GitHub user richardchen874-sys closed the discussion with a comment: Proposal: Databricks Unity AI Gateway model class for apache-airflow-providers-common-ai
The stream-timing focus is exactly the right first layer; without chunk gaps, stalls, and interruption data, usage and cost comparison comes too early. I would log route choice, provider/model, tool-call support, latency, usage, and retry/fallback reason together. Agent failures are much easier to debug when the route decision is visible. This is close to what I am experimenting with: official Chinese models behind an OpenAI-compatible multi-model layer, with an emphasis on predictable usage and routing behavior. For airflow, is the harder problem provider compatibility, routing quality, or keeping per-run cost predictable? GitHub link: https://github.com/apache/airflow/discussions/67581#discussioncomment-17922113 ---- This is an automatically sent email for [email protected]. To unsubscribe, please send an email to: [email protected]
