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surrealdb/surrealdb

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Surrealdb

SurrealDB is a multi-model database engine designed to store and query document, graph, relational, and vector data within a single ACID-compliant platform. It functions as an AI-native data store, integrating vector search, graph traversal, and machine learning model execution directly into its query layer. By providing a unified declarative query language, the platform eliminates the need for external middleware to synchronize data across different storage models.

The platform distinguishes itself through its ability to manage agent memory and complex workflows natively. It allows developers to store agent memory, knowledge graphs, and structured data within a single transaction boundary, ensuring consistent state and permissions. Furthermore, the engine supports real-time reactive applications by pushing data updates directly to connected clients through live queries, removing the requirement for external message brokers or polling mechanisms.

SurrealDB is built for versatility, operating as a portable database runtime that maintains a consistent interface across embedded, edge, and cloud environments. Its architecture includes a granular, record-level permission model that enforces security and multi-tenant isolation directly at the data layer. The system also features an isolated sandboxing environment for custom extensions, allowing for specialized data processing without compromising system stability or security.

The project provides extensive documentation and learning resources, including a structured curriculum and hands-on projects, to assist with onboarding and architectural mastery. It is distributed as a single binary, facilitating deployment across diverse infrastructure ranging from resource-constrained devices to large-scale distributed cloud clusters.

Features

  • Multi-Model Databases - Stores and queries document, graph, relational, and vector data within a single ACID-compliant engine.
  • ACID Transactional Cores - Groups multiple operations into atomic, isolated units to ensure data integrity and consistency across complex distributed database workloads.
  • Database Engines - Maintains data integrity using ACID-compliant transactions, multi-tenancy isolation, and flexible schema modes.
  • Declarative Query Languages - Provides a unified declarative syntax for CRUD, graph traversal, and vector search.
  • Access Control Systems - Enforces granular, record-level permissions and multi-tenant isolation directly at the data layer.

