Merge branch 'main' into chore/runtime-state-event-bus

This commit is contained in:
Greyson LaLonde
2026-04-04 04:12:18 +08:00
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---
title: "تدريب الطواقم"
description: "قم بتدريب طواقمك المنشورة مباشرة من منصة CrewAI AMP لتحسين أداء الوكلاء بمرور الوقت"
icon: "dumbbell"
mode: "wide"
---
يتيح لك التدريب تحسين أداء الطاقم من خلال تشغيل جلسات تدريب تكرارية مباشرة من علامة تبويب **Training** في CrewAI AMP. تستخدم المنصة **وضع التدريب التلقائي** — حيث تتولى العملية التكرارية تلقائياً، على عكس تدريب CLI الذي يتطلب ملاحظات بشرية تفاعلية لكل تكرار.
بعد اكتمال التدريب، يقوم CrewAI بتقييم مخرجات الوكلاء ودمج الملاحظات في اقتراحات قابلة للتنفيذ لكل وكيل. يتم بعد ذلك تطبيق هذه الاقتراحات على تشغيلات الطاقم المستقبلية لتحسين جودة المخرجات.
<Tip>
للحصول على تفاصيل حول كيفية عمل تدريب CrewAI، راجع صفحة [مفاهيم التدريب](/ar/concepts/training).
</Tip>
## المتطلبات الأساسية
<CardGroup cols={2}>
<Card title="نشر نشط" icon="rocket">
تحتاج إلى حساب CrewAI AMP مع نشر نشط في حالة **Ready** (نوع Crew).
</Card>
<Card title="صلاحية التشغيل" icon="key">
يجب أن يكون لحسابك صلاحية تشغيل للنشر الذي تريد تدريبه.
</Card>
</CardGroup>
## كيفية تدريب طاقم
<Steps>
<Step title="افتح علامة تبويب Training">
انتقل إلى **Deployments**، انقر على نشرك، ثم اختر علامة تبويب **Training**.
</Step>
<Step title="أدخل اسم التدريب">
قدم **Training Name** — سيصبح هذا اسم ملف `.pkl` المستخدم لتخزين نتائج التدريب. على سبيل المثال، "Expert Mode Training" ينتج `expert_mode_training.pkl`.
</Step>
<Step title="املأ مدخلات الطاقم">
أدخل حقول إدخال الطاقم. هذه هي نفس المدخلات التي ستقدمها للتشغيل العادي — يتم تحميلها ديناميكياً بناءً على تكوين طاقمك.
</Step>
<Step title="ابدأ التدريب">
انقر على **Train Crew**. يتغير الزر إلى "Training..." مع مؤشر دوران أثناء تشغيل العملية.
خلف الكواليس:
- يتم إنشاء سجل تدريب للنشر الخاص بك
- تستدعي المنصة نقطة نهاية التدريب التلقائي للنشر
- يقوم الطاقم بتشغيل تكراراته تلقائياً — لا حاجة لملاحظات يدوية
</Step>
<Step title="راقب التقدم">
تعرض لوحة **Current Training Status**:
- **Status** — الحالة الحالية لجلسة التدريب
- **Nº Iterations** — عدد تكرارات التدريب المُهيأة
- **Filename** — ملف `.pkl` الذي يتم إنشاؤه
- **Started At** — وقت بدء التدريب
- **Training Inputs** — المدخلات التي قدمتها
</Step>
</Steps>
## فهم نتائج التدريب
بمجرد اكتمال التدريب، سترى بطاقات نتائج لكل وكيل تحتوي على المعلومات التالية:
- **Agent Role** — اسم/دور الوكيل في طاقمك
- **Final Quality** — درجة من 0 إلى 10 تقيّم جودة مخرجات الوكيل
- **Final Summary** — ملخص لأداء الوكيل أثناء التدريب
- **Suggestions** — توصيات قابلة للتنفيذ لتحسين سلوك الوكيل
### تحرير الاقتراحات
يمكنك تحسين الاقتراحات لأي وكيل:
<Steps>
<Step title="انقر على Edit">
في بطاقة نتائج أي وكيل، انقر على زر **Edit** بجوار الاقتراحات.
