Lee-W opened a new pull request, #72938:
URL: https://github.com/apache/airflow/pull/72938
Provider batch APIs run at roughly half price with a 24h SLA, but a pipeline
built on @task.llm had no way to use them. Switching meant the author owned
JSONL construction, upload, chunking to provider limits, polling, and result
retrieval by hand.
pydantic-ai exposes no batch abstraction, so there is no Agent path for
batch work. Batch is submit/poll/fetch spanning hours rather than
request/response in a loop, which means the agent loop, tool execution, HITL
approval, and message_history have no meaning there. A dedicated deferrable
operator and decorator therefore fit better than a mode flag on @task.llm.
Results land in object storage as JSONL with only a small manifest in XCom,
because a batch can hold 100k items. Submission state is written before the
paid call and keyed on the run so that a retry reattaches to the in-flight
batch instead of buying a second one; XCom cannot hold that state because the
server clears a task instance's XCom at the start of every attempt.
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