magnett-automation-core VS conductor

Why use a library dedicated to process automation in particular? After having worked for more than 20 years and dedicating so much of this time to make integrations, either with internal or external systems, such as payment gateways, and seeing several strategies from dedicated applications to make these integrations, such as BPM's, ESB's, custom systems, etc., I believe that in one way or another, when that integration was not direct "A" calls "B" but that it had a certain logic "A" calls "B" with the result of "C", or the error control involves transaction rollback, was when the problems began. The code that was in charge of the orchestration often lost that separation of responsibilities between integration (data mapping, security, etc.) and the orchestration of the same. The code lost readability and became difficult to reuse and test, among other things. The creation of this library intends to alleviate all these weaknesses. It covers all kinds of scenarios and allows to have the orchestration encapsulated and tested independently. It also allows the creation of flows as if it were a single service and facilitates the reuse of code, as we can create our own libraries with the definition of our flows. For more details regarding this library, please visit its GitHub page: [https://github.com/LHPiney/magnett-automation-core]

We celebrated a remarkable milestone in September when the Netflix Conductor GitHub repository reached 10k stars. It was a momentous achievement for our DevRel team. Just a month later, we're thrilled to announce that we've surpassed 12k stars! ⭐🎉

Also, don’t forget to give us a ⭐ on our Netflix Conductor repo.

One of my favorite workflow engines that has a really simple way to do things was not listed here, so I'll call it out - Netflix Conductor (https://github.com/Netflix/conductor).

Its capabilities comes to light when you model really complex workflows and one real value is how its all very visual not just during modeling but when running it. The history remains visible and you can even see how the whole flow evolved.

Yet another significant milestone on our journey: we've proudly reached the 10,000-star mark on our Netflix Conductor GitHub repository! 🌟

Give something like https://github.com/Netflix/conductor a try to solve this -- makes it very easy to do what you are trying to achieve.

Checkout Conductor https://github.com/Netflix/conductor which is far more scalable and easy on the resources with its own Celery like queues. Fully supports writing task workers in python: