retworkx
A High-Performance Graph Library for Python
Decision gist · record as of 2026-08-14
No—retworkx is deprecated and retired. Install rustworkx instead, which is the active successor and is described as a drop-in replacement. The fact sheet itself recommends rustworkx over retworkx. If you are already using retworkx, migration to rustworkx is the appropriate path forward.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python >= 3.9.
- Building from source requires Rust >= 1.79 and cargo.
- Precompiled binaries available for x86_64, i686, ppc64le, s390x, aarch64 Linux; x86_64 macOS; and 32/64-bit Windows.
License · maintenance · safety
Apache-2.0 (permissive) — Licensed under Apache-2.0 (permissive), allowing commercial and private use with minimal restrictions.
last release 2025-09-15 (333 days) · last repo commit 2026-08-07 · 1,738 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 98,881 downloads/mo, #13,053 on PyPI
Alternatives
Verify before relying
pip install retworkx
import rustworkx
graph = rustworkx.PyGraph()
a = graph.add_node("A")
b = graph.add_node("B")
graph.add_edges_from([(a, b, 1.5)])- Whether retworkx will continue to receive security updates or if users must migrate to rustworkx immediately
- Exact compatibility guarantees between retworkx 0.17.1 and rustworkx as a drop-in replacement
What it is and what it does
retworkx is the deprecated package name for rustworkx, a high-performance graph library for Python implemented in Rust. It provides graph data structures (directed and undirected) and a suite of graph algorithms including shortest-path calculations, traversals, and other network operations. The package is designed for performance and is suitable for applications ranging from quantum computing transpilers to general network analysis.
The package has been retired in favor of rustworkx, which is described as a drop-in replacement. Users installing retworkx will actually get rustworkx as its runtime dependency. The library supports Python 3.9 through 3.13 and offers optional visualization modules via matplotlib or graphviz when those dependencies are installed separately.
Use it for
- Computing shortest paths and other algorithms on directed or undirected graphs
- Building and manipulating network structures for quantum computing transpilers or DAG-based workflows
- Analyzing network topology and connectivity in scientific or research applications
- Visualizing graphs using matplotlib or graphviz integration when optional dependencies are present
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
No—retworkx is deprecated and retired.
Install rustworkx instead, which is the active successor and is described as a drop-in replacement. The fact sheet itself recommends rustworkx over retworkx. If you are already using retworkx, migration to rustworkx is the appropriate path forward.
Install
retworkx on PyPI
Before you install
Low install friction with precompiled wheels available for common platforms. Active maintenance with last commit on 2026-08-07. However, this is the deprecated package name; rustworkx is the actively maintained successor and recommended replacement.
Requires Python >= 3.9. Building from source requires Rust >= 1.79 and cargo. Precompiled binaries available for x86_64, i686, ppc64le, s390x, aarch64 Linux; x86_64 macOS; and 32/64-bit Windows.
License in practice
Licensed under Apache-2.0 (permissive), allowing commercial and private use with minimal restrictions.
Quickstart
pip install retworkx
import rustworkx
graph = rustworkx.PyGraph()
a = graph.add_node("A")
b = graph.add_node("B")
graph.add_edges_from([(a, b, 1.5)])Verify before relying
- Whether retworkx will continue to receive security updates or if users must migrate to rustworkx immediately
- Exact compatibility guarantees between retworkx 0.17.1 and rustworkx as a drop-in replacement
Package facts
| License | Apache-2.0 permissive |
| Python support | Supports the current Python release >=3.9 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 1 packagerustworkx |
| Maintenance | Actively maintained 333 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 98,881 / month, #13,053 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Intended Audience :: DevelopersIntended Audience :: Science/ResearchOperating System :: MacOS :: MacOS XOperating System :: Microsoft :: WindowsOperating System :: POSIX :: LinuxProgramming Language :: Python :: 3 :: OnlyProgramming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13Programming Language :: Python :: 3.9Programming Language :: Rust |
Evidence: retworkx-0.17.1-py3-none-any.whl
Tags
Let your AI agent find packages like this
Example. Real query, live index.
You found this page by searching. An agent finds it by wishing: SkillFed indexes 14,416 PyPI packages by what they can do, searchable in plain language.
wish › “graph library python”
- retworkxA deprecated package name for rustworkx, a high-performance graph…
- objectgraphObjectgraph provides a Python class for representing directed graphs…
- rustworkxA high-performance graph library for Python, written in Rust,…
Give your agent the search over MCP, or paste the wish link into any chat.
More Scientific/Engineering packages
NumPy provides an N-dimensional array object and a comprehensive suite of mathematical, linear algebra, Fourier transform, and random number functions for scientific computing in Python.
pandas provides fast, flexible data structures (Series and DataFrame) for loading, cleaning, transforming, and analyzing labeled or relational data in Python.
scipy provides numerical algorithms for mathematics, science, and engineering—including optimization, integration, linear algebra, Fourier transforms, signal and image processing, and ODE solvers—built on numpy arrays.
scikit-learn provides a comprehensive Python library for supervised and unsupervised machine learning, including classification, regression, clustering, dimensionality reduction, and model evaluation tools built on NumPy and SciPy.
Install it if you need to train, evaluate, or deploy supervised or unsupervised learning models.
dill extends Python's pickle module to serialize and deserialize a much wider range of Python objects, including functions, lambdas, classes, and interpreter sessions, to byte streams for storage or network transmission.
Multiprocess is an enhanced fork of Python's standard multiprocessing library that uses dill for better serialization, allowing you to spawn processes with a threading-like API and share complex objects between them.
Install it if you use multiprocessing and encounter pickle serialization limits with lambdas or complex objects.
See also rustworkx · chalkpy-rs · networkx · setuptools-rust · kailash-enterprise · altgraph · graspologic-native · tensor-grep · moviepilot-rust · baseten-performance-client