Merge branch 'gl/feat/async-crew-support' into gl/feat/async-flow-kickoff

docs/en/concepts/llms.mdx

@@ -283,54 +283,11 @@ In this section, you'll find detailed examples that help you select, configure,
     )
     ```
 
-    **Extended Thinking (Claude Sonnet 4 and Beyond):**
-
-    CrewAI supports Anthropic's Extended Thinking feature, which allows Claude to think through problems in a more human-like way before responding. This is particularly useful for complex reasoning, analysis, and problem-solving tasks.
-
-    ```python Code
-    from crewai import LLM
-
-    # Enable extended thinking with default settings
-    llm = LLM(
-        model="anthropic/claude-sonnet-4",
-        thinking={"type": "enabled"},
-        max_tokens=10000
-    )
-
-    # Configure thinking with budget control
-    llm = LLM(
-        model="anthropic/claude-sonnet-4",
-        thinking={
-            "type": "enabled",
-            "budget_tokens": 5000  # Limit thinking tokens
-        },
-        max_tokens=10000
-    )
-    ```
-
-    **Thinking Configuration Options:**
-    - `type`: Set to `"enabled"` to activate extended thinking mode
-    - `budget_tokens` (optional): Maximum tokens to use for thinking (helps control costs)
-
-    **Models Supporting Extended Thinking:**
-    - `claude-sonnet-4` and newer models
-    - `claude-3-7-sonnet` (with extended thinking capabilities)
-
-    **When to Use Extended Thinking:**
-    - Complex reasoning and multi-step problem solving
-    - Mathematical calculations and proofs
-    - Code analysis and debugging
-    - Strategic planning and decision making
-    - Research and analytical tasks
-
-    **Note:** Extended thinking consumes additional tokens but can significantly improve response quality for complex tasks.
-
     **Supported Environment Variables:**
     - `ANTHROPIC_API_KEY`: Your Anthropic API key (required)
 
     **Features:**
     - Native tool use support for Claude 3+ models
-    - Extended Thinking support for Claude Sonnet 4+
     - Streaming support for real-time responses
     - Automatic system message handling
     - Stop sequences for controlled output
@@ -348,7 +305,6 @@ In this section, you'll find detailed examples that help you select, configure,
 
     | Model                        | Context Window | Best For                                      |
     |------------------------------|----------------|-----------------------------------------------|
-    | claude-sonnet-4              | 200,000 tokens | Latest with extended thinking capabilities    |
     | claude-3-7-sonnet            | 200,000 tokens | Advanced reasoning and agentic tasks          |
     | claude-3-5-sonnet-20241022   | 200,000 tokens | Latest Sonnet with best performance           |
     | claude-3-5-haiku             | 200,000 tokens | Fast, compact model for quick responses       |

docs/en/concepts/memory.mdx

@@ -515,7 +515,8 @@ crew = Crew(
         "provider": "huggingface",
         "config": {
             "api_key": "your-hf-token",  # Optional for public models
-            "model": "sentence-transformers/all-MiniLM-L6-v2"
+            "model": "sentence-transformers/all-MiniLM-L6-v2",
+            "api_url": "https://api-inference.huggingface.co"  # or your custom endpoint
         }
     }
 )

docs/ko/concepts/memory.mdx

@@ -515,7 +515,8 @@ crew = Crew(
         "provider": "huggingface",
         "config": {
             "api_key": "your-hf-token",  # Optional for public models
-            "model": "sentence-transformers/all-MiniLM-L6-v2"
+            "model": "sentence-transformers/all-MiniLM-L6-v2",
+            "api_url": "https://api-inference.huggingface.co"  # or your custom endpoint
         }
     }
 )

docs/pt-BR/concepts/memory.mdx

@@ -515,7 +515,8 @@ crew = Crew(
         "provider": "huggingface",
         "config": {
             "api_key": "your-hf-token",  # Opcional para modelos públicos
-            "model": "sentence-transformers/all-MiniLM-L6-v2"
+            "model": "sentence-transformers/all-MiniLM-L6-v2",
+            "api_url": "https://api-inference.huggingface.co"  # ou seu endpoint customizado
         }
     }
 )

lib/crewai-tools/pyproject.toml

@@ -12,7 +12,7 @@ dependencies = [
     "pytube~=15.0.0",
     "requests~=2.32.5",
     "docker~=7.1.0",
-    "crewai==1.7.0",
+    "crewai==1.6.1",
     "lancedb~=0.5.4",
     "tiktoken~=0.8.0",
     "beautifulsoup4~=4.13.4",

lib/crewai-tools/src/crewai_tools/__init__.py

@@ -291,4 +291,4 @@ __all__ = [
     "ZapierActionTools",
 ]
 
-__version__ = "1.7.0"
+__version__ = "1.6.1"

lib/crewai/pyproject.toml

@@ -49,7 +49,7 @@ Repository = "https://github.com/crewAIInc/crewAI"
 
 [project.optional-dependencies]
 tools = [
-    "crewai-tools==1.7.0",
+    "crewai-tools==1.6.1",
 ]
 embeddings = [
     "tiktoken~=0.8.0"
@@ -96,7 +96,6 @@ a2a = [
      "a2a-sdk~=0.3.10",
      "httpx-auth~=0.23.1",
      "httpx-sse~=0.4.0",
-     "aiocache[redis,memcached]~=0.12.3",
 ]
 
 

lib/crewai/src/crewai/__init__.py

@@ -40,7 +40,7 @@ def _suppress_pydantic_deprecation_warnings() -> None:
 
 _suppress_pydantic_deprecation_warnings()
 
-__version__ = "1.7.0"
+__version__ = "1.6.1"
 _telemetry_submitted = False
 
 

lib/crewai/src/crewai/a2a/extensions/__init__.py

@@ -1,4 +0,0 @@
-"""A2A Protocol Extensions for CrewAI.
-
-This module contains extensions to the A2A (Agent-to-Agent) protocol.
-"""

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

@@ -1,193 +0,0 @@
-"""Base extension interface for A2A wrapper integrations.
-
-This module defines the protocol for extending A2A wrapper functionality
-with custom logic for conversation processing, prompt augmentation, and
-agent response handling.
-"""
-
-from __future__ import annotations
-
-from collections.abc import Sequence
-from typing import TYPE_CHECKING, Any, Protocol
-
-
-if TYPE_CHECKING:
-    from a2a.types import Message
-
-    from crewai.agent.core import Agent
-
-
-class ConversationState(Protocol):
-    """Protocol for extension-specific conversation state.
-
-    Extensions can define their own state classes that implement this protocol
-    to track conversation-specific data extracted from message history.
-    """
-
-    def is_ready(self) -> bool:
-        """Check if the state indicates readiness for some action.
-
-        Returns:
-            True if the state is ready, False otherwise.
-        """
-        ...
-
-
-class A2AExtension(Protocol):
-    """Protocol for A2A wrapper extensions.
-
-    Extensions can implement this protocol to inject custom logic into
-    the A2A conversation flow at various integration points.
-    """
-
-    def inject_tools(self, agent: Agent) -> None:
-        """Inject extension-specific tools into the agent.
-
-        Called when an agent is wrapped with A2A capabilities. Extensions
-        can add tools that enable extension-specific functionality.
-
-        Args:
-            agent: The agent instance to inject tools into.
-        """
-        ...
-
-    def extract_state_from_history(
-        self, conversation_history: Sequence[Message]
-    ) -> ConversationState | None:
-        """Extract extension-specific state from conversation history.
-
-        Called during prompt augmentation to allow extensions to analyze
-        the conversation history and extract relevant state information.
-
-        Args:
-            conversation_history: The sequence of A2A messages exchanged.
-
-        Returns:
-            Extension-specific conversation state, or None if no relevant state.
-        """
-        ...
-
-    def augment_prompt(
-        self,
-        base_prompt: str,
-        conversation_state: ConversationState | None,
-    ) -> str:
-        """Augment the task prompt with extension-specific instructions.
-
-        Called during prompt augmentation to allow extensions to add
-        custom instructions based on conversation state.
-
-        Args:
-            base_prompt: The base prompt to augment.
-            conversation_state: Extension-specific state from extract_state_from_history.
-
-        Returns:
-            The augmented prompt with extension-specific instructions.
-        """
-        ...
-
-    def process_response(
-        self,
-        agent_response: Any,
-        conversation_state: ConversationState | None,
-    ) -> Any:
-        """Process and potentially modify the agent response.
-
-        Called after parsing the agent's response, allowing extensions to
-        enhance or modify the response based on conversation state.
-
-        Args:
-            agent_response: The parsed agent response.
-            conversation_state: Extension-specific state from extract_state_from_history.
-
-        Returns:
-            The processed agent response (may be modified or original).
-        """
-        ...
-
-
-class ExtensionRegistry:
-    """Registry for managing A2A extensions.
-
-    Maintains a collection of extensions and provides methods to invoke
-    their hooks at various integration points.
-    """
-
-    def __init__(self) -> None:
-        """Initialize the extension registry."""
-        self._extensions: list[A2AExtension] = []
-
-    def register(self, extension: A2AExtension) -> None:
-        """Register an extension.
-
-        Args:
-            extension: The extension to register.
-        """
-        self._extensions.append(extension)
-
-    def inject_all_tools(self, agent: Agent) -> None:
-        """Inject tools from all registered extensions.
-
-        Args:
-            agent: The agent instance to inject tools into.
-        """
-        for extension in self._extensions:
-            extension.inject_tools(agent)
-
-    def extract_all_states(
-        self, conversation_history: Sequence[Message]
-    ) -> dict[type[A2AExtension], ConversationState]:
-        """Extract conversation states from all registered extensions.
-
-        Args:
-            conversation_history: The sequence of A2A messages exchanged.
-
-        Returns:
-            Mapping of extension types to their conversation states.
-        """
-        states: dict[type[A2AExtension], ConversationState] = {}
-        for extension in self._extensions:
-            state = extension.extract_state_from_history(conversation_history)
-            if state is not None:
-                states[type(extension)] = state
-        return states
-
-    def augment_prompt_with_all(
-        self,
-        base_prompt: str,
-        extension_states: dict[type[A2AExtension], ConversationState],
-    ) -> str:
-        """Augment prompt with instructions from all registered extensions.
-
-        Args:
-            base_prompt: The base prompt to augment.
-            extension_states: Mapping of extension types to conversation states.
-
-        Returns:
-            The fully augmented prompt.
-        """
-        augmented = base_prompt
-        for extension in self._extensions:
-            state = extension_states.get(type(extension))
-            augmented = extension.augment_prompt(augmented, state)
-        return augmented
-
-    def process_response_with_all(
-        self,
-        agent_response: Any,
-        extension_states: dict[type[A2AExtension], ConversationState],
-    ) -> Any:
-        """Process response through all registered extensions.
-
-        Args:
-            agent_response: The parsed agent response.
-            extension_states: Mapping of extension types to conversation states.
-
-        Returns:
-            The processed agent response.
-        """
-        processed = agent_response
-        for extension in self._extensions:
-            state = extension_states.get(type(extension))
-            processed = extension.process_response(processed, state)
-        return processed

lib/crewai/src/crewai/a2a/extensions/registry.py

@@ -1,34 +0,0 @@
-"""Extension registry factory for A2A configurations.
-
-This module provides utilities for creating extension registries from A2A configurations.
-"""
-
-from __future__ import annotations
-
-from typing import TYPE_CHECKING
-
-from crewai.a2a.extensions.base import ExtensionRegistry
-
-
-if TYPE_CHECKING:
-    from crewai.a2a.config import A2AConfig
-
-
-def create_extension_registry_from_config(
-    a2a_config: list[A2AConfig] | A2AConfig,
-) -> ExtensionRegistry:
-    """Create an extension registry from A2A configuration.
-
-    Args:
-        a2a_config: A2A configuration (single or list)
-
-    Returns:
-        Configured extension registry with all applicable extensions
-    """
-    registry = ExtensionRegistry()
-    configs = a2a_config if isinstance(a2a_config, list) else [a2a_config]
-
-    for _ in configs:
-        pass
-
-    return registry

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

@@ -23,8 +23,6 @@ from a2a.types import (
     TextPart,
     TransportProtocol,
 )
-from aiocache import cached  # type: ignore[import-untyped]
-from aiocache.serializers import PickleSerializer  # type: ignore[import-untyped]
 import httpx
 from pydantic import BaseModel, Field, create_model
 
@@ -67,7 +65,7 @@ def _fetch_agent_card_cached(
         endpoint: A2A agent endpoint URL
         auth_hash: Hash of the auth object
         timeout: Request timeout
-        _ttl_hash: Time-based hash for cache invalidation
+        _ttl_hash: Time-based hash for cache invalidation (unused in body)
 
     Returns:
         Cached AgentCard
@@ -108,18 +106,7 @@ def fetch_agent_card(
         A2AClientHTTPError: If authentication fails
     """
     if use_cache:
-        if auth:
-            auth_data = auth.model_dump_json(
-                exclude={
-                    "_access_token",
-                    "_token_expires_at",
-                    "_refresh_token",
-                    "_authorization_callback",
-                }
-            )
-            auth_hash = hash((type(auth).__name__, auth_data))
-        else:
-            auth_hash = 0
+        auth_hash = hash((type(auth).__name__, id(auth))) if auth else 0
         _auth_store[auth_hash] = auth
         ttl_hash = int(time.time() // cache_ttl)
         return _fetch_agent_card_cached(endpoint, auth_hash, timeout, ttl_hash)
@@ -134,26 +121,6 @@ def fetch_agent_card(
         loop.close()
 
 
-@cached(ttl=300, serializer=PickleSerializer())  # type: ignore[untyped-decorator]
-async def _fetch_agent_card_async_cached(
-    endpoint: str,
-    auth_hash: int,
-    timeout: int,
-) -> AgentCard:
-    """Cached async implementation of AgentCard fetching.
-
-    Args:
-        endpoint: A2A agent endpoint URL
-        auth_hash: Hash of the auth object
-        timeout: Request timeout in seconds
-
-    Returns:
-        Cached AgentCard object
-    """
-    auth = _auth_store.get(auth_hash)
-    return await _fetch_agent_card_async(endpoint=endpoint, auth=auth, timeout=timeout)
-
-
 async def _fetch_agent_card_async(
     endpoint: str,
     auth: AuthScheme | None,
@@ -372,22 +339,7 @@ async def _execute_a2a_delegation_async(
     Returns:
         Dictionary with status, result/error, and new history
     """
-    if auth:
-        auth_data = auth.model_dump_json(
-            exclude={
-                "_access_token",
-                "_token_expires_at",
-                "_refresh_token",
-                "_authorization_callback",
-            }
-        )
-        auth_hash = hash((type(auth).__name__, auth_data))
-    else:
-        auth_hash = 0
-    _auth_store[auth_hash] = auth
-    agent_card = await _fetch_agent_card_async_cached(
-        endpoint=endpoint, auth_hash=auth_hash, timeout=timeout
-    )
+    agent_card = await _fetch_agent_card_async(endpoint, auth, timeout)
 
     validate_auth_against_agent_card(agent_card, auth)
 
@@ -604,34 +556,6 @@ async def _execute_a2a_delegation_async(
                         }
                         break
         except Exception as e:
-            if isinstance(e, A2AClientHTTPError):
-                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,
-                    task_id=task_id,
-                )
-                new_messages.append(error_message)
-
-                crewai_event_bus.emit(
-                    None,
-                    A2AResponseReceivedEvent(
-                        response=error_msg,
-                        turn_number=turn_number,
-                        is_multiturn=is_multiturn,
-                        status="failed",
-                        agent_role=agent_role,
-                    ),
-                )
-                return {
-                    "status": "failed",
-                    "error": error_msg,
-                    "history": new_messages,
-                }
-
             current_exception: Exception | BaseException | None = e
             while current_exception:
                 if hasattr(current_exception, "response"):
@@ -828,5 +752,4 @@ def get_a2a_agents_and_response_model(
         Tuple of A2A agent IDs and response model
     """
     a2a_agents, agent_ids = extract_a2a_agent_ids_from_config(a2a_config=a2a_config)
-
     return a2a_agents, create_agent_response_model(agent_ids)

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

@@ -15,7 +15,6 @@ from a2a.types import Role
 from pydantic import BaseModel, ValidationError
 
 from crewai.a2a.config import A2AConfig
-from crewai.a2a.extensions.base import ExtensionRegistry
 from crewai.a2a.templates import (
     AVAILABLE_AGENTS_TEMPLATE,
     CONVERSATION_TURN_INFO_TEMPLATE,
@@ -43,9 +42,7 @@ if TYPE_CHECKING:
     from crewai.tools.base_tool import BaseTool
 
 
-def wrap_agent_with_a2a_instance(
-    agent: Agent, extension_registry: ExtensionRegistry | None = None
-) -> None:
+def wrap_agent_with_a2a_instance(agent: Agent) -> None:
     """Wrap an agent instance's execute_task method with A2A support.
 
