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* feat: aggregate LLM token usage at the flow level Introduces `flow.usage_metrics`, a snapshot of every LLMCallCompletedEvent emitted under the flow's `current_flow_id` for the duration of one kickoff (or resume) call. Aggregation happens on the singleton event bus so it covers crews, direct `LLM.call`s, and nested listener calls — solving the mismatch where the SDK reported only the last crew's usage while the Enterprise UI showed the correct full total. Co-authored-by: Cursor <cursoragent@cursor.com> * refactor: centralize provider key normalization in UsageMetrics Add UsageMetrics.from_provider_dict to normalize raw LLM usage dicts across providers (LiteLLM, native Anthropic, native Gemini, OpenAI nested cached). BaseLLM._track_token_usage_internal and the flow-level aggregator now share this single source of truth, so `flow.usage_metrics` agrees with per-LLM totals on every provider — including the native Anthropic path that emits `input_tokens`/`output_tokens` instead of `prompt_tokens`/`completion_tokens`. * fix: flush event bus before reading aggregated usage_metrics `crewai_event_bus.emit` dispatches LLMCallCompletedEvent handlers on a ThreadPoolExecutor (fire-and-forget), so a flow whose last LLM call completes right before kickoff_async/resume_async returns can detach the usage listener while that handler is still queued, leaving its tokens off `flow.usage_metrics`. Match `Crew.kickoff()` and call `crewai_event_bus.flush()` in both finally blocks so every handler drains before the listener is detached. --------- Co-authored-by: Cursor <cursoragent@cursor.com>