koheesio
The steps-based Koheesio framework
Decision gist · record as of 2026-08-14
Yes. Koheesio is actively maintained, has low install friction, carries no known vulnerabilities, and is licensed permissively. It fills a clear niche for developers building modular, testable data tasks—particularly those who plan to integrate with orchestration tools. The framework's reliance on well-established dependencies (Pydantic, cryptography, PyYAML) and its production-stable classifier make it a sound choice for data engineering work.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.9 or later (supports 3.9, 3.10, 3.11, 3.12; capped below 3.13).
- Low install friction with a pure-Python wheel.
- Actively maintained with a recent release (18 days ago) and active CI/CD.
License · maintenance · safety
Apache-2.0 (permissive) — Licensed under Apache 2.0 (permissive), allowing commercial use, modification, and distribution with minimal restrictions—suitable for most organizational and open-source contexts.
last release 2026-07-27 (18 days) · last repo commit 2026-08-01 · 817 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 665,001 downloads/mo, #5,430 on PyPI
Alternatives
Verify before relying
pip install koheesio
from koheesio import Step
from pydantic import Field
class MyStep(Step):
input_data: str = Field(..., description="Input data")
def execute(self):
self.output = self.input_data.upper()
step = MyStep(input_data="hello")
step.run()- Whether the framework integrates with specific orchestration tools (Airflow, Luigi, Databricks) beyond the stated design goal.
- Performance characteristics and scalability limits for large-scale data processing tasks.
- Availability and maturity of built-in readers, writers, and transformations mentioned in the description.
What it is and what it does
Koheesio is a Python framework for constructing data pipelines from reusable, step-based components. It emphasizes modularity, testability, and strong typing through Pydantic, enabling developers to build robust data tasks that can be composed into larger workflows. The framework is designed to work with multiple data processing libraries and frameworks, making it adaptable to various data scales and technologies.
Unlike workflow orchestration tools (Airflow, Luigi, Databricks), Koheesio focuses on making individual data tasks resilient, observable, and maintainable. It provides built-in logging, flexible context customization, and a foundation for data validation, transformation, and ETL work. The framework is positioned as a complement to orchestration tools rather than a replacement, allowing teams to write well-engineered data tasks that can be orchestrated separately.
Use it for
- Build reusable ETL components that can be tested independently and composed into larger data workflows.
- Write data transformation and validation logic with strong typing and structured configuration using Pydantic.
- Create modular data tasks for use within orchestration frameworks like Apache Airflow or Databricks Workflows.
- Develop data quality checks and data processing steps with built-in logging and error handling.
- Implement step-based data pipelines where each task is a manageable, testable unit of work.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes.
Koheesio is actively maintained, has low install friction, carries no known vulnerabilities, and is licensed permissively. It fills a clear niche for developers building modular, testable data tasks—particularly those who plan to integrate with orchestration tools. The framework's reliance on well-established dependencies (Pydantic, cryptography, PyYAML) and its production-stable classifier make it a sound choice for data engineering work.
Install
koheesio on PyPI
Before you install
Low install friction with a pure-Python wheel. Actively maintained with a recent release (18 days ago) and active CI/CD. Seven runtime dependencies are all well-established libraries (pydantic, cryptography, pyyaml, pytz, jsonpickle, python-decouple, tomli).
Requires Python 3.9 or later (supports 3.9, 3.10, 3.11, 3.12; capped below 3.13).
License in practice
Licensed under Apache 2.0 (permissive), allowing commercial use, modification, and distribution with minimal restrictions—suitable for most organizational and open-source contexts.
Quickstart
pip install koheesio
from koheesio import Step
from pydantic import Field
class MyStep(Step):
input_data: str = Field(..., description="Input data")
def execute(self):
self.output = self.input_data.upper()
step = MyStep(input_data="hello")
step.run()Verify before relying
- Whether the framework integrates with specific orchestration tools (Airflow, Luigi, Databricks) beyond the stated design goal.
- Performance characteristics and scalability limits for large-scale data processing tasks.
- Availability and maturity of built-in readers, writers, and transformations mentioned in the description.
Package facts
| License | Apache-2.0 permissive |
| Python support | Capped below the current Python release <3.13,>=3.9 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 7 packagescryptographyjsonpicklepydanticpython-decouplepytzpyyamltomli |
| Maintenance | Actively maintained 18 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 665,001 / month, #5,430 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 :: DevelopersLicense :: OSI Approved :: Apache Software LicenseNatural Language :: EnglishOperating System :: OS IndependentProgramming Language :: PythonProgramming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: Implementation :: CPythonProgramming Language :: Python :: Implementation :: PyPyTopic :: Software Development :: Libraries :: Python Modules |
Evidence: koheesio-0.11.0-py3-none-any.whl
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