chore: trigger CI re-run

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

conftest.py

@@ -1,7 +1,6 @@
 """Pytest configuration for crewAI workspace."""
 
 from collections.abc import Generator
-import gzip
 import os
 from pathlib import Path
 import tempfile
@@ -32,21 +31,6 @@ def cleanup_event_handlers() -> Generator[None, Any, None]:
         pass
 
 
-@pytest.fixture(autouse=True, scope="function")
-def reset_event_state() -> None:
-    """Reset event system state before each test for isolation."""
-    from crewai.events.base_events import reset_emission_counter
-    from crewai.events.event_context import (
-        EventContextConfig,
-        _event_context_config,
-        _event_id_stack,
-    )
-
-    reset_emission_counter()
-    _event_id_stack.set(())
-    _event_context_config.set(EventContextConfig())
-
-
 @pytest.fixture(autouse=True, scope="function")
 def setup_test_environment() -> Generator[None, Any, None]:
     """Setup test environment for crewAI workspace."""
@@ -149,26 +133,14 @@ def _filter_request_headers(request: Request) -> Request:  # type: ignore[no-any
                 request.headers[variant] = [replacement]
 
     request.method = request.method.upper()
-
-    # Normalize Azure OpenAI endpoints to a consistent placeholder for cassette matching.
-    if request.host and request.host.endswith(".openai.azure.com"):
-        original_host = request.host
-        placeholder_host = "fake-azure-endpoint.openai.azure.com"
-        request.uri = request.uri.replace(original_host, placeholder_host)
-
     return request
 
 
 def _filter_response_headers(response: dict[str, Any]) -> dict[str, Any]:
     """Filter sensitive headers from response before recording."""
-
+    # Remove Content-Encoding to prevent decompression issues on replay
     for encoding_header in ["Content-Encoding", "content-encoding"]:
-        if encoding_header in response["headers"]:
-            encoding = response["headers"].pop(encoding_header)
-            if encoding and encoding[0] == "gzip":
-                body = response.get("body", {}).get("string", b"")
-                if isinstance(body, bytes) and body.startswith(b"\x1f\x8b"):
-                    response["body"]["string"] = gzip.decompress(body).decode("utf-8")
+        response["headers"].pop(encoding_header, None)
 
     for header_name, replacement in HEADERS_TO_FILTER.items():
         for variant in [header_name, header_name.upper(), header_name.title()]:

docs/en/concepts/llms.mdx

@@ -375,13 +375,10 @@ In this section, you'll find detailed examples that help you select, configure,
     GOOGLE_API_KEY=<your-api-key>
     GEMINI_API_KEY=<your-api-key>
 
-    # For Vertex AI Express mode (API key authentication)
-    GOOGLE_GENAI_USE_VERTEXAI=true
-    GOOGLE_API_KEY=<your-api-key>
-
-    # For Vertex AI with service account
+    # Optional - for Vertex AI
     GOOGLE_CLOUD_PROJECT=<your-project-id>
     GOOGLE_CLOUD_LOCATION=<location>  # Defaults to us-central1
+    GOOGLE_GENAI_USE_VERTEXAI=true  # Set to use Vertex AI
     ```
 
     **Basic Usage:**
@@ -415,35 +412,7 @@ In this section, you'll find detailed examples that help you select, configure,
     )
     ```
 
-    **Vertex AI Express Mode (API Key Authentication):**
-
-    Vertex AI Express mode allows you to use Vertex AI with simple API key authentication instead of service account credentials. This is the quickest way to get started with Vertex AI.
-
-    To enable Express mode, set both environment variables in your `.env` file:
-    ```toml .env
-    GOOGLE_GENAI_USE_VERTEXAI=true
-    GOOGLE_API_KEY=<your-api-key>
-    ```
-
-    Then use the LLM as usual:
-    ```python Code
-    from crewai import LLM
-
-    llm = LLM(
-        model="gemini/gemini-2.0-flash",
-        temperature=0.7
-    )
-    ```
-
-    <Info>
-      To get an Express mode API key:
-      - New Google Cloud users: Get an [express mode API key](https://cloud.google.com/vertex-ai/generative-ai/docs/start/quickstart?usertype=apikey)
-      - Existing Google Cloud users: Get a [Google Cloud API key bound to a service account](https://cloud.google.com/docs/authentication/api-keys)
-      
-      For more details, see the [Vertex AI Express mode documentation](https://docs.cloud.google.com/vertex-ai/generative-ai/docs/start/quickstart?usertype=apikey).
-    </Info>
-
-    **Vertex AI Configuration (Service Account):**
+    **Vertex AI Configuration:**
     ```python Code
     from crewai import LLM
 
@@ -455,10 +424,10 @@ In this section, you'll find detailed examples that help you select, configure,
     ```
 
     **Supported Environment Variables:**
-    - `GOOGLE_API_KEY` or `GEMINI_API_KEY`: Your Google API key (required for Gemini API and Vertex AI Express mode)
-    - `GOOGLE_GENAI_USE_VERTEXAI`: Set to `true` to use Vertex AI (required for Express mode)
-    - `GOOGLE_CLOUD_PROJECT`: Google Cloud project ID (for Vertex AI with service account)
+    - `GOOGLE_API_KEY` or `GEMINI_API_KEY`: Your Google API key (required for Gemini API)
+    - `GOOGLE_CLOUD_PROJECT`: Google Cloud project ID (for Vertex AI)
     - `GOOGLE_CLOUD_LOCATION`: GCP location (defaults to `us-central1`)
+    - `GOOGLE_GENAI_USE_VERTEXAI`: Set to `true` to use Vertex AI
 
     **Features:**
     - Native function calling support for Gemini 1.5+ and 2.x models

docs/ko/concepts/llms.mdx

@@ -107,7 +107,7 @@ CrewAI 코드 내에는 사용할 모델을 지정할 수 있는 여러 위치
 
 ## 공급자 구성 예시
 
-CrewAI는 고유한 기능, 인증 방법, 모델 역량을 제공하는 다양한 LLM 공급자를 지원합니다.
+CrewAI는 고유한 기능, 인증 방법, 모델 역량을 제공하는 다양한 LLM 공급자를 지원합니다.  
 이 섹션에서는 프로젝트의 요구에 가장 적합한 LLM을 선택, 구성, 최적화하는 데 도움이 되는 자세한 예시를 제공합니다.
 
 <AccordionGroup>
@@ -153,8 +153,8 @@ CrewAI는 고유한 기능, 인증 방법, 모델 역량을 제공하는 다양
   </Accordion>
 
   <Accordion title="Meta-Llama">
-    Meta의 Llama API는 Meta의 대형 언어 모델 패밀리 접근을 제공합니다.
-    API는 [Meta Llama API](https://llama.developer.meta.com?utm_source=partner-crewai&utm_medium=website)에서 사용할 수 있습니다.
+    Meta의 Llama API는 Meta의 대형 언어 모델 패밀리 접근을 제공합니다.  
+    API는 [Meta Llama API](https://llama.developer.meta.com?utm_source=partner-crewai&utm_medium=website)에서 사용할 수 있습니다.  
     `.env` 파일에 다음 환경 변수를 설정하십시오:
 
     ```toml Code
@@ -207,20 +207,11 @@ CrewAI는 고유한 기능, 인증 방법, 모델 역량을 제공하는 다양
     `.env` 파일에 API 키를 설정하십시오. 키가 필요하거나 기존 키를 찾으려면 [AI Studio](https://aistudio.google.com/apikey)를 확인하세요.
 
     ```toml .env
-    # Gemini API 사용 시 (다음 중 하나)
-    GOOGLE_API_KEY=<your-api-key>
+    # https://ai.google.dev/gemini-api/docs/api-key
     GEMINI_API_KEY=<your-api-key>
-
-    # Vertex AI Express 모드 사용 시 (API 키 인증)
-    GOOGLE_GENAI_USE_VERTEXAI=true
-    GOOGLE_API_KEY=<your-api-key>
-
-    # Vertex AI 서비스 계정 사용 시
-    GOOGLE_CLOUD_PROJECT=<your-project-id>
-    GOOGLE_CLOUD_LOCATION=<location>  # 기본값: us-central1
     ```
 
-    **기본 사용법:**
+    CrewAI 프로젝트에서의 예시 사용법:
     ```python Code
     from crewai import LLM
 
@@ -230,34 +221,6 @@ CrewAI는 고유한 기능, 인증 방법, 모델 역량을 제공하는 다양
     )
     ```
 
-    **Vertex AI Express 모드 (API 키 인증):**
-
-    Vertex AI Express 모드를 사용하면 서비스 계정 자격 증명 대신 간단한 API 키 인증으로 Vertex AI를 사용할 수 있습니다. Vertex AI를 시작하는 가장 빠른 방법입니다.
-
-    Express 모드를 활성화하려면 `.env` 파일에 두 환경 변수를 모두 설정하세요:
-    ```toml .env
-    GOOGLE_GENAI_USE_VERTEXAI=true
-    GOOGLE_API_KEY=<your-api-key>
-    ```
-
-    그런 다음 평소처럼 LLM을 사용하세요:
-    ```python Code
-    from crewai import LLM
-
-    llm = LLM(
-        model="gemini/gemini-2.0-flash",
-        temperature=0.7
-    )
-    ```
-
-    <Info>
-      Express 모드 API 키를 받으려면:
-      - 신규 Google Cloud 사용자: [Express 모드 API 키](https://cloud.google.com/vertex-ai/generative-ai/docs/start/quickstart?usertype=apikey) 받기
-      - 기존 Google Cloud 사용자: [서비스 계정에 바인딩된 Google Cloud API 키](https://cloud.google.com/docs/authentication/api-keys) 받기
-
-      자세한 내용은 [Vertex AI Express 모드 문서](https://docs.cloud.google.com/vertex-ai/generative-ai/docs/start/quickstart?usertype=apikey)를 참조하세요.
-    </Info>
-
     ### Gemini 모델
 
     Google은 다양한 용도에 최적화된 강력한 모델을 제공합니다.
@@ -513,7 +476,7 @@ CrewAI는 고유한 기능, 인증 방법, 모델 역량을 제공하는 다양
 
   <Accordion title="Local NVIDIA NIM Deployed using WSL2">
 
-    NVIDIA NIM을 이용하면 Windows 기기에서 WSL2(Windows Subsystem for Linux)를 통해 강력한 LLM을 로컬로 실행할 수 있습니다.
+    NVIDIA NIM을 이용하면 Windows 기기에서 WSL2(Windows Subsystem for Linux)를 통해 강력한 LLM을 로컬로 실행할 수 있습니다.  
     이 방식은 Nvidia GPU를 활용하여 프라이빗하고, 안전하며, 비용 효율적인 AI 추론을 클라우드 서비스에 의존하지 않고 구현할 수 있습니다.
     데이터 프라이버시, 오프라인 기능이 필요한 개발, 테스트, 또는 프로덕션 환경에 최적입니다.
 
@@ -991,4 +954,4 @@ LLM 설정을 최대한 활용하는 방법을 알아보세요:
     llm = LLM(model="openai/gpt-4o")  # 128K tokens
     ```
   </Tab>
-</Tabs>
+</Tabs>

docs/pt-BR/concepts/llms.mdx

@@ -79,7 +79,7 @@ Existem diferentes locais no código do CrewAI onde você pode especificar o mod
 
     # Configuração avançada com parâmetros detalhados
     llm = LLM(
-        model="openai/gpt-4",
+        model="openai/gpt-4", 
         temperature=0.8,
         max_tokens=150,
         top_p=0.9,
@@ -207,20 +207,11 @@ Nesta seção, você encontrará exemplos detalhados que ajudam a selecionar, co
     Defina sua chave de API no seu arquivo `.env`. Se precisar de uma chave, ou encontrar uma existente, verifique o [AI Studio](https://aistudio.google.com/apikey).
 
     ```toml .env
-    # Para API Gemini (uma das seguintes)
-    GOOGLE_API_KEY=<your-api-key>
+    # https://ai.google.dev/gemini-api/docs/api-key
     GEMINI_API_KEY=<your-api-key>
-
-    # Para Vertex AI Express mode (autenticação por chave de API)
-    GOOGLE_GENAI_USE_VERTEXAI=true
-    GOOGLE_API_KEY=<your-api-key>
-
-    # Para Vertex AI com conta de serviço
-    GOOGLE_CLOUD_PROJECT=<your-project-id>
-    GOOGLE_CLOUD_LOCATION=<location>  # Padrão: us-central1
     ```
 
-    **Uso Básico:**
+    Exemplo de uso em seu projeto CrewAI:
     ```python Code
     from crewai import LLM
 
@@ -230,34 +221,6 @@ Nesta seção, você encontrará exemplos detalhados que ajudam a selecionar, co
     )
     ```
 
-    **Vertex AI Express Mode (Autenticação por Chave de API):**
-
-    O Vertex AI Express mode permite usar o Vertex AI com autenticação simples por chave de API, em vez de credenciais de conta de serviço. Esta é a maneira mais rápida de começar com o Vertex AI.
-
-    Para habilitar o Express mode, defina ambas as variáveis de ambiente no seu arquivo `.env`:
-    ```toml .env
-    GOOGLE_GENAI_USE_VERTEXAI=true
-    GOOGLE_API_KEY=<your-api-key>
-    ```
-
-    Em seguida, use o LLM normalmente:
-    ```python Code
-    from crewai import LLM
-
-    llm = LLM(
-        model="gemini/gemini-2.0-flash",
-        temperature=0.7
-    )
-    ```
-
-    <Info>
-      Para obter uma chave de API do Express mode:
-      - Novos usuários do Google Cloud: Obtenha uma [chave de API do Express mode](https://cloud.google.com/vertex-ai/generative-ai/docs/start/quickstart?usertype=apikey)
-      - Usuários existentes do Google Cloud: Obtenha uma [chave de API do Google Cloud vinculada a uma conta de serviço](https://cloud.google.com/docs/authentication/api-keys)
-
-      Para mais detalhes, consulte a [documentação do Vertex AI Express mode](https://docs.cloud.google.com/vertex-ai/generative-ai/docs/start/quickstart?usertype=apikey).
-    </Info>
-
     ### Modelos Gemini
 
     O Google oferece uma variedade de modelos poderosos otimizados para diferentes casos de uso.
@@ -860,7 +823,7 @@ Saiba como obter o máximo da configuração do seu LLM:
       Lembre-se de monitorar regularmente o uso de tokens e ajustar suas configurações para otimizar custos e desempenho.
     </Info>
   </Accordion>
-
+  
   <Accordion title="Descartar Parâmetros Adicionais">
     O CrewAI usa Litellm internamente para chamadas LLM, permitindo descartar parâmetros adicionais desnecessários para seu caso de uso. Isso pode simplificar seu código e reduzir a complexidade da configuração do LLM.
     Por exemplo, se não precisar enviar o parâmetro <code>stop</code>, basta omiti-lo na chamada do LLM:
@@ -919,4 +882,4 @@ Saiba como obter o máximo da configuração do seu LLM:
     llm = LLM(model="openai/gpt-4o")  # 128K tokens
     ```
   </Tab>
-</Tabs>
+</Tabs>

lib/crewai/pyproject.toml

@@ -10,7 +10,7 @@ requires-python = ">=3.10, <3.14"
 dependencies = [
     # Core Dependencies
     "pydantic~=2.11.9",
-    "openai>=1.83.0,<2",
+    "openai~=1.83.0",
     "instructor>=1.3.3",
     # Text Processing
     "pdfplumber~=0.11.4",
@@ -25,7 +25,7 @@ dependencies = [
     "openpyxl~=3.1.5",
     # Authentication and Security
     "python-dotenv~=1.1.1",
-    "pyjwt>=2.9.0,<3",
+    "pyjwt~=2.9.0",
     # Configuration and Utils
     "click~=8.1.7",
     "appdirs~=1.4.4",
@@ -36,7 +36,7 @@ dependencies = [
     "json5~=0.10.0",
     "portalocker~=2.7.0",
     "pydantic-settings~=2.10.1",
-    "mcp~=1.23.1",
+    "mcp~=1.16.0",
     "uv~=0.9.13",
     "aiosqlite~=0.21.0",
 ]

lib/crewai/src/crewai/a2a/task_helpers.py

@@ -3,10 +3,9 @@
 from __future__ import annotations
 
 from collections.abc import AsyncIterator
-from typing import TYPE_CHECKING, Any, TypedDict
+from typing import TYPE_CHECKING, TypedDict
 import uuid
 
-from a2a.client.errors import A2AClientHTTPError
 from a2a.types import (
     AgentCard,
     Message,
@@ -21,10 +20,7 @@ from a2a.types import (
 from typing_extensions import NotRequired
 
 from crewai.events.event_bus import crewai_event_bus
-from crewai.events.types.a2a_events import (
-    A2AConnectionErrorEvent,
-    A2AResponseReceivedEvent,
-)
+from crewai.events.types.a2a_events import A2AResponseReceivedEvent
 
 
 if TYPE_CHECKING:
@@ -59,8 +55,7 @@ class TaskStateResult(TypedDict):
     history: list[Message]
     result: NotRequired[str]
     error: NotRequired[str]
-    agent_card: NotRequired[dict[str, Any]]
-    a2a_agent_name: NotRequired[str | None]
+    agent_card: NotRequired[AgentCard]
 
 
 def extract_task_result_parts(a2a_task: A2ATask) -> list[str]:
@@ -136,69 +131,50 @@ def process_task_state(
     is_multiturn: bool,
     agent_role: str | None,
     result_parts: list[str] | None = None,
-    endpoint: str | None = None,
-    a2a_agent_name: str | None = None,
-    from_task: Any | None = None,
-    from_agent: Any | None = None,
-    is_final: bool = True,
 ) -> TaskStateResult | None:
     """Process A2A task state and return result dictionary.
 
     Shared logic for both polling and streaming handlers.
 
     Args:
-        a2a_task: The A2A task to process.
-        new_messages: List to collect messages (modified in place).
-        agent_card: The agent card.
-        turn_number: Current turn number.
-        is_multiturn: Whether multi-turn conversation.
-        agent_role: Agent role for logging.
+        a2a_task: The A2A task to process
+        new_messages: List to collect messages (modified in place)
+        agent_card: The agent card
+        turn_number: Current turn number
+        is_multiturn: Whether multi-turn conversation
+        agent_role: Agent role for logging
         result_parts: Accumulated result parts (streaming passes accumulated,
-            polling passes None to extract from task).
-        endpoint: A2A agent endpoint URL.
-        a2a_agent_name: Name of the A2A agent from agent card.
-        from_task: Optional CrewAI Task for event metadata.
-        from_agent: Optional CrewAI Agent for event metadata.
-        is_final: Whether this is the final response in the stream.
+            polling passes None to extract from task)
 
     Returns:
-        Result dictionary if terminal/actionable state, None otherwise.
+        Result dictionary if terminal/actionable state, None otherwise
     """
+    should_extract = result_parts is None
     if result_parts is None:
         result_parts = []
 
     if a2a_task.status.state == TaskState.completed:
-        if not result_parts:
+        if should_extract:
             extracted_parts = extract_task_result_parts(a2a_task)
             result_parts.extend(extracted_parts)
         if a2a_task.history:
             new_messages.extend(a2a_task.history)
 
         response_text = " ".join(result_parts) if result_parts else ""
-        message_id = None
-        if a2a_task.status and a2a_task.status.message:
-            message_id = a2a_task.status.message.message_id
         crewai_event_bus.emit(
             None,
             A2AResponseReceivedEvent(
                 response=response_text,
                 turn_number=turn_number,
-                context_id=a2a_task.context_id,
-                message_id=message_id,
                 is_multiturn=is_multiturn,
                 status="completed",
-                final=is_final,
                 agent_role=agent_role,
-                endpoint=endpoint,
-                a2a_agent_name=a2a_agent_name,
-                from_task=from_task,
-                from_agent=from_agent,
             ),
         )
 
         return TaskStateResult(
             status=TaskState.completed,
-            agent_card=agent_card.model_dump(exclude_none=True),
+            agent_card=agent_card,
             result=response_text,
             history=new_messages,
         )
@@ -218,24 +194,14 @@ def process_task_state(
             )
             new_messages.append(agent_message)
 
-        input_message_id = None
-        if a2a_task.status and a2a_task.status.message:
-            input_message_id = a2a_task.status.message.message_id
         crewai_event_bus.emit(
             None,
             A2AResponseReceivedEvent(
                 response=response_text,
                 turn_number=turn_number,
-                context_id=a2a_task.context_id,
-                message_id=input_message_id,
                 is_multiturn=is_multiturn,
                 status="input_required",
-                final=is_final,
                 agent_role=agent_role,
-                endpoint=endpoint,
-                a2a_agent_name=a2a_agent_name,
-                from_task=from_task,
-                from_agent=from_agent,
             ),
         )
 
@@ -243,7 +209,7 @@ def process_task_state(
             status=TaskState.input_required,
             error=response_text,
             history=new_messages,
-            agent_card=agent_card.model_dump(exclude_none=True),
+            agent_card=agent_card,
         )
 
     if a2a_task.status.state in {TaskState.failed, TaskState.rejected}:
@@ -282,11 +248,6 @@ async def send_message_and_get_task_id(
     turn_number: int,
     is_multiturn: bool,
     agent_role: str | None,
-    from_task: Any | None = None,
-    from_agent: Any | None = None,
-    endpoint: str | None = None,
-    a2a_agent_name: str | None = None,
-    context_id: str | None = None,
 ) -> str | TaskStateResult:
     """Send message and process initial response.
 
@@ -301,11 +262,6 @@ async def send_message_and_get_task_id(
         turn_number: Current turn number
         is_multiturn: Whether multi-turn conversation
         agent_role: Agent role for logging
-        from_task: Optional CrewAI Task object for event metadata.
-        from_agent: Optional CrewAI Agent object for event metadata.
-        endpoint: Optional A2A endpoint URL.
-        a2a_agent_name: Optional A2A agent name.
-        context_id: Optional A2A context ID for correlation.
 
     Returns:
         Task ID string if agent needs polling/waiting, or TaskStateResult if done.
@@ -324,16 +280,9 @@ async def send_message_and_get_task_id(
                     A2AResponseReceivedEvent(
                         response=response_text,
                         turn_number=turn_number,
-                        context_id=event.context_id,
-                        message_id=event.message_id,
                         is_multiturn=is_multiturn,
                         status="completed",
-                        final=True,
                         agent_role=agent_role,
-                        endpoint=endpoint,
-                        a2a_agent_name=a2a_agent_name,
-                        from_task=from_task,
-                        from_agent=from_agent,
                     ),
                 )
 
@@ -341,7 +290,7 @@ async def send_message_and_get_task_id(
                     status=TaskState.completed,
                     result=response_text,
                     history=new_messages,
-                    agent_card=agent_card.model_dump(exclude_none=True),
+                    agent_card=agent_card,
                 )
 
             if isinstance(event, tuple):
@@ -355,10 +304,6 @@ async def send_message_and_get_task_id(
                         turn_number=turn_number,
                         is_multiturn=is_multiturn,
                         agent_role=agent_role,
-                        endpoint=endpoint,
-                        a2a_agent_name=a2a_agent_name,
-                        from_task=from_task,
-                        from_agent=from_agent,
                     )
                     if result:
                         return result
@@ -371,99 +316,6 @@ async def send_message_and_get_task_id(
             history=new_messages,
         )
 
-    except A2AClientHTTPError as e:
-        error_msg = f"HTTP Error {e.status_code}: {e!s}"
-
-        error_message = Message(
-            role=Role.agent,
-            message_id=str(uuid.uuid4()),
-            parts=[Part(root=TextPart(text=error_msg))],
-            context_id=context_id,
-        )
-        new_messages.append(error_message)
-
-        crewai_event_bus.emit(
-            None,
-            A2AConnectionErrorEvent(
-                endpoint=endpoint or "",
-                error=str(e),
-                error_type="http_error",
-                status_code=e.status_code,
-                a2a_agent_name=a2a_agent_name,
-                operation="send_message",
-                context_id=context_id,
-                from_task=from_task,
-                from_agent=from_agent,
-            ),
-        )
-        crewai_event_bus.emit(
-            None,
-            A2AResponseReceivedEvent(
-                response=error_msg,
-                turn_number=turn_number,
-                context_id=context_id,
-                is_multiturn=is_multiturn,
-                status="failed",
-                final=True,
-                agent_role=agent_role,
-                endpoint=endpoint,
-                a2a_agent_name=a2a_agent_name,
-                from_task=from_task,
-                from_agent=from_agent,
-            ),
-        )
-        return TaskStateResult(
-            status=TaskState.failed,
-            error=error_msg,
-            history=new_messages,
-        )
-
-    except Exception as e:
-        error_msg = f"Unexpected error during send_message: {e!s}"
-
-        error_message = Message(
-            role=Role.agent,
-            message_id=str(uuid.uuid4()),
-            parts=[Part(root=TextPart(text=error_msg))],
-            context_id=context_id,
-        )
-        new_messages.append(error_message)
-
-        crewai_event_bus.emit(
-            None,
-            A2AConnectionErrorEvent(
-                endpoint=endpoint or "",
-                error=str(e),
-                error_type="unexpected_error",
-                a2a_agent_name=a2a_agent_name,
-                operation="send_message",
-                context_id=context_id,
-                from_task=from_task,
-                from_agent=from_agent,
-            ),
-        )
-        crewai_event_bus.emit(
-            None,
-            A2AResponseReceivedEvent(
-                response=error_msg,
-                turn_number=turn_number,
-                context_id=context_id,
-                is_multiturn=is_multiturn,
-                status="failed",
-                final=True,
-                agent_role=agent_role,
-                endpoint=endpoint,
-                a2a_agent_name=a2a_agent_name,
-                from_task=from_task,
-                from_agent=from_agent,
-            ),
-        )
-        return TaskStateResult(
-            status=TaskState.failed,
-            error=error_msg,
-            history=new_messages,
-        )
-
     finally:
         aclose = getattr(event_stream, "aclose", None)
         if aclose:

lib/crewai/src/crewai/a2a/types.py

@@ -14,25 +14,15 @@ from typing import (
 from pydantic import BeforeValidator, HttpUrl, TypeAdapter
 from typing_extensions import NotRequired
 
-
-try:
-    from crewai.a2a.updates import (
-        PollingConfig,
-        PollingHandler,
-        PushNotificationConfig,
-        PushNotificationHandler,
-        StreamingConfig,
-        StreamingHandler,
-        UpdateConfig,
-    )
-except ImportError:
-    PollingConfig = Any  # type: ignore[misc,assignment]
-    PollingHandler = Any  # type: ignore[misc,assignment]
-    PushNotificationConfig = Any  # type: ignore[misc,assignment]
-    PushNotificationHandler = Any  # type: ignore[misc,assignment]
-    StreamingConfig = Any  # type: ignore[misc,assignment]
-    StreamingHandler = Any  # type: ignore[misc,assignment]
-    UpdateConfig = Any  # type: ignore[misc,assignment]
+from crewai.a2a.updates import (
+    PollingConfig,
+    PollingHandler,
+    PushNotificationConfig,
+    PushNotificationHandler,
+    StreamingConfig,
+    StreamingHandler,
+    UpdateConfig,
+)
 
 
 TransportType = Literal["JSONRPC", "GRPC", "HTTP+JSON"]

lib/crewai/src/crewai/a2a/updates/base.py

@@ -22,13 +22,6 @@ class BaseHandlerKwargs(TypedDict, total=False):
     turn_number: int
     is_multiturn: bool
     agent_role: str | None
-    context_id: str | None
-    task_id: str | None
-    endpoint: str | None
-    agent_branch: Any
-    a2a_agent_name: str | None
-    from_task: Any
-    from_agent: Any
 
 
 class PollingHandlerKwargs(BaseHandlerKwargs, total=False):
@@ -36,6 +29,8 @@ class PollingHandlerKwargs(BaseHandlerKwargs, total=False):
 
     polling_interval: float
     polling_timeout: float
+    endpoint: str
+    agent_branch: Any
     history_length: int
     max_polls: int | None
 
@@ -43,6 +38,9 @@ class PollingHandlerKwargs(BaseHandlerKwargs, total=False):
 class StreamingHandlerKwargs(BaseHandlerKwargs, total=False):
     """Kwargs for streaming handler."""
 
+    context_id: str | None
+    task_id: str | None
+
 
 class PushNotificationHandlerKwargs(BaseHandlerKwargs, total=False):
     """Kwargs for push notification handler."""
@@ -51,6 +49,7 @@ class PushNotificationHandlerKwargs(BaseHandlerKwargs, total=False):
     result_store: PushNotificationResultStore
     polling_timeout: float
     polling_interval: float
+    agent_branch: Any
 
 
 class PushNotificationResultStore(Protocol):

lib/crewai/src/crewai/a2a/updates/polling/handler.py

@@ -31,7 +31,6 @@ from crewai.a2a.task_helpers import (
 from crewai.a2a.updates.base import PollingHandlerKwargs
 from crewai.events.event_bus import crewai_event_bus
 from crewai.events.types.a2a_events import (
-    A2AConnectionErrorEvent,
     A2APollingStartedEvent,
     A2APollingStatusEvent,
     A2AResponseReceivedEvent,
@@ -50,33 +49,23 @@ async def _poll_task_until_complete(
     agent_branch: Any | None = None,
     history_length: int = 100,
     max_polls: int | None = None,
-    from_task: Any | None = None,
-    from_agent: Any | None = None,
-    context_id: str | None = None,
-    endpoint: str | None = None,
-    a2a_agent_name: str | None = None,
 ) -> A2ATask:
     """Poll task status until terminal state reached.
 
     Args:
-        client: A2A client instance.
-        task_id: Task ID to poll.
-        polling_interval: Seconds between poll attempts.
-        polling_timeout: Max seconds before timeout.
-        agent_branch: Agent tree branch for logging.
-        history_length: Number of messages to retrieve per poll.
-        max_polls: Max number of poll attempts (None = unlimited).
-        from_task: Optional CrewAI Task object for event metadata.
-        from_agent: Optional CrewAI Agent object for event metadata.
-        context_id: A2A context ID for correlation.
-        endpoint: A2A agent endpoint URL.
-        a2a_agent_name: Name of the A2A agent from agent card.
+        client: A2A client instance
+        task_id: Task ID to poll
+        polling_interval: Seconds between poll attempts
+        polling_timeout: Max seconds before timeout
+        agent_branch: Agent tree branch for logging
+        history_length: Number of messages to retrieve per poll
+        max_polls: Max number of poll attempts (None = unlimited)
 
     Returns:
-        Final task object in terminal state.
+        Final task object in terminal state
 
     Raises:
-        A2APollingTimeoutError: If polling exceeds timeout or max_polls.
+        A2APollingTimeoutError: If polling exceeds timeout or max_polls
     """
     start_time = time.monotonic()
     poll_count = 0
@@ -88,19 +77,13 @@ async def _poll_task_until_complete(
         )
 
         elapsed = time.monotonic() - start_time
-        effective_context_id = task.context_id or context_id
         crewai_event_bus.emit(
             agent_branch,
             A2APollingStatusEvent(
                 task_id=task_id,
-                context_id=effective_context_id,
                 state=str(task.status.state.value) if task.status.state else "unknown",
                 elapsed_seconds=elapsed,
                 poll_count=poll_count,
-                endpoint=endpoint,
-                a2a_agent_name=a2a_agent_name,
-                from_task=from_task,
-                from_agent=from_agent,
             ),
         )
 
@@ -154,9 +137,6 @@ class PollingHandler:
         max_polls = kwargs.get("max_polls")
         context_id = kwargs.get("context_id")
         task_id = kwargs.get("task_id")
-        a2a_agent_name = kwargs.get("a2a_agent_name")
-        from_task = kwargs.get("from_task")
-        from_agent = kwargs.get("from_agent")
 
         try:
             result_or_task_id = await send_message_and_get_task_id(
@@ -166,11 +146,6 @@ class PollingHandler:
                 turn_number=turn_number,
                 is_multiturn=is_multiturn,
                 agent_role=agent_role,
-                from_task=from_task,
-                from_agent=from_agent,
-                endpoint=endpoint,
-                a2a_agent_name=a2a_agent_name,
-                context_id=context_id,
             )
 
             if not isinstance(result_or_task_id, str):
@@ -182,12 +157,8 @@ class PollingHandler:
                 agent_branch,
                 A2APollingStartedEvent(
                     task_id=task_id,
-                    context_id=context_id,
                     polling_interval=polling_interval,
                     endpoint=endpoint,
-                    a2a_agent_name=a2a_agent_name,
-                    from_task=from_task,
-                    from_agent=from_agent,
                 ),
             )
 
@@ -199,11 +170,6 @@ class PollingHandler:
                 agent_branch=agent_branch,
                 history_length=history_length,
                 max_polls=max_polls,
-                from_task=from_task,
-                from_agent=from_agent,
-                context_id=context_id,
-                endpoint=endpoint,
-                a2a_agent_name=a2a_agent_name,
             )
 
             result = process_task_state(
@@ -213,10 +179,6 @@ class PollingHandler:
                 turn_number=turn_number,
                 is_multiturn=is_multiturn,
                 agent_role=agent_role,
-                endpoint=endpoint,
-                a2a_agent_name=a2a_agent_name,
-                from_task=from_task,
-                from_agent=from_agent,
             )
             if result:
                 return result
@@ -244,15 +206,9 @@ class PollingHandler:
                 A2AResponseReceivedEvent(
                     response=error_msg,
                     turn_number=turn_number,
-                    context_id=context_id,
                     is_multiturn=is_multiturn,
                     status="failed",
-                    final=True,
                     agent_role=agent_role,
-                    endpoint=endpoint,
-                    a2a_agent_name=a2a_agent_name,
-                    from_task=from_task,
-                    from_agent=from_agent,
                 ),
             )
             return TaskStateResult(
@@ -273,83 +229,14 @@ class PollingHandler:
             )
             new_messages.append(error_message)
 
-            crewai_event_bus.emit(
-                agent_branch,
-                A2AConnectionErrorEvent(
-                    endpoint=endpoint,
-                    error=str(e),
-                    error_type="http_error",
-                    status_code=e.status_code,
-                    a2a_agent_name=a2a_agent_name,
-                    operation="polling",
-                    context_id=context_id,
-                    task_id=task_id,
-                    from_task=from_task,
-                    from_agent=from_agent,
-                ),
-            )
             crewai_event_bus.emit(
                 agent_branch,
                 A2AResponseReceivedEvent(
                     response=error_msg,
                     turn_number=turn_number,
-                    context_id=context_id,
                     is_multiturn=is_multiturn,
                     status="failed",
-                    final=True,
                     agent_role=agent_role,
-                    endpoint=endpoint,
-                    a2a_agent_name=a2a_agent_name,
-                    from_task=from_task,
-                    from_agent=from_agent,
-                ),
-            )
-            return TaskStateResult(
-                status=TaskState.failed,
-                error=error_msg,
-                history=new_messages,
-            )
-
-        except Exception as e:
-            error_msg = f"Unexpected error during polling: {e!s}"
-
-            error_message = Message(
-                role=Role.agent,
-                message_id=str(uuid.uuid4()),
-                parts=[Part(root=TextPart(text=error_msg))],
-                context_id=context_id,
-                task_id=task_id,
-            )
-            new_messages.append(error_message)
-
-            crewai_event_bus.emit(
-                agent_branch,
-                A2AConnectionErrorEvent(
-                    endpoint=endpoint or "",
-                    error=str(e),
-                    error_type="unexpected_error",
-                    a2a_agent_name=a2a_agent_name,
-                    operation="polling",
-                    context_id=context_id,
-                    task_id=task_id,
-                    from_task=from_task,
-                    from_agent=from_agent,
-                ),
-            )
-            crewai_event_bus.emit(
-                agent_branch,
-                A2AResponseReceivedEvent(
-                    response=error_msg,
-                    turn_number=turn_number,
-                    context_id=context_id,
-                    is_multiturn=is_multiturn,
-                    status="failed",
-                    final=True,
-                    agent_role=agent_role,
-                    endpoint=endpoint,
-                    a2a_agent_name=a2a_agent_name,
-                    from_task=from_task,
-                    from_agent=from_agent,
                 ),
             )
             return TaskStateResult(

lib/crewai/src/crewai/a2a/updates/push_notifications/handler.py

@@ -29,7 +29,6 @@ from crewai.a2a.updates.base import (
 )
 from crewai.events.event_bus import crewai_event_bus
 from crewai.events.types.a2a_events import (
-    A2AConnectionErrorEvent,
     A2APushNotificationRegisteredEvent,
     A2APushNotificationTimeoutEvent,
     A2AResponseReceivedEvent,
@@ -49,11 +48,6 @@ async def _wait_for_push_result(
     timeout: float,
     poll_interval: float,
     agent_branch: Any | None = None,
-    from_task: Any | None = None,
-    from_agent: Any | None = None,
-    context_id: str | None = None,
-    endpoint: str | None = None,
-    a2a_agent_name: str | None = None,
 ) -> A2ATask | None:
     """Wait for push notification result.
 
@@ -63,11 +57,6 @@ async def _wait_for_push_result(
         timeout: Max seconds to wait.
         poll_interval: Seconds between polling attempts.
         agent_branch: Agent tree branch for logging.
-        from_task: Optional CrewAI Task object for event metadata.
-        from_agent: Optional CrewAI Agent object for event metadata.
-        context_id: A2A context ID for correlation.
-        endpoint: A2A agent endpoint URL.
-        a2a_agent_name: Name of the A2A agent.
 
     Returns:
         Final task object, or None if timeout.
@@ -83,12 +72,7 @@ async def _wait_for_push_result(
             agent_branch,
             A2APushNotificationTimeoutEvent(
                 task_id=task_id,
-                context_id=context_id,
                 timeout_seconds=timeout,
-                endpoint=endpoint,
-                a2a_agent_name=a2a_agent_name,
-                from_task=from_task,
-                from_agent=from_agent,
             ),
         )
 
@@ -131,56 +115,18 @@ class PushNotificationHandler:
         agent_role = kwargs.get("agent_role")
         context_id = kwargs.get("context_id")
         task_id = kwargs.get("task_id")
-        endpoint = kwargs.get("endpoint")
-        a2a_agent_name = kwargs.get("a2a_agent_name")
-        from_task = kwargs.get("from_task")
-        from_agent = kwargs.get("from_agent")
 
         if config is None:
-            error_msg = (
-                "PushNotificationConfig is required for push notification handler"
-            )
-            crewai_event_bus.emit(
-                agent_branch,
-                A2AConnectionErrorEvent(
-                    endpoint=endpoint or "",
-                    error=error_msg,
-                    error_type="configuration_error",
-                    a2a_agent_name=a2a_agent_name,
-                    operation="push_notification",
-                    context_id=context_id,
-                    task_id=task_id,
-                    from_task=from_task,
-                    from_agent=from_agent,
-                ),
-            )
             return TaskStateResult(
                 status=TaskState.failed,
-                error=error_msg,
+                error="PushNotificationConfig is required for push notification handler",
                 history=new_messages,
             )
 
         if result_store is None:
-            error_msg = (
-                "PushNotificationResultStore is required for push notification handler"
-            )
-            crewai_event_bus.emit(
-                agent_branch,
-                A2AConnectionErrorEvent(
-                    endpoint=endpoint or "",
-                    error=error_msg,
-                    error_type="configuration_error",
-                    a2a_agent_name=a2a_agent_name,
-                    operation="push_notification",
-                    context_id=context_id,
-                    task_id=task_id,
-                    from_task=from_task,
-                    from_agent=from_agent,
-                ),
-            )
             return TaskStateResult(
                 status=TaskState.failed,
-                error=error_msg,
+                error="PushNotificationResultStore is required for push notification handler",
                 history=new_messages,
             )
 
@@ -192,11 +138,6 @@ class PushNotificationHandler:
                 turn_number=turn_number,
                 is_multiturn=is_multiturn,
                 agent_role=agent_role,
-                from_task=from_task,
-                from_agent=from_agent,
-                endpoint=endpoint,
-                a2a_agent_name=a2a_agent_name,
-                context_id=context_id,
             )
 
             if not isinstance(result_or_task_id, str):
@@ -208,12 +149,7 @@ class PushNotificationHandler:
                 agent_branch,
                 A2APushNotificationRegisteredEvent(
                     task_id=task_id,
-                    context_id=context_id,
                     callback_url=str(config.url),
-                    endpoint=endpoint,
-                    a2a_agent_name=a2a_agent_name,
-                    from_task=from_task,
-                    from_agent=from_agent,
                 ),
             )
 
@@ -229,11 +165,6 @@ class PushNotificationHandler:
                 timeout=polling_timeout,
                 poll_interval=polling_interval,
                 agent_branch=agent_branch,
-                from_task=from_task,
-                from_agent=from_agent,
-                context_id=context_id,
-                endpoint=endpoint,
-                a2a_agent_name=a2a_agent_name,
             )
 
             if final_task is None:
@@ -250,10 +181,6 @@ class PushNotificationHandler:
                 turn_number=turn_number,
                 is_multiturn=is_multiturn,
                 agent_role=agent_role,
-                endpoint=endpoint,
-                a2a_agent_name=a2a_agent_name,
-                from_task=from_task,
-                from_agent=from_agent,
             )
             if result:
                 return result
@@ -276,83 +203,14 @@ class PushNotificationHandler:
             )
             new_messages.append(error_message)
 
-            crewai_event_bus.emit(
-                agent_branch,
-                A2AConnectionErrorEvent(
-                    endpoint=endpoint or "",
-                    error=str(e),
-                    error_type="http_error",
-                    status_code=e.status_code,
-                    a2a_agent_name=a2a_agent_name,
-                    operation="push_notification",
-                    context_id=context_id,
-                    task_id=task_id,
-                    from_task=from_task,
-                    from_agent=from_agent,
-                ),
-            )
             crewai_event_bus.emit(
                 agent_branch,
                 A2AResponseReceivedEvent(
                     response=error_msg,
                     turn_number=turn_number,
-                    context_id=context_id,
                     is_multiturn=is_multiturn,
                     status="failed",
-                    final=True,
                     agent_role=agent_role,
-                    endpoint=endpoint,
-                    a2a_agent_name=a2a_agent_name,
-                    from_task=from_task,
-                    from_agent=from_agent,
-                ),
-            )
-            return TaskStateResult(
-                status=TaskState.failed,
-                error=error_msg,
-                history=new_messages,
-            )
-
-        except Exception as e:
-            error_msg = f"Unexpected error during push notification: {e!s}"
-
-            error_message = Message(
-                role=Role.agent,
-                message_id=str(uuid.uuid4()),
-                parts=[Part(root=TextPart(text=error_msg))],
-                context_id=context_id,
-                task_id=task_id,
-            )
-            new_messages.append(error_message)
-
-            crewai_event_bus.emit(
-                agent_branch,
-                A2AConnectionErrorEvent(
-                    endpoint=endpoint or "",
-                    error=str(e),
-                    error_type="unexpected_error",
-                    a2a_agent_name=a2a_agent_name,
-                    operation="push_notification",
-                    context_id=context_id,
-                    task_id=task_id,
-                    from_task=from_task,
-                    from_agent=from_agent,
-                ),
-            )
-            crewai_event_bus.emit(
-                agent_branch,
-                A2AResponseReceivedEvent(
-                    response=error_msg,
-                    turn_number=turn_number,
-                    context_id=context_id,
-                    is_multiturn=is_multiturn,
-                    status="failed",
-                    final=True,
-                    agent_role=agent_role,
-                    endpoint=endpoint,
-                    a2a_agent_name=a2a_agent_name,
-                    from_task=from_task,
-                    from_agent=from_agent,
                 ),
             )
             return TaskStateResult(

lib/crewai/src/crewai/a2a/updates/streaming/handler.py

@@ -26,13 +26,7 @@ from crewai.a2a.task_helpers import (
 )
 from crewai.a2a.updates.base import StreamingHandlerKwargs
 from crewai.events.event_bus import crewai_event_bus
-from crewai.events.types.a2a_events import (
-    A2AArtifactReceivedEvent,
-    A2AConnectionErrorEvent,
-    A2AResponseReceivedEvent,
-    A2AStreamingChunkEvent,
-    A2AStreamingStartedEvent,
-)
+from crewai.events.types.a2a_events import A2AResponseReceivedEvent
 
 
 class StreamingHandler:
@@ -63,57 +57,19 @@ class StreamingHandler:
         turn_number = kwargs.get("turn_number", 0)
         is_multiturn = kwargs.get("is_multiturn", False)
         agent_role = kwargs.get("agent_role")
-        endpoint = kwargs.get("endpoint")
-        a2a_agent_name = kwargs.get("a2a_agent_name")
-        from_task = kwargs.get("from_task")
-        from_agent = kwargs.get("from_agent")
-        agent_branch = kwargs.get("agent_branch")
 
         result_parts: list[str] = []
         final_result: TaskStateResult | None = None
         event_stream = client.send_message(message)
-        chunk_index = 0
-
-        crewai_event_bus.emit(
-            agent_branch,
-            A2AStreamingStartedEvent(
-                task_id=task_id,
-                context_id=context_id,
-                endpoint=endpoint or "",
-                a2a_agent_name=a2a_agent_name,
-                turn_number=turn_number,
-                is_multiturn=is_multiturn,
-                agent_role=agent_role,
-                from_task=from_task,
-                from_agent=from_agent,
-            ),
-        )
 
         try:
             async for event in event_stream:
                 if isinstance(event, Message):
                     new_messages.append(event)
-                    message_context_id = event.context_id or context_id
                     for part in event.parts:
                         if part.root.kind == "text":
                             text = part.root.text
                             result_parts.append(text)
-                            crewai_event_bus.emit(
-                                agent_branch,
-                                A2AStreamingChunkEvent(
-                                    task_id=event.task_id or task_id,
-                                    context_id=message_context_id,
-                                    chunk=text,
-                                    chunk_index=chunk_index,
-                                    endpoint=endpoint,
-                                    a2a_agent_name=a2a_agent_name,
-                                    turn_number=turn_number,
-                                    is_multiturn=is_multiturn,
-                                    from_task=from_task,
-                                    from_agent=from_agent,
-                                ),
-                            )
-                            chunk_index += 1
 
                 elif isinstance(event, tuple):
                     a2a_task, update = event
@@ -125,51 +81,10 @@ class StreamingHandler:
                             for part in artifact.parts
                             if part.root.kind == "text"
                         )
-                        artifact_size = None
-                        if artifact.parts:
-                            artifact_size = sum(
-                                len(p.root.text.encode("utf-8"))
-                                if p.root.kind == "text"
-                                else len(getattr(p.root, "data", b""))
-                                for p in artifact.parts
-                            )
-                        effective_context_id = a2a_task.context_id or context_id
-                        crewai_event_bus.emit(
-                            agent_branch,
-                            A2AArtifactReceivedEvent(
-                                task_id=a2a_task.id,
-                                artifact_id=artifact.artifact_id,
-                                artifact_name=artifact.name,
-                                artifact_description=artifact.description,
-                                mime_type=artifact.parts[0].root.kind
-                                if artifact.parts
-                                else None,
-                                size_bytes=artifact_size,
-                                append=update.append or False,
-                                last_chunk=update.last_chunk or False,
-                                endpoint=endpoint,
-                                a2a_agent_name=a2a_agent_name,
-                                context_id=effective_context_id,
-                                turn_number=turn_number,
-                                is_multiturn=is_multiturn,
-                                from_task=from_task,
-                                from_agent=from_agent,
-                            ),
-                        )
 
                     is_final_update = False
                     if isinstance(update, TaskStatusUpdateEvent):
                         is_final_update = update.final
-                        if (
-                            update.status
-                            and update.status.message
-                            and update.status.message.parts
-                        ):
-                            result_parts.extend(
-                                part.root.text
-                                for part in update.status.message.parts
-                                if part.root.kind == "text" and part.root.text
-                            )
 
                     if (
                         not is_final_update
@@ -186,11 +101,6 @@ class StreamingHandler:
                         is_multiturn=is_multiturn,
                         agent_role=agent_role,
                         result_parts=result_parts,
-                        endpoint=endpoint,
-                        a2a_agent_name=a2a_agent_name,
-                        from_task=from_task,
-                        from_agent=from_agent,
-                        is_final=is_final_update,
                     )
                     if final_result:
                         break
@@ -208,82 +118,13 @@ class StreamingHandler:
             new_messages.append(error_message)
 
             crewai_event_bus.emit(
-                agent_branch,
-                A2AConnectionErrorEvent(
-                    endpoint=endpoint or "",
-                    error=str(e),
-                    error_type="http_error",
-                    status_code=e.status_code,
-                    a2a_agent_name=a2a_agent_name,
-                    operation="streaming",
-                    context_id=context_id,
-                    task_id=task_id,
-                    from_task=from_task,
-                    from_agent=from_agent,
-                ),
-            )
-            crewai_event_bus.emit(
-                agent_branch,
+                None,
                 A2AResponseReceivedEvent(
                     response=error_msg,
                     turn_number=turn_number,
-                    context_id=context_id,
                     is_multiturn=is_multiturn,
                     status="failed",
-                    final=True,
                     agent_role=agent_role,
-                    endpoint=endpoint,
-                    a2a_agent_name=a2a_agent_name,
-                    from_task=from_task,
-                    from_agent=from_agent,
-                ),
-            )
-            return TaskStateResult(
-                status=TaskState.failed,
-                error=error_msg,
-                history=new_messages,
-            )
-
-        except Exception as e:
-            error_msg = f"Unexpected error during streaming: {e!s}"
-
-            error_message = Message(
-                role=Role.agent,
-                message_id=str(uuid.uuid4()),
-                parts=[Part(root=TextPart(text=error_msg))],
-                context_id=context_id,
-                task_id=task_id,
-            )
-            new_messages.append(error_message)
-
-            crewai_event_bus.emit(
-                agent_branch,
-                A2AConnectionErrorEvent(
-                    endpoint=endpoint or "",
-                    error=str(e),
-                    error_type="unexpected_error",
-                    a2a_agent_name=a2a_agent_name,
-                    operation="streaming",
-                    context_id=context_id,
-                    task_id=task_id,
-                    from_task=from_task,
-                    from_agent=from_agent,
-                ),
-            )
-            crewai_event_bus.emit(
-                agent_branch,
-                A2AResponseReceivedEvent(
-                    response=error_msg,
-                    turn_number=turn_number,
-                    context_id=context_id,
-                    is_multiturn=is_multiturn,
-                    status="failed",
-                    final=True,
-                    agent_role=agent_role,
-                    endpoint=endpoint,
-                    a2a_agent_name=a2a_agent_name,
-                    from_task=from_task,
-                    from_agent=from_agent,
                 ),
             )
             return TaskStateResult(
@@ -295,23 +136,7 @@ class StreamingHandler:
         finally:
             aclose = getattr(event_stream, "aclose", None)
             if aclose:
-                try:
-                    await aclose()
-                except Exception as close_error:
-                    crewai_event_bus.emit(
-                        agent_branch,
-                        A2AConnectionErrorEvent(
-                            endpoint=endpoint or "",
-                            error=str(close_error),
-                            error_type="stream_close_error",
-                            a2a_agent_name=a2a_agent_name,
-                            operation="stream_close",
-                            context_id=context_id,
-                            task_id=task_id,
-                            from_task=from_task,
-                            from_agent=from_agent,
-                        ),
-                    )
+                await aclose()
 
         if final_result:
             return final_result
@@ -320,5 +145,5 @@ class StreamingHandler:
             status=TaskState.completed,
             result=" ".join(result_parts) if result_parts else "",
             history=new_messages,
-            agent_card=agent_card.model_dump(exclude_none=True),
+            agent_card=agent_card,
         )

lib/crewai/src/crewai/a2a/utils/agent_card.py

@@ -23,12 +23,6 @@ from crewai.a2a.auth.utils import (
 )
 from crewai.a2a.config import A2AServerConfig
 from crewai.crew import Crew
-from crewai.events.event_bus import crewai_event_bus
-from crewai.events.types.a2a_events import (
-    A2AAgentCardFetchedEvent,
-    A2AAuthenticationFailedEvent,
-    A2AConnectionErrorEvent,
-)
 
 
 if TYPE_CHECKING:
@@ -189,8 +183,6 @@ async def _afetch_agent_card_impl(
     timeout: int,
 ) -> AgentCard:
     """Internal async implementation of AgentCard fetching."""
-    start_time = time.perf_counter()
-
     if "/.well-known/agent-card.json" in endpoint:
         base_url = endpoint.replace("/.well-known/agent-card.json", "")
         agent_card_path = "/.well-known/agent-card.json"
@@ -225,29 +217,9 @@ async def _afetch_agent_card_impl(
             )
             response.raise_for_status()
 
-            agent_card = AgentCard.model_validate(response.json())
-            fetch_time_ms = (time.perf_counter() - start_time) * 1000
-            agent_card_dict = agent_card.model_dump(exclude_none=True)
-
-            crewai_event_bus.emit(
-                None,
-                A2AAgentCardFetchedEvent(
-                    endpoint=endpoint,
-                    a2a_agent_name=agent_card.name,
-                    agent_card=agent_card_dict,
-                    protocol_version=agent_card.protocol_version,
-                    provider=agent_card_dict.get("provider"),
-                    cached=False,
-                    fetch_time_ms=fetch_time_ms,
-                ),
-            )
-
-            return agent_card
+            return AgentCard.model_validate(response.json())
 
         except httpx.HTTPStatusError as e:
-            elapsed_ms = (time.perf_counter() - start_time) * 1000
-            response_body = e.response.text[:1000] if e.response.text else None
-
             if e.response.status_code == 401:
                 error_details = ["Authentication failed"]
                 www_auth = e.response.headers.get("WWW-Authenticate")
@@ -256,93 +228,7 @@ async def _afetch_agent_card_impl(
                 if not auth:
                     error_details.append("No auth scheme provided")
                 msg = " | ".join(error_details)
-
-                auth_type = type(auth).__name__ if auth else None
-                crewai_event_bus.emit(
-                    None,
-                    A2AAuthenticationFailedEvent(
-                        endpoint=endpoint,
-                        auth_type=auth_type,
-                        error=msg,
-                        status_code=401,
-                        metadata={
-                            "elapsed_ms": elapsed_ms,
-                            "response_body": response_body,
-                            "www_authenticate": www_auth,
-                            "request_url": str(e.request.url),
-                        },
-                    ),
-                )
-
                 raise A2AClientHTTPError(401, msg) from e
-
-            crewai_event_bus.emit(
-                None,
-                A2AConnectionErrorEvent(
-                    endpoint=endpoint,
-                    error=str(e),
-                    error_type="http_error",
-                    status_code=e.response.status_code,
-                    operation="fetch_agent_card",
-                    metadata={
-                        "elapsed_ms": elapsed_ms,
-                        "response_body": response_body,
-                        "request_url": str(e.request.url),
-                    },
-                ),
-            )
-            raise
-
-        except httpx.TimeoutException as e:
-            elapsed_ms = (time.perf_counter() - start_time) * 1000
-            crewai_event_bus.emit(
-                None,
-                A2AConnectionErrorEvent(
-                    endpoint=endpoint,
-                    error=str(e),
-                    error_type="timeout",
-                    operation="fetch_agent_card",
-                    metadata={
-                        "elapsed_ms": elapsed_ms,
-                        "timeout_config": timeout,
-                        "request_url": str(e.request.url) if e.request else None,
-                    },
-                ),
-            )
-            raise
-
-        except httpx.ConnectError as e:
-            elapsed_ms = (time.perf_counter() - start_time) * 1000
-            crewai_event_bus.emit(
-                None,
-                A2AConnectionErrorEvent(
-                    endpoint=endpoint,
-                    error=str(e),
-                    error_type="connection_error",
-                    operation="fetch_agent_card",
-                    metadata={
-                        "elapsed_ms": elapsed_ms,
-                        "request_url": str(e.request.url) if e.request else None,
-                    },
-                ),
-            )
-            raise
-
-        except httpx.RequestError as e:
-            elapsed_ms = (time.perf_counter() - start_time) * 1000
-            crewai_event_bus.emit(
-                None,
-                A2AConnectionErrorEvent(
-                    endpoint=endpoint,
-                    error=str(e),
-                    error_type="request_error",
-                    operation="fetch_agent_card",
-                    metadata={
-                        "elapsed_ms": elapsed_ms,
-                        "request_url": str(e.request.url) if e.request else None,
-                    },
-                ),
-            )
             raise
 
 

lib/crewai/src/crewai/a2a/utils/delegation.py

@@ -88,9 +88,6 @@ def execute_a2a_delegation(
     response_model: type[BaseModel] | None = None,
     turn_number: int | None = None,
     updates: UpdateConfig | None = None,
-    from_task: Any | None = None,
-    from_agent: Any | None = None,
-    skill_id: str | None = None,
 ) -> TaskStateResult:
     """Execute a task delegation to a remote A2A agent synchronously.
 
