fix: convert tool_calls to string when no available_functions provided

Fixes #4036 - Models with native function calling (like Ollama) fail when
the model returns tool_calls but no available_functions are provided.

Previously, when a model returned tool_calls without text content and no
available_functions were provided, the raw tool_calls list was returned.
This list would then cause a "'list' object has no attribute 'rstrip'"
error when passed to format_message_for_llm().

Now, tool_calls are converted to a human-readable string representation
containing the tool name and arguments. This allows the agent to see what
tool the model wanted to call and handle it through text-based parsing.

Changes:
- Modified _handle_non_streaming_response to convert tool_calls to string
- Modified _ahandle_non_streaming_response with the same fix
- Added warning log when this conversion occurs
- Added tests covering the fix for both sync and async paths

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

docs/en/concepts/memory.mdx

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

docs/en/learn/create-custom-tools.mdx

@@ -66,5 +66,55 @@ def my_cache_strategy(arguments: dict, result: str) -> bool:
 cached_tool.cache_function = my_cache_strategy
 ```
 
+### Creating Async Tools
+
+CrewAI supports async tools for non-blocking I/O operations. This is useful when your tool needs to make HTTP requests, database queries, or other I/O-bound operations.
+
+#### Using the `@tool` Decorator with Async Functions
+
+The simplest way to create an async tool is using the `@tool` decorator with an async function:
+
+```python Code
+import aiohttp
+from crewai.tools import tool
+
+@tool("Async Web Fetcher")
+async def fetch_webpage(url: str) -> str:
+    """Fetch content from a webpage asynchronously."""
+    async with aiohttp.ClientSession() as session:
+        async with session.get(url) as response:
+            return await response.text()
+```
+
+#### Subclassing `BaseTool` with Async Support
+
+For more control, subclass `BaseTool` and implement both `_run` (sync) and `_arun` (async) methods:
+
+```python Code
+import requests
+import aiohttp
+from crewai.tools import BaseTool
+from pydantic import BaseModel, Field
+
+class WebFetcherInput(BaseModel):
+    """Input schema for WebFetcher."""
+    url: str = Field(..., description="The URL to fetch")
+
+class WebFetcherTool(BaseTool):
+    name: str = "Web Fetcher"
+    description: str = "Fetches content from a URL"
+    args_schema: type[BaseModel] = WebFetcherInput
+
+    def _run(self, url: str) -> str:
+        """Synchronous implementation."""
+        return requests.get(url).text
+
+    async def _arun(self, url: str) -> str:
+        """Asynchronous implementation for non-blocking I/O."""
+        async with aiohttp.ClientSession() as session:
+            async with session.get(url) as response:
+                return await response.text()
+```
+
 By adhering to these guidelines and incorporating new functionalities and collaboration tools into your tool creation and management processes,
 you can leverage the full capabilities of the CrewAI framework, enhancing both the development experience and the efficiency of your AI agents.

docs/ko/concepts/memory.mdx

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

docs/ko/learn/create-custom-tools.mdx

@@ -63,5 +63,55 @@ def my_cache_strategy(arguments: dict, result: str) -> bool:
 cached_tool.cache_function = my_cache_strategy
 ```
 
+### 비동기 도구 생성하기
+
+CrewAI는 논블로킹 I/O 작업을 위한 비동기 도구를 지원합니다. 이는 HTTP 요청, 데이터베이스 쿼리 또는 기타 I/O 바운드 작업이 필요한 경우에 유용합니다.
+
+#### `@tool` 데코레이터와 비동기 함수 사용하기
+
+비동기 도구를 만드는 가장 간단한 방법은 `@tool` 데코레이터와 async 함수를 사용하는 것입니다:
+
+```python Code
+import aiohttp
+from crewai.tools import tool
+
+@tool("Async Web Fetcher")
+async def fetch_webpage(url: str) -> str:
+    """Fetch content from a webpage asynchronously."""
+    async with aiohttp.ClientSession() as session:
+        async with session.get(url) as response:
+            return await response.text()
+```
+
+#### 비동기 지원으로 `BaseTool` 서브클래싱하기
+
+더 많은 제어를 위해 `BaseTool`을 상속하고 `_run`(동기) 및 `_arun`(비동기) 메서드를 모두 구현할 수 있습니다:
+
+```python Code
+import requests
+import aiohttp
+from crewai.tools import BaseTool
+from pydantic import BaseModel, Field
+
+class WebFetcherInput(BaseModel):
+    """Input schema for WebFetcher."""
+    url: str = Field(..., description="The URL to fetch")
+
+class WebFetcherTool(BaseTool):
+    name: str = "Web Fetcher"
+    description: str = "Fetches content from a URL"
+    args_schema: type[BaseModel] = WebFetcherInput
+
+    def _run(self, url: str) -> str:
+        """Synchronous implementation."""
+        return requests.get(url).text
+
+    async def _arun(self, url: str) -> str:
+        """Asynchronous implementation for non-blocking I/O."""
+        async with aiohttp.ClientSession() as session:
+            async with session.get(url) as response:
+                return await response.text()
+```
+
 이 가이드라인을 준수하고 새로운 기능과 협업 도구를 도구 생성 및 관리 프로세스에 통합함으로써,
-CrewAI 프레임워크의 모든 기능을 활용할 수 있으며, AI agent의 개발 경험과 효율성을 모두 높일 수 있습니다.
+CrewAI 프레임워크의 모든 기능을 활용할 수 있으며, AI agent의 개발 경험과 효율성을 모두 높일 수 있습니다.

docs/pt-BR/concepts/memory.mdx

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

docs/pt-BR/learn/create-custom-tools.mdx

@@ -66,5 +66,55 @@ def my_cache_strategy(arguments: dict, result: str) -> bool:
 cached_tool.cache_function = my_cache_strategy
 ```
 
+### Criando Ferramentas Assíncronas
+
+O CrewAI suporta ferramentas assíncronas para operações de I/O não bloqueantes. Isso é útil quando sua ferramenta precisa fazer requisições HTTP, consultas a banco de dados ou outras operações de I/O.
+
+#### Usando o Decorador `@tool` com Funções Assíncronas
+
+A maneira mais simples de criar uma ferramenta assíncrona é usando o decorador `@tool` com uma função async:
+
+```python Code
+import aiohttp
+from crewai.tools import tool
+
+@tool("Async Web Fetcher")
+async def fetch_webpage(url: str) -> str:
+    """Fetch content from a webpage asynchronously."""
+    async with aiohttp.ClientSession() as session:
+        async with session.get(url) as response:
+            return await response.text()
+```
+
+#### Subclassificando `BaseTool` com Suporte Assíncrono
+
+Para maior controle, herde de `BaseTool` e implemente os métodos `_run` (síncrono) e `_arun` (assíncrono):
+
+```python Code
+import requests
+import aiohttp
+from crewai.tools import BaseTool
+from pydantic import BaseModel, Field
+
+class WebFetcherInput(BaseModel):
+    """Input schema for WebFetcher."""
+    url: str = Field(..., description="The URL to fetch")
+
+class WebFetcherTool(BaseTool):
+    name: str = "Web Fetcher"
+    description: str = "Fetches content from a URL"
+    args_schema: type[BaseModel] = WebFetcherInput
+
+    def _run(self, url: str) -> str:
+        """Synchronous implementation."""
+        return requests.get(url).text
+
+    async def _arun(self, url: str) -> str:
+        """Asynchronous implementation for non-blocking I/O."""
+        async with aiohttp.ClientSession() as session:
+            async with session.get(url) as response:
+                return await response.text()
+```
+
 Seguindo essas orientações e incorporando novas funcionalidades e ferramentas de colaboração nos seus processos de criação e gerenciamento de ferramentas,
-você pode aproveitar ao máximo as capacidades do framework CrewAI, aprimorando tanto a experiência de desenvolvimento quanto a eficiência dos seus agentes de IA.
+você pode aproveitar ao máximo as capacidades do framework CrewAI, aprimorando tanto a experiência de desenvolvimento quanto a eficiência dos seus agentes de IA.

lib/crewai/pyproject.toml

@@ -38,6 +38,7 @@ dependencies = [
     "pydantic-settings~=2.10.1",
     "mcp~=1.16.0",
     "uv~=0.9.13",
+    "aiosqlite~=0.21.0",
 ]
 
 [project.urls]
@@ -95,6 +96,7 @@ 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/a2a/extensions/__init__.py

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

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

@@ -0,0 +1,193 @@
+"""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

@@ -0,0 +1,34 @@
+"""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

@@ -23,6 +23,8 @@ 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
 
@@ -65,7 +67,7 @@ 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 (unused in body)
+        _ttl_hash: Time-based hash for cache invalidation
 
     Returns:
         Cached AgentCard
@@ -106,7 +108,18 @@ def fetch_agent_card(
         A2AClientHTTPError: If authentication fails
     """
     if use_cache:
-        auth_hash = hash((type(auth).__name__, id(auth))) if auth else 0
+        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
         ttl_hash = int(time.time() // cache_ttl)
         return _fetch_agent_card_cached(endpoint, auth_hash, timeout, ttl_hash)
@@ -121,6 +134,26 @@ 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,
@@ -339,7 +372,22 @@ async def _execute_a2a_delegation_async(
     Returns:
         Dictionary with status, result/error, and new history
     """
-    agent_card = await _fetch_agent_card_async(endpoint, auth, timeout)
+    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
+    )
 
     validate_auth_against_agent_card(agent_card, auth)
 
@@ -556,6 +604,34 @@ 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"):
@@ -752,4 +828,5 @@ 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

@@ -15,6 +15,7 @@ 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,
@@ -42,7 +43,9 @@ if TYPE_CHECKING:
     from crewai.tools.base_tool import BaseTool
 
 
-def wrap_agent_with_a2a_instance(agent: Agent) -> None:
+def wrap_agent_with_a2a_instance(
+    agent: Agent, extension_registry: ExtensionRegistry | None = None
+) -> None:
     """Wrap an agent instance's execute_task method with A2A support.
 
     This function modifies the agent instance by wrapping its execute_task
@@ -51,7 +54,13 @@ def wrap_agent_with_a2a_instance(agent: Agent) -> None:
 
     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)
@@ -85,6 +94,7 @@ def wrap_agent_with_a2a_instance(agent: Agent) -> None:
             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))
@@ -154,6 +164,7 @@ 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.
 
@@ -165,6 +176,7 @@ 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)
@@ -190,11 +202,12 @@ 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
 
@@ -204,6 +217,11 @@ 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
         ):
@@ -217,6 +235,7 @@ 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)
 
@@ -235,7 +254,8 @@ def _augment_prompt_with_a2a(
     turn_num: int = 0,
     max_turns: int | None = None,
     failed_agents: dict[str, str] | None = None,
-) -> str:
+    extension_registry: ExtensionRegistry | None = None,
+) -> tuple[str, bool]:
     """Add A2A delegation instructions to prompt.
 
     Args:
@@ -246,13 +266,14 @@ 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:
-        Augmented task description with A2A instructions
+        Tuple of (augmented prompt, disable_structured_output flag)
     """
 
     if not agent_cards:
-        return task_description
+        return task_description, False
 
     agents_text = ""
 
@@ -270,6 +291,7 @@ 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"
@@ -277,6 +299,15 @@ 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:
@@ -296,16 +327,22 @@ def _augment_prompt_with_a2a(
             warning=warning,
         )
 
-    return f"""{task_description}
+    augmented_prompt = 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]
@@ -373,7 +410,7 @@ 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 = _augment_prompt_with_a2a(
+    task.description, disable_structured_output = _augment_prompt_with_a2a(
         a2a_agents=a2a_agents,
         task_description=original_task_description,
         conversation_history=conversation_history,
@@ -382,7 +419,38 @@ 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
     )
@@ -425,6 +493,7 @@ 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.