</Step>
<Step title="عدّل الاقتراحات">
حدّث نص الاقتراحات ليعكس التحسينات التي تريدها بشكل أفضل.
</Step>
<Step title="احفظ التغييرات">
انقر على **Save**. تتم مزامنة الاقتراحات المُعدّلة مع النشر وتُستخدم في جميع التشغيلات المستقبلية.
</Step>
</Steps>
## استخدام بيانات التدريب
لتطبيق نتائج التدريب على طاقمك:
1. لاحظ **Training Filename** (ملف `.pkl`) من جلسة التدريب المكتملة.
2. حدد اسم الملف هذا في تكوين kickoff أو التشغيل الخاص بنشرك.
3. يقوم الطاقم تلقائياً بتحميل ملف التدريب وتطبيق الاقتراحات المخزنة على كل وكيل.
هذا يعني أن الوكلاء يستفيدون من الملاحظات المُنشأة أثناء التدريب في كل تشغيل لاحق.
## التدريبات السابقة
يعرض الجزء السفلي من علامة تبويب Training **سجل جميع جلسات التدريب السابقة** للنشر. استخدم هذا لمراجعة التدريبات السابقة، ومقارنة النتائج، أو اختيار ملف تدريب مختلف للاستخدام.
## معالجة الأخطاء
إذا فشل تشغيل التدريب، تعرض لوحة الحالة حالة خطأ مع رسالة تصف ما حدث خطأ.
الأسباب الشائعة لفشل التدريب:
- **لم يتم تحديث وقت تشغيل النشر** — تأكد من أن نشرك يعمل بأحدث إصدار
- **أخطاء تنفيذ الطاقم** — مشاكل في منطق مهام الطاقم أو تكوين الوكيل
- **مشاكل الشبكة** — مشاكل الاتصال بين المنصة والنشر
## القيود
<Info>
ضع هذه القيود في الاعتبار عند التخطيط لسير عمل التدريب الخاص بك:
- **تدريب نشط واحد في كل مرة** لكل نشر — انتظر حتى ينتهي التشغيل الحالي قبل بدء آخر
- **وضع التدريب التلقائي فقط** — لا تدعم المنصة الملاحظات التفاعلية لكل تكرار مثل CLI
- **بيانات التدريب خاصة بالنشر** — ترتبط نتائج التدريب بمثيل وإصدار النشر المحدد
</Info>
## الموارد ذات الصلة
<CardGroup cols={3}>
<Card title="مفاهيم التدريب" icon="book" href="/ar/concepts/training">
تعلم كيف يعمل تدريب CrewAI.
</Card>
<Card title="تشغيل الطاقم" icon="play" href="/ar/enterprise/guides/kickoff-crew">
قم بتشغيل طاقمك المنشور من منصة AMP.
</Card>
<Card title="النشر على AMP" icon="cloud-arrow-up" href="/ar/enterprise/guides/deploy-to-amp">
انشر طاقمك واجعله جاهزاً للتدريب.
</Card>
</CardGroup>

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@@ -2342,6 +2342,7 @@
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---
title: "Training Crews"
description: "Train your deployed crews directly from the CrewAI AMP platform to improve agent performance over time"
icon: "dumbbell"
mode: "wide"
---
Training lets you improve crew performance by running iterative training sessions directly from the **Training** tab in CrewAI AMP. The platform uses **auto-train mode** — it handles the iterative process automatically, unlike CLI training which requires interactive human feedback per iteration.
After training completes, CrewAI evaluates agent outputs and consolidates feedback into actionable suggestions for each agent. These suggestions are then applied to future crew runs to improve output quality.
<Tip>
For details on how CrewAI training works under the hood, see the [Training Concepts](/en/concepts/training) page.
</Tip>
## Prerequisites
<CardGroup cols={2}>
<Card title="Active deployment" icon="rocket">
You need a CrewAI AMP account with an active deployment in **Ready** status (Crew type).
</Card>
<Card title="Run permission" icon="key">
Your account must have run permission for the deployment you want to train.