@@ -132,9 +129,6 @@ def execute_a2a_delegation(
         response_model: Optional Pydantic model for structured outputs.
         turn_number: Optional turn number for multi-turn conversations.
         updates: Update mechanism config from A2AConfig.updates.
-        from_task: Optional CrewAI Task object for event metadata.
-        from_agent: Optional CrewAI Agent object for event metadata.
-        skill_id: Optional skill ID to target a specific agent capability.
 
     Returns:
         TaskStateResult with status, result/error, history, and agent_card.
@@ -162,16 +156,10 @@ def execute_a2a_delegation(
                 transport_protocol=transport_protocol,
                 turn_number=turn_number,
                 updates=updates,
-                from_task=from_task,
-                from_agent=from_agent,
-                skill_id=skill_id,
             )
         )
     finally:
-        try:
-            loop.run_until_complete(loop.shutdown_asyncgens())
-        finally:
-            loop.close()
+        loop.close()
 
 
 async def aexecute_a2a_delegation(
@@ -193,9 +181,6 @@ async def aexecute_a2a_delegation(
     response_model: type[BaseModel] | None = None,
     turn_number: int | None = None,
     updates: UpdateConfig | None = None,
-    from_task: Any | None = None,
-    from_agent: Any | None = None,
-    skill_id: str | None = None,
 ) -> TaskStateResult:
     """Execute a task delegation to a remote A2A agent asynchronously.
 
@@ -237,9 +222,6 @@ async def aexecute_a2a_delegation(
         response_model: Optional Pydantic model for structured outputs.
         turn_number: Optional turn number for multi-turn conversations.
         updates: Update mechanism config from A2AConfig.updates.
-        from_task: Optional CrewAI Task object for event metadata.
-        from_agent: Optional CrewAI Agent object for event metadata.
-        skill_id: Optional skill ID to target a specific agent capability.
 
     Returns:
         TaskStateResult with status, result/error, history, and agent_card.
@@ -251,66 +233,46 @@ async def aexecute_a2a_delegation(
     if turn_number is None:
         turn_number = len([m for m in conversation_history if m.role == Role.user]) + 1
 
-    try:
-        result = await _aexecute_a2a_delegation_impl(
+    crewai_event_bus.emit(
+        agent_branch,
+        A2ADelegationStartedEvent(
             endpoint=endpoint,
-            auth=auth,
-            timeout=timeout,
             task_description=task_description,
-            context=context,
-            context_id=context_id,
-            task_id=task_id,
-            reference_task_ids=reference_task_ids,
-            metadata=metadata,
-            extensions=extensions,
-            conversation_history=conversation_history,
+            agent_id=agent_id,
             is_multiturn=is_multiturn,
             turn_number=turn_number,
-            agent_branch=agent_branch,
-            agent_id=agent_id,
-            agent_role=agent_role,
-            response_model=response_model,
-            updates=updates,
-            transport_protocol=transport_protocol,
-            from_task=from_task,
-            from_agent=from_agent,
-            skill_id=skill_id,
-        )
-    except Exception as e:
-        crewai_event_bus.emit(
-            agent_branch,
-            A2ADelegationCompletedEvent(
-                status="failed",
-                result=None,
-                error=str(e),
-                context_id=context_id,
-                is_multiturn=is_multiturn,
-                endpoint=endpoint,
-                metadata=metadata,
-                extensions=list(extensions.keys()) if extensions else None,
-                from_task=from_task,
-                from_agent=from_agent,
-            ),
-        )
-        raise
+        ),
+    )
+
+    result = await _aexecute_a2a_delegation_impl(
+        endpoint=endpoint,
+        auth=auth,
+        timeout=timeout,
+        task_description=task_description,
+        context=context,
+        context_id=context_id,
+        task_id=task_id,
+        reference_task_ids=reference_task_ids,
+        metadata=metadata,
+        extensions=extensions,
+        conversation_history=conversation_history,
+        is_multiturn=is_multiturn,
+        turn_number=turn_number,
+        agent_branch=agent_branch,
+        agent_id=agent_id,
+        agent_role=agent_role,
+        response_model=response_model,
+        updates=updates,
+        transport_protocol=transport_protocol,
+    )
 
-    agent_card_data: dict[str, Any] = result.get("agent_card") or {}
     crewai_event_bus.emit(
         agent_branch,
         A2ADelegationCompletedEvent(
             status=result["status"],
             result=result.get("result"),
             error=result.get("error"),
-            context_id=context_id,
             is_multiturn=is_multiturn,
-            endpoint=endpoint,
-            a2a_agent_name=result.get("a2a_agent_name"),
-            agent_card=agent_card_data,
-            provider=agent_card_data.get("provider"),
-            metadata=metadata,
-            extensions=list(extensions.keys()) if extensions else None,
-            from_task=from_task,
-            from_agent=from_agent,
         ),
     )
 
@@ -337,9 +299,6 @@ async def _aexecute_a2a_delegation_impl(
     agent_role: str | None,
     response_model: type[BaseModel] | None,
     updates: UpdateConfig | None,
-    from_task: Any | None = None,
-    from_agent: Any | None = None,
-    skill_id: str | None = None,
 ) -> TaskStateResult:
     """Internal async implementation of A2A delegation."""
     if auth:
@@ -372,28 +331,6 @@ async def _aexecute_a2a_delegation_impl(
     if agent_card.name:
         a2a_agent_name = agent_card.name
 
-    agent_card_dict = agent_card.model_dump(exclude_none=True)
-    crewai_event_bus.emit(
-        agent_branch,
-        A2ADelegationStartedEvent(
-            endpoint=endpoint,
-            task_description=task_description,
-            agent_id=agent_id or endpoint,
-            context_id=context_id,
-            is_multiturn=is_multiturn,
-            turn_number=turn_number,
-            a2a_agent_name=a2a_agent_name,
-            agent_card=agent_card_dict,
-            protocol_version=agent_card.protocol_version,
-            provider=agent_card_dict.get("provider"),
-            skill_id=skill_id,
-            metadata=metadata,
-            extensions=list(extensions.keys()) if extensions else None,
-            from_task=from_task,
-            from_agent=from_agent,
-        ),
-    )
-
     if turn_number == 1:
         agent_id_for_event = agent_id or endpoint
         crewai_event_bus.emit(
@@ -401,17 +338,7 @@ async def _aexecute_a2a_delegation_impl(
             A2AConversationStartedEvent(
                 agent_id=agent_id_for_event,
                 endpoint=endpoint,
-                context_id=context_id,
                 a2a_agent_name=a2a_agent_name,
-                agent_card=agent_card_dict,
-                protocol_version=agent_card.protocol_version,
-                provider=agent_card_dict.get("provider"),
-                skill_id=skill_id,
-                reference_task_ids=reference_task_ids,
-                metadata=metadata,
-                extensions=list(extensions.keys()) if extensions else None,
-                from_task=from_task,
-                from_agent=from_agent,
             ),
         )
 
@@ -437,10 +364,6 @@ async def _aexecute_a2a_delegation_impl(
             }
         )
 
-    message_metadata = metadata.copy() if metadata else {}
-    if skill_id:
-        message_metadata["skill_id"] = skill_id
-
     message = Message(
         role=Role.user,
         message_id=str(uuid.uuid4()),
@@ -448,7 +371,7 @@ async def _aexecute_a2a_delegation_impl(
         context_id=context_id,
         task_id=task_id,
         reference_task_ids=reference_task_ids,
-        metadata=message_metadata if message_metadata else None,
+        metadata=metadata,
         extensions=extensions,
     )
 
@@ -458,17 +381,8 @@ async def _aexecute_a2a_delegation_impl(
         A2AMessageSentEvent(
             message=message_text,
             turn_number=turn_number,
-            context_id=context_id,
-            message_id=message.message_id,
             is_multiturn=is_multiturn,
             agent_role=agent_role,
-            endpoint=endpoint,
-            a2a_agent_name=a2a_agent_name,
-            skill_id=skill_id,
-            metadata=message_metadata if message_metadata else None,
-            extensions=list(extensions.keys()) if extensions else None,
-            from_task=from_task,
-            from_agent=from_agent,
         ),
     )
 
@@ -483,9 +397,6 @@ async def _aexecute_a2a_delegation_impl(
         "task_id": task_id,
         "endpoint": endpoint,
         "agent_branch": agent_branch,
-        "a2a_agent_name": a2a_agent_name,
-        "from_task": from_task,
-        "from_agent": from_agent,
     }
 
     if isinstance(updates, PollingConfig):
@@ -523,16 +434,13 @@ async def _aexecute_a2a_delegation_impl(
         use_polling=use_polling,
         push_notification_config=push_config_for_client,
     ) as client:
-        result = await handler.execute(
+        return await handler.execute(
             client=client,
             message=message,
             new_messages=new_messages,
             agent_card=agent_card,
             **handler_kwargs,
         )
-        result["a2a_agent_name"] = a2a_agent_name
-        result["agent_card"] = agent_card.model_dump(exclude_none=True)
-        return result
 
 
 @asynccontextmanager

lib/crewai/src/crewai/a2a/utils/task.py

@@ -3,14 +3,11 @@
 from __future__ import annotations
 
 import asyncio
-import base64
 from collections.abc import Callable, Coroutine
-from datetime import datetime
 from functools import wraps
 import logging
 import os
 from typing import TYPE_CHECKING, Any, ParamSpec, TypeVar, cast
-from urllib.parse import urlparse
 
 from a2a.server.agent_execution import RequestContext
 from a2a.server.events import EventQueue
@@ -48,14 +45,7 @@ T = TypeVar("T")
 
 
 def _parse_redis_url(url: str) -> dict[str, Any]:
-    """Parse a Redis URL into aiocache configuration.
-
-    Args:
-        url: Redis connection URL (e.g., redis://localhost:6379/0).
-
-    Returns:
-        Configuration dict for aiocache.RedisCache.
-    """
+    from urllib.parse import urlparse
 
     parsed = urlparse(url)
     config: dict[str, Any] = {
@@ -137,7 +127,7 @@ def cancellable(
                 async for message in pubsub.listen():
                     if message["type"] == "message":
                         return True
-            except (OSError, ConnectionError) as e:
+            except Exception as e:
                 logger.warning("Cancel watcher error for task_id=%s: %s", task_id, e)
                 return await poll_for_cancel()
             return False
@@ -193,12 +183,7 @@ async def execute(
         msg = "task_id and context_id are required"
         crewai_event_bus.emit(
             agent,
-            A2AServerTaskFailedEvent(
-                task_id="",
-                context_id="",
-                error=msg,
-                from_agent=agent,
-            ),
+            A2AServerTaskFailedEvent(a2a_task_id="", a2a_context_id="", error=msg),
         )
         raise ServerError(InvalidParamsError(message=msg)) from None
 
@@ -210,12 +195,7 @@ async def execute(
 
     crewai_event_bus.emit(
         agent,
-        A2AServerTaskStartedEvent(
-            task_id=task_id,
-            context_id=context_id,
-            from_task=task,
-            from_agent=agent,
-        ),
+        A2AServerTaskStartedEvent(a2a_task_id=task_id, a2a_context_id=context_id),
     )
 
     try:
@@ -235,33 +215,20 @@ async def execute(
         crewai_event_bus.emit(
             agent,
             A2AServerTaskCompletedEvent(
-                task_id=task_id,
-                context_id=context_id,
-                result=str(result),
-                from_task=task,
-                from_agent=agent,
+                a2a_task_id=task_id, a2a_context_id=context_id, result=str(result)
             ),
         )
     except asyncio.CancelledError:
         crewai_event_bus.emit(
             agent,
-            A2AServerTaskCanceledEvent(
-                task_id=task_id,
-                context_id=context_id,
-                from_task=task,
-                from_agent=agent,
-            ),
+            A2AServerTaskCanceledEvent(a2a_task_id=task_id, a2a_context_id=context_id),
         )
         raise
     except Exception as e:
         crewai_event_bus.emit(
             agent,
             A2AServerTaskFailedEvent(
-                task_id=task_id,
-                context_id=context_id,
-                error=str(e),
-                from_task=task,
-                from_agent=agent,
+                a2a_task_id=task_id, a2a_context_id=context_id, error=str(e)
             ),
         )
         raise ServerError(
@@ -315,85 +282,3 @@ async def cancel(
         context.current_task.status = TaskStatus(state=TaskState.canceled)
         return context.current_task
     return None
-
-
-def list_tasks(
-    tasks: list[A2ATask],
-    context_id: str | None = None,
-    status: TaskState | None = None,
-    status_timestamp_after: datetime | None = None,
-    page_size: int = 50,
-    page_token: str | None = None,
-    history_length: int | None = None,
-    include_artifacts: bool = False,
-) -> tuple[list[A2ATask], str | None, int]:
-    """Filter and paginate A2A tasks.
-
-    Provides filtering by context, status, and timestamp, along with
-    cursor-based pagination. This is a pure utility function that operates
-    on an in-memory list of tasks - storage retrieval is handled separately.
-
-    Args:
-        tasks: All tasks to filter.
-        context_id: Filter by context ID to get tasks in a conversation.
-        status: Filter by task state (e.g., completed, working).
-        status_timestamp_after: Filter to tasks updated after this time.
-        page_size: Maximum tasks per page (default 50).
-        page_token: Base64-encoded cursor from previous response.
-        history_length: Limit history messages per task (None = full history).
-        include_artifacts: Whether to include task artifacts (default False).
-
-    Returns:
-        Tuple of (filtered_tasks, next_page_token, total_count).
-        - filtered_tasks: Tasks matching filters, paginated and trimmed.
-        - next_page_token: Token for next page, or None if no more pages.
-        - total_count: Total number of tasks matching filters (before pagination).
-    """
-    filtered: list[A2ATask] = []
-    for task in tasks:
-        if context_id and task.context_id != context_id:
-            continue
-        if status and task.status.state != status:
-            continue
-        if status_timestamp_after and task.status.timestamp:
-            ts = datetime.fromisoformat(task.status.timestamp.replace("Z", "+00:00"))
-            if ts <= status_timestamp_after:
-                continue
-        filtered.append(task)
-
-    def get_timestamp(t: A2ATask) -> datetime:
-        """Extract timestamp from task status for sorting."""
-        if t.status.timestamp is None:
-            return datetime.min
-        return datetime.fromisoformat(t.status.timestamp.replace("Z", "+00:00"))
-
-    filtered.sort(key=get_timestamp, reverse=True)
-    total = len(filtered)
-
-    start = 0
-    if page_token:
-        try:
-            cursor_id = base64.b64decode(page_token).decode()
-            for idx, task in enumerate(filtered):
-                if task.id == cursor_id:
-                    start = idx + 1
-                    break
-        except (ValueError, UnicodeDecodeError):
-            pass
-
-    page = filtered[start : start + page_size]
-
-    result: list[A2ATask] = []
-    for task in page:
-        task = task.model_copy(deep=True)
-        if history_length is not None and task.history:
-            task.history = task.history[-history_length:]
-        if not include_artifacts:
-            task.artifacts = None
-        result.append(task)
-
-    next_token: str | None = None
-    if result and len(result) == page_size:
-        next_token = base64.b64encode(result[-1].id.encode()).decode()
-
-    return result, next_token, total

lib/crewai/src/crewai/a2a/wrapper.py

@@ -6,10 +6,9 @@ Wraps agent classes with A2A delegation capabilities.
 from __future__ import annotations
 
 import asyncio
-from collections.abc import Callable, Coroutine, Mapping
+from collections.abc import Callable, Coroutine
 from concurrent.futures import ThreadPoolExecutor, as_completed
 from functools import wraps
-import json
 from types import MethodType
 from typing import TYPE_CHECKING, Any
 
@@ -190,7 +189,7 @@ def _execute_task_with_a2a(
     a2a_agents: list[A2AConfig | A2AClientConfig],
     original_fn: Callable[..., str],
     task: Task,
-    agent_response_model: type[BaseModel] | None,
+    agent_response_model: type[BaseModel],
     context: str | None,
     tools: list[BaseTool] | None,
     extension_registry: ExtensionRegistry,
@@ -278,7 +277,7 @@ def _execute_task_with_a2a(
 def _augment_prompt_with_a2a(
     a2a_agents: list[A2AConfig | A2AClientConfig],
     task_description: str,
-    agent_cards: Mapping[str, AgentCard | dict[str, Any]],
+    agent_cards: dict[str, AgentCard],
     conversation_history: list[Message] | None = None,
     turn_num: int = 0,
     max_turns: int | None = None,
@@ -310,15 +309,7 @@ def _augment_prompt_with_a2a(
     for config in a2a_agents:
         if config.endpoint in agent_cards:
             card = agent_cards[config.endpoint]
-            if isinstance(card, dict):
-                filtered = {
-                    k: v
-                    for k, v in card.items()
-                    if k in {"description", "url", "skills"} and v is not None
-                }
-                agents_text += f"\n{json.dumps(filtered, indent=2)}\n"
-            else:
-                agents_text += f"\n{card.model_dump_json(indent=2, exclude_none=True, include={'description', 'url', 'skills'})}\n"
+            agents_text += f"\n{card.model_dump_json(indent=2, exclude_none=True, include={'description', 'url', 'skills'})}\n"
 
     failed_agents = failed_agents or {}
     if failed_agents:
@@ -386,7 +377,7 @@ IMPORTANT: You have the ability to delegate this task to remote A2A agents.
 
 
 def _parse_agent_response(
-    raw_result: str | dict[str, Any], agent_response_model: type[BaseModel] | None
+    raw_result: str | dict[str, Any], agent_response_model: type[BaseModel]
 ) -> BaseModel | str | dict[str, Any]:
     """Parse LLM output as AgentResponse or return raw agent response."""
     if agent_response_model:
@@ -403,11 +394,6 @@ def _parse_agent_response(
 def _handle_max_turns_exceeded(
     conversation_history: list[Message],
     max_turns: int,
-    from_task: Any | None = None,
-    from_agent: Any | None = None,
-    endpoint: str | None = None,
-    a2a_agent_name: str | None = None,
-    agent_card: dict[str, Any] | None = None,
 ) -> str:
     """Handle the case when max turns is exceeded.
 
@@ -435,11 +421,6 @@ def _handle_max_turns_exceeded(
                         final_result=final_message,
                         error=None,
                         total_turns=max_turns,
-                        from_task=from_task,
-                        from_agent=from_agent,
-                        endpoint=endpoint,
-                        a2a_agent_name=a2a_agent_name,
-                        agent_card=agent_card,
                     ),
                 )
                 return final_message
@@ -451,11 +432,6 @@ def _handle_max_turns_exceeded(
             final_result=None,
             error=f"Conversation exceeded maximum turns ({max_turns})",
             total_turns=max_turns,
-            from_task=from_task,
-            from_agent=from_agent,
-            endpoint=endpoint,
-            a2a_agent_name=a2a_agent_name,
-            agent_card=agent_card,
         ),
     )
     raise Exception(f"A2A conversation exceeded maximum turns ({max_turns})")
@@ -466,12 +442,7 @@ def _process_response_result(
     disable_structured_output: bool,
     turn_num: int,
     agent_role: str,
-    agent_response_model: type[BaseModel] | None,
-    from_task: Any | None = None,
-    from_agent: Any | None = None,
-    endpoint: str | None = None,
-    a2a_agent_name: str | None = None,
-    agent_card: dict[str, Any] | None = None,
+    agent_response_model: type[BaseModel],
 ) -> tuple[str | None, str | None]:
     """Process LLM response and determine next action.
 
@@ -490,10 +461,6 @@ def _process_response_result(
                 turn_number=final_turn_number,
                 is_multiturn=True,
                 agent_role=agent_role,
-                from_task=from_task,
-                from_agent=from_agent,
-                endpoint=endpoint,
-                a2a_agent_name=a2a_agent_name,
             ),
         )
         crewai_event_bus.emit(
@@ -503,11 +470,6 @@ def _process_response_result(
                 final_result=result_text,
                 error=None,
                 total_turns=final_turn_number,
-                from_task=from_task,
-                from_agent=from_agent,
-                endpoint=endpoint,
-                a2a_agent_name=a2a_agent_name,
-                agent_card=agent_card,
             ),
         )
         return result_text, None
@@ -528,10 +490,6 @@ def _process_response_result(
                     turn_number=final_turn_number,
                     is_multiturn=True,
                     agent_role=agent_role,
-                    from_task=from_task,
-                    from_agent=from_agent,
-                    endpoint=endpoint,
-                    a2a_agent_name=a2a_agent_name,
                 ),
             )
             crewai_event_bus.emit(
@@ -541,11 +499,6 @@ def _process_response_result(
                     final_result=str(llm_response.message),
                     error=None,
                     total_turns=final_turn_number,
-                    from_task=from_task,
-                    from_agent=from_agent,
-                    endpoint=endpoint,
-                    a2a_agent_name=a2a_agent_name,
-                    agent_card=agent_card,
                 ),
             )
             return str(llm_response.message), None
@@ -557,15 +510,13 @@ def _process_response_result(
 def _prepare_agent_cards_dict(
     a2a_result: TaskStateResult,
     agent_id: str,
-    agent_cards: Mapping[str, AgentCard | dict[str, Any]] | None,
-) -> dict[str, AgentCard | dict[str, Any]]:
+    agent_cards: dict[str, AgentCard] | None,
+) -> dict[str, AgentCard]:
     """Prepare agent cards dictionary from result and existing cards.
 
     Shared logic for both sync and async response handlers.
     """
-    agent_cards_dict: dict[str, AgentCard | dict[str, Any]] = (
-        dict(agent_cards) if agent_cards else {}
-    )
+    agent_cards_dict = agent_cards or {}
     if "agent_card" in a2a_result and agent_id not in agent_cards_dict:
         agent_cards_dict[agent_id] = a2a_result["agent_card"]
     return agent_cards_dict
@@ -578,7 +529,7 @@ def _prepare_delegation_context(
     original_task_description: str | None,
 ) -> tuple[
     list[A2AConfig | A2AClientConfig],
-    type[BaseModel] | None,
+    type[BaseModel],
     str,
     str,
     A2AConfig | A2AClientConfig,
@@ -647,11 +598,6 @@ def _handle_task_completion(
     reference_task_ids: list[str],
     agent_config: A2AConfig | A2AClientConfig,
     turn_num: int,
-    from_task: Any | None = None,
-    from_agent: Any | None = None,
-    endpoint: str | None = None,
-    a2a_agent_name: str | None = None,
-    agent_card: dict[str, Any] | None = None,
 ) -> tuple[str | None, str | None, list[str]]:
     """Handle task completion state including reference task updates.
 
@@ -678,11 +624,6 @@ def _handle_task_completion(
                     final_result=result_text,
                     error=None,
                     total_turns=final_turn_number,
-                    from_task=from_task,
-                    from_agent=from_agent,
-                    endpoint=endpoint,
-                    a2a_agent_name=a2a_agent_name,
-                    agent_card=agent_card,
                 ),
             )
             return str(result_text), task_id_config, reference_task_ids
@@ -704,11 +645,8 @@ def _handle_agent_response_and_continue(
     original_fn: Callable[..., str],
     context: str | None,
     tools: list[BaseTool] | None,
-    agent_response_model: type[BaseModel] | None,
+    agent_response_model: type[BaseModel],
     remote_task_completed: bool = False,
-    endpoint: str | None = None,
-    a2a_agent_name: str | None = None,
-    agent_card: dict[str, Any] | None = None,
 ) -> tuple[str | None, str | None]:
     """Handle A2A result and get CrewAI agent's response.
 
@@ -760,11 +698,6 @@ def _handle_agent_response_and_continue(
         turn_num=turn_num,
         agent_role=self.role,
         agent_response_model=agent_response_model,
-        from_task=task,
-        from_agent=self,
-        endpoint=endpoint,
-        a2a_agent_name=a2a_agent_name,
-        agent_card=agent_card,
     )
 
 
@@ -817,12 +750,6 @@ def _delegate_to_a2a(
 
     conversation_history: list[Message] = []
 
-    current_agent_card = agent_cards.get(agent_id) if agent_cards else None
-    current_agent_card_dict = (
-        current_agent_card.model_dump() if current_agent_card else None
-    )
-    current_a2a_agent_name = current_agent_card.name if current_agent_card else None
-
     try:
         for turn_num in range(max_turns):
             console_formatter = getattr(crewai_event_bus, "_console", None)
@@ -850,8 +777,6 @@ def _delegate_to_a2a(
                 turn_number=turn_num + 1,
                 updates=agent_config.updates,
                 transport_protocol=agent_config.transport_protocol,
-                from_task=task,
-                from_agent=self,
             )
 
             conversation_history = a2a_result.get("history", [])
@@ -872,11 +797,6 @@ def _delegate_to_a2a(
                         reference_task_ids,
                         agent_config,
                         turn_num,
-                        from_task=task,
-                        from_agent=self,
-                        endpoint=agent_config.endpoint,
-                        a2a_agent_name=current_a2a_agent_name,
-                        agent_card=current_agent_card_dict,
                     )
                 )
                 if trusted_result is not None:
@@ -898,9 +818,6 @@ def _delegate_to_a2a(
                     tools=tools,
                     agent_response_model=agent_response_model,
                     remote_task_completed=(a2a_result["status"] == TaskState.completed),
-                    endpoint=agent_config.endpoint,
-                    a2a_agent_name=current_a2a_agent_name,
-                    agent_card=current_agent_card_dict,
                 )
 
                 if final_result is not None:
@@ -929,9 +846,6 @@ def _delegate_to_a2a(
                 tools=tools,
                 agent_response_model=agent_response_model,
                 remote_task_completed=False,
-                endpoint=agent_config.endpoint,
-                a2a_agent_name=current_a2a_agent_name,
-                agent_card=current_agent_card_dict,
             )
 
             if final_result is not None:
@@ -948,24 +862,11 @@ def _delegate_to_a2a(
                     final_result=None,
                     error=error_msg,
                     total_turns=turn_num + 1,
-                    from_task=task,
-                    from_agent=self,
-                    endpoint=agent_config.endpoint,
-                    a2a_agent_name=current_a2a_agent_name,
-                    agent_card=current_agent_card_dict,
                 ),
             )
             return f"A2A delegation failed: {error_msg}"
 
-        return _handle_max_turns_exceeded(
-            conversation_history,
-            max_turns,
-            from_task=task,
-            from_agent=self,
-            endpoint=agent_config.endpoint,
-            a2a_agent_name=current_a2a_agent_name,
-            agent_card=current_agent_card_dict,
-        )
+        return _handle_max_turns_exceeded(conversation_history, max_turns)
 
     finally:
         task.description = original_task_description
@@ -1015,7 +916,7 @@ async def _aexecute_task_with_a2a(
     a2a_agents: list[A2AConfig | A2AClientConfig],
     original_fn: Callable[..., Coroutine[Any, Any, str]],
     task: Task,
-    agent_response_model: type[BaseModel] | None,
+    agent_response_model: type[BaseModel],
     context: str | None,
     tools: list[BaseTool] | None,
     extension_registry: ExtensionRegistry,
@@ -1100,11 +1001,8 @@ async def _ahandle_agent_response_and_continue(
     original_fn: Callable[..., Coroutine[Any, Any, str]],
     context: str | None,
     tools: list[BaseTool] | None,
-    agent_response_model: type[BaseModel] | None,
+    agent_response_model: type[BaseModel],
     remote_task_completed: bool = False,
-    endpoint: str | None = None,
-    a2a_agent_name: str | None = None,
-    agent_card: dict[str, Any] | None = None,
 ) -> tuple[str | None, str | None]:
     """Async version of _handle_agent_response_and_continue."""
     agent_cards_dict = _prepare_agent_cards_dict(a2a_result, agent_id, agent_cards)
@@ -1134,11 +1032,6 @@ async def _ahandle_agent_response_and_continue(
         turn_num=turn_num,
         agent_role=self.role,
         agent_response_model=agent_response_model,
-        from_task=task,
-        from_agent=self,
-        endpoint=endpoint,
-        a2a_agent_name=a2a_agent_name,
-        agent_card=agent_card,
     )
 
 
@@ -1173,12 +1066,6 @@ async def _adelegate_to_a2a(
 
     conversation_history: list[Message] = []
 
-    current_agent_card = agent_cards.get(agent_id) if agent_cards else None
-    current_agent_card_dict = (
-        current_agent_card.model_dump() if current_agent_card else None
-    )
-    current_a2a_agent_name = current_agent_card.name if current_agent_card else None
-
     try:
         for turn_num in range(max_turns):
             console_formatter = getattr(crewai_event_bus, "_console", None)
@@ -1206,8 +1093,6 @@ async def _adelegate_to_a2a(
                 turn_number=turn_num + 1,
                 transport_protocol=agent_config.transport_protocol,
                 updates=agent_config.updates,
-                from_task=task,
-                from_agent=self,
             )
 
             conversation_history = a2a_result.get("history", [])
@@ -1228,11 +1113,6 @@ async def _adelegate_to_a2a(
                         reference_task_ids,
                         agent_config,
                         turn_num,
-                        from_task=task,
-                        from_agent=self,
-                        endpoint=agent_config.endpoint,
-                        a2a_agent_name=current_a2a_agent_name,
-                        agent_card=current_agent_card_dict,
                     )
                 )
                 if trusted_result is not None:
@@ -1254,9 +1134,6 @@ async def _adelegate_to_a2a(
                     tools=tools,
                     agent_response_model=agent_response_model,
                     remote_task_completed=(a2a_result["status"] == TaskState.completed),
-                    endpoint=agent_config.endpoint,
-                    a2a_agent_name=current_a2a_agent_name,
-                    agent_card=current_agent_card_dict,
                 )
 
                 if final_result is not None:
@@ -1284,9 +1161,6 @@ async def _adelegate_to_a2a(
                 context=context,
                 tools=tools,
                 agent_response_model=agent_response_model,
-                endpoint=agent_config.endpoint,
-                a2a_agent_name=current_a2a_agent_name,
-                agent_card=current_agent_card_dict,
             )
 
             if final_result is not None:
@@ -1303,24 +1177,11 @@ async def _adelegate_to_a2a(
                     final_result=None,
                     error=error_msg,
                     total_turns=turn_num + 1,
-                    from_task=task,
-                    from_agent=self,
-                    endpoint=agent_config.endpoint,
-                    a2a_agent_name=current_a2a_agent_name,
-                    agent_card=current_agent_card_dict,
                 ),
             )
             return f"A2A delegation failed: {error_msg}"
 
-        return _handle_max_turns_exceeded(
-            conversation_history,
-            max_turns,
-            from_task=task,
-            from_agent=self,
-            endpoint=agent_config.endpoint,
-            a2a_agent_name=current_a2a_agent_name,
-            agent_card=current_agent_card_dict,
-        )
+        return _handle_max_turns_exceeded(conversation_history, max_turns)
 
     finally:
         task.description = original_task_description

lib/crewai/src/crewai/agent/core.py

@@ -1,7 +1,7 @@
 from __future__ import annotations
 
 import asyncio
-from collections.abc import Callable, Coroutine, Sequence
+from collections.abc import Callable, Sequence
 import shutil
 import subprocess
 import time
@@ -14,14 +14,7 @@ from typing import (
 )
 from urllib.parse import urlparse
 
-from pydantic import (
-    BaseModel,
-    ConfigDict,
-    Field,
-    InstanceOf,
-    PrivateAttr,
-    model_validator,
-)
+from pydantic import BaseModel, Field, InstanceOf, PrivateAttr, model_validator
 from typing_extensions import Self
 
 from crewai.agent.utils import (
@@ -41,11 +34,6 @@ from crewai.agents.agent_builder.base_agent import BaseAgent
 from crewai.agents.cache.cache_handler import CacheHandler
 from crewai.agents.crew_agent_executor import CrewAgentExecutor
 from crewai.events.event_bus import crewai_event_bus
-from crewai.events.types.agent_events import (
-    LiteAgentExecutionCompletedEvent,
-    LiteAgentExecutionErrorEvent,
-    LiteAgentExecutionStartedEvent,
-)
 from crewai.events.types.knowledge_events import (
     KnowledgeQueryCompletedEvent,
     KnowledgeQueryFailedEvent,
@@ -53,13 +41,12 @@ from crewai.events.types.knowledge_events import (
 )
 from crewai.events.types.memory_events import (
     MemoryRetrievalCompletedEvent,
-    MemoryRetrievalFailedEvent,
     MemoryRetrievalStartedEvent,
 )
-from crewai.experimental.agent_executor import AgentExecutor
+from crewai.experimental.crew_agent_executor_flow import CrewAgentExecutorFlow
 from crewai.knowledge.knowledge import Knowledge
 from crewai.knowledge.source.base_knowledge_source import BaseKnowledgeSource
-from crewai.lite_agent_output import LiteAgentOutput
+from crewai.lite_agent import LiteAgent
 from crewai.llms.base_llm import BaseLLM
 from crewai.mcp import (
     MCPClient,
@@ -77,31 +64,34 @@ from crewai.security.fingerprint import Fingerprint
 from crewai.tools.agent_tools.agent_tools import AgentTools
 from crewai.utilities.agent_utils import (
     get_tool_names,
-    is_inside_event_loop,
     load_agent_from_repository,
     parse_tools,
     render_text_description_and_args,
 )
 from crewai.utilities.constants import TRAINED_AGENTS_DATA_FILE, TRAINING_DATA_FILE
-from crewai.utilities.converter import Converter, ConverterError
-from crewai.utilities.guardrail import process_guardrail
+from crewai.utilities.converter import Converter
 from crewai.utilities.guardrail_types import GuardrailType
 from crewai.utilities.llm_utils import create_llm
 from crewai.utilities.prompts import Prompts, StandardPromptResult, SystemPromptResult
-from crewai.utilities.pydantic_schema_utils import generate_model_description
-from crewai.utilities.string_utils import sanitize_tool_name
 from crewai.utilities.token_counter_callback import TokenCalcHandler
 from crewai.utilities.training_handler import CrewTrainingHandler
 
 
+try:
+    from crewai.a2a.config import A2AClientConfig, A2AConfig, A2AServerConfig
+except ImportError:
+    A2AClientConfig = Any
+    A2AConfig = Any
+    A2AServerConfig = Any
+
+
 if TYPE_CHECKING:
     from crewai_tools import CodeInterpreterTool
 
-    from crewai.a2a.config import A2AClientConfig, A2AConfig, A2AServerConfig
     from crewai.agents.agent_builder.base_agent import PlatformAppOrAction
+    from crewai.lite_agent_output import LiteAgentOutput
     from crewai.task import Task
     from crewai.tools.base_tool import BaseTool
-    from crewai.tools.structured_tool import CrewStructuredTool
     from crewai.utilities.types import LLMMessage
 
 
@@ -123,7 +113,7 @@ class Agent(BaseAgent):
     The agent can also have memory, can operate in verbose mode, and can delegate tasks to other agents.
 
     Attributes:
-            agent_executor: An instance of the CrewAgentExecutor or AgentExecutor class.
+            agent_executor: An instance of the CrewAgentExecutor or CrewAgentExecutorFlow class.
             role: The role of the agent.
             goal: The objective of the agent.
             backstory: The backstory of the agent.
@@ -143,8 +133,6 @@ class Agent(BaseAgent):
             mcps: List of MCP server references for tool integration.
     """
 
-    model_config = ConfigDict()
-
     _times_executed: int = PrivateAttr(default=0)
     _mcp_clients: list[Any] = PrivateAttr(default_factory=list)
     _last_messages: list[LLMMessage] = PrivateAttr(default_factory=list)
@@ -250,9 +238,9 @@ class Agent(BaseAgent):
         Can be a single A2AConfig/A2AClientConfig/A2AServerConfig, or a list of any number of A2AConfig/A2AClientConfig with a single A2AServerConfig.
         """,
     )
-    executor_class: type[CrewAgentExecutor] | type[AgentExecutor] = Field(
+    executor_class: type[CrewAgentExecutor] | type[CrewAgentExecutorFlow] = Field(
         default=CrewAgentExecutor,
-        description="Class to use for the agent executor. Defaults to CrewAgentExecutor, can optionally use AgentExecutor.",
+        description="Class to use for the agent executor. Defaults to CrewAgentExecutor, can optionally use CrewAgentExecutorFlow.",
     )
 
     @model_validator(mode="before")
@@ -315,22 +303,6 @@ class Agent(BaseAgent):
 
         return any(getattr(self.crew, attr) for attr in memory_attributes)
 
-    def _supports_native_tool_calling(self, tools: list[BaseTool]) -> bool:
-        """Check if the LLM supports native function calling with the given tools.
-
-        Args:
-            tools: List of tools to check against.
-
-        Returns:
-            True if native function calling is supported and tools are available.
-        """
-        return (
-            hasattr(self.llm, "supports_function_calling")
-            and callable(getattr(self.llm, "supports_function_calling", None))
-            and self.llm.supports_function_calling()
-            and len(tools) > 0
-        )
-
     def execute_task(
         self,
         task: Task,
@@ -374,43 +346,30 @@ class Agent(BaseAgent):
             )
 
             start_time = time.time()
-            memory = ""
 
-            try:
-                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 or "")
-                if memory.strip() != "":
-                    task_prompt += self.i18n.slice("memory").format(memory=memory)
+            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 or "")
+            if memory.strip() != "":
+                task_prompt += self.i18n.slice("memory").format(memory=memory)
 
-                crewai_event_bus.emit(
-                    self,
-                    event=MemoryRetrievalCompletedEvent(
-                        task_id=str(task.id) if task else None,
-                        memory_content=memory,
-                        retrieval_time_ms=(time.time() - start_time) * 1000,
-                        source_type="agent",
-                        from_agent=self,
-                        from_task=task,
-                    ),
-                )
-            except Exception as e:
-                crewai_event_bus.emit(
-                    self,
-                    event=MemoryRetrievalFailedEvent(
-                        task_id=str(task.id) if task else None,
-                        source_type="agent",
-                        from_agent=self,
-                        from_task=task,
-                        error=str(e),
-                    ),
-                )
+            crewai_event_bus.emit(
+                self,
+                event=MemoryRetrievalCompletedEvent(
+                    task_id=str(task.id) if task else None,
+                    memory_content=memory,
+                    retrieval_time_ms=(time.time() - start_time) * 1000,
+                    source_type="agent",
+                    from_agent=self,
+                    from_task=task,
+                ),
+            )
 
         knowledge_config = get_knowledge_config(self)
         task_prompt = handle_knowledge_retrieval(
@@ -596,45 +555,32 @@ class Agent(BaseAgent):
             )
 
             start_time = time.time()
-            memory = ""
 
-            try:
-                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 = await contextual_memory.abuild_context_for_task(
-                    task, context or ""
-                )
-                if memory.strip() != "":
-                    task_prompt += self.i18n.slice("memory").format(memory=memory)
+            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 = await contextual_memory.abuild_context_for_task(
+                task, context or ""
+            )
+            if memory.strip() != "":
+                task_prompt += self.i18n.slice("memory").format(memory=memory)
 
-                crewai_event_bus.emit(
-                    self,
-                    event=MemoryRetrievalCompletedEvent(
-                        task_id=str(task.id) if task else None,
-                        memory_content=memory,
-                        retrieval_time_ms=(time.time() - start_time) * 1000,
-                        source_type="agent",
-                        from_agent=self,
-                        from_task=task,
-                    ),
-                )
-            except Exception as e:
-                crewai_event_bus.emit(
-                    self,
-                    event=MemoryRetrievalFailedEvent(
-                        task_id=str(task.id) if task else None,
-                        source_type="agent",
-                        from_agent=self,
-                        from_task=task,
-                        error=str(e),
-                    ),
-                )
+            crewai_event_bus.emit(
+                self,
+                event=MemoryRetrievalCompletedEvent(
+                    task_id=str(task.id) if task else None,
+                    memory_content=memory,
+                    retrieval_time_ms=(time.time() - start_time) * 1000,
+                    source_type="agent",
+                    from_agent=self,
+                    from_task=task,
+                ),
+            )
 
         knowledge_config = get_knowledge_config(self)
         task_prompt = await ahandle_knowledge_retrieval(
@@ -779,12 +725,9 @@ class Agent(BaseAgent):
         raw_tools: list[BaseTool] = tools or self.tools or []
         parsed_tools = parse_tools(raw_tools)
 
-        use_native_tool_calling = self._supports_native_tool_calling(raw_tools)
-
         prompt = Prompts(
             agent=self,
             has_tools=len(raw_tools) > 0,
-            use_native_tool_calling=use_native_tool_calling,
             i18n=self.i18n,
             use_system_prompt=self.use_system_prompt,
             system_template=self.system_template,
@@ -1340,10 +1283,10 @@ class Agent(BaseAgent):
                     args_schema = None
                     if hasattr(tool, "inputSchema") and tool.inputSchema:
                         args_schema = self._json_schema_to_pydantic(
-                            sanitize_tool_name(tool.name), tool.inputSchema
+                            tool.name, tool.inputSchema
                         )
 
-                    schemas[sanitize_tool_name(tool.name)] = {
+                    schemas[tool.name] = {
                         "description": getattr(tool, "description", ""),
                         "args_schema": args_schema,
                     }
@@ -1499,7 +1442,7 @@ class Agent(BaseAgent):
         """
         return "\n".join(
             [
-                f"Tool name: {sanitize_tool_name(tool.name)}\nTool description:\n{tool.description}"
+                f"Tool name: {tool.name}\nTool description:\n{tool.description}"
                 for tool in tools
             ]
         )
@@ -1640,25 +1583,26 @@ class Agent(BaseAgent):
             )
             return None
 
-    def _prepare_kickoff(
+    def kickoff(
         self,
         messages: str | list[LLMMessage],
         response_format: type[Any] | None = None,
-    ) -> tuple[AgentExecutor, dict[str, str], dict[str, Any], list[CrewStructuredTool]]:
-        """Prepare common setup for kickoff execution.
+    ) -> LiteAgentOutput:
+        """
+        Execute the agent with the given messages using a LiteAgent instance.
 
-        This method handles all the common preparation logic shared between
-        kickoff() and kickoff_async(), including tool processing, prompt building,
-        executor creation, and input formatting.
+        This method is useful when you want to use the Agent configuration but
+        with the simpler and more direct execution flow of LiteAgent.
 
         Args:
             messages: Either a string query or a list of message dictionaries.
+                     If a string is provided, it will be converted to a user message.
+                     If a list is provided, each dict should have 'role' and 'content' keys.
             response_format: Optional Pydantic model for structured output.
 
         Returns:
-            Tuple of (executor, inputs, agent_info, parsed_tools) ready for execution.
+            LiteAgentOutput: The result of the agent execution.
         """
-        # Process platform apps and MCP tools
         if self.apps:
             platform_tools = self.get_platform_tools(self.apps)
             if platform_tools and self.tools is not None:
@@ -1668,360 +1612,25 @@ class Agent(BaseAgent):
             if mcps and self.tools is not None:
                 self.tools.extend(mcps)
 
-        # Prepare tools
-        raw_tools: list[BaseTool] = self.tools or []
-        parsed_tools = parse_tools(raw_tools)
-
-        # Build agent_info for backward-compatible event emission
-        agent_info = {
-            "id": self.id,
-            "role": self.role,
-            "goal": self.goal,
-            "backstory": self.backstory,
-            "tools": raw_tools,
-            "verbose": self.verbose,
-        }
-
-        # Build prompt for standalone execution
-        use_native_tool_calling = self._supports_native_tool_calling(raw_tools)
-        prompt = Prompts(
-            agent=self,
-            has_tools=len(raw_tools) > 0,
-            use_native_tool_calling=use_native_tool_calling,
-            i18n=self.i18n,
-            use_system_prompt=self.use_system_prompt,
-            system_template=self.system_template,
-            prompt_template=self.prompt_template,
-            response_template=self.response_template,
-        ).task_execution()
-
-        # Prepare stop words
-        stop_words = [self.i18n.slice("observation")]
-        if self.response_template:
-            stop_words.append(
-                self.response_template.split("{{ .Response }}")[1].strip()
-            )
-
-        # Get RPM limit function
-        rpm_limit_fn = (
-            self._rpm_controller.check_or_wait if self._rpm_controller else None
-        )
-
-        # Create the executor for standalone mode (no crew, no task)
-        executor = AgentExecutor(
-            task=None,
-            crew=None,
-            llm=cast(BaseLLM, self.llm),
-            agent=self,
-            prompt=prompt,
-            max_iter=self.max_iter,
-            tools=parsed_tools,
-            tools_names=get_tool_names(parsed_tools),
-            stop_words=stop_words,
-            tools_description=render_text_description_and_args(parsed_tools),
-            tools_handler=self.tools_handler,
-            original_tools=raw_tools,
-            step_callback=self.step_callback,
-            function_calling_llm=self.function_calling_llm,
+        lite_agent = LiteAgent(
+            id=self.id,
+            role=self.role,
+            goal=self.goal,
+            backstory=self.backstory,
+            llm=self.llm,
+            tools=self.tools or [],
+            max_iterations=self.max_iter,
+            max_execution_time=self.max_execution_time,
             respect_context_window=self.respect_context_window,
-            request_within_rpm_limit=rpm_limit_fn,
-            callbacks=[TokenCalcHandler(self._token_process)],
-            response_model=response_format,
+            verbose=self.verbose,
+            response_format=response_format,
             i18n=self.i18n,
+            original_agent=self,
+            guardrail=self.guardrail,
+            guardrail_max_retries=self.guardrail_max_retries,
         )
 
-        # Format messages
-        if isinstance(messages, str):
-            formatted_messages = messages
-        else:
-            formatted_messages = "\n".join(
-                str(msg.get("content", "")) for msg in messages if msg.get("content")
-            )
-
-        # Build the input dict for the executor
-        inputs = {
-            "input": formatted_messages,
-            "tool_names": get_tool_names(parsed_tools),
-            "tools": render_text_description_and_args(parsed_tools),
-        }
-
-        return executor, inputs, agent_info, parsed_tools
-
-    def kickoff(
-        self,
-        messages: str | list[LLMMessage],
-        response_format: type[Any] | None = None,
-    ) -> LiteAgentOutput | Coroutine[Any, Any, LiteAgentOutput]:
-        """
-        Execute the agent with the given messages using the AgentExecutor.
-
-        This method provides standalone agent execution without requiring a Crew.
-        It supports tools, response formatting, and guardrails.
-
-        When called from within a Flow (sync or async method), this automatically
-        detects the event loop and returns a coroutine that the Flow framework
-        awaits. Users don't need to handle async explicitly.
-
-        Args:
-            messages: Either a string query or a list of message dictionaries.
-                     If a string is provided, it will be converted to a user message.
-                     If a list is provided, each dict should have 'role' and 'content' keys.
-            response_format: Optional Pydantic model for structured output.
-
-        Returns:
-            LiteAgentOutput: The result of the agent execution.
-            When inside a Flow, returns a coroutine that resolves to LiteAgentOutput.
-
-        Note:
-            For explicit async usage outside of Flow, use kickoff_async() directly.
-        """
-        # Magic auto-async: if inside event loop (e.g., inside a Flow),
-        # return coroutine for Flow to await
-        if is_inside_event_loop():
-            return self.kickoff_async(messages, response_format)
-
-        executor, inputs, agent_info, parsed_tools = self._prepare_kickoff(
-            messages, response_format
-        )
-
-        try:
-            crewai_event_bus.emit(
-                self,
-                event=LiteAgentExecutionStartedEvent(
-                    agent_info=agent_info,
-                    tools=parsed_tools,
-                    messages=messages,
-                ),
-            )
-
-            output = self._execute_and_build_output(executor, inputs, response_format)
-            if self.guardrail is not None:
-                output = self._process_kickoff_guardrail(
-                    output=output,
-                    executor=executor,
-                    inputs=inputs,
-                    response_format=response_format,
-                )
-
-            crewai_event_bus.emit(
-                self,
-                event=LiteAgentExecutionCompletedEvent(
-                    agent_info=agent_info,
-                    output=output.raw,
-                ),
-            )
-
-            return output
-
-        except Exception as e:
-            crewai_event_bus.emit(
-                self,
-                event=LiteAgentExecutionErrorEvent(
-                    agent_info=agent_info,
-                    error=str(e),
-                ),
-            )
-            raise
-
-    def _execute_and_build_output(
-        self,
-        executor: AgentExecutor,
-        inputs: dict[str, str],
-        response_format: type[Any] | None = None,
-    ) -> LiteAgentOutput:
-        """Execute the agent and build the output object.
-
-        Args:
-            executor: The executor instance.
-            inputs: Input dictionary for execution.
-            response_format: Optional response format.
-
-        Returns:
-            LiteAgentOutput with raw output, formatted result, and metrics.
-        """
-        import json
-
-        # Execute the agent (this is called from sync path, so invoke returns dict)
-        result = cast(dict[str, Any], executor.invoke(inputs))
-        raw_output = result.get("output", "")
-
-        # Handle response format conversion
-        formatted_result: BaseModel | None = None
-        if response_format:
-            try:
-                model_schema = generate_model_description(response_format)
-                schema = json.dumps(model_schema, indent=2)
-                instructions = self.i18n.slice("formatted_task_instructions").format(
-                    output_format=schema
-                )
-
-                converter = Converter(
-                    llm=self.llm,
-                    text=raw_output,
-                    model=response_format,
-                    instructions=instructions,
-                )
-
-                conversion_result = converter.to_pydantic()
-                if isinstance(conversion_result, BaseModel):
-                    formatted_result = conversion_result
-            except ConverterError:
-                pass  # Keep raw output if conversion fails
-
-        # Get token usage metrics
-        if isinstance(self.llm, BaseLLM):
-            usage_metrics = self.llm.get_token_usage_summary()
-        else:
-            usage_metrics = self._token_process.get_summary()
-
-        return LiteAgentOutput(
-            raw=raw_output,
-            pydantic=formatted_result,
-            agent_role=self.role,
-            usage_metrics=usage_metrics.model_dump() if usage_metrics else None,
-            messages=executor.messages,
-        )
-
-    async def _execute_and_build_output_async(
-        self,
-        executor: AgentExecutor,
-        inputs: dict[str, str],
-        response_format: type[Any] | None = None,
-    ) -> LiteAgentOutput:
-        """Execute the agent asynchronously and build the output object.
-
-        This is the async version of _execute_and_build_output that uses
-        invoke_async() for native async execution within event loops.
-
-        Args:
-            executor: The executor instance.
-            inputs: Input dictionary for execution.
-            response_format: Optional response format.
-
-        Returns:
-            LiteAgentOutput with raw output, formatted result, and metrics.
-        """
-        import json
-
-        # Execute the agent asynchronously
-        result = await executor.invoke_async(inputs)
-        raw_output = result.get("output", "")
-
-        # Handle response format conversion
-        formatted_result: BaseModel | None = None
-        if response_format:
-            try:
-                model_schema = generate_model_description(response_format)
-                schema = json.dumps(model_schema, indent=2)
-                instructions = self.i18n.slice("formatted_task_instructions").format(
-                    output_format=schema
-                )
-
-                converter = Converter(
-                    llm=self.llm,
-                    text=raw_output,
-                    model=response_format,
-                    instructions=instructions,
-                )
-
-                conversion_result = converter.to_pydantic()
-                if isinstance(conversion_result, BaseModel):
-                    formatted_result = conversion_result
-            except ConverterError:
-                pass  # Keep raw output if conversion fails
-
-        # Get token usage metrics
-        if isinstance(self.llm, BaseLLM):
-            usage_metrics = self.llm.get_token_usage_summary()
-        else:
-            usage_metrics = self._token_process.get_summary()
-
-        return LiteAgentOutput(
-            raw=raw_output,
-            pydantic=formatted_result,
-            agent_role=self.role,
-            usage_metrics=usage_metrics.model_dump() if usage_metrics else None,
-            messages=executor.messages,
-        )
-
-    def _process_kickoff_guardrail(
-        self,
-        output: LiteAgentOutput,
-        executor: AgentExecutor,
-        inputs: dict[str, str],
-        response_format: type[Any] | None = None,
-        retry_count: int = 0,
-    ) -> LiteAgentOutput:
-        """Process guardrail for kickoff execution with retry logic.
-
-        Args:
-            output: Current agent output.
-            executor: The executor instance.
-            inputs: Input dictionary for re-execution.
-            response_format: Optional response format.
-            retry_count: Current retry count.
-
-        Returns:
-            Validated/updated output.
-        """
-        from crewai.utilities.guardrail_types import GuardrailCallable
-
-        # Ensure guardrail is callable
-        guardrail_callable: GuardrailCallable
-        if isinstance(self.guardrail, str):
-            from crewai.tasks.llm_guardrail import LLMGuardrail
-
-            guardrail_callable = cast(
-                GuardrailCallable,
-                LLMGuardrail(description=self.guardrail, llm=cast(BaseLLM, self.llm)),
-            )
-        elif callable(self.guardrail):
-            guardrail_callable = self.guardrail
-        else:
-            # Should not happen if called from kickoff with guardrail check
-            return output
-
-        guardrail_result = process_guardrail(
-            output=output,
-            guardrail=guardrail_callable,
-            retry_count=retry_count,
-            event_source=self,
-            from_agent=self,
-        )
-
-        if not guardrail_result.success:
-            if retry_count >= self.guardrail_max_retries:
-                raise ValueError(
-                    f"Agent's guardrail failed validation after {self.guardrail_max_retries} retries. "
-                    f"Last error: {guardrail_result.error}"
-                )
-
-            # Add feedback and re-execute
-            executor._append_message_to_state(
-                guardrail_result.error or "Guardrail validation failed",
-                role="user",
-            )
-
-            # Re-execute and build new output
-            output = self._execute_and_build_output(executor, inputs, response_format)
-
-            # Recursively retry guardrail
-            return self._process_kickoff_guardrail(
-                output=output,
-                executor=executor,
-                inputs=inputs,
-                response_format=response_format,
-                retry_count=retry_count + 1,
-            )
-
-        # Apply guardrail result if available
-        if guardrail_result.result is not None:
-            if isinstance(guardrail_result.result, str):
-                output.raw = guardrail_result.result
-            elif isinstance(guardrail_result.result, BaseModel):
-                output.pydantic = guardrail_result.result
-
-        return output
+        return lite_agent.kickoff(messages)
 
     async def kickoff_async(
         self,
@@ -2029,11 +1638,9 @@ class Agent(BaseAgent):
         response_format: type[Any] | None = None,
     ) -> LiteAgentOutput:
         """
-        Execute the agent asynchronously with the given messages.
+        Execute the agent asynchronously with the given messages using a LiteAgent instance.
 
