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ViditOstwal
2026-08-06 20:38:28 +05:30
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@@ -322,7 +322,7 @@ Caches can be employed to store the results of tools' execution, making the proc
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.
`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.
`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.
```python Code
# 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,
# prompt_tokens includes cache read + cache write for Anthropic
```
See [UsageMetrics field semantics](/concepts/flows#usagemetrics-field-semantics)
for the provider-neutral contract used by `crew.usage_metrics` and
`flow.usage_metrics`.
See the **UsageMetrics field semantics** section in the Flows concept
documentation for the provider-neutral contract used by `crew.usage_metrics`
and `flow.usage_metrics`.
**Important Notes:**
- `max_tokens` is a **required** parameter for all Anthropic models