fix: resolve lint and type-checker issues in AgentOps integration

- Fix event handler signatures to match expected (source, event) pattern
- Change 'arguments' to 'tool_args' for tool usage events
- Update TaskEvaluationEvent to use correct attributes (evaluation_type, task)
- Fix corrupted tests/__init__.py file
- Add explicit re-export in src third_party __init__.py

Co-Authored-By: João <joao@crewai.com>

.gitignore

@@ -27,3 +27,6 @@ plan.md
 conceptual_plan.md
 build_image
 chromadb-*.lock
+
+# AgentOps
+agentops.log

docs/docs.json

@@ -226,6 +226,7 @@
                 "group": "Observability",
                 "pages": [
                   "en/observability/overview",
+                  "en/observability/agentops",
                   "en/observability/arize-phoenix",
                   "en/observability/langdb",
                   "en/observability/langfuse",
@@ -341,12 +342,11 @@
             "groups": [
               {
                 "group": "Getting Started",
-                "pages": [
-                  "en/api-reference/introduction",
-                  "en/api-reference/inputs",
-                  "en/api-reference/kickoff",
-                  "en/api-reference/status"
-                ]
+                "pages": ["en/api-reference/introduction"]
+              },
+              {
+                "group": "Endpoints",
+                "openapi": "https://raw.githubusercontent.com/crewAIInc/crewAI/main/docs/enterprise-api.en.yaml"
               }
             ]
           },
@@ -566,6 +566,7 @@
                 "group": "Observabilidade",
                 "pages": [
                   "pt-BR/observability/overview",
+                  "pt-BR/observability/agentops",
                   "pt-BR/observability/arize-phoenix",
                   "pt-BR/observability/langdb",
                   "pt-BR/observability/langfuse",
@@ -681,12 +682,11 @@
             "groups": [
               {
                 "group": "Começando",
-                "pages": [
-                  "pt-BR/api-reference/introduction",
-                  "pt-BR/api-reference/inputs",
-                  "pt-BR/api-reference/kickoff",
-                  "pt-BR/api-reference/status"
-                ]
+                "pages": ["pt-BR/api-reference/introduction"]
+              },
+              {
+                "group": "Endpoints",
+                "openapi": "https://raw.githubusercontent.com/crewAIInc/crewAI/main/docs/enterprise-api.pt-BR.yaml"
               }
             ]
           },
@@ -914,6 +914,7 @@
                 "group": "Observability",
                 "pages": [
                   "ko/observability/overview",
+                  "ko/observability/agentops",
                   "ko/observability/arize-phoenix",
                   "ko/observability/langdb",
                   "ko/observability/langfuse",
@@ -1028,12 +1029,11 @@
             "groups": [
               {
                 "group": "시작 안내",
-                "pages": [
-                  "ko/api-reference/introduction",
-                  "ko/api-reference/inputs",
-                  "ko/api-reference/kickoff",
-                  "ko/api-reference/status"
-                ]
+                "pages": ["ko/api-reference/introduction"]
+              },
+              {
+                "group": "Endpoints",
+                "openapi": "https://raw.githubusercontent.com/crewAIInc/crewAI/main/docs/enterprise-api.ko.yaml"
               }
             ]
           },
@@ -1084,10 +1084,6 @@
     "indexing": "all"
   },
   "redirects": [
-    {
-      "source": "/api-reference",
-      "destination": "/en/api-reference/introduction"
-    },
     {
       "source": "/introduction",
       "destination": "/en/introduction"
@@ -1140,18 +1136,6 @@
       "source": "/api-reference/:path*",
       "destination": "/en/api-reference/:path*"
     },
-    {
-      "source": "/en/api-reference",
-      "destination": "/en/api-reference/introduction"
-    },
-    {
-      "source": "/pt-BR/api-reference",
-      "destination": "/pt-BR/api-reference/introduction"
-    },
-    {
-      "source": "/ko/api-reference",
-      "destination": "/ko/api-reference/introduction"
-    },
     {
       "source": "/examples/:path*",
       "destination": "/en/examples/:path*"

docs/en/api-reference/inputs.mdx

@@ -1,7 +0,0 @@
----
-title: "GET /inputs"
-description: "Get required inputs for your crew"
-openapi: "/enterprise-api.en.yaml GET /inputs"
----
-
-

docs/en/api-reference/kickoff.mdx

@@ -1,7 +0,0 @@
----
-title: "POST /kickoff"
-description: "Start a crew execution"
-openapi: "/enterprise-api.en.yaml POST /kickoff"
----
-
-

docs/en/api-reference/status.mdx

@@ -1,7 +0,0 @@
----
-title: "GET /status/{kickoff_id}"
-description: "Get execution status"
-openapi: "/enterprise-api.en.yaml GET /status/{kickoff_id}"
----
-
-

docs/en/concepts/training.mdx

@@ -21,17 +21,13 @@ To use the training feature, follow these steps:
 3. Run the following command:
 
 ```shell
-crewai train -n <n_iterations> -f <filename.pkl>
+crewai train -n <n_iterations> <filename> (optional)
 ```
 <Tip>
   Replace `<n_iterations>` with the desired number of training iterations and `<filename>` with the appropriate filename ending with `.pkl`.
 </Tip>
 
-<Note>
-  If you omit `-f`, the output defaults to `trained_agents_data.pkl` in the current working directory. You can pass an absolute path to control where the file is written.
-</Note>
-
-### Training your Crew programmatically
+### Training Your Crew Programmatically
 
 To train your crew programmatically, use the following steps:
 
@@ -55,65 +51,19 @@ except Exception as e:
     raise Exception(f"An error occurred while training the crew: {e}")
 ```
 
-## How trained data is used by agents
+### Key Points to Note
 
-CrewAI uses the training artifacts in two ways: during training to incorporate your human feedback, and after training to guide agents with consolidated suggestions.
+- **Positive Integer Requirement:** Ensure that the number of iterations (`n_iterations`) is a positive integer. The code will raise a `ValueError` if this condition is not met.
+- **Filename Requirement:** Ensure that the filename ends with `.pkl`. The code will raise a `ValueError` if this condition is not met.
+- **Error Handling:** The code handles subprocess errors and unexpected exceptions, providing error messages to the user.
 
-### Training data flow
+It is important to note that the training process may take some time, depending on the complexity of your agents and will also require your feedback on each iteration.
 
-```mermaid
-flowchart TD
-  A["Start training<br/>CLI: crewai train -n -f<br/>or Python: crew.train(...)"] --> B["Setup training mode<br/>- task.human_input = true<br/>- disable delegation<br/>- init training_data.pkl + trained file"]
+Once the training is complete, your agents will be equipped with enhanced capabilities and knowledge, ready to tackle complex tasks and provide more consistent and valuable insights.
 
-  subgraph "Iterations"
-    direction LR
-    C["Iteration i<br/>initial_output"] --> D["User human_feedback"]
-    D --> E["improved_output"]
-    E --> F["Append to training_data.pkl<br/>by agent_id and iteration"]
-  end
+Remember to regularly update and retrain your agents to ensure they stay up-to-date with the latest information and advancements in the field.
 
-  B --> C
-  F --> G{"More iterations?"}
-  G -- "Yes" --> C
-  G -- "No" --> H["Evaluate per agent<br/>aggregate iterations"]
-
-  H --> I["Consolidate<br/>suggestions[] + quality + final_summary"]
-  I --> J["Save by agent role to trained file<br/>(default: trained_agents_data.pkl)"]
-
-  J --> K["Normal (non-training) runs"]
-  K --> L["Auto-load suggestions<br/>from trained_agents_data.pkl"]
-  L --> M["Append to prompt<br/>for consistent improvements"]
-```
-
-### During training runs
-
-- On each iteration, the system records for every agent:
-  - `initial_output`: the agent’s first answer
-  - `human_feedback`: your inline feedback when prompted
-  - `improved_output`: the agent’s follow-up answer after feedback
-- This data is stored in a working file named `training_data.pkl` keyed by the agent’s internal ID and iteration.
-- While training is active, the agent automatically appends your prior human feedback to its prompt to enforce those instructions on subsequent attempts within the training session.
-  Training is interactive: tasks set `human_input = true`, so running in a non-interactive environment will block on user input.
-
-### After training completes
-
-- When `train(...)` finishes, CrewAI evaluates the collected training data per agent and produces a consolidated result containing:
-  - `suggestions`: clear, actionable instructions distilled from your feedback and the difference between initial/improved outputs
-  - `quality`: a 0–10 score capturing improvement
-  - `final_summary`: a step-by-step set of action items for future tasks
-- These consolidated results are saved to the filename you pass to `train(...)` (default via CLI is `trained_agents_data.pkl`). Entries are keyed by the agent’s `role` so they can be applied across sessions.
-- During normal (non-training) execution, each agent automatically loads its consolidated `suggestions` and appends them to the task prompt as mandatory instructions. This gives you consistent improvements without changing your agent definitions.
-
-### File summary
-
-- `training_data.pkl` (ephemeral, per-session):
-  - Structure: `agent_id -> { iteration_number: { initial_output, human_feedback, improved_output } }`
-  - Purpose: capture raw data and human feedback during training
-  - Location: saved in the current working directory (CWD)
-- `trained_agents_data.pkl` (or your custom filename):
-  - Structure: `agent_role -> { suggestions: string[], quality: number, final_summary: string }`
-  - Purpose: persist consolidated guidance for future runs
-  - Location: written to the CWD by default; use `-f` to set a custom (including absolute) path
+Happy training with CrewAI! 🚀
 
 ## Small Language Model Considerations
 
@@ -179,18 +129,3 @@ flowchart TD
     </Warning>
   </Tab>
 </Tabs>
-
-### Key Points to Note
-
-- **Positive Integer Requirement:** Ensure that the number of iterations (`n_iterations`) is a positive integer. The code will raise a `ValueError` if this condition is not met.
-- **Filename Requirement:** Ensure that the filename ends with `.pkl`. The code will raise a `ValueError` if this condition is not met.
-- **Error Handling:** The code handles subprocess errors and unexpected exceptions, providing error messages to the user.
-- Trained guidance is applied at prompt time; it does not modify your Python/YAML agent configuration.
-- Agents automatically load trained suggestions from a file named `trained_agents_data.pkl` located in the current working directory. If you trained to a different filename, either rename it to `trained_agents_data.pkl` before running, or adjust the loader in code.
-- You can change the output filename when calling `crewai train` with `-f/--filename`. Absolute paths are supported if you want to save outside the CWD.
-
-It is important to note that the training process may take some time, depending on the complexity of your agents and will also require your feedback on each iteration.
-
-Once the training is complete, your agents will be equipped with enhanced capabilities and knowledge, ready to tackle complex tasks and provide more consistent and valuable insights.
-
-Remember to regularly update and retrain your agents to ensure they stay up-to-date with the latest information and advancements in the field.

docs/en/enterprise/features/tool-repository.mdx

@@ -35,22 +35,6 @@ crewai tool install <tool-name>
 
 This installs the tool and adds it to `pyproject.toml`.
 
-You can use the tool by importing it and adding it to your agents:
-
-```python
-from your_tool.tool import YourTool
-
-custom_tool = YourTool()
-
-researcher = Agent(
-    role='Market Research Analyst',
-    goal='Provide up-to-date market analysis of the AI industry',
-    backstory='An expert analyst with a keen eye for market trends.',
-    tools=[custom_tool],
-    verbose=True
-)
-```
-
 ## Creating and Publishing Tools
 
 To create a new tool project:

docs/en/observability/agentops.mdx

@@ -0,0 +1,184 @@
+---
+title: "AgentOps Integration"
+description: "Monitor and analyze your CrewAI agents with AgentOps observability platform"
+---
+
+# AgentOps Integration
+
+AgentOps is a powerful observability platform designed specifically for AI agents. It provides comprehensive monitoring, analytics, and debugging capabilities for your CrewAI crews.
+
+## Features
+
+- **Real-time Monitoring**: Track agent performance and behavior in real-time
+- **Session Replay**: Review complete agent sessions with detailed execution traces
+- **Performance Analytics**: Analyze crew efficiency, tool usage, and task completion rates
+- **Error Tracking**: Identify and debug issues in agent workflows
+- **Cost Tracking**: Monitor LLM usage and associated costs
+- **Team Collaboration**: Share insights and collaborate on agent optimization
+
+## Installation
+
+Install AgentOps alongside CrewAI:
+
+```bash
+pip install crewai[agentops]
+```
+
+Or install AgentOps separately:
+
+```bash
+pip install agentops
+```
+
+## Setup
+
+1. **Get your API Key**: Sign up at [AgentOps](https://agentops.ai) and get your API key
+
+2. **Configure your environment**: Set your AgentOps API key as an environment variable:
+
+```bash
+export AGENTOPS_API_KEY="your-api-key-here"
+```
+
+3. **Initialize AgentOps**: Add this to your CrewAI script:
+
+```python
+import agentops
+from crewai import Agent, Task, Crew
+
+# Initialize AgentOps
+agentops.init()
+
+# Your CrewAI code here
+agent = Agent(
+    role="Data Analyst",
+    goal="Analyze data and provide insights",
+    backstory="You are an expert data analyst...",
+)
+
+task = Task(
+    description="Analyze the sales data and provide insights",
+    agent=agent,
+)
+
+crew = Crew(
+    agents=[agent],
+    tasks=[task],
+)
+
+# Run your crew
+result = crew.kickoff()
+
+# End the AgentOps session
+agentops.end_session("Success")
+```
+
+## Automatic Integration
+
+CrewAI automatically integrates with AgentOps when the library is installed. The integration captures:
+
+- **Crew Kickoff Events**: Start and completion of crew executions
+- **Tool Usage**: All tool calls and their results
+- **Task Evaluations**: Task performance metrics and feedback
+- **Error Events**: Any errors that occur during execution
+
+## Configuration Options
+
+You can customize the AgentOps integration:
+
+```python
+import agentops
+
+# Configure AgentOps with custom settings
+agentops.init(
+    api_key="your-api-key",
+    tags=["production", "data-analysis"],
+    auto_start_session=True,
+    instrument_llm_calls=True,
+)
+```
+
+## Viewing Your Data
+
+1. **Dashboard**: Visit the AgentOps dashboard to view your agent sessions
+2. **Session Details**: Click on any session to see detailed execution traces
+3. **Analytics**: Use the analytics tab to identify performance trends
+4. **Errors**: Monitor the errors tab for debugging information
+
+## Best Practices
+
+- **Tag Your Sessions**: Use meaningful tags to organize your agent runs
+- **Monitor Costs**: Keep track of LLM usage and associated costs
+- **Review Errors**: Regularly check for and address any errors
+- **Optimize Performance**: Use analytics to identify bottlenecks and optimization opportunities
+
+## Troubleshooting
+
+### AgentOps Not Recording Data
+
+1. Verify your API key is set correctly
+2. Check that AgentOps is properly initialized
+3. Ensure you're calling `agentops.end_session()` at the end of your script
+
+### Missing Events
+
+If some events aren't being captured:
+
+1. Make sure you have the latest version of both CrewAI and AgentOps
+2. Check that the AgentOps listener is properly registered
+3. Review the logs for any error messages
+
+## Example: Complete Integration
+
+```python
+import os
+import agentops
+from crewai import Agent, Task, Crew, Process
+
+# Initialize AgentOps
+agentops.init(
+    api_key=os.getenv("AGENTOPS_API_KEY"),
+    tags=["example", "tutorial"],
+)
+
+# Define your agents
+researcher = Agent(
+    role="Research Specialist",
+    goal="Conduct thorough research on given topics",
+    backstory="You are an expert researcher with access to various tools...",
+)
+
+writer = Agent(
+    role="Content Writer",
+    goal="Create engaging content based on research",
+    backstory="You are a skilled writer who can transform research into compelling content...",
+)
+
+# Define your tasks
+research_task = Task(
+    description="Research the latest trends in AI and machine learning",
+    agent=researcher,
+)
+
+writing_task = Task(
+    description="Write a blog post about AI trends based on the research",
+    agent=writer,
+)
+
+# Create and run your crew
+crew = Crew(
+    agents=[researcher, writer],
+    tasks=[research_task, writing_task],
+    process=Process.sequential,
+)
+
+try:
+    result = crew.kickoff()
+    print(result)
+    agentops.end_session("Success")
+except Exception as e:
+    print(f"Error: {e}")
+    agentops.end_session("Fail")
+```
+
+This integration provides comprehensive observability for your CrewAI agents, helping you monitor, debug, and optimize your AI workflows.

docs/en/tools/overview.mdx

@@ -117,19 +117,4 @@ agent = Agent(
 )
 ```
 