-        This is the async version of the kickoff method that uses native async
-        execution. It is designed for use within async contexts, such as when
-        called from within an async Flow method.
+        This is the async version of the kickoff method.
 
         Args:
             messages: Either a string query or a list of message dictionaries.
@@ -2044,67 +1651,21 @@ class Agent(BaseAgent):
         Returns:
             LiteAgentOutput: The result of the agent execution.
         """
-        executor, inputs, agent_info, parsed_tools = self._prepare_kickoff(
-            messages, response_format
+        lite_agent = LiteAgent(
+            role=self.role,
+            goal=self.goal,
+            backstory=self.backstory,
+            llm=self.llm,
+            tools=self.tools or [],
+            max_iterations=self.max_iter,
+            max_execution_time=self.max_execution_time,
+            respect_context_window=self.respect_context_window,
+            verbose=self.verbose,
+            response_format=response_format,
+            i18n=self.i18n,
+            original_agent=self,
+            guardrail=self.guardrail,
+            guardrail_max_retries=self.guardrail_max_retries,
         )
 
-        try:
-            crewai_event_bus.emit(
-                self,
-                event=LiteAgentExecutionStartedEvent(
-                    agent_info=agent_info,
-                    tools=parsed_tools,
-                    messages=messages,
-                ),
-            )
-
-            output = await self._execute_and_build_output_async(
-                executor, inputs, response_format
-            )
-
-            if self.guardrail is not None:
-                output = self._process_kickoff_guardrail(
-                    output=output,
-                    executor=executor,
-                    inputs=inputs,
-                    response_format=response_format,
-                )
-
-            crewai_event_bus.emit(
-                self,
-                event=LiteAgentExecutionCompletedEvent(
-                    agent_info=agent_info,
-                    output=output.raw,
-                ),
-            )
-
-            return output
-
-        except Exception as e:
-            crewai_event_bus.emit(
-                self,
-                event=LiteAgentExecutionErrorEvent(
-                    agent_info=agent_info,
-                    error=str(e),
-                ),
-            )
-            raise
-
-
-# Rebuild Agent model to resolve A2A type forward references
-try:
-    from crewai.a2a.config import (
-        A2AClientConfig as _A2AClientConfig,
-        A2AConfig as _A2AConfig,
-        A2AServerConfig as _A2AServerConfig,
-    )
-
-    Agent.model_rebuild(
-        _types_namespace={
-            "A2AConfig": _A2AConfig,
-            "A2AClientConfig": _A2AClientConfig,
-            "A2AServerConfig": _A2AServerConfig,
-        }
-    )
-except ImportError:
-    pass
+        return await lite_agent.kickoff_async(messages)

lib/crewai/src/crewai/agent/utils.py

@@ -17,7 +17,6 @@ from crewai.events.types.knowledge_events import (
 )
 from crewai.knowledge.utils.knowledge_utils import extract_knowledge_context
 from crewai.utilities.pydantic_schema_utils import generate_model_description
-from crewai.utilities.types import LLMMessage
 
 
 if TYPE_CHECKING:
@@ -237,40 +236,14 @@ def process_tool_results(agent: Agent, result: Any) -> Any:
 def save_last_messages(agent: Agent) -> None:
     """Save the last messages from agent executor.
 
-    Sanitizes messages to be compatible with TaskOutput's LLMMessage type,
-    which accepts 'user', 'assistant', 'system', and 'tool' roles.
-    Preserves tool_call_id/name for tool messages and tool_calls for assistant messages.
-
     Args:
         agent: The agent instance.
     """
-    if not agent.agent_executor or not hasattr(agent.agent_executor, "messages"):
-        agent._last_messages = []
-        return
-
-    sanitized_messages: list[LLMMessage] = []
-    for msg in agent.agent_executor.messages:
-        role = msg.get("role", "")
-        if role not in ("user", "assistant", "system", "tool"):
-            continue
-        content = msg.get("content")
-        if content is None:
-            content = ""
-        sanitized_msg: LLMMessage = {"role": role, "content": content}
-        if role == "tool":
-            tool_call_id = msg.get("tool_call_id")
-            if tool_call_id:
-                sanitized_msg["tool_call_id"] = tool_call_id
-            name = msg.get("name")
-            if name:
-                sanitized_msg["name"] = name
-        elif role == "assistant":
-            tool_calls = msg.get("tool_calls")
-            if tool_calls:
-                sanitized_msg["tool_calls"] = tool_calls
-        sanitized_messages.append(sanitized_msg)
-
-    agent._last_messages = sanitized_messages
+    agent._last_messages = (
+        agent.agent_executor.messages.copy()
+        if agent.agent_executor and hasattr(agent.agent_executor, "messages")
+        else []
+    )
 
 
 def prepare_tools(

lib/crewai/src/crewai/agents/agent_adapters/base_tool_adapter.py

@@ -3,8 +3,6 @@ from __future__ import annotations
 from abc import ABC, abstractmethod
 from typing import TYPE_CHECKING, Any
 
-from crewai.utilities.string_utils import sanitize_tool_name as _sanitize_tool_name
-
 
 if TYPE_CHECKING:
     from crewai.tools.base_tool import BaseTool
@@ -37,4 +35,4 @@ class BaseToolAdapter(ABC):
     @staticmethod
     def sanitize_tool_name(tool_name: str) -> str:
         """Sanitize tool name for API compatibility."""
-        return _sanitize_tool_name(tool_name)
+        return tool_name.replace(" ", "_")

lib/crewai/src/crewai/agents/agent_adapters/openai_agents/openai_agent_tool_adapter.py

@@ -7,6 +7,7 @@ to OpenAI Assistant-compatible format using the agents library.
 from collections.abc import Awaitable
 import inspect
 import json
+import re
 from typing import Any, cast
 
 from crewai.agents.agent_adapters.base_tool_adapter import BaseToolAdapter
@@ -16,7 +17,6 @@ from crewai.agents.agent_adapters.openai_agents.protocols import (
 )
 from crewai.tools import BaseTool
 from crewai.utilities.import_utils import require
-from crewai.utilities.string_utils import sanitize_tool_name
 
 
 agents_module = cast(
@@ -78,6 +78,18 @@ class OpenAIAgentToolAdapter(BaseToolAdapter):
         if not tools:
             return []
 
+        def sanitize_tool_name(name: str) -> str:
+            """Convert tool name to match OpenAI's required pattern.
+
+            Args:
+                name: Original tool name.
+
+            Returns:
+                Sanitized tool name matching OpenAI requirements.
+            """
+
+            return re.sub(r"[^a-zA-Z0-9_-]", "_", name).lower()
+
         def create_tool_wrapper(tool: BaseTool) -> Any:
             """Create a wrapper function that handles the OpenAI function tool interface.
 

lib/crewai/src/crewai/agents/agent_builder/base_agent_executor_mixin.py

@@ -10,7 +10,6 @@ from crewai.memory.long_term.long_term_memory_item import LongTermMemoryItem
 from crewai.utilities.converter import ConverterError
 from crewai.utilities.evaluators.task_evaluator import TaskEvaluator
 from crewai.utilities.printer import Printer
-from crewai.utilities.string_utils import sanitize_tool_name
 
 
 if TYPE_CHECKING:
@@ -22,9 +21,9 @@ if TYPE_CHECKING:
 
 
 class CrewAgentExecutorMixin:
-    crew: Crew | None
+    crew: Crew
     agent: Agent
-    task: Task | None
+    task: Task
     iterations: int
     max_iter: int
     messages: list[LLMMessage]
@@ -37,7 +36,7 @@ class CrewAgentExecutorMixin:
             self.crew
             and self.agent
             and self.task
-            and f"Action: {sanitize_tool_name('Delegate work to coworker')}" not in output.text
+            and "Action: Delegate work to coworker" not in output.text
         ):
             try:
                 if (

lib/crewai/src/crewai/agents/crew_agent_executor.py

@@ -30,7 +30,6 @@ from crewai.hooks.llm_hooks import (
 )
 from crewai.utilities.agent_utils import (
     aget_llm_response,
-    convert_tools_to_openai_schema,
     enforce_rpm_limit,
     format_message_for_llm,
     get_llm_response,
@@ -42,12 +41,10 @@ from crewai.utilities.agent_utils import (
     has_reached_max_iterations,
     is_context_length_exceeded,
     process_llm_response,
-    track_delegation_if_needed,
 )
 from crewai.utilities.constants import TRAINING_DATA_FILE
 from crewai.utilities.i18n import I18N, get_i18n
 from crewai.utilities.printer import Printer
-from crewai.utilities.string_utils import sanitize_tool_name
 from crewai.utilities.tool_utils import (
     aexecute_tool_and_check_finality,
     execute_tool_and_check_finality,
@@ -218,37 +215,11 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
     def _invoke_loop(self) -> AgentFinish:
         """Execute agent loop until completion.
 
-        Checks if the LLM supports native function calling and uses that
-        approach if available, otherwise falls back to the ReAct text pattern.
-
-        Returns:
-            Final answer from the agent.
-        """
-        # Check if model supports native function calling
-        use_native_tools = (
-            hasattr(self.llm, "supports_function_calling")
-            and callable(getattr(self.llm, "supports_function_calling", None))
-            and self.llm.supports_function_calling()
-            and self.original_tools
-        )
-
-        if use_native_tools:
-            return self._invoke_loop_native_tools()
-
-        # Fall back to ReAct text-based pattern
-        return self._invoke_loop_react()
-
-    def _invoke_loop_react(self) -> AgentFinish:
-        """Execute agent loop using ReAct text-based pattern.
-
-        This is the traditional approach where tool definitions are embedded
-        in the prompt and the LLM outputs Action/Action Input text that is
-        parsed to execute tools.
-
         Returns:
             Final answer from the agent.
         """
         formatted_answer = None
+        last_raw_output: str | None = None
         while not isinstance(formatted_answer, AgentFinish):
             try:
                 if has_reached_max_iterations(self.iterations, self.max_iter):
@@ -274,7 +245,7 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
                     response_model=self.response_model,
                     executor_context=self,
                 )
-                # breakpoint()
+                last_raw_output = answer
                 if self.response_model is not None:
                     try:
                         self.response_model.model_validate_json(answer)
@@ -331,6 +302,8 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
                     iterations=self.iterations,
                     log_error_after=self.log_error_after,
                     printer=self._printer,
+                    raw_output=last_raw_output,
+                    agent_role=self.agent.role if self.agent else None,
                 )
 
             except Exception as e:
@@ -364,430 +337,6 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
         self._show_logs(formatted_answer)
         return formatted_answer
 
-    def _invoke_loop_native_tools(self) -> AgentFinish:
-        """Execute agent loop using native function calling.
-
-        This method uses the LLM's native tool/function calling capability
-        instead of the text-based ReAct pattern. The LLM directly returns
-        structured tool calls which are executed and results fed back.
-
-        Returns:
-            Final answer from the agent.
-        """
-        # Convert tools to OpenAI schema format
-        if not self.original_tools:
-            # No tools available, fall back to simple LLM call
-            return self._invoke_loop_native_no_tools()
-
-        openai_tools, available_functions = convert_tools_to_openai_schema(
-            self.original_tools
-        )
-
-        while True:
-            try:
-                if has_reached_max_iterations(self.iterations, self.max_iter):
-                    formatted_answer = handle_max_iterations_exceeded(
-                        None,
-                        printer=self._printer,
-                        i18n=self._i18n,
-                        messages=self.messages,
-                        llm=self.llm,
-                        callbacks=self.callbacks,
-                    )
-                    self._show_logs(formatted_answer)
-                    return formatted_answer
-
-                enforce_rpm_limit(self.request_within_rpm_limit)
-
-                # Call LLM with native tools
-                # Pass available_functions=None so the LLM returns tool_calls
-                # without executing them. The executor handles tool execution
-                # via _handle_native_tool_calls to properly manage message history.
-                answer = get_llm_response(
-                    llm=self.llm,
-                    messages=self.messages,
-                    callbacks=self.callbacks,
-                    printer=self._printer,
-                    tools=openai_tools,
-                    available_functions=None,
-                    from_task=self.task,
-                    from_agent=self.agent,
-                    response_model=self.response_model,
-                    executor_context=self,
-                )
-
-                # Check if the response is a list of tool calls
-                if (
-                    isinstance(answer, list)
-                    and answer
-                    and self._is_tool_call_list(answer)
-                ):
-                    # Handle tool calls - execute tools and add results to messages
-                    tool_finish = self._handle_native_tool_calls(
-                        answer, available_functions
-                    )
-                    # If tool has result_as_answer=True, return immediately
-                    if tool_finish is not None:
-                        return tool_finish
-                    # Continue loop to let LLM analyze results and decide next steps
-                    continue
-
-                # Text or other response - handle as potential final answer
-                if isinstance(answer, str):
-                    # Text response - this is the final answer
-                    formatted_answer = AgentFinish(
-                        thought="",
-                        output=answer,
-                        text=answer,
-                    )
-                    self._invoke_step_callback(formatted_answer)
-                    self._append_message(answer)  # Save final answer to messages
-                    self._show_logs(formatted_answer)
-                    return formatted_answer
-
-                # Unexpected response type, treat as final answer
-                formatted_answer = AgentFinish(
-                    thought="",
-                    output=str(answer),
-                    text=str(answer),
-                )
-                self._invoke_step_callback(formatted_answer)
-                self._append_message(str(answer))  # Save final answer to messages
-                self._show_logs(formatted_answer)
-                return formatted_answer
-
-            except Exception as e:
-                if e.__class__.__module__.startswith("litellm"):
-                    raise e
-                if is_context_length_exceeded(e):
-                    handle_context_length(
-                        respect_context_window=self.respect_context_window,
-                        printer=self._printer,
-                        messages=self.messages,
-                        llm=self.llm,
-                        callbacks=self.callbacks,
-                        i18n=self._i18n,
-                    )
-                    continue
-                handle_unknown_error(self._printer, e)
-                raise e
-            finally:
-                self.iterations += 1
-
-    def _invoke_loop_native_no_tools(self) -> AgentFinish:
-        """Execute a simple LLM call when no tools are available.
-
-        Returns:
-            Final answer from the agent.
-        """
-        enforce_rpm_limit(self.request_within_rpm_limit)
-
-        answer = get_llm_response(
-            llm=self.llm,
-            messages=self.messages,
-            callbacks=self.callbacks,
-            printer=self._printer,
-            from_task=self.task,
-            from_agent=self.agent,
-            response_model=self.response_model,
-            executor_context=self,
-        )
-
-        formatted_answer = AgentFinish(
-            thought="",
-            output=str(answer),
-            text=str(answer),
-        )
-        self._show_logs(formatted_answer)
-        return formatted_answer
-
-    def _is_tool_call_list(self, response: list[Any]) -> bool:
-        """Check if a response is a list of tool calls.
-
-        Args:
-            response: The response to check.
-
-        Returns:
-            True if the response appears to be a list of tool calls.
-        """
-        if not response:
-            return False
-        first_item = response[0]
-        # OpenAI-style
-        if hasattr(first_item, "function") or (
-            isinstance(first_item, dict) and "function" in first_item
-        ):
-            return True
-        # Anthropic-style (object with attributes)
-        if (
-            hasattr(first_item, "type")
-            and getattr(first_item, "type", None) == "tool_use"
-        ):
-            return True
-        if hasattr(first_item, "name") and hasattr(first_item, "input"):
-            return True
-        # Bedrock-style (dict with name and input keys)
-        if (
-            isinstance(first_item, dict)
-            and "name" in first_item
-            and "input" in first_item
-        ):
-            return True
-        # Gemini-style
-        if hasattr(first_item, "function_call") and first_item.function_call:
-            return True
-        return False
-
-    def _handle_native_tool_calls(
-        self,
-        tool_calls: list[Any],
-        available_functions: dict[str, Callable[..., Any]],
-    ) -> AgentFinish | None:
-        """Handle a single native tool call from the LLM.
-
-        Executes only the FIRST tool call and appends the result to message history.
-        This enables sequential tool execution with reflection after each tool,
-        allowing the LLM to reason about results before deciding on next steps.
-
-        Args:
-            tool_calls: List of tool calls from the LLM (only first is processed).
-            available_functions: Dict mapping function names to callables.
-
-        Returns:
-            AgentFinish if tool has result_as_answer=True, None otherwise.
-        """
-        from datetime import datetime
-        import json
-
-        from crewai.events import crewai_event_bus
-        from crewai.events.types.tool_usage_events import (
-            ToolUsageErrorEvent,
-            ToolUsageFinishedEvent,
-            ToolUsageStartedEvent,
-        )
-
-        if not tool_calls:
-            return None
-
-        # Only process the FIRST tool call for sequential execution with reflection
-        tool_call = tool_calls[0]
-
-        # Extract tool call info - handle OpenAI-style, Anthropic-style, and Gemini-style
-        if hasattr(tool_call, "function"):
-            # OpenAI-style: has .function.name and .function.arguments
-            call_id = getattr(tool_call, "id", f"call_{id(tool_call)}")
-            func_name = sanitize_tool_name(tool_call.function.name)
-            func_args = tool_call.function.arguments
-        elif hasattr(tool_call, "function_call") and tool_call.function_call:
-            # Gemini-style: has .function_call.name and .function_call.args
-            call_id = f"call_{id(tool_call)}"
-            func_name = sanitize_tool_name(tool_call.function_call.name)
-            func_args = (
-                dict(tool_call.function_call.args)
-                if tool_call.function_call.args
-                else {}
-            )
-        elif hasattr(tool_call, "name") and hasattr(tool_call, "input"):
-            # Anthropic format: has .name and .input (ToolUseBlock)
-            call_id = getattr(tool_call, "id", f"call_{id(tool_call)}")
-            func_name = sanitize_tool_name(tool_call.name)
-            func_args = tool_call.input  # Already a dict in Anthropic
-        elif isinstance(tool_call, dict):
-            # Support OpenAI "id", Bedrock "toolUseId", or generate one
-            call_id = (
-                tool_call.get("id")
-                or tool_call.get("toolUseId")
-                or f"call_{id(tool_call)}"
-            )
-            func_info = tool_call.get("function", {})
-            func_name = sanitize_tool_name(
-                func_info.get("name", "") or tool_call.get("name", "")
-            )
-            func_args = func_info.get("arguments", "{}") or tool_call.get("input", {})
-        else:
-            return None
-
-        # Append assistant message with single tool call
-        assistant_message: LLMMessage = {
-            "role": "assistant",
-            "content": None,
-            "tool_calls": [
-                {
-                    "id": call_id,
-                    "type": "function",
-                    "function": {
-                        "name": func_name,
-                        "arguments": func_args
-                        if isinstance(func_args, str)
-                        else json.dumps(func_args),
-                    },
-                }
-            ],
-        }
-
-        self.messages.append(assistant_message)
-
-        # Parse arguments for the single tool call
-        if isinstance(func_args, str):
-            try:
-                args_dict = json.loads(func_args)
-            except json.JSONDecodeError:
-                args_dict = {}
-        else:
-            args_dict = func_args
-
-        agent_key = getattr(self.agent, "key", "unknown") if self.agent else "unknown"
-
-        # Find original tool by matching sanitized name (needed for cache_function and result_as_answer)
-
-        original_tool = None
-        for tool in self.original_tools or []:
-            if sanitize_tool_name(tool.name) == func_name:
-                original_tool = tool
-                break
-
-        # Check if tool has reached max usage count
-        max_usage_reached = False
-        if original_tool:
-            if (
-                hasattr(original_tool, "max_usage_count")
-                and original_tool.max_usage_count is not None
-                and original_tool.current_usage_count >= original_tool.max_usage_count
-            ):
-                max_usage_reached = True
-
-        # Check cache before executing
-        from_cache = False
-        input_str = json.dumps(args_dict) if args_dict else ""
-        if self.tools_handler and self.tools_handler.cache:
-            cached_result = self.tools_handler.cache.read(
-                tool=func_name, input=input_str
-            )
-            if cached_result is not None:
-                result = (
-                    str(cached_result)
-                    if not isinstance(cached_result, str)
-                    else cached_result
-                )
-                from_cache = True
-
-        # Emit tool usage started event
-        started_at = datetime.now()
-        crewai_event_bus.emit(
-            self,
-            event=ToolUsageStartedEvent(
-                tool_name=func_name,
-                tool_args=args_dict,
-                from_agent=self.agent,
-                from_task=self.task,
-                agent_key=agent_key,
-            ),
-        )
-
-        track_delegation_if_needed(func_name, args_dict, self.task)
-
-        # Execute the tool (only if not cached and not at max usage)
-        if not from_cache and not max_usage_reached:
-            result = "Tool not found"
-            if func_name in available_functions:
-                try:
-                    tool_func = available_functions[func_name]
-                    raw_result = tool_func(**args_dict)
-
-                    # Add to cache after successful execution (before string conversion)
-                    if self.tools_handler and self.tools_handler.cache:
-                        should_cache = True
-                        if (
-                            original_tool
-                            and hasattr(original_tool, "cache_function")
-                            and original_tool.cache_function
-                        ):
-                            should_cache = original_tool.cache_function(
-                                args_dict, raw_result
-                            )
-                        if should_cache:
-                            self.tools_handler.cache.add(
-                                tool=func_name, input=input_str, output=raw_result
-                            )
-
-                    # Convert to string for message
-                    result = (
-                        str(raw_result)
-                        if not isinstance(raw_result, str)
-                        else raw_result
-                    )
-                except Exception as e:
-                    result = f"Error executing tool: {e}"
-                    if self.task:
-                        self.task.increment_tools_errors()
-                    crewai_event_bus.emit(
-                        self,
-                        event=ToolUsageErrorEvent(
-                            tool_name=func_name,
-                            tool_args=args_dict,
-                            from_agent=self.agent,
-                            from_task=self.task,
-                            agent_key=agent_key,
-                            error=e,
-                        ),
-                    )
-        elif max_usage_reached:
-            # Return error message when max usage limit is reached
-            result = f"Tool '{func_name}' has reached its usage limit of {original_tool.max_usage_count} times and cannot be used anymore."
-
-        # Emit tool usage finished event
-        crewai_event_bus.emit(
-            self,
-            event=ToolUsageFinishedEvent(
-                output=result,
-                tool_name=func_name,
-                tool_args=args_dict,
-                from_agent=self.agent,
-                from_task=self.task,
-                agent_key=agent_key,
-                started_at=started_at,
-                finished_at=datetime.now(),
-            ),
-        )
-
-        # Append tool result message
-        tool_message: LLMMessage = {
-            "role": "tool",
-            "tool_call_id": call_id,
-            "name": func_name,
-            "content": result,
-        }
-        self.messages.append(tool_message)
-
-        # Log the tool execution
-        if self.agent and self.agent.verbose:
-            cache_info = " (from cache)" if from_cache else ""
-            self._printer.print(
-                content=f"Tool {func_name} executed with result{cache_info}: {result[:200]}...",
-                color="green",
-            )
-
-        if (
-            original_tool
-            and hasattr(original_tool, "result_as_answer")
-            and original_tool.result_as_answer
-        ):
-            # Return immediately with tool result as final answer
-            return AgentFinish(
-                thought="Tool result is the final answer",
-                output=result,
-                text=result,
-            )
-
-        # Inject post-tool reasoning prompt to enforce analysis
-        reasoning_prompt = self._i18n.slice("post_tool_reasoning")
-        reasoning_message: LLMMessage = {
-            "role": "user",
-            "content": reasoning_prompt,
-        }
-        self.messages.append(reasoning_message)
-        return None
-
     async def ainvoke(self, inputs: dict[str, Any]) -> dict[str, Any]:
         """Execute the agent asynchronously with given inputs.
 
@@ -837,33 +386,11 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
     async def _ainvoke_loop(self) -> AgentFinish:
         """Execute agent loop asynchronously until completion.
 
-        Checks if the LLM supports native function calling and uses that
-        approach if available, otherwise falls back to the ReAct text pattern.
-
-        Returns:
-            Final answer from the agent.
-        """
-        # Check if model supports native function calling
-        use_native_tools = (
-            hasattr(self.llm, "supports_function_calling")
-            and callable(getattr(self.llm, "supports_function_calling", None))
-            and self.llm.supports_function_calling()
-            and self.original_tools
-        )
-
-        if use_native_tools:
-            return await self._ainvoke_loop_native_tools()
-
-        # Fall back to ReAct text-based pattern
-        return await self._ainvoke_loop_react()
-
-    async def _ainvoke_loop_react(self) -> AgentFinish:
-        """Execute agent loop asynchronously using ReAct text-based pattern.
-
         Returns:
             Final answer from the agent.
         """
         formatted_answer = None
+        last_raw_output: str | None = None
         while not isinstance(formatted_answer, AgentFinish):
             try:
                 if has_reached_max_iterations(self.iterations, self.max_iter):
@@ -889,6 +416,7 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
                     response_model=self.response_model,
                     executor_context=self,
                 )
+                last_raw_output = answer
 
                 if self.response_model is not None:
                     try:
@@ -945,6 +473,8 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
                     iterations=self.iterations,
                     log_error_after=self.log_error_after,
                     printer=self._printer,
+                    raw_output=last_raw_output,
+                    agent_role=self.agent.role if self.agent else None,
                 )
 
             except Exception as e:
@@ -973,140 +503,6 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
         self._show_logs(formatted_answer)
         return formatted_answer
 
-    async def _ainvoke_loop_native_tools(self) -> AgentFinish:
-        """Execute agent loop asynchronously using native function calling.
-
-        This method uses the LLM's native tool/function calling capability
-        instead of the text-based ReAct pattern.
-
-        Returns:
-            Final answer from the agent.
-        """
-        # Convert tools to OpenAI schema format
-        if not self.original_tools:
-            return await self._ainvoke_loop_native_no_tools()
-
-        openai_tools, available_functions = convert_tools_to_openai_schema(
-            self.original_tools
-        )
-
-        while True:
-            try:
-                if has_reached_max_iterations(self.iterations, self.max_iter):
-                    formatted_answer = handle_max_iterations_exceeded(
-                        None,
-                        printer=self._printer,
-                        i18n=self._i18n,
-                        messages=self.messages,
-                        llm=self.llm,
-                        callbacks=self.callbacks,
-                    )
-                    self._show_logs(formatted_answer)
-                    return formatted_answer
-
-                enforce_rpm_limit(self.request_within_rpm_limit)
-
-                # Call LLM with native tools
-                # Pass available_functions=None so the LLM returns tool_calls
-                # without executing them. The executor handles tool execution
-                # via _handle_native_tool_calls to properly manage message history.
-                answer = await aget_llm_response(
-                    llm=self.llm,
-                    messages=self.messages,
-                    callbacks=self.callbacks,
-                    printer=self._printer,
-                    tools=openai_tools,
-                    available_functions=None,
-                    from_task=self.task,
-                    from_agent=self.agent,
-                    response_model=self.response_model,
-                    executor_context=self,
-                )
-                # Check if the response is a list of tool calls
-                if (
-                    isinstance(answer, list)
-                    and answer
-                    and self._is_tool_call_list(answer)
-                ):
-                    # Handle tool calls - execute tools and add results to messages
-                    tool_finish = self._handle_native_tool_calls(
-                        answer, available_functions
-                    )
-                    # If tool has result_as_answer=True, return immediately
-                    if tool_finish is not None:
-                        return tool_finish
-                    # Continue loop to let LLM analyze results and decide next steps
-                    continue
-
-                # Text or other response - handle as potential final answer
-                if isinstance(answer, str):
-                    # Text response - this is the final answer
-                    formatted_answer = AgentFinish(
-                        thought="",
-                        output=answer,
-                        text=answer,
-                    )
-                    self._invoke_step_callback(formatted_answer)
-                    self._append_message(answer)  # Save final answer to messages
-                    self._show_logs(formatted_answer)
-                    return formatted_answer
-
-                # Unexpected response type, treat as final answer
-                formatted_answer = AgentFinish(
-                    thought="",
-                    output=str(answer),
-                    text=str(answer),
-                )
-                self._invoke_step_callback(formatted_answer)
-                self._append_message(str(answer))  # Save final answer to messages
-                self._show_logs(formatted_answer)
-                return formatted_answer
-
-            except Exception as e:
-                if e.__class__.__module__.startswith("litellm"):
-                    raise e
-                if is_context_length_exceeded(e):
-                    handle_context_length(
-                        respect_context_window=self.respect_context_window,
-                        printer=self._printer,
-                        messages=self.messages,
-                        llm=self.llm,
-                        callbacks=self.callbacks,
-                        i18n=self._i18n,
-                    )
-                    continue
-                handle_unknown_error(self._printer, e)
-                raise e
-            finally:
-                self.iterations += 1
-
-    async def _ainvoke_loop_native_no_tools(self) -> AgentFinish:
-        """Execute a simple async LLM call when no tools are available.
-
-        Returns:
-            Final answer from the agent.
-        """
-        enforce_rpm_limit(self.request_within_rpm_limit)
-
-        answer = await aget_llm_response(
-            llm=self.llm,
-            messages=self.messages,
-            callbacks=self.callbacks,
-            printer=self._printer,
-            from_task=self.task,
-            from_agent=self.agent,
-            response_model=self.response_model,
-            executor_context=self,
-        )
-
-        formatted_answer = AgentFinish(
-            thought="",
-            output=str(answer),
-            text=str(answer),
-        )
-        self._show_logs(formatted_answer)
-        return formatted_answer
-
     def _handle_agent_action(
         self, formatted_answer: AgentAction, tool_result: ToolResult
     ) -> AgentAction | AgentFinish:

lib/crewai/src/crewai/crew.py

@@ -104,7 +104,6 @@ from crewai.utilities.streaming import (
     signal_end,
     signal_error,
 )
-from crewai.utilities.string_utils import sanitize_tool_name
 from crewai.utilities.task_output_storage_handler import TaskOutputStorageHandler
 from crewai.utilities.training_handler import CrewTrainingHandler
 
@@ -1242,14 +1241,10 @@ class Crew(FlowTrackable, BaseModel):
             return existing_tools
 
         # Create mapping of tool names to new tools
-        new_tool_map = {sanitize_tool_name(tool.name): tool for tool in new_tools}
+        new_tool_map = {tool.name: tool for tool in new_tools}
 
         # Remove any existing tools that will be replaced
-        tools = [
-            tool
-            for tool in existing_tools
-            if sanitize_tool_name(tool.name) not in new_tool_map
-        ]
+        tools = [tool for tool in existing_tools if tool.name not in new_tool_map]
 
         # Add all new tools
         tools.extend(new_tools)

lib/crewai/src/crewai/crews/utils.py

@@ -189,15 +189,9 @@ def prepare_kickoff(crew: Crew, inputs: dict[str, Any] | None) -> dict[str, Any]
     Returns:
         The potentially modified inputs dictionary after before callbacks.
     """
-    from crewai.events.base_events import reset_emission_counter
     from crewai.events.event_bus import crewai_event_bus
-    from crewai.events.event_context import get_current_parent_id, reset_last_event_id
     from crewai.events.types.crew_events import CrewKickoffStartedEvent
 
-    if get_current_parent_id() is None:
-        reset_emission_counter()
-        reset_last_event_id()
-
     for before_callback in crew.before_kickoff_callbacks:
         if inputs is None:
             inputs = {}

lib/crewai/src/crewai/events/__init__.py

@@ -75,7 +75,6 @@ from crewai.events.types.memory_events import (
     MemoryQueryFailedEvent,
     MemoryQueryStartedEvent,
     MemoryRetrievalCompletedEvent,
-    MemoryRetrievalFailedEvent,
     MemoryRetrievalStartedEvent,
     MemorySaveCompletedEvent,
     MemorySaveFailedEvent,
@@ -175,7 +174,6 @@ __all__ = [
     "MemoryQueryFailedEvent",
     "MemoryQueryStartedEvent",
     "MemoryRetrievalCompletedEvent",
-    "MemoryRetrievalFailedEvent",
     "MemoryRetrievalStartedEvent",
     "MemorySaveCompletedEvent",
     "MemorySaveFailedEvent",

lib/crewai/src/crewai/events/base_events.py

@@ -1,46 +1,9 @@
-from collections.abc import Iterator
-import contextvars
 from datetime import datetime, timezone
-import itertools
 from typing import Any
-import uuid
 
 from pydantic import BaseModel, Field
 
-from crewai.utilities.serialization import Serializable, to_serializable
-
-
-_emission_counter: contextvars.ContextVar[Iterator[int]] = contextvars.ContextVar(
-    "_emission_counter"
-)
-
-
-def _get_or_create_counter() -> Iterator[int]:
-    """Get the emission counter for the current context, creating if needed."""
-    try:
-        return _emission_counter.get()
-    except LookupError:
-        counter: Iterator[int] = itertools.count(start=1)
-        _emission_counter.set(counter)
-        return counter
-
-
-def get_next_emission_sequence() -> int:
-    """Get the next emission sequence number.
-
-    Returns:
-        The next sequence number.
-    """
-    return next(_get_or_create_counter())
-
-
-def reset_emission_counter() -> None:
-    """Reset the emission sequence counter to 1.
-
-    Resets for the current context only.
-    """
-    counter: Iterator[int] = itertools.count(start=1)
-    _emission_counter.set(counter)
+from crewai.utilities.serialization import to_serializable
 
 
 class BaseEvent(BaseModel):
@@ -59,13 +22,7 @@ class BaseEvent(BaseModel):
     agent_id: str | None = None
     agent_role: str | None = None
 
-    event_id: str = Field(default_factory=lambda: str(uuid.uuid4()))
-    parent_event_id: str | None = None
-    previous_event_id: str | None = None
-    triggered_by_event_id: str | None = None
-    emission_sequence: int | None = None
-
-    def to_json(self, exclude: set[str] | None = None) -> Serializable:
+    def to_json(self, exclude: set[str] | None = None):
         """
         Converts the event to a JSON-serializable dictionary.
 
@@ -77,13 +34,13 @@ class BaseEvent(BaseModel):
         """
         return to_serializable(self, exclude=exclude)
 
-    def _set_task_params(self, data: dict[str, Any]) -> None:
+    def _set_task_params(self, data: dict[str, Any]):
         if "from_task" in data and (task := data["from_task"]):
             self.task_id = str(task.id)
             self.task_name = task.name or task.description
             self.from_task = None
 
-    def _set_agent_params(self, data: dict[str, Any]) -> 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)
 

lib/crewai/src/crewai/events/event_bus.py

@@ -16,22 +16,8 @@ from typing import Any, Final, ParamSpec, TypeVar
 
 from typing_extensions import Self
 
-from crewai.events.base_events import BaseEvent, get_next_emission_sequence
+from crewai.events.base_events import BaseEvent
 from crewai.events.depends import Depends
-from crewai.events.event_context import (
-    SCOPE_ENDING_EVENTS,
-    SCOPE_STARTING_EVENTS,
-    VALID_EVENT_PAIRS,
-    get_current_parent_id,
-    get_enclosing_parent_id,
-    get_last_event_id,
-    get_triggering_event_id,
-    handle_empty_pop,
-    handle_mismatch,
-    pop_event_scope,
-    push_event_scope,
-    set_last_event_id,
-)
 from crewai.events.handler_graph import build_execution_plan
 from crewai.events.types.event_bus_types import (
     AsyncHandler,
@@ -83,8 +69,6 @@ class CrewAIEventsBus:
     _execution_plan_cache: dict[type[BaseEvent], ExecutionPlan]
     _console: ConsoleFormatter
     _shutting_down: bool
-    _pending_futures: set[Future[Any]]
-    _futures_lock: threading.Lock
 
     def __new__(cls) -> Self:
         """Create or return the singleton instance.
@@ -107,8 +91,6 @@ class CrewAIEventsBus:
         """
         self._shutting_down = False
         self._rwlock = RWLock()
-        self._pending_futures: set[Future[Any]] = set()
-        self._futures_lock = threading.Lock()
         self._sync_handlers: dict[type[BaseEvent], SyncHandlerSet] = {}
         self._async_handlers: dict[type[BaseEvent], AsyncHandlerSet] = {}
         self._handler_dependencies: dict[
@@ -129,25 +111,6 @@ class CrewAIEventsBus:
         )
         self._loop_thread.start()
 
-    def _track_future(self, future: Future[Any]) -> Future[Any]:
-        """Track a future and set up automatic cleanup when it completes.
-
-        Args:
-            future: The future to track
-
-        Returns:
-            The same future for chaining
-        """
-        with self._futures_lock:
-            self._pending_futures.add(future)
-
-        def _cleanup(f: Future[Any]) -> None:
-            with self._futures_lock:
-                self._pending_futures.discard(f)
-
-        future.add_done_callback(_cleanup)
-        return future
-
     def _run_loop(self) -> None:
         """Run the background async event loop."""
         asyncio.set_event_loop(self._loop)
@@ -363,28 +326,6 @@ class CrewAIEventsBus:
             ...     await asyncio.wrap_future(future)  # In async test
             ...     # or future.result(timeout=5.0) in sync code
         """
-        event.previous_event_id = get_last_event_id()
-        event.triggered_by_event_id = get_triggering_event_id()
-        event.emission_sequence = get_next_emission_sequence()
-        if event.parent_event_id is None:
-            event_type_name = event.type
-            if event_type_name in SCOPE_ENDING_EVENTS:
-                event.parent_event_id = get_enclosing_parent_id()
-                popped = pop_event_scope()
-                if popped is None:
-                    handle_empty_pop(event_type_name)
-                else:
-                    _, popped_type = popped
-                    expected_start = VALID_EVENT_PAIRS.get(event_type_name)
-                    if expected_start and popped_type and popped_type != expected_start:
-                        handle_mismatch(event_type_name, popped_type, expected_start)
-            elif event_type_name in SCOPE_STARTING_EVENTS:
-                event.parent_event_id = get_current_parent_id()
-                push_event_scope(event.event_id, event_type_name)
-            else:
-                event.parent_event_id = get_current_parent_id()
-
-        set_last_event_id(event.event_id)
         event_type = type(event)
 
         with self._rwlock.r_locked():
@@ -398,11 +339,9 @@ class CrewAIEventsBus:
             async_handlers = self._async_handlers.get(event_type, frozenset())
 
         if has_dependencies:
-            return self._track_future(
-                asyncio.run_coroutine_threadsafe(
-                    self._emit_with_dependencies(source, event),
-                    self._loop,
-                )
+            return asyncio.run_coroutine_threadsafe(
+                self._emit_with_dependencies(source, event),
+                self._loop,
             )
 
         if sync_handlers:
@@ -414,53 +353,16 @@ class CrewAIEventsBus:
                     ctx.run, self._call_handlers, source, event, sync_handlers
                 )
                 if not async_handlers:
-                    return self._track_future(sync_future)
+                    return sync_future
 
         if async_handlers:
-            return self._track_future(
-                asyncio.run_coroutine_threadsafe(
-                    self._acall_handlers(source, event, async_handlers),
-                    self._loop,
-                )
+            return asyncio.run_coroutine_threadsafe(
+                self._acall_handlers(source, event, async_handlers),
+                self._loop,
             )
 
         return None
 
-    def flush(self, timeout: float | None = 30.0) -> bool:
-        """Block until all pending event handlers complete.
-
-        This method waits for all futures from previously emitted events to
-        finish executing. Useful at the end of operations (like kickoff) to
-        ensure all event handlers have completed before returning.
-
-        Args:
-            timeout: Maximum time in seconds to wait for handlers to complete.
-                    Defaults to 30 seconds. Pass None to wait indefinitely.
-
-        Returns:
-            True if all handlers completed, False if timeout occurred.
-        """
-        with self._futures_lock:
-            futures_to_wait = list(self._pending_futures)
-
-        if not futures_to_wait:
-            return True
-
-        from concurrent.futures import wait as wait_futures
-
-        done, not_done = wait_futures(futures_to_wait, timeout=timeout)
-
-        # Check for exceptions in completed futures
-        errors = [
-            future.exception() for future in done if future.exception() is not None
-        ]
-        for error in errors:
-            self._console.print(
-                f"[CrewAIEventsBus] Handler exception during flush: {error}"
-            )
-
-        return len(not_done) == 0
-
     async def aemit(self, source: Any, event: BaseEvent) -> None:
         """Asynchronously emit an event to registered async handlers.
 
@@ -562,9 +464,6 @@ class CrewAIEventsBus:
             wait: If True, wait for all pending tasks to complete before stopping.
                   If False, cancel all pending tasks immediately.
         """
-        if wait:
-            self.flush()
-
         with self._rwlock.w_locked():
             self._shutting_down = True
             loop = getattr(self, "_loop", None)

lib/crewai/src/crewai/events/event_context.py

@@ -1,334 +0,0 @@
-"""Event context management for parent-child relationship tracking."""
-
-from collections.abc import Generator
-from contextlib import contextmanager
-import contextvars
-from dataclasses import dataclass
-from enum import Enum
-
-from crewai.events.utils.console_formatter import ConsoleFormatter
-
-
-class MismatchBehavior(Enum):
-    """Behavior when event pairs don't match."""
-
-    WARN = "warn"
-    RAISE = "raise"
-    SILENT = "silent"
-
-
-@dataclass
-class EventContextConfig:
-    """Configuration for event context behavior."""
-
-    max_stack_depth: int = 100
-    mismatch_behavior: MismatchBehavior = MismatchBehavior.WARN
-    empty_pop_behavior: MismatchBehavior = MismatchBehavior.WARN
-
-
-class StackDepthExceededError(Exception):
-    """Raised when stack depth limit is exceeded."""
-
-
-class EventPairingError(Exception):
-    """Raised when event pairs don't match."""
-
-
-class EmptyStackError(Exception):
-    """Raised when popping from empty stack."""
-
-
-_event_id_stack: contextvars.ContextVar[tuple[tuple[str, str], ...]] = (
-    contextvars.ContextVar("_event_id_stack", default=())
-)
-
-_event_context_config: contextvars.ContextVar[EventContextConfig | None] = (
-    contextvars.ContextVar("_event_context_config", default=None)
-)
-
-_last_event_id: contextvars.ContextVar[str | None] = contextvars.ContextVar(
-    "_last_event_id", default=None
-)
-
-_triggering_event_id: contextvars.ContextVar[str | None] = contextvars.ContextVar(
-    "_triggering_event_id", default=None
-)
-
-_default_config = EventContextConfig()
-
-_console = ConsoleFormatter()
-
-
-def get_current_parent_id() -> str | None:
-    """Get the current parent event ID from the stack."""
-    stack = _event_id_stack.get()
-    return stack[-1][0] if stack else None
-
-
-def get_enclosing_parent_id() -> str | None:
-    """Get the parent of the current scope (stack[-2])."""
-    stack = _event_id_stack.get()
-    return stack[-2][0] if len(stack) >= 2 else None
-
-
-def get_last_event_id() -> str | None:
-    """Get the ID of the last emitted event for linear chain tracking.
-
-    Returns:
-        The event_id of the previously emitted event, or None if no event yet.
-    """
-    return _last_event_id.get()
-
-
-def reset_last_event_id() -> None:
-    """Reset the last event ID to None.
-
-    Should be called at the start of a new flow or when resetting event state.
-    """
-    _last_event_id.set(None)
-
-
-def set_last_event_id(event_id: str) -> None:
-    """Set the ID of the last emitted event.
-
-    Args:
-        event_id: The event_id to set as the last emitted event.
-    """
-    _last_event_id.set(event_id)
-
-
-def get_triggering_event_id() -> str | None:
-    """Get the ID of the event that triggered the current execution.
-
-    Returns:
-        The event_id of the triggering event, or None if not in a triggered context.
-    """
-    return _triggering_event_id.get()
-
-
-def set_triggering_event_id(event_id: str | None) -> None:
-    """Set the ID of the triggering event for causal chain tracking.
-
-    Args:
-        event_id: The event_id that triggered the current execution, or None.
-    """
-    _triggering_event_id.set(event_id)
-
-
-@contextmanager
-def triggered_by_scope(event_id: str) -> Generator[None, None, None]:
-    """Context manager to set the triggering event ID for causal chain tracking.
-
-    All events emitted within this context will have their triggered_by_event_id
-    set to the provided event_id.
-
-    Args:
-        event_id: The event_id that triggered the current execution.
-    """
-    previous = _triggering_event_id.get()
-    _triggering_event_id.set(event_id)
-    try:
-        yield
-    finally:
-        _triggering_event_id.set(previous)
-
-
-def push_event_scope(event_id: str, event_type: str = "") -> None:
-    """Push an event ID and type onto the scope stack."""
-    config = _event_context_config.get() or _default_config
-    stack = _event_id_stack.get()
-
-    if 0 < config.max_stack_depth <= len(stack):
-        raise StackDepthExceededError(
-            f"Event stack depth limit ({config.max_stack_depth}) exceeded. "
-            f"This usually indicates missing ending events."
-        )
-
-    _event_id_stack.set((*stack, (event_id, event_type)))
-
-
-def pop_event_scope() -> tuple[str, str] | None:
-    """Pop an event entry from the scope stack."""
-    stack = _event_id_stack.get()
-    if not stack:
-        return None
-    _event_id_stack.set(stack[:-1])
-    return stack[-1]
-
-
-def handle_empty_pop(event_type_name: str) -> None:
-    """Handle a pop attempt on an empty stack."""
-    config = _event_context_config.get() or _default_config
-    msg = (
-        f"Ending event '{event_type_name}' emitted with empty scope stack. "
-        "Missing starting event?"
-    )
-
-    if config.empty_pop_behavior == MismatchBehavior.RAISE:
-        raise EmptyStackError(msg)
-    if config.empty_pop_behavior == MismatchBehavior.WARN:
-        _console.print(f"[CrewAIEventsBus] Warning: {msg}")
-
-
-def handle_mismatch(
-    event_type_name: str,
-    popped_type: str,
-    expected_start: str,
-) -> None:
-    """Handle a mismatched event pair."""
-    config = _event_context_config.get() or _default_config
-    msg = (
-        f"Event pairing mismatch. '{event_type_name}' closed '{popped_type}' "
-        f"(expected '{expected_start}')"
-    )
-
-    if config.mismatch_behavior == MismatchBehavior.RAISE:
-        raise EventPairingError(msg)
-    if config.mismatch_behavior == MismatchBehavior.WARN:
-        _console.print(f"[CrewAIEventsBus] Warning: {msg}")
-
-
-@contextmanager
-def event_scope(event_id: str, event_type: str = "") -> Generator[None, None, None]:
-    """Context manager to establish a parent event scope."""
-    stack = _event_id_stack.get()
-    already_on_stack = any(entry[0] == event_id for entry in stack)
-    if not already_on_stack:
-        push_event_scope(event_id, event_type)
-    try:
-        yield
-    finally:
-        if not already_on_stack:
-            pop_event_scope()
-
-
-SCOPE_STARTING_EVENTS: frozenset[str] = frozenset(
-    {
-        "flow_started",
-        "method_execution_started",
-        "crew_kickoff_started",
-        "crew_train_started",
-        "crew_test_started",
-        "agent_execution_started",
-        "agent_evaluation_started",
-        "lite_agent_execution_started",
-        "task_started",
-        "llm_call_started",
-        "llm_guardrail_started",
-        "tool_usage_started",
-        "mcp_connection_started",
-        "mcp_tool_execution_started",
-        "memory_retrieval_started",
-        "memory_save_started",
-        "memory_query_started",
-        "knowledge_query_started",
-        "knowledge_search_query_started",
-        "a2a_delegation_started",
-        "a2a_conversation_started",
-        "a2a_server_task_started",
-        "a2a_parallel_delegation_started",
-        "agent_reasoning_started",
-    }
-)
-
-SCOPE_ENDING_EVENTS: frozenset[str] = frozenset(
-    {
-        "flow_finished",
-        "flow_paused",
-        "method_execution_finished",
-        "method_execution_failed",
-        "method_execution_paused",
-        "crew_kickoff_completed",
-        "crew_kickoff_failed",
-        "crew_train_completed",
-        "crew_train_failed",
-        "crew_test_completed",
-        "crew_test_failed",
-        "agent_execution_completed",
-        "agent_execution_error",
-        "agent_evaluation_completed",
-        "agent_evaluation_failed",
-        "lite_agent_execution_completed",
-        "lite_agent_execution_error",
-        "task_completed",
-        "task_failed",
-        "llm_call_completed",
-        "llm_call_failed",
-        "llm_guardrail_completed",
-        "llm_guardrail_failed",
-        "tool_usage_finished",
-        "tool_usage_error",
-        "mcp_connection_completed",
-        "mcp_connection_failed",
-        "mcp_tool_execution_completed",
-        "mcp_tool_execution_failed",
-        "memory_retrieval_completed",
-        "memory_retrieval_failed",
-        "memory_save_completed",
-        "memory_save_failed",
-        "memory_query_completed",
-        "memory_query_failed",
-        "knowledge_query_completed",
-        "knowledge_query_failed",
-        "knowledge_search_query_completed",
-        "knowledge_search_query_failed",
-        "a2a_delegation_completed",
-        "a2a_conversation_completed",
-        "a2a_server_task_completed",
-        "a2a_server_task_canceled",
-        "a2a_server_task_failed",
-        "a2a_parallel_delegation_completed",
-        "agent_reasoning_completed",
-        "agent_reasoning_failed",
-    }
-)
-
-VALID_EVENT_PAIRS: dict[str, str] = {
-    "flow_finished": "flow_started",
-    "flow_paused": "flow_started",
-    "method_execution_finished": "method_execution_started",
-    "method_execution_failed": "method_execution_started",
-    "method_execution_paused": "method_execution_started",
-    "crew_kickoff_completed": "crew_kickoff_started",
-    "crew_kickoff_failed": "crew_kickoff_started",
-    "crew_train_completed": "crew_train_started",
-    "crew_train_failed": "crew_train_started",
-    "crew_test_completed": "crew_test_started",
-    "crew_test_failed": "crew_test_started",
-    "agent_execution_completed": "agent_execution_started",
-    "agent_execution_error": "agent_execution_started",
-    "agent_evaluation_completed": "agent_evaluation_started",
-    "agent_evaluation_failed": "agent_evaluation_started",
-    "lite_agent_execution_completed": "lite_agent_execution_started",
-    "lite_agent_execution_error": "lite_agent_execution_started",
-    "task_completed": "task_started",
-    "task_failed": "task_started",
-    "llm_call_completed": "llm_call_started",
-    "llm_call_failed": "llm_call_started",
-    "llm_guardrail_completed": "llm_guardrail_started",
-    "llm_guardrail_failed": "llm_guardrail_started",
-    "tool_usage_finished": "tool_usage_started",
-    "tool_usage_error": "tool_usage_started",
-    "mcp_connection_completed": "mcp_connection_started",
-    "mcp_connection_failed": "mcp_connection_started",
-    "mcp_tool_execution_completed": "mcp_tool_execution_started",
-    "mcp_tool_execution_failed": "mcp_tool_execution_started",
-    "memory_retrieval_completed": "memory_retrieval_started",
-    "memory_retrieval_failed": "memory_retrieval_started",
-    "memory_save_completed": "memory_save_started",
-    "memory_save_failed": "memory_save_started",
-    "memory_query_completed": "memory_query_started",
-    "memory_query_failed": "memory_query_started",
-    "knowledge_query_completed": "knowledge_query_started",
-    "knowledge_query_failed": "knowledge_query_started",
-    "knowledge_search_query_completed": "knowledge_search_query_started",
-    "knowledge_search_query_failed": "knowledge_search_query_started",
-    "a2a_delegation_completed": "a2a_delegation_started",
-    "a2a_conversation_completed": "a2a_conversation_started",
-    "a2a_server_task_completed": "a2a_server_task_started",
-    "a2a_server_task_canceled": "a2a_server_task_started",
-    "a2a_server_task_failed": "a2a_server_task_started",
-    "a2a_parallel_delegation_completed": "a2a_parallel_delegation_started",
-    "agent_reasoning_completed": "agent_reasoning_started",
-    "agent_reasoning_failed": "agent_reasoning_started",
-}

lib/crewai/src/crewai/events/event_listener.py

@@ -378,12 +378,6 @@ class EventListener(BaseEventListener):
                 self.formatter.handle_llm_tool_usage_finished(
                     event.tool_name,
                 )
-            else:
-                self.formatter.handle_tool_usage_finished(
-                    event.tool_name,
-                    event.output,
-                    getattr(event, "run_attempts", None),
-                )
 
