The initial version of groundwork was created inside an environment of cross-company development teams, with very different skills on Python and its ecosystem.

Main challenges were the needed understanding of already existing code, missing responsibilities for artefacts besides the code (like tests and documentation) and a tight project plan, which never contained time slots needed to teach and update team members about important changes on the code.

Besides these challenges on the project level, there were also a lot of challenges on code level when it came to understanding architecture, databases, algorithms, interactions and more of a running application with dynamic and extensible behavior.


groundwork was created to take most of these challenges and to provide easy, understandable and plugin-able solutions.

The groundwork team has defined goals, which shall be applicable for all groundwork based applications:

A plugin bundles everything

Besides the code itself, a plugin also provide tests, documentation and meta data for its functionality.

Like other application/plugin frameworks, groundwork is responsible to “glue” all plugin code together to a single application.

But it also cares about test cases of a plugin and makes tests of all used plugins available inside a single test suite. Furthermore, it also collects all plugin documentation and creates a single, overall documentation for developers and users. The meta data of a plugin is collected as well and made available inside the documentation and - if desired - also in the application.


To lower the learning curve, commonly used libraries and their core functions can directly be injected into groundwork plugins by using groundwork patterns.

For instance: Instead of initialising and configuring Blinker for signals and Click for command line interfaces by yourself, groundworks provides self.signal.send("Yehaa") and self.commands.register(...) directly inside plugin classes.

By defining own patterns, it is very easy to provide team members additional injected functions. E.g. self.web.route() for registering a web route or self.db.sql() to execute a SQL statement.

However, the library and its objects can still be made available and directly accessible to support uncommon or not yet supported use cases.

Realtime documentation

Nowadays it’s really hard to get the big picture of an existing application. Normally only some kind of documentation and the code itself are available as information source. However, the former is rarely well maintained and the later gives you a structured, but too deep view which takes hours or even days to understand.

groundwork tries to retrieve and provide a lot of information from the executed code directly during runtime. For instance, it is able to show registered and used signals or to create a list of available commands.

These information depends on the activated plugins, which may change during runtime and affect the documentation as well.

Technical background

groundwork was created to glue code-snippets from various developers together and make their nested functions easily available.

In Python this is commonly achieved by importing modules, initialising a class of them and storing the class instance in a local or global variable. However, these mechanism aren’t really dynamic and the relationship between different classes and objects is hard-coded, without any chance to change it during runtime.

groundwork uses plugins, based on cooperative-multi-inheritance to load and manage needed attributes and functions from reusable patterns. It also enforces the usage of the groundwork GwBasePattern for all plugins to make common attributes and functions available.

For more information about cooperative-multi-inheritance see: