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Laboro is a NO-Code / Low-Code workflow automation tool that helps you to build and run automated tasks with the least amount of effort and technical knowledge:
Busy developers and system administrators have many task automation needs, but often don't have enough time to write and maintain their scripts and, even worse, when there are scripts, they rarely are consistent with each other, which makes their maintenance tedious.
Laboro provides a unique way to quickly write and execute standardized, easy to maintain automated workflows without any need to install a specific environment or dependencies of any kind.
It also:
Advanced users shall find Laboro as a mix between:
Anyway, the main purpose of Laboro is to provide a tool to quickly write highly standardized and easy to maintain automation tasks with the least amount of effort and technical knowledge.
Laboro is the latin expression for "I am working".
Your time is valuable and tedious tasks are for robots. Laboro is always pleased to perform all your repetitive tasks for you while allowing you to stay focused on the part of your job where you bring real added value.
Laboro is currently well tested and "without known bug" but is still in beta stage and will not be considered suitable for production until Laboro-1.0.0 release.
This documentation is a work in progress and changes may occur until the release of the Laboro-1.0.0 version.
Laboro leverages modern container environments such as Podman or Docker.
Follow the quick installation guide for all needed details.
Since Laboro is run as a container, it does not require any configuration tweaks.
However, advanced users may have a look on how to configure Laboro to suit specific needs.
Laboro lays on the following fundamental concepts:
Laboro capabilities are extended through modules delivered as Python packages.
All information about available modules and how to write you own module are available here.
Configuring a Laboro workflow is basically writing a YAML file.
You may want to first have a look at the underlying concepts or if you feel brave enough go straight to step by step guide and configure you first workflow
Once all your workflows are configured and saved in the /path/to/local/workflowdir
, you can run them by issuing the following command:
Example:
podman run --name laboro \
-e TZ=Pacific/Noumea \
-v /path/to/local/workflowdir:/opt/laboro/workflows:Z \
-v /path/to/local/workspacesdir:/opt/laboro/workspaces:Z \
-v /path/to/local/logs:/opt/laboro/log:Z \
mcosystem/laboro:latest \
-w workflow_demo_1.yml workflow_demo_2.yml workflow_demo_3.yml
In this example the 3 workflows workflow_demo_1.yml
, workflow_demo_2.yml
, workflow_demo_3.yml
will be run sequentially in the specified order within a container which time zone will be set to Pacific/Noumea
.
The container also has three host directories mounted:
/path/to/local/workflowdir
mounted on /opt/laboro/workflows
/path/to/local/workspacesdir
mounted on /opt/laboro/workspaces
/path/to/local/logs
mounted on /opt/laboro/log
You may find more specific examples of how to run you workflow
This repository host all the necessary bits to install, configure and run Laboro for demonstration and testing purpose: https://codeberg.org/laboro/laboro_poc
You will find all needed instructions to install a simulated basic infrastructure based on 5 containers running the following services:
The instructions will guide you through all steps to run a workflow using mostly all Laboro features such as loops, variables and iterables, conditional execution, split workflows and encrypted data, data files etc.
A lot of fun to be discovered for whom would like to understand Laboro and wants its daily cumbersome tasks automated once for all 🙂
Example of a Laboro container running a real life workflow named workflow_demo.yml
:
FAQs
The Laboro workflow framework
We found that laboro demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
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