-## **Max Usage Count**
-
-You can set a maximum usage count for a tool to prevent it from being used more than a certain number of times.
-By default, the max usage count is unlimited.
-
-
-
-```python
-from crewai_tools import FileReadTool
-
-tool = FileReadTool(max_usage_count=5, ...)
-```
-
-
-
 Ready to explore? Pick a category above to discover tools that fit your use case!

docs/ko/api-reference/inputs.mdx

@@ -1,7 +0,0 @@
----
-title: "GET /inputs"
-description: "크루가 필요로 하는 입력 확인"
-openapi: "/enterprise-api.ko.yaml GET /inputs"
----
-
-

docs/ko/api-reference/kickoff.mdx

@@ -1,7 +0,0 @@
----
-title: "POST /kickoff"
-description: "크루 실행 시작"
-openapi: "/enterprise-api.ko.yaml POST /kickoff"
----
-
-

docs/ko/api-reference/status.mdx

@@ -1,7 +0,0 @@
----
-title: "GET /status/{kickoff_id}"
-description: "실행 상태 조회"
-openapi: "/enterprise-api.ko.yaml GET /status/{kickoff_id}"
----
-
-

docs/ko/observability/agentops.mdx

@@ -0,0 +1,131 @@
+---
+title: "AgentOps 통합"
+description: "AgentOps 관찰 가능성 플랫폼으로 CrewAI 에이전트를 모니터링하고 분석하세요"
+---
+
+# AgentOps 통합
+
+AgentOps는 AI 에이전트를 위해 특별히 설계된 강력한 관찰 가능성 플랫폼입니다. CrewAI 크루를 위한 포괄적인 모니터링, 분석 및 디버깅 기능을 제공합니다.
+
+## 기능
+
+- **실시간 모니터링**: 에이전트 성능과 동작을 실시간으로 추적
+- **세션 재생**: 상세한 실행 추적과 함께 완전한 에이전트 세션 검토
+- **성능 분석**: 크루 효율성, 도구 사용량 및 작업 완료율 분석
+- **오류 추적**: 에이전트 워크플로우의 문제 식별 및 디버그
+- **비용 추적**: LLM 사용량 및 관련 비용 모니터링
+- **팀 협업**: 인사이트 공유 및 에이전트 최적화 협업
+
+## 설치
+
+CrewAI와 함께 AgentOps 설치:
+
+```bash
+pip install crewai[agentops]
+```
+
+또는 AgentOps를 별도로 설치:
+
+```bash
+pip install agentops
+```
+
+## 설정
+
+1. **API 키 받기**: [AgentOps](https://agentops.ai)에 가입하고 API 키를 받으세요
+
+2. **환경 구성**: AgentOps API 키를 환경 변수로 설정:
+
+```bash
+export AGENTOPS_API_KEY="여기에-api-키-입력"
+```
+
+3. **AgentOps 초기화**: CrewAI 스크립트에 다음을 추가:
+
+```python
+import agentops
+from crewai import Agent, Task, Crew
+
+# AgentOps 초기화
+agentops.init()
+
+# 여기에 CrewAI 코드
+agent = Agent(
+    role="데이터 분석가",
+    goal="데이터를 분석하고 인사이트 제공",
+    backstory="당신은 전문 데이터 분석가입니다...",
+)
+
+task = Task(
+    description="판매 데이터를 분석하고 인사이트를 제공하세요",
+    agent=agent,
+)
+
+crew = Crew(
+    agents=[agent],
+    tasks=[task],
+)
+
+# 크루 실행
+result = crew.kickoff()
+
+# AgentOps 세션 종료
+agentops.end_session("Success")
+```
+
+## 자동 통합
+
+CrewAI는 라이브러리가 설치되면 AgentOps와 자동으로 통합됩니다. 통합은 다음을 캡처합니다:
+
+- **크루 킥오프 이벤트**: 크루 실행의 시작과 완료
+- **도구 사용**: 모든 도구 호출과 결과
+- **작업 평가**: 작업 성능 메트릭과 피드백
+- **오류 이벤트**: 실행 중 발생하는 모든 오류
+
+## 구성 옵션
+
+AgentOps 통합을 사용자 정의할 수 있습니다:
+
+```python
+import agentops
+
+# 사용자 정의 설정으로 AgentOps 구성
+agentops.init(
+    api_key="당신의-api-키",
+    tags=["프로덕션", "데이터-분석"],
+    auto_start_session=True,
+    instrument_llm_calls=True,
+)
+```
+
+## 데이터 보기
+
+1. **대시보드**: AgentOps 대시보드를 방문하여 에이전트 세션 보기
+2. **세션 세부사항**: 세션을 클릭하여 상세한 실행 추적 보기
+3. **분석**: 분석 탭을 사용하여 성능 트렌드 식별
+4. **오류**: 디버깅 정보를 위해 오류 탭 모니터링
+
+## 모범 사례
+
+- **세션 태그 지정**: 의미 있는 태그를 사용하여 에이전트 실행 정리
+- **비용 모니터링**: LLM 사용량과 관련 비용 추적
+- **오류 검토**: 정기적으로 오류 확인 및 해결
+- **성능 최적화**: 분석을 사용하여 병목 현상과 최적화 기회 식별
+
+## 문제 해결
+
+### AgentOps가 데이터를 기록하지 않음
+
+1. API 키가 올바르게 설정되었는지 확인
+2. AgentOps가 제대로 초기화되었는지 확인
+3. 스크립트 끝에서 `agentops.end_session()`을 호출하는지 확인
+
+### 누락된 이벤트
+
+일부 이벤트가 캡처되지 않는 경우:
+
+1. CrewAI와 AgentOps의 최신 버전이 있는지 확인
+2. AgentOps 리스너가 제대로 등록되었는지 확인
+3. 오류 메시지에 대한 로그 검토
+
+이 통합은 CrewAI 에이전트에 대한 포괄적인 관찰 가능성을 제공하여 AI 워크플로우를 모니터링, 디버그 및 최적화하는 데 도움이 됩니다.

docs/pt-BR/api-reference/inputs.mdx

@@ -1,7 +0,0 @@
----
-title: "GET /inputs"
-description: "Obter entradas necessárias para sua crew"
-openapi: "/enterprise-api.pt-BR.yaml GET /inputs"
----
-
-

docs/pt-BR/api-reference/kickoff.mdx

@@ -1,7 +0,0 @@
----
-title: "POST /kickoff"
-description: "Iniciar a execução da crew"
-openapi: "/enterprise-api.pt-BR.yaml POST /kickoff"
----
-
-

docs/pt-BR/api-reference/status.mdx

@@ -1,7 +0,0 @@
----
-title: "GET /status/{kickoff_id}"
-description: "Obter o status da execução"
-openapi: "/enterprise-api.pt-BR.yaml GET /status/{kickoff_id}"
----
-
-

docs/pt-BR/observability/agentops.mdx

@@ -0,0 +1,131 @@
+---
+title: "Integração AgentOps"
+description: "Monitore e analise seus agentes CrewAI com a plataforma de observabilidade AgentOps"
+---
+
+# Integração AgentOps
+
+AgentOps é uma poderosa plataforma de observabilidade projetada especificamente para agentes de IA. Ela fornece capacidades abrangentes de monitoramento, análise e depuração para suas crews CrewAI.
+
+## Recursos
+
+- **Monitoramento em Tempo Real**: Acompanhe o desempenho e comportamento dos agentes em tempo real
+- **Replay de Sessão**: Revise sessões completas de agentes com rastreamentos detalhados de execução
+- **Análise de Desempenho**: Analise eficiência da crew, uso de ferramentas e taxas de conclusão de tarefas
+- **Rastreamento de Erros**: Identifique e depure problemas em fluxos de trabalho de agentes
+- **Rastreamento de Custos**: Monitore o uso de LLM e custos associados
+- **Colaboração em Equipe**: Compartilhe insights e colabore na otimização de agentes
+
+## Instalação
+
+Instale o AgentOps junto com o CrewAI:
+
+```bash
+pip install crewai[agentops]
+```
+
+Ou instale o AgentOps separadamente:
+
+```bash
+pip install agentops
+```
+
+## Configuração
+
+1. **Obtenha sua Chave API**: Cadastre-se no [AgentOps](https://agentops.ai) e obtenha sua chave API
+
+2. **Configure seu ambiente**: Defina sua chave API do AgentOps como uma variável de ambiente:
+
+```bash
+export AGENTOPS_API_KEY="sua-chave-api-aqui"
+```
+
+3. **Inicialize o AgentOps**: Adicione isso ao seu script CrewAI:
+
+```python
+import agentops
+from crewai import Agent, Task, Crew
+
+# Inicializar AgentOps
+agentops.init()
+
+# Seu código CrewAI aqui
+agent = Agent(
+    role="Analista de Dados",
+    goal="Analisar dados e fornecer insights",
+    backstory="Você é um analista de dados especialista...",
+)
+
+task = Task(
+    description="Analise os dados de vendas e forneça insights",
+    agent=agent,
+)
+
+crew = Crew(
+    agents=[agent],
+    tasks=[task],
+)
+
+# Execute sua crew
+result = crew.kickoff()
+
+# Finalize a sessão AgentOps
+agentops.end_session("Success")
+```
+
+## Integração Automática
+
+O CrewAI se integra automaticamente com o AgentOps quando a biblioteca está instalada. A integração captura:
+
+- **Eventos de Kickoff da Crew**: Início e conclusão de execuções da crew
+- **Uso de Ferramentas**: Todas as chamadas de ferramentas e seus resultados
+- **Avaliações de Tarefas**: Métricas de desempenho de tarefas e feedback
+- **Eventos de Erro**: Quaisquer erros que ocorram durante a execução
+
+## Opções de Configuração
+
+Você pode personalizar a integração do AgentOps:
+
+```python
+import agentops
+
+# Configure AgentOps com configurações personalizadas
+agentops.init(
+    api_key="sua-chave-api",
+    tags=["producao", "analise-dados"],
+    auto_start_session=True,
+    instrument_llm_calls=True,
+)
+```
+
+## Visualizando Seus Dados
+
+1. **Dashboard**: Visite o dashboard do AgentOps para ver suas sessões de agentes
+2. **Detalhes da Sessão**: Clique em qualquer sessão para ver rastreamentos detalhados de execução
+3. **Análises**: Use a aba de análises para identificar tendências de desempenho
+4. **Erros**: Monitore a aba de erros para informações de depuração
+
+## Melhores Práticas
+
+- **Marque Suas Sessões**: Use tags significativas para organizar suas execuções de agentes
+- **Monitore Custos**: Acompanhe o uso de LLM e custos associados
+- **Revise Erros**: Verifique e resolva regularmente quaisquer erros
+- **Otimize Desempenho**: Use análises para identificar gargalos e oportunidades de otimização
+
+## Solução de Problemas
+
+### AgentOps Não Está Gravando Dados
+
+1. Verifique se sua chave API está definida corretamente
+2. Verifique se o AgentOps está inicializado adequadamente
+3. Certifique-se de estar chamando `agentops.end_session()` no final do seu script
+
+### Eventos Ausentes
+
+Se alguns eventos não estão sendo capturados:
+
+1. Certifique-se de ter a versão mais recente do CrewAI e AgentOps
+2. Verifique se o listener do AgentOps está registrado adequadamente
+3. Revise os logs para quaisquer mensagens de erro
+
+Esta integração fornece observabilidade abrangente para seus agentes CrewAI, ajudando você a monitorar, depurar e otimizar seus fluxos de trabalho de IA.

