Summary mode invokes an auxiliary LLM (same Opus-tier model in default
'auto' routing) once per session summarised, with up to ~28K input
tokens (MAX_SESSION_CHARS=100K chars) and up to 10K output tokens
(MAX_SUMMARY_TOKENS) per call. That cost was being silently discarded:
_summarize_session() consumed response.usage only for the content string
and threw the usage data away. Smoke-test cost reporting showed
summary-mode scenarios at a fraction of their real spend because of it.
This patch:
- Changes _summarize_session() to return (content, usage) where usage
is a normalised dict {model, input_tokens, output_tokens,
cache_read_tokens, cache_creation_tokens} or None when the provider
didn't surface usage.
- Adds _extract_aux_usage() that handles both OpenAI-style
(prompt_tokens/completion_tokens, prompt_tokens_details.cached_tokens)
and Anthropic-style (input_tokens/output_tokens,
cache_read_input_tokens, cache_creation_input_tokens) usage shapes.
- The summary-mode caller aggregates per-session usage into both an
entry-level 'aux_usage' field and a top-level 'aux_usage_total'
carrying a call_count. The aggregate is omitted from the payload
entirely when no usage data was captured (test mocks, providers that
don't report it) so consumers can distinguish 'no data' from
'all zero'.
Note: this surfaces aux cost in the tool RESPONSE, where downstream
metrics extraction can pick it up. It does NOT yet attribute the cost
back to the parent session row (sessions.input_tokens / output_tokens /
estimated_cost_usd) — that's a wider fix to async_call_llm and the
session DB, out of scope here. Aggregator scripts (smoke-test
extractor, dashboards) get the data they need from the tool payload
without that wider change.
yoniebans
8709e1ebec