About GDB-Engines
What is GDB-Engines?
GDB-Engines is an open source comparison of graph databases, query engines, extensions, and embedded libraries. It covers property graph (LPG), RDF, and multi-model databases, providing a comprehensive reference for developers, researchers, and architects evaluating graph technology.
The project aims to maintain an accurate, up-to-date catalog of every notable graph database — from established industry leaders to emerging experimental projects — with structured metadata and quantitative feature scores derived from academic research.
Methodology
Databases are included based on community contributions and editorial review. The catalog is open to additions via pull request. Each database is classified along several dimensions:
Categories:
- Established — Widely adopted databases with large user bases and mature ecosystems.
- Enterprise — Major vendor platforms typically backed by large organizations.
- Growing — Actively developed databases with a growing user community.
- Emerging — New or experimental projects in early stages of development.
Types:
- Property Graph — Databases using the labeled property graph model.
- RDF — Databases built on the Resource Description Framework and SPARQL.
- Multiple — Multi-model databases supporting more than one graph paradigm.
- Extension — Graph capabilities added to an existing database system.
- Query Engine — Standalone engines that query graph data from external sources.
- Embedded — Databases designed for in-process graph storage and querying.
- Library — Programming libraries for RDF or graph data manipulation.
Status:
- Active — Under active development and maintenance.
- Inactive — No longer actively maintained but still available.
- Deprecated — Officially end-of-life or superseded.
Rankings
GDB-Engines Rankings is a separate monthly popularity ranking covering every active engine in the catalog. Engines are ranked overall and across boards split by data model, engine kind, license, query language, and implementation language, plus a movers board surfacing the biggest changes month-on-month.
Scores blend signals across four pillars — adoption, activity, community, and research — drawing on public data sources across the web. The methodology is fixed in advance and is not adjusted for sponsors.
API
A JSON API is available at /api.json containing all database entries and their metadata. Example usage:
curl https://gdb-engines.com/api.jsonChangelog
- 2026-06-01
- Added RelationalAI, a relational knowledge graph system queried with Rel and built in Julia.
- 2026-05-28
- Launched GDB-Engines Rankings: monthly popularity boards across data model, engine kind, license, query language, and implementation language, plus an overall board and a movers board.
- 2026-05-27
- Added a Language column showing each engine's primary implementation language. Filled in missing licenses for 16 proprietary engines (Cosmos DB, Neptune, SAP HANA, DataStax, and others).
- 2026-05-26
- Added Google Cloud BigQuery Graph, Microsoft Fabric Graph, Prometheux, pgGraph, PostgreSQL SQL/PGQ, and Data Graphs.
- 2026-05-09
- Added GraphScope, ONgDB, OxiRS, TribleSpace, OmniGraph, CodeMix Graph, MemGQL, CogDB, agdb, Attean, RDF::Trine, and DozerDB.
- 2026-03-31
- Added Apache Rya. Updated gdotv links to dedicated database pages.
- 2026-03-19
- Added Rocketgraph, Strabon, Marmotta KiWi, Parliament, Halyard, NornicDB, 4store, Redland, Dydra, SparkleDB, RedStore, FalkorDBLite, Kinetica, Quine, Raphtory. Updated AgensGraph URLs for SKAI Worldwide migration.
- 2026-03-18
- SEO improvements: self-hosted fonts and CSS, individual database pages, security headers.
- 2026-03-17
- Added GFQL, Lance Graph openCypher support, additional graph databases.
- 2026-03-16
- Removed reasoning languages that can't be used for querying, added SQL for Stardog.
Contributing
GDB-Engines is open source. Database entries are stored as TOML files in the GitHub repository. To add or update a database, open a pull request with changes to the relevant TOML file. Contributions for new databases, corrections, and feature improvements are welcome.
Research Features
Feature scores are derived from: Coimbra, M. E., Svitakova, L., Francisco, A. P., & Veiga, L. (2025). Survey: Graph Databases. arXiv:2505.24758.
The study evaluates 43 features across categories including data model capabilities, query language support, scalability, and administration. Each score ranges from 0 to 1, where 0 indicates no support, 1 indicates full support, and intermediate values reflect partial or conditional support as assessed by the survey authors.