Best Graph Databases for Linux of 2025

Data engineers and scientists can be 95% better at their jobs with ready access to the most flexible semantic layer, explainable AI and reusable data modelling. They can create and expand semantic models, understand data interrelationships, and run federated query to speed up time to insight. Stardog's graph data virtualization and high performance graph database are the best available -- at a price that is up to 57x less than competitors -- to connect any data source, warehouse, or enterprise data lakehouse without copying or moving data. Scale users and use cases at a lower infrastructure cost. Stardog's intelligent inference engine applies expert knowledge dynamically at query times to uncover hidden patterns and unexpected insights in relationships that lead to better data-informed business decisions and outcomes.

Graphlytic is a web-based BI platform that allows knowledge graph visualization and analysis. Interactively explore the graph and look for patterns using the Cypher query language or query templates for non-technical users. Users can also use filters to find answers to any graph question. The graph visualization provides deep insights into industries such as scientific research and anti-fraud investigation. Even users with little knowledge of graph theory can quickly explore the data. Cytoscape.js allows graph rendering. It can render tens to thousands of nodes and hundreds upon thousands of relationships. The application is available in three formats: Desktop, Cloud, or Server. Graphlytic Desktop is a Neo4j Desktop app that can be installed in just a few mouse clicks. Cloud instances are great for small teams who don't want or need to worry about installing and need to be up and running quickly.

InfiniteGraph is a massively scalable graph database specifically designed to excel at high-speed ingest of massive volumes of data (billions of nodes and edges per hour) while supporting complex queries. InfiniteGraph can seamlessly distribute connected graph data across a global enterprise. InfiniteGraph is a schema-based graph database that supports highly complex data models. It also has an advanced schema evolution capability that allows you to modify and evolve the schema of an existing database. InfiniteGraph’s Placement Management Capability allows you to optimize the placement of data items resulting in tremendous performance improvements in both query and ingest. InfiniteGraph has client-side caching which caches frequently used node and edges. This can allow InfiniteGraph to perform like an in-memory graph database. InfiniteGraph's DO query language enables complex "beyond graph" queries not supported by other graph databases.

*GraphDB allows the creation of large knowledge graphs by linking diverse data and indexing it for semantic search. * GraphDB is a robust and efficient graph database that supports RDF and SPARQL. The GraphDB database supports a highly accessible replication cluster. This has been demonstrated in a variety of enterprise use cases that required resilience for data loading and query answering. Visit the GraphDB product page for a quick overview and a link to download the latest releases. GraphDB uses RDF4J to store and query data. It also supports a wide range of query languages (e.g. SPARQL and SeRQL), and RDF syntaxes such as RDF/XML and Turtle.

Memgraph offers a light and powerful graph platform comprising the Memgraph Graph Database, MAGE Library, and Memgraph Lab Visualization. Memgraph is a dynamic, lightweight graph database optimized for analyzing data, relationships, and dependencies quickly and efficiently. It comes with a rich suite of pre-built deep path traversal algorithms and a library of traditional, dynamic, and ML algorithms tailored for advanced graph analysis, making Memgraph an excellent choice in critical decision-making scenarios such as risk assessment (fraud detection, cybersecurity threat analysis, and criminal risk assessment), 360-degree data and network exploration (Identity and Access Management (IAM), Master Data Management (MDM), Bill of Materials (BOM)), and logistics and network optimization. Memgraph's vibrant open-source community brings together over 150,000 developers in more than 100 countries to exchange ideas and optimize the next generation of in-memory data-driven applications across GenAI/ LLMs and real-time analytics performed with streaming data.

VelocityDB is a database platform unlike any other. It stores data faster and more efficiently than other databases engines at a fraction the cost. It stores.NET objects in their original form without any mapping to tables, JSON, or XML. VelocityGraph, an open-source property graph database, can be used in conjunction the VelocityDB object data base. Object and graph database engine VelocityDB, a C#.NET NoSQL object database, can be extended to VelocityGraph. World's fastest most scalable & flexible database. A bug reported with a reproducible case is usually fixed within one week. This database system offers the greatest benefit, flexibility. You can fine-tune your application like no other database system. You can choose the most suitable data structure for your application with VelocityDB. You can choose where and how the data is indexed and accessed.