     This function modifies the agent instance by wrapping its execute_task
@@ -54,13 +51,7 @@ def wrap_agent_with_a2a_instance(
 
     Args:
         agent: The agent instance to wrap
-        extension_registry: Optional registry of A2A extensions for injecting tools and custom logic
     """
-    if extension_registry is None:
-        extension_registry = ExtensionRegistry()
-
-    extension_registry.inject_all_tools(agent)
-
     original_execute_task = agent.execute_task.__func__  # type: ignore[attr-defined]
 
     @wraps(original_execute_task)
@@ -94,7 +85,6 @@ def wrap_agent_with_a2a_instance(
             agent_response_model=agent_response_model,
             context=context,
             tools=tools,
-            extension_registry=extension_registry,
         )
 
     object.__setattr__(agent, "execute_task", MethodType(execute_task_with_a2a, agent))
@@ -164,7 +154,6 @@ def _execute_task_with_a2a(
     agent_response_model: type[BaseModel],
     context: str | None,
     tools: list[BaseTool] | None,
-    extension_registry: ExtensionRegistry,
 ) -> str:
     """Wrap execute_task with A2A delegation logic.
 
@@ -176,7 +165,6 @@ def _execute_task_with_a2a(
         context: Optional context for task execution
         tools: Optional tools available to the agent
         agent_response_model: Optional agent response model
-        extension_registry: Registry of A2A extensions
 
     Returns:
         Task execution result (either from LLM or A2A agent)
@@ -202,12 +190,11 @@ def _execute_task_with_a2a(
         finally:
             task.description = original_description
 
-    task.description, _ = _augment_prompt_with_a2a(
+    task.description = _augment_prompt_with_a2a(
         a2a_agents=a2a_agents,
         task_description=original_description,
         agent_cards=agent_cards,
         failed_agents=failed_agents,
-        extension_registry=extension_registry,
     )
     task.response_model = agent_response_model
 
@@ -217,11 +204,6 @@ def _execute_task_with_a2a(
             raw_result=raw_result, agent_response_model=agent_response_model
         )
 
-        if extension_registry and isinstance(agent_response, BaseModel):
-            agent_response = extension_registry.process_response_with_all(
-                agent_response, {}
-            )
-
         if isinstance(agent_response, BaseModel) and isinstance(
             agent_response, AgentResponseProtocol
         ):
@@ -235,7 +217,6 @@ def _execute_task_with_a2a(
                     tools=tools,
                     agent_cards=agent_cards,
                     original_task_description=original_description,
-                    extension_registry=extension_registry,
                 )
             return str(agent_response.message)
 
@@ -254,8 +235,7 @@ def _augment_prompt_with_a2a(
     turn_num: int = 0,
     max_turns: int | None = None,
     failed_agents: dict[str, str] | None = None,
-    extension_registry: ExtensionRegistry | None = None,
-) -> tuple[str, bool]:
+) -> str:
     """Add A2A delegation instructions to prompt.
 
     Args:
@@ -266,14 +246,13 @@ def _augment_prompt_with_a2a(
         turn_num: Current turn number (0-indexed)
         max_turns: Maximum allowed turns (from config)
         failed_agents: Dictionary mapping failed agent endpoints to error messages
-        extension_registry: Optional registry of A2A extensions
 
     Returns:
-        Tuple of (augmented prompt, disable_structured_output flag)
+        Augmented task description with A2A instructions
     """
 
     if not agent_cards:
-        return task_description, False
+        return task_description
 
     agents_text = ""
 
@@ -291,7 +270,6 @@ def _augment_prompt_with_a2a(
     agents_text = AVAILABLE_AGENTS_TEMPLATE.substitute(available_a2a_agents=agents_text)
 
     history_text = ""
-
     if conversation_history:
         for msg in conversation_history:
             history_text += f"\n{msg.model_dump_json(indent=2, exclude_none=True, exclude={'message_id'})}\n"
@@ -299,15 +277,6 @@ def _augment_prompt_with_a2a(
     history_text = PREVIOUS_A2A_CONVERSATION_TEMPLATE.substitute(
         previous_a2a_conversation=history_text
     )
-
-    extension_states = {}
-    disable_structured_output = False
-    if extension_registry and conversation_history:
-        extension_states = extension_registry.extract_all_states(conversation_history)
-        for state in extension_states.values():
-            if state.is_ready():
-                disable_structured_output = True
-                break
     turn_info = ""
 
     if max_turns is not None and conversation_history:
@@ -327,22 +296,16 @@ def _augment_prompt_with_a2a(
             warning=warning,
         )
 
-    augmented_prompt = f"""{task_description}
+    return f"""{task_description}
 
 IMPORTANT: You have the ability to delegate this task to remote A2A agents.
+
 {agents_text}
 {history_text}{turn_info}
 
 
 """
 
-    if extension_registry:
-        augmented_prompt = extension_registry.augment_prompt_with_all(
-            augmented_prompt, extension_states
-        )
-
-    return augmented_prompt, disable_structured_output
-
 
 def _parse_agent_response(
     raw_result: str | dict[str, Any], agent_response_model: type[BaseModel]
@@ -410,7 +373,7 @@ def _handle_agent_response_and_continue(
     if "agent_card" in a2a_result and agent_id not in agent_cards_dict:
         agent_cards_dict[agent_id] = a2a_result["agent_card"]
 
-    task.description, disable_structured_output = _augment_prompt_with_a2a(
+    task.description = _augment_prompt_with_a2a(
         a2a_agents=a2a_agents,
         task_description=original_task_description,
         conversation_history=conversation_history,
@@ -419,38 +382,7 @@ def _handle_agent_response_and_continue(
         agent_cards=agent_cards_dict,
     )
 
-    original_response_model = task.response_model
-    if disable_structured_output:
-        task.response_model = None
-
     raw_result = original_fn(self, task, context, tools)
-
-    if disable_structured_output:
-        task.response_model = original_response_model
-
-    if disable_structured_output:
-        final_turn_number = turn_num + 1
-        result_text = str(raw_result)
-        crewai_event_bus.emit(
-            None,
-            A2AMessageSentEvent(
-                message=result_text,
-                turn_number=final_turn_number,
-                is_multiturn=True,
-                agent_role=self.role,
-            ),
-        )
-        crewai_event_bus.emit(
-            None,
-            A2AConversationCompletedEvent(
-                status="completed",
-                final_result=result_text,
-                error=None,
-                total_turns=final_turn_number,
-            ),
-        )
-        return result_text, None
-
     llm_response = _parse_agent_response(
         raw_result=raw_result, agent_response_model=agent_response_model
     )
@@ -493,7 +425,6 @@ def _delegate_to_a2a(
     tools: list[BaseTool] | None,
     agent_cards: dict[str, AgentCard] | None = None,
     original_task_description: str | None = None,
-    extension_registry: ExtensionRegistry | None = None,
 ) -> str:
     """Delegate to A2A agent with multi-turn conversation support.
 
@@ -506,7 +437,6 @@ def _delegate_to_a2a(
         tools: Optional tools available to the agent
         agent_cards: Pre-fetched agent cards from _execute_task_with_a2a
         original_task_description: The original task description before A2A augmentation
-        extension_registry: Optional registry of A2A extensions
 
     Returns:
         Result from A2A agent
@@ -517,13 +447,9 @@ def _delegate_to_a2a(
     a2a_agents, agent_response_model = get_a2a_agents_and_response_model(self.a2a)
     agent_ids = tuple(config.endpoint for config in a2a_agents)
     current_request = str(agent_response.message)
+    agent_id = agent_response.a2a_ids[0]
 
-    if hasattr(agent_response, "a2a_ids") and agent_response.a2a_ids:
-        agent_id = agent_response.a2a_ids[0]
-    else:
-        agent_id = agent_ids[0] if agent_ids else ""
-
-    if agent_id and agent_id not in agent_ids:
+    if agent_id not in agent_ids:
         raise ValueError(
             f"Unknown A2A agent ID(s): {agent_response.a2a_ids} not in {agent_ids}"
         )
@@ -532,11 +458,10 @@ def _delegate_to_a2a(
     task_config = task.config or {}
     context_id = task_config.get("context_id")
     task_id_config = task_config.get("task_id")
+    reference_task_ids = task_config.get("reference_task_ids")
     metadata = task_config.get("metadata")
     extensions = task_config.get("extensions")
 
-    reference_task_ids = task_config.get("reference_task_ids", [])
-
     if original_task_description is None:
         original_task_description = task.description
 
@@ -572,27 +497,11 @@ def _delegate_to_a2a(
 
             conversation_history = a2a_result.get("history", [])
 
-            if conversation_history:
-                latest_message = conversation_history[-1]
-                if latest_message.task_id is not None:
-                    task_id_config = latest_message.task_id
-                if latest_message.context_id is not None:
-                    context_id = latest_message.context_id
-
             if a2a_result["status"] in ["completed", "input_required"]:
                 if (
                     a2a_result["status"] == "completed"
                     and agent_config.trust_remote_completion_status
                 ):
-                    if (
-                        task_id_config is not None
-                        and task_id_config not in reference_task_ids
-                    ):
-                        reference_task_ids.append(task_id_config)
-                        if task.config is None:
-                            task.config = {}
-                        task.config["reference_task_ids"] = reference_task_ids
-
                     result_text = a2a_result.get("result", "")
                     final_turn_number = turn_num + 1
                     crewai_event_bus.emit(
@@ -604,7 +513,7 @@ def _delegate_to_a2a(
                             total_turns=final_turn_number,
                         ),
                     )
-                    return cast(str, result_text)
+                    return result_text  # type: ignore[no-any-return]
 
                 final_result, next_request = _handle_agent_response_and_continue(
                     self=self,
@@ -632,31 +541,6 @@ def _delegate_to_a2a(
                 continue
 
             error_msg = a2a_result.get("error", "Unknown error")
-
-            final_result, next_request = _handle_agent_response_and_continue(
-                self=self,
-                a2a_result=a2a_result,
-                agent_id=agent_id,
-                agent_cards=agent_cards,
-                a2a_agents=a2a_agents,
-                original_task_description=original_task_description,
-                conversation_history=conversation_history,
-                turn_num=turn_num,
-                max_turns=max_turns,
-                task=task,
-                original_fn=original_fn,
-                context=context,
-                tools=tools,
-                agent_response_model=agent_response_model,
-            )
-
-            if final_result is not None:
-                return final_result
-
-            if next_request is not None:
-                current_request = next_request
-                continue
-
             crewai_event_bus.emit(
                 None,
                 A2AConversationCompletedEvent(
@@ -666,7 +550,7 @@ def _delegate_to_a2a(
                     total_turns=turn_num + 1,
                 ),
             )
-            return f"A2A delegation failed: {error_msg}"
+            raise Exception(f"A2A delegation failed: {error_msg}")
 
         if conversation_history:
             for msg in reversed(conversation_history):

lib/crewai/src/crewai/agent/internal/meta.py

@@ -4,8 +4,9 @@ This metaclass enables extension capabilities for agents by detecting
 extension fields in class annotations and applying appropriate wrappers.
 """
 
-from typing import Any
 import warnings
+from functools import wraps
+from typing import Any
 
 from pydantic import model_validator
 from pydantic._internal._model_construction import ModelMetaclass
@@ -58,15 +59,9 @@ class AgentMeta(ModelMetaclass):
 
                 a2a_value = getattr(self, "a2a", None)
                 if a2a_value is not None:
-                    from crewai.a2a.extensions.registry import (
-                        create_extension_registry_from_config,
-                    )
                     from crewai.a2a.wrapper import wrap_agent_with_a2a_instance
 
-                    extension_registry = create_extension_registry_from_config(
-                        a2a_value
-                    )
-                    wrap_agent_with_a2a_instance(self, extension_registry)
+                    wrap_agent_with_a2a_instance(self)
 
                 return result
 

lib/crewai/src/crewai/cli/crew_chat.py

@@ -14,8 +14,7 @@ import tomli
 from crewai.cli.utils import read_toml
 from crewai.cli.version import get_crewai_version
 from crewai.crew import Crew
-from crewai.llm import LLM
-from crewai.llms.base_llm import BaseLLM
+from crewai.llm import LLM, BaseLLM
 from crewai.types.crew_chat import ChatInputField, ChatInputs
 from crewai.utilities.llm_utils import create_llm
 from crewai.utilities.printer import Printer
@@ -28,7 +27,7 @@ MIN_REQUIRED_VERSION: Final[Literal["0.98.0"]] = "0.98.0"
 
 
 def check_conversational_crews_version(
-    crewai_version: str, pyproject_data: dict[str, Any]
+    crewai_version: str, pyproject_data: dict
 ) -> bool:
     """
     Check if the installed crewAI version supports conversational crews.
@@ -54,7 +53,7 @@ def check_conversational_crews_version(
     return True
 
 
-def run_chat() -> None:
+def run_chat():
     """
     Runs an interactive chat loop using the Crew's chat LLM with function calling.
     Incorporates crew_name, crew_description, and input fields to build a tool schema.
@@ -102,7 +101,7 @@ def run_chat() -> None:
 
     click.secho(f"Assistant: {introductory_message}\n", fg="green")
 
-    messages: list[LLMMessage] = [
+    messages = [
         {"role": "system", "content": system_message},
         {"role": "assistant", "content": introductory_message},
     ]
@@ -114,7 +113,7 @@ def run_chat() -> None:
     chat_loop(chat_llm, messages, crew_tool_schema, available_functions)
 
 
-def show_loading(event: threading.Event) -> None:
+def show_loading(event: threading.Event):
     """Display animated loading dots while processing."""
     while not event.is_set():
         _printer.print(".", end="")
@@ -163,23 +162,23 @@ def build_system_message(crew_chat_inputs: ChatInputs) -> str:
     )
 
 
-def create_tool_function(crew: Crew, messages: list[LLMMessage]) -> Any:
+def create_tool_function(crew: Crew, messages: list[dict[str, str]]) -> Any:
     """Creates a wrapper function for running the crew tool with messages."""
 
-    def run_crew_tool_with_messages(**kwargs: Any) -> str:
+    def run_crew_tool_with_messages(**kwargs):
         return run_crew_tool(crew, messages, **kwargs)
 
     return run_crew_tool_with_messages
 
 
-def flush_input() -> None:
+def flush_input():
     """Flush any pending input from the user."""
     if platform.system() == "Windows":
         # Windows platform
         import msvcrt
 
-        while msvcrt.kbhit():  # type: ignore[attr-defined]
-            msvcrt.getch()  # type: ignore[attr-defined]
+        while msvcrt.kbhit():
+            msvcrt.getch()
     else:
         # Unix-like platforms (Linux, macOS)
         import termios
@@ -187,12 +186,7 @@ def flush_input() -> None:
         termios.tcflush(sys.stdin, termios.TCIFLUSH)
 
 
-def chat_loop(
-    chat_llm: LLM | BaseLLM,
-    messages: list[LLMMessage],
-    crew_tool_schema: dict[str, Any],
-    available_functions: dict[str, Any],
-) -> None:
+def chat_loop(chat_llm, messages, crew_tool_schema, available_functions):
     """Main chat loop for interacting with the user."""
     while True:
         try:
@@ -231,7 +225,7 @@ def get_user_input() -> str:
 
 def handle_user_input(
     user_input: str,
-    chat_llm: LLM | BaseLLM,
+    chat_llm: LLM,
     messages: list[LLMMessage],
     crew_tool_schema: dict[str, Any],
     available_functions: dict[str, Any],
@@ -261,7 +255,7 @@ def handle_user_input(
     click.secho(f"\nAssistant: {final_response}\n", fg="green")
 
 
-def generate_crew_tool_schema(crew_inputs: ChatInputs) -> dict[str, Any]:
+def generate_crew_tool_schema(crew_inputs: ChatInputs) -> dict:
     """
     Dynamically build a Littellm 'function' schema for the given crew.
 
@@ -292,7 +286,7 @@ def generate_crew_tool_schema(crew_inputs: ChatInputs) -> dict[str, Any]:
     }
 
 
-def run_crew_tool(crew: Crew, messages: list[LLMMessage], **kwargs: Any) -> str:
+def run_crew_tool(crew: Crew, messages: list[dict[str, str]], **kwargs):
     """
     Runs the crew using crew.kickoff(inputs=kwargs) and returns the output.
 
@@ -378,9 +372,7 @@ def load_crew_and_name() -> tuple[Crew, str]:
     return crew_instance, crew_class_name
 
 
-def generate_crew_chat_inputs(
-    crew: Crew, crew_name: str, chat_llm: LLM | BaseLLM
-) -> ChatInputs:
+def generate_crew_chat_inputs(crew: Crew, crew_name: str, chat_llm) -> ChatInputs:
     """
     Generates the ChatInputs required for the crew by analyzing the tasks and agents.
 
@@ -418,12 +410,23 @@ def fetch_required_inputs(crew: Crew) -> set[str]:
     Returns:
         Set[str]: A set of placeholder names.
     """
-    return crew.fetch_inputs()
+    placeholder_pattern = re.compile(r"\{(.+?)}")
+    required_inputs: set[str] = set()
+
+    # Scan tasks
+    for task in crew.tasks:
+        text = f"{task.description or ''} {task.expected_output or ''}"
+        required_inputs.update(placeholder_pattern.findall(text))
+
+    # Scan agents
+    for agent in crew.agents:
+        text = f"{agent.role or ''} {agent.goal or ''} {agent.backstory or ''}"
+        required_inputs.update(placeholder_pattern.findall(text))
+
+    return required_inputs
 
 
-def generate_input_description_with_ai(
-    input_name: str, crew: Crew, chat_llm: LLM | BaseLLM
-) -> str:
+def generate_input_description_with_ai(input_name: str, crew: Crew, chat_llm) -> str:
     """
     Generates an input description using AI based on the context of the crew.
 