</Card>
</CardGroup>
## How to train a crew
<Steps>
<Step title="Open the Training tab">
Navigate to **Deployments**, click your deployment, then select the **Training** tab.
</Step>
<Step title="Enter a training name">
Provide a **Training Name** — this becomes the `.pkl` filename used to store training results. For example, "Expert Mode Training" produces `expert_mode_training.pkl`.
</Step>
<Step title="Fill in the crew inputs">
Enter the crew's input fields. These are the same inputs you'd provide for a normal kickoff — they're dynamically loaded based on your crew's configuration.
</Step>
<Step title="Start training">
Click **Train Crew**. The button changes to "Training..." with a spinner while the process runs.
Behind the scenes:
- A training record is created for your deployment
- The platform calls the deployment's auto-train endpoint
- The crew runs its iterations automatically — no manual feedback required
</Step>
<Step title="Monitor progress">
The **Current Training Status** panel displays:
- **Status** — Current state of the training run
- **Nº Iterations** — Number of training iterations configured
- **Filename** — The `.pkl` file being generated
- **Started At** — When training began
- **Training Inputs** — The inputs you provided
</Step>
</Steps>
## Understanding training results
Once training completes, you'll see per-agent result cards with the following information:
- **Agent Role** — The name/role of the agent in your crew
- **Final Quality** — A score from 0 to 10 evaluating the agent's output quality
- **Final Summary** — A summary of the agent's performance during training
- **Suggestions** — Actionable recommendations for improving the agent's behavior
### Editing suggestions
You can refine the suggestions for any agent:
<Steps>
<Step title="Click Edit">
On any agent's result card, click the **Edit** button next to the suggestions.
</Step>
<Step title="Modify suggestions">
Update the suggestions text to better reflect the improvements you want.
</Step>
<Step title="Save changes">
Click **Save**. The edited suggestions sync back to the deployment and are used in all future runs.
</Step>
</Steps>
## Using trained data
To apply training results to your crew:
1. Note the **Training Filename** (the `.pkl` file) from your completed training session.
2. Specify this filename in your deployment's kickoff or run configuration.
3. The crew automatically loads the training file and applies the stored suggestions to each agent.
This means agents benefit from the feedback generated during training on every subsequent run.
## Previous trainings
The bottom of the Training tab displays a **history of all past training sessions** for the deployment. Use this to review previous training runs, compare results, or select a different training file to use.
## Error handling
If a training run fails, the status panel shows an error state along with a message describing what went wrong.
Common causes of training failures:
- **Deployment runtime not updated** — Ensure your deployment is running the latest version
- **Crew execution errors** — Issues within the crew's task logic or agent configuration
- **Network issues** — Connectivity problems between the platform and the deployment
## Limitations
<Info>
Keep these constraints in mind when planning your training workflow:
- **One active training at a time** per deployment — wait for the current run to finish before starting another
- **Auto-train mode only** — the platform does not support interactive per-iteration feedback like the CLI does
- **Training data is deployment-specific** — training results are tied to the specific deployment instance and version
</Info>
## Related resources
<CardGroup cols={3}>
<Card title="Training Concepts" icon="book" href="/en/concepts/training">
Learn how CrewAI training works under the hood.
</Card>
<Card title="Kickoff Crew" icon="play" href="/en/enterprise/guides/kickoff-crew">
Run your deployed crew from the AMP platform.
</Card>
<Card title="Deploy to AMP" icon="cloud-arrow-up" href="/en/enterprise/guides/deploy-to-amp">
Get your crew deployed and ready for training.
</Card>
</CardGroup>

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---
title: "Crew 훈련"
description: "CrewAI AMP 플랫폼에서 직접 배포된 Crew를 훈련하여 시간이 지남에 따라 에이전트 성능을 개선하세요"
icon: "dumbbell"
mode: "wide"
---
훈련을 통해 CrewAI AMP의 **Training** 탭에서 직접 반복 훈련 세션을 실행하여 Crew 성능을 개선할 수 있습니다. 플랫폼은 **자동 훈련 모드**를 사용합니다 — 반복 프로세스를 자동으로 처리하며, 반복마다 대화형 피드백이 필요한 CLI 훈련과는 다릅니다.
