Ross ROSS = Recommend OSS · open-source software intelligence for agents

apache/hugegraph

A graph database that supports more than 100+ billion data, high performance and scalability (Include OLTP Engine & REST-API & Backends) observed · 2026-08-28

github.com/apache/hugegraph · homepage · Java · Apache-2.0 (permissive) observed · 2026-08-28

Health v2 · maintenance only

73/100

  • Activity 99
  • Release rhythm 25
  • Longevity 100
How is this computed?

round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10) — computed 2026-09-02. Adoption (stars, forks) is never an input.

  • gap_med: 341
  • age_days: 2968
  • days_rel: 290
  • days_push: 7
  • n_releases_24m: 2

Full methodology

Adoption not part of the score

3157 stars · 631 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

Apache HugeGraph is a fast, highly scalable graph database supporting tens of billions of vertices and edges with OLTP real-time queries via Gremlin, Cypher, and REST APIs. It forms a full-stack graph ecosystem including distributed OLAP graph computing (HugeGraph-Computer), GraphRAG/AI tooling (HugeGraph-AI), and a toolchain for loading, visualizing, and managing graph data.

Use cases

  • store and query billions of vertices and edges in real time
  • run gremlin or cypher queries against a property graph
  • build a knowledge graph for fraud detection or recommendations
  • implement GraphRAG question answering over connected data
  • run distributed graph algorithms like PageRank at scale
  • import massive datasets into a graph database
  • visualize and explore graph data in a web UI

When to choose

  • you need millisecond-level OLTP queries over very large graphs
  • you want TinkerPop/Gremlin compatibility with pluggable backends
  • you need both OLTP querying and OLAP graph computing in one ecosystem
  • you are building GraphRAG or LLM-powered graph applications
  • you need horizontal scaling from standalone to PB-level clusters

When to avoid

  • you need relational or document data models rather than property graphs
  • you rely on MySQL, PostgreSQL, Cassandra, or ScyllaDB backends (removed in 1.7.0)
  • your graph is small enough for simpler embedded options like SQLite-based stores
  • you require full TinkerPop feature coverage such as threaded transactions
  • you cannot run Java 11+ or operate JVM-based server infrastructure

Facets

service · maturity active

database search-engine rag machine-learning api-framework etl data-visualization databases big-data graph-processing artificial-intelligence jvm self-hosted cloud graph-database gremlin cypher tinkerpop oltp olap graphrag knowledge-graph rocksdb hbase distributed graph-computing pregel text2gremlin retrieval-augmented-generation knowledge-graphs linux docker kubernetes

10 sources

Member repositories

RepositoryRoleHealth v2
apache/hugegraphmain73

For agents

markdown · JSON · MCP: product_card(name="apache/hugegraph")

Data as of 2026-08-30T08:39:29.467469+00:00 · Report a problem