@@ -481,10 +484,10 @@ def generate_input_description_with_ai(
         f"{context}"
     )
     response = chat_llm.call(messages=[{"role": "user", "content": prompt}])
-    return str(response).strip()
+    return response.strip()
 
 
-def generate_crew_description_with_ai(crew: Crew, chat_llm: LLM | BaseLLM) -> str:
+def generate_crew_description_with_ai(crew: Crew, chat_llm) -> str:
     """
     Generates a brief description of the crew using AI.
 
@@ -531,4 +534,4 @@ def generate_crew_description_with_ai(crew: Crew, chat_llm: LLM | BaseLLM) -> st
         f"{context}"
     )
     response = chat_llm.call(messages=[{"role": "user", "content": prompt}])
-    return str(response).strip()
+    return response.strip()

lib/crewai/src/crewai/cli/shared/token_manager.py

@@ -3,56 +3,103 @@ import json
 import os
 from pathlib import Path
 import sys
-import tempfile
-from typing import Final, Literal, cast
+from typing import BinaryIO, cast
 
 from cryptography.fernet import Fernet
 
 
-_FERNET_KEY_LENGTH: Final[Literal[44]] = 44
+if sys.platform == "win32":
+    import msvcrt
+else:
+    import fcntl
 
 
 class TokenManager:
-    """Manages encrypted token storage."""
-
     def __init__(self, file_path: str = "tokens.enc") -> None:
-        """Initialize the TokenManager.
+        """
+        Initialize the TokenManager class.
 
-        Args:
-            file_path: The file path to store encrypted tokens.
+        :param file_path: The file path to store the encrypted tokens. Default is "tokens.enc".
         """
         self.file_path = file_path
         self.key = self._get_or_create_key()
         self.fernet = Fernet(self.key)
 
-    def _get_or_create_key(self) -> bytes:
-        """Get or create the encryption key.
-
-        Returns:
-            The encryption key as bytes.
+    @staticmethod
+    def _acquire_lock(file_handle: BinaryIO) -> None:
         """
-        key_filename: str = "secret.key"
-
-        key = self._read_secure_file(key_filename)
-        if key is not None and len(key) == _FERNET_KEY_LENGTH:
-            return key
-
-        new_key = Fernet.generate_key()
-        if self._atomic_create_secure_file(key_filename, new_key):
-            return new_key
-
-        key = self._read_secure_file(key_filename)
-        if key is not None and len(key) == _FERNET_KEY_LENGTH:
-            return key
-
-        raise RuntimeError("Failed to create or read encryption key")
-
-    def save_tokens(self, access_token: str, expires_at: int) -> None:
-        """Save the access token and its expiration time.
+        Acquire an exclusive lock on a file handle.
 
         Args:
-            access_token: The access token to save.
-            expires_at: The UNIX timestamp of the expiration time.
+            file_handle: Open file handle to lock.
+        """
+        if sys.platform == "win32":
+            msvcrt.locking(file_handle.fileno(), msvcrt.LK_LOCK, 1)
+        else:
+            fcntl.flock(file_handle.fileno(), fcntl.LOCK_EX)
+
+    @staticmethod
+    def _release_lock(file_handle: BinaryIO) -> None:
+        """
+        Release the lock on a file handle.
+
+        Args:
+            file_handle: Open file handle to unlock.
+        """
+        if sys.platform == "win32":
+            msvcrt.locking(file_handle.fileno(), msvcrt.LK_UNLCK, 1)
+        else:
+            fcntl.flock(file_handle.fileno(), fcntl.LOCK_UN)
+
+    def _get_or_create_key(self) -> bytes:
+        """
+        Get or create the encryption key with file locking to prevent race conditions.
+
+        Returns:
+            The encryption key.
+        """
+        key_filename = "secret.key"
+        storage_path = self.get_secure_storage_path()
+
+        key = self.read_secure_file(key_filename)
+        if key is not None and len(key) == 44:
+            return key
+
+        lock_file_path = storage_path / f"{key_filename}.lock"
+
+        try:
+            lock_file_path.touch()
+
+            with open(lock_file_path, "r+b") as lock_file:
+                self._acquire_lock(lock_file)
+                try:
+                    key = self.read_secure_file(key_filename)
+                    if key is not None and len(key) == 44:
+                        return key
+
+                    new_key = Fernet.generate_key()
+                    self.save_secure_file(key_filename, new_key)
+                    return new_key
+                finally:
+                    try:
+                        self._release_lock(lock_file)
+                    except OSError:
+                        pass
+        except OSError:
+            key = self.read_secure_file(key_filename)
+            if key is not None and len(key) == 44:
+                return key
+
+            new_key = Fernet.generate_key()
+            self.save_secure_file(key_filename, new_key)
+            return new_key
+
+    def save_tokens(self, access_token: str, expires_at: int) -> None:
+        """
+        Save the access token and its expiration time.
+
+        :param access_token: The access token to save.
+        :param expires_at: The UNIX timestamp of the expiration time.
         """
         expiration_time = datetime.fromtimestamp(expires_at)
         data = {
@@ -60,15 +107,15 @@ class TokenManager:
             "expiration": expiration_time.isoformat(),
         }
         encrypted_data = self.fernet.encrypt(json.dumps(data).encode())
-        self._atomic_write_secure_file(self.file_path, encrypted_data)
+        self.save_secure_file(self.file_path, encrypted_data)
 
     def get_token(self) -> str | None:
-        """Get the access token if it is valid and not expired.
-
-        Returns:
-            The access token if valid and not expired, otherwise None.
         """
-        encrypted_data = self._read_secure_file(self.file_path)
+        Get the access token if it is valid and not expired.
+
+        :return: The access token if valid and not expired, otherwise None.
+        """
+        encrypted_data = self.read_secure_file(self.file_path)
         if encrypted_data is None:
             return None
 
@@ -79,18 +126,20 @@ class TokenManager:
         if expiration <= datetime.now():
             return None
 
-        return cast(str | None, data.get("access_token"))
+        return cast(str | None, data["access_token"])
 
     def clear_tokens(self) -> None:
-        """Clear the stored tokens."""
-        self._delete_secure_file(self.file_path)
+        """
+        Clear the tokens.
+        """
+        self.delete_secure_file(self.file_path)
 
     @staticmethod
-    def _get_secure_storage_path() -> Path:
-        """Get the secure storage path based on the operating system.
+    def get_secure_storage_path() -> Path:
+        """
+        Get the secure storage path based on the operating system.
 
-        Returns:
-            The secure storage path.
+        :return: The secure storage path.
         """
         if sys.platform == "win32":
             base_path = os.environ.get("LOCALAPPDATA")
@@ -106,81 +155,44 @@ class TokenManager:
 
         return storage_path
 
-    def _atomic_create_secure_file(self, filename: str, content: bytes) -> bool:
-        """Create a file only if it doesn't exist.
-
-        Args:
-            filename: The name of the file.
-            content: The content to write.
-
-        Returns:
-            True if file was created, False if it already exists.
+    def save_secure_file(self, filename: str, content: bytes) -> None:
         """
-        storage_path = self._get_secure_storage_path()
+        Save the content to a secure file.
+
+        :param filename: The name of the file.
+        :param content: The content to save.
+        """
+        storage_path = self.get_secure_storage_path()
         file_path = storage_path / filename
 
-        try:
-            fd = os.open(file_path, os.O_CREAT | os.O_EXCL | os.O_WRONLY, 0o600)
-            try:
-                os.write(fd, content)
-            finally:
-                os.close(fd)
-            return True
-        except FileExistsError:
-            return False
+        with open(file_path, "wb") as f:
+            f.write(content)
 
-    def _atomic_write_secure_file(self, filename: str, content: bytes) -> None:
-        """Write content to a secure file.
+        os.chmod(file_path, 0o600)
 
-        Args:
-            filename: The name of the file.
-            content: The content to write.
+    def read_secure_file(self, filename: str) -> bytes | None:
         """
-        storage_path = self._get_secure_storage_path()
+        Read the content of a secure file.
+
+        :param filename: The name of the file.
+        :return: The content of the file if it exists, otherwise None.
+        """
+        storage_path = self.get_secure_storage_path()
         file_path = storage_path / filename
 
-        fd, temp_path = tempfile.mkstemp(dir=storage_path, prefix=f".{filename}.")
-        fd_closed = False
-        try:
-            os.write(fd, content)
-            os.close(fd)
-            fd_closed = True
-            os.chmod(temp_path, 0o600)
-            os.replace(temp_path, file_path)
-        except Exception:
-            if not fd_closed:
-                os.close(fd)
-            if os.path.exists(temp_path):
-                os.unlink(temp_path)
-            raise
-
-    def _read_secure_file(self, filename: str) -> bytes | None:
-        """Read the content of a secure file.
-
-        Args:
-            filename: The name of the file.
-
-        Returns:
-            The content of the file if it exists, otherwise None.
-        """
-        storage_path = self._get_secure_storage_path()
-        file_path = storage_path / filename
-
-        try:
-            with open(file_path, "rb") as f:
-                return f.read()
-        except FileNotFoundError:
+        if not file_path.exists():
             return None
 
-    def _delete_secure_file(self, filename: str) -> None:
-        """Delete a secure file.
+        with open(file_path, "rb") as f:
+            return f.read()
 
-        Args:
-            filename: The name of the file.
+    def delete_secure_file(self, filename: str) -> None:
         """
-        storage_path = self._get_secure_storage_path()
+        Delete the secure file.
+
+        :param filename: The name of the file.
+        """
+        storage_path = self.get_secure_storage_path()
         file_path = storage_path / filename
-        try:
-            file_path.unlink()
-        except FileNotFoundError:
-            pass
+        if file_path.exists():
+            file_path.unlink(missing_ok=True)

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

@@ -5,7 +5,7 @@ description = "{{name}} using crewAI"
 authors = [{ name = "Your Name", email = "you@example.com" }]
 requires-python = ">=3.10,<3.14"
 dependencies = [
-    "crewai[tools]==1.7.0"
+    "crewai[tools]==1.6.1"
 ]
 
 [project.scripts]

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

@@ -5,7 +5,7 @@ description = "{{name}} using crewAI"
 authors = [{ name = "Your Name", email = "you@example.com" }]
 requires-python = ">=3.10,<3.14"
 dependencies = [
-    "crewai[tools]==1.7.0"
+    "crewai[tools]==1.6.1"
 ]
 
 [project.scripts]

lib/crewai/src/crewai/crew.py

@@ -35,14 +35,6 @@ from crewai.agent import Agent
 from crewai.agents.agent_builder.base_agent import BaseAgent
 from crewai.agents.cache.cache_handler import CacheHandler
 from crewai.crews.crew_output import CrewOutput
-from crewai.crews.utils import (
-    StreamingContext,
-    check_conditional_skip,
-    enable_agent_streaming,
-    prepare_kickoff,
-    prepare_task_execution,
-    run_for_each_async,
-)
 from crewai.events.event_bus import crewai_event_bus
 from crewai.events.event_listener import EventListener
 from crewai.events.listeners.tracing.trace_listener import (
@@ -55,6 +47,7 @@ from crewai.events.listeners.tracing.utils import (
 from crewai.events.types.crew_events import (
     CrewKickoffCompletedEvent,
     CrewKickoffFailedEvent,
+    CrewKickoffStartedEvent,
     CrewTestCompletedEvent,
     CrewTestFailedEvent,
     CrewTestStartedEvent,
@@ -81,7 +74,7 @@ from crewai.tasks.conditional_task import ConditionalTask
 from crewai.tasks.task_output import TaskOutput
 from crewai.tools.agent_tools.agent_tools import AgentTools
 from crewai.tools.base_tool import BaseTool
-from crewai.types.streaming import CrewStreamingOutput
+from crewai.types.streaming import CrewStreamingOutput, FlowStreamingOutput
 from crewai.types.usage_metrics import UsageMetrics
 from crewai.utilities.constants import NOT_SPECIFIED, TRAINING_DATA_FILE
 from crewai.utilities.crew.models import CrewContext
@@ -99,8 +92,10 @@ from crewai.utilities.planning_handler import CrewPlanner
 from crewai.utilities.printer import PrinterColor
 from crewai.utilities.rpm_controller import RPMController
 from crewai.utilities.streaming import (
+    TaskInfo,
     create_async_chunk_generator,
     create_chunk_generator,
+    create_streaming_state,
     signal_end,
     signal_error,
 )
@@ -273,7 +268,7 @@ class Crew(FlowTrackable, BaseModel):
         description="list of file paths for task execution JSON files.",
     )
     execution_logs: list[dict[str, Any]] = Field(
-        default_factory=list,
+        default=[],
         description="list of execution logs for tasks",
     )
     knowledge_sources: list[BaseKnowledgeSource] | None = Field(
@@ -409,7 +404,8 @@ class Crew(FlowTrackable, BaseModel):
                 raise PydanticCustomError(
                     "missing_manager_llm_or_manager_agent",
                     (
-                        "Attribute `manager_llm` or `manager_agent` is required when using hierarchical process."
+                        "Attribute `manager_llm` or `manager_agent` is required "
+                        "when using hierarchical process."
                     ),
                     {},
                 )
@@ -515,9 +511,10 @@ class Crew(FlowTrackable, BaseModel):
                 raise PydanticCustomError(
                     "invalid_async_conditional_task",
                     (
-                        "Conditional Task: {description}, cannot be executed asynchronously."
+                        f"Conditional Task: {task.description}, "
+                        f"cannot be executed asynchronously."
                     ),
-                    {"description": task.description},
+                    {},
                 )
         return self
 
@@ -678,8 +675,21 @@ class Crew(FlowTrackable, BaseModel):
         inputs: dict[str, Any] | None = None,
     ) -> CrewOutput | CrewStreamingOutput:
         if self.stream:
-            enable_agent_streaming(self.agents)
-            ctx = StreamingContext()
+            for agent in self.agents:
+                if agent.llm is not None:
+                    agent.llm.stream = True
+
+            result_holder: list[CrewOutput] = []
+            current_task_info: TaskInfo = {
+                "index": 0,
+                "name": "",
+                "id": "",
+                "agent_role": "",
+                "agent_id": "",
+            }
+
+            state = create_streaming_state(current_task_info, result_holder)
+            output_holder: list[CrewStreamingOutput | FlowStreamingOutput] = []
 
             def run_crew() -> None:
                 """Execute the crew and capture the result."""
@@ -687,28 +697,59 @@ class Crew(FlowTrackable, BaseModel):
                     self.stream = False
                     crew_result = self.kickoff(inputs=inputs)
                     if isinstance(crew_result, CrewOutput):
-                        ctx.result_holder.append(crew_result)
+                        result_holder.append(crew_result)
                 except Exception as exc:
-                    signal_error(ctx.state, exc)
+                    signal_error(state, exc)
                 finally:
                     self.stream = True
-                    signal_end(ctx.state)
+                    signal_end(state)
 
             streaming_output = CrewStreamingOutput(
-                sync_iterator=create_chunk_generator(
-                    ctx.state, run_crew, ctx.output_holder
-                )
+                sync_iterator=create_chunk_generator(state, run_crew, output_holder)
             )
-            ctx.output_holder.append(streaming_output)
+            output_holder.append(streaming_output)
             return streaming_output
 
-        baggage_ctx = baggage.set_baggage(
+        ctx = baggage.set_baggage(
             "crew_context", CrewContext(id=str(self.id), key=self.key)
         )
-        token = attach(baggage_ctx)
+        token = attach(ctx)
 
         try:
-            inputs = prepare_kickoff(self, inputs)
+            for before_callback in self.before_kickoff_callbacks:
+                if inputs is None:
+                    inputs = {}
+                inputs = before_callback(inputs)
+
+            crewai_event_bus.emit(
+                self,
+                CrewKickoffStartedEvent(crew_name=self.name, inputs=inputs),
+            )
+
+            # Starts the crew to work on its assigned tasks.
+            self._task_output_handler.reset()
+            self._logging_color = "bold_purple"
+
+            if inputs is not None:
+                self._inputs = inputs
+                self._interpolate_inputs(inputs)
+            self._set_tasks_callbacks()
+            self._set_allow_crewai_trigger_context_for_first_task()
+
+            for agent in self.agents:
+                agent.crew = self
+                agent.set_knowledge(crew_embedder=self.embedder)
+                # TODO: Create an AgentFunctionCalling protocol for future refactoring
+                if not agent.function_calling_llm:  # type: ignore # "BaseAgent" has no attribute "function_calling_llm"
+                    agent.function_calling_llm = self.function_calling_llm  # type: ignore # "BaseAgent" has no attribute "function_calling_llm"
+
+                if not agent.step_callback:  # type: ignore # "BaseAgent" has no attribute "step_callback"
+                    agent.step_callback = self.step_callback  # type: ignore # "BaseAgent" has no attribute "step_callback"
+
+                agent.create_agent_executor()
+
+            if self.planning:
+                self._handle_crew_planning()
 
             if self.process == Process.sequential:
                 result = self._run_sequential_process()
@@ -773,27 +814,42 @@ class Crew(FlowTrackable, BaseModel):
         inputs = inputs or {}
 
         if self.stream:
-            enable_agent_streaming(self.agents)
-            ctx = StreamingContext(use_async=True)
+            for agent in self.agents:
+                if agent.llm is not None:
+                    agent.llm.stream = True
+
+            result_holder: list[CrewOutput] = []
+            current_task_info: TaskInfo = {
+                "index": 0,
+                "name": "",
+                "id": "",
+                "agent_role": "",
+                "agent_id": "",
+            }
+
+            state = create_streaming_state(
+                current_task_info, result_holder, use_async=True
+            )
+            output_holder: list[CrewStreamingOutput | FlowStreamingOutput] = []
 
             async def run_crew() -> None:
                 try:
                     self.stream = False
                     result = await asyncio.to_thread(self.kickoff, inputs)
                     if isinstance(result, CrewOutput):
-                        ctx.result_holder.append(result)
+                        result_holder.append(result)
                 except Exception as e:
-                    signal_error(ctx.state, e, is_async=True)
+                    signal_error(state, e, is_async=True)
                 finally:
                     self.stream = True
-                    signal_end(ctx.state, is_async=True)
+                    signal_end(state, is_async=True)
 
             streaming_output = CrewStreamingOutput(
                 async_iterator=create_async_chunk_generator(
-                    ctx.state, run_crew, ctx.output_holder
+                    state, run_crew, output_holder
                 )
             )
-            ctx.output_holder.append(streaming_output)
+            output_holder.append(streaming_output)
 
             return streaming_output
 
@@ -808,13 +864,89 @@ class Crew(FlowTrackable, BaseModel):
         from all crews as they arrive. After iteration, access results via .results
         (list of CrewOutput).
         """
+        crew_copies = [self.copy() for _ in inputs]
 
