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@@ -322,7 +322,7 @@ Caches can be employed to store the results of tools' execution, making the proc
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After the crew execution, you can access the `usage_metrics` attribute to view the language model (LLM) usage metrics for all tasks executed by the crew. This provides insights into operational efficiency and areas for improvement.
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`total_tokens` is the billed total (`prompt_tokens + completion_tokens`). Breakdown fields such as `cached_prompt_tokens` describe subsets already included in those totals — see [Flow Usage Metrics](/concepts/flows#flow-usage-metrics) for the full field semantics.
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`total_tokens` is the billed total (`prompt_tokens + completion_tokens`). Breakdown fields such as `cached_prompt_tokens` and `cache_creation_tokens` describe subsets already included in those totals and are not added on top of `total_tokens`. See the **UsageMetrics field semantics** section in the Flows concept documentation for the full contract.
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```python Code
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# Access the crew's usage metrics
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@@ -435,9 +435,9 @@ In this section, you'll find detailed examples that help you select, configure,
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# prompt_tokens includes cache read + cache write for Anthropic
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```
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See [UsageMetrics field semantics](/concepts/flows#usagemetrics-field-semantics)
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for the provider-neutral contract used by `crew.usage_metrics` and
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`flow.usage_metrics`.
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See the **UsageMetrics field semantics** section in the Flows concept
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documentation for the provider-neutral contract used by `crew.usage_metrics`
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and `flow.usage_metrics`.
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**Important Notes:**
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- `max_tokens` is a **required** parameter for all Anthropic models
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