fast and accessible.
Model knowledge and relationships
Create entities and connect them with a typed edge containing metadata.
1-- Create entities (nodes) 2CREATEperson:marieCONTENT{name: "Marie Curie", occupation: "Scientist"}; 3CREATEaward:nobel_prizeCONTENT{category: "Chemistry", year: 1911}; 4 5-- Create a relationship (edge) with metadata 6RELATEperson:marie->won->award:nobel_prizeSETreason="Discovery of Radium and Polonium";
Graph traversal: querying linked data
Traverse the graph to find all awards won by a person or all people who have won a specific category of award.
1-- Find all awards won by Marie Curie 2SELECT->won->award.* FROMperson:marie; 3 4-- Find all people who have won a Nobel Prize in Chemistry 5SELECT<-won<-person.* FROMawardWHEREcategory="Chemistry";
Combining graph with document data
Store rich document-style information on nodes and link them to other entities via relationships.
1-- Each node can store rich document data 2CREATEperson:einsteinCONTENT{ 3name: "Albert Einstein", 4biography: { 5birth: "1879-03-14", 6nationality: "Swiss", 7summary: "Developed the theory of relativity." 8}, 9tags: ["physics", "relativity", "nobel laureate"] 10}; 11 12-- Create a graph edge from Albert Einstein to the Nobel Prize 13RELATEperson:einstein->won->award:nobel_prizeSETyear=1921; 14 15-- Graph traversal with metadata filtering to find all people who won a Nobel Prize after 1910 16SELECT<-won<-person.* FROMawardWHEREyear>1910;
Enterprise-ready by design
Security, scalability, and reliability built directly into the core of SurrealDB.
Built-in security
Fine-grained access control, authentication, and permissions are enforced directly at the database level to protect data across teams and applications.
Scale with confidence
Designed to scale horizontally and handle high-throughput, real-time workloads without sacrificing performance or reliability.
Flexible deployment
Run SurrealDB on-premises, in the cloud, or at the edge - giving enterprises full control over data residency and infrastructure.
Operational reliability
Strong consistency, fault tolerance, and predictable behaviour ensure SurrealDB can support mission-critical production systems.
Real-world scenarios
From fraud prevention to supply chain visibility, knowledge graphs power critical business applications
Fraud prevention
Link card transactions, devices, IPs, and identity documents. Run multi-hop traversals on every authorization call to flag suspicious loops.
Knowledge-driven LLM applications
Store documents with embeddings, chunk hierarchy, and citation edges. Plugins call local vector search or external OpenAI APIs to ground answers.
Supply chain visibility
Track parts, suppliers, shipments, and compliance certificates with full lineage. Time-travel queries reconstruct the exact chain for any delivered unit.
Recommendation engines
Traverse user, content, and behavioural signals in real time to deliver personalized recommendations with context.
Real-time that moves the business
Faster time to market
Simplify your architecture and reduce dependencies, allowing teams to ship real-time features faster and iterate with confidence.
Lower TCO
Replace multiple databases, caches, and streaming systems with a single platform - reducing infrastructure, maintenance, and operational costs.
Better user experience
Instant updates and live data synchronization create responsive, engaging applications that increase user satisfaction and retention.
Future-proof architecture
A flexible, multi-model database with built-in real-time capabilities adapts easily as product requirements and business needs evolve.
Powering innovation across industries
Unlocking insights with knowledge graphs
Samsung uses SurrealDB to power a knowledge graph for real-time audience insights and ad targeting in its ad division.