-        async def kickoff_fn(
-            crew: Crew, input_data: dict[str, Any]
-        ) -> CrewOutput | CrewStreamingOutput:
-            return await crew.kickoff_async(inputs=input_data)
+        if self.stream:
+            result_holder: list[list[CrewOutput]] = [[]]
+            current_task_info: TaskInfo = {
+                "index": 0,
+                "name": "",
+                "id": "",
+                "agent_role": "",
+                "agent_id": "",
+            }
 
-        return await run_for_each_async(self, inputs, kickoff_fn)
+            state = create_streaming_state(
+                current_task_info, result_holder, use_async=True
+            )
+            output_holder: list[CrewStreamingOutput | FlowStreamingOutput] = []
+
+            async def run_all_crews() -> None:
+                """Run all crew copies and aggregate their streaming outputs."""
+                try:
+                    streaming_outputs: list[CrewStreamingOutput] = []
+                    for i, crew in enumerate(crew_copies):
+                        streaming = await crew.kickoff_async(inputs=inputs[i])
+                        if isinstance(streaming, CrewStreamingOutput):
+                            streaming_outputs.append(streaming)
+
+                    async def consume_stream(
+                        stream_output: CrewStreamingOutput,
+                    ) -> CrewOutput:
+                        """Consume stream chunks and forward to parent queue.
+
+                        Args:
+                            stream_output: The streaming output to consume.
+
+                        Returns:
+                            The final CrewOutput result.
+                        """
+                        async for chunk in stream_output:
+                            if state.async_queue is not None and state.loop is not None:
+                                state.loop.call_soon_threadsafe(
+                                    state.async_queue.put_nowait, chunk
+                                )
+                        return stream_output.result
+
+                    crew_results = await asyncio.gather(
+                        *[consume_stream(s) for s in streaming_outputs]
+                    )
+                    result_holder[0] = list(crew_results)
+                except Exception as e:
+                    signal_error(state, e, is_async=True)
+                finally:
+                    signal_end(state, is_async=True)
+
+            streaming_output = CrewStreamingOutput(
+                async_iterator=create_async_chunk_generator(
+                    state, run_all_crews, output_holder
+                )
+            )
+
+            def set_results_wrapper(result: Any) -> None:
+                """Wrap _set_results to match _set_result signature."""
+                streaming_output._set_results(result)
+
+            streaming_output._set_result = set_results_wrapper  # type: ignore[method-assign]
+            output_holder.append(streaming_output)
+
+            return streaming_output
+
+        tasks = [
+            asyncio.create_task(crew_copy.kickoff_async(inputs=input_data))
+            for crew_copy, input_data in zip(crew_copies, inputs, strict=True)
+        ]
+
+        results = await asyncio.gather(*tasks)
+
+        total_usage_metrics = UsageMetrics()
+        for crew_copy in crew_copies:
+            if crew_copy.usage_metrics:
+                total_usage_metrics.add_usage_metrics(crew_copy.usage_metrics)
+        self.usage_metrics = total_usage_metrics
+
+        self._task_output_handler.reset()
+        return list(results)
 
     async def akickoff(
         self, inputs: dict[str, Any] | None = None
@@ -826,37 +958,83 @@ class Crew(FlowTrackable, BaseModel):
         memory operations, and knowledge queries.
         """
         if self.stream:
-            enable_agent_streaming(self.agents)
-            ctx = StreamingContext(use_async=True)
+            for agent in self.agents:
+                if agent.llm is not None:
+                    agent.llm.stream = True
+
+            result_holder: list[CrewOutput] = []
+            current_task_info: TaskInfo = {
+                "index": 0,
+                "name": "",
+                "id": "",
+                "agent_role": "",
+                "agent_id": "",
+            }
+
+            state = create_streaming_state(
+                current_task_info, result_holder, use_async=True
+            )
+            output_holder: list[CrewStreamingOutput | FlowStreamingOutput] = []
 
             async def run_crew() -> None:
                 try:
                     self.stream = False
-                    inner_result = await self.akickoff(inputs)
-                    if isinstance(inner_result, CrewOutput):
-                        ctx.result_holder.append(inner_result)
-                except Exception as exc:
-                    signal_error(ctx.state, exc, is_async=True)
+                    result = await self.akickoff(inputs)
+                    if isinstance(result, CrewOutput):
+                        result_holder.append(result)
+                except Exception as e:
+                    signal_error(state, e, is_async=True)
                 finally:
                     self.stream = True
-                    signal_end(ctx.state, is_async=True)
+                    signal_end(state, is_async=True)
 
             streaming_output = CrewStreamingOutput(
                 async_iterator=create_async_chunk_generator(
-                    ctx.state, run_crew, ctx.output_holder
+                    state, run_crew, output_holder
                 )
             )
-            ctx.output_holder.append(streaming_output)
+            output_holder.append(streaming_output)
 
             return streaming_output
 
-        baggage_ctx = baggage.set_baggage(
+        ctx = baggage.set_baggage(
             "crew_context", CrewContext(id=str(self.id), key=self.key)
         )
-        token = attach(baggage_ctx)
+        token = attach(ctx)
 
         try:
-            inputs = prepare_kickoff(self, inputs)
+            for before_callback in self.before_kickoff_callbacks:
+                if inputs is None:
+                    inputs = {}
+                inputs = before_callback(inputs)
+
+            crewai_event_bus.emit(
+                self,
+                CrewKickoffStartedEvent(crew_name=self.name, inputs=inputs),
+            )
+
+            self._task_output_handler.reset()
+            self._logging_color = "bold_purple"
+
+            if inputs is not None:
+                self._inputs = inputs
+                self._interpolate_inputs(inputs)
+            self._set_tasks_callbacks()
+            self._set_allow_crewai_trigger_context_for_first_task()
+
+            for agent in self.agents:
+                agent.crew = self
+                agent.set_knowledge(crew_embedder=self.embedder)
+                if not agent.function_calling_llm:  # type: ignore[attr-defined]
+                    agent.function_calling_llm = self.function_calling_llm  # type: ignore[attr-defined]
+
+                if not agent.step_callback:  # type: ignore[attr-defined]
+                    agent.step_callback = self.step_callback  # type: ignore[attr-defined]
+
+                agent.create_agent_executor()
+
+            if self.planning:
+                self._handle_crew_planning()
 
             if self.process == Process.sequential:
                 result = await self._arun_sequential_process()
@@ -891,13 +1069,79 @@ class Crew(FlowTrackable, BaseModel):
         If stream=True, returns a single CrewStreamingOutput that yields chunks
         from all crews as they arrive.
         """
+        crew_copies = [self.copy() for _ in inputs]
 
-        async def kickoff_fn(
-            crew: Crew, input_data: dict[str, Any]
-        ) -> CrewOutput | CrewStreamingOutput:
-            return await crew.akickoff(inputs=input_data)
+        if self.stream:
+            result_holder: list[list[CrewOutput]] = [[]]
+            current_task_info: TaskInfo = {
+                "index": 0,
+                "name": "",
+                "id": "",
+                "agent_role": "",
+                "agent_id": "",
+            }
 
-        return await run_for_each_async(self, inputs, kickoff_fn)
+            state = create_streaming_state(
+                current_task_info, result_holder, use_async=True
+            )
+            output_holder: list[CrewStreamingOutput | FlowStreamingOutput] = []
+
+            async def run_all_crews() -> None:
+                try:
+                    streaming_outputs: list[CrewStreamingOutput] = []
+                    for i, crew in enumerate(crew_copies):
+                        streaming = await crew.akickoff(inputs=inputs[i])
+                        if isinstance(streaming, CrewStreamingOutput):
+                            streaming_outputs.append(streaming)
+
+                    async def consume_stream(
+                        stream_output: CrewStreamingOutput,
+                    ) -> CrewOutput:
+                        async for chunk in stream_output:
+                            if state.async_queue is not None and state.loop is not None:
+                                state.loop.call_soon_threadsafe(
+                                    state.async_queue.put_nowait, chunk
+                                )
+                        return stream_output.result
+
+                    crew_results = await asyncio.gather(
+                        *[consume_stream(s) for s in streaming_outputs]
+                    )
+                    result_holder[0] = list(crew_results)
+                except Exception as e:
+                    signal_error(state, e, is_async=True)
+                finally:
+                    signal_end(state, is_async=True)
+
+            streaming_output = CrewStreamingOutput(
+                async_iterator=create_async_chunk_generator(
+                    state, run_all_crews, output_holder
+                )
+            )
+
+            def set_results_wrapper(result: Any) -> None:
+                streaming_output._set_results(result)
+
+            streaming_output._set_result = set_results_wrapper  # type: ignore[method-assign]
+            output_holder.append(streaming_output)
+
+            return streaming_output
+
+        tasks = [
+            asyncio.create_task(crew_copy.akickoff(inputs=input_data))
+            for crew_copy, input_data in zip(crew_copies, inputs, strict=True)
+        ]
+
+        results = await asyncio.gather(*tasks)
+
+        total_usage_metrics = UsageMetrics()
+        for crew_copy in crew_copies:
+            if crew_copy.usage_metrics:
+                total_usage_metrics.add_usage_metrics(crew_copy.usage_metrics)
+        self.usage_metrics = total_usage_metrics
+
+        self._task_output_handler.reset()
+        return list(results)
 
     async def _arun_sequential_process(self) -> CrewOutput:
         """Executes tasks sequentially using native async and returns the final output."""
@@ -929,12 +1173,32 @@ class Crew(FlowTrackable, BaseModel):
         last_sync_output: TaskOutput | None = None
 
         for task_index, task in enumerate(tasks):
-            exec_data, task_outputs, last_sync_output = prepare_task_execution(
-                self, task, task_index, start_index, task_outputs, last_sync_output
-            )
-            if exec_data.should_skip:
+            if start_index is not None and task_index < start_index:
+                if task.output:
+                    if task.async_execution:
+                        task_outputs.append(task.output)
+                    else:
+                        task_outputs = [task.output]
+                        last_sync_output = task.output
                 continue
 
+            agent_to_use = self._get_agent_to_use(task)
+            if agent_to_use is None:
+                raise ValueError(
+                    f"No agent available for task: {task.description}. "
+                    f"Ensure that either the task has an assigned agent "
+                    f"or a manager agent is provided."
+                )
+
+            tools_for_task = task.tools or agent_to_use.tools or []
+            tools_for_task = self._prepare_tools(
+                agent_to_use,
+                task,
+                tools_for_task,
+            )
+
+            self._log_task_start(task, agent_to_use.role)
+
             if isinstance(task, ConditionalTask):
                 skipped_task_output = await self._ahandle_conditional_task(
                     task, task_outputs, pending_tasks, task_index, was_replayed
@@ -949,9 +1213,9 @@ class Crew(FlowTrackable, BaseModel):
                 )
                 async_task = asyncio.create_task(
                     task.aexecute_sync(
-                        agent=exec_data.agent,
+                        agent=agent_to_use,
                         context=context,
-                        tools=exec_data.tools,
+                        tools=tools_for_task,
                     )
                 )
                 pending_tasks.append((task, async_task, task_index))
@@ -964,9 +1228,9 @@ class Crew(FlowTrackable, BaseModel):
 
                 context = self._get_context(task, task_outputs)
                 task_output = await task.aexecute_sync(
-                    agent=exec_data.agent,
+                    agent=agent_to_use,
                     context=context,
-                    tools=exec_data.tools,
+                    tools=tools_for_task,
                 )
                 task_outputs.append(task_output)
                 self._process_task_result(task, task_output)
@@ -990,9 +1254,19 @@ class Crew(FlowTrackable, BaseModel):
             task_outputs = await self._aprocess_async_tasks(pending_tasks, was_replayed)
             pending_tasks.clear()
 
-        return check_conditional_skip(
-            self, task, task_outputs, task_index, was_replayed
-        )
+        previous_output = task_outputs[-1] if task_outputs else None
+        if previous_output is not None and not task.should_execute(previous_output):
+            self._logger.log(
+                "debug",
+                f"Skipping conditional task: {task.description}",
+                color="yellow",
+            )
+            skipped_task_output = task.get_skipped_task_output()
+
+            if not was_replayed:
+                self._store_execution_log(task, skipped_task_output, task_index)
+            return skipped_task_output
+        return None
 
     async def _aprocess_async_tasks(
         self,
@@ -1110,12 +1384,34 @@ class Crew(FlowTrackable, BaseModel):
         last_sync_output: TaskOutput | None = None
 
         for task_index, task in enumerate(tasks):
-            exec_data, task_outputs, last_sync_output = prepare_task_execution(
-                self, task, task_index, start_index, task_outputs, last_sync_output
-            )
-            if exec_data.should_skip:
+            if start_index is not None and task_index < start_index:
+                if task.output:
+                    if task.async_execution:
+                        task_outputs.append(task.output)
+                    else:
+                        task_outputs = [task.output]
+                        last_sync_output = task.output
                 continue
 
+            agent_to_use = self._get_agent_to_use(task)
+            if agent_to_use is None:
+                raise ValueError(
+                    f"No agent available for task: {task.description}. "
+                    f"Ensure that either the task has an assigned agent "
+                    f"or a manager agent is provided."
+                )
+
+            # Determine which tools to use - task tools take precedence over agent tools
+            tools_for_task = task.tools or agent_to_use.tools or []
+            # Prepare tools and ensure they're compatible with task execution
+            tools_for_task = self._prepare_tools(
+                agent_to_use,
+                task,
+                tools_for_task,
+            )
+
+            self._log_task_start(task, agent_to_use.role)
+
             if isinstance(task, ConditionalTask):
                 skipped_task_output = self._handle_conditional_task(
                     task, task_outputs, futures, task_index, was_replayed
@@ -1129,9 +1425,9 @@ class Crew(FlowTrackable, BaseModel):
                     task, [last_sync_output] if last_sync_output else []
                 )
                 future = task.execute_async(
-                    agent=exec_data.agent,
+                    agent=agent_to_use,
                     context=context,
-                    tools=exec_data.tools,
+                    tools=tools_for_task,
                 )
                 futures.append((task, future, task_index))
             else:
@@ -1141,9 +1437,9 @@ class Crew(FlowTrackable, BaseModel):
 
                 context = self._get_context(task, task_outputs)
                 task_output = task.execute_sync(
-                    agent=exec_data.agent,
+                    agent=agent_to_use,
                     context=context,
-                    tools=exec_data.tools,
+                    tools=tools_for_task,
                 )
                 task_outputs.append(task_output)
                 self._process_task_result(task, task_output)
@@ -1166,9 +1462,19 @@ class Crew(FlowTrackable, BaseModel):
             task_outputs = self._process_async_tasks(futures, was_replayed)
             futures.clear()
 
-        return check_conditional_skip(
-            self, task, task_outputs, task_index, was_replayed
-        )
+        previous_output = task_outputs[-1] if task_outputs else None
+        if previous_output is not None and not task.should_execute(previous_output):
+            self._logger.log(
+                "debug",
+                f"Skipping conditional task: {task.description}",
+                color="yellow",
+            )
+            skipped_task_output = task.get_skipped_task_output()
+
+            if not was_replayed:
+                self._store_execution_log(task, skipped_task_output, task_index)
+            return skipped_task_output
+        return None
 
     def _prepare_tools(
         self, agent: BaseAgent, task: Task, tools: list[BaseTool]
@@ -1332,8 +1638,7 @@ class Crew(FlowTrackable, BaseModel):
                 )
         return tools
 
-    @staticmethod
-    def _get_context(task: Task, task_outputs: list[TaskOutput]) -> str:
+    def _get_context(self, task: Task, task_outputs: list[TaskOutput]) -> str:
         if not task.context:
             return ""
 
@@ -1402,8 +1707,7 @@ class Crew(FlowTrackable, BaseModel):
             )
         return task_outputs
 
-    @staticmethod
-    def _find_task_index(task_id: str, stored_outputs: list[Any]) -> int | None:
+    def _find_task_index(self, task_id: str, stored_outputs: list[Any]) -> int | None:
         return next(
             (
                 index
@@ -1481,7 +1785,7 @@ class Crew(FlowTrackable, BaseModel):
 
