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fix: aggregate token usage across all LLM calls (#6122)
* 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>
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Lucas Gomide
@@ -226,6 +226,48 @@ counter=2 message='Hello from first_method - updated by second_method'
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من خلال ضمان إعادة مخرجات الدالة الأخيرة وتوفير الوصول إلى الحالة، تجعل تدفقات CrewAI من السهل دمج نتائج سير عمل الذكاء الاصطناعي في التطبيقات أو الأنظمة الأكبر،
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مع الحفاظ على الوصول إلى الحالة طوال تنفيذ التدفق.
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## مقاييس استخدام التدفق
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بعد اكتمال تنفيذ التدفق، يمكنك الوصول إلى الخاصية `usage_metrics` لعرض إجمالي استخدام التوكنات عبر **كل استدعاء لنموذج اللغة** يتم خلال التشغيل — بما في ذلك الاستدعاءات من كل فريق (Crew) ينظمه التدفق، والاستدعاءات داخل أدوات الـ Agents، والاستدعاءات المباشرة لـ `LLM.call(...)` من دوال التدفق. هذا هو المكافئ على جانب الـ SDK للإجماليات المعروضة في واجهة CrewAI Enterprise.
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```python Code
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from crewai import LLM
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from crewai.flow.flow import Flow, listen, start
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class UsageMetricsFlow(Flow):
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@start()
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def run_first_crew(self):
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self.state.first_result = FirstCrew().crew().kickoff()
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@listen(run_first_crew)
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def call_llm_directly(self):
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# استدعاء مباشر لنموذج اللغة — يُحسب أيضًا ضمن flow.usage_metrics
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llm = LLM(model="openai/gpt-4o-mini")
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self.state.summary = llm.call("لخّص النقاط الرئيسية.")
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@listen(call_llm_directly)
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def run_second_crew(self):
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self.state.second_result = SecondCrew().crew().kickoff()
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flow = UsageMetricsFlow()
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flow.kickoff()
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print(flow.usage_metrics)
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# UsageMetrics(total_tokens=8579, prompt_tokens=6210, completion_tokens=2369,
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# cached_prompt_tokens=0, reasoning_tokens=0,
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# cache_creation_tokens=0, successful_requests=5)
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```
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<Note>
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`flow.usage_metrics` **ليست** نفس `flow.kickoff().token_usage`. هذه الأخيرة
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ترجع فقط `CrewOutput.token_usage` لـ **آخر** دالة `@listen` أعادت
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`CrewOutput`، مما يعني أنها تعكس فقط الفريق الأخير وتتجاهل الفرق السابقة
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وكذلك أي استدعاءات مباشرة لـ `LLM.call(...)`. استخدم `flow.usage_metrics`
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كلما احتجت إلى الإجمالي **الكامل** للتوكنات لتنفيذ التدفق.
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</Note>
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كل حقل في [`UsageMetrics`](https://github.com/crewAIInc/crewAI/blob/main/lib/crewai/src/crewai/types/usage_metrics.py) المُعاد هو مجموع جميع استدعاءات نموذج اللغة التي حدثت خلال استدعاء واحد لـ `flow.kickoff()`. تتم إعادة تعيين العدادات عند الاستدعاء التالي لـ `kickoff()` (وفي كل تكرار من `kickoff_for_each`)، لذلك لن تتكرر العدّات عبر التشغيلات المتتالية. يمكن قراءة هذه الخاصية بأمان في أي وقت بعد اكتمال `kickoff()`؛ قراءتها أثناء التنفيذ تُرجع المجموع الجزئي المتراكم حتى تلك اللحظة.
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## إدارة حالة التدفق
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إدارة الحالة بفعالية أمر بالغ الأهمية لبناء سير عمل ذكاء اصطناعي موثوق وقابل للصيانة. توفر تدفقات CrewAI آليات قوية لإدارة الحالة غير المهيكلة والمهيكلة،
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@@ -226,6 +226,49 @@ After the Flow has run, you can access the final state to see the updates made b
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By ensuring that the final method's output is returned and providing access to the state, CrewAI Flows make it easy to integrate the results of your AI workflows into larger applications or systems,
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while also maintaining and accessing the state throughout the Flow's execution.
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## Flow Usage Metrics
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After a Flow execution completes, you can access the `usage_metrics` property to view aggregated token usage across **every LLM call** made during the run — including calls from every Crew the Flow orchestrated, calls inside Agent tools, and bare `LLM.call(...)` invocations from Flow methods. This is the SDK-side equivalent of the totals shown in the CrewAI Enterprise UI.
