docs(mintlify): remove OpenAPI Endpoints groups; add localized MDX endpoint pages for pt-BR and ko

docs/docs.json

@@ -320,7 +320,6 @@
                   "en/enterprise/guides/update-crew",
                   "en/enterprise/guides/enable-crew-studio",
                   "en/enterprise/guides/azure-openai-setup",
-                  "en/enterprise/guides/automation-triggers",
                   "en/enterprise/guides/hubspot-trigger",
                   "en/enterprise/guides/react-component-export",
                   "en/enterprise/guides/salesforce-trigger",
@@ -659,7 +658,6 @@
                   "pt-BR/enterprise/guides/update-crew",
                   "pt-BR/enterprise/guides/enable-crew-studio",
                   "pt-BR/enterprise/guides/azure-openai-setup",
-                  "pt-BR/enterprise/guides/automation-triggers",
                   "pt-BR/enterprise/guides/hubspot-trigger",
                   "pt-BR/enterprise/guides/react-component-export",
                   "pt-BR/enterprise/guides/salesforce-trigger",
@@ -1009,7 +1007,6 @@
                   "ko/enterprise/guides/update-crew",
                   "ko/enterprise/guides/enable-crew-studio",
                   "ko/enterprise/guides/azure-openai-setup",
-                  "ko/enterprise/guides/automation-triggers",
                   "ko/enterprise/guides/hubspot-trigger",
                   "ko/enterprise/guides/react-component-export",
                   "ko/enterprise/guides/salesforce-trigger",

docs/en/concepts/tasks.mdx

@@ -59,7 +59,6 @@ crew = Crew(
 | **Output Pydantic** _(optional)_ | `output_pydantic` | `Optional[Type[BaseModel]]`   | A Pydantic model for task output.                                                                                    |
 | **Callback** _(optional)_        | `callback`        | `Optional[Any]`               | Function/object to be executed after task completion.                                                                |
 | **Guardrail** _(optional)_       | `guardrail`       | `Optional[Callable]`             | Function to validate task output before proceeding to next task.                                                  |
-| **Guardrail Max Retries** _(optional)_ | `guardrail_max_retries` | `Optional[int]`     | Maximum number of retries when guardrail validation fails. Defaults to 3.                                         |
 
 ## Creating Tasks
 
@@ -453,7 +452,7 @@ task = Task(
     expected_output="A valid JSON object",
     agent=analyst,
     guardrail=validate_json_output,
-    guardrail_max_retries=3  # Limit retry attempts
+    max_retries=3  # Limit retry attempts
 )
 ```
 

docs/en/enterprise/guides/automation-triggers.mdx

@@ -1,171 +0,0 @@
----
-title: "Automation Triggers"
-description: "Automatically execute your CrewAI workflows when specific events occur in connected integrations"
-icon: "bolt"
----
-
-Automation triggers enable you to automatically run your CrewAI deployments when specific events occur in your connected integrations, creating powerful event-driven workflows that respond to real-time changes in your business systems.
-
-## Overview
-
-With automation triggers, you can:
-
-- **Respond to real-time events** - Automatically execute workflows when specific conditions are met
-- **Integrate with external systems** - Connect with platforms like Gmail, Outlook, OneDrive, JIRA, Slack, Stripe and more
-- **Scale your automation** - Handle high-volume events without manual intervention
-- **Maintain context** - Access trigger data within your crews and flows
-
-## Managing Automation Triggers
-
-### Viewing Available Triggers
-
-To access and manage your automation triggers:
-
-1. Navigate to your deployment in the CrewAI dashboard
-2. Click on the **Triggers** tab to view all available trigger integrations
-
-<Frame>
-  <img src="/images/enterprise/list-available-triggers.png" alt="List of available automation triggers" />
-</Frame>
-
-This view shows all the trigger integrations available for your deployment, along with their current connection status.
-
-### Enabling and Disabling Triggers
-
-Each trigger can be easily enabled or disabled using the toggle switch:
-
-<Frame>
-  <img src="/images/enterprise/trigger-selected.png" alt="Enable or disable triggers with toggle" />
-</Frame>
-
-- **Enabled (blue toggle)**: The trigger is active and will automatically execute your deployment when the specified events occur
-- **Disabled (gray toggle)**: The trigger is inactive and will not respond to events
-
-Simply click the toggle to change the trigger state. Changes take effect immediately.
-
-### Monitoring Trigger Executions
-
-Track the performance and history of your triggered executions:
-
-<Frame>
-  <img src="/images/enterprise/list-executions.png" alt="List of executions triggered by automation" />
-</Frame>
-
-## Building Automation
-
-Before building your automation, it's helpful to understand the structure of trigger payloads that your crews and flows will receive.
-
-### Payload Samples Repository
-
-We maintain a comprehensive repository with sample payloads from various trigger sources to help you build and test your automations:
-
-**🔗 [CrewAI Enterprise Trigger Payload Samples](https://github.com/crewAIInc/crewai-enterprise-trigger-payload-samples)**
-
-This repository contains:
-
-- **Real payload examples** from different trigger sources (Gmail, Google Drive, etc.)
-- **Payload structure documentation** showing the format and available fields
-
-### Triggers with Crew
-
-Your existing crew definitions work seamlessly with triggers, you just need to have a task to parse the received payload:
-
-```python
-@CrewBase
-class MyAutomatedCrew:
-    @agent
-    def researcher(self) -> Agent:
-        return Agent(
-            config=self.agents_config['researcher'],
-        )
-
-    @task
-    def parse_trigger_payload(self) -> Task:
-        return Task(
-            config=self.tasks_config['parse_trigger_payload'],
-            agent=self.researcher(),
-        )
-
-    @task
-    def analyze_trigger_content(self) -> Task:
-        return Task(
-            config=self.tasks_config['analyze_trigger_data'],
-            agent=self.researcher(),
-        )
-```
-
-The crew will automatically receive and can access the trigger payload through the standard CrewAI context mechanisms.
-
-### Integration with Flows
-
-For flows, you have more control over how trigger data is handled:
-
-#### Accessing Trigger Payload
-
-All `@start()` methods in your flows will accept an additional parameter called `crewai_trigger_payload`:
-
-```python
-from crewai.flow import Flow, start, listen
-
-class MyAutomatedFlow(Flow):
-    @start()
-    def handle_trigger(self, crewai_trigger_payload: dict = None):
-        """
-        This start method can receive trigger data
-        """
-        if crewai_trigger_payload:
-            # Process the trigger data
-            trigger_id = crewai_trigger_payload.get('id')
-            event_data = crewai_trigger_payload.get('payload', {})
-
-            # Store in flow state for use by other methods
-            self.state.trigger_id = trigger_id
-            self.state.trigger_type = event_data
-
-            return event_data
-
-        # Handle manual execution
-        return None
-
-    @listen(handle_trigger)
-    def process_data(self, trigger_data):
-        """
-        Process the data from the trigger
-        """
-        # ... process the trigger
-```
-
-#### Triggering Crews from Flows
-
-When kicking off a crew within a flow that was triggered, pass the trigger payload as it:
-
-```python
-@start()
-def delegate_to_crew(self, crewai_trigger_payload: dict = None):
-    """
-    Delegate processing to a specialized crew
-    """
-    crew = MySpecializedCrew()
-
-    # Pass the trigger payload to the crew
-    result = crew.crew().kickoff(
-        inputs={
-            'a_custom_parameter': "custom_value",
-            'crewai_trigger_payload': crewai_trigger_payload
-        },
-    )
-
-    return result
-```
-
-## Troubleshooting
-
-**Trigger not firing:**
-- Verify the trigger is enabled
-- Check integration connection status
-
-**Execution failures:**
-- Check the execution logs for error details
-- If you are developing, make sure the inputs include the `crewai_trigger_payload` parameter with the correct payload
-
-Automation triggers transform your CrewAI deployments into responsive, event-driven systems that can seamlessly integrate with your existing business processes and tools.

docs/en/tools/database-data/weaviatevectorsearchtool.mdx

@@ -1,13 +1,13 @@
 ---
 title: Weaviate Vector Search
-description: The `WeaviateVectorSearchTool` is designed to search a Weaviate vector database for semantically similar documents using hybrid search.
+description: The `WeaviateVectorSearchTool` is designed to search a Weaviate vector database for semantically similar documents.
 icon: network-wired
 ---
 
 ## Overview
 
 
-The `WeaviateVectorSearchTool` is specifically crafted for conducting semantic searches within documents stored in a Weaviate vector database. This tool allows you to find semantically similar documents to a given query, leveraging the power of vector and keyword search for more accurate and contextually relevant search results.
+The `WeaviateVectorSearchTool` is specifically crafted for conducting semantic searches within documents stored in a Weaviate vector database. This tool allows you to find semantically similar documents to a given query, leveraging the power of vector embeddings for more accurate and contextually relevant search results.
 
 [Weaviate](https://weaviate.io/) is a vector database that stores and queries vector embeddings, enabling semantic search capabilities.
 
@@ -39,7 +39,6 @@ from crewai_tools import WeaviateVectorSearchTool
 tool = WeaviateVectorSearchTool(
     collection_name='example_collections',
     limit=3,
-    alpha=0.75,
     weaviate_cluster_url="https://your-weaviate-cluster-url.com",
     weaviate_api_key="your-weaviate-api-key",
 )
@@ -64,7 +63,6 @@ The `WeaviateVectorSearchTool` accepts the following parameters:
 - **weaviate_cluster_url**: Required. The URL of the Weaviate cluster.
 - **weaviate_api_key**: Required. The API key for the Weaviate cluster.
 - **limit**: Optional. The number of results to return. Default is `3`.
-- **alpha**: Optional. Controls the weighting between vector and keyword (BM25) search. alpha = 0 -> BM25 only, alpha = 1 -> vector search only. Default is `0.75`.
 - **vectorizer**: Optional. The vectorizer to use. If not provided, it will use `text2vec_openai` with the `nomic-embed-text` model.
 - **generative_model**: Optional. The generative model to use. If not provided, it will use OpenAI's `gpt-4o`.
 
@@ -80,7 +78,6 @@ from weaviate.classes.config import Configure
 tool = WeaviateVectorSearchTool(
     collection_name='example_collections',
     limit=3,
-    alpha=0.75,
     vectorizer=Configure.Vectorizer.text2vec_openai(model="nomic-embed-text"),
     generative_model=Configure.Generative.openai(model="gpt-4o-mini"),
     weaviate_cluster_url="https://your-weaviate-cluster-url.com",
@@ -131,7 +128,6 @@ with test_docs.batch.dynamic() as batch:
 tool = WeaviateVectorSearchTool(
     collection_name='example_collections', 
     limit=3,
-    alpha=0.75,
     weaviate_cluster_url="https://your-weaviate-cluster-url.com",
     weaviate_api_key="your-weaviate-api-key",
 )
@@ -149,7 +145,6 @@ from crewai_tools import WeaviateVectorSearchTool
 weaviate_tool = WeaviateVectorSearchTool(
     collection_name='example_collections',
     limit=3,
-    alpha=0.75,
     weaviate_cluster_url="https://your-weaviate-cluster-url.com",
     weaviate_api_key="your-weaviate-api-key",
 )

docs/ko/concepts/tasks.mdx

@@ -59,7 +59,6 @@ crew = Crew(
 | **Pydantic 출력** _(선택 사항)_   | `output_pydantic`     | `Optional[Type[BaseModel]]`       | 태스크 출력용 Pydantic 모델입니다.                                                                             |
 | **콜백** _(선택 사항)_            | `callback`            | `Optional[Any]`                  | 태스크 완료 후 실행할 함수/객체입니다.                                                                         |
 | **가드레일** _(선택 사항)_        | `guardrail`           | `Optional[Callable]`                | 다음 태스크로 진행하기 전에 태스크 출력을 검증하는 함수입니다.                                               |
-| **가드레일 최대 재시도** _(선택 사항)_ | `guardrail_max_retries` | `Optional[int]`          | 가드레일 검증 실패 시 최대 재시도 횟수입니다. 기본값은 3입니다.                                                |
 
 ## 작업 생성하기
 
@@ -449,7 +448,7 @@ task = Task(
     expected_output="A valid JSON object",
     agent=analyst,
     guardrail=validate_json_output,
-    guardrail_max_retries=3  # 재시도 횟수 제한
+    max_retries=3  # Limit retry attempts
 )
 ```
 
@@ -900,4 +899,4 @@ except RuntimeError as e:
 작업(task)은 CrewAI 에이전트의 행동을 이끄는 원동력입니다.
 작업과 그 결과를 적절하게 정의함으로써, 에이전트가 독립적으로 또는 협업 단위로 효과적으로 작동할 수 있는 기반을 마련할 수 있습니다.
 작업에 적합한 도구를 장착하고, 실행 과정을 이해하며, 견고한 검증 절차를 따르는 것은 CrewAI의 잠재력을 극대화하는 데 필수적입니다.
-이를 통해 에이전트가 할당된 작업에 효과적으로 준비되고, 작업이 의도대로 수행될 수 있습니다.
+이를 통해 에이전트가 할당된 작업에 효과적으로 준비되고, 작업이 의도대로 수행될 수 있습니다.

docs/ko/enterprise/guides/automation-triggers.mdx

@@ -1,171 +0,0 @@
----
-title: "자동화 트리거"
-description: "연결된 통합에서 특정 이벤트가 발생할 때 CrewAI 워크플로우를 자동으로 실행합니다"
-icon: "bolt"
----
-
-자동화 트리거를 사용하면 연결된 통합에서 특정 이벤트가 발생할 때 CrewAI 배포를 자동으로 실행할 수 있어, 비즈니스 시스템의 실시간 변화에 반응하는 강력한 이벤트 기반 워크플로우를 만들 수 있습니다.
-
-## 개요
-
-자동화 트리거를 사용하면 다음을 수행할 수 있습니다:
-
-- **실시간 이벤트에 응답** - 특정 조건이 충족될 때 워크플로우를 자동으로 실행
-- **외부 시스템과 통합** - Gmail, Outlook, OneDrive, JIRA, Slack, Stripe 등의 플랫폼과 연결
-- **자동화 확장** - 수동 개입 없이 대용량 이벤트 처리
-- **컨텍스트 유지** - crew와 flow 내에서 트리거 데이터에 액세스
-
-## 자동화 트리거 관리
-
-### 사용 가능한 트리거 보기
-
-자동화 트리거에 액세스하고 관리하려면:
-
-1. CrewAI 대시보드에서 배포로 이동
-2. **트리거** 탭을 클릭하여 사용 가능한 모든 트리거 통합 보기
-
-<Frame>
-  <img src="/images/enterprise/list-available-triggers.png" alt="사용 가능한 자동화 트리거 목록" />
-</Frame>
-
-이 보기는 배포에 사용 가능한 모든 트리거 통합과 현재 연결 상태를 보여줍니다.
-
-### 트리거 활성화 및 비활성화
-
-각 트리거는 토글 스위치를 사용하여 쉽게 활성화하거나 비활성화할 수 있습니다:
-
-<Frame>
-  <img src="/images/enterprise/trigger-selected.png" alt="토글로 트리거 활성화 또는 비활성화" />
-</Frame>
-
-- **활성화됨 (파란색 토글)**: 트리거가 활성 상태이며 지정된 이벤트가 발생할 때 배포를 자동으로 실행합니다
-- **비활성화됨 (회색 토글)**: 트리거가 비활성 상태이며 이벤트에 응답하지 않습니다
-
-토글을 클릭하기만 하면 트리거 상태를 변경할 수 있습니다. 변경 사항은 즉시 적용됩니다.
-
-### 트리거 실행 모니터링
-
-트리거된 실행의 성능과 기록을 추적합니다:
-
-<Frame>
-  <img src="/images/enterprise/list-executions.png" alt="자동화에 의해 트리거된 실행 목록" />
-</Frame>
-
-## 자동화 구축
-
-자동화를 구축하기 전에 crew와 flow가 받을 트리거 페이로드의 구조를 이해하는 것이 도움이 됩니다.
-
-### 페이로드 샘플 저장소
-
-자동화를 구축하고 테스트하는 데 도움이 되도록 다양한 트리거 소스의 샘플 페이로드가 포함된 포괄적인 저장소를 유지 관리하고 있습니다:
-
-**🔗 [CrewAI Enterprise 트리거 페이로드 샘플](https://github.com/crewAIInc/crewai-enterprise-trigger-payload-samples)**
-
-이 저장소에는 다음이 포함되어 있습니다:
-
-- **실제 페이로드 예제** - 다양한 트리거 소스(Gmail, Google Drive 등)에서 가져온 예제
-- **페이로드 구조 문서** - 형식과 사용 가능한 필드를 보여주는 문서
-
-### Crew와 트리거
-
-기존 crew 정의는 트리거와 완벽하게 작동하며, 받은 페이로드를 분석하는 작업만 있으면 됩니다:
-
-```python
-@CrewBase
-class MyAutomatedCrew:
-    @agent
-    def researcher(self) -> Agent:
-        return Agent(
-            config=self.agents_config['researcher'],
-        )
-
-    @task
-    def parse_trigger_payload(self) -> Task:
-        return Task(
-            config=self.tasks_config['parse_trigger_payload'],
-            agent=self.researcher(),
-        )
-
-    @task
-    def analyze_trigger_content(self) -> Task:
-        return Task(
-            config=self.tasks_config['analyze_trigger_data'],
-            agent=self.researcher(),
-        )
-```
-
-crew는 자동으로 트리거 페이로드를 받고 표준 CrewAI 컨텍스트 메커니즘을 통해 액세스할 수 있습니다.
-
-### Flow와의 통합
-
-flow의 경우 트리거 데이터 처리 방법을 더 세밀하게 제어할 수 있습니다:
-
-#### 트리거 페이로드 액세스
-
-flow의 모든 `@start()` 메서드는 `crewai_trigger_payload`라는 추가 매개변수를 허용합니다:
-
-```python
-from crewai.flow import Flow, start, listen
-
-class MyAutomatedFlow(Flow):
-    @start()
-    def handle_trigger(self, crewai_trigger_payload: dict = None):
-        """
-        이 start 메서드는 트리거 데이터를 받을 수 있습니다
-        """
-        if crewai_trigger_payload:
-            # 트리거 데이터 처리
-            trigger_id = crewai_trigger_payload.get('id')
-            event_data = crewai_trigger_payload.get('payload', {})
-
-            # 다른 메서드에서 사용할 수 있도록 flow 상태에 저장
-            self.state.trigger_id = trigger_id
-            self.state.trigger_type = event_data
-
-            return event_data
-
-        # 수동 실행 처리
-        return None
-
-    @listen(handle_trigger)
-    def process_data(self, trigger_data):
-        """
-        트리거 데이터 처리
-        """
-        # ... 트리거 처리
-```
-
-#### Flow에서 Crew 트리거하기
-
-트리거된 flow 내에서 crew를 시작할 때 트리거 페이로드를 전달합니다:
-
-```python
-@start()
-def delegate_to_crew(self, crewai_trigger_payload: dict = None):
-    """
-    전문 crew에 처리 위임
-    """
-    crew = MySpecializedCrew()
-
-    # crew에 트리거 페이로드 전달
-    result = crew.crew().kickoff(
-        inputs={
-            'a_custom_parameter': "custom_value",
-            'crewai_trigger_payload': crewai_trigger_payload
-        },
-    )
-
-    return result
-```
-
-## 문제 해결
-
-**트리거가 작동하지 않는 경우:**
-- 트리거가 활성화되어 있는지 확인
-- 통합 연결 상태 확인
-
-**실행 실패:**
-- 오류 세부 정보는 실행 로그 확인
-- 개발 중인 경우 입력에 올바른 페이로드가 포함된 `crewai_trigger_payload` 매개변수가 포함되어 있는지 확인
-
-자동화 트리거는 CrewAI 배포를 기존 비즈니스 프로세스 및 도구와 완벽하게 통합할 수 있는 반응형 이벤트 기반 시스템으로 변환합니다.

docs/pt-BR/concepts/tasks.mdx

@@ -59,7 +59,6 @@ crew = Crew(
 | **Output Pydantic** _(opcional)_ | `output_pydantic` | `Optional[Type[BaseModel]]`  | Um modelo Pydantic para a saída da tarefa.                                                                         |
 | **Callback** _(opcional)_        | `callback`        | `Optional[Any]`              | Função/objeto a ser executado após a conclusão da tarefa.                                                          |
 | **Guardrail** _(opcional)_       | `guardrail`       | `Optional[Callable]`             | Função para validar a saída da tarefa antes de prosseguir para a próxima tarefa.                                |
-| **Max Tentativas Guardrail** _(opcional)_ | `guardrail_max_retries` | `Optional[int]`    | Número máximo de tentativas quando a validação do guardrail falha. Padrão é 3.                                   |
 
 ## Criando Tarefas
 
@@ -451,7 +450,7 @@ task = Task(
     expected_output="Um objeto JSON válido",
     agent=analyst,
     guardrail=validate_json_output,
-    guardrail_max_retries=3  # Limite de tentativas
+    max_retries=3  # Limite de tentativas
 )
 ```
 
@@ -936,7 +935,7 @@ task = Task(
     description="Gerar dados",
     expected_output="Dados válidos",
     guardrail=validate_data,
-    guardrail_max_retries=5  # Sobrescreve o limite padrão de tentativas
+    max_retries=5  # Sobrescreve o limite padrão de tentativas
 )
 ```
 

docs/pt-BR/enterprise/guides/automation-triggers.mdx

@@ -1,171 +0,0 @@
----
-title: "Triggers de Automação"
-description: "Execute automaticamente seus workflows CrewAI quando eventos específicos ocorrem em integrações conectadas"
-icon: "bolt"
----
-
-Os triggers de automação permitem executar automaticamente suas implantações CrewAI quando eventos específicos ocorrem em suas integrações conectadas, criando workflows poderosos orientados por eventos que respondem a mudanças em tempo real em seus sistemas de negócio.
-
-## Visão Geral
-
-Com triggers de automação, você pode:
-
-- **Responder a eventos em tempo real** - Execute workflows automaticamente quando condições específicas forem atendidas
-- **Integrar com sistemas externos** - Conecte com plataformas como Gmail, Outlook, OneDrive, JIRA, Slack, Stripe e muito mais
-- **Escalar sua automação** - Lide com eventos de alto volume sem intervenção manual
-- **Manter contexto** - Acesse dados do trigger dentro de suas crews e flows
-
-## Gerenciando Triggers de Automação
-
-### Visualizando Triggers Disponíveis
-
-Para acessar e gerenciar seus triggers de automação:
-
-1. Navegue até sua implantação no painel do CrewAI
-2. Clique na aba **Triggers** para visualizar todas as integrações de trigger disponíveis
-
-<Frame>
-  <img src="/images/enterprise/list-available-triggers.png" alt="Lista de triggers de automação disponíveis" />
-</Frame>
-
-Esta visualização mostra todas as integrações de trigger disponíveis para sua implantação, junto com seus status de conexão atuais.
-
-### Habilitando e Desabilitando Triggers
-
-Cada trigger pode ser facilmente habilitado ou desabilitado usando o botão de alternância:
-
-<Frame>
-  <img src="/images/enterprise/trigger-selected.png" alt="Habilitar ou desabilitar triggers com alternância" />
-</Frame>
-
-- **Habilitado (alternância azul)**: O trigger está ativo e executará automaticamente sua implantação quando os eventos especificados ocorrerem
-- **Desabilitado (alternância cinza)**: O trigger está inativo e não responderá a eventos
-
-Simplesmente clique na alternância para mudar o estado do trigger. As alterações entram em vigor imediatamente.
-
-### Monitorando Execuções de Trigger
-
-Acompanhe o desempenho e histórico de suas execuções acionadas:
-
-<Frame>
-  <img src="/images/enterprise/list-executions.png" alt="Lista de execuções acionadas por automação" />
-</Frame>
-
-## Construindo Automação
-
-Antes de construir sua automação, é útil entender a estrutura dos payloads de trigger que suas crews e flows receberão.
-
-### Repositório de Amostras de Payload
-
-Mantemos um repositório abrangente com amostras de payload de várias fontes de trigger para ajudá-lo a construir e testar suas automações:
-
-**🔗 [Amostras de Payload de Trigger CrewAI Enterprise](https://github.com/crewAIInc/crewai-enterprise-trigger-payload-samples)**
-
-Este repositório contém:
-
-- **Exemplos reais de payload** de diferentes fontes de trigger (Gmail, Google Drive, etc.)
-- **Documentação da estrutura de payload** mostrando o formato e campos disponíveis
-
-### Triggers com Crew
-
-Suas definições de crew existentes funcionam perfeitamente com triggers, você só precisa ter uma tarefa para analisar o payload recebido:
-
-```python
-@CrewBase
-class MinhaCrewAutomatizada:
-    @agent
-    def pesquisador(self) -> Agent:
-        return Agent(
-            config=self.agents_config['pesquisador'],
-        )
-
-    @task
-    def analisar_payload_trigger(self) -> Task:
-        return Task(
-            config=self.tasks_config['analisar_payload_trigger'],
-            agent=self.pesquisador(),
-        )
-
-    @task
-    def analisar_conteudo_trigger(self) -> Task:
-        return Task(
-            config=self.tasks_config['analisar_dados_trigger'],
-            agent=self.pesquisador(),
-        )
-```
-
-A crew receberá automaticamente e pode acessar o payload do trigger através dos mecanismos de contexto padrão do CrewAI.
-
-### Integração com Flows
-
-Para flows, você tem mais controle sobre como os dados do trigger são tratados:
-
-#### Acessando Payload do Trigger
-
-Todos os métodos `@start()` em seus flows aceitarão um parâmetro adicional chamado `crewai_trigger_payload`:
-
-```python
-from crewai.flow import Flow, start, listen
-
-class MeuFlowAutomatizado(Flow):
-    @start()
-    def lidar_com_trigger(self, crewai_trigger_payload: dict = None):
-        """
-        Este método start pode receber dados do trigger
-        """
-        if crewai_trigger_payload:
-            # Processa os dados do trigger
-            trigger_id = crewai_trigger_payload.get('id')
-            dados_evento = crewai_trigger_payload.get('payload', {})
-
-            # Armazena no estado do flow para uso por outros métodos
-            self.state.trigger_id = trigger_id
-            self.state.trigger_type = dados_evento
-
-            return dados_evento
-
-        # Lida com execução manual
-        return None
-
-    @listen(lidar_com_trigger)
-    def processar_dados(self, dados_trigger):
-        """
-        Processa os dados do trigger
-        """
-        # ... processa o trigger
-```
-
-#### Acionando Crews a partir de Flows
-
-Ao iniciar uma crew dentro de um flow que foi acionado, passe o payload do trigger conforme ele:
-
-```python
-@start()
-def delegar_para_crew(self, crewai_trigger_payload: dict = None):
-    """
-    Delega processamento para uma crew especializada
-    """
-    crew = MinhaCrewEspecializada()
-
-    # Passa o payload do trigger para a crew
-    resultado = crew.crew().kickoff(
-        inputs={
-            'parametro_personalizado': "valor_personalizado",
-            'crewai_trigger_payload': crewai_trigger_payload
-        },
-    )
-
-    return resultado
-```
-
-## Solução de Problemas
-
-**Trigger não está sendo disparado:**
-- Verifique se o trigger está habilitado
-- Verifique o status de conexão da integração
-
-**Falhas de execução:**
-- Verifique os logs de execução para detalhes do erro
-- Se você está desenvolvendo, certifique-se de que as entradas incluem o parâmetro `crewai_trigger_payload` com o payload correto
-
-Os triggers de automação transformam suas implantações CrewAI em sistemas responsivos orientados por eventos que podem se integrar perfeitamente com seus processos de negócio e ferramentas existentes.

pyproject.toml

@@ -48,7 +48,7 @@ Documentation = "https://docs.crewai.com"
 Repository = "https://github.com/crewAIInc/crewAI"
 
 [project.optional-dependencies]
-tools = ["crewai-tools~=0.65.0"]
+tools = ["crewai-tools~=0.62.1"]
 embeddings = [
     "tiktoken~=0.8.0"
 ]
@@ -68,9 +68,6 @@ docling = [
 aisuite = [
     "aisuite>=0.1.10",
 ]
-qdrant = [
-    "qdrant-client[fastembed]>=1.14.3",
-]
 
 [tool.uv]
 dev-dependencies = [

src/crewai/__init__.py

@@ -54,7 +54,7 @@ def _track_install_async():
 
 _track_install_async()
 
-__version__ = "0.175.0"
+__version__ = "0.165.1"
 __all__ = [
     "Agent",
     "Crew",

src/crewai/agent.py

@@ -1,18 +1,7 @@
 import shutil
 import subprocess
 import time
-from typing import (
+from typing import Any, Callable, Dict, List, Literal, Optional, Sequence, Tuple, Type, Union
-    Any,
-    Callable,
-    Dict,
-    List,
-    Literal,
-    Optional,
-    Sequence,
-    Tuple,
-    Type,
-    Union,
-)
 
 from pydantic import Field, InstanceOf, PrivateAttr, model_validator
 
@@ -173,7 +162,7 @@ class Agent(BaseAgent):
     )
     guardrail: Optional[Union[Callable[[Any], Tuple[bool, Any]], str]] = Field(
         default=None,
-        description="Function or string description of a guardrail to validate agent output",
+        description="Function or string description of a guardrail to validate agent output"
     )
     guardrail_max_retries: int = Field(
         default=3, description="Maximum number of retries when guardrail fails"
@@ -287,7 +276,7 @@ class Agent(BaseAgent):
         self._inject_date_to_task(task)
 
         if self.tools_handler:
-            self.tools_handler.last_used_tool = None
+            self.tools_handler.last_used_tool = {}  # type: ignore # Incompatible types in assignment (expression has type "dict[Never, Never]", variable has type "ToolCalling")
 
         task_prompt = task.prompt()
 
@@ -320,20 +309,15 @@ class Agent(BaseAgent):
                 event=MemoryRetrievalStartedEvent(
                     task_id=str(task.id) if task else None,
                     source_type="agent",
-                    from_agent=self,
-                    from_task=task,
                 ),
             )
 
             start_time = time.time()
-
             contextual_memory = ContextualMemory(
                 self.crew._short_term_memory,
                 self.crew._long_term_memory,
                 self.crew._entity_memory,
                 self.crew._external_memory,
-                agent=self,
-                task=task,
             )
             memory = contextual_memory.build_context_for_task(task, context)
             if memory.strip() != "":
@@ -346,14 +330,13 @@ class Agent(BaseAgent):
                     memory_content=memory,
                     retrieval_time_ms=(time.time() - start_time) * 1000,
                     source_type="agent",
-                    from_agent=self,
-                    from_task=task,
                 ),
             )
         knowledge_config = (
             self.knowledge_config.model_dump() if self.knowledge_config else {}
         )
 
+
         if self.knowledge or (self.crew and self.crew.knowledge):
             crewai_event_bus.emit(
                 self,

src/crewai/agents/agent_builder/base_agent_executor_mixin.py

@@ -43,6 +43,7 @@ class CrewAgentExecutorMixin:
                         metadata={
                             "observation": self.task.description,
                         },
+                        agent=self.agent.role,
                     )
             except Exception as e:
                 print(f"Failed to add to short term memory: {e}")
@@ -64,6 +65,7 @@ class CrewAgentExecutorMixin:
                         "description": self.task.description,
                         "messages": self.messages,
                     },
+                    agent=self.agent.role,
                 )
             except Exception as e:
                 print(f"Failed to add to external memory: {e}")
@@ -98,8 +100,8 @@ class CrewAgentExecutorMixin:
                 )
                 self.crew._long_term_memory.save(long_term_memory)
 
-                entity_memories = [
+                for entity in evaluation.entities:
-                    EntityMemoryItem(
+                    entity_memory = EntityMemoryItem(
                         name=entity.name,
                         type=entity.type,
                         description=entity.description,
@@ -107,10 +109,7 @@ class CrewAgentExecutorMixin:
                             [f"- {r}" for r in entity.relationships]
                         ),
                     )
-                    for entity in evaluation.entities
+                    self.crew._entity_memory.save(entity_memory)
-                ]
-                if entity_memories:
-                    self.crew._entity_memory.save(entity_memories)
             except AttributeError as e:
                 print(f"Missing attributes for long term memory: {e}")
                 pass
@@ -159,9 +158,7 @@ class CrewAgentExecutorMixin:
             self._printer.print(content=prompt, color="bold_yellow")
             response = input()
             if response.strip() != "":
-                self._printer.print(
+                self._printer.print(content="\nProcessing your feedback...", color="cyan")
-                    content="\nProcessing your feedback...", color="cyan"
-                )
             return response
         finally:
             event_listener.formatter.resume_live_updates()

src/crewai/agents/tools_handler.py

@@ -8,13 +8,13 @@ from .cache.cache_handler import CacheHandler
 class ToolsHandler:
     """Callback handler for tool usage."""
 