         Returns a set of all discovered placeholder names.
         """
-        placeholder_pattern = re.compile(r"\{(.+?)}")
+        placeholder_pattern = re.compile(r"\{(.+?)\}")
         required_inputs: set[str] = set()
 
         # Scan tasks for inputs
@@ -1729,32 +2033,6 @@ class Crew(FlowTrackable, BaseModel):
             self._logger.log("error", error_msg)
             raise RuntimeError(error_msg) from e
 
-    def _reset_memory_system(
-        self, system: Any, name: str, reset_fn: Callable[[Any], Any]
-    ) -> None:
-        """Reset a single memory system.
-
-        Args:
-            system: The memory system instance to reset.
-            name: Display name of the memory system for logging.
-            reset_fn: Function to call to reset the system.
-
-        Raises:
-            RuntimeError: If the reset operation fails.
-        """
-        try:
-            reset_fn(system)
-            self._logger.log(
-                "info",
-                f"[Crew ({self.name if self.name else self.id})] "
-                f"{name} memory has been reset",
-            )
-        except Exception as e:
-            raise RuntimeError(
-                f"[Crew ({self.name if self.name else self.id})] "
-                f"Failed to reset {name} memory: {e!s}"
-            ) from e
-
     def _reset_all_memories(self) -> None:
         """Reset all available memory systems."""
         memory_systems = self._get_memory_systems()
@@ -1762,10 +2040,21 @@ class Crew(FlowTrackable, BaseModel):
         for config in memory_systems.values():
             if (system := config.get("system")) is not None:
                 name = config.get("name")
-                reset_fn: Callable[[Any], Any] = cast(
-                    Callable[[Any], Any], config.get("reset")
-                )
-                self._reset_memory_system(system, name, reset_fn)
+                try:
+                    reset_fn: Callable[[Any], Any] = cast(
+                        Callable[[Any], Any], config.get("reset")
+                    )
+                    reset_fn(system)
+                    self._logger.log(
+                        "info",
+                        f"[Crew ({self.name if self.name else self.id})] "
+                        f"{name} memory has been reset",
+                    )
+                except Exception as e:
+                    raise RuntimeError(
+                        f"[Crew ({self.name if self.name else self.id})] "
+                        f"Failed to reset {name} memory: {e!s}"
+                    ) from e
 
     def _reset_specific_memory(self, memory_type: str) -> None:
         """Reset a specific memory system.
@@ -1784,8 +2073,21 @@ class Crew(FlowTrackable, BaseModel):
         if system is None:
             raise RuntimeError(f"{name} memory system is not initialized")
 
-        reset_fn: Callable[[Any], Any] = cast(Callable[[Any], Any], config.get("reset"))
-        self._reset_memory_system(system, name, reset_fn)
+        try:
+            reset_fn: Callable[[Any], Any] = cast(
+                Callable[[Any], Any], config.get("reset")
+            )
+            reset_fn(system)
+            self._logger.log(
+                "info",
+                f"[Crew ({self.name if self.name else self.id})] "
+                f"{name} memory has been reset",
+            )
+        except Exception as e:
+            raise RuntimeError(
+                f"[Crew ({self.name if self.name else self.id})] "
+                f"Failed to reset {name} memory: {e!s}"
+            ) from e
 
     def _get_memory_systems(self) -> dict[str, Any]:
         """Get all available memory systems with their configuration.
@@ -1873,8 +2175,7 @@ class Crew(FlowTrackable, BaseModel):
         ):
             self.tasks[0].allow_crewai_trigger_context = True
 
-    @staticmethod
-    def _show_tracing_disabled_message() -> None:
+    def _show_tracing_disabled_message(self) -> None:
         """Show a message when tracing is disabled."""
         from crewai.events.listeners.tracing.utils import has_user_declined_tracing
 

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

@@ -1,363 +0,0 @@
-"""Utility functions for crew operations."""
-
-from __future__ import annotations
-
-import asyncio
-from collections.abc import Callable, Coroutine, Iterable
-from typing import TYPE_CHECKING, Any
-
-from crewai.agents.agent_builder.base_agent import BaseAgent
-from crewai.crews.crew_output import CrewOutput
-from crewai.rag.embeddings.types import EmbedderConfig
-from crewai.types.streaming import CrewStreamingOutput, FlowStreamingOutput
-from crewai.utilities.streaming import (
-    StreamingState,
-    TaskInfo,
-    create_streaming_state,
-)
-
-
-if TYPE_CHECKING:
-    from crewai.crew import Crew
-
-
-def enable_agent_streaming(agents: Iterable[BaseAgent]) -> None:
-    """Enable streaming on all agents that have an LLM configured.
-
-    Args:
-        agents: Iterable of agents to enable streaming on.
-    """
-    for agent in agents:
-        if agent.llm is not None:
-            agent.llm.stream = True
-
-
-def setup_agents(
-    crew: Crew,
-    agents: Iterable[BaseAgent],
-    embedder: EmbedderConfig | None,
-    function_calling_llm: Any,
-    step_callback: Callable[..., Any] | None,
-) -> None:
-    """Set up agents for crew execution.
-
-    Args:
-        crew: The crew instance agents belong to.
-        agents: Iterable of agents to set up.
-        embedder: Embedder configuration for knowledge.
-        function_calling_llm: Default function calling LLM for agents.
-        step_callback: Default step callback for agents.
-    """
-    for agent in agents:
-        agent.crew = crew
-        agent.set_knowledge(crew_embedder=embedder)
-        if not agent.function_calling_llm:  # type: ignore[attr-defined]
-            agent.function_calling_llm = function_calling_llm  # type: ignore[attr-defined]
-        if not agent.step_callback:  # type: ignore[attr-defined]
-            agent.step_callback = step_callback  # type: ignore[attr-defined]
-        agent.create_agent_executor()
-
-
-class TaskExecutionData:
-    """Data container for prepared task execution information."""
-
-    def __init__(
-        self,
-        agent: BaseAgent | None,
-        tools: list[Any],
-        should_skip: bool = False,
-    ) -> None:
-        """Initialize task execution data.
-
-        Args:
-            agent: The agent to use for task execution (None if skipped).
-            tools: Prepared tools for the task.
-            should_skip: Whether the task should be skipped (replay).
-        """
-        self.agent = agent
-        self.tools = tools
-        self.should_skip = should_skip
-
-
-def prepare_task_execution(
-    crew: Crew,
-    task: Any,
-    task_index: int,
-    start_index: int | None,
-    task_outputs: list[Any],
-    last_sync_output: Any | None,
-) -> tuple[TaskExecutionData, list[Any], Any | None]:
-    """Prepare a task for execution, handling replay skip logic and agent/tool setup.
-
-    Args:
-        crew: The crew instance.
-        task: The task to prepare.
-        task_index: Index of the current task.
-        start_index: Index to start execution from (for replay).
-        task_outputs: Current list of task outputs.
-        last_sync_output: Last synchronous task output.
-
-    Returns:
-        A tuple of (TaskExecutionData or None if skipped, updated task_outputs, updated last_sync_output).
-        If the task should be skipped, TaskExecutionData will have should_skip=True.
-
-    Raises:
-        ValueError: If no agent is available for the task.
-    """
-    # Handle replay skip
-    if start_index is not None and task_index < start_index:
-        if task.output:
-            if task.async_execution:
-                task_outputs.append(task.output)
-            else:
-                task_outputs = [task.output]
-                last_sync_output = task.output
-        return (
-            TaskExecutionData(agent=None, tools=[], should_skip=True),
-            task_outputs,
-            last_sync_output,
-        )
-
-    agent_to_use = crew._get_agent_to_use(task)
-    if agent_to_use is None:
-        raise ValueError(
-            f"No agent available for task: {task.description}. "
-            f"Ensure that either the task has an assigned agent "
-            f"or a manager agent is provided."
-        )
-
-    tools_for_task = task.tools or agent_to_use.tools or []
-    tools_for_task = crew._prepare_tools(
-        agent_to_use,
-        task,
-        tools_for_task,
-    )
-
-    crew._log_task_start(task, agent_to_use.role)
-
-    return (
-        TaskExecutionData(agent=agent_to_use, tools=tools_for_task),
-        task_outputs,
-        last_sync_output,
-    )
-
-
-def check_conditional_skip(
-    crew: Crew,
-    task: Any,
-    task_outputs: list[Any],
-    task_index: int,
-    was_replayed: bool,
-) -> Any | None:
-    """Check if a conditional task should be skipped.
-
-    Args:
-        crew: The crew instance.
-        task: The conditional task to check.
-        task_outputs: List of previous task outputs.
-        task_index: Index of the current task.
-        was_replayed: Whether this is a replayed execution.
-
-    Returns:
-        The skipped task output if the task should be skipped, None otherwise.
-    """
-    previous_output = task_outputs[-1] if task_outputs else None
-    if previous_output is not None and not task.should_execute(previous_output):
-        crew._logger.log(
-            "debug",
-            f"Skipping conditional task: {task.description}",
-            color="yellow",
-        )
-        skipped_task_output = task.get_skipped_task_output()
-
-        if not was_replayed:
-            crew._store_execution_log(task, skipped_task_output, task_index)
-        return skipped_task_output
-    return None
-
-
-def prepare_kickoff(crew: Crew, inputs: dict[str, Any] | None) -> dict[str, Any] | None:
-    """Prepare crew for kickoff execution.
-
-    Handles before callbacks, event emission, task handler reset, input
-    interpolation, task callbacks, agent setup, and planning.
-
-    Args:
-        crew: The crew instance to prepare.
-        inputs: Optional input dictionary to pass to the crew.
-
-    Returns:
-        The potentially modified inputs dictionary after before callbacks.
-    """
-    from crewai.events.event_bus import crewai_event_bus
-    from crewai.events.types.crew_events import CrewKickoffStartedEvent
-
-    for before_callback in crew.before_kickoff_callbacks:
-        if inputs is None:
-            inputs = {}
-        inputs = before_callback(inputs)
-
-    future = crewai_event_bus.emit(
-        crew,
-        CrewKickoffStartedEvent(crew_name=crew.name, inputs=inputs),
-    )
-    if future is not None:
-        try:
-            future.result()
-        except Exception:  # noqa: S110
-            pass
-
-    crew._task_output_handler.reset()
-    crew._logging_color = "bold_purple"
-
-    if inputs is not None:
-        crew._inputs = inputs
-        crew._interpolate_inputs(inputs)
-    crew._set_tasks_callbacks()
-    crew._set_allow_crewai_trigger_context_for_first_task()
-
-    setup_agents(
-        crew,
-        crew.agents,
-        crew.embedder,
-        crew.function_calling_llm,
-        crew.step_callback,
-    )
-
-    if crew.planning:
-        crew._handle_crew_planning()
-
-    return inputs
-
-
-class StreamingContext:
-    """Container for streaming state and holders used during crew execution."""
-
-    def __init__(self, use_async: bool = False) -> None:
-        """Initialize streaming context.
-
-        Args:
-            use_async: Whether to use async streaming mode.
-        """
-        self.result_holder: list[CrewOutput] = []
-        self.current_task_info: TaskInfo = {
-            "index": 0,
-            "name": "",
-            "id": "",
-            "agent_role": "",
-            "agent_id": "",
-        }
-        self.state: StreamingState = create_streaming_state(
-            self.current_task_info, self.result_holder, use_async=use_async
-        )
-        self.output_holder: list[CrewStreamingOutput | FlowStreamingOutput] = []
-
-
-class ForEachStreamingContext:
-    """Container for streaming state used in for_each crew execution methods."""
-
-    def __init__(self) -> None:
-        """Initialize for_each streaming context."""
-        self.result_holder: list[list[CrewOutput]] = [[]]
-        self.current_task_info: TaskInfo = {
-            "index": 0,
-            "name": "",
-            "id": "",
-            "agent_role": "",
-            "agent_id": "",
-        }
-        self.state: StreamingState = create_streaming_state(
-            self.current_task_info, self.result_holder, use_async=True
-        )
-        self.output_holder: list[CrewStreamingOutput | FlowStreamingOutput] = []
-
-
-async def run_for_each_async(
-    crew: Crew,
-    inputs: list[dict[str, Any]],
-    kickoff_fn: Callable[
-        [Crew, dict[str, Any]], Coroutine[Any, Any, CrewOutput | CrewStreamingOutput]
-    ],
-) -> list[CrewOutput | CrewStreamingOutput] | CrewStreamingOutput:
-    """Execute crew workflow for each input asynchronously.
-
-    Args:
-        crew: The crew instance to execute.
-        inputs: List of input dictionaries for each execution.
-        kickoff_fn: Async function to call for each crew copy (kickoff_async or akickoff).
-
-    Returns:
-        If streaming, a single CrewStreamingOutput that yields chunks from all crews.
-        Otherwise, a list of CrewOutput results.
-    """
-    from crewai.types.usage_metrics import UsageMetrics
-    from crewai.utilities.streaming import (
-        create_async_chunk_generator,
-        signal_end,
-        signal_error,
-    )
-
-    crew_copies = [crew.copy() for _ in inputs]
-
-    if crew.stream:
-        ctx = ForEachStreamingContext()
-
-        async def run_all_crews() -> None:
-            try:
-                streaming_outputs: list[CrewStreamingOutput] = []
-                for i, crew_copy in enumerate(crew_copies):
-                    streaming = await kickoff_fn(crew_copy, inputs[i])
-                    if isinstance(streaming, CrewStreamingOutput):
-                        streaming_outputs.append(streaming)
-
-                async def consume_stream(
-                    stream_output: CrewStreamingOutput,
-                ) -> CrewOutput:
-                    async for chunk in stream_output:
-                        if (
-                            ctx.state.async_queue is not None
-                            and ctx.state.loop is not None
-                        ):
-                            ctx.state.loop.call_soon_threadsafe(
-                                ctx.state.async_queue.put_nowait, chunk
-                            )
-                    return stream_output.result
-
-                crew_results = await asyncio.gather(
-                    *[consume_stream(s) for s in streaming_outputs]
-                )
-                ctx.result_holder[0] = list(crew_results)
-            except Exception as e:
-                signal_error(ctx.state, e, is_async=True)
-            finally:
-                signal_end(ctx.state, is_async=True)
-
-        streaming_output = CrewStreamingOutput(
-            async_iterator=create_async_chunk_generator(
-                ctx.state, run_all_crews, ctx.output_holder
-            )
-        )
-
-        def set_results_wrapper(result: Any) -> None:
-            streaming_output._set_results(result)
-
-        streaming_output._set_result = set_results_wrapper  # type: ignore[method-assign]
-        ctx.output_holder.append(streaming_output)
-
-        return streaming_output
-
-    async_tasks: list[asyncio.Task[CrewOutput | CrewStreamingOutput]] = [
-        asyncio.create_task(kickoff_fn(crew_copy, input_data))
-        for crew_copy, input_data in zip(crew_copies, inputs, strict=True)
-    ]
-
-    results = await asyncio.gather(*async_tasks)
-
-    total_usage_metrics = UsageMetrics()
-    for crew_copy in crew_copies:
-        if crew_copy.usage_metrics:
-            total_usage_metrics.add_usage_metrics(crew_copy.usage_metrics)
-    crew.usage_metrics = total_usage_metrics
-
-    crew._task_output_handler.reset()
-    return list(results)

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

@@ -140,9 +140,7 @@ class EventListener(BaseEventListener):
         def on_crew_started(source: Any, event: CrewKickoffStartedEvent) -> None:
             with self._crew_tree_lock:
                 self.formatter.create_crew_tree(event.crew_name or "Crew", source.id)
-                source._execution_span = self._telemetry.crew_execution_span(
-                    source, event.inputs
-                )
+                self._telemetry.crew_execution_span(source, event.inputs)
                 self._crew_tree_lock.notify_all()
 
         @crewai_event_bus.on(CrewKickoffCompletedEvent)

lib/crewai/src/crewai/hooks/llm_hooks.py

@@ -1,6 +1,6 @@
 from __future__ import annotations
 
-from typing import TYPE_CHECKING, Any, cast
+from typing import TYPE_CHECKING
 
 from crewai.events.event_listener import event_listener
 from crewai.hooks.types import AfterLLMCallHookType, BeforeLLMCallHookType
@@ -9,22 +9,17 @@ from crewai.utilities.printer import Printer
 
 if TYPE_CHECKING:
     from crewai.agents.crew_agent_executor import CrewAgentExecutor
-    from crewai.lite_agent import LiteAgent
-    from crewai.llms.base_llm import BaseLLM
-    from crewai.utilities.types import LLMMessage
 
 
 class LLMCallHookContext:
-    """Context object passed to LLM call hooks.
+    """Context object passed to LLM call hooks with full executor access.
 
-    Provides hooks with complete access to the execution state, allowing
+    Provides hooks with complete access to the executor state, allowing
     modification of messages, responses, and executor attributes.
 