OrigoDB allows you to create high-quality, mission-critical systems in a fraction of time and cost. This isn't marketing gibberish! For a detailed description of our features, please read on. Contact us if you have any questions. You can also download the software and start it right away! In-memory operations are a lot faster than disk operations. One OrigoDB engine can execute millions upon millions of read transactions per minute and thousands upon thousands of write transactions every second. Asynchronous command journaling to local SSDs is also available. This is why OrigoDB was built. A single object-oriented domain model is much simpler than a full stack that includes a relational model, object/relational map, data access code and views, as well as stored procedures. This is a lot of waste that can easily be eliminated. The OrigoDB engine runs 100% ACID right out of the box. Each command executes one at a moment, transitioning the in memory model from one consistent state into another.

RecallGraph is a versioned graph data store. It retains all changes its data (vertices, edges) have undergone to get to their current state. It supports point-in time graph traversals that allow the user to query any past state of a graph as well as the present. RecallGraph can be used in situations where data is best represented using a network of edges and vertices (i.e., as a graph). 1. Both edges and vertices can contain properties in the form attribute/value pairs (equivalent of JSON objects). 2. Documents (vertices/edges), can change throughout their lives (both in their individual attributes/values as well as in their relationships to each other). 3. Documents from the past are just as important as their current states, so it is essential to retain and queryable their change history. Also see this blog post for an intro - https://blog.recallgraph.tech/never-lose-your-old-data-again.

Apache TinkerPop™, a graph computing framework, is available for graph databases (OLTP), and graph analytic system (OLAP). Apache TinkerPop's graph traversal language is Gremlin. Gremlin allows users to express complex traversals (or queries) on their application's property diagram in a concise, data-flow language. Each Gremlin traversal consists of a sequence (potentially nested). A graph is a structure that is composed of vertices or edges. Each edge and vertices can have an unlimited number of key/value pairs, called properties. Vertices can be used to denote discrete objects, such as a person or a place or an event. Edges denote relationships between vertices. A person might know another person, be involved in an event, or have been to a specific place recently. If a domain contains a heterogeneous set objects (vertices), that can be linked to one another in many ways (edges), it is called a domain.

ArcadeDB allows you to manage complex models without any compromises. Polyglot Persistence is gone. There is no need to have multiple databases. ArcadeDB Multi-Model databases can store graphs and documents, key values, time series, and key values. Each model is native to the database engine so you don't need to worry about translations slowing down your computer. ArcadeDB's engine was developed with Alien Technology. It can crunch millions upon millions of records per second. ArcadeDB's traversing speed does not depend on the size of the database. It doesn't matter if your database contains a few records or a billion. ArcadeDB can be used as an embedded database on a single server. It can scale up by using Kubernetes to connect multiple servers. It is flexible enough to run on any platform that has a small footprint. Your data is protected. Our unbreakable fully transactional engine ensures durability for mission-critical production database databases. ArcadeDB uses the Raft Consensus Algorithm in order to maintain consistency across multiple servers.

PuppyGraph allows you to query multiple data stores in a single graph model. Graph databases can be expensive, require months of setup, and require a dedicated team. Traditional graph databases struggle to handle data beyond 100GB and can take hours to run queries with multiple hops. A separate graph database complicates architecture with fragile ETLs, and increases your total cost ownership (TCO). Connect to any data source, anywhere. Cross-cloud and cross region graph analytics. No ETLs are required, nor is data replication. PuppyGraph allows you to query data as a graph directly from your data lakes and warehouses. This eliminates the need for time-consuming ETL processes that are required with a traditional graph databases setup. No more data delays or failed ETL processes. PuppyGraph eliminates graph scaling issues by separating computation from storage.

Neo4j's graph platform is designed to help you leverage data and data relationships. Developers can create intelligent applications that use Neo4j to traverse today's interconnected, large datasets in real-time. Neo4j's graph database is powered by a native graph storage engine and processing engine. It provides unique, actionable insights through an intuitive, flexible, and secure database.