-    last_used_tool: Optional[ToolCalling] = None
+    last_used_tool: ToolCalling = {}  # type: ignore # BUG?: Incompatible types in assignment (expression has type "Dict[...]", variable has type "ToolCalling")
     cache: Optional[CacheHandler]
 
     def __init__(self, cache: Optional[CacheHandler] = None):
         """Initialize the callback handler."""
         self.cache = cache
-        self.last_used_tool = None
+        self.last_used_tool = {}  # type: ignore # BUG?: same as above
 
     def on_tool_use(
         self,

src/crewai/cli/authentication/constants.py

@@ -1 +1,6 @@
 ALGORITHMS = ["RS256"]
+
+#TODO: The AUTH0 constants should be removed after WorkOS migration is completed
+AUTH0_DOMAIN = "crewai.us.auth0.com"
+AUTH0_CLIENT_ID = "DEVC5Fw6NlRoSzmDCcOhVq85EfLBjKa8"
+AUTH0_AUDIENCE = "https://crewai.us.auth0.com/api/v2/"

src/crewai/cli/authentication/main.py

@@ -7,27 +7,24 @@ from rich.console import Console
 from pydantic import BaseModel, Field
 
 
-from .utils import validate_jwt_token
+from .utils import TokenManager, validate_jwt_token
-from crewai.cli.shared.token_manager import TokenManager
+from urllib.parse import quote
+from crewai.cli.plus_api import PlusAPI
 from crewai.cli.config import Settings
+from crewai.cli.authentication.constants import (
+    AUTH0_AUDIENCE,
+    AUTH0_CLIENT_ID,
+    AUTH0_DOMAIN,
+)
 
 console = Console()
 
 
 class Oauth2Settings(BaseModel):
-    provider: str = Field(
+    provider: str = Field(description="OAuth2 provider used for authentication (e.g., workos, okta, auth0).")
-        description="OAuth2 provider used for authentication (e.g., workos, okta, auth0)."
+    client_id: str = Field(description="OAuth2 client ID issued by the provider, used during authentication requests.")
-    )
+    domain: str = Field(description="OAuth2 provider's domain (e.g., your-org.auth0.com) used for issuing tokens.")
-    client_id: str = Field(
+    audience: Optional[str] = Field(description="OAuth2 audience value, typically used to identify the target API or resource.", default=None)
-        description="OAuth2 client ID issued by the provider, used during authentication requests."
-    )
-    domain: str = Field(
-        description="OAuth2 provider's domain (e.g., your-org.auth0.com) used for issuing tokens."
-    )
-    audience: Optional[str] = Field(
-        description="OAuth2 audience value, typically used to identify the target API or resource.",
-        default=None,
-    )
 
     @classmethod
     def from_settings(cls):
@@ -47,15 +44,11 @@ class ProviderFactory:
         settings = settings or Oauth2Settings.from_settings()
 
         import importlib
-
+        module = importlib.import_module(f"crewai.cli.authentication.providers.{settings.provider.lower()}")
-        module = importlib.import_module(
-            f"crewai.cli.authentication.providers.{settings.provider.lower()}"
-        )
         provider = getattr(module, f"{settings.provider.capitalize()}Provider")
 
         return provider(settings)
 
-
 class AuthenticationCommand:
     def __init__(self):
         self.token_manager = TokenManager()
@@ -65,12 +58,26 @@ class AuthenticationCommand:
         """Sign up to CrewAI+"""
         console.print("Signing in to CrewAI Enterprise...\n", style="bold blue")
 
+        # TODO: WORKOS - Next line and conditional are temporary until migration to WorkOS is complete.
+        user_provider = self._determine_user_provider()
+        if user_provider == "auth0":
+            settings = Oauth2Settings(
+                provider="auth0",
+                client_id=AUTH0_CLIENT_ID,
+                domain=AUTH0_DOMAIN,
+                audience=AUTH0_AUDIENCE
+            )
+            self.oauth2_provider = ProviderFactory.from_settings(settings)
+        # End of temporary code.
+
         device_code_data = self._get_device_code()
         self._display_auth_instructions(device_code_data)
 
         return self._poll_for_token(device_code_data)
 
-    def _get_device_code(self) -> Dict[str, Any]:
+    def _get_device_code(
+        self
+    ) -> Dict[str, Any]:
         """Get the device code to authenticate the user."""
 
         device_code_payload = {
@@ -79,9 +86,7 @@ class AuthenticationCommand:
             "audience": self.oauth2_provider.get_audience(),
         }
         response = requests.post(
-            url=self.oauth2_provider.get_authorize_url(),
+            url=self.oauth2_provider.get_authorize_url(), data=device_code_payload, timeout=20
-            data=device_code_payload,
-            timeout=20,
         )
         response.raise_for_status()
         return response.json()
@@ -92,7 +97,9 @@ class AuthenticationCommand:
         console.print("2. Enter the following code: ", device_code_data["user_code"])
         webbrowser.open(device_code_data["verification_uri_complete"])
 
-    def _poll_for_token(self, device_code_data: Dict[str, Any]) -> None:
+    def _poll_for_token(
+        self, device_code_data: Dict[str, Any]
+    ) -> None:
         """Polls the server for the token until it is received, or max attempts are reached."""
 
         token_payload = {
@@ -105,9 +112,7 @@ class AuthenticationCommand:
 
         attempts = 0
         while True and attempts < 10:
-            response = requests.post(
+            response = requests.post(self.oauth2_provider.get_token_url(), data=token_payload, timeout=30)
-                self.oauth2_provider.get_token_url(), data=token_payload, timeout=30
-            )
             token_data = response.json()
 
             if response.status_code == 200:
@@ -187,3 +192,30 @@ class AuthenticationCommand:
                 "\nRun [bold]crewai login[/bold] to try logging in again.\n",
                 style="yellow",
             )
+
+    # TODO: WORKOS - This method is temporary until migration to WorkOS is complete.
+    def _determine_user_provider(self) -> str:
+        """Determine which provider to use for authentication."""
+
+        console.print(
+            "Enter your CrewAI Enterprise account email: ", style="bold blue", end=""
+        )
+        email = input()
+        email_encoded = quote(email)
+
+        # It's not correct to call this method directly, but it's temporary until migration is complete.
+        response = PlusAPI("")._make_request(
+            "GET", f"/crewai_plus/api/v1/me/provider?email={email_encoded}"
+        )
+
+        if response.status_code == 200:
+            if response.json().get("provider") == "auth0":
+                return "auth0"
+            else:
+                return "workos"
+        else:
+            console.print(
+                "Error: Failed to authenticate with crewai enterprise. Ensure that you are using the latest crewai version and please try again. If the problem persists, contact support@crewai.com.",
+                style="red",
+            )
+            raise SystemExit

src/crewai/cli/authentication/token.py

@@ -1,4 +1,4 @@
-from crewai.cli.shared.token_manager import TokenManager
+from .utils import TokenManager
 
 
 class AuthError(Exception):

src/crewai/cli/authentication/utils.py

@@ -1,5 +1,12 @@
+import json
+import os
+import sys
+from datetime import datetime
+from pathlib import Path
+from typing import Optional
 import jwt
 from jwt import PyJWKClient
+from cryptography.fernet import Fernet
 
 
 def validate_jwt_token(
@@ -60,3 +67,118 @@ def validate_jwt_token(
         raise Exception(f"JWKS or key processing error: {str(e)}")
     except jwt.InvalidTokenError as e:
         raise Exception(f"Invalid token: {str(e)}")
+
+
+class TokenManager:
+    def __init__(self, file_path: str = "tokens.enc") -> None:
+        """
+        Initialize the TokenManager class.
+
+        :param file_path: The file path to store the encrypted tokens. Default is "tokens.enc".
+        """
+        self.file_path = file_path
+        self.key = self._get_or_create_key()
+        self.fernet = Fernet(self.key)
+
+    def _get_or_create_key(self) -> bytes:
+        """
+        Get or create the encryption key.
+
+        :return: The encryption key.
+        """
+        key_filename = "secret.key"
+        key = self.read_secure_file(key_filename)
+
+        if key is not None:
+            return key
+
+        new_key = Fernet.generate_key()
+        self.save_secure_file(key_filename, new_key)
+        return new_key
+
+    def save_tokens(self, access_token: str, expires_at: int) -> None:
+        """
+        Save the access token and its expiration time.
+
+        :param access_token: The access token to save.
+        :param expires_at: The UNIX timestamp of the expiration time.
+        """
+        expiration_time = datetime.fromtimestamp(expires_at)
+        data = {
+            "access_token": access_token,
+            "expiration": expiration_time.isoformat(),
+        }
+        encrypted_data = self.fernet.encrypt(json.dumps(data).encode())
+        self.save_secure_file(self.file_path, encrypted_data)
+
+    def get_token(self) -> Optional[str]:
+        """
+        Get the access token if it is valid and not expired.
+
+        :return: The access token if valid and not expired, otherwise None.
+        """
+        encrypted_data = self.read_secure_file(self.file_path)
+
+        decrypted_data = self.fernet.decrypt(encrypted_data)  # type: ignore
+        data = json.loads(decrypted_data)
+
+        expiration = datetime.fromisoformat(data["expiration"])
+        if expiration <= datetime.now():
+            return None
+
+        return data["access_token"]
+
+    def get_secure_storage_path(self) -> Path:
+        """
+        Get the secure storage path based on the operating system.
+
+        :return: The secure storage path.
+        """
+        if sys.platform == "win32":
+            # Windows: Use %LOCALAPPDATA%
+            base_path = os.environ.get("LOCALAPPDATA")
+        elif sys.platform == "darwin":
+            # macOS: Use ~/Library/Application Support
+            base_path = os.path.expanduser("~/Library/Application Support")
+        else:
+            # Linux and other Unix-like: Use ~/.local/share
+            base_path = os.path.expanduser("~/.local/share")
+
+        app_name = "crewai/credentials"
+        storage_path = Path(base_path) / app_name
+
+        storage_path.mkdir(parents=True, exist_ok=True)
+
+        return storage_path
+
+    def save_secure_file(self, filename: str, content: bytes) -> None:
+        """
+        Save the content to a secure file.
+
+        :param filename: The name of the file.
+        :param content: The content to save.
+        """
+        storage_path = self.get_secure_storage_path()
+        file_path = storage_path / filename
+
+        with open(file_path, "wb") as f:
+            f.write(content)
+
+        # Set appropriate permissions (read/write for owner only)
+        os.chmod(file_path, 0o600)
+
+    def read_secure_file(self, filename: str) -> Optional[bytes]:
+        """
+        Read the content of a secure file.
+
+        :param filename: The name of the file.
+        :return: The content of the file if it exists, otherwise None.
+        """
+        storage_path = self.get_secure_storage_path()
+        file_path = storage_path / filename
+
+        if not file_path.exists():
+            return None
+
+        with open(file_path, "rb") as f:
+            return f.read()

src/crewai/cli/config.py

@@ -11,7 +11,6 @@ from crewai.cli.constants import (
     CREWAI_ENTERPRISE_DEFAULT_OAUTH2_CLIENT_ID,
     CREWAI_ENTERPRISE_DEFAULT_OAUTH2_DOMAIN,
 )
-from crewai.cli.shared.token_manager import TokenManager
 
 DEFAULT_CONFIG_PATH = Path.home() / ".config" / "crewai" / "settings.json"
 
@@ -54,7 +53,6 @@ HIDDEN_SETTINGS_KEYS = [
     "tool_repository_password",
 ]
 
-
 class Settings(BaseModel):
     enterprise_base_url: Optional[str] = Field(
         default=DEFAULT_CLI_SETTINGS["enterprise_base_url"],
@@ -76,12 +74,12 @@ class Settings(BaseModel):
 
     oauth2_provider: str = Field(
         description="OAuth2 provider used for authentication (e.g., workos, okta, auth0).",
-        default=DEFAULT_CLI_SETTINGS["oauth2_provider"],
+        default=DEFAULT_CLI_SETTINGS["oauth2_provider"]
     )
 
     oauth2_audience: Optional[str] = Field(
         description="OAuth2 audience value, typically used to identify the target API or resource.",
-        default=DEFAULT_CLI_SETTINGS["oauth2_audience"],
+        default=DEFAULT_CLI_SETTINGS["oauth2_audience"]
     )
 
     oauth2_client_id: str = Field(
@@ -91,7 +89,7 @@ class Settings(BaseModel):
 
     oauth2_domain: str = Field(
         description="OAuth2 provider's domain (e.g., your-org.auth0.com) used for issuing tokens.",
-        default=DEFAULT_CLI_SETTINGS["oauth2_domain"],
+        default=DEFAULT_CLI_SETTINGS["oauth2_domain"]
     )
 
     def __init__(self, config_path: Path = DEFAULT_CONFIG_PATH, **data):
@@ -118,7 +116,6 @@ class Settings(BaseModel):
         """Reset all settings to default values"""
         self._reset_user_settings()
         self._reset_cli_settings()
-        self._clear_auth_tokens()
         self.dump()
 
     def dump(self) -> None:
@@ -142,7 +139,3 @@ class Settings(BaseModel):
         """Reset all CLI settings to default values"""
         for key in CLI_SETTINGS_KEYS:
             setattr(self, key, DEFAULT_CLI_SETTINGS.get(key))
-
-    def _clear_auth_tokens(self) -> None:
-        """Clear all authentication tokens"""
-        TokenManager().clear_tokens()

src/crewai/cli/plus_api.py

@@ -117,19 +117,17 @@ class PlusAPI:
     def get_organizations(self) -> requests.Response:
         return self._make_request("GET", self.ORGANIZATIONS_RESOURCE)
 
+    def send_trace_batch(self, payload) -> requests.Response:
+        return self._make_request("POST", self.TRACING_RESOURCE, json=payload)
+
     def initialize_trace_batch(self, payload) -> requests.Response:
         return self._make_request(
-            "POST",
+            "POST", f"{self.TRACING_RESOURCE}/batches", json=payload
-            f"{self.TRACING_RESOURCE}/batches",
-            json=payload,
-            timeout=30,
         )
 
     def initialize_ephemeral_trace_batch(self, payload) -> requests.Response:
         return self._make_request(
-            "POST",
+            "POST", f"{self.EPHEMERAL_TRACING_RESOURCE}/batches", json=payload
-            f"{self.EPHEMERAL_TRACING_RESOURCE}/batches",
-            json=payload,
         )
 
     def send_trace_events(self, trace_batch_id: str, payload) -> requests.Response:
@@ -137,7 +135,6 @@ class PlusAPI:
             "POST",
             f"{self.TRACING_RESOURCE}/batches/{trace_batch_id}/events",
             json=payload,
-            timeout=30,
         )
 
     def send_ephemeral_trace_events(
@@ -147,7 +144,6 @@ class PlusAPI:
             "POST",
             f"{self.EPHEMERAL_TRACING_RESOURCE}/batches/{trace_batch_id}/events",
             json=payload,
-            timeout=30,
         )
 
     def finalize_trace_batch(self, trace_batch_id: str, payload) -> requests.Response:
@@ -155,7 +151,6 @@ class PlusAPI:
             "PATCH",
             f"{self.TRACING_RESOURCE}/batches/{trace_batch_id}/finalize",
             json=payload,
-            timeout=30,
         )
 
     def finalize_ephemeral_trace_batch(
@@ -165,5 +160,4 @@ class PlusAPI:
             "PATCH",
             f"{self.EPHEMERAL_TRACING_RESOURCE}/batches/{trace_batch_id}/finalize",
             json=payload,
-            timeout=30,
         )

src/crewai/cli/shared/token_manager.py

@@ -1,141 +0,0 @@
-import json
-import os
-import sys
-from datetime import datetime
-from pathlib import Path
-from typing import Optional
-from cryptography.fernet import Fernet
-
-
-class TokenManager:
-    def __init__(self, file_path: str = "tokens.enc") -> None:
-        """
-        Initialize the TokenManager class.
-
-        :param file_path: The file path to store the encrypted tokens. Default is "tokens.enc".
-        """
-        self.file_path = file_path
-        self.key = self._get_or_create_key()
-        self.fernet = Fernet(self.key)
-
-    def _get_or_create_key(self) -> bytes:
-        """
-        Get or create the encryption key.
-
-        :return: The encryption key.
-        """
-        key_filename = "secret.key"
-        key = self.read_secure_file(key_filename)
-
-        if key is not None:
-            return key
-
-        new_key = Fernet.generate_key()
-        self.save_secure_file(key_filename, new_key)
-        return new_key
-
-    def save_tokens(self, access_token: str, expires_at: int) -> None:
-        """
-        Save the access token and its expiration time.
-
-        :param access_token: The access token to save.
-        :param expires_at: The UNIX timestamp of the expiration time.
-        """
-        expiration_time = datetime.fromtimestamp(expires_at)
-        data = {
-            "access_token": access_token,
-            "expiration": expiration_time.isoformat(),
-        }
-        encrypted_data = self.fernet.encrypt(json.dumps(data).encode())
-        self.save_secure_file(self.file_path, encrypted_data)
-
-    def get_token(self) -> Optional[str]:
-        """
-        Get the access token if it is valid and not expired.
-
-        :return: The access token if valid and not expired, otherwise None.
-        """
-        encrypted_data = self.read_secure_file(self.file_path)
-        if encrypted_data is None:
-            return None
-
-        decrypted_data = self.fernet.decrypt(encrypted_data)  # type: ignore
-        data = json.loads(decrypted_data)
-
-        expiration = datetime.fromisoformat(data["expiration"])
-        if expiration <= datetime.now():
-            return None
-
-        return data["access_token"]
-
-    def clear_tokens(self) -> None:
-        """
-        Clear the tokens.
-        """
-        self.delete_secure_file(self.file_path)
-
-    def get_secure_storage_path(self) -> Path:
-        """
-        Get the secure storage path based on the operating system.
-
-        :return: The secure storage path.
-        """
-        if sys.platform == "win32":
-            # Windows: Use %LOCALAPPDATA%
-            base_path = os.environ.get("LOCALAPPDATA")
-        elif sys.platform == "darwin":
-            # macOS: Use ~/Library/Application Support
-            base_path = os.path.expanduser("~/Library/Application Support")
-        else:
-            # Linux and other Unix-like: Use ~/.local/share
-            base_path = os.path.expanduser("~/.local/share")
-
-        app_name = "crewai/credentials"
-        storage_path = Path(base_path) / app_name
-
-        storage_path.mkdir(parents=True, exist_ok=True)
-
-        return storage_path
-
-    def save_secure_file(self, filename: str, content: bytes) -> None:
-        """
-        Save the content to a secure file.
-
-        :param filename: The name of the file.
-        :param content: The content to save.
-        """
-        storage_path = self.get_secure_storage_path()
-        file_path = storage_path / filename
-
-        with open(file_path, "wb") as f:
-            f.write(content)
-
-        # Set appropriate permissions (read/write for owner only)
-        os.chmod(file_path, 0o600)
-
-    def read_secure_file(self, filename: str) -> Optional[bytes]:
-        """
-        Read the content of a secure file.
-
-        :param filename: The name of the file.
-        :return: The content of the file if it exists, otherwise None.
-        """
-        storage_path = self.get_secure_storage_path()
-        file_path = storage_path / filename
-
-        if not file_path.exists():
-            return None
-
-        with open(file_path, "rb") as f:
-            return f.read()
-
-    def delete_secure_file(self, filename: str) -> None:
-        """
-        Delete the secure file.
-
-        :param filename: The name of the file.
-        """
-        storage_path = self.get_secure_storage_path()
-        file_path = storage_path / filename
-        if file_path.exists():
-            file_path.unlink(missing_ok=True)

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]>=0.175.0,<1.0.0"
+    "crewai[tools]>=0.165.1,<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]>=0.175.0,<1.0.0",
+    "crewai[tools]>=0.165.1,<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]>=0.175.0"
+    "crewai[tools]>=0.165.1"
 ]
 
 [tool.crewai]

src/crewai/experimental/evaluation/agent_evaluator.py

@@ -1,5 +1,5 @@
 import threading
-from typing import Any, Optional
+from typing import Any
 
 from crewai.experimental.evaluation.base_evaluator import AgentEvaluationResult, AggregationStrategy
 from crewai.agent import Agent
@@ -15,11 +15,10 @@ from crewai.utilities.events.agent_events import LiteAgentExecutionCompletedEven
 from crewai.experimental.evaluation.base_evaluator import AgentAggregatedEvaluationResult, EvaluationScore, MetricCategory
 
 class ExecutionState:
-    current_agent_id: Optional[str] = None
-    current_task_id: Optional[str] = None
-
     def __init__(self):
         self.traces = {}
+        self.current_agent_id: str | None = None
+        self.current_task_id: str | None = None
         self.iteration = 1
         self.iterations_results = {}
         self.agent_evaluators = {}

src/crewai/flow/flow.py

@@ -474,7 +474,6 @@ class Flow(Generic[T], metaclass=FlowMeta):
         self._method_outputs: List[Any] = []  # List to store all method outputs
         self._completed_methods: Set[str] = set()  # Track completed methods for reload
         self._persistence: Optional[FlowPersistence] = persistence
-        self._is_execution_resuming: bool = False
 
         # Initialize state with initial values
         self._state = self._create_initial_state()
@@ -830,9 +829,6 @@ class Flow(Generic[T], metaclass=FlowMeta):
                 # Clear completed methods and outputs for a fresh start
                 self._completed_methods.clear()
                 self._method_outputs.clear()
-            else:
-                # We're restoring from persistence, set the flag
-                self._is_execution_resuming = True
 
             if inputs:
                 # Override the id in the state if it exists in inputs
@@ -884,9 +880,6 @@ class Flow(Generic[T], metaclass=FlowMeta):
             ]
             await asyncio.gather(*tasks)
 
-            # Clear the resumption flag after initial execution completes
-            self._is_execution_resuming = False
-
             final_output = self._method_outputs[-1] if self._method_outputs else None
 
             crewai_event_bus.emit(
@@ -923,13 +916,9 @@ class Flow(Generic[T], metaclass=FlowMeta):
         - Automatically injects crewai_trigger_payload if available in flow inputs
         """
         if start_method_name in self._completed_methods:
-            if self._is_execution_resuming:
+            last_output = self._method_outputs[-1] if self._method_outputs else None
-                # During resumption, skip execution but continue listeners
+            await self._execute_listeners(start_method_name, last_output)
-                last_output = self._method_outputs[-1] if self._method_outputs else None
+            return
-                await self._execute_listeners(start_method_name, last_output)
-                return
-            # For cyclic flows, clear from completed to allow re-execution
-            self._completed_methods.discard(start_method_name)
 
         method = self._methods[start_method_name]
         enhanced_method = self._inject_trigger_payload_for_start_method(method)
@@ -1061,15 +1050,11 @@ class Flow(Generic[T], metaclass=FlowMeta):
             for router_name in routers_triggered:
                 await self._execute_single_listener(router_name, result)
                 # After executing router, the router's result is the path
-                router_result = (
+                router_result = self._method_outputs[-1]
-                    self._method_outputs[-1] if self._method_outputs else None
-                )
                 if router_result:  # Only add non-None results
                     router_results.append(router_result)
                 current_trigger = (
-                    str(router_result)
+                    router_result  # Update for next iteration of router chain
-                    if router_result is not None
-                    else ""  # Update for next iteration of router chain
                 )
 
         # Now execute normal listeners for all router results and the original trigger
@@ -1087,24 +1072,6 @@ class Flow(Generic[T], metaclass=FlowMeta):
                     ]
                     await asyncio.gather(*tasks)
 
-                if current_trigger in router_results:
-                    # Find start methods triggered by this router result
-                    for method_name in self._start_methods:
-                        # Check if this start method is triggered by the current trigger
-                        if method_name in self._listeners:
-                            condition_type, trigger_methods = self._listeners[
-                                method_name
-                            ]
-                            if current_trigger in trigger_methods:
-                                # Only execute if this is a cycle (method was already completed)
-                                if method_name in self._completed_methods:
-                                    # For router-triggered start methods in cycles, temporarily clear resumption flag
-                                    # to allow cyclic execution
-                                    was_resuming = self._is_execution_resuming
-                                    self._is_execution_resuming = False
-                                    await self._execute_start_method(method_name)
-                                    self._is_execution_resuming = was_resuming
-
     def _find_triggered_methods(
         self, trigger_method: str, router_only: bool
     ) -> List[str]:
@@ -1142,9 +1109,6 @@ class Flow(Generic[T], metaclass=FlowMeta):
             if router_only != is_router:
                 continue
 
-            if not router_only and listener_name in self._start_methods:
-                continue
-
             if condition_type == "OR":
                 # If the trigger_method matches any in methods, run this
                 if trigger_method in methods:
@@ -1194,13 +1158,10 @@ class Flow(Generic[T], metaclass=FlowMeta):
         Catches and logs any exceptions during execution, preventing
         individual listener failures from breaking the entire flow.
         """
-        if listener_name in self._completed_methods:
+        # TODO: greyson fix
-            if self._is_execution_resuming:
+        # if listener_name in self._completed_methods:
-                # During resumption, skip execution but continue listeners
+        #     await self._execute_listeners(listener_name, None)
-                await self._execute_listeners(listener_name, None)
+        #     return
-                return
-            # For cyclic flows, clear from completed to allow re-execution
-            self._completed_methods.discard(listener_name)
 
         try:
             method = self._methods[listener_name]

src/crewai/lite_agent.py

@@ -69,7 +69,12 @@ from crewai.utilities.events.agent_events import (
     LiteAgentExecutionStartedEvent,
 )
 from crewai.utilities.events.crewai_event_bus import crewai_event_bus
-
+from crewai.utilities.events.llm_events import (
+    LLMCallCompletedEvent,
+    LLMCallFailedEvent,
+    LLMCallStartedEvent,
+    LLMCallType,
+)
 from crewai.utilities.llm_utils import create_llm
 from crewai.utilities.printer import Printer
 from crewai.utilities.token_counter_callback import TokenCalcHandler
@@ -514,6 +519,19 @@ class LiteAgent(FlowTrackable, BaseModel):
 
                 enforce_rpm_limit(self.request_within_rpm_limit)
 
+                llm = cast(LLM, self.llm)
+                model = llm.model if hasattr(llm, "model") else "unknown"
+                crewai_event_bus.emit(
+                    self,
+                    event=LLMCallStartedEvent(
+                        messages=self._messages,
+                        tools=None,
+                        callbacks=self._callbacks,
+                        from_agent=self,
+                        model=model,
+                    ),
+                )
+
                 try:
                     answer = get_llm_response(
                         llm=cast(LLM, self.llm),
@@ -523,7 +541,23 @@ class LiteAgent(FlowTrackable, BaseModel):
                         from_agent=self,
                     )
 
+                    # Emit LLM call completed event
+                    crewai_event_bus.emit(
+                        self,
+                        event=LLMCallCompletedEvent(
+                            messages=self._messages,
+                            response=answer,
+                            call_type=LLMCallType.LLM_CALL,
+                            from_agent=self,
+                            model=model,
+                        ),
+                    )
                 except Exception as e:
+                    # Emit LLM call failed event
+                    crewai_event_bus.emit(
+                        self,
+                        event=LLMCallFailedEvent(error=str(e), from_agent=self),
+                    )
                     raise e
 
                 formatted_answer = process_llm_response(answer, self.use_stop_words)

src/crewai/llm.py

@@ -851,9 +851,7 @@ class LLM(BaseLLM):
             return tool_calls
 
         # --- 7) Handle tool calls if present
-        tool_result = self._handle_tool_call(
+        tool_result = self._handle_tool_call(tool_calls, available_functions)
-            tool_calls, available_functions, from_task, from_agent
-        )
         if tool_result is not None:
             return tool_result
         # --- 8) If tool call handling didn't return a result, emit completion event and return text response
@@ -870,8 +868,6 @@ class LLM(BaseLLM):
         self,
         tool_calls: List[Any],
         available_functions: Optional[Dict[str, Any]] = None,
-        from_task: Optional[Any] = None,
-        from_agent: Optional[Any] = None,
     ) -> Optional[str]:
         """Handle a tool call from the LLM.
 