 
@@ -437,6 +506,7 @@ 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
@@ -447,9 +517,13 @@ 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 agent_id not in agent_ids:
+    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:
         raise ValueError(
             f"Unknown A2A agent ID(s): {agent_response.a2a_ids} not in {agent_ids}"
         )
@@ -458,10 +532,11 @@ 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
 
@@ -497,11 +572,27 @@ 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(
@@ -513,7 +604,7 @@ def _delegate_to_a2a(
                             total_turns=final_turn_number,
                         ),
                     )
-                    return result_text  # type: ignore[no-any-return]
+                    return cast(str, result_text)
 
                 final_result, next_request = _handle_agent_response_and_continue(
                     self=self,
@@ -541,6 +632,31 @@ 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(
@@ -550,7 +666,7 @@ def _delegate_to_a2a(
                     total_turns=turn_num + 1,
                 ),
             )
-            raise Exception(f"A2A delegation failed: {error_msg}")
+            return f"A2A delegation failed: {error_msg}"
 
         if conversation_history:
             for msg in reversed(conversation_history):

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

@@ -2,7 +2,6 @@ from __future__ import annotations
 
 import asyncio
 from collections.abc import Sequence
-import json
 import shutil
 import subprocess
 import time
@@ -19,6 +18,19 @@ from pydantic import BaseModel, Field, InstanceOf, PrivateAttr, model_validator
 from typing_extensions import Self
 
 from crewai.a2a.config import A2AConfig
+from crewai.agent.utils import (
+    ahandle_knowledge_retrieval,
+    apply_training_data,
+    build_task_prompt_with_schema,
+    format_task_with_context,
+    get_knowledge_config,
+    handle_knowledge_retrieval,
+    handle_reasoning,
+    prepare_tools,
+    process_tool_results,
+    save_last_messages,
+    validate_max_execution_time,
+)
 from crewai.agents.agent_builder.base_agent import BaseAgent
 from crewai.agents.cache.cache_handler import CacheHandler
 from crewai.agents.crew_agent_executor import CrewAgentExecutor
@@ -27,9 +39,6 @@ from crewai.events.types.knowledge_events import (
     KnowledgeQueryCompletedEvent,
     KnowledgeQueryFailedEvent,
     KnowledgeQueryStartedEvent,
-    KnowledgeRetrievalCompletedEvent,
-    KnowledgeRetrievalStartedEvent,
-    KnowledgeSearchQueryFailedEvent,
 )
 from crewai.events.types.memory_events import (
     MemoryRetrievalCompletedEvent,
@@ -37,7 +46,6 @@ from crewai.events.types.memory_events import (
 )
 from crewai.knowledge.knowledge import Knowledge
 from crewai.knowledge.source.base_knowledge_source import BaseKnowledgeSource
-from crewai.knowledge.utils.knowledge_utils import extract_knowledge_context
 from crewai.lite_agent import LiteAgent
 from crewai.llms.base_llm import BaseLLM
 from crewai.mcp import (
@@ -61,7 +69,7 @@ from crewai.utilities.agent_utils import (
     render_text_description_and_args,
 )
 from crewai.utilities.constants import TRAINED_AGENTS_DATA_FILE, TRAINING_DATA_FILE
-from crewai.utilities.converter import Converter, generate_model_description
+from crewai.utilities.converter import Converter
 from crewai.utilities.guardrail_types import GuardrailType
 from crewai.utilities.llm_utils import create_llm
 from crewai.utilities.prompts import Prompts
@@ -295,53 +303,15 @@ class Agent(BaseAgent):
             ValueError: If the max execution time is not a positive integer.
             RuntimeError: If the agent execution fails for other reasons.
         """
-        if self.reasoning:
-            try:
-                from crewai.utilities.reasoning_handler import (
-                    AgentReasoning,
-                    AgentReasoningOutput,
-                )
-
-                reasoning_handler = AgentReasoning(task=task, agent=self)
-                reasoning_output: AgentReasoningOutput = (
-                    reasoning_handler.handle_agent_reasoning()
-                )
-
-                # Add the reasoning plan to the task description
-                task.description += f"\n\nReasoning Plan:\n{reasoning_output.plan.plan}"
-            except Exception as e:
-                self._logger.log("error", f"Error during reasoning process: {e!s}")
+        handle_reasoning(self, task)
         self._inject_date_to_task(task)
 
         if self.tools_handler:
             self.tools_handler.last_used_tool = None
 
         task_prompt = task.prompt()
-
-        # If the task requires output in JSON or Pydantic format,
-        # append specific instructions to the task prompt to ensure
-        # that the final answer does not include any code block markers
-        # Skip this if task.response_model is set, as native structured outputs handle schema automatically
-        if (task.output_json or task.output_pydantic) and not task.response_model:
-            # Generate the schema based on the output format
-            if task.output_json:
-                schema_dict = generate_model_description(task.output_json)
-                schema = json.dumps(schema_dict["json_schema"]["schema"], indent=2)
-                task_prompt += "\n" + self.i18n.slice(
-                    "formatted_task_instructions"
-                ).format(output_format=schema)
-
-            elif task.output_pydantic:
-                schema_dict = generate_model_description(task.output_pydantic)
-                schema = json.dumps(schema_dict["json_schema"]["schema"], indent=2)
-                task_prompt += "\n" + self.i18n.slice(
-                    "formatted_task_instructions"
-                ).format(output_format=schema)
-
-        if context:
-            task_prompt = self.i18n.slice("task_with_context").format(
-                task=task_prompt, context=context
-            )
+        task_prompt = build_task_prompt_with_schema(task, task_prompt, self.i18n)
+        task_prompt = format_task_with_context(task_prompt, context, self.i18n)
 
         if self._is_any_available_memory():
             crewai_event_bus.emit(
@@ -379,84 +349,20 @@ class Agent(BaseAgent):
                     from_task=task,
                 ),
             )
-        knowledge_config = (
-            self.knowledge_config.model_dump() if self.knowledge_config else {}
+
+        knowledge_config = get_knowledge_config(self)
+        task_prompt = handle_knowledge_retrieval(
+            self,
+            task,
+            task_prompt,
+            knowledge_config,
+            self.knowledge.query if self.knowledge else lambda *a, **k: None,
+            self.crew.query_knowledge if self.crew else lambda *a, **k: None,
         )
 
-        if self.knowledge or (self.crew and self.crew.knowledge):
-            crewai_event_bus.emit(
-                self,
-                event=KnowledgeRetrievalStartedEvent(
-                    from_task=task,
-                    from_agent=self,
-                ),
-            )
-            try:
-                self.knowledge_search_query = self._get_knowledge_search_query(
-                    task_prompt, task
-                )
-                if self.knowledge_search_query:
-                    # Quering agent specific knowledge
-                    if self.knowledge:
-                        agent_knowledge_snippets = self.knowledge.query(
-                            [self.knowledge_search_query], **knowledge_config
-                        )
-                        if agent_knowledge_snippets:
-                            self.agent_knowledge_context = extract_knowledge_context(
-                                agent_knowledge_snippets
-                            )
-                            if self.agent_knowledge_context:
-                                task_prompt += self.agent_knowledge_context
+        prepare_tools(self, tools, task)
+        task_prompt = apply_training_data(self, task_prompt)
 
-                    # Quering crew specific knowledge
-                    knowledge_snippets = self.crew.query_knowledge(
-                        [self.knowledge_search_query], **knowledge_config
-                    )
-                    if knowledge_snippets:
-                        self.crew_knowledge_context = extract_knowledge_context(
-                            knowledge_snippets
-                        )
-                        if self.crew_knowledge_context:
-                            task_prompt += self.crew_knowledge_context
-
-                    crewai_event_bus.emit(
-                        self,
-                        event=KnowledgeRetrievalCompletedEvent(
-                            query=self.knowledge_search_query,
-                            from_task=task,
-                            from_agent=self,
-                            retrieved_knowledge=(
-                                (self.agent_knowledge_context or "")
-                                + (
-                                    "\n"
-                                    if self.agent_knowledge_context
-                                    and self.crew_knowledge_context
-                                    else ""
-                                )
-                                + (self.crew_knowledge_context or "")
-                            ),
-                        ),
-                    )
-            except Exception as e:
-                crewai_event_bus.emit(
-                    self,
-                    event=KnowledgeSearchQueryFailedEvent(
-                        query=self.knowledge_search_query or "",
-                        error=str(e),
-                        from_task=task,
-                        from_agent=self,
-                    ),
-                )
-
-        tools = tools or self.tools or []
-        self.create_agent_executor(tools=tools, task=task)
-
-        if self.crew and self.crew._train:
-            task_prompt = self._training_handler(task_prompt=task_prompt)
-        else:
-            task_prompt = self._use_trained_data(task_prompt=task_prompt)
-
-        # Import agent events locally to avoid circular imports
         from crewai.events.types.agent_events import (
             AgentExecutionCompletedEvent,
             AgentExecutionErrorEvent,
@@ -474,15 +380,8 @@ class Agent(BaseAgent):
                 ),
             )
 
-            # Determine execution method based on timeout setting
+            validate_max_execution_time(self.max_execution_time)
             if self.max_execution_time is not None:
-                if (
-                    not isinstance(self.max_execution_time, int)
-                    or self.max_execution_time <= 0
-                ):
-                    raise ValueError(
-                        "Max Execution time must be a positive integer greater than zero"
-                    )
                 result = self._execute_with_timeout(
                     task_prompt, task, self.max_execution_time
                 )
@@ -490,7 +389,6 @@ class Agent(BaseAgent):
                 result = self._execute_without_timeout(task_prompt, task)
 
         except TimeoutError as e:
-            # Propagate TimeoutError without retry
             crewai_event_bus.emit(
                 self,
                 event=AgentExecutionErrorEvent(
@@ -502,7 +400,6 @@ class Agent(BaseAgent):
             raise e
         except Exception as e:
             if e.__class__.__module__.startswith("litellm"):
-                # Do not retry on litellm errors
                 crewai_event_bus.emit(
                     self,
                     event=AgentExecutionErrorEvent(
@@ -528,23 +425,13 @@ class Agent(BaseAgent):
         if self.max_rpm and self._rpm_controller:
             self._rpm_controller.stop_rpm_counter()
 
-        # If there was any tool in self.tools_results that had result_as_answer
-        # set to True, return the results of the last tool that had
-        # result_as_answer set to True
-        for tool_result in self.tools_results:
-            if tool_result.get("result_as_answer", False):
-                result = tool_result["result"]
+        result = process_tool_results(self, result)
         crewai_event_bus.emit(
             self,
             event=AgentExecutionCompletedEvent(agent=self, task=task, output=result),
         )
 
-        self._last_messages = (
-            self.agent_executor.messages.copy()
-            if self.agent_executor and hasattr(self.agent_executor, "messages")
-            else []
-        )
-
+        save_last_messages(self)
         self._cleanup_mcp_clients()
 
         return result
@@ -604,6 +491,208 @@ class Agent(BaseAgent):
             }
         )["output"]
 
+    async def aexecute_task(
+        self,
+        task: Task,
+        context: str | None = None,
+        tools: list[BaseTool] | None = None,
+    ) -> Any:
+        """Execute a task with the agent asynchronously.
+
+        Args:
+            task: Task to execute.
+            context: Context to execute the task in.
+            tools: Tools to use for the task.
+
+        Returns:
+            Output of the agent.
+
+        Raises:
+            TimeoutError: If execution exceeds the maximum execution time.
+            ValueError: If the max execution time is not a positive integer.
+            RuntimeError: If the agent execution fails for other reasons.
