First Working Version of Flowrra

🚀 I just pushed the first working version of Flowrra!

After months of experimenting, building, and learning along the way, I finally have a small but fully functional background task execution framework that is async-first, Python-native, and designed with simplicity and clarity in mind.

Why Flowrra?

I’ve been using Celery in production for years. It’s powerful, but it can also feel a bit opaque; tasks run in processes or threads, async support isn’t native, and integrating it tightly with custom frameworks can get tricky.

On top of that, using Celery usually means you have to:

• Run a separate worker service just to execute tasks.


• Start a separate Flower service if you want to monitor them.

Flowrra is different:

• No separate worker service needed: tasks are executed directly by the scheduler/worker integration, reducing setup complexity.


• Async-native: built around Python’s asyncio for truly non-blocking, I/O-heavy tasks.


• Pluggable result backends: Redis now, but swapping or adding new backends is straightforward.


• Built-in UI / framework integration: monitor tasks, view results, and integrate directly with your framework without starting a separate service.


• Transparent lifecycle: every task’s state is explicit — perfect for debugging and learning.

For developers exploring async workloads or wanting a simple, understandable task system, Flowrra could be a more natural fit than Celery.

The learning journey

Building Flowrra wasn’t just about writing code; it was a deep dive into distributed systems and Python concurrency: Understanding how tasks are scheduled, executed, and their results stored. Learning the subtle complexities of retries, state management, and task lifecycles. Seeing how even simple abstractions leak complexity when scaled. Every line of code taught me something I could never fully grasp just by reading docs or tutorials.

What’s next

Flowrra is still early-stage. Next steps include:

• Expanding broker options


• Expanding backend options


• Enhancing the built-in UI for better monitoring


• Testing distributed task execution scalability


• Testing multiple scheduler instances' scalability

It’s exciting to see something that started as a curiosity turn into a working system.

Contribution

Flowrra is fully open-source. If you’re curious about async Python, distributed systems, or just want to experiment with a task runner, you can contribute or test it today.

Links to get started

• GitHub repo: https://github.com/flowrra/flowrra
  


• PyPI package: https://pypi.org/project/flowrra
  


• Documentation (ReadTheDocs): https://flowrra.readthedocs.io/en/latest/index.html