@@ -906,8 +902,6 @@ class LLM(BaseLLM):
                     event=ToolUsageStartedEvent(
                         tool_name=function_name,
                         tool_args=function_args,
-                        from_agent=from_agent,
-                        from_task=from_task,
                     ),
                 )
 
@@ -920,17 +914,12 @@ class LLM(BaseLLM):
                         tool_args=function_args,
                         started_at=started_at,
                         finished_at=datetime.now(),
-                        from_task=from_task,
-                        from_agent=from_agent,
                     ),
                 )
 
                 # --- 3.3) Emit success event
                 self._handle_emit_call_events(
-                    response=result,
+                    response=result, call_type=LLMCallType.TOOL_CALL
-                    call_type=LLMCallType.TOOL_CALL,
-                    from_task=from_task,
-                    from_agent=from_agent,
                 )
                 return result
             except Exception as e:
@@ -1150,11 +1139,7 @@ class LLM(BaseLLM):
 
         # TODO: Remove this code after merging PR https://github.com/BerriAI/litellm/pull/10917
         # Ollama doesn't supports last message to be 'assistant'
-        if (
+        if "ollama" in self.model.lower() and messages and messages[-1]["role"] == "assistant":
-            "ollama" in self.model.lower()
-            and messages
-            and messages[-1]["role"] == "assistant"
-        ):
             return messages + [{"role": "user", "content": ""}]
 
         # Handle Anthropic models

src/crewai/memory/contextual/contextual_memory.py

@@ -1,4 +1,4 @@
-from typing import Optional, TYPE_CHECKING
+from typing import Optional
 
 from crewai.memory import (
     EntityMemory,
@@ -7,10 +7,6 @@ from crewai.memory import (
     ShortTermMemory,
 )
 
-if TYPE_CHECKING:
-    from crewai.agent import Agent
-    from crewai.task import Task
-
 
 class ContextualMemory:
     def __init__(
@@ -19,28 +15,11 @@ class ContextualMemory:
         ltm: LongTermMemory,
         em: EntityMemory,
         exm: ExternalMemory,
-        agent: Optional["Agent"] = None,
-        task: Optional["Task"] = None,
     ):
         self.stm = stm
         self.ltm = ltm
         self.em = em
         self.exm = exm
-        self.agent = agent
-        self.task = task
-
-        if self.stm is not None:
-            self.stm.agent = self.agent
-            self.stm.task = self.task
-        if self.ltm is not None:
-            self.ltm.agent = self.agent
-            self.ltm.task = self.task
-        if self.em is not None:
-            self.em.agent = self.agent
-            self.em.task = self.task
-        if self.exm is not None:
-            self.exm.agent = self.agent
-            self.exm.task = self.task
 
     def build_context_for_task(self, task, context) -> str:
         """
@@ -70,7 +49,10 @@ class ContextualMemory:
 
         stm_results = self.stm.search(query)
         formatted_results = "\n".join(
-            [f"- {result['context']}" for result in stm_results]
+            [
+                f"- {result['context']}"
+                for result in stm_results
+            ]
         )
         return f"Recent Insights:\n{formatted_results}" if stm_results else ""
 
@@ -107,7 +89,10 @@ class ContextualMemory:
 
         em_results = self.em.search(query)
         formatted_results = "\n".join(
-            [f"- {result['context']}" for result in em_results]  # type: ignore #  Invalid index type "str" for "str"; expected type "SupportsIndex | slice"
+            [
+                f"- {result['context']}"
+                for result in em_results
+            ]  # type: ignore #  Invalid index type "str" for "str"; expected type "SupportsIndex | slice"
         )
         return f"Entities:\n{formatted_results}" if em_results else ""
 

src/crewai/memory/entity/entity_memory.py

@@ -1,4 +1,4 @@
-from typing import Any
+from typing import Optional
 import time
 
 from pydantic import PrivateAttr
@@ -24,7 +24,7 @@ class EntityMemory(Memory):
     Inherits from the Memory class.
     """
 
-    _memory_provider: str | None = PrivateAttr()
+    _memory_provider: Optional[str] = PrivateAttr()
 
     def __init__(self, crew=None, embedder_config=None, storage=None, path=None):
         memory_provider = embedder_config.get("provider") if embedder_config else None
@@ -35,7 +35,7 @@ class EntityMemory(Memory):
                 raise ImportError(
                     "Mem0 is not installed. Please install it with `pip install mem0ai`."
                 )
-            config = embedder_config.get("config") if embedder_config else None
+            config = embedder_config.get("config")
             storage = Mem0Storage(type="short_term", crew=crew, config=config)
         else:
             storage = (
@@ -53,99 +53,47 @@ class EntityMemory(Memory):
         super().__init__(storage=storage)
         self._memory_provider = memory_provider
 
-    def save(
+    def save(self, item: EntityMemoryItem) -> None:  # type: ignore # BUG?: Signature of "save" incompatible with supertype "Memory"
-        self,
+        """Saves an entity item into the SQLite storage."""
-        value: EntityMemoryItem | list[EntityMemoryItem],
-        metadata: dict[str, Any] | None = None,
-    ) -> None:
-        """Saves one or more entity items into the SQLite storage.
-
-        Args:
-            value: Single EntityMemoryItem or list of EntityMemoryItems to save.
-            metadata: Optional metadata dict (included for supertype compatibility but not used).
-
-        Notes:
-            The metadata parameter is included to satisfy the supertype signature but is not
-            used - entity metadata is extracted from the EntityMemoryItem objects themselves.
-        """
-
-        if not value:
-            return
-
-        items = value if isinstance(value, list) else [value]
-        is_batch = len(items) > 1
-
-        metadata = {"entity_count": len(items)} if is_batch else items[0].metadata
         crewai_event_bus.emit(
             self,
             event=MemorySaveStartedEvent(
-                metadata=metadata,
+                metadata=item.metadata,
                 source_type="entity_memory",
-                from_agent=self.agent,
-                from_task=self.task,
             ),
         )
 
         start_time = time.time()
-        saved_count = 0
-        errors = []
-
         try:
-            for item in items:
+            if self._memory_provider == "mem0":
-                try:
+                data = f"""
-                    if self._memory_provider == "mem0":
+                Remember details about the following entity:
-                        data = f"""
+                Name: {item.name}
-                        Remember details about the following entity:
+                Type: {item.type}
-                        Name: {item.name}
+                Entity Description: {item.description}
-                        Type: {item.type}
+                """
-                        Entity Description: {item.description}
-                        """
-                    else:
-                        data = f"{item.name}({item.type}): {item.description}"
-
-                    super().save(data, item.metadata)
-                    saved_count += 1
-                except Exception as e:
-                    errors.append(f"{item.name}: {str(e)}")
-
-            if is_batch:
-                emit_value = f"Saved {saved_count} entities"
-                metadata = {"entity_count": saved_count, "errors": errors}
             else:
-                emit_value = f"{items[0].name}({items[0].type}): {items[0].description}"
+                data = f"{item.name}({item.type}): {item.description}"
-                metadata = items[0].metadata
 
+            super().save(data, item.metadata)
+
+            # Emit memory save completed event
             crewai_event_bus.emit(
                 self,
                 event=MemorySaveCompletedEvent(
-                    value=emit_value,
+                    value=data,
-                    metadata=metadata,
+                    metadata=item.metadata,
                     save_time_ms=(time.time() - start_time) * 1000,
                     source_type="entity_memory",
-                    from_agent=self.agent,
-                    from_task=self.task,
                 ),
             )
-
-            if errors:
-                raise Exception(
-                    f"Partial save: {len(errors)} failed out of {len(items)}"
-                )
-
         except Exception as e:
-            fail_metadata = (
-                {"entity_count": len(items), "saved": saved_count}
-                if is_batch
-                else items[0].metadata
-            )
             crewai_event_bus.emit(
                 self,
                 event=MemorySaveFailedEvent(
-                    metadata=fail_metadata,
+                    metadata=item.metadata,
                     error=str(e),
                     source_type="entity_memory",
-                    from_agent=self.agent,
-                    from_task=self.task,
                 ),
             )
             raise
@@ -163,8 +111,6 @@ class EntityMemory(Memory):
                 limit=limit,
                 score_threshold=score_threshold,
                 source_type="entity_memory",
-                from_agent=self.agent,
-                from_task=self.task,
             ),
         )
 
@@ -183,8 +129,6 @@ class EntityMemory(Memory):
                     score_threshold=score_threshold,
                     query_time_ms=(time.time() - start_time) * 1000,
                     source_type="entity_memory",
-                    from_agent=self.agent,
-                    from_task=self.task,
                 ),
             )
 

src/crewai/memory/external/external_memory.py

@@ -53,6 +53,7 @@ class ExternalMemory(Memory):
         self,
         value: Any,
         metadata: Optional[Dict[str, Any]] = None,
+        agent: Optional[str] = None,
     ) -> None:
         """Saves a value into the external storage."""
         crewai_event_bus.emit(
@@ -60,30 +61,24 @@ class ExternalMemory(Memory):
             event=MemorySaveStartedEvent(
                 value=value,
                 metadata=metadata,
+                agent_role=agent,
                 source_type="external_memory",
-                from_agent=self.agent,
-                from_task=self.task,
             ),
         )
 
         start_time = time.time()
         try:
-            item = ExternalMemoryItem(
+            item = ExternalMemoryItem(value=value, metadata=metadata, agent=agent)
-                value=value,
+            super().save(value=item.value, metadata=item.metadata, agent=item.agent)
-                metadata=metadata,
-                agent=self.agent.role if self.agent else None,
-            )
-            super().save(value=item.value, metadata=item.metadata)
 
             crewai_event_bus.emit(
                 self,
                 event=MemorySaveCompletedEvent(
                     value=value,
                     metadata=metadata,
+                    agent_role=agent,
                     save_time_ms=(time.time() - start_time) * 1000,
                     source_type="external_memory",
-                    from_agent=self.agent,
-                    from_task=self.task,
                 ),
             )
         except Exception as e:
@@ -92,10 +87,9 @@ class ExternalMemory(Memory):
                 event=MemorySaveFailedEvent(
                     value=value,
                     metadata=metadata,
+                    agent_role=agent,
                     error=str(e),
                     source_type="external_memory",
-                    from_agent=self.agent,
-                    from_task=self.task,
                 ),
             )
             raise
@@ -113,8 +107,6 @@ class ExternalMemory(Memory):
                 limit=limit,
                 score_threshold=score_threshold,
                 source_type="external_memory",
-                from_agent=self.agent,
-                from_task=self.task,
             ),
         )
 
@@ -133,8 +125,6 @@ class ExternalMemory(Memory):
                     score_threshold=score_threshold,
                     query_time_ms=(time.time() - start_time) * 1000,
                     source_type="external_memory",
-                    from_agent=self.agent,
-                    from_task=self.task,
                 ),
             )
 

src/crewai/memory/long_term/long_term_memory.py

@@ -37,17 +37,13 @@ class LongTermMemory(Memory):
                 metadata=item.metadata,
                 agent_role=item.agent,
                 source_type="long_term_memory",
-                from_agent=self.agent,
-                from_task=self.task,
             ),
         )
 
         start_time = time.time()
         try:
             metadata = item.metadata
-            metadata.update(
+            metadata.update({"agent": item.agent, "expected_output": item.expected_output})
-                {"agent": item.agent, "expected_output": item.expected_output}
-            )
             self.storage.save(  # type: ignore # BUG?: Unexpected keyword argument "task_description","score","datetime" for "save" of "Storage"
                 task_description=item.task,
                 score=metadata["quality"],
@@ -63,8 +59,6 @@ class LongTermMemory(Memory):
                     agent_role=item.agent,
                     save_time_ms=(time.time() - start_time) * 1000,
                     source_type="long_term_memory",
-                    from_agent=self.agent,
-                    from_task=self.task,
                 ),
             )
         except Exception as e:
@@ -80,19 +74,13 @@ class LongTermMemory(Memory):
             )
             raise
 
-    def search(  # type: ignore # signature of "search" incompatible with supertype "Memory"
+    def search(self, task: str, latest_n: int = 3) -> List[Dict[str, Any]]:  # type: ignore # signature of "search" incompatible with supertype "Memory"
-        self,
-        task: str,
-        latest_n: int = 3,
-    ) -> List[Dict[str, Any]]:  # type: ignore # signature of "search" incompatible with supertype "Memory"
         crewai_event_bus.emit(
             self,
             event=MemoryQueryStartedEvent(
                 query=task,
                 limit=latest_n,
                 source_type="long_term_memory",
-                from_agent=self.agent,
-                from_task=self.task,
             ),
         )
 
@@ -108,8 +96,6 @@ class LongTermMemory(Memory):
                     limit=latest_n,
                     query_time_ms=(time.time() - start_time) * 1000,
                     source_type="long_term_memory",
-                    from_agent=self.agent,
-                    from_task=self.task,
                 ),
             )
 

src/crewai/memory/memory.py

@@ -1,11 +1,7 @@
-from typing import Any, Dict, List, Optional, TYPE_CHECKING
+from typing import Any, Dict, List, Optional
 
 from pydantic import BaseModel
 
-if TYPE_CHECKING:
-    from crewai.agent import Agent
-    from crewai.task import Task
-
 
 class Memory(BaseModel):
     """
@@ -16,38 +12,19 @@ class Memory(BaseModel):
     crew: Optional[Any] = None
 
     storage: Any
-    _agent: Optional["Agent"] = None
-    _task: Optional["Task"] = None
 
     def __init__(self, storage: Any, **data: Any):
         super().__init__(storage=storage, **data)
 
-    @property
-    def task(self) -> Optional["Task"]:
-        """Get the current task associated with this memory."""
-        return self._task
-
-    @task.setter
-    def task(self, task: Optional["Task"]) -> None:
-        """Set the current task associated with this memory."""
-        self._task = task
-
-    @property
-    def agent(self) -> Optional["Agent"]:
-        """Get the current agent associated with this memory."""
-        return self._agent
-
-    @agent.setter
-    def agent(self, agent: Optional["Agent"]) -> None:
-        """Set the current agent associated with this memory."""
-        self._agent = agent
-
     def save(
         self,
         value: Any,
         metadata: Optional[Dict[str, Any]] = None,
+        agent: Optional[str] = None,
     ) -> None:
         metadata = metadata or {}
+        if agent:
+            metadata["agent"] = agent
 
         self.storage.save(value, metadata)
 

src/crewai/memory/short_term/short_term_memory.py

@@ -37,7 +37,7 @@ class ShortTermMemory(Memory):
                 raise ImportError(
                     "Mem0 is not installed. Please install it with `pip install mem0ai`."
                 )
-            config = embedder_config.get("config") if embedder_config else None
+            config = embedder_config.get("config")
             storage = Mem0Storage(type="short_term", crew=crew, config=config)
         else:
             storage = (
@@ -57,42 +57,34 @@ class ShortTermMemory(Memory):
         self,
         value: Any,
         metadata: Optional[Dict[str, Any]] = None,
+        agent: Optional[str] = None,
     ) -> None:
         crewai_event_bus.emit(
             self,
             event=MemorySaveStartedEvent(
                 value=value,
                 metadata=metadata,
+                agent_role=agent,
                 source_type="short_term_memory",
-                from_agent=self.agent,
-                from_task=self.task,
             ),
         )
 
         start_time = time.time()
         try:
-            item = ShortTermMemoryItem(
+            item = ShortTermMemoryItem(data=value, metadata=metadata, agent=agent)
-                data=value,
-                metadata=metadata,
-                agent=self.agent.role if self.agent else None,
-            )
             if self._memory_provider == "mem0":
-                item.data = (
+                item.data = f"Remember the following insights from Agent run: {item.data}"
-                    f"Remember the following insights from Agent run: {item.data}"
-                )
 
-            super().save(value=item.data, metadata=item.metadata)
+            super().save(value=item.data, metadata=item.metadata, agent=item.agent)
 
             crewai_event_bus.emit(
                 self,
                 event=MemorySaveCompletedEvent(
                     value=value,
                     metadata=metadata,
-                    # agent_role=agent,
+                    agent_role=agent,
                     save_time_ms=(time.time() - start_time) * 1000,
                     source_type="short_term_memory",
-                    from_agent=self.agent,
-                    from_task=self.task,
                 ),
             )
         except Exception as e:
@@ -101,10 +93,9 @@ class ShortTermMemory(Memory):
                 event=MemorySaveFailedEvent(
                     value=value,
                     metadata=metadata,
+                    agent_role=agent,
                     error=str(e),
                     source_type="short_term_memory",
-                    from_agent=self.agent,
-                    from_task=self.task,
                 ),
             )
             raise
@@ -122,8 +113,6 @@ class ShortTermMemory(Memory):
                 limit=limit,
                 score_threshold=score_threshold,
                 source_type="short_term_memory",
-                from_agent=self.agent,
-                from_task=self.task,
             ),
         )
 
@@ -142,8 +131,6 @@ class ShortTermMemory(Memory):
                     score_threshold=score_threshold,
                     query_time_ms=(time.time() - start_time) * 1000,
                     source_type="short_term_memory",
-                    from_agent=self.agent,
-                    from_task=self.task,
                 ),
             )
 

src/crewai/rag/__init__.py

@@ -1,58 +1 @@
-"""RAG (Retrieval-Augmented Generation) infrastructure for CrewAI."""
+"""RAG (Retrieval-Augmented Generation) infrastructure for CrewAI."""
-
-import sys
-import importlib
-from types import ModuleType
-from typing import Any
-
-from crewai.rag.config.types import RagConfigType
-from crewai.rag.config.utils import set_rag_config
-
-
-_module_path = __path__
-_module_file = __file__
-
-class _RagModule(ModuleType):
-    """Module wrapper to intercept attribute setting for config."""
-
-    __path__ = _module_path
-    __file__ = _module_file
-
-    def __init__(self, module_name: str):
-        """Initialize the module wrapper.
-
-        Args:
-            module_name: Name of the module.
-        """
-        super().__init__(module_name)
-
-    def __setattr__(self, name: str, value: RagConfigType) -> None:
-        """Set module attributes.
-
-        Args:
-            name: Attribute name.
-            value: Attribute value.
-        """
-        if name == "config":
-            return set_rag_config(value)
-        raise AttributeError(f"Setting attribute '{name}' is not allowed.")
-
-    def __getattr__(self, name: str) -> Any:
-        """Get module attributes.
-
-        Args:
-            name: Attribute name.
-
-        Returns:
-            The requested attribute.
-
-        Raises:
-            AttributeError: If attribute doesn't exist.
-        """
-        try:
-            return importlib.import_module(f"{self.__name__}.{name}")
-        except ImportError:
-            raise AttributeError(f"module '{self.__name__}' has no attribute '{name}'")
-
-
-sys.modules[__name__] = _RagModule(__name__)