+        """
+        handle_reasoning(self, task)
+        self._inject_date_to_task(task)
+
+        if self.tools_handler:
+            self.tools_handler.last_used_tool = None
+
+        task_prompt = task.prompt()
+        task_prompt = build_task_prompt_with_schema(task, task_prompt, self.i18n)
+        task_prompt = format_task_with_context(task_prompt, context, self.i18n)
+
+        if self._is_any_available_memory():
+            crewai_event_bus.emit(
+                self,
+                event=MemoryRetrievalStartedEvent(
+                    task_id=str(task.id) if task else None,
+                    source_type="agent",
+                    from_agent=self,
+                    from_task=task,
+                ),
+            )
+
+            start_time = time.time()
+
+            contextual_memory = ContextualMemory(
+                self.crew._short_term_memory,
+                self.crew._long_term_memory,
+                self.crew._entity_memory,
+                self.crew._external_memory,
+                agent=self,
+                task=task,
+            )
+            memory = await contextual_memory.abuild_context_for_task(
+                task, context or ""
+            )
+            if memory.strip() != "":
+                task_prompt += self.i18n.slice("memory").format(memory=memory)
+
+            crewai_event_bus.emit(
+                self,
+                event=MemoryRetrievalCompletedEvent(
+                    task_id=str(task.id) if task else None,
+                    memory_content=memory,
+                    retrieval_time_ms=(time.time() - start_time) * 1000,
+                    source_type="agent",
+                    from_agent=self,
+                    from_task=task,
+                ),
+            )
+
+        knowledge_config = get_knowledge_config(self)
+        task_prompt = await ahandle_knowledge_retrieval(
+            self, task, task_prompt, knowledge_config
+        )
+
+        prepare_tools(self, tools, task)
+        task_prompt = apply_training_data(self, task_prompt)
+
+        from crewai.events.types.agent_events import (
+            AgentExecutionCompletedEvent,
+            AgentExecutionErrorEvent,
+            AgentExecutionStartedEvent,
+        )
+
+        try:
+            crewai_event_bus.emit(
+                self,
+                event=AgentExecutionStartedEvent(
+                    agent=self,
+                    tools=self.tools,
+                    task_prompt=task_prompt,
+                    task=task,
+                ),
+            )
+
+            validate_max_execution_time(self.max_execution_time)
+            if self.max_execution_time is not None:
+                result = await self._aexecute_with_timeout(
+                    task_prompt, task, self.max_execution_time
+                )
+            else:
+                result = await self._aexecute_without_timeout(task_prompt, task)
+
+        except TimeoutError as e:
+            crewai_event_bus.emit(
+                self,
+                event=AgentExecutionErrorEvent(
+                    agent=self,
+                    task=task,
+                    error=str(e),
+                ),
+            )
+            raise e
+        except Exception as e:
+            if e.__class__.__module__.startswith("litellm"):
+                crewai_event_bus.emit(
+                    self,
+                    event=AgentExecutionErrorEvent(
+                        agent=self,
+                        task=task,
+                        error=str(e),
+                    ),
+                )
+                raise e
+            self._times_executed += 1
+            if self._times_executed > self.max_retry_limit:
+                crewai_event_bus.emit(
+                    self,
+                    event=AgentExecutionErrorEvent(
+                        agent=self,
+                        task=task,
+                        error=str(e),
+                    ),
+                )
+                raise e
+            result = await self.aexecute_task(task, context, tools)
+
+        if self.max_rpm and self._rpm_controller:
+            self._rpm_controller.stop_rpm_counter()
+
+        result = process_tool_results(self, result)
+        crewai_event_bus.emit(
+            self,
+            event=AgentExecutionCompletedEvent(agent=self, task=task, output=result),
+        )
+
+        save_last_messages(self)
+        self._cleanup_mcp_clients()
+
+        return result
+
+    async def _aexecute_with_timeout(
+        self, task_prompt: str, task: Task, timeout: int
+    ) -> Any:
+        """Execute a task with a timeout asynchronously.
+
+        Args:
+            task_prompt: The prompt to send to the agent.
+            task: The task being executed.
+            timeout: Maximum execution time in seconds.
+
+        Returns:
+            The output of the agent.
+
+        Raises:
+            TimeoutError: If execution exceeds the timeout.
+            RuntimeError: If execution fails for other reasons.
+        """
+        try:
+            return await asyncio.wait_for(
+                self._aexecute_without_timeout(task_prompt, task),
+                timeout=timeout,
+            )
+        except asyncio.TimeoutError as e:
+            raise TimeoutError(
+                f"Task '{task.description}' execution timed out after {timeout} seconds. "
+                "Consider increasing max_execution_time or optimizing the task."
+            ) from e
+
+    async def _aexecute_without_timeout(self, task_prompt: str, task: Task) -> Any:
+        """Execute a task without a timeout asynchronously.
+
+        Args:
+            task_prompt: The prompt to send to the agent.
+            task: The task being executed.
+
+        Returns:
+            The output of the agent.
+        """
+        if not self.agent_executor:
+            raise RuntimeError("Agent executor is not initialized.")
+
+        result = await self.agent_executor.ainvoke(
+            {
+                "input": task_prompt,
+                "tool_names": self.agent_executor.tools_names,
+                "tools": self.agent_executor.tools_description,
+                "ask_for_human_input": task.human_input,
+            }
+        )
+        return result["output"]
+
     def create_agent_executor(
         self, tools: list[BaseTool] | None = None, task: Task | None = None
     ) -> None:
@@ -633,7 +722,7 @@ class Agent(BaseAgent):
             )
 
         self.agent_executor = CrewAgentExecutor(
-            llm=self.llm,
+            llm=self.llm,  # type: ignore[arg-type]
             task=task,  # type: ignore[arg-type]
             agent=self,
             crew=self.crew,
@@ -810,6 +899,7 @@ class Agent(BaseAgent):
         from crewai.tools.base_tool import BaseTool
         from crewai.tools.mcp_native_tool import MCPNativeTool
 
+        transport: StdioTransport | HTTPTransport | SSETransport
         if isinstance(mcp_config, MCPServerStdio):
             transport = StdioTransport(
                 command=mcp_config.command,
@@ -903,10 +993,10 @@ class Agent(BaseAgent):
                                 server_name=server_name,
                                 run_context=None,
                             )
-                            if mcp_config.tool_filter(context, tool):
+                            if mcp_config.tool_filter(context, tool):  # type: ignore[call-arg, arg-type]
                                 filtered_tools.append(tool)
                         except (TypeError, AttributeError):
-                            if mcp_config.tool_filter(tool):
+                            if mcp_config.tool_filter(tool):  # type: ignore[call-arg, arg-type]
                                 filtered_tools.append(tool)
                     else:
                         # Not callable - include tool
@@ -981,7 +1071,9 @@ class Agent(BaseAgent):
         path = parsed.path.replace("/", "_").strip("_")
         return f"{domain}_{path}" if path else domain
 
-    def _get_mcp_tool_schemas(self, server_params: dict) -> dict[str, dict]:
+    def _get_mcp_tool_schemas(
+        self, server_params: dict[str, Any]
+    ) -> dict[str, dict[str, Any]]:
         """Get tool schemas from MCP server for wrapper creation with caching."""
         server_url = server_params["url"]
 
@@ -995,7 +1087,7 @@ class Agent(BaseAgent):
                 self._logger.log(
                     "debug", f"Using cached MCP tool schemas for {server_url}"
                 )
-                return cached_data
+                return cached_data  # type: ignore[no-any-return]
 
         try:
             schemas = asyncio.run(self._get_mcp_tool_schemas_async(server_params))
@@ -1013,7 +1105,7 @@ class Agent(BaseAgent):
 
     async def _get_mcp_tool_schemas_async(
         self, server_params: dict[str, Any]
-    ) -> dict[str, dict]:
+    ) -> dict[str, dict[str, Any]]:
         """Async implementation of MCP tool schema retrieval with timeouts and retries."""
         server_url = server_params["url"]
         return await self._retry_mcp_discovery(
@@ -1021,7 +1113,7 @@ class Agent(BaseAgent):
         )
 
     async def _retry_mcp_discovery(
-        self, operation_func, server_url: str
+        self, operation_func: Any, server_url: str
     ) -> dict[str, dict[str, Any]]:
         """Retry MCP discovery operation with exponential backoff, avoiding try-except in loop."""
         last_error = None
@@ -1052,7 +1144,7 @@ class Agent(BaseAgent):
 
     @staticmethod
     async def _attempt_mcp_discovery(
-        operation_func, server_url: str
+        operation_func: Any, server_url: str
     ) -> tuple[dict[str, dict[str, Any]] | None, str, bool]:
         """Attempt single MCP discovery operation and return (result, error_message, should_retry)."""
         try:
@@ -1142,7 +1234,7 @@ class Agent(BaseAgent):
         properties = json_schema.get("properties", {})
         required_fields = json_schema.get("required", [])
 
-        field_definitions = {}
+        field_definitions: dict[str, Any] = {}
 
         for field_name, field_schema in properties.items():
             field_type = self._json_type_to_python(field_schema)
@@ -1162,7 +1254,7 @@ class Agent(BaseAgent):
                 )
 
         model_name = f"{tool_name.replace('-', '_').replace(' ', '_')}Schema"
-        return create_model(model_name, **field_definitions)
+        return create_model(model_name, **field_definitions)  # type: ignore[no-any-return]
 
     def _json_type_to_python(self, field_schema: dict[str, Any]) -> type:
         """Convert JSON Schema type to Python type.
@@ -1177,7 +1269,7 @@ class Agent(BaseAgent):
         json_type = field_schema.get("type")
 
         if "anyOf" in field_schema:
-            types = []
+            types: list[type] = []
             for option in field_schema["anyOf"]:
                 if "const" in option:
                     types.append(str)
@@ -1185,13 +1277,13 @@ class Agent(BaseAgent):
                     types.append(self._json_type_to_python(option))
             unique_types = list(set(types))
             if len(unique_types) > 1:
-                result = unique_types[0]
+                result: Any = unique_types[0]
                 for t in unique_types[1:]:
                     result = result | t
-                return result
+                return result  # type: ignore[no-any-return]
             return unique_types[0]
 
-        type_mapping = {
+        type_mapping: dict[str | None, type] = {
             "string": str,
             "number": float,
             "integer": int,
@@ -1203,7 +1295,7 @@ class Agent(BaseAgent):
         return type_mapping.get(json_type, Any)
 
     @staticmethod
-    def _fetch_amp_mcp_servers(mcp_name: str) -> list[dict]:
+    def _fetch_amp_mcp_servers(mcp_name: str) -> list[dict[str, Any]]:
         """Fetch MCP server configurations from CrewAI AOP API."""
         # TODO: Implement AMP API call to "integrations/mcps" endpoint
         # Should return list of server configs with URLs
@@ -1438,11 +1530,11 @@ class Agent(BaseAgent):
         """
         if self.apps:
             platform_tools = self.get_platform_tools(self.apps)
-            if platform_tools:
+            if platform_tools and self.tools is not None:
                 self.tools.extend(platform_tools)
         if self.mcps:
             mcps = self.get_mcp_tools(self.mcps)
-            if mcps:
+            if mcps and self.tools is not None:
                 self.tools.extend(mcps)
 
         lite_agent = LiteAgent(

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

@@ -4,9 +4,8 @@ This metaclass enables extension capabilities for agents by detecting
 extension fields in class annotations and applying appropriate wrappers.
 """
 
-import warnings
-from functools import wraps
 from typing import Any
+import warnings
 
 from pydantic import model_validator
 from pydantic._internal._model_construction import ModelMetaclass
@@ -59,9 +58,15 @@ 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
 
-                    wrap_agent_with_a2a_instance(self)
+                    extension_registry = create_extension_registry_from_config(
+                        a2a_value
+                    )
+                    wrap_agent_with_a2a_instance(self, extension_registry)
 
                 return result
 

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

@@ -0,0 +1,355 @@
+"""Utility functions for agent task execution.
+
+This module contains shared logic extracted from the Agent's execute_task
+and aexecute_task methods to reduce code duplication.
+"""
+
+from __future__ import annotations
+
+import json
+from typing import TYPE_CHECKING, Any
+
+from crewai.events.event_bus import crewai_event_bus
+from crewai.events.types.knowledge_events import (
+    KnowledgeRetrievalCompletedEvent,
+    KnowledgeRetrievalStartedEvent,
+    KnowledgeSearchQueryFailedEvent,
+)
+from crewai.knowledge.utils.knowledge_utils import extract_knowledge_context
+from crewai.utilities.converter import generate_model_description
+
+
+if TYPE_CHECKING:
+    from crewai.agent.core import Agent
+    from crewai.task import Task
+    from crewai.tools.base_tool import BaseTool
+    from crewai.utilities.i18n import I18N
+
+
+def handle_reasoning(agent: Agent, task: Task) -> None:
+    """Handle the reasoning process for an agent before task execution.
+
+    Args:
+        agent: The agent performing the task.
