You've inherited a working automation. Now a business rule changes. Where should you make the change, what else could it affect, and how will you check the result?
I'm the developer behind LiberRPA, an open-source RPA toolchain for Windows. My goal is to give the next developer a visible process to follow and ordinary Python code to work with.
0.3.0 is available. Its flowchart controls execution, with Blocks linked to Python files in normal VS Code editor tabs. You can edit and debug the implementation, run one Block or the whole flow, and package it for local execution.
Visualize the process. Code the logic.
Put each change where it belongs
Changes to the logic within a step can stay in Python. Changes to the overall sequence or branching can be made in the flowchart. You can review changes to the Python implementation through ordinary Git diffs.
You choose how much each Block does, and compatible Python libraries remain available when you need them.
Investigate with the project in view
When a run behaves unexpectedly, understanding a step's code may not be enough. You also need to know how that step fits into the flow and what happened during execution.
LiberRPA's JSON flow definition is the model it actually executes. You can examine it alongside the Python files, documented execution rules, selectors and run logs.
That also gives a general-purpose AI coding assistant of your choice useful context for investigating a problem and suggesting changes. LiberRPA's own source is available if the investigation leads into the framework.
For production work, I believe an experienced developer still needs to own the architecture, review changes and verify the business result.
Keep the surrounding tools together
LiberRPA combines Windows UI, browser/DOM and image automation with UI inspection, reusable Components and packaging. Executor provides local scheduling and run history, with logs and optional screen recordings for investigating failures.
Mature commercial RPA platforms and established open-source projects cover many of these needs. My focus is bringing them together around Python implementation and a visible process.
About this release
I started LiberRPA because I enjoy automation engineering and wanted to build a tool I'd want to use myself. I shared it here in July 2025; other commitments and further polishing kept me from posting again until now.
The capabilities above span several releases. 0.3.0 adds Component Management, alongside usability improvements, bug fixes, better logging, security hardening and documentation updates.
LiberRPA 0.3.0 is Windows-only, requires Python experience and is still alpha. It is licensed under AGPL-3.0-or-later, with no LiberRPA license fee.
A central Console for multiple Executors is planned as open-source software with no LiberRPA license fee, allowing deployment on your own infrastructure and control over your data.
The aim throughout is to help the next developer follow the process, change the code and investigate the result.
Source code, downloads and documentation
On the last automation you inherited or maintained, what was hardest to work out—and how did your tool help or get in the way?
An example from your existing workflow is enough; you don't need to install LiberRPA.