+
+
+### Email Notifications
+
+Toggle to enable or disable email notifications for HITL requests.
+
+| Setting | Default | Description |
+|---------|---------|-------------|
+| Email Notifications | Enabled | Send emails when feedback is requested |
+
+
+
+
+### Rule Structure
+
+```json
+{
+ "name": "Approvals to Finance",
+ "match": {
+ "method_name": "approve_*"
+ },
+ "assign_to_email": "finance@company.com",
+ "assign_from_input": "manager_email"
+}
+```
+
+### Matching Patterns
+
+| Pattern | Description | Example Match |
+|---------|-------------|---------------|
+| `approve_*` | Wildcard (any chars) | `approve_payment`, `approve_vendor` |
+| `review_?` | Single char | `review_a`, `review_1` |
+| `validate_payment` | Exact match | `validate_payment` only |
+
+### Assignment Priority
+
+1. **Dynamic assignment** (`assign_from_input`): If configured, pulls email from flow state
+2. **Static email** (`assign_to_email`): Falls back to configured email
+3. **Deployment creator**: If no rule matches, the deployment creator's email is used
+
+### Dynamic Assignment Example
+
+If your flow state contains `{"sales_rep_email": "alice@company.com"}`, configure:
+
+```json
+{
+ "name": "Route to Sales Rep",
+ "match": {
+ "method_name": "review_*"
+ },
+ "assign_from_input": "sales_rep_email"
+}
+```
+
+The request will be assigned to `alice@company.com` automatically.
+
+
+
+
+### Use Cases
+
+- **SLA compliance**: Ensure flows don't hang indefinitely
+- **Default approval**: Auto-approve low-risk requests after timeout
+- **Graceful degradation**: Continue with a safe default when reviewers are unavailable
+
+
+
+
+### Response Methods
+
+Reviewers can respond via three channels:
+
+| Method | Description |
+|--------|-------------|
+| **Email Reply** | Reply directly to the notification email |
+| **Dashboard** | Use the Enterprise dashboard UI |
+| **API/Webhook** | Programmatic response via API |
+
+### History & Audit Trail
+
+Every HITL interaction is tracked with a complete timeline:
+
+- Decision history (approve/reject/revise)
+- Reviewer identity and timestamp
+- Feedback and comments provided
+- Response method (email/dashboard/API)
+- Response time metrics
+
+## Analytics & Monitoring
+
+Track HITL performance with comprehensive analytics.
+
+### Performance Dashboard
+
+
+ 
+
+
+
+
+
+### Configuring Webhooks
+
+
+
This view shows all the trigger integrations available for your deployment, along with their current connection status.
@@ -86,7 +113,10 @@ This view shows all the trigger integrations available for your deployment, alon
Each trigger can be easily enabled or disabled using the toggle switch:
- 
+
- **Enabled (blue toggle)**: The trigger is active and will automatically execute your deployment when the specified events occur
@@ -99,7 +129,10 @@ Simply click the toggle to change the trigger state. Changes take effect immedia
Track the performance and history of your triggered executions:
- 
+
## Building Trigger-Driven Automations
@@ -130,6 +163,7 @@ crewai triggers list
```
This command displays all triggers available based on your connected integrations, showing:
+
- Integration name and connection status
- Available trigger types
- Trigger names and descriptions
@@ -149,6 +183,7 @@ crewai triggers run microsoft_onedrive/file_changed
```
This command:
+
- Executes your crew locally
- Passes a complete, realistic trigger payload
- Simulates exactly how your crew will be called in production
@@ -161,7 +196,6 @@ This command:
- If your crew expects parameters that aren't in the trigger payload, execution may fail
-
### Triggers with Crew
Your existing crew definitions work seamlessly with triggers, you just need to have a task to parse the received payload:
@@ -193,10 +227,12 @@ class MyAutomatedCrew:
The crew will automatically receive and can access the trigger payload through the standard CrewAI context mechanisms.
+
## Example: Process new emails
@@ -62,13 +66,15 @@ Test your Gmail trigger integration locally using the CrewAI CLI:
crewai triggers list
# Simulate a Gmail trigger with realistic payload
-crewai triggers run gmail/new_email
+crewai triggers run gmail/new_email_received
```
The `crewai triggers run` command will execute your crew with a complete Gmail payload, allowing you to test your parsing logic before deployment.
+
## Troubleshooting
- Ensure Gmail is connected in Tools & Integrations
- Verify the Gmail Trigger is enabled on the Triggers tab
-- Test locally with `crewai triggers run gmail/new_email` to see the exact payload structure
+- Test locally with `crewai triggers run gmail/new_email_received` to see the exact payload structure
- Check the execution logs and confirm the payload is passed as `crewai_trigger_payload`
- Remember: use `crewai triggers run` (not `crewai run`) to simulate trigger execution
diff --git a/docs/en/enterprise/guides/google-calendar-trigger.mdx b/docs/en/enterprise/guides/google-calendar-trigger.mdx
index 4dee7a3dd..5a5f66a2b 100644
--- a/docs/en/enterprise/guides/google-calendar-trigger.mdx
+++ b/docs/en/enterprise/guides/google-calendar-trigger.mdx
@@ -10,7 +10,8 @@ mode: "wide"
Use the Google Calendar trigger to launch automations whenever calendar events change. Common use cases include briefing a team before a meeting, notifying stakeholders when a critical event is cancelled, or summarizing daily schedules.
+
## Example: Summarize meeting details
@@ -54,7 +58,9 @@ crewai triggers run google_calendar/event_changed
The `crewai triggers run` command will execute your crew with a complete Calendar payload, allowing you to test your parsing logic before deployment.
+
## Troubleshooting
diff --git a/docs/en/enterprise/guides/google-drive-trigger.mdx b/docs/en/enterprise/guides/google-drive-trigger.mdx
index f0fc4e938..0baf1cae3 100644
--- a/docs/en/enterprise/guides/google-drive-trigger.mdx
+++ b/docs/en/enterprise/guides/google-drive-trigger.mdx
@@ -10,7 +10,8 @@ mode: "wide"
Trigger your automations when files are created, updated, or removed in Google Drive. Typical workflows include summarizing newly uploaded content, enforcing sharing policies, or notifying owners when critical files change.
+
## Example: Summarize file activity
@@ -51,7 +55,9 @@ crewai triggers run google_drive/file_changed
The `crewai triggers run` command will execute your crew with a complete Drive payload, allowing you to test your parsing logic before deployment.
+
## Troubleshooting
diff --git a/docs/en/enterprise/guides/hubspot-trigger.mdx b/docs/en/enterprise/guides/hubspot-trigger.mdx
index 0c95db0f6..9dfae4fba 100644
--- a/docs/en/enterprise/guides/hubspot-trigger.mdx
+++ b/docs/en/enterprise/guides/hubspot-trigger.mdx
@@ -15,38 +15,47 @@ This guide provides a step-by-step process to set up HubSpot triggers for CrewAI
## Setup Steps
-
- 
-
- - Configure any additional actions as needed
- - Review your workflow steps to ensure everything is set up correctly
- - Activate the workflow
-
- 
-
-
+
+
+
+ - Configure any additional actions as needed - Review your workflow
+ steps to ensure everything is set up correctly - Activate the workflow
+
+
+
+ 
+
## Example: Summarize a new chat thread
@@ -52,7 +56,9 @@ crewai triggers run microsoft_teams/teams_message_created
The `crewai triggers run` command will execute your crew with a complete Teams payload, allowing you to test your parsing logic before deployment.
+
## Example: Audit file permissions
@@ -51,7 +55,9 @@ crewai triggers run microsoft_onedrive/file_changed
The `crewai triggers run` command will execute your crew with a complete OneDrive payload, allowing you to test your parsing logic before deployment.
+
## Example: Summarize a new email
@@ -51,7 +55,9 @@ crewai triggers run microsoft_outlook/email_received
The `crewai triggers run` command will execute your crew with a complete Outlook payload, allowing you to test your parsing logic before deployment.
+
- 
+
## Next Steps
diff --git a/docs/en/enterprise/guides/team-management.mdx b/docs/en/enterprise/guides/team-management.mdx
index cc51f1824..c9258cd4d 100644
--- a/docs/en/enterprise/guides/team-management.mdx
+++ b/docs/en/enterprise/guides/team-management.mdx
@@ -10,31 +10,30 @@ As an administrator of a CrewAI AMP account, you can easily invite new team memb
## Inviting Team Members
-
- 
-
-
+
+
+
+
-
- 
-
- - Click on the `Add Role` button to add a new role.
- - Enter the details and permissions of the role and click the `Create Role` button to create the role.
-
- 
-
- 
-
- - Once the member has accepted the invitation, you can add a role to them.
- - Navigate back to `Roles` tab
- - Go to the member you want to add a role to and under the `Role` column, click on the dropdown
- - Select the role you want to add to the member
- - Click the `Update` button to save the role
-
- 
-
-
+
+
+
+ - Click on the `Add Role` button to add a new role. - Enter the
+ details and permissions of the role and click the `Create Role` button to
+ create the role.