+        task: The task to execute.
+    """
+    if not agent.reasoning:
+        return
+
+    try:
+        from crewai.utilities.reasoning_handler import (
+            AgentReasoning,
+            AgentReasoningOutput,
+        )
+
+        reasoning_handler = AgentReasoning(task=task, agent=agent)
+        reasoning_output: AgentReasoningOutput = (
+            reasoning_handler.handle_agent_reasoning()
+        )
+        task.description += f"\n\nReasoning Plan:\n{reasoning_output.plan.plan}"
+    except Exception as e:
+        agent._logger.log("error", f"Error during reasoning process: {e!s}")
+
+
+def build_task_prompt_with_schema(task: Task, task_prompt: str, i18n: I18N) -> str:
+    """Build task prompt with JSON/Pydantic schema instructions if applicable.
+
+    Args:
+        task: The task being executed.
+        task_prompt: The initial task prompt.
+        i18n: Internationalization instance.
+
+    Returns:
+        The task prompt potentially augmented with schema instructions.
+    """
+    if (task.output_json or task.output_pydantic) and not task.response_model:
+        if task.output_json:
+            schema_dict = generate_model_description(task.output_json)
+            schema = json.dumps(schema_dict["json_schema"]["schema"], indent=2)
+            task_prompt += "\n" + i18n.slice("formatted_task_instructions").format(
+                output_format=schema
+            )
+        elif task.output_pydantic:
+            schema_dict = generate_model_description(task.output_pydantic)
+            schema = json.dumps(schema_dict["json_schema"]["schema"], indent=2)
+            task_prompt += "\n" + i18n.slice("formatted_task_instructions").format(
+                output_format=schema
+            )
+    return task_prompt
+
+
+def format_task_with_context(task_prompt: str, context: str | None, i18n: I18N) -> str:
+    """Format task prompt with context if provided.
+
+    Args:
+        task_prompt: The task prompt.
+        context: Optional context string.
+        i18n: Internationalization instance.
+
+    Returns:
+        The task prompt formatted with context if provided.
+    """
+    if context:
+        return i18n.slice("task_with_context").format(task=task_prompt, context=context)
+    return task_prompt
+
+
+def get_knowledge_config(agent: Agent) -> dict[str, Any]:
+    """Get knowledge configuration from agent.
+
+    Args:
+        agent: The agent instance.
+
+    Returns:
+        Dictionary of knowledge configuration.
+    """
+    return agent.knowledge_config.model_dump() if agent.knowledge_config else {}
+
+
+def handle_knowledge_retrieval(
+    agent: Agent,
+    task: Task,
+    task_prompt: str,
+    knowledge_config: dict[str, Any],
+    query_func: Any,
+    crew_query_func: Any,
+) -> str:
+    """Handle knowledge retrieval for task execution.
+
+    This function handles both agent-specific and crew-specific knowledge queries.
+
+    Args:
+        agent: The agent performing the task.
+        task: The task being executed.
+        task_prompt: The current task prompt.
+        knowledge_config: Knowledge configuration dictionary.
+        query_func: Function to query agent knowledge (sync or async).
+        crew_query_func: Function to query crew knowledge (sync or async).
+
+    Returns:
+        The task prompt potentially augmented with knowledge context.
+    """
+    if not (agent.knowledge or (agent.crew and agent.crew.knowledge)):
+        return task_prompt
+
+    crewai_event_bus.emit(
+        agent,
+        event=KnowledgeRetrievalStartedEvent(
+            from_task=task,
+            from_agent=agent,
+        ),
+    )
+    try:
+        agent.knowledge_search_query = agent._get_knowledge_search_query(
+            task_prompt, task
+        )
+        if agent.knowledge_search_query:
+            if agent.knowledge:
+                agent_knowledge_snippets = query_func(
+                    [agent.knowledge_search_query], **knowledge_config
+                )
+                if agent_knowledge_snippets:
+                    agent.agent_knowledge_context = extract_knowledge_context(
+                        agent_knowledge_snippets
+                    )
+                    if agent.agent_knowledge_context:
+                        task_prompt += agent.agent_knowledge_context
+
+            knowledge_snippets = crew_query_func(
+                [agent.knowledge_search_query], **knowledge_config
+            )
+            if knowledge_snippets:
+                agent.crew_knowledge_context = extract_knowledge_context(
+                    knowledge_snippets
+                )
+                if agent.crew_knowledge_context:
+                    task_prompt += agent.crew_knowledge_context
+
+            crewai_event_bus.emit(
+                agent,
+                event=KnowledgeRetrievalCompletedEvent(
+                    query=agent.knowledge_search_query,
+                    from_task=task,
+                    from_agent=agent,
+                    retrieved_knowledge=_combine_knowledge_context(agent),
+                ),
+            )
+    except Exception as e:
+        crewai_event_bus.emit(
+            agent,
+            event=KnowledgeSearchQueryFailedEvent(
+                query=agent.knowledge_search_query or "",
+                error=str(e),
+                from_task=task,
+                from_agent=agent,
+            ),
+        )
+    return task_prompt
+
+
+def _combine_knowledge_context(agent: Agent) -> str:
+    """Combine agent and crew knowledge contexts into a single string.
+
+    Args:
+        agent: The agent with knowledge contexts.
+
+    Returns:
+        Combined knowledge context string.
+    """
+    agent_ctx = agent.agent_knowledge_context or ""
+    crew_ctx = agent.crew_knowledge_context or ""
+    separator = "\n" if agent_ctx and crew_ctx else ""
+    return agent_ctx + separator + crew_ctx
+
+
+def apply_training_data(agent: Agent, task_prompt: str) -> str:
+    """Apply training data to the task prompt.
+
+    Args:
+        agent: The agent performing the task.
+        task_prompt: The task prompt.
+
+    Returns:
+        The task prompt with training data applied.
+    """
+    if agent.crew and agent.crew._train:
+        return agent._training_handler(task_prompt=task_prompt)
+    return agent._use_trained_data(task_prompt=task_prompt)
+
+
+def process_tool_results(agent: Agent, result: Any) -> Any:
+    """Process tool results, returning result_as_answer if applicable.
+
+    Args:
+        agent: The agent with tool results.
+        result: The current result.
+
+    Returns:
+        The final result, potentially overridden by tool result_as_answer.
+    """
+    for tool_result in agent.tools_results:
+        if tool_result.get("result_as_answer", False):
+            result = tool_result["result"]
+    return result
+
+
+def save_last_messages(agent: Agent) -> None:
+    """Save the last messages from agent executor.
+
+    Args:
+        agent: The agent instance.
+    """
+    agent._last_messages = (
+        agent.agent_executor.messages.copy()
+        if agent.agent_executor and hasattr(agent.agent_executor, "messages")
+        else []
+    )
+
+
+def prepare_tools(
+    agent: Agent, tools: list[BaseTool] | None, task: Task
+) -> list[BaseTool]:
+    """Prepare tools for task execution and create agent executor.
+
+    Args:
+        agent: The agent instance.
+        tools: Optional list of tools.
+        task: The task being executed.
+
+    Returns:
+        The list of tools to use.
+    """
+    final_tools = tools or agent.tools or []
+    agent.create_agent_executor(tools=final_tools, task=task)
+    return final_tools
+
+
+def validate_max_execution_time(max_execution_time: int | None) -> None:
+    """Validate max_execution_time parameter.
+
+    Args:
+        max_execution_time: The maximum execution time to validate.
+
+    Raises:
+        ValueError: If max_execution_time is not a positive integer.
+    """
+    if max_execution_time is not None:
+        if not isinstance(max_execution_time, int) or max_execution_time <= 0:
+            raise ValueError(
+                "Max Execution time must be a positive integer greater than zero"
+            )
+
+
+async def ahandle_knowledge_retrieval(
+    agent: Agent,
+    task: Task,
+    task_prompt: str,
+    knowledge_config: dict[str, Any],
+) -> str:
+    """Handle async knowledge retrieval for task execution.
+
+    Args:
+        agent: The agent performing the task.
+        task: The task being executed.
+        task_prompt: The current task prompt.
+        knowledge_config: Knowledge configuration dictionary.
+
+    Returns:
+        The task prompt potentially augmented with knowledge context.
+    """
+    if not (agent.knowledge or (agent.crew and agent.crew.knowledge)):
+        return task_prompt
+
+    crewai_event_bus.emit(
+        agent,
+        event=KnowledgeRetrievalStartedEvent(
+            from_task=task,
+            from_agent=agent,
+        ),
+    )
+    try:
+        agent.knowledge_search_query = agent._get_knowledge_search_query(
+            task_prompt, task
+        )
+        if agent.knowledge_search_query:
+            if agent.knowledge:
+                agent_knowledge_snippets = await agent.knowledge.aquery(
+                    [agent.knowledge_search_query], **knowledge_config
+                )
+                if agent_knowledge_snippets:
+                    agent.agent_knowledge_context = extract_knowledge_context(
+                        agent_knowledge_snippets
+                    )
+                    if agent.agent_knowledge_context:
+                        task_prompt += agent.agent_knowledge_context
+
+            knowledge_snippets = await agent.crew.aquery_knowledge(
+                [agent.knowledge_search_query], **knowledge_config
+            )
+            if knowledge_snippets:
+                agent.crew_knowledge_context = extract_knowledge_context(
+                    knowledge_snippets
+                )
+                if agent.crew_knowledge_context:
+                    task_prompt += agent.crew_knowledge_context
+
+            crewai_event_bus.emit(
+                agent,
+                event=KnowledgeRetrievalCompletedEvent(
+                    query=agent.knowledge_search_query,
+                    from_task=task,
+                    from_agent=agent,
+                    retrieved_knowledge=_combine_knowledge_context(agent),
+                ),
+            )
+    except Exception as e:
+        crewai_event_bus.emit(
+            agent,
+            event=KnowledgeSearchQueryFailedEvent(
+                query=agent.knowledge_search_query or "",
+                error=str(e),
+                from_task=task,
+                from_agent=agent,
+            ),
+        )
+    return task_prompt

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

@@ -265,7 +265,7 @@ class BaseAgent(BaseModel, ABC, metaclass=AgentMeta):
         if not mcps:
             return mcps
 
-        validated_mcps = []
+        validated_mcps: list[str | MCPServerConfig] = []
         for mcp in mcps:
             if isinstance(mcp, str):
                 if mcp.startswith(("https://", "crewai-amp:")):
@@ -347,6 +347,15 @@ class BaseAgent(BaseModel, ABC, metaclass=AgentMeta):
     ) -> str:
         pass
 
+    @abstractmethod
+    async def aexecute_task(
+        self,
+        task: Any,
+        context: str | None = None,
+        tools: list[BaseTool] | None = None,
+    ) -> str:
+        """Execute a task asynchronously."""
+
     @abstractmethod
     def create_agent_executor(self, tools: list[BaseTool] | None = None) -> None:
         pass

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

@@ -28,6 +28,7 @@ from crewai.hooks.llm_hooks import (
     get_before_llm_call_hooks,
 )
 from crewai.utilities.agent_utils import (
+    aget_llm_response,
     enforce_rpm_limit,
     format_message_for_llm,
     get_llm_response,
@@ -43,7 +44,10 @@ from crewai.utilities.agent_utils import (
 from crewai.utilities.constants import TRAINING_DATA_FILE
 from crewai.utilities.i18n import I18N, get_i18n
 from crewai.utilities.printer import Printer
-from crewai.utilities.tool_utils import execute_tool_and_check_finality
+from crewai.utilities.tool_utils import (
+    aexecute_tool_and_check_finality,
+    execute_tool_and_check_finality,
+)
 from crewai.utilities.training_handler import CrewTrainingHandler
 
 
@@ -134,8 +138,8 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
         self.messages: list[LLMMessage] = []
         self.iterations = 0
         self.log_error_after = 3
-        self.before_llm_call_hooks: list[Callable] = []
-        self.after_llm_call_hooks: list[Callable] = []
+        self.before_llm_call_hooks: list[Callable[..., Any]] = []
+        self.after_llm_call_hooks: list[Callable[..., Any]] = []
         self.before_llm_call_hooks.extend(get_before_llm_call_hooks())
         self.after_llm_call_hooks.extend(get_after_llm_call_hooks())
         if self.llm:
@@ -312,6 +316,154 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
         self._show_logs(formatted_answer)
         return formatted_answer
 
+    async def ainvoke(self, inputs: dict[str, Any]) -> dict[str, Any]:
+        """Execute the agent asynchronously with given inputs.