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```python Code
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from crewai import LLM
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from crewai.flow.flow import Flow, listen, start
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class UsageMetricsFlow(Flow):
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@start()
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def run_first_crew(self):
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self.state.first_result = FirstCrew().crew().kickoff()
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@listen(run_first_crew)
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def call_llm_directly(self):
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# Bare LLM call — still counted by flow.usage_metrics
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llm = LLM(model="openai/gpt-4o-mini")
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self.state.summary = llm.call("Summarize the key takeaways.")
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@listen(call_llm_directly)
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def run_second_crew(self):
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self.state.second_result = SecondCrew().crew().kickoff()
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flow = UsageMetricsFlow()
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flow.kickoff()
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print(flow.usage_metrics)
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# UsageMetrics(total_tokens=8579, prompt_tokens=6210, completion_tokens=2369,
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# cached_prompt_tokens=0, reasoning_tokens=0,
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# cache_creation_tokens=0, successful_requests=5)
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```
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<Note>
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`flow.usage_metrics` is **not** the same as `flow.kickoff().token_usage`. The
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latter returns the `CrewOutput.token_usage` of the **last** `@listen` method
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that returned a `CrewOutput`, which means it only reflects the final Crew and
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ignores prior Crews and bare `LLM.call(...)` invocations entirely. Use
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`flow.usage_metrics` whenever you need the **full** token rollup for the Flow
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execution.
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</Note>
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Each entry in the returned [`UsageMetrics`](https://github.com/crewAIInc/crewAI/blob/main/lib/crewai/src/crewai/types/usage_metrics.py) is the sum across all LLM calls made within a single `flow.kickoff()` invocation. Counters reset on the next `kickoff()` call (or on each iteration of `kickoff_for_each`), so successive runs don't double-count. The property is safe to read at any point after `kickoff()` completes; reading it during execution returns the partial total accumulated so far.
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## Flow State Management
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Managing state effectively is crucial for building reliable and maintainable AI workflows. CrewAI Flows provides robust mechanisms for both unstructured and structured state management,
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@@ -221,6 +221,48 @@ Flow가 실행된 후, 이러한 메소드들에 의해 수행된 업데이트
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최종 메소드의 출력이 반환되고 상태에 접근할 수 있도록 함으로써, CrewAI Flow는 AI 워크플로우의 결과를 더 큰 애플리케이션이나 시스템에 쉽게 통합할 수 있게 하며,
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Flow 실행 과정 전반에 걸쳐 상태를 유지하고 접근하면서도 이를 용이하게 만듭니다.
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## 플로우 사용 메트릭
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Flow 실행이 완료된 후, `usage_metrics` 속성에 접근하여 실행 동안 발생한 **모든 LLM 호출**의 토큰 사용량 집계를 확인할 수 있습니다. 여기에는 Flow가 오케스트레이션한 모든 Crew의 호출, Agent의 도구 내부에서 발생한 호출, 그리고 Flow 메서드에서 직접 호출한 `LLM.call(...)`이 모두 포함됩니다. 이는 CrewAI Enterprise UI에 표시되는 총량과 동등한 SDK 측 값입니다.
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```python Code
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from crewai import LLM
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from crewai.flow.flow import Flow, listen, start
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class UsageMetricsFlow(Flow):
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@start()
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def run_first_crew(self):
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self.state.first_result = FirstCrew().crew().kickoff()
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@listen(run_first_crew)
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def call_llm_directly(self):
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# 직접 LLM 호출 — flow.usage_metrics에서도 집계됩니다
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llm = LLM(model="openai/gpt-4o-mini")
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self.state.summary = llm.call("핵심 내용을 요약해 주세요.")
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@listen(call_llm_directly)
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def run_second_crew(self):
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self.state.second_result = SecondCrew().crew().kickoff()
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flow = UsageMetricsFlow()
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flow.kickoff()
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print(flow.usage_metrics)
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# UsageMetrics(total_tokens=8579, prompt_tokens=6210, completion_tokens=2369,
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# cached_prompt_tokens=0, reasoning_tokens=0,
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# cache_creation_tokens=0, successful_requests=5)
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```
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<Note>
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`flow.usage_metrics`는 `flow.kickoff().token_usage`와 **동일하지 않습니다**.
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후자는 `CrewOutput`을 반환한 **마지막** `@listen` 메서드의
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`CrewOutput.token_usage`만 반환하므로, 이전에 실행된 Crew들과 Flow 메서드에서
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직접 호출한 `LLM.call(...)`은 전혀 포함되지 않습니다. Flow 실행에 대한
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**전체** 토큰 집계가 필요할 때는 항상 `flow.usage_metrics`를 사용하십시오.