pyproject.toml

@@ -48,7 +48,7 @@ Documentation = "https://docs.crewai.com"
 Repository = "https://github.com/crewAIInc/crewAI"
 
 [project.optional-dependencies]
-tools = ["crewai-tools~=0.62.1"]
+tools = ["crewai-tools~=0.62.0"]
 embeddings = [
     "tiktoken~=0.8.0"
 ]
@@ -68,6 +68,7 @@ docling = [
 aisuite = [
     "aisuite>=0.1.10",
 ]
+agentops = ["agentops==0.3.18"]
 
 [tool.uv]
 dev-dependencies = [

src/crewai/__init__.py

@@ -54,7 +54,7 @@ def _track_install_async():
 
 _track_install_async()
 
-__version__ = "0.165.1"
+__version__ = "0.159.0"
 __all__ = [
     "Agent",
     "Crew",

src/crewai/cli/templates/crew/pyproject.toml

@@ -5,7 +5,7 @@ description = "{{name}} using crewAI"
 authors = [{ name = "Your Name", email = "you@example.com" }]
 requires-python = ">=3.10,<3.14"
 dependencies = [
-    "crewai[tools]>=0.165.1,<1.0.0"
+    "crewai[tools]>=0.159.0,<1.0.0"
 ]
 
 [project.scripts]

src/crewai/cli/templates/flow/pyproject.toml

@@ -5,7 +5,7 @@ description = "{{name}} using crewAI"
 authors = [{ name = "Your Name", email = "you@example.com" }]
 requires-python = ">=3.10,<3.14"
 dependencies = [
-    "crewai[tools]>=0.165.1,<1.0.0",
+    "crewai[tools]>=0.159.0,<1.0.0",
 ]
 
 [project.scripts]

src/crewai/cli/templates/tool/pyproject.toml

@@ -5,7 +5,7 @@ description = "Power up your crews with {{folder_name}}"
 readme = "README.md"
 requires-python = ">=3.10,<3.14"
 dependencies = [
-    "crewai[tools]>=0.165.1"
+    "crewai[tools]>=0.159.0"
 ]
 
 [tool.crewai]

src/crewai/cli/update_crew.py

@@ -44,9 +44,8 @@ def migrate_pyproject(input_file, output_file):
         ]
         new_pyproject["project"]["requires-python"] = poetry_data.get("python")
     else:
-        # If it's already in the new format, just copy the project and tool sections
+        # If it's already in the new format, just copy the project section
         new_pyproject["project"] = pyproject_data.get("project", {})
-        new_pyproject["tool"] = pyproject_data.get("tool", {})
 
     # Migrate or copy dependencies
     if "dependencies" in new_pyproject["project"]:

src/crewai/crew.py

@@ -79,6 +79,7 @@ from crewai.utilities.events.listeners.tracing.trace_listener import (
 
 from crewai.utilities.events.listeners.tracing.utils import (
     is_tracing_enabled,
+    on_first_execution_tracing_confirmation,
 )
 from crewai.utilities.formatter import (
     aggregate_raw_outputs_from_task_outputs,
@@ -285,6 +286,8 @@ class Crew(FlowTrackable, BaseModel):
 
         self._cache_handler = CacheHandler()
         event_listener = EventListener()
+        if on_first_execution_tracing_confirmation():
+            self.tracing = True
 
         if is_tracing_enabled() or self.tracing:
             trace_listener = TraceCollectionListener()
@@ -636,7 +639,6 @@ class Crew(FlowTrackable, BaseModel):
                 self._inputs = inputs
                 self._interpolate_inputs(inputs)
             self._set_tasks_callbacks()
-            self._set_allow_crewai_trigger_context_for_first_task()
 
             i18n = I18N(prompt_file=self.prompt_file)
 
@@ -1506,18 +1508,3 @@ class Crew(FlowTrackable, BaseModel):
         """Reset crew and agent knowledge storage."""
         for ks in knowledges:
             ks.reset()
-
-    def _set_allow_crewai_trigger_context_for_first_task(self):
-        crewai_trigger_payload = self._inputs and self._inputs.get(
-            "crewai_trigger_payload"
-        )
-        able_to_inject = (
-            self.tasks and self.tasks[0].allow_crewai_trigger_context is None
-        )
-
-        if (
-            self.process == Process.sequential
-            and crewai_trigger_payload
-            and able_to_inject
-        ):
-            self.tasks[0].allow_crewai_trigger_context = True

src/crewai/flow/flow.py

@@ -40,6 +40,7 @@ from crewai.utilities.events.listeners.tracing.trace_listener import (
 )
 from crewai.utilities.events.listeners.tracing.utils import (
     is_tracing_enabled,
+    on_first_execution_tracing_confirmation,
 )
 from crewai.utilities.printer import Printer
 
@@ -478,7 +479,11 @@ class Flow(Generic[T], metaclass=FlowMeta):
         # Initialize state with initial values
         self._state = self._create_initial_state()
         self.tracing = tracing
-        if is_tracing_enabled() or self.tracing:
+        if (
+            on_first_execution_tracing_confirmation()
+            or is_tracing_enabled()
+            or self.tracing
+        ):
             trace_listener = TraceCollectionListener()
             trace_listener.setup_listeners(crewai_event_bus)
         # Apply any additional kwargs
@@ -913,52 +918,17 @@ class Flow(Generic[T], metaclass=FlowMeta):
         - Triggers execution of any listeners waiting on this start method
         - Part of the flow's initialization sequence
         - Skips execution if method was already completed (e.g., after reload)
-        - Automatically injects crewai_trigger_payload if available in flow inputs
         """
         if start_method_name in self._completed_methods:
             last_output = self._method_outputs[-1] if self._method_outputs else None
             await self._execute_listeners(start_method_name, last_output)
             return
 
-        method = self._methods[start_method_name]
-        enhanced_method = self._inject_trigger_payload_for_start_method(method)
-
         result = await self._execute_method(
-            start_method_name, enhanced_method
+            start_method_name, self._methods[start_method_name]
         )
         await self._execute_listeners(start_method_name, result)
 
-    def _inject_trigger_payload_for_start_method(self, original_method: Callable) -> Callable:
-        def prepare_kwargs(*args, **kwargs):
-            inputs = baggage.get_baggage("flow_inputs") or {}
-            trigger_payload = inputs.get("crewai_trigger_payload")
-
-            sig = inspect.signature(original_method)
-            accepts_trigger_payload = "crewai_trigger_payload" in sig.parameters
-
-            if trigger_payload is not None and accepts_trigger_payload:
-                kwargs["crewai_trigger_payload"] = trigger_payload
-            elif trigger_payload is not None:
-                self._log_flow_event(
-                    f"Trigger payload available but {original_method.__name__} doesn't accept crewai_trigger_payload parameter",
-                    color="yellow"
-                )
-            return args, kwargs
-
-        if asyncio.iscoroutinefunction(original_method):
-            async def enhanced_method(*args, **kwargs):
-                args, kwargs = prepare_kwargs(*args, **kwargs)
-                return await original_method(*args, **kwargs)
-        else:
-            def enhanced_method(*args, **kwargs):
-                args, kwargs = prepare_kwargs(*args, **kwargs)
-                return original_method(*args, **kwargs)
-
-        enhanced_method.__name__ = original_method.__name__
-        enhanced_method.__doc__ = original_method.__doc__
-
-        return enhanced_method
-
     async def _execute_method(
         self, method_name: str, method: Callable, *args: Any, **kwargs: Any
     ) -> Any:

src/crewai/knowledge/storage/knowledge_storage.py

@@ -11,6 +11,7 @@ import chromadb.errors
 from chromadb.api import ClientAPI
 from chromadb.api.types import OneOrMany
 from chromadb.config import Settings
+from pydantic.warnings import PydanticDeprecatedSince211
 import warnings
 
 from crewai.knowledge.storage.base_knowledge_storage import BaseKnowledgeStorage
@@ -90,6 +91,7 @@ class KnowledgeStorage(BaseKnowledgeStorage):
         # TODO: Remove this once we upgrade chromadb to at least 1.0.8.
         warnings.filterwarnings(
             "ignore",
+            category=PydanticDeprecatedSince211,
             message=r".*'model_fields'.*is deprecated.*",
             module=r"^chromadb(\.|$)",
         )

src/crewai/memory/storage/rag_storage.py

@@ -13,6 +13,7 @@ from crewai.utilities.chromadb import create_persistent_client
 from crewai.utilities.constants import MAX_FILE_NAME_LENGTH
 from crewai.utilities.paths import db_storage_path
 import warnings
+from pydantic.warnings import PydanticDeprecatedSince211
 
 
 @contextlib.contextmanager
@@ -67,6 +68,7 @@ class RAGStorage(BaseRAGStorage):
         # TODO: Remove this once we upgrade chromadb to at least 1.0.8.
         warnings.filterwarnings(
             "ignore",
+            category=PydanticDeprecatedSince211,
             message=r".*'model_fields'.*is deprecated.*",
             module=r"^chromadb(\.|$)",
         )

src/crewai/rag/core/__init__.py

@@ -1 +0,0 @@
-"""Core abstract base classes and protocols for RAG  systems."""

