Lee-W opened a new pull request, #71403:
URL: https://github.com/apache/airflow/pull/71403
Dag authors had no way to cap what a single LLM or Agent task is allowed to
spend, and no way to see what one cost after the fact. pydantic-ai made both
first-class in 2.23.0 via RunUsage.cost and UsageLimits.cost_limit, but this
provider's floor was >=2.0.0, so neither existed for users here.
Raising the floor is what unlocks the enforcement path — the operators
already forward usage_limits untouched to run_sync, so cost_limit takes effect
with no interception logic of our own. The max_cost parameter exists for the
one thing usage_limits structurally cannot do: a UsageLimits object cannot be
templated, so a budget that follows the environment had to be expressible as a
scalar.
The docs deliberately spell out where the cap stops being a guarantee, since
each of these reads as a broken promise to anyone who assumed otherwise: the
request that crosses the limit still completes and still bills, self-hosted or
unpriced models report no cost at all and the limit silently does nothing, and
a durable replay re-counts the cached spend against a fresh attempt's budget.
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