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  • Agent Memory Storage - Keeps agent memory inside the database to eliminate middleware layers while ensuring consistent permissions.
  • Vector Databases - Integrates vector search, graph traversal, and machine learning model execution into the query layer.
  • Database Client Libraries - Provides a strongly-typed client interface for consistent communication with the database instance.
  • Distributed Transaction Processing - Processes write transactions through a quorum consensus mechanism to ensure data durability across multiple nodes.
  • Graph Querying - Supports complex graph traversal and nested object projection for connected data.
  • Multi-Model Vector Storage - Keeps vector embeddings alongside structured data and graph relationships within a single database to simplify data management.
  • Query Languages - Manipulates data using a multi-model language that supports standard CRUD operations and graph-based relationship traversal.
  • Access Control Policies - Evaluates granular security and access control rules directly against individual data records during query execution.
  • Agent Context Management - Provides a unified memory layer that maintains consistent state and permissions across multiple database models for intelligent agents.
  • Agent Memory Systems - Maintains persistent, ACID-consistent memory and knowledge graphs for intelligent agents.
  • Atomic Agent Contexts - Handles memory, knowledge graphs, and structured data within a single transaction boundary to ensure data consistency.
  • In-Database Model Execution - Runs trained machine learning models directly within database queries to perform real-time predictions on stored data.
  • Browser Databases - Provides high-performance data storage directly within the browser environment.
  • Consensus Protocols - Processes write transactions through a distributed agreement mechanism that ensures data durability and consistency.
  • Database Clients - Instantiates database clients to handle connections using multiple protocols.
  • Database Execution Engines - Performs document storage, search, vector processing, and graph querying natively within a single unified engine.
  • Distributed Databases - Operates across embedded, edge, and cloud environments using a consistent binary and API.
  • Event-Driven Subscription Systems - Pushes real-time data updates directly to connected clients via live queries to eliminate the need for external message brokers.
  • Indexing and Search - Executes complex data lookups using unique, compound, and array indexes alongside full-text and vector indexing.
  • Real-Time Data Streaming - Powers reactive, real-time experiences using built-in subscriptions, event triggers, and streaming updates.
  • Relationship Management - Connects related data records and performs vector similarity searches natively within a single database.
  • Transaction Management - Manages query execution flow and manual transactions to ensure data consistency.
  • Model Context Protocol Integrations - Links AI models directly to database services using standard protocol tool calls to avoid custom integration code.
  • Multi-Agent Coordination - Synchronizes multiple agents using shared memory and event-driven handoffs to maintain consistent state across complex workflows.
  • Database Management Tools - Scalable document-graph database for real-time web applications.
  • Database Systems - Scalable, distributed document-graph database.
  • Bases de données - Listed in the “Databases” section of the Awesome Mac awesome list.
  • Databases & Data - Scalable distributed document-graph database.
  • Vector Databases - Scalable multi-model database optimized for time-series data.
  • Data Change Subscriptions - Pushes data changes to connected clients instantly using live queries.
  • Database Administration - Organizes database resources by managing schema elements and access grants.
  • Edge Databases - Deploys a consistent database engine across environments ranging from edge devices to cloud clusters.
  • Graph Databases - Handles continuously updated graph data and multi-model workloads at scale.
  • Hybrid Search - Merges vector similarity searches with graph traversal and structured data filters in a single query.
  • In-Memory Databases - Manages application state using a high-speed embedded in-memory engine.
  • Reactive Databases - Pushes real-time updates to connected clients through live queries and event triggers.
  • Schema Definition Tools - Creates type-safe table references to ensure language compatibility and consistent data access patterns.
  • Storage-Compute Architectures - Decouples the database processing layer from underlying storage nodes to enable independent scaling.
  • Distributed Systems - Distributes database instances across industrial environments from edge devices to cloud infrastructure.
  • Fault Tolerance - Restores failed nodes by retrieving state from object storage and replaying transaction logs.
  • Data Security Frameworks - Enforces granular, record-level permissions and multi-tenant isolation across all stored data.
  • Agent Memory Architectures - Structures agent data across working, semantic, and episodic storage using graph relationships and temporal awareness to support specialized recall.
  • Database Assistants - Generates database queries, designs schemas, and troubleshoots errors using an integrated assistant that understands workspace context.
  • Multi-Axis Memory Querying - Searches memory across semantic, relational, and temporal axes using a unified substrate that fuses signals into a single ranking.
  • Unified Data Querying - Retrieves diverse data types including documents, graphs, and vectors through a single query language and protocol interface.
  • Connection Establishment - Connects to database instances using network or embedded protocols with authentication.
  • Data Modeling - Represents diverse data structures using native support for arrays, objects, datetimes, and geometry types.
  • Key-Value Stores - Stores information in an embedded database using a reliable key-value interface.
  • Remote Query Execution Protocols - Executes multiple complex database queries within a single network request using persistent websocket connections.
  • Versioned Storage - Maintains historical data snapshots using an immutable tree structure.
  • Single-Binary Distributions - Deploys a single-binary database engine across diverse environments while maintaining a consistent interface.
  • Object Storage Integration - Saves transactional data directly in cloud object storage to enable stateless, elastic compute.
  • WebAssembly - Compiles the core database engine into portable bytecode to enable high-performance execution within browser and edge environments.
  • Access Governance - Restricts extension execution using custom permission expressions to ensure secure multi-tenant access.
  • Reactive Data Streams - Streams database updates directly to clients to power live, event-driven user experiences.
  • Agent Memory Categorization - Organizes memory into distinct categories like episodic, identity, and knowledge with specific schemas and retrieval weights.
  • Agent Memory Maintenance - Runs background processes that autonomously discover connections between entities and consolidate fragmented knowledge for intelligent agent applications.
  • Memory Provenance Tracking - Records fact provenance as first-class data using independent system clocks to maintain an auditable history of information.
  • Semantic Substrates - Creates a multi-model substrate that anchors canonical meaning and governs access across an organization.
  • Cloud Database Provisioning - Enables automated deployment and configuration of cloud-hosted database instances with specific performance and scaling requirements.
  • Connection Pooling - Establishes persistent connections to database instances for efficient data access.
  • Data Insertion Interfaces - Maps application objects to database records while supporting auto-generated and custom identifiers for data integrity.
  • Data Retrieval Interfaces - Fetches specific records using unique identifiers or custom query statements with bound parameters.
  • Schema Management - Structures domain ontologies directly within the database schema using tables and typed relations.
  • Semantic Search - Improves product discovery by matching items using semantic search and AI embeddings.
  • Managed Database Services - Operates database clusters in the cloud with automated infrastructure and independent compute scaling.
  • Execution Isolation - Executes custom modules within isolated memory sandboxes to maintain system stability and security.
  • Sandboxing Environments - Executes custom user-defined modules within restricted memory environments to prevent unauthorized system access.
  • Schema Migration Tools - Tracks and manages schema changes to ensure compatibility during data evolution.

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    Questions fréquentes

    Que fait surrealdb/surrealdb ?

    SurrealDB is a multi-model database engine designed to store and query document, graph, relational, and vector data within a single ACID-compliant platform. It functions as an AI-native data store, integrating vector search, graph traversal, and machine learning model execution directly into its query layer. By providing a unified declarative query language, the platform eliminates the need for external middleware to synchronize data across different storage models.

    Quelles sont les fonctionnalités principales de surrealdb/surrealdb ?

    Les fonctionnalités principales de surrealdb/surrealdb sont : Multi-Model Databases, ACID Transactional Cores, Database Engines, Declarative Query Languages, Access Control Systems, Agent Memory Storage, Vector Databases, Database Client Libraries.

    Quelles sont les alternatives open-source à surrealdb/surrealdb ?

    Les alternatives open-source à surrealdb/surrealdb incluent : neo4j/neo4j — Neo4j is a native graph database management system designed to store and query highly connected data using a… vonng/ddia — This project serves as a comprehensive technical reference for the architecture and design of data-intensive… kuzudb/kuzu — Kùzu is an embedded property graph database engine designed for high-performance analytical queries and local data… pubkey/rxdb — This project is a reactive, offline-first NoSQL database engine designed for JavaScript applications. It provides a… arangodb/arangodb — This project is a multi-model database system designed to store and manage information as documents, graphs, and… memgraph/memgraph — Memgraph is an in-memory, distributed graph database designed for high-performance labeled property graph management.…