src/crewai/rag/chromadb/client.py

@@ -1,567 +0,0 @@
-"""ChromaDB client implementation."""
-
-from typing import Any
-
-from chromadb.api.types import (
-    Embeddable,
-    EmbeddingFunction as ChromaEmbeddingFunction,
-    QueryResult,
-)
-from typing_extensions import Unpack
-
-from crewai.rag.chromadb.types import (
-    ChromaDBClientType,
-    ChromaDBCollectionCreateParams,
-    ChromaDBCollectionSearchParams,
-)
-from crewai.rag.chromadb.utils import (
-    _extract_search_params,
-    _is_async_client,
-    _is_sync_client,
-    _prepare_documents_for_chromadb,
-    _process_query_results,
-)
-from crewai.rag.core.base_client import (
-    BaseClient,
-    BaseCollectionParams,
-    BaseCollectionAddParams,
-)
-from crewai.rag.types import SearchResult
-
-
-class ChromaDBClient(BaseClient):
-    """ChromaDB implementation of the BaseClient protocol.
-
-    Provides vector database operations for ChromaDB, supporting both
-    synchronous and asynchronous clients.
-
-    Attributes:
-        client: ChromaDB client instance (ClientAPI or AsyncClientAPI).
-        embedding_function: Function to generate embeddings for documents.
-    """
-
-    def __init__(
-        self,
-        client: ChromaDBClientType,
-        embedding_function: ChromaEmbeddingFunction[Embeddable],
-    ) -> None:
-        """Initialize ChromaDBClient with client and embedding function.
-
-        Args:
-            client: Pre-configured ChromaDB client instance.
-            embedding_function: Embedding function for text to vector conversion.
-        """
-        self.client = client
-        self.embedding_function = embedding_function
-
-    def create_collection(
-        self, **kwargs: Unpack[ChromaDBCollectionCreateParams]
-    ) -> None:
-        """Create a new collection in ChromaDB.
-
-        Uses the client's default embedding function if none provided.
-
-        Keyword Args:
-            collection_name: Name of the collection to create. Must be unique.
-            configuration: Optional collection configuration specifying distance metrics,
-                HNSW parameters, or other backend-specific settings.
-            metadata: Optional metadata dictionary to attach to the collection.
-            embedding_function: Optional custom embedding function. If not provided,
-                uses the client's default embedding function.
-            data_loader: Optional data loader for batch loading data into the collection.
-            get_or_create: If True, returns existing collection if it already exists
-                instead of raising an error. Defaults to False.
-
-        Raises:
-            TypeError: If AsyncClientAPI is used instead of ClientAPI for sync operations.
-            ValueError: If collection with the same name already exists and get_or_create
-                is False.
-            ConnectionError: If unable to connect to ChromaDB server.
-
-        Example:
-            >>> client = ChromaDBClient()
-            >>> client.create_collection(
-            ...     collection_name="documents",
-            ...     metadata={"description": "Product documentation"},
-            ...     get_or_create=True
-            ... )
-        """
-        if not _is_sync_client(self.client):
-            raise TypeError(
-                "Synchronous method create_collection() requires a ClientAPI. "
-                "Use acreate_collection() for AsyncClientAPI."
-            )
-
-        metadata = kwargs.get("metadata", {})
-        if "hnsw:space" not in metadata:
-            metadata["hnsw:space"] = "cosine"
-
-        self.client.create_collection(
-            name=kwargs["collection_name"],
-            configuration=kwargs.get("configuration"),
-            metadata=metadata,
-            embedding_function=kwargs.get(
-                "embedding_function", self.embedding_function
-            ),
-            data_loader=kwargs.get("data_loader"),
-            get_or_create=kwargs.get("get_or_create", False),
-        )
-
-    async def acreate_collection(
-        self, **kwargs: Unpack[ChromaDBCollectionCreateParams]
-    ) -> None:
-        """Create a new collection in ChromaDB asynchronously.
-
-        Creates a new collection with the specified name and optional configuration.
-        If an embedding function is not provided, uses the client's default embedding function.
-
-        Keyword Args:
-            collection_name: Name of the collection to create. Must be unique.
-            configuration: Optional collection configuration specifying distance metrics,
-                HNSW parameters, or other backend-specific settings.
-            metadata: Optional metadata dictionary to attach to the collection.
-            embedding_function: Optional custom embedding function. If not provided,
-                uses the client's default embedding function.
-            data_loader: Optional data loader for batch loading data into the collection.
-            get_or_create: If True, returns existing collection if it already exists
-                instead of raising an error. Defaults to False.
-
-        Raises:
-            TypeError: If ClientAPI is used instead of AsyncClientAPI for async operations.
-            ValueError: If collection with the same name already exists and get_or_create
-                is False.
-            ConnectionError: If unable to connect to ChromaDB server.
-
-        Example:
-            >>> import asyncio
-            >>> async def main():
-            ...     client = ChromaDBClient()
-            ...     await client.acreate_collection(
-            ...         collection_name="documents",
-            ...         metadata={"description": "Product documentation"},
-            ...         get_or_create=True
-            ...     )
-            >>> asyncio.run(main())
-        """
-        if not _is_async_client(self.client):
-            raise TypeError(
-                "Asynchronous method acreate_collection() requires an AsyncClientAPI. "
-                "Use create_collection() for ClientAPI."
-            )
-
-        metadata = kwargs.get("metadata", {})
-        if "hnsw:space" not in metadata:
-            metadata["hnsw:space"] = "cosine"
-
-        await self.client.create_collection(
-            name=kwargs["collection_name"],
-            configuration=kwargs.get("configuration"),
-            metadata=metadata,
-            embedding_function=kwargs.get(
-                "embedding_function", self.embedding_function
-            ),
-            data_loader=kwargs.get("data_loader"),
-            get_or_create=kwargs.get("get_or_create", False),
-        )
-
-    def get_or_create_collection(
-        self, **kwargs: Unpack[ChromaDBCollectionCreateParams]
-    ) -> Any:
-        """Get an existing collection or create it if it doesn't exist.
-
-        Returns existing collection if found, otherwise creates a new one.
-
-        Keyword Args:
-            collection_name: Name of the collection to get or create.
-            configuration: Optional collection configuration specifying distance metrics,
-                HNSW parameters, or other backend-specific settings.
-            metadata: Optional metadata dictionary to attach to the collection.
-            embedding_function: Optional custom embedding function. If not provided,
-                uses the client's default embedding function.
-            data_loader: Optional data loader for batch loading data into the collection.
-
-        Returns:
-            A ChromaDB Collection object.
-
-        Raises:
-            TypeError: If AsyncClientAPI is used instead of ClientAPI for sync operations.
-            ConnectionError: If unable to connect to ChromaDB server.
-
-        Example:
-            >>> client = ChromaDBClient()
-            >>> collection = client.get_or_create_collection(
-            ...     collection_name="documents",
-            ...     metadata={"description": "Product documentation"}
-            ... )
-        """
-        if not _is_sync_client(self.client):
-            raise TypeError(
-                "Synchronous method get_or_create_collection() requires a ClientAPI. "
-                "Use aget_or_create_collection() for AsyncClientAPI."
-            )
-
-        metadata = kwargs.get("metadata", {})
-        if "hnsw:space" not in metadata:
-            metadata["hnsw:space"] = "cosine"
-
-        return self.client.get_or_create_collection(
-            name=kwargs["collection_name"],
-            configuration=kwargs.get("configuration"),
-            metadata=metadata,
-            embedding_function=kwargs.get(
-                "embedding_function", self.embedding_function
-            ),
-            data_loader=kwargs.get("data_loader"),
-        )
-
-    async def aget_or_create_collection(
-        self, **kwargs: Unpack[ChromaDBCollectionCreateParams]
-    ) -> Any:
-        """Get an existing collection or create it if it doesn't exist asynchronously.
-
-        Returns existing collection if found, otherwise creates a new one.
-
-        Keyword Args:
-            collection_name: Name of the collection to get or create.
-            configuration: Optional collection configuration specifying distance metrics,
-                HNSW parameters, or other backend-specific settings.
-            metadata: Optional metadata dictionary to attach to the collection.
-            embedding_function: Optional custom embedding function. If not provided,
-                uses the client's default embedding function.
-            data_loader: Optional data loader for batch loading data into the collection.
-
-        Returns:
-            A ChromaDB AsyncCollection object.
-
-        Raises:
-            TypeError: If ClientAPI is used instead of AsyncClientAPI for async operations.
-            ConnectionError: If unable to connect to ChromaDB server.
-
-        Example:
-            >>> import asyncio
-            >>> async def main():
-            ...     client = ChromaDBClient()
-            ...     collection = await client.aget_or_create_collection(
-            ...         collection_name="documents",
-            ...         metadata={"description": "Product documentation"}
-            ...     )
-            >>> asyncio.run(main())
-        """
-        if not _is_async_client(self.client):
-            raise TypeError(
-                "Asynchronous method aget_or_create_collection() requires an AsyncClientAPI. "
-                "Use get_or_create_collection() for ClientAPI."
-            )
-
-        metadata = kwargs.get("metadata", {})
-        if "hnsw:space" not in metadata:
-            metadata["hnsw:space"] = "cosine"
-
-        return await self.client.get_or_create_collection(
-            name=kwargs["collection_name"],
-            configuration=kwargs.get("configuration"),
-            metadata=metadata,
-            embedding_function=kwargs.get(
-                "embedding_function", self.embedding_function
-            ),
-            data_loader=kwargs.get("data_loader"),
-        )
-
-    def add_documents(self, **kwargs: Unpack[BaseCollectionAddParams]) -> None:
-        """Add documents with their embeddings to a collection.
-
-        Performs an upsert operation - documents with existing IDs are updated.
-        Generates embeddings automatically using 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 if missing)
-                - metadata: Optional metadata dictionary
-
-        Raises:
-            TypeError: If AsyncClientAPI is used instead of ClientAPI for sync operations.
-            ValueError: If collection doesn't exist or documents list is empty.
-            ConnectionError: If unable to connect to ChromaDB server.
-        """
-        if not _is_sync_client(self.client):
-            raise TypeError(
-                "Synchronous method add_documents() requires a ClientAPI. "
-                "Use aadd_documents() for AsyncClientAPI."
-            )
-
-        collection_name = kwargs["collection_name"]
-        documents = kwargs["documents"]
-
-        if not documents:
-            raise ValueError("Documents list cannot be empty")
-
-        collection = self.client.get_collection(
-            name=collection_name,
-            embedding_function=self.embedding_function,
-        )
-
-        prepared = _prepare_documents_for_chromadb(documents)
-        collection.add(
-            ids=prepared.ids,
-            documents=prepared.texts,
-            metadatas=prepared.metadatas,
-        )
-
-    async def aadd_documents(self, **kwargs: Unpack[BaseCollectionAddParams]) -> None:
-        """Add documents with their embeddings to a collection asynchronously.
-
-        Performs an upsert operation - documents with existing IDs are updated.
-        Generates embeddings automatically using 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 if missing)
-                - metadata: Optional metadata dictionary
-
-        Raises:
-            TypeError: If ClientAPI is used instead of AsyncClientAPI for async operations.
-            ValueError: If collection doesn't exist or documents list is empty.
-            ConnectionError: If unable to connect to ChromaDB server.
-        """
-        if not _is_async_client(self.client):
-            raise TypeError(
-                "Asynchronous method aadd_documents() requires an AsyncClientAPI. "
-                "Use add_documents() for ClientAPI."
-            )
-
-        collection_name = kwargs["collection_name"]
-        documents = kwargs["documents"]
-
-        if not documents:
-            raise ValueError("Documents list cannot be empty")
-
-        collection = await self.client.get_collection(
-            name=collection_name,
-            embedding_function=self.embedding_function,
-        )
-        prepared = _prepare_documents_for_chromadb(documents)
-        await collection.add(
-            ids=prepared.ids,
-            documents=prepared.texts,
-            metadatas=prepared.metadatas,
-        )
-
-    def search(
-        self, **kwargs: Unpack[ChromaDBCollectionSearchParams]
-    ) -> list[SearchResult]:
-        """Search for similar documents using a query.
-
-        Performs semantic search to find documents similar to the query text.
-        Uses the configured embedding function to generate query embeddings.
-
-        Keyword Args:
-            collection_name: Name of the collection to search in.
-            query: The text query to search for.
-            limit: Maximum number of results to return (default: 10).
-            metadata_filter: Optional filter for metadata fields.
-            score_threshold: Optional minimum similarity score (0-1) for results.
-            where: Optional ChromaDB where clause for metadata filtering.
-            where_document: Optional ChromaDB where clause for document content filtering.
-            include: Optional list of fields to include in results.
-
-        Returns:
-            List of SearchResult dicts containing id, content, metadata, and score.
-
-        Raises:
-            TypeError: If AsyncClientAPI is used instead of ClientAPI for sync operations.
-            ValueError: If collection doesn't exist.
-            ConnectionError: If unable to connect to ChromaDB server.
-        """
-        if not _is_sync_client(self.client):
-            raise TypeError(
-                "Synchronous method search() requires a ClientAPI. "
-                "Use asearch() for AsyncClientAPI."
-            )
-
-        params = _extract_search_params(kwargs)
-
-        collection = self.client.get_collection(
-            name=params.collection_name,
-            embedding_function=self.embedding_function,
-        )
-
-        where = params.where if params.where is not None else params.metadata_filter
-
-        results: QueryResult = collection.query(
-            query_texts=[params.query],
-            n_results=params.limit,
-            where=where,
-            where_document=params.where_document,
-            include=params.include,
-        )
-
-        return _process_query_results(
-            collection=collection,
-            results=results,
-            params=params,
-        )
-
-    async def asearch(
-        self, **kwargs: Unpack[ChromaDBCollectionSearchParams]
-    ) -> list[SearchResult]:
-        """Search for similar documents using a query asynchronously.
-
-        Performs semantic search to find documents similar to the query text.
-        Uses the configured embedding function to generate query embeddings.
-
-        Keyword Args:
-            collection_name: Name of the collection to search in.
-            query: The text query to search for.
-            limit: Maximum number of results to return (default: 10).
-            metadata_filter: Optional filter for metadata fields.
-            score_threshold: Optional minimum similarity score (0-1) for results.
-            where: Optional ChromaDB where clause for metadata filtering.
-            where_document: Optional ChromaDB where clause for document content filtering.
-            include: Optional list of fields to include in results.
-
-        Returns:
-            List of SearchResult dicts containing id, content, metadata, and score.
-
-        Raises:
-            TypeError: If ClientAPI is used instead of AsyncClientAPI for async operations.
-            ValueError: If collection doesn't exist.
-            ConnectionError: If unable to connect to ChromaDB server.
-        """
-        if not _is_async_client(self.client):
-            raise TypeError(
-                "Asynchronous method asearch() requires an AsyncClientAPI. "
-                "Use search() for ClientAPI."
-            )
-
-        params = _extract_search_params(kwargs)
-
-        collection = await self.client.get_collection(
-            name=params.collection_name,
-            embedding_function=self.embedding_function,
-        )
-
-        where = params.where if params.where is not None else params.metadata_filter
-
-        results: QueryResult = await collection.query(
-            query_texts=[params.query],
-            n_results=params.limit,
-            where=where,
-            where_document=params.where_document,
-            include=params.include,
-        )
-
-        return _process_query_results(
-            collection=collection,
-            results=results,
-            params=params,
-        )
-
-    def delete_collection(self, **kwargs: Unpack[BaseCollectionParams]) -> None:
-        """Delete a collection and all its data.
-
-        Permanently removes a collection and all documents, embeddings, and metadata it contains.
-        This operation cannot be undone.
-
-        Keyword Args:
-            collection_name: Name of the collection to delete.
-
-        Raises:
-            TypeError: If AsyncClientAPI is used instead of ClientAPI for sync operations.
-            ValueError: If collection doesn't exist.
-            ConnectionError: If unable to connect to ChromaDB server.
-
-        Example:
-            >>> client = ChromaDBClient()
-            >>> client.delete_collection(collection_name="old_documents")
-        """
-        if not _is_sync_client(self.client):
-            raise TypeError(
-                "Synchronous method delete_collection() requires a ClientAPI. "
-                "Use adelete_collection() for AsyncClientAPI."
-            )
-
-        collection_name = kwargs["collection_name"]
-        self.client.delete_collection(name=collection_name)
-
-    async def adelete_collection(self, **kwargs: Unpack[BaseCollectionParams]) -> None:
-        """Delete a collection and all its data asynchronously.
-
-        Permanently removes a collection and all documents, embeddings, and metadata it contains.
-        This operation cannot be undone.
-
-        Keyword Args:
-            collection_name: Name of the collection to delete.
-
-        Raises:
-            TypeError: If ClientAPI is used instead of AsyncClientAPI for async operations.
-            ValueError: If collection doesn't exist.
-            ConnectionError: If unable to connect to ChromaDB server.
-
-        Example:
-            >>> import asyncio
-            >>> async def main():
-            ...     client = ChromaDBClient()
-            ...     await client.adelete_collection(collection_name="old_documents")
-            >>> asyncio.run(main())
-        """
-        if not _is_async_client(self.client):
-            raise TypeError(
-                "Asynchronous method adelete_collection() requires an AsyncClientAPI. "
-                "Use delete_collection() for ClientAPI."
-            )
-
-        collection_name = kwargs["collection_name"]
-        await self.client.delete_collection(name=collection_name)
-
-    def reset(self) -> None:
-        """Reset the vector database by deleting all collections and data.
-
-        Completely clears the ChromaDB instance, removing all collections,
-        documents, embeddings, and metadata. This operation cannot be undone.
-        Use with extreme caution in production environments.
-
-        Raises:
-            TypeError: If AsyncClientAPI is used instead of ClientAPI for sync operations.
-            ConnectionError: If unable to connect to ChromaDB server.
-
-        Example:
-            >>> client = ChromaDBClient()
-            >>> client.reset()  # Removes ALL data from ChromaDB
-        """
-        if not _is_sync_client(self.client):
-            raise TypeError(
-                "Synchronous method reset() requires a ClientAPI. "
-                "Use areset() for AsyncClientAPI."
-            )
-
-        self.client.reset()
-
-    async def areset(self) -> None:
-        """Reset the vector database by deleting all collections and data asynchronously.
-
-        Completely clears the ChromaDB instance, removing all collections,
-        documents, embeddings, and metadata. This operation cannot be undone.
-        Use with extreme caution in production environments.
-
-        Raises:
-            TypeError: If ClientAPI is used instead of AsyncClientAPI for async operations.
-            ConnectionError: If unable to connect to ChromaDB server.
-
-        Example:
-            >>> import asyncio
-            >>> async def main():
-            ...     client = ChromaDBClient()
-            ...     await client.areset()  # Removes ALL data from ChromaDB
-            >>> asyncio.run(main())
-        """
-        if not _is_async_client(self.client):
-            raise TypeError(
-                "Asynchronous method areset() requires an AsyncClientAPI. "
-                "Use reset() for ClientAPI."
-            )
-
-        await self.client.reset()

src/crewai/rag/chromadb/config.py

@@ -1,59 +0,0 @@
-"""ChromaDB configuration model."""
-
-import warnings
-from dataclasses import field
-from typing import Literal, cast
-from pydantic.dataclasses import dataclass as pyd_dataclass
-from chromadb.config import Settings
-from chromadb.utils.embedding_functions import DefaultEmbeddingFunction
-
-from crewai.rag.chromadb.types import ChromaEmbeddingFunctionWrapper
-from crewai.rag.config.base import BaseRagConfig
-from crewai.rag.chromadb.constants import (
-    DEFAULT_TENANT,
-    DEFAULT_DATABASE,
-    DEFAULT_STORAGE_PATH,
-)
-
-
-warnings.filterwarnings(
-    "ignore",
-    message=".*Mixing V1 models and V2 models.*",
-    category=UserWarning,
-    module="pydantic._internal._generate_schema",
-)
-
-
-def _default_settings() -> Settings:
-    """Create default ChromaDB settings.
-
-    Returns:
-        Settings with persistent storage and reset enabled.
-    """
-    return Settings(
-        persist_directory=DEFAULT_STORAGE_PATH,
-        allow_reset=True,
-        is_persistent=True,
-    )
-
-
-def _default_embedding_function() -> ChromaEmbeddingFunctionWrapper:
-    """Create default ChromaDB embedding function.
-
-    Returns:
-        Default embedding function using all-MiniLM-L6-v2 via ONNX.
-    """
-    return cast(ChromaEmbeddingFunctionWrapper, DefaultEmbeddingFunction())
-
-
-@pyd_dataclass(frozen=True)
-class ChromaDBConfig(BaseRagConfig):
-    """Configuration for ChromaDB client."""
-
-    provider: Literal["chromadb"] = field(default="chromadb", init=False)
-    tenant: str = DEFAULT_TENANT
-    database: str = DEFAULT_DATABASE
-    settings: Settings = field(default_factory=_default_settings)
-    embedding_function: ChromaEmbeddingFunctionWrapper = field(
-        default_factory=_default_embedding_function
-    )

src/crewai/rag/chromadb/constants.py

@@ -1,10 +0,0 @@
-"""Constants for ChromaDB configuration."""
-
-import os
-from typing import Final
-
-from crewai.utilities.paths import db_storage_path
-
-DEFAULT_TENANT: Final[str] = "default_tenant"
-DEFAULT_DATABASE: Final[str] = "default_database"
-DEFAULT_STORAGE_PATH: Final[str] = os.path.join(db_storage_path(), "chromadb")

src/crewai/rag/chromadb/factory.py

@@ -1,24 +0,0 @@
-"""Factory functions for creating ChromaDB clients."""
-
-from chromadb import Client
-
-from crewai.rag.chromadb.config import ChromaDBConfig
-from crewai.rag.chromadb.client import ChromaDBClient
-
-
-def create_client(config: ChromaDBConfig) -> ChromaDBClient:
-    """Create a ChromaDBClient from configuration.
-
-    Args:
-        config: ChromaDB configuration object.
-
-    Returns:
-        Configured ChromaDBClient instance.
-    """
-
-    return ChromaDBClient(
-        client=Client(
-            settings=config.settings, tenant=config.tenant, database=config.database
-        ),
-        embedding_function=config.embedding_function,
-    )

src/crewai/rag/chromadb/types.py

@@ -1,102 +0,0 @@
-"""Type definitions specific to ChromaDB implementation."""
-
-from collections.abc import Mapping
-from typing import Any, NamedTuple
-
-from pydantic import GetCoreSchemaHandler
-from pydantic_core import CoreSchema, core_schema
-from chromadb.api import ClientAPI, AsyncClientAPI
-from chromadb.api.configuration import CollectionConfigurationInterface
-from chromadb.api.types import (
-    CollectionMetadata,
-    DataLoader,
-    Embeddable,
-    EmbeddingFunction as ChromaEmbeddingFunction,
-    Include,
-    Loadable,
-    Where,
-    WhereDocument,
-)
-
-from crewai.rag.core.base_client import BaseCollectionParams, BaseCollectionSearchParams
-
-ChromaDBClientType = ClientAPI | AsyncClientAPI
-
-
-class ChromaEmbeddingFunctionWrapper(ChromaEmbeddingFunction[Embeddable]):
-    """Base class for ChromaDB EmbeddingFunction to work with Pydantic validation."""
-
-    @classmethod
-    def __get_pydantic_core_schema__(
-        cls, _source_type: Any, _handler: GetCoreSchemaHandler
-    ) -> CoreSchema:
-        """Generate Pydantic core schema for ChromaDB EmbeddingFunction.
-
-        This allows Pydantic to handle ChromaDB's EmbeddingFunction type
-        without requiring arbitrary_types_allowed=True.
-        """
-        return core_schema.any_schema()
-
-
-class PreparedDocuments(NamedTuple):
-    """Prepared documents ready for ChromaDB insertion.
-
-    Attributes:
-        ids: List of document IDs
-        texts: List of document texts
-        metadatas: List of document metadata mappings
-    """
-
-    ids: list[str]
-    texts: list[str]
-    metadatas: list[Mapping[str, str | int | float | bool]]
-
-
-class ExtractedSearchParams(NamedTuple):
-    """Extracted search parameters for ChromaDB queries.
-
-    Attributes:
-        collection_name: Name of the collection to search
-        query: Search query text
-        limit: Maximum number of results
-        metadata_filter: Optional metadata filter
-        score_threshold: Optional minimum similarity score
-        where: Optional ChromaDB where clause
-        where_document: Optional ChromaDB document filter
-        include: Fields to include in results
-    """
-
-    collection_name: str
-    query: str
-    limit: int
-    metadata_filter: dict[str, Any] | None
-    score_threshold: float | None
-    where: Where | None
-    where_document: WhereDocument | None
-    include: Include
-
-
-class ChromaDBCollectionCreateParams(BaseCollectionParams, total=False):
-    """Parameters for creating a ChromaDB collection.
-
-    This class extends BaseCollectionParams to include any additional
-    parameters specific to ChromaDB collection creation.
-    """
-
-    configuration: CollectionConfigurationInterface
-    metadata: CollectionMetadata
-    embedding_function: ChromaEmbeddingFunction[Embeddable]
-    data_loader: DataLoader[Loadable]
-    get_or_create: bool
-
-
-class ChromaDBCollectionSearchParams(BaseCollectionSearchParams, total=False):
-    """Parameters for searching a ChromaDB collection.
-
-    This class extends BaseCollectionSearchParams to include ChromaDB-specific
-    search parameters like where clauses and include options.
-    """
-
-    where: Where
-    where_document: WhereDocument
-    include: Include

src/crewai/rag/chromadb/utils.py

@@ -1,218 +0,0 @@
-"""Utility functions for ChromaDB client implementation."""
-
-import hashlib
-from collections.abc import Mapping
-from typing import Literal, TypeGuard, cast
-
-from chromadb.api import AsyncClientAPI, ClientAPI
-from chromadb.api.types import (
-    Include,
-    IncludeEnum,
-    QueryResult,
-)
-from chromadb.api.models.AsyncCollection import AsyncCollection
-from chromadb.api.models.Collection import Collection
-from crewai.rag.chromadb.types import (
-    ChromaDBClientType,
-    ChromaDBCollectionSearchParams,
-    ExtractedSearchParams,
-    PreparedDocuments,
-)
-from crewai.rag.types import BaseRecord, SearchResult
-
-
-def _is_sync_client(client: ChromaDBClientType) -> TypeGuard[ClientAPI]:
-    """Type guard to check if the client is a synchronous ClientAPI.
-
-    Args:
-        client: The client to check.
-
-    Returns:
-        True if the client is a ClientAPI, False otherwise.
-    """
-    return isinstance(client, ClientAPI)
-
-
-def _is_async_client(client: ChromaDBClientType) -> TypeGuard[AsyncClientAPI]:
-    """Type guard to check if the client is an asynchronous AsyncClientAPI.
-
-    Args:
-        client: The client to check.
-
-    Returns:
-        True if the client is an AsyncClientAPI, False otherwise.
-    """
-    return isinstance(client, AsyncClientAPI)
-
-
-def _prepare_documents_for_chromadb(
-    documents: list[BaseRecord],
-) -> PreparedDocuments:
-    """Prepare documents for ChromaDB by extracting IDs, texts, and metadata.
-
-    Args:
-        documents: List of BaseRecord documents to prepare.
-
-    Returns:
-        PreparedDocuments with ids, texts, and metadatas ready for ChromaDB.
-    """
-    ids: list[str] = []
-    texts: list[str] = []
-    metadatas: list[Mapping[str, str | int | float | bool]] = []
-
-    for doc in documents:
-        if "doc_id" in doc:
-            ids.append(doc["doc_id"])
-        else:
-            content_hash = hashlib.sha256(doc["content"].encode()).hexdigest()[:16]
-            ids.append(content_hash)
-
-        texts.append(doc["content"])
-        metadata = doc.get("metadata")
-        if metadata:
-            if isinstance(metadata, list):
-                metadatas.append(metadata[0] if metadata else {})
-            else:
-                metadatas.append(metadata)
-        else:
-            metadatas.append({})
-
-    return PreparedDocuments(ids, texts, metadatas)
-
-
-def _extract_search_params(
-    kwargs: ChromaDBCollectionSearchParams,
-) -> ExtractedSearchParams:
-    """Extract search parameters from kwargs.
-
-    Args:
-        kwargs: Keyword arguments containing search parameters.
-
-    Returns:
-        ExtractedSearchParams with all extracted parameters.
-    """
-    return ExtractedSearchParams(
-        collection_name=kwargs["collection_name"],
-        query=kwargs["query"],
-        limit=kwargs.get("limit", 10),
-        metadata_filter=kwargs.get("metadata_filter"),
-        score_threshold=kwargs.get("score_threshold"),
-        where=kwargs.get("where"),
-        where_document=kwargs.get("where_document"),
-        include=kwargs.get(
-            "include",
-            [IncludeEnum.metadatas, IncludeEnum.documents, IncludeEnum.distances],
-        ),
-    )
-
-
-def _convert_distance_to_score(
-    distance: float,
-    distance_metric: Literal["l2", "cosine", "ip"],
-) -> float:
-    """Convert ChromaDB distance to similarity score.
-
-    Notes:
-        Assuming all embedding are unit-normalized for now, including custom embeddings.
-
-    Args:
-        distance: The distance value from ChromaDB.
-        distance_metric: The distance metric used ("l2", "cosine", or "ip").
-
-    Returns:
-        Similarity score in range [0, 1] where 1 is most similar.
-    """
-    if distance_metric == "cosine":
-        score = 1.0 - 0.5 * distance
-        return max(0.0, min(1.0, score))
-    raise ValueError(f"Unsupported distance metric: {distance_metric}")
-
-
-def _convert_chromadb_results_to_search_results(
-    results: QueryResult,
-    include: Include,
-    distance_metric: Literal["l2", "cosine", "ip"],
-    score_threshold: float | None = None,
-) -> list[SearchResult]:
-    """Convert ChromaDB query results to SearchResult format.
-
-    Args:
-        results: ChromaDB query results.
-        include: List of fields that were included in the query.
-        distance_metric: The distance metric used by the collection.
-        score_threshold: Optional minimum similarity score (0-1) for results.
-
-    Returns:
-        List of SearchResult dicts containing id, content, metadata, and score.
-    """
-    search_results: list[SearchResult] = []
-
-    include_strings = [item.value for item in include]
-
-    ids = results["ids"][0] if results.get("ids") else []
-
-    documents_list = results.get("documents")
-    documents = (
-        documents_list[0] if documents_list and "documents" in include_strings else []
-    )
-
-    metadatas_list = results.get("metadatas")
-    metadatas = (
-        metadatas_list[0] if metadatas_list and "metadatas" in include_strings else []
-    )
-
-    distances_list = results.get("distances")
-    distances = (
-        distances_list[0] if distances_list and "distances" in include_strings else []
-    )
-
-    for i, doc_id in enumerate(ids):
-        if not distances or i >= len(distances):
-            continue
-
-        distance = distances[i]
-        score = _convert_distance_to_score(
-            distance=distance, distance_metric=distance_metric
-        )
-
-        if score_threshold and score < score_threshold:
-            continue
-
-        result: SearchResult = {
-            "id": doc_id,
-            "content": documents[i] if documents and i < len(documents) else "",
-            "metadata": dict(metadatas[i]) if metadatas and i < len(metadatas) else {},
-            "score": score,
-        }
-        search_results.append(result)
-
-    return search_results
-
-
-def _process_query_results(
-    collection: Collection | AsyncCollection,
-    results: QueryResult,
-    params: ExtractedSearchParams,
-) -> list[SearchResult]:
-    """Process ChromaDB query results and convert to SearchResult format.
-
-    Args:
-        collection: The ChromaDB collection (sync or async) that was queried.
-        results: Raw query results from ChromaDB.
-        params: The search parameters used for the query.
-
-    Returns:
-        List of SearchResult dicts containing id, content, metadata, and score.
-    """
-
-    distance_metric = cast(
-        Literal["l2", "cosine", "ip"],
-        collection.metadata.get("hnsw:space", "l2") if collection.metadata else "l2",
-    )
-
-    return _convert_chromadb_results_to_search_results(
-        results=results,
-        include=params.include,
-        distance_metric=distance_metric,
-        score_threshold=params.score_threshold,
-    )

src/crewai/rag/config/__init__.py

@@ -1 +0,0 @@
-"""RAG client configuration management using ContextVars for thread-safe provider switching."""

src/crewai/rag/config/base.py

@@ -1,16 +0,0 @@
-"""Base configuration class for RAG providers."""
-
-from dataclasses import field
-from typing import Any
-
-from pydantic.dataclasses import dataclass as pyd_dataclass
-
-from crewai.rag.config.optional_imports.types import SupportedProvider
-
-
-@pyd_dataclass(frozen=True)
-class BaseRagConfig:
-    """Base class for RAG configuration with Pydantic serialization support."""
-
-    provider: SupportedProvider = field(init=False)
-    embedding_function: Any | None = field(default=None)

src/crewai/rag/config/constants.py

@@ -1,8 +0,0 @@
-"""Constants for RAG configuration."""
-
-from typing import Final
-
-DISCRIMINATOR: Final[str] = "provider"
-
-DEFAULT_RAG_CONFIG_PATH: Final[str] = "crewai.rag.chromadb.config"
-DEFAULT_RAG_CONFIG_CLASS: Final[str] = "ChromaDBConfig"

src/crewai/rag/config/optional_imports/__init__.py

@@ -1 +0,0 @@
-"""Optional imports for RAG configuration providers."""

src/crewai/rag/config/optional_imports/base.py

@@ -1,26 +0,0 @@
-"""Base classes for missing provider configurations."""
-
-from typing import Literal
-from dataclasses import field
-
-from pydantic import ConfigDict
-from pydantic.dataclasses import dataclass as pyd_dataclass
-
-
-@pyd_dataclass(config=ConfigDict(extra="forbid"))
-class _MissingProvider:
-    """Base class for missing provider configurations.
-
-    Raises RuntimeError when instantiated to indicate missing dependencies.
-    """
-
-    provider: Literal["chromadb", "qdrant", "__missing__"] = field(
-        default="__missing__"
-    )
-
-    def __post_init__(self) -> None:
-        """Raises error indicating the provider is not installed."""
-        raise RuntimeError(
-            f"provider '{self.provider}' requested but not installed. "
-            f"Install the extra: `uv add crewai'[{self.provider}]'`."
-        )

src/crewai/rag/config/optional_imports/protocols.py

@@ -1,27 +0,0 @@
-"""Protocol definitions for RAG factory modules."""
-
-from __future__ import annotations
-
-from typing import Protocol, TYPE_CHECKING
-
-if TYPE_CHECKING:
-    from crewai.rag.chromadb.client import ChromaDBClient
-    from crewai.rag.chromadb.config import ChromaDBConfig
-    from crewai.rag.qdrant.client import QdrantClient
-    from crewai.rag.qdrant.config import QdrantConfig
-
-
-class ChromaFactoryModule(Protocol):
-    """Protocol for ChromaDB factory module."""
-
-    def create_client(self, config: ChromaDBConfig) -> ChromaDBClient:
-        """Creates a ChromaDB client from configuration."""
-        ...
-
-
-class QdrantFactoryModule(Protocol):
-    """Protocol for Qdrant factory module."""
-
-    def create_client(self, config: QdrantConfig) -> QdrantClient:
-        """Creates a Qdrant client from configuration."""
-        ...

