# rapidsai/cugraph

cuGraph - RAPIDS Graph Analytics Library

Repository: https://github.com/rapidsai/cugraph
Canonical: https://ross.abutalabs.com/products/cugraph
Homepage: https://docs.rapids.ai/api/cugraph/stable/
Language: Cuda
License: Apache-2.0
License Family: permissive
Topics: rapids, nvidia, gpu, cuda, graph, graph-algorithms, graph-analysis, graph-framework, graphml, complex-networks
Last push: 2026-08-26T20:46:37+00:00

## Health v2 (maintenance only)
Score: 94/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 99, release rhythm 84, longevity 100
- inputs: {"age_days": 2848, "days_push": 7, "days_rel": 28, "gap_med": 56, "n_releases_24m": 16}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 2225, forks 365 (observed 2026-08-28T04:06:27.994033+00:00)

## What it is
cuGraph is NVIDIA's RAPIDS collection of GPU-accelerated graph analytics libraries, offering Python, C, and C++ APIs for building graphs and running scalable graph algorithms like PageRank, centrality, community detection, and traversal. It integrates with cuDF/pandas DataFrames, NetworkX (via nx-cugraph), and PyTorch Geometric for graph neural network workflows.

## Use cases
- run pagerank on large graphs on gpu
- accelerate networkx algorithms with zero code changes
- detect communities with louvain or leiden at scale
- compute shortest paths and centrality on massive graphs
- train graph neural networks with pytorch geometric on gpu
- multi-gpu distributed graph analytics
- find connected components in billion-edge graphs

## When to choose
- you have NVIDIA GPUs and need fast graph algorithms on large graphs
- your NetworkX workflows are too slow and you want a drop-in backend
- you're doing GNN training and need GPU graph sampling and storage
- you want seamless integration with RAPIDS cuDF ETL pipelines

## When to avoid
- you have no CUDA-capable GPU hardware
- your graphs are small enough that CPU libraries like NetworkX suffice
- you need algorithms not in cuGraph's supported list
- you need a pure-CPU or portable solution

## Facets
- artifact type: library
- maturity: active
- function: machine-learning, data-science, etl, graphics
- domain: graph-processing, machine-learning, data-science, gpu-computing
- platform: python, cpp
- tags: graph-analytics, cuda, rapids, networkx-backend, graph-neural-networks, pagerank, community-detection, multi-gpu, algorithms, gpu, linux, docker

## Member repositories
- rapidsai/cugraph (main) score 94

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:06:27.994033+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 2026-08-30T02:44:56.050796+00:00, confidence not recorded.
  - readme: https://github.com/rapidsai/cugraph (fetched 2026-08-28T04:06:27.994033+00:00, sha 6f77ff934497)
  - homepage: https://docs.rapids.ai/api/cugraph/stable/ (fetched 2026-08-29T10:25:41.845502+00:00, sha 8bab271b8c77)
  - site_page: https://docs.rapids.ai/api/cugraph/stable/installation (fetched 2026-08-29T10:25:41.854492+00:00, sha 51848e7c9dec)
  - site_page: https://docs.rapids.ai/api/cugraph/stable/api_docs (fetched 2026-08-29T10:25:41.856387+00:00, sha e714f8364efb)
  - site_page: https://docs.rapids.ai/api/cugraph/stable/api_docs/cugraph (fetched 2026-08-29T10:25:41.858623+00:00, sha 4b6354dbd1b3)
  - site_page: https://docs.rapids.ai/api/cugraph/stable/api_docs/plc/pylibcugraph (fetched 2026-08-29T10:25:41.860357+00:00, sha 6a5b48f029b2)
  - site_page: https://docs.rapids.ai/api/cugraph/stable/api_docs/cugraph_c (fetched 2026-08-29T10:25:41.863058+00:00, sha dd4315c457f2)
  - site_page: https://docs.rapids.ai/api/cugraph/stable/api_docs/cugraph_cpp (fetched 2026-08-29T10:25:41.864939+00:00, sha 1ae6e44c8520)
  - site_page: https://docs.rapids.ai/api/cugraph/stable/api_docs/cugraph-pyg/cugraph_pyg (fetched 2026-08-29T10:25:41.866779+00:00, sha c8bd486dc9b0)
  - site_page: https://docs.rapids.ai/api/cugraph/stable/api_docs/wholegraph/pylibwholegraph (fetched 2026-08-29T10:25:41.868472+00:00, sha 0744b5414188)
- Data as of 2026-08-30T08:39:29.467469+00:00.