Python Workflow engine

Open-source Python projects categorized as Workflow engine

Top 14 Python Workflow engine Projects

  • Airflow

    Apache Airflow - A platform to programmatically author, schedule, and monitor workflows

  • Project mention: AI Strategy Guide: How to Scale AI Across Your Business | | 2024-05-11

    Level 1 of MLOps is when you've put each lifecycle stage and their intefaces in an automated pipeline. The pipeline could be a python or bash script, or it could be a directed acyclic graph run by some orchestration framework like Airflow, dagster or one of the cloud-provider offerings. AI- or data-specific platforms like MLflow, ClearML and dvc also feature pipeline capabilities.

  • luigi

    Luigi is a Python module that helps you build complex pipelines of batch jobs. It handles dependency resolution, workflow management, visualization etc. It also comes with Hadoop support built in.

  • Project mention: Ask HN: What is the correct way to deal with pipelines? | | 2023-09-21

    I agree there are many options in this space. Two others to consider:



    There are also many Kubernetes based options out there. For the specific use case you specified, you might even consider a plain old Makefile and incrond if you expect these all to run on a single host and be triggered by a new file showing up in a directory…

  • InfluxDB

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  • Prefect

    The easiest way to build, run, and monitor data pipelines at scale.

  • Project mention: Prefect: A workflow orchestration tool for data pipelines | | 2024-03-13
  • Kedro

    Kedro is a toolbox for production-ready data science. It uses software engineering best practices to help you create data engineering and data science pipelines that are reproducible, maintainable, and modular.

  • Project mention: Nextflow: Data-Driven Computational Pipelines | | 2023-08-10

    Interesting, thanks for sharing. I'll definitely take a look, although at this point I am so comfortable with Snakemake, it is a bit hard to imagine what would convince me to move to another tool. But I like the idea of composable pipelines: I am building a tool (too early to share) that would allow to lay Snakemake pipelines on top of each other using semi-automatic data annotations similar to how it is done in kedro (

  • viewflow

    Reusable workflow library for Django

  • Project mention: Ask HN: Anyone use a code to mindmap/flowchart tool? | | 2024-02-24

    viewflow supports BPMN:

  • galaxy

    Data intensive science for everyone.

  • Project mention: Need for GUIs for bioinformatic tools? | /r/bioinformatics | 2023-06-17

    Maybe it would help you to look at the galaxy project: GitHub main site

  • couler

    Unified Interface for Constructing and Managing Workflows on different workflow engines, such as Argo Workflows, Tekton Pipelines, and Apache Airflow.

  • Project mention: (Not) to Write a Pipeline | | 2023-06-27

    author seems to be describing the kind of patterns you might make with . or see for example , which is an sdk for describing tasks that may be submitted to different workflow engines on the backend.

    it's a little confusing to me that the author seems to object to "pipelines" and then equate them with messaging-queues. for me at least, "pipeline" vs "workflow-engine" vs "scheduler" are all basically synonyms in this context. those things may or may not be implemented with a message-queue for persistence, but the persistence layer itself is usually below the level of abstraction that $current_problem is really concerned with. like the author says, eventually you have to track state/timestamps/logs, but you get that from the beginning if you start with a workflow engine.

    i agree with author that message-queues should not be a knee-jerk response to most problems because the LoE for edge-cases/observability/monitoring is huge. (maybe reach for a queue only if you may actually overwhelm whatever the "scheduler" can handle.) but don't build the scheduler from scratch either.. use argowf, kubeflow, or a more opinionated framework like airflow, mlflow, databricks, aws lamda or step-functions. all/any of these should have config or api that's robust enough to express rate-limit/retry stuff. almost any of these choices has better observability out-of-the-box than you can easily get from a queue. but most importantly.. they provide idioms for handling failure that data-science folks and junior devs can work with. the right way to structure code is just much more clear and things like structuring messages/events, subclassing workers, repeating/retrying tasks, is just harder to mess up.

  • SaaSHub

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  • NIPY

    Workflows and interfaces for neuroimaging packages

  • redun

    Yet another redundant workflow engine

  • Project mention: Redun: Yet another redundant workflow engine | | 2023-08-11
  • jug

    Parallel programming with Python

  • flowsaber

    Dataflow based workflow framework


    Robotic Process Automation in Windows and Linux by using BPMN diagrams.

  • typhoon-orchestrator

    Create elegant data pipelines and deploy to AWS Lambda or Airflow

  • pyDag

    Scheduling Big Data Workloads and Data Pipelines in the Cloud with pyDag

  • SaaSHub

    SaaSHub - Software Alternatives and Reviews. SaaSHub helps you find the best software and product alternatives

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NOTE: The open source projects on this list are ordered by number of github stars. The number of mentions indicates repo mentiontions in the last 12 Months or since we started tracking (Dec 2020).

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What are some of the best open-source Workflow engine projects in Python? This list will help you:

Project Stars
1 Airflow 34,705
2 luigi 17,372
3 Prefect 14,829
4 Kedro 9,398
5 viewflow 2,573
6 galaxy 1,319
7 couler 891
8 NIPY 734
9 redun 489
10 jug 405
11 flowsaber 40
12 BPMN_RPA 37
13 typhoon-orchestrator 29
14 pyDag 24

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SaaSHub helps you find the best software and product alternatives