src/crewai/rag/config/optional_imports/providers.py

@@ -1,22 +0,0 @@
-"""Provider-specific missing configuration classes."""
-
-from typing import Literal
-from dataclasses import field
-from pydantic import ConfigDict
-from pydantic.dataclasses import dataclass as pyd_dataclass
-
-from crewai.rag.config.optional_imports.base import _MissingProvider
-
-
-@pyd_dataclass(config=ConfigDict(extra="forbid"))
-class MissingChromaDBConfig(_MissingProvider):
-    """Placeholder for missing ChromaDB configuration."""
-
-    provider: Literal["chromadb"] = field(default="chromadb")
-
-
-@pyd_dataclass(config=ConfigDict(extra="forbid"))
-class MissingQdrantConfig(_MissingProvider):
-    """Placeholder for missing Qdrant configuration."""
-
-    provider: Literal["qdrant"] = field(default="qdrant")

src/crewai/rag/config/optional_imports/types.py

@@ -1,8 +0,0 @@
-"""Type definitions for optional imports."""
-
-from typing import Annotated, Literal
-
-SupportedProvider = Annotated[
-    Literal["chromadb", "qdrant"],
-    "Supported RAG provider types, add providers here as they become available",
-]

src/crewai/rag/config/types.py

@@ -1,34 +0,0 @@
-"""Type definitions for RAG configuration."""
-
-from typing import Annotated, TypeAlias, TYPE_CHECKING
-from pydantic import Field
-
-from crewai.rag.config.constants import DISCRIMINATOR
-
-# Linter freaks out on conditional imports, assigning in the type checking fixes it
-if TYPE_CHECKING:
-    from crewai.rag.chromadb.config import ChromaDBConfig as ChromaDBConfig_
-
-    ChromaDBConfig = ChromaDBConfig_
-    from crewai.rag.qdrant.config import QdrantConfig as QdrantConfig_
-
-    QdrantConfig = QdrantConfig_
-else:
-    try:
-        from crewai.rag.chromadb.config import ChromaDBConfig
-    except ImportError:
-        from crewai.rag.config.optional_imports.providers import (
-            MissingChromaDBConfig as ChromaDBConfig,
-        )
-
-    try:
-        from crewai.rag.qdrant.config import QdrantConfig
-    except ImportError:
-        from crewai.rag.config.optional_imports.providers import (
-            MissingQdrantConfig as QdrantConfig,
-        )
-
-SupportedProviderConfig: TypeAlias = ChromaDBConfig | QdrantConfig
-RagConfigType: TypeAlias = Annotated[
-    SupportedProviderConfig, Field(discriminator=DISCRIMINATOR)
-]

src/crewai/rag/config/utils.py

@@ -1,86 +0,0 @@
-"""RAG client configuration utilities."""
-
-from contextvars import ContextVar
-
-from pydantic import BaseModel, Field
-
-from crewai.utilities.import_utils import require
-from crewai.rag.core.base_client import BaseClient
-from crewai.rag.config.types import RagConfigType
-from crewai.rag.config.constants import (
-    DEFAULT_RAG_CONFIG_PATH,
-    DEFAULT_RAG_CONFIG_CLASS,
-)
-from crewai.rag.factory import create_client
-
-
-class RagContext(BaseModel):
-    """Context holding RAG configuration and client instance."""
-
-    config: RagConfigType = Field(..., description="RAG provider configuration")
-    client: BaseClient | None = Field(
-        default=None, description="Instantiated RAG client"
-    )
-
-
-_rag_context: ContextVar[RagContext | None] = ContextVar("_rag_context", default=None)
-
-
-def set_rag_config(config: RagConfigType) -> None:
-    """Set global RAG client configuration and instantiate the client.
-
-    Args:
-        config: The RAG client configuration (ChromaDBConfig).
-    """
-    client = create_client(config)
-    context = RagContext(config=config, client=client)
-    _rag_context.set(context)
-
-
-def get_rag_config() -> RagConfigType:
-    """Get current RAG configuration.
-
-    Returns:
-        The current RAG configuration object.
-    """
-    context = _rag_context.get()
-    if context is None:
-        module = require(DEFAULT_RAG_CONFIG_PATH, purpose="RAG configuration")
-        config_class = getattr(module, DEFAULT_RAG_CONFIG_CLASS)
-        default_config = config_class()
-        set_rag_config(default_config)
-        context = _rag_context.get()
-
-    if context is None or context.config is None:
-        raise ValueError(
-            "RAG configuration is not set. Please set the RAG config first."
-        )
-
-    return context.config
-
-
-def get_rag_client() -> BaseClient:
-    """Get the current RAG client instance.
-
-    Returns:
-        The current RAG client, creating one if needed.
-    """
-    context = _rag_context.get()
-    if context is None:
-        get_rag_config()
-        context = _rag_context.get()
-
-    if context and context.client is None:
-        context.client = create_client(context.config)
-
-    if context is None or context.client is None:
-        raise ValueError(
-            "RAG client is not configured. Please set the RAG config first."
-        )
-
-    return context.client
-
-
-def clear_rag_config() -> None:
-    """Clear the current RAG configuration and client, reverting to defaults."""
-    _rag_context.set(None)

src/crewai/rag/core/base_client.py

@@ -1,10 +1,8 @@
 """Protocol for vector database client implementations."""
 
 from abc import abstractmethod
-from typing import Any, Protocol, runtime_checkable, Annotated
+from typing import Any, Protocol, runtime_checkable, TypedDict, Annotated
-from typing_extensions import Unpack, Required, TypedDict
+from typing_extensions import Unpack, Required
-from pydantic import GetCoreSchemaHandler
-from pydantic_core import CoreSchema, core_schema
 
 
 from crewai.rag.types import (
@@ -98,17 +96,6 @@ class BaseClient(Protocol):
     client: Any
     embedding_function: EmbeddingFunction
 
-    @classmethod
-    def __get_pydantic_core_schema__(
-        cls, _source_type: Any, _handler: GetCoreSchemaHandler
-    ) -> CoreSchema:
-        """Generate Pydantic core schema for BaseClient Protocol.
-
-        This allows the Protocol to be used in Pydantic models without
-        requiring arbitrary_types_allowed=True.
-        """
-        return core_schema.any_schema()
-
     @abstractmethod
     def create_collection(self, **kwargs: Unpack[BaseCollectionParams]) -> None:
         """Create a new collection/index in the vector database.

src/crewai/rag/core/base_provider.py

@@ -0,0 +1,30 @@
+"""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/core/exceptions.py

@@ -1,26 +0,0 @@
-"""Core exceptions for RAG module."""
-
-
-class ClientMethodMismatchError(TypeError):
-    """Raised when a method is called with the wrong client type.
-
-    Typically used when a sync method is called with an async client,
-    or vice versa.
-    """
-
-    def __init__(
-        self, method_name: str, expected_client: str, alt_method: str, alt_client: str
-    ) -> None:
-        """Create a ClientMethodMismatchError.
-
-        Args:
-            method_name: Method that was called incorrectly.
-            expected_client: Required client type.
-            alt_method: Suggested alternative method.
-            alt_client: Client type for the alternative method.
-        """
-        message = (
-            f"Method {method_name}() requires a {expected_client}. "
-            f"Use {alt_method}() for {alt_client}."
-        )
-        super().__init__(message)

src/crewai/rag/factory.py

@@ -1,45 +0,0 @@
-"""Factory functions for creating RAG clients from configuration."""
-
-from typing import cast
-
-from crewai.rag.config.optional_imports.protocols import (
-    ChromaFactoryModule,
-    QdrantFactoryModule,
-)
-from crewai.rag.core.base_client import BaseClient
-from crewai.rag.config.types import RagConfigType
-from crewai.utilities.import_utils import require
-
-
-def create_client(config: RagConfigType) -> BaseClient:
-    """Create a client from configuration using the appropriate factory.
-
-    Args:
-        config: The RAG client configuration.
-
-    Returns:
-        The created client instance.
-
-    Raises:
-        ValueError: If the configuration provider is not supported.
-    """
-
-    if config.provider == "chromadb":
-        chromadb_mod = cast(
-            ChromaFactoryModule,
-            require(
-                "crewai.rag.chromadb.factory",
-                purpose="The 'chromadb' provider",
-            ),
-        )
-        return chromadb_mod.create_client(config)
-
-    if config.provider == "qdrant":
-        qdrant_mod = cast(
-            QdrantFactoryModule,
-            require(
-                "crewai.rag.qdrant.factory",
-                purpose="The 'qdrant' provider",
-            ),
-        )
-        return qdrant_mod.create_client(config)

src/crewai/rag/qdrant/__init__.py

@@ -1 +0,0 @@
-"""Qdrant vector database client implementation."""

src/crewai/rag/qdrant/client.py

@@ -1,501 +0,0 @@
-"""Qdrant client implementation."""
-
-from typing import Any, cast
-
-from typing_extensions import Unpack
-
-from crewai.rag.core.base_client import (
-    BaseClient,
-    BaseCollectionParams,
-    BaseCollectionAddParams,
-    BaseCollectionSearchParams,
-)
-from crewai.rag.core.exceptions import ClientMethodMismatchError
-from crewai.rag.qdrant.types import (
-    AsyncEmbeddingFunction,
-    EmbeddingFunction,
-    QdrantClientType,
-    QdrantCollectionCreateParams,
-)
-from crewai.rag.qdrant.utils import (
-    _is_async_client,
-    _is_async_embedding_function,
-    _is_sync_client,
-    _create_point_from_document,
-    _get_collection_params,
-    _prepare_search_params,
-    _process_search_results,
-)
-from crewai.rag.types import SearchResult
-
-
-class QdrantClient(BaseClient):
-    """Qdrant implementation of the BaseClient protocol.
-
-    Provides vector database operations for Qdrant, supporting both
-    synchronous and asynchronous clients.
-
-    Attributes:
-        client: Qdrant client instance (QdrantClient or AsyncQdrantClient).
-        embedding_function: Function to generate embeddings for documents.
-    """
-
-    def __init__(
-        self,
-        client: QdrantClientType,
-        embedding_function: EmbeddingFunction | AsyncEmbeddingFunction,
-    ) -> None:
-        """Initialize QdrantClient with client and embedding function.
-
-        Args:
-            client: Pre-configured Qdrant client instance.
-            embedding_function: Embedding function for text to vector conversion.
-        """
-        self.client = client
-        self.embedding_function = embedding_function
-
-    def create_collection(self, **kwargs: Unpack[QdrantCollectionCreateParams]) -> None:
-        """Create a new collection in Qdrant.
-
-        Keyword Args:
-            collection_name: Name of the collection to create. Must be unique.
-            vectors_config: Optional vector configuration. Defaults to 1536 dimensions with cosine distance.
-            sparse_vectors_config: Optional sparse vector configuration.
-            shard_number: Optional number of shards.
-            replication_factor: Optional replication factor.
-            write_consistency_factor: Optional write consistency factor.
-            on_disk_payload: Optional flag to store payload on disk.
-            hnsw_config: Optional HNSW index configuration.
-            optimizers_config: Optional optimizer configuration.
-            wal_config: Optional write-ahead log configuration.
-            quantization_config: Optional quantization configuration.
-            init_from: Optional collection to initialize from.
-            timeout: Optional timeout for the operation.
-
-        Raises:
-            ValueError: If collection with the same name already exists.
-            ConnectionError: If unable to connect to Qdrant server.
-        """
-        if not _is_sync_client(self.client):
-            raise ClientMethodMismatchError(
-                method_name="create_collection",
-                expected_client="QdrantClient",
-                alt_method="acreate_collection",
-                alt_client="AsyncQdrantClient",
-            )
-
-        collection_name = kwargs["collection_name"]
-
-        if self.client.collection_exists(collection_name):
-            raise ValueError(f"Collection '{collection_name}' already exists")
-
-        params = _get_collection_params(kwargs)
-        self.client.create_collection(**params)
-
-    async def acreate_collection(
-        self, **kwargs: Unpack[QdrantCollectionCreateParams]
-    ) -> None:
-        """Create a new collection in Qdrant asynchronously.
-
-        Keyword Args:
-            collection_name: Name of the collection to create. Must be unique.
-            vectors_config: Optional vector configuration. Defaults to 1536 dimensions with cosine distance.
-            sparse_vectors_config: Optional sparse vector configuration.
-            shard_number: Optional number of shards.
-            replication_factor: Optional replication factor.
-            write_consistency_factor: Optional write consistency factor.
-            on_disk_payload: Optional flag to store payload on disk.
-            hnsw_config: Optional HNSW index configuration.
-            optimizers_config: Optional optimizer configuration.
-            wal_config: Optional write-ahead log configuration.
-            quantization_config: Optional quantization configuration.
-            init_from: Optional collection to initialize from.
-            timeout: Optional timeout for the operation.
-
-        Raises:
-            ValueError: If collection with the same name already exists.
-            ConnectionError: If unable to connect to Qdrant server.
-        """
-        if not _is_async_client(self.client):
-            raise ClientMethodMismatchError(
-                method_name="acreate_collection",
-                expected_client="AsyncQdrantClient",
-                alt_method="create_collection",
-                alt_client="QdrantClient",
-            )
-
-        collection_name = kwargs["collection_name"]
-
-        if await self.client.collection_exists(collection_name):
-            raise ValueError(f"Collection '{collection_name}' already exists")
-
-        params = _get_collection_params(kwargs)
-        await self.client.create_collection(**params)
-
-    def get_or_create_collection(
-        self, **kwargs: Unpack[QdrantCollectionCreateParams]
-    ) -> Any:
-        """Get an existing collection or create it if it doesn't exist.
-
-        Keyword Args:
-            collection_name: Name of the collection to get or create.
-            vectors_config: Optional vector configuration. Defaults to 1536 dimensions with cosine distance.
-            sparse_vectors_config: Optional sparse vector configuration.
-            shard_number: Optional number of shards.
-            replication_factor: Optional replication factor.
-            write_consistency_factor: Optional write consistency factor.
-            on_disk_payload: Optional flag to store payload on disk.
-            hnsw_config: Optional HNSW index configuration.
-            optimizers_config: Optional optimizer configuration.
-            wal_config: Optional write-ahead log configuration.
-            quantization_config: Optional quantization configuration.
-            init_from: Optional collection to initialize from.
-            timeout: Optional timeout for the operation.
-
-        Returns:
-            Collection info dict with name and other metadata.
-
-        Raises:
-            ConnectionError: If unable to connect to Qdrant server.
-        """
-        if not _is_sync_client(self.client):
-            raise ClientMethodMismatchError(
-                method_name="get_or_create_collection",
-                expected_client="QdrantClient",
-                alt_method="aget_or_create_collection",
-                alt_client="AsyncQdrantClient",
-            )
-
-        collection_name = kwargs["collection_name"]
-
-        if self.client.collection_exists(collection_name):
-            return self.client.get_collection(collection_name)
-
-        params = _get_collection_params(kwargs)
-        self.client.create_collection(**params)
-
-        return self.client.get_collection(collection_name)
-
-    async def aget_or_create_collection(
-        self, **kwargs: Unpack[QdrantCollectionCreateParams]
-    ) -> Any:
-        """Get an existing collection or create it if it doesn't exist asynchronously.
-
-        Keyword Args:
-            collection_name: Name of the collection to get or create.
-            vectors_config: Optional vector configuration. Defaults to 1536 dimensions with cosine distance.
-            sparse_vectors_config: Optional sparse vector configuration.
-            shard_number: Optional number of shards.
-            replication_factor: Optional replication factor.
-            write_consistency_factor: Optional write consistency factor.
-            on_disk_payload: Optional flag to store payload on disk.
-            hnsw_config: Optional HNSW index configuration.
-            optimizers_config: Optional optimizer configuration.
-            wal_config: Optional write-ahead log configuration.
-            quantization_config: Optional quantization configuration.
-            init_from: Optional collection to initialize from.
-            timeout: Optional timeout for the operation.
-
-        Returns:
-            Collection info dict with name and other metadata.
-
-        Raises:
-            ConnectionError: If unable to connect to Qdrant server.
-        """
-        if not _is_async_client(self.client):
-            raise ClientMethodMismatchError(
-                method_name="aget_or_create_collection",
-                expected_client="AsyncQdrantClient",
-                alt_method="get_or_create_collection",
-                alt_client="QdrantClient",
-            )
-
-        collection_name = kwargs["collection_name"]
-
-        if await self.client.collection_exists(collection_name):
-            return await self.client.get_collection(collection_name)
-
-        params = _get_collection_params(kwargs)
-        await self.client.create_collection(**params)
-
-        return await self.client.get_collection(collection_name)
-
-    def add_documents(self, **kwargs: Unpack[BaseCollectionAddParams]) -> None:
-        """Add documents with their embeddings to a collection.
-
-        Keyword Args:
-            collection_name: The name of the collection to add documents to.
-            documents: List of BaseRecord dicts containing document data.
-
-        Raises:
-            ValueError: If collection doesn't exist or documents list is empty.
-            ConnectionError: If unable to connect to Qdrant server.
-        """
-        if not _is_sync_client(self.client):
-            raise ClientMethodMismatchError(
-                method_name="add_documents",
-                expected_client="QdrantClient",
-                alt_method="aadd_documents",
-                alt_client="AsyncQdrantClient",
-            )
-
-        collection_name = kwargs["collection_name"]
-        documents = kwargs["documents"]
-
-        if not documents:
-            raise ValueError("Documents list cannot be empty")
-
-        if not self.client.collection_exists(collection_name):
-            raise ValueError(f"Collection '{collection_name}' does not exist")
-
-        points = []
-        for doc in documents:
-            if _is_async_embedding_function(self.embedding_function):
-                raise TypeError(
-                    "Async embedding function cannot be used with sync add_documents. "
-                    "Use aadd_documents instead."
-                )
-            sync_fn = cast(EmbeddingFunction, self.embedding_function)
-            embedding = sync_fn(doc["content"])
-            point = _create_point_from_document(doc, embedding)
-            points.append(point)
-
-        self.client.upsert(collection_name=collection_name, points=points)
-
-    async def aadd_documents(self, **kwargs: Unpack[BaseCollectionAddParams]) -> None:
-        """Add documents with their embeddings to a collection asynchronously.
-
-        Keyword Args:
-            collection_name: The name of the collection to add documents to.
-            documents: List of BaseRecord dicts containing document data.
-
-        Raises:
-            ValueError: If collection doesn't exist or documents list is empty.
-            ConnectionError: If unable to connect to Qdrant server.
-        """
-        if not _is_async_client(self.client):
-            raise ClientMethodMismatchError(
-                method_name="aadd_documents",
-                expected_client="AsyncQdrantClient",
-                alt_method="add_documents",
-                alt_client="QdrantClient",
-            )
-
-        collection_name = kwargs["collection_name"]
-        documents = kwargs["documents"]
-
-        if not documents:
-            raise ValueError("Documents list cannot be empty")
-
-        if not await self.client.collection_exists(collection_name):
-            raise ValueError(f"Collection '{collection_name}' does not exist")
-
-        points = []
-        for doc in documents:
-            if _is_async_embedding_function(self.embedding_function):
-                async_fn = cast(AsyncEmbeddingFunction, self.embedding_function)
-                embedding = await async_fn(doc["content"])
-            else:
-                sync_fn = cast(EmbeddingFunction, self.embedding_function)
-                embedding = sync_fn(doc["content"])
-            point = _create_point_from_document(doc, embedding)
-            points.append(point)
-
-        await self.client.upsert(collection_name=collection_name, points=points)
-
-    def search(
-        self, **kwargs: Unpack[BaseCollectionSearchParams]
-    ) -> list[SearchResult]:
-        """Search for similar documents using a query.
-
-        Keyword Args:
-            collection_name: Name of the collection to search in.
-            query: The text query to search for.
-            limit: Maximum number of results to return (default: 10).
-            metadata_filter: Optional filter for metadata fields.
-            score_threshold: Optional minimum similarity score (0-1) for results.
-
-        Returns:
-            List of SearchResult dicts containing id, content, metadata, and score.
-
-        Raises:
-            ValueError: If collection doesn't exist.
-            ConnectionError: If unable to connect to Qdrant server.
-        """
-        if not _is_sync_client(self.client):
-            raise ClientMethodMismatchError(
-                method_name="search",
-                expected_client="QdrantClient",
-                alt_method="asearch",
-                alt_client="AsyncQdrantClient",
-            )
-
-        collection_name = kwargs["collection_name"]
-        query = kwargs["query"]
-        limit = kwargs.get("limit", 10)
-        metadata_filter = kwargs.get("metadata_filter")
-        score_threshold = kwargs.get("score_threshold")
-
-        if not self.client.collection_exists(collection_name):
-            raise ValueError(f"Collection '{collection_name}' does not exist")
-
-        if _is_async_embedding_function(self.embedding_function):
-            raise TypeError(
-                "Async embedding function cannot be used with sync search. "
-                "Use asearch instead."
-            )
-        sync_fn = cast(EmbeddingFunction, self.embedding_function)
-        query_embedding = sync_fn(query)
-
-        search_kwargs = _prepare_search_params(
-            collection_name=collection_name,
-            query_embedding=query_embedding,
-            limit=limit,
-            score_threshold=score_threshold,
-            metadata_filter=metadata_filter,
-        )
-
-        response = self.client.query_points(**search_kwargs)
-        return _process_search_results(response)
-
-    async def asearch(
-        self, **kwargs: Unpack[BaseCollectionSearchParams]
-    ) -> list[SearchResult]:
-        """Search for similar documents using a query asynchronously.
-
-        Keyword Args:
-            collection_name: Name of the collection to search in.
-            query: The text query to search for.
-            limit: Maximum number of results to return (default: 10).
-            metadata_filter: Optional filter for metadata fields.
-            score_threshold: Optional minimum similarity score (0-1) for results.
-
-        Returns:
-            List of SearchResult dicts containing id, content, metadata, and score.
-
-        Raises:
-            ValueError: If collection doesn't exist.
-            ConnectionError: If unable to connect to Qdrant server.
-        """
-        if not _is_async_client(self.client):
-            raise ClientMethodMismatchError(
-                method_name="asearch",
-                expected_client="AsyncQdrantClient",
-                alt_method="search",
-                alt_client="QdrantClient",
-            )
-
-        collection_name = kwargs["collection_name"]
-        query = kwargs["query"]
-        limit = kwargs.get("limit", 10)
-        metadata_filter = kwargs.get("metadata_filter")
-        score_threshold = kwargs.get("score_threshold")
-
-        if not await self.client.collection_exists(collection_name):
-            raise ValueError(f"Collection '{collection_name}' does not exist")
-
-        if _is_async_embedding_function(self.embedding_function):
-            async_fn = cast(AsyncEmbeddingFunction, self.embedding_function)
-            query_embedding = await async_fn(query)
-        else:
-            sync_fn = cast(EmbeddingFunction, self.embedding_function)
-            query_embedding = sync_fn(query)
-
-        search_kwargs = _prepare_search_params(
-            collection_name=collection_name,
-            query_embedding=query_embedding,
-            limit=limit,
-            score_threshold=score_threshold,
-            metadata_filter=metadata_filter,
-        )
-
-        response = await self.client.query_points(**search_kwargs)
-        return _process_search_results(response)
-
-    def delete_collection(self, **kwargs: Unpack[BaseCollectionParams]) -> None:
-        """Delete a collection and all its data.
-
-        Keyword Args:
-            collection_name: Name of the collection to delete.
-
-        Raises:
-            ValueError: If collection doesn't exist.
-            ConnectionError: If unable to connect to Qdrant server.
-        """
-        if not _is_sync_client(self.client):
-            raise ClientMethodMismatchError(
-                method_name="delete_collection",
-                expected_client="QdrantClient",
-                alt_method="adelete_collection",
-                alt_client="AsyncQdrantClient",
-            )
-
-        collection_name = kwargs["collection_name"]
-
-        if not self.client.collection_exists(collection_name):
-            raise ValueError(f"Collection '{collection_name}' does not exist")
-
-        self.client.delete_collection(collection_name=collection_name)
-
-    async def adelete_collection(self, **kwargs: Unpack[BaseCollectionParams]) -> None:
-        """Delete a collection and all its data asynchronously.
-
-        Keyword Args:
-            collection_name: Name of the collection to delete.
-
-        Raises:
-            ValueError: If collection doesn't exist.
-            ConnectionError: If unable to connect to Qdrant server.
-        """
-        if not _is_async_client(self.client):
-            raise ClientMethodMismatchError(
-                method_name="adelete_collection",
-                expected_client="AsyncQdrantClient",
-                alt_method="delete_collection",
-                alt_client="QdrantClient",
-            )
-
-        collection_name = kwargs["collection_name"]
-
-        if not await self.client.collection_exists(collection_name):
-            raise ValueError(f"Collection '{collection_name}' does not exist")
-
-        await self.client.delete_collection(collection_name=collection_name)
-
-    def reset(self) -> None:
-        """Reset the vector database by deleting all collections and data.
-
-        Raises:
-            ConnectionError: If unable to connect to Qdrant server.
-        """
-        if not _is_sync_client(self.client):
-            raise ClientMethodMismatchError(
-                method_name="reset",
-                expected_client="QdrantClient",
-                alt_method="areset",
-                alt_client="AsyncQdrantClient",
-            )
-
-        collections_response = self.client.get_collections()
-
-        for collection in collections_response.collections:
-            self.client.delete_collection(collection_name=collection.name)
-
-    async def areset(self) -> None:
-        """Reset the vector database by deleting all collections and data asynchronously.
-
-        Raises:
-            ConnectionError: If unable to connect to Qdrant server.
-        """
-        if not _is_async_client(self.client):
-            raise ClientMethodMismatchError(
-                method_name="areset",
-                expected_client="AsyncQdrantClient",
-                alt_method="reset",
-                alt_client="QdrantClient",
-            )
-
-        collections_response = await self.client.get_collections()
-
-        for collection in collections_response.collections:
-            await self.client.delete_collection(collection_name=collection.name)

src/crewai/rag/qdrant/config.py

@@ -1,54 +0,0 @@
-"""Qdrant configuration model."""
-
-from dataclasses import field
-from typing import Literal, cast
-from pydantic.dataclasses import dataclass as pyd_dataclass
-
-from crewai.rag.config.base import BaseRagConfig
-from crewai.rag.qdrant.types import QdrantClientParams, QdrantEmbeddingFunctionWrapper
-from crewai.rag.qdrant.constants import DEFAULT_EMBEDDING_MODEL, DEFAULT_STORAGE_PATH
-
-
-def _default_options() -> QdrantClientParams:
-    """Create default Qdrant client options.
-
-    Returns:
-        Default options with file-based storage.
-    """
-    return QdrantClientParams(path=DEFAULT_STORAGE_PATH)
-
-
-def _default_embedding_function() -> QdrantEmbeddingFunctionWrapper:
-    """Create default Qdrant embedding function.
-
-    Returns:
-        Default embedding function using fastembed with all-MiniLM-L6-v2.
-    """
-    from fastembed import TextEmbedding
-
-    model = TextEmbedding(model_name=DEFAULT_EMBEDDING_MODEL)
-
-    def embed_fn(text: str) -> list[float]:
-        """Embed a single text string.
-
-        Args:
-            text: Text to embed.
-
-        Returns:
-            Embedding vector as list of floats.
-        """
-        embeddings = list(model.embed([text]))
-        return embeddings[0].tolist() if embeddings else []
-
-    return cast(QdrantEmbeddingFunctionWrapper, embed_fn)
-
-
-@pyd_dataclass(frozen=True)
-class QdrantConfig(BaseRagConfig):
-    """Configuration for Qdrant client."""
-
-    provider: Literal["qdrant"] = field(default="qdrant", init=False)
-    options: QdrantClientParams = field(default_factory=_default_options)
-    embedding_function: QdrantEmbeddingFunctionWrapper = field(
-        default_factory=_default_embedding_function
-    )

src/crewai/rag/qdrant/constants.py

@@ -1,12 +0,0 @@
-"""Constants for Qdrant implementation."""
-
-import os
-from typing import Final
-
-from qdrant_client.models import Distance, VectorParams
-
-from crewai.utilities.paths import db_storage_path
-
-DEFAULT_VECTOR_PARAMS: Final = VectorParams(size=384, distance=Distance.COSINE)
-DEFAULT_EMBEDDING_MODEL: Final[str] = "sentence-transformers/all-MiniLM-L6-v2"
-DEFAULT_STORAGE_PATH: Final[str] = os.path.join(db_storage_path(), "qdrant")

src/crewai/rag/qdrant/factory.py

@@ -1,21 +0,0 @@
-"""Factory functions for creating Qdrant clients from configuration."""
-
-from qdrant_client import QdrantClient as SyncQdrantClientBase
-from crewai.rag.qdrant.client import QdrantClient
-from crewai.rag.qdrant.config import QdrantConfig
-
-
-def create_client(config: QdrantConfig) -> QdrantClient:
-    """Create a Qdrant client from configuration.
-
-    Args:
-        config: The Qdrant configuration.
-
-    Returns:
-        A configured QdrantClient instance.
-    """
-
-    qdrant_client = SyncQdrantClientBase(**config.options)
-    return QdrantClient(
-        client=qdrant_client, embedding_function=config.embedding_function
-    )