+
+
+
+
+
+ - Once the member has accepted the invitation, you can add a role to
+ them. - Navigate back to `Roles` tab - Go to the member you want to add a
+ role to and under the `Role` column, click on the dropdown - Select the role
+ you want to add to the member - Click the `Update` button to save the role
+
+
+
+ 
+
## Webhook Output Examples
@@ -152,4 +153,5 @@ CrewAI AMP allows you to automate your workflow using webhooks. This article wil
}
```
+
diff --git a/docs/en/enterprise/guides/zapier-trigger.mdx b/docs/en/enterprise/guides/zapier-trigger.mdx
index df586a781..74400b884 100644
--- a/docs/en/enterprise/guides/zapier-trigger.mdx
+++ b/docs/en/enterprise/guides/zapier-trigger.mdx
@@ -93,6 +93,7 @@ This guide will walk you through the process of setting up Zapier triggers for C
+
## Tips for Success
diff --git a/docs/en/enterprise/integrations/asana.mdx b/docs/en/enterprise/integrations/asana.mdx
index dd9e7bb3d..4b025b674 100644
--- a/docs/en/enterprise/integrations/asana.mdx
+++ b/docs/en/enterprise/integrations/asana.mdx
@@ -36,7 +36,9 @@ uv add crewai-tools
### 3. Environment Variable Setup
+
CrewAI AMP extends the power of the open-source framework with features designed for production deployments, collaboration, and scalability. Deploy your crews to a managed infrastructure and monitor their execution in real-time.
@@ -22,7 +25,8 @@ CrewAI AMP extends the power of the open-source framework with features designed
Deploy your crews to a managed infrastructure with a few clicks
-
-
-| Component | Description | Key Features |
-|:----------|:-----------:|:------------|
-| **Crew** | The top-level organization | • Manages AI agent teams• Oversees workflows
• Ensures collaboration
• Delivers outcomes | -| **AI Agents** | Specialized team members | • Have specific roles (researcher, writer)
• Use designated tools
• Can delegate tasks
• Make autonomous decisions | -| **Process** | Workflow management system | • Defines collaboration patterns
• Controls task assignments
• Manages interactions
• Ensures efficient execution | -| **Tasks** | Individual assignments | • Have clear objectives
• Use specific tools
• Feed into larger process
• Produce actionable results | - -### How It All Works Together - -1. The **Crew** organizes the overall operation -2. **AI Agents** work on their specialized tasks -3. The **Process** ensures smooth collaboration -4. **Tasks** get completed to achieve the goal - -## Key Features - -
-| Component | Description | Key Features |
-|:----------|:-----------:|:------------|
-| **Flow** | Structured workflow orchestration | • Manages execution paths• Handles state transitions
• Controls task sequencing
• Ensures reliable execution | -| **Events** | Triggers for workflow actions | • Initiate specific processes
• Enable dynamic responses
• Support conditional branching
• Allow for real-time adaptation | -| **States** | Workflow execution contexts | • Maintain execution data
• Enable persistence
• Support resumability
• Ensure execution integrity | -| **Crew Support** | Enhances workflow automation | • Injects pockets of agency when needed
• Complements structured workflows
• Balances automation with intelligence
• Enables adaptive decision-making | +Flows provide: +- **State Management**: Persist data across steps and executions. +- **Event-Driven Execution**: Trigger actions based on events or external inputs. +- **Control Flow**: Use conditional logic, loops, and branching. -### Key Capabilities +### 2. Crews: The Intelligence + +
+
+
+Crews provide:
+- **Role-Playing Agents**: Specialized agents with specific goals and tools.
+- **Autonomous Collaboration**: Agents work together to solve tasks.
+- **Task Delegation**: Tasks are assigned and executed based on agent capabilities.
+
+## How It All Works Together
+
+1. **The Flow** triggers an event or starts a process.
+2. **The Flow** manages the state and decides what to do next.
+3. **The Flow** delegates a complex task to a **Crew**.
+4. **The Crew**'s agents collaborate to complete the task.
+5. **The Crew** returns the result to the **Flow**.
+6. **The Flow** continues execution based on the result.
+
+## Key Features
+
+
+### 이메일 알림
+
+HITL 요청에 대한 이메일 알림을 활성화하거나 비활성화하는 토글입니다.
+
+| 설정 | 기본값 | 설명 |
+|-----|-------|------|
+| 이메일 알림 | 활성화됨 | 피드백 요청 시 이메일 전송 |
+
+
+
+
+### 규칙 구조
+
+```json
+{
+ "name": "재무팀으로 승인",
+ "match": {
+ "method_name": "approve_*"
+ },
+ "assign_to_email": "finance@company.com",
+ "assign_from_input": "manager_email"
+}
+```
+
+### 매칭 패턴
+
+| 패턴 | 설명 | 매칭 예시 |
+|-----|------|----------|
+| `approve_*` | 와일드카드 (모든 문자) | `approve_payment`, `approve_vendor` |
+| `review_?` | 단일 문자 | `review_a`, `review_1` |
+| `validate_payment` | 정확히 일치 | `validate_payment`만 |
+
+### 할당 우선순위
+
+1. **동적 할당** (`assign_from_input`): 구성된 경우 Flow 상태에서 이메일 가져옴
+2. **정적 이메일** (`assign_to_email`): 구성된 이메일로 대체
+3. **배포 생성자**: 규칙이 일치하지 않으면 배포 생성자의 이메일이 사용됨
+
+### 동적 할당 예제
+
+Flow 상태에 `{"sales_rep_email": "alice@company.com"}`이 포함된 경우:
+
+```json
+{
+ "name": "영업 담당자에게 라우팅",
+ "match": {
+ "method_name": "review_*"
+ },
+ "assign_from_input": "sales_rep_email"
+}
+```
+
+요청이 자동으로 `alice@company.com`에 할당됩니다.
+
+
+
+
+### 사용 사례
+
+- **SLA 준수**: Flow가 무한정 중단되지 않도록 보장
+- **기본 승인**: 타임아웃 후 저위험 요청 자동 승인
+- **우아한 저하**: 검토자가 없을 때 안전한 기본값으로 계속
+
+
+
+
+### 응답 방법
+
+검토자는 세 가지 채널을 통해 응답할 수 있습니다:
+
+| 방법 | 설명 |
+|-----|------|
+| **이메일 회신** | 알림 이메일에 직접 회신 |
+| **대시보드** | Enterprise 대시보드 UI 사용 |
+| **API/Webhook** | API를 통한 프로그래밍 방식 응답 |
+
+### 기록 및 감사 추적
+
+모든 HITL 상호작용은 완전한 타임라인으로 추적됩니다:
+
+- 결정 기록 (승인/거부/수정)
+- 검토자 신원 및 타임스탬프
+- 제공된 피드백 및 코멘트
+- 응답 방법 (이메일/대시보드/API)
+- 응답 시간 메트릭
+
+## 분석 및 모니터링
+
+포괄적인 분석으로 HITL 성능을 추적합니다.
+
+### 성능 대시보드
+
+
+
+
+
+
+
+
+### Webhook 구성
+
+
@@ -31,7 +31,8 @@ CrewAI AMP의 RBAC는 **조직 수준 역할**과 **자동화(Automation) 수준
Settings → Roles로 이동합니다.
+
### 트리거 활성화/비활성화
@@ -123,6 +150,7 @@ crewai triggers list
```
이 명령은 연결된 통합을 기반으로 사용 가능한 모든 트리거를 표시합니다:
+
- 통합 이름 및 연결 상태
- 사용 가능한 트리거 유형
- 트리거 이름 및 설명
@@ -142,6 +170,7 @@ crewai triggers run microsoft_onedrive/file_changed
```
이 명령은:
+
- 로컬에서 크루를 실행합니다
- 완전하고 실제적인 트리거 payload를 전달합니다
- 프로덕션에서 크루가 호출되는 방식을 정확히 시뮬레이션합니다
@@ -221,17 +250,20 @@ def delegate_to_crew(self, crewai_trigger_payload: dict = None):
## 문제 해결
**트리거가 실행되지 않나요?**
+
- 배포의 Triggers 탭에서 트리거가 활성화되어 있는지 확인하세요
- Tools & Integrations에서 통합 연결 상태를 확인하세요
- 필요한 모든 환경 변수가 올바르게 구성되어 있는지 확인하세요
**실행 중 오류가 발생하나요?**
+
- 실행 로그에서 오류 세부 정보를 확인하세요
- `crewai triggers run <트리거_이름>`을 사용하여 로컬에서 테스트하고 정확한 payload 구조를 확인하세요
- 크루가 `crewai_trigger_payload` 매개변수를 처리할 수 있는지 확인하세요
- 크루가 트리거 payload에 포함되지 않은 매개변수를 기대하지 않는지 확인하세요
**개발 문제:**
+
- 배포하기 전에 항상 `crewai triggers run
+
## Example: Process new emails
@@ -62,13 +66,14 @@ CrewAI CLI를 사용하여 Gmail 트리거 통합을 로컬에서 테스트하
crewai triggers list
# 실제 payload로 Gmail 트리거 시뮬레이션
-crewai triggers run gmail/new_email
+crewai triggers run gmail/new_email_received
```
`crewai triggers run` 명령은 완전한 Gmail payload로 크루를 실행하여 배포 전에 파싱 로직을 테스트할 수 있게 해줍니다.