훈련이 완료되면 CrewAI는 에이전트 출력을 평가하고 각 에이전트에 대한 실행 가능한 제안으로 피드백을 통합합니다. 이러한 제안은 향후 Crew 실행에 적용되어 출력 품질을 개선합니다.
<Tip>
CrewAI 훈련이 내부적으로 어떻게 작동하는지에 대한 자세한 내용은 [훈련 개념](/ko/concepts/training) 페이지를 참조하세요.
</Tip>
## 사전 요구 사항
<CardGroup cols={2}>
<Card title="활성 배포" icon="rocket">
**Ready** 상태의 활성 배포(Crew 유형)가 있는 CrewAI AMP 계정이 필요합니다.
</Card>
<Card title="실행 권한" icon="key">
훈련하려는 배포에 대한 실행 권한이 계정에 있어야 합니다.
</Card>
</CardGroup>
## Crew 훈련 방법
<Steps>
<Step title="Training 탭 열기">
**Deployments**로 이동하여 배포를 클릭한 다음 **Training** 탭을 선택합니다.
</Step>
<Step title="훈련 이름 입력">
**Training Name**을 입력합니다 — 이것은 훈련 결과를 저장하는 데 사용되는 `.pkl` 파일 이름이 됩니다. 예를 들어, "Expert Mode Training"은 `expert_mode_training.pkl`을 생성합니다.
</Step>
<Step title="Crew 입력값 작성">
Crew의 입력 필드를 입력합니다. 이는 일반 kickoff에 제공하는 것과 동일한 입력값입니다 — Crew 구성에 따라 동적으로 로드됩니다.
</Step>
<Step title="훈련 시작">
**Train Crew**를 클릭합니다. 프로세스가 실행되는 동안 버튼이 스피너와 함께 "Training..."으로 변경됩니다.
내부적으로:
- 배포에 대한 훈련 레코드가 생성됩니다
- 플랫폼이 배포의 자동 훈련 엔드포인트를 호출합니다
- Crew가 자동으로 반복을 실행합니다 — 수동 피드백이 필요하지 않습니다
</Step>
<Step title="진행 상황 모니터링">
**Current Training Status** 패널에 다음이 표시됩니다:
- **Status** — 훈련 실행의 현재 상태
- **Nº Iterations** — 구성된 훈련 반복 횟수
- **Filename** — 생성 중인 `.pkl` 파일
- **Started At** — 훈련 시작 시간
- **Training Inputs** — 제공한 입력값
</Step>
</Steps>
## 훈련 결과 이해
훈련이 완료되면 다음 정보가 포함된 에이전트별 결과 카드가 표시됩니다:
- **Agent Role** — Crew에서 에이전트의 이름/역할
- **Final Quality** — 에이전트 출력 품질을 평가하는 0~10점 점수
- **Final Summary** — 훈련 중 에이전트 성능 요약
- **Suggestions** — 에이전트 동작 개선을 위한 실행 가능한 권장 사항
### 제안 편집
모든 에이전트의 제안을 개선할 수 있습니다:
<Steps>
<Step title="Edit 클릭">
에이전트의 결과 카드에서 제안 옆에 있는 **Edit** 버튼을 클릭합니다.
</Step>
<Step title="제안 수정">
원하는 개선 사항을 더 잘 반영하도록 제안 텍스트를 업데이트합니다.
</Step>
<Step title="변경 사항 저장">
**Save**를 클릭합니다. 편집된 제안이 배포에 다시 동기화되고 이후 모든 실행에 사용됩니다.
</Step>
</Steps>
## 훈련 데이터 사용
Crew에 훈련 결과를 적용하려면:
1. 완료된 훈련 세션에서 **Training Filename**(`.pkl` 파일)을 확인합니다.
2. 배포의 kickoff 또는 실행 구성에서 이 파일 이름을 지정합니다.
3. Crew가 자동으로 훈련 파일을 로드하고 저장된 제안을 각 에이전트에 적용합니다.
이는 에이전트가 이후 모든 실행에서 훈련 중에 생성된 피드백의 혜택을 받는다는 것을 의미합니다.