+
+        Args:
+            inputs: Input dictionary containing prompt variables.
+
+        Returns:
+            Dictionary with agent output.
+        """
+        if "system" in self.prompt:
+            system_prompt = self._format_prompt(
+                cast(str, self.prompt.get("system", "")), inputs
+            )
+            user_prompt = self._format_prompt(
+                cast(str, self.prompt.get("user", "")), inputs
+            )
+            self.messages.append(format_message_for_llm(system_prompt, role="system"))
+            self.messages.append(format_message_for_llm(user_prompt))
+        else:
+            user_prompt = self._format_prompt(self.prompt.get("prompt", ""), inputs)
+            self.messages.append(format_message_for_llm(user_prompt))
+
+        self._show_start_logs()
+
+        self.ask_for_human_input = bool(inputs.get("ask_for_human_input", False))
+
+        try:
+            formatted_answer = await self._ainvoke_loop()
+        except AssertionError:
+            self._printer.print(
+                content="Agent failed to reach a final answer. This is likely a bug - please report it.",
+                color="red",
+            )
+            raise
+        except Exception as e:
+            handle_unknown_error(self._printer, e)
+            raise
+
+        if self.ask_for_human_input:
+            formatted_answer = self._handle_human_feedback(formatted_answer)
+
+        self._create_short_term_memory(formatted_answer)
+        self._create_long_term_memory(formatted_answer)
+        self._create_external_memory(formatted_answer)
+        return {"output": formatted_answer.output}
+
+    async def _ainvoke_loop(self) -> AgentFinish:
+        """Execute agent loop asynchronously until completion.
+
+        Returns:
+            Final answer from the agent.
+        """
+        formatted_answer = None
+        while not isinstance(formatted_answer, AgentFinish):
+            try:
+                if has_reached_max_iterations(self.iterations, self.max_iter):
+                    formatted_answer = handle_max_iterations_exceeded(
+                        formatted_answer,
+                        printer=self._printer,
+                        i18n=self._i18n,
+                        messages=self.messages,
+                        llm=self.llm,
+                        callbacks=self.callbacks,
+                    )
+                    break
+
+                enforce_rpm_limit(self.request_within_rpm_limit)
+
+                answer = await aget_llm_response(
+                    llm=self.llm,
+                    messages=self.messages,
+                    callbacks=self.callbacks,
+                    printer=self._printer,
+                    from_task=self.task,
+                    from_agent=self.agent,
+                    response_model=self.response_model,
+                    executor_context=self,
+                )
+                formatted_answer = process_llm_response(answer, self.use_stop_words)  # type: ignore[assignment]
+
+                if isinstance(formatted_answer, AgentAction):
+                    fingerprint_context = {}
+                    if (
+                        self.agent
+                        and hasattr(self.agent, "security_config")
+                        and hasattr(self.agent.security_config, "fingerprint")
+                    ):
+                        fingerprint_context = {
+                            "agent_fingerprint": str(
+                                self.agent.security_config.fingerprint
+                            )
+                        }
+
+                    tool_result = await aexecute_tool_and_check_finality(
+                        agent_action=formatted_answer,
+                        fingerprint_context=fingerprint_context,
+                        tools=self.tools,
+                        i18n=self._i18n,
+                        agent_key=self.agent.key if self.agent else None,
+                        agent_role=self.agent.role if self.agent else None,
+                        tools_handler=self.tools_handler,
+                        task=self.task,
+                        agent=self.agent,
+                        function_calling_llm=self.function_calling_llm,
+                        crew=self.crew,
+                    )
+                    formatted_answer = self._handle_agent_action(
+                        formatted_answer, tool_result
+                    )
+
+                self._invoke_step_callback(formatted_answer)  # type: ignore[arg-type]
+                self._append_message(formatted_answer.text)  # type: ignore[union-attr,attr-defined]
+
+            except OutputParserError as e:
+                formatted_answer = handle_output_parser_exception(  # type: ignore[assignment]
+                    e=e,
+                    messages=self.messages,
+                    iterations=self.iterations,
+                    log_error_after=self.log_error_after,
+                    printer=self._printer,
+                )
+
+            except Exception as e:
+                if e.__class__.__module__.startswith("litellm"):
+                    raise e
+                if is_context_length_exceeded(e):
+                    handle_context_length(
+                        respect_context_window=self.respect_context_window,
+                        printer=self._printer,
+                        messages=self.messages,
+                        llm=self.llm,
+                        callbacks=self.callbacks,
+                        i18n=self._i18n,
+                    )
+                    continue
+                handle_unknown_error(self._printer, e)
+                raise e
+            finally:
+                self.iterations += 1
+
+        if not isinstance(formatted_answer, AgentFinish):
+            raise RuntimeError(
+                "Agent execution ended without reaching a final answer. "
+                f"Got {type(formatted_answer).__name__} instead of AgentFinish."
+            )
+        self._show_logs(formatted_answer)
+        return formatted_answer
+
     def _handle_agent_action(
         self, formatted_answer: AgentAction, tool_result: ToolResult
     ) -> AgentAction | AgentFinish:

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

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

lib/crewai/src/crewai/crew.py

@@ -35,6 +35,14 @@ 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 (
@@ -47,7 +55,6 @@ from crewai.events.listeners.tracing.utils import (
 from crewai.events.types.crew_events import (
     CrewKickoffCompletedEvent,
     CrewKickoffFailedEvent,
-    CrewKickoffStartedEvent,
     CrewTestCompletedEvent,
     CrewTestFailedEvent,
     CrewTestStartedEvent,
@@ -74,7 +81,7 @@ 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, FlowStreamingOutput
+from crewai.types.streaming import CrewStreamingOutput
 from crewai.types.usage_metrics import UsageMetrics
 from crewai.utilities.constants import NOT_SPECIFIED, TRAINING_DATA_FILE
 from crewai.utilities.crew.models import CrewContext
@@ -92,10 +99,8 @@ 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,
 )
@@ -268,7 +273,7 @@ class Crew(FlowTrackable, BaseModel):
         description="list of file paths for task execution JSON files.",
     )
     execution_logs: list[dict[str, Any]] = Field(
-        default=[],
+        default_factory=list,
         description="list of execution logs for tasks",
     )
     knowledge_sources: list[BaseKnowledgeSource] | None = Field(
@@ -327,7 +332,7 @@ class Crew(FlowTrackable, BaseModel):
     def set_private_attrs(self) -> Crew:
         """set private attributes."""
         self._cache_handler = CacheHandler()
-        event_listener = EventListener()  # type: ignore[no-untyped-call]
+        event_listener = EventListener()
 
         # Determine and set tracing state once for this execution
         tracing_enabled = should_enable_tracing(override=self.tracing)
@@ -348,12 +353,12 @@ class Crew(FlowTrackable, BaseModel):
         return self
 
     def _initialize_default_memories(self) -> None:
-        self._long_term_memory = self._long_term_memory or LongTermMemory()  # type: ignore[no-untyped-call]
-        self._short_term_memory = self._short_term_memory or ShortTermMemory(  # type: ignore[no-untyped-call]
+        self._long_term_memory = self._long_term_memory or LongTermMemory()
+        self._short_term_memory = self._short_term_memory or ShortTermMemory(
             crew=self,
             embedder_config=self.embedder,
         )
-        self._entity_memory = self.entity_memory or EntityMemory(  # type: ignore[no-untyped-call]
+        self._entity_memory = self.entity_memory or EntityMemory(
             crew=self, embedder_config=self.embedder
         )
 
@@ -404,8 +409,7 @@ 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."
                     ),
                     {},
                 )
@@ -511,10 +515,9 @@ class Crew(FlowTrackable, BaseModel):
                 raise PydanticCustomError(
                     "invalid_async_conditional_task",
                     (
-                        f"Conditional Task: {task.description}, "
-                        f"cannot be executed asynchronously."
+                        "Conditional Task: {description}, cannot be executed asynchronously."
                     ),
-                    {},
+                    {"description": task.description},
                 )
         return self
 
@@ -675,21 +678,8 @@ class Crew(FlowTrackable, BaseModel):
         inputs: dict[str, Any] | None = None,
     ) -> CrewOutput | CrewStreamingOutput:
         if self.stream:
-            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] = []
+            enable_agent_streaming(self.agents)
+            ctx = StreamingContext()
 
             def run_crew() -> None:
                 """Execute the crew and capture the result."""
@@ -697,59 +687,28 @@ class Crew(FlowTrackable, BaseModel):
                     self.stream = False
                     crew_result = self.kickoff(inputs=inputs)
                     if isinstance(crew_result, CrewOutput):
-                        result_holder.append(crew_result)
+                        ctx.result_holder.append(crew_result)
                 except Exception as exc:
-                    signal_error(state, exc)
+                    signal_error(ctx.state, exc)
                 finally:
                     self.stream = True
-                    signal_end(state)
+                    signal_end(ctx.state)
 
             streaming_output = CrewStreamingOutput(
-                sync_iterator=create_chunk_generator(state, run_crew, output_holder)
+                sync_iterator=create_chunk_generator(
+                    ctx.state, run_crew, ctx.output_holder
+                )
             )
-            output_holder.append(streaming_output)
+            ctx.output_holder.append(streaming_output)
             return streaming_output
 
-        ctx = baggage.set_baggage(
+        baggage_ctx = baggage.set_baggage(
             "crew_context", CrewContext(id=str(self.id), key=self.key)
         )
-        token = attach(ctx)
+        token = attach(baggage_ctx)
 
         try:
-            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()
+            inputs = prepare_kickoff(self, inputs)
 
             if self.process == Process.sequential:
                 result = self._run_sequential_process()
@@ -814,42 +773,27 @@ class Crew(FlowTrackable, BaseModel):
         inputs = inputs or {}
 
         if self.stream:
-            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] = []
+            enable_agent_streaming(self.agents)
+            ctx = StreamingContext(use_async=True)
 
             async def run_crew() -> None:
                 try:
                     self.stream = False
                     result = await asyncio.to_thread(self.kickoff, inputs)
                     if isinstance(result, CrewOutput):
-                        result_holder.append(result)
+                        ctx.result_holder.append(result)
                 except Exception as e:
-                    signal_error(state, e, is_async=True)
+                    signal_error(ctx.state, e, is_async=True)
                 finally:
                     self.stream = True
-                    signal_end(state, is_async=True)
+                    signal_end(ctx.state, is_async=True)
 
             streaming_output = CrewStreamingOutput(
                 async_iterator=create_async_chunk_generator(
-                    state, run_crew, output_holder
+                    ctx.state, run_crew, ctx.output_holder
                 )
             )
-            output_holder.append(streaming_output)
+            ctx.output_holder.append(streaming_output)
 
             return streaming_output
 
@@ -864,89 +808,207 @@ 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)
+
+        return await run_for_each_async(self, inputs, kickoff_fn)
+
+    async def akickoff(
+        self, inputs: dict[str, Any] | None = None
+    ) -> CrewOutput | CrewStreamingOutput:
+        """Native async kickoff method using async task execution throughout.
+
+        Unlike kickoff_async which wraps sync kickoff in a thread, this method
+        uses native async/await for all operations including task execution,
+        memory operations, and knowledge queries.
+        """
         if self.stream:
-            result_holder: list[list[CrewOutput]] = [[]]
-            current_task_info: TaskInfo = {
-                "index": 0,
-                "name": "",
-                "id": "",
-                "agent_role": "",
-                "agent_id": "",
-            }
+            enable_agent_streaming(self.agents)
+            ctx = StreamingContext(use_async=True)
 
-            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."""
