The high-performance database for modern applications
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Updated
May 28, 2024 - Go
The high-performance database for modern applications
Embeddable property graph database management system built for query speed and scalability. Implements Cypher.
Deterministic LLMs Outputs for AI Applications and AI Agents
React-based web application that enables users to visualize both property graph and RDF data and explore connections between data without having to write graph queries.
Blazingly fast, vectorised, parallel, and scalable temporal graph engine for Rust, Python and JavaScript
A scalable, distributed, collaborative, document-graph database, for the realtime web
Search and browse documents and data; find the people and companies you look for.
🥑 ArangoDB is a native multi-model database with flexible data models for documents, graphs, and key-values. Build high performance applications using a convenient SQL-like query language or JavaScript extensions.
The official documentation for Memgraph open-source graph database.
Śivadharma Database is a Neo4j web application built in Node.js. The project is ongoing and under development.
OrientDB is the most versatile DBMS supporting Graph, Document, Reactive, Full-Text and Geospatial models in one Multi-Model product. OrientDB can run distributed (Multi-Master), supports SQL, ACID Transactions, Full-Text indexing and Reactive Queries.
A Python client for the Neo4j Graph Data Science (GDS) library
A distributed, fast open-source graph database featuring horizontal scalability and high availability
Open-source graph database, tuned for dynamic analytics environments. Easy to adopt, scale and own.
Extends of Apache Ignite, add feathers:Mongodb protocol,Gremlin protocol, vector search,fulltext search,graph compute
Eclipse JNoSQL is a framework which has the goal to help Java developers to create Jakarta EE applications with NoSQL.
Structr is an integrated low-code development and runtime environment that uses a graph database.
A super fast Graph Database uses GraphBLAS under the hood for its sparse adjacency matrix graph representation. Our goal is to provide the best Knowledge Graph for LLM (GraphRAG).
CNCF Landscape Graph, data model, and applications.
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