+
## Troubleshooting
- Ensure Gmail is connected in Tools & Integrations
- Verify the Gmail Trigger is enabled on the Triggers tab
-- `crewai triggers run gmail/new_email`로 로컬 테스트하여 정확한 payload 구조를 확인하세요
+- `crewai triggers run gmail/new_email_received`로 로컬 테스트하여 정확한 payload 구조를 확인하세요
- Check the execution logs and confirm the payload is passed as `crewai_trigger_payload`
- 주의: 트리거 실행을 시뮬레이션하려면 `crewai triggers run`을 사용하세요 (`crewai run`이 아님)
diff --git a/docs/ko/enterprise/guides/google-calendar-trigger.mdx b/docs/ko/enterprise/guides/google-calendar-trigger.mdx
index 6f279602e..c6b250a49 100644
--- a/docs/ko/enterprise/guides/google-calendar-trigger.mdx
+++ b/docs/ko/enterprise/guides/google-calendar-trigger.mdx
@@ -10,7 +10,8 @@ mode: "wide"
Use the Google Calendar trigger to launch automations whenever calendar events change. Common use cases include briefing a team before a meeting, notifying stakeholders when a critical event is cancelled, or summarizing daily schedules.
+
## Example: Summarize meeting details
@@ -54,7 +58,8 @@ crewai triggers run google_calendar/event_changed
`crewai triggers run` 명령은 완전한 Calendar payload로 크루를 실행하여 배포 전에 파싱 로직을 테스트할 수 있게 해줍니다.
+
## Troubleshooting
diff --git a/docs/ko/enterprise/guides/google-drive-trigger.mdx b/docs/ko/enterprise/guides/google-drive-trigger.mdx
index 3fd27bcd6..9a05c7c4f 100644
--- a/docs/ko/enterprise/guides/google-drive-trigger.mdx
+++ b/docs/ko/enterprise/guides/google-drive-trigger.mdx
@@ -10,7 +10,8 @@ mode: "wide"
Trigger your automations when files are created, updated, or removed in Google Drive. Typical workflows include summarizing newly uploaded content, enforcing sharing policies, or notifying owners when critical files change.
+
## Example: Summarize file activity
@@ -51,7 +55,8 @@ crewai triggers run google_drive/file_changed
`crewai triggers run` 명령은 완전한 Drive payload로 크루를 실행하여 배포 전에 파싱 로직을 테스트할 수 있게 해줍니다.
+
## Troubleshooting
diff --git a/docs/ko/enterprise/guides/hubspot-trigger.mdx b/docs/ko/enterprise/guides/hubspot-trigger.mdx
index 1818e48b1..a21c52f9d 100644
--- a/docs/ko/enterprise/guides/hubspot-trigger.mdx
+++ b/docs/ko/enterprise/guides/hubspot-trigger.mdx
@@ -5,7 +5,7 @@ icon: "hubspot"
mode: "wide"
---
-이 가이드는 HubSpot Workflows에서 직접 crew를 시작할 수 있도록 CrewAI AMP용 HubSpot 트리거를 설정하는 단계별 과정을 제공합니다.
+이 가이드는 HubSpot Workflows에서 직접 crew를 시작할 수 있도록 CrewAI AOP용 HubSpot 트리거를 설정하는 단계별 과정을 제공합니다.
## 사전 준비 사항
@@ -15,38 +15,47 @@ mode: "wide"
## 설정 단계
-
-
-
- - 필요에 따라 추가 작업을 구성합니다.
- - 모든 워크플로우 단계를 검토하여 올바르게 설정되었는지 확인합니다.
- - 워크플로우를 활성화합니다.
-
-
-
-
+
+
+
+ - 필요에 따라 추가 작업을 구성합니다. - 모든 워크플로우 단계를
+ 검토하여 올바르게 설정되었는지 확인합니다. - 워크플로우를 활성화합니다.
+
+
+
+
+
## Example: Summarize a new chat thread
@@ -52,7 +56,9 @@ crewai triggers run microsoft_teams/teams_message_created
`crewai triggers run` 명령은 완전한 Teams payload로 크루를 실행하여 배포 전에 파싱 로직을 테스트할 수 있게 해줍니다.
+
## Example: Audit file permissions
@@ -51,7 +55,8 @@ crewai triggers run microsoft_onedrive/file_changed
`crewai triggers run` 명령은 완전한 OneDrive payload로 크루를 실행하여 배포 전에 파싱 로직을 테스트할 수 있게 해줍니다.
+
## Example: Summarize a new email
@@ -51,7 +55,9 @@ crewai triggers run microsoft_outlook/email_received
`crewai triggers run` 명령은 완전한 Outlook payload로 크루를 실행하여 배포 전에 파싱 로직을 테스트할 수 있게 해줍니다.
+
-
+
## 다음 단계
diff --git a/docs/ko/enterprise/guides/salesforce-trigger.mdx b/docs/ko/enterprise/guides/salesforce-trigger.mdx
index 9d4e92e58..f2c7d7c0a 100644
--- a/docs/ko/enterprise/guides/salesforce-trigger.mdx
+++ b/docs/ko/enterprise/guides/salesforce-trigger.mdx
@@ -5,7 +5,7 @@ icon: "salesforce"
mode: "wide"
---
-CrewAI AMP는 Salesforce에서 트리거되어 고객 관계 관리 워크플로우를 자동화하고 영업 운영을 강화할 수 있습니다.
+CrewAI AOP는 Salesforce에서 트리거되어 고객 관계 관리 워크플로우를 자동화하고 영업 운영을 강화할 수 있습니다.
## 개요
diff --git a/docs/ko/enterprise/guides/team-management.mdx b/docs/ko/enterprise/guides/team-management.mdx
index b1fa9a570..2017d7d32 100644
--- a/docs/ko/enterprise/guides/team-management.mdx
+++ b/docs/ko/enterprise/guides/team-management.mdx
@@ -10,31 +10,29 @@ CrewAI AMP 계정의 관리자라면 새로운 팀원을 조직에 쉽게 초대
## 팀 멤버 초대하기
-
-
-
-
+
+
+
+
-
-
-
- - 새로운 역할을 추가하려면 `Add Role` 버튼을 클릭하세요.
- - 역할의 세부 정보와 권한을 입력한 후 `Create Role` 버튼을 클릭하여 역할을 생성하세요.
-
-
-
-
-
- - 멤버가 초대를 수락하면 역할을 추가할 수 있습니다.
- - 다시 `Roles` 탭으로 이동하세요
- - 역할을 추가할 멤버로 이동한 후 `Role` 열에서 드롭다운을 클릭하세요
- - 멤버에게 추가할 역할을 선택하세요
- - `Update` 버튼을 클릭하여 역할을 저장하세요
-
-
-
-
+
+
+
+ - 새로운 역할을 추가하려면 `Add Role` 버튼을 클릭하세요. - 역할의
+ 세부 정보와 권한을 입력한 후 `Create Role` 버튼을 클릭하여 역할을
+ 생성하세요.
+
+
+
+
+
+ - 멤버가 초대를 수락하면 역할을 추가할 수 있습니다. - 다시 `Roles`
+ 탭으로 이동하세요 - 역할을 추가할 멤버로 이동한 후 `Role` 열에서 드롭다운을
+ 클릭하세요 - 멤버에게 추가할 역할을 선택하세요 - `Update` 버튼을 클릭하여
+ 역할을 저장하세요
+
+
+
+
+
## Webhook 출력 예시
@@ -121,4 +122,5 @@ CrewAI AMP를 사용하면 웹훅을 통해 워크플로우를 자동화할 수
}
```
+
diff --git a/docs/ko/enterprise/guides/zapier-trigger.mdx b/docs/ko/enterprise/guides/zapier-trigger.mdx
index 36d414460..5b939496c 100644
--- a/docs/ko/enterprise/guides/zapier-trigger.mdx
+++ b/docs/ko/enterprise/guides/zapier-trigger.mdx
@@ -5,7 +5,7 @@ icon: "bolt"
mode: "wide"
---
-이 가이드는 CrewAI AMP용 Zapier 트리거를 설정하는 과정을 안내합니다. 이를 통해 CrewAI AMP와 기타 애플리케이션 간의 워크플로우를 자동화할 수 있습니다.
+이 가이드는 CrewAI AOP용 Zapier 트리거를 설정하는 과정을 안내합니다. 이를 통해 CrewAI AOP와 기타 애플리케이션 간의 워크플로우를 자동화할 수 있습니다.
## 사전 요구 사항
@@ -52,7 +52,7 @@ mode: "wide"
+
-CrewAI AMP는 오픈 소스 프레임워크의 강력함에 프로덕션 배포, 협업, 확장성을 위한 기능을 더했습니다. crew를 관리형 인프라에 배포하고, 실행을 실시간으로 모니터링하세요.
+CrewAI AOP는 오픈 소스 프레임워크의 강력함에 프로덕션 배포, 협업, 확장성을 위한 기능을 더했습니다. crew를 관리형 인프라에 배포하고, 실행을 실시간으로 모니터링하세요.
