# alibaba/GraphScope

🔨 🍇 💻 🚀 GraphScope: A One-Stop Large-Scale Graph Computing System from Alibaba | 一站式图计算系统

Repository: https://github.com/alibaba/GraphScope
Canonical: https://ross.abutalabs.com/products/graphscope
Homepage: https://graphscope.io
Language: C++
License: Apache-2.0
License Family: permissive
Topics: graph, graph-computation, graph-neural-networks, gremlin, graph-analytics, graph-data, graph-computing, analytics, big-data, data-science
Last push: 2026-08-04T06:47:25+00:00

## Health v2 (maintenance only)
Score: 73/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 96, release rhythm 28, longevity 100
- inputs: {"age_days": 2135, "days_push": 29, "days_rel": 583, "gap_med": 59.5, "n_releases_24m": 3}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 3556, forks 470 (observed 2026-08-28T04:08:09.680450+00:00)

## What it is
GraphScope is a unified distributed graph computing platform from Alibaba that combines graph analytics (GRAPE), interactive graph queries (MaxGraph/Gremlin/Cypher), and graph neural network training (Graph-Learn) in one system, backed by the Vineyard in-memory store. It exposes a Python interface and deploys on Kubernetes clusters for processing very large graphs.

## Use cases
- run distributed graph analytics algorithms on large graphs
- query large graphs interactively with Gremlin or Cypher
- train graph neural networks on big graphs
- process graph data across a Kubernetes cluster from Python
- explore graphs in a Jupyter notebook
- benchmark interactive graph queries on LDBC SNB
- embed a lightweight graph database in a data lake pipeline

## When to choose
- you need analytics, interactive queries, and GNN training over large graphs in one platform
- your graphs are too large for a single machine and you have a Kubernetes cluster
- you want a Python-first API for distributed graph computing
- you need high-performance interactive graph query throughput

## When to avoid
- you only need a small single-node graph database for an application backend
- your team has no Kubernetes or distributed infrastructure experience
- you need a simple embedded graph store without distributed computing
- your workload is primarily relational or tabular rather than graph-shaped

## Facets
- artifact type: framework
- maturity: active
- function: database, analytics, machine-learning, data-science, streaming
- domain: big-data, data-science, graph-processing, microservices, machine-learning, databases, analytics
- platform: python, cloud, self-hosted
- tags: graph-analytics, graph-neural-networks, gremlin, cypher, distributed-graph-computing, vineyard, graph-database, networkx, kubernetes, docker, linux, macos

## Member repositories
- alibaba/GraphScope (main) score 73

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:08:09.680450+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 2026-08-29T18:34:16.121327+00:00, confidence not recorded.
  - readme: https://github.com/alibaba/GraphScope (fetched 2026-08-28T04:08:09.680450+00:00, sha 9fbb1b5c197e)
  - homepage: https://graphscope.io (fetched 2026-08-29T09:28:05.007034+00:00, sha b1d975c539b5)
  - site_page: https://graphscope.io/docs (fetched 2026-08-29T09:28:05.016715+00:00, sha ee7a35823ccb)
  - site_page: https://graphscope.io/blog/tech/2026/06/01/NeuG-v0.1.2-Data-Pipeline.html (fetched 2026-08-29T09:28:05.019217+00:00, sha bfaa39f09d0c)
- Data as of 2026-08-30T08:39:29.467469+00:00.