-    Supports both executor-based calls (agents in crews/flows) and direct LLM calls.
-
     Attributes:
-        executor: Reference to the executor (CrewAgentExecutor/LiteAgent) or None for direct calls
-        messages: Direct reference to messages (mutable list).
+        executor: Full reference to the CrewAgentExecutor instance
+        messages: Direct reference to executor.messages (mutable list).
             Can be modified in both before_llm_call and after_llm_call hooks.
             Modifications in after_llm_call hooks persist to the next iteration,
             allowing hooks to modify conversation history for subsequent LLM calls.
@@ -32,75 +27,33 @@ class LLMCallHookContext:
             Do NOT replace the list (e.g., context.messages = []), as this will break
             the executor. Use context.messages.append() or context.messages.extend()
             instead of assignment.
-        agent: Reference to the agent executing the task (None for direct LLM calls)
-        task: Reference to the task being executed (None for direct LLM calls or LiteAgent)
-        crew: Reference to the crew instance (None for direct LLM calls or LiteAgent)
+        agent: Reference to the agent executing the task
+        task: Reference to the task being executed
+        crew: Reference to the crew instance
         llm: Reference to the LLM instance
-        iterations: Current iteration count (0 for direct LLM calls)
+        iterations: Current iteration count
         response: LLM response string (only set for after_llm_call hooks).
             Can be modified by returning a new string from after_llm_call hook.
     """
 
-    executor: CrewAgentExecutor | LiteAgent | None
-    messages: list[LLMMessage]
-    agent: Any
-    task: Any
-    crew: Any
-    llm: BaseLLM | None | str | Any
-    iterations: int
-    response: str | None
-
     def __init__(
         self,
-        executor: CrewAgentExecutor | LiteAgent | None = None,
+        executor: CrewAgentExecutor,
         response: str | None = None,
-        messages: list[LLMMessage] | None = None,
-        llm: BaseLLM | str | Any | None = None,  # TODO: look into
-        agent: Any | None = None,
-        task: Any | None = None,
-        crew: Any | None = None,
     ) -> None:
-        """Initialize hook context with executor reference or direct parameters.
+        """Initialize hook context with executor reference.
 
         Args:
-            executor: The CrewAgentExecutor or LiteAgent instance (None for direct LLM calls)
+            executor: The CrewAgentExecutor instance
             response: Optional response string (for after_llm_call hooks)
-            messages: Optional messages list (for direct LLM calls when executor is None)
-            llm: Optional LLM instance (for direct LLM calls when executor is None)
-            agent: Optional agent reference (for direct LLM calls when executor is None)
-            task: Optional task reference (for direct LLM calls when executor is None)
-            crew: Optional crew reference (for direct LLM calls when executor is None)
         """
-        if executor is not None:
-            # Existing path: extract from executor
-            self.executor = executor
-            self.messages = executor.messages
-            self.llm = executor.llm
-            self.iterations = executor.iterations
-            # Handle CrewAgentExecutor vs LiteAgent differences
-            if hasattr(executor, "agent"):
-                self.agent = executor.agent
-                self.task = cast("CrewAgentExecutor", executor).task
-                self.crew = cast("CrewAgentExecutor", executor).crew
-            else:
-                # LiteAgent case - is the agent itself, doesn't have task/crew
-                self.agent = (
-                    executor.original_agent
-                    if hasattr(executor, "original_agent")
-                    else executor
-                )
-                self.task = None
-                self.crew = None
-        else:
-            # New path: direct LLM call with explicit parameters
-            self.executor = None
-            self.messages = messages or []
-            self.llm = llm
-            self.agent = agent
-            self.task = task
-            self.crew = crew
-            self.iterations = 0
-
+        self.executor = executor
+        self.messages = executor.messages
+        self.agent = executor.agent
+        self.task = executor.task
+        self.crew = executor.crew
+        self.llm = executor.llm
+        self.iterations = executor.iterations
         self.response = response
 
     def request_human_input(

lib/crewai/src/crewai/lite_agent.py

@@ -38,8 +38,6 @@ from crewai.events.types.agent_events import (
 )
 from crewai.events.types.logging_events import AgentLogsExecutionEvent
 from crewai.flow.flow_trackable import FlowTrackable
-from crewai.hooks.llm_hooks import get_after_llm_call_hooks, get_before_llm_call_hooks
-from crewai.hooks.types import AfterLLMCallHookType, BeforeLLMCallHookType
 from crewai.lite_agent_output import LiteAgentOutput
 from crewai.llm import LLM
 from crewai.llms.base_llm import BaseLLM
@@ -157,12 +155,6 @@ class LiteAgent(FlowTrackable, BaseModel):
     _guardrail: GuardrailCallable | None = PrivateAttr(default=None)
     _guardrail_retry_count: int = PrivateAttr(default=0)
     _callbacks: list[TokenCalcHandler] = PrivateAttr(default_factory=list)
-    _before_llm_call_hooks: list[BeforeLLMCallHookType] = PrivateAttr(
-        default_factory=get_before_llm_call_hooks
-    )
-    _after_llm_call_hooks: list[AfterLLMCallHookType] = PrivateAttr(
-        default_factory=get_after_llm_call_hooks
-    )
 
     @model_validator(mode="after")
     def setup_llm(self) -> Self:
@@ -254,26 +246,6 @@ class LiteAgent(FlowTrackable, BaseModel):
         """Return the original role for compatibility with tool interfaces."""
         return self.role
 
-    @property
-    def before_llm_call_hooks(self) -> list[BeforeLLMCallHookType]:
-        """Get the before_llm_call hooks for this agent."""
-        return self._before_llm_call_hooks
-
-    @property
-    def after_llm_call_hooks(self) -> list[AfterLLMCallHookType]:
-        """Get the after_llm_call hooks for this agent."""
-        return self._after_llm_call_hooks
-
-    @property
-    def messages(self) -> list[LLMMessage]:
-        """Get the messages list for hook context compatibility."""
-        return self._messages
-
-    @property
-    def iterations(self) -> int:
-        """Get the current iteration count for hook context compatibility."""
-        return self._iterations
-
     def kickoff(
         self,
         messages: str | list[LLMMessage],
@@ -532,7 +504,7 @@ class LiteAgent(FlowTrackable, BaseModel):
             AgentFinish: The final result of the agent execution.
         """
         # Execute the agent loop
-        formatted_answer: AgentAction | AgentFinish | None = None
+        formatted_answer = None
         while not isinstance(formatted_answer, AgentFinish):
             try:
                 if has_reached_max_iterations(self._iterations, self.max_iterations):
@@ -554,7 +526,6 @@ class LiteAgent(FlowTrackable, BaseModel):
                         callbacks=self._callbacks,
                         printer=self._printer,
                         from_agent=self,
-                        executor_context=self,
                     )
 
                 except Exception as e:

lib/crewai/src/crewai/llm.py

@@ -67,7 +67,6 @@ if TYPE_CHECKING:
 
     from crewai.agent.core import Agent
     from crewai.llms.hooks.base import BaseInterceptor
-    from crewai.llms.providers.anthropic.completion import AnthropicThinkingConfig
     from crewai.task import Task
     from crewai.tools.base_tool import BaseTool
     from crewai.utilities.types import LLMMessage
@@ -586,7 +585,6 @@ class LLM(BaseLLM):
         reasoning_effort: Literal["none", "low", "medium", "high"] | None = None,
         stream: bool = False,
         interceptor: BaseInterceptor[httpx.Request, httpx.Response] | None = None,
-        thinking: AnthropicThinkingConfig | dict[str, Any] | None = None,
         **kwargs: Any,
     ) -> None:
         """Initialize LLM instance.
@@ -1644,10 +1642,6 @@ class LLM(BaseLLM):
                 if message.get("role") == "system":
                     msg_role: Literal["assistant"] = "assistant"
                     message["role"] = msg_role
-
-        if not self._invoke_before_llm_call_hooks(messages, from_agent):
-            raise ValueError("LLM call blocked by before_llm_call hook")
-
         # --- 5) Set up callbacks if provided
         with suppress_warnings():
             if callbacks and len(callbacks) > 0:
@@ -1657,16 +1651,7 @@ class LLM(BaseLLM):
                 params = self._prepare_completion_params(messages, tools)
                 # --- 7) Make the completion call and handle response
                 if self.stream:
-                    result = self._handle_streaming_response(
-                        params=params,
-                        callbacks=callbacks,
-                        available_functions=available_functions,
-                        from_task=from_task,
-                        from_agent=from_agent,
-                        response_model=response_model,
-                    )
-                else:
-                    result = self._handle_non_streaming_response(
+                    return self._handle_streaming_response(
                         params=params,
                         callbacks=callbacks,
                         available_functions=available_functions,
@@ -1675,12 +1660,14 @@ class LLM(BaseLLM):
                         response_model=response_model,
                     )
 
-                if isinstance(result, str):
-                    result = self._invoke_after_llm_call_hooks(
-                        messages, result, from_agent
-                    )
-
-                return result
+                return self._handle_non_streaming_response(
+                    params=params,
+                    callbacks=callbacks,
+                    available_functions=available_functions,
+                    from_task=from_task,
+                    from_agent=from_agent,
+                    response_model=response_model,
+                )
             except LLMContextLengthExceededError:
                 # Re-raise LLMContextLengthExceededError as it should be handled
                 # by the CrewAgentExecutor._invoke_loop method, which can then decide

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

@@ -314,7 +314,7 @@ class BaseLLM(ABC):
         call_type: LLMCallType,
         from_task: Task | None = None,
         from_agent: Agent | None = None,
-        messages: str | list[LLMMessage] | None = None,
+        messages: str | list[dict[str, Any]] | None = None,
     ) -> None:
         """Emit LLM call completed event."""
         crewai_event_bus.emit(
@@ -586,134 +586,3 @@ class BaseLLM(ABC):
             Dictionary with token usage totals
         """
         return UsageMetrics(**self._token_usage)
-
-    def _invoke_before_llm_call_hooks(
-        self,
-        messages: list[LLMMessage],
-        from_agent: Agent | None = None,
-    ) -> bool:
-        """Invoke before_llm_call hooks for direct LLM calls (no agent context).
-
-        This method should be called by native provider implementations before
-        making the actual LLM call when from_agent is None (direct calls).
-
-        Args:
-            messages: The messages being sent to the LLM
-            from_agent: The agent making the call (None for direct calls)
-
-        Returns:
-            True if LLM call should proceed, False if blocked by hook
-
-        Example:
-            >>> # In a native provider's call() method:
-            >>> if from_agent is None and not self._invoke_before_llm_call_hooks(
-            ...     messages, from_agent
-            ... ):
-            ...     raise ValueError("LLM call blocked by hook")
-        """
-        # Only invoke hooks for direct calls (no agent context)
-        if from_agent is not None:
-            return True
-
-        from crewai.hooks.llm_hooks import (
-            LLMCallHookContext,
-            get_before_llm_call_hooks,
-        )
-        from crewai.utilities.printer import Printer
-
-        before_hooks = get_before_llm_call_hooks()
-        if not before_hooks:
-            return True
-
-        hook_context = LLMCallHookContext(
-            executor=None,
-            messages=messages,
-            llm=self,
-            agent=None,
-            task=None,
-            crew=None,
-        )
-        printer = Printer()
-
-        try:
-            for hook in before_hooks:
-                result = hook(hook_context)
-                if result is False:
-                    printer.print(
-                        content="LLM call blocked by before_llm_call hook",
-                        color="yellow",
-                    )
-                    return False
-        except Exception as e:
-            printer.print(
-                content=f"Error in before_llm_call hook: {e}",
-                color="yellow",
-            )
-
-        return True
-
-    def _invoke_after_llm_call_hooks(
-        self,
-        messages: list[LLMMessage],
-        response: str,
-        from_agent: Agent | None = None,
-    ) -> str:
-        """Invoke after_llm_call hooks for direct LLM calls (no agent context).
-
-        This method should be called by native provider implementations after
-        receiving the LLM response when from_agent is None (direct calls).
-
-        Args:
-            messages: The messages that were sent to the LLM
-            response: The response from the LLM
-            from_agent: The agent that made the call (None for direct calls)
-
-        Returns:
-            The potentially modified response string
-
-        Example:
-            >>> # In a native provider's call() method:
-            >>> if from_agent is None and isinstance(result, str):
-            ...     result = self._invoke_after_llm_call_hooks(
-            ...         messages, result, from_agent
-            ...     )
-        """
-        # Only invoke hooks for direct calls (no agent context)
-        if from_agent is not None or not isinstance(response, str):
-            return response
-
-        from crewai.hooks.llm_hooks import (
-            LLMCallHookContext,
-            get_after_llm_call_hooks,
-        )
-        from crewai.utilities.printer import Printer
-
-        after_hooks = get_after_llm_call_hooks()
-        if not after_hooks:
-            return response
-
-        hook_context = LLMCallHookContext(
-            executor=None,
-            messages=messages,
-            llm=self,
-            agent=None,
-            task=None,
-            crew=None,
-            response=response,
-        )
-        printer = Printer()
-        modified_response = response
-
-        try:
-            for hook in after_hooks:
-                result = hook(hook_context)
-                if result is not None and isinstance(result, str):
-                    modified_response = result
-                    hook_context.response = modified_response
-        except Exception as e:
-            printer.print(
-                content=f"Error in after_llm_call hook: {e}",
-                color="yellow",
-            )
-
-        return modified_response

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

@@ -3,9 +3,8 @@ from __future__ import annotations
 import json
 import logging
 import os
-from typing import TYPE_CHECKING, Any, Literal, cast
+from typing import TYPE_CHECKING, Any, cast
 
-from anthropic.types import ThinkingBlock
 from pydantic import BaseModel
 
 from crewai.events.types.llm_events import LLMCallType
@@ -23,7 +22,8 @@ if TYPE_CHECKING:
 
 try:
     from anthropic import Anthropic, AsyncAnthropic
-    from anthropic.types import Message, TextBlock, ThinkingBlock, ToolUseBlock
+    from anthropic.types import Message
+    from anthropic.types.tool_use_block import ToolUseBlock
     import httpx
 except ImportError:
     raise ImportError(
@@ -31,11 +31,6 @@ except ImportError:
     ) from None
 
 
-class AnthropicThinkingConfig(BaseModel):
-    type: Literal["enabled", "disabled"]
-    budget_tokens: int | None = None
-
-
 class AnthropicCompletion(BaseLLM):
     """Anthropic native completion implementation.
 
@@ -57,7 +52,6 @@ class AnthropicCompletion(BaseLLM):
         stream: bool = False,
         client_params: dict[str, Any] | None = None,
         interceptor: BaseInterceptor[httpx.Request, httpx.Response] | None = None,
-        thinking: AnthropicThinkingConfig | None = None,
         **kwargs: Any,
     ):
         """Initialize Anthropic chat completion client.
@@ -103,10 +97,6 @@ class AnthropicCompletion(BaseLLM):
         self.top_p = top_p
         self.stream = stream
         self.stop_sequences = stop_sequences or []
-        self.thinking = thinking
-        self.previous_thinking_blocks: list[ThinkingBlock] = []
-        # Model-specific settings
-        self.is_claude_3 = "claude-3" in model.lower()
         self.supports_tools = True
 
     @property
@@ -197,9 +187,6 @@ class AnthropicCompletion(BaseLLM):
                 messages
             )
 
-            if not self._invoke_before_llm_call_hooks(formatted_messages, from_agent):
-                raise ValueError("LLM call blocked by before_llm_call hook")
-
             # Prepare completion parameters
             completion_params = self._prepare_completion_params(
                 formatted_messages, system_message, tools
@@ -336,12 +323,6 @@ class AnthropicCompletion(BaseLLM):
         if tools and self.supports_tools:
             params["tools"] = self._convert_tools_for_interference(tools)
 
-        if self.thinking:
-            if isinstance(self.thinking, AnthropicThinkingConfig):
-                params["thinking"] = self.thinking.model_dump()
-            else:
-                params["thinking"] = self.thinking
-
         return params
 
     def _convert_tools_for_interference(
@@ -381,34 +362,6 @@ class AnthropicCompletion(BaseLLM):
 
         return anthropic_tools
 
-    def _extract_thinking_block(
-        self, content_block: Any
-    ) -> ThinkingBlock | dict[str, Any] | None:
-        """Extract and format thinking block from content block.
-
-        Args:
-            content_block: Content block from Anthropic response
-
-        Returns:
-            Dictionary with thinking block data including signature, or None if not a thinking block
-        """
-        if content_block.type == "thinking":
-            thinking_block = {
-                "type": "thinking",
-                "thinking": content_block.thinking,
-            }
-            if hasattr(content_block, "signature"):
-                thinking_block["signature"] = content_block.signature
-            return thinking_block
-        if content_block.type == "redacted_thinking":
-            redacted_block = {"type": "redacted_thinking"}
-            if hasattr(content_block, "thinking"):
-                redacted_block["thinking"] = content_block.thinking
-            if hasattr(content_block, "signature"):
-                redacted_block["signature"] = content_block.signature
-            return redacted_block
-        return None
-
     def _format_messages_for_anthropic(
         self, messages: str | list[LLMMessage]
     ) -> tuple[list[LLMMessage], str | None]:
@@ -418,7 +371,6 @@ class AnthropicCompletion(BaseLLM):
         - System messages are separate from conversation messages
         - Messages must alternate between user and assistant
         - First message must be from user
-        - When thinking is enabled, assistant messages must start with thinking blocks
 
         Args:
             messages: Input messages
@@ -443,29 +395,8 @@ class AnthropicCompletion(BaseLLM):
                     system_message = cast(str, content)
             else:
                 role_str = role if role is not None else "user"
-
-                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]],
-                        [
-                            *self.previous_thinking_blocks,
-                            {"type": "text", "text": content if content else ""},
-                        ],
-                    )
-                    formatted_messages.append(
-                        LLMMessage(role=role_str, content=structured_content)
-                    )
-                else:
-                    content_str = content if content is not None else ""
-                    formatted_messages.append(
-                        LLMMessage(role=role_str, content=content_str)
-                    )
+                content_str = content if content is not None else ""
+                formatted_messages.append({"role": role_str, "content": content_str})
 
