LLM Workflows then Agents: Getting Started with Apache Airflow
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I'm currently building pgflow, which is a simple, postgres-first engine that uses task queues to perform real work.
Have explicit DAG approach, strong typesafety, nice DSL in TypeScript and a dedicated task queue worker that allows it to run solely on Supabase without any external tools.
I'm super close to the alpha release, if you guys want more info, check out the readme for SQL core (https://github.com/pgflow-dev/pgflow/tree/main/pkgs/core#rea...) or my Twitter (https://x.com/pgflow_dev).
Hope that grabs someone attention :-) Cheers
I'm very close to releasing an alpha, will post here when ready!
That said, my impression is that Airflow is a really dated choice for a greenfield project. There isn't a clear successor though. I looked into this recently, and was quickly overwhelmed by Prefect, Dagster, Temporal, and even newer ones like Hatchet and Hamilton
Most of these frameworks now have docs / plugins / sister libraries geared around AI agents
It would be really helpful to read a good technical blog doing a landscape of design patterns in these different approaches, and thoughts on how to fit things together well into a pipeline given various quirks of LLMs (e.g. nondeterminism).
This page is a good start, even if it is written as an airflow-specific how-to!
Related possibly, my dags get kill -9 for no apparent reason. The RAM usage is not that high, maybe 2gb out of 8gb system RAM in use. No reason is given in the logs.
I am trying to switch to dagster, not because it's awesome, but because it hasn't crashed randomly on me.
I realize Amazon is taking an open source project and making a ton of money on it (the instance prices are ridiculous for what you get) and the incentives are misaligned for the Airflow team to help AWS make it better unless AWS paid them to help fix it.
It's crap all around, and Airflow gets a bad rap from AWS's terrible MWAA product based on it.
Their operational perspective is catastrophic; how does one view the logs for a dag through the UI[1]? Why can't it store the python in the database they have attached to their deployment, versus making me jump through 80,000 hoops to put the files in the right magic directory on disk of every worker[2]?
1: no, not <https://airflow.apache.org/docs/apache-airflow/stable/ui.htm...> I mean the log, you know, like in the old days of $(tail -f /var/log/the.thing). I'm open to the answer hiding somewhere in this gobbledygook <https://airflow.apache.org/docs/apache-airflow/stable/admini...> but who is the target audience for having such a fancy UI and omitting log viewing from it, doubly so if there's some alleged http just for viewing logs
2: https://airflow.apache.org/docs/apache-airflow/stable/core-c... and double-plus-good anytime python software mentions PYTHONPATH -- that's how you know you're in for a hot good time https://airflow.apache.org/docs/apache-airflow/stable/admini...
WRT your 1, above any DAG output to stdout/err is available via the logs tab from the graph view of the individual tasks. Almost all our DAGs leverage on the PythonOperator though, not sure if that standardises this for us and your experience is muddied by more complexity than we currently have?
WRT 2. we generate an uber requirements.txt running pyreqs from the pipeline and install everything in the container automatically. Again no issues currently - although we do need to manually add the installation of test libraries to the pipeline job as for some reason auto-discovery is flakier for unit-tests frameworks.
You either get simplicity with the caveate that your systems need to perfectly align.
Or you get complexity but will work with basically anything (airflow).
If you are building a expandable long term system and you want all the goodies baked in choose airflow.
Pretty much the same as any architecture choice. Ugly/hard often means control and features, pretty/easy means less of both.
On the surface the differences are not very noticable other than the learning curve of getting started.
https://www.dbos.dev/blog/durable-execution-crashproof-ai-ag...
I'd clarify this to say "Temporal is absolutely not limited to a static graph." It can certainly handle a static graph, but it can also handle a dynamic one. Here is an example in Go (https://github.com/temporalio/samples-go/tree/main/choice-mu...), there are similar ones for other languages.
I think the confusion might stem from the determinism requirement in Temporal (and other replay-based Durable Execution platforms). It's not the Workflow Definition (i.e., the code) that must be deterministic, it's the Workflow Execution (i.e., a specific running instance of that code) that must be deterministic. Each running instance is allowed to take a different path through that code, so long as it does so consistently when executed with the same input.
Disclosure, I'm the CEO of DBOS.
[0] https://github.com/dbos-inc/dbos-transact-py
[1] https://www.dbos.dev/blog/durable-execution-coding-compariso...
If you want a product hint from me, I think that adding integrations natively into the platform that would allow vibe coders to build asynchronous agents easier would really boost revenue. Like email, text, etc.
Probably not your vision, just a suggestion
We've experimented with that actually! Six months ago it was terrible, but the new models are getting pretty good.
And it's definitely easier for an AI to generate DBOS code to make a fully formed distributed system than a fully formed distributed system somewhere else.
[1]: Limited to what decorator arguments can do. I suspect it could become an issue with `@task.branch` if some post-processing would be needed to adjust for smaller models' finickinesses.
[2]: As the final step is described at the top of the function.
It is _potentially_ more restrictive than writing pure Python functions, but the plus side is that we can interject certain Airflow-specific features into how the agent runs. And this isn't mean for someone who knows agents inside & out / wants the low-level customizability.
The best example of this today is log groups: Airflow lets you log things out as part of a "group" which has some UI abstractions to make it easier. This SDK takes the raw agent tool calls and turns them each into a log group, so you can see a) at a high level what the agent is doing, and b) drill down into a specific tool call to understand what's happening within the tool call.
To your point about the `@task.llm_branch`, the SDK & Pydantic AI (which the SDK uses under the hood) will re-prompt the LLM up to a certain number of attempts if it receives output that isn't the name of a downstream task. So there shouldn't be much finickiness.
It just exposes set of functions to propagate the DAG through states and queues tasks for a task worker to perform the actual work and acknowledge completion or failure back to the sql orchestrator.
I was working on it for last few months and it will be ready in upcoming weeks, first version is dedicated to work on Supabase but I plan to make it agnostic.
If you want to learn more, check out the SQL Core readme which explains the whole concept (https://github.com/pgflow-dev/pgflow/tree/main/pkgs/core#rea...) or my Twitter for updates and some demos (https://x.com/pgflow_dev).
In real life program, I don't think that you will have hundreds of calls to LLM or agent in your app so much that you have any code gains to decorator but at the opposite the decorator will make it very hard to have parametric values or values not hard coded but from config that you don't set up upfront at application startup like globals. That is a bad practice...
Airflow actually uses decorators to indicate something is an explicit task in a data pipeline vs just a utility function, so this follows that pattern!
It also uses an "operator" under the hood (Airflow's term for a pre-built, parameterized task) which can be subclassed and customized if you want to do any customization.