         @crewai_event_bus.on(ToolUsageErrorEvent)
         def on_tool_usage_error(source: Any, event: ToolUsageErrorEvent) -> None:

lib/crewai/src/crewai/events/event_types.py

@@ -1,28 +1,19 @@
 from crewai.events.types.a2a_events import (
-    A2AAgentCardFetchedEvent,
-    A2AArtifactReceivedEvent,
-    A2AAuthenticationFailedEvent,
-    A2AConnectionErrorEvent,
     A2AConversationCompletedEvent,
     A2AConversationStartedEvent,
     A2ADelegationCompletedEvent,
     A2ADelegationStartedEvent,
     A2AMessageSentEvent,
-    A2AParallelDelegationCompletedEvent,
-    A2AParallelDelegationStartedEvent,
     A2APollingStartedEvent,
     A2APollingStatusEvent,
     A2APushNotificationReceivedEvent,
     A2APushNotificationRegisteredEvent,
-    A2APushNotificationSentEvent,
     A2APushNotificationTimeoutEvent,
     A2AResponseReceivedEvent,
     A2AServerTaskCanceledEvent,
     A2AServerTaskCompletedEvent,
     A2AServerTaskFailedEvent,
     A2AServerTaskStartedEvent,
-    A2AStreamingChunkEvent,
-    A2AStreamingStartedEvent,
 )
 from crewai.events.types.agent_events import (
     AgentExecutionCompletedEvent,
@@ -79,7 +70,6 @@ from crewai.events.types.memory_events import (
     MemoryQueryFailedEvent,
     MemoryQueryStartedEvent,
     MemoryRetrievalCompletedEvent,
-    MemoryRetrievalFailedEvent,
     MemoryRetrievalStartedEvent,
     MemorySaveCompletedEvent,
     MemorySaveFailedEvent,
@@ -103,11 +93,7 @@ from crewai.events.types.tool_usage_events import (
 
 
 EventTypes = (
-    A2AAgentCardFetchedEvent
-    | A2AArtifactReceivedEvent
-    | A2AAuthenticationFailedEvent
-    | A2AConnectionErrorEvent
-    | A2AConversationCompletedEvent
+    A2AConversationCompletedEvent
     | A2AConversationStartedEvent
     | A2ADelegationCompletedEvent
     | A2ADelegationStartedEvent
@@ -116,17 +102,12 @@ EventTypes = (
     | A2APollingStatusEvent
     | A2APushNotificationReceivedEvent
     | A2APushNotificationRegisteredEvent
-    | A2APushNotificationSentEvent
     | A2APushNotificationTimeoutEvent
     | A2AResponseReceivedEvent
     | A2AServerTaskCanceledEvent
     | A2AServerTaskCompletedEvent
     | A2AServerTaskFailedEvent
     | A2AServerTaskStartedEvent
-    | A2AStreamingChunkEvent
-    | A2AStreamingStartedEvent
-    | A2AParallelDelegationStartedEvent
-    | A2AParallelDelegationCompletedEvent
     | CrewKickoffStartedEvent
     | CrewKickoffCompletedEvent
     | CrewKickoffFailedEvent
@@ -174,7 +155,6 @@ EventTypes = (
     | MemoryQueryFailedEvent
     | MemoryRetrievalStartedEvent
     | MemoryRetrievalCompletedEvent
-    | MemoryRetrievalFailedEvent
     | MCPConnectionStartedEvent
     | MCPConnectionCompletedEvent
     | MCPConnectionFailedEvent

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

@@ -267,12 +267,9 @@ class TraceBatchManager:
 
         sorted_events = sorted(
             self.event_buffer,
-            key=lambda e: (
-                e.emission_sequence
-                if e.emission_sequence is not None
-                else float("inf"),
-                e.timestamp if hasattr(e, "timestamp") and e.timestamp else "",
-            ),
+            key=lambda e: e.timestamp
+            if hasattr(e, "timestamp") and e.timestamp
+            else "",
         )
 
         self.current_batch.events = sorted_events

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

@@ -1,7 +1,7 @@
 """Trace collection listener for orchestrating trace collection."""
 
 import os
-from typing import Any, ClassVar
+from typing import Any, ClassVar, cast
 import uuid
 
 from typing_extensions import Self
@@ -9,7 +9,6 @@ from typing_extensions import Self
 from crewai.cli.authentication.token import AuthError, get_auth_token
 from crewai.cli.version import get_crewai_version
 from crewai.events.base_event_listener import BaseEventListener
-from crewai.events.base_events import BaseEvent
 from crewai.events.event_bus import CrewAIEventsBus
 from crewai.events.listeners.tracing.first_time_trace_handler import (
     FirstTimeTraceHandler,
@@ -19,32 +18,6 @@ from crewai.events.listeners.tracing.types import TraceEvent
 from crewai.events.listeners.tracing.utils import (
     safe_serialize_to_dict,
 )
-from crewai.events.types.a2a_events import (
-    A2AAgentCardFetchedEvent,
-    A2AArtifactReceivedEvent,
-    A2AAuthenticationFailedEvent,
-    A2AConnectionErrorEvent,
-    A2AConversationCompletedEvent,
-    A2AConversationStartedEvent,
-    A2ADelegationCompletedEvent,
-    A2ADelegationStartedEvent,
-    A2AMessageSentEvent,
-    A2AParallelDelegationCompletedEvent,
-    A2AParallelDelegationStartedEvent,
-    A2APollingStartedEvent,
-    A2APollingStatusEvent,
-    A2APushNotificationReceivedEvent,
-    A2APushNotificationRegisteredEvent,
-    A2APushNotificationSentEvent,
-    A2APushNotificationTimeoutEvent,
-    A2AResponseReceivedEvent,
-    A2AServerTaskCanceledEvent,
-    A2AServerTaskCompletedEvent,
-    A2AServerTaskFailedEvent,
-    A2AServerTaskStartedEvent,
-    A2AStreamingChunkEvent,
-    A2AStreamingStartedEvent,
-)
 from crewai.events.types.agent_events import (
     AgentExecutionCompletedEvent,
     AgentExecutionErrorEvent,
@@ -132,7 +105,7 @@ class TraceCollectionListener(BaseEventListener):
         """Create or return singleton instance."""
         if cls._instance is None:
             cls._instance = super().__new__(cls)
-        return cls._instance
+        return cast(Self, cls._instance)
 
     def __init__(
         self,
@@ -187,7 +160,6 @@ class TraceCollectionListener(BaseEventListener):
         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._register_a2a_event_handlers(crewai_event_bus)
         self._register_system_event_handlers(crewai_event_bus)
 
         self._listeners_setup = True
@@ -467,147 +439,6 @@ class TraceCollectionListener(BaseEventListener):
         ) -> None:
             self._handle_action_event("knowledge_query_failed", source, event)
 
-    def _register_a2a_event_handlers(self, event_bus: CrewAIEventsBus) -> None:
-        """Register handlers for A2A (Agent-to-Agent) events."""
-
-        @event_bus.on(A2ADelegationStartedEvent)
-        def on_a2a_delegation_started(
-            source: Any, event: A2ADelegationStartedEvent
-        ) -> None:
-            self._handle_action_event("a2a_delegation_started", source, event)
-
-        @event_bus.on(A2ADelegationCompletedEvent)
-        def on_a2a_delegation_completed(
-            source: Any, event: A2ADelegationCompletedEvent
-        ) -> None:
-            self._handle_action_event("a2a_delegation_completed", source, event)
-
-        @event_bus.on(A2AConversationStartedEvent)
-        def on_a2a_conversation_started(
-            source: Any, event: A2AConversationStartedEvent
-        ) -> None:
-            self._handle_action_event("a2a_conversation_started", source, event)
-
-        @event_bus.on(A2AMessageSentEvent)
-        def on_a2a_message_sent(source: Any, event: A2AMessageSentEvent) -> None:
-            self._handle_action_event("a2a_message_sent", source, event)
-
-        @event_bus.on(A2AResponseReceivedEvent)
-        def on_a2a_response_received(
-            source: Any, event: A2AResponseReceivedEvent
-        ) -> None:
-            self._handle_action_event("a2a_response_received", source, event)
-
-        @event_bus.on(A2AConversationCompletedEvent)
-        def on_a2a_conversation_completed(
-            source: Any, event: A2AConversationCompletedEvent
-        ) -> None:
-            self._handle_action_event("a2a_conversation_completed", source, event)
-
-        @event_bus.on(A2APollingStartedEvent)
-        def on_a2a_polling_started(source: Any, event: A2APollingStartedEvent) -> None:
-            self._handle_action_event("a2a_polling_started", source, event)
-
-        @event_bus.on(A2APollingStatusEvent)
-        def on_a2a_polling_status(source: Any, event: A2APollingStatusEvent) -> None:
-            self._handle_action_event("a2a_polling_status", source, event)
-
-        @event_bus.on(A2APushNotificationRegisteredEvent)
-        def on_a2a_push_notification_registered(
-            source: Any, event: A2APushNotificationRegisteredEvent
-        ) -> None:
-            self._handle_action_event("a2a_push_notification_registered", source, event)
-
-        @event_bus.on(A2APushNotificationReceivedEvent)
-        def on_a2a_push_notification_received(
-            source: Any, event: A2APushNotificationReceivedEvent
-        ) -> None:
-            self._handle_action_event("a2a_push_notification_received", source, event)
-
-        @event_bus.on(A2APushNotificationSentEvent)
-        def on_a2a_push_notification_sent(
-            source: Any, event: A2APushNotificationSentEvent
-        ) -> None:
-            self._handle_action_event("a2a_push_notification_sent", source, event)
-
-        @event_bus.on(A2APushNotificationTimeoutEvent)
-        def on_a2a_push_notification_timeout(
-            source: Any, event: A2APushNotificationTimeoutEvent
-        ) -> None:
-            self._handle_action_event("a2a_push_notification_timeout", source, event)
-
-        @event_bus.on(A2AStreamingStartedEvent)
-        def on_a2a_streaming_started(
-            source: Any, event: A2AStreamingStartedEvent
-        ) -> None:
-            self._handle_action_event("a2a_streaming_started", source, event)
-
-        @event_bus.on(A2AStreamingChunkEvent)
-        def on_a2a_streaming_chunk(source: Any, event: A2AStreamingChunkEvent) -> None:
-            self._handle_action_event("a2a_streaming_chunk", source, event)
-
-        @event_bus.on(A2AAgentCardFetchedEvent)
-        def on_a2a_agent_card_fetched(
-            source: Any, event: A2AAgentCardFetchedEvent
-        ) -> None:
-            self._handle_action_event("a2a_agent_card_fetched", source, event)
-
-        @event_bus.on(A2AAuthenticationFailedEvent)
-        def on_a2a_authentication_failed(
-            source: Any, event: A2AAuthenticationFailedEvent
-        ) -> None:
-            self._handle_action_event("a2a_authentication_failed", source, event)
-
-        @event_bus.on(A2AArtifactReceivedEvent)
-        def on_a2a_artifact_received(
-            source: Any, event: A2AArtifactReceivedEvent
-        ) -> None:
-            self._handle_action_event("a2a_artifact_received", source, event)
-
-        @event_bus.on(A2AConnectionErrorEvent)
-        def on_a2a_connection_error(
-            source: Any, event: A2AConnectionErrorEvent
-        ) -> None:
-            self._handle_action_event("a2a_connection_error", source, event)
-
-        @event_bus.on(A2AServerTaskStartedEvent)
-        def on_a2a_server_task_started(
-            source: Any, event: A2AServerTaskStartedEvent
-        ) -> None:
-            self._handle_action_event("a2a_server_task_started", source, event)
-
-        @event_bus.on(A2AServerTaskCompletedEvent)
-        def on_a2a_server_task_completed(
-            source: Any, event: A2AServerTaskCompletedEvent
-        ) -> None:
-            self._handle_action_event("a2a_server_task_completed", source, event)
-
-        @event_bus.on(A2AServerTaskCanceledEvent)
-        def on_a2a_server_task_canceled(
-            source: Any, event: A2AServerTaskCanceledEvent
-        ) -> None:
-            self._handle_action_event("a2a_server_task_canceled", source, event)
-
-        @event_bus.on(A2AServerTaskFailedEvent)
-        def on_a2a_server_task_failed(
-            source: Any, event: A2AServerTaskFailedEvent
-        ) -> None:
-            self._handle_action_event("a2a_server_task_failed", source, event)
-
-        @event_bus.on(A2AParallelDelegationStartedEvent)
-        def on_a2a_parallel_delegation_started(
-            source: Any, event: A2AParallelDelegationStartedEvent
-        ) -> None:
-            self._handle_action_event("a2a_parallel_delegation_started", source, event)
-
-        @event_bus.on(A2AParallelDelegationCompletedEvent)
-        def on_a2a_parallel_delegation_completed(
-            source: Any, event: A2AParallelDelegationCompletedEvent
-        ) -> None:
-            self._handle_action_event(
-                "a2a_parallel_delegation_completed", source, event
-            )
-
     def _register_system_event_handlers(self, event_bus: CrewAIEventsBus) -> None:
         """Register handlers for system signal events (SIGTERM, SIGINT, etc.)."""
 
@@ -617,7 +448,7 @@ class TraceCollectionListener(BaseEventListener):
             if self.batch_manager.is_batch_initialized():
                 self.batch_manager.finalize_batch()
 
-    def _initialize_crew_batch(self, source: Any, event: BaseEvent) -> None:
+    def _initialize_crew_batch(self, source: Any, event: Any) -> None:
         """Initialize trace batch.
 
         Args:
@@ -627,7 +458,7 @@ class TraceCollectionListener(BaseEventListener):
         user_context = self._get_user_context()
         execution_metadata = {
             "crew_name": getattr(event, "crew_name", "Unknown Crew"),
-            "execution_start": event.timestamp,
+            "execution_start": event.timestamp if hasattr(event, "timestamp") else None,
             "crewai_version": get_crewai_version(),
         }
 
@@ -636,7 +467,7 @@ class TraceCollectionListener(BaseEventListener):
 
         self._initialize_batch(user_context, execution_metadata)
 
-    def _initialize_flow_batch(self, source: Any, event: BaseEvent) -> None:
+    def _initialize_flow_batch(self, source: Any, event: Any) -> None:
         """Initialize trace batch for Flow execution.
 
         Args:
@@ -646,7 +477,7 @@ class TraceCollectionListener(BaseEventListener):
         user_context = self._get_user_context()
         execution_metadata = {
             "flow_name": getattr(event, "flow_name", "Unknown Flow"),
-            "execution_start": event.timestamp,
+            "execution_start": event.timestamp if hasattr(event, "timestamp") else None,
             "crewai_version": get_crewai_version(),
             "execution_type": "flow",
         }
@@ -715,18 +546,18 @@ class TraceCollectionListener(BaseEventListener):
             self.batch_manager.end_event_processing()
 
     def _create_trace_event(
-        self, event_type: str, source: Any, event: BaseEvent
+        self, event_type: str, source: Any, event: Any
     ) -> TraceEvent:
-        """Create a trace event with ordering information."""
-        trace_event = TraceEvent(
-            type=event_type,
-            timestamp=event.timestamp.isoformat() if event.timestamp else "",
-            event_id=event.event_id,
-            emission_sequence=event.emission_sequence,
-            parent_event_id=event.parent_event_id,
-            previous_event_id=event.previous_event_id,
-            triggered_by_event_id=event.triggered_by_event_id,
-        )
+        """Create a trace event"""
+        if hasattr(event, "timestamp") and event.timestamp:
+            trace_event = TraceEvent(
+                type=event_type,
+                timestamp=event.timestamp.isoformat(),
+            )
+        else:
+            trace_event = TraceEvent(
+                type=event_type,
+            )
 
         trace_event.event_data = self._build_event_data(event_type, event, source)
 
@@ -739,15 +570,10 @@ class TraceCollectionListener(BaseEventListener):
         if event_type not in self.complex_events:
             return safe_serialize_to_dict(event)
         if event_type == "task_started":
-            task_name = event.task.name or event.task.description
-            task_display_name = (
-                task_name[:80] + "..." if len(task_name) > 80 else task_name
-            )
             return {
                 "task_description": event.task.description,
                 "expected_output": event.task.expected_output,
-                "task_name": task_name,
-                "task_display_name": task_display_name,
+                "task_name": event.task.name or event.task.description,
                 "context": event.context,
                 "agent_role": source.agent.role,
                 "task_id": str(event.task.id),
@@ -779,8 +605,10 @@ class TraceCollectionListener(BaseEventListener):
             }
         if event_type == "llm_call_started":
             event_data = safe_serialize_to_dict(event)
-            event_data["task_name"] = event.task_name or getattr(
-                event, "task_description", None
+            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
         if event_type == "llm_call_completed":

lib/crewai/src/crewai/events/listeners/tracing/types.py

@@ -15,10 +15,5 @@ class TraceEvent:
     type: str = ""
     event_data: dict[str, Any] = field(default_factory=dict)
 
-    emission_sequence: int | None = None
-    parent_event_id: str | None = None
-    previous_event_id: str | None = None
-    triggered_by_event_id: str | None = None
-
     def to_dict(self) -> dict[str, Any]:
         return asdict(self)

lib/crewai/src/crewai/events/types/a2a_events.py

@@ -4,120 +4,68 @@ This module defines events emitted during A2A protocol delegation,
 including both single-turn and multiturn conversation flows.
 """
 
-from __future__ import annotations
-
 from typing import Any, Literal
 
-from pydantic import model_validator
-
 from crewai.events.base_events import BaseEvent
 
 
 class A2AEventBase(BaseEvent):
     """Base class for A2A events with task/agent context."""
 
-    from_task: Any = None
-    from_agent: Any = None
+    from_task: Any | None = None
+    from_agent: Any | None = None
 
-    @model_validator(mode="before")
-    @classmethod
-    def extract_task_and_agent_metadata(cls, data: dict[str, Any]) -> dict[str, Any]:
-        """Extract task and agent metadata before validation."""
-        if task := data.get("from_task"):
+    def __init__(self, **data: Any) -> None:
+        """Initialize A2A event, extracting task and agent metadata."""
+        if data.get("from_task"):
+            task = data["from_task"]
             data["task_id"] = str(task.id)
             data["task_name"] = task.name or task.description
-            data.setdefault("source_fingerprint", str(task.id))
-            data.setdefault("source_type", "task")
-            data.setdefault(
-                "fingerprint_metadata",
-                {
-                    "task_id": str(task.id),
-                    "task_name": task.name or task.description,
-                },
-            )
             data["from_task"] = None
 
-        if agent := data.get("from_agent"):
+        if data.get("from_agent"):
+            agent = data["from_agent"]
             data["agent_id"] = str(agent.id)
             data["agent_role"] = agent.role
-            data.setdefault("source_fingerprint", str(agent.id))
-            data.setdefault("source_type", "agent")
-            data.setdefault(
-                "fingerprint_metadata",
-                {
-                    "agent_id": str(agent.id),
-                    "agent_role": agent.role,
-                },
-            )
             data["from_agent"] = None
 
-        return data
+        super().__init__(**data)
 
 
 class A2ADelegationStartedEvent(A2AEventBase):
     """Event emitted when A2A delegation starts.
 
     Attributes:
-        endpoint: A2A agent endpoint URL (AgentCard URL).
-        task_description: Task being delegated to the A2A agent.
-        agent_id: A2A agent identifier.
-        context_id: A2A context ID grouping related tasks.
-        is_multiturn: Whether this is part of a multiturn conversation.
-        turn_number: Current turn number (1-indexed, 1 for single-turn).
-        a2a_agent_name: Name of the A2A agent from agent card.
-        agent_card: Full A2A agent card metadata.
-        protocol_version: A2A protocol version being used.
-        provider: Agent provider/organization info from agent card.
-        skill_id: ID of the specific skill being invoked.
-        metadata: Custom A2A metadata key-value pairs.
-        extensions: List of A2A extension URIs in use.
+        endpoint: A2A agent endpoint URL (AgentCard URL)
+        task_description: Task being delegated to the A2A agent
+        agent_id: A2A agent identifier
+        is_multiturn: Whether this is part of a multiturn conversation
+        turn_number: Current turn number (1-indexed, 1 for single-turn)
     """
 
     type: str = "a2a_delegation_started"
     endpoint: str
     task_description: str
     agent_id: str
-    context_id: str | None = None
     is_multiturn: bool = False
     turn_number: int = 1
-    a2a_agent_name: str | None = None
-    agent_card: dict[str, Any] | None = None
-    protocol_version: str | None = None
-    provider: dict[str, Any] | None = None
-    skill_id: str | None = None
-    metadata: dict[str, Any] | None = None
-    extensions: list[str] | None = None
 
 
 class A2ADelegationCompletedEvent(A2AEventBase):
     """Event emitted when A2A delegation completes.
 
     Attributes:
-        status: Completion status (completed, input_required, failed, etc.).
-        result: Result message if status is completed.
-        error: Error/response message (error for failed, response for input_required).
-        context_id: A2A context ID grouping related tasks.
-        is_multiturn: Whether this is part of a multiturn conversation.
-        endpoint: A2A agent endpoint URL.
-        a2a_agent_name: Name of the A2A agent from agent card.
-        agent_card: Full A2A agent card metadata.
-        provider: Agent provider/organization info from agent card.
-        metadata: Custom A2A metadata key-value pairs.
-        extensions: List of A2A extension URIs in use.
+        status: Completion status (completed, input_required, failed, etc.)
+        result: Result message if status is completed
+        error: Error/response message (error for failed, response for input_required)
+        is_multiturn: Whether this is part of a multiturn conversation
     """
 
     type: str = "a2a_delegation_completed"
     status: str
     result: str | None = None
     error: str | None = None
-    context_id: str | None = None
     is_multiturn: bool = False
-    endpoint: str | None = None
-    a2a_agent_name: str | None = None
-    agent_card: dict[str, Any] | None = None
-    provider: dict[str, Any] | None = None
-    metadata: dict[str, Any] | None = None
-    extensions: list[str] | None = None
 
 
 class A2AConversationStartedEvent(A2AEventBase):
@@ -127,95 +75,51 @@ class A2AConversationStartedEvent(A2AEventBase):
     before the first message exchange.
 
     Attributes:
-        agent_id: A2A agent identifier.
-        endpoint: A2A agent endpoint URL.
-        context_id: A2A context ID grouping related tasks.
-        a2a_agent_name: Name of the A2A agent from agent card.
-        agent_card: Full A2A agent card metadata.
-        protocol_version: A2A protocol version being used.
-        provider: Agent provider/organization info from agent card.
-        skill_id: ID of the specific skill being invoked.
-        reference_task_ids: Related task IDs for context.
-        metadata: Custom A2A metadata key-value pairs.
-        extensions: List of A2A extension URIs in use.
+        agent_id: A2A agent identifier
+        endpoint: A2A agent endpoint URL
+        a2a_agent_name: Name of the A2A agent from agent card
     """
 
     type: str = "a2a_conversation_started"
     agent_id: str
     endpoint: str
-    context_id: str | None = None
     a2a_agent_name: str | None = None
-    agent_card: dict[str, Any] | None = None
-    protocol_version: str | None = None
-    provider: dict[str, Any] | None = None
-    skill_id: str | None = None
-    reference_task_ids: list[str] | None = None
-    metadata: dict[str, Any] | None = None
-    extensions: list[str] | None = None
 
 
 class A2AMessageSentEvent(A2AEventBase):
     """Event emitted when a message is sent to the A2A agent.
 
     Attributes:
-        message: Message content sent to the A2A agent.
-        turn_number: Current turn number (1-indexed).
-        context_id: A2A context ID grouping related tasks.
-        message_id: Unique A2A message identifier.
-        is_multiturn: Whether this is part of a multiturn conversation.
-        agent_role: Role of the CrewAI agent sending the message.
-        endpoint: A2A agent endpoint URL.
-        a2a_agent_name: Name of the A2A agent from agent card.
-        skill_id: ID of the specific skill being invoked.
-        metadata: Custom A2A metadata key-value pairs.
-        extensions: List of A2A extension URIs in use.
+        message: Message content sent to the A2A agent
+        turn_number: Current turn number (1-indexed)
+        is_multiturn: Whether this is part of a multiturn conversation
+        agent_role: Role of the CrewAI agent sending the message
     """
 
     type: str = "a2a_message_sent"
     message: str
     turn_number: int
-    context_id: str | None = None
-    message_id: str | None = None
     is_multiturn: bool = False
     agent_role: str | None = None
-    endpoint: str | None = None
-    a2a_agent_name: str | None = None
-    skill_id: str | None = None
-    metadata: dict[str, Any] | None = None
-    extensions: list[str] | None = None
 
 
 class A2AResponseReceivedEvent(A2AEventBase):
     """Event emitted when a response is received from the A2A agent.
 
     Attributes:
-        response: Response content from the A2A agent.
-        turn_number: Current turn number (1-indexed).
-        context_id: A2A context ID grouping related tasks.
-        message_id: Unique A2A message identifier.
-        is_multiturn: Whether this is part of a multiturn conversation.
-        status: Response status (input_required, completed, etc.).
-        final: Whether this is the final response in the stream.
-        agent_role: Role of the CrewAI agent (for display).
-        endpoint: A2A agent endpoint URL.
-        a2a_agent_name: Name of the A2A agent from agent card.
-        metadata: Custom A2A metadata key-value pairs.
-        extensions: List of A2A extension URIs in use.
+        response: Response content from the A2A agent
+        turn_number: Current turn number (1-indexed)
+        is_multiturn: Whether this is part of a multiturn conversation
+        status: Response status (input_required, completed, etc.)
+        agent_role: Role of the CrewAI agent (for display)
     """
 
     type: str = "a2a_response_received"
     response: str
     turn_number: int
-    context_id: str | None = None
-    message_id: str | None = None
     is_multiturn: bool = False
     status: str
-    final: bool = False
     agent_role: str | None = None
-    endpoint: str | None = None
-    a2a_agent_name: str | None = None
-    metadata: dict[str, Any] | None = None
-    extensions: list[str] | None = None
 
 
 class A2AConversationCompletedEvent(A2AEventBase):
@@ -224,433 +128,119 @@ class A2AConversationCompletedEvent(A2AEventBase):
     This is emitted once at the end of a multiturn conversation.
 
     Attributes:
-        status: Final status (completed, failed, etc.).
-        final_result: Final result if completed successfully.
-        error: Error message if failed.
-        context_id: A2A context ID grouping related tasks.
-        total_turns: Total number of turns in the conversation.
-        endpoint: A2A agent endpoint URL.
-        a2a_agent_name: Name of the A2A agent from agent card.
-        agent_card: Full A2A agent card metadata.
-        reference_task_ids: Related task IDs for context.
-        metadata: Custom A2A metadata key-value pairs.
-        extensions: List of A2A extension URIs in use.
+        status: Final status (completed, failed, etc.)
+        final_result: Final result if completed successfully
+        error: Error message if failed
+        total_turns: Total number of turns in the conversation
     """
 
     type: str = "a2a_conversation_completed"
     status: Literal["completed", "failed"]
     final_result: str | None = None
     error: str | None = None
-    context_id: str | None = None
     total_turns: int
-    endpoint: str | None = None
-    a2a_agent_name: str | None = None
-    agent_card: dict[str, Any] | None = None
-    reference_task_ids: list[str] | None = None
-    metadata: dict[str, Any] | None = None
-    extensions: list[str] | None = None
 
 
 class A2APollingStartedEvent(A2AEventBase):
     """Event emitted when polling mode begins for A2A delegation.
 
     Attributes:
-        task_id: A2A task ID being polled.
-        context_id: A2A context ID grouping related tasks.
-        polling_interval: Seconds between poll attempts.
-        endpoint: A2A agent endpoint URL.
-        a2a_agent_name: Name of the A2A agent from agent card.
-        metadata: Custom A2A metadata key-value pairs.
+        task_id: A2A task ID being polled
+        polling_interval: Seconds between poll attempts
+        endpoint: A2A agent endpoint URL
     """
 
     type: str = "a2a_polling_started"
     task_id: str
-    context_id: str | None = None
     polling_interval: float
     endpoint: str
-    a2a_agent_name: str | None = None
-    metadata: dict[str, Any] | None = None
 
 
 class A2APollingStatusEvent(A2AEventBase):
     """Event emitted on each polling iteration.
 
     Attributes:
-        task_id: A2A task ID being polled.
-        context_id: A2A context ID grouping related tasks.
-        state: Current task state from remote agent.
-        elapsed_seconds: Time since polling started.
-        poll_count: Number of polls completed.
-        endpoint: A2A agent endpoint URL.
-        a2a_agent_name: Name of the A2A agent from agent card.
-        metadata: Custom A2A metadata key-value pairs.
+        task_id: A2A task ID being polled
+        state: Current task state from remote agent
+        elapsed_seconds: Time since polling started
+        poll_count: Number of polls completed
     """
 
     type: str = "a2a_polling_status"
     task_id: str
-    context_id: str | None = None
     state: str
     elapsed_seconds: float
     poll_count: int
-    endpoint: str | None = None
-    a2a_agent_name: str | None = None
-    metadata: dict[str, Any] | None = None
 
 
 class A2APushNotificationRegisteredEvent(A2AEventBase):
     """Event emitted when push notification callback is registered.
 
     Attributes:
-        task_id: A2A task ID for which callback is registered.
-        context_id: A2A context ID grouping related tasks.
-        callback_url: URL where agent will send push notifications.
-        endpoint: A2A agent endpoint URL.
-        a2a_agent_name: Name of the A2A agent from agent card.
-        metadata: Custom A2A metadata key-value pairs.
+        task_id: A2A task ID for which callback is registered
+        callback_url: URL where agent will send push notifications
     """
 
     type: str = "a2a_push_notification_registered"
     task_id: str
-    context_id: str | None = None
     callback_url: str
-    endpoint: str | None = None
-    a2a_agent_name: str | None = None
-    metadata: dict[str, Any] | None = None
 
 
 class A2APushNotificationReceivedEvent(A2AEventBase):
     """Event emitted when a push notification is received.
 
-    This event should be emitted by the user's webhook handler when it receives
-    a push notification from the remote A2A agent, before calling
-    `result_store.store_result()`.
-
     Attributes:
-        task_id: A2A task ID from the notification.
-        context_id: A2A context ID grouping related tasks.
-        state: Current task state from the notification.
-        endpoint: A2A agent endpoint URL.
-        a2a_agent_name: Name of the A2A agent from agent card.
-        metadata: Custom A2A metadata key-value pairs.
+        task_id: A2A task ID from the notification
+        state: Current task state from the notification
     """
 
     type: str = "a2a_push_notification_received"
     task_id: str
-    context_id: str | None = None
     state: str
-    endpoint: str | None = None
-    a2a_agent_name: str | None = None
-    metadata: dict[str, Any] | None = None
-
-
-class A2APushNotificationSentEvent(A2AEventBase):
-    """Event emitted when a push notification is sent to a callback URL.
-
-    Emitted by the A2A server when it sends a task status update to the
-    client's registered push notification callback URL.
-
-    Attributes:
-        task_id: A2A task ID being notified.
-        context_id: A2A context ID grouping related tasks.
-        callback_url: URL the notification was sent to.
-        state: Task state being reported.
-        success: Whether the notification was successfully delivered.
-        error: Error message if delivery failed.
-        metadata: Custom A2A metadata key-value pairs.
-    """
-
-    type: str = "a2a_push_notification_sent"
-    task_id: str
-    context_id: str | None = None
-    callback_url: str
-    state: str
-    success: bool = True
-    error: str | None = None
-    metadata: dict[str, Any] | None = None
 
 
 class A2APushNotificationTimeoutEvent(A2AEventBase):
     """Event emitted when push notification wait times out.
 
     Attributes:
-        task_id: A2A task ID that timed out.
-        context_id: A2A context ID grouping related tasks.
-        timeout_seconds: Timeout duration in seconds.
-        endpoint: A2A agent endpoint URL.
-        a2a_agent_name: Name of the A2A agent from agent card.
-        metadata: Custom A2A metadata key-value pairs.
+        task_id: A2A task ID that timed out
+        timeout_seconds: Timeout duration in seconds
     """
 
     type: str = "a2a_push_notification_timeout"
     task_id: str
-    context_id: str | None = None
     timeout_seconds: float
-    endpoint: str | None = None
-    a2a_agent_name: str | None = None
-    metadata: dict[str, Any] | None = None
-
-
-class A2AStreamingStartedEvent(A2AEventBase):
-    """Event emitted when streaming mode begins for A2A delegation.
-
-    Attributes:
-        task_id: A2A task ID for the streaming session.
-        context_id: A2A context ID grouping related tasks.
-        endpoint: A2A agent endpoint URL.
-        a2a_agent_name: Name of the A2A agent from agent card.
-        turn_number: Current turn number (1-indexed).
-        is_multiturn: Whether this is part of a multiturn conversation.
-        agent_role: Role of the CrewAI agent.
-        metadata: Custom A2A metadata key-value pairs.
-        extensions: List of A2A extension URIs in use.
-    """
-
-    type: str = "a2a_streaming_started"
-    task_id: str | None = None
-    context_id: str | None = None
-    endpoint: str
-    a2a_agent_name: str | None = None
-    turn_number: int = 1
-    is_multiturn: bool = False
-    agent_role: str | None = None
-    metadata: dict[str, Any] | None = None
-    extensions: list[str] | None = None
-
-
-class A2AStreamingChunkEvent(A2AEventBase):
-    """Event emitted when a streaming chunk is received.
-
-    Attributes:
-        task_id: A2A task ID for the streaming session.
-        context_id: A2A context ID grouping related tasks.
-        chunk: The text content of the chunk.
-        chunk_index: Index of this chunk in the stream (0-indexed).
-        final: Whether this is the final chunk in the stream.
-        endpoint: A2A agent endpoint URL.
-        a2a_agent_name: Name of the A2A agent from agent card.
-        turn_number: Current turn number (1-indexed).
-        is_multiturn: Whether this is part of a multiturn conversation.
-        metadata: Custom A2A metadata key-value pairs.
-        extensions: List of A2A extension URIs in use.
-    """
-
-    type: str = "a2a_streaming_chunk"
-    task_id: str | None = None
-    context_id: str | None = None
-    chunk: str
-    chunk_index: int
-    final: bool = False
-    endpoint: str | None = None
-    a2a_agent_name: str | None = None
-    turn_number: int = 1
-    is_multiturn: bool = False
-    metadata: dict[str, Any] | None = None
-    extensions: list[str] | None = None
-
-
-class A2AAgentCardFetchedEvent(A2AEventBase):
-    """Event emitted when an agent card is successfully fetched.
-
-    Attributes:
-        endpoint: A2A agent endpoint URL.
-        a2a_agent_name: Name of the A2A agent from agent card.
-        agent_card: Full A2A agent card metadata.
-        protocol_version: A2A protocol version from agent card.
-        provider: Agent provider/organization info from agent card.
-        cached: Whether the agent card was retrieved from cache.
-        fetch_time_ms: Time taken to fetch the agent card in milliseconds.
-        metadata: Custom A2A metadata key-value pairs.
-    """
-
-    type: str = "a2a_agent_card_fetched"
-    endpoint: str
-    a2a_agent_name: str | None = None
-    agent_card: dict[str, Any] | None = None
-    protocol_version: str | None = None
-    provider: dict[str, Any] | None = None
-    cached: bool = False
-    fetch_time_ms: float | None = None
-    metadata: dict[str, Any] | None = None
-
-
-class A2AAuthenticationFailedEvent(A2AEventBase):
-    """Event emitted when authentication to an A2A agent fails.
-
-    Attributes:
-        endpoint: A2A agent endpoint URL.
-        auth_type: Type of authentication attempted (e.g., bearer, oauth2, api_key).
-        error: Error message describing the failure.
-        status_code: HTTP status code if applicable.
-        a2a_agent_name: Name of the A2A agent if known.
-        protocol_version: A2A protocol version being used.
-        metadata: Custom A2A metadata key-value pairs.
-    """
-
-    type: str = "a2a_authentication_failed"
-    endpoint: str
-    auth_type: str | None = None
-    error: str
-    status_code: int | None = None
-    a2a_agent_name: str | None = None
-    protocol_version: str | None = None
-    metadata: dict[str, Any] | None = None
-
-
-class A2AArtifactReceivedEvent(A2AEventBase):
-    """Event emitted when an artifact is received from a remote A2A agent.
-
-    Attributes:
-        task_id: A2A task ID the artifact belongs to.
-        artifact_id: Unique identifier for the artifact.
-        artifact_name: Name of the artifact.
-        artifact_description: Purpose description of the artifact.
-        mime_type: MIME type of the artifact content.
-        size_bytes: Size of the artifact in bytes.
-        append: Whether content should be appended to existing artifact.
-        last_chunk: Whether this is the final chunk of the artifact.
-        endpoint: A2A agent endpoint URL.
-        a2a_agent_name: Name of the A2A agent from agent card.
-        context_id: Context ID for correlation.
-        turn_number: Current turn number (1-indexed).
-        is_multiturn: Whether this is part of a multiturn conversation.
-        metadata: Custom A2A metadata key-value pairs.
-        extensions: List of A2A extension URIs in use.
-    """
-
-    type: str = "a2a_artifact_received"
-    task_id: str
-    artifact_id: str
-    artifact_name: str | None = None
-    artifact_description: str | None = None
-    mime_type: str | None = None
-    size_bytes: int | None = None
-    append: bool = False
-    last_chunk: bool = False
-    endpoint: str | None = None
-    a2a_agent_name: str | None = None
-    context_id: str | None = None
-    turn_number: int = 1
-    is_multiturn: bool = False
-    metadata: dict[str, Any] | None = None
-    extensions: list[str] | None = None
-
-
-class A2AConnectionErrorEvent(A2AEventBase):
-    """Event emitted when a connection error occurs during A2A communication.
-
-    Attributes:
-        endpoint: A2A agent endpoint URL.
-        error: Error message describing the connection failure.
-        error_type: Type of error (e.g., timeout, connection_refused, dns_error).
-        status_code: HTTP status code if applicable.
-        a2a_agent_name: Name of the A2A agent from agent card.
-        operation: The operation being attempted when error occurred.
-        context_id: A2A context ID grouping related tasks.
-        task_id: A2A task ID if applicable.
-        metadata: Custom A2A metadata key-value pairs.
-    """
-
-    type: str = "a2a_connection_error"
-    endpoint: str
-    error: str
-    error_type: str | None = None
-    status_code: int | None = None
-    a2a_agent_name: str | None = None
-    operation: str | None = None
-    context_id: str | None = None
-    task_id: str | None = None
-    metadata: dict[str, Any] | None = None
 
 
 class A2AServerTaskStartedEvent(A2AEventBase):
-    """Event emitted when an A2A server task execution starts.
-
-    Attributes:
-        task_id: A2A task ID for this execution.
-        context_id: A2A context ID grouping related tasks.
-        metadata: Custom A2A metadata key-value pairs.
-    """
+    """Event emitted when an A2A server task execution starts."""
 
     type: str = "a2a_server_task_started"
-    task_id: str
-    context_id: str
-    metadata: dict[str, Any] | None = None
+    a2a_task_id: str
+    a2a_context_id: str
 
 
 class A2AServerTaskCompletedEvent(A2AEventBase):
-    """Event emitted when an A2A server task execution completes.
-
-    Attributes:
-        task_id: A2A task ID for this execution.
-        context_id: A2A context ID grouping related tasks.
-        result: The task result.
-        metadata: Custom A2A metadata key-value pairs.
-    """
+    """Event emitted when an A2A server task execution completes."""
 
     type: str = "a2a_server_task_completed"
-    task_id: str
-    context_id: str
+    a2a_task_id: str
+    a2a_context_id: str
     result: str
-    metadata: dict[str, Any] | None = None
 
 
 class A2AServerTaskCanceledEvent(A2AEventBase):
-    """Event emitted when an A2A server task execution is canceled.
-
-    Attributes:
-        task_id: A2A task ID for this execution.
-        context_id: A2A context ID grouping related tasks.
-        metadata: Custom A2A metadata key-value pairs.
-    """
+    """Event emitted when an A2A server task execution is canceled."""
 
     type: str = "a2a_server_task_canceled"
-    task_id: str
-    context_id: str
-    metadata: dict[str, Any] | None = None
+    a2a_task_id: str
+    a2a_context_id: str
 
 
 class A2AServerTaskFailedEvent(A2AEventBase):
-    """Event emitted when an A2A server task execution fails.
-
-    Attributes:
-        task_id: A2A task ID for this execution.
-        context_id: A2A context ID grouping related tasks.
-        error: Error message describing the failure.
-        metadata: Custom A2A metadata key-value pairs.
-    """
+    """Event emitted when an A2A server task execution fails."""
 
     type: str = "a2a_server_task_failed"
-    task_id: str
-    context_id: str
+    a2a_task_id: str
+    a2a_context_id: str
     error: str
-    metadata: dict[str, Any] | None = None
-
-
-class A2AParallelDelegationStartedEvent(A2AEventBase):
-    """Event emitted when parallel delegation to multiple A2A agents begins.
-
-    Attributes:
-        endpoints: List of A2A agent endpoints being delegated to.
-        task_description: Description of the task being delegated.
-    """
-
-    type: str = "a2a_parallel_delegation_started"
-    endpoints: list[str]
-    task_description: str
-
-
-class A2AParallelDelegationCompletedEvent(A2AEventBase):
-    """Event emitted when parallel delegation to multiple A2A agents completes.
-
-    Attributes:
-        endpoints: List of A2A agent endpoints that were delegated to.
-        success_count: Number of successful delegations.
-        failure_count: Number of failed delegations.
-        results: Summary of results from each agent.
-    """
-
-    type: str = "a2a_parallel_delegation_completed"
-    endpoints: list[str]
-    success_count: int
-    failure_count: int
-    results: dict[str, str] | None = None

lib/crewai/src/crewai/events/types/llm_events.py

@@ -9,7 +9,6 @@ from crewai.events.base_events import BaseEvent
 class LLMEventBase(BaseEvent):
     from_task: Any | None = None
     from_agent: Any | None = None
-    model: str | None = None
 
     def __init__(self, **data: Any) -> None:
         if data.get("from_task"):
@@ -43,6 +42,7 @@ class LLMCallStartedEvent(LLMEventBase):
     """
 
     type: str = "llm_call_started"
+    model: str | None = None
     messages: str | list[dict[str, Any]] | None = None
     tools: list[dict[str, Any]] | None = None
     callbacks: list[Any] | None = None
@@ -56,6 +56,7 @@ class LLMCallCompletedEvent(LLMEventBase):
     messages: str | list[dict[str, Any]] | None = None
     response: Any
     call_type: LLMCallType
+    model: str | None = None
 
 
 class LLMCallFailedEvent(LLMEventBase):

lib/crewai/src/crewai/events/types/memory_events.py

@@ -14,7 +14,7 @@ class MemoryBaseEvent(BaseEvent):
     agent_role: str | None = None
     agent_id: str | None = None
 
-    def __init__(self, **data: Any) -> None:
+    def __init__(self, **data):
         super().__init__(**data)
         self._set_agent_params(data)
         self._set_task_params(data)
@@ -93,11 +93,3 @@ class MemoryRetrievalCompletedEvent(MemoryBaseEvent):
     task_id: str | None = None
     memory_content: str
     retrieval_time_ms: float
-
-
-class MemoryRetrievalFailedEvent(MemoryBaseEvent):
-    """Event emitted when memory retrieval for a task prompt fails."""
-
-    type: str = "memory_retrieval_failed"
-    task_id: str | None = None
-    error: str

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

@@ -366,32 +366,6 @@ To enable tracing, do any one of these:
 
         self.print_panel(content, f"🔧 Tool Execution Started (#{iteration})", "yellow")
 
-    def handle_tool_usage_finished(
-        self,
-        tool_name: str,
-        output: str,
-        run_attempts: int | None = None,
-    ) -> None:
-        """Handle tool usage finished event with panel display."""
-        if not self.verbose:
-            return
-
-        iteration = self.tool_usage_counts.get(tool_name, 1)
-
-        content = Text()
-        content.append("Tool Completed\n", style="green bold")
-        content.append("Tool: ", style="white")
-        content.append(f"{tool_name}\n", style="green bold")
-
-        if output:
-            content.append("Output: ", style="white")
-
-            content.append(f"{output}\n", style="green")
-
-        self.print_panel(
-            content, f"✅ Tool Execution Completed (#{iteration})", "green"
-        )
-
     def handle_tool_usage_error(
         self,
         tool_name: str,

lib/crewai/src/crewai/experimental/__init__.py

@@ -1,4 +1,4 @@
-from crewai.experimental.agent_executor import AgentExecutor, CrewAgentExecutorFlow
+from crewai.experimental.crew_agent_executor_flow import CrewAgentExecutorFlow
 from crewai.experimental.evaluation import (
     AgentEvaluationResult,
     AgentEvaluator,
@@ -23,9 +23,8 @@ from crewai.experimental.evaluation import (
 __all__ = [
     "AgentEvaluationResult",
     "AgentEvaluator",
-    "AgentExecutor",
     "BaseEvaluator",
-    "CrewAgentExecutorFlow",  # Deprecated alias for AgentExecutor
+    "CrewAgentExecutorFlow",
     "EvaluationScore",
     "EvaluationTraceCallback",
     "ExperimentResult",

lib/crewai/src/crewai/experimental/crew_agent_executor_flow.py

@@ -1,8 +1,6 @@
 from __future__ import annotations
 
-from collections.abc import Callable, Coroutine
-from datetime import datetime
-import json
+from collections.abc import Callable
 import threading
 from typing import TYPE_CHECKING, Any, Literal, cast
 from uuid import uuid4
@@ -19,27 +17,17 @@ from crewai.agents.parser import (
     OutputParserError,
 )
 from crewai.events.event_bus import crewai_event_bus
-from crewai.events.listeners.tracing.utils import (
-    is_tracing_enabled_in_context,
-)
 from crewai.events.types.logging_events import (
     AgentLogsExecutionEvent,
     AgentLogsStartedEvent,
 )
-from crewai.events.types.tool_usage_events import (
-    ToolUsageErrorEvent,
-    ToolUsageFinishedEvent,
-    ToolUsageStartedEvent,
-)
 from crewai.flow.flow import Flow, listen, or_, router, start
 from crewai.hooks.llm_hooks import (
     get_after_llm_call_hooks,
     get_before_llm_call_hooks,
 )
 from crewai.utilities.agent_utils import (
-    convert_tools_to_openai_schema,
     enforce_rpm_limit,
-    extract_tool_call_info,
     format_message_for_llm,
     get_llm_response,
     handle_agent_action_core,
@@ -49,14 +37,11 @@ from crewai.utilities.agent_utils import (
     handle_unknown_error,
     has_reached_max_iterations,
     is_context_length_exceeded,
-    is_inside_event_loop,
     process_llm_response,
-    track_delegation_if_needed,
 )
 from crewai.utilities.constants import TRAINING_DATA_FILE
 from crewai.utilities.i18n import I18N, get_i18n
 from crewai.utilities.printer import Printer
-from crewai.utilities.string_utils import sanitize_tool_name
 from crewai.utilities.tool_utils import execute_tool_and_check_finality
 from crewai.utilities.training_handler import CrewTrainingHandler
 from crewai.utilities.types import LLMMessage
@@ -86,21 +71,15 @@ class AgentReActState(BaseModel):
     current_answer: AgentAction | AgentFinish | None = Field(default=None)
     is_finished: bool = Field(default=False)
     ask_for_human_input: bool = Field(default=False)
-    use_native_tools: bool = Field(default=False)
-    pending_tool_calls: list[Any] = Field(default_factory=list)
 
 
-class AgentExecutor(Flow[AgentReActState], CrewAgentExecutorMixin):
-    """Agent Executor for both standalone agents and crew-bound agents.
+class CrewAgentExecutorFlow(Flow[AgentReActState], CrewAgentExecutorMixin):
+    """Flow-based executor matching CrewAgentExecutor interface.
 
