Graph Database Management Systems

This project is a multi-model database system designed to store and manage information as documents, graphs, and key-value pairs within a single engine. It functions as a graph database and knowledge graph platform, providing the infrastructure to build, query, and visualize structured data models. By integrating vector search capabilities, the system serves as a vector database that supports retrieval-augmented generation for artificial intelligence applications. The platform distinguishes itself through a unified query language that allows users to perform document lookups, graph traversals, and vector searches across diverse data models simultaneously. It includes a dedicated graph analytics engine capable of executing structural algorithms, such as pathfinding and centrality analysis, to identify patterns and influential nodes within complex networks. These features enable the construction of knowledge graphs that ground generative AI models in verified enterprise context, reducing hallucinations and improving response accuracy. Beyond its core storage and retrieval capabilities, the system supports predictive machine learning by leveraging stored relationship data to classify elements and forecast connections. It provides an interactive web interface for the visual exploration and navigation of graph structures, facilitating the analysis of complex information networks. The software is documented and distributed as a comprehensive environment for managing multi-model data and building intelligent, context-aware systems.

Dgraph is a distributed graph database designed to store and query highly connected data. It organizes information as nodes and edges to represent complex relationships between entities, providing a platform for managing and analyzing deeply linked datasets. The system functions as a horizontally scalable cluster that partitions data across multiple nodes to maintain performance and availability as information volume increases. It utilizes a specialized query language built for low-latency navigation of interconnected data points, allowing for the execution of complex queries across large-scale information networks. The platform incorporates a graph-oriented storage engine and in-memory indexing to facilitate efficient traversal of relationships. It manages state changes and data consistency through a distributed consensus algorithm and predicate-based sharding, which enables the system to decompose and execute queries in parallel across the cluster.

Cayley is a graph database and query engine designed to store and retrieve interconnected data. It functions as a quad store, persisting information as four-element tuples to maintain complex relationships and semantic linked data. The system features a backend-agnostic storage layer that decouples the graph API from the underlying data store. This allows for the integration of external backends through a modular adapter system, enabling the synchronization of data across different storage engines. The project provides a pattern-matching query engine for extracting specific nodes and relationships. It also includes a built-in visual editor for graph exploration and the mapping of data connections.

Neo4j is a native graph database management system designed to store and query highly connected data using a property-graph model. It provides an ACID-compliant transaction engine that ensures data integrity, supported by a distributed cluster architecture that maintains causal consistency across nodes. Users interact with the system through a declarative query language, which allows for complex pattern matching and path traversal without requiring manual traversal logic. The platform distinguishes itself through its hybrid approach to data retrieval, combining traditional graph-based queries with high-dimensional vector indexing. This integration enables simultaneous semantic similarity searches and relational data analysis within a single environment. By supporting both structured graph patterns and vector embeddings, the system facilitates advanced analytical tasks such as community detection, pathfinding, and centrality calculations. The project covers a broad capability surface, including comprehensive database administration, security controls, and performance optimization tools. It provides extensive support for AI-augmented workflows, enabling the integration of large language models for retrieval-augmented generation, natural language query translation, and autonomous agent memory management. These features are accessible through standardized language drivers, HTTP interfaces, and native schema enforcement mechanisms. The software is distributed as a database engine with support for both self-managed and cloud-hosted infrastructure, offering command-line tools for provisioning, monitoring, and lifecycle management.

Helix DB is a distributed graph database and knowledge graph platform that persists nodes and edges on object storage for durable and unlimited scaling. It operates as an ACID-compliant system, ensuring data consistency through serializable snapshot isolation during concurrent operations. The project distinguishes itself by combining a vector search engine and a property graph, utilizing hybrid vector and full-text search to locate entry points for graph traversals. It enables dynamic graph querying through a domain-specific language, allowing complex logic and recursive queries to be executed via an API without redeploying application code. The system provides high availability through a distributed cluster of gateways and reader nodes that scale automatically based on load. Its broader capabilities include graph data mutation, multi-hop relationship traversal, and query output shaping with filtering and pagination. A command-line interface is provided for cluster management and project bootstrapping.

FalkorDB is a high-performance graph database management system and vector graph database. It serves as a knowledge graph construction tool and a GraphRAG knowledge store, integrating structured property graphs with vector search to provide grounded context for large language models. The engine is designed as a multi-tenant graph engine, capable of hosting thousands of isolated datasets within a single instance. The system distinguishes itself by using linear algebra for query execution, treating relationship tensors as matrix multiplications to achieve low-latency multi-hop traversals. It utilizes sparse-matrix graph storage and vectorized traversals to process thousands of relationships simultaneously. These capabilities are combined with hybrid vector-graph indexing to unify semantic similarity search with structural graph exploration. The platform covers a broad range of capabilities, including GraphRAG orchestration, AI agent memory implementation, and advanced graph analytics such as community detection and centrality ranking. It supports OpenCypher query execution and provides connectivity via the Bolt and RESP protocols. Additional functionality includes automated ontology loading, temporal data tracking, and real-time binary replication for high availability. The database supports migration from Neo4j and can be deployed as a distributed cluster or as an embedded graph engine.

