SurrealDB is a multi-model database designed to simplify building modern applications. It combines the capabilities of traditional relational databases, document stores, graph databases, and more into a single, unified platform. Whether you are building real-time applications, working with complex data relationships, or deploying AI-powered workflows, SurrealDB provides the tools you need without requiring multiple database systems.
This documentation covers the full SurrealDB ecosystem, from core database concepts and query language reference to SDK guides, deployment options, and third-party integrations. Use the sections below to navigate to the area most relevant to your needs.
Guides
The SurrealDB ecosystem consists of several guides, each designed to address different aspects of database management and application development. Together, they provide a complete platform for storing, querying, managing, and visualising your data.
Query Language
The powerful query language for SurrealDB, combining the familiarity of SQL with modern data capabilities.
SurrealDB Cloud
A fully managed cloud service for running SurrealDB without managing infrastructure.
Surrealist
A graphical interface for exploring, querying, and managing your SurrealDB databases.
Extensions
Machine learning integration for training and running ML models directly within the database.
Features
SurrealDB is built around a rich feature set that supports a wide variety of application requirements. Rather than requiring you to stitch together separate systems for different data models or capabilities, SurrealDB provides them all within a single engine. The following features each have dedicated documentation covering concepts, configuration, and practical usage.
Multi-model data
Work with document, graph, and relational data in a single database.
Real-time queries
Subscribe to live queries and changefeeds for real-time data updates.
Authentication
Built-in authentication and access control with scoped permissions.
Graph relationships
Model and traverse complex relationships using native graph edges.
Full-text search
Search and rank text content with analyzers, tokenizers, and scoring.
Vector search
Store embeddings and perform similarity searches for AI and RAG workflows.
Geospatial queries
Query location data with geometry types, distance calculations, and spatial functions.
Time-series data
Ingest and query time-stamped data with windowing and aggregation patterns.
Schema management
Define and enforce schemas, tables, fields, indexes, and computed data.
SDKs
SurrealDB provides official SDKs for a range of programming languages, allowing you to integrate the database into your applications regardless of your technology stack. Each SDK offers methods for connecting to SurrealDB, executing queries, managing authentication, and subscribing to real-time updates. Some SDKs also support embedding SurrealDB directly within your application process, either in-memory or persisted to disk.
Refer to the documentation for your preferred language to get started with installation, configuration, and usage examples.
Deployment
SurrealDB offers flexible deployment options to suit your infrastructure requirements. You can run the database as a fully managed cloud service, self-host it on your own servers, deploy it within containers, or embed it directly into your application. Each deployment model is documented with step-by-step guides for installation, configuration, and operations.
SurrealDB Cloud
Provision and manage cloud-hosted SurrealDB instances with automatic scaling and backups.
Self-hosted
Install and run SurrealDB on your own infrastructure with full control over configuration.
Docker
Run SurrealDB in a Docker container for rapid setup and consistent environments.
Embedding
Embed SurrealDB directly into your application as an in-process database engine.
Integrations
SurrealDB integrates with a variety of third-party tools and platforms, enabling you to connect the database to your existing workflows and infrastructure. The integrations documentation covers data management tools, AI frameworks, embeddings providers, and more. SurrealDB also provides dedicated support for AI agent architectures, with guides on connecting agents to the database for knowledge retrieval and stateful workflows.
Integrations
Connect SurrealDB to third-party tools, AI frameworks, embeddings providers, and data management platforms.
AI agents
Build AI agent workflows that use SurrealDB for knowledge storage, retrieval, and state management.
Reference
The reference documentation provides comprehensive, detailed specifications for all SurrealDB interfaces. This includes the full SurrealQL query language with syntax definitions for every statement, clause, function, and data type, as well as the command-line interface for managing SurrealDB instances and the HTTP-based REST API for programmatic access.
SurrealQL
Complete query language reference covering statements, functions, data types, and operators.
CLI tools
Command-line interface reference for starting, managing, and interacting with SurrealDB.
REST API
HTTP API reference for querying and managing SurrealDB over REST endpoints.
Tutorials and resources
If you prefer learning through practical examples, the tutorials and demos section provides hands-on guides that walk you through common use cases and application patterns. The labs section offers experimental projects and community-contributed content for exploring SurrealDB in different contexts.