         # Ensure first message is from user (Anthropic requirement)
         if not formatted_messages:
@@ -515,6 +446,7 @@ class AnthropicCompletion(BaseLLM):
             if tool_uses and tool_uses[0].name == "structured_output":
                 structured_data = tool_uses[0].input
                 structured_json = json.dumps(structured_data)
+
                 self._emit_call_completed_event(
                     response=structured_json,
                     call_type=LLMCallType.LLM_CALL,
@@ -542,22 +474,15 @@ class AnthropicCompletion(BaseLLM):
                     from_agent,
                 )
 
+        # Extract text content
         content = ""
-        thinking_blocks: list[ThinkingBlock] = []
-
         if response.content:
             for content_block in response.content:
                 if hasattr(content_block, "text"):
                     content += content_block.text
-                else:
-                    thinking_block = self._extract_thinking_block(content_block)
-                    if thinking_block:
-                        thinking_blocks.append(cast(ThinkingBlock, thinking_block))
-
-        if thinking_blocks:
-            self.previous_thinking_blocks = thinking_blocks
 
         content = self._apply_stop_words(content)
+
         self._emit_call_completed_event(
             response=content,
             call_type=LLMCallType.LLM_CALL,
@@ -569,9 +494,7 @@ class AnthropicCompletion(BaseLLM):
         if usage.get("total_tokens", 0) > 0:
             logging.info(f"Anthropic API usage: {usage}")
 
-        return self._invoke_after_llm_call_hooks(
-            params["messages"], content, from_agent
-        )
+        return content
 
     def _handle_streaming_completion(
         self,
@@ -612,16 +535,6 @@ class AnthropicCompletion(BaseLLM):
 
             final_message: Message = stream.get_final_message()
 
-        thinking_blocks: list[ThinkingBlock] = []
-        if final_message.content:
-            for content_block in final_message.content:
-                thinking_block = self._extract_thinking_block(content_block)
-                if thinking_block:
-                    thinking_blocks.append(cast(ThinkingBlock, thinking_block))
-
-        if thinking_blocks:
-            self.previous_thinking_blocks = thinking_blocks
-
         usage = self._extract_anthropic_token_usage(final_message)
         self._track_token_usage_internal(usage)
 
@@ -675,9 +588,7 @@ class AnthropicCompletion(BaseLLM):
             messages=params["messages"],
         )
 
-        return self._invoke_after_llm_call_hooks(
-            params["messages"], full_response, from_agent
-        )
+        return full_response
 
     def _handle_tool_use_conversation(
         self,
@@ -726,26 +637,7 @@ class AnthropicCompletion(BaseLLM):
         follow_up_params = params.copy()
 
         # Add Claude's tool use response to conversation
-        assistant_content: list[
-            ThinkingBlock | ToolUseBlock | TextBlock | dict[str, Any]
-        ] = []
-        for block in initial_response.content:
-            thinking_block = self._extract_thinking_block(block)
-            if thinking_block:
-                assistant_content.append(thinking_block)
-            elif block.type == "tool_use":
-                assistant_content.append(
-                    {
-                        "type": "tool_use",
-                        "id": block.id,
-                        "name": block.name,
-                        "input": block.input,
-                    }
-                )
-            elif hasattr(block, "text"):
-                assistant_content.append({"type": "text", "text": block.text})
-
-        assistant_message = {"role": "assistant", "content": assistant_content}
+        assistant_message = {"role": "assistant", "content": initial_response.content}
 
         # Add user message with tool results
         user_message = {"role": "user", "content": tool_results}
@@ -764,20 +656,12 @@ class AnthropicCompletion(BaseLLM):
             follow_up_usage = self._extract_anthropic_token_usage(final_response)
             self._track_token_usage_internal(follow_up_usage)
 
+            # Extract final text content
             final_content = ""
-            thinking_blocks: list[ThinkingBlock] = []
-
             if final_response.content:
                 for content_block in final_response.content:
                     if hasattr(content_block, "text"):
                         final_content += content_block.text
-                    else:
-                        thinking_block = self._extract_thinking_block(content_block)
-                        if thinking_block:
-                            thinking_blocks.append(cast(ThinkingBlock, thinking_block))
-
-            if thinking_blocks:
-                self.previous_thinking_blocks = thinking_blocks
 
             final_content = self._apply_stop_words(final_content)
 

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

@@ -216,9 +216,6 @@ class AzureCompletion(BaseLLM):
             # Format messages for Azure
             formatted_messages = self._format_messages_for_azure(messages)
 
-            if not self._invoke_before_llm_call_hooks(formatted_messages, from_agent):
-                raise ValueError("LLM call blocked by before_llm_call hook")
-
             # Prepare completion parameters
             completion_params = self._prepare_completion_params(
                 formatted_messages, tools, response_model
@@ -553,10 +550,6 @@ class AzureCompletion(BaseLLM):
                 messages=params["messages"],
             )
 
-            content = self._invoke_after_llm_call_hooks(
-                params["messages"], content, from_agent
-            )
-
         except Exception as e:
             if is_context_length_exceeded(e):
                 logging.error(f"Context window exceeded: {e}")
@@ -649,9 +642,7 @@ class AzureCompletion(BaseLLM):
             messages=params["messages"],
         )
 
-        return self._invoke_after_llm_call_hooks(
-            params["messages"], full_response, from_agent
-        )
+        return full_response
 
     async def _ahandle_completion(
         self,

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

@@ -312,14 +312,9 @@ class BedrockCompletion(BaseLLM):
 
             # Format messages for Converse API
             formatted_messages, system_message = self._format_messages_for_converse(
-                messages
+                messages  # type: ignore[arg-type]
             )
 
-            if not self._invoke_before_llm_call_hooks(
-                cast(list[LLMMessage], formatted_messages), from_agent
-            ):
-                raise ValueError("LLM call blocked by before_llm_call hook")
-
             # Prepare request body
             body: BedrockConverseRequestBody = {
                 "inferenceConfig": self._get_inference_config(),
@@ -361,19 +356,11 @@ class BedrockCompletion(BaseLLM):
 
             if self.stream:
                 return self._handle_streaming_converse(
-                    cast(list[LLMMessage], formatted_messages),
-                    body,
-                    available_functions,
-                    from_task,
-                    from_agent,
+                    formatted_messages, body, available_functions, from_task, from_agent
                 )
 
             return self._handle_converse(
-                cast(list[LLMMessage], formatted_messages),
-                body,
-                available_functions,
-                from_task,
-                from_agent,
+                formatted_messages, body, available_functions, from_task, from_agent
             )
 
         except Exception as e:
@@ -494,7 +481,7 @@ class BedrockCompletion(BaseLLM):
 
     def _handle_converse(
         self,
-        messages: list[LLMMessage],
+        messages: list[dict[str, Any]],
         body: BedrockConverseRequestBody,
         available_functions: Mapping[str, Any] | None = None,
         from_task: Any | None = None,
@@ -618,11 +605,7 @@ class BedrockCompletion(BaseLLM):
                 messages=messages,
             )
 
-            return self._invoke_after_llm_call_hooks(
-                messages,
-                text_content,
-                from_agent,
-            )
+            return text_content
 
         except ClientError as e:
             # Handle all AWS ClientError exceptions as per documentation
@@ -679,7 +662,7 @@ class BedrockCompletion(BaseLLM):
 
     def _handle_streaming_converse(
         self,
-        messages: list[LLMMessage],
+        messages: list[dict[str, Any]],
         body: BedrockConverseRequestBody,
         available_functions: dict[str, Any] | None = None,
         from_task: Any | None = None,
@@ -1166,25 +1149,16 @@ class BedrockCompletion(BaseLLM):
             messages=messages,
         )
 
-        return self._invoke_after_llm_call_hooks(
-            messages,
-            full_response,
-            from_agent,
-        )
+        return full_response
 
     def _format_messages_for_converse(
-        self, messages: str | list[LLMMessage]
+        self, messages: str | list[dict[str, str]]
     ) -> tuple[list[dict[str, Any]], str | None]:
-        """Format messages for Converse API following AWS documentation.
-
-        Note: Returns dict[str, Any] instead of LLMMessage because Bedrock uses
-        a different content structure: {"role": str, "content": [{"text": str}]}
-        rather than the standard {"role": str, "content": str}.
-        """
+        """Format messages for Converse API following AWS documentation."""
         # Use base class formatting first
-        formatted_messages = self._format_messages(messages)
+        formatted_messages = self._format_messages(messages)  # type: ignore[arg-type]
 
-        converse_messages: list[dict[str, Any]] = []
+        converse_messages = []
         system_message: str | None = None
 
         for message in formatted_messages:

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

@@ -246,11 +246,6 @@ class GeminiCompletion(BaseLLM):
                 messages
             )
 
-            messages_for_hooks = self._convert_contents_to_dict(formatted_content)
-
-            if not self._invoke_before_llm_call_hooks(messages_for_hooks, from_agent):
-                raise ValueError("LLM call blocked by before_llm_call hook")
-
             config = self._prepare_generation_config(
                 system_instruction, tools, response_model
             )
@@ -564,9 +559,7 @@ class GeminiCompletion(BaseLLM):
             messages=messages_for_event,
         )
 
-        return self._invoke_after_llm_call_hooks(
-            messages_for_event, content, from_agent
-        )
+        return content
 
     def _handle_streaming_completion(
         self,
@@ -646,9 +639,7 @@ class GeminiCompletion(BaseLLM):
             messages=messages_for_event,
         )
 
-        return self._invoke_after_llm_call_hooks(
-            messages_for_event, full_response, from_agent
-        )
+        return full_response
 
     async def _ahandle_completion(
         self,
@@ -796,159 +787,7 @@ class GeminiCompletion(BaseLLM):
             messages=messages_for_event,
         )
 
-        return self._invoke_after_llm_call_hooks(
-            messages_for_event, full_response, from_agent
-        )
-
-    async def _ahandle_completion(
-        self,
-        contents: list[types.Content],
-        system_instruction: str | None,
-        config: types.GenerateContentConfig,
-        available_functions: dict[str, Any] | None = None,
-        from_task: Any | None = None,
-        from_agent: Any | None = None,
-        response_model: type[BaseModel] | None = None,
-    ) -> str | Any:
-        """Handle async non-streaming content generation."""
-        try:
-            # The API accepts list[Content] but mypy is overly strict about variance
-            contents_for_api: Any = contents
-            response = await self.client.aio.models.generate_content(
-                model=self.model,
-                contents=contents_for_api,
-                config=config,
-            )
-
-            usage = self._extract_token_usage(response)
-        except Exception as e:
-            if is_context_length_exceeded(e):
-                logging.error(f"Context window exceeded: {e}")
-                raise LLMContextLengthExceededError(str(e)) from e
-            raise e from e
-
-        self._track_token_usage_internal(usage)
-
-        if response.candidates and (self.tools or available_functions):
-            candidate = response.candidates[0]
-            if candidate.content and candidate.content.parts:
-                for part in candidate.content.parts:
-                    if hasattr(part, "function_call") and part.function_call:
-                        function_name = part.function_call.name
-                        if function_name is None:
-                            continue
-                        function_args = (
-                            dict(part.function_call.args)
-                            if part.function_call.args
-                            else {}
-                        )
-
-                        result = self._handle_tool_execution(
-                            function_name=function_name,
-                            function_args=function_args,
-                            available_functions=available_functions or {},
-                            from_task=from_task,
-                            from_agent=from_agent,
-                        )
-
-                        if result is not None:
-                            return result
-
-        content = response.text or ""
-        content = self._apply_stop_words(content)
-
-        messages_for_event = self._convert_contents_to_dict(contents)
-
-        self._emit_call_completed_event(
-            response=content,
-            call_type=LLMCallType.LLM_CALL,
-            from_task=from_task,
-            from_agent=from_agent,
-            messages=messages_for_event,
-        )
-
-        return content
-
-    async def _ahandle_streaming_completion(
-        self,
-        contents: list[types.Content],
-        config: types.GenerateContentConfig,
-        available_functions: dict[str, Any] | None = None,
-        from_task: Any | None = None,
-        from_agent: Any | None = None,
-        response_model: type[BaseModel] | None = None,
-    ) -> str:
-        """Handle async streaming content generation."""
-        full_response = ""
-        function_calls: dict[str, dict[str, Any]] = {}
-
-        # The API accepts list[Content] but mypy is overly strict about variance
-        contents_for_api: Any = contents
-        stream = await self.client.aio.models.generate_content_stream(
-            model=self.model,
-            contents=contents_for_api,
-            config=config,
-        )
-        async for chunk in stream:
-            if chunk.text:
-                full_response += chunk.text
-                self._emit_stream_chunk_event(
-                    chunk=chunk.text,
-                    from_task=from_task,
-                    from_agent=from_agent,
-                )
-
-            if chunk.candidates:
-                candidate = chunk.candidates[0]
-                if candidate.content and candidate.content.parts:
-                    for part in candidate.content.parts:
-                        if hasattr(part, "function_call") and part.function_call:
-                            call_id = part.function_call.name or "default"
-                            if call_id not in function_calls:
-                                function_calls[call_id] = {
-                                    "name": part.function_call.name,
-                                    "args": dict(part.function_call.args)
-                                    if part.function_call.args
-                                    else {},
-                                }
-
-        if function_calls and available_functions:
-            for call_data in function_calls.values():
-                function_name = call_data["name"]
-                function_args = call_data["args"]
-
-                # Skip if function_name is None
-                if not isinstance(function_name, str):
-                    continue
-
-                # Ensure function_args is a dict
-                if not isinstance(function_args, dict):
-                    function_args = {}
-
-                result = self._handle_tool_execution(
-                    function_name=function_name,
-                    function_args=function_args,
-                    available_functions=available_functions,
-                    from_task=from_task,
-                    from_agent=from_agent,
-                )
-
-                if result is not None:
-                    return result
-
-        messages_for_event = self._convert_contents_to_dict(contents)
-
-        self._emit_call_completed_event(
-            response=full_response,
-            call_type=LLMCallType.LLM_CALL,
-            from_task=from_task,
-            from_agent=from_agent,
-            messages=messages_for_event,
-        )
-
-        return self._invoke_after_llm_call_hooks(
-            messages_for_event, full_response, from_agent
-        )
+        return full_response
 
     def supports_function_calling(self) -> bool:
         """Check if the model supports function calling."""
@@ -1012,7 +851,7 @@ class GeminiCompletion(BaseLLM):
     def _convert_contents_to_dict(
         self,
         contents: list[types.Content],
-    ) -> list[LLMMessage]:
+    ) -> list[dict[str, str]]:
         """Convert contents to dict format."""
         result: list[dict[str, str]] = []
         for content_obj in contents:

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

@@ -1,14 +1,13 @@
 from __future__ import annotations
 
-from collections.abc import AsyncIterator
+from collections.abc import AsyncIterator, Iterator
 import json
 import logging
 import os
 from typing import TYPE_CHECKING, Any
 
 import httpx
-from openai import APIConnectionError, AsyncOpenAI, NotFoundError, OpenAI, Stream
-from openai.lib.streaming.chat import ChatCompletionStream
+from openai import APIConnectionError, AsyncOpenAI, NotFoundError, OpenAI
 from openai.types.chat import ChatCompletion, ChatCompletionChunk
 from openai.types.chat.chat_completion import Choice
 from openai.types.chat.chat_completion_chunk import ChoiceDelta
@@ -190,9 +189,6 @@ class OpenAICompletion(BaseLLM):
 
             formatted_messages = self._format_messages(messages)
 
-            if not self._invoke_before_llm_call_hooks(formatted_messages, from_agent):
-                raise ValueError("LLM call blocked by before_llm_call hook")
-
             completion_params = self._prepare_completion_params(
                 messages=formatted_messages, tools=tools
             )
@@ -477,10 +473,6 @@ class OpenAICompletion(BaseLLM):
 
             if usage.get("total_tokens", 0) > 0:
                 logging.info(f"OpenAI API usage: {usage}")
-
-            content = self._invoke_after_llm_call_hooks(
-                params["messages"], content, from_agent
-            )
         except NotFoundError as e:
             error_msg = f"Model {self.model} not found: {e}"
             logging.error(error_msg)
@@ -523,52 +515,59 @@ class OpenAICompletion(BaseLLM):
         tool_calls = {}
 
         if response_model:
-            parse_params = {
-                k: v
-                for k, v in params.items()
-                if k not in ("response_format", "stream")
-            }
+            completion_stream: Iterator[ChatCompletionChunk] = (
+                self.client.chat.completions.create(**params)
+            )
 
-            stream: ChatCompletionStream[BaseModel]
-            with self.client.beta.chat.completions.stream(
-                **parse_params, response_format=response_model
-            ) as stream:
-                for chunk in stream:
-                    if chunk.type == "content.delta":
-                        delta_content = chunk.delta
-                        if delta_content:
-                            self._emit_stream_chunk_event(
-                                chunk=delta_content,
-                                from_task=from_task,
-                                from_agent=from_agent,
-                            )
+            accumulated_content = ""
+            for chunk in completion_stream:
+                if not chunk.choices:
+                    continue
 