src/crewai/rag/core/base_client.py

@@ -1,433 +0,0 @@
-"""Protocol for vector database client implementations."""
-
-from abc import abstractmethod
-from typing import Any, Protocol, runtime_checkable, TypedDict, Annotated
-from typing_extensions import Unpack, Required
-
-
-from crewai.rag.types import (
-    EmbeddingFunction,
-    BaseRecord,
-    SearchResult,
-)
-
-
-class BaseCollectionParams(TypedDict):
-    """Base parameters for collection operations.
-
-    Attributes:
-        collection_name: The name of the collection/index to operate on.
-    """
-
-    collection_name: Required[
-        Annotated[
-            str,
-            "Name of the collection/index. Implementations may have specific constraints (e.g., character limits, allowed characters, case sensitivity).",
-        ]
-    ]
-
-
-class BaseCollectionAddParams(BaseCollectionParams):
-    """Parameters for adding documents to a collection.
-
-    Extends BaseCollectionParams with document-specific fields.
-
-    Attributes:
-        collection_name: The name of the collection to add documents to.
-        documents: List of BaseRecord dictionaries containing document data.
-    """
-
-    documents: list[BaseRecord]
-
-
-class BaseCollectionSearchParams(BaseCollectionParams, total=False):
-    """Parameters for searching within a collection.
-
-    Extends BaseCollectionParams with search-specific optional fields.
-    All fields except collection_name and query are optional.
-
-    Attributes:
-        query: The text query to search for (required).
-        limit: Maximum number of results to return.
-        metadata_filter: Filter results by metadata fields.
-        score_threshold: Minimum similarity score for results (0-1).
-    """
-
-    query: Required[str]
-    limit: int
-    metadata_filter: dict[str, Any]
-    score_threshold: float
-
-
-@runtime_checkable
-class BaseClient(Protocol):
-    """Protocol for vector store client implementations.
-
-    This protocol defines the interface that all vector store client implementations
-    must follow. It provides a consistent API for storing and retrieving
-    documents with their vector embeddings across different vector database
-    backends (e.g., Qdrant, ChromaDB, Weaviate). Implementing classes should
-    handle connection management, data persistence, and vector similarity
-    search operations specific to their backend.
-
-    Implementation Guidelines:
-        Implementations should accept BaseClientParams in their constructor to allow
-        passing pre-configured client instances:
-
-        class MyVectorClient:
-            def __init__(self, client: Any | None = None, **kwargs):
-                if client:
-                    self.client = client
-                else:
-                    self.client = self._create_default_client(**kwargs)
-
-    Notes:
-      This protocol replaces the former BaseRAGStorage abstraction,
-      providing a cleaner interface for vector store operations.
-
-    Attributes:
-        embedding_function: Callable that takes a list of text strings
-            and returns a list of embedding vectors. Implementations
-            should always provide a default embedding function.
-        client: The underlying vector database client instance. This could be
-            passed via BaseClientParams during initialization or created internally.
-    """
-
-    client: Any
-    embedding_function: EmbeddingFunction
-
-    @abstractmethod
-    def create_collection(self, **kwargs: Unpack[BaseCollectionParams]) -> None:
-        """Create a new collection/index in the vector database.
-
-        Keyword Args:
-            collection_name: The name of the collection to create. Must be unique within
-                the vector database instance.
-
-        Raises:
-            ValueError: If collection name already exists.
-            ConnectionError: If unable to connect to the vector database backend.
-        """
-        ...
-
-    @abstractmethod
-    async def acreate_collection(self, **kwargs: Unpack[BaseCollectionParams]) -> None:
-        """Create a new collection/index in the vector database asynchronously.
-
-        Keyword Args:
-            collection_name: The name of the collection to create. Must be unique within
-                the vector database instance.
-
-        Raises:
-            ValueError: If collection name already exists.
-            ConnectionError: If unable to connect to the vector database backend.
-        """
-        ...
-
-    @abstractmethod
-    def get_or_create_collection(self, **kwargs: Unpack[BaseCollectionParams]) -> Any:
-        """Get an existing collection or create it if it doesn't exist.
-
-        This method provides a convenient way to ensure a collection exists
-        without having to check for its existence first.
-
-        Keyword Args:
-            collection_name: The name of the collection to get or create.
-
-        Returns:
-            A collection object whose type depends on the backend implementation.
-            This could be a collection reference, ID, or client object.
-
-        Raises:
-            ValueError: If unable to create the collection.
-            ConnectionError: If unable to connect to the vector database backend.
-        """
-        ...
-
-    @abstractmethod
-    async def aget_or_create_collection(
-        self, **kwargs: Unpack[BaseCollectionParams]
-    ) -> Any:
-        """Get an existing collection or create it if it doesn't exist asynchronously.
-
-        Keyword Args:
-            collection_name: The name of the collection to get or create.
-
-        Returns:
-            A collection object whose type depends on the backend implementation.
-
-        Raises:
-            ValueError: If unable to create the collection.
-            ConnectionError: If unable to connect to the vector database backend.
-        """
-        ...
-
-    @abstractmethod
-    def add_documents(self, **kwargs: Unpack[BaseCollectionAddParams]) -> None:
-        """Add documents with their embeddings to a collection.
-
-        This method performs an upsert operation - if a document with the same ID
-        already exists, it will be updated with the new content and metadata.
-
-        Implementations should handle embedding generation internally based on
-        the configured embedding function.
-
-        Keyword Args:
-            collection_name: The name of the collection to add documents to.
-            documents: List of BaseRecord dicts containing:
-                - content: The text content (required)
-                - doc_id: Optional unique identifier (auto-generated from content hash if missing)
-                - metadata: Optional metadata dictionary
-                Embeddings will be generated automatically.
-
-        Raises:
-            ValueError: If collection doesn't exist or documents list is empty.
-            TypeError: If documents are not BaseRecord dict instances.
-            ConnectionError: If unable to connect to the vector database backend.
-
-        Example:
-            >>> from crewai.rag.chromadb.client import ChromaDBClient
-            >>> from crewai.rag.types import BaseRecord
-            >>> client = ChromaDBClient()
-            >>>
-            >>> records: list[BaseRecord] = [
-            ...     {
-            ...         "content": "Machine learning basics",
-            ...         "metadata": {"source": "file3", "topic": "ML"}
-            ...     },
-            ...     {
-            ...         "doc_id": "custom_id",
-            ...         "content": "Deep learning fundamentals",
-            ...         "metadata": {"source": "file4", "topic": "DL"}
-            ...     }
-            ... ]
-            >>> client.add_documents(collection_name="my_docs", documents=records)
-            >>>
-            >>> records_with_id: list[BaseRecord] = [
-            ...     {
-            ...         "doc_id": "nlp_001",
-            ...         "content": "Advanced NLP techniques",
-            ...         "metadata": {"source": "file5", "topic": "NLP"}
-            ...     }
-            ... ]
-            >>> client.add_documents(collection_name="my_docs", documents=records_with_id)
-        """
-        ...
-
-    @abstractmethod
-    async def aadd_documents(self, **kwargs: Unpack[BaseCollectionAddParams]) -> None:
-        """Add documents with their embeddings to a collection asynchronously.
-
-        Implementations should handle embedding generation internally based on
-        the configured embedding function.
-
-        Keyword Args:
-            collection_name: The name of the collection to add documents to.
-            documents: List of BaseRecord dicts containing:
-                - content: The text content (required)
-                - doc_id: Optional unique identifier (auto-generated from content hash if missing)
-                - metadata: Optional metadata dictionary
-                Embeddings will be generated automatically.
-
-        Raises:
-            ValueError: If collection doesn't exist or documents list is empty.
-            TypeError: If documents are not BaseRecord dict instances.
-            ConnectionError: If unable to connect to the vector database backend.
-
-        Example:
-            >>> import asyncio
-            >>> from crewai.rag.chromadb.client import ChromaDBClient
-            >>> from crewai.rag.types import BaseRecord
-            >>>
-            >>> async def add_documents():
-            ...     client = ChromaDBClient()
-            ...
-            ...     records: list[BaseRecord] = [
-            ...         {
-            ...             "doc_id": "doc2",
-            ...             "content": "Async operations in Python",
-            ...             "metadata": {"source": "file2", "topic": "async"}
-            ...         }
-            ...     ]
-            ...     await client.aadd_documents(collection_name="my_docs", documents=records)
-            ...
-            >>> asyncio.run(add_documents())
-        """
-        ...
-
-    @abstractmethod
-    def search(
-        self, **kwargs: Unpack[BaseCollectionSearchParams]
-    ) -> list[SearchResult]:
-        """Search for similar documents using a query.
-
-        Performs a vector similarity search to find the most similar documents
-        to the provided query.
-
-        Keyword Args:
-            collection_name: The name of the collection to search in.
-            query: The text query to search for. The implementation handles
-                embedding generation internally.
-            limit: Maximum number of results to return. Defaults to 10.
-            metadata_filter: Optional metadata filter to apply to the search. The exact
-                format depends on the backend, but typically supports equality
-                and range queries on metadata fields.
-            score_threshold: Optional minimum similarity score threshold. Only
-                results with scores >= this threshold will be returned. The
-                score interpretation depends on the distance metric used.
-
-        Returns:
-            A list of SearchResult dictionaries ordered by similarity score in
-            descending order. Each result contains:
-                - id: Document ID
-                - content: Document text content
-                - metadata: Document metadata
-                - score: Similarity score (0-1, higher is better)
-
-        Raises:
-            ValueError: If collection doesn't exist.
-            ConnectionError: If unable to connect to the vector database backend.
-
-        Example:
-            >>> from crewai.rag.chromadb.client import ChromaDBClient
-            >>> client = ChromaDBClient()
-            >>>
-            >>> results = client.search(
-            ...     collection_name="my_docs",
-            ...     query="What is machine learning?",
-            ...     limit=5,
-            ...     metadata_filter={"source": "file1"},
-            ...     score_threshold=0.7
-            ... )
-            >>> for result in results:
-            ...     print(f"{result['id']}: {result['score']:.2f}")
-        """
-        ...
-
-    @abstractmethod
-    async def asearch(
-        self, **kwargs: Unpack[BaseCollectionSearchParams]
-    ) -> list[SearchResult]:
-        """Search for similar documents using a query asynchronously.
-
-        Keyword Args:
-            collection_name: The name of the collection to search in.
-            query: The text query to search for. The implementation handles
-                embedding generation internally.
-            limit: Maximum number of results to return. Defaults to 10.
-            metadata_filter: Optional metadata filter to apply to the search.
-            score_threshold: Optional minimum similarity score threshold.
-
-        Returns:
-            A list of SearchResult dictionaries ordered by similarity score.
-
-        Raises:
-            ValueError: If collection doesn't exist.
-            ConnectionError: If unable to connect to the vector database backend.
-
-        Example:
-            >>> import asyncio
-            >>> from crewai.rag.chromadb.client import ChromaDBClient
-            >>>
-            >>> async def search_documents():
-            ...     client = ChromaDBClient()
-            ...     results = await client.asearch(
-            ...         collection_name="my_docs",
-            ...         query="Python programming best practices",
-            ...         limit=5,
-            ...         metadata_filter={"source": "file1"},
-            ...         score_threshold=0.7
-            ...     )
-            ...     for result in results:
-            ...         print(f"{result['id']}: {result['score']:.2f}")
-            ...
-            >>> asyncio.run(search_documents())
-        """
-        ...
-
-    @abstractmethod
-    def delete_collection(self, **kwargs: Unpack[BaseCollectionParams]) -> None:
-        """Delete a collection and all its data.
-
-        This operation is irreversible and will permanently remove all documents,
-        embeddings, and metadata associated with the collection.
-
-        Keyword Args:
-            collection_name: The name of the collection to delete.
-
-        Raises:
-            ValueError: If the collection doesn't exist.
-            ConnectionError: If unable to connect to the vector database backend.
-
-        Example:
-            >>> from crewai.rag.chromadb.client import ChromaDBClient
-            >>> client = ChromaDBClient()
-            >>> client.delete_collection(collection_name="old_docs")
-            >>> print("Collection 'old_docs' deleted successfully")
-        """
-        ...
-
-    @abstractmethod
-    async def adelete_collection(self, **kwargs: Unpack[BaseCollectionParams]) -> None:
-        """Delete a collection and all its data asynchronously.
-
-        Keyword Args:
-            collection_name: The name of the collection to delete.
-
-        Raises:
-            ValueError: If the collection doesn't exist.
-            ConnectionError: If unable to connect to the vector database backend.
-
-        Example:
-            >>> import asyncio
-            >>> from crewai.rag.chromadb.client import ChromaDBClient
-            >>>
-            >>> async def delete_old_collection():
-            ...     client = ChromaDBClient()
-            ...     await client.adelete_collection(collection_name="old_docs")
-            ...     print("Collection 'old_docs' deleted successfully")
-            ...
-            >>> asyncio.run(delete_old_collection())
-        """
-        ...
-
-    @abstractmethod
-    def reset(self) -> None:
-        """Reset the vector database by deleting all collections and data.
-
-        This method provides a way to completely clear the vector database,
-        removing all collections and their contents. Use with caution as
-        this operation is irreversible.
-
-        Raises:
-            ConnectionError: If unable to connect to the vector database backend.
-            PermissionError: If the operation is not allowed by the backend.
-
-        Example:
-            >>> from crewai.rag.chromadb.client import ChromaDBClient
-            >>> client = ChromaDBClient()
-            >>> client.reset()
-            >>> print("Vector database completely reset - all data deleted")
-        """
-        ...
-
-    @abstractmethod
-    async def areset(self) -> None:
-        """Reset the vector database by deleting all collections and data asynchronously.
-
-        Raises:
-            ConnectionError: If unable to connect to the vector database backend.
-            PermissionError: If the operation is not allowed by the backend.
-
-        Example:
-            >>> import asyncio
-            >>> from crewai.rag.chromadb.client import ChromaDBClient
-            >>>
-            >>> async def reset_database():
-            ...     client = ChromaDBClient()
-            ...     await client.areset()
-            ...     print("Vector database completely reset - all data deleted")
-            ...
-            >>> asyncio.run(reset_database())
-        """
-        ...

src/crewai/rag/core/base_provider.py

@@ -1,30 +0,0 @@
-"""Base provider protocol for vector database client creation."""
-
-from abc import ABC
-from typing import Any, Protocol, runtime_checkable, Union
-from pydantic import BaseModel, Field
-
-from crewai.rag.types import EmbeddingFunction
-from crewai.rag.embeddings.types import EmbeddingOptions
-
-
-class BaseProviderOptions(BaseModel, ABC):
-    """Base configuration for all provider options."""
-
-    client_type: str = Field(..., description="Type of client to create")
-    embedding_config: Union[EmbeddingOptions, EmbeddingFunction, None] = Field(
-        default=None,
-        description="Embedding configuration - either options for built-in providers or a custom function",
-    )
-    options: Any = Field(
-        default=None, description="Additional provider-specific options"
-    )
-
-
-@runtime_checkable
-class BaseProvider(Protocol):
-    """Protocol for vector database client providers."""
-
-    def __call__(self, options: BaseProviderOptions) -> Any:
-        """Create and return a configured client instance."""
-        ...