## 주요 기능
@@ -57,11 +60,7 @@ CrewAI AMP는 오픈 소스 프레임워크의 강력함에 프로덕션 배포,
-
-
-| 구성 요소 | 설명 | 주요 특징 |
-|:----------|:----:|:----------|
-| **Crew** | 최상위 조직 | • AI agent 팀 관리• workflow 감독
• 협업 보장
• 결과 전달 | -| **AI agents** | 전문 팀원 | • 특정 역할 보유(Researcher, Writer 등)
• 지정된 도구 사용
• 작업 위임 가능
• 자율적 의사결정 가능 | -| **Process** | workflow 관리 시스템 | • 협업 패턴 정의
• 작업 할당 제어
• 상호작용 관리
• 효율적 실행 보장 | -| **Task** | 개별 할당 | • 명확한 목표 보유
• 특정 도구 사용
• 더 큰 프로세스에 기여
• 실행 가능한 결과 도출 | - -### 전체 구조의 동작 방식 - -1. **Crew**가 전체 운영을 조직합니다 -2. **AI agents**가 자신들의 전문 작업을 수행합니다 -3. **Process**가 원활한 협업을 보장합니다 -4. **Tasks**가 완료되어 목표를 달성합니다 - -## 주요 기능 - -
-| 구성 요소 | 설명 | 주요 기능 |
-|:----------|:-----------:|:------------|
-| **Flow** | 구조화된 workflow orchestration | • 실행 경로 관리• 상태 전환 처리
• 작업 순서 제어
• 신뢰성 있는 실행 보장 | -| **Events** | workflow 액션 트리거 | • 특정 프로세스 시작
• 동적 응답 가능
• 조건부 분기 지원
• 실시간 적응 허용 | -| **States** | workflow 실행 컨텍스트 | • 실행 데이터 유지
• 데이터 영속성 지원
• 재개 가능성 보장
• 실행 무결성 확보 | -| **Crew Support** | workflow 자동화 강화 | • 필요할 때 agency 삽입
• 구조화된 workflow 보완
• 자동화와 인텔리전스의 균형
• 적응적 의사결정 지원 | +Flows의 기능: +- **상태 관리**: 단계 및 실행 전반에 걸쳐 데이터를 유지합니다. +- **이벤트 기반 실행**: 이벤트 또는 외부 입력을 기반으로 작업을 트리거합니다. +- **제어 흐름**: 조건부 로직, 반복문, 분기를 사용합니다. -### 주요 기능 +### 2. Crews: 지능 (Intelligence) + +
+
+
+Crews의 기능:
+- **역할 수행 Agent**: 특정 목표와 도구를 가진 전문 agent입니다.
+- **자율 협업**: agent들이 협력하여 작업을 해결합니다.
+- **작업 위임**: agent의 능력에 따라 작업이 할당되고 실행됩니다.
+
+## 전체 작동 방식
+
+1. **Flow**가 이벤트를 트리거하거나 프로세스를 시작합니다.
+2. **Flow**가 상태를 관리하고 다음에 무엇을 할지 결정합니다.
+3. **Flow**가 복잡한 작업을 **Crew**에게 위임합니다.
+4. **Crew**의 agent들이 협력하여 작업을 완료합니다.
+5. **Crew**가 결과를 **Flow**에 반환합니다.
+6. **Flow**가 결과를 바탕으로 실행을 계속합니다.
+
+## 주요 기능
stop, basta omiti-lo na chamada do LLM:
@@ -882,4 +950,4 @@ Saiba como obter o máximo da configuração do seu LLM:
llm = LLM(model="openai/gpt-4o") # 128K tokens
```
-
\ No newline at end of file
+
diff --git a/docs/pt-BR/concepts/memory.mdx b/docs/pt-BR/concepts/memory.mdx
index 05301ccaf..f7daa1560 100644
--- a/docs/pt-BR/concepts/memory.mdx
+++ b/docs/pt-BR/concepts/memory.mdx
@@ -515,8 +515,7 @@ crew = Crew(
"provider": "huggingface",
"config": {
"api_key": "your-hf-token", # Opcional para modelos públicos
- "model": "sentence-transformers/all-MiniLM-L6-v2",
- "api_url": "https://api-inference.huggingface.co" # ou seu endpoint customizado
+ "model": "sentence-transformers/all-MiniLM-L6-v2"
}
}
)
diff --git a/docs/pt-BR/concepts/production-architecture.mdx b/docs/pt-BR/concepts/production-architecture.mdx
new file mode 100644
index 000000000..ac1e17801
--- /dev/null
+++ b/docs/pt-BR/concepts/production-architecture.mdx
@@ -0,0 +1,154 @@
+---
+title: Arquitetura de Produção
+description: Melhores práticas para construir aplicações de IA prontas para produção com CrewAI
+icon: server
+mode: "wide"
+---
+
+# A Mentalidade Flow-First
+
+Ao construir aplicações de IA de produção com CrewAI, **recomendamos começar com um Flow**.
+
+Embora seja possível executar Crews ou Agentes individuais, envolvê-los em um Flow fornece a estrutura necessária para uma aplicação robusta e escalável.
+
+## Por que Flows?
+
+1. **Gerenciamento de Estado**: Flows fornecem uma maneira integrada de gerenciar o estado em diferentes etapas da sua aplicação. Isso é crucial para passar dados entre Crews, manter o contexto e lidar com entradas do usuário.
+2. **Controle**: Flows permitem definir caminhos de execução precisos, incluindo loops, condicionais e lógica de ramificação. Isso é essencial para lidar com casos extremos e garantir que sua aplicação se comporte de maneira previsível.
+3. **Observabilidade**: Flows fornecem uma estrutura clara que facilita o rastreamento da execução, a depuração de problemas e o monitoramento do desempenho. Recomendamos o uso do [CrewAI Tracing](/pt-BR/observability/tracing) para insights detalhados. Basta executar `crewai login` para habilitar recursos de observabilidade gratuitos.
+
+## A Arquitetura
+
+Uma aplicação CrewAI de produção típica se parece com isso:
+
+```mermaid
+graph TD
+ Start((Início)) --> Flow[Orquestrador de Flow]
+ Flow --> State{Gerenciamento de Estado}
+ State --> Step1[Etapa 1: Coleta de Dados]
+ Step1 --> Crew1[Crew de Pesquisa]
+ Crew1 --> State
+ State --> Step2{Verificação de Condição}
+ Step2 -- "Válido" --> Step3[Etapa 3: Execução]
+ Step3 --> Crew2[Crew de Ação]
+ Step2 -- "Inválido" --> End((Fim))
+ Crew2 --> End
+```
+
+### 1. A Classe Flow
+Sua classe `Flow` é o ponto de entrada. Ela define o esquema de estado e os métodos que executam sua lógica.
+
+```python
+from crewai.flow.flow import Flow, listen, start
+from pydantic import BaseModel
+
+class AppState(BaseModel):
+ user_input: str = ""
+ research_results: str = ""
+ final_report: str = ""
+
+class ProductionFlow(Flow[AppState]):
+ @start()
+ def gather_input(self):
+ # ... lógica para obter entrada ...
+ pass
+
+ @listen(gather_input)
+ def run_research_crew(self):
+ # ... acionar um Crew ...
+ pass
+```
+
+### 2. Gerenciamento de Estado
+Use modelos Pydantic para definir seu estado. Isso garante a segurança de tipos e deixa claro quais dados estão disponíveis em cada etapa.
+
+- **Mantenha o mínimo**: Armazene apenas o que você precisa persistir entre as etapas.
+- **Use dados estruturados**: Evite dicionários não estruturados quando possível.
+
+### 3. Crews como Unidades de Trabalho
+Delegue tarefas complexas para Crews. Um Crew deve ser focado em um objetivo específico (por exemplo, "Pesquisar um tópico", "Escrever uma postagem no blog").
+
+- **Não superengendre Crews**: Mantenha-os focados.
+- **Passe o estado explicitamente**: Passe os dados necessários do estado do Flow para as entradas do Crew.
+
+```python
+ @listen(gather_input)
+ def run_research_crew(self):
+ crew = ResearchCrew()
+ result = crew.kickoff(inputs={"topic": self.state.user_input})
+ self.state.research_results = result.raw
+```
+
+## Primitivas de Controle
+
+Aproveite as primitivas de controle do CrewAI para adicionar robustez e controle aos seus Crews.
+
+### 1. Task Guardrails
+Use [Task Guardrails](/pt-BR/concepts/tasks#task-guardrails) para validar as saídas das tarefas antes que sejam aceitas. Isso garante que seus agentes produzam resultados de alta qualidade.
+
+```python
+def validate_content(result: TaskOutput) -> Tuple[bool, Any]:
+ if len(result.raw) < 100:
+ return (False, "Content is too short. Please expand.")
+ return (True, result.raw)
+
+task = Task(
+ ...,
+ guardrail=validate_content
+)
+```
+
+### 2. Saídas Estruturadas
+Sempre use saídas estruturadas (`output_pydantic` ou `output_json`) ao passar dados entre tarefas ou para sua aplicação. Isso evita erros de análise e garante a segurança de tipos.
+
+```python
+class ResearchResult(BaseModel):
+ summary: str
+ sources: List[str]
+
+task = Task(
+ ...,
+ output_pydantic=ResearchResult
+)
+```
+
+### 3. LLM Hooks
+Use [LLM Hooks](/pt-BR/learn/llm-hooks) para inspecionar ou modificar mensagens antes que elas sejam enviadas para o LLM, ou para higienizar respostas.
+
+```python
+@before_llm_call
+def log_request(context):
+ print(f"Agent {context.agent.role} is calling the LLM...")