src/crewai/rag/qdrant/types.py

@@ -1,155 +0,0 @@
-"""Type definitions specific to Qdrant implementation."""
-
-from collections.abc import Awaitable, Callable
-from typing import Annotated, Any, Protocol, TypeAlias
-from typing_extensions import NotRequired, TypedDict
-
-import numpy as np
-from pydantic import GetCoreSchemaHandler
-from pydantic_core import CoreSchema, core_schema
-from qdrant_client import AsyncQdrantClient, QdrantClient as SyncQdrantClient
-from qdrant_client.models import (
-    FieldCondition,
-    Filter,
-    HasIdCondition,
-    HasVectorCondition,
-    HnswConfigDiff,
-    InitFrom,
-    IsEmptyCondition,
-    IsNullCondition,
-    NestedCondition,
-    OptimizersConfigDiff,
-    QuantizationConfig,
-    ShardingMethod,
-    SparseVectorsConfig,
-    VectorsConfig,
-    WalConfigDiff,
-)
-
-from crewai.rag.core.base_client import BaseCollectionParams
-
-QdrantClientType = SyncQdrantClient | AsyncQdrantClient
-
-QueryEmbedding: TypeAlias = list[float] | np.ndarray[Any, np.dtype[np.floating[Any]]]
-
-BasicConditions = FieldCondition | IsEmptyCondition | IsNullCondition
-StructuralConditions = HasIdCondition | HasVectorCondition | NestedCondition
-FilterCondition = BasicConditions | StructuralConditions | Filter
-
-MetadataFilterValue = bool | int | str
-MetadataFilter = dict[str, MetadataFilterValue]
-
-
-class EmbeddingFunction(Protocol):
-    """Protocol for embedding functions that convert text to vectors."""
-
-    def __call__(self, text: str) -> QueryEmbedding:
-        """Convert text to embedding vector.
-
-        Args:
-            text: Input text to embed.
-
-        Returns:
-            Embedding vector as list of floats or numpy array.
-        """
-        ...
-
-
-class QdrantEmbeddingFunctionWrapper(EmbeddingFunction):
-    """Base class for Qdrant EmbeddingFunction to work with Pydantic validation."""
-
-    @classmethod
-    def __get_pydantic_core_schema__(
-        cls, _source_type: Any, _handler: GetCoreSchemaHandler
-    ) -> CoreSchema:
-        """Generate Pydantic core schema for Qdrant EmbeddingFunction.
-
-        This allows Pydantic to handle Qdrant's EmbeddingFunction type
-        without requiring arbitrary_types_allowed=True.
-        """
-        return core_schema.any_schema()
-
-
-class AsyncEmbeddingFunction(Protocol):
-    """Protocol for async embedding functions that convert text to vectors."""
-
-    async def __call__(self, text: str) -> QueryEmbedding:
-        """Convert text to embedding vector asynchronously.
-
-        Args:
-            text: Input text to embed.
-
-        Returns:
-            Embedding vector as list of floats or numpy array.
-        """
-        ...
-
-
-class QdrantClientParams(TypedDict, total=False):
-    """Parameters for QdrantClient initialization.
-
-    Notes:
-        Need to implement in factory or remove.
-    """
-
-    location: str | None
-    url: str | None
-    port: int
-    grpc_port: int
-    prefer_grpc: bool
-    https: bool | None
-    api_key: str | None
-    prefix: str | None
-    timeout: int | None
-    host: str | None
-    path: str | None
-    force_disable_check_same_thread: bool
-    grpc_options: dict[str, Any] | None
-    auth_token_provider: Callable[[], str] | Callable[[], Awaitable[str]] | None
-    cloud_inference: bool
-    local_inference_batch_size: int | None
-    check_compatibility: bool
-
-
-class CommonCreateFields(TypedDict, total=False):
-    """Fields shared between high-level and direct create_collection params."""
-
-    vectors_config: VectorsConfig
-    sparse_vectors_config: SparseVectorsConfig
-    shard_number: Annotated[int, "Number of shards (default: 1)"]
-    sharding_method: ShardingMethod
-    replication_factor: Annotated[int, "Number of replicas per shard (default: 1)"]
-    write_consistency_factor: Annotated[int, "Await N replicas on write (default: 1)"]
-    on_disk_payload: Annotated[bool, "Store payload on disk instead of RAM"]
-    hnsw_config: HnswConfigDiff
-    optimizers_config: OptimizersConfigDiff
-    wal_config: WalConfigDiff
-    quantization_config: QuantizationConfig
-    init_from: InitFrom | str
-    timeout: Annotated[int, "Operation timeout in seconds"]
-
-
-class QdrantCollectionCreateParams(
-    BaseCollectionParams, CommonCreateFields, total=False
-):
-    """High-level parameters for creating a Qdrant collection."""
-
-    pass
-
-
-class CreateCollectionParams(CommonCreateFields, total=False):
-    """Parameters for qdrant_client.create_collection."""
-
-    collection_name: str
-
-
-class PreparedSearchParams(TypedDict):
-    """Type definition for prepared Qdrant search parameters."""
-
-    collection_name: str
-    query: list[float]
-    limit: Annotated[int, "Max results to return"]
-    with_payload: Annotated[bool, "Include payload in results"]
-    with_vectors: Annotated[bool, "Include vectors in results"]
-    score_threshold: NotRequired[Annotated[float, "Min similarity score (0-1)"]]
-    query_filter: NotRequired[Filter]

src/crewai/rag/qdrant/utils.py

@@ -1,228 +0,0 @@
-"""Utility functions for Qdrant operations."""
-
-import asyncio
-from typing import TypeGuard
-from uuid import uuid4
-
-from qdrant_client import AsyncQdrantClient, QdrantClient as SyncQdrantClient
-from qdrant_client.models import (
-    FieldCondition,
-    Filter,
-    MatchValue,
-    PointStruct,
-    QueryResponse,
-)
-
-from crewai.rag.qdrant.constants import DEFAULT_VECTOR_PARAMS
-from crewai.rag.qdrant.types import (
-    AsyncEmbeddingFunction,
-    CreateCollectionParams,
-    EmbeddingFunction,
-    FilterCondition,
-    MetadataFilter,
-    PreparedSearchParams,
-    QdrantClientType,
-    QdrantCollectionCreateParams,
-    QueryEmbedding,
-)
-from crewai.rag.types import SearchResult, BaseRecord
-
-
-def _ensure_list_embedding(embedding: QueryEmbedding) -> list[float]:
-    """Convert embedding to list[float] format if needed.
-
-    Args:
-        embedding: Embedding vector as list or numpy array.
-
-    Returns:
-        Embedding as list[float].
-    """
-    if not isinstance(embedding, list):
-        return embedding.tolist()
-    return embedding
-
-
-def _is_sync_client(client: QdrantClientType) -> TypeGuard[SyncQdrantClient]:
-    """Type guard to check if the client is a synchronous QdrantClient.
-
-    Args:
-        client: The client to check.
-
-    Returns:
-        True if the client is a QdrantClient, False otherwise.
-    """
-    return isinstance(client, SyncQdrantClient)
-
-
-def _is_async_client(client: QdrantClientType) -> TypeGuard[AsyncQdrantClient]:
-    """Type guard to check if the client is an asynchronous AsyncQdrantClient.
-
-    Args:
-        client: The client to check.
-
-    Returns:
-        True if the client is an AsyncQdrantClient, False otherwise.
-    """
-    return isinstance(client, AsyncQdrantClient)
-
-
-def _is_async_embedding_function(
-    func: EmbeddingFunction | AsyncEmbeddingFunction,
-) -> TypeGuard[AsyncEmbeddingFunction]:
-    """Type guard to check if the embedding function is async.
-
-    Args:
-        func: The embedding function to check.
-
-    Returns:
-        True if the function is async, False otherwise.
-    """
-    return asyncio.iscoroutinefunction(func)
-
-
-def _get_collection_params(
-    kwargs: QdrantCollectionCreateParams,
-) -> CreateCollectionParams:
-    """Extract collection creation parameters from kwargs."""
-    params: CreateCollectionParams = {
-        "collection_name": kwargs["collection_name"],
-        "vectors_config": kwargs.get("vectors_config", DEFAULT_VECTOR_PARAMS),
-    }
-
-    if "sparse_vectors_config" in kwargs:
-        params["sparse_vectors_config"] = kwargs["sparse_vectors_config"]
-    if "shard_number" in kwargs:
-        params["shard_number"] = kwargs["shard_number"]
-    if "sharding_method" in kwargs:
-        params["sharding_method"] = kwargs["sharding_method"]
-    if "replication_factor" in kwargs:
-        params["replication_factor"] = kwargs["replication_factor"]
-    if "write_consistency_factor" in kwargs:
-        params["write_consistency_factor"] = kwargs["write_consistency_factor"]
-    if "on_disk_payload" in kwargs:
-        params["on_disk_payload"] = kwargs["on_disk_payload"]
-    if "hnsw_config" in kwargs:
-        params["hnsw_config"] = kwargs["hnsw_config"]
-    if "optimizers_config" in kwargs:
-        params["optimizers_config"] = kwargs["optimizers_config"]
-    if "wal_config" in kwargs:
-        params["wal_config"] = kwargs["wal_config"]
-    if "quantization_config" in kwargs:
-        params["quantization_config"] = kwargs["quantization_config"]
-    if "init_from" in kwargs:
-        params["init_from"] = kwargs["init_from"]
-    if "timeout" in kwargs:
-        params["timeout"] = kwargs["timeout"]
-
-    return params
-
-
-def _prepare_search_params(
-    collection_name: str,
-    query_embedding: QueryEmbedding,
-    limit: int,
-    score_threshold: float | None,
-    metadata_filter: MetadataFilter | None,
-) -> PreparedSearchParams:
-    """Prepare search parameters for Qdrant query_points.
-
-    Args:
-        collection_name: Name of the collection to search.
-        query_embedding: Embedding vector for the query.
-        limit: Maximum number of results.
-        score_threshold: Optional minimum similarity score.
-        metadata_filter: Optional metadata filters.
-
-    Returns:
-        Dictionary of parameters for query_points method.
-    """
-    query_vector = _ensure_list_embedding(query_embedding)
-
-    search_kwargs: PreparedSearchParams = {
-        "collection_name": collection_name,
-        "query": query_vector,
-        "limit": limit,
-        "with_payload": True,
-        "with_vectors": False,
-    }
-
-    if score_threshold is not None:
-        search_kwargs["score_threshold"] = score_threshold
-
-    if metadata_filter:
-        filter_conditions: list[FilterCondition] = []
-        for key, value in metadata_filter.items():
-            filter_conditions.append(
-                FieldCondition(key=key, match=MatchValue(value=value))
-            )
-
-        search_kwargs["query_filter"] = Filter(must=filter_conditions)
-
-    return search_kwargs
-
-
-def _normalize_qdrant_score(score: float) -> float:
-    """Normalize Qdrant cosine similarity score to [0, 1] range.
-
-    Converts from Qdrant's [-1, 1] cosine similarity range to [0, 1] range for standardization across clients.
-
-    Args:
-        score: Raw cosine similarity score from Qdrant [-1, 1].
-
-    Returns:
-        Normalized score in [0, 1] range where 1 is most similar.
-    """
-    normalized = (score + 1.0) / 2.0
-    return max(0.0, min(1.0, normalized))
-
-
-def _process_search_results(response: QueryResponse) -> list[SearchResult]:
-    """Process Qdrant search response into SearchResult format.
-
-    Args:
-        response: Response from Qdrant query_points method.
-
-    Returns:
-        List of SearchResult dictionaries.
-    """
-    results: list[SearchResult] = []
-    for point in response.points:
-        payload = point.payload or {}
-        score = _normalize_qdrant_score(score=point.score)
-        result: SearchResult = {
-            "id": str(point.id),
-            "content": payload.get("content", ""),
-            "metadata": {k: v for k, v in payload.items() if k != "content"},
-            "score": score,
-        }
-        results.append(result)
-
-    return results
-
-
-def _create_point_from_document(
-    doc: BaseRecord, embedding: QueryEmbedding
-) -> PointStruct:
-    """Create a PointStruct from a document and its embedding.
-
-    Args:
-        doc: Document dictionary containing content, metadata, and optional doc_id.
-        embedding: The embedding vector for the document content.
-
-    Returns:
-        PointStruct ready to be upserted to Qdrant.
-    """
-    doc_id = doc.get("doc_id", str(uuid4()))
-    vector = _ensure_list_embedding(embedding)
-
-    metadata = doc.get("metadata", {})
-    if isinstance(metadata, list):
-        metadata = metadata[0] if metadata else {}
-    elif not isinstance(metadata, dict):
-        metadata = dict(metadata) if metadata else {}
-
-    return PointStruct(
-        id=doc_id,
-        vector=vector,
-        payload={"content": doc["content"], **metadata},
-    )

src/crewai/rag/types.py

@@ -1,9 +1,9 @@
 """Type definitions for RAG (Retrieval-Augmented Generation) systems."""
 
 from collections.abc import Callable, Mapping
-from typing import TypeAlias, Any
+from typing import TypeAlias, TypedDict, Any
 
-from typing_extensions import Required, TypedDict
+from typing_extensions import Required
 
 
 class BaseRecord(TypedDict, total=False):

src/crewai/task.py

@@ -4,7 +4,6 @@ import json
 import logging
 import threading
 import uuid
-import warnings
 from concurrent.futures import Future
 from copy import copy
 from hashlib import md5
@@ -158,13 +157,8 @@ class Task(BaseModel):
         default=None,
         description="Function or string description of a guardrail to validate task output before proceeding to next task",
     )
-    max_retries: Optional[int] = Field(
+    max_retries: int = Field(
-        default=None,
+        default=3, description="Maximum number of retries when guardrail fails"
-        description="[DEPRECATED] Maximum number of retries when guardrail fails. Use guardrail_max_retries instead. Will be removed in v1.0.0"
-    )
-    guardrail_max_retries: int = Field(
-        default=3,
-        description="Maximum number of retries when guardrail fails"
     )
     retry_count: int = Field(default=0, description="Current number of retries")
     start_time: Optional[datetime.datetime] = Field(
@@ -360,18 +354,6 @@ class Task(BaseModel):
             )
         return self
 
-    @model_validator(mode="after")
-    def handle_max_retries_deprecation(self):
-        if self.max_retries is not None:
-            warnings.warn(
-                "The 'max_retries' parameter is deprecated and will be removed in CrewAI v1.0.0. "
-                "Please use 'guardrail_max_retries' instead.",
-                DeprecationWarning,
-                stacklevel=2
-            )
-            self.guardrail_max_retries = self.max_retries
-        return self
-
     def execute_sync(
         self,
         agent: Optional[BaseAgent] = None,
@@ -451,7 +433,7 @@ class Task(BaseModel):
 
             pydantic_output, json_output = self._export_output(result)
             task_output = TaskOutput(
-                name=self.name or self.description,
+                name=self.name,
                 description=self.description,
                 expected_output=self.expected_output,
                 raw=result,
@@ -468,9 +450,9 @@ class Task(BaseModel):
                     retry_count=self.retry_count,
                 )
                 if not guardrail_result.success:
-                    if self.retry_count >= self.guardrail_max_retries:
+                    if self.retry_count >= self.max_retries:
                         raise Exception(
-                            f"Task failed guardrail validation after {self.guardrail_max_retries} retries. "
+                            f"Task failed guardrail validation after {self.max_retries} retries. "
                             f"Last error: {guardrail_result.error}"
                         )
 
@@ -579,8 +561,8 @@ class Task(BaseModel):
         should_inject = self.allow_crewai_trigger_context
 
         if should_inject and self.agent:
-            crew = getattr(self.agent, "crew", None)
+            crew = getattr(self.agent, 'crew', None)
-            if crew and hasattr(crew, "_inputs") and crew._inputs:
+            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}"
@@ -798,9 +780,7 @@ Follow these guidelines:
             if self.create_directory and not directory.exists():
                 directory.mkdir(parents=True, exist_ok=True)
             elif not self.create_directory and not directory.exists():
-                raise RuntimeError(
+                raise RuntimeError(f"Directory {directory} does not exist and create_directory is False")
-                    f"Directory {directory} does not exist and create_directory is False"
-                )
 
             with resolved_path.open("w", encoding="utf-8") as file:
                 if isinstance(result, dict):

src/crewai/tools/structured_tool.py

@@ -16,12 +16,6 @@ if TYPE_CHECKING:
     from crewai.tools.base_tool import BaseTool
 
 
-class ToolUsageLimitExceeded(Exception):
-    """Exception raised when a tool has reached its maximum usage limit."""
-
-    pass
-
-
 class CrewStructuredTool:
     """A structured tool that can operate on any number of inputs.
 
@@ -233,25 +227,17 @@ class CrewStructuredTool:
         """
         parsed_args = self._parse_args(input)
 
-        if self.has_reached_max_usage_count():
-            raise ToolUsageLimitExceeded(
-                f"Tool '{self.name}' has reached its maximum usage limit of {self.max_usage_count}. You should not use the {self.name} tool again."
-            )
-
         self._increment_usage_count()
 
-        try:
+        if inspect.iscoroutinefunction(self.func):
-            if inspect.iscoroutinefunction(self.func):
+            return await self.func(**parsed_args, **kwargs)
-                return await self.func(**parsed_args, **kwargs)
+        else:
-            else:
+            # Run sync functions in a thread pool
-                # Run sync functions in a thread pool
+            import asyncio
-                import asyncio
 
-                return await asyncio.get_event_loop().run_in_executor(
+            return await asyncio.get_event_loop().run_in_executor(
-                    None, lambda: self.func(**parsed_args, **kwargs)
+                None, lambda: self.func(**parsed_args, **kwargs)
-                )
+            )
-        except Exception:
-            raise
 
     def _run(self, *args, **kwargs) -> Any:
         """Legacy method for compatibility."""
@@ -266,22 +252,12 @@ class CrewStructuredTool:
         """Main method for tool execution."""
         parsed_args = self._parse_args(input)
 
-        if self.has_reached_max_usage_count():
-            raise ToolUsageLimitExceeded(
-                f"Tool '{self.name}' has reached its maximum usage limit of {self.max_usage_count}. You should not use the {self.name} tool again."
-            )
-
         self._increment_usage_count()
 
         if inspect.iscoroutinefunction(self.func):
             result = asyncio.run(self.func(**parsed_args, **kwargs))
             return result
 
-        try:
-            result = self.func(**parsed_args, **kwargs)
-        except Exception:
-            raise
-
         result = self.func(**parsed_args, **kwargs)
 
         if asyncio.iscoroutine(result):
@@ -289,13 +265,6 @@ class CrewStructuredTool:
 
         return result
 
-    def has_reached_max_usage_count(self) -> bool:
-        """Check if the tool has reached its maximum usage count."""
-        return (
-            self.max_usage_count is not None
-            and self.current_usage_count >= self.max_usage_count
-        )
-
     def _increment_usage_count(self) -> None:
         """Increment the usage count."""
         self.current_usage_count += 1

src/crewai/tools/tool_usage.py

@@ -178,11 +178,9 @@ class ToolUsage:
 
             if self.agent.fingerprint:
                 event_data.update(self.agent.fingerprint)
-            if self.task:
-                event_data["task_name"] = self.task.name or self.task.description
-                event_data["task_id"] = str(self.task.id)
-            crewai_event_bus.emit(self, ToolUsageStartedEvent(**event_data))
 
+            crewai_event_bus.emit(self,ToolUsageStartedEvent(**event_data))
+            
         started_at = time.time()
         from_cache = False
         result = None  # type: ignore
@@ -313,15 +311,12 @@ class ToolUsage:
         if self.agent and hasattr(self.agent, "tools_results"):
             self.agent.tools_results.append(data)
 
-        if available_tool and hasattr(available_tool, "current_usage_count"):
+        if available_tool and hasattr(available_tool, 'current_usage_count'):
             available_tool.current_usage_count += 1
-            if (
+            if hasattr(available_tool, 'max_usage_count') and available_tool.max_usage_count is not None:
-                hasattr(available_tool, "max_usage_count")
-                and available_tool.max_usage_count is not None
-            ):
                 self._printer.print(
                     content=f"Tool '{available_tool.name}' usage: {available_tool.current_usage_count}/{available_tool.max_usage_count}",
-                    color="blue",
+                    color="blue"
                 )
 
         return result
@@ -355,20 +350,20 @@ class ToolUsage:
                 calling.arguments == last_tool_usage.arguments
             )
         return False
-
+        
     def _check_usage_limit(self, tool: Any, tool_name: str) -> str | None:
         """Check if tool has reached its usage limit.
-
+        
         Args:
             tool: The tool to check
             tool_name: The name of the tool (used for error message)
-
+            
         Returns:
             Error message if limit reached, None otherwise
         """
         if (
-            hasattr(tool, "max_usage_count")
+            hasattr(tool, 'max_usage_count') 
-            and tool.max_usage_count is not None
+            and tool.max_usage_count is not None 
             and tool.current_usage_count >= tool.max_usage_count
         ):
             return f"Tool '{tool_name}' has reached its usage limit of {tool.max_usage_count} times and cannot be used anymore."
@@ -610,9 +605,6 @@ class ToolUsage:
                 "output": result,
             }
         )
-        if self.task:
-            event_data["task_id"] = str(self.task.id)
-            event_data["task_name"] = self.task.name or self.task.description
         crewai_event_bus.emit(self, ToolUsageFinishedEvent(**event_data))
 
     def _prepare_event_data(

src/crewai/utilities/events/base_events.py

@@ -11,9 +11,7 @@ class BaseEvent(BaseModel):
     timestamp: datetime = Field(default_factory=lambda: datetime.now(timezone.utc))
     type: str
     source_fingerprint: Optional[str] = None  # UUID string of the source entity
-    source_type: Optional[str] = (
+    source_type: Optional[str] = None  # "agent", "task", "crew", "memory", "entity_memory", "short_term_memory", "long_term_memory", "external_memory"
-        None  # "agent", "task", "crew", "memory", "entity_memory", "short_term_memory", "long_term_memory", "external_memory"
-    )
     fingerprint_metadata: Optional[Dict[str, Any]] = None  # Any relevant metadata
 
     def to_json(self, exclude: set[str] | None = None):
@@ -27,20 +25,3 @@ class BaseEvent(BaseModel):
             dict: A JSON-serializable dictionary.
         """
         return to_serializable(self, exclude=exclude)
-
-    def _set_task_params(self, data: Dict[str, Any]):
-        if "from_task" in data and (task := data["from_task"]):
-            self.task_id = task.id
-            self.task_name = task.name or task.description
-            self.from_task = None
-
-    def _set_agent_params(self, data: Dict[str, Any]):
-        task = data.get("from_task", None)
-        agent = task.agent if task else data.get("from_agent", None)
-
-        if not agent:
-            return
-
-        self.agent_id = agent.id
-        self.agent_role = agent.role
-        self.from_agent = None

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

@@ -41,21 +41,18 @@ class TraceBatchManager:
     """Single responsibility: Manage batches and event buffering"""
 
     is_current_batch_ephemeral: bool = False
-    trace_batch_id: Optional[str] = None
-    current_batch: Optional[TraceBatch] = None
-    event_buffer: List[TraceEvent] = []
-    execution_start_times: Dict[str, datetime] = {}
-    batch_owner_type: Optional[str] = None
-    batch_owner_id: Optional[str] = None
 
     def __init__(self):
         try:
-            self.plus_api = PlusAPI(
+            self.plus_api = PlusAPI(api_key=get_auth_token())
-                api_key=get_auth_token(),
-            )
         except AuthError:
             self.plus_api = PlusAPI(api_key="")
 
+        self.trace_batch_id: Optional[str] = None  # Backend ID
+        self.current_batch: Optional[TraceBatch] = None
+        self.event_buffer: List[TraceEvent] = []
+        self.execution_start_times: Dict[str, datetime] = {}
+
     def initialize_batch(
         self,
         user_context: Dict[str, str],
@@ -116,13 +113,7 @@ class TraceBatchManager:
                 else self.plus_api.initialize_trace_batch(payload)
             )
 
-            if response is None:
+            if response.status_code == 201 or response.status_code == 200:
-                logger.warning(
-                    "Trace batch initialization failed gracefully. Continuing without tracing."
-                )
-                return
-
-            if response.status_code in [201, 200]:
                 response_data = response.json()
                 self.trace_batch_id = (
                     response_data["trace_id"]
@@ -137,21 +128,19 @@ class TraceBatchManager:
                 )
                 console.print(panel)
             else:
-                logger.warning(
+                logger.error(
-                    f"Trace batch initialization returned status {response.status_code}. Continuing without tracing."
+                    f"❌ Failed to initialize trace batch: {response.status_code} - {response.text}"
                 )
 
         except Exception as e:
-            logger.warning(
+            logger.error(f"❌ Error initializing trace batch: {str(e)}")
-                f"Error initializing trace batch: {str(e)}. Continuing without tracing."
-            )
 
     def add_event(self, trace_event: TraceEvent):
         """Add event to buffer"""
         self.event_buffer.append(trace_event)
 
     def _send_events_to_backend(self):
-        """Send buffered events to backend with graceful failure handling"""
+        """Send buffered events to backend"""
         if not self.plus_api or not self.trace_batch_id or not self.event_buffer:
             return
 
@@ -165,42 +154,37 @@ class TraceBatchManager:
                 },
             }
 
+            if not self.trace_batch_id:
+                raise Exception("❌ Trace batch ID not found")
+
             response = (
                 self.plus_api.send_ephemeral_trace_events(self.trace_batch_id, payload)
                 if self.is_current_batch_ephemeral
                 else self.plus_api.send_trace_events(self.trace_batch_id, payload)
             )
 
-            if response is None:
+            if response.status_code == 200 or response.status_code == 201:
-                logger.warning("Failed to send trace events. Events will be lost.")
-                return
-
-            if response.status_code in [200, 201]:
                 self.event_buffer.clear()
             else:
-                logger.warning(
+                logger.error(
-                    f"Failed to send events: {response.status_code}. Events will be lost."
+                    f"❌ Failed to send events: {response.status_code} - {response.text}"
                 )
 
         except Exception as e:
-            logger.warning(
+            logger.error(f"❌ Error sending events to backend: {str(e)}")
-                f"Error sending events to backend: {str(e)}. Events will be lost."
-            )
 
     def finalize_batch(self) -> Optional[TraceBatch]:
         """Finalize batch and return it for sending"""
         if not self.current_batch:
             return None
 
-        self.current_batch.events = self.event_buffer.copy()
         if self.event_buffer:
             self._send_events_to_backend()
         self._finalize_backend_batch()
 
-        finalized_batch = self.current_batch
+        self.current_batch.events = self.event_buffer.copy()
 
-        self.batch_owner_type = None
+        finalized_batch = self.current_batch
-        self.batch_owner_id = None
 
         self.current_batch = None
         self.event_buffer.clear()

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

@@ -75,18 +75,10 @@ class TraceCollectionListener(BaseEventListener):
     Trace collection listener that orchestrates trace collection
     """
 
-    complex_events = [
+    complex_events = ["task_started", "llm_call_started", "llm_call_completed"]
-        "task_started",
-        "task_completed",
-        "llm_call_started",
-        "llm_call_completed",
-        "agent_execution_started",
-        "agent_execution_completed",
-    ]
 
     _instance = None
     _initialized = False
-    _listeners_setup = False
 
     def __new__(cls, batch_manager=None):
         if cls._instance is None:
@@ -124,15 +116,10 @@ class TraceCollectionListener(BaseEventListener):
     def setup_listeners(self, crewai_event_bus):
         """Setup event listeners - delegates to specific handlers"""
 
-        if self._listeners_setup:
-            return
-
         self._register_flow_event_handlers(crewai_event_bus)
         self._register_context_event_handlers(crewai_event_bus)
         self._register_action_event_handlers(crewai_event_bus)
 
-        self._listeners_setup = True
-
     def _register_flow_event_handlers(self, event_bus):
         """Register handlers for flow events"""
 
@@ -161,8 +148,7 @@ class TraceCollectionListener(BaseEventListener):
         @event_bus.on(FlowFinishedEvent)
         def on_flow_finished(source, event):
             self._handle_trace_event("flow_finished", source, event)
-            if self.batch_manager.batch_owner_type == "flow":
+            self.batch_manager.finalize_batch()
-                self.batch_manager.finalize_batch()
 
         @event_bus.on(FlowPlotEvent)
         def on_flow_plot(source, event):
@@ -180,8 +166,7 @@ class TraceCollectionListener(BaseEventListener):
         @event_bus.on(CrewKickoffCompletedEvent)
         def on_crew_completed(source, event):
             self._handle_trace_event("crew_kickoff_completed", source, event)
-            if self.batch_manager.batch_owner_type == "crew":
+            self.batch_manager.finalize_batch()
-                self.batch_manager.finalize_batch()
 
         @event_bus.on(CrewKickoffFailedEvent)
         def on_crew_failed(source, event):
@@ -233,7 +218,7 @@ class TraceCollectionListener(BaseEventListener):
             self._handle_trace_event("llm_guardrail_completed", source, event)
 
     def _register_action_event_handlers(self, event_bus):
-        """Register handlers for action events (LLM calls, tool usage)"""
+        """Register handlers for action events (LLM calls, tool usage, memory)"""
 
         @event_bus.on(LLMCallStartedEvent)
         def on_llm_call_started(source, event):
@@ -304,9 +289,6 @@ class TraceCollectionListener(BaseEventListener):
             "crewai_version": get_crewai_version(),
         }
 
-        self.batch_manager.batch_owner_type = "crew"
-        self.batch_manager.batch_owner_id = getattr(source, "id", str(uuid.uuid4()))
-
         self._initialize_batch(user_context, execution_metadata)
 
     def _initialize_flow_batch(self, source: Any, event: Any):
@@ -319,9 +301,6 @@ class TraceCollectionListener(BaseEventListener):
             "execution_type": "flow",
         }
 
-        self.batch_manager.batch_owner_type = "flow"
-        self.batch_manager.batch_owner_id = getattr(source, "id", str(uuid.uuid4()))
-
         self._initialize_batch(user_context, execution_metadata)
 
     def _initialize_batch(
@@ -379,44 +358,12 @@ class TraceCollectionListener(BaseEventListener):
             return {
                 "task_description": event.task.description,
                 "expected_output": event.task.expected_output,
-                "task_name": event.task.name or event.task.description,
+                "task_name": event.task.name,
                 "context": event.context,
-                "agent_role": source.agent.role,
+                "agent": source.agent.role,
-                "task_id": str(event.task.id),
-            }
-        elif event_type == "task_completed":
-            return {
-                "task_description": event.task.description if event.task else None,
-                "task_name": event.task.name or event.task.description
-                if event.task
-                else None,
-                "task_id": str(event.task.id) if event.task else None,
-                "output_raw": event.output.raw if event.output else None,
-                "output_format": str(event.output.output_format)
-                if event.output
-                else None,
-                "agent_role": event.output.agent if event.output else None,
-            }
-        elif event_type == "agent_execution_started":
-            return {
-                "agent_role": event.agent.role,
-                "agent_goal": event.agent.goal,
-                "agent_backstory": event.agent.backstory,
-            }
-        elif event_type == "agent_execution_completed":
-            return {
-                "agent_role": event.agent.role,
-                "agent_goal": event.agent.goal,
-                "agent_backstory": event.agent.backstory,
             }
         elif event_type == "llm_call_started":
-            event_data = self._safe_serialize_to_dict(event)
+            return self._safe_serialize_to_dict(event)
-            event_data["task_name"] = (
-                event.task_name or event.task_description
-                if hasattr(event, "task_name") and event.task_name
-                else None
-            )
-            return event_data
         elif event_type == "llm_call_completed":
             return self._safe_serialize_to_dict(event)
         else:

src/crewai/utilities/events/llm_events.py

@@ -13,14 +13,26 @@ class LLMEventBase(BaseEvent):
     agent_id: Optional[str] = None
     agent_role: Optional[str] = None
 
