Persist fork token usage under session_model_usage task=background_review,
emit a per-fork completion log line, and expose enabled/max_iterations/
prompt_file so operators can see and bound the automatic review cost.
Address review feedback: load auxiliary.background_review once per spawn,
classify completion logs by summarize action prefixes, treat explicit
api_call_count=None as the documented default of 1, and WARNING on the
fail-open enabled-gate path.
The overview's total_input/output/cache token counts summed only the
sessions counters (main-loop usage), while the per-model breakdown
already included auxiliary usage rows (task dimension from #65537) and
reconciled residuals. Result: hermes insights top-line totals
undercounted aux spend (compression summarizer, vision, titles) and
disagreed with the per-model table below them — the symptom reported
in #58592 and requested in #9979.
When the per-model breakdown is available, derive the overview token
totals from it (same pattern total_cost already used). Verified no
double-count across incremental CLI deltas, gateway absolute
overwrites, and aux rows.
* feat(analytics): record auxiliary model usage per task in session accounting
Auxiliary LLM calls (vision, compression, title_generation, web_extract,
session_search, ...) discarded their token usage, leaving dashboard
analytics blind to aux model spend (issue #23270).
- hermes_state.py: session_model_usage gains a task PK dimension
(''=main loop) via v22 table-rebuild migration (SQLite can't alter a
PK); record_auxiliary_usage() writes per-(model,provider,task) deltas
WITHOUT touching sessions counters (gateway overwrites those with
absolute main-loop totals — folding aux in would double-count or be
clobbered). Aux rows never inherit the session's main-loop route.
- agent/aux_accounting.py: ContextVar ambient accounting context
(mirrors the portal_tags conversation context); record_aux_usage()
normalizes usage via usage_pricing.normalize_usage, estimates cost,
and is strictly best-effort. moa_reference/moa_aggregator excluded —
conversation_loop already folds MoA usage+cost into the main delta.
- agent/auxiliary_client.py: _validate_llm_response is the recording
chokepoint — every successful non-streaming aux response passes
through it exactly once, sync and async, including fallback paths
(model read from the response itself stays accurate across
fallbacks).
- run_agent.py: run_conversation publishes/resets the accounting
context; agent/title_generator.py republishes on its bare thread.
- hermes_cli/web_server.py: /api/analytics/usage folds aux rows into
by_model (aux-only models finally appear) and adds a by_task
summary; /api/analytics/models surfaces aux rows on the Models page.
Design per review of PR #62850 by @eeksock (thread-local + separate
auxiliary_usage table): rebuilt on ContextVar (async-safe — thread-local
cross-attributes concurrent coroutines on one event loop) and the
existing session_model_usage table instead of a parallel accounting
path, extended beyond vision to every aux task, and wired the analytics
endpoints so the dashboard actually shows it. Credit to @eeksock for
the approach and @tboatman for the detailed root-cause analysis.
* test(moa): match _validate_llm_response mock to new accounting-hint signature
* test(aux): accept accounting-hint kwargs in remaining _validate_llm_response mocks
Ojas Sharma