FilterHN

DAG Workflow Engine

28 points

by blobmty

4 hours ago

| past

| 8 comments

| github.com

| HN

▲

peterkelly

20 minutes ago

[-]

I've always been of the view that for a workflow language, you should use a proper, turing-complete functional language which gives you all the usual flexiblity for transformations on intermediate data, while also supporting things like automatic parallelisation of things like external, compute-intensive tasks.

I recommend checking out https://github.com/peterkelly/rex and also my PhD thesis on the topic https://www.pmkelly.net/publications/thesis.pdf.

The gap in flexiblity between DAG-only and a full language designed for the task is a significant one.

▲

tedchs

1 minute ago

[-]

How does this compare to Temporal? That seems to be the current baseline for application-oriented workflow engines.

▲

purpleidea

11 minutes ago

[-]

Here's a different kind of workflow engine with a proper DSL. It turns out config management is the same problem as workflow engines, if you use my modern definition of config management.

https://github.com/purpleidea/mgmt/

▲

tibbar

1 hour ago

[-]

I was expecting to see some verbose LLM output, but actually the code has a distinctly hand-crafted feel. Nice to see! I'm not sure if "production ready" is a safe claim 7 commits in to a project ;)

▲

afshinmeh

1 hour ago

[-]

https://github.com/vivekg13186/Daisy-DAG/blob/main/backend/s...

▲

antonvs

19 minutes ago

[-]

I've seen LLMs include that exact "production-ready" claim on code they generate. But of course it gets that from its training data.

▲

taybin

1 hour ago

[-]

What makes it production ready? What's the code coverage on your tests? There are only seven commits in this repo as of this comment.

▲

_ZeD_

49 minutes ago

[-]

how it compares to airflow?

▲

colton_padden

35 minutes ago

[-]

Was going to ask the same thing. The orchestration space already has some very well established frameworks like Airflow and Dagster. Would be curious to see the pros and cons.

▲

saltyoldman

30 minutes ago

[-]

I think the future of replacements to well established frameworks written in Python/etc.. are zero dependency binaries (from Rust or Go) that require so little configuration and tuning and they "just work".

That being said, that's not this project.

▲

esafak

1 hour ago

[-]

I don't see any references to existing orchestrators, which are way more complete, so I presume you did this as an exercise?

Just seeing YAML used for workflows in this age makes me automatically nope out.

▲

afshinmeh

1 hour ago

[-]

Curious, what format would you prefer to use to represent a workflow instead of YAML?

▲

esafak

1 hour ago

[-]

Type-safe code. Workflows are not configuration! If I wanted YAML hell I could stick to Github Actions.

But that's only the start. There are a lot of other things I would expect of a new workflow orchestrator in 2026 so if you are not comparing yourself to the competition you probably don't know what you're getting yourself into.

▲

afshinmeh

54 minutes ago

[-]

Yeah, that makes sense. I looked at a few workflow orchestrators and I'm building something that I will release soon, but my thinking is that the "workflow engine" should be an abstraction that takes the input and executes the steps. "What" you use to define that workflow is probably the SDK layer though, but I can certainly see the value in using type safe code to define as opposed to a YAML file.

I'm mainly focusing on the portability aspect of it (e.g. use TS/Python/etc. to define the workflow/steps or just simple a simple YAML file).

▲

verdverm

37 minutes ago

[-]

Are you planning to map those varied definitions onto varied orchestrators?

▲

afshinmeh

27 minutes ago

[-]

Sort of. My thinking is that the input to define the workflow should be anything you prefer to use (TS, Go, YAML, etc.) and the orchestrator's job is to model that and execute the job, given your deployment model.

▲

blobmty

4 hours ago

[-]

DAG Workflow Engine A production-ready DAG (Directed Acyclic Graph) workflow engine driven by a YAML DSL. Validates, executes, and visualizes workflows with support for parallel execution, retries, conditional branching, batch iteration, and pluggable actions.