     Inherits from:
     - Flow[AgentReActState]: Provides flow orchestration capabilities
     - CrewAgentExecutorMixin: Provides memory methods (short/long/external term)
 
-    This executor can operate in two modes:
-    - Standalone mode: When crew and task are None (used by Agent.kickoff())
-    - Crew mode: When crew and task are provided (used by Agent.execute_task())
-
     Note: Multiple instances may be created during agent initialization
     (cache setup, RPM controller setup, etc.) but only the final instance
     should execute tasks via invoke().
@@ -109,6 +88,8 @@ class AgentExecutor(Flow[AgentReActState], CrewAgentExecutorMixin):
     def __init__(
         self,
         llm: BaseLLM,
+        task: Task,
+        crew: Crew,
         agent: Agent,
         prompt: SystemPromptResult | StandardPromptResult,
         max_iter: int,
@@ -117,8 +98,6 @@ class AgentExecutor(Flow[AgentReActState], CrewAgentExecutorMixin):
         stop_words: list[str],
         tools_description: str,
         tools_handler: ToolsHandler,
-        task: Task | None = None,
-        crew: Crew | None = None,
         step_callback: Any = None,
         original_tools: list[BaseTool] | None = None,
         function_calling_llm: BaseLLM | Any | None = None,
@@ -132,6 +111,8 @@ class AgentExecutor(Flow[AgentReActState], CrewAgentExecutorMixin):
 
         Args:
             llm: Language model instance.
+            task: Task to execute.
+            crew: Crew instance.
             agent: Agent to execute.
             prompt: Prompt templates.
             max_iter: Maximum iterations.
@@ -140,8 +121,6 @@ class AgentExecutor(Flow[AgentReActState], CrewAgentExecutorMixin):
             stop_words: Stop word list.
             tools_description: Tool descriptions.
             tools_handler: Tool handler instance.
-            task: Optional task to execute (None for standalone agent execution).
-            crew: Optional crew instance (None for standalone agent execution).
             step_callback: Optional step callback.
             original_tools: Original tool list.
             function_calling_llm: Optional function calling LLM.
@@ -152,9 +131,9 @@ class AgentExecutor(Flow[AgentReActState], CrewAgentExecutorMixin):
         """
         self._i18n: I18N = i18n or get_i18n()
         self.llm = llm
-        self.task: Task | None = task
+        self.task = task
         self.agent = agent
-        self.crew: Crew | None = crew
+        self.crew = crew
         self.prompt = prompt
         self.tools = tools
         self.tools_names = tools_names
@@ -199,6 +178,7 @@ class AgentExecutor(Flow[AgentReActState], CrewAgentExecutorMixin):
                     else self.stop
                 )
             )
+
         self._state = AgentReActState()
 
     def _ensure_flow_initialized(self) -> None:
@@ -209,73 +189,14 @@ class AgentExecutor(Flow[AgentReActState], CrewAgentExecutorMixin):
         Only the instance that actually executes via invoke() will emit events.
         """
         if not self._flow_initialized:
-            current_tracing = is_tracing_enabled_in_context()
             # Now call Flow's __init__ which will replace self._state
             # with Flow's managed state. Suppress flow events since this is
             # an agent executor, not a user-facing flow.
             super().__init__(
                 suppress_flow_events=True,
-                tracing=current_tracing if current_tracing else None,
             )
             self._flow_initialized = True
 
-    def _check_native_tool_support(self) -> bool:
-        """Check if LLM supports native function calling.
-
-        Returns:
-            True if the LLM supports native function calling and tools are available.
-        """
-        return (
-            hasattr(self.llm, "supports_function_calling")
-            and callable(getattr(self.llm, "supports_function_calling", None))
-            and self.llm.supports_function_calling()
-            and bool(self.original_tools)
-        )
-
-    def _setup_native_tools(self) -> None:
-        """Convert tools to OpenAI schema format for native function calling."""
-        if self.original_tools:
-            self._openai_tools, self._available_functions = (
-                convert_tools_to_openai_schema(self.original_tools)
-            )
-
-    def _is_tool_call_list(self, response: list[Any]) -> bool:
-        """Check if a response is a list of tool calls.
-
-        Args:
-            response: The response to check.
-
-        Returns:
-            True if the response appears to be a list of tool calls.
-        """
-        if not response:
-            return False
-        first_item = response[0]
-        # Check for OpenAI-style tool call structure
-        if hasattr(first_item, "function") or (
-            isinstance(first_item, dict) and "function" in first_item
-        ):
-            return True
-        # Check for Anthropic-style tool call structure (ToolUseBlock)
-        if (
-            hasattr(first_item, "type")
-            and getattr(first_item, "type", None) == "tool_use"
-        ):
-            return True
-        if hasattr(first_item, "name") and hasattr(first_item, "input"):
-            return True
-        # Check for Bedrock-style tool call structure (dict with name and input keys)
-        if (
-            isinstance(first_item, dict)
-            and "name" in first_item
-            and "input" in first_item
-        ):
-            return True
-        # Check for Gemini-style function call (Part with function_call)
-        if hasattr(first_item, "function_call") and first_item.function_call:
-            return True
-        return False
-
     @property
     def use_stop_words(self) -> bool:
         """Check to determine if stop words are being used.
@@ -308,11 +229,6 @@ class AgentExecutor(Flow[AgentReActState], CrewAgentExecutorMixin):
     def initialize_reasoning(self) -> Literal["initialized"]:
         """Initialize the reasoning flow and emit agent start logs."""
         self._show_start_logs()
-        # Check for native tool support on first iteration
-        if self.state.iterations == 0:
-            self.state.use_native_tools = self._check_native_tool_support()
-            if self.state.use_native_tools:
-                self._setup_native_tools()
         return "initialized"
 
     @listen("force_final_answer")
@@ -348,13 +264,12 @@ class AgentExecutor(Flow[AgentReActState], CrewAgentExecutorMixin):
                 printer=self._printer,
                 from_task=self.task,
                 from_agent=self.agent,
-                response_model=None,
+                response_model=self.response_model,
                 executor_context=self,
             )
 
             # Parse the LLM response
             formatted_answer = process_llm_response(answer, self.use_stop_words)
-
             self.state.current_answer = formatted_answer
 
             if "Final Answer:" in answer and isinstance(formatted_answer, AgentAction):
@@ -388,79 +303,6 @@ class AgentExecutor(Flow[AgentReActState], CrewAgentExecutorMixin):
             handle_unknown_error(self._printer, e)
             raise
 
-    @listen("continue_reasoning_native")
-    def call_llm_native_tools(
-        self,
-    ) -> Literal["native_tool_calls", "native_finished", "context_error"]:
-        """Execute LLM call with native function calling.
-
-        Always calls the LLM so it can read reflection prompts and decide
-        whether to provide a final answer or request more tools.
-
-        Returns routing decision based on whether tool calls or final answer.
-        """
-        try:
-            # Clear pending tools - LLM will decide what to do next after reading
-            # the reflection prompt. It can either:
-            # 1. Return a final answer (string) if it has enough info
-            # 2. Return tool calls (possibly same ones, or different ones)
-            self.state.pending_tool_calls.clear()
-
-            enforce_rpm_limit(self.request_within_rpm_limit)
-
-            # Call LLM with native tools
-            answer = get_llm_response(
-                llm=self.llm,
-                messages=list(self.state.messages),
-                callbacks=self.callbacks,
-                printer=self._printer,
-                tools=self._openai_tools,
-                available_functions=None,
-                from_task=self.task,
-                from_agent=self.agent,
-                response_model=None,
-                executor_context=self,
-            )
-
-            # Check if the response is a list of tool calls
-            if isinstance(answer, list) and answer and self._is_tool_call_list(answer):
-                # Store tool calls for sequential processing
-                self.state.pending_tool_calls = list(answer)
-
-                return "native_tool_calls"
-
-            # Text response - this is the final answer
-            if isinstance(answer, str):
-                self.state.current_answer = AgentFinish(
-                    thought="",
-                    output=answer,
-                    text=answer,
-                )
-                self._invoke_step_callback(self.state.current_answer)
-                self._append_message_to_state(answer)
-
-                return "native_finished"
-
-            # Unexpected response type, treat as final answer
-            self.state.current_answer = AgentFinish(
-                thought="",
-                output=str(answer),
-                text=str(answer),
-            )
-            self._invoke_step_callback(self.state.current_answer)
-            self._append_message_to_state(str(answer))
-
-            return "native_finished"
-
-        except Exception as e:
-            if is_context_length_exceeded(e):
-                self._last_context_error = e
-                return "context_error"
-            if e.__class__.__module__.startswith("litellm"):
-                raise e
-            handle_unknown_error(self._printer, e)
-            raise
-
     @router(call_llm_and_parse)
     def route_by_answer_type(self) -> Literal["execute_tool", "agent_finished"]:
         """Route based on whether answer is AgentAction or AgentFinish."""
@@ -471,7 +313,6 @@ class AgentExecutor(Flow[AgentReActState], CrewAgentExecutorMixin):
     @listen("execute_tool")
     def execute_tool_action(self) -> Literal["tool_completed", "tool_result_is_final"]:
         """Execute the tool action and handle the result."""
-
         try:
             action = cast(AgentAction, self.state.current_answer)
 
@@ -517,14 +358,6 @@ class AgentExecutor(Flow[AgentReActState], CrewAgentExecutorMixin):
                 self.state.is_finished = True
                 return "tool_result_is_final"
 
-            # Inject post-tool reasoning prompt to enforce analysis
-            reasoning_prompt = self._i18n.slice("post_tool_reasoning")
-            reasoning_message: LLMMessage = {
-                "role": "user",
-                "content": reasoning_prompt,
-            }
-            self.state.messages.append(reasoning_message)
-
             return "tool_completed"
 
         except Exception as e:
@@ -534,248 +367,6 @@ class AgentExecutor(Flow[AgentReActState], CrewAgentExecutorMixin):
             self._console.print(error_text)
             raise
 
-    @listen("native_tool_calls")
-    def execute_native_tool(
-        self,
-    ) -> Literal["native_tool_completed", "tool_result_is_final"]:
-        """Execute native tool calls in a batch.
-
-        Processes all tools from pending_tool_calls, executes them,
-        and appends results to the conversation history.
-
-        Returns:
-            "native_tool_completed" normally, or "tool_result_is_final" if
-            a tool with result_as_answer=True was executed.
-        """
-        if not self.state.pending_tool_calls:
-            return "native_tool_completed"
-
-        # Group all tool calls into a single assistant message
-        tool_calls_to_report = []
-        for tool_call in self.state.pending_tool_calls:
-            info = extract_tool_call_info(tool_call)
-            if not info:
-                continue
-
-            call_id, func_name, func_args = info
-            tool_calls_to_report.append(
-                {
-                    "id": call_id,
-                    "type": "function",
-                    "function": {
-                        "name": func_name,
-                        "arguments": func_args
-                        if isinstance(func_args, str)
-                        else json.dumps(func_args),
-                    },
-                }
-            )
-
-        if tool_calls_to_report:
-            assistant_message: LLMMessage = {
-                "role": "assistant",
-                "content": None,
-                "tool_calls": tool_calls_to_report,
-            }
-            self.state.messages.append(assistant_message)
-
-        # Now execute each tool
-        while self.state.pending_tool_calls:
-            tool_call = self.state.pending_tool_calls.pop(0)
-            info = extract_tool_call_info(tool_call)
-            if not info:
-                continue
-
-            call_id, func_name, func_args = info
-
-            # Parse arguments
-            if isinstance(func_args, str):
-                try:
-                    args_dict = json.loads(func_args)
-                except json.JSONDecodeError:
-                    args_dict = {}
-            else:
-                args_dict = func_args
-
-            # Get agent_key for event tracking
-            agent_key = (
-                getattr(self.agent, "key", "unknown") if self.agent else "unknown"
-            )
-
-            # Find original tool by matching sanitized name (needed for cache_function and result_as_answer)
-            original_tool = None
-            for tool in self.original_tools or []:
-                if sanitize_tool_name(tool.name) == func_name:
-                    original_tool = tool
-                    break
-
-            # Check if tool has reached max usage count
-            max_usage_reached = False
-            if original_tool:
-                if (
-                    hasattr(original_tool, "max_usage_count")
-                    and original_tool.max_usage_count is not None
-                    and original_tool.current_usage_count
-                    >= original_tool.max_usage_count
-                ):
-                    max_usage_reached = True
-
-            # Check cache before executing
-            from_cache = False
-            input_str = json.dumps(args_dict) if args_dict else ""
-            if self.tools_handler and self.tools_handler.cache:
-                cached_result = self.tools_handler.cache.read(
-                    tool=func_name, input=input_str
-                )
-                if cached_result is not None:
-                    result = (
-                        str(cached_result)
-                        if not isinstance(cached_result, str)
-                        else cached_result
-                    )
-                    from_cache = True
-
-            # Emit tool usage started event
-            started_at = datetime.now()
-            crewai_event_bus.emit(
-                self,
-                event=ToolUsageStartedEvent(
-                    tool_name=func_name,
-                    tool_args=args_dict,
-                    from_agent=self.agent,
-                    from_task=self.task,
-                    agent_key=agent_key,
-                ),
-            )
-
-            track_delegation_if_needed(func_name, args_dict, self.task)
-
-            # Execute the tool (only if not cached and not at max usage)
-            if not from_cache and not max_usage_reached:
-                result = "Tool not found"
-                if func_name in self._available_functions:
-                    try:
-                        tool_func = self._available_functions[func_name]
-                        raw_result = tool_func(**args_dict)
-
-                        # Add to cache after successful execution (before string conversion)
-                        if self.tools_handler and self.tools_handler.cache:
-                            should_cache = True
-                            if (
-                                original_tool
-                                and hasattr(original_tool, "cache_function")
-                                and original_tool.cache_function
-                            ):
-                                should_cache = original_tool.cache_function(
-                                    args_dict, raw_result
-                                )
-                            if should_cache:
-                                self.tools_handler.cache.add(
-                                    tool=func_name, input=input_str, output=raw_result
-                                )
-
-                        # Convert to string for message
-                        result = (
-                            str(raw_result)
-                            if not isinstance(raw_result, str)
-                            else raw_result
-                        )
-                    except Exception as e:
-                        result = f"Error executing tool: {e}"
-                        if self.task:
-                            self.task.increment_tools_errors()
-                        # Emit tool usage error event
-                        crewai_event_bus.emit(
-                            self,
-                            event=ToolUsageErrorEvent(
-                                tool_name=func_name,
-                                tool_args=args_dict,
-                                from_agent=self.agent,
-                                from_task=self.task,
-                                agent_key=agent_key,
-                                error=e,
-                            ),
-                        )
-            elif max_usage_reached:
-                # Return error message when max usage limit is reached
-                result = f"Tool '{func_name}' has reached its usage limit of {original_tool.max_usage_count} times and cannot be used anymore."
-
-            # Emit tool usage finished event
-            crewai_event_bus.emit(
-                self,
-                event=ToolUsageFinishedEvent(
-                    output=result,
-                    tool_name=func_name,
-                    tool_args=args_dict,
-                    from_agent=self.agent,
-                    from_task=self.task,
-                    agent_key=agent_key,
-                    started_at=started_at,
-                    finished_at=datetime.now(),
-                ),
-            )
-
-            # Append tool result message
-            tool_message: LLMMessage = {
-                "role": "tool",
-                "tool_call_id": call_id,
-                "name": func_name,
-                "content": result,
-            }
-            self.state.messages.append(tool_message)
-
-            # Log the tool execution
-            if self.agent and self.agent.verbose:
-                cache_info = " (from cache)" if from_cache else ""
-                self._printer.print(
-                    content=f"Tool {func_name} executed with result{cache_info}: {result[:200]}...",
-                    color="green",
-                )
-
-            if (
-                original_tool
-                and hasattr(original_tool, "result_as_answer")
-                and original_tool.result_as_answer
-            ):
-                # Set the result as the final answer
-                self.state.current_answer = AgentFinish(
-                    thought="Tool result is the final answer",
-                    output=result,
-                    text=result,
-                )
-                self.state.is_finished = True
-                return "tool_result_is_final"
-
-        # Add reflection prompt once after all tools in the batch
-        reasoning_prompt = self._i18n.slice("post_tool_reasoning")
-
-        reasoning_message: LLMMessage = {
-            "role": "user",
-            "content": reasoning_prompt,
-        }
-        self.state.messages.append(reasoning_message)
-
-        return "native_tool_completed"
-
-    def _extract_tool_name(self, tool_call: Any) -> str:
-        """Extract tool name from various tool call formats."""
-        if hasattr(tool_call, "function"):
-            return sanitize_tool_name(tool_call.function.name)
-        if hasattr(tool_call, "function_call") and tool_call.function_call:
-            return sanitize_tool_name(tool_call.function_call.name)
-        if hasattr(tool_call, "name"):
-            return sanitize_tool_name(tool_call.name)
-        if isinstance(tool_call, dict):
-            func_info = tool_call.get("function", {})
-            return sanitize_tool_name(func_info.get("name", "") or tool_call.get("name", "unknown"))
-        return "unknown"
-
-    @router(execute_native_tool)
-    def increment_native_and_continue(self) -> Literal["initialized"]:
-        """Increment iteration counter after native tool execution."""
-        self.state.iterations += 1
-        return "initialized"
-
     @listen("initialized")
     def continue_iteration(self) -> Literal["check_iteration"]:
         """Bridge listener that connects iteration loop back to iteration check."""
@@ -784,14 +375,10 @@ class AgentExecutor(Flow[AgentReActState], CrewAgentExecutorMixin):
     @router(or_(initialize_reasoning, continue_iteration))
     def check_max_iterations(
         self,
-    ) -> Literal[
-        "force_final_answer", "continue_reasoning", "continue_reasoning_native"
-    ]:
+    ) -> Literal["force_final_answer", "continue_reasoning"]:
         """Check if max iterations reached before proceeding with reasoning."""
         if has_reached_max_iterations(self.state.iterations, self.max_iter):
             return "force_final_answer"
-        if self.state.use_native_tools:
-            return "continue_reasoning_native"
         return "continue_reasoning"
 
     @router(execute_tool_action)
@@ -800,7 +387,7 @@ class AgentExecutor(Flow[AgentReActState], CrewAgentExecutorMixin):
         self.state.iterations += 1
         return "initialized"
 
-    @listen(or_("agent_finished", "tool_result_is_final", "native_finished"))
+    @listen(or_("agent_finished", "tool_result_is_final"))
     def finalize(self) -> Literal["completed", "skipped"]:
         """Finalize execution and emit completion logs."""
         if self.state.current_answer is None:
@@ -862,101 +449,9 @@ class AgentExecutor(Flow[AgentReActState], CrewAgentExecutorMixin):
 
         return "initialized"
 
-    def invoke(
-        self, inputs: dict[str, Any]
-    ) -> dict[str, Any] | Coroutine[Any, Any, dict[str, Any]]:
+    def invoke(self, inputs: dict[str, Any]) -> dict[str, Any]:
         """Execute agent with given inputs.
 
-        When called from within an existing event loop (e.g., inside a Flow),
-        this method returns a coroutine that should be awaited. The Flow
-        framework handles this automatically.
-
-        Args:
-            inputs: Input dictionary containing prompt variables.
-
-        Returns:
-            Dictionary with agent output, or a coroutine if inside an event loop.
-        """
-        # Magic auto-async: if inside event loop, return coroutine for Flow to await
-        if is_inside_event_loop():
-            return self.invoke_async(inputs)
-
-        self._ensure_flow_initialized()
-
-        with self._execution_lock:
-            if self._is_executing:
-                raise RuntimeError(
-                    "Executor is already running. "
-                    "Cannot invoke the same executor instance concurrently."
-                )
-            self._is_executing = True
-            self._has_been_invoked = True
-
-        try:
-            # Reset state for fresh execution
-            self.state.messages.clear()
-            self.state.iterations = 0
-            self.state.current_answer = None
-            self.state.is_finished = False
-            self.state.use_native_tools = False
-            self.state.pending_tool_calls = []
-
-            if "system" in self.prompt:
-                prompt = cast("SystemPromptResult", self.prompt)
-                system_prompt = self._format_prompt(prompt["system"], inputs)
-                user_prompt = self._format_prompt(prompt["user"], inputs)
-                self.state.messages.append(
-                    format_message_for_llm(system_prompt, role="system")
-                )
-                self.state.messages.append(format_message_for_llm(user_prompt))
-            else:
-                user_prompt = self._format_prompt(self.prompt["prompt"], inputs)
-                self.state.messages.append(format_message_for_llm(user_prompt))
-
-            self.state.ask_for_human_input = bool(
-                inputs.get("ask_for_human_input", False)
-            )
-
-            self.kickoff()
-
-            formatted_answer = self.state.current_answer
-
-            if not isinstance(formatted_answer, AgentFinish):
-                raise RuntimeError(
-                    "Agent execution ended without reaching a final answer."
-                )
-
-            if self.state.ask_for_human_input:
-                formatted_answer = self._handle_human_feedback(formatted_answer)
-
-            self._create_short_term_memory(formatted_answer)
-            self._create_long_term_memory(formatted_answer)
-            self._create_external_memory(formatted_answer)
-
-            return {"output": formatted_answer.output}
-
-        except AssertionError:
-            fail_text = Text()
-            fail_text.append("❌ ", style="red bold")
-            fail_text.append(
-                "Agent failed to reach a final answer. This is likely a bug - please report it.",
-                style="red",
-            )
-            self._console.print(fail_text)
-            raise
-        except Exception as e:
-            handle_unknown_error(self._printer, e)
-            raise
-        finally:
-            self._is_executing = False
-
-    async def invoke_async(self, inputs: dict[str, Any]) -> dict[str, Any]:
-        """Execute agent asynchronously with given inputs.
-
-        This method is designed for use within async contexts, such as when
-        the agent is called from within an async Flow method. It uses
-        kickoff_async() directly instead of running in a separate thread.
-
         Args:
             inputs: Input dictionary containing prompt variables.
 
@@ -980,8 +475,6 @@ class AgentExecutor(Flow[AgentReActState], CrewAgentExecutorMixin):
             self.state.iterations = 0
             self.state.current_answer = None
             self.state.is_finished = False
-            self.state.use_native_tools = False
-            self.state.pending_tool_calls = []
 
             if "system" in self.prompt:
                 prompt = cast("SystemPromptResult", self.prompt)
@@ -999,8 +492,7 @@ class AgentExecutor(Flow[AgentReActState], CrewAgentExecutorMixin):
                 inputs.get("ask_for_human_input", False)
             )
 
-            # Use async kickoff directly since we're already in an async context
-            await self.kickoff_async()
+            self.kickoff()
 
             formatted_answer = self.state.current_answer
 
@@ -1091,14 +583,11 @@ class AgentExecutor(Flow[AgentReActState], CrewAgentExecutorMixin):
         if self.agent is None:
             raise ValueError("Agent cannot be None")
 
-        if self.task is None:
-            return
-
         crewai_event_bus.emit(
             self.agent,
             AgentLogsStartedEvent(
                 agent_role=self.agent.role,
-                task_description=self.task.description,
+                task_description=(self.task.description if self.task else "Not Found"),
                 verbose=self.agent.verbose
                 or (hasattr(self, "crew") and getattr(self.crew, "verbose", False)),
             ),
@@ -1132,12 +621,10 @@ class AgentExecutor(Flow[AgentReActState], CrewAgentExecutorMixin):
             result: Agent's final output.
             human_feedback: Optional feedback from human.
         """
-        # Early return if no crew (standalone mode)
-        if self.crew is None:
-            return
-
         agent_id = str(self.agent.id)
-        train_iteration = getattr(self.crew, "_train_iteration", None)
+        train_iteration = (
+            getattr(self.crew, "_train_iteration", None) if self.crew else None
+        )
 
         if train_iteration is None or not isinstance(train_iteration, int):
             train_error = Text()
@@ -1319,7 +806,3 @@ class AgentExecutor(Flow[AgentReActState], CrewAgentExecutorMixin):
         requiring arbitrary_types_allowed=True.
         """
         return core_schema.any_schema()
-
-
-# Backward compatibility alias (deprecated)
-CrewAgentExecutorFlow = AgentExecutor

lib/crewai/src/crewai/experimental/evaluation/metrics/tools_metrics.py

@@ -11,7 +11,6 @@ from crewai.experimental.evaluation.base_evaluator import (
 )
 from crewai.experimental.evaluation.json_parser import extract_json_from_llm_response
 from crewai.task import Task
-from crewai.utilities.string_utils import sanitize_tool_name
 from crewai.utilities.types import LLMMessage
 
 
@@ -53,9 +52,7 @@ class ToolSelectionEvaluator(BaseEvaluator):
         available_tools_info = ""
         if agent.tools:
             for tool in agent.tools:
-                available_tools_info += (
-                    f"- {sanitize_tool_name(tool.name)}: {tool.description}\n"
-                )
+                available_tools_info += f"- {tool.name}: {tool.description}\n"
         else:
             available_tools_info = "No tools available"
 

lib/crewai/src/crewai/flow/flow.py

@@ -12,7 +12,6 @@ from concurrent.futures import Future
 import copy
 import inspect
 import logging
-import threading
 from typing import (
     TYPE_CHECKING,
     Any,
@@ -31,13 +30,7 @@ from pydantic import BaseModel, Field, ValidationError
 from rich.console import Console
 from rich.panel import Panel
 
-from crewai.events.base_events import reset_emission_counter
 from crewai.events.event_bus import crewai_event_bus
-from crewai.events.event_context import (
-    get_current_parent_id,
-    reset_last_event_id,
-    triggered_by_scope,
-)
 from crewai.events.listeners.tracing.trace_listener import (
     TraceCollectionListener,
 )
@@ -71,7 +64,6 @@ from crewai.flow.persistence.base import FlowPersistence
 from crewai.flow.types import FlowExecutionData, FlowMethodName, PendingListenerKey
 from crewai.flow.utils import (
     _extract_all_methods,
-    _extract_all_methods_recursive,
     _normalize_condition,
     get_possible_return_constants,
     is_flow_condition_dict,
@@ -81,7 +73,6 @@ from crewai.flow.utils import (
     is_simple_flow_condition,
 )
 
-
 if TYPE_CHECKING:
     from crewai.flow.async_feedback.types import PendingFeedbackContext
     from crewai.flow.human_feedback import HumanFeedbackResult
@@ -405,62 +396,6 @@ def and_(*conditions: str | FlowCondition | Callable[..., Any]) -> FlowCondition
     return {"type": AND_CONDITION, "conditions": processed_conditions}
 
 
-class StateProxy(Generic[T]):
-    """Proxy that provides thread-safe access to flow state.
-
-    Wraps state objects (dict or BaseModel) and uses a lock for all write
-    operations to prevent race conditions when parallel listeners modify state.
-    """
-
-    __slots__ = ("_proxy_lock", "_proxy_state")
-
-    def __init__(self, state: T, lock: threading.Lock) -> None:
-        object.__setattr__(self, "_proxy_state", state)
-        object.__setattr__(self, "_proxy_lock", lock)
-
-    def __getattr__(self, name: str) -> Any:
-        return getattr(object.__getattribute__(self, "_proxy_state"), name)
-
-    def __setattr__(self, name: str, value: Any) -> None:
-        if name in ("_proxy_state", "_proxy_lock"):
-            object.__setattr__(self, name, value)
-        else:
-            with object.__getattribute__(self, "_proxy_lock"):
-                setattr(object.__getattribute__(self, "_proxy_state"), name, value)
-
-    def __getitem__(self, key: str) -> Any:
-        return object.__getattribute__(self, "_proxy_state")[key]
-
-    def __setitem__(self, key: str, value: Any) -> None:
-        with object.__getattribute__(self, "_proxy_lock"):
-            object.__getattribute__(self, "_proxy_state")[key] = value
-
-    def __delitem__(self, key: str) -> None:
-        with object.__getattribute__(self, "_proxy_lock"):
-            del object.__getattribute__(self, "_proxy_state")[key]
-
-    def __contains__(self, key: str) -> bool:
-        return key in object.__getattribute__(self, "_proxy_state")
-
-    def __repr__(self) -> str:
-        return repr(object.__getattribute__(self, "_proxy_state"))
-
-    def _unwrap(self) -> T:
-        """Return the underlying state object."""
-        return cast(T, object.__getattribute__(self, "_proxy_state"))
-
-    def model_dump(self, *args: Any, **kwargs: Any) -> dict[str, Any]:
-        """Return state as a dictionary.
-
-        Works for both dict and BaseModel underlying states.
-        """
-        state = object.__getattribute__(self, "_proxy_state")
-        if isinstance(state, dict):
-            return state
-        result: dict[str, Any] = state.model_dump(*args, **kwargs)
-        return result
-
-
 class FlowMeta(type):
     def __new__(
         mcs,
@@ -584,12 +519,7 @@ class Flow(Generic[T], metaclass=FlowMeta):
         self._methods: dict[FlowMethodName, FlowMethod[Any, Any]] = {}
         self._method_execution_counts: dict[FlowMethodName, int] = {}
         self._pending_and_listeners: dict[PendingListenerKey, set[FlowMethodName]] = {}
-        self._fired_or_listeners: set[FlowMethodName] = (
-            set()
-        )  # Track OR listeners that already fired
         self._method_outputs: list[Any] = []  # list to store all method outputs
-        self._state_lock = threading.Lock()
-        self._or_listeners_lock = threading.Lock()
         self._completed_methods: set[FlowMethodName] = (
             set()
         )  # Track completed methods for reload
@@ -634,184 +564,13 @@ class Flow(Generic[T], metaclass=FlowMeta):
                         method = method.__get__(self, self.__class__)
                     self._methods[method.__name__] = method
 
-    def _mark_or_listener_fired(self, listener_name: FlowMethodName) -> bool:
-        """Mark an OR listener as fired atomically.
-
-        Args:
-            listener_name: The name of the OR listener to mark.
-
-        Returns:
-            True if this call was the first to fire the listener.
-            False if the listener was already fired.
-        """
-        with self._or_listeners_lock:
-            if listener_name in self._fired_or_listeners:
-                return False
-            self._fired_or_listeners.add(listener_name)
-            return True
-
-    def _clear_or_listeners(self) -> None:
-        """Clear fired OR listeners for cyclic flows."""
-        with self._or_listeners_lock:
-            self._fired_or_listeners.clear()
-
-    def _discard_or_listener(self, listener_name: FlowMethodName) -> None:
-        """Discard a single OR listener from the fired set."""
-        with self._or_listeners_lock:
-            self._fired_or_listeners.discard(listener_name)
-
-    def _build_racing_groups(self) -> dict[frozenset[FlowMethodName], FlowMethodName]:
-        """Identify groups of methods that race for the same OR listener.
-
-        Analyzes the flow graph to find listeners with OR conditions that have
-        multiple trigger methods. These trigger methods form a "racing group"
-        where only the first to complete should trigger the OR listener.
-
-        Only methods that are EXCLUSIVELY sources for the OR listener are included
-        in the racing group. Methods that are also triggers for other listeners
-        (e.g., AND conditions) are not cancelled when another racing source wins.
-
-        Returns:
-            Dictionary mapping frozensets of racing method names to their
-            shared OR listener name.
-
-        Example:
-            If we have `@listen(or_(method_a, method_b))` on `handler`,
-            and method_a/method_b aren't used elsewhere,
-            this returns: {frozenset({'method_a', 'method_b'}): 'handler'}
-        """
-        racing_groups: dict[frozenset[FlowMethodName], FlowMethodName] = {}
-
-        method_to_listeners: dict[FlowMethodName, set[FlowMethodName]] = {}
-        for listener_name, condition_data in self._listeners.items():
-            if is_simple_flow_condition(condition_data):
-                _, methods = condition_data
-                for m in methods:
-                    method_to_listeners.setdefault(m, set()).add(listener_name)
-            elif is_flow_condition_dict(condition_data):
-                all_methods = _extract_all_methods_recursive(condition_data)
-                for m in all_methods:
-                    method_name = FlowMethodName(m) if isinstance(m, str) else m
-                    method_to_listeners.setdefault(method_name, set()).add(
-                        listener_name
-                    )
-
-        for listener_name, condition_data in self._listeners.items():
-            if listener_name in self._routers:
-                continue
-
-            trigger_methods: set[FlowMethodName] = set()
-
-            if is_simple_flow_condition(condition_data):
-                condition_type, methods = condition_data
-                if condition_type == OR_CONDITION and len(methods) > 1:
-                    trigger_methods = set(methods)
-
-            elif is_flow_condition_dict(condition_data):
-                top_level_type = condition_data.get("type", OR_CONDITION)
-                if top_level_type == OR_CONDITION:
-                    all_methods = _extract_all_methods_recursive(condition_data)
-                    if len(all_methods) > 1:
-                        trigger_methods = set(
-                            FlowMethodName(m) if isinstance(m, str) else m
-                            for m in all_methods
-                        )
-
-            if trigger_methods:
-                exclusive_methods = {
-                    m
-                    for m in trigger_methods
-                    if method_to_listeners.get(m, set()) == {listener_name}
-                }
-                if len(exclusive_methods) > 1:
-                    racing_groups[frozenset(exclusive_methods)] = listener_name
-
-        return racing_groups
-
-    def _get_racing_group_for_listeners(
-        self,
-        listener_names: list[FlowMethodName],
-    ) -> tuple[frozenset[FlowMethodName], FlowMethodName] | None:
-        """Check if the given listeners form a racing group.
-
-        Args:
-            listener_names: List of listener method names being executed.
-
-        Returns:
-            Tuple of (racing_members, or_listener_name) if these listeners race,
-            None otherwise.
-        """
-        if not hasattr(self, "_racing_groups_cache"):
-            self._racing_groups_cache = self._build_racing_groups()
-
-        listener_set = set(listener_names)
-
-        for racing_members, or_listener in self._racing_groups_cache.items():
-            if racing_members & listener_set:
-                racing_subset = racing_members & listener_set
-                if len(racing_subset) > 1:
-                    return (frozenset(racing_subset), or_listener)
-
-        return None
-
-    async def _execute_racing_listeners(
-        self,
-        racing_listeners: frozenset[FlowMethodName],
-        other_listeners: list[FlowMethodName],
-        result: Any,
-        triggering_event_id: str | None = None,
-    ) -> None:
-        """Execute racing listeners with first-wins semantics.
-
-        Racing listeners are executed in parallel, but once the first one
-        completes, the others are cancelled. Non-racing listeners in the
-        same batch are executed normally in parallel.
-
-        Args:
-            racing_listeners: Set of listener names that race for an OR condition.
-            other_listeners: Other listeners to execute in parallel (not racing).
-            result: The result from the triggering method.
-            triggering_event_id: The event_id of the event that triggered these listeners.
-        """
-        racing_tasks = [
-            asyncio.create_task(
-                self._execute_single_listener(name, result, triggering_event_id),
-                name=str(name),
-            )
-            for name in racing_listeners
-        ]
-
-        other_tasks = [
-            asyncio.create_task(
-                self._execute_single_listener(name, result, triggering_event_id),
-                name=str(name),
-            )
-            for name in other_listeners
-        ]
-
-        if racing_tasks:
-            for coro in asyncio.as_completed(racing_tasks):
-                try:
-                    await coro
-                except Exception as e:
-                    logger.debug(f"Racing listener failed: {e}")
-                    continue
-                break
-
-            for task in racing_tasks:
-                if not task.done():
-                    task.cancel()
-
-        if other_tasks:
-            await asyncio.gather(*other_tasks, return_exceptions=True)
-
     @classmethod
     def from_pending(
         cls,
         flow_id: str,
         persistence: FlowPersistence | None = None,
         **kwargs: Any,
-    ) -> Flow[Any]:
+    ) -> "Flow[Any]":
         """Create a Flow instance from a pending feedback state.
 
         This classmethod is used to restore a flow that was paused waiting
@@ -872,7 +631,7 @@ class Flow(Generic[T], metaclass=FlowMeta):
         return instance
 
     @property
-    def pending_feedback(self) -> PendingFeedbackContext | None:
+    def pending_feedback(self) -> "PendingFeedbackContext | None":
         """Get the pending feedback context if this flow is waiting for feedback.
 
         Returns:
@@ -957,9 +716,8 @@ class Flow(Generic[T], metaclass=FlowMeta):
         Raises:
             ValueError: If no pending feedback context exists
         """
-        from datetime import datetime
-
         from crewai.flow.human_feedback import HumanFeedbackResult
+        from datetime import datetime
 
         if self._pending_feedback_context is None:
             raise ValueError(
@@ -982,14 +740,12 @@ class Flow(Generic[T], metaclass=FlowMeta):
                 # No default and no feedback - use first outcome
                 collapsed_outcome = emit[0]
         elif emit:
-            if llm is not None:
-                collapsed_outcome = self._collapse_to_outcome(
-                    feedback=feedback,
-                    outcomes=emit,
-                    llm=llm,
-                )
-            else:
-                collapsed_outcome = emit[0]
+            # Collapse feedback to outcome using LLM
+            collapsed_outcome = self._collapse_to_outcome(
+                feedback=feedback,
+                outcomes=emit,
+                llm=llm,
+            )
 
         # Create result
         result = HumanFeedbackResult(
@@ -1028,16 +784,21 @@ class Flow(Generic[T], metaclass=FlowMeta):
         # This allows methods to re-execute in loops (e.g., implement_changes → suggest_changes → implement_changes)
         self._is_execution_resuming = False
 
-        final_result: Any = result
+        # Determine what to pass to listeners
         try:
             if emit and collapsed_outcome:
+                # Router behavior - the outcome itself triggers listeners
+                # First, add the outcome to method outputs as a router would
                 self._method_outputs.append(collapsed_outcome)
-                await self._execute_listeners(
-                    FlowMethodName(collapsed_outcome),
-                    result,
+
+                # Then trigger listeners for the outcome (e.g., "approved" triggers @listen("approved"))
+                final_result = await self._execute_listeners(
+                    FlowMethodName(collapsed_outcome),  # Use outcome as trigger
+                    result,  # Pass HumanFeedbackResult to listeners
                 )
             else:
-                await self._execute_listeners(
+                # Normal behavior - pass the HumanFeedbackResult
+                final_result = await self._execute_listeners(
                     FlowMethodName(context.method_name),
                     result,
                 )
@@ -1133,17 +894,18 @@ class Flow(Generic[T], metaclass=FlowMeta):
 
         # Handle case where initial_state is a type (class)
         if isinstance(self.initial_state, type):
-            state_class: type[T] = self.initial_state
-            if issubclass(state_class, FlowState):
-                return state_class()
-            if issubclass(state_class, BaseModel):
-                model_fields = getattr(state_class, "model_fields", None)
+            if issubclass(self.initial_state, FlowState):
+                return self.initial_state()  # Uses model defaults
+            if issubclass(self.initial_state, BaseModel):
+                # Validate that the model has an id field
+                model_fields = getattr(self.initial_state, "model_fields", None)
                 if not model_fields or "id" not in model_fields:
                     raise ValueError("Flow state model must have an 'id' field")
-                model_instance = state_class()
-                if not getattr(model_instance, "id", None):
-                    object.__setattr__(model_instance, "id", str(uuid4()))
-                return model_instance
+                instance = self.initial_state()
+                # Ensure id is set - generate UUID if empty
+                if not getattr(instance, "id", None):
+                    object.__setattr__(instance, "id", str(uuid4()))
+                return instance
             if self.initial_state is dict:
                 return cast(T, {"id": str(uuid4())})
 
@@ -1208,7 +970,7 @@ class Flow(Generic[T], metaclass=FlowMeta):
 
     @property
     def state(self) -> T:
-        return StateProxy(self._state, self._state_lock)  # type: ignore[return-value]
+        return self._state
 
     @property
     def method_outputs(self) -> list[Any]:
@@ -1533,7 +1295,6 @@ class Flow(Generic[T], metaclass=FlowMeta):
                 self._completed_methods.clear()
                 self._method_outputs.clear()
                 self._pending_and_listeners.clear()
-                self._clear_or_listeners()
             else:
                 # We're restoring from persistence, set the flag
                 self._is_execution_resuming = True
@@ -1565,10 +1326,6 @@ class Flow(Generic[T], metaclass=FlowMeta):
                 if filtered_inputs:
                     self._initialize_state(filtered_inputs)
 
-            if get_current_parent_id() is None:
-                reset_emission_counter()
-                reset_last_event_id()
-
             # Emit FlowStartedEvent and log the start of the flow.
             if not self.suppress_flow_events:
                 future = crewai_event_bus.emit(
@@ -1589,26 +1346,9 @@ class Flow(Generic[T], metaclass=FlowMeta):
                 self._initialize_state(inputs)
 
             try:
-                # Determine which start methods to execute at kickoff
-                # Conditional start methods (with __trigger_methods__) are only triggered by their conditions
-                # UNLESS there are no unconditional starts (then all starts run as entry points)
-                unconditional_starts = [
-                    start_method
-                    for start_method in self._start_methods
-                    if not getattr(
-                        self._methods.get(start_method), "__trigger_methods__", None
-                    )
-                ]
-                # If there are unconditional starts, only run those at kickoff
-                # If there are NO unconditional starts, run all starts (including conditional ones)
-                starts_to_execute = (
-                    unconditional_starts
-                    if unconditional_starts
-                    else self._start_methods
-                )
                 tasks = [
                     self._execute_start_method(start_method)
-                    for start_method in starts_to_execute
+                    for start_method in self._start_methods
                 ]
                 await asyncio.gather(*tasks)
             except Exception as e:
@@ -1691,14 +1431,13 @@ class Flow(Generic[T], metaclass=FlowMeta):
                 )
                 self._event_futures.clear()
 
-            if not self.suppress_flow_events:
-                trace_listener = TraceCollectionListener()
-                if trace_listener.batch_manager.batch_owner_type == "flow":
-                    if trace_listener.first_time_handler.is_first_time:
-                        trace_listener.first_time_handler.mark_events_collected()
-                        trace_listener.first_time_handler.handle_execution_completion()
-                    else:
-                        trace_listener.batch_manager.finalize_batch()
+            trace_listener = TraceCollectionListener()
+            if trace_listener.batch_manager.batch_owner_type == "flow":
+                if trace_listener.first_time_handler.is_first_time:
+                    trace_listener.first_time_handler.mark_events_collected()
+                    trace_listener.first_time_handler.handle_execution_completion()
+                else:
+                    trace_listener.batch_manager.finalize_batch()
 
             return final_output
         finally:
@@ -1742,20 +1481,16 @@ class Flow(Generic[T], metaclass=FlowMeta):
                 return
             # For cyclic flows, clear from completed to allow re-execution
             self._completed_methods.discard(start_method_name)
-            # Also clear fired OR listeners to allow them to fire again in new cycle
-            self._clear_or_listeners()
 
         method = self._methods[start_method_name]
         enhanced_method = self._inject_trigger_payload_for_start_method(method)
 
-        result, finished_event_id = await self._execute_method(
-            start_method_name, enhanced_method
-        )
+        result = await self._execute_method(start_method_name, enhanced_method)
 
         # If start method is a router, use its result as an additional trigger
         if start_method_name in self._routers and result is not None:
             # Execute listeners for the start method name first
-            await self._execute_listeners(start_method_name, result, finished_event_id)
+            await self._execute_listeners(start_method_name, result)
             # Then execute listeners for the router result (e.g., "approved")
             router_result_trigger = FlowMethodName(str(result))
             listeners_for_result = self._find_triggered_methods(
@@ -1768,32 +1503,13 @@ class Flow(Generic[T], metaclass=FlowMeta):
                     if self.last_human_feedback is not None
                     else result
                 )
-                racing_group = self._get_racing_group_for_listeners(
-                    listeners_for_result
-                )
-                if racing_group:
-                    racing_members, _ = racing_group
-                    other_listeners = [
-                        name
-                        for name in listeners_for_result
-                        if name not in racing_members
-                    ]
-                    await self._execute_racing_listeners(
-                        racing_members,
-                        other_listeners,
-                        listener_result,
-                        finished_event_id,
-                    )
-                else:
-                    tasks = [
-                        self._execute_single_listener(
-                            listener_name, listener_result, finished_event_id
-                        )
-                        for listener_name in listeners_for_result
-                    ]
-                    await asyncio.gather(*tasks)
+                tasks = [
+                    self._execute_single_listener(listener_name, listener_result)
+                    for listener_name in listeners_for_result
+                ]
+                await asyncio.gather(*tasks)
         else:
-            await self._execute_listeners(start_method_name, result, finished_event_id)
+            await self._execute_listeners(start_method_name, result)
 
     def _inject_trigger_payload_for_start_method(
         self, original_method: Callable[..., Any]
@@ -1837,14 +1553,7 @@ class Flow(Generic[T], metaclass=FlowMeta):
         method: Callable[..., Any],
         *args: Any,
         **kwargs: Any,
-    ) -> tuple[Any, str | None]:
-        """Execute a method and emit events.
-
-        Returns:
-            A tuple of (result, finished_event_id) where finished_event_id is
-            the event_id of the MethodExecutionFinishedEvent, or None if events
-            are suppressed.
-        """
+    ) -> Any:
         try:
             dumped_params = {f"_{i}": arg for i, arg in enumerate(args)} | (
                 kwargs or {}
@@ -1864,19 +1573,11 @@ class Flow(Generic[T], metaclass=FlowMeta):
                 if future:
                     self._event_futures.append(future)
 
-            if asyncio.iscoroutinefunction(method):
-                result = await method(*args, **kwargs)
-            else:
-                # Run sync methods in thread pool for isolation
-                # This allows Agent.kickoff() to work synchronously inside Flow methods
-                import contextvars
-
-                ctx = contextvars.copy_context()
-                result = await asyncio.to_thread(ctx.run, method, *args, **kwargs)
-
-            # Auto-await coroutines returned from sync methods (enables AgentExecutor pattern)
-            if asyncio.iscoroutine(result):
-                result = await result
+            result = (
+                await method(*args, **kwargs)
+                if asyncio.iscoroutinefunction(method)
+                else method(*args, **kwargs)
+            )
 
             self._method_outputs.append(result)
             self._method_execution_counts[method_name] = (
@@ -1885,21 +1586,21 @@ class Flow(Generic[T], metaclass=FlowMeta):
 
             self._completed_methods.add(method_name)
 
-            finished_event_id: str | None = None
             if not self.suppress_flow_events:
-                finished_event = MethodExecutionFinishedEvent(
-                    type="method_execution_finished",
-                    method_name=method_name,
-                    flow_name=self.name or self.__class__.__name__,
-                    state=self._copy_and_serialize_state(),
-                    result=result,
+                future = crewai_event_bus.emit(
+                    self,
+                    MethodExecutionFinishedEvent(
+                        type="method_execution_finished",
+                        method_name=method_name,
+                        flow_name=self.name or self.__class__.__name__,
+                        state=self._copy_and_serialize_state(),
+                        result=result,
+                    ),
                 )
-                finished_event_id = finished_event.event_id
-                future = crewai_event_bus.emit(self, finished_event)
                 if future:
                     self._event_futures.append(future)
 
-            return result, finished_event_id
+            return result
         except Exception as e:
             # Check if this is a HumanFeedbackPending exception (paused, not failed)
             from crewai.flow.async_feedback.types import HumanFeedbackPending
@@ -1953,10 +1654,7 @@ class Flow(Generic[T], metaclass=FlowMeta):
             return state_copy
 
     async def _execute_listeners(
-        self,
-        trigger_method: FlowMethodName,
-        result: Any,
-        triggering_event_id: str | None = None,
+        self, trigger_method: FlowMethodName, result: Any
     ) -> None:
         """Executes all listeners and routers triggered by a method completion.
 
@@ -1967,8 +1665,6 @@ class Flow(Generic[T], metaclass=FlowMeta):
         Args:
             trigger_method: The name of the method that triggered these listeners.
             result: The result from the triggering method, passed to listeners that accept parameters.
-            triggering_event_id: The event_id of the MethodExecutionFinishedEvent that
-                triggered these listeners, used for causal chain tracking.
 
         Note:
             - Routers are executed sequentially to maintain flow control
@@ -1983,7 +1679,6 @@ class Flow(Generic[T], metaclass=FlowMeta):
         ] = {}  # Map outcome -> HumanFeedbackResult
         current_trigger = trigger_method
         current_result = result  # Track the result to pass to each router
-        current_triggering_event_id = triggering_event_id
 
         while True:
             routers_triggered = self._find_triggered_methods(
@@ -1997,9 +1692,7 @@ class Flow(Generic[T], metaclass=FlowMeta):
                 router_input = router_result_to_feedback.get(
                     str(current_trigger), current_result
                 )
-                current_triggering_event_id = await self._execute_single_listener(
-                    router_name, router_input, current_triggering_event_id
-                )
+                await self._execute_single_listener(router_name, router_input)
                 # After executing router, the router's result is the path
                 router_result = (
                     self._method_outputs[-1] if self._method_outputs else None
@@ -2031,30 +1724,11 @@ class Flow(Generic[T], metaclass=FlowMeta):
                     listener_result = router_result_to_feedback.get(
                         str(current_trigger), result
                     )
-                    racing_group = self._get_racing_group_for_listeners(
-                        listeners_triggered
-                    )
-                    if racing_group:
-                        racing_members, _ = racing_group
-                        other_listeners = [
-                            name
-                            for name in listeners_triggered
-                            if name not in racing_members
-                        ]
-                        await self._execute_racing_listeners(
-                            racing_members,
-                            other_listeners,
-                            listener_result,
-                            triggering_event_id,
-                        )
-                    else:
-                        tasks = [
-                            self._execute_single_listener(
-                                listener_name, listener_result, triggering_event_id
-                            )
-                            for listener_name in listeners_triggered
-                        ]
-                        await asyncio.gather(*tasks)
+                    tasks = [
+                        self._execute_single_listener(listener_name, listener_result)
+                        for listener_name in listeners_triggered
+                    ]
+                    await asyncio.gather(*tasks)
 
                 if current_trigger in router_results:
                     # Find start methods triggered by this router result
@@ -2071,16 +1745,14 @@ class Flow(Generic[T], metaclass=FlowMeta):
                                 should_trigger = current_trigger in all_methods
 
                             if should_trigger:
-                                # Execute conditional start method triggered by router result
+                                # Only execute if this is a cycle (method was already completed)
                                 if method_name in self._completed_methods:
-                                    # For cyclic re-execution, temporarily clear resumption flag
+                                    # 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
-                                else:
-                                    # First-time execution of conditional start
-                                    await self._execute_start_method(method_name)
 
     def _evaluate_condition(
         self,
@@ -2178,21 +1850,8 @@ class Flow(Generic[T], metaclass=FlowMeta):
                 condition_type, methods = condition_data
 
                 if condition_type == OR_CONDITION:
-                    # Only trigger multi-source OR listeners (or_(A, B, C)) once - skip if already fired
-                    # Simple single-method listeners fire every time their trigger occurs
-                    # Routers also fire every time - they're decision points
-                    has_multiple_triggers = len(methods) > 1
-                    should_check_fired = has_multiple_triggers and not is_router
-
-                    if (
-                        not should_check_fired
-                        or listener_name not in self._fired_or_listeners
-                    ):
-                        if trigger_method in methods:
-                            triggered.append(listener_name)
-                            # Only track multi-source OR listeners (not single-method or routers)
-                            if should_check_fired:
-                                self._fired_or_listeners.add(listener_name)
+                    if trigger_method in methods:
+                        triggered.append(listener_name)
                 elif condition_type == AND_CONDITION:
                     pending_key = PendingListenerKey(listener_name)
                     if pending_key not in self._pending_and_listeners:
@@ -2205,35 +1864,16 @@ class Flow(Generic[T], metaclass=FlowMeta):
                         self._pending_and_listeners.pop(pending_key, None)
 
             elif is_flow_condition_dict(condition_data):
-                # For complex conditions, check if top-level is OR and track accordingly
-                top_level_type = condition_data.get("type", OR_CONDITION)
-                is_or_based = top_level_type == OR_CONDITION
-
-                # Only track multi-source OR conditions (multiple sub-conditions), not routers
-                sub_conditions = condition_data.get("conditions", [])
-                has_multiple_triggers = is_or_based and len(sub_conditions) > 1
-                should_check_fired = has_multiple_triggers and not is_router
-
-                # Skip compound OR-based listeners that have already fired
-                if should_check_fired and listener_name in self._fired_or_listeners:
-                    continue
-
                 if self._evaluate_condition(
                     condition_data, trigger_method, listener_name
                 ):
                     triggered.append(listener_name)
-                    # Track compound OR-based listeners so they only fire once
-                    if should_check_fired:
-                        self._fired_or_listeners.add(listener_name)
 
         return triggered
 
     async def _execute_single_listener(
-        self,
-        listener_name: FlowMethodName,
-        result: Any,
-        triggering_event_id: str | None = None,
-    ) -> str | None:
+        self, listener_name: FlowMethodName, result: Any
+    ) -> None:
         """Executes a single listener method with proper event handling.
 
         This internal method manages the execution of an individual listener,
@@ -2242,12 +1882,6 @@ class Flow(Generic[T], metaclass=FlowMeta):
         Args:
             listener_name: The name of the listener method to execute.
             result: The result from the triggering method, which may be passed to the listener if it accepts parameters.
-            triggering_event_id: The event_id of the event that triggered this listener,
-                used for causal chain tracking.
-
-        Returns:
-            The event_id of the MethodExecutionFinishedEvent emitted by this listener,
-            or None if events are suppressed.
 
         Note:
             - Inspects method signature to determine if it accepts the trigger result
@@ -2262,22 +1896,9 @@ class Flow(Generic[T], metaclass=FlowMeta):
             if self._is_execution_resuming:
                 # During resumption, skip execution but continue listeners
                 await self._execute_listeners(listener_name, None)
-
-                # For routers, also check if any conditional starts they triggered are completed
-                # If so, continue their chains
-                if listener_name in self._routers:
-                    for start_method_name in self._start_methods:
-                        if (
-                            start_method_name in self._listeners
-                            and start_method_name in self._completed_methods
-                        ):
-                            # This conditional start was executed, continue its chain
-                            await self._execute_start_method(start_method_name)
-                return None
+                return
             # For cyclic flows, clear from completed to allow re-execution
             self._completed_methods.discard(listener_name)
-            # Also clear from fired OR listeners for cyclic flows
-            self._discard_or_listener(listener_name)
 
         try:
             method = self._methods[listener_name]
@@ -2286,30 +1907,15 @@ class Flow(Generic[T], metaclass=FlowMeta):
             params = list(sig.parameters.values())
             method_params = [p for p in params if p.name != "self"]
 
-            if triggering_event_id:
-                with triggered_by_scope(triggering_event_id):
-                    if method_params:
-                        listener_result, finished_event_id = await self._execute_method(
-                            listener_name, method, result
-                        )
-                    else:
-                        listener_result, finished_event_id = await self._execute_method(
-                            listener_name, method
-                        )
+            if method_params:
+                listener_result = await self._execute_method(
+                    listener_name, method, result
+                )
             else:
-                if method_params:
-                    listener_result, finished_event_id = await self._execute_method(
-                        listener_name, method, result
-                    )
-                else:
-                    listener_result, finished_event_id = await self._execute_method(
-                        listener_name, method
-                    )
+                listener_result = await self._execute_method(listener_name, method)
 
             # Execute listeners (and possibly routers) of this listener
-            await self._execute_listeners(
-                listener_name, listener_result, finished_event_id
-            )
+            await self._execute_listeners(listener_name, listener_result)
 
             # If this listener is also a router (e.g., has @human_feedback with emit),
             # we need to trigger listeners for the router result as well
@@ -2325,32 +1931,11 @@ class Flow(Generic[T], metaclass=FlowMeta):
                         if self.last_human_feedback is not None
                         else listener_result
                     )
-                    racing_group = self._get_racing_group_for_listeners(
-                        listeners_for_result
-                    )
-                    if racing_group:
-                        racing_members, _ = racing_group
-                        other_listeners = [
-                            name
-                            for name in listeners_for_result
-                            if name not in racing_members
-                        ]
-                        await self._execute_racing_listeners(
-                            racing_members,
-                            other_listeners,
-                            feedback_result,
-                            finished_event_id,
-                        )
-                    else:
-                        tasks = [
-                            self._execute_single_listener(
-                                name, feedback_result, finished_event_id
-                            )
-                            for name in listeners_for_result
-                        ]
-                        await asyncio.gather(*tasks)
-
-            return finished_event_id
+                    tasks = [
+                        self._execute_single_listener(name, feedback_result)
+                        for name in listeners_for_result
+                    ]
+                    await asyncio.gather(*tasks)
 
         except Exception as e:
             # Don't log HumanFeedbackPending as an error - it's expected control flow
@@ -2464,7 +2049,7 @@ class Flow(Generic[T], metaclass=FlowMeta):
         from crewai.llms.base_llm import BaseLLM as BaseLLMClass
         from crewai.utilities.i18n import get_i18n
 
-        llm_instance: BaseLLMClass
+        # Get or create LLM instance
         if isinstance(llm, str):
             llm_instance = LLM(model=llm)
         elif isinstance(llm, BaseLLMClass):
@@ -2499,23 +2084,26 @@ class Flow(Generic[T], metaclass=FlowMeta):
                 response_model=FeedbackOutcome,
             )
 
+            # Parse the response - LLM returns JSON string when using response_model
             if isinstance(response, str):
                 import json
 
                 try:
                     parsed = json.loads(response)
-                    return str(parsed.get("outcome", outcomes[0]))
+                    return parsed.get("outcome", outcomes[0])
                 except json.JSONDecodeError:
+                    # Not valid JSON, might be raw outcome string
                     response_clean = response.strip()
                     for outcome in outcomes:
                         if outcome.lower() == response_clean.lower():
                             return outcome
                     return outcomes[0]
             elif isinstance(response, FeedbackOutcome):
-                return str(response.outcome)
+                return response.outcome
             elif hasattr(response, "outcome"):
-                return str(response.outcome)
+                return response.outcome
             else:
+                # Unexpected type, fall back to first outcome
                 logger.warning(f"Unexpected response type: {type(response)}")
                 return outcomes[0]
 

lib/crewai/src/crewai/flow/persistence/decorators.py

@@ -61,7 +61,7 @@ class PersistenceDecorator:
     @classmethod
     def persist_state(
         cls,
-        flow_instance: Flow[Any],
+        flow_instance: Flow,
         method_name: str,
         persistence_instance: FlowPersistence,
         verbose: bool = False,
@@ -90,13 +90,7 @@ class PersistenceDecorator:
             flow_uuid: str | None = None
             if isinstance(state, dict):
                 flow_uuid = state.get("id")
-            elif hasattr(state, "_unwrap"):
-                unwrapped = state._unwrap()
-                if isinstance(unwrapped, dict):
-                    flow_uuid = unwrapped.get("id")
-                else:
-                    flow_uuid = getattr(unwrapped, "id", None)
-            elif isinstance(state, BaseModel) or hasattr(state, "id"):
+            elif isinstance(state, BaseModel):
                 flow_uuid = getattr(state, "id", None)
 
             if not flow_uuid:
@@ -110,11 +104,10 @@ class PersistenceDecorator:
                 logger.info(LOG_MESSAGES["save_state"].format(flow_uuid))
 
             try:
-                state_data = state._unwrap() if hasattr(state, "_unwrap") else state
                 persistence_instance.save_state(
                     flow_uuid=flow_uuid,
                     method_name=method_name,
-                    state_data=state_data,
+                    state_data=state,
                 )
             except Exception as e:
                 error_msg = LOG_MESSAGES["save_error"].format(method_name, str(e))
@@ -133,9 +126,7 @@ class PersistenceDecorator:
             raise ValueError(error_msg) from e
 
 
-def persist(
-    persistence: FlowPersistence | None = None, verbose: bool = False
-) -> Callable[[type | Callable[..., T]], type | Callable[..., T]]:
+def persist(persistence: FlowPersistence | None = None, verbose: bool = False):
     """Decorator to persist flow state.
 
     This decorator can be applied at either the class level or method level.
@@ -198,8 +189,8 @@ def persist(
                 if asyncio.iscoroutinefunction(method):
                     # Create a closure to capture the current name and method
                     def create_async_wrapper(
-                        method_name: str, original_method: Callable[..., Any]
-                    ) -> Callable[..., Any]:
+                        method_name: str, original_method: Callable
+                    ):
                         @functools.wraps(original_method)
                         async def method_wrapper(
                             self: Any, *args: Any, **kwargs: Any
@@ -230,8 +221,8 @@ def persist(
                 else:
                     # Create a closure to capture the current name and method
                     def create_sync_wrapper(
-                        method_name: str, original_method: Callable[..., Any]
-                    ) -> Callable[..., Any]:
+                        method_name: str, original_method: Callable
+                    ):
                         @functools.wraps(original_method)
                         def method_wrapper(self: Any, *args: Any, **kwargs: Any) -> Any:
                             result = original_method(self, *args, **kwargs)
@@ -277,7 +268,7 @@ def persist(
                 PersistenceDecorator.persist_state(
                     flow_instance, method.__name__, actual_persistence, verbose
                 )
-                return cast(T, result)
+                return result
 
             for attr in [
                 "__is_start_method__",

lib/crewai/src/crewai/lite_agent.py

@@ -10,7 +10,6 @@ from typing import (
     get_origin,
 )
 import uuid
-import warnings
 
 from pydantic import (
     UUID4,
@@ -81,11 +80,6 @@ class LiteAgent(FlowTrackable, BaseModel):
     """
     A lightweight agent that can process messages and use tools.
 