+            async def run_crew() -> None:
                 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)
+                    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)
                 finally:
-                    signal_end(state, is_async=True)
+                    self.stream = True
+                    signal_end(ctx.state, is_async=True)
 
             streaming_output = CrewStreamingOutput(
                 async_iterator=create_async_chunk_generator(
-                    state, run_all_crews, output_holder
+                    ctx.state, run_crew, ctx.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)
+            ctx.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)
-        ]
+        baggage_ctx = baggage.set_baggage(
+            "crew_context", CrewContext(id=str(self.id), key=self.key)
+        )
+        token = attach(baggage_ctx)
 
-        results = await asyncio.gather(*tasks)
+        try:
+            inputs = prepare_kickoff(self, inputs)
 
-        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
+            if self.process == Process.sequential:
+                result = await self._arun_sequential_process()
+            elif self.process == Process.hierarchical:
+                result = await self._arun_hierarchical_process()
+            else:
+                raise NotImplementedError(
+                    f"The process '{self.process}' is not implemented yet."
+                )
 
-        self._task_output_handler.reset()
-        return list(results)
+            for after_callback in self.after_kickoff_callbacks:
+                result = after_callback(result)
+
+            self.usage_metrics = self.calculate_usage_metrics()
+
+            return result
+        except Exception as e:
+            crewai_event_bus.emit(
+                self,
+                CrewKickoffFailedEvent(error=str(e), crew_name=self.name),
+            )
+            raise
+        finally:
+            detach(token)
+
+    async def akickoff_for_each(
+        self, inputs: list[dict[str, Any]]
+    ) -> list[CrewOutput | CrewStreamingOutput] | CrewStreamingOutput:
+        """Native async execution of the Crew's workflow for each input.
+
+        Uses native async throughout rather than thread-based async.
+        If stream=True, returns a single CrewStreamingOutput that yields chunks
+        from all crews as they arrive.
+        """
+
+        async def kickoff_fn(
+            crew: Crew, input_data: dict[str, Any]
+        ) -> CrewOutput | CrewStreamingOutput:
+            return await crew.akickoff(inputs=input_data)
+
+        return await run_for_each_async(self, inputs, kickoff_fn)
+
+    async def _arun_sequential_process(self) -> CrewOutput:
+        """Executes tasks sequentially using native async and returns the final output."""
+        return await self._aexecute_tasks(self.tasks)
+
+    async def _arun_hierarchical_process(self) -> CrewOutput:
+        """Creates and assigns a manager agent to complete the tasks using native async."""
+        self._create_manager_agent()
+        return await self._aexecute_tasks(self.tasks)
+
+    async def _aexecute_tasks(
+        self,
+        tasks: list[Task],
+        start_index: int | None = 0,
+        was_replayed: bool = False,
+    ) -> CrewOutput:
+        """Executes tasks using native async and returns the final output.
+
+        Args:
+            tasks: List of tasks to execute
+            start_index: Index to start execution from (for replay)
+            was_replayed: Whether this is a replayed execution
+
+        Returns:
+            CrewOutput: Final output of the crew
+        """
+        task_outputs: list[TaskOutput] = []
+        pending_tasks: list[tuple[Task, asyncio.Task[TaskOutput], int]] = []
+        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:
+                continue
+
+            if isinstance(task, ConditionalTask):
+                skipped_task_output = await self._ahandle_conditional_task(
+                    task, task_outputs, pending_tasks, task_index, was_replayed
+                )
+                if skipped_task_output:
+                    task_outputs.append(skipped_task_output)
+                    continue
+
+            if task.async_execution:
+                context = self._get_context(
+                    task, [last_sync_output] if last_sync_output else []
+                )
+                async_task = asyncio.create_task(
+                    task.aexecute_sync(
+                        agent=exec_data.agent,
+                        context=context,
+                        tools=exec_data.tools,
+                    )
+                )
+                pending_tasks.append((task, async_task, task_index))
+            else:
+                if pending_tasks:
+                    task_outputs = await self._aprocess_async_tasks(
+                        pending_tasks, was_replayed
+                    )
+                    pending_tasks.clear()
+
+                context = self._get_context(task, task_outputs)
+                task_output = await task.aexecute_sync(
+                    agent=exec_data.agent,
+                    context=context,
+                    tools=exec_data.tools,
+                )
+                task_outputs.append(task_output)
+                self._process_task_result(task, task_output)
+                self._store_execution_log(task, task_output, task_index, was_replayed)
+
+        if pending_tasks:
+            task_outputs = await self._aprocess_async_tasks(pending_tasks, was_replayed)
+
+        return self._create_crew_output(task_outputs)
+
+    async def _ahandle_conditional_task(
+        self,
+        task: ConditionalTask,
+        task_outputs: list[TaskOutput],
+        pending_tasks: list[tuple[Task, asyncio.Task[TaskOutput], int]],
+        task_index: int,
+        was_replayed: bool,
+    ) -> TaskOutput | None:
+        """Handle conditional task evaluation using native async."""
+        if pending_tasks:
+            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
+        )
+
+    async def _aprocess_async_tasks(
+        self,
+        pending_tasks: list[tuple[Task, asyncio.Task[TaskOutput], int]],
+        was_replayed: bool = False,
+    ) -> list[TaskOutput]:
+        """Process pending async tasks and return their outputs."""
+        task_outputs: list[TaskOutput] = []
+        for future_task, async_task, task_index in pending_tasks:
+            task_output = await async_task
+            task_outputs.append(task_output)
+            self._process_task_result(future_task, task_output)
+            self._store_execution_log(
+                future_task, task_output, task_index, was_replayed
+            )
+        return task_outputs
 
     def _handle_crew_planning(self) -> None:
         """Handles the Crew planning."""
@@ -1048,33 +1110,11 @@ class Crew(FlowTrackable, BaseModel):
         last_sync_output: TaskOutput | None = None
 
         for task_index, task in enumerate(tasks):
-            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,
+            exec_data, task_outputs, last_sync_output = prepare_task_execution(
+                self, task, task_index, start_index, task_outputs, last_sync_output
             )
-
-            self._log_task_start(task, agent_to_use.role)
+            if exec_data.should_skip:
+                continue
 
             if isinstance(task, ConditionalTask):
                 skipped_task_output = self._handle_conditional_task(
@@ -1089,9 +1129,9 @@ class Crew(FlowTrackable, BaseModel):
                     task, [last_sync_output] if last_sync_output else []
                 )
                 future = task.execute_async(
-                    agent=agent_to_use,
+                    agent=exec_data.agent,
                     context=context,
-                    tools=tools_for_task,
+                    tools=exec_data.tools,
                 )
                 futures.append((task, future, task_index))
             else:
@@ -1101,9 +1141,9 @@ class Crew(FlowTrackable, BaseModel):
 
                 context = self._get_context(task, task_outputs)
                 task_output = task.execute_sync(
-                    agent=agent_to_use,
+                    agent=exec_data.agent,
                     context=context,
-                    tools=tools_for_task,
+                    tools=exec_data.tools,
                 )
                 task_outputs.append(task_output)
                 self._process_task_result(task, task_output)
@@ -1126,19 +1166,9 @@ class Crew(FlowTrackable, BaseModel):
             task_outputs = self._process_async_tasks(futures, was_replayed)
             futures.clear()
 
-        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
+        return check_conditional_skip(
+            self, task, task_outputs, task_index, was_replayed
+        )
 
     def _prepare_tools(
         self, agent: BaseAgent, task: Task, tools: list[BaseTool]
@@ -1302,7 +1332,8 @@ class Crew(FlowTrackable, BaseModel):
                 )
         return tools
 
-    def _get_context(self, task: Task, task_outputs: list[TaskOutput]) -> str:
+    @staticmethod
+    def _get_context(task: Task, task_outputs: list[TaskOutput]) -> str:
         if not task.context:
             return ""
 
@@ -1371,7 +1402,8 @@ class Crew(FlowTrackable, BaseModel):
             )
         return task_outputs
 
-    def _find_task_index(self, task_id: str, stored_outputs: list[Any]) -> int | None:
+    @staticmethod
+    def _find_task_index(task_id: str, stored_outputs: list[Any]) -> int | None:
         return next(
             (
                 index
@@ -1431,6 +1463,16 @@ class Crew(FlowTrackable, BaseModel):
             )
         return None
 
+    async def aquery_knowledge(
+        self, query: list[str], results_limit: int = 3, score_threshold: float = 0.35
+    ) -> list[SearchResult] | None:
+        """Query the crew's knowledge base for relevant information asynchronously."""
+        if self.knowledge:
+            return await self.knowledge.aquery(
+                query, results_limit=results_limit, score_threshold=score_threshold
+            )
+        return None
+
     def fetch_inputs(self) -> set[str]:
         """
         Gathers placeholders (e.g., {something}) referenced in tasks or agents.
@@ -1439,7 +1481,7 @@ 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
@@ -1687,6 +1729,32 @@ 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()
@@ -1694,21 +1762,10 @@ class Crew(FlowTrackable, BaseModel):
         for config in memory_systems.values():
             if (system := config.get("system")) is not None:
                 name = config.get("name")
-                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
+                reset_fn: Callable[[Any], Any] = cast(
+                    Callable[[Any], Any], config.get("reset")
+                )
+                self._reset_memory_system(system, name, reset_fn)
 
     def _reset_specific_memory(self, memory_type: str) -> None:
         """Reset a specific memory system.
@@ -1727,21 +1784,8 @@ class Crew(FlowTrackable, BaseModel):
         if system is None:
             raise RuntimeError(f"{name} memory system is not initialized")
 
-        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
+        reset_fn: Callable[[Any], Any] = cast(Callable[[Any], Any], config.get("reset"))
+        self._reset_memory_system(system, name, reset_fn)
 
     def _get_memory_systems(self) -> dict[str, Any]:
         """Get all available memory systems with their configuration.
@@ -1829,7 +1873,8 @@ class Crew(FlowTrackable, BaseModel):
         ):
             self.tasks[0].allow_crewai_trigger_context = True
 
-    def _show_tracing_disabled_message(self) -> None:
+    @staticmethod
+    def _show_tracing_disabled_message() -> 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

@@ -0,0 +1,363 @@
+"""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

@@ -140,7 +140,9 @@ 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)
-                self._telemetry.crew_execution_span(source, event.inputs)
+                source._execution_span = self._telemetry.crew_execution_span(
+                    source, event.inputs
+                )
                 self._crew_tree_lock.notify_all()
 
         @crewai_event_bus.on(CrewKickoffCompletedEvent)

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

@@ -1032,6 +1032,20 @@ class Flow(Generic[T], metaclass=FlowMeta):
         finally:
             detach(flow_token)
 
+    async def akickoff(
+        self, inputs: dict[str, Any] | None = None
+    ) -> Any | FlowStreamingOutput:
+        """Native async method to start the flow execution. Alias for kickoff_async.
+
+
+        Args:
+            inputs: Optional dictionary containing input values and/or a state ID for restoration.
+
+        Returns:
+            The final output from the flow, which is the result of the last executed method.
+        """
+        return await self.kickoff_async(inputs)
+
     async def _execute_start_method(self, start_method_name: FlowMethodName) -> None:
         """Executes a flow's start method and its triggered listeners.
 

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

@@ -1,6 +1,6 @@
 from __future__ import annotations
 
-from typing import TYPE_CHECKING
+from typing import TYPE_CHECKING, Any, cast
 
 from crewai.events.event_listener import event_listener
 from crewai.hooks.types import AfterLLMCallHookType, BeforeLLMCallHookType
@@ -9,17 +9,22 @@ 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 with full executor access.
+    """Context object passed to LLM call hooks.
 
-    Provides hooks with complete access to the executor state, allowing
+    Provides hooks with complete access to the execution state, allowing
     modification of messages, responses, and executor attributes.
 
+    Supports both executor-based calls (agents in crews/flows) and direct LLM calls.
+
     Attributes:
-        executor: Full reference to the CrewAgentExecutor instance
-        messages: Direct reference to executor.messages (mutable list).
+        executor: Reference to the executor (CrewAgentExecutor/LiteAgent) or None for direct calls
+        messages: Direct reference to 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.
@@ -27,33 +32,75 @@ 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
-        task: Reference to the task being executed
-        crew: Reference to the crew instance
+        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)
         llm: Reference to the LLM instance
-        iterations: Current iteration count
+        iterations: Current iteration count (0 for direct LLM calls)
         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,
+        executor: CrewAgentExecutor | LiteAgent | None = None,
         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.
