weasel
Weasel: A small and easy workflow system
Decision gist · record as of 2026-08-14
Yes. Weasel is actively maintained, has low install friction, carries a permissive MIT license, and no known vulnerabilities. It is well-suited if you need structured workflow management for machine learning or NLP projects. Start with it if you want template-driven reproducibility and remote storage integration; avoid it if you need a general-purpose task orchestration tool unrelated to model training or data science workflows.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.7 or later.
- Project templates and remote storage operations may require additional system dependencies or cloud credentials depending on the specific workflow.
- Low install friction with a pure-Python wheel.
License · maintenance · safety
MIT (permissive) — MIT license (permissive) places no restrictions on use, modification, or distribution. You can integrate this freely into commercial or proprietary projects.
last release 2026-03-20 (147 days) · last repo commit 2026-03-27 · 93 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 23,354,862 downloads/mo, #948 on PyPI
Alternatives
Verify before relying
pip install weasel
python -m weasel clone pipelines/tagger_parser_ud
cd tagger_parser_ud
python -m weasel run all- Whether pre-built project templates in the explosion/projects repo are actively maintained and compatible with version 1.0.0
- Performance characteristics and scalability limits for large-scale training workflows
- Specific cloud storage providers and authentication methods supported by cloudpathlib integration
What it is and what it does
Weasel is a standalone workflow system designed to streamline machine learning and NLP project execution. It provides a command-line interface and configuration-driven approach to managing reproducible end-to-end workflows, including data preparation, model training, packaging, and deployment. The system integrates with remote storage (via cloudpathlib and smart-open) to enable team collaboration and result sharing.
The package is built on top of established dependencies like pydantic for configuration validation, typer for CLI generation, and httpx for HTTP operations. It replaces the earlier spaCy Projects system with a domain-agnostic workflow engine. Users typically start by cloning a project template, modifying it for their specific needs, and then executing the workflow via the command line. Weasel handles orchestration of custom scripts, asset management, and remote storage uploads.
Use it for
- Clone and run a pre-configured NLP training template to train a part-of-speech tagger on Universal Dependencies data
- Define a custom multi-stage workflow (data prep → training → evaluation → export) in YAML and execute it reproducibly
- Train a machine learning pipeline locally and automatically upload trained artifacts to cloud storage for team access
- Package a trained model as a Python package and share it with collaborators via remote storage
- Orchestrate complex data processing and model training scripts with dependency management and configuration overrides
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes.
Weasel is actively maintained, has low install friction, carries a permissive MIT license, and no known vulnerabilities. It is well-suited if you need structured workflow management for machine learning or NLP projects. Start with it if you want template-driven reproducibility and remote storage integration; avoid it if you need a general-purpose task orchestration tool unrelated to model training or data science workflows.
Install
weasel on PyPI
Before you install
Low install friction with a pure-Python wheel. Actively maintained as of March 2026 with recent activity. Nine runtime dependencies are all established packages (confection, pydantic, typer, httpx, cloudpathlib, smart-open, srsly, wasabi, packaging), suggesting a stable dependency chain.
Requires Python 3.7 or later. Project templates and remote storage operations may require additional system dependencies or cloud credentials depending on the specific workflow.
License in practice
MIT license (permissive) places no restrictions on use, modification, or distribution. You can integrate this freely into commercial or proprietary projects.
Quickstart
pip install weasel
python -m weasel clone pipelines/tagger_parser_ud
cd tagger_parser_ud
python -m weasel run allVerify before relying
- Whether pre-built project templates in the explosion/projects repo are actively maintained and compatible with version 1.0.0
- Performance characteristics and scalability limits for large-scale training workflows
- Specific cloud storage providers and authentication methods supported by cloudpathlib integration
Package facts
| License | MIT permissive |
| Python support | Supports the current Python release >=3.7 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 9 packagesconfectionpackagingwasabisrslytypercloudpathlibsmart-openhttpxpydantic |
| Maintenance | Actively maintained 147 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 23,354,862 / month, #948 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Environment :: ConsoleIntended Audience :: DevelopersIntended Audience :: Science/ResearchLicense :: OSI Approved :: MIT LicenseOperating System :: MacOS :: MacOS XOperating System :: Microsoft :: WindowsOperating System :: POSIX :: LinuxProgramming Language :: Python :: 3Programming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.7Programming Language :: Python :: 3.8Programming Language :: Python :: 3.9Topic :: Scientific/Engineering |
Evidence: weasel-1.0.0-py3-none-any.whl
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See also sagemaker-mlops · azureml-pipeline · outerbounds · farm-haystack · azureml-pipeline-steps · kfp-pipeline-spec · zenml · kfp · metaflow · spark-nlp