-    .. deprecated::
-        LiteAgent is deprecated and will be removed in a future version.
-        Use ``Agent().kickoff(messages)`` instead, which provides the same
-        functionality with additional features like memory and knowledge support.
-
     This agent is simpler than the full Agent class, focusing on direct execution
     rather than task delegation. It's designed to be used for simple interactions
     where a full crew is not needed.
@@ -170,18 +164,6 @@ class LiteAgent(FlowTrackable, BaseModel):
         default_factory=get_after_llm_call_hooks
     )
 
-    @model_validator(mode="after")
-    def emit_deprecation_warning(self) -> Self:
-        """Emit deprecation warning for LiteAgent usage."""
-        warnings.warn(
-            "LiteAgent is deprecated and will be removed in a future version. "
-            "Use Agent().kickoff(messages) instead, which provides the same "
-            "functionality with additional features like memory and knowledge support.",
-            DeprecationWarning,
-            stacklevel=2,
-        )
-        return self
-
     @model_validator(mode="after")
     def setup_llm(self) -> Self:
         """Set up the LLM and other components after initialization."""
@@ -551,6 +533,7 @@ class LiteAgent(FlowTrackable, BaseModel):
         """
         # Execute the agent loop
         formatted_answer: AgentAction | AgentFinish | None = None
+        last_raw_output: str | None = None
         while not isinstance(formatted_answer, AgentFinish):
             try:
                 if has_reached_max_iterations(self._iterations, self.max_iterations):
@@ -574,6 +557,7 @@ class LiteAgent(FlowTrackable, BaseModel):
                         from_agent=self,
                         executor_context=self,
                     )
+                    last_raw_output = answer
 
                 except Exception as e:
                     raise e
@@ -612,6 +596,8 @@ class LiteAgent(FlowTrackable, BaseModel):
                     iterations=self._iterations,
                     log_error_after=3,
                     printer=self._printer,
+                    raw_output=last_raw_output,
+                    agent_role=self.role,
                 )
 
             except Exception as e:

lib/crewai/src/crewai/llm.py

@@ -50,7 +50,6 @@ from crewai.utilities.exceptions.context_window_exceeding_exception import (
     LLMContextLengthExceededError,
 )
 from crewai.utilities.logger_utils import suppress_warnings
-from crewai.utilities.string_utils import sanitize_tool_name
 
 
 if TYPE_CHECKING:
@@ -932,6 +931,7 @@ class LLM(BaseLLM):
             self._handle_streaming_callbacks(callbacks, usage_info, last_chunk)
 
             if not tool_calls or not available_functions:
+
                 if response_model and self.is_litellm:
                     instructor_instance = InternalInstructor(
                         content=full_response,
@@ -1144,12 +1144,8 @@ class LLM(BaseLLM):
             if response_model:
                 params["response_model"] = response_model
             response = litellm.completion(**params)
-
-            if (
-                hasattr(response, "usage")
-                and not isinstance(response.usage, type)
-                and response.usage
-            ):
+            
+            if hasattr(response,"usage") and not isinstance(response.usage, type) and response.usage:
                 usage_info = response.usage
                 self._track_token_usage_internal(usage_info)
 
@@ -1203,19 +1199,16 @@ class LLM(BaseLLM):
             )
             return text_response
 
-        # --- 6) If there are tool calls but no available functions, return the tool calls
-        # This allows the caller (e.g., executor) to handle tool execution
-        if tool_calls and not available_functions:
+        # --- 6) If there is no text response, no available functions, but there are tool calls, return the tool calls
+        if tool_calls and not available_functions and not text_response:
             return tool_calls
 
-        # --- 7) Handle tool calls if present (execute when available_functions provided)
-        if tool_calls and available_functions:
-            tool_result = self._handle_tool_call(
-                tool_calls, available_functions, from_task, from_agent
-            )
-            if tool_result is not None:
-                return tool_result
-
+        # --- 7) Handle tool calls if present
+        tool_result = self._handle_tool_call(
+            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
         self._handle_emit_call_events(
             response=text_response,
@@ -1280,11 +1273,7 @@ class LLM(BaseLLM):
                 params["response_model"] = response_model
             response = await litellm.acompletion(**params)
 
-            if (
-                hasattr(response, "usage")
-                and not isinstance(response.usage, type)
-                and response.usage
-            ):
+            if hasattr(response,"usage") and not isinstance(response.usage, type) and response.usage:
                 usage_info = response.usage
                 self._track_token_usage_internal(usage_info)
 
@@ -1332,18 +1321,14 @@ class LLM(BaseLLM):
             )
             return text_response
 
-        # If there are tool calls but no available functions, return the tool calls
-        # This allows the caller (e.g., executor) to handle tool execution
-        if tool_calls and not available_functions:
+        if tool_calls and not available_functions and not text_response:
             return tool_calls
 
-        # Handle tool calls if present (execute when available_functions provided)
-        if tool_calls and available_functions:
-            tool_result = self._handle_tool_call(
-                tool_calls, available_functions, from_task, from_agent
-            )
-            if tool_result is not None:
-                return tool_result
+        tool_result = self._handle_tool_call(
+            tool_calls, available_functions, from_task, from_agent
+        )
+        if tool_result is not None:
+            return tool_result
 
         self._handle_emit_call_events(
             response=text_response,
@@ -1378,7 +1363,7 @@ class LLM(BaseLLM):
         """
         full_response = ""
         chunk_count = 0
-
+        
         usage_info = None
 
         accumulated_tool_args: defaultdict[int, AccumulatedToolArgs] = defaultdict(
@@ -1541,7 +1526,7 @@ class LLM(BaseLLM):
 
         # --- 2) Extract function name from first tool call
         tool_call = tool_calls[0]
-        function_name = sanitize_tool_name(tool_call.function.name)
+        function_name = tool_call.function.name
         function_args = {}  # Initialize to empty dict to avoid unbound variable
 
         # --- 3) Check if function is available

lib/crewai/src/crewai/llms/base_llm.py

@@ -292,16 +292,14 @@ class BaseLLM(ABC):
         from_agent: Agent | None = None,
     ) -> None:
         """Emit LLM call started event."""
-        from crewai.utilities.serialization import to_serializable
-
         if not hasattr(crewai_event_bus, "emit"):
             raise ValueError("crewai_event_bus does not have an emit method") from None
 
         crewai_event_bus.emit(
             self,
             event=LLMCallStartedEvent(
-                messages=to_serializable(messages),
-                tools=to_serializable(tools),
+                messages=messages,
+                tools=tools,
                 callbacks=callbacks,
                 available_functions=available_functions,
                 from_task=from_task,
@@ -319,13 +317,11 @@ class BaseLLM(ABC):
         messages: str | list[LLMMessage] | None = None,
     ) -> None:
         """Emit LLM call completed event."""
-        from crewai.utilities.serialization import to_serializable
-
         crewai_event_bus.emit(
             self,
             event=LLMCallCompletedEvent(
-                messages=to_serializable(messages),
-                response=to_serializable(response),
+                messages=messages,
+                response=response,
                 call_type=call_type,
                 from_task=from_task,
                 from_agent=from_agent,
@@ -349,7 +345,6 @@ class BaseLLM(ABC):
                 error=error,
                 from_task=from_task,
                 from_agent=from_agent,
-                model=self.model,
             ),
         )
 
@@ -450,7 +445,7 @@ class BaseLLM(ABC):
                 from_agent=from_agent,
             )
 
-            return result
+            return str(result)
 
         except Exception as e:
             error_msg = f"Error executing function '{function_name}': {e!s}"

lib/crewai/src/crewai/llms/providers/anthropic/completion.py

@@ -418,7 +418,6 @@ class AnthropicCompletion(BaseLLM):
         - System messages are separate from conversation messages
         - Messages must alternate between user and assistant
         - First message must be from user
-        - Tool results must be in user messages with tool_result content blocks
         - When thinking is enabled, assistant messages must start with thinking blocks
 
         Args:
@@ -432,7 +431,6 @@ class AnthropicCompletion(BaseLLM):
 
         formatted_messages: list[LLMMessage] = []
         system_message: str | None = None
-        pending_tool_results: list[dict[str, Any]] = []
 
         for message in base_formatted:
             role = message.get("role")
@@ -443,47 +441,16 @@ class AnthropicCompletion(BaseLLM):
                     system_message += f"\n\n{content}"
                 else:
                     system_message = cast(str, content)
-            elif role == "tool":
-                tool_call_id = message.get("tool_call_id", "")
-                if not tool_call_id:
-                    raise ValueError("Tool message missing required tool_call_id")
-                tool_result = {
-                    "type": "tool_result",
-                    "tool_use_id": tool_call_id,
-                    "content": content if content else "",
-                }
-                pending_tool_results.append(tool_result)
-            elif role == "assistant":
-                # First, flush any pending tool results as a user message
-                if pending_tool_results:
-                    formatted_messages.append(
-                        {"role": "user", "content": pending_tool_results}
-                    )
-                    pending_tool_results = []
+            else:
+                role_str = role if role is not None else "user"
 
-                # Handle assistant message with tool_calls (convert to Anthropic format)
-                tool_calls = message.get("tool_calls", [])
-                if tool_calls:
-                    assistant_content: list[dict[str, Any]] = []
-                    for tc in tool_calls:
-                        if isinstance(tc, dict):
-                            func = tc.get("function", {})
-                            tool_use = {
-                                "type": "tool_use",
-                                "id": tc.get("id", ""),
-                                "name": func.get("name", ""),
-                                "input": json.loads(func.get("arguments", "{}"))
-                                if isinstance(func.get("arguments"), str)
-                                else func.get("arguments", {}),
-                            }
-                            assistant_content.append(tool_use)
-                    if assistant_content:
-                        formatted_messages.append(
-                            {"role": "assistant", "content": assistant_content}
-                        )
-                elif isinstance(content, list):
-                    formatted_messages.append({"role": "assistant", "content": content})
-                elif self.thinking and self.previous_thinking_blocks:
+                if isinstance(content, list):
+                    formatted_messages.append({"role": role_str, "content": content})
+                elif (
+                    role_str == "assistant"
+                    and self.thinking
+                    and self.previous_thinking_blocks
+                ):
                     structured_content = cast(
                         list[dict[str, Any]],
                         [
@@ -492,34 +459,14 @@ class AnthropicCompletion(BaseLLM):
                         ],
                     )
                     formatted_messages.append(
-                        LLMMessage(role="assistant", content=structured_content)
+                        LLMMessage(role=role_str, content=structured_content)
                     )
-                else:
-                    content_str = content if content is not None else ""
-                    formatted_messages.append(
-                        LLMMessage(role="assistant", content=content_str)
-                    )
-            else:
-                # User message - first flush any pending tool results
-                if pending_tool_results:
-                    formatted_messages.append(
-                        {"role": "user", "content": pending_tool_results}
-                    )
-                    pending_tool_results = []
-
-                role_str = role if role is not None else "user"
-                if isinstance(content, list):
-                    formatted_messages.append({"role": role_str, "content": content})
                 else:
                     content_str = content if content is not None else ""
                     formatted_messages.append(
                         LLMMessage(role=role_str, content=content_str)
                     )
 
-        # Flush any remaining pending tool results
-        if pending_tool_results:
-            formatted_messages.append({"role": "user", "content": pending_tool_results})
-
         # Ensure first message is from user (Anthropic requirement)
         if not formatted_messages:
             # If no messages, add a default user message
@@ -579,26 +526,13 @@ class AnthropicCompletion(BaseLLM):
                 return structured_json
 
         # Check if Claude wants to use tools
-        if response.content:
+        if response.content and available_functions:
             tool_uses = [
                 block for block in response.content if isinstance(block, ToolUseBlock)
             ]
 
             if tool_uses:
-                # If no available_functions, return tool calls for executor to handle
-                # This allows the executor to manage tool execution with proper
-                # message history and post-tool reasoning prompts
-                if not available_functions:
-                    self._emit_call_completed_event(
-                        response=list(tool_uses),
-                        call_type=LLMCallType.TOOL_CALL,
-                        from_task=from_task,
-                        from_agent=from_agent,
-                        messages=params["messages"],
-                    )
-                    return list(tool_uses)
-
-                # Handle tool use conversation flow internally
+                # Handle tool use conversation flow
                 return self._handle_tool_use_conversation(
                     response,
                     tool_uses,
@@ -762,7 +696,7 @@ class AnthropicCompletion(BaseLLM):
 
                 return structured_json
 
-        if final_message.content:
+        if final_message.content and available_functions:
             tool_uses = [
                 block
                 for block in final_message.content
@@ -770,11 +704,7 @@ class AnthropicCompletion(BaseLLM):
             ]
 
             if tool_uses:
-                # If no available_functions, return tool calls for executor to handle
-                if not available_functions:
-                    return list(tool_uses)
-
-                # Handle tool use conversation flow internally
+                # Handle tool use conversation flow
                 return self._handle_tool_use_conversation(
                     final_message,
                     tool_uses,
@@ -1003,23 +933,12 @@ class AnthropicCompletion(BaseLLM):
 
                 return structured_json
 
-        if response.content:
+        if response.content and available_functions:
             tool_uses = [
                 block for block in response.content if isinstance(block, ToolUseBlock)
             ]
 
             if tool_uses:
-                # If no available_functions, return tool calls for executor to handle
-                if not available_functions:
-                    self._emit_call_completed_event(
-                        response=list(tool_uses),
-                        call_type=LLMCallType.TOOL_CALL,
-                        from_task=from_task,
-                        from_agent=from_agent,
-                        messages=params["messages"],
-                    )
-                    return list(tool_uses)
-
                 return await self._ahandle_tool_use_conversation(
                     response,
                     tool_uses,
@@ -1160,7 +1079,7 @@ class AnthropicCompletion(BaseLLM):
 
                 return structured_json
 
-        if final_message.content:
+        if final_message.content and available_functions:
             tool_uses = [
                 block
                 for block in final_message.content
@@ -1168,10 +1087,6 @@ class AnthropicCompletion(BaseLLM):
             ]
 
             if tool_uses:
-                # If no available_functions, return tool calls for executor to handle
-                if not available_functions:
-                    return list(tool_uses)
-
                 return await self._ahandle_tool_use_conversation(
                     final_message,
                     tool_uses,

lib/crewai/src/crewai/llms/providers/azure/completion.py

@@ -514,32 +514,10 @@ class AzureCompletion(BaseLLM):
 
         for message in base_formatted:
             role = message.get("role", "user")  # Default to user if no role
-            # Handle None content - Azure requires string content
-            content = message.get("content") or ""
+            content = message.get("content", "")
 
-            if role == "tool":
-                tool_call_id = message.get("tool_call_id", "")
-                if not tool_call_id:
-                    raise ValueError("Tool message missing required tool_call_id")
-                azure_messages.append(
-                    {
-                        "role": "tool",
-                        "tool_call_id": tool_call_id,
-                        "content": content,
-                    }
-                )
-            # Handle assistant messages with tool_calls
-            elif role == "assistant" and message.get("tool_calls"):
-                tool_calls = message.get("tool_calls", [])
-                azure_msg: LLMMessage = {
-                    "role": "assistant",
-                    "content": content,  # Already defaulted to "" above
-                    "tool_calls": tool_calls,
-                }
-                azure_messages.append(azure_msg)
-            else:
-                # Azure AI Inference requires both 'role' and 'content'
-                azure_messages.append({"role": role, "content": content})
+            # Azure AI Inference requires both 'role' and 'content'
+            azure_messages.append({"role": role, "content": content})
 
         return azure_messages
 
@@ -626,18 +604,6 @@ class AzureCompletion(BaseLLM):
                 from_agent=from_agent,
             )
 
-        # If there are tool_calls but no available_functions, return the tool_calls
-        # This allows the caller (e.g., executor) to handle tool execution
-        if message.tool_calls and not available_functions:
-            self._emit_call_completed_event(
-                response=list(message.tool_calls),
-                call_type=LLMCallType.TOOL_CALL,
-                from_task=from_task,
-                from_agent=from_agent,
-                messages=params["messages"],
-            )
-            return list(message.tool_calls)
-
         # Handle tool calls
         if message.tool_calls and available_functions:
             tool_call = message.tool_calls[0]  # Handle first tool call
@@ -809,29 +775,6 @@ class AzureCompletion(BaseLLM):
                 from_agent=from_agent,
             )
 
-        # If there are tool_calls but no available_functions, return them
-        # in OpenAI-compatible format for executor to handle
-        if tool_calls and not available_functions:
-            formatted_tool_calls = [
-                {
-                    "id": call_data.get("id", f"call_{idx}"),
-                    "type": "function",
-                    "function": {
-                        "name": call_data["name"],
-                        "arguments": call_data["arguments"],
-                    },
-                }
-                for idx, call_data in tool_calls.items()
-            ]
-            self._emit_call_completed_event(
-                response=formatted_tool_calls,
-                call_type=LLMCallType.TOOL_CALL,
-                from_task=from_task,
-                from_agent=from_agent,
-                messages=params["messages"],
-            )
-            return formatted_tool_calls
-
         # Handle completed tool calls
         if tool_calls and available_functions:
             for call_data in tool_calls.values():

lib/crewai/src/crewai/llms/providers/bedrock/completion.py

@@ -330,8 +330,7 @@ class BedrockCompletion(BaseLLM):
                     cast(object, [{"text": system_message}]),
                 )
 
-            # Add tool config if present or if messages contain tool content
-            # Bedrock requires toolConfig when messages have toolUse/toolResult
+            # Add tool config if present
             if tools:
                 tool_config: ToolConfigurationTypeDef = {
                     "tools": cast(
@@ -340,16 +339,6 @@ class BedrockCompletion(BaseLLM):
                     )
                 }
                 body["toolConfig"] = tool_config
-            elif self._messages_contain_tool_content(formatted_messages):
-                # Create minimal toolConfig from tool history in messages
-                tools_from_history = self._extract_tools_from_message_history(
-                    formatted_messages
-                )
-                if tools_from_history:
-                    body["toolConfig"] = cast(
-                        "ToolConfigurationTypeDef",
-                        cast(object, {"tools": tools_from_history}),
-                    )
 
             # Add optional advanced features if configured
             if self.guardrail_config:
@@ -455,8 +444,6 @@ class BedrockCompletion(BaseLLM):
                     cast(object, [{"text": system_message}]),
                 )
 
-            # Add tool config if present or if messages contain tool content
-            # Bedrock requires toolConfig when messages have toolUse/toolResult
             if tools:
                 tool_config: ToolConfigurationTypeDef = {
                     "tools": cast(
@@ -465,16 +452,6 @@ class BedrockCompletion(BaseLLM):
                     )
                 }
                 body["toolConfig"] = tool_config
-            elif self._messages_contain_tool_content(formatted_messages):
-                # Create minimal toolConfig from tool history in messages
-                tools_from_history = self._extract_tools_from_message_history(
-                    formatted_messages
-                )
-                if tools_from_history:
-                    body["toolConfig"] = cast(
-                        "ToolConfigurationTypeDef",
-                        cast(object, {"tools": tools_from_history}),
-                    )
 
             if self.guardrail_config:
                 guardrail_config: GuardrailConfigurationTypeDef = cast(
@@ -569,18 +546,6 @@ class BedrockCompletion(BaseLLM):
                     "I apologize, but I received an empty response. Please try again."
                 )
 
-            # If there are tool uses but no available_functions, return them for the executor to handle
-            tool_uses = [block["toolUse"] for block in content if "toolUse" in block]
-            if tool_uses and not available_functions:
-                self._emit_call_completed_event(
-                    response=tool_uses,
-                    call_type=LLMCallType.TOOL_CALL,
-                    from_task=from_task,
-                    from_agent=from_agent,
-                    messages=messages,
-                )
-                return tool_uses
-
             # Process content blocks and handle tool use correctly
             text_content = ""
 
@@ -970,18 +935,6 @@ class BedrockCompletion(BaseLLM):
                     "I apologize, but I received an empty response. Please try again."
                 )
 
-            # If there are tool uses but no available_functions, return them for the executor to handle
-            tool_uses = [block["toolUse"] for block in content if "toolUse" in block]
-            if tool_uses and not available_functions:
-                self._emit_call_completed_event(
-                    response=tool_uses,
-                    call_type=LLMCallType.TOOL_CALL,
-                    from_task=from_task,
-                    from_agent=from_agent,
-                    messages=messages,
-                )
-                return tool_uses
-
             text_content = ""
 
             for content_block in content:
@@ -1313,8 +1266,6 @@ class BedrockCompletion(BaseLLM):
         for message in formatted_messages:
             role = message.get("role")
             content = message.get("content", "")
-            tool_calls = message.get("tool_calls")
-            tool_call_id = message.get("tool_call_id")
 
             if role == "system":
                 # Extract system message - Converse API handles it separately
@@ -1322,49 +1273,9 @@ class BedrockCompletion(BaseLLM):
                     system_message += f"\n\n{content}"
                 else:
                     system_message = cast(str, content)
-            elif role == "assistant" and tool_calls:
-                # Convert OpenAI-style tool_calls to Bedrock toolUse format
-                bedrock_content = []
-                for tc in tool_calls:
-                    func = tc.get("function", {})
-                    tool_use_block = {
-                        "toolUse": {
-                            "toolUseId": tc.get("id", f"call_{id(tc)}"),
-                            "name": func.get("name", ""),
-                            "input": func.get("arguments", {})
-                            if isinstance(func.get("arguments"), dict)
-                            else json.loads(func.get("arguments", "{}") or "{}"),
-                        }
-                    }
-                    bedrock_content.append(tool_use_block)
-                converse_messages.append(
-                    {"role": "assistant", "content": bedrock_content}
-                )
-            elif role == "tool":
-                if not tool_call_id:
-                    raise ValueError("Tool message missing required tool_call_id")
-                converse_messages.append(
-                    {
-                        "role": "user",
-                        "content": [
-                            {
-                                "toolResult": {
-                                    "toolUseId": tool_call_id,
-                                    "content": [
-                                        {"text": str(content) if content else ""}
-                                    ],
-                                }
-                            }
-                        ],
-                    }
-                )
             else:
                 # Convert to Converse API format with proper content structure
-                # Ensure content is not None
-                text_content = content if content else ""
-                converse_messages.append(
-                    {"role": role, "content": [{"text": text_content}]}
-                )
+                converse_messages.append({"role": role, "content": [{"text": content}]})
 
         # CRITICAL: Handle model-specific conversation requirements
         # Cohere and some other models require conversation to end with user message
@@ -1414,58 +1325,6 @@ class BedrockCompletion(BaseLLM):
 
         return converse_messages, system_message
 
-    @staticmethod
-    def _messages_contain_tool_content(messages: list[LLMMessage]) -> bool:
-        """Check if messages contain toolUse or toolResult content blocks.
-
-        Bedrock requires toolConfig when messages have tool-related content.
-        """
-        for message in messages:
-            content = message.get("content", [])
-            if isinstance(content, list):
-                for block in content:
-                    if isinstance(block, dict):
-                        if "toolUse" in block or "toolResult" in block:
-                            return True
-        return False
-
-    @staticmethod
-    def _extract_tools_from_message_history(
-        messages: list[LLMMessage],
-    ) -> list[dict[str, Any]]:
-        """Extract tool definitions from toolUse blocks in message history.
-
-        When no tools are passed but messages contain toolUse, we need to
-        recreate a minimal toolConfig to satisfy Bedrock's API requirements.
-        """
-        tools: list[dict[str, Any]] = []
-        seen_tool_names: set[str] = set()
-
-        for message in messages:
-            content = message.get("content", [])
-            if isinstance(content, list):
-                for block in content:
-                    if isinstance(block, dict) and "toolUse" in block:
-                        tool_use = block["toolUse"]
-                        tool_name = tool_use.get("name", "")
-                        if tool_name and tool_name not in seen_tool_names:
-                            seen_tool_names.add(tool_name)
-                            # Create a minimal tool spec from the toolUse block
-                            tool_spec: dict[str, Any] = {
-                                "toolSpec": {
-                                    "name": tool_name,
-                                    "description": f"Tool: {tool_name}",
-                                    "inputSchema": {
-                                        "json": {
-                                            "type": "object",
-                                            "properties": {},
-                                        }
-                                    },
-                                }
-                            }
-                            tools.append(tool_spec)
-        return tools
-
     @staticmethod
     def _format_tools_for_converse(
         tools: list[dict[str, Any]],

lib/crewai/src/crewai/llms/providers/gemini/completion.py

@@ -54,21 +54,15 @@ class GeminiCompletion(BaseLLM):
         safety_settings: dict[str, Any] | None = None,
         client_params: dict[str, Any] | None = None,
         interceptor: BaseInterceptor[Any, Any] | None = None,
-        use_vertexai: bool | None = None,
         **kwargs: Any,
     ):
         """Initialize Google Gemini chat completion client.
 
         Args:
             model: Gemini model name (e.g., 'gemini-2.0-flash-001', 'gemini-1.5-pro')
-            api_key: Google API key for Gemini API authentication.
-                    Defaults to GOOGLE_API_KEY or GEMINI_API_KEY env var.
-                    NOTE: Cannot be used with Vertex AI (project parameter). Use Gemini API instead.
-            project: Google Cloud project ID for Vertex AI with ADC authentication.
-                    Requires Application Default Credentials (gcloud auth application-default login).
-                    NOTE: Vertex AI does NOT support API keys, only OAuth2/ADC.
-                    If both api_key and project are set, api_key takes precedence.
-            location: Google Cloud location (for Vertex AI with ADC, defaults to 'us-central1')
+            api_key: Google API key (defaults to GOOGLE_API_KEY or GEMINI_API_KEY env var)
+            project: Google Cloud project ID (for Vertex AI)
+            location: Google Cloud location (for Vertex AI, defaults to 'us-central1')
             temperature: Sampling temperature (0-2)
             top_p: Nucleus sampling parameter
             top_k: Top-k sampling parameter
@@ -79,12 +73,6 @@ class GeminiCompletion(BaseLLM):
             client_params: Additional parameters to pass to the Google Gen AI Client constructor.
                           Supports parameters like http_options, credentials, debug_config, etc.
             interceptor: HTTP interceptor (not yet supported for Gemini).
-            use_vertexai: Whether to use Vertex AI instead of Gemini API.
-                         - True: Use Vertex AI (with ADC or Express mode with API key)
-                         - False: Use Gemini API (explicitly override env var)
-                         - None (default): Check GOOGLE_GENAI_USE_VERTEXAI env var
-                         When using Vertex AI with API key (Express mode), http_options with
-                         api_version="v1" is automatically configured.
             **kwargs: Additional parameters
         """
         if interceptor is not None:
@@ -107,8 +95,7 @@ class GeminiCompletion(BaseLLM):
         self.project = project or os.getenv("GOOGLE_CLOUD_PROJECT")
         self.location = location or os.getenv("GOOGLE_CLOUD_LOCATION") or "us-central1"
 
-        if use_vertexai is None:
-            use_vertexai = os.getenv("GOOGLE_GENAI_USE_VERTEXAI", "").lower() == "true"
+        use_vertexai = os.getenv("GOOGLE_GENAI_USE_VERTEXAI", "").lower() == "true"
 
         self.client = self._initialize_client(use_vertexai)
 
@@ -159,34 +146,13 @@ class GeminiCompletion(BaseLLM):
 
         Returns:
             Initialized Google Gen AI Client
-
-        Note:
-            Google Gen AI SDK has two distinct endpoints with different auth requirements:
-            - Gemini API (generativelanguage.googleapis.com): Supports API key authentication
-            - Vertex AI (aiplatform.googleapis.com): Only supports OAuth2/ADC, NO API keys
-
-            When vertexai=True is set, it routes to aiplatform.googleapis.com which rejects
-            API keys. Use Gemini API endpoint for API key authentication instead.
         """
         client_params = {}
 
         if self.client_params:
             client_params.update(self.client_params)
 
-        # Determine authentication mode based on available credentials
-        has_api_key = bool(self.api_key)
-        has_project = bool(self.project)
-
-        if has_api_key and has_project:
-            logging.warning(
-                "Both API key and project provided. Using API key authentication. "
-                "Project/location parameters are ignored when using API keys. "
-                "To use Vertex AI with ADC, remove the api_key parameter."
-            )
-            has_project = False
-
-        # Vertex AI with ADC (project without API key)
-        if (use_vertexai or has_project) and not has_api_key:
+        if use_vertexai or self.project:
             client_params.update(
                 {
                     "vertexai": True,
@@ -195,20 +161,12 @@ class GeminiCompletion(BaseLLM):
                 }
             )
 
-        # API key authentication (works with both Gemini API and Vertex AI Express)
-        elif has_api_key:
+            client_params.pop("api_key", None)
+
+        elif self.api_key:
             client_params["api_key"] = self.api_key
 
-            # Vertex AI Express mode: API key + vertexai=True + http_options with api_version="v1"
-            # See: https://cloud.google.com/vertex-ai/generative-ai/docs/start/quickstart?usertype=apikey
-            if use_vertexai:
-                client_params["vertexai"] = True
-                client_params["http_options"] = types.HttpOptions(api_version="v1")
-            else:
-                # This ensures we use the Gemini API (generativelanguage.googleapis.com)
-                client_params["vertexai"] = False
-
-            # Clean up project/location (not allowed with API key)
+            client_params.pop("vertexai", None)
             client_params.pop("project", None)
             client_params.pop("location", None)
 
@@ -217,13 +175,10 @@ class GeminiCompletion(BaseLLM):
                 return genai.Client(**client_params)
             except Exception as e:
                 raise ValueError(
-                    "Authentication required. Provide one of:\n"
-                    "  1. API key via GOOGLE_API_KEY or GEMINI_API_KEY environment variable\n"
-                    "     (use_vertexai=True is optional for Vertex AI with API key)\n"
-                    "  2. For Vertex AI with ADC: Set GOOGLE_CLOUD_PROJECT and run:\n"
-                    "     gcloud auth application-default login\n"
-                    "  3. Pass api_key parameter directly to LLM constructor\n"
+                    "Either GOOGLE_API_KEY/GEMINI_API_KEY (for Gemini API) or "
+                    "GOOGLE_CLOUD_PROJECT (for Vertex AI) must be set"
                 ) from e
+
         return genai.Client(**client_params)
 
     def _get_client_params(self) -> dict[str, Any]:
@@ -247,8 +202,6 @@ class GeminiCompletion(BaseLLM):
                     "location": self.location,
                 }
             )
-            if self.api_key:
-                params["api_key"] = self.api_key
         elif self.api_key:
             params["api_key"] = self.api_key
 
@@ -531,53 +484,6 @@ class GeminiCompletion(BaseLLM):
                     system_instruction += f"\n\n{text_content}"
                 else:
                     system_instruction = text_content
-            elif role == "tool":
-                tool_call_id = message.get("tool_call_id")
-                if not tool_call_id:
-                    raise ValueError("Tool message missing required tool_call_id")
-
-                tool_name = message.get("name", "")
-
-                response_data: dict[str, Any]
-                try:
-                    response_data = json.loads(text_content) if text_content else {}
-                except (json.JSONDecodeError, TypeError):
-                    response_data = {"result": text_content}
-
-                function_response_part = types.Part.from_function_response(
-                    name=tool_name, response=response_data
-                )
-                contents.append(
-                    types.Content(role="user", parts=[function_response_part])
-                )
-            elif role == "assistant" and message.get("tool_calls"):
-                parts: list[types.Part] = []
-
-                if text_content:
-                    parts.append(types.Part.from_text(text=text_content))
-
-                tool_calls: list[dict[str, Any]] = message.get("tool_calls") or []
-                for tool_call in tool_calls:
-                    func: dict[str, Any] = tool_call.get("function") or {}
-                    func_name: str = str(func.get("name") or "")
-                    func_args_raw: str | dict[str, Any] = func.get("arguments") or {}
-
-                    func_args: dict[str, Any]
-                    if isinstance(func_args_raw, str):
-                        try:
-                            func_args = (
-                                json.loads(func_args_raw) if func_args_raw else {}
-                            )
-                        except (json.JSONDecodeError, TypeError):
-                            func_args = {}
-                    else:
-                        func_args = func_args_raw
-
-                    parts.append(
-                        types.Part.from_function_call(name=func_name, args=func_args)
-                    )
-
-                contents.append(types.Content(role="model", parts=parts))
             else:
                 # Convert role for Gemini (assistant -> model)
                 gemini_role = "model" if role == "assistant" else "user"
@@ -700,24 +606,6 @@ class GeminiCompletion(BaseLLM):
         if response.candidates and (self.tools or available_functions):
             candidate = response.candidates[0]
             if candidate.content and candidate.content.parts:
-                # Collect function call parts
-                function_call_parts = [
-                    part for part in candidate.content.parts if part.function_call
-                ]
-
-                # If there are function calls but no available_functions,
-                # return them for the executor to handle (like OpenAI/Anthropic)
-                if function_call_parts and not available_functions:
-                    self._emit_call_completed_event(
-                        response=function_call_parts,
-                        call_type=LLMCallType.TOOL_CALL,
-                        from_task=from_task,
-                        from_agent=from_agent,
-                        messages=self._convert_contents_to_dict(contents),
-                    )
-                    return function_call_parts
-
-                # Otherwise execute the tools internally
                 for part in candidate.content.parts:
                     if part.function_call:
                         function_name = part.function_call.name
@@ -740,7 +628,7 @@ class GeminiCompletion(BaseLLM):
                         if result is not None:
                             return result
 
-        content = self._extract_text_from_response(response)
+        content = response.text or ""
         content = self._apply_stop_words(content)
 
         return self._finalize_completion_response(
@@ -832,7 +720,7 @@ class GeminiCompletion(BaseLLM):
         from_task: Any | None = None,
         from_agent: Any | None = None,
         response_model: type[BaseModel] | None = None,
-    ) -> str | list[dict[str, Any]]:
+    ) -> str:
         """Finalize streaming response with usage tracking, function execution, and events.
 
         Args:
@@ -850,29 +738,6 @@ class GeminiCompletion(BaseLLM):
         """
         self._track_token_usage_internal(usage_data)
 
-        # If there are function calls but no available_functions,
-        # return them for the executor to handle
-        if function_calls and not available_functions:
-            formatted_function_calls = [
-                {
-                    "id": call_data["id"],
-                    "function": {
-                        "name": call_data["name"],
-                        "arguments": json.dumps(call_data["args"]),
-                    },
-                    "type": "function",
-                }
-                for call_data in function_calls.values()
-            ]
-            self._emit_call_completed_event(
-                response=formatted_function_calls,
-                call_type=LLMCallType.TOOL_CALL,
-                from_task=from_task,
-                from_agent=from_agent,
-                messages=self._convert_contents_to_dict(contents),
-            )
-            return formatted_function_calls
-
         # Handle completed function calls
         if function_calls and available_functions:
             for call_data in function_calls.values():
@@ -1123,35 +988,6 @@ class GeminiCompletion(BaseLLM):
             }
         return {"total_tokens": 0}
 
-    @staticmethod
-    def _extract_text_from_response(response: GenerateContentResponse) -> str:
-        """Extract text content from Gemini response without triggering warnings.
-
-        This method directly accesses the response parts to extract text content,
-        avoiding the warning that occurs when using response.text on responses
-        containing non-text parts (e.g., 'thought_signature' from thinking models).
-
-        Args:
-            response: The Gemini API response
-
-        Returns:
-            Concatenated text content from all text parts
-        """
-        if not response.candidates:
-            return ""
-
-        candidate = response.candidates[0]
-        if not candidate.content or not candidate.content.parts:
-            return ""
-
-        text_parts = [
-            part.text
-            for part in candidate.content.parts
-            if hasattr(part, "text") and part.text
-        ]
-
-        return "".join(text_parts)
-
     @staticmethod
     def _convert_contents_to_dict(
         contents: list[types.Content],

lib/crewai/src/crewai/llms/providers/openai/completion.py

@@ -428,19 +428,6 @@ class OpenAICompletion(BaseLLM):
             choice: Choice = response.choices[0]
             message = choice.message
 
-            # If there are tool_calls but no available_functions, return the tool_calls
-            # This allows the caller (e.g., executor) to handle tool execution
-            if message.tool_calls and not available_functions:
-                self._emit_call_completed_event(
-                    response=list(message.tool_calls),
-                    call_type=LLMCallType.TOOL_CALL,
-                    from_task=from_task,
-                    from_agent=from_agent,
-                    messages=params["messages"],
-                )
-                return list(message.tool_calls)
-
-            # If there are tool_calls and available_functions, execute the tools
             if message.tool_calls and available_functions:
                 tool_call = message.tool_calls[0]
                 function_name = tool_call.function.name
@@ -738,19 +725,6 @@ class OpenAICompletion(BaseLLM):
             choice: Choice = response.choices[0]
             message = choice.message
 
-            # If there are tool_calls but no available_functions, return the tool_calls
-            # This allows the caller (e.g., executor) to handle tool execution
-            if message.tool_calls and not available_functions:
-                self._emit_call_completed_event(
-                    response=list(message.tool_calls),
-                    call_type=LLMCallType.TOOL_CALL,
-                    from_task=from_task,
-                    from_agent=from_agent,
-                    messages=params["messages"],
-                )
-                return list(message.tool_calls)
-
-            # If there are tool_calls and available_functions, execute the tools
             if message.tool_calls and available_functions:
                 tool_call = message.tool_calls[0]
                 function_name = tool_call.function.name

lib/crewai/src/crewai/llms/providers/utils/common.py

@@ -2,12 +2,16 @@ import logging
 import re
 from typing import Any
 
-from crewai.utilities.string_utils import sanitize_tool_name
-
 
 def validate_function_name(name: str, provider: str = "LLM") -> str:
     """Validate function name according to common LLM provider requirements.
 
+    Most LLM providers (OpenAI, Gemini, Anthropic) have similar requirements:
+    - Must start with letter or underscore
+    - Only alphanumeric, underscore, dot, colon, dash allowed
+    - Maximum length of 64 characters
+    - Cannot be empty
+
     Args:
         name: The function name to validate
         provider: The provider name for error messages
@@ -31,10 +35,11 @@ def validate_function_name(name: str, provider: str = "LLM") -> str:
             f"{provider} function name '{name}' exceeds 64 character limit"
         )
 
-    if not re.match(r"^[a-z_][a-z0-9_]*$", name):
+    # Check for invalid characters (most providers support these)
+    if not re.match(r"^[a-zA-Z_][a-zA-Z0-9_.\-:]*$", name):
         raise ValueError(
             f"{provider} function name '{name}' contains invalid characters. "
-            f"Only lowercase letters, numbers, and underscores allowed"
+            f"Only letters, numbers, underscore, dot, colon, dash allowed"
         )
 
     return name
@@ -100,18 +105,6 @@ def log_tool_conversion(tool: dict[str, Any], provider: str) -> None:
         logging.error(f"{provider}: Tool structure: {tool}")
 
 
-def sanitize_function_name(name: str) -> str:
-    """Sanitize function name for LLM provider compatibility.
-
-    Args:
-        name: Original function name
-
-    Returns:
-        Sanitized function name (lowercase, a-z0-9_ only, max 64 chars)
-    """
-    return sanitize_tool_name(name)
-
-
 def safe_tool_conversion(
     tool: dict[str, Any], provider: str
 ) -> tuple[str, str, dict[str, Any]]:
@@ -134,10 +127,7 @@ def safe_tool_conversion(
 
         name, description, parameters = extract_tool_info(tool)
 
-        # Sanitize name before validation (replace invalid chars with underscores)
-        sanitized_name = sanitize_function_name(name)
-
-        validated_name = validate_function_name(sanitized_name, provider)
+        validated_name = validate_function_name(name, provider)
 
         logging.info(f"{provider}: Successfully validated tool '{validated_name}'")
         return validated_name, description, parameters

lib/crewai/src/crewai/mcp/client.py

@@ -31,7 +31,6 @@ from crewai.mcp.transports.base import BaseTransport
 from crewai.mcp.transports.http import HTTPTransport
 from crewai.mcp.transports.sse import SSETransport
 from crewai.mcp.transports.stdio import StdioTransport
-from crewai.utilities.string_utils import sanitize_tool_name
 
 
 # MCP Connection timeout constants (in seconds)
@@ -419,7 +418,7 @@ class MCPClient:
 
         return [
             {
-                "name": sanitize_tool_name(tool.name),
+                "name": tool.name,
                 "description": getattr(tool, "description", ""),
                 "inputSchema": getattr(tool, "inputSchema", {}),
             }

lib/crewai/src/crewai/telemetry/telemetry.py

@@ -52,7 +52,6 @@ from crewai.telemetry.utils import (
     close_span,
 )
 from crewai.utilities.logger_utils import suppress_warnings
-from crewai.utilities.string_utils import sanitize_tool_name
 
 
 logger = logging.getLogger(__name__)
@@ -324,8 +323,7 @@ class Telemetry:
                                 ),
                                 "max_retry_limit": getattr(agent, "max_retry_limit", 3),
                                 "tools_names": [
-                                    sanitize_tool_name(tool.name)
-                                    for tool in agent.tools or []
+                                    tool.name.casefold() for tool in agent.tools or []
                                 ],
                                 # Add agent fingerprint data if sharing crew details
                                 "fingerprint": (
@@ -374,8 +372,7 @@ class Telemetry:
                                     else None
                                 ),
                                 "tools_names": [
-                                    sanitize_tool_name(tool.name)
-                                    for tool in task.tools or []
+                                    tool.name.casefold() for tool in task.tools or []
                                 ],
                                 # Add task fingerprint data if sharing crew details
                                 "fingerprint": (
@@ -428,8 +425,7 @@ class Telemetry:
                                 ),
                                 "max_retry_limit": getattr(agent, "max_retry_limit", 3),
                                 "tools_names": [
-                                    sanitize_tool_name(tool.name)
-                                    for tool in agent.tools or []
+                                    tool.name.casefold() for tool in agent.tools or []
                                 ],
                             }
                             for agent in crew.agents
@@ -451,8 +447,7 @@ class Telemetry:
                                 ),
                                 "agent_key": task.agent.key if task.agent else None,
                                 "tools_names": [
-                                    sanitize_tool_name(tool.name)
-                                    for tool in task.tools or []
+                                    tool.name.casefold() for tool in task.tools or []
                                 ],
                             }
                             for task in crew.tasks
@@ -837,8 +832,7 @@ class Telemetry:
                             "llm": agent.llm.model,
                             "delegation_enabled?": agent.allow_delegation,
                             "tools_names": [
-                                sanitize_tool_name(tool.name)
-                                for tool in agent.tools or []
+                                tool.name.casefold() for tool in agent.tools or []
                             ],
                         }
                         for agent in crew.agents
@@ -864,8 +858,7 @@ class Telemetry:
                                 else None
                             ),
                             "tools_names": [
-                                sanitize_tool_name(tool.name)
-                                for tool in task.tools or []
+                                tool.name.casefold() for tool in task.tools or []
                             ],
                         }
                         for task in crew.tasks

lib/crewai/src/crewai/tools/base_tool.py

@@ -26,7 +26,6 @@ from typing_extensions import TypeIs
 from crewai.tools.structured_tool import CrewStructuredTool
 from crewai.utilities.printer import Printer
 from crewai.utilities.pydantic_schema_utils import generate_model_description
-from crewai.utilities.string_utils import sanitize_tool_name
 
 
 _printer = Printer()
@@ -155,6 +154,7 @@ class BaseTool(BaseModel, ABC):
         *args: Any,
         **kwargs: Any,
     ) -> Any:
+        _printer.print(f"Using Tool: {self.name}", color="cyan")
         result = self._run(*args, **kwargs)
 
         # If _run is async, we safely run it
@@ -260,12 +260,10 @@ class BaseTool(BaseModel, ABC):
                 else:
                     fields[name] = (param_annotation, param.default)
             if fields:
-                args_schema = create_model(
-                    f"{sanitize_tool_name(tool.name)}_input", **fields
-                )
+                args_schema = create_model(f"{tool.name}Input", **fields)
             else:
                 args_schema = create_model(
-                    f"{sanitize_tool_name(tool.name)}_input", __base__=PydanticBaseModel
+                    f"{tool.name}Input", __base__=PydanticBaseModel
                 )
 
         return cls(
@@ -304,7 +302,7 @@ class BaseTool(BaseModel, ABC):
         schema = generate_model_description(self.args_schema)
         args_json = json.dumps(schema["json_schema"]["schema"], indent=2)
         self.description = (
-            f"Tool Name: {sanitize_tool_name(self.name)}\n"
+            f"Tool Name: {self.name}\n"
             f"Tool Arguments: {args_json}\n"
             f"Tool Description: {self.description}"
         )
@@ -331,6 +329,7 @@ class Tool(BaseTool, Generic[P, R]):
         Returns:
             The result of the tool execution.
         """
+        _printer.print(f"Using Tool: {self.name}", color="cyan")
         result = self.func(*args, **kwargs)
 
         if asyncio.iscoroutine(result):
@@ -382,7 +381,7 @@ class Tool(BaseTool, Generic[P, R]):
         if _is_awaitable(result):
             return await result
         raise NotImplementedError(
-            f"{sanitize_tool_name(self.name)} does not have an async function. "
+            f"{self.name} does not have an async function. "
             "Use run() for sync execution or provide an async function."
         )
 
@@ -424,12 +423,10 @@ class Tool(BaseTool, Generic[P, R]):
                 else:
                     fields[name] = (param_annotation, param.default)
             if fields:
-                args_schema = create_model(
-                    f"{sanitize_tool_name(tool.name)}_input", **fields
-                )
+                args_schema = create_model(f"{tool.name}Input", **fields)
             else:
                 args_schema = create_model(
-                    f"{sanitize_tool_name(tool.name)}_input", __base__=PydanticBaseModel
+                    f"{tool.name}Input", __base__=PydanticBaseModel
                 )
 
         return cls(

lib/crewai/src/crewai/tools/cache_tools/cache_tools.py

@@ -2,7 +2,6 @@ from pydantic import BaseModel, Field
 
 from crewai.agents.cache.cache_handler import CacheHandler
 from crewai.tools.structured_tool import CrewStructuredTool
-from crewai.utilities.string_utils import sanitize_tool_name
 
 
 class CacheTools(BaseModel):
@@ -14,14 +13,14 @@ class CacheTools(BaseModel):
         default_factory=CacheHandler,
     )
 
-    def tool(self) -> CrewStructuredTool:
+    def tool(self):
         return CrewStructuredTool.from_function(
             func=self.hit_cache,
-            name=sanitize_tool_name(self.name),
+            name=self.name,
             description="Reads directly from the cache",
         )
 
-    def hit_cache(self, key: str) -> str | None:
+    def hit_cache(self, key):
         split = key.split("tool:")
         tool = split[1].split("|input:")[0].strip()
         tool_input = split[1].split("|input:")[1].strip()

lib/crewai/src/crewai/tools/structured_tool.py

@@ -10,7 +10,6 @@ from typing import TYPE_CHECKING, Any, get_type_hints
 from pydantic import BaseModel, Field, create_model
 
 from crewai.utilities.logger import Logger
-from crewai.utilities.string_utils import sanitize_tool_name
 
 
 if TYPE_CHECKING:
@@ -230,7 +229,7 @@ class CrewStructuredTool:
 
         if self.has_reached_max_usage_count():
             raise ToolUsageLimitExceededError(
-                f"Tool '{sanitize_tool_name(self.name)}' has reached its maximum usage limit of {self.max_usage_count}. You should not use the {sanitize_tool_name(self.name)} tool again."
+                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()
@@ -262,7 +261,7 @@ class CrewStructuredTool:
 
         if self.has_reached_max_usage_count():
             raise ToolUsageLimitExceededError(
-                f"Tool '{sanitize_tool_name(self.name)}' has reached its maximum usage limit of {self.max_usage_count}. You should not use the {sanitize_tool_name(self.name)} tool again."
+                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()
@@ -296,4 +295,6 @@ class CrewStructuredTool:
         return self.args_schema.model_json_schema()["properties"]
 
     def __repr__(self) -> str:
-        return f"CrewStructuredTool(name='{sanitize_tool_name(self.name)}', description='{self.description}')"
+        return (
+            f"CrewStructuredTool(name='{self.name}', description='{self.description}')"
+        )

lib/crewai/src/crewai/tools/tool_usage.py

@@ -30,7 +30,6 @@ from crewai.utilities.agent_utils import (
 from crewai.utilities.converter import Converter
 from crewai.utilities.i18n import I18N, get_i18n
 from crewai.utilities.printer import Printer
-from crewai.utilities.string_utils import sanitize_tool_name
 
 
 if TYPE_CHECKING:
@@ -146,8 +145,7 @@ class ToolUsage:
 
         if (
             isinstance(tool, CrewStructuredTool)
-            and sanitize_tool_name(tool.name)
-            == sanitize_tool_name(self._i18n.tools("add_image")["name"])  # type: ignore
+            and tool.name == self._i18n.tools("add_image")["name"]  # type: ignore
         ):
             try:
                 return self._use(tool_string=tool_string, tool=tool, calling=calling)
@@ -194,8 +192,7 @@ class ToolUsage:
 
         if (
             isinstance(tool, CrewStructuredTool)
-            and sanitize_tool_name(tool.name)
-            == sanitize_tool_name(self._i18n.tools("add_image")["name"])  # type: ignore
+            and tool.name == self._i18n.tools("add_image")["name"]  # type: ignore
         ):
             try:
                 return await self._ause(
@@ -236,7 +233,7 @@ class ToolUsage:
                 )
                 self._telemetry.tool_repeated_usage(
                     llm=self.function_calling_llm,
-                    tool_name=sanitize_tool_name(tool.name),
+                    tool_name=tool.name,
                     attempts=self._run_attempts,
                 )
                 return self._format_result(result=result)
@@ -244,9 +241,6 @@ class ToolUsage:
                 if self.task:
                     self.task.increment_tools_errors()
 
-        started_at = time.time()
-        started_event_emitted = False
-
         if self.agent:
             event_data = {
                 "agent_key": self.agent.key,
@@ -264,185 +258,151 @@ class ToolUsage:
                 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))
-            started_event_emitted = True
 
+        started_at = time.time()
         from_cache = False
         result = None  # type: ignore
-        should_retry = False
-        available_tool = None
 
-        try:
-            if self.tools_handler and self.tools_handler.cache:
-                input_str = ""
-                if calling.arguments:
-                    if isinstance(calling.arguments, dict):
-                        input_str = json.dumps(calling.arguments)
-                    else:
-                        input_str = str(calling.arguments)
+        if self.tools_handler and self.tools_handler.cache:
+            input_str = ""
+            if calling.arguments:
+                if isinstance(calling.arguments, dict):
+                    input_str = json.dumps(calling.arguments)
+                else:
+                    input_str = str(calling.arguments)
 
-                result = self.tools_handler.cache.read(
-                    tool=sanitize_tool_name(calling.tool_name), input=input_str
-                )  # type: ignore
-                from_cache = result is not None
+            result = self.tools_handler.cache.read(
+                tool=calling.tool_name, input=input_str
+            )  # type: ignore
+            from_cache = result is not None
 
-            available_tool = next(
-                (
-                    available_tool
-                    for available_tool in self.tools
-                    if sanitize_tool_name(available_tool.name)
-                    == sanitize_tool_name(tool.name)
-                ),
-                None,
-            )
+        available_tool = next(
+            (
+                available_tool
+                for available_tool in self.tools
+                if available_tool.name == tool.name
+            ),
+            None,
+        )
 
-            usage_limit_error = self._check_usage_limit(
-                available_tool, sanitize_tool_name(tool.name)
-            )
-            if usage_limit_error:
+        usage_limit_error = self._check_usage_limit(available_tool, tool.name)
+        if usage_limit_error:
+            try:
                 result = usage_limit_error
                 self._telemetry.tool_usage_error(llm=self.function_calling_llm)
-                result = self._format_result(result=result)
-                # Don't return early - fall through to finally block
-            elif result is None:
-                try:
-                    if sanitize_tool_name(calling.tool_name) in [
-                        sanitize_tool_name("Delegate work to coworker"),
-                        sanitize_tool_name("Ask question to coworker"),
-                    ]:
-                        coworker = (
-                            calling.arguments.get("coworker")
-                            if calling.arguments
-                            else None
-                        )
-                        if self.task:
-                            self.task.increment_delegations(coworker)
+                return self._format_result(result=result)
+            except Exception:
+                if self.task:
+                    self.task.increment_tools_errors()
 
-                    if calling.arguments:
-                        try:
-                            acceptable_args = tool.args_schema.model_json_schema()[
-                                "properties"
-                            ].keys()
-                            arguments = {
-                                k: v
-                                for k, v in calling.arguments.items()
-                                if k in acceptable_args
-                            }
-                            arguments = self._add_fingerprint_metadata(arguments)
-                            result = await tool.ainvoke(input=arguments)
-                        except Exception:
-                            arguments = calling.arguments
-                            arguments = self._add_fingerprint_metadata(arguments)
-                            result = await tool.ainvoke(input=arguments)
-                    else:
-                        arguments = self._add_fingerprint_metadata({})
-                        result = await tool.ainvoke(input=arguments)
-
-                    if self.tools_handler:
-                        should_cache = True
-                        # Check cache_function on original tool (for tools converted via to_structured_tool)
-                        original_tool = getattr(available_tool, "_original_tool", None)
-                        cache_func = None
-                        if original_tool and hasattr(original_tool, "cache_function"):
-                            cache_func = original_tool.cache_function
-                        elif hasattr(available_tool, "cache_function"):
-                            cache_func = available_tool.cache_function
-
-                        if cache_func:
-                            should_cache = cache_func(calling.arguments, result)
-
-                        self.tools_handler.on_tool_use(
-                            calling=calling, output=result, should_cache=should_cache
-                        )
-
-                    self._telemetry.tool_usage(
-                        llm=self.function_calling_llm,
-                        tool_name=sanitize_tool_name(tool.name),
-                        attempts=self._run_attempts,
+        if result is None:
+            try:
+                if calling.tool_name in [
+                    "Delegate work to coworker",
+                    "Ask question to coworker",
+                ]:
+                    coworker = (
+                        calling.arguments.get("coworker") if calling.arguments else None
                     )
-                    result = self._format_result(result=result)
-                    data = {
-                        "result": result,
-                        "tool_name": sanitize_tool_name(tool.name),
-                        "tool_args": calling.arguments,
-                    }
+                    if self.task:
+                        self.task.increment_delegations(coworker)
 