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</Note>
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반환되는 [`UsageMetrics`](https://github.com/crewAIInc/crewAI/blob/main/lib/crewai/src/crewai/types/usage_metrics.py)의 각 항목은 단일 `flow.kickoff()` 실행 동안 발생한 모든 LLM 호출의 합계입니다. 다음 `kickoff()` 호출(및 `kickoff_for_each`의 각 반복)에서 카운터가 초기화되므로 연속 실행이 이중으로 집계되지 않습니다. 이 속성은 `kickoff()` 완료 후 언제든지 안전하게 읽을 수 있으며, 실행 중에 읽으면 그 시점까지 누적된 부분 합계를 반환합니다.
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## 플로우 상태 관리
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상태를 효과적으로 관리하는 것은 신뢰할 수 있고 유지 보수가 용이한 AI 워크플로를 구축하는 데 매우 중요합니다. CrewAI 플로우는 비정형 및 정형 상태 관리를 위한 강력한 메커니즘을 제공하여, 개발자가 자신의 애플리케이션에 가장 적합한 접근 방식을 선택할 수 있도록 합니다.
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@@ -219,6 +219,49 @@ Após o término da execução, é possível acessar o estado final e observar a
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Ao garantir que a saída do método final seja retornada e oferecer acesso ao estado, o CrewAI Flows facilita a integração dos resultados dos seus workflows de IA em aplicações maiores,
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além de permitir o gerenciamento e o acesso ao estado durante toda a execução do Flow.
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## Métricas de Uso do Flow
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Após a execução de um Flow, você pode acessar a propriedade `usage_metrics` para visualizar o consumo agregado de tokens em **todas as chamadas de LLM** realizadas durante a execução — incluindo chamadas das Crews orquestradas pelo Flow, chamadas dentro de tools de Agents, e invocações diretas de `LLM.call(...)` feitas a partir de métodos do Flow. Esse é o equivalente, do lado do SDK, ao total exibido na interface do CrewAI Enterprise.
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```python Code
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from crewai import LLM
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from crewai.flow.flow import Flow, listen, start
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class UsageMetricsFlow(Flow):
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@start()
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def run_first_crew(self):
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self.state.first_result = FirstCrew().crew().kickoff()
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@listen(run_first_crew)
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def call_llm_directly(self):
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# Chamada direta de LLM — também contabilizada por flow.usage_metrics
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llm = LLM(model="openai/gpt-4o-mini")
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self.state.summary = llm.call("Resuma os principais pontos.")
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@listen(call_llm_directly)
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def run_second_crew(self):
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self.state.second_result = SecondCrew().crew().kickoff()
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flow = UsageMetricsFlow()
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flow.kickoff()
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print(flow.usage_metrics)
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# UsageMetrics(total_tokens=8579, prompt_tokens=6210, completion_tokens=2369,
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# cached_prompt_tokens=0, reasoning_tokens=0,
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# cache_creation_tokens=0, successful_requests=5)
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```
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<Note>
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`flow.usage_metrics` **não** é o mesmo que `flow.kickoff().token_usage`. Este
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último retorna apenas o `CrewOutput.token_usage` do **último** método
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`@listen` que retornou um `CrewOutput`, ou seja, reflete somente a Crew
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final e ignora completamente as Crews anteriores e quaisquer chamadas
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diretas de `LLM.call(...)`. Use `flow.usage_metrics` sempre que precisar do
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rollup **completo** de tokens da execução do Flow.
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</Note>
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Cada campo do [`UsageMetrics`](https://github.com/crewAIInc/crewAI/blob/main/lib/crewai/src/crewai/types/usage_metrics.py) retornado representa a soma de todas as chamadas de LLM feitas em uma única invocação de `flow.kickoff()`. Os contadores são resetados a cada novo `kickoff()` (e em cada iteração de `kickoff_for_each`), de modo que execuções sucessivas não duplicam o total. A propriedade é segura para ser lida em qualquer momento após o `kickoff()`; lê-la durante a execução retorna o total parcial acumulado até aquele instante.
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## Gerenciamento de Estado em Flows
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Gerenciar o estado de forma eficaz é fundamental para construir fluxos de trabalho de IA confiáveis e de fácil manutenção. O CrewAI Flows oferece mecanismos robustos para o gerenciamento de estado tanto não estruturado quanto estruturado,
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