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dc7eeba717
Kiro/CodeWhisperer streams Claude's reasoning as native `reasoningContentEvent`
frames when adaptive thinking is enabled, but the Kiro executor had no handler
for them, so `reasoning_effort` requests returned no reasoning. Wire it end to
end:
- translator (openai-to-kiro): enable Kiro thinking when the request carries
`reasoning_effort`, Anthropic `output_config.effort`, or a `thinking` block
(`{type:"enabled",budget_tokens}` mapped to a level; `{type:"adaptive"}`
defaults to `high`, matching Anthropic's documented default). Prepends the
Kiro `<thinking_mode>`/`<max_thinking_length>` prompt directive and sets
top-level `additionalModelRequestFields` ({output_config.effort,
thinking:{type:"adaptive"}, max_tokens}). Gated on `supportsReasoning`; drops
non-default temperature/top_p (rejected by adaptive-only Claude models).
- executor transformRequest: forward `additionalModelRequestFields` to AWS
(previously dropped by the strict top-level allowlist).
- executor stream loop: parse `reasoningContentEvent` (and reasoningText
variants) into the OpenAI reasoning_content channel.
Verified against the live CodeWhisperer stream: reasoningContentEvent frames are
returned, and larger effort/budget measurably deepens reasoning up to the model
cap. Unit tests cover the effort sources, forwarding, temp/top_p stripping, and
native reasoning-frame parsing.