Cayley is a graph database engine designed for storing and querying interconnected data using a quad-based data model. It functions as an RDF quad store, managing information through subjects, predicates, objects, and labels. The system features a modular graph store architecture with pluggable backends, allowing it to swap between in-memory storage and various external persistent databases. It includes a GraphQL-inspired API and a dedicated data visualizer for the interactive exploration of nodes and edges. Query capabilities cover bidirectional path traversal and multi-syntax execution using JSON or GraphQL. The engine supports complex node and result filtering, set operations, and stateful query management to handle large-scale data traversal. The project provides options for containerized deployment and integration with managed cloud hosting environments.

Memgraph is an in-memory, distributed graph database designed for high-performance labeled property graph management. It utilizes a Cypher query engine for declarative data retrieval and manipulation, providing a scalable knowledge graph backend that integrates vector search and graph traversals. The system distinguishes itself as a real-time graph analytics platform, employing native C++ and CUDA implementations to execute complex network analysis and dynamic community detection on streaming data. It provides specialized support for AI integration, including GraphRAG capabilities, the construction of knowledge graphs from unstructured text, and the orchestration of AI agents with long-term memory storage. The platform covers a broad range of capabilities, including advanced graph analytics for path discovery, node centrality, and topology analysis. It also features machine learning workflows for graph neural networks, hybrid indexing for semantic and geospatial search, and comprehensive data migration tools for importing relational and flat-file data. Deployment is supported across containerized environments, Kubernetes, and managed cloud instances, with high availability ensured via the Raft consensus protocol.

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.

Apache AGE is a graph database extension for PostgreSQL that adds openCypher graph query capabilities directly within the relational database environment. It functions as a loadable extension that translates Cypher graph traversal queries into SQL expressions, enabling users to run pattern matching and path analysis alongside standard SQL operations within a single database instance. The extension stores labeled, directed property graphs as isolated schemas with internal relational tables for vertices, edges, and labels, preventing cross-graph interference. It supports hybrid query execution that embeds Cypher patterns inside SQL common table expressions, joins, and subqueries, allowing graph traversals and relational joins to run in a single query plan. Variable-length path traversal is implemented through recursive SQL constructs, and a dedicated CSV bulk import pipeline loads vertex and edge data transactionally. Users can create, modify, and query graph elements using standard openCypher clauses like MATCH, CREATE, SET, DELETE, and MERGE, with support for aggregation, sorting, filtering, and property management. The extension also allows user-defined PL/pgSQL functions to be registered as graph query functions, extending Cypher with custom logic. Multiple graphs can be referenced within a single SQL statement, and graph query results can be joined with relational tables for combined analysis.

Cozo is a logic-based database engine that functions as a relational data store, an embedded graph database, and a temporal vector database. It utilizes a Datalog-inspired query language to execute relational, recursive, and graph queries. The system distinguishes itself through specialized indexing for high-dimensional vector similarity searches and near-duplicate detection using locality sensitive hashing. It also provides built-in temporal versioning, allowing for historical state retrieval and time-travel queries to access data as it existed at specific points in time. Its broader capabilities include full-text search with tokenization, recursive graph analysis for pathfinding and community detection, and the execution of complex relational joins. The engine supports atomic transactions, persistent stored relations with schema definitions, and pluggable storage backends. The database can be integrated directly into an application as an embedded library to manage concurrency without a separate server.

Cytoscape.js is a JavaScript library designed for rendering interactive node-link diagrams and analyzing complex network structures directly within a web browser. It functions as a comprehensive framework for building responsive graph interfaces, providing the tools necessary to visualize relational datasets and manage hierarchical data models. The library distinguishes itself through a modular architecture that supports custom layout algorithms and rendering styles, allowing for the integration of physics-based engines to organize complex network structures automatically. It utilizes an event-driven interaction layer that captures user gestures, such as panning and zooming, to facilitate navigation within large diagrams. Furthermore, the system includes a tween-based animation engine that enables smooth transitions between different graph states, ensuring that visual updates remain responsive during dynamic data manipulation. Beyond core visualization, the library provides a suite of analytical utilities for computing network metrics, including shortest path calculations and traversal sequences. It supports the management of nested, hierarchical groupings and allows for the attachment of custom metadata to individual nodes and edges. Users can also export network visualizations into static image files or structured text formats for external use.