Hi HN,
We've built an SDK for building DAGs / data pipelines with LLMs in Apache Airflow [1] using Pydantic AI [2] under the hood. I've seen success across the board with Airflow users building simple LLM workflows before moving on to "AI agents". In my experience, the noise around building agents means that people forget that there are other ways to get more immediate value out of LLMs.
Coupling Airflow for orchestration and Pydantic AI for LLM interactions has turned out to be a very pragmatic approach to building these workflows (and agents). Neither tool "gets in the way" of what you're trying to do. Airflow's been around for 10+ years and has a very well-built orchestration engine rich with everything you need to write production grade data pipelines, and Pydantic AI's been a refreshing take on working with LLMs.
Would love some feedback from this community!
[1] https://github.com/apache/airflow
[2] https://ai.pydantic.dev