-    from_task: Optional[Any] = None
-    from_agent: Optional[Any] = None
-
     def __init__(self, **data):
         super().__init__(**data)
         self._set_agent_params(data)
         self._set_task_params(data)
 
+    def _set_agent_params(self, data: Dict[str, Any]):
+        task = data.get("from_task", None)
+        agent = task.agent if task else data.get("from_agent", None)
+
+        if not agent:
+            return
+
+        self.agent_id = agent.id
+        self.agent_role = agent.role
+
+    def _set_task_params(self, data: Dict[str, Any]):
+        if "from_task" in data and (task := data["from_task"]):
+            self.task_id = task.id
+            self.task_name = task.name
+
 
 class LLMCallType(Enum):
     """Type of LLM call being made"""

src/crewai/utilities/events/memory_events.py

@@ -3,24 +3,7 @@ from typing import Any, Dict, Optional
 from crewai.utilities.events.base_events import BaseEvent
 
 
-class MemoryBaseEvent(BaseEvent):
+class MemoryQueryStartedEvent(BaseEvent):
-    """Base event for memory operations"""
-
-    type: str
-    task_id: Optional[str] = None
-    task_name: Optional[str] = None
-    from_task: Optional[Any] = None
-    from_agent: Optional[Any] = None
-    agent_role: Optional[str] = None
-    agent_id: Optional[str] = None
-
-    def __init__(self, **data):
-        super().__init__(**data)
-        self._set_agent_params(data)
-        self._set_task_params(data)
-
-
-class MemoryQueryStartedEvent(MemoryBaseEvent):
     """Event emitted when a memory query is started"""
 
     type: str = "memory_query_started"
@@ -29,7 +12,7 @@ class MemoryQueryStartedEvent(MemoryBaseEvent):
     score_threshold: Optional[float] = None
 
 
-class MemoryQueryCompletedEvent(MemoryBaseEvent):
+class MemoryQueryCompletedEvent(BaseEvent):
     """Event emitted when a memory query is completed successfully"""
 
     type: str = "memory_query_completed"
@@ -40,7 +23,7 @@ class MemoryQueryCompletedEvent(MemoryBaseEvent):
     query_time_ms: float
 
 
-class MemoryQueryFailedEvent(MemoryBaseEvent):
+class MemoryQueryFailedEvent(BaseEvent):
     """Event emitted when a memory query fails"""
 
     type: str = "memory_query_failed"
@@ -50,7 +33,7 @@ class MemoryQueryFailedEvent(MemoryBaseEvent):
     error: str
 
 
-class MemorySaveStartedEvent(MemoryBaseEvent):
+class MemorySaveStartedEvent(BaseEvent):
     """Event emitted when a memory save operation is started"""
 
     type: str = "memory_save_started"
@@ -59,7 +42,7 @@ class MemorySaveStartedEvent(MemoryBaseEvent):
     agent_role: Optional[str] = None
 
 
-class MemorySaveCompletedEvent(MemoryBaseEvent):
+class MemorySaveCompletedEvent(BaseEvent):
     """Event emitted when a memory save operation is completed successfully"""
 
     type: str = "memory_save_completed"
@@ -69,7 +52,7 @@ class MemorySaveCompletedEvent(MemoryBaseEvent):
     save_time_ms: float
 
 
-class MemorySaveFailedEvent(MemoryBaseEvent):
+class MemorySaveFailedEvent(BaseEvent):
     """Event emitted when a memory save operation fails"""
 
     type: str = "memory_save_failed"
@@ -79,14 +62,14 @@ class MemorySaveFailedEvent(MemoryBaseEvent):
     error: str
 
 
-class MemoryRetrievalStartedEvent(MemoryBaseEvent):
+class MemoryRetrievalStartedEvent(BaseEvent):
     """Event emitted when memory retrieval for a task prompt starts"""
 
     type: str = "memory_retrieval_started"
     task_id: Optional[str] = None
 
 
-class MemoryRetrievalCompletedEvent(MemoryBaseEvent):
+class MemoryRetrievalCompletedEvent(BaseEvent):
     """Event emitted when memory retrieval for a task prompt completes successfully"""
 
     type: str = "memory_retrieval_completed"

src/crewai/utilities/events/reasoning_events.py

@@ -1,34 +1,16 @@
 from crewai.utilities.events.base_events import BaseEvent
-from typing import Any, Optional
 
 
-class ReasoningEvent(BaseEvent):
+class AgentReasoningStartedEvent(BaseEvent):
-    """Base event for reasoning events."""
-
-    type: str
-    attempt: int = 1
-    agent_role: str
-    task_id: str
-    task_name: Optional[str] = None
-    from_task: Optional[Any] = None
-    agent_id: Optional[str] = None
-    from_agent: Optional[Any] = None
-
-    def __init__(self, **data):
-        super().__init__(**data)
-        self._set_task_params(data)
-        self._set_agent_params(data)
-
-
-class AgentReasoningStartedEvent(ReasoningEvent):
     """Event emitted when an agent starts reasoning about a task."""
 
     type: str = "agent_reasoning_started"
     agent_role: str
     task_id: str
+    attempt: int = 1  # The current reasoning/refinement attempt
 
 
-class AgentReasoningCompletedEvent(ReasoningEvent):
+class AgentReasoningCompletedEvent(BaseEvent):
     """Event emitted when an agent finishes its reasoning process."""
 
     type: str = "agent_reasoning_completed"
@@ -36,12 +18,14 @@ class AgentReasoningCompletedEvent(ReasoningEvent):
     task_id: str
     plan: str
     ready: bool
+    attempt: int = 1
 
 
-class AgentReasoningFailedEvent(ReasoningEvent):
+class AgentReasoningFailedEvent(BaseEvent):
     """Event emitted when the reasoning process fails."""
 
     type: str = "agent_reasoning_failed"
     agent_role: str
     task_id: str
     error: str
+    attempt: int = 1

src/crewai/utilities/events/tool_usage_events.py

@@ -9,24 +9,17 @@ class ToolUsageEvent(BaseEvent):
 
     agent_key: Optional[str] = None
     agent_role: Optional[str] = None
-    agent_id: Optional[str] = None
     tool_name: str
     tool_args: Dict[str, Any] | str
     tool_class: Optional[str] = None
     run_attempts: int | None = None
     delegations: int | None = None
     agent: Optional[Any] = None
-    task_name: Optional[str] = None
-    task_id: Optional[str] = None
-    from_task: Optional[Any] = None
-    from_agent: Optional[Any] = None
 
     model_config = {"arbitrary_types_allowed": True}
 
     def __init__(self, **data):
         super().__init__(**data)
-        self._set_agent_params(data)
-        self._set_task_params(data)
         # Set fingerprint data from the agent
         if self.agent and hasattr(self.agent, "fingerprint") and self.agent.fingerprint:
             self.source_fingerprint = self.agent.fingerprint.uuid_str

src/crewai/utilities/import_utils.py

@@ -1,32 +0,0 @@
-"""Import utilities for optional dependencies."""
-
-import importlib
-from types import ModuleType
-
-
-class OptionalDependencyError(ImportError):
-    """Exception raised when an optional dependency is not installed."""
-
-    pass
-
-
-def require(name: str, *, purpose: str) -> ModuleType:
-    """Import a module, raising a helpful error if it's not installed.
-
-    Args:
-        name: The module name to import.
-        purpose: Description of what requires this dependency.
-
-    Returns:
-        The imported module.
-
-    Raises:
-        OptionalDependencyError: If the module is not installed.
-    """
-    try:
-        return importlib.import_module(name)
-    except ImportError as exc:
-        raise OptionalDependencyError(
-            f"{purpose} requires the optional dependency '{name}'.\n"
-            f"Install it with: uv add {name}"
-        ) from exc

src/crewai/utilities/reasoning_handler.py

@@ -18,20 +18,17 @@ from crewai.utilities.events.reasoning_events import (
 
 class ReasoningPlan(BaseModel):
     """Model representing a reasoning plan for a task."""
-
     plan: str = Field(description="The detailed reasoning plan for the task.")
     ready: bool = Field(description="Whether the agent is ready to execute the task.")
 
 
 class AgentReasoningOutput(BaseModel):
     """Model representing the output of the agent reasoning process."""
-
     plan: ReasoningPlan = Field(description="The reasoning plan for the task.")
 
 
 class ReasoningFunction(BaseModel):
     """Model for function calling with reasoning."""
-
     plan: str = Field(description="The detailed reasoning plan for the task.")
     ready: bool = Field(description="Whether the agent is ready to execute the task.")
 
@@ -41,7 +38,6 @@ class AgentReasoning:
     Handles the agent reasoning process, enabling an agent to reflect and create a plan
     before executing a task.
     """
-
     def __init__(self, task: Task, agent: Agent):
         if not task or not agent:
             raise ValueError("Both task and agent must be provided.")
@@ -67,7 +63,6 @@ class AgentReasoning:
                     agent_role=self.agent.role,
                     task_id=str(self.task.id),
                     attempt=1,
-                    from_task=self.task,
                 ),
             )
         except Exception:
@@ -87,7 +82,6 @@ class AgentReasoning:
                         plan=output.plan.plan,
                         ready=output.plan.ready,
                         attempt=1,
-                        from_task=self.task,
                     ),
                 )
             except Exception:
@@ -104,7 +98,6 @@ class AgentReasoning:
                         task_id=str(self.task.id),
                         error=str(e),
                         attempt=1,
-                        from_task=self.task,
                     ),
                 )
             except Exception:
@@ -142,16 +135,14 @@ class AgentReasoning:
             system_prompt = self.i18n.retrieve("reasoning", "initial_plan").format(
                 role=self.agent.role,
                 goal=self.agent.goal,
-                backstory=self.__get_agent_backstory(),
+                backstory=self.__get_agent_backstory()
             )
 
             response = self.llm.call(
                 [
                     {"role": "system", "content": system_prompt},
-                    {"role": "user", "content": reasoning_prompt},
+                    {"role": "user", "content": reasoning_prompt}
-                ],
+                ]
-                from_task=self.task,
-                from_agent=self.agent,
             )
 
             return self.__parse_reasoning_response(str(response))
@@ -179,7 +170,6 @@ class AgentReasoning:
                         agent_role=self.agent.role,
                         task_id=str(self.task.id),
                         attempt=attempt + 1,
-                        from_task=self.task,
                     ),
                 )
             except Exception:
@@ -193,16 +183,14 @@ class AgentReasoning:
                 system_prompt = self.i18n.retrieve("reasoning", "refine_plan").format(
                     role=self.agent.role,
                     goal=self.agent.goal,
-                    backstory=self.__get_agent_backstory(),
+                    backstory=self.__get_agent_backstory()
                 )
 
                 response = self.llm.call(
                     [
                         {"role": "system", "content": system_prompt},
-                        {"role": "user", "content": refine_prompt},
+                        {"role": "user", "content": refine_prompt}
-                    ],
+                    ]
-                    from_task=self.task,
-                    from_agent=self.agent,
                 )
                 plan, ready = self.__parse_reasoning_response(str(response))
 
@@ -239,23 +227,23 @@ class AgentReasoning:
                     "properties": {
                         "plan": {
                             "type": "string",
-                            "description": "The detailed reasoning plan for the task.",
+                            "description": "The detailed reasoning plan for the task."
                         },
                         "ready": {
                             "type": "boolean",
-                            "description": "Whether the agent is ready to execute the task.",
+                            "description": "Whether the agent is ready to execute the task."
-                        },
+                        }
                     },
-                    "required": ["plan", "ready"],
+                    "required": ["plan", "ready"]
-                },
+                }
-            },
+            }
         }
 
         try:
             system_prompt = self.i18n.retrieve("reasoning", prompt_type).format(
                 role=self.agent.role,
                 goal=self.agent.goal,
-                backstory=self.__get_agent_backstory(),
+                backstory=self.__get_agent_backstory()
             )
 
             # Prepare a simple callable that just returns the tool arguments as JSON
@@ -266,12 +254,10 @@ class AgentReasoning:
             response = self.llm.call(
                 [
                     {"role": "system", "content": system_prompt},
-                    {"role": "user", "content": prompt},
+                    {"role": "user", "content": prompt}
                 ],
                 tools=[function_schema],
                 available_functions={"create_reasoning_plan": _create_reasoning_plan},
-                from_task=self.task,
-                from_agent=self.agent,
             )
 
             self.logger.debug(f"Function calling response: {response[:100]}...")
@@ -284,43 +270,30 @@ class AgentReasoning:
                 pass
 
             response_str = str(response)
-            return (
+            return response_str, "READY: I am ready to execute the task." in response_str
-                response_str,
-                "READY: I am ready to execute the task." in response_str,
-            )
 
         except Exception as e:
-            self.logger.warning(
+            self.logger.warning(f"Error during function calling: {str(e)}. Falling back to text parsing.")
-                f"Error during function calling: {str(e)}. Falling back to text parsing."
-            )
 
             try:
                 system_prompt = self.i18n.retrieve("reasoning", prompt_type).format(
                     role=self.agent.role,
                     goal=self.agent.goal,
-                    backstory=self.__get_agent_backstory(),
+                    backstory=self.__get_agent_backstory()
                 )
 
                 fallback_response = self.llm.call(
                     [
                         {"role": "system", "content": system_prompt},
-                        {"role": "user", "content": prompt},
+                        {"role": "user", "content": prompt}
-                    ],
+                    ]
-                    from_task=self.task,
-                    from_agent=self.agent,
                 )
 
                 fallback_str = str(fallback_response)
-                return (
+                return fallback_str, "READY: I am ready to execute the task." in fallback_str
-                    fallback_str,
-                    "READY: I am ready to execute the task." in fallback_str,
-                )
             except Exception as inner_e:
                 self.logger.error(f"Error during fallback text parsing: {str(inner_e)}")
-                return (
+                return "Failed to generate a plan due to an error.", True  # Default to ready to avoid getting stuck
-                    "Failed to generate a plan due to an error.",
-                    True,
-                )  # Default to ready to avoid getting stuck
 
     def __get_agent_backstory(self) -> str:
         """
@@ -346,7 +319,7 @@ class AgentReasoning:
             backstory=self.__get_agent_backstory(),
             description=self.task.description,
             expected_output=self.task.expected_output,
-            tools=available_tools,
+            tools=available_tools
         )
 
     def __format_available_tools(self) -> str:
@@ -357,7 +330,7 @@ class AgentReasoning:
             str: Comma-separated list of tool names.
         """
         try:
-            return ", ".join([tool.name for tool in (self.task.tools or [])])
+            return ', '.join([tool.name for tool in (self.task.tools or [])])
         except (AttributeError, TypeError):
             return "No tools available"
 
@@ -375,7 +348,7 @@ class AgentReasoning:
             role=self.agent.role,
             goal=self.agent.goal,
             backstory=self.__get_agent_backstory(),
-            current_plan=current_plan,
+            current_plan=current_plan
         )
 
     def __parse_reasoning_response(self, response: str) -> Tuple[str, bool]:

tests/agent_reasoning_test.py

@@ -15,37 +15,37 @@ def mock_llm_responses():
         "ready": "I'll solve this simple math problem.\n\nREADY: I am ready to execute the task.\n\n",
         "not_ready": "I need to think about derivatives.\n\nNOT READY: I need to refine my plan because I'm not sure about the derivative rules.",
         "ready_after_refine": "I'll use the power rule for derivatives where d/dx(x^n) = n*x^(n-1).\n\nREADY: I am ready to execute the task.",
-        "execution": "4",
+        "execution": "4"
     }
 
 
 def test_agent_with_reasoning(mock_llm_responses):
     """Test agent with reasoning."""
     llm = LLM("gpt-3.5-turbo")
-
+    
     agent = Agent(
         role="Test Agent",
         goal="To test the reasoning feature",
         backstory="I am a test agent created to verify the reasoning feature works correctly.",
         llm=llm,
         reasoning=True,
-        verbose=True,
+        verbose=True
     )
-
+    
     task = Task(
         description="Simple math task: What's 2+2?",
         expected_output="The answer should be a number.",
-        agent=agent,
+        agent=agent
     )
-
+    
     agent.llm.call = lambda messages, *args, **kwargs: (
         mock_llm_responses["ready"]
         if any("create a detailed plan" in msg.get("content", "") for msg in messages)
         else mock_llm_responses["execution"]
     )
-
+    
     result = agent.execute_task(task)
-
+    
     assert result == mock_llm_responses["execution"]
     assert "Reasoning Plan:" in task.description
 
@@ -53,7 +53,7 @@ def test_agent_with_reasoning(mock_llm_responses):
 def test_agent_with_reasoning_not_ready_initially(mock_llm_responses):
     """Test agent with reasoning that requires refinement."""
     llm = LLM("gpt-3.5-turbo")
-
+    
     agent = Agent(
         role="Test Agent",
         goal="To test the reasoning feature",
@@ -61,21 +61,19 @@ def test_agent_with_reasoning_not_ready_initially(mock_llm_responses):
         llm=llm,
         reasoning=True,
         max_reasoning_attempts=2,
-        verbose=True,
+        verbose=True
     )
-
+    
     task = Task(
         description="Complex math task: What's the derivative of x²?",
         expected_output="The answer should be a mathematical expression.",
-        agent=agent,
+        agent=agent
     )
-
+    
     call_count = [0]
-
+    
     def mock_llm_call(messages, *args, **kwargs):
-        if any(
+        if any("create a detailed plan" in msg.get("content", "") for msg in messages) or any("refine your plan" in msg.get("content", "") for msg in messages):
-            "create a detailed plan" in msg.get("content", "") for msg in messages
-        ) or any("refine your plan" in msg.get("content", "") for msg in messages):
             call_count[0] += 1
             if call_count[0] == 1:
                 return mock_llm_responses["not_ready"]
@@ -83,11 +81,11 @@ def test_agent_with_reasoning_not_ready_initially(mock_llm_responses):
                 return mock_llm_responses["ready_after_refine"]
         else:
             return "2x"
-
+    
     agent.llm.call = mock_llm_call
-
+    
     result = agent.execute_task(task)
-
+    
     assert result == "2x"
     assert call_count[0] == 2  # Should have made 2 reasoning calls
     assert "Reasoning Plan:" in task.description
@@ -96,7 +94,7 @@ def test_agent_with_reasoning_not_ready_initially(mock_llm_responses):
 def test_agent_with_reasoning_max_attempts_reached():
     """Test agent with reasoning that reaches max attempts without being ready."""
     llm = LLM("gpt-3.5-turbo")
-
+    
     agent = Agent(
         role="Test Agent",
         goal="To test the reasoning feature",
@@ -104,53 +102,52 @@ def test_agent_with_reasoning_max_attempts_reached():
         llm=llm,
         reasoning=True,
         max_reasoning_attempts=2,
-        verbose=True,
+        verbose=True
     )
-
+    
     task = Task(
         description="Complex math task: Solve the Riemann hypothesis.",
         expected_output="A proof or disproof of the hypothesis.",
-        agent=agent,
+        agent=agent
     )
-
+    
     call_count = [0]
-
+    
     def mock_llm_call(messages, *args, **kwargs):
-        if any(
+        if any("create a detailed plan" in msg.get("content", "") for msg in messages) or any("refine your plan" in msg.get("content", "") for msg in messages):
-            "create a detailed plan" in msg.get("content", "") for msg in messages
-        ) or any("refine your plan" in msg.get("content", "") for msg in messages):
             call_count[0] += 1
             return f"Attempt {call_count[0]}: I need more time to think.\n\nNOT READY: I need to refine my plan further."
         else:
             return "This is an unsolved problem in mathematics."
-
+    
     agent.llm.call = mock_llm_call
-
+    
     result = agent.execute_task(task)
-
+    
     assert result == "This is an unsolved problem in mathematics."
-    assert (
+    assert call_count[0] == 2  # Should have made exactly 2 reasoning calls (max_attempts)
-        call_count[0] == 2
-    )  # Should have made exactly 2 reasoning calls (max_attempts)
     assert "Reasoning Plan:" in task.description
 
 
 def test_agent_reasoning_input_validation():
     """Test input validation in AgentReasoning."""
     llm = LLM("gpt-3.5-turbo")
-
+    
     agent = Agent(
         role="Test Agent",
         goal="To test the reasoning feature",
         backstory="I am a test agent created to verify the reasoning feature works correctly.",
         llm=llm,
-        reasoning=True,
+        reasoning=True
     )
-
+    
     with pytest.raises(ValueError, match="Both task and agent must be provided"):
         AgentReasoning(task=None, agent=agent)
-
+    
-    task = Task(description="Simple task", expected_output="Simple output")
+    task = Task(
+        description="Simple task",
+        expected_output="Simple output"
+    )
     with pytest.raises(ValueError, match="Both task and agent must be provided"):
         AgentReasoning(task=task, agent=None)
 
@@ -158,33 +155,33 @@ def test_agent_reasoning_input_validation():
 def test_agent_reasoning_error_handling():
     """Test error handling during the reasoning process."""
     llm = LLM("gpt-3.5-turbo")
-
+    
     agent = Agent(
         role="Test Agent",
         goal="To test the reasoning feature",
         backstory="I am a test agent created to verify the reasoning feature works correctly.",
         llm=llm,
-        reasoning=True,
+        reasoning=True
     )
-
+    
     task = Task(
         description="Task that will cause an error",
         expected_output="Output that will never be generated",
-        agent=agent,
+        agent=agent
     )
-
+    
     call_count = [0]
-
+    
     def mock_llm_call_error(*args, **kwargs):
         call_count[0] += 1
         if call_count[0] <= 2:  # First calls are for reasoning
             raise Exception("LLM error during reasoning")
         return "Fallback execution result"  # Return a value for task execution
-
+    
     agent.llm.call = mock_llm_call_error
-
+    
     result = agent.execute_task(task)
-
+    
     assert result == "Fallback execution result"
     assert call_count[0] > 2  # Ensure we called the mock multiple times
 
@@ -192,36 +189,37 @@ def test_agent_reasoning_error_handling():
 def test_agent_with_function_calling():
     """Test agent with reasoning using function calling."""
     llm = LLM("gpt-3.5-turbo")
-
+    
     agent = Agent(
         role="Test Agent",
         goal="To test the reasoning feature",
         backstory="I am a test agent created to verify the reasoning feature works correctly.",
         llm=llm,
         reasoning=True,
-        verbose=True,
+        verbose=True
     )
-
+    
     task = Task(
         description="Simple math task: What's 2+2?",
         expected_output="The answer should be a number.",
-        agent=agent,
+        agent=agent
     )
-
+    
     agent.llm.supports_function_calling = lambda: True
-
+    
     def mock_function_call(messages, *args, **kwargs):
         if "tools" in kwargs:
-            return json.dumps(
+            return json.dumps({
-                {"plan": "I'll solve this simple math problem: 2+2=4.", "ready": True}
+                "plan": "I'll solve this simple math problem: 2+2=4.",
-            )
+                "ready": True
+            })
         else:
             return "4"
-
+    
     agent.llm.call = mock_function_call
-
+    
     result = agent.execute_task(task)
-
+    
     assert result == "4"
     assert "Reasoning Plan:" in task.description
     assert "I'll solve this simple math problem: 2+2=4." in task.description
@@ -230,34 +228,34 @@ def test_agent_with_function_calling():
 def test_agent_with_function_calling_fallback():
     """Test agent with reasoning using function calling that falls back to text parsing."""
     llm = LLM("gpt-3.5-turbo")
-
+    
     agent = Agent(
         role="Test Agent",
         goal="To test the reasoning feature",
         backstory="I am a test agent created to verify the reasoning feature works correctly.",
         llm=llm,
         reasoning=True,
-        verbose=True,
+        verbose=True
     )
-
+    
     task = Task(
         description="Simple math task: What's 2+2?",
         expected_output="The answer should be a number.",
-        agent=agent,
+        agent=agent
     )
-
+    
     agent.llm.supports_function_calling = lambda: True
-
+    
     def mock_function_call(messages, *args, **kwargs):
         if "tools" in kwargs:
             return "Invalid JSON that will trigger fallback. READY: I am ready to execute the task."
         else:
             return "4"
-
+    
     agent.llm.call = mock_function_call
-
+    
     result = agent.execute_task(task)
-
+    
     assert result == "4"
     assert "Reasoning Plan:" in task.description
     assert "Invalid JSON that will trigger fallback" in task.description

tests/agent_test.py

@@ -23,7 +23,6 @@ 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
     original_api_key = os.environ.get("OPENAI_API_KEY")
@@ -236,7 +235,7 @@ def test_logging_tool_usage():
     )
 
     assert agent.llm.model == "gpt-4o-mini"
-    assert agent.tools_handler.last_used_tool is None
+    assert agent.tools_handler.last_used_tool == {}
     task = Task(
         description="What is 3 times 4?",
         agent=agent,
@@ -594,17 +593,42 @@ def test_agent_repeated_tool_usage_check_even_with_disabled_cache(capsys):
     )
 
     captured = capsys.readouterr()
+    output = (
+        captured.out.replace("\n", " ")
+        .replace("  ", " ")
+        .strip()
+        .replace("╭", "")
+        .replace("╮", "")
+        .replace("╯", "")
+        .replace("╰", "")
+        .replace("│", "")
+        .replace("─", "")
+        .replace("[", "")
+        .replace("]", "")
+        .replace("bold", "")
+        .replace("blue", "")
+        .replace("yellow", "")
+        .replace("green", "")
+        .replace("red", "")
+        .replace("dim", "")
+        .replace("🤖", "")
+        .replace("🔧", "")
+        .replace("✅", "")
+        .replace("\x1b[93m", "")
+        .replace("\x1b[00m", "")
+        .replace("\\", "")
+        .replace('"', "")
+        .replace("'", "")
+    )
 
-    # More flexible check, look for either the repeated usage message or verification that max iterations was reached
+    # Look for the message in the normalized output, handling the apostrophe difference
-    output_lower = captured.out.lower()
+    expected_message = (
-
+        "I tried reusing the same input, I must stop using this action input"
-    has_repeated_usage_message = "tried reusing the same input" in output_lower
+    )
-    has_max_iterations = "maximum iterations reached" in output_lower
-    has_final_answer = "final answer" in output_lower or "42" in captured.out
 
     assert (
-        has_repeated_usage_message or (has_max_iterations and has_final_answer)
+        expected_message in output
-    ), f"Expected repeated tool usage handling or proper max iteration handling. Output was: {captured.out[:500]}..."
+    ), f"Expected message not found in output. Output was: {output}"
 
 
 @pytest.mark.vcr(filter_headers=["authorization"])
@@ -759,10 +783,10 @@ def test_agent_without_max_rpm_respects_crew_rpm(capsys):
 
     with patch.object(RPMController, "_wait_for_next_minute") as moveon:
         moveon.return_value = True
-        result = crew.kickoff()
+        crew.kickoff()
-        # Verify the crew executed and RPM limit was triggered
+        captured = capsys.readouterr()
-        assert result is not None
+        assert "get_final_answer" in captured.out
-        assert moveon.called
+        assert "Max RPM reached, waiting for next minute to start." in captured.out
         moveon.assert_called_once()
 
 
@@ -1189,13 +1213,17 @@ Thought:<|eot_id|>
 def test_task_allow_crewai_trigger_context():
     from crewai import Crew
 
-    agent = Agent(role="test role", goal="test goal", backstory="test backstory")
+    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,
+        allow_crewai_trigger_context=True
     )
     crew = Crew(agents=[agent], tasks=[task])
     crew.kickoff({"crewai_trigger_payload": "Important context data"})
@@ -1210,13 +1238,17 @@ def test_task_allow_crewai_trigger_context():
 def test_task_without_allow_crewai_trigger_context():
     from crewai import Crew
 
-    agent = Agent(role="test role", goal="test goal", backstory="test backstory")
+    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,
+        allow_crewai_trigger_context=False
     )
 
     crew = Crew(agents=[agent], tasks=[task])
@@ -1233,18 +1265,23 @@ def test_task_without_allow_crewai_trigger_context():
 def test_task_allow_crewai_trigger_context_no_payload():
     from crewai import Crew
 
-    agent = Agent(role="test role", goal="test goal", backstory="test backstory")
+    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,
+        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
@@ -1256,9 +1293,7 @@ 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(
+    agent2 = Agent(role="Second Agent", goal="Second goal", backstory="Second backstory")
-        role="Second Agent", goal="Second goal", backstory="Second backstory"
-    )
 
     first_task = Task(
         description="Process initial data",
@@ -1266,11 +1301,12 @@ def test_do_not_allow_crewai_trigger_context_for_first_task_hierarchical():
         agent=agent1,
     )
 