+```
+
+## Padrões de Implantação
+
+Ao implantar seu Flow, considere o seguinte:
+
+### CrewAI Enterprise
+A maneira mais fácil de implantar seu Flow é usando o CrewAI Enterprise. Ele lida com a infraestrutura, autenticação e monitoramento para você.
+
+Confira o [Guia de Implantação](/pt-BR/enterprise/guides/deploy-to-amp) para começar.
+
+```bash
+crewai deploy create
+```
+
+### Execução Assíncrona
+Para tarefas de longa duração, use `kickoff_async` para evitar bloquear sua API.
+
+### Persistência
+Use o decorador `@persist` para salvar o estado do seu Flow em um banco de dados. Isso permite retomar a execução se o processo falhar ou se você precisar esperar pela entrada humana.
+
+```python
+@persist
+class ProductionFlow(Flow[AppState]):
+ # ...
+```
+
+## Resumo
+
+- **Comece com um Flow.**
+- **Defina um Estado claro.**
+- **Use Crews para tarefas complexas.**
+- **Implante com uma API e persistência.**
diff --git a/docs/pt-BR/concepts/tasks.mdx b/docs/pt-BR/concepts/tasks.mdx
index 153150833..4ef324d90 100644
--- a/docs/pt-BR/concepts/tasks.mdx
+++ b/docs/pt-BR/concepts/tasks.mdx
@@ -19,6 +19,7 @@ O CrewAI AMP inclui um Construtor Visual de Tarefas no Crew Studio, que simplifi
O Construtor Visual de Tarefas permite:
+
- Criação de tarefas via arrastar-e-soltar
- Visualização de dependências e fluxo de tarefas
- Testes e validações em tempo real
@@ -28,10 +29,12 @@ O Construtor Visual de Tarefas permite:
### Fluxo de Execução de Tarefas
As tarefas podem ser executadas de duas maneiras:
+
- **Sequencial**: As tarefas são executadas na ordem em que são definidas
- **Hierárquica**: As tarefas são atribuídas aos agentes com base em seus papéis e especialidades
O fluxo de execução é definido ao criar o crew:
+
```python Code
crew = Crew(
agents=[agent1, agent2],
@@ -42,25 +45,25 @@ crew = Crew(
## Atributos da Tarefa
-| Atributo | Parâmetros | Tipo | Descrição |
-| :------------------------------- | :---------------- | :--------------------------- | :----------------------------------------------------------------------------------------------------------------- |
-| **Descrição** | `description` | `str` | Uma declaração clara e concisa do que a tarefa envolve. |
-| **Saída Esperada** | `expected_output` | `str` | Uma descrição detalhada de como deve ser o resultado da tarefa concluída. |
-| **Nome** _(opcional)_ | `name` | `Optional[str]` | Um identificador de nome para a tarefa. |
-| **Agente** _(opcional)_ | `agent` | `Optional[BaseAgent]` | O agente responsável por executar a tarefa. |
-| **Ferramentas** _(opcional)_ | `tools` | `List[BaseTool]` | As ferramentas/recursos que o agente pode usar para esta tarefa. |
-| **Contexto** _(opcional)_ | `context` | `Optional[List["Task"]]` | Outras tarefas cujas saídas serão usadas como contexto para esta tarefa. |
-| **Execução Assíncrona** _(opc.)_ | `async_execution` | `Optional[bool]` | Se a tarefa deve ser executada de forma assíncrona. O padrão é False. |
-| **Input Humano** _(opcional)_ | `human_input` | `Optional[bool]` | Se a tarefa deve ter uma revisão humana da resposta final do agente. O padrão é False. |
-| **Markdown** _(opcional)_ | `markdown` | `Optional[bool]` | Se a tarefa deve instruir o agente a retornar a resposta final formatada em Markdown. O padrão é False. |
-| **Config** _(opcional)_ | `config` | `Optional[Dict[str, Any]]` | Parâmetros de configuração específicos da tarefa. |
-| **Arquivo de Saída** _(opcional)_| `output_file` | `Optional[str]` | Caminho do arquivo para armazenar a saída da tarefa. |
-| **Criar Diretório** _(opcional)_ | `create_directory` | `Optional[bool]` | Se deve criar o diretório para output_file caso não exista. O padrão é True. |
-| **Saída JSON** _(opcional)_ | `output_json` | `Optional[Type[BaseModel]]` | Um modelo Pydantic para estruturar a saída em JSON. |
-| **Output Pydantic** _(opcional)_ | `output_pydantic` | `Optional[Type[BaseModel]]` | Um modelo Pydantic para a saída da tarefa. |
-| **Callback** _(opcional)_ | `callback` | `Optional[Any]` | Função/objeto a ser executado após a conclusão da tarefa. |
-| **Guardrail** _(opcional)_ | `guardrail` | `Optional[Callable]` | Função para validar a saída da tarefa antes de prosseguir para a próxima tarefa. |
-| **Max Tentativas Guardrail** _(opcional)_ | `guardrail_max_retries` | `Optional[int]` | Número máximo de tentativas quando a validação do guardrail falha. Padrão é 3. |
+| Atributo | Parâmetros | Tipo | Descrição |
+| :---------------------------------------- | :---------------------- | :-------------------------- | :------------------------------------------------------------------------------------------------------ |
+| **Descrição** | `description` | `str` | Uma declaração clara e concisa do que a tarefa envolve. |
+| **Saída Esperada** | `expected_output` | `str` | Uma descrição detalhada de como deve ser o resultado da tarefa concluída. |
+| **Nome** _(opcional)_ | `name` | `Optional[str]` | Um identificador de nome para a tarefa. |
+| **Agente** _(opcional)_ | `agent` | `Optional[BaseAgent]` | O agente responsável por executar a tarefa. |
+| **Ferramentas** _(opcional)_ | `tools` | `List[BaseTool]` | As ferramentas/recursos que o agente pode usar para esta tarefa. |
+| **Contexto** _(opcional)_ | `context` | `Optional[List["Task"]]` | Outras tarefas cujas saídas serão usadas como contexto para esta tarefa. |
+| **Execução Assíncrona** _(opc.)_ | `async_execution` | `Optional[bool]` | Se a tarefa deve ser executada de forma assíncrona. O padrão é False. |
+| **Input Humano** _(opcional)_ | `human_input` | `Optional[bool]` | Se a tarefa deve ter uma revisão humana da resposta final do agente. O padrão é False. |
+| **Markdown** _(opcional)_ | `markdown` | `Optional[bool]` | Se a tarefa deve instruir o agente a retornar a resposta final formatada em Markdown. O padrão é False. |
+| **Config** _(opcional)_ | `config` | `Optional[Dict[str, Any]]` | Parâmetros de configuração específicos da tarefa. |
+| **Arquivo de Saída** _(opcional)_ | `output_file` | `Optional[str]` | Caminho do arquivo para armazenar a saída da tarefa. |
+| **Criar Diretório** _(opcional)_ | `create_directory` | `Optional[bool]` | Se deve criar o diretório para output_file caso não exista. O padrão é True. |
+| **Saída JSON** _(opcional)_ | `output_json` | `Optional[Type[BaseModel]]` | Um modelo Pydantic para estruturar a saída em JSON. |
+| **Output Pydantic** _(opcional)_ | `output_pydantic` | `Optional[Type[BaseModel]]` | Um modelo Pydantic para a saída da tarefa. |
+| **Callback** _(opcional)_ | `callback` | `Optional[Any]` | Função/objeto a ser executado após a conclusão da tarefa. |
+| **Guardrail** _(opcional)_ | `guardrail` | `Optional[Callable]` | Função para validar a saída da tarefa antes de prosseguir para a próxima tarefa. |
+| **Max Tentativas Guardrail** _(opcional)_ | `guardrail_max_retries` | `Optional[int]` | Número máximo de tentativas quando a validação do guardrail falha. Padrão é 3. |
## Criando Tarefas
@@ -81,7 +84,7 @@ crew.kickoff(inputs={'topic': 'AI Agents'})
Veja um exemplo de configuração de tarefas usando YAML:
-```yaml tasks.yaml
+````yaml tasks.yaml
research_task:
description: >
Realize uma pesquisa detalhada sobre {topic}
@@ -101,7 +104,7 @@ reporting_task:
agent: reporting_analyst
markdown: true
output_file: report.md
-```
+````
Para usar essa configuração YAML em seu código, crie uma classe crew que herda de `CrewBase`:
@@ -159,7 +162,8 @@ class LatestAiDevelopmentCrew():
```
+
+
+### Notificações por Email
+
+Toggle para ativar ou desativar notificações por email para solicitações HITL.