## 이전 훈련
Training 탭 하단에는 배포에 대한 **모든 과거 훈련 세션 기록**이 표시됩니다. 이전 훈련 실행을 검토하거나 결과를 비교하거나 사용할 다른 훈련 파일을 선택하는 데 사용합니다.
## 오류 처리
훈련 실행이 실패하면 상태 패널에 무엇이 잘못되었는지 설명하는 메시지와 함께 오류 상태가 표시됩니다.
훈련 실패의 일반적인 원인:
- **배포 런타임이 업데이트되지 않음** — 배포가 최신 버전을 실행하고 있는지 확인하세요
- **Crew 실행 오류** — Crew의 작업 로직 또는 에이전트 구성 내 문제
- **네트워크 문제** — 플랫폼과 배포 간의 연결 문제
## 제한 사항
<Info>
훈련 워크플로를 계획할 때 다음 제약 사항을 염두에 두세요:
- **배포당 한 번에 하나의 활성 훈련** — 다른 훈련을 시작하기 전에 현재 실행이 완료될 때까지 기다리세요
- **자동 훈련 모드만** — 플랫폼은 CLI처럼 반복당 대화형 피드백을 지원하지 않습니다
- **훈련 데이터는 배포별** — 훈련 결과는 특정 배포 인스턴스 및 버전에 연결됩니다
</Info>
## 관련 리소스
<CardGroup cols={3}>
<Card title="훈련 개념" icon="book" href="/ko/concepts/training">
CrewAI 훈련이 내부적으로 어떻게 작동하는지 알아보세요.
</Card>
<Card title="Crew 시작" icon="play" href="/ko/enterprise/guides/kickoff-crew">
AMP 플랫폼에서 배포된 Crew를 실행하세요.
</Card>
<Card title="AMP에 배포" icon="cloud-arrow-up" href="/ko/enterprise/guides/deploy-to-amp">
Crew를 배포하고 훈련 준비를 완료하세요.
</Card>
</CardGroup>

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---
title: "Treinamento de Crews"
description: "Treine seus crews implantados diretamente da plataforma CrewAI AMP para melhorar o desempenho dos agentes ao longo do tempo"
icon: "dumbbell"
mode: "wide"
---
O treinamento permite que você melhore o desempenho do crew executando sessões de treinamento iterativas diretamente da aba **Training** no CrewAI AMP. A plataforma usa o **modo de auto-treinamento** — ela gerencia o processo iterativo automaticamente, diferente do treinamento via CLI que requer feedback humano interativo por iteração.
Após a conclusão do treinamento, o CrewAI avalia as saídas dos agentes e consolida o feedback em sugestões acionáveis para cada agente. Essas sugestões são então aplicadas às execuções futuras do crew para melhorar a qualidade das saídas.
<Tip>
Para detalhes sobre como o treinamento do CrewAI funciona internamente, consulte a página [Conceitos de Treinamento](/pt-BR/concepts/training).
</Tip>
## Pré-requisitos
<CardGroup cols={2}>
<Card title="Implantação ativa" icon="rocket">
Você precisa de uma conta CrewAI AMP com uma implantação ativa em status **Ready** (tipo Crew).
</Card>
<Card title="Permissão de execução" icon="key">
Sua conta deve ter permissão de execução para a implantação que deseja treinar.
</Card>
</CardGroup>
## Como treinar um crew
<Steps>
<Step title="Abra a aba Training">
Navegue até **Deployments**, clique na sua implantação e selecione a aba **Training**.
</Step>
<Step title="Insira um nome de treinamento">
Forneça um **Training Name** — este será o nome do arquivo `.pkl` usado para armazenar os resultados do treinamento. Por exemplo, "Expert Mode Training" produz `expert_mode_training.pkl`.
</Step>
<Step title="Preencha as entradas do crew">
Insira os campos de entrada do crew. Estas são as mesmas entradas que você forneceria para um kickoff normal — elas são carregadas dinamicamente com base na configuração do seu crew.