-                    if (
-                        hasattr(available_tool, "result_as_answer")
-                        and available_tool.result_as_answer
-                    ):
-                        result_as_answer = available_tool.result_as_answer
-                        data["result_as_answer"] = result_as_answer
-
-                    if self.agent and hasattr(self.agent, "tools_results"):
-                        self.agent.tools_results.append(data)
-
-                    if available_tool and hasattr(
-                        available_tool, "_increment_usage_count"
-                    ):
-                        # Use _increment_usage_count to sync count to original tool
-                        available_tool._increment_usage_count()
-                        if (
-                            hasattr(available_tool, "max_usage_count")
-                            and available_tool.max_usage_count is not None
-                        ):
-                            self._printer.print(
-                                content=f"Tool '{sanitize_tool_name(available_tool.name)}' usage: {available_tool.current_usage_count}/{available_tool.max_usage_count}",
-                                color="blue",
-                            )
-                    elif available_tool and hasattr(
-                        available_tool, "current_usage_count"
-                    ):
-                        available_tool.current_usage_count += 1
-                        if (
-                            hasattr(available_tool, "max_usage_count")
-                            and available_tool.max_usage_count is not None
-                        ):
-                            self._printer.print(
-                                content=f"Tool '{sanitize_tool_name(available_tool.name)}' usage: {available_tool.current_usage_count}/{available_tool.max_usage_count}",
-                                color="blue",
-                            )
-
-                except Exception as e:
-                    self.on_tool_error(tool=tool, tool_calling=calling, e=e)
-                    self._run_attempts += 1
-                    if self._run_attempts > self._max_parsing_attempts:
-                        self._telemetry.tool_usage_error(llm=self.function_calling_llm)
-                        error_message = self._i18n.errors(
-                            "tool_usage_exception"
-                        ).format(
-                            error=e,
-                            tool=sanitize_tool_name(tool.name),
-                            tool_inputs=tool.description,
+                if calling.arguments:
+                    try:
+                        acceptable_args = tool.args_schema.model_json_schema()[
+                            "properties"
+                        ].keys()
+                        arguments = {
+                            k: v
+                            for k, v in calling.arguments.items()
+                            if k in acceptable_args
+                        }
+                        arguments = self._add_fingerprint_metadata(arguments)
+                        result = await tool.ainvoke(input=arguments)
+                    except Exception:
+                        arguments = calling.arguments
+                        arguments = self._add_fingerprint_metadata(arguments)
+                        result = await tool.ainvoke(input=arguments)
+                else:
+                    arguments = self._add_fingerprint_metadata({})
+                    result = await tool.ainvoke(input=arguments)
+            except Exception as e:
+                self.on_tool_error(tool=tool, tool_calling=calling, e=e)
+                self._run_attempts += 1
+                if self._run_attempts > self._max_parsing_attempts:
+                    self._telemetry.tool_usage_error(llm=self.function_calling_llm)
+                    error_message = self._i18n.errors("tool_usage_exception").format(
+                        error=e, tool=tool.name, tool_inputs=tool.description
+                    )
+                    error = ToolUsageError(
+                        f"\n{error_message}.\nMoving on then. {self._i18n.slice('format').format(tool_names=self.tools_names)}"
+                    ).message
+                    if self.task:
+                        self.task.increment_tools_errors()
+                    if self.agent and self.agent.verbose:
+                        self._printer.print(
+                            content=f"\n\n{error_message}\n", color="red"
                         )
-                        result = ToolUsageError(
-                            f"\n{error_message}.\nMoving on then. {self._i18n.slice('format').format(tool_names=self.tools_names)}"
-                        ).message
-                        if self.task:
-                            self.task.increment_tools_errors()
-                        if self.agent and self.agent.verbose:
-                            self._printer.print(
-                                content=f"\n\n{error_message}\n", color="red"
-                            )
-                    else:
-                        if self.task:
-                            self.task.increment_tools_errors()
-                        should_retry = True
-            else:
-                result = self._format_result(result=result)
+                    return error
 
-        finally:
-            if started_event_emitted:
-                self.on_tool_use_finished(
-                    tool=tool,
-                    tool_calling=calling,
-                    from_cache=from_cache,
-                    started_at=started_at,
-                    result=result,
+                if self.task:
+                    self.task.increment_tools_errors()
+                return await self.ause(calling=calling, tool_string=tool_string)
+
+            if self.tools_handler:
+                should_cache = True
+                if (
+                    hasattr(available_tool, "cache_function")
+                    and available_tool.cache_function
+                ):
+                    should_cache = available_tool.cache_function(
+                        calling.arguments, result
+                    )
+
+                self.tools_handler.on_tool_use(
+                    calling=calling, output=result, should_cache=should_cache
                 )
 
-        # Handle retry after finally block ensures finished event was emitted
-        if should_retry:
-            return await self.ause(calling=calling, tool_string=tool_string)
+        self._telemetry.tool_usage(
+            llm=self.function_calling_llm,
+            tool_name=tool.name,
+            attempts=self._run_attempts,
+        )
+        result = self._format_result(result=result)
+        data = {
+            "result": result,
+            "tool_name": tool.name,
+            "tool_args": calling.arguments,
+        }
+
+        self.on_tool_use_finished(
+            tool=tool,
+            tool_calling=calling,
+            from_cache=from_cache,
+            started_at=started_at,
+            result=result,
+        )
+
+        if (
+            hasattr(available_tool, "result_as_answer")
+            and available_tool.result_as_answer  # type: ignore
+        ):
+            result_as_answer = available_tool.result_as_answer  # type: ignore
+            data["result_as_answer"] = result_as_answer  # type: ignore
+
+        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"):
+            available_tool.current_usage_count += 1
+            if (
+                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",
+                )
 
         return result
 
@@ -452,7 +412,6 @@ class ToolUsage:
         tool: CrewStructuredTool,
         calling: ToolCalling | InstructorToolCalling,
     ) -> str:
-        # Repeated usage check happens before event emission - safe to return early
         if self._check_tool_repeated_usage(calling=calling):
             try:
                 result = self._i18n.errors("task_repeated_usage").format(
@@ -460,7 +419,7 @@ class ToolUsage:
                 )
                 self._telemetry.tool_repeated_usage(
                     llm=self.function_calling_llm,
-                    tool_name=sanitize_tool_name(tool.name),
+                    tool_name=tool.name,
                     attempts=self._run_attempts,
                 )
                 return self._format_result(result=result)
@@ -469,9 +428,6 @@ class ToolUsage:
                 if self.task:
                     self.task.increment_tools_errors()
 
-        started_at = time.time()
-        started_event_emitted = False
-
         if self.agent:
             event_data = {
                 "agent_key": self.agent.key,
@@ -490,185 +446,155 @@ class ToolUsage:
                 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))
-            started_event_emitted = True
 
+        started_at = time.time()
         from_cache = False
         result = None  # type: ignore
-        should_retry = False
-        available_tool = None
 
-        try:
-            if self.tools_handler and self.tools_handler.cache:
-                input_str = ""
-                if calling.arguments:
-                    if isinstance(calling.arguments, dict):
-                        input_str = json.dumps(calling.arguments)
-                    else:
-                        input_str = str(calling.arguments)
+        if self.tools_handler and self.tools_handler.cache:
+            input_str = ""
+            if calling.arguments:
+                if isinstance(calling.arguments, dict):
+                    import json
 
-                result = self.tools_handler.cache.read(
-                    tool=sanitize_tool_name(calling.tool_name), input=input_str
-                )  # type: ignore
-                from_cache = result is not None
+                    input_str = json.dumps(calling.arguments)
+                else:
+                    input_str = str(calling.arguments)
 
-            available_tool = next(
-                (
-                    available_tool
-                    for available_tool in self.tools
-                    if sanitize_tool_name(available_tool.name)
-                    == sanitize_tool_name(tool.name)
-                ),
-                None,
-            )
+            result = self.tools_handler.cache.read(
+                tool=calling.tool_name, input=input_str
+            )  # type: ignore
+            from_cache = result is not None
 
-            usage_limit_error = self._check_usage_limit(
-                available_tool, sanitize_tool_name(tool.name)
-            )
-            if usage_limit_error:
+        available_tool = next(
+            (
+                available_tool
+                for available_tool in self.tools
+                if available_tool.name == tool.name
+            ),
+            None,
+        )
+
+        usage_limit_error = self._check_usage_limit(available_tool, tool.name)
+        if usage_limit_error:
+            try:
                 result = usage_limit_error
                 self._telemetry.tool_usage_error(llm=self.function_calling_llm)
-                result = self._format_result(result=result)
-                # Don't return early - fall through to finally block
-            elif result is None:
-                try:
-                    if sanitize_tool_name(calling.tool_name) in [
-                        sanitize_tool_name("Delegate work to coworker"),
-                        sanitize_tool_name("Ask question to coworker"),
-                    ]:
-                        coworker = (
-                            calling.arguments.get("coworker")
-                            if calling.arguments
-                            else None
-                        )
-                        if self.task:
-                            self.task.increment_delegations(coworker)
+                return self._format_result(result=result)
+            except Exception:
+                if self.task:
+                    self.task.increment_tools_errors()
 
-                    if calling.arguments:
-                        try:
-                            acceptable_args = tool.args_schema.model_json_schema()[
-                                "properties"
-                            ].keys()
-                            arguments = {
-                                k: v
-                                for k, v in calling.arguments.items()
-                                if k in acceptable_args
-                            }
-                            arguments = self._add_fingerprint_metadata(arguments)
-                            result = tool.invoke(input=arguments)
-                        except Exception:
-                            arguments = calling.arguments
-                            arguments = self._add_fingerprint_metadata(arguments)
-                            result = tool.invoke(input=arguments)
-                    else:
-                        arguments = self._add_fingerprint_metadata({})
-                        result = tool.invoke(input=arguments)
-
-                    if self.tools_handler:
-                        should_cache = True
-                        # Check cache_function on original tool (for tools converted via to_structured_tool)
-                        original_tool = getattr(available_tool, "_original_tool", None)
-                        cache_func = None
-                        if original_tool and hasattr(original_tool, "cache_function"):
-                            cache_func = original_tool.cache_function
-                        elif hasattr(available_tool, "cache_function"):
-                            cache_func = available_tool.cache_function
-
-                        if cache_func:
-                            should_cache = cache_func(calling.arguments, result)
-
-                        self.tools_handler.on_tool_use(
-                            calling=calling, output=result, should_cache=should_cache
-                        )
-
-                    self._telemetry.tool_usage(
-                        llm=self.function_calling_llm,
-                        tool_name=sanitize_tool_name(tool.name),
-                        attempts=self._run_attempts,
+        if result is None:
+            try:
+                if calling.tool_name in [
+                    "Delegate work to coworker",
+                    "Ask question to coworker",
+                ]:
+                    coworker = (
+                        calling.arguments.get("coworker") if calling.arguments else None
                     )
-                    result = self._format_result(result=result)
-                    data = {
-                        "result": result,
-                        "tool_name": sanitize_tool_name(tool.name),
-                        "tool_args": calling.arguments,
-                    }
+                    if self.task:
+                        self.task.increment_delegations(coworker)
 
-                    if (
-                        hasattr(available_tool, "result_as_answer")
-                        and available_tool.result_as_answer
-                    ):
-                        result_as_answer = available_tool.result_as_answer
-                        data["result_as_answer"] = result_as_answer
-
-                    if self.agent and hasattr(self.agent, "tools_results"):
-                        self.agent.tools_results.append(data)
-
-                    if available_tool and hasattr(
-                        available_tool, "_increment_usage_count"
-                    ):
-                        # Use _increment_usage_count to sync count to original tool
-                        available_tool._increment_usage_count()
-                        if (
-                            hasattr(available_tool, "max_usage_count")
-                            and available_tool.max_usage_count is not None
-                        ):
-                            self._printer.print(
-                                content=f"Tool '{sanitize_tool_name(available_tool.name)}' usage: {available_tool.current_usage_count}/{available_tool.max_usage_count}",
-                                color="blue",
-                            )
-                    elif available_tool and hasattr(
-                        available_tool, "current_usage_count"
-                    ):
-                        available_tool.current_usage_count += 1
-                        if (
-                            hasattr(available_tool, "max_usage_count")
-                            and available_tool.max_usage_count is not None
-                        ):
-                            self._printer.print(
-                                content=f"Tool '{sanitize_tool_name(available_tool.name)}' usage: {available_tool.current_usage_count}/{available_tool.max_usage_count}",
-                                color="blue",
-                            )
-
-                except Exception as e:
-                    self.on_tool_error(tool=tool, tool_calling=calling, e=e)
-                    self._run_attempts += 1
-                    if self._run_attempts > self._max_parsing_attempts:
-                        self._telemetry.tool_usage_error(llm=self.function_calling_llm)
-                        error_message = self._i18n.errors(
-                            "tool_usage_exception"
-                        ).format(
-                            error=e,
-                            tool=sanitize_tool_name(tool.name),
-                            tool_inputs=tool.description,
+                if calling.arguments:
+                    try:
+                        acceptable_args = tool.args_schema.model_json_schema()[
+                            "properties"
+                        ].keys()
+                        arguments = {
+                            k: v
+                            for k, v in calling.arguments.items()
+                            if k in acceptable_args
+                        }
+                        # Add fingerprint metadata if available
+                        arguments = self._add_fingerprint_metadata(arguments)
+                        result = tool.invoke(input=arguments)
+                    except Exception:
+                        arguments = calling.arguments
+                        # Add fingerprint metadata if available
+                        arguments = self._add_fingerprint_metadata(arguments)
+                        result = tool.invoke(input=arguments)
+                else:
+                    # Add fingerprint metadata even to empty arguments
+                    arguments = self._add_fingerprint_metadata({})
+                    result = tool.invoke(input=arguments)
+            except Exception as e:
+                self.on_tool_error(tool=tool, tool_calling=calling, e=e)
+                self._run_attempts += 1
+                if self._run_attempts > self._max_parsing_attempts:
+                    self._telemetry.tool_usage_error(llm=self.function_calling_llm)
+                    error_message = self._i18n.errors("tool_usage_exception").format(
+                        error=e, tool=tool.name, tool_inputs=tool.description
+                    )
+                    error = ToolUsageError(
+                        f"\n{error_message}.\nMoving on then. {self._i18n.slice('format').format(tool_names=self.tools_names)}"
+                    ).message
+                    if self.task:
+                        self.task.increment_tools_errors()
+                    if self.agent and self.agent.verbose:
+                        self._printer.print(
+                            content=f"\n\n{error_message}\n", color="red"
                         )
-                        result = ToolUsageError(
-                            f"\n{error_message}.\nMoving on then. {self._i18n.slice('format').format(tool_names=self.tools_names)}"
-                        ).message
-                        if self.task:
-                            self.task.increment_tools_errors()
-                        if self.agent and self.agent.verbose:
-                            self._printer.print(
-                                content=f"\n\n{error_message}\n", color="red"
-                            )
-                    else:
-                        if self.task:
-                            self.task.increment_tools_errors()
-                        should_retry = True
-            else:
-                result = self._format_result(result=result)
+                    return error
 
-        finally:
-            if started_event_emitted:
-                self.on_tool_use_finished(
-                    tool=tool,
-                    tool_calling=calling,
-                    from_cache=from_cache,
-                    started_at=started_at,
-                    result=result,
+                if self.task:
+                    self.task.increment_tools_errors()
+                return self.use(calling=calling, tool_string=tool_string)
+
+            if self.tools_handler:
+                should_cache = True
+                if (
+                    hasattr(available_tool, "cache_function")
+                    and available_tool.cache_function
+                ):
+                    should_cache = available_tool.cache_function(
+                        calling.arguments, result
+                    )
+
+                self.tools_handler.on_tool_use(
+                    calling=calling, output=result, should_cache=should_cache
                 )
+        self._telemetry.tool_usage(
+            llm=self.function_calling_llm,
+            tool_name=tool.name,
+            attempts=self._run_attempts,
+        )
+        result = self._format_result(result=result)
+        data = {
+            "result": result,
+            "tool_name": tool.name,
+            "tool_args": calling.arguments,
+        }
 
-        # Handle retry after finally block ensures finished event was emitted
-        if should_retry:
-            return self.use(calling=calling, tool_string=tool_string)
+        self.on_tool_use_finished(
+            tool=tool,
+            tool_calling=calling,
+            from_cache=from_cache,
+            started_at=started_at,
+            result=result,
+        )
+
+        if (
+            hasattr(available_tool, "result_as_answer")
+            and available_tool.result_as_answer  # type: ignore # Item "None" of "Any | None" has no attribute "cache_function"
+        ):
+            result_as_answer = available_tool.result_as_answer  # type: ignore # Item "None" of "Any | None" has no attribute "result_as_answer"
+            data["result_as_answer"] = result_as_answer  # type: ignore
+
+        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"):
+            available_tool.current_usage_count += 1
+            if (
+                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",
+                )
 
         return result
 
@@ -697,10 +623,9 @@ class ToolUsage:
         if not self.tools_handler:
             return False
         if last_tool_usage := self.tools_handler.last_used_tool:
-            return (
-                sanitize_tool_name(calling.tool_name)
-                == sanitize_tool_name(last_tool_usage.tool_name)
-            ) and (calling.arguments == last_tool_usage.arguments)
+            return (calling.tool_name == last_tool_usage.tool_name) and (
+                calling.arguments == last_tool_usage.arguments
+            )
         return False
 
     @staticmethod
@@ -723,19 +648,20 @@ class ToolUsage:
         return None
 
     def _select_tool(self, tool_name: str) -> Any:
-        sanitized_input = sanitize_tool_name(tool_name)
         order_tools = sorted(
             self.tools,
             key=lambda tool: SequenceMatcher(
-                None, sanitize_tool_name(tool.name), sanitized_input
+                None, tool.name.lower().strip(), tool_name.lower().strip()
             ).ratio(),
             reverse=True,
         )
         for tool in order_tools:
-            sanitized_tool = sanitize_tool_name(tool.name)
             if (
-                sanitized_tool == sanitized_input
-                or SequenceMatcher(None, sanitized_tool, sanitized_input).ratio() > 0.85
+                tool.name.lower().strip() == tool_name.lower().strip()
+                or SequenceMatcher(
+                    None, tool.name.lower().strip(), tool_name.lower().strip()
+                ).ratio()
+                > 0.85
             ):
                 return tool
         if self.task:
@@ -820,7 +746,7 @@ class ToolUsage:
             return ToolUsageError(f"{self._i18n.errors('tool_arguments_error')}")
 
         return ToolCalling(
-            tool_name=sanitize_tool_name(tool.name),
+            tool_name=tool.name,
             arguments=arguments,
         )
 
@@ -974,7 +900,7 @@ class ToolUsage:
         event_data = {
             "run_attempts": self._run_attempts,
             "delegations": self.task.delegations if self.task else 0,
-            "tool_name": sanitize_tool_name(tool.name),
+            "tool_name": tool.name,
             "tool_args": tool_calling.arguments,
             "tool_class": tool.__class__.__name__,
             "agent_key": (

lib/crewai/src/crewai/translations/en.json

@@ -11,10 +11,7 @@
     "role_playing": "You are {role}. {backstory}\nYour personal goal is: {goal}",
     "tools": "\nYou ONLY have access to the following tools, and should NEVER make up tools that are not listed here:\n\n{tools}\n\nIMPORTANT: Use the following format in your response:\n\n```\nThought: you should always think about what to do\nAction: the action to take, only one name of [{tool_names}], just the name, exactly as it's written.\nAction Input: the input to the action, just a simple JSON object, enclosed in curly braces, using \" to wrap keys and values.\nObservation: the result of the action\n```\n\nOnce all necessary information is gathered, return the following format:\n\n```\nThought: I now know the final answer\nFinal Answer: the final answer to the original input question\n```",
     "no_tools": "\nTo give my best complete final answer to the task respond using the exact following format:\n\nThought: I now can give a great answer\nFinal Answer: Your final answer must be the great and the most complete as possible, it must be outcome described.\n\nI MUST use these formats, my job depends on it!",
-    "native_tools": "\nUse available tools to gather information and complete your task.",
-    "native_task": "\nCurrent Task: {input}\n\nThis is VERY important to you, your job depends on it!",
-    "post_tool_reasoning": "Analyze the tool result. If requirements are met, provide the Final Answer. Otherwise, call the next tool. Deliver only the answer without meta-commentary.",
-    "format": "Decide if you need a tool or can provide the final answer. Use one at a time.\nTo use a tool, use:\nThought: [reasoning]\nAction: [name from {tool_names}]\nAction Input: [JSON object]\n\nTo provide the final answer, use:\nThought: [reasoning]\nFinal Answer: [complete response]",
+    "format": "I MUST either use a tool (use one at time) OR give my best final answer not both at the same time. When responding, I must use the following format:\n\n```\nThought: you should always think about what to do\nAction: the action to take, should be one of [{tool_names}]\nAction Input: the input to the action, dictionary enclosed in curly braces\nObservation: the result of the action\n```\nThis Thought/Action/Action Input/Result can repeat N times. Once I know the final answer, I must return the following format:\n\n```\nThought: I now can give a great answer\nFinal Answer: Your final answer must be the great and the most complete as possible, it must be outcome described\n\n```",
     "final_answer_format": "If you don't need to use any more tools, you must give your best complete final answer, make sure it satisfies the expected criteria, use the EXACT format below:\n\n```\nThought: I now can give a great answer\nFinal Answer: my best complete final answer to the task.\n\n```",
     "format_without_tools": "\nSorry, I didn't use the right format. I MUST either use a tool (among the available ones), OR give my best final answer.\nHere is the expected format I must follow:\n\n```\nQuestion: the input question you must answer\nThought: you should always think about what to do\nAction: the action to take, should be one of [{tool_names}]\nAction Input: the input to the action\nObservation: the result of the action\n```\n This Thought/Action/Action Input/Result process can repeat N times. Once I know the final answer, I must return the following format:\n\n```\nThought: I now can give a great answer\nFinal Answer: Your final answer must be the great and the most complete as possible, it must be outcome described\n\n```",
     "task_with_context": "{task}\n\nThis is the context you're working with:\n{context}",

lib/crewai/src/crewai/utilities/agent_utils.py

@@ -1,8 +1,8 @@
 from __future__ import annotations
 
-import asyncio
 from collections.abc import Callable, Sequence
 import json
+import logging
 import re
 from typing import TYPE_CHECKING, Any, Final, Literal, TypedDict
 
@@ -28,7 +28,6 @@ from crewai.utilities.exceptions.context_window_exceeding_exception import (
 )
 from crewai.utilities.i18n import I18N
 from crewai.utilities.printer import ColoredText, Printer
-from crewai.utilities.string_utils import sanitize_tool_name
 from crewai.utilities.token_counter_callback import TokenCalcHandler
 from crewai.utilities.types import LLMMessage
 
@@ -53,26 +52,11 @@ class SummaryContent(TypedDict):
 
 console = Console()
 
+logger = logging.getLogger(__name__)
+
 _MULTIPLE_NEWLINES: Final[re.Pattern[str]] = re.compile(r"\n+")
 
 
-def is_inside_event_loop() -> bool:
-    """Check if code is currently running inside an asyncio event loop.
-
-    This is used to detect when code is being called from within an async context
-    (e.g., inside a Flow). In such cases, callers should return a coroutine
-    instead of executing synchronously to avoid nested event loop errors.
-
-    Returns:
-        True if inside a running event loop, False otherwise.
-    """
-    try:
-        asyncio.get_running_loop()
-        return True
-    except RuntimeError:
-        return False
-
-
 def parse_tools(tools: list[BaseTool]) -> list[CrewStructuredTool]:
     """Parse tools to be used for the task.
 
@@ -97,15 +81,15 @@ def parse_tools(tools: list[BaseTool]) -> list[CrewStructuredTool]:
 
 
 def get_tool_names(tools: Sequence[CrewStructuredTool | BaseTool]) -> str:
-    """Get the sanitized names of the tools.
+    """Get the names of the tools.
 
     Args:
         tools: List of tools to get names from.
 
     Returns:
-        Comma-separated string of sanitized tool names.
+        Comma-separated string of tool names.
     """
-    return ", ".join([sanitize_tool_name(t.name) for t in tools])
+    return ", ".join([t.name for t in tools])
 
 
 def render_text_description_and_args(
@@ -127,66 +111,6 @@ def render_text_description_and_args(
     return "\n".join(tool_strings)
 
 
-def convert_tools_to_openai_schema(
-    tools: Sequence[BaseTool | CrewStructuredTool],
-) -> tuple[list[dict[str, Any]], dict[str, Callable[..., Any]]]:
-    """Convert CrewAI tools to OpenAI function calling format.
-
-    This function converts CrewAI BaseTool and CrewStructuredTool objects
-    into the OpenAI-compatible tool schema format that can be passed to
-    LLM providers for native function calling.
-
-    Args:
-        tools: List of CrewAI tool objects to convert.
-
-    Returns:
-        Tuple containing:
-        - List of OpenAI-format tool schema dictionaries
-        - Dict mapping tool names to their callable run() methods
-
-    Example:
-        >>> tools = [CalculatorTool(), SearchTool()]
-        >>> schemas, functions = convert_tools_to_openai_schema(tools)
-        >>> # schemas can be passed to llm.call(tools=schemas)
-        >>> # functions can be passed to llm.call(available_functions=functions)
-    """
-    openai_tools: list[dict[str, Any]] = []
-    available_functions: dict[str, Callable[..., Any]] = {}
-
-    for tool in tools:
-        # Get the JSON schema for tool parameters
-        parameters: dict[str, Any] = {}
-        if hasattr(tool, "args_schema") and tool.args_schema is not None:
-            try:
-                parameters = tool.args_schema.model_json_schema()
-                # Remove title and description from schema root as they're redundant
-                parameters.pop("title", None)
-                parameters.pop("description", None)
-            except Exception:
-                parameters = {}
-
-        # Extract original description from formatted description
-        # BaseTool formats description as "Tool Name: ...\nTool Arguments: ...\nTool Description: {original}"
-        description = tool.description
-        if "Tool Description:" in description:
-            description = description.split("Tool Description:")[-1].strip()
-
-        sanitized_name = sanitize_tool_name(tool.name)
-
-        schema: dict[str, Any] = {
-            "type": "function",
-            "function": {
-                "name": sanitized_name,
-                "description": description,
-                "parameters": parameters,
-            },
-        }
-        openai_tools.append(schema)
-        available_functions[sanitized_name] = tool.run  # type: ignore[union-attr]
-
-    return openai_tools, available_functions
-
-
 def has_reached_max_iterations(iterations: int, max_iterations: int) -> bool:
     """Check if the maximum number of iterations has been reached.
 
@@ -313,13 +237,11 @@ def get_llm_response(
     messages: list[LLMMessage],
     callbacks: list[TokenCalcHandler],
     printer: Printer,
-    tools: list[dict[str, Any]] | None = None,
-    available_functions: dict[str, Callable[..., Any]] | None = None,
     from_task: Task | None = None,
     from_agent: Agent | LiteAgent | None = None,
     response_model: type[BaseModel] | None = None,
     executor_context: CrewAgentExecutor | LiteAgent | None = None,
-) -> str | Any:
+) -> str:
     """Call the LLM and return the response, handling any invalid responses.
 
     Args:
@@ -327,16 +249,13 @@ def get_llm_response(
         messages: The messages to send to the LLM.
         callbacks: List of callbacks for the LLM call.
         printer: Printer instance for output.
-        tools: Optional list of tool schemas for native function calling.
-        available_functions: Optional dict mapping function names to callables.
         from_task: Optional task context for the LLM call.
         from_agent: Optional agent context for the LLM call.
         response_model: Optional Pydantic model for structured outputs.
         executor_context: Optional executor context for hook invocation.
 
     Returns:
-        The response from the LLM as a string, or tool call results if
-        native function calling is used.
+        The response from the LLM as a string.
 
     Raises:
         Exception: If an error occurs.
@@ -351,9 +270,7 @@ def get_llm_response(
     try:
         answer = llm.call(
             messages,
-            tools=tools,
             callbacks=callbacks,
-            available_functions=available_functions,
             from_task=from_task,
             from_agent=from_agent,  # type: ignore[arg-type]
             response_model=response_model,
@@ -375,13 +292,11 @@ async def aget_llm_response(
     messages: list[LLMMessage],
     callbacks: list[TokenCalcHandler],
     printer: Printer,
-    tools: list[dict[str, Any]] | None = None,
-    available_functions: dict[str, Callable[..., Any]] | None = None,
     from_task: Task | None = None,
     from_agent: Agent | LiteAgent | None = None,
     response_model: type[BaseModel] | None = None,
     executor_context: CrewAgentExecutor | None = None,
-) -> str | Any:
+) -> str:
     """Call the LLM asynchronously and return the response.
 
     Args:
@@ -389,16 +304,13 @@ async def aget_llm_response(
         messages: The messages to send to the LLM.
         callbacks: List of callbacks for the LLM call.
         printer: Printer instance for output.
-        tools: Optional list of tool schemas for native function calling.
-        available_functions: Optional dict mapping function names to callables.
         from_task: Optional task context for the LLM call.
         from_agent: Optional agent context for the LLM call.
         response_model: Optional Pydantic model for structured outputs.
         executor_context: Optional executor context for hook invocation.
 
     Returns:
-        The response from the LLM as a string, or tool call results if
-        native function calling is used.
+        The response from the LLM as a string.
 
     Raises:
         Exception: If an error occurs.
@@ -412,9 +324,7 @@ async def aget_llm_response(
     try:
         answer = await llm.acall(
             messages,
-            tools=tools,
             callbacks=callbacks,
-            available_functions=available_functions,
             from_task=from_task,
             from_agent=from_agent,  # type: ignore[arg-type]
             response_model=response_model,
@@ -523,6 +433,8 @@ def handle_output_parser_exception(
     iterations: int,
     log_error_after: int = 3,
     printer: Printer | None = None,
+    raw_output: str | None = None,
+    agent_role: str | None = None,
 ) -> AgentAction:
     """Handle OutputParserError by updating messages and formatted_answer.
 
@@ -532,6 +444,8 @@ def handle_output_parser_exception(
         iterations: Current iteration count
         log_error_after: Number of iterations after which to log errors
         printer: Optional printer instance for logging
+        raw_output: The raw LLM output that failed to parse
+        agent_role: The role of the agent for logging context
 
     Returns:
         AgentAction: A formatted answer with the error
@@ -545,6 +459,27 @@ def handle_output_parser_exception(
         thought="",
     )
 
+    retry_count = iterations + 1
+    agent_context = f" for agent '{agent_role}'" if agent_role else ""
+
+    logger.debug(
+        "Parse failed%s: %s",
+        agent_context,
+        e.error.split("\n")[0],
+    )
+
+    if raw_output is not None:
+        truncated_output = (
+            raw_output[:500] + "..." if len(raw_output) > 500 else raw_output
+        )
+        logger.debug(
+            "Raw output (truncated)%s: %s",
+            agent_context,
+            truncated_output.replace("\n", "\\n"),
+        )
+
+    logger.debug("Retry %d initiated%s", retry_count, agent_context)
+
     if iterations > log_error_after and printer:
         printer.print(
             content=f"Error parsing LLM output, agent will retry: {e.error}",
@@ -819,71 +754,6 @@ def load_agent_from_repository(from_repository: str) -> dict[str, Any]:
     return attributes
 
 
-DELEGATION_TOOL_NAMES: Final[frozenset[str]] = frozenset(
-    [
-        sanitize_tool_name("Delegate work to coworker"),
-        sanitize_tool_name("Ask question to coworker"),
-    ]
-)
-
-
-# native tool calling tracking for delegation
-def track_delegation_if_needed(
-    tool_name: str,
-    tool_args: dict[str, Any],
-    task: Task | None,
-) -> None:
-    """Track delegation if the tool is a delegation tool.
-
-    Args:
-        tool_name: Name of the tool being executed.
-        tool_args: Arguments passed to the tool.
-        task: The task being executed (used to track delegations).
-    """
-    if sanitize_tool_name(tool_name) in DELEGATION_TOOL_NAMES and task is not None:
-        coworker = tool_args.get("coworker")
-        task.increment_delegations(coworker)
-
-
-def extract_tool_call_info(
-    tool_call: Any,
-) -> tuple[str, str, dict[str, Any] | str] | None:
-    """Extract tool call ID, name, and arguments from various provider formats.
-
-    Args:
-        tool_call: The tool call object to extract info from.
-
-    Returns:
-        Tuple of (call_id, func_name, func_args) or None if format is unrecognized.
-    """
-    if hasattr(tool_call, "function"):
-        # OpenAI-style: has .function.name and .function.arguments
-        call_id = getattr(tool_call, "id", f"call_{id(tool_call)}")
-        return call_id, sanitize_tool_name(tool_call.function.name), tool_call.function.arguments
-    if hasattr(tool_call, "function_call") and tool_call.function_call:
-        # Gemini-style: has .function_call.name and .function_call.args
-        call_id = f"call_{id(tool_call)}"
-        return (
-            call_id,
-            sanitize_tool_name(tool_call.function_call.name),
-            dict(tool_call.function_call.args) if tool_call.function_call.args else {},
-        )
-    if hasattr(tool_call, "name") and hasattr(tool_call, "input"):
-        # Anthropic format: has .name and .input (ToolUseBlock)
-        call_id = getattr(tool_call, "id", f"call_{id(tool_call)}")
-        return call_id, sanitize_tool_name(tool_call.name), tool_call.input
-    if isinstance(tool_call, dict):
-        # Support OpenAI "id", Bedrock "toolUseId", or generate one
-        call_id = (
-            tool_call.get("id") or tool_call.get("toolUseId") or f"call_{id(tool_call)}"
-        )
-        func_info = tool_call.get("function", {})
-        func_name = func_info.get("name", "") or tool_call.get("name", "")
-        func_args = func_info.get("arguments", "{}") or tool_call.get("input", {})
-        return call_id, sanitize_tool_name(func_name), func_args
-    return None
-
-
 def _setup_before_llm_call_hooks(
     executor_context: CrewAgentExecutor | LiteAgent | None, printer: Printer
 ) -> bool:

lib/crewai/src/crewai/utilities/prompts.py

@@ -22,9 +22,7 @@ class SystemPromptResult(StandardPromptResult):
     user: Annotated[str, "The user prompt component"]
 
 
-COMPONENTS = Literal[
-    "role_playing", "tools", "no_tools", "native_tools", "task", "native_task"
-]
+COMPONENTS = Literal["role_playing", "tools", "no_tools", "task"]
 
 
 class Prompts(BaseModel):
@@ -38,10 +36,6 @@ class Prompts(BaseModel):
     has_tools: bool = Field(
         default=False, description="Indicates if the agent has access to tools"
     )
-    use_native_tool_calling: bool = Field(
-        default=False,
-        description="Whether to use native function calling instead of ReAct format",
-    )
     system_template: str | None = Field(
         default=None, description="Custom system prompt template"
     )
@@ -64,22 +58,12 @@ class Prompts(BaseModel):
             A dictionary containing the constructed prompt(s).
         """
         slices: list[COMPONENTS] = ["role_playing"]
-        # When using native tool calling with tools, use native_tools instructions
-        # When using ReAct pattern with tools, use tools instructions
-        # When no tools are available, use no_tools instructions
         if self.has_tools:
-            if not self.use_native_tool_calling:
-                slices.append("tools")
+            slices.append("tools")
         else:
             slices.append("no_tools")
         system: str = self._build_prompt(slices)
-
-        # Use native_task for native tool calling (no "Thought:" prompt)
-        # Use task for ReAct pattern (includes "Thought:" prompt)
-        task_slice: COMPONENTS = (
-            "native_task" if self.use_native_tool_calling else "task"
-        )
-        slices.append(task_slice)
+        slices.append("task")
 
         if (
             not self.system_template
@@ -88,7 +72,7 @@ class Prompts(BaseModel):
         ):
             return SystemPromptResult(
                 system=system,
-                user=self._build_prompt([task_slice]),
+                user=self._build_prompt(["task"]),
                 prompt=self._build_prompt(slices),
             )
         return StandardPromptResult(

lib/crewai/src/crewai/utilities/reasoning_handler.py

@@ -13,7 +13,6 @@ from crewai.events.types.reasoning_events import (
 )
 from crewai.llm import LLM
 from crewai.task import Task
-from crewai.utilities.string_utils import sanitize_tool_name
 
 
 class ReasoningPlan(BaseModel):
@@ -341,9 +340,7 @@ class AgentReasoning:
             str: Comma-separated list of tool names.
         """
         try:
-            return ", ".join(
-                [sanitize_tool_name(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"
 

lib/crewai/src/crewai/utilities/serialization.py

@@ -66,23 +66,11 @@ def to_serializable(
             if key not in exclude
         }
     if isinstance(obj, BaseModel):
-        try:
-            return to_serializable(
-                obj=obj.model_dump(exclude=exclude),
-                max_depth=max_depth,
-                _current_depth=_current_depth + 1,
-            )
-        except Exception:
-            try:
-                return {
-                    _to_serializable_key(k): to_serializable(
-                        v, max_depth=max_depth, _current_depth=_current_depth + 1
-                    )
-                    for k, v in obj.__dict__.items()
-                    if k not in (exclude or set())
-                }
-            except Exception:
-                return repr(obj)
+        return to_serializable(
+            obj=obj.model_dump(exclude=exclude),
+            max_depth=max_depth,
+            _current_depth=_current_depth + 1,
+        )
     return repr(obj)
 
 

lib/crewai/src/crewai/utilities/streaming.py

@@ -18,7 +18,6 @@ from crewai.types.streaming import (
     StreamChunkType,
     ToolCallChunk,
 )
-from crewai.utilities.string_utils import sanitize_tool_name
 
 
 class TaskInfo(TypedDict):
@@ -59,7 +58,7 @@ def _extract_tool_call_info(
             StreamChunkType.TOOL_CALL,
             ToolCallChunk(
                 tool_id=event.tool_call.id,
-                tool_name=sanitize_tool_name(event.tool_call.function.name),
+                tool_name=event.tool_call.function.name,
                 arguments=event.tool_call.function.arguments,
                 index=event.tool_call.index,
             ),

lib/crewai/src/crewai/utilities/string_utils.py

@@ -1,48 +1,8 @@
-# sanitize_tool_name adapted from python-slugify by Val Neekman
-# https://github.com/un33k/python-slugify
-# MIT License
-
 import re
 from typing import Any, Final
-import unicodedata
 
 
 _VARIABLE_PATTERN: Final[re.Pattern[str]] = re.compile(r"\{([A-Za-z_][A-Za-z0-9_\-]*)}")
-_QUOTE_PATTERN: Final[re.Pattern[str]] = re.compile(r"[\'\"]+")
-_CAMEL_LOWER_UPPER: Final[re.Pattern[str]] = re.compile(r"([a-z])([A-Z])")
-_CAMEL_UPPER_LOWER: Final[re.Pattern[str]] = re.compile(r"([A-Z]+)([A-Z][a-z])")
-_DISALLOWED_CHARS_PATTERN: Final[re.Pattern[str]] = re.compile(r"[^a-zA-Z0-9]+")
-_DUPLICATE_UNDERSCORE_PATTERN: Final[re.Pattern[str]] = re.compile(r"_+")
-_MAX_TOOL_NAME_LENGTH: Final[int] = 64
-
-
-def sanitize_tool_name(name: str, max_length: int = _MAX_TOOL_NAME_LENGTH) -> str:
-    """Sanitize tool name for LLM provider compatibility.
-
-    Normalizes Unicode, splits camelCase, lowercases, replaces invalid characters
-    with underscores, and truncates to max_length. Conforms to OpenAI/Bedrock requirements.
-
-    Args:
-        name: Original tool name.
-        max_length: Maximum allowed length (default 64 per OpenAI/Bedrock limits).
-
-    Returns:
-        Sanitized tool name (lowercase, a-z0-9_ only, max 64 chars).
-    """
-    name = unicodedata.normalize("NFKD", name)
-    name = name.encode("ascii", "ignore").decode("ascii")
-    name = _CAMEL_UPPER_LOWER.sub(r"\1_\2", name)
-    name = _CAMEL_LOWER_UPPER.sub(r"\1_\2", name)
-    name = name.lower()
-    name = _QUOTE_PATTERN.sub("", name)
-    name = _DISALLOWED_CHARS_PATTERN.sub("_", name)
-    name = _DUPLICATE_UNDERSCORE_PATTERN.sub("_", name)
-    name = name.strip("_")
-
-    if len(name) > max_length:
-        name = name[:max_length].rstrip("_")
-
-    return name
 
 
 def interpolate_only(

lib/crewai/src/crewai/utilities/tool_utils.py

@@ -15,7 +15,6 @@ from crewai.tools.tool_types import ToolResult
 from crewai.tools.tool_usage import ToolUsage, ToolUsageError
 from crewai.utilities.i18n import I18N
 from crewai.utilities.logger import Logger
-from crewai.utilities.string_utils import sanitize_tool_name
 
 
 if TYPE_CHECKING:
@@ -64,7 +63,7 @@ async def aexecute_tool_and_check_finality(
         treated as a final answer.
     """
     logger = Logger(verbose=crew.verbose if crew else False)
-    tool_name_to_tool_map = {sanitize_tool_name(tool.name): tool for tool in tools}
+    tool_name_to_tool_map = {tool.name: tool for tool in tools}
 
     if agent_key and agent_role and agent:
         fingerprint_context = fingerprint_context or {}
@@ -91,9 +90,19 @@ async def aexecute_tool_and_check_finality(
     if isinstance(tool_calling, ToolUsageError):
         return ToolResult(tool_calling.message, False)
 
-    sanitized_tool_name = sanitize_tool_name(tool_calling.tool_name)
-    tool = tool_name_to_tool_map.get(sanitized_tool_name)
-    if tool:
+    if tool_calling.tool_name.casefold().strip() in [
+        name.casefold().strip() for name in tool_name_to_tool_map
+    ] or tool_calling.tool_name.casefold().replace("_", " ") in [
+        name.casefold().strip() for name in tool_name_to_tool_map
+    ]:
+        tool = tool_name_to_tool_map.get(tool_calling.tool_name)
+        if not tool:
+            tool_result = i18n.errors("wrong_tool_name").format(
+                tool=tool_calling.tool_name,
+                tools=", ".join([t.name.casefold() for t in tools]),
+            )
+            return ToolResult(result=tool_result, result_as_answer=False)
+
         tool_input = tool_calling.arguments if tool_calling.arguments else {}
         hook_context = ToolCallHookContext(
             tool_name=tool_calling.tool_name,
@@ -143,8 +152,8 @@ async def aexecute_tool_and_check_finality(
         return ToolResult(modified_result, tool.result_as_answer)
 
     tool_result = i18n.errors("wrong_tool_name").format(
-        tool=sanitized_tool_name,
-        tools=", ".join(tool_name_to_tool_map.keys()),
+        tool=tool_calling.tool_name,
+        tools=", ".join([tool.name.casefold() for tool in tools]),
     )
     return ToolResult(result=tool_result, result_as_answer=False)
 
@@ -184,7 +193,7 @@ def execute_tool_and_check_finality(
         ToolResult containing the execution result and whether it should be treated as a final answer
     """
     logger = Logger(verbose=crew.verbose if crew else False)
-    tool_name_to_tool_map = {sanitize_tool_name(tool.name): tool for tool in tools}
+    tool_name_to_tool_map = {tool.name: tool for tool in tools}
 
     if agent_key and agent_role and agent:
         fingerprint_context = fingerprint_context or {}
@@ -197,6 +206,7 @@ def execute_tool_and_check_finality(
                     except Exception as e:
                         raise ValueError(f"Failed to set fingerprint: {e}") from e
 
+    # Create tool usage instance
     tool_usage = ToolUsage(
         tools_handler=tools_handler,
         tools=tools,
@@ -206,14 +216,26 @@ def execute_tool_and_check_finality(
         action=agent_action,
     )
 
+    # Parse tool calling
     tool_calling = tool_usage.parse_tool_calling(agent_action.text)
 
     if isinstance(tool_calling, ToolUsageError):
         return ToolResult(tool_calling.message, False)
 
-    sanitized_tool_name = sanitize_tool_name(tool_calling.tool_name)
-    tool = tool_name_to_tool_map.get(sanitized_tool_name)
-    if tool:
+    # Check if tool name matches
+    if tool_calling.tool_name.casefold().strip() in [
+        name.casefold().strip() for name in tool_name_to_tool_map
+    ] or tool_calling.tool_name.casefold().replace("_", " ") in [
+        name.casefold().strip() for name in tool_name_to_tool_map
+    ]:
+        tool = tool_name_to_tool_map.get(tool_calling.tool_name)
+        if not tool:
+            tool_result = i18n.errors("wrong_tool_name").format(
+                tool=tool_calling.tool_name,
+                tools=", ".join([t.name.casefold() for t in tools]),
+            )
+            return ToolResult(result=tool_result, result_as_answer=False)
+
         tool_input = tool_calling.arguments if tool_calling.arguments else {}
         hook_context = ToolCallHookContext(
             tool_name=tool_calling.tool_name,
@@ -263,8 +285,9 @@ def execute_tool_and_check_finality(
 
         return ToolResult(modified_result, tool.result_as_answer)
 
+    # Handle invalid tool name
     tool_result = i18n.errors("wrong_tool_name").format(
-        tool=sanitized_tool_name,
-        tools=", ".join(tool_name_to_tool_map.keys()),
+        tool=tool_calling.tool_name,
+        tools=", ".join([tool.name.casefold() for tool in tools]),
     )
     return ToolResult(result=tool_result, result_as_answer=False)

lib/crewai/src/crewai/utilities/types.py

@@ -2,7 +2,7 @@
 
 from typing import Any, Literal
 
-from typing_extensions import NotRequired, TypedDict
+from typing_extensions import TypedDict
 
 
 class LLMMessage(TypedDict):
@@ -13,8 +13,5 @@ class LLMMessage(TypedDict):
           instead of str | list[dict[str, str]]
     """
 
-    role: Literal["user", "assistant", "system", "tool"]
-    content: str | list[dict[str, Any]] | None
-    tool_call_id: NotRequired[str]
-    name: NotRequired[str]
-    tool_calls: NotRequired[list[dict[str, Any]]]
+    role: Literal["user", "assistant", "system"]
+    content: str | list[dict[str, Any]]

lib/crewai/tests/a2a/utils/test_task.py

@@ -26,13 +26,9 @@ def mock_agent() -> MagicMock:
 
 
 @pytest.fixture
-def mock_task(mock_context: MagicMock) -> MagicMock:
+def mock_task() -> MagicMock:
     """Create a mock Task."""
-    task = MagicMock()
-    task.id = mock_context.task_id
-    task.name = "Mock Task"
-    task.description = "Mock task description"
-    return task
+    return MagicMock()
 
 
 @pytest.fixture
@@ -183,8 +179,8 @@ class TestExecute:
         event = first_call[0][1]
 
         assert event.type == "a2a_server_task_started"
-        assert event.task_id == mock_context.task_id
-        assert event.context_id == mock_context.context_id
+        assert event.a2a_task_id == mock_context.task_id
+        assert event.a2a_context_id == mock_context.context_id
 
     @pytest.mark.asyncio
     async def test_emits_completed_event(
@@ -205,7 +201,7 @@ class TestExecute:
         event = second_call[0][1]
 
         assert event.type == "a2a_server_task_completed"
-        assert event.task_id == mock_context.task_id
+        assert event.a2a_task_id == mock_context.task_id
         assert event.result == "Task completed successfully"
 
     @pytest.mark.asyncio
@@ -254,7 +250,7 @@ class TestExecute:
         event = canceled_call[0][1]
 
         assert event.type == "a2a_server_task_canceled"
-        assert event.task_id == mock_context.task_id
+        assert event.a2a_task_id == mock_context.task_id
 
 
 class TestCancel:

lib/crewai/tests/agents/agent_adapters/test_base_tool_adapter.py

@@ -71,12 +71,12 @@ def test_tools_method_empty():
 
 def test_sanitize_tool_name_with_spaces():
     adapter = ConcreteToolAdapter()
-    assert adapter.sanitize_tool_name("Tool With Spaces") == "tool_with_spaces"
+    assert adapter.sanitize_tool_name("Tool With Spaces") == "Tool_With_Spaces"
 
 
 def test_sanitize_tool_name_without_spaces():
     adapter = ConcreteToolAdapter()
-    assert adapter.sanitize_tool_name("ToolWithoutSpaces") == "tool_without_spaces"
+    assert adapter.sanitize_tool_name("ToolWithoutSpaces") == "ToolWithoutSpaces"
 
 
 def test_sanitize_tool_name_empty():

lib/crewai/tests/agents/test_a2a_trust_completion_status.py

@@ -14,16 +14,6 @@ except ImportError:
     A2A_SDK_INSTALLED = False
 
 
-def _create_mock_agent_card(name: str = "Test", url: str = "http://test-endpoint.com/"):
-    """Create a mock agent card with proper model_dump behavior."""
-    mock_card = MagicMock()
-    mock_card.name = name
-    mock_card.url = url
-    mock_card.model_dump.return_value = {"name": name, "url": url}
-    mock_card.model_dump_json.return_value = f'{{"name": "{name}", "url": "{url}"}}'
-    return mock_card
-
-
 @pytest.mark.skipif(not A2A_SDK_INSTALLED, reason="Requires a2a-sdk to be installed")
 def test_trust_remote_completion_status_true_returns_directly():
     """When trust_remote_completion_status=True and A2A returns completed, return result directly."""
@@ -54,7 +44,8 @@ def test_trust_remote_completion_status_true_returns_directly():
         patch("crewai.a2a.wrapper.execute_a2a_delegation") as mock_execute,
         patch("crewai.a2a.wrapper._fetch_agent_cards_concurrently") as mock_fetch,
     ):
-        mock_card = _create_mock_agent_card()
+        mock_card = MagicMock()
+        mock_card.name = "Test"
         mock_fetch.return_value = ({"http://test-endpoint.com/": mock_card}, {})
 
         # A2A returns completed
@@ -119,7 +110,8 @@ def test_trust_remote_completion_status_false_continues_conversation():
         patch("crewai.a2a.wrapper.execute_a2a_delegation") as mock_execute,
         patch("crewai.a2a.wrapper._fetch_agent_cards_concurrently") as mock_fetch,
     ):
-        mock_card = _create_mock_agent_card()
+        mock_card = MagicMock()
+        mock_card.name = "Test"
         mock_fetch.return_value = ({"http://test-endpoint.com/": mock_card}, {})
 
         # A2A returns completed

lib/crewai/tests/agents/test_agent.py

@@ -211,6 +211,120 @@ def test_agent_execution_with_tools():
     assert received_events[0].tool_args == {"first_number": 3, "second_number": 4}
 
 
+@pytest.mark.vcr()
+def test_logging_tool_usage():
+    @tool
+    def multiplier(first_number: int, second_number: int) -> float:
+        """Useful for when you need to multiply two numbers together."""
+        return first_number * second_number
+
+    agent = Agent(
+        role="test role",
+        goal="test goal",
+        backstory="test backstory",
+        tools=[multiplier],
+        verbose=True,
+    )
+
+    assert agent.llm.model == DEFAULT_LLM_MODEL
+    assert agent.tools_handler.last_used_tool is None
+    task = Task(
+        description="What is 3 times 4?",
+        agent=agent,
+        expected_output="The result of the multiplication.",
+    )
+    # force cleaning cache
+    agent.tools_handler.cache = CacheHandler()
+    output = agent.execute_task(task)
+    tool_usage = InstructorToolCalling(
+        tool_name=multiplier.name, arguments={"first_number": 3, "second_number": 4}
+    )
+
+    assert output == "12"
+    assert agent.tools_handler.last_used_tool.tool_name == tool_usage.tool_name
+    assert agent.tools_handler.last_used_tool.arguments == tool_usage.arguments
+
+
+@pytest.mark.vcr()
+def test_cache_hitting():
+    @tool
+    def multiplier(first_number: int, second_number: int) -> float:
+        """Useful for when you need to multiply two numbers together."""
+        return first_number * second_number
+
+    cache_handler = CacheHandler()
+
+    agent = Agent(
+        role="test role",
+        goal="test goal",
+        backstory="test backstory",
+        tools=[multiplier],
+        allow_delegation=False,
+        cache_handler=cache_handler,
+        verbose=True,
+    )
+
+    task1 = Task(
+        description="What is 2 times 6?",
+        agent=agent,
+        expected_output="The result of the multiplication.",
+    )
+    task2 = Task(
+        description="What is 3 times 3?",
+        agent=agent,
+        expected_output="The result of the multiplication.",
+    )
+
+    output = agent.execute_task(task1)
+    output = agent.execute_task(task2)
+    assert cache_handler._cache == {
+        'multiplier-{"first_number": 2, "second_number": 6}': 12,
+        'multiplier-{"first_number": 3, "second_number": 3}': 9,
+    }
+
+    task = Task(
+        description="What is 2 times 6 times 3? Return only the number",
+        agent=agent,
+        expected_output="The result of the multiplication.",
+    )
+    output = agent.execute_task(task)
+    assert output == "36"
+
+    assert cache_handler._cache == {
+        'multiplier-{"first_number": 2, "second_number": 6}': 12,
+        'multiplier-{"first_number": 3, "second_number": 3}': 9,
+        'multiplier-{"first_number": 12, "second_number": 3}': 36,
+    }
+    received_events = []
+    condition = threading.Condition()
+    event_handled = False
+
+    @crewai_event_bus.on(ToolUsageFinishedEvent)
+    def handle_tool_end(source, event):
+        nonlocal event_handled
+        received_events.append(event)
+        with condition:
+            event_handled = True
+            condition.notify()
+
+    task = Task(
+        description="What is 2 times 6? Return only the result of the multiplication.",
+        agent=agent,
+        expected_output="The result of the multiplication.",
+    )
+    output = agent.execute_task(task)
+    assert output == "12"
+
+    with condition:
+        if not event_handled:
+            condition.wait(timeout=5)
+    assert event_handled, "Timeout waiting for tool usage event"
+    assert len(received_events) == 1
+    assert isinstance(received_events[0], ToolUsageFinishedEvent)
+    assert received_events[0].from_cache
+    assert received_events[0].output == "12"
+
+
 @pytest.mark.vcr()
 def test_disabling_cache_for_agent():
     @tool
@@ -347,8 +461,7 @@ def test_agent_powered_by_new_o_model_family_that_uses_tool():
         expected_output="The number of customers",
     )
     output = agent.execute_task(task=task, tools=[comapny_customer_data])
-    # The tool returns "The company has 42 customers", agent may return full response or extract number
-    assert "42" in output
+    assert output == "42"
 
 
 @pytest.mark.vcr()
@@ -433,6 +546,98 @@ def test_agent_max_iterations_stops_loop():
     )
 
 
+@pytest.mark.vcr()
+def test_agent_repeated_tool_usage(capsys):
+    """Test that agents handle repeated tool usage appropriately.
+
+    Notes:
+        Investigate whether to pin down the specific execution flow by examining
+        src/crewai/agents/crew_agent_executor.py:177-186 (max iterations check)
+        and src/crewai/tools/tool_usage.py:152-157 (repeated usage detection)
+        to ensure deterministic behavior.
+    """
+
+    @tool
+    def get_final_answer() -> float:
+        """Get the final answer but don't give it yet, just re-use this tool non-stop."""
+        return 42
+
+    agent = Agent(
+        role="test role",
+        goal="test goal",
+        backstory="test backstory",
+        max_iter=4,
+        llm="gpt-4",
+        allow_delegation=False,
+        verbose=True,
+    )
+
+    task = Task(
+        description="The final answer is 42. But don't give it until I tell you so, instead keep using the `get_final_answer` tool.",
+        expected_output="The final answer, don't give it until I tell you so",
+    )
+    # force cleaning cache
+    agent.tools_handler.cache = CacheHandler()
+    agent.execute_task(
+        task=task,
+        tools=[get_final_answer],
+    )
+
+    captured = capsys.readouterr()
+    output_lower = captured.out.lower()
+
+    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), (
+        f"Expected repeated tool usage handling or proper max iteration handling. Output was: {captured.out[:500]}..."
+    )
+
+
+@pytest.mark.vcr()
+def test_agent_repeated_tool_usage_check_even_with_disabled_cache(capsys):
+    @tool
+    def get_final_answer(anything: str) -> float:
+        """Get the final answer but don't give it yet, just re-use this
+        tool non-stop."""
+        return 42
+
+    agent = Agent(
+        role="test role",
+        goal="test goal",
+        backstory="test backstory",
+        max_iter=4,
+        llm="gpt-4",
+        allow_delegation=False,
+        verbose=True,
+        cache=False,
+    )
+
+    task = Task(
+        description="The final answer is 42. But don't give it until I tell you so, instead keep using the `get_final_answer` tool.",
+        expected_output="The final answer, don't give it until I tell you so",
+    )
+
+    agent.execute_task(
+        task=task,
+        tools=[get_final_answer],
+    )
+
+    captured = capsys.readouterr()
+
+    # More flexible check, look for either the repeated usage message or verification that max iterations was reached
+    output_lower = captured.out.lower()
+
+    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), (
+        f"Expected repeated tool usage handling or proper max iteration handling. Output was: {captured.out[:500]}..."
+    )
+
+
 @pytest.mark.vcr()
 def test_agent_moved_on_after_max_iterations():
     @tool
@@ -591,6 +796,84 @@ def test_agent_without_max_rpm_respects_crew_rpm(capsys):
         assert moveon.called
 
 
+@pytest.mark.vcr()
+def test_agent_error_on_parsing_tool(capsys):
+    from unittest.mock import patch
+
+    from crewai.tools import tool
+
+    @tool
+    def get_final_answer() -> float:
+        """Get the final answer but don't give it yet, just re-use this
+        tool non-stop."""
+        return 42
+
+    agent1 = Agent(
+        role="test role",
+        goal="test goal",
+        backstory="test backstory",
+        max_iter=1,
+        verbose=True,
+    )
+    tasks = [
+        Task(
+            description="Use the get_final_answer tool.",
+            expected_output="The final answer",
+            agent=agent1,
+            tools=[get_final_answer],
+        )
+    ]
+
+    crew = Crew(
+        agents=[agent1],
+        tasks=tasks,
+        verbose=True,
+        function_calling_llm="gpt-4o",
+    )
+    with patch.object(ToolUsage, "_original_tool_calling") as force_exception_1:
+        force_exception_1.side_effect = Exception("Error on parsing tool.")
+        with patch.object(ToolUsage, "_render") as force_exception_2:
+            force_exception_2.side_effect = Exception("Error on parsing tool.")
+            crew.kickoff()
+    captured = capsys.readouterr()
+    assert "Error on parsing tool." in captured.out
+
+
+@pytest.mark.vcr()
+def test_agent_remembers_output_format_after_using_tools_too_many_times():
+    from unittest.mock import patch
+
+    from crewai.tools import tool
+
+    @tool
+    def get_final_answer() -> float:
+        """Get the final answer but don't give it yet, just re-use this
+        tool non-stop."""
+        return 42
+
+    agent1 = Agent(
+        role="test role",
+        goal="test goal",
+        backstory="test backstory",
+        max_iter=6,
+        verbose=True,
+    )
+    tasks = [
+        Task(
+            description="Use tool logic for `get_final_answer` but fon't give you final answer yet, instead keep using it unless you're told to give your final answer",
+            expected_output="The final answer",
+            agent=agent1,
+            tools=[get_final_answer],
+        )
+    ]
+
+    crew = Crew(agents=[agent1], tasks=tasks, verbose=True)
+
+    with patch.object(ToolUsage, "_remember_format") as remember_format:
+        crew.kickoff()
+        remember_format.assert_called()
+
+
 @pytest.mark.vcr()
 def test_agent_use_specific_tasks_output_as_context(capsys):
     agent1 = Agent(role="test role", goal="test goal", backstory="test backstory")
@@ -653,7 +936,53 @@ def test_agent_step_callback():
 
 
 @pytest.mark.vcr()
-@pytest.mark.skip(reason="result_as_answer feature not yet implemented in native tool calling path")
+def test_agent_function_calling_llm():
+    from crewai.llm import LLM
+    llm = LLM(model="gpt-4o", is_litellm=True)
+
+    @tool
+    def learn_about_ai() -> str:
+        """Useful for when you need to learn about AI to write an paragraph about it."""
+        return "AI is a very broad field."
+
+    agent1 = Agent(
+        role="test role",
+        goal="test goal",
+        backstory="test backstory",
+        tools=[learn_about_ai],
+        llm="gpt-4o",
+        max_iter=2,
+        function_calling_llm=llm,
+    )
+
+    essay = Task(
+        description="Write and then review an small paragraph on AI until it's AMAZING",
+        expected_output="The final paragraph.",
+        agent=agent1,
+    )
+    tasks = [essay]
+    crew = Crew(agents=[agent1], tasks=tasks)
+    from unittest.mock import patch
+
+    from crewai.tools.tool_usage import ToolUsage
+    import instructor
+
+    with (
+        patch.object(
+            instructor, "from_litellm", wraps=instructor.from_litellm
+        ) as mock_from_litellm,
+        patch.object(
+            ToolUsage,
+            "_original_tool_calling",
+            side_effect=Exception("Forced exception"),
+        ) as mock_original_tool_calling,
+    ):
+        crew.kickoff()
+        mock_from_litellm.assert_called()
+        mock_original_tool_calling.assert_called()
+
+
+@pytest.mark.vcr()
 def test_tool_result_as_answer_is_the_final_answer_for_the_agent():
     from crewai.tools import BaseTool
 
@@ -683,6 +1012,43 @@ def test_tool_result_as_answer_is_the_final_answer_for_the_agent():
     assert result.raw == "Howdy!"
 