+
+| Configuração | Padrão | Descrição |
+|--------------|--------|-----------|
+| Notificações por Email | Ativado | Enviar emails quando feedback for solicitado |
+
+
+
+
+### Estrutura da Regra
+
+```json
+{
+ "name": "Aprovações para Financeiro",
+ "match": {
+ "method_name": "approve_*"
+ },
+ "assign_to_email": "financeiro@empresa.com",
+ "assign_from_input": "manager_email"
+}
+```
+
+### Padrões de Correspondência
+
+| Padrão | Descrição | Exemplo de Correspondência |
+|--------|-----------|---------------------------|
+| `approve_*` | Wildcard (qualquer caractere) | `approve_payment`, `approve_vendor` |
+| `review_?` | Caractere único | `review_a`, `review_1` |
+| `validate_payment` | Correspondência exata | apenas `validate_payment` |
+
+### Prioridade de Atribuição
+
+1. **Atribuição dinâmica** (`assign_from_input`): Se configurado, obtém email do estado do flow
+2. **Email estático** (`assign_to_email`): Fallback para email configurado
+3. **Criador do deployment**: Se nenhuma regra corresponder, o email do criador do deployment é usado
+
+### Exemplo de Atribuição Dinâmica
+
+Se seu estado do flow contém `{"sales_rep_email": "alice@empresa.com"}`, configure:
+
+```json
+{
+ "name": "Direcionar para Representante de Vendas",
+ "match": {
+ "method_name": "review_*"
+ },
+ "assign_from_input": "sales_rep_email"
+}
+```
+
+A solicitação será atribuída automaticamente para `alice@empresa.com`.
+
+
+
+
+### Casos de Uso
+
+- **Conformidade com SLA**: Garante que flows não fiquem travados indefinidamente
+- **Aprovação padrão**: Aprove automaticamente solicitações de baixo risco após timeout
+- **Degradação graciosa**: Continue com um padrão seguro quando revisores não estiverem disponíveis
+
+
+
+
+### Métodos de Resposta
+
+Revisores podem responder por três canais:
+
+| Método | Descrição |
+|--------|-----------|
+| **Resposta por Email** | Responda diretamente ao email de notificação |
+| **Dashboard** | Use a UI do dashboard Enterprise |
+| **API/Webhook** | Resposta programática via API |
+
+### Histórico e Trilha de Auditoria
+
+Toda interação HITL é rastreada com uma linha do tempo completa:
+
+- Histórico de decisões (aprovar/rejeitar/revisar)
+- Identidade do revisor e timestamp
+- Feedback e comentários fornecidos
+- Método de resposta (email/dashboard/API)
+- Métricas de tempo de resposta
+
+## Análise e Monitoramento
+
+Acompanhe o desempenho HITL com análises abrangentes.
+
+### Dashboard de Desempenho
+
+
+
+
+
+
+
+
+### Configurando Webhooks
+
+
+
### Habilitando e Desabilitando
@@ -87,7 +115,10 @@ Com triggers você pode:
Cada trigger possui uma chave de ativação:
-
+
- **Habilitado (azul)** – Executa a automação quando o evento ocorrer
@@ -100,7 +131,10 @@ As alterações são aplicadas imediatamente.
Use a lista de execuções para acompanhar histórico, status e payloads:
-
+
## Construindo Automações Orientadas por Trigger
@@ -129,6 +163,7 @@ crewai triggers list
```
Este comando exibe todos os triggers disponíveis baseados nas suas integrações conectadas, mostrando:
+
- Nome da integração e status de conexão
- Tipos de triggers disponíveis
- Nomes e descrições dos triggers
@@ -148,6 +183,7 @@ crewai triggers run microsoft_onedrive/file_changed
```
Este comando:
+
- Executa sua crew localmente
- Passa um payload de trigger completo e realista
- Simula exatamente como sua crew será chamada em produção
@@ -233,17 +269,20 @@ def delegar_para_crew(self, crewai_trigger_payload: dict = None):
## Solução de Problemas
**Trigger não dispara:**
+
- Verifique se o trigger está habilitado na aba Triggers do seu deployment
- Confira o status da conexão em Tools & Integrations
- Garanta que todas as variáveis de ambiente necessárias estão configuradas
**Falhas de execução:**
+
- Consulte os logs de execução para detalhes do erro
- Use `crewai triggers run
+
## Example: Process new emails
@@ -62,13 +66,15 @@ Teste sua integração de trigger do Gmail localmente usando a CLI da CrewAI:
crewai triggers list
# Simule um trigger do Gmail com payload realista
-crewai triggers run gmail/new_email
+crewai triggers run gmail/new_email_received
```
O comando `crewai triggers run` executará sua crew com um payload completo do Gmail, permitindo que você teste sua lógica de parsing antes do deployment.
+
## Troubleshooting
- Ensure Gmail is connected in Tools & Integrations
- Verify the Gmail Trigger is enabled on the Triggers tab
-- Teste localmente com `crewai triggers run gmail/new_email` para ver a estrutura exata do payload
+- Teste localmente com `crewai triggers run gmail/new_email_received` para ver a estrutura exata do payload
- Check the execution logs and confirm the payload is passed as `crewai_trigger_payload`
- Lembre-se: use `crewai triggers run` (não `crewai run`) para simular execução de trigger
diff --git a/docs/pt-BR/enterprise/guides/google-calendar-trigger.mdx b/docs/pt-BR/enterprise/guides/google-calendar-trigger.mdx
index 0d9a7dbc8..852d3a2a8 100644
--- a/docs/pt-BR/enterprise/guides/google-calendar-trigger.mdx
+++ b/docs/pt-BR/enterprise/guides/google-calendar-trigger.mdx
@@ -10,7 +10,8 @@ mode: "wide"
Use the Google Calendar trigger to launch automations whenever calendar events change. Common use cases include briefing a team before a meeting, notifying stakeholders when a critical event is cancelled, or summarizing daily schedules.
+
## Example: Summarize meeting details
@@ -54,7 +58,9 @@ crewai triggers run google_calendar/event_changed
O comando `crewai triggers run` executará sua crew com um payload completo do Calendar, permitindo que você teste sua lógica de parsing antes do deployment.
+
## Troubleshooting
diff --git a/docs/pt-BR/enterprise/guides/google-drive-trigger.mdx b/docs/pt-BR/enterprise/guides/google-drive-trigger.mdx
index d4f2f2ed8..90f8f4ff8 100644
--- a/docs/pt-BR/enterprise/guides/google-drive-trigger.mdx
+++ b/docs/pt-BR/enterprise/guides/google-drive-trigger.mdx
@@ -10,7 +10,8 @@ mode: "wide"
Trigger your automations when files are created, updated, or removed in Google Drive. Typical workflows include summarizing newly uploaded content, enforcing sharing policies, or notifying owners when critical files change.
+
## Example: Summarize file activity
@@ -51,7 +55,9 @@ crewai triggers run google_drive/file_changed
O comando `crewai triggers run` executará sua crew com um payload completo do Drive, permitindo que você teste sua lógica de parsing antes do deployment.
+
## Troubleshooting
diff --git a/docs/pt-BR/enterprise/guides/hubspot-trigger.mdx b/docs/pt-BR/enterprise/guides/hubspot-trigger.mdx
index 849fe97cd..8bc1a1340 100644
--- a/docs/pt-BR/enterprise/guides/hubspot-trigger.mdx
+++ b/docs/pt-BR/enterprise/guides/hubspot-trigger.mdx
@@ -15,38 +15,49 @@ Este guia fornece um processo passo a passo para configurar gatilhos do HubSpot
## Etapas de Configuração
-
-
-
- - Configure quaisquer ações adicionais necessárias
- - Revise as etapas do seu workflow para garantir que tudo está configurado corretamente
- - Ative o workflow
-
-
-
-
+
+
+
+ - Configure quaisquer ações adicionais necessárias - Revise as
+ etapas do seu workflow para garantir que tudo está configurado corretamente
+ - Ative o workflow
+
+
+
+
+
## Example: Summarize a new chat thread
@@ -52,7 +56,9 @@ crewai triggers run microsoft_teams/teams_message_created
O comando `crewai triggers run` executará sua crew com um payload completo do Teams, permitindo que você teste sua lógica de parsing antes do deployment.
+
## Example: Audit file permissions
@@ -51,7 +55,9 @@ crewai triggers run microsoft_onedrive/file_changed
O comando `crewai triggers run` executará sua crew com um payload completo do OneDrive, permitindo que você teste sua lógica de parsing antes do deployment.
+
## Example: Summarize a new email
@@ -51,7 +55,9 @@ crewai triggers run microsoft_outlook/email_received
O comando `crewai triggers run` executará sua crew com um payload completo do Outlook, permitindo que você teste sua lógica de parsing antes do deployment.
+
-
+
## Próximos Passos
diff --git a/docs/pt-BR/enterprise/guides/team-management.mdx b/docs/pt-BR/enterprise/guides/team-management.mdx
index 41aed304c..b92956541 100644
--- a/docs/pt-BR/enterprise/guides/team-management.mdx
+++ b/docs/pt-BR/enterprise/guides/team-management.mdx
@@ -10,31 +10,35 @@ Como administrador de uma conta CrewAI AMP, você pode facilmente convidar novos
## Convidando Membros da Equipe
-
-
-
-
+
+
+
+
-
-
-
- - Clique no botão `Add Role` para adicionar uma nova função.
- - Insira os detalhes e as permissões da função e clique no botão `Create Role` para criar a função.
-
-
-
-
-
- - Após o membro aceitar o convite, você poderá adicionar uma função a ele.
- - Volte para a aba `Roles`
- - Vá até o membro ao qual deseja adicionar uma função e, na coluna `Role`, clique no menu suspenso
- - Selecione a função que deseja atribuir ao membro
- - Clique no botão `Update` para salvar a função
-
-
-
-
+
+
+
+ - Clique no botão `Add Role` para adicionar uma nova função. -
+ Insira os detalhes e as permissões da função e clique no botão `Create Role`
+ para criar a função.
