# vesoft-inc/nebula

A distributed, fast open-source graph database featuring horizontal scalability and high availability

Repository: https://github.com/vesoft-inc/nebula
Canonical: https://ross.abutalabs.com/products/vesoft-inc-nebula
Homepage: https://nebula-graph.io
Language: C++
License: Apache-2.0
License Family: permissive
Topics: graph-database, distributed, database, graphdb, raft, cpp, nebula-graph, nebula, graph, nebulagraph, big-data, distributed-systems, scalability, hacktoberfest, graphrag, knowledge-base, knowledge-graph
Last push: 2026-05-18T09:51:50+00:00

## Health v2 (maintenance only)
Score: 60/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 83, release rhythm 8, longevity 100
- inputs: {"age_days": 2927, "days_push": 107, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 12365, forks 1327 (observed 2026-08-28T04:10:52.552979+00:00)

## What it is
NebulaGraph is a distributed, open-source graph database written in C++ that handles large graph datasets with millisecond latency and horizontal scalability. It uses RAFT for strong consistency, supports an openCypher-compatible query language, and includes built-in graph analytics algorithms.

## Use cases
- store and query billions of nodes and edges in a distributed graph database
- run multi-hop fraud detection and anti-money-laundering analysis in real time
- build a knowledge graph for RAG and GenAI applications
- power recommendation engines with relationship queries
- analyze social networks and security threat graphs
- run graph analytics algorithms on large-scale connected data

## When to choose
- you need a horizontally scalable graph database for very large datasets
- you want millisecond-latency queries on highly connected data
- you need openCypher-compatible querying with strong consistency
- you are building knowledge graphs, fraud detection, or recommendation systems

## When to avoid
- your graph fits comfortably in a single node and simpler databases suffice
- you need a lightweight embedded graph store
- your team cannot operate a distributed multi-component cluster

## Facets
- artifact type: application
- maturity: active
- function: database, search-engine, rag
- domain: databases, big-data, microservices, artificial-intelligence
- platform: self-hosted, cpp
- tags: graph-database, opencypher, raft, knowledge-graph, graphrag, horizontal-scalability, linux, docker, kubernetes

## Member repositories
- vesoft-inc/nebula (main) score 60

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:10:52.552979+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-29T17:14:07.601279+00:00, confidence not recorded.
  - readme: https://github.com/vesoft-inc/nebula (fetched 2026-08-28T04:10:52.552979+00:00, sha eb0eab0eff3e)
  - homepage: https://nebula-graph.io (fetched 2026-08-29T08:11:09.658750+00:00, sha 6888e315db37)
  - site_page: https://nebula-graph.io/about-us (fetched 2026-08-29T08:11:09.670891+00:00, sha bc09b8df496e)
  - site_page: https://nebula-graph.io/pricing (fetched 2026-08-29T08:11:09.668407+00:00, sha 84e9e34136a0)
- Data as of 2026-08-30T08:39:29.467469+00:00.