-                final_completion = stream.get_final_completion()
-                if final_completion and final_completion.choices:
-                    parsed_result = final_completion.choices[0].message.parsed
-                    if parsed_result:
-                        structured_json = parsed_result.model_dump_json()
-                        self._emit_call_completed_event(
-                            response=structured_json,
-                            call_type=LLMCallType.LLM_CALL,
-                            from_task=from_task,
-                            from_agent=from_agent,
-                            messages=params["messages"],
-                        )
-                        return structured_json
+                choice = chunk.choices[0]
+                delta: ChoiceDelta = choice.delta
 
-            logging.error("Failed to get parsed result from stream")
-            return ""
+                if delta.content:
+                    accumulated_content += delta.content
+                    self._emit_stream_chunk_event(
+                        chunk=delta.content,
+                        from_task=from_task,
+                        from_agent=from_agent,
+                    )
 
-        completion_stream: Stream[ChatCompletionChunk] = (
-            self.client.chat.completions.create(**params)
+            try:
+                parsed_object = response_model.model_validate_json(accumulated_content)
+                structured_json = parsed_object.model_dump_json()
+
+                self._emit_call_completed_event(
+                    response=structured_json,
+                    call_type=LLMCallType.LLM_CALL,
+                    from_task=from_task,
+                    from_agent=from_agent,
+                    messages=params["messages"],
+                )
+
+                return structured_json
+            except Exception as e:
+                logging.error(f"Failed to parse structured output from stream: {e}")
+                self._emit_call_completed_event(
+                    response=accumulated_content,
+                    call_type=LLMCallType.LLM_CALL,
+                    from_task=from_task,
+                    from_agent=from_agent,
+                    messages=params["messages"],
+                )
+                return accumulated_content
+
+        stream: Iterator[ChatCompletionChunk] = self.client.chat.completions.create(
+            **params
         )
 
-        for completion_chunk in completion_stream:
-            if not completion_chunk.choices:
+        for chunk in stream:
+            if not chunk.choices:
                 continue
 
-            choice = completion_chunk.choices[0]
+            choice = chunk.choices[0]
             chunk_delta: ChoiceDelta = choice.delta
 
             if chunk_delta.content:
@@ -636,9 +635,7 @@ class OpenAICompletion(BaseLLM):
             messages=params["messages"],
         )
 
-        return self._invoke_after_llm_call_hooks(
-            params["messages"], full_response, from_agent
-        )
+        return full_response
 
     async def _ahandle_completion(
         self,

lib/crewai/src/crewai/rag/embeddings/providers/huggingface/huggingface_provider.py

@@ -1,35 +1,21 @@
 """HuggingFace embeddings provider."""
 
 from chromadb.utils.embedding_functions.huggingface_embedding_function import (
-    HuggingFaceEmbeddingFunction,
+    HuggingFaceEmbeddingServer,
 )
 from pydantic import AliasChoices, Field
 
 from crewai.rag.core.base_embeddings_provider import BaseEmbeddingsProvider
 
 
-class HuggingFaceProvider(BaseEmbeddingsProvider[HuggingFaceEmbeddingFunction]):
-    """HuggingFace embeddings provider for the HuggingFace Inference API."""
+class HuggingFaceProvider(BaseEmbeddingsProvider[HuggingFaceEmbeddingServer]):
+    """HuggingFace embeddings provider."""
 
-    embedding_callable: type[HuggingFaceEmbeddingFunction] = Field(
-        default=HuggingFaceEmbeddingFunction,
+    embedding_callable: type[HuggingFaceEmbeddingServer] = Field(
+        default=HuggingFaceEmbeddingServer,
         description="HuggingFace embedding function class",
     )
-    api_key: str | None = Field(
-        default=None,
-        description="HuggingFace API key",
-        validation_alias=AliasChoices(
-            "EMBEDDINGS_HUGGINGFACE_API_KEY",
-            "HUGGINGFACE_API_KEY",
-            "HF_TOKEN",
-        ),
-    )
-    model_name: str = Field(
-        default="sentence-transformers/all-MiniLM-L6-v2",
-        description="Model name to use for embeddings",
-        validation_alias=AliasChoices(
-            "EMBEDDINGS_HUGGINGFACE_MODEL_NAME",
-            "HUGGINGFACE_MODEL_NAME",
-            "model",
-        ),
+    url: str = Field(
+        description="HuggingFace API URL",
+        validation_alias=AliasChoices("EMBEDDINGS_HUGGINGFACE_URL", "HUGGINGFACE_URL"),
     )

lib/crewai/src/crewai/rag/embeddings/providers/huggingface/types.py

@@ -1,6 +1,6 @@
 """Type definitions for HuggingFace embedding providers."""
 
-from typing import Annotated, Literal
+from typing import Literal
 
 from typing_extensions import Required, TypedDict
 
@@ -8,11 +8,7 @@ from typing_extensions import Required, TypedDict
 class HuggingFaceProviderConfig(TypedDict, total=False):
     """Configuration for HuggingFace provider."""
 
-    api_key: str
-    model: Annotated[
-        str, "sentence-transformers/all-MiniLM-L6-v2"
-    ]  # alias for model_name for backward compat
-    model_name: Annotated[str, "sentence-transformers/all-MiniLM-L6-v2"]
+    url: str
 
 
 class HuggingFaceProviderSpec(TypedDict, total=False):

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

@@ -392,7 +392,9 @@ class Telemetry:
                 self._add_attribute(span, "platform_system", platform.system())
                 self._add_attribute(span, "platform_version", platform.version())
                 self._add_attribute(span, "cpus", os.cpu_count())
-                self._add_attribute(span, "crew_inputs", json.dumps(inputs or {}))
+                self._add_attribute(
+                    span, "crew_inputs", json.dumps(inputs) if inputs else None
+                )
             else:
                 self._add_attribute(
                     span,
@@ -705,7 +707,9 @@ class Telemetry:
             self._add_attribute(span, "model_name", model_name)
 
             if crew.share_crew:
-                self._add_attribute(span, "inputs", json.dumps(inputs or {}))
+                self._add_attribute(
+                    span, "inputs", json.dumps(inputs) if inputs else None
+                )
 
             close_span(span)
 
@@ -810,7 +814,9 @@ class Telemetry:
             add_crew_attributes(
                 span, crew, self._add_attribute, include_fingerprint=False
             )
-            self._add_attribute(span, "crew_inputs", json.dumps(inputs or {}))
+            self._add_attribute(
+                span, "crew_inputs", json.dumps(inputs) if inputs else None
+            )
             self._add_attribute(
                 span,
                 "crew_agents",

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

@@ -237,7 +237,7 @@ def get_llm_response(
     from_task: Task | None = None,
     from_agent: Agent | LiteAgent | None = None,
     response_model: type[BaseModel] | None = None,
-    executor_context: CrewAgentExecutor | LiteAgent | None = None,
+    executor_context: CrewAgentExecutor | None = None,
 ) -> str:
     """Call the LLM and return the response, handling any invalid responses.
 
@@ -727,7 +727,7 @@ def load_agent_from_repository(from_repository: str) -> dict[str, Any]:
 
 
 def _setup_before_llm_call_hooks(
-    executor_context: CrewAgentExecutor | LiteAgent | None, printer: Printer
+    executor_context: CrewAgentExecutor | None, printer: Printer
 ) -> bool:
     """Setup and invoke before_llm_call hooks for the executor context.
 
@@ -777,7 +777,7 @@ def _setup_before_llm_call_hooks(
 
 
 def _setup_after_llm_call_hooks(
-    executor_context: CrewAgentExecutor | LiteAgent | None,
+    executor_context: CrewAgentExecutor | None,
     answer: str,
     printer: Printer,
 ) -> str:

lib/crewai/tests/agents/test_agent.py

@@ -163,7 +163,7 @@ def test_agent_execution():
     )
 
     output = agent.execute_task(task)
-    assert output == "The result of the math operation 1 + 1 is 2."
+    assert output == "1 + 1 is 2"
 
 
 @pytest.mark.vcr()
@@ -199,7 +199,7 @@ def test_agent_execution_with_tools():
             condition.notify()
 
     output = agent.execute_task(task)
-    assert output == "12"
+    assert output == "The result of the multiplication is 12."
 
     with condition:
         if not event_handled:
@@ -240,7 +240,7 @@ def test_logging_tool_usage():
         tool_name=multiplier.name, arguments={"first_number": 3, "second_number": 4}
     )
 
-    assert output == "12"
+    assert output == "The result of the multiplication is 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
 
@@ -409,7 +409,7 @@ def test_agent_execution_with_specific_tools():
         expected_output="The result of the multiplication.",
     )
     output = agent.execute_task(task=task, tools=[multiplier])
-    assert output == "12"
+    assert output == "The result of the multiplication is 12."
 
 
 @pytest.mark.vcr()
@@ -693,7 +693,7 @@ def test_agent_respect_the_max_rpm_set(capsys):
             task=task,
             tools=[get_final_answer],
         )
-        assert "42" in output or "final answer" in output.lower()
+        assert output == "42"
         captured = capsys.readouterr()
         assert "Max RPM reached, waiting for next minute to start." in captured.out
         moveon.assert_called()
@@ -794,6 +794,7 @@ def test_agent_without_max_rpm_respects_crew_rpm(capsys):
         # Verify the crew executed and RPM limit was triggered
         assert result is not None
         assert moveon.called
+        moveon.assert_called_once()
 
 
 @pytest.mark.vcr()
@@ -1712,7 +1713,6 @@ def test_llm_call_with_all_attributes():
 
 
 @pytest.mark.vcr()
-@pytest.mark.skip(reason="Requires local Ollama instance")
 def test_agent_with_ollama_llama3():
     agent = Agent(
         role="test role",
@@ -1734,7 +1734,6 @@ def test_agent_with_ollama_llama3():
 
 
 @pytest.mark.vcr()
-@pytest.mark.skip(reason="Requires local Ollama instance")
 def test_llm_call_with_ollama_llama3():
     llm = LLM(
         model="ollama/llama3.2:3b",
@@ -1816,7 +1815,7 @@ def test_agent_execute_task_with_tool():
     )
 
     result = agent.execute_task(task)
-    assert "you should always think about what to do" in result
+    assert "Dummy result for: test query" in result
 
 
 @pytest.mark.vcr()
@@ -1835,13 +1834,12 @@ def test_agent_execute_task_with_custom_llm():
     )
 
     result = agent.execute_task(task)
-    assert "In circuits they thrive" in result
-    assert "Artificial minds awake" in result
-    assert "Future's coded drive" in result
+    assert result.startswith(
+        "Artificial minds,\nCoding thoughts in circuits bright,\nAI's silent might."
+    )
 
 
 @pytest.mark.vcr()
-@pytest.mark.skip(reason="Requires local Ollama instance")
 def test_agent_execute_task_with_ollama():
     agent = Agent(
         role="test role",
@@ -2119,7 +2117,6 @@ def test_agent_with_knowledge_sources_generate_search_query():
 
 
 @pytest.mark.vcr()
-@pytest.mark.skip(reason="Requires OpenRouter API key")
 def test_agent_with_knowledge_with_no_crewai_knowledge():
     mock_knowledge = MagicMock(spec=Knowledge)
 
@@ -2172,7 +2169,6 @@ def test_agent_with_only_crewai_knowledge():
 
 
 @pytest.mark.vcr()
-@pytest.mark.skip(reason="Requires OpenRouter API key")
 def test_agent_knowledege_with_crewai_knowledge():
     crew_knowledge = MagicMock(spec=Knowledge)
     agent_knowledge = MagicMock(spec=Knowledge)

lib/crewai/tests/cassettes/TestLLMHooksIntegration.test_direct_llm_call_hooks_integration.yaml

@@ -1,82 +0,0 @@
-interactions:
-- request:
-    body: '{"messages":[{"role":"user","content":"Say hello"}],"model":"gpt-4o-mini"}'
-    headers:
-      accept:
-      - application/json
-      accept-encoding:
-      - gzip, deflate, zstd
-      authorization:
-      - AUTHORIZATION-XXX
-      connection:
-      - keep-alive
-      content-length:
-      - '74'
-      content-type:
-      - application/json
-      host:
-      - api.openai.com
-      user-agent:
-      - OpenAI/Python 1.109.1
-      x-stainless-arch:
-      - arm64
-      x-stainless-async:
-      - 'false'
-      x-stainless-lang:
-      - python
-      x-stainless-os:
-      - MacOS
-      x-stainless-package-version:
-      - 1.109.1
-      x-stainless-read-timeout:
-      - '600'
-      x-stainless-retry-count:
-      - '0'
-      x-stainless-runtime:
-      - CPython
-      x-stainless-runtime-version:
-      - 3.13.3
-    method: POST
-    uri: https://api.openai.com/v1/chat/completions
-  response:
-    body:
-      string: !!binary |
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-      Server:
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-      Strict-Transport-Security:
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-      Transfer-Encoding:
-      - chunked
-      X-Content-Type-Options:
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-      access-control-expose-headers:
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-      alt-svc:
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-      cf-cache-status:
-      - DYNAMIC
-      openai-version:
-      - '2020-10-01'
-      x-openai-proxy-wasm:
-      - v0.1
-    status:
-      code: 200
-      message: OK
-version: 1

lib/crewai/tests/cassettes/TestLLMHooksIntegration.test_lite_agent_hooks_integration_with_real_llm.yaml

@@ -1,87 +0,0 @@
-interactions:
-- request:
-    body: '{"messages":[{"role":"system","content":"You are Test Assistant. You are
-      a helpful test assistant\nYour personal goal is: Answer questions briefly\n\nTo
-      give my best complete final answer to the task respond using the exact following
-      format:\n\nThought: I now can give a great answer\nFinal Answer: Your final
-      answer must be the great and the most complete as possible, it must be outcome
-      described.\n\nI MUST use these formats, my job depends on it!"},{"role":"user","content":"Say
-      ''Hello World'' and nothing else"}],"model":"gpt-4.1-mini"}'
-    headers:
-      accept:
-      - application/json
-      accept-encoding:
-      - gzip, deflate, zstd
-      connection:
-      - keep-alive
-      content-length:
-      - '540'
-      content-type:
-      - application/json
-      host:
-      - api.openai.com
-      user-agent:
-      - OpenAI/Python 1.109.1
-      x-stainless-arch:
-      - arm64
-      x-stainless-async:
-      - 'false'
-      x-stainless-lang:
-      - python
-      x-stainless-os:
-      - MacOS
-      x-stainless-package-version:
-      - 1.109.1
-      x-stainless-read-timeout:
-      - '600'
-      x-stainless-retry-count:
-      - '0'
-      x-stainless-runtime:
-      - CPython
-      x-stainless-runtime-version:
-      - 3.13.3
-    method: POST
-    uri: https://api.openai.com/v1/chat/completions
-  response:
-    body:
-      string: !!binary |
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-      Server:
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-      Strict-Transport-Security:
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-      Transfer-Encoding:
-      - chunked
-      X-Content-Type-Options:
-      - nosniff
-      access-control-expose-headers:
-      - X-Request-ID
-      alt-svc:
-      - h3=":443"; ma=86400
-      cf-cache-status:
-      - DYNAMIC
-      openai-version:
-      - '2020-10-01'
-      x-openai-proxy-wasm:
-      - v0.1
-    status:
-      code: 200
-      message: OK
-version: 1

lib/crewai/tests/cassettes/agents/test_agent_custom_max_iterations.yaml

@@ -1,6 +1,6 @@
 interactions:
 - request:
-    body: '{"messages":[{"role":"system","content":"You are test role. test backstory\nYour
+    body: '{"messages": [{"role": "system", "content": "You are test role. test backstory\nYour
       personal goal is: test goal\nYou ONLY have access to the following tools, and
       should NEVER make up tools that are not listed here:\n\nTool Name: get_final_answer\nTool
       Arguments: {}\nTool Description: Get the final answer but don''t give it yet,
@@ -11,66 +11,62 @@ interactions:
       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```"},{"role":"user","content":"\nCurrent
-      Task: The final answer is 42. But don''t give it yet, instead keep using the
-      `get_final_answer` tool.\n\nThis is the expected criteria for your final answer:
-      The final answer\nyou MUST return the actual complete content as the final answer,
-      not a summary.\n\nBegin! This is VERY important to you, use the tools available
-      and give your best Final Answer, your job depends on it!\n\nThought:"}],"model":"gpt-4.1-mini"}'
+      Answer: the final answer to the original input question\n```"}, {"role": "user",
+      "content": "\nCurrent Task: The final answer is 42. But don''t give it yet,
+      instead keep using the `get_final_answer` tool.\n\nThis is the expected criteria
+      for your final answer: The final answer\nyou MUST return the actual complete
+      content as the final answer, not a summary.\n\nBegin! This is VERY important
+      to you, use the tools available and give your best Final Answer, your job depends
+      on it!\n\nThought:"}], "model": "gpt-4o-mini", "stop": ["\nObservation:"], "stream":
+      false}'
     headers:
-      User-Agent:
-      - X-USER-AGENT-XXX
       accept:
       - application/json
       accept-encoding:
-      - ACCEPT-ENCODING-XXX
-      authorization:
-      - AUTHORIZATION-XXX
+      - gzip, deflate
       connection:
       - keep-alive
       content-length:
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lib/crewai/tests/cassettes/agents/test_agent_with_ollama_llama3.yaml

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