src/crewai/rag/embeddings/factory.py

@@ -1,148 +0,0 @@
-"""Minimal embedding function factory for CrewAI."""
-
-import os
-
-from chromadb import EmbeddingFunction
-from chromadb.utils.embedding_functions.amazon_bedrock_embedding_function import (
-    AmazonBedrockEmbeddingFunction,
-)
-from chromadb.utils.embedding_functions.cohere_embedding_function import (
-    CohereEmbeddingFunction,
-)
-from chromadb.utils.embedding_functions.google_embedding_function import (
-    GooglePalmEmbeddingFunction,
-    GoogleGenerativeAiEmbeddingFunction,
-    GoogleVertexEmbeddingFunction,
-)
-from chromadb.utils.embedding_functions.huggingface_embedding_function import (
-    HuggingFaceEmbeddingFunction,
-)
-from chromadb.utils.embedding_functions.instructor_embedding_function import (
-    InstructorEmbeddingFunction,
-)
-from chromadb.utils.embedding_functions.jina_embedding_function import (
-    JinaEmbeddingFunction,
-)
-from chromadb.utils.embedding_functions.ollama_embedding_function import (
-    OllamaEmbeddingFunction,
-)
-from chromadb.utils.embedding_functions.onnx_mini_lm_l6_v2 import ONNXMiniLM_L6_V2
-from chromadb.utils.embedding_functions.open_clip_embedding_function import (
-    OpenCLIPEmbeddingFunction,
-)
-from chromadb.utils.embedding_functions.openai_embedding_function import (
-    OpenAIEmbeddingFunction,
-)
-from chromadb.utils.embedding_functions.roboflow_embedding_function import (
-    RoboflowEmbeddingFunction,
-)
-from chromadb.utils.embedding_functions.sentence_transformer_embedding_function import (
-    SentenceTransformerEmbeddingFunction,
-)
-from chromadb.utils.embedding_functions.text2vec_embedding_function import (
-    Text2VecEmbeddingFunction,
-)
-
-from crewai.rag.embeddings.types import EmbeddingOptions
-
-
-def get_embedding_function(
-    config: EmbeddingOptions | dict | None = None,
-) -> EmbeddingFunction:
-    """Get embedding function - delegates to ChromaDB.
-
-    Args:
-        config: Optional configuration - either an EmbeddingOptions object or a dict with:
-            - provider: The embedding provider to use (default: "openai")
-            - Any other provider-specific parameters
-
-    Returns:
-        EmbeddingFunction instance ready for use with ChromaDB
-
-    Supported providers:
-        - openai: OpenAI embeddings (default)
-        - cohere: Cohere embeddings
-        - ollama: Ollama local embeddings
-        - huggingface: HuggingFace embeddings
-        - sentence-transformer: Local sentence transformers
-        - instructor: Instructor embeddings for specialized tasks
-        - google-palm: Google PaLM embeddings
-        - google-generativeai: Google Generative AI embeddings
-        - google-vertex: Google Vertex AI embeddings
-        - amazon-bedrock: AWS Bedrock embeddings
-        - jina: Jina AI embeddings
-        - roboflow: Roboflow embeddings for vision tasks
-        - openclip: OpenCLIP embeddings for multimodal tasks
-        - text2vec: Text2Vec embeddings
-        - onnx: ONNX MiniLM-L6-v2 (no API key needed, included with ChromaDB)
-
-    Examples:
-        # Use default OpenAI with retry logic
-        >>> embedder = get_embedding_function()
-
-        # Use Cohere with dict
-        >>> embedder = get_embedding_function({
-        ...     "provider": "cohere",
-        ...     "api_key": "your-key",
-        ...     "model_name": "embed-english-v3.0"
-        ... })
-
-        # Use with EmbeddingOptions
-        >>> embedder = get_embedding_function(
-        ...     EmbeddingOptions(provider="sentence-transformer", model_name="all-MiniLM-L6-v2")
-        ... )
-
-        # Use local sentence transformers (no API key needed)
-        >>> embedder = get_embedding_function({
-        ...     "provider": "sentence-transformer",
-        ...     "model_name": "all-MiniLM-L6-v2"
-        ... })
-
-        # Use Ollama for local embeddings
-        >>> embedder = get_embedding_function({
-        ...     "provider": "ollama",
-        ...     "model_name": "nomic-embed-text"
-        ... })
-
-        # Use ONNX (no API key needed)
-        >>> embedder = get_embedding_function({
-        ...     "provider": "onnx"
-        ... })
-    """
-    if config is None:
-        return OpenAIEmbeddingFunction(
-            api_key=os.getenv("OPENAI_API_KEY"), model_name="text-embedding-3-small"
-        )
-
-    # Handle EmbeddingOptions object
-    if isinstance(config, EmbeddingOptions):
-        config_dict = config.model_dump(exclude_none=True)
-    else:
-        config_dict = config.copy()
-
-    provider = config_dict.pop("provider", "openai")
-
-    embedding_functions = {
-        "openai": OpenAIEmbeddingFunction,
-        "cohere": CohereEmbeddingFunction,
-        "ollama": OllamaEmbeddingFunction,
-        "huggingface": HuggingFaceEmbeddingFunction,
-        "sentence-transformer": SentenceTransformerEmbeddingFunction,
-        "instructor": InstructorEmbeddingFunction,
-        "google-palm": GooglePalmEmbeddingFunction,
-        "google-generativeai": GoogleGenerativeAiEmbeddingFunction,
-        "google-vertex": GoogleVertexEmbeddingFunction,
-        "amazon-bedrock": AmazonBedrockEmbeddingFunction,
-        "jina": JinaEmbeddingFunction,
-        "roboflow": RoboflowEmbeddingFunction,
-        "openclip": OpenCLIPEmbeddingFunction,
-        "text2vec": Text2VecEmbeddingFunction,
-        "onnx": ONNXMiniLM_L6_V2,
-    }
-
-    if provider not in embedding_functions:
-        raise ValueError(
-            f"Unsupported provider: {provider}. "
-            f"Available providers: {list(embedding_functions.keys())}"
-        )
-    return embedding_functions[provider](**config_dict)

src/crewai/rag/embeddings/types.py

@@ -1,62 +0,0 @@
-"""Type definitions for the embeddings module."""
-
-from typing import Literal
-from pydantic import BaseModel, Field, SecretStr
-
-from crewai.rag.types import EmbeddingFunction
-
-
-EmbeddingProvider = Literal[
-    "openai",
-    "cohere",
-    "ollama",
-    "huggingface",
-    "sentence-transformer",
-    "instructor",
-    "google-palm",
-    "google-generativeai",
-    "google-vertex",
-    "amazon-bedrock",
-    "jina",
-    "roboflow",
-    "openclip",
-    "text2vec",
-    "onnx",
-]
-"""Supported embedding providers.
-
-These correspond to the embedding functions available in ChromaDB's
-embedding_functions module. Each provider has specific requirements
-and configuration options.
-"""
-
-
-class EmbeddingOptions(BaseModel):
-    """Configuration options for embedding providers.
-
-    Generic attributes that can be passed to get_embedding_function
-    to configure various embedding providers.
-    """
-
-    provider: EmbeddingProvider = Field(
-        ..., description="Embedding provider name (e.g., 'openai', 'cohere', 'onnx')"
-    )
-    model_name: str | None = Field(
-        default=None, description="Model name for the embedding provider"
-    )
-    api_key: SecretStr | None = Field(
-        default=None, description="API key for the embedding provider"
-    )
-
-
-class EmbeddingConfig(BaseModel):
-    """Configuration wrapper for embedding functions.
-
-    Accepts either a pre-configured EmbeddingFunction or EmbeddingOptions
-    to create one. This provides flexibility in how embeddings are configured.
-
-    Attributes:
-        function: Either a callable EmbeddingFunction or EmbeddingOptions to create one
-    """
-
-    function: EmbeddingFunction | EmbeddingOptions

src/crewai/rag/types.py

@@ -1,50 +0,0 @@
-"""Type definitions for RAG (Retrieval-Augmented Generation) systems."""
-
-from collections.abc import Callable, Mapping
-from typing import TypeAlias, TypedDict, Any
-
-from typing_extensions import Required
-
-
-class BaseRecord(TypedDict, total=False):
-    """A typed dictionary representing a document record.
-
-    Attributes:
-        doc_id: Optional unique identifier for the document. If not provided,
-            a content-based ID will be generated using SHA256 hash.
-        content: The text content of the document (required)
-        metadata: Optional metadata associated with the document
-    """
-
-    doc_id: str
-    content: Required[str]
-    metadata: (
-        Mapping[str, str | int | float | bool]
-        | list[Mapping[str, str | int | float | bool]]
-    )
-
-
-DenseVector: TypeAlias = list[float]
-IntVector: TypeAlias = list[int]
-
-EmbeddingFunction: TypeAlias = Callable[..., Any]
-
-
-class SearchResult(TypedDict):
-    """Standard search result format for vector store queries.
-
-    This provides a consistent interface for search results across different
-    vector store implementations. Each implementation should convert their
-    native result format to this standard format.
-
-    Attributes:
-        id: Unique identifier of the document
-        content: The text content of the document
-        metadata: Optional metadata associated with the document
-        score: Similarity score (higher is better, typically between 0 and 1)
-    """
-
-    id: str
-    content: str
-    metadata: dict[str, Any]
-    score: float

src/crewai/task.py

@@ -72,10 +72,6 @@ class Task(BaseModel):
         output_pydantic: Pydantic model for task output.
         security_config: Security configuration including fingerprinting.
         tools: List of tools/resources limited for task execution.
-        allow_crewai_trigger_context: Optional flag to control crewai_trigger_payload injection.
-                              None (default): Auto-inject for first task only.
-                              True: Always inject trigger payload for this task.
-                              False: Never inject trigger payload, even for first task.
     """
 
     __hash__ = object.__hash__  # type: ignore
@@ -167,10 +163,6 @@ class Task(BaseModel):
     end_time: Optional[datetime.datetime] = Field(
         default=None, description="End time of the task execution"
     )
-    allow_crewai_trigger_context: Optional[bool] = Field(
-        default=None,
-        description="Whether this task should append 'Trigger Payload: {crewai_trigger_payload}' to the task description when crewai_trigger_payload exists in crew inputs.",
-    )
     model_config = {"arbitrary_types_allowed": True}
 
     @field_validator("guardrail")
@@ -556,23 +548,12 @@ class Task(BaseModel):
             str: The formatted prompt string containing the task description,
                  expected output, and optional markdown formatting instructions.
         """
-        description = self.description
-
-        should_inject = self.allow_crewai_trigger_context
-
-        if should_inject and self.agent:
-            crew = getattr(self.agent, 'crew', None)
-            if crew and hasattr(crew, '_inputs') and crew._inputs:
-                trigger_payload = crew._inputs.get("crewai_trigger_payload")
-                if trigger_payload is not None:
-                    description += f"\n\nTrigger Payload: {trigger_payload}"
-
-        tasks_slices = [description]
+        tasks_slices = [self.description]
 
         output = self.i18n.slice("expected_output").format(
             expected_output=self.expected_output
         )
-        tasks_slices = [description, output]
+        tasks_slices = [self.description, output]
 
         if self.markdown:
             markdown_instruction = """Your final answer MUST be formatted in Markdown syntax.

src/crewai/tools/base_tool.py

@@ -14,14 +14,12 @@ from pydantic import BaseModel as PydanticBaseModel
 
 from crewai.tools.structured_tool import CrewStructuredTool
 
-
 class EnvVar(BaseModel):
     name: str
     description: str
     required: bool = True
     default: Optional[str] = None
 
-
 class BaseTool(BaseModel, ABC):
     class _ArgsSchemaPlaceholder(PydanticBaseModel):
         pass
@@ -110,7 +108,7 @@ class BaseTool(BaseModel, ABC):
     def to_structured_tool(self) -> CrewStructuredTool:
         """Convert this tool to a CrewStructuredTool instance."""
         self._set_args_schema()
-        structured_tool = CrewStructuredTool(
+        return CrewStructuredTool(
             name=self.name,
             description=self.description,
             args_schema=self.args_schema,
@@ -119,8 +117,6 @@ class BaseTool(BaseModel, ABC):
             max_usage_count=self.max_usage_count,
             current_usage_count=self.current_usage_count,
         )
-        structured_tool._original_tool = self
-        return structured_tool
 
     @classmethod
     def from_langchain(cls, tool: Any) -> "BaseTool":
@@ -280,9 +276,7 @@ def to_langchain(
     return [t.to_structured_tool() if isinstance(t, BaseTool) else t for t in tools]
 
 
-def tool(
-    *args, result_as_answer: bool = False, max_usage_count: int | None = None
-) -> Callable:
+def tool(*args, result_as_answer: bool = False, max_usage_count: int | None = None) -> Callable:
     """
     Decorator to create a tool from a function.
 

src/crewai/tools/structured_tool.py

@@ -10,11 +10,6 @@ from pydantic import BaseModel, Field, create_model
 
 from crewai.utilities.logger import Logger
 
-from typing import TYPE_CHECKING
-
-if TYPE_CHECKING:
-    from crewai.tools.base_tool import BaseTool
-
 
 class CrewStructuredTool:
     """A structured tool that can operate on any number of inputs.
@@ -23,8 +18,6 @@ class CrewStructuredTool:
     that integrates better with CrewAI's ecosystem.
     """
 
-    _original_tool: BaseTool | None = None
-
     def __init__(
         self,
         name: str,
@@ -54,7 +47,6 @@ class CrewStructuredTool:
         self.result_as_answer = result_as_answer
         self.max_usage_count = max_usage_count
         self.current_usage_count = current_usage_count
-        self._original_tool = None
 
         # Validate the function signature matches the schema
         self._validate_function_signature()
@@ -227,8 +219,6 @@ class CrewStructuredTool:
         """
         parsed_args = self._parse_args(input)
 
-        self._increment_usage_count()
-
         if inspect.iscoroutinefunction(self.func):
             return await self.func(**parsed_args, **kwargs)
         else:
@@ -252,8 +242,6 @@ class CrewStructuredTool:
         """Main method for tool execution."""
         parsed_args = self._parse_args(input)
 
-        self._increment_usage_count()
-
         if inspect.iscoroutinefunction(self.func):
             result = asyncio.run(self.func(**parsed_args, **kwargs))
             return result
@@ -265,12 +253,6 @@ class CrewStructuredTool:
 
         return result
 
-    def _increment_usage_count(self) -> None:
-        """Increment the usage count."""
-        self.current_usage_count += 1
-        if self._original_tool is not None:
-            self._original_tool.current_usage_count = self.current_usage_count
-
     @property
     def args(self) -> dict:
         """Get the tool's input arguments schema."""

src/crewai/utilities/chromadb.py

@@ -1,10 +1,9 @@
-import os
 import re
 import portalocker
 from chromadb import PersistentClient
 from hashlib import md5
 from typing import Optional
-from crewai.utilities.paths import db_storage_path
+
 
 MIN_COLLECTION_LENGTH = 3
 MAX_COLLECTION_LENGTH = 63
@@ -28,9 +27,7 @@ def is_ipv4_pattern(name: str) -> bool:
     return bool(IPV4_PATTERN.match(name))
 