+
     crew = Crew(
         agents=[agent1, agent2],
         tasks=[first_task],
         process=Process.hierarchical,
-        manager_llm="gpt-4o",
+        manager_llm="gpt-4o"
     )
 
     crew.kickoff({"crewai_trigger_payload": "Initial context data"})
@@ -1285,9 +1321,7 @@ def test_first_task_auto_inject_trigger():
     from crewai import Crew
 
     agent1 = Agent(role="First Agent", goal="First goal", backstory="First backstory")
-    agent2 = Agent(
+    agent2 = Agent(role="Second Agent", goal="Second goal", backstory="Second backstory")
-        role="Second Agent", goal="Second goal", backstory="Second backstory"
-    )
 
     first_task = Task(
         description="Process initial data",
@@ -1301,7 +1335,10 @@ def test_first_task_auto_inject_trigger():
         agent=agent2,
     )
 
-    crew = Crew(agents=[agent1, agent2], tasks=[first_task, second_task])
+    crew = Crew(
+        agents=[agent1, agent2],
+        tasks=[first_task, second_task]
+    )
     crew.kickoff({"crewai_trigger_payload": "Initial context data"})
 
     first_prompt = first_task.prompt()
@@ -1312,31 +1349,31 @@ def test_first_task_auto_inject_trigger():
     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(
+    agent2 = Agent(role="Second Agent", goal="Second goal", backstory="Second backstory")
-        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,
+        allow_crewai_trigger_context=False
     )
 
     second_task = Task(
         description="Process secondary data",
         expected_output="Secondary analysis",
         agent=agent2,
-        allow_crewai_trigger_context=True,
+        allow_crewai_trigger_context=True
     )
 
-    crew = Crew(agents=[agent1, agent2], tasks=[first_task, second_task])
+    crew = Crew(
+        agents=[agent1, agent2],
+        tasks=[first_task, second_task]
+    )
     crew.kickoff({"crewai_trigger_payload": "Context data"})
 
     first_prompt = first_task.prompt()
@@ -1346,6 +1383,7 @@ def test_ensure_first_task_allow_crewai_trigger_context_is_false_does_not_inject
     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?"
@@ -2309,13 +2347,12 @@ def mock_get_auth_token():
 
 @patch("crewai.cli.plus_api.PlusAPI.get_agent")
 def test_agent_from_repository(mock_get_agent, mock_get_auth_token):
-    # Mock embedchain initialization to prevent race conditions in parallel CI execution
+    from crewai_tools import (
-    with patch("embedchain.client.Client.setup"):
+        SerperDevTool,
-        from crewai_tools import (
+        XMLSearchTool,
-            SerperDevTool,
+        CSVSearchTool,
-            FileReadTool,
+        EnterpriseActionTool,
-            EnterpriseActionTool,
+    )
-        )
 
     mock_get_response = MagicMock()
     mock_get_response.status_code = 200
@@ -2331,9 +2368,10 @@ def test_agent_from_repository(mock_get_agent, mock_get_auth_token):
             },
             {
                 "module": "crewai_tools",
-                "name": "FileReadTool",
+                "name": "XMLSearchTool",
-                "init_params": {"file_path": "test.txt"},
+                "init_params": {"summarize": "true"},
             },
+            {"module": "crewai_tools", "name": "CSVSearchTool", "init_params": {}},
             # using a tools that returns a list of BaseTools
             {
                 "module": "crewai_tools",
@@ -2358,22 +2396,23 @@ def test_agent_from_repository(mock_get_agent, mock_get_auth_token):
     assert agent.role == "test role"
     assert agent.goal == "test goal"
     assert agent.backstory == "test backstory"
-    assert len(agent.tools) == 3
+    assert len(agent.tools) == 4
 
     assert isinstance(agent.tools[0], SerperDevTool)
     assert agent.tools[0].n_results == 30
-    assert isinstance(agent.tools[1], FileReadTool)
+    assert isinstance(agent.tools[1], XMLSearchTool)
-    assert agent.tools[1].file_path == "test.txt"
+    assert agent.tools[1].summarize
 
-    assert isinstance(agent.tools[2], EnterpriseActionTool)
+    assert isinstance(agent.tools[2], CSVSearchTool)
-    assert agent.tools[2].name == "test_name"
+    assert not agent.tools[2].summarize
+
+    assert isinstance(agent.tools[3], EnterpriseActionTool)
+    assert agent.tools[3].name == "test_name"
 
 
 @patch("crewai.cli.plus_api.PlusAPI.get_agent")
 def test_agent_from_repository_override_attributes(mock_get_agent, mock_get_auth_token):
-    # Mock embedchain initialization to prevent race conditions in parallel CI execution
+    from crewai_tools import SerperDevTool
-    with patch("embedchain.client.Client.setup"):
-        from crewai_tools import SerperDevTool
 
     mock_get_response = MagicMock()
     mock_get_response.status_code = 200

tests/cassettes/test_agent_usage_metrics_are_captured_for_hierarchical_process.yaml

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tests/cassettes/test_do_not_allow_crewai_trigger_context_for_first_task_hierarchical.yaml

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tests/cli/authentication/test_auth_main.py

@@ -3,6 +3,11 @@ from datetime import datetime, timedelta
 import requests
 from unittest.mock import MagicMock, patch, call
 from crewai.cli.authentication.main import AuthenticationCommand
+from crewai.cli.authentication.constants import (
+    AUTH0_AUDIENCE,
+    AUTH0_CLIENT_ID,
+    AUTH0_DOMAIN
+)
 from crewai.cli.constants import (
     CREWAI_ENTERPRISE_DEFAULT_OAUTH2_DOMAIN,
     CREWAI_ENTERPRISE_DEFAULT_OAUTH2_CLIENT_ID,
@@ -17,6 +22,16 @@ class TestAuthenticationCommand:
     @pytest.mark.parametrize(
         "user_provider,expected_urls",
         [
+            (
+                "auth0",
+                {
+                    "device_code_url": f"https://{AUTH0_DOMAIN}/oauth/device/code",
+                    "token_url": f"https://{AUTH0_DOMAIN}/oauth/token",
+                    "client_id": AUTH0_CLIENT_ID,
+                    "audience": AUTH0_AUDIENCE,
+                    "domain": AUTH0_DOMAIN,
+                },
+            ),
             (
                 "workos",
                 {
@@ -29,6 +44,9 @@ class TestAuthenticationCommand:
             ),
         ],
     )
+    @patch(
+        "crewai.cli.authentication.main.AuthenticationCommand._determine_user_provider"
+    )
     @patch("crewai.cli.authentication.main.AuthenticationCommand._get_device_code")
     @patch(
         "crewai.cli.authentication.main.AuthenticationCommand._display_auth_instructions"
@@ -41,9 +59,11 @@ class TestAuthenticationCommand:
         mock_poll,
         mock_display,
         mock_get_device,
+        mock_determine_provider,
         user_provider,
         expected_urls,
     ):
+        mock_determine_provider.return_value = user_provider
         mock_get_device.return_value = {
             "device_code": "test_code",
             "user_code": "123456",
@@ -54,6 +74,7 @@ class TestAuthenticationCommand:
         mock_console_print.assert_called_once_with(
             "Signing in to CrewAI Enterprise...\n", style="bold blue"
         )
+        mock_determine_provider.assert_called_once()
         mock_get_device.assert_called_once()
         mock_display.assert_called_once_with(
             {"device_code": "test_code", "user_code": "123456"}
@@ -61,17 +82,9 @@ class TestAuthenticationCommand:
         mock_poll.assert_called_once_with(
             {"device_code": "test_code", "user_code": "123456"},
         )
-        assert (
+        assert self.auth_command.oauth2_provider.get_client_id() == expected_urls["client_id"]
-            self.auth_command.oauth2_provider.get_client_id()
+        assert self.auth_command.oauth2_provider.get_audience() == expected_urls["audience"]
-            == expected_urls["client_id"]
+        assert self.auth_command.oauth2_provider._get_domain() == expected_urls["domain"]
-        )
-        assert (
-            self.auth_command.oauth2_provider.get_audience()
-            == expected_urls["audience"]
-        )
-        assert (
-            self.auth_command.oauth2_provider._get_domain() == expected_urls["domain"]
-        )
 
     @patch("crewai.cli.authentication.main.webbrowser")
     @patch("crewai.cli.authentication.main.console.print")
@@ -93,6 +106,14 @@ class TestAuthenticationCommand:
     @pytest.mark.parametrize(
         "user_provider,jwt_config",
         [
+            (
+                "auth0",
+                {
+                    "jwks_url": f"https://{AUTH0_DOMAIN}/.well-known/jwks.json",
+                    "issuer": f"https://{AUTH0_DOMAIN}/",
+                    "audience": AUTH0_AUDIENCE,
+                },
+            ),
             (
                 "workos",
                 {
@@ -114,18 +135,14 @@ class TestAuthenticationCommand:
         jwt_config,
         has_expiration,
     ):
+        from crewai.cli.authentication.providers.auth0 import Auth0Provider
         from crewai.cli.authentication.providers.workos import WorkosProvider
         from crewai.cli.authentication.main import Oauth2Settings
 
-        if user_provider == "workos":
+        if user_provider == "auth0":
-            self.auth_command.oauth2_provider = WorkosProvider(
+            self.auth_command.oauth2_provider = Auth0Provider(settings=Oauth2Settings(provider=user_provider, client_id="test-client-id", domain=AUTH0_DOMAIN, audience=jwt_config["audience"]))
-                settings=Oauth2Settings(
+        elif user_provider == "workos":
-                    provider=user_provider,
+            self.auth_command.oauth2_provider = WorkosProvider(settings=Oauth2Settings(provider=user_provider, client_id="test-client-id", domain=CREWAI_ENTERPRISE_DEFAULT_OAUTH2_DOMAIN, audience=jwt_config["audience"]))
-                    client_id="test-client-id",
-                    domain=CREWAI_ENTERPRISE_DEFAULT_OAUTH2_DOMAIN,
-                    audience=jwt_config["audience"],
-                )
-            )
 
         token_data = {"access_token": "test_access_token", "id_token": "test_id_token"}
 
@@ -217,6 +234,83 @@ class TestAuthenticationCommand:
         ]
         mock_console_print.assert_has_calls(expected_calls)
 
+    @pytest.mark.parametrize(
+        "api_response,expected_provider",
+        [
+            ({"provider": "auth0"}, "auth0"),
+            ({"provider": "workos"}, "workos"),
+            ({"provider": "none"}, "workos"),  # Default to workos for any other value
+            (
+                {},
+                "workos",
+            ),  # Default to workos if no provider key is sent in the response
+        ],
+    )
+    @patch("crewai.cli.authentication.main.PlusAPI")
+    @patch("crewai.cli.authentication.main.console.print")
+    @patch("builtins.input", return_value="test@example.com")
+    def test_determine_user_provider_success(
+        self,
+        mock_input,
+        mock_console_print,
+        mock_plus_api,
+        api_response,
+        expected_provider,
+    ):
+        mock_api_instance = MagicMock()
+        mock_response = MagicMock()
+        mock_response.status_code = 200
+        mock_response.json.return_value = api_response
+        mock_api_instance._make_request.return_value = mock_response
+        mock_plus_api.return_value = mock_api_instance
+
+        result = self.auth_command._determine_user_provider()
+
+        mock_input.assert_called_once()
+
+        mock_plus_api.assert_called_once_with("")
+        mock_api_instance._make_request.assert_called_once_with(
+            "GET", "/crewai_plus/api/v1/me/provider?email=test%40example.com"
+        )
+
+        assert result == expected_provider
+
+    @patch("crewai.cli.authentication.main.PlusAPI")
+    @patch("crewai.cli.authentication.main.console.print")
+    @patch("builtins.input", return_value="test@example.com")
+    def test_determine_user_provider_error(
+        self, mock_input, mock_console_print, mock_plus_api
+    ):
+        mock_api_instance = MagicMock()
+        mock_response = MagicMock()
+        mock_response.status_code = 500
+        mock_api_instance._make_request.return_value = mock_response
+        mock_plus_api.return_value = mock_api_instance
+
+        with pytest.raises(SystemExit):
+            self.auth_command._determine_user_provider()
+
+        mock_input.assert_called_once()
+
+        mock_plus_api.assert_called_once_with("")
+        mock_api_instance._make_request.assert_called_once_with(
+            "GET", "/crewai_plus/api/v1/me/provider?email=test%40example.com"
+        )
+
+        mock_console_print.assert_has_calls(
+            [
+                call(
+                    "Enter your CrewAI Enterprise account email: ",
+                    style="bold blue",
+                    end="",
+                ),
+                call(
+                    "Error: Failed to authenticate with crewai enterprise. Ensure that you are using the latest crewai version and please try again. If the problem persists, contact support@crewai.com.",
+                    style="red",
+                ),
+            ]
+        )
+
     @patch("requests.post")
     def test_get_device_code(self, mock_post):
         mock_response = MagicMock()
@@ -229,9 +323,7 @@ class TestAuthenticationCommand:
 
         self.auth_command.oauth2_provider = MagicMock()
         self.auth_command.oauth2_provider.get_client_id.return_value = "test_client"
-        self.auth_command.oauth2_provider.get_authorize_url.return_value = (
+        self.auth_command.oauth2_provider.get_authorize_url.return_value = "https://example.com/device"
-            "https://example.com/device"
-        )
         self.auth_command.oauth2_provider.get_audience.return_value = "test_audience"
 
         result = self.auth_command._get_device_code()
@@ -274,12 +366,12 @@ class TestAuthenticationCommand:
             ) as mock_tool_login,
         ):
             self.auth_command.oauth2_provider = MagicMock()
-            self.auth_command.oauth2_provider.get_token_url.return_value = (
+            self.auth_command.oauth2_provider.get_token_url.return_value = "https://example.com/token"
-                "https://example.com/token"
-            )
             self.auth_command.oauth2_provider.get_client_id.return_value = "test_client"
 
-            self.auth_command._poll_for_token(device_code_data)
+            self.auth_command._poll_for_token(
+                device_code_data
+            )
 
             mock_post.assert_called_once_with(
                 "https://example.com/token",
@@ -314,7 +406,9 @@ class TestAuthenticationCommand:
             "interval": 0.1,  # Short interval for testing
         }
 
-        self.auth_command._poll_for_token(device_code_data)
+        self.auth_command._poll_for_token(
+            device_code_data
+        )
 
         mock_console_print.assert_any_call(
             "Timeout: Failed to get the token. Please try again.", style="bold red"
@@ -335,4 +429,15 @@ class TestAuthenticationCommand:
         device_code_data = {"device_code": "test_device_code", "interval": 1}
 
         with pytest.raises(requests.HTTPError):
-            self.auth_command._poll_for_token(device_code_data)
+            self.auth_command._poll_for_token(
+                device_code_data
+            )
+    # @patch(
+    #     "crewai.cli.authentication.main.AuthenticationCommand._determine_user_provider"
+    # )
+    # def test_login_with_auth0(self, mock_determine_provider):
+    #     from crewai.cli.authentication.providers.auth0 import Auth0Provider
+    #     from crewai.cli.authentication.main import Oauth2Settings
+
+    #     self.auth_command.oauth2_provider = Auth0Provider(settings=Oauth2Settings(provider="auth0", client_id=AUTH0_CLIENT_ID, domain=AUTH0_DOMAIN, audience=AUTH0_AUDIENCE))
+    #     self.auth_command.login()

tests/cli/authentication/test_utils.py

@@ -1,14 +1,17 @@
+import json
 import jwt
 import unittest
+from datetime import datetime, timedelta
 from unittest.mock import MagicMock, patch
 
+from cryptography.fernet import Fernet
 
-from crewai.cli.authentication.utils import validate_jwt_token
+from crewai.cli.authentication.utils import TokenManager, validate_jwt_token
 
 
 @patch("crewai.cli.authentication.utils.PyJWKClient", return_value=MagicMock())
 @patch("crewai.cli.authentication.utils.jwt")
-class TestUtils(unittest.TestCase):
+class TestValidateToken(unittest.TestCase):
     def test_validate_jwt_token(self, mock_jwt, mock_pyjwkclient):
         mock_jwt.decode.return_value = {"exp": 1719859200}
 
@@ -102,3 +105,119 @@ class TestUtils(unittest.TestCase):
                 issuer="https://mock_issuer",
                 audience="app_id_xxxx",
             )
+
+
+class TestTokenManager(unittest.TestCase):
+    def setUp(self):
+        self.token_manager = TokenManager()
+
+    @patch("crewai.cli.authentication.utils.TokenManager.read_secure_file")
+    @patch("crewai.cli.authentication.utils.TokenManager.save_secure_file")
+    @patch("crewai.cli.authentication.utils.TokenManager._get_or_create_key")
+    def test_get_or_create_key_existing(self, mock_get_or_create, mock_save, mock_read):
+        mock_key = Fernet.generate_key()
+        mock_get_or_create.return_value = mock_key
+
+        token_manager = TokenManager()
+        result = token_manager.key
+
+        self.assertEqual(result, mock_key)
+
+    @patch("crewai.cli.authentication.utils.Fernet.generate_key")
+    @patch("crewai.cli.authentication.utils.TokenManager.read_secure_file")
+    @patch("crewai.cli.authentication.utils.TokenManager.save_secure_file")
+    def test_get_or_create_key_new(self, mock_save, mock_read, mock_generate):
+        mock_key = b"new_key"
+        mock_read.return_value = None
+        mock_generate.return_value = mock_key
+
+        result = self.token_manager._get_or_create_key()
+
+        self.assertEqual(result, mock_key)
+        mock_read.assert_called_once_with("secret.key")
+        mock_generate.assert_called_once()
+        mock_save.assert_called_once_with("secret.key", mock_key)
+
+    @patch("crewai.cli.authentication.utils.TokenManager.save_secure_file")
+    def test_save_tokens(self, mock_save):
+        access_token = "test_token"
+        expires_at = int((datetime.now() + timedelta(seconds=3600)).timestamp())
+
+        self.token_manager.save_tokens(access_token, expires_at)
+
+        mock_save.assert_called_once()
+        args = mock_save.call_args[0]
+        self.assertEqual(args[0], "tokens.enc")
+        decrypted_data = self.token_manager.fernet.decrypt(args[1])
+        data = json.loads(decrypted_data)
+        self.assertEqual(data["access_token"], access_token)
+        expiration = datetime.fromisoformat(data["expiration"])
+        self.assertEqual(expiration, datetime.fromtimestamp(expires_at))
+
+    @patch("crewai.cli.authentication.utils.TokenManager.read_secure_file")
+    def test_get_token_valid(self, mock_read):
+        access_token = "test_token"
+        expiration = (datetime.now() + timedelta(hours=1)).isoformat()
+        data = {"access_token": access_token, "expiration": expiration}
+        encrypted_data = self.token_manager.fernet.encrypt(json.dumps(data).encode())
+        mock_read.return_value = encrypted_data
+
+        result = self.token_manager.get_token()
+
+        self.assertEqual(result, access_token)
+
+    @patch("crewai.cli.authentication.utils.TokenManager.read_secure_file")
+    def test_get_token_expired(self, mock_read):
+        access_token = "test_token"
+        expiration = (datetime.now() - timedelta(hours=1)).isoformat()
+        data = {"access_token": access_token, "expiration": expiration}
+        encrypted_data = self.token_manager.fernet.encrypt(json.dumps(data).encode())
+        mock_read.return_value = encrypted_data
+
+        result = self.token_manager.get_token()
+
+        self.assertIsNone(result)
+
+    @patch("crewai.cli.authentication.utils.TokenManager.get_secure_storage_path")
+    @patch("builtins.open", new_callable=unittest.mock.mock_open)
+    @patch("crewai.cli.authentication.utils.os.chmod")
+    def test_save_secure_file(self, mock_chmod, mock_open, mock_get_path):
+        mock_path = MagicMock()
+        mock_get_path.return_value = mock_path
+        filename = "test_file.txt"
+        content = b"test_content"
+
+        self.token_manager.save_secure_file(filename, content)
+
+        mock_path.__truediv__.assert_called_once_with(filename)
+        mock_open.assert_called_once_with(mock_path.__truediv__.return_value, "wb")
+        mock_open().write.assert_called_once_with(content)
+        mock_chmod.assert_called_once_with(mock_path.__truediv__.return_value, 0o600)
+
+    @patch("crewai.cli.authentication.utils.TokenManager.get_secure_storage_path")
+    @patch(
+        "builtins.open", new_callable=unittest.mock.mock_open, read_data=b"test_content"
+    )
+    def test_read_secure_file_exists(self, mock_open, mock_get_path):
+        mock_path = MagicMock()
+        mock_get_path.return_value = mock_path
+        mock_path.__truediv__.return_value.exists.return_value = True
+        filename = "test_file.txt"
+
+        result = self.token_manager.read_secure_file(filename)
+
+        self.assertEqual(result, b"test_content")
+        mock_path.__truediv__.assert_called_once_with(filename)
+        mock_open.assert_called_once_with(mock_path.__truediv__.return_value, "rb")
+
+    @patch("crewai.cli.authentication.utils.TokenManager.get_secure_storage_path")
+    def test_read_secure_file_not_exists(self, mock_get_path):
+        mock_path = MagicMock()
+        mock_get_path.return_value = mock_path
+        mock_path.__truediv__.return_value.exists.return_value = False
+        filename = "test_file.txt"
+
+        result = self.token_manager.read_secure_file(filename)
+
+        self.assertIsNone(result)
+        mock_path.__truediv__.assert_called_once_with(filename)

tests/cli/config_test.py

@@ -3,7 +3,6 @@ import shutil
 import tempfile
 import unittest
 from pathlib import Path
-from unittest.mock import patch, MagicMock
 
 from crewai.cli.config import (
     Settings,
@@ -11,8 +10,6 @@ from crewai.cli.config import (
     CLI_SETTINGS_KEYS,
     DEFAULT_CLI_SETTINGS,
 )
-from crewai.cli.shared.token_manager import TokenManager
-from datetime import datetime, timedelta
 
 
 class TestSettings(unittest.TestCase):
@@ -69,8 +66,7 @@ class TestSettings(unittest.TestCase):
         for key in user_settings.keys():
             self.assertEqual(getattr(settings, key), None)
 
-    @patch("crewai.cli.config.TokenManager")
+    def test_reset_settings(self):
-    def test_reset_settings(self, mock_token_manager):
         user_settings = {key: f"value_for_{key}" for key in USER_SETTINGS_KEYS}
         cli_settings = {key: f"value_for_{key}" for key in CLI_SETTINGS_KEYS}
 
@@ -78,11 +74,6 @@ class TestSettings(unittest.TestCase):
             config_path=self.config_path, **user_settings, **cli_settings
         )
 
-        mock_token_manager.return_value = MagicMock()
-        TokenManager().save_tokens(
-            "aaa.bbb.ccc", (datetime.now() + timedelta(seconds=36000)).timestamp()
-        )
-
         settings.reset()
 
         for key in user_settings.keys():
@@ -90,8 +81,6 @@ class TestSettings(unittest.TestCase):
         for key in cli_settings.keys():
             self.assertEqual(getattr(settings, key), DEFAULT_CLI_SETTINGS.get(key))
 
-        mock_token_manager.return_value.clear_tokens.assert_called_once()
-
     def test_dump_new_settings(self):
         settings = Settings(
             config_path=self.config_path, tool_repository_username="user1"

tests/cli/test_token_manager.py

@@ -1,138 +0,0 @@
-import json
-import unittest
-from datetime import datetime, timedelta
-from unittest.mock import MagicMock, patch
-
-from cryptography.fernet import Fernet
-
-from crewai.cli.shared.token_manager import TokenManager
-
-
-class TestTokenManager(unittest.TestCase):
-    @patch("crewai.cli.shared.token_manager.TokenManager._get_or_create_key")
-    def setUp(self, mock_get_key):
-        mock_get_key.return_value = Fernet.generate_key()
-        self.token_manager = TokenManager()
-
-    @patch("crewai.cli.shared.token_manager.TokenManager.read_secure_file")
-    @patch("crewai.cli.shared.token_manager.TokenManager.save_secure_file")
-    @patch("crewai.cli.shared.token_manager.TokenManager._get_or_create_key")
-    def test_get_or_create_key_existing(self, mock_get_or_create, mock_save, mock_read):
-        mock_key = Fernet.generate_key()
-        mock_get_or_create.return_value = mock_key
-
-        token_manager = TokenManager()
-        result = token_manager.key
-
-        self.assertEqual(result, mock_key)
-
-    @patch("crewai.cli.shared.token_manager.Fernet.generate_key")
-    @patch("crewai.cli.shared.token_manager.TokenManager.read_secure_file")
-    @patch("crewai.cli.shared.token_manager.TokenManager.save_secure_file")
-    def test_get_or_create_key_new(self, mock_save, mock_read, mock_generate):
-        mock_key = b"new_key"
-        mock_read.return_value = None
-        mock_generate.return_value = mock_key
-
-        result = self.token_manager._get_or_create_key()
-
-        self.assertEqual(result, mock_key)
-        mock_read.assert_called_once_with("secret.key")
-        mock_generate.assert_called_once()
-        mock_save.assert_called_once_with("secret.key", mock_key)
-
-    @patch("crewai.cli.shared.token_manager.TokenManager.save_secure_file")
-    def test_save_tokens(self, mock_save):
-        access_token = "test_token"
-        expires_at = int((datetime.now() + timedelta(seconds=3600)).timestamp())
-
-        self.token_manager.save_tokens(access_token, expires_at)
-
-        mock_save.assert_called_once()
-        args = mock_save.call_args[0]
-        self.assertEqual(args[0], "tokens.enc")
-        decrypted_data = self.token_manager.fernet.decrypt(args[1])
-        data = json.loads(decrypted_data)
-        self.assertEqual(data["access_token"], access_token)
-        expiration = datetime.fromisoformat(data["expiration"])
-        self.assertEqual(expiration, datetime.fromtimestamp(expires_at))
-
-    @patch("crewai.cli.shared.token_manager.TokenManager.read_secure_file")
-    def test_get_token_valid(self, mock_read):
-        access_token = "test_token"
-        expiration = (datetime.now() + timedelta(hours=1)).isoformat()
-        data = {"access_token": access_token, "expiration": expiration}
-        encrypted_data = self.token_manager.fernet.encrypt(json.dumps(data).encode())
-        mock_read.return_value = encrypted_data
-
-        result = self.token_manager.get_token()
-
-        self.assertEqual(result, access_token)
-
-    @patch("crewai.cli.shared.token_manager.TokenManager.read_secure_file")
-    def test_get_token_expired(self, mock_read):
-        access_token = "test_token"
-        expiration = (datetime.now() - timedelta(hours=1)).isoformat()
-        data = {"access_token": access_token, "expiration": expiration}
-        encrypted_data = self.token_manager.fernet.encrypt(json.dumps(data).encode())
-        mock_read.return_value = encrypted_data
-
-        result = self.token_manager.get_token()
-
-        self.assertIsNone(result)
-
-    @patch("crewai.cli.shared.token_manager.TokenManager.get_secure_storage_path")
-    @patch("builtins.open", new_callable=unittest.mock.mock_open)
-    @patch("crewai.cli.shared.token_manager.os.chmod")
-    def test_save_secure_file(self, mock_chmod, mock_open, mock_get_path):
-        mock_path = MagicMock()
-        mock_get_path.return_value = mock_path
-        filename = "test_file.txt"
-        content = b"test_content"
-
-        self.token_manager.save_secure_file(filename, content)
-
-        mock_path.__truediv__.assert_called_once_with(filename)
-        mock_open.assert_called_once_with(mock_path.__truediv__.return_value, "wb")
-        mock_open().write.assert_called_once_with(content)
-        mock_chmod.assert_called_once_with(mock_path.__truediv__.return_value, 0o600)
-
-    @patch("crewai.cli.shared.token_manager.TokenManager.get_secure_storage_path")
-    @patch(
-        "builtins.open", new_callable=unittest.mock.mock_open, read_data=b"test_content"
-    )
-    def test_read_secure_file_exists(self, mock_open, mock_get_path):
-        mock_path = MagicMock()
-        mock_get_path.return_value = mock_path
-        mock_path.__truediv__.return_value.exists.return_value = True
-        filename = "test_file.txt"
-
-        result = self.token_manager.read_secure_file(filename)
-
-        self.assertEqual(result, b"test_content")
-        mock_path.__truediv__.assert_called_once_with(filename)
-        mock_open.assert_called_once_with(mock_path.__truediv__.return_value, "rb")
-
-    @patch("crewai.cli.shared.token_manager.TokenManager.get_secure_storage_path")
-    def test_read_secure_file_not_exists(self, mock_get_path):
-        mock_path = MagicMock()
-        mock_get_path.return_value = mock_path
-        mock_path.__truediv__.return_value.exists.return_value = False
-        filename = "test_file.txt"
-
-        result = self.token_manager.read_secure_file(filename)
-
-        self.assertIsNone(result)
-        mock_path.__truediv__.assert_called_once_with(filename)
-
-    @patch("crewai.cli.shared.token_manager.TokenManager.get_secure_storage_path")
-    def test_clear_tokens(self, mock_get_path):
-        mock_path = MagicMock()
-        mock_get_path.return_value = mock_path
-
-        self.token_manager.clear_tokens()
-
-        mock_path.__truediv__.assert_called_once_with("tokens.enc")
-        mock_path.__truediv__.return_value.unlink.assert_called_once_with(
-            missing_ok=True
-        )