</Step>
<Step title="Inicie o treinamento">
Clique em **Train Crew**. O botão muda para "Training..." com um spinner enquanto o processo é executado.
Por trás dos panos:
- Um registro de treinamento é criado para sua implantação
- A plataforma chama o endpoint de auto-treinamento da implantação
- O crew executa suas iterações automaticamente — nenhum feedback manual é necessário
</Step>
<Step title="Monitore o progresso">
O painel **Current Training Status** exibe:
- **Status** — Estado atual da execução do treinamento
- **Nº Iterations** — Número de iterações de treinamento configuradas
- **Filename** — O arquivo `.pkl` sendo gerado
- **Started At** — Quando o treinamento começou
- **Training Inputs** — As entradas que você forneceu
</Step>
</Steps>
## Entendendo os resultados do treinamento
Uma vez que o treinamento for concluído, você verá cards de resultado por agente com as seguintes informações:
- **Agent Role** — O nome/função do agente no seu crew
- **Final Quality** — Uma pontuação de 0 a 10 avaliando a qualidade da saída do agente
- **Final Summary** — Um resumo do desempenho do agente durante o treinamento
- **Suggestions** — Recomendações acionáveis para melhorar o comportamento do agente
### Editando sugestões
Você pode refinar as sugestões para qualquer agente:
<Steps>
<Step title="Clique em Edit">
No card de resultado de qualquer agente, clique no botão **Edit** ao lado das sugestões.
</Step>
<Step title="Modifique as sugestões">
Atualize o texto das sugestões para refletir melhor as melhorias que você deseja.
</Step>
<Step title="Salve as alterações">
Clique em **Save**. As sugestões editadas são sincronizadas de volta à implantação e usadas em todas as execuções futuras.
</Step>
</Steps>
## Usando dados de treinamento
Para aplicar os resultados do treinamento ao seu crew:
1. Anote o **Training Filename** (o arquivo `.pkl`) da sua sessão de treinamento concluída.
2. Especifique este nome de arquivo na configuração de kickoff ou execução da sua implantação.
3. O crew carrega automaticamente o arquivo de treinamento e aplica as sugestões armazenadas a cada agente.
Isso significa que os agentes se beneficiam do feedback gerado durante o treinamento em cada execução subsequente.
## Treinamentos anteriores
A parte inferior da aba Training exibe um **histórico de todas as sessões de treinamento anteriores** da implantação. Use isso para revisar execuções de treinamento anteriores, comparar resultados ou selecionar um arquivo de treinamento diferente para usar.
## Tratamento de erros
Se uma execução de treinamento falhar, o painel de status mostra um estado de erro junto com uma mensagem descrevendo o que deu errado.
Causas comuns de falhas de treinamento:
- **Runtime da implantação não atualizado** — Certifique-se de que sua implantação está executando a versão mais recente
- **Erros de execução do crew** — Problemas na lógica de tarefas do crew ou configuração do agente
- **Problemas de rede** — Problemas de conectividade entre a plataforma e a implantação
## Limitações
<Info>
Tenha estas restrições em mente ao planejar seu fluxo de trabalho de treinamento:
- **Um treinamento ativo por vez** por implantação — aguarde a execução atual terminar antes de iniciar outra
- **Apenas modo de auto-treinamento** — a plataforma não suporta feedback interativo por iteração como o CLI
- **Dados de treinamento são específicos da implantação** — os resultados do treinamento estão vinculados à instância e versão específicas da implantação
</Info>
## Recursos relacionados
<CardGroup cols={3}>
<Card title="Conceitos de Treinamento" icon="book" href="/pt-BR/concepts/training">
Aprenda como o treinamento do CrewAI funciona internamente.
</Card>
<Card title="Kickoff Crew" icon="play" href="/pt-BR/enterprise/guides/kickoff-crew">
Execute seu crew implantado a partir da plataforma AMP.
</Card>
<Card title="Implantar no AMP" icon="cloud-arrow-up" href="/pt-BR/enterprise/guides/deploy-to-amp">
Faça a implantação do seu crew e deixe-o pronto para treinamento.
</Card>
</CardGroup>