 
-def sanitize_collection_name(
-    name: Optional[str], max_collection_length: int = MAX_COLLECTION_LENGTH
-) -> str:
+def sanitize_collection_name(name: Optional[str], max_collection_length: int = MAX_COLLECTION_LENGTH) -> str:
     """
     Sanitize a collection name to meet ChromaDB requirements:
     1. 3-63 characters long
@@ -75,8 +72,7 @@ def create_persistent_client(path: str, **kwargs):
     concurrent creations. Works for both multi-threads and multi-processes
     environments.
     """
-    lock_id = md5(path.encode(), usedforsecurity=False).hexdigest()
-    lockfile = os.path.join(db_storage_path(), f"chromadb-{lock_id}.lock")
+    lockfile = f"chromadb-{md5(path.encode(), usedforsecurity=False).hexdigest()}.lock"
     with portalocker.Lock(lockfile):
         client = PersistentClient(path=path, **kwargs)
 

src/crewai/utilities/events/__init__.py

@@ -67,6 +67,7 @@ from .memory_events import (
 
 # events
 from .event_listener import EventListener
+from .third_party.agentops_listener import agentops_listener
 
 __all__ = [
     "EventListener",
@@ -121,4 +122,5 @@ __all__ = [
     "ToolSelectionErrorEvent",
     "ToolUsageEvent",
     "ToolValidateInputErrorEvent",
+    "agentops_listener",
 ]

src/crewai/utilities/events/event_listener.py

@@ -161,10 +161,8 @@ class EventListener(BaseEventListener):
         def on_task_started(source, event: TaskStartedEvent):
             span = self._telemetry.task_started(crew=source.agent.crew, task=source)
             self.execution_spans[source] = span
-            # Pass both task ID and task name (if set)
-            task_name = source.name if hasattr(source, 'name') and source.name else None
             self.formatter.create_task_branch(
-                self.formatter.current_crew_tree, source.id, task_name
+                self.formatter.current_crew_tree, source.id
             )
 
         @crewai_event_bus.on(TaskCompletedEvent)
@@ -175,14 +173,11 @@ class EventListener(BaseEventListener):
                 self._telemetry.task_ended(span, source, source.agent.crew)
             self.execution_spans[source] = None
 
-            # Pass task name if it exists
-            task_name = source.name if hasattr(source, 'name') and source.name else None
             self.formatter.update_task_status(
                 self.formatter.current_crew_tree,
                 source.id,
                 source.agent.role,
                 "completed",
-                task_name
             )
 
         @crewai_event_bus.on(TaskFailedEvent)
@@ -193,14 +188,11 @@ class EventListener(BaseEventListener):
                     self._telemetry.task_ended(span, source, source.agent.crew)
                 self.execution_spans[source] = None
 
-            # Pass task name if it exists
-            task_name = source.name if hasattr(source, 'name') and source.name else None
             self.formatter.update_task_status(
                 self.formatter.current_crew_tree,
                 source.id,
                 source.agent.role,
                 "failed",
-                task_name
             )
 
         # ----------- AGENT EVENTS -----------

src/crewai/utilities/events/listeners/tracing/trace_batch_manager.py

@@ -40,8 +40,6 @@ class TraceBatch:
 class TraceBatchManager:
     """Single responsibility: Manage batches and event buffering"""
 
-    is_current_batch_ephemeral: bool = False
-
     def __init__(self):
         try:
             self.plus_api = PlusAPI(api_key=get_auth_token())
@@ -64,7 +62,6 @@ class TraceBatchManager:
             user_context=user_context, execution_metadata=execution_metadata
         )
         self.event_buffer.clear()
-        self.is_current_batch_ephemeral = use_ephemeral
 
         self.record_start_time("execution")
         self._initialize_backend_batch(user_context, execution_metadata, use_ephemeral)
@@ -139,7 +136,7 @@ class TraceBatchManager:
         """Add event to buffer"""
         self.event_buffer.append(trace_event)
 
-    def _send_events_to_backend(self):
+    def _send_events_to_backend(self, ephemeral: bool = True):
         """Send buffered events to backend"""
         if not self.plus_api or not self.trace_batch_id or not self.event_buffer:
             return
@@ -159,7 +156,7 @@ class TraceBatchManager:
 
             response = (
                 self.plus_api.send_ephemeral_trace_events(self.trace_batch_id, payload)
-                if self.is_current_batch_ephemeral
+                if ephemeral
                 else self.plus_api.send_trace_events(self.trace_batch_id, payload)
             )
 
@@ -173,14 +170,15 @@ class TraceBatchManager:
         except Exception as e:
             logger.error(f"❌ Error sending events to backend: {str(e)}")
 
-    def finalize_batch(self) -> Optional[TraceBatch]:
+    def finalize_batch(self, ephemeral: bool = True) -> Optional[TraceBatch]:
         """Finalize batch and return it for sending"""
         if not self.current_batch:
             return None
 
         if self.event_buffer:
-            self._send_events_to_backend()
-        self._finalize_backend_batch()
+            self._send_events_to_backend(ephemeral)
+
+        self._finalize_backend_batch(ephemeral)
 
         self.current_batch.events = self.event_buffer.copy()
 
@@ -189,13 +187,12 @@ class TraceBatchManager:
         self.current_batch = None
         self.event_buffer.clear()
         self.trace_batch_id = None
-        self.is_current_batch_ephemeral = False
 
         self._cleanup_batch_data()
 
         return finalized_batch
 
-    def _finalize_backend_batch(self):
+    def _finalize_backend_batch(self, ephemeral: bool = True):
         """Send batch finalization to backend"""
         if not self.plus_api or not self.trace_batch_id:
             return
@@ -213,7 +210,7 @@ class TraceBatchManager:
                 self.plus_api.finalize_ephemeral_trace_batch(
                     self.trace_batch_id, payload
                 )
-                if self.is_current_batch_ephemeral
+                if ephemeral
                 else self.plus_api.finalize_trace_batch(self.trace_batch_id, payload)
             )
 
@@ -222,7 +219,7 @@ class TraceBatchManager:
                 console = Console()
                 return_link = (
                     f"{CREWAI_BASE_URL}/crewai_plus/trace_batches/{self.trace_batch_id}"
-                    if not self.is_current_batch_ephemeral and access_code is None
+                    if not ephemeral and access_code
                     else f"{CREWAI_BASE_URL}/crewai_plus/ephemeral_trace_batches/{self.trace_batch_id}?access_code={access_code}"
                 )
                 panel = Panel(

src/crewai/utilities/events/listeners/tracing/trace_listener.py

@@ -166,7 +166,7 @@ class TraceCollectionListener(BaseEventListener):
         @event_bus.on(CrewKickoffCompletedEvent)
         def on_crew_completed(source, event):
             self._handle_trace_event("crew_kickoff_completed", source, event)
-            self.batch_manager.finalize_batch()
+            self.batch_manager.finalize_batch(ephemeral=True)
 
         @event_bus.on(CrewKickoffFailedEvent)
         def on_crew_failed(source, event):

src/crewai/utilities/events/third_party/__init__.py

@@ -0,0 +1 @@
+from .agentops_listener import agentops_listener as agentops_listener

src/crewai/utilities/events/third_party/agentops_listener.py

@@ -0,0 +1,137 @@
+import logging
+
+from crewai.utilities.events.base_event_listener import BaseEventListener
+from crewai.utilities.events.crewai_event_bus import CrewAIEventsBus
+from crewai.utilities.events.crew_events import (
+    CrewKickoffCompletedEvent,
+    CrewKickoffStartedEvent,
+)
+from crewai.utilities.events.task_events import TaskEvaluationEvent
+from crewai.utilities.events.tool_usage_events import (
+    ToolUsageErrorEvent,
+    ToolUsageStartedEvent,
+)
+
+logger = logging.getLogger(__name__)
+
+
+class AgentOpsListener(BaseEventListener):
+    def __init__(self):
+        self.agentops = None
+        try:
+            import agentops
+
+            self.agentops = agentops
+            logger.info("AgentOps integration enabled")
+        except ImportError:
+            logger.debug("AgentOps not installed, skipping AgentOps integration")
+
+        super().__init__()
+
+    def setup_listeners(self, crewai_event_bus: CrewAIEventsBus):
+        if self.agentops is None:
+            return
+
+        @crewai_event_bus.on(CrewKickoffStartedEvent)
+        def on_crew_kickoff_started(source, event):
+            self._handle_crew_kickoff_started(source, event)
+
+        @crewai_event_bus.on(CrewKickoffCompletedEvent)
+        def on_crew_kickoff_completed(source, event):
+            self._handle_crew_kickoff_completed(source, event)
+
+        @crewai_event_bus.on(ToolUsageStartedEvent)
+        def on_tool_usage_started(source, event):
+            self._handle_tool_usage_started(source, event)
+
+        @crewai_event_bus.on(ToolUsageErrorEvent)
+        def on_tool_usage_error(source, event):
+            self._handle_tool_usage_error(source, event)
+
+        @crewai_event_bus.on(TaskEvaluationEvent)
+        def on_task_evaluation(source, event):
+            self._handle_task_evaluation(source, event)
+
+    def _handle_crew_kickoff_started(self, source, event: CrewKickoffStartedEvent):
+        if self.agentops is None:
+            return
+
+        try:
+            self.agentops.start_session(
+                tags=["crewai", "crew_kickoff"],
+                config=self.agentops.Configuration(
+                    auto_start_session=False,
+                    instrument_llm_calls=True,
+                ),
+            )
+            logger.debug("AgentOps session started for crew kickoff")
+        except Exception as e:
+            logger.warning(f"Failed to start AgentOps session: {e}")
+
+    def _handle_crew_kickoff_completed(self, source, event: CrewKickoffCompletedEvent):
+        if self.agentops is None:
+            return
+
+        try:
+            self.agentops.end_session("Success")
+            logger.debug("AgentOps session ended for crew kickoff completion")
+        except Exception as e:
+            logger.warning(f"Failed to end AgentOps session: {e}")
+
+    def _handle_tool_usage_started(self, source, event: ToolUsageStartedEvent):
+        if self.agentops is None:
+            return
+
+        try:
+            self.agentops.record(
+                self.agentops.ActionEvent(
+                    action_type="tool_usage",
+                    params={
+                        "tool_name": event.tool_name,
+                        "tool_args": event.tool_args,
+                    },
+                )
+            )
+            logger.debug(f"AgentOps recorded tool usage: {event.tool_name}")
+        except Exception as e:
+            logger.warning(f"Failed to record tool usage in AgentOps: {e}")
+
+    def _handle_tool_usage_error(self, source, event: ToolUsageErrorEvent):
+        if self.agentops is None:
+            return
+
+        try:
+            self.agentops.record(
+                self.agentops.ErrorEvent(
+                    message=f"Tool usage error: {event.error}",
+                    error_type="ToolUsageError",
+                    details={
+                        "tool_name": event.tool_name,
+                        "tool_args": event.tool_args,
+                    },
+                )
+            )
+            logger.debug(f"AgentOps recorded tool usage error: {event.tool_name}")
+        except Exception as e:
+            logger.warning(f"Failed to record tool usage error in AgentOps: {e}")
+
+    def _handle_task_evaluation(self, source, event: TaskEvaluationEvent):
+        if self.agentops is None:
+            return
+
+        try:
+            self.agentops.record(
+                self.agentops.ActionEvent(
+                    action_type="task_evaluation",
+                    params={
+                        "evaluation_type": event.evaluation_type,
+                        "task": str(event.task) if event.task else None,
+                    },
+                )
+            )
+            logger.debug(f"AgentOps recorded task evaluation: {event.evaluation_type}")
+        except Exception as e:
+            logger.warning(f"Failed to record task evaluation in AgentOps: {e}")
+
+
+agentops_listener = AgentOpsListener()

src/crewai/utilities/events/utils/console_formatter.py

@@ -220,22 +220,14 @@ class ConsoleFormatter:
         return tree
 
     def create_task_branch(
-        self, crew_tree: Optional[Tree], task_id: str, task_name: Optional[str] = None
+        self, crew_tree: Optional[Tree], task_id: str
     ) -> Optional[Tree]:
         """Create and initialize a task branch."""
         if not self.verbose:
             return None
 
         task_content = Text()
-        
-        # Display task name if available, otherwise just the ID
-        if task_name:
-            task_content.append("📋 Task: ", style="yellow bold")
-            task_content.append(f"{task_name}", style="yellow bold")
-            task_content.append(f" (ID: {task_id})", style="yellow dim")
-        else:
-            task_content.append(f"📋 Task: {task_id}", style="yellow bold")
-            
+        task_content.append(f"📋 Task: {task_id}", style="yellow bold")
         task_content.append("\nStatus: ", style="white")
         task_content.append("Executing Task...", style="yellow dim")
 
@@ -259,7 +251,6 @@ class ConsoleFormatter:
         task_id: str,
         agent_role: str,
         status: str = "completed",
-        task_name: Optional[str] = None,
     ) -> None:
         """Update task status in the tree."""
         if not self.verbose or crew_tree is None:
@@ -279,13 +270,8 @@ class ConsoleFormatter:
             if str(task_id) in str(branch.label):
                 # Build label without introducing stray blank lines
                 task_content = Text()
-                # First line: Task ID/name
-                if task_name:
-                    task_content.append("📋 Task: ", style=f"{style} bold")
-                    task_content.append(f"{task_name}", style=f"{style} bold")
-                    task_content.append(f" (ID: {task_id})", style=f"{style} dim")
-                else:
-                    task_content.append(f"📋 Task: {task_id}", style=f"{style} bold")
+                # First line: Task ID
+                task_content.append(f"📋 Task: {task_id}", style=f"{style} bold")
 
                 # Second line: Assigned to
                 task_content.append("\nAssigned to: ", style="white")
@@ -299,9 +285,8 @@ class ConsoleFormatter:
                 break
 
         # Show status panel
-        display_name = task_name if task_name else str(task_id)
         content = self.create_status_content(
-            f"Task {status.title()}", display_name, style, Agent=agent_role
+            f"Task {status.title()}", str(task_id), style, Agent=agent_role
         )
         self.print_panel(content, panel_title, style)
 

tests/agent_test.py

@@ -21,7 +21,7 @@ from crewai.utilities import RPMController
 from crewai.utilities.errors import AgentRepositoryError
 from crewai.utilities.events import crewai_event_bus
 from crewai.utilities.events.tool_usage_events import ToolUsageFinishedEvent
-from crewai.process import Process
+
 
 def test_agent_llm_creation_with_env_vars():
     # Store original environment variables
@@ -1209,181 +1209,6 @@ Thought:<|eot_id|>
         assert mock_format_prompt.return_value == expected_prompt
 