+        """Initialize hook context with executor reference or direct parameters.
 
         Args:
-            executor: The CrewAgentExecutor instance
+            executor: The CrewAgentExecutor or LiteAgent instance (None for direct LLM calls)
             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)
         """
-        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
+        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.response = response
 
     def request_human_input(

lib/crewai/src/crewai/knowledge/knowledge.py

@@ -32,8 +32,8 @@ class Knowledge(BaseModel):
         sources: list[BaseKnowledgeSource],
         embedder: EmbedderConfig | None = None,
         storage: KnowledgeStorage | None = None,
-        **data,
-    ):
+        **data: object,
+    ) -> None:
         super().__init__(**data)
         if storage:
             self.storage = storage
@@ -75,3 +75,44 @@ class Knowledge(BaseModel):
             self.storage.reset()
         else:
             raise ValueError("Storage is not initialized.")
+
+    async def aquery(
+        self, query: list[str], results_limit: int = 5, score_threshold: float = 0.6
+    ) -> list[SearchResult]:
+        """Query across all knowledge sources asynchronously.
+
+        Args:
+            query: List of query strings.
+            results_limit: Maximum number of results to return.
+            score_threshold: Minimum similarity score for results.
+
+        Returns:
+            The top results matching the query.
+
+        Raises:
+            ValueError: If storage is not initialized.
+        """
+        if self.storage is None:
+            raise ValueError("Storage is not initialized.")
+
+        return await self.storage.asearch(
+            query,
+            limit=results_limit,
+            score_threshold=score_threshold,
+        )
+
+    async def aadd_sources(self) -> None:
+        """Add all knowledge sources to storage asynchronously."""
+        try:
+            for source in self.sources:
+                source.storage = self.storage
+                await source.aadd()
+        except Exception as e:
+            raise e
+
+    async def areset(self) -> None:
+        """Reset the knowledge base asynchronously."""
+        if self.storage:
+            await self.storage.areset()
+        else:
+            raise ValueError("Storage is not initialized.")

lib/crewai/src/crewai/knowledge/source/base_file_knowledge_source.py

@@ -1,5 +1,6 @@
 from abc import ABC, abstractmethod
 from pathlib import Path
+from typing import Any
 
 from pydantic import Field, field_validator
 
@@ -25,7 +26,10 @@ class BaseFileKnowledgeSource(BaseKnowledgeSource, ABC):
     safe_file_paths: list[Path] = Field(default_factory=list)
 
     @field_validator("file_path", "file_paths", mode="before")
-    def validate_file_path(cls, v, info):  # noqa: N805
+    @classmethod
+    def validate_file_path(
+        cls, v: Path | list[Path] | str | list[str] | None, info: Any
+    ) -> Path | list[Path] | str | list[str] | None:
         """Validate that at least one of file_path or file_paths is provided."""
         # Single check if both are None, O(1) instead of nested conditions
         if (
@@ -38,7 +42,7 @@ class BaseFileKnowledgeSource(BaseKnowledgeSource, ABC):
             raise ValueError("Either file_path or file_paths must be provided")
         return v
 
-    def model_post_init(self, _):
+    def model_post_init(self, _: Any) -> None:
         """Post-initialization method to load content."""
         self.safe_file_paths = self._process_file_paths()
         self.validate_content()
@@ -48,7 +52,7 @@ class BaseFileKnowledgeSource(BaseKnowledgeSource, ABC):
     def load_content(self) -> dict[Path, str]:
         """Load and preprocess file content. Should be overridden by subclasses. Assume that the file path is relative to the project root in the knowledge directory."""
 
-    def validate_content(self):
+    def validate_content(self) -> None:
         """Validate the paths."""
         for path in self.safe_file_paths:
             if not path.exists():
@@ -65,13 +69,20 @@ class BaseFileKnowledgeSource(BaseKnowledgeSource, ABC):
                     color="red",
                 )
 
-    def _save_documents(self):
+    def _save_documents(self) -> None:
         """Save the documents to the storage."""
         if self.storage:
             self.storage.save(self.chunks)
         else:
             raise ValueError("No storage found to save documents.")
 
+    async def _asave_documents(self) -> None:
+        """Save the documents to the storage asynchronously."""
+        if self.storage:
+            await self.storage.asave(self.chunks)
+        else:
+            raise ValueError("No storage found to save documents.")
+
     def convert_to_path(self, path: Path | str) -> Path:
         """Convert a path to a Path object."""
         return Path(KNOWLEDGE_DIRECTORY + "/" + path) if isinstance(path, str) else path

lib/crewai/src/crewai/knowledge/source/base_knowledge_source.py

@@ -39,12 +39,32 @@ class BaseKnowledgeSource(BaseModel, ABC):
             for i in range(0, len(text), self.chunk_size - self.chunk_overlap)
         ]
 
-    def _save_documents(self):
-        """
-        Save the documents to the storage.
+    def _save_documents(self) -> None:
+        """Save the documents to the storage.
+
         This method should be called after the chunks and embeddings are generated.
+
+        Raises:
+            ValueError: If no storage is configured.
         """
         if self.storage:
             self.storage.save(self.chunks)
         else:
             raise ValueError("No storage found to save documents.")
+
+    @abstractmethod
+    async def aadd(self) -> None:
+        """Process content, chunk it, compute embeddings, and save them asynchronously."""
+
+    async def _asave_documents(self) -> None:
+        """Save the documents to the storage asynchronously.
+
+        This method should be called after the chunks and embeddings are generated.
+
+        Raises:
+            ValueError: If no storage is configured.
+        """
+        if self.storage:
+            await self.storage.asave(self.chunks)
+        else:
+            raise ValueError("No storage found to save documents.")

lib/crewai/src/crewai/knowledge/source/crew_docling_source.py

@@ -2,27 +2,24 @@ from __future__ import annotations
 
 from collections.abc import Iterator
 from pathlib import Path
+from typing import TYPE_CHECKING, Any
 from urllib.parse import urlparse
 
 
 try:
-    from docling.datamodel.base_models import (  # type: ignore[import-not-found]
-        InputFormat,
-    )
-    from docling.document_converter import (  # type: ignore[import-not-found]
-        DocumentConverter,
-    )
-    from docling.exceptions import ConversionError  # type: ignore[import-not-found]
-    from docling_core.transforms.chunker.hierarchical_chunker import (  # type: ignore[import-not-found]
-        HierarchicalChunker,
-    )
-    from docling_core.types.doc.document import (  # type: ignore[import-not-found]
-        DoclingDocument,
-    )
+    from docling.datamodel.base_models import InputFormat
+    from docling.document_converter import DocumentConverter
+    from docling.exceptions import ConversionError
+    from docling_core.transforms.chunker.hierarchical_chunker import HierarchicalChunker
+    from docling_core.types.doc.document import DoclingDocument
 
     DOCLING_AVAILABLE = True
 except ImportError:
     DOCLING_AVAILABLE = False
+    # Provide type stubs for when docling is not available
+    if TYPE_CHECKING:
+        from docling.document_converter import DocumentConverter
+        from docling_core.types.doc.document import DoclingDocument
 
 from pydantic import Field
 
@@ -32,11 +29,13 @@ from crewai.utilities.logger import Logger
 
 
 class CrewDoclingSource(BaseKnowledgeSource):
-    """Default Source class for converting documents to markdown or json
-    This will auto support PDF, DOCX, and TXT, XLSX, Images, and HTML files without any additional dependencies and follows the docling package as the source of truth.
+    """Default Source class for converting documents to markdown or json.
+
+    This will auto support PDF, DOCX, and TXT, XLSX, Images, and HTML files without
+    any additional dependencies and follows the docling package as the source of truth.
     """
 
-    def __init__(self, *args, **kwargs):
+    def __init__(self, *args: Any, **kwargs: Any) -> None:
         if not DOCLING_AVAILABLE:
             raise ImportError(
                 "The docling package is required to use CrewDoclingSource. "
@@ -66,7 +65,7 @@ class CrewDoclingSource(BaseKnowledgeSource):
         )
     )
 
-    def model_post_init(self, _) -> None:
+    def model_post_init(self, _: Any) -> None:
         if self.file_path:
             self._logger.log(
                 "warning",
@@ -99,6 +98,15 @@ class CrewDoclingSource(BaseKnowledgeSource):
             self.chunks.extend(list(new_chunks_iterable))
         self._save_documents()
 
+    async def aadd(self) -> None:
+        """Add docling content asynchronously."""
+        if self.content is None:
+            return
+        for doc in self.content:
+            new_chunks_iterable = self._chunk_doc(doc)
+            self.chunks.extend(list(new_chunks_iterable))
+        await self._asave_documents()
+
     def _convert_source_to_docling_documents(self) -> list[DoclingDocument]:
         conv_results_iter = self.document_converter.convert_all(self.safe_file_paths)
         return [result.document for result in conv_results_iter]

lib/crewai/src/crewai/knowledge/source/csv_knowledge_source.py

@@ -31,6 +31,15 @@ class CSVKnowledgeSource(BaseFileKnowledgeSource):
         self.chunks.extend(new_chunks)
         self._save_documents()
 
+    async def aadd(self) -> None:
+        """Add CSV file content asynchronously."""
+        content_str = (
+            str(self.content) if isinstance(self.content, dict) else self.content
+        )
+        new_chunks = self._chunk_text(content_str)
+        self.chunks.extend(new_chunks)
+        await self._asave_documents()
+
     def _chunk_text(self, text: str) -> list[str]:
         """Utility method to split text into chunks."""
         return [

lib/crewai/src/crewai/knowledge/source/excel_knowledge_source.py

@@ -1,4 +1,6 @@
 from pathlib import Path
+from types import ModuleType
+from typing import Any
 
 from pydantic import Field, field_validator
 
@@ -26,7 +28,10 @@ class ExcelKnowledgeSource(BaseKnowledgeSource):
     safe_file_paths: list[Path] = Field(default_factory=list)
 
     @field_validator("file_path", "file_paths", mode="before")
-    def validate_file_path(cls, v, info):  # noqa: N805
+    @classmethod
+    def validate_file_path(
+        cls, v: Path | list[Path] | str | list[str] | None, info: Any
+    ) -> Path | list[Path] | str | list[str] | None:
         """Validate that at least one of file_path or file_paths is provided."""
         # Single check if both are None, O(1) instead of nested conditions
         if (
@@ -69,7 +74,7 @@ class ExcelKnowledgeSource(BaseKnowledgeSource):
 
         return [self.convert_to_path(path) for path in path_list]
 
-    def validate_content(self):
+    def validate_content(self) -> None:
         """Validate the paths."""
         for path in self.safe_file_paths:
             if not path.exists():
@@ -86,7 +91,7 @@ class ExcelKnowledgeSource(BaseKnowledgeSource):
                     color="red",
                 )
 
-    def model_post_init(self, _) -> None:
+    def model_post_init(self, _: Any) -> None:
         if self.file_path:
             self._logger.log(
                 "warning",
@@ -128,12 +133,12 @@ class ExcelKnowledgeSource(BaseKnowledgeSource):
         """Convert a path to a Path object."""
         return Path(KNOWLEDGE_DIRECTORY + "/" + path) if isinstance(path, str) else path
 
-    def _import_dependencies(self):
+    def _import_dependencies(self) -> ModuleType:
         """Dynamically import dependencies."""
         try:
-            import pandas as pd  # type: ignore[import-untyped,import-not-found]
+            import pandas as pd  # type: ignore[import-untyped]
 
-            return pd
+            return pd  # type: ignore[no-any-return]
         except ImportError as e:
             missing_package = str(e).split()[-1]
             raise ImportError(
@@ -159,6 +164,20 @@ class ExcelKnowledgeSource(BaseKnowledgeSource):
         self.chunks.extend(new_chunks)
         self._save_documents()
 
+    async def aadd(self) -> None:
+        """Add Excel file content asynchronously."""