Cayley is an open source database for Linked Data. It was inspired by Google's Knowledge Graph graph database (formerly Freebase). Cayley is an open source graph database that allows you to store complex data and makes it easy to use. Built-in query editor, visualizer, and REPL. Cayley supports multiple query languages, including Gizmo, a query engine inspired by Gremlin and GraphQL-inspired query languages, MQL, a simplified version for Freebase lovers, and MQL. Cayley is modular and easy to connect with your favorite programming languages. It can also be used by back-end stores. Cayley has been well tested and used by many companies for their production workloads. It is also fast and optimized for use in applications. Rough performance testing has shown that on 2014 consumer hardware, 134m quads of LevelDB are not a problem, and a multi-hop intersection query - films starring X or Y - takes 150ms. Cayley is set up to run in memory by default (that's what backendmemstore means).

You can create high-quality, mission-critical applications in a fraction of the time and costs. Memstate is new. It is extremely inefficient to move data between RAM and disk. Additionally, it requires complex software that can be eliminated. Memstate allows you to manage and structure your data in-memory. It also provides transparent persistence, concurrency control, and transactions with strong ACID warranties. This is too technical. Make your applications faster and your developers more productive. Memstate can be used in many ways, but it is designed to handle complex OLTP workloads within an enterprise application. In-memory operations are a factor of ten times faster than disk operations. A single Memstate engine can execute millions upon millions of read transactions, and tens to thousands upon thousands of write transactions per second. All this at submillisecond latency.

HyperGraphDB is an open-source, general-purpose data storage system that uses a powerful knowledge management approach called directed hypergraphs. Although it is a persistent memory model, it can also serve as an embedded object-oriented data base for Java projects of any size. Or a graph database or a (non SQLL) relational database. HyperGraphDB is a storage system that uses generalized hypergraphs for its underlying data model. A tuple is a collection of 0 or more tuples. Each atom is a tuple of this type. The data model can be viewed as either relational, where higher-order, non-ary relationships are permitted, or graph-oriented where edges point to an arbitrary set nodes. Each atom is assigned a strongly-typed, arbitrary value. The hypergraph that manages these values is embedded in the type system and can be customized from the ground up.

JanusGraph is an optimized graph database that can store and query graphs with hundreds of billions of edges and vertices distributed across a multi-machine cluster. JanusGraph is a project of The Linux Foundation and includes participants from Expero and Google, GRAKN.AI., Hortonworks. IBM, and Amazon. Linear and elastic scaling for growing data and users. Data replication and data distribution for performance and fault tolerance. Hot backups and high availability for multi-datacenters All functionality is completely free. There is no need to purchase commercial licenses. JanusGraph is completely open source under the Apache 2 License. JanusGraph is an open source transactional database that can handle thousands of concurrent users performing complex graph traversals in real-time. ACID and eventual consistency support. JanusGraph offers online transactional processing (OLTP) and global graph analytics (OLAP), through its Apache Spark integration.

The graph database is designed for graphs up to super large scale with very low latency. We continue to work with the community to promote, popularize, and prepare the graph database. Nebula Graph allows only authenticated access through role-based access control. Nebula Graph can support multiple storage engines and the query language is extensible to support new algorithms. Nebula Graph offers low latency read/write while maintaining high throughput to simplify complex data sets. Nebula Graph's distributed, shared-nothing architecture allows for linear scaling. Nebula Graph's SQL query language is similar to SQL and can be used to address complex business requirements. Nebula Graph's horizontal scalability, snapshot feature and high availability guarantee that there will be no downtime. Nebula Graph has been used in production environments by large Internet companies such as JD, Meituan and Xiaohongshu.

Sparksee, formerly known as DEX, is space- and performance-friendly. It has a small footprint and can quickly analyze large networks. It is natively compatible with.Net, C++ and Python and Objective-C. The graph is represented using bitmap data structures, which allow for high compression rates. Each bitmap is divided into chunks that can be placed on disk pages to increase I/O location. Bitmaps allow operations to be computed using binary logic instructions, which simplify execution in pipelined processors. Full native indexing allows for extremely fast access to all graph data structures. Bitmaps are used to represent node adjacencies in order to reduce their footprint. Advanced I/O policies reduce the number of times each page is brought into memory. Each value in the database can only be represented once, which prevents unnecessary replication.