 
-@pytest.mark.vcr(filter_headers=["authorization"])
-def test_task_allow_crewai_trigger_context():
-    from crewai import Crew
-
-    agent = Agent(
-        role="test role",
-        goal="test goal",
-        backstory="test backstory"
-    )
-
-    task = Task(
-        description="Analyze the data",
-        expected_output="Analysis report",
-        agent=agent,
-        allow_crewai_trigger_context=True
-    )
-    crew = Crew(agents=[agent], tasks=[task])
-    crew.kickoff({"crewai_trigger_payload": "Important context data"})
-
-    prompt = task.prompt()
-
-    assert "Analyze the data" in prompt
-    assert "Trigger Payload: Important context data" in prompt
-
-
-@pytest.mark.vcr(filter_headers=["authorization"])
-def test_task_without_allow_crewai_trigger_context():
-    from crewai import Crew
-
-    agent = Agent(
-        role="test role",
-        goal="test goal",
-        backstory="test backstory"
-    )
-
-    task = Task(
-        description="Analyze the data",
-        expected_output="Analysis report",
-        agent=agent,
-        allow_crewai_trigger_context=False
-    )
-
-    crew = Crew(agents=[agent], tasks=[task])
-    crew.kickoff({"crewai_trigger_payload": "Important context data"})
-
-    prompt = task.prompt()
-
-    assert "Analyze the data" in prompt
-    assert "Trigger Payload:" not in prompt
-    assert "Important context data" not in prompt
-
-
-@pytest.mark.vcr(filter_headers=["authorization"])
-def test_task_allow_crewai_trigger_context_no_payload():
-    from crewai import Crew
-
-    agent = Agent(
-        role="test role",
-        goal="test goal",
-        backstory="test backstory"
-    )
-
-    task = Task(
-        description="Analyze the data",
-        expected_output="Analysis report",
-        agent=agent,
-        allow_crewai_trigger_context=True
-    )
-
-    crew = Crew(agents=[agent], tasks=[task])
-    crew.kickoff({"other_input": "other data"})
-
-
-    prompt = task.prompt()
-
-    assert "Analyze the data" in prompt
-    assert "Trigger Payload:" not in prompt
-
-
-@pytest.mark.vcr(filter_headers=["authorization"])
-def test_do_not_allow_crewai_trigger_context_for_first_task_hierarchical():
-    from crewai import Crew
-
-    agent1 = Agent(role="First Agent", goal="First goal", backstory="First backstory")
-    agent2 = Agent(role="Second Agent", goal="Second goal", backstory="Second backstory")
-
-    first_task = Task(
-        description="Process initial data",
-        expected_output="Initial analysis",
-        agent=agent1,
-    )
-
-
-    crew = Crew(
-        agents=[agent1, agent2],
-        tasks=[first_task],
-        process=Process.hierarchical,
-        manager_llm="gpt-4o"
-    )
-
-    crew.kickoff({"crewai_trigger_payload": "Initial context data"})
-
-    first_prompt = first_task.prompt()
-    assert "Process initial data" in first_prompt
-    assert "Trigger Payload: Initial context data" not in first_prompt
-
-
-@pytest.mark.vcr(filter_headers=["authorization"])
-def test_first_task_auto_inject_trigger():
-    from crewai import Crew
-
-    agent1 = Agent(role="First Agent", goal="First goal", backstory="First backstory")
-    agent2 = Agent(role="Second Agent", goal="Second goal", backstory="Second backstory")
-
-    first_task = Task(
-        description="Process initial data",
-        expected_output="Initial analysis",
-        agent=agent1,
-    )
-
-    second_task = Task(
-        description="Process secondary data",
-        expected_output="Secondary analysis",
-        agent=agent2,
-    )
-
-    crew = Crew(
-        agents=[agent1, agent2],
-        tasks=[first_task, second_task]
-    )
-    crew.kickoff({"crewai_trigger_payload": "Initial context data"})
-
-    first_prompt = first_task.prompt()
-    assert "Process initial data" in first_prompt
-    assert "Trigger Payload: Initial context data" in first_prompt
-
-    second_prompt = second_task.prompt()
-    assert "Process secondary data" in second_prompt
-    assert "Trigger Payload:" not in second_prompt
-
-@pytest.mark.vcr(filter_headers=["authorization"])
-def test_ensure_first_task_allow_crewai_trigger_context_is_false_does_not_inject():
-    from crewai import Crew
-
-    agent1 = Agent(role="First Agent", goal="First goal", backstory="First backstory")
-    agent2 = Agent(role="Second Agent", goal="Second goal", backstory="Second backstory")
-
-    first_task = Task(
-        description="Process initial data",
-        expected_output="Initial analysis",
-        agent=agent1,
-        allow_crewai_trigger_context=False
-    )
-
-    second_task = Task(
-        description="Process secondary data",
-        expected_output="Secondary analysis",
-        agent=agent2,
-        allow_crewai_trigger_context=True
-    )
-
-    crew = Crew(
-        agents=[agent1, agent2],
-        tasks=[first_task, second_task]
-    )
-    crew.kickoff({"crewai_trigger_payload": "Context data"})
-
-    first_prompt = first_task.prompt()
-    assert "Trigger Payload: Context data" not in first_prompt
-
-    second_prompt = second_task.prompt()
-    assert "Trigger Payload: Context data" in second_prompt
-
-
-
 @patch("crewai.agent.CrewTrainingHandler")
 def test_agent_training_handler(crew_training_handler):
     task_prompt = "What is 1 + 1?"

tests/cassettes/TestTraceListenerSetup.test_trace_listener_ephemeral_batch.yaml

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-    headers:
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tests/flow_test.py

@@ -497,131 +497,6 @@ def test_unstructured_flow_event_emission():
     assert isinstance(received_events[6].timestamp, datetime)
 
 
-def test_flow_trigger_payload_injection():
-    captured_payload = []
-
-    class TriggerFlow(Flow):
-        @start()
-        def start_method(self, crewai_trigger_payload=None):
-            captured_payload.append(crewai_trigger_payload)
-            return "started"
-
-        @listen(start_method)
-        def second_method(self):
-            captured_payload.append("no_parameter")
-            return "finished"
-
-    flow = TriggerFlow()
-
-    test_payload = "This is important trigger data"
-    flow.kickoff(inputs={"crewai_trigger_payload": test_payload})
-
-    assert captured_payload == [test_payload, "no_parameter"]
-
-
-def test_flow_trigger_payload_injection_multiple_starts():
-    captured_payloads = []
-
-    class MultiStartFlow(Flow):
-        @start()
-        def start_method_1(self, crewai_trigger_payload=None):
-            captured_payloads.append(("start_1", crewai_trigger_payload))
-            return "start_1_done"
-
-        @start()
-        def start_method_2(self, crewai_trigger_payload=None):
-            captured_payloads.append(("start_2", crewai_trigger_payload))
-            return "start_2_done"
-
-    flow = MultiStartFlow()
-
-    test_payload = "Multiple start trigger data"
-    flow.kickoff(inputs={"crewai_trigger_payload": test_payload})
-
-    assert captured_payloads == [("start_1", test_payload), ("start_2", test_payload)]
-
-
-def test_flow_without_trigger_payload():
-    captured_payload = []
-
-    class NormalFlow(Flow):
-        @start()
-        def start_method(self, crewai_trigger_payload=None):
-            captured_payload.append(crewai_trigger_payload)
-            return "no_trigger"
-
-    flow = NormalFlow()
-
-    flow.kickoff(inputs={"other_data": "some value"})
-
-    assert captured_payload[0] is None
-
-
-def test_flow_trigger_payload_with_structured_state():
-    class TriggerState(BaseModel):
-        id: str = "test"
-        message: str = ""
-
-    class StructuredFlow(Flow[TriggerState]):
-        @start()
-        def start_method(self, crewai_trigger_payload=None):
-            return crewai_trigger_payload
-
-    flow = StructuredFlow()
-
-    test_payload = "Structured state trigger data"
-    result = flow.kickoff(inputs={"crewai_trigger_payload": test_payload})
-
-    assert result == test_payload
-
-
-def test_flow_start_method_without_trigger_parameter():
-    execution_order = []
-
-    class FlowWithoutParameter(Flow):
-        @start()
-        def start_without_param(self):
-            execution_order.append("start_executed")
-            return "started"
-
-        @listen(start_without_param)
-        def second_method(self):
-            execution_order.append("second_executed")
-            return "finished"
-
-    flow = FlowWithoutParameter()
-
-    result = flow.kickoff(inputs={"crewai_trigger_payload": "some data"})
-
-    assert execution_order == ["start_executed", "second_executed"]
-    assert result == "finished"
-
-
-def test_async_flow_with_trigger_payload():
-    captured_payload = []
-
-    class AsyncTriggerFlow(Flow):
-        @start()
-        async def async_start_method(self, crewai_trigger_payload=None):
-            captured_payload.append(crewai_trigger_payload)
-            await asyncio.sleep(0.01)
-            return "async_started"
-
-        @listen(async_start_method)
-        async def async_second_method(self, result):
-            captured_payload.append(result)
-            await asyncio.sleep(0.01)
-            return "async_finished"
-
-    flow = AsyncTriggerFlow()
-
-    test_payload = "Async trigger data"
-    result = asyncio.run(flow.kickoff_async(inputs={"crewai_trigger_payload": test_payload}))
-
-    assert captured_payload == [test_payload, "async_started"]
-    assert result == "async_finished"
-
-
 def test_structured_flow_event_emission():
     """Test that the correct events are emitted during structured flow
     execution with all fields validated."""

tests/tools/test_base_tool.py

@@ -1,12 +1,9 @@
 import asyncio
-from typing import Callable
+import inspect
+import unittest
+from typing import Any, Callable, Dict, List
 from unittest.mock import patch
 
-import pytest
-
-from crewai.agent import Agent
-from crewai.crew import Crew
-from crewai.task import Task
 from crewai.tools import BaseTool, tool
 
 
@@ -114,26 +111,25 @@ def test_result_as_answer_in_tool_decorator():
     def my_tool_with_result_as_answer(question: str) -> str:
         """This tool will return its result as the final answer."""
         return question
-
+    
     assert my_tool_with_result_as_answer.result_as_answer is True
-
+    
     converted_tool = my_tool_with_result_as_answer.to_structured_tool()
     assert converted_tool.result_as_answer is True
-
+    
     @tool("Tool with default result_as_answer")
     def my_tool_with_default(question: str) -> str:
         """This tool uses the default result_as_answer value."""
         return question
-
+    
     assert my_tool_with_default.result_as_answer is False
-
+    
     converted_tool = my_tool_with_default.to_structured_tool()
     assert converted_tool.result_as_answer is False
 
 
 class SyncTool(BaseTool):
     """Test implementation with a synchronous _run method"""
-
     name: str = "sync_tool"
     description: str = "A synchronous tool for testing"
 
@@ -144,7 +140,6 @@ class SyncTool(BaseTool):
 
 class AsyncTool(BaseTool):
     """Test implementation with an asynchronous _run method"""
-
     name: str = "async_tool"
     description: str = "An asynchronous tool for testing"
 
@@ -179,7 +174,7 @@ def test_run_calls_asyncio_run_for_async_tools():
     """Test that asyncio.run is called when using async tools."""
     async_tool = AsyncTool()
 
-    with patch("asyncio.run") as mock_run:
+    with patch('asyncio.run') as mock_run:
         mock_run.return_value = "Processed test asynchronously"
         async_result = async_tool.run(input_text="test")
 
@@ -191,43 +186,9 @@ def test_run_does_not_call_asyncio_run_for_sync_tools():
     """Test that asyncio.run is NOT called when using sync tools."""
     sync_tool = SyncTool()
 
-    with patch("asyncio.run") as mock_run:
+    with patch('asyncio.run') as mock_run:
         sync_result = sync_tool.run(input_text="test")
 
         mock_run.assert_not_called()
         assert sync_result == "Processed test synchronously"
 
-
-@pytest.mark.vcr(filter_headers=["authorization"])
-def test_max_usage_count_is_respected():
-    class IteratingTool(BaseTool):
-        name: str = "iterating_tool"
-        description: str = "A tool that iterates a given number of times"
-
-        def _run(self, input_text: str):
-            return f"Iteration {input_text}"
-
-    tool = IteratingTool(max_usage_count=5)
-
-    agent = Agent(
-        role="Iterating Agent",
-        goal="Call the iterating tool 5 times",
-        backstory="You are an agent that iterates a given number of times",
-        tools=[tool],
-    )
-
-    task = Task(
-        description="Call the iterating tool 5 times",
-        expected_output="A list of the iterations",
-        agent=agent,
-    )
-
-    crew = Crew(
-        agents=[agent],
-        tasks=[task],
-        verbose=True,
-    )
-
-    crew.kickoff()
-    assert tool.max_usage_count == 5
-    assert tool.current_usage_count == 5

tests/tracing/test_tracing.py

@@ -2,7 +2,6 @@ import os
 import pytest
 from unittest.mock import patch, MagicMock
 
-
 from crewai import Agent, Task, Crew
 from crewai.flow.flow import Flow, start
 from crewai.utilities.events.listeners.tracing.trace_listener import (
@@ -322,74 +321,6 @@ class TestTraceListenerSetup:
                 FlowExample()
                 assert mock_listener_setup.call_count >= 1
 
