snakemake
Workflow management system to create reproducible and scalable data analyses
Decision gist · record as of 2026-08-14
Yes. Snakemake is actively maintained, production-stable, has no known vulnerabilities, and is widely adopted in scientific computing. Install friction is low and the MIT license is permissive. It is worth installing if you need to build reproducible, scalable data pipelines—particularly in scientific research—or if you want to manage complex multi-step workflows with automatic parallelization and cloud portability.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.11 or later.
- Workflows typically need a Snakefile in the working directory to execute.
- Low install friction with a pure-wheel distribution.
License · maintenance · safety
MIT (permissive) — MIT license is permissive; you can use, modify, and distribute Snakemake with minimal restrictions in both open and proprietary projects.
last release 2026-08-04 (10 days) · last repo commit 2026-08-14 · 2,847 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 262,218 downloads/mo, #8,378 on PyPI
Alternatives
Verify before relying
pip install snakemake
import snakemake
snakemake.snakemake('Snakefile')- Whether the 31 runtime dependencies are all required for basic use or only for specific features.
- Concrete performance characteristics or scalability limits when running on large clusters or cloud platforms.
- Whether all interface plugins (executor, storage, report, logger, scheduler) are mandatory or optional for typical workflows.
- Specific parallelization and job scheduling capabilities across different execution backends.
What it is and what it does
Snakemake is a Python-based workflow management system designed to make data analysis pipelines reproducible and scalable. You write workflows in a human-readable Python dialect, defining rules that specify inputs, outputs, and commands. Snakemake handles dependency resolution, parallelization, and job scheduling automatically. It can execute workflows locally, on compute clusters, or in cloud environments without requiring changes to the workflow definition itself.
The system integrates with conda for automatic software deployment, supports multiple execution backends through its plugin interface, and includes built-in support for common data formats and storage systems. With 31 runtime dependencies including jinja2, pyyaml, jsonschema, and specialized snakemake interface plugins, it provides a comprehensive framework for orchestrating complex scientific and data-engineering pipelines. It has been in active development since 2012 and is widely used in scientific research.
Use it for
- Automate multi-step bioinformatics analyses that need to run on HPC clusters with automatic parallelization.
- Define reproducible data processing pipelines that scale from laptop to cloud without code changes.
- Manage complex workflows with conditional execution, dynamic rule generation, and automatic dependency tracking.
- Coordinate software environments using conda, ensuring tools and versions deploy consistently across platforms.
- Build data analysis pipelines that can be version-controlled, shared, and re-run with identical results.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes.
Snakemake is actively maintained, production-stable, has no known vulnerabilities, and is widely adopted in scientific computing. Install friction is low and the MIT license is permissive. It is worth installing if you need to build reproducible, scalable data pipelines—particularly in scientific research—or if you want to manage complex multi-step workflows with automatic parallelization and cloud portability.
Install
snakemake on PyPI
Before you install
Low install friction with a pure-wheel distribution. Actively maintained with a release 10 days ago and 2847 repository stars. Supports current Python versions and has been in production use since 2012.
Requires Python 3.11 or later. Workflows typically need a Snakefile in the working directory to execute.
License in practice
MIT license is permissive; you can use, modify, and distribute Snakemake with minimal restrictions in both open and proprietary projects.
Quickstart
pip install snakemake
import snakemake
snakemake.snakemake('Snakefile')Verify before relying
- Whether the 31 runtime dependencies are all required for basic use or only for specific features.
- Concrete performance characteristics or scalability limits when running on large clusters or cloud platforms.
- Whether all interface plugins (executor, storage, report, logger, scheduler) are mandatory or optional for typical workflows.
- Specific parallelization and job scheduling capabilities across different execution backends.
Package facts
| License | MIT permissive |
| Python support | Supports the current Python release >=3.11 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 31 packagesplatformdirsimmutablesconfigargparseconnection_pooldocutilsgitpythonhumanfriendlyjinja2jsonschemanbformatpackagingpsutilpulppyyamlreferencingrequeststenacitysmart-opensnakemake-interface-executor-pluginssnakemake-interface-commonsnakemake-interface-storage-pluginssnakemake-interface-report-pluginssnakemake-interface-logger-pluginssnakemake-interface-scheduler-pluginstabulatethrottlerwraptytedpathconda-inject |
| Maintenance | Actively maintained 10 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 262,218 / month, #8,378 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/ResearchNatural Language :: EnglishProgramming Language :: Python :: 3 :: OnlyProgramming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13Programming Language :: Python :: 3.14Topic :: Scientific/Engineering |
Evidence: snakemake-9.25.1-py3-none-any.whl
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See also snakemake-storage-plugin-gcs · snakemake-interface-executor-plugins · snakemake-storage-plugin-s3 · snakemake-interface-scheduler-plugins · snakemake-interface-common · repo2rocrate · argo-workflows · snakemake-interface-logger-plugins · snakemake-interface-report-plugins