nipype
Neuroimaging in Python: Pipelines and Interfaces
Decision gist · record as of 2026-08-14
Yes, if you work with neuroimaging data and use multiple software packages. Nipype is actively maintained, has no known vulnerabilities, and is production-stable. The low install friction and permissive license make it practical for research and clinical pipelines. Install only if you have external neuroimaging software already available on your system.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.10 or later; external neuroimaging software must be installed separately on the system to use their interfaces.
- Low friction: pure Python wheel with 18 runtime dependencies including standard scientific stack (numpy, scipy, networkx) and neuroimaging libraries (nibabel).
- Active maintenance as of July 2026 with stable production status.
License · maintenance · safety
Apache-2.0 (permissive) — Apache 2.0 permissive license allows commercial and private use with minimal restrictions; suitable for most research and commercial neuroimaging workflows.
last release 2026-03-02 (165 days) · last repo commit 2026-07-20 · 834 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 457,040 downloads/mo, #6,550 on PyPI
Alternatives
Verify before relying
pip install nipype
from nipype.interfaces.fsl import BET
from nipype.pipeline import Node, Workflow
bet_node = Node(BET(frac=0.5), name='bet')
workflow = Workflow(name='my_pipeline')
workflow.add_nodes([bet_node])- Whether all 18 runtime dependencies are required for basic use or if some are optional for specific interfaces.
- Typical memory and disk requirements for processing large neuroimaging datasets.
- Performance characteristics when running pipelines in parallel across multiple cores or machines.
What it is and what it does
Nipype is a Python framework that solves the problem of integrating heterogeneous neuroimaging software into unified, reproducible analysis pipelines. Rather than learning separate command-line interfaces for different tools, you write Python code that orchestrates these tools together, combines processing steps across packages, and runs computations in parallel. It abstracts away the differences between software packages so you can focus on your analysis logic instead of tool-specific syntax.
The package is built on a foundation of scientific Python libraries (numpy, scipy, networkx) and neuroimaging-specific tools (nibabel for image I/O, prov for provenance tracking, rdflib for semantic metadata). It's designed for researchers who need to build complex, multi-step brain imaging workflows and want those workflows to be shareable, reproducible, and efficient enough to process large datasets.
Use it for
- Build a multi-stage preprocessing pipeline combining tools from different packages in a single reproducible workflow.
- Process a cohort of brain scans in parallel across a compute cluster to extract morphometry measures.
- Share a complete neuroimaging analysis pipeline with collaborators so results can be exactly reproduced.
- Prototype and iterate on analysis designs by reusing common pipeline components across multiple studies.
- Automate quality control and provenance tracking by logging all processing steps and parameters.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you work with neuroimaging data and use multiple software packages.
Nipype is actively maintained, has no known vulnerabilities, and is production-stable. The low install friction and permissive license make it practical for research and clinical pipelines. Install only if you have external neuroimaging software already available on your system.
Install
nipype on PyPI
Before you install
Low friction: pure Python wheel with 18 runtime dependencies including standard scientific stack (numpy, scipy, networkx) and neuroimaging libraries (nibabel). Active maintenance as of July 2026 with stable production status.
Requires Python 3.10 or later; external neuroimaging software must be installed separately on the system to use their interfaces.
License in practice
Apache 2.0 permissive license allows commercial and private use with minimal restrictions; suitable for most research and commercial neuroimaging workflows.
Quickstart
pip install nipype
from nipype.interfaces.fsl import BET
from nipype.pipeline import Node, Workflow
bet_node = Node(BET(frac=0.5), name='bet')
workflow = Workflow(name='my_pipeline')
workflow.add_nodes([bet_node])Verify before relying
- Whether all 18 runtime dependencies are required for basic use or if some are optional for specific interfaces.
- Typical memory and disk requirements for processing large neuroimaging datasets.
- Performance characteristics when running pipelines in parallel across multiple cores or machines.
Package facts
| License | Apache-2.0 permissive |
| Python support | Supports the current Python release >=3.10 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 18 packagesacresclicketelemetryfilelocklooseversionlxmlnetworkxnibabelnumpypackagingprovpuremagicpydotpython-dateutilrdflibscipysimplejsontraits |
| Maintenance | Actively maintained 165 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 457,040 / month, #6,550 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/StableEnvironment :: ConsoleIntended Audience :: Science/ResearchLicense :: OSI Approved :: Apache Software LicenseOperating System :: MacOS :: MacOS XOperating System :: POSIX :: LinuxProgramming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13Topic :: Scientific/Engineering |
Evidence: nipype-1.11.0-py3-none-any.whl
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See also nibabel · mne-bids · mne · pybids · dipy · nilearn · pyxnat · pyannote-pipeline · bidsschematools · neo