Apache TinkerPop is a graph computing framework for both graph databases (OLTP) and graph analytic systems (OLAP). When a data system is TinkerPop-enabled, its users are able to model their domain as a graph and analyze that graph using the Gremlin graph traversal language. Furthermore, all TinkerPop-enabled systems integrate with one another allowing them to easily expand their offerings as well as allowing users to choose the appropriate graph technology for their application. 

Apache Tinkerpop - A graph computing framework for both graph databases (OLTP) and graph analytic systems (OLAP)

GUN is a realtime, distributed, offline-first, graph database engine. Lightweight and powerful. GUN does state synchronization out of the box. It is peer-to-peer by design, meaning you have no centralized database server to maintain. It has offline support, works even without internet. Users can save data offline and when when the network comes back online GUN will automatically synchronize the data. 
 
GUN is a data­base en­gine that runs every­where JavaScript does — browsers, mo­bile de­vices and servers, al­low­ing you to build ex­act­ly the data sys­tem you want. GUN's se­cu­ri­ty mod­ule is built on strong en­cryp­tion stan­dards and works com­plete­ly de­cen­tral­ized. In oth­er words — real pri­va­cy with no sin­gle point of fail­ure.

GUN is a realtime, distributed, offline-first, graph database engine. Lightweight and powerful. GUN does state synchronization out of the box.  It is peer-to-peer by design, meaning you have no centralized database server to maintain. It has offline support, works even without internet. Users can save data offline and when when the network comes back online GUN will automatically synchronize the data. 

GUN - A realtime, decentralized, offline-first, graph database engine

STINGER is a package designed to support streaming graph analytics by using in-memory parallel computation to accelerate the computation. STINGER is composed of the core data structure and the STINGER server, algorithms, and an RPC server that can be used to run queries and serve visualizations. 
 
The data structure used is similar to an adjacency list except that the adjacencies of a vertex are stored in a linked list of semi-dense blocks of edges rather than a linked list of individual edges. Also, the vertex array contains more metadata per each vertex than just a pointer to the linked of edge blocks.

STINGER is a package designed to support streaming graph analytics by using in-memory parallel computation to accelerate the computation. STINGER is composed of the core data structure and the STINGER server, algorithms, and an RPC server that can be used to run queries and serve visualizations. 

STINGER - In-memory graph store and dynamic graph analysis platform

Memgraph is a streaming graph application platform that helps you wrangle your streaming data, build sophisticated models that you can query in real-time, and develop graph applications.
 
You can ingest data from sources like Kafka, SQL, or plain CSV files. Memgraph provides a standard interface to query your data with Cypher, a widely-used and declarative query language that is easy to write, understand and optimize for performance. This is achieved by using the property graph data model, which stores data in terms of objects, their attributes, and the relationships that connect them. This is a natural and effective way to model many real-world problems without relying on complex SQL schemas.
 
Memgraph is a platform for graph applications, not just for graph analytics. From Social networks to user event tracking, permissions modeling, recommendation systems, and so much more, you can build it on Memgraph.

Memgraph is a streaming graph application platform that helps you wrangle your streaming data, build sophisticated models that you can query in real-time, and develop graph applications.



Memgraph - Build modern, graph-based applications on top of your streaming data in minutes

InfoGrid is a Web Graph Database with a many additional software components that make the development of REST-ful web applications on a graph foundation easy. GraphDatabase is the heart of InfoGrid. Graph database could be replicated across servers. It provides an common interface to store data either in file system or relational or non-relational database.

Infogrid - Web Graph Database

Bio4j is a bioinformatics graph based DB including most data available in Uniprot KB (SwissProt + Trembl), Gene Ontology (GO), UniRef (50,90,100), RefSeq, NCBI Taxonomy, and Expasy Enzyme DB.
Bio4j provides a completely new and powerful framework for protein related information querying and management. Since it relies on a  high-performance graph engine, data is stored in a way that semantically represents its own structure. 

Bio4j - Bioinformatics Graph based DB

Graphlite is an embedded graph databases for Python. Graphlite aims to change that by building a simple and fast graph layer over SQLite. Similar to FlockDB, Graphlite only stores adjacency lists, but they can be queried in the style of normal graph databases, e.g. with traversals.

Graphlite - Embedded graph databases for Python

HugeGraph is a fast-speed and highly-scalable graph database. Billions of vertices and edges can be easily stored into and queried from HugeGraph due to its excellent OLTP ability. As compliance to Apache TinkerPop 3 framework, various complicated graph queries can be accomplished through Gremlin(a powerful graph traversal language).



HugeGraph - Fast-speed and highly-scalable graph database

Fallen-8 is an in-memory graph database implemented in C#. Its focus is to provide raw speed for heavy graph algorithms. It provides semantic adhoc queries on multi-dimensional graphs, Plugins for indexes, algorithms and services.

fallen-8 - Fallen-8 is an in-memory graph database implemented in C#.

Eclipse JNoSQL is a Java framework that streamlines the integration of Java applications with NoSQL databases. It defines a set of APIs and provides a standard implementation for most NoSQL databases. This clearly helps to achieve very low coupling with the underlying NoSQL technologies used in applications.
 
It provide a set of APIs that defines communication with NoSQL databases. Compared with traditional the RDBMS world, they are like the JDBC API. It contains four modules, one for each NoSQL database type: Key-Value, Column Family, Document, and Graph.

Eclipse JNoSQL is a Java framework that streamlines the integration of Java applications with NoSQL databases. It defines a set of APIs and provides a standard implementation for most NoSQL databases. This clearly helps to achieve very low coupling with the underlying NoSQL technologies used in applications.

Eclipse JNoSQL - Framework which has the goal to help Java developers to create Jakarta EE applications with NoSQL

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