orchest VS Node RED

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

Good question.

I expect that we're moving into a phase of AIs talking to AIs, and initially it'll be wasteful (because it'll be mostly English), but eventually, they'll derive their own language and seamlessly upgrade protocols when they determine they're talking to an AI. No clue how that will come about or what that language will look like, but honestly, it's kind of exciting.

Really interesting to think about how they might handle context, as well. Even though we have much bigger context windows (and they'll only get larger), context management is still a resource-management issue, which we'll probably continue to refine, as well. Imagine different strategies for managing both what is brought into the context of each request, as well as what form it could take (level of detail, additional references or commentary on it, etc). Things could get really unreadable even in English, and still be very interpretable for an LLM.

W.r.t. the graph-oriented interfaces, are you thinking something like Node-RED [1]? I'm seeing more and more people mention having LLMs produce non-text or structured outputs, like JSON, UI, and other things. Easy to imagine an LLM that wires together various open-source platforms, on-demand. Something like Node-RED for pipelines/functions, some UI tools for visualization/interactivity, other platforms for messaging, etc...

[1] https://nodered.org/

I skipped to chapter 9 in the article ("Clogged"), and it looked like Pipes failed because it didn't have a large enough team or a well-defined mission. As a result they couldn't offer a super robust product that would lure in enterprise users. "You could not purchase some number of guaranteed-to-work Pipes calls per month" is the quote from the article.

The reason I think that interesting is because that's the model these days for everything from AI tokens to Monday.com seats. It makes me feel like Pipes was before its time.

That said I've been collecting different "business glue" products that are similar to Pipes. To me, like you say, they aren't as interesting, exciting and intuitive as Pipes was, but maybe it just takes a little more digging. I tried to focus on open source tools but some aren't.

- n8n io: https://n8n.io/integrations/mondaycom/

- Node-RED: https://nodered.org/ (just read about this one in this thread)

- trigger dev: trigger.dev

- automatisch.io: https://automatisch.io/docs/

- Activepieces: https://www.activepieces.com/docs/getting-started/introducti...

- Huginn: https://github.com/huginn/huginn

- budibase: https://budibase.com/

- windmill: https://www.windmill.dev/

- tooljet: https://www.tooljet.com/workflows

- Bracket: https://www.usebracket.com/pricing (just SalesForce <-> PostgreSQL)

- Zapier: zapier.com/

Anyway I hope some of these are fun!

Source: https://github.com/node-red/node-red

I believe Node-RED (https://nodered.org/) the way to go. It's just an NPM package to install and you can run it how ever you wish (even on Windows). It has a friendly and helpful community with even the main developers tirelessly answering even beginner level questions. In fact the community forum its THE friendliest forum I've ever been a member of by a large margin. Node-RED's development is supported by the JS Foundation and it's completely free and open source. It's widely used in the industrial automation industry and even integrated by some PLC manufacturers such as Siemens.

Does anyone know if their are plans to implement something like this (or if there are already custom nodes out there). I'd like to experiment with things like looping and incrementing values (like a for loop) for a Ksampler for example. It's only an example though, so I am not looking for a ksampler specific solution; just a generic way to have a variable (e.g. Seed value), run some nodes that use that value, increment the value, and then loop back to the beginning until some sort of condition is met. Node-Red (an event driven node based programming language) has this functionality so it could defintely work in a node based environment such as ComfyUI (see here).