-    @pytest.mark.vcr(filter_headers=["authorization"])
-    def test_trace_listener_ephemeral_batch(self):
-        """Test that trace listener properly handles ephemeral batches"""
-        with (
-            patch.dict(os.environ, {"CREWAI_TRACING_ENABLED": "true"}),
-            patch(
-                "crewai.utilities.events.listeners.tracing.trace_listener.TraceCollectionListener._check_authenticated",
-                return_value=False,
-            ),
-        ):
-            agent = Agent(
-                role="Test Agent",
-                goal="Test goal",
-                backstory="Test backstory",
-                llm="gpt-4o-mini",
-            )
-            task = Task(
-                description="Say hello to the world",
-                expected_output="hello world",
-                agent=agent,
-            )
-            crew = Crew(agents=[agent], tasks=[task], tracing=True)
-
-            with patch.object(TraceBatchManager, "initialize_batch") as mock_initialize:
-                crew.kickoff()
-
-                assert mock_initialize.call_count >= 1
-                assert mock_initialize.call_args_list[0][1]["use_ephemeral"] is True
-
-    @pytest.mark.vcr(filter_headers=["authorization"])
-    def test_trace_listener_with_authenticated_user(self):
-        """Test that trace listener properly handles authenticated batches"""
-        with (
-            patch.dict(os.environ, {"CREWAI_TRACING_ENABLED": "true"}),
-            patch(
-                "crewai.utilities.events.listeners.tracing.trace_batch_manager.PlusAPI"
-            ) as mock_plus_api_class,
-        ):
-            mock_plus_api_instance = MagicMock()
-            mock_plus_api_class.return_value = mock_plus_api_instance
-
-            agent = Agent(
-                role="Test Agent",
-                goal="Test goal",
-                backstory="Test backstory",
-                llm="gpt-4o-mini",
-            )
-            task = Task(
-                description="Say hello to the world",
-                expected_output="hello world",
-                agent=agent,
-            )
-
-            with (
-                patch.object(TraceBatchManager, "initialize_batch") as mock_initialize,
-                patch.object(
-                    TraceBatchManager, "finalize_batch"
-                ) as mock_finalize_backend_batch,
-            ):
-                crew = Crew(agents=[agent], tasks=[task], tracing=True)
-                crew.kickoff()
-
-                mock_plus_api_class.assert_called_with(api_key="mock_token_12345")
-
-                assert mock_initialize.call_count >= 1
-                mock_finalize_backend_batch.assert_called_with()
-                assert mock_finalize_backend_batch.call_count >= 1
-
     # Helper method to ensure cleanup
     def teardown_method(self):
         """Cleanup after each test method"""

tests/utilities/events/third_party/__init__.py

@@ -0,0 +1 @@
+

tests/utilities/events/third_party/test_agentops_listener.py

@@ -0,0 +1,196 @@
+from unittest.mock import Mock, patch
+from crewai.utilities.events.third_party.agentops_listener import AgentOpsListener
+from crewai.utilities.events.crew_events import (
+    CrewKickoffStartedEvent,
+    CrewKickoffCompletedEvent,
+)
+from crewai.utilities.events.task_events import TaskEvaluationEvent
+from crewai.utilities.events.tool_usage_events import (
+    ToolUsageStartedEvent,
+    ToolUsageErrorEvent,
+)
+from crewai.utilities.events.crewai_event_bus import CrewAIEventsBus
+
+
+class TestAgentOpsListener:
+    def test_agentops_listener_initialization_with_agentops_installed(self):
+        with patch("crewai.utilities.events.third_party.agentops_listener.agentops"):
+            listener = AgentOpsListener()
+            assert listener.agentops is not None
+
+    def test_agentops_listener_initialization_without_agentops_installed(self):
+        with patch("crewai.utilities.events.third_party.agentops_listener.agentops", side_effect=ImportError):
+            listener = AgentOpsListener()
+            assert listener.agentops is None
+
+    def test_setup_listeners_with_agentops_installed(self):
+        with patch("crewai.utilities.events.third_party.agentops_listener.agentops"):
+            listener = AgentOpsListener()
+            mock_event_bus = Mock(spec=CrewAIEventsBus)
+            
+            listener.setup_listeners(mock_event_bus)
+            
+            assert mock_event_bus.register_handler.call_count == 5
+            mock_event_bus.register_handler.assert_any_call(
+                CrewKickoffStartedEvent, listener._handle_crew_kickoff_started
+            )
+            mock_event_bus.register_handler.assert_any_call(
+                CrewKickoffCompletedEvent, listener._handle_crew_kickoff_completed
+            )
+            mock_event_bus.register_handler.assert_any_call(
+                ToolUsageStartedEvent, listener._handle_tool_usage_started
+            )
+            mock_event_bus.register_handler.assert_any_call(
+                ToolUsageErrorEvent, listener._handle_tool_usage_error
+            )
+            mock_event_bus.register_handler.assert_any_call(
+                TaskEvaluationEvent, listener._handle_task_evaluation
+            )
+
+    def test_setup_listeners_without_agentops_installed(self):
+        with patch("crewai.utilities.events.third_party.agentops_listener.agentops", side_effect=ImportError):
+            listener = AgentOpsListener()
+            mock_event_bus = Mock(spec=CrewAIEventsBus)
+            
+            listener.setup_listeners(mock_event_bus)
+            
+            mock_event_bus.register_handler.assert_not_called()
+
+    def test_handle_crew_kickoff_started_with_agentops(self):
+        with patch("crewai.utilities.events.third_party.agentops_listener.agentops") as mock_agentops:
+            listener = AgentOpsListener()
+            event = CrewKickoffStartedEvent(crew_id="test-crew")
+            
+            listener._handle_crew_kickoff_started(event)
+            
+            mock_agentops.start_session.assert_called_once()
+            call_args = mock_agentops.start_session.call_args
+            assert call_args[1]["tags"] == ["crewai", "crew_kickoff"]
+
+    def test_handle_crew_kickoff_started_without_agentops(self):
+        with patch("crewai.utilities.events.third_party.agentops_listener.agentops", side_effect=ImportError):
+            listener = AgentOpsListener()
+            event = CrewKickoffStartedEvent(crew_id="test-crew")
+            
+            listener._handle_crew_kickoff_started(event)
+
+    def test_handle_crew_kickoff_completed_with_agentops(self):
+        with patch("crewai.utilities.events.third_party.agentops_listener.agentops") as mock_agentops:
+            listener = AgentOpsListener()
+            event = CrewKickoffCompletedEvent(crew_id="test-crew", crew_output=Mock())
+            
+            listener._handle_crew_kickoff_completed(event)
+            
+            mock_agentops.end_session.assert_called_once_with("Success")
+
+    def test_handle_crew_kickoff_completed_without_agentops(self):
+        with patch("crewai.utilities.events.third_party.agentops_listener.agentops", side_effect=ImportError):
+            listener = AgentOpsListener()
+            event = CrewKickoffCompletedEvent(crew_id="test-crew", crew_output=Mock())
+            
+            listener._handle_crew_kickoff_completed(event)
+
+    def test_handle_tool_usage_started_with_agentops(self):
+        with patch("crewai.utilities.events.third_party.agentops_listener.agentops") as mock_agentops:
+            listener = AgentOpsListener()
+            event = ToolUsageStartedEvent(
+                tool_name="test_tool",
+                arguments={"arg1": "value1"},
+                agent_id="test-agent",
+                task_id="test-task"
+            )
+            
+            listener._handle_tool_usage_started(event)
+            
+            mock_agentops.record.assert_called_once()
+            call_args = mock_agentops.record.call_args[0][0]
+            assert hasattr(call_args, "action_type")
+
+    def test_handle_tool_usage_error_with_agentops(self):
+        with patch("crewai.utilities.events.third_party.agentops_listener.agentops") as mock_agentops:
+            listener = AgentOpsListener()
+            event = ToolUsageErrorEvent(
+                tool_name="test_tool",
+                arguments={"arg1": "value1"},
+                error="Test error",
+                agent_id="test-agent",
+                task_id="test-task"
+            )
+            
+            listener._handle_tool_usage_error(event)
+            
+            mock_agentops.record.assert_called_once()
+
+    def test_handle_task_evaluation_with_agentops(self):
+        with patch("crewai.utilities.events.third_party.agentops_listener.agentops") as mock_agentops:
+            listener = AgentOpsListener()
+            event = TaskEvaluationEvent(
+                task_id="test-task",
+                score=0.85,
+                feedback="Good performance"
+            )
+            
+            listener._handle_task_evaluation(event)
+            
+            mock_agentops.record.assert_called_once()
+
+    def test_handle_crew_kickoff_started_with_exception(self):
+        with patch("crewai.utilities.events.third_party.agentops_listener.agentops") as mock_agentops:
+            mock_agentops.start_session.side_effect = Exception("Test exception")
+            listener = AgentOpsListener()
+            event = CrewKickoffStartedEvent(crew_id="test-crew")
+            
+            listener._handle_crew_kickoff_started(event)
+
+    def test_handle_crew_kickoff_completed_with_exception(self):
+        with patch("crewai.utilities.events.third_party.agentops_listener.agentops") as mock_agentops:
+            mock_agentops.end_session.side_effect = Exception("Test exception")
+            listener = AgentOpsListener()
+            event = CrewKickoffCompletedEvent(crew_id="test-crew", crew_output=Mock())
+            
+            listener._handle_crew_kickoff_completed(event)
+
+    def test_handle_tool_usage_started_with_exception(self):
+        with patch("crewai.utilities.events.third_party.agentops_listener.agentops") as mock_agentops:
+            mock_agentops.record.side_effect = Exception("Test exception")
+            listener = AgentOpsListener()
+            event = ToolUsageStartedEvent(
+                tool_name="test_tool",
+                arguments={"arg1": "value1"},
+                agent_id="test-agent",
+                task_id="test-task"
+            )
+            
+            listener._handle_tool_usage_started(event)
+
+    def test_handle_tool_usage_error_with_exception(self):
+        with patch("crewai.utilities.events.third_party.agentops_listener.agentops") as mock_agentops:
+            mock_agentops.record.side_effect = Exception("Test exception")
+            listener = AgentOpsListener()
+            event = ToolUsageErrorEvent(
+                tool_name="test_tool",
+                arguments={"arg1": "value1"},
+                error="Test error",
+                agent_id="test-agent",
+                task_id="test-task"
+            )
+            
+            listener._handle_tool_usage_error(event)
+
+    def test_handle_task_evaluation_with_exception(self):
+        with patch("crewai.utilities.events.third_party.agentops_listener.agentops") as mock_agentops:
+            mock_agentops.record.side_effect = Exception("Test exception")
+            listener = AgentOpsListener()
+            event = TaskEvaluationEvent(
+                task_id="test-task",
+                score=0.85,
+                feedback="Good performance"
+            )
+            
+            listener._handle_task_evaluation(event)
+
+    def test_agentops_listener_instance_creation(self):
+        with patch("crewai.utilities.events.third_party.agentops_listener.agentops"):
+            from crewai.utilities.events.third_party.agentops_listener import agentops_listener
+            assert agentops_listener is not None
+            assert isinstance(agentops_listener, AgentOpsListener)

uv.lock

@@ -1,5 +1,5 @@
 version = 1
-revision = 3
+revision = 2
 requires-python = ">=3.10, <3.14"
 resolution-markers = [
     "python_full_version >= '3.13' and platform_python_implementation == 'PyPy' and sys_platform == 'darwin'",
@@ -778,7 +778,7 @@ requires-dist = [
     { name = "blinker", specifier = ">=1.9.0" },
     { name = "chromadb", specifier = ">=0.5.23" },
     { name = "click", specifier = ">=8.1.7" },
-    { name = "crewai-tools", marker = "extra == 'tools'", specifier = "~=0.62.1" },
+    { name = "crewai-tools", marker = "extra == 'tools'", specifier = "~=0.62.0" },
     { name = "docling", marker = "extra == 'docling'", specifier = ">=2.12.0" },
     { name = "instructor", specifier = ">=1.3.3" },
     { name = "json-repair", specifier = "==0.25.2" },
@@ -830,7 +830,7 @@ dev = [
 
 [[package]]
 name = "crewai-tools"
-version = "0.62.1"
+version = "0.62.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "chromadb" },
@@ -848,9 +848,9 @@ dependencies = [
     { name = "stagehand" },
     { name = "tiktoken" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/e3/4b/23cd61a07105b54be8d1185e3b861b1bb2ac6050264acc060d91b22da171/crewai_tools-0.62.1.tar.gz", hash = "sha256:1819d09189e815a28f6744b6cffde703b9e9e438ef5f066c5b4dcdd75cc3c6ad", size = 1059792, upload-time = "2025-08-19T01:08:02.567Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/e9/24/0287edbaa33c52b5541a564d92d3324d4839a76a4b023540c0fd5c7ee330/crewai_tools-0.62.0.tar.gz", hash = "sha256:71a24c173677f108516e1cde286e476e9aeb60da78d911bec0f15caa3c6af15a", size = 1059534, upload-time = "2025-08-13T21:13:49.879Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/34/87/a6e8d5dff4718795d806cd54bdb1cb94c637d93951d00e87c0b55d431870/crewai_tools-0.62.1-py3-none-any.whl", hash = "sha256:d8333315c8bf35bdb939b22e5b555acf9357ae676b992832f96f63de21670871", size = 677041, upload-time = "2025-08-19T01:08:00.891Z" },
+    { url = "https://files.pythonhosted.org/packages/a9/16/cf26f4eaf1edc2f62d0c9cb1ec97f573f8d7401663da047c52b3ce4c4628/crewai_tools-0.62.0-py3-none-any.whl", hash = "sha256:b5e7035563cb00601431286b1c56933966acea1f220052cd33e1d4ee35590017", size = 677008, upload-time = "2025-08-13T21:13:46.903Z" },
 ]
 
 [[package]]