A graph database as a Redis module
Graph and network visualization using tabular data in R
Distributed Temporal Graph Analytics with Apache Flink
Bio4j abstract model and general entry point to the project
JAW: A Graph-based Security Analysis Framework for Client-side JavaScript
This tool transpiles openCypher graph query language to a relational database query language. This project publishes a T-SQL for Microsoft SQL Server as the target query language.
Gremlinator: An effort towards converting SPARQL queries to Gremlin Graph Pattern Matching Traversals
A persistent, local-first AI development daemon built in Elixir/OTP. AST-level code intelligence, Property Graph, runtime BEAM introspection, and semantic search — all via a 80+ endpoint REST API.
Specification of the LDBC Social Network Benchmark suite
grim brings property graphs to the Nim language. Look around you: everything is a graph!
KuzuDB fork maintained by Vela Partners. Embedded graph database for AI agent memory with concurrent multi-writer support. 374x faster than Neo4j on path queries. Cypher, vector search, full-text search. In-process, zero infrastructure. MIT licensed.
Manifest-driven schema & ingestion for labeled property graphs (GSTL): define once in YAML/Python, ingest from CSV/SQL/RDF/SPARQL/API, load to ArangoDB, Neo4j, TigerGraph, FalkorDB, Memgraph, or NebulaGraph.
Amazon Neptune CSV to RDF Converter is a tool for Amazon Neptune that converts property graphs stored as comma separated values into RDF graphs.
Python framework for graph analytics and co-occurrence analysis
Expressive Reasoning Graph Store (ERGS) is an OWL reasoner and an RDF triple store built on top of a Property Graph architecture.
Absurdly fast embedded graph database with built-in vector search. Rust core, Node.js and Python connectors, GQL, graph algorithms, all in-process with no server.
Demo application for GRADOOP operators
The Internet Memes Knowledge Graph
V1: A Visual Query Language for Property Graphs
ISONGraph is a multi-language property graph library designed specifically for LLM and AI applications. It provides the most token-efficient way to represent graph data in LLM context windows while maintaining high accuracy for graph-based reasoning tasks.
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