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Posted by blobmty 5 days ago

DAG Workflow Engine(github.com)
70 points | 55 commentspage 2
bognition 5 days ago|
Production Ready?

That is a is a pretty bold claim for a repo that existed for a few days, has 0 issues, PRs, etc...

halfcat 5 days ago|
0 open issues. That means it’s production ready, right?

Right??

philipodonnell 5 days ago||
This particular example aside, I don’t think it being derivative and simplified is necessarily bad. Libraries that are popular today were written for humans and reinforced by LLMs via training. It’s unlikely they represent the ideal interaction surface for an agent.

There was a study recently that LLms prefer resumes written by LLMs rather than by humans. Stands to reason they would prefer apis written by LLMs.

This is probably the early days of such intentionally simplified agentic semantic primitives like “DAG Workflow” where the answer for why not Temporal is that LLMs prefer different things than humans.

zaptheimpaler 5 days ago||
I have several sources of data I want to fetch, retry, process periodically. Like exporting Claude chats into .md files that go to Obsidian, fetching Garmin data from the API and processing it for a custom tool, exporting replays for a game, maybe even running some browser automation to get bank CSVs. I have some ad-hoc python scripts for all of this but no central way to manage them, schedule, handle errors and retries, store the original data and processed versions, resume from the last point etc.. is a workflow engine useful for something like that?
halfcat 5 days ago||
Agree with other response, look at Dagster for this.

If you want to roll your own, you build a dependency graph (a dict) of the functions you want to call, Python already has graphlib.TopologicalSorter built in that can do this for you. Throw in logging and the tenacity library for retries and you’re set.

momojo 5 days ago||
Check out Airflow and Dagster.

I've used Dagster but I can't compare to airflow. But in terms of DX, I've found Dagster pretty easy to use. Instead of writing their own DSL, they have a python library that lets yo tag your pre-made methods as @ops and and string them together into a DAG.

barelysapient 5 days ago||
My version of a similar tool; but written in Go with a compile time guarantee.

https://github.com/swetjen/daggo

taybin 5 days 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.
Hasnep 5 days ago|
The LLM generated the words "production ready" so it must be true!
zackham 5 days ago||
I have a project in this space that I've run many thousands of jobs through. It's solid and full featured. Feel free to connect: https://stepwise.run/
kamikazechaser 5 days ago|
Just looking at the features, this is pretty cool!
_ZeD_ 5 days ago||
how it compares to airflow?
colton_padden 5 days 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 5 days 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.

topspin 5 days ago||
Agreed. Right now, if I needed "workflow" for a greenfield that could tolerate some risk, I'd look at https://www.restate.dev/ which matches your model of a self contained binary.
snthpy 5 days ago||
YAML no thanks.

I want something that uses BPML for actual business workflows.

blobmty 5 days 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.
lixtra 5 days ago|
Ideally, you submit your own project as https://news.ycombinator.com/showhn.html
tbrownaw 5 days ago|
These are always a fun couple-day project. :)
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