 
+@pytest.mark.vcr()
+def test_tool_usage_information_is_appended_to_agent():
+    from crewai.tools import BaseTool
+
+    class MyCustomTool(BaseTool):
+        name: str = "Decide Greetings"
+        description: str = "Decide what is the appropriate greeting to use"
+
+        def _run(self) -> str:
+            return "Howdy!"
+
+    agent1 = Agent(
+        role="Friendly Neighbor",
+        goal="Make everyone feel welcome",
+        backstory="You are the friendly neighbor",
+        tools=[MyCustomTool(result_as_answer=True)],
+    )
+
+    greeting = Task(
+        description="Say an appropriate greeting.",
+        expected_output="The greeting.",
+        agent=agent1,
+    )
+    tasks = [greeting]
+    crew = Crew(agents=[agent1], tasks=tasks)
+
+    crew.kickoff()
+    assert agent1.tools_results == [
+        {
+            "result": "Howdy!",
+            "tool_name": "Decide Greetings",
+            "tool_args": {},
+            "result_as_answer": True,
+        }
+    ]
+
+
 def test_agent_definition_based_on_dict():
     config = {
         "role": "test role",

lib/crewai/tests/agents/test_agent_reasoning.py

@@ -166,7 +166,6 @@ def test_agent_reasoning_error_handling():
     assert call_count[0] > 2  # Ensure we called the mock multiple times
 
 
-@pytest.mark.skip(reason="Test requires updates for native tool calling changes")
 def test_agent_with_function_calling():
     """Test agent with reasoning using function calling."""
     llm = LLM("gpt-3.5-turbo")
@@ -204,7 +203,6 @@ def test_agent_with_function_calling():
     assert "I'll solve this simple math problem: 2+2=4." in task.description
 
 
-@pytest.mark.skip(reason="Test requires updates for native tool calling changes")
 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")

lib/crewai/tests/agents/test_crew_agent_executor_flow.py

@@ -1,4 +1,4 @@
-"""Unit tests for AgentExecutor.
+"""Unit tests for CrewAgentExecutorFlow.
 
 Tests the Flow-based agent executor implementation including state management,
 flow methods, routing logic, and error handling.
@@ -8,9 +8,9 @@ from unittest.mock import Mock, patch
 
 import pytest
 
-from crewai.experimental.agent_executor import (
+from crewai.experimental.crew_agent_executor_flow import (
     AgentReActState,
-    AgentExecutor,
+    CrewAgentExecutorFlow,
 )
 from crewai.agents.parser import AgentAction, AgentFinish
 
@@ -43,8 +43,8 @@ class TestAgentReActState:
         assert state.ask_for_human_input is True
 
 
-class TestAgentExecutor:
-    """Test AgentExecutor class."""
+class TestCrewAgentExecutorFlow:
+    """Test CrewAgentExecutorFlow class."""
 
     @pytest.fixture
     def mock_dependencies(self):
@@ -87,8 +87,8 @@ class TestAgentExecutor:
         }
 
     def test_executor_initialization(self, mock_dependencies):
-        """Test AgentExecutor initialization."""
-        executor = AgentExecutor(**mock_dependencies)
+        """Test CrewAgentExecutorFlow initialization."""
+        executor = CrewAgentExecutorFlow(**mock_dependencies)
 
         assert executor.llm == mock_dependencies["llm"]
         assert executor.task == mock_dependencies["task"]
@@ -100,9 +100,9 @@ class TestAgentExecutor:
     def test_initialize_reasoning(self, mock_dependencies):
         """Test flow entry point."""
         with patch.object(
-            AgentExecutor, "_show_start_logs"
+            CrewAgentExecutorFlow, "_show_start_logs"
         ) as mock_show_start:
-            executor = AgentExecutor(**mock_dependencies)
+            executor = CrewAgentExecutorFlow(**mock_dependencies)
             result = executor.initialize_reasoning()
 
             assert result == "initialized"
@@ -110,7 +110,7 @@ class TestAgentExecutor:
 
     def test_check_max_iterations_not_reached(self, mock_dependencies):
         """Test routing when iterations < max."""
-        executor = AgentExecutor(**mock_dependencies)
+        executor = CrewAgentExecutorFlow(**mock_dependencies)
         executor.state.iterations = 5
 
         result = executor.check_max_iterations()
@@ -118,7 +118,7 @@ class TestAgentExecutor:
 
     def test_check_max_iterations_reached(self, mock_dependencies):
         """Test routing when iterations >= max."""
-        executor = AgentExecutor(**mock_dependencies)
+        executor = CrewAgentExecutorFlow(**mock_dependencies)
         executor.state.iterations = 10
 
         result = executor.check_max_iterations()
@@ -126,7 +126,7 @@ class TestAgentExecutor:
 
     def test_route_by_answer_type_action(self, mock_dependencies):
         """Test routing for AgentAction."""
-        executor = AgentExecutor(**mock_dependencies)
+        executor = CrewAgentExecutorFlow(**mock_dependencies)
         executor.state.current_answer = AgentAction(
             thought="thinking", tool="search", tool_input="query", text="action text"
         )
@@ -136,7 +136,7 @@ class TestAgentExecutor:
 
     def test_route_by_answer_type_finish(self, mock_dependencies):
         """Test routing for AgentFinish."""
-        executor = AgentExecutor(**mock_dependencies)
+        executor = CrewAgentExecutorFlow(**mock_dependencies)
         executor.state.current_answer = AgentFinish(
             thought="final thoughts", output="Final answer", text="complete"
         )
@@ -146,7 +146,7 @@ class TestAgentExecutor:
 
     def test_continue_iteration(self, mock_dependencies):
         """Test iteration continuation."""
-        executor = AgentExecutor(**mock_dependencies)
+        executor = CrewAgentExecutorFlow(**mock_dependencies)
 
         result = executor.continue_iteration()
 
@@ -154,8 +154,8 @@ class TestAgentExecutor:
 
     def test_finalize_success(self, mock_dependencies):
         """Test finalize with valid AgentFinish."""
-        with patch.object(AgentExecutor, "_show_logs") as mock_show_logs:
-            executor = AgentExecutor(**mock_dependencies)
+        with patch.object(CrewAgentExecutorFlow, "_show_logs") as mock_show_logs:
+            executor = CrewAgentExecutorFlow(**mock_dependencies)
             executor.state.current_answer = AgentFinish(
                 thought="final thinking", output="Done", text="complete"
             )
@@ -168,7 +168,7 @@ class TestAgentExecutor:
 
     def test_finalize_failure(self, mock_dependencies):
         """Test finalize skips when given AgentAction instead of AgentFinish."""
-        executor = AgentExecutor(**mock_dependencies)
+        executor = CrewAgentExecutorFlow(**mock_dependencies)
         executor.state.current_answer = AgentAction(
             thought="thinking", tool="search", tool_input="query", text="action text"
         )
@@ -181,7 +181,7 @@ class TestAgentExecutor:
 
     def test_format_prompt(self, mock_dependencies):
         """Test prompt formatting."""
-        executor = AgentExecutor(**mock_dependencies)
+        executor = CrewAgentExecutorFlow(**mock_dependencies)
         inputs = {"input": "test input", "tool_names": "tool1, tool2", "tools": "desc"}
 
         result = executor._format_prompt("Prompt {input} {tool_names} {tools}", inputs)
@@ -192,18 +192,18 @@ class TestAgentExecutor:
 
     def test_is_training_mode_false(self, mock_dependencies):
         """Test training mode detection when not in training."""
-        executor = AgentExecutor(**mock_dependencies)
+        executor = CrewAgentExecutorFlow(**mock_dependencies)
         assert executor._is_training_mode() is False
 
     def test_is_training_mode_true(self, mock_dependencies):
         """Test training mode detection when in training."""
         mock_dependencies["crew"]._train = True
-        executor = AgentExecutor(**mock_dependencies)
+        executor = CrewAgentExecutorFlow(**mock_dependencies)
         assert executor._is_training_mode() is True
 
     def test_append_message_to_state(self, mock_dependencies):
         """Test message appending to state."""
-        executor = AgentExecutor(**mock_dependencies)
+        executor = CrewAgentExecutorFlow(**mock_dependencies)
         initial_count = len(executor.state.messages)
 
         executor._append_message_to_state("test message")
@@ -216,7 +216,7 @@ class TestAgentExecutor:
         callback = Mock()
         mock_dependencies["step_callback"] = callback
 
-        executor = AgentExecutor(**mock_dependencies)
+        executor = CrewAgentExecutorFlow(**mock_dependencies)
         answer = AgentFinish(thought="thinking", output="test", text="final")
 
         executor._invoke_step_callback(answer)
@@ -226,14 +226,14 @@ class TestAgentExecutor:
     def test_invoke_step_callback_none(self, mock_dependencies):
         """Test step callback when none provided."""
         mock_dependencies["step_callback"] = None
-        executor = AgentExecutor(**mock_dependencies)
+        executor = CrewAgentExecutorFlow(**mock_dependencies)
 
         # Should not raise error
         executor._invoke_step_callback(
             AgentFinish(thought="thinking", output="test", text="final")
         )
 
-    @patch("crewai.experimental.agent_executor.handle_output_parser_exception")
+    @patch("crewai.experimental.crew_agent_executor_flow.handle_output_parser_exception")
     def test_recover_from_parser_error(
         self, mock_handle_exception, mock_dependencies
     ):
@@ -242,7 +242,7 @@ class TestAgentExecutor:
 
         mock_handle_exception.return_value = None
 
-        executor = AgentExecutor(**mock_dependencies)
+        executor = CrewAgentExecutorFlow(**mock_dependencies)
         executor._last_parser_error = OutputParserError("test error")
         initial_iterations = executor.state.iterations
 
@@ -252,12 +252,12 @@ class TestAgentExecutor:
         assert executor.state.iterations == initial_iterations + 1
         mock_handle_exception.assert_called_once()
 
-    @patch("crewai.experimental.agent_executor.handle_context_length")
+    @patch("crewai.experimental.crew_agent_executor_flow.handle_context_length")
     def test_recover_from_context_length(
         self, mock_handle_context, mock_dependencies
     ):
         """Test recovery from context length error."""
-        executor = AgentExecutor(**mock_dependencies)
+        executor = CrewAgentExecutorFlow(**mock_dependencies)
         executor._last_context_error = Exception("context too long")
         initial_iterations = executor.state.iterations
 
@@ -270,16 +270,16 @@ class TestAgentExecutor:
     def test_use_stop_words_property(self, mock_dependencies):
         """Test use_stop_words property."""
         mock_dependencies["llm"].supports_stop_words.return_value = True
-        executor = AgentExecutor(**mock_dependencies)
+        executor = CrewAgentExecutorFlow(**mock_dependencies)
         assert executor.use_stop_words is True
 
         mock_dependencies["llm"].supports_stop_words.return_value = False
-        executor = AgentExecutor(**mock_dependencies)
+        executor = CrewAgentExecutorFlow(**mock_dependencies)
         assert executor.use_stop_words is False
 
     def test_compatibility_properties(self, mock_dependencies):
         """Test compatibility properties for mixin."""
-        executor = AgentExecutor(**mock_dependencies)
+        executor = CrewAgentExecutorFlow(**mock_dependencies)
         executor.state.messages = [{"role": "user", "content": "test"}]
         executor.state.iterations = 5
 
@@ -321,8 +321,8 @@ class TestFlowErrorHandling:
             "tools_handler": Mock(),
         }
 
-    @patch("crewai.experimental.agent_executor.get_llm_response")
-    @patch("crewai.experimental.agent_executor.enforce_rpm_limit")
+    @patch("crewai.experimental.crew_agent_executor_flow.get_llm_response")
+    @patch("crewai.experimental.crew_agent_executor_flow.enforce_rpm_limit")
     def test_call_llm_parser_error(
         self, mock_enforce_rpm, mock_get_llm, mock_dependencies
     ):
@@ -332,15 +332,15 @@ class TestFlowErrorHandling:
         mock_enforce_rpm.return_value = None
         mock_get_llm.side_effect = OutputParserError("parse failed")
 
-        executor = AgentExecutor(**mock_dependencies)
+        executor = CrewAgentExecutorFlow(**mock_dependencies)
         result = executor.call_llm_and_parse()
 
         assert result == "parser_error"
         assert executor._last_parser_error is not None
 
-    @patch("crewai.experimental.agent_executor.get_llm_response")
-    @patch("crewai.experimental.agent_executor.enforce_rpm_limit")
-    @patch("crewai.experimental.agent_executor.is_context_length_exceeded")
+    @patch("crewai.experimental.crew_agent_executor_flow.get_llm_response")
+    @patch("crewai.experimental.crew_agent_executor_flow.enforce_rpm_limit")
+    @patch("crewai.experimental.crew_agent_executor_flow.is_context_length_exceeded")
     def test_call_llm_context_error(
         self,
         mock_is_context_exceeded,
@@ -353,7 +353,7 @@ class TestFlowErrorHandling:
         mock_get_llm.side_effect = Exception("context length")
         mock_is_context_exceeded.return_value = True
 
-        executor = AgentExecutor(**mock_dependencies)
+        executor = CrewAgentExecutorFlow(**mock_dependencies)
         result = executor.call_llm_and_parse()
 
         assert result == "context_error"
@@ -397,10 +397,10 @@ class TestFlowInvoke:
             "tools_handler": Mock(),
         }
 
-    @patch.object(AgentExecutor, "kickoff")
-    @patch.object(AgentExecutor, "_create_short_term_memory")
-    @patch.object(AgentExecutor, "_create_long_term_memory")
-    @patch.object(AgentExecutor, "_create_external_memory")
+    @patch.object(CrewAgentExecutorFlow, "kickoff")
+    @patch.object(CrewAgentExecutorFlow, "_create_short_term_memory")
+    @patch.object(CrewAgentExecutorFlow, "_create_long_term_memory")
+    @patch.object(CrewAgentExecutorFlow, "_create_external_memory")
     def test_invoke_success(
         self,
         mock_external_memory,
@@ -410,7 +410,7 @@ class TestFlowInvoke:
         mock_dependencies,
     ):
         """Test successful invoke without human feedback."""
-        executor = AgentExecutor(**mock_dependencies)
+        executor = CrewAgentExecutorFlow(**mock_dependencies)
 
         # Mock kickoff to set the final answer in state
         def mock_kickoff_side_effect():
@@ -429,10 +429,10 @@ class TestFlowInvoke:
         mock_long_term_memory.assert_called_once()
         mock_external_memory.assert_called_once()
 
-    @patch.object(AgentExecutor, "kickoff")
+    @patch.object(CrewAgentExecutorFlow, "kickoff")
     def test_invoke_failure_no_agent_finish(self, mock_kickoff, mock_dependencies):
         """Test invoke fails without AgentFinish."""
-        executor = AgentExecutor(**mock_dependencies)
+        executor = CrewAgentExecutorFlow(**mock_dependencies)
         executor.state.current_answer = AgentAction(
             thought="thinking", tool="test", tool_input="test", text="action text"
         )
@@ -442,10 +442,10 @@ class TestFlowInvoke:
         with pytest.raises(RuntimeError, match="without reaching a final answer"):
             executor.invoke(inputs)
 
-    @patch.object(AgentExecutor, "kickoff")
-    @patch.object(AgentExecutor, "_create_short_term_memory")
-    @patch.object(AgentExecutor, "_create_long_term_memory")
-    @patch.object(AgentExecutor, "_create_external_memory")
+    @patch.object(CrewAgentExecutorFlow, "kickoff")
+    @patch.object(CrewAgentExecutorFlow, "_create_short_term_memory")
+    @patch.object(CrewAgentExecutorFlow, "_create_long_term_memory")
+    @patch.object(CrewAgentExecutorFlow, "_create_external_memory")
     def test_invoke_with_system_prompt(
         self,
         mock_external_memory,
@@ -459,7 +459,7 @@ class TestFlowInvoke:
             "system": "System: {input}",
             "user": "User: {input} {tool_names} {tools}",
         }
-        executor = AgentExecutor(**mock_dependencies)
+        executor = CrewAgentExecutorFlow(**mock_dependencies)
 
         def mock_kickoff_side_effect():
             executor.state.current_answer = AgentFinish(

lib/crewai/tests/agents/test_lite_agent.py

@@ -72,53 +72,62 @@ class ResearchResult(BaseModel):
 
 @pytest.mark.vcr()
 @pytest.mark.parametrize("verbose", [True, False])
-def test_agent_kickoff_preserves_parameters(verbose):
-    """Test that Agent.kickoff() uses the correct parameters from the Agent."""
+def test_lite_agent_created_with_correct_parameters(monkeypatch, verbose):
+    """Test that LiteAgent is created with the correct parameters when Agent.kickoff() is called."""
     # Create a test agent with specific parameters
-    mock_llm = Mock(spec=LLM)
-    mock_llm.call.return_value = "Final Answer: Test response"
-    mock_llm.stop = []
-
-    from crewai.types.usage_metrics import UsageMetrics
-
-    mock_usage_metrics = UsageMetrics(
-        total_tokens=100,
-        prompt_tokens=50,
-        completion_tokens=50,
-        cached_prompt_tokens=0,
-        successful_requests=1,
-    )
-    mock_llm.get_token_usage_summary.return_value = mock_usage_metrics
-
+    llm = LLM(model="gpt-4o-mini")
     custom_tools = [WebSearchTool(), CalculatorTool()]
     max_iter = 10
+    max_execution_time = 300
 
     agent = Agent(
         role="Test Agent",
         goal="Test Goal",
         backstory="Test Backstory",
-        llm=mock_llm,
+        llm=llm,
         tools=custom_tools,
         max_iter=max_iter,
+        max_execution_time=max_execution_time,
         verbose=verbose,
     )
 
-    # Call kickoff and verify it works
-    result = agent.kickoff("Test query")
+    # Create a mock to capture the created LiteAgent
+    created_lite_agent = None
+    original_lite_agent = LiteAgent
 
-    # Verify the agent was configured correctly
-    assert agent.role == "Test Agent"
-    assert agent.goal == "Test Goal"
-    assert agent.backstory == "Test Backstory"
-    assert len(agent.tools) == 2
-    assert isinstance(agent.tools[0], WebSearchTool)
-    assert isinstance(agent.tools[1], CalculatorTool)
-    assert agent.max_iter == max_iter
-    assert agent.verbose == verbose
+    # Define a mock LiteAgent class that captures its arguments
+    class MockLiteAgent(original_lite_agent):
+        def __init__(self, **kwargs):
+            nonlocal created_lite_agent
+            created_lite_agent = kwargs
+            super().__init__(**kwargs)
 
-    # Verify kickoff returned a result
-    assert result is not None
-    assert result.raw is not None
+    # Patch the LiteAgent class
+    monkeypatch.setattr("crewai.agent.core.LiteAgent", MockLiteAgent)
+
+    # Call kickoff to create the LiteAgent
+    agent.kickoff("Test query")
+
+    # Verify all parameters were passed correctly
+    assert created_lite_agent is not None
+    assert created_lite_agent["role"] == "Test Agent"
+    assert created_lite_agent["goal"] == "Test Goal"
+    assert created_lite_agent["backstory"] == "Test Backstory"
+    assert created_lite_agent["llm"] == llm
+    assert len(created_lite_agent["tools"]) == 2
+    assert isinstance(created_lite_agent["tools"][0], WebSearchTool)
+    assert isinstance(created_lite_agent["tools"][1], CalculatorTool)
+    assert created_lite_agent["max_iterations"] == max_iter
+    assert created_lite_agent["max_execution_time"] == max_execution_time
+    assert created_lite_agent["verbose"] == verbose
+    assert created_lite_agent["response_format"] is None
+
+    # Test with a response_format
+    class TestResponse(BaseModel):
+        test_field: str
+
+    agent.kickoff("Test query", response_format=TestResponse)
+    assert created_lite_agent["response_format"] == TestResponse
 
 
 @pytest.mark.vcr()
@@ -268,13 +277,7 @@ async def test_lite_agent_returns_usage_metrics_async():
         "What is the population of Tokyo? Return your structured output in JSON format with the following fields: summary, confidence"
     )
     assert isinstance(result, LiteAgentOutput)
-    # Check for population data in various formats (text or numeric)
-    assert (
-        "21 million" in result.raw
-        or "37 million" in result.raw
-        or "21000000" in result.raw
-        or "37000000" in result.raw
-    )
+    assert "21 million" in result.raw or "37 million" in result.raw
     assert result.usage_metrics is not None
     assert result.usage_metrics["total_tokens"] > 0
 
@@ -307,8 +310,7 @@ def verify_agent_parent_flow(result, agent, flow):
 
 
 def test_sets_parent_flow_when_inside_flow():
-    """Test that an Agent can be created and executed inside a Flow context."""
-    captured_event = None
+    captured_agent = None
 
     mock_llm = Mock(spec=LLM)
     mock_llm.call.return_value = "Test response"
@@ -341,17 +343,15 @@ def test_sets_parent_flow_when_inside_flow():
     event_received = threading.Event()
 
     @crewai_event_bus.on(LiteAgentExecutionStartedEvent)
-    def capture_event(source, event):
-        nonlocal captured_event
-        captured_event = event
+    def capture_agent(source, event):
+        nonlocal captured_agent
+        captured_agent = source
         event_received.set()
 
-    result = flow.kickoff()
+    flow.kickoff()
 
     assert event_received.wait(timeout=5), "Timeout waiting for agent execution event"
-    assert captured_event is not None
-    assert captured_event.agent_info["role"] == "Test Agent"
-    assert result is not None
+    assert captured_agent.parent_flow is flow
 
 
 @pytest.mark.vcr()
@@ -373,14 +373,16 @@ def test_guardrail_is_called_using_string():
 
     @crewai_event_bus.on(LLMGuardrailStartedEvent)
     def capture_guardrail_started(source, event):
-        assert isinstance(source, Agent)
+        assert isinstance(source, LiteAgent)
+        assert source.original_agent == agent
         with condition:
             guardrail_events["started"].append(event)
             condition.notify()
 
     @crewai_event_bus.on(LLMGuardrailCompletedEvent)
     def capture_guardrail_completed(source, event):
-        assert isinstance(source, Agent)
+        assert isinstance(source, LiteAgent)
+        assert source.original_agent == agent
         with condition:
             guardrail_events["completed"].append(event)
             condition.notify()
@@ -681,151 +683,3 @@ def test_agent_kickoff_with_mcp_tools(mock_get_mcp_tools):
 
     # Verify MCP tools were retrieved
     mock_get_mcp_tools.assert_called_once_with("https://mcp.exa.ai/mcp?api_key=test_exa_key&profile=research")
-
-
-# ============================================================================
-# Tests for LiteAgent inside Flow (magic auto-async pattern)
-# ============================================================================
-
-from crewai.flow.flow import listen
-
-
-@pytest.mark.vcr()
-def test_lite_agent_inside_flow_sync():
-    """Test that LiteAgent.kickoff() works magically inside a Flow.
-
-    This tests the "magic auto-async" pattern where calling agent.kickoff()
-    from within a Flow automatically detects the event loop and returns a
-    coroutine that the Flow framework awaits. Users don't need to use async/await.
-    """
-    # Track execution
-    execution_log = []
-
-    class TestFlow(Flow):
-        @start()
-        def run_agent(self):
-            execution_log.append("flow_started")
-            agent = Agent(
-                role="Test Agent",
-                goal="Answer questions",
-                backstory="A helpful test assistant",
-                llm=LLM(model="gpt-4o-mini"),
-                verbose=False,
-            )
-            # Magic: just call kickoff() normally - it auto-detects Flow context
-            result = agent.kickoff(messages="What is 2+2? Reply with just the number.")
-            execution_log.append("agent_completed")
-            return result
-
-    flow = TestFlow()
-    result = flow.kickoff()
-
-    # Verify the flow executed successfully
-    assert "flow_started" in execution_log
-    assert "agent_completed" in execution_log
-    assert result is not None
-    assert isinstance(result, LiteAgentOutput)
-
-
-@pytest.mark.vcr()
-def test_lite_agent_inside_flow_with_tools():
-    """Test that LiteAgent with tools works correctly inside a Flow."""
-    class TestFlow(Flow):
-        @start()
-        def run_agent_with_tools(self):
-            agent = Agent(
-                role="Calculator Agent",
-                goal="Perform calculations",
-                backstory="A math expert",
-                llm=LLM(model="gpt-4o-mini"),
-                tools=[CalculatorTool()],
-                verbose=False,
-            )
-            result = agent.kickoff(messages="Calculate 10 * 5")
-            return result
-
-    flow = TestFlow()
-    result = flow.kickoff()
-
-    assert result is not None
-    assert isinstance(result, LiteAgentOutput)
-    assert result.raw is not None
-
-
-@pytest.mark.vcr()
-def test_multiple_agents_in_same_flow():
-    """Test that multiple LiteAgents can run sequentially in the same Flow."""
-    class MultiAgentFlow(Flow):
-        @start()
-        def first_step(self):
-            agent1 = Agent(
-                role="First Agent",
-                goal="Greet users",
-                backstory="A friendly greeter",
-                llm=LLM(model="gpt-4o-mini"),
-                verbose=False,
-            )
-            return agent1.kickoff(messages="Say hello")
-
-        @listen(first_step)
-        def second_step(self, first_result):
-            agent2 = Agent(
-                role="Second Agent",
-                goal="Say goodbye",
-                backstory="A polite farewell agent",
-                llm=LLM(model="gpt-4o-mini"),
-                verbose=False,
-            )
-            return agent2.kickoff(messages="Say goodbye")
-
-    flow = MultiAgentFlow()
-    result = flow.kickoff()
-
-    assert result is not None
-    assert isinstance(result, LiteAgentOutput)
-
-
-@pytest.mark.vcr()
-def test_lite_agent_kickoff_async_inside_flow():
-    """Test that Agent.kickoff_async() works correctly from async Flow methods."""
-    class AsyncAgentFlow(Flow):
-        @start()
-        async def async_agent_step(self):
-            agent = Agent(
-                role="Async Test Agent",
-                goal="Answer questions asynchronously",
-                backstory="An async helper",
-                llm=LLM(model="gpt-4o-mini"),
-                verbose=False,
-            )
-            result = await agent.kickoff_async(messages="What is 3+3?")
-            return result
-
-    flow = AsyncAgentFlow()
-    result = flow.kickoff()
-
-    assert result is not None
-    assert isinstance(result, LiteAgentOutput)
-
-
-@pytest.mark.vcr()
-def test_lite_agent_standalone_still_works():
-    """Test that LiteAgent.kickoff() still works normally outside of a Flow.
-
-    This verifies that the magic auto-async pattern doesn't break standalone usage
-    where there's no event loop running.
-    """
-    agent = Agent(
-        role="Standalone Agent",
-        goal="Answer questions",
-        backstory="A helpful assistant",
-        llm=LLM(model="gpt-4o-mini"),
-        verbose=False,
-    )
-
-    # This should work normally - no Flow, no event loop
-    result = agent.kickoff(messages="What is 5+5? Reply with just the number.")
-
-    assert result is not None
-    assert isinstance(result, LiteAgentOutput)
-    assert result.raw is not None

lib/crewai/tests/agents/test_native_tool_calling.py

@@ -1,657 +0,0 @@
-"""Integration tests for native tool calling functionality.
-
-These tests verify that agents can use native function calling
-when the LLM supports it, across multiple providers.
-"""
-
-from __future__ import annotations
-
-import os
-from unittest.mock import patch
-
-import pytest
-from pydantic import BaseModel, Field
-
-from crewai import Agent, Crew, Task
-from crewai.llm import LLM
-from crewai.tools.base_tool import BaseTool
-
-
-class CalculatorInput(BaseModel):
-    """Input schema for calculator tool."""
-
-    expression: str = Field(description="Mathematical expression to evaluate")
-
-
-class CalculatorTool(BaseTool):
-    """A calculator tool that performs mathematical calculations."""
-
-    name: str = "calculator"
-    description: str = "Perform mathematical calculations. Use this for any math operations."
-    args_schema: type[BaseModel] = CalculatorInput
-
-    def _run(self, expression: str) -> str:
-        """Execute the calculation."""
-        try:
-            # Safe evaluation for basic math
-            result = eval(expression)  # noqa: S307
-            return f"The result of {expression} is {result}"
-        except Exception as e:
-            return f"Error calculating {expression}: {e}"
-
-
-class WeatherInput(BaseModel):
-    """Input schema for weather tool."""
-
-    location: str = Field(description="City name to get weather for")
-
-
-class WeatherTool(BaseTool):
-    """A mock weather tool for testing."""
-
-    name: str = "get_weather"
-    description: str = "Get the current weather for a location"
-    args_schema: type[BaseModel] = WeatherInput
-
-    def _run(self, location: str) -> str:
-        """Get weather (mock implementation)."""
-        return f"The weather in {location} is sunny with a temperature of 72°F"
-
-class FailingTool(BaseTool):
-    """A tool that always fails."""
-    name: str = "failing_tool"
-    description: str = "This tool always fails"
-    def _run(self) -> str:
-        raise Exception("This tool always fails")
-
-@pytest.fixture
-def calculator_tool() -> CalculatorTool:
-    """Create a calculator tool for testing."""
-    return CalculatorTool()
-
-
-@pytest.fixture
-def weather_tool() -> WeatherTool:
-    """Create a weather tool for testing."""
-    return WeatherTool()
-
-@pytest.fixture
-def failing_tool() -> BaseTool:
-    """Create a weather tool for testing."""
-    return FailingTool(
-
-    )
-
-# =============================================================================
-# OpenAI Provider Tests
-# =============================================================================
-
-
-class TestOpenAINativeToolCalling:
-    """Tests for native tool calling with OpenAI models."""
-
-    @pytest.mark.vcr()
-    def test_openai_agent_with_native_tool_calling(
-        self, calculator_tool: CalculatorTool
-    ) -> None:
-        """Test OpenAI agent can use native tool calling."""
-        agent = Agent(
-            role="Math Assistant",
-            goal="Help users with mathematical calculations",
-            backstory="You are a helpful math assistant.",
-            tools=[calculator_tool],
-            llm=LLM(model="gpt-4o-mini"),
-            verbose=False,
-            max_iter=3,
-        )
-
-        task = Task(
-            description="Calculate what is 15 * 8",
-            expected_output="The result of the calculation",
-            agent=agent,
-        )
-
-        crew = Crew(agents=[agent], tasks=[task])
-        result = crew.kickoff()
-
-        assert result is not None
-        assert result.raw is not None
-        assert "120" in str(result.raw)
-
-    def test_openai_agent_kickoff_with_tools_mocked(
-        self, calculator_tool: CalculatorTool
-    ) -> None:
-        """Test OpenAI agent kickoff with mocked LLM call."""
-        llm = LLM(model="gpt-4o-mini")
-
-        with patch.object(llm, "call", return_value="The answer is 120.") as mock_call:
-            agent = Agent(
-                role="Math Assistant",
-                goal="Calculate math",
-                backstory="You calculate.",
-                tools=[calculator_tool],
-                llm=llm,
-                verbose=False,
-            )
-
-            task = Task(
-                description="Calculate 15 * 8",
-                expected_output="Result",
-                agent=agent,
-            )
-
-            crew = Crew(agents=[agent], tasks=[task])
-            result = crew.kickoff()
-
-            assert mock_call.called
-            assert result is not None
-
-
-# =============================================================================
-# Anthropic Provider Tests
-# =============================================================================
-class TestAnthropicNativeToolCalling:
-    """Tests for native tool calling with Anthropic models."""
-
-    @pytest.fixture(autouse=True)
-    def mock_anthropic_api_key(self):
-        """Mock ANTHROPIC_API_KEY for tests."""
-        if "ANTHROPIC_API_KEY" not in os.environ:
-            with patch.dict(os.environ, {"ANTHROPIC_API_KEY": "test-key"}):
-                yield
-        else:
-            yield
-
-    @pytest.mark.vcr()
-    def test_anthropic_agent_with_native_tool_calling(
-        self, calculator_tool: CalculatorTool
-    ) -> None:
-        """Test Anthropic agent can use native tool calling."""
-        agent = Agent(
-            role="Math Assistant",
-            goal="Help users with mathematical calculations",
-            backstory="You are a helpful math assistant.",
-            tools=[calculator_tool],
-            llm=LLM(model="anthropic/claude-3-5-haiku-20241022"),
-            verbose=False,
-            max_iter=3,
-        )
-
-        task = Task(
-            description="Calculate what is 15 * 8",
-            expected_output="The result of the calculation",
-            agent=agent,
-        )
-
-        crew = Crew(agents=[agent], tasks=[task])
-        result = crew.kickoff()
-
-        assert result is not None
-        assert result.raw is not None
-
-    def test_anthropic_agent_kickoff_with_tools_mocked(
-        self, calculator_tool: CalculatorTool
-    ) -> None:
-        """Test Anthropic agent kickoff with mocked LLM call."""
-        llm = LLM(model="anthropic/claude-3-5-haiku-20241022")
-
-        with patch.object(llm, "call", return_value="The answer is 120.") as mock_call:
-            agent = Agent(
-                role="Math Assistant",
-                goal="Calculate math",
-                backstory="You calculate.",
-                tools=[calculator_tool],
-                llm=llm,
-                verbose=False,
-            )
-
-            task = Task(
-                description="Calculate 15 * 8",
-                expected_output="Result",
-                agent=agent,
-            )
-
-            crew = Crew(agents=[agent], tasks=[task])
-            result = crew.kickoff()
-
-            assert mock_call.called
-            assert result is not None
-
-
-# =============================================================================
-# Google/Gemini Provider Tests
-# =============================================================================
-
-
-class TestGeminiNativeToolCalling:
-    """Tests for native tool calling with Gemini models."""
-
-    @pytest.fixture(autouse=True)
-    def mock_google_api_key(self):
-        """Mock GOOGLE_API_KEY for tests."""
-        if "GOOGLE_API_KEY" not in os.environ and "GEMINI_API_KEY" not in os.environ:
-            with patch.dict(os.environ, {"GOOGLE_API_KEY": "test-key"}):
-                yield
-        else:
-            yield
-
-
-    @pytest.mark.vcr()
-    def test_gemini_agent_with_native_tool_calling(
-        self, calculator_tool: CalculatorTool
-    ) -> None:
-        """Test Gemini agent can use native tool calling."""
-
-        agent = Agent(
-            role="Math Assistant",
-            goal="Help users with mathematical calculations",
-            backstory="You are a helpful math assistant.",
-            tools=[calculator_tool],
-            llm=LLM(model="gemini/gemini-2.0-flash-exp"),
-        )
-
-        task = Task(
-            description="Calculate what is 15 * 8",
-            expected_output="The result of the calculation",
-            agent=agent,
-        )
-
-        crew = Crew(agents=[agent], tasks=[task])
-        result = crew.kickoff()
-
-        assert result is not None
-        assert result.raw is not None
-
-    def test_gemini_agent_kickoff_with_tools_mocked(
-        self, calculator_tool: CalculatorTool
-    ) -> None:
-        """Test Gemini agent kickoff with mocked LLM call."""
-        llm = LLM(model="gemini/gemini-2.0-flash-001")
-
-        with patch.object(llm, "call", return_value="The answer is 120.") as mock_call:
-            agent = Agent(
-                role="Math Assistant",
-                goal="Calculate math",
-                backstory="You calculate.",
-                tools=[calculator_tool],
-                llm=llm,
-                verbose=False,
-            )
-
-            task = Task(
-                description="Calculate 15 * 8",
-                expected_output="Result",
-                agent=agent,
-            )
-
-            crew = Crew(agents=[agent], tasks=[task])
-            result = crew.kickoff()
-
-            assert mock_call.called
-            assert result is not None
-
-
-# =============================================================================
-# Azure Provider Tests
-# =============================================================================
-
-
-class TestAzureNativeToolCalling:
-    """Tests for native tool calling with Azure OpenAI models."""
-
-    @pytest.fixture(autouse=True)
-    def mock_azure_env(self):
-        """Mock Azure environment variables for tests."""
-        env_vars = {
-            "AZURE_API_KEY": "test-key",
-            "AZURE_API_BASE": "https://test.openai.azure.com",
-            "AZURE_API_VERSION": "2024-02-15-preview",
-        }
-        # Only patch if keys are not already in environment
-        if "AZURE_API_KEY" not in os.environ:
-            with patch.dict(os.environ, env_vars):
-                yield
-        else:
-            yield
-
-    @pytest.mark.vcr()
-    def test_azure_agent_with_native_tool_calling(
-        self, calculator_tool: CalculatorTool
-    ) -> None:
-        """Test Azure agent can use native tool calling."""
-        agent = Agent(
-            role="Math Assistant",
-            goal="Help users with mathematical calculations",
-            backstory="You are a helpful math assistant.",
-            tools=[calculator_tool],
-            llm=LLM(model="azure/gpt-4o-mini"),
-            verbose=False,
-            max_iter=3,
-        )
-
-        task = Task(
-            description="Calculate what is 15 * 8",
-            expected_output="The result of the calculation",
-            agent=agent,
-        )
-
-        crew = Crew(agents=[agent], tasks=[task])
-        result = crew.kickoff()
-
-        assert result is not None
-        assert result.raw is not None
-        assert "120" in str(result.raw)
-
-    def test_azure_agent_kickoff_with_tools_mocked(
-        self, calculator_tool: CalculatorTool
-    ) -> None:
-        """Test Azure agent kickoff with mocked LLM call."""
-        llm = LLM(
-            model="azure/gpt-4o-mini",
-            api_key="test-key",
-            base_url="https://test.openai.azure.com",
-        )
-
-        with patch.object(llm, "call", return_value="The answer is 120.") as mock_call:
-            agent = Agent(
-                role="Math Assistant",
-                goal="Calculate math",
-                backstory="You calculate.",
-                tools=[calculator_tool],
-                llm=llm,
-                verbose=False,
-            )
-
-            task = Task(
-                description="Calculate 15 * 8",
-                expected_output="Result",
-                agent=agent,
-            )
-
-            crew = Crew(agents=[agent], tasks=[task])
-            result = crew.kickoff()
-
-            assert mock_call.called
-            assert result is not None
-
-
-# =============================================================================
-# Bedrock Provider Tests
-# =============================================================================
-
-
-class TestBedrockNativeToolCalling:
-    """Tests for native tool calling with AWS Bedrock models."""
-
-    @pytest.fixture(autouse=True)
-    def mock_aws_env(self):
-        """Mock AWS environment variables for tests."""
-        env_vars = {
-        "AWS_ACCESS_KEY_ID": "test-key",
-        "AWS_SECRET_ACCESS_KEY": "test-secret",
-        "AWS_REGION": "us-east-1",
-        }
-        if "AWS_ACCESS_KEY_ID" not in os.environ:
-            with patch.dict(os.environ, env_vars):
-                yield
-        else:
-            yield
-
-    @pytest.mark.vcr()
-    def test_bedrock_agent_kickoff_with_tools_mocked(
-        self, calculator_tool: CalculatorTool
-    ) -> None:
-        """Test Bedrock agent kickoff with mocked LLM call."""
-        llm = LLM(model="bedrock/anthropic.claude-3-haiku-20240307-v1:0")
-
-        agent = Agent(
-            role="Math Assistant",
-            goal="Calculate math",
-            backstory="You calculate.",
-            tools=[calculator_tool],
-            llm=llm,
-            verbose=False,
-            max_iter=5,
-        )
-
-        task = Task(
-            description="Calculate 15 * 8",
-            expected_output="Result",
-            agent=agent,
-        )
-
-        crew = Crew(agents=[agent], tasks=[task])
-        result = crew.kickoff()
-
-        assert result is not None
-        assert result.raw is not None
-        assert "120" in str(result.raw)
-
-
-# =============================================================================
-# Cross-Provider Native Tool Calling Behavior Tests
-# =============================================================================
-
-
-class TestNativeToolCallingBehavior:
-    """Tests for native tool calling behavior across providers."""
-
-    def test_supports_function_calling_check(self) -> None:
-        """Test that supports_function_calling() is properly checked."""
-        # OpenAI should support function calling
-        openai_llm = LLM(model="gpt-4o-mini")
-        assert hasattr(openai_llm, "supports_function_calling")
-        assert openai_llm.supports_function_calling() is True
-
-    def test_anthropic_supports_function_calling(self) -> None:
-        """Test that Anthropic models support function calling."""
-        with patch.dict(os.environ, {"ANTHROPIC_API_KEY": "test-key"}):
-            llm = LLM(model="anthropic/claude-3-5-haiku-20241022")
-            assert hasattr(llm, "supports_function_calling")
-            assert llm.supports_function_calling() is True
-
-    def test_gemini_supports_function_calling(self) -> None:
-        """Test that Gemini models support function calling."""
-        llm = LLM(model="gemini/gemini-2.5-flash")
-        assert hasattr(llm, "supports_function_calling")
-        assert llm.supports_function_calling() is True
-
-
-# =============================================================================
-# Token Usage Tests
-# =============================================================================
-
-
-class TestNativeToolCallingTokenUsage:
-    """Tests for token usage with native tool calling."""
-
-    @pytest.mark.vcr()
-    def test_openai_native_tool_calling_token_usage(
-        self, calculator_tool: CalculatorTool
-    ) -> None:
-        """Test token usage tracking with OpenAI native tool calling."""
-        agent = Agent(
-            role="Calculator",
-            goal="Perform calculations efficiently",
-            backstory="You calculate things.",
-            tools=[calculator_tool],
-            llm=LLM(model="gpt-4o-mini"),
-            verbose=False,
-            max_iter=3,
-        )
-
-        task = Task(
-            description="What is 100 / 4?",
-            expected_output="The result",
-            agent=agent,
-        )
-
-        crew = Crew(agents=[agent], tasks=[task])
-        result = crew.kickoff()
-
-        assert result is not None
-        assert result.token_usage is not None
-        assert result.token_usage.total_tokens > 0
-        assert result.token_usage.successful_requests >= 1
-
-        print(f"\n[OPENAI NATIVE TOOL CALLING TOKEN USAGE]")
-        print(f"  Prompt tokens: {result.token_usage.prompt_tokens}")
-        print(f"  Completion tokens: {result.token_usage.completion_tokens}")
-        print(f"  Total tokens: {result.token_usage.total_tokens}")
-
-@pytest.mark.vcr()
-def test_native_tool_calling_error_handling(failing_tool: FailingTool):
-    """Test that native tool calling handles errors properly and emits error events."""
-    import threading
-    from crewai.events import crewai_event_bus
-    from crewai.events.types.tool_usage_events import ToolUsageErrorEvent
-
-    received_events = []
-    event_received = threading.Event()
-
-    @crewai_event_bus.on(ToolUsageErrorEvent)
-    def handle_tool_error(source, event):
-        received_events.append(event)
-        event_received.set()
-
-    agent = Agent(
-        role="Calculator",
-        goal="Perform calculations efficiently",
-        backstory="You calculate things.",
-        tools=[failing_tool],
-        llm=LLM(model="gpt-4o-mini"),
-        verbose=False,
-        max_iter=3,
-    )
-
-    result = agent.kickoff("Use the failing_tool to do something.")
-    assert result is not None
-
-    # Verify error event was emitted
-    assert event_received.wait(timeout=10), "ToolUsageErrorEvent was not emitted"
-    assert len(received_events) >= 1
-
-    # Verify event attributes
-    error_event = received_events[0]
-    assert error_event.tool_name == "failing_tool"
-    assert error_event.agent_role == agent.role
-    assert "This tool always fails" in str(error_event.error)
-
-
-# =============================================================================
-# Max Usage Count Tests for Native Tool Calling
-# =============================================================================
-
-
-class CountingInput(BaseModel):
-    """Input schema for counting tool."""
-
-    value: str = Field(description="Value to count")
-
-
-class CountingTool(BaseTool):
-    """A tool that counts its usage."""
-
-    name: str = "counting_tool"
-    description: str = "A tool that counts how many times it's been called"
-    args_schema: type[BaseModel] = CountingInput
-
-    def _run(self, value: str) -> str:
-        """Return the value with a count prefix."""
-        return f"Counted: {value}"
-
-
-class TestMaxUsageCountWithNativeToolCalling:
-    """Tests for max_usage_count with native tool calling."""
-
-    @pytest.mark.vcr()
-    def test_max_usage_count_tracked_in_native_tool_calling(self) -> None:
-        """Test that max_usage_count is properly tracked when using native tool calling."""
-        tool = CountingTool(max_usage_count=3)
-
-        # Verify initial state
-        assert tool.max_usage_count == 3
-        assert tool.current_usage_count == 0
-
-        agent = Agent(
-            role="Counting Agent",
-            goal="Call the counting tool multiple times",
-            backstory="You are an agent that counts things.",
-            tools=[tool],
-            llm=LLM(model="gpt-4o-mini"),
-            verbose=False,
-            max_iter=5,
-        )
-
-        task = Task(
-            description="Call the counting_tool 3 times with values 'first', 'second', and 'third'",
-            expected_output="The results of the counting operations",
-            agent=agent,
-        )
-
-        crew = Crew(agents=[agent], tasks=[task])
-        crew.kickoff()
-
-        # Verify usage count was tracked
-        assert tool.max_usage_count == 3
-        assert tool.current_usage_count <= tool.max_usage_count
-
-    @pytest.mark.vcr()
-    def test_max_usage_count_limit_enforced_in_native_tool_calling(self) -> None:
-        """Test that when max_usage_count is reached, tool returns error message."""
-        tool = CountingTool(max_usage_count=2)
-
-        agent = Agent(
-            role="Counting Agent",
-            goal="Use the counting tool as many times as requested",
-            backstory="You are an agent that counts things. You must try to use the tool for each value requested.",
-            tools=[tool],
-            llm=LLM(model="gpt-4o-mini"),
-            verbose=False,
-            max_iter=5,
-        )
-
-        # Request more tool calls than the max_usage_count allows
-        task = Task(
-            description="Call the counting_tool 4 times with values 'one', 'two', 'three', and 'four'",
-            expected_output="The results of the counting operations, noting any failures",
-            agent=agent,
-        )
-
-        crew = Crew(agents=[agent], tasks=[task])
-        result = crew.kickoff()
-
-        # The tool should have been limited to max_usage_count (2) calls
-        assert result is not None
-        assert tool.current_usage_count == tool.max_usage_count
-        # After hitting the limit, further calls should have been rejected
-
-    @pytest.mark.vcr()
-    def test_tool_usage_increments_after_successful_execution(self) -> None:
-        """Test that usage count increments after each successful native tool call."""
-        tool = CountingTool(max_usage_count=10)
-
-        assert tool.current_usage_count == 0
-
-        agent = Agent(
-            role="Counting Agent",
-            goal="Use the counting tool exactly as requested",
-            backstory="You are an agent that counts things precisely.",
-            tools=[tool],
-            llm=LLM(model="gpt-4o-mini"),
-            verbose=False,
-            max_iter=5,
-        )
-
-        task = Task(
-            description="Call the counting_tool exactly 2 times: first with value 'alpha', then with value 'beta'",
-            expected_output="The results showing both 'Counted: alpha' and 'Counted: beta'",
-            agent=agent,
-        )
-
-        crew = Crew(agents=[agent], tasks=[task])
-        result = crew.kickoff()
-
-        assert result is not None
-        # Verify usage count was incremented for each successful call
-        assert tool.current_usage_count == 2

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