+
+
+
+
+
+ - Após o membro aceitar o convite, você poderá adicionar uma função
+ a ele. - Volte para a aba `Roles` - Vá até o membro ao qual deseja adicionar
+ uma função e, na coluna `Role`, clique no menu suspenso - Selecione a função
+ que deseja atribuir ao membro - Clique no botão `Update` para salvar a
+ função
+
+
+
+
+
## Exemplos de Output do Webhook
@@ -121,4 +122,5 @@ O CrewAI AMP permite que você automatize seu fluxo de trabalho usando webhooks.
}
```
+
diff --git a/docs/pt-BR/enterprise/guides/zapier-trigger.mdx b/docs/pt-BR/enterprise/guides/zapier-trigger.mdx
index dba720a1f..70f562998 100644
--- a/docs/pt-BR/enterprise/guides/zapier-trigger.mdx
+++ b/docs/pt-BR/enterprise/guides/zapier-trigger.mdx
@@ -93,6 +93,7 @@ Este guia irá conduzi-lo pelo processo de configuração de triggers no Zapier
+
## Dicas para o Sucesso
diff --git a/docs/pt-BR/enterprise/integrations/asana.mdx b/docs/pt-BR/enterprise/integrations/asana.mdx
index 364ee9f60..8a0f45ffa 100644
--- a/docs/pt-BR/enterprise/integrations/asana.mdx
+++ b/docs/pt-BR/enterprise/integrations/asana.mdx
@@ -36,7 +36,8 @@ uv add crewai-tools
### 3. Configuração de variável de ambiente
+
CrewAI AMP expande o poder do framework open-source com funcionalidades projetadas para implantações em produção, colaboração e escalabilidade. Implemente seus crews em uma infraestrutura gerenciada e monitore sua execução em tempo real.
@@ -19,10 +22,12 @@ CrewAI AMP expande o poder do framework open-source com funcionalidades projetad
-
-
-| Componente | Descrição | Principais Funcionalidades |
-|:-----------|:-----------:|:-------------------------|
-| **Crew** | Organização de mais alto nível | • Gerencia equipes de agentes de IA• Supervisiona fluxos de trabalho
• Garante colaboração
• Entrega resultados | -| **Agentes de IA** | Membros especializados da equipe | • Possuem funções específicas (pesquisador, escritor)
• Utilizam ferramentas designadas
• Podem delegar tarefas
• Tomam decisões autônomas | -| **Process** | Sistema de gestão do fluxo de trabalho | • Define padrões de colaboração
• Controla designação de tarefas
• Gerencia interações
• Garante execução eficiente | -| **Tasks** | Atribuições individuais | • Objetivos claros
• Utilizam ferramentas específicas
• Alimentam processos maiores
• Geram resultados acionáveis | - -### Como tudo trabalha junto - -1. O **Crew** organiza toda a operação -2. **Agentes de IA** realizam tarefas especializadas -3. O **Process** garante colaboração fluida -4. **Tasks** são concluídas para alcançar o objetivo - -## Principais Funcionalidades - -
-| Componente | Descrição | Principais Funcionalidades |
-|:-----------|:-----------:|:-------------------------|
-| **Flow** | Orquestração de fluxo de trabalho estruturada | • Gerencia caminhos de execução• Lida com transições de estado
• Controla a sequência de tarefas
• Garante execução confiável | -| **Events** | Gatilhos para ações nos fluxos | • Iniciam processos específicos
• Permitem respostas dinâmicas
• Suportam ramificações condicionais
• Adaptam-se em tempo real | -| **States** | Contextos de execução dos fluxos | • Mantêm dados de execução
• Permitem persistência
• Suportam retomada
• Garantem integridade na execução | -| **Crew Support** | Aprimora automação de fluxos | • Injeta autonomia quando necessário
• Complementa fluxos estruturados
• Equilibra automação e inteligência
• Permite tomada de decisão adaptativa | +Flows fornecem: +- **Gerenciamento de Estado**: Persistem dados através de etapas e execuções. +- **Execução Orientada a Eventos**: Acionam ações com base em eventos ou entradas externas. +- **Controle de Fluxo**: Usam lógica condicional, loops e ramificações. -### Capacidades-Chave +### 2. Crews: A Inteligência + +
+
+
+Crews fornecem:
+- **Agentes com Funções**: Agentes especializados com objetivos e ferramentas específicas.
+- **Colaboração Autônoma**: Agentes trabalham juntos para resolver tarefas.
+- **Delegação de Tarefas**: Tarefas são atribuídas e executadas com base nas capacidades dos agentes.
+
+## Como Tudo Funciona Junto
+
+1. **O Flow** aciona um evento ou inicia um processo.
+2. **O Flow** gerencia o estado e decide o que fazer a seguir.
+3. **O Flow** delega uma tarefa complexa para um **Crew**.
+4. Os agentes do **Crew** colaboram para completar a tarefa.
+5. **O Crew** retorna o resultado para o **Flow**.
+6. **O Flow** continua a execução com base no resultado.
+
+## Principais Funcionalidades
\n
\n\n\n"
- headers:
- Connection:
- - keep-alive
- Content-Length:
- - '1722'
- Content-Type:
- - text/html; charset=UTF-8
- Date:
- - Wed, 06 Aug 2025 19:30:52 GMT
- strict-transport-security:
- - max-age=63072000; includeSubDomains
- x-request-id:
- - bec0cf39-af9c-4955-b600-607187a7b10b
- x-runtime:
- - '0.005352'
- status:
- code: 404
- message: Not Found
-- request:
- body: '{"version": "0.152.0", "batch_id": "eb9e0ee1-15ed-4044-b84b-f17e493a1e28",
- "user_context": {"user_id": "anonymous", "organization_id": "", "session_id":
- "e7e7a716-e64b-490b-96db-5c5367042114", "trace_id": "54e95e1f-cd41-4ece-9e5e-21984d635e6a"},
- "execution_metadata": {"crew_name": "crew", "execution_start": "2025-08-06T19:30:52.209750+00:00",
- "crewai_version": "0.152.0"}, "events": [{"event_id": "98b2a833-63fc-457c-a2e0-6ce228a8214c",
- "timestamp": "2025-08-06T19:30:52.328066+00:00", "type": "crew_kickoff_started",
- "event_data": {"timestamp": "2025-08-06T19:30:52.209750+00:00", "type": "crew_kickoff_started",
- "source_fingerprint": null, "source_type": null, "fingerprint_metadata": null,
- "crew_name": "crew", "crew": null, "inputs": null}}, {"event_id": "4abf563c-d35f-4a09-867d-75c1c54b3fed",
- "timestamp": "2025-08-06T19:30:52.328113+00:00", "type": "crew_kickoff_started",
- "event_data": {"timestamp": "2025-08-06T19:30:52.209750+00:00", "type": "crew_kickoff_started",
- "source_fingerprint": null, "source_type": null, "fingerprint_metadata": null,
- "crew_name": "crew", "crew": null, "inputs": null}}, {"event_id": "60bdc932-6b56-4f1d-bcc2-5b3b57c8dc94",
- "timestamp": "2025-08-06T19:30:52.330079+00:00", "type": "task_started", "event_data":
- {"task_description": "Say hello to the world", "task_name": null, "context":
- "", "agent": "Test Agent"}}, {"event_id": "97761b9f-d132-47e7-8857-5fdda8c80b65",
- "timestamp": "2025-08-06T19:30:52.330089+00:00", "type": "task_started", "event_data":
- {"task_description": "Say hello to the world", "task_name": null, "context":
- "", "agent": "Test Agent"}}, {"event_id": "cdaa47c1-448f-476e-9761-14a25f26c481",
- "timestamp": "2025-08-06T19:30:52.330477+00:00", "type": "agent_execution_started",
- "event_data": {"serialization_error": "Circular reference detected (id repeated)",
- "object_type": "AgentExecutionStartedEvent"}}, {"event_id": "7aa43738-3903-44cf-8416-d47542469537",
- "timestamp": "2025-08-06T19:30:52.330612+00:00", "type": "agent_execution_started",
- "event_data": {"serialization_error": "Circular reference detected (id repeated)",
- "object_type": "AgentExecutionStartedEvent"}}, {"event_id": "6eb42795-be95-4f1c-b70f-385c59483e43",
- "timestamp": "2025-08-06T19:30:52.330751+00:00", "type": "llm_call_started",
- "event_data": {"timestamp": "2025-08-06T19:30:52.330725+00:00", "type": "llm_call_started",
- "source_fingerprint": null, "source_type": null, "fingerprint_metadata": null,
- "task_name": null, "task_id": "1bfe4b49-ba6a-464d-9b6a-ca2eb8e965d8", "agent_id":
- "5d6dbe70-71fc-42e2-ba0d-61b460542dad", "agent_role": "Test Agent", "model":
- "gpt-4o-mini", "messages": [{"role": "system", "content": "You are Test Agent.
- Test backstory\nYour personal goal is: Test goal\nTo give my best complete final
- answer to the task respond using the exact following format:\n\nThought: I now
- can give a great answer\n..."}, {"role": "user", "content": "\nCurrent Task:
- Say hello to the world\n\nThis is the expected criteria for your final answer:
- hello world\nyou MUST return the actual complete content as the final answer,
- not a summary.\n\nBegin! This is ..."}], "tools": null, "callbacks": ["\n
\n The - page you were looking for doesn't exist.
