graphdatascience
A Python client for the Neo4j Graph Data Science (GDS) library
Decision gist · record as of 2026-08-14
Yes, if you are already using Neo4j Graph Data Science and want to work with graphs from Python. The low install friction, active maintenance, permissive license, and lack of known vulnerabilities make it a safe choice. No if you don't have a GDS instance or are looking for a standalone graph library—this is a client, not a self-contained engine.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires a running Neo4j Graph Data Science instance (GDS 2.0+) and network access to it; cannot operate standalone.
- Low friction: pure Python wheel with no compiled dependencies.
- Active maintenance with recent releases; last commit 2026-08-13.
License · maintenance · safety
Apache-2.0 (permissive) — Apache-2.0 permissive license allows commercial and private use with minimal restrictions; suitable for most projects.
last release 2026-06-04 (71 days) · last repo commit 2026-08-13 · 244 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 74,484 downloads/mo, #14,825 on PyPI
Alternatives
Verify before relying
pip install graphdatascience
from graphdatascience import GraphDataScience
gds = GraphDataScience("neo4j+s://your-instance:7687", auth=("user", "pass"), aura_ds=True)
G = gds.graph.load_cora()
result = gds.pageRank.mutate(G, tolerance=0.5, mutateProperty="pagerank")- Whether numeric utility functions are truly unsupported or if this limitation has changed since the description was written.
- Exact compatibility matrix between graphdatascience versions and GDS server versions beyond the stated 2.0+ requirement.
What it is and what it does
graphdatascience is a Python wrapper around Neo4j's Graph Data Science library, designed to let you work with graph algorithms and machine learning pipelines without writing Cypher. It abstracts the Neo4j Python driver and provides object-oriented access to graph projections, algorithm execution, and model training. The client mimics GDS's Cypher procedure API but surfaces it as Python methods, reducing boilerplate and making graph workflows feel more native to Python developers.
You use it to load or project graphs from a Neo4j database, run algorithms like PageRank or community detection, and construct machine learning pipelines for tasks like node classification or link prediction. It depends on neo4j (the driver), pandas, numpy, and several utility libraries (pydantic, tenacity, requests) to handle data transformation, retry logic, and communication. The package is production-stable and actively maintained, with support for Python 3.10 through 3.14.
Use it for
- Build and train node classification pipelines on graph data without writing Cypher queries.
- Run graph algorithms (PageRank, centrality, community detection) and materialize results back to the database.
- Develop recommendation systems using graph embeddings and similarity algorithms like kNN on FastRP.
- Perform end-to-end machine learning on heterogeneous graphs with feature engineering and model inference.
- Load and manipulate graph projections programmatically as part of a Python data science workflow.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you are already using Neo4j Graph Data Science and want to work with graphs from Python.
The low install friction, active maintenance, permissive license, and lack of known vulnerabilities make it a safe choice. No if you don't have a GDS instance or are looking for a standalone graph library—this is a client, not a self-contained engine.
Install
graphdatascience on PyPI
Before you install
Low friction: pure Python wheel with no compiled dependencies. Active maintenance with recent releases; last commit 2026-08-13. Requires Python 3.10 or later and a running Neo4j GDS instance.
Requires a running Neo4j Graph Data Science instance (GDS 2.0+) and network access to it; cannot operate standalone.
License in practice
Apache-2.0 permissive license allows commercial and private use with minimal restrictions; suitable for most projects.
Quickstart
pip install graphdatascience
from graphdatascience import GraphDataScience
gds = GraphDataScience("neo4j+s://your-instance:7687", auth=("user", "pass"), aura_ds=True)
G = gds.graph.load_cora()
result = gds.pageRank.mutate(G, tolerance=0.5, mutateProperty="pagerank")Verify before relying
- Whether numeric utility functions are truly unsupported or if this limitation has changed since the description was written.
- Exact compatibility matrix between graphdatascience versions and GDS server versions beyond the stated 2.0+ requirement.
Package facts
| License | Apache-2.0 permissive |
| Python support | Supports the current Python release >=3.10 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 11 packagesmultimethodneo4jnumpypandaspyarrowtextdistancetqdmtyping-extensionsrequeststenacitypydantic |
| Maintenance | Actively maintained 71 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 74,484 / month, #14,825 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 5 - Production/StableIntended Audience :: DevelopersIntended Audience :: Science/ResearchOperating System :: OS IndependentProgramming Language :: Python :: 3Programming Language :: Python :: 3 :: OnlyProgramming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13Programming Language :: Python :: 3.14Topic :: DatabaseTopic :: Scientific/EngineeringTopic :: Software DevelopmentTyping :: Typed |
Evidence: graphdatascience-1.22-py3-none-any.whl
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See also graphlib · neo4j-graphrag · neo4j-driver · neo4j · py2neo · neomodel · graphiti-core · apache-airflow-providers-neo4j · graphframes-py