Metadata-Version: 2.4
Name: pipeline-toolkit
Version: 0.6.11
Summary: A small functional pipeline toolkit for Python.
Keywords: pipeline,functional programming,functional pipeline,functional toolkit,sequence pipeline,execute object,workflow,async,asynchronous,threading,utilities
Author: Hoàng Long
Author-email: Hoàng Long <hoanglongcodes@gmail.com>
License-Expression: MIT
License-File: LICENSE
Classifier: Development Status :: 5 - Production/Stable
Classifier: Intended Audience :: Developers
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3 :: Only
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Programming Language :: Python :: 3.13
Classifier: Programming Language :: Python :: 3.14
Classifier: Programming Language :: Python :: 3.15
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Classifier: Topic :: Utilities
Requires-Python: >=3.8
Project-URL: Homepage, https://pipeline-toolkit.pages.dev
Project-URL: Repository, https://github.com/Hoang-Long2012/pipeline-toolkit
Project-URL: Documentation, https://pipeline-toolkit.pages.dev
Project-URL: Issues, https://github.com/Hoang-Long2012/pipeline-toolkit/issues
Project-URL: Changelog, https://pipeline-toolkit.pages.dev/changelog
Project-URL: Contributing, https://pipeline-toolkit.pages.dev/contributing
Description-Content-Type: text/markdown

# Pipeline Toolkit

A small functional pipeline toolkit for Python.

`pipeline-toolkit` provides a simple way to build sequential pipelines from ordinary Python callables.

It also provides small utilities for composing functions, configuring callable steps, applying side effects, and managing pipeline state.

A pipeline is created from an iterable of steps. Each step is a tuple containing a callable and optional arguments.

```python
from pipeline import Pipeline

def add(value, amount):
	return value + amount

def multiply(value, factor):
	return value * factor

pipeline = Pipeline([
	(add, (5,)),
	(multiply, (2,)),
])

pipeline.run(10).wait()

print(pipeline.result)  # 30
```

The value flows through each step sequentially:

```text
10
 ↓
add(10, 5)
 ↓
15
 ↓
multiply(15, 2)
 ↓
30
```

See the [API Reference](https://pipeline-toolkit.pages.dev) for detailed documentation.

Copyright (C) 2026 Hoàng Long
