What it does This is an awesome-list that catalogs knowledge-graph infrastructure: graph databases, triple stores, visualization libraries, query languages, managed cloud services, datasets, and learning materials. Think of it as a map for developers who need to store, query, or visualize highly connected data and don’t want to evaluate 47 options from scratch.

The interesting bit The list reveals how fragmented the ecosystem still is. Property-graph people speak Cypher or Gremlin; RDF folks swear by SPARQL; and a few brave souls are pushing GQL as a unifying standard. The README doesn’t pick sides—it just dumps the battlefield in front of you.

Key highlights

• Databases: 20+ entries ranging from household names (Neo4j, Dgraph, JanusGraph) to niche players (Kuzu, CogDB, Atomic-Server in Rust)
• Triple stores: AllegroGraph, Apache Jena, Oxigraph, and CLever—which claims to handle 100B+ triples on a single server
• Cloud hosting: Every major provider is here, including some you might not expect (Huawei Cloud GES, Tencent Knowledge Graph, Baidu AI Platform)
• Construction tools: Morph-KGC and Ontop for RML/R2RML mapping, plus Termboard for quick ChatGPT-assisted sketches
• Datasets & learning: Sections for academic and general KG datasets, plus official docs and community guides

Caveats

• Descriptions are mostly one-line marketing blurbs from the projects themselves; there’s no comparative analysis or curation beyond inclusion
• Some entries are truncated or cut off mid-description (the README appears to end abruptly in the dataset section)
• No indication of maintenance cadence; a few linked projects (Apache Marmotta) are explicitly noted as retired, but others may be stale

Verdict Worth bookmarking if you’re surveying the knowledge-graph landscape or need to justify a technology choice with a link dump. Skip it if you already know your stack and just want deep dives—this is a phone book, not a review.