\nYou may have mistyped - the address or the page may have moved.
\nIf you are - the application owner check the logs for more information.
\n\n
\n\n\n"
- headers:
- Connection:
- - keep-alive
- Content-Length:
- - '1722'
- Content-Type:
- - text/html; charset=UTF-8
- Date:
- - Wed, 06 Aug 2025 19:30:52 GMT
- strict-transport-security:
- - max-age=63072000; includeSubDomains
- x-request-id:
- - 78674bcb-6c8a-4eaf-8577-5cb27cac4089
- x-runtime:
- - '0.006009'
- status:
- code: 404
- message: Not Found
-version: 1
diff --git a/lib/crewai/tests/cassettes/TestTraceListenerSetup.test_events_collection_batch_manager.yaml b/lib/crewai/tests/cassettes/TestTraceListenerSetup.test_events_collection_batch_manager.yaml
deleted file mode 100644
index 1b1c78ffe..000000000
--- a/lib/crewai/tests/cassettes/TestTraceListenerSetup.test_events_collection_batch_manager.yaml
+++ /dev/null
@@ -1,558 +0,0 @@
-interactions:
-- request:
- body: '{"messages": [{"role": "system", "content": "You are Test Agent. Test backstory\nYour
- personal goal is: Test goal\nTo give my best complete final answer to the task
- respond using the exact following format:\n\nThought: I now can give a great
- answer\nFinal Answer: Your final answer must be the great and the most complete
- as possible, it must be outcome described.\n\nI MUST use these formats, my job
- depends on it!"}, {"role": "user", "content": "\nCurrent Task: Say hello to
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+ method: GET
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diff --git a/lib/crewai/tests/cassettes/a2a/TestA2APollingIntegration.test_polling_completes_task.yaml b/lib/crewai/tests/cassettes/a2a/TestA2APollingIntegration.test_polling_completes_task.yaml
new file mode 100644
index 000000000..3832dc7da
--- /dev/null
+++ b/lib/crewai/tests/cassettes/a2a/TestA2APollingIntegration.test_polling_completes_task.yaml
@@ -0,0 +1,126 @@
+interactions:
+- request:
+ body: ''
+ headers:
+ User-Agent:
+ - X-USER-AGENT-XXX
+ accept:
+ - '*/*'
+ accept-encoding:
+ - ACCEPT-ENCODING-XXX
+ connection:
+ - keep-alive
+ host:
+ - localhost:9999
+ method: GET
+ uri: http://localhost:9999/.well-known/agent-card.json
+ response:
+ body:
+ string: '{"capabilities":{"streaming":true},"defaultInputModes":["text"],"defaultOutputModes":["text"],"description":"An
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+ headers:
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+ message: OK
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+ message: OK
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+ message: OK
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diff --git a/lib/crewai/tests/cassettes/a2a/TestA2AStreamingIntegration.test_streaming_completes_task.yaml b/lib/crewai/tests/cassettes/a2a/TestA2AStreamingIntegration.test_streaming_completes_task.yaml
new file mode 100644
index 000000000..e98e61c2b
--- /dev/null
+++ b/lib/crewai/tests/cassettes/a2a/TestA2AStreamingIntegration.test_streaming_completes_task.yaml
@@ -0,0 +1,90 @@
+interactions:
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+ x-accel-buffering:
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+ status:
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+ message: OK
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diff --git a/lib/crewai/tests/cassettes/a2a/TestA2ATaskOperations.test_send_message_and_get_response.yaml b/lib/crewai/tests/cassettes/a2a/TestA2ATaskOperations.test_send_message_and_get_response.yaml
new file mode 100644
index 000000000..e3623e8da
--- /dev/null
+++ b/lib/crewai/tests/cassettes/a2a/TestA2ATaskOperations.test_send_message_and_get_response.yaml
@@ -0,0 +1,90 @@
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diff --git a/lib/crewai/tests/cassettes/agents/TestAgentA2AKickoff.test_agent_kickoff_delegates_to_a2a.yaml b/lib/crewai/tests/cassettes/agents/TestAgentA2AKickoff.test_agent_kickoff_delegates_to_a2a.yaml
new file mode 100644
index 000000000..79c5ebe3f
--- /dev/null
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diff --git a/lib/crewai/tests/cassettes/agents/TestAgentA2AKickoff.test_agent_kickoff_handles_multi_turn_conversation.yaml b/lib/crewai/tests/cassettes/agents/TestAgentA2AKickoff.test_agent_kickoff_handles_multi_turn_conversation.yaml
new file mode 100644
index 000000000..3d28a179d
--- /dev/null
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diff --git a/lib/crewai/tests/cassettes/agents/TestAgentA2AKickoff.test_agent_kickoff_returns_lite_agent_output.yaml b/lib/crewai/tests/cassettes/agents/TestAgentA2AKickoff.test_agent_kickoff_returns_lite_agent_output.yaml
new file mode 100644
index 000000000..aae06f24e
--- /dev/null
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diff --git a/lib/crewai/tests/cassettes/agents/TestAgentA2AKickoff.test_agent_kickoff_with_calculator_skill.yaml b/lib/crewai/tests/cassettes/agents/TestAgentA2AKickoff.test_agent_kickoff_with_calculator_skill.yaml
new file mode 100644
index 000000000..256c8e71d
--- /dev/null
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new file mode 100644
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diff --git a/lib/crewai/tests/cassettes/agents/TestAgentA2AKickoff.test_agent_without_a2a_works_normally.yaml b/lib/crewai/tests/cassettes/agents/TestAgentA2AKickoff.test_agent_without_a2a_works_normally.yaml
new file mode 100644
index 000000000..ce86df05c
--- /dev/null
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diff --git a/lib/crewai/tests/cassettes/agents/TestAgentA2AKickoffAsync.test_agent_kickoff_async_delegates_to_a2a.yaml b/lib/crewai/tests/cassettes/agents/TestAgentA2AKickoffAsync.test_agent_kickoff_async_delegates_to_a2a.yaml
new file mode 100644
index 000000000..79c154e1c
--- /dev/null
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new file mode 100644
index 000000000..672889884
--- /dev/null
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diff --git a/lib/crewai/tests/cassettes/agents/TestAnthropicNativeToolCalling.test_anthropic_agent_with_native_tool_calling.yaml b/lib/crewai/tests/cassettes/agents/TestAnthropicNativeToolCalling.test_anthropic_agent_with_native_tool_calling.yaml
new file mode 100644
index 000000000..6783a8e27
--- /dev/null
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new file mode 100644
index 000000000..cfec2e992
--- /dev/null
+++ b/lib/crewai/tests/cassettes/agents/TestAzureNativeToolCalling.test_azure_agent_with_native_tool_calling.yaml
@@ -0,0 +1,164 @@
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diff --git a/lib/crewai/tests/cassettes/agents/TestBedrockNativeToolCalling.test_bedrock_agent_kickoff_with_tools_mocked.yaml b/lib/crewai/tests/cassettes/agents/TestBedrockNativeToolCalling.test_bedrock_agent_kickoff_with_tools_mocked.yaml
new file mode 100644
index 000000000..1e7565eb1
--- /dev/null
+++ b/lib/crewai/tests/cassettes/agents/TestBedrockNativeToolCalling.test_bedrock_agent_kickoff_with_tools_mocked.yaml
@@ -0,0 +1,485 @@
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diff --git a/lib/crewai/tests/cassettes/agents/TestGeminiNativeToolCalling.test_gemini_agent_with_native_tool_calling.yaml b/lib/crewai/tests/cassettes/agents/TestGeminiNativeToolCalling.test_gemini_agent_with_native_tool_calling.yaml
new file mode 100644
index 000000000..3682cdf68
--- /dev/null
+++ b/lib/crewai/tests/cassettes/agents/TestGeminiNativeToolCalling.test_gemini_agent_with_native_tool_calling.yaml
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diff --git a/lib/crewai/tests/cassettes/agents/TestMaxUsageCountWithNativeToolCalling.test_max_usage_count_limit_enforced_in_native_tool_calling.yaml b/lib/crewai/tests/cassettes/agents/TestMaxUsageCountWithNativeToolCalling.test_max_usage_count_limit_enforced_in_native_tool_calling.yaml
new file mode 100644
index 000000000..4301b22f3
--- /dev/null
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diff --git a/lib/crewai/tests/cassettes/agents/TestMaxUsageCountWithNativeToolCalling.test_max_usage_count_tracked_in_native_tool_calling.yaml b/lib/crewai/tests/cassettes/agents/TestMaxUsageCountWithNativeToolCalling.test_max_usage_count_tracked_in_native_tool_calling.yaml
new file mode 100644
index 000000000..f30ae5874
--- /dev/null
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diff --git a/lib/crewai/tests/cassettes/agents/TestMaxUsageCountWithNativeToolCalling.test_tool_usage_increments_after_successful_execution.yaml b/lib/crewai/tests/cassettes/agents/TestMaxUsageCountWithNativeToolCalling.test_tool_usage_increments_after_successful_execution.yaml
new file mode 100644
index 000000000..49ccc8302
--- /dev/null
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