orchest VS windmill

You can check out our OSS https://github.com/orchest/orchest

The code example is part of our ELT/data pipeline tool called Orchest: https://github.com/orchest/orchest/

First want to say congrats to the Patterns team for creating a gorgeous looking tool. Very minimal and approachable. Massive kudos!

Disclaimer: we're building something very similar and I'm curious about a couple of things.

One of the questions our users have asked us often is how to minimize the dependence on "product specific" components/nodes/steps. For example, if you write CI for GitHub Actions you may use a bunch of GitHub Action references.

Looking at the `graph.yml` in some of the examples you shared you use a similar approach (e.g. patterns/openai-completion@v4). That means that whenever you depend on such components your automation/data pipeline becomes more tied to the specific tool (GitHub Actions/Patterns), effectively locking in users.

How are you helping users feel comfortable with that problem (I don't want to invest in something that's not portable)? It's something we've struggled with ourselves as we're expanding the "out of the box" capabilities you get.

Furthermore, would have loved to see this as an open source project. But I guess the second best thing to open source is some open source contributions and `dcp` and `common-model` look quite interesting!

For those who are curious, I'm one of the authors of https://github.com/orchest/orchest

Haven't tried it. In its favor, Argo is vendor neutral and is really easy to set up in a local k8s environment like docker for desktop or minikube. If you already use k8s for configuration, service discovery, secret management, etc, it's dead simple to set up and use (avoiding configuration having to learn a whole new workflow configuration language in addition to k8s). The big downside is that it doesn't have a visual DAG editor (although that might be a positive for engineers having to fix workflows written by non-programmers), but the relatively bare-metal nature of Argo means that it's fairly easy to use it as an underlying engine for a more opinionated or lower-code framework (orchest is a notable one out now).

If you’d like to try something for you data workflows that’s vendor agnostic (k8s based) and open source you can check out our project: https://github.com/orchest/orchest

You can find it here: https://github.com/orchest/orchest Convenience install script: https://github.com/orchest/orchest#installation

We automatically provide container level parallelism in Orchest: https://github.com/orchest/orchest

For people in this thread interested in what this tool is an alternative to: Airflow, Luigi, Kubeflow, Kedro, Flyte, Metaflow, Sagemaker Pipelines, GCP Vertex Workbench, Azure Data Factory, Azure ML, Dagster, DVC, ClearML, Prefect, Pachyderm, and Orchest.

Disclaimer: author of Orchest https://github.com/orchest/orchest

Look very similar to the script builder portion of https://github.com/windmill-labs/windmill, but not open-source, not self-hostable, and without open-source integrations (https://hub.windmill.dev/)

disclaimer: I'm founder of ^

If you need a job queue on Postgres, https://windmill.dev provide an all-integrated developer platform with a Pg queue at its core that support jobs defined in python/typescript/sql

windmill.dev - Windmill is an open-source developer platform to quickly build production-grade multi-step automation and internal apps from minimal Python and Typescript scripts. As a free user, you can create and be a member of at most three non-premium workspaces.

For an alternative to airplane.dev, you can checkout Windmill.

https://github.com/windmill-labs/windmill

"Open-source developer infrastructure for internal tools (APIs, background jobs, workflows and UIs). Self-hostable alternative to Airplane, Pipedream, Superblocks and a simplified Temporal with autogenerated UIsm and custom UIs to trigger workflows and scripts as internal apps.

Scripts are turned into sharable UIs automatically, and can be composed together into flows or used into richer apps built with low-code. Supported script languages supported are: Python, TypeScript, Go, Bash, SQL, and GraphQL. "

If you search HN, you'll find the creator of Windmill comment on comparisons to airplane.dev:

https://hn.algolia.com/?dateRange=all&page=0&prefix=false&qu...

https://windmill.dev is a self-hostable OSS alternative to pipedream

(disclaimer: I'm founder)

I'm genuinely curious what server-side stuff on BC you are referring to. That may have been something added after our assessment. The way I'd generally approach something like that for any of the platforms would be using an external low/no code solution to process webhook data. But it would depend heavily on the use case. For a more developer friendly option I've been really impressed by windmill.dev. We use a mix of n8n and windmill for various needs.

Yes it goes in that direction, however note that you can already do this in a not too hard way.

Our openflow spec is both open-source and has a full openapi definition: https://github.com/windmill-labs/windmill/blob/main/openflow...

you can use that to generate client sdks in any languages and build your own dag with it. That's what one of our customer did building a reactflow to openflow library: https://github.com/Devessier/reactflow-to-windmill

It's not as good as the decorator way but we move fast and if you still have interest for it we could prioritize it (and ask for feedbacks :))

We have built an open-source generic workflow engine to run arbitrary scripts (https://windmill.dev) with a vscode extension to build the yaml using a low-code builder and each individual script in their dedicated python/ts files so you get your full editor assistants https://youtu.be/aSOF6AzyDr8?t=116

One of the area we are expanding next is a github app so you get exactly the same UX as github actions but running windmill workflows on your windmill workers.