+        content_str = ""
+        for value in self.content.values():
+            if isinstance(value, dict):
+                for sheet_value in value.values():
+                    content_str += str(sheet_value) + "\n"
+            else:
+                content_str += str(value) + "\n"
+
+        new_chunks = self._chunk_text(content_str)
+        self.chunks.extend(new_chunks)
+        await self._asave_documents()
+
     def _chunk_text(self, text: str) -> list[str]:
         """Utility method to split text into chunks."""
         return [

lib/crewai/src/crewai/knowledge/source/json_knowledge_source.py

@@ -44,6 +44,15 @@ class JSONKnowledgeSource(BaseFileKnowledgeSource):
         self.chunks.extend(new_chunks)
         self._save_documents()
 
+    async def aadd(self) -> None:
+        """Add JSON file content asynchronously."""
+        content_str = (
+            str(self.content) if isinstance(self.content, dict) else self.content
+        )
+        new_chunks = self._chunk_text(content_str)
+        self.chunks.extend(new_chunks)
+        await self._asave_documents()
+
     def _chunk_text(self, text: str) -> list[str]:
         """Utility method to split text into chunks."""
         return [

lib/crewai/src/crewai/knowledge/source/pdf_knowledge_source.py

@@ -1,4 +1,5 @@
 from pathlib import Path
+from types import ModuleType
 
 from crewai.knowledge.source.base_file_knowledge_source import BaseFileKnowledgeSource
 
@@ -23,7 +24,7 @@ class PDFKnowledgeSource(BaseFileKnowledgeSource):
             content[path] = text
         return content
 
-    def _import_pdfplumber(self):
+    def _import_pdfplumber(self) -> ModuleType:
         """Dynamically import pdfplumber."""
         try:
             import pdfplumber
@@ -44,6 +45,13 @@ class PDFKnowledgeSource(BaseFileKnowledgeSource):
             self.chunks.extend(new_chunks)
         self._save_documents()
 
+    async def aadd(self) -> None:
+        """Add PDF file content asynchronously."""
+        for text in self.content.values():
+            new_chunks = self._chunk_text(text)
+            self.chunks.extend(new_chunks)
+        await self._asave_documents()
+
     def _chunk_text(self, text: str) -> list[str]:
         """Utility method to split text into chunks."""
         return [

lib/crewai/src/crewai/knowledge/source/string_knowledge_source.py

@@ -1,3 +1,5 @@
+from typing import Any
+
 from pydantic import Field
 
 from crewai.knowledge.source.base_knowledge_source import BaseKnowledgeSource
@@ -9,11 +11,11 @@ class StringKnowledgeSource(BaseKnowledgeSource):
     content: str = Field(...)
     collection_name: str | None = Field(default=None)
 
-    def model_post_init(self, _):
+    def model_post_init(self, _: Any) -> None:
         """Post-initialization method to validate content."""
         self.validate_content()
 
-    def validate_content(self):
+    def validate_content(self) -> None:
         """Validate string content."""
         if not isinstance(self.content, str):
             raise ValueError("StringKnowledgeSource only accepts string content")
@@ -24,6 +26,12 @@ class StringKnowledgeSource(BaseKnowledgeSource):
         self.chunks.extend(new_chunks)
         self._save_documents()
 
+    async def aadd(self) -> None:
+        """Add string content asynchronously."""
+        new_chunks = self._chunk_text(self.content)
+        self.chunks.extend(new_chunks)
+        await self._asave_documents()
+
     def _chunk_text(self, text: str) -> list[str]:
         """Utility method to split text into chunks."""
         return [

lib/crewai/src/crewai/knowledge/source/text_file_knowledge_source.py

@@ -25,6 +25,13 @@ class TextFileKnowledgeSource(BaseFileKnowledgeSource):
             self.chunks.extend(new_chunks)
         self._save_documents()
 
+    async def aadd(self) -> None:
+        """Add text file content asynchronously."""
+        for text in self.content.values():
+            new_chunks = self._chunk_text(text)
+            self.chunks.extend(new_chunks)
+        await self._asave_documents()
+
     def _chunk_text(self, text: str) -> list[str]:
         """Utility method to split text into chunks."""
         return [

lib/crewai/src/crewai/knowledge/storage/base_knowledge_storage.py

@@ -21,10 +21,28 @@ class BaseKnowledgeStorage(ABC):
     ) -> list[SearchResult]:
         """Search for documents in the knowledge base."""
 
+    @abstractmethod
+    async def asearch(
+        self,
+        query: list[str],
+        limit: int = 5,
+        metadata_filter: dict[str, Any] | None = None,
+        score_threshold: float = 0.6,
+    ) -> list[SearchResult]:
+        """Search for documents in the knowledge base asynchronously."""
+
     @abstractmethod
     def save(self, documents: list[str]) -> None:
         """Save documents to the knowledge base."""
 
+    @abstractmethod
+    async def asave(self, documents: list[str]) -> None:
+        """Save documents to the knowledge base asynchronously."""
+
     @abstractmethod
     def reset(self) -> None:
         """Reset the knowledge base."""
+
+    @abstractmethod
+    async def areset(self) -> None:
+        """Reset the knowledge base asynchronously."""

lib/crewai/src/crewai/knowledge/storage/knowledge_storage.py

@@ -25,8 +25,8 @@ class KnowledgeStorage(BaseKnowledgeStorage):
     def __init__(
         self,
         embedder: ProviderSpec
-        | BaseEmbeddingsProvider
-        | type[BaseEmbeddingsProvider]
+        | BaseEmbeddingsProvider[Any]
+        | type[BaseEmbeddingsProvider[Any]]
         | None = None,
         collection_name: str | None = None,
     ) -> None:
@@ -127,3 +127,96 @@ class KnowledgeStorage(BaseKnowledgeStorage):
                 ) from e
             Logger(verbose=True).log("error", f"Failed to upsert documents: {e}", "red")
             raise
+
+    async def asearch(
+        self,
+        query: list[str],
+        limit: int = 5,
+        metadata_filter: dict[str, Any] | None = None,
+        score_threshold: float = 0.6,
+    ) -> list[SearchResult]:
+        """Search for documents in the knowledge base asynchronously.
+
+        Args:
+            query: List of query strings.
+            limit: Maximum number of results to return.
+            metadata_filter: Optional metadata filter for the search.
+            score_threshold: Minimum similarity score for results.
+
+        Returns:
+            List of search results.
+        """
+        try:
+            if not query:
+                raise ValueError("Query cannot be empty")
+
+            client = self._get_client()
+            collection_name = (
+                f"knowledge_{self.collection_name}"
+                if self.collection_name
+                else "knowledge"
+            )
+            query_text = " ".join(query) if len(query) > 1 else query[0]
+
+            return await client.asearch(
+                collection_name=collection_name,
+                query=query_text,
+                limit=limit,
+                metadata_filter=metadata_filter,
+                score_threshold=score_threshold,
+            )
+        except Exception as e:
+            logging.error(
+                f"Error during knowledge search: {e!s}\n{traceback.format_exc()}"
+            )
+            return []
+
+    async def asave(self, documents: list[str]) -> None:
+        """Save documents to the knowledge base asynchronously.
+
+        Args:
+            documents: List of document strings to save.
+        """
+        try:
+            client = self._get_client()
+            collection_name = (
+                f"knowledge_{self.collection_name}"
+                if self.collection_name
+                else "knowledge"
+            )
+            await client.aget_or_create_collection(collection_name=collection_name)
+
+            rag_documents: list[BaseRecord] = [{"content": doc} for doc in documents]
+
+            await client.aadd_documents(
+                collection_name=collection_name, documents=rag_documents
+            )
+        except Exception as e:
+            if "dimension mismatch" in str(e).lower():
+                Logger(verbose=True).log(
+                    "error",
+                    "Embedding dimension mismatch. This usually happens when mixing different embedding models. Try resetting the collection using `crewai reset-memories -a`",
+                    "red",
+                )
+                raise ValueError(
+                    "Embedding dimension mismatch. Make sure you're using the same embedding model "
+                    "across all operations with this collection."
+                    "Try resetting the collection using `crewai reset-memories -a`"
+                ) from e
+            Logger(verbose=True).log("error", f"Failed to upsert documents: {e}", "red")
+            raise
+
+    async def areset(self) -> None:
+        """Reset the knowledge base asynchronously."""
+        try:
+            client = self._get_client()
+            collection_name = (
+                f"knowledge_{self.collection_name}"
+                if self.collection_name
+                else "knowledge"
+            )
+            await client.adelete_collection(collection_name=collection_name)
+        except Exception as e:
+            logging.error(
+                f"Error during knowledge reset: {e!s}\n{traceback.format_exc()}"
+            )

lib/crewai/src/crewai/lite_agent.py

@@ -38,6 +38,8 @@ 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
@@ -155,6 +157,12 @@ 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:
@@ -246,6 +254,26 @@ 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],
@@ -504,7 +532,7 @@ class LiteAgent(FlowTrackable, BaseModel):
             AgentFinish: The final result of the agent execution.
         """
         # Execute the agent loop
-        formatted_answer = None
+        formatted_answer: AgentAction | AgentFinish | None = None
         while not isinstance(formatted_answer, AgentFinish):
             try:
                 if has_reached_max_iterations(self._iterations, self.max_iterations):
@@ -526,6 +554,7 @@ 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

@@ -1197,9 +1197,36 @@ class LLM(BaseLLM):
             )
             return text_response
 
-        # --- 6) If there is no text response, no available functions, but there are tool calls, return the tool calls
+        # --- 6) If there is no text response, no available functions, but there are tool calls,
+        # convert tool calls to a string representation instead of returning raw list
         if tool_calls and not available_functions and not text_response:
-            return tool_calls
+            try:
+                formatted_calls = []
+                for call in tool_calls:
+                    fn = getattr(call, "function", None)
+                    name = getattr(fn, "name", None) if fn else None
+                    args = getattr(fn, "arguments", None) if fn else None
+                    formatted_calls.append(
+                        f"Tool: {name or 'unknown'}\n"
+                        f"Arguments: {args or '{}'}"
+                    )
+                text_response = "\n\n".join(formatted_calls)
+            except Exception:
+                text_response = str(tool_calls)
+
+            logging.warning(
+                "Model returned tool_calls but no available_functions were provided. "
+                "Returning a string representation of the tool calls."
+            )
+
+            self._handle_emit_call_events(
+                response=text_response,
+                call_type=LLMCallType.LLM_CALL,
+                from_task=from_task,
+                from_agent=from_agent,
+                messages=params["messages"],
+            )
+            return text_response
 
         # --- 7) Handle tool calls if present
         tool_result = self._handle_tool_call(
@@ -1315,8 +1342,35 @@ class LLM(BaseLLM):
             )
             return text_response
 
+        # Convert tool calls to string representation when no available_functions
         if tool_calls and not available_functions and not text_response:
-            return tool_calls
+            try:
+                formatted_calls = []
+                for call in tool_calls:
+                    fn = getattr(call, "function", None)
+                    name = getattr(fn, "name", None) if fn else None
+                    args = getattr(fn, "arguments", None) if fn else None
+                    formatted_calls.append(
+                        f"Tool: {name or 'unknown'}\n"
+                        f"Arguments: {args or '{}'}"
+                    )
+                text_response = "\n\n".join(formatted_calls)
+            except Exception:
+                text_response = str(tool_calls)
+
+            logging.warning(
+                "Model returned tool_calls but no available_functions were provided. "
+                "Returning a string representation of the tool calls."
+            )
+
+            self._handle_emit_call_events(
+                response=text_response,
+                call_type=LLMCallType.LLM_CALL,
+                from_task=from_task,
+                from_agent=from_agent,
+                messages=params["messages"],
+            )
+            return text_response
 
         tool_result = self._handle_tool_call(
             tool_calls, available_functions, from_task, from_agent
@@ -1642,6 +1696,10 @@ 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:
@@ -1651,7 +1709,16 @@ class LLM(BaseLLM):
                 params = self._prepare_completion_params(messages, tools)
                 # --- 7) Make the completion call and handle response
                 if self.stream:
-                    return self._handle_streaming_response(
+                    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(
                         params=params,
                         callbacks=callbacks,
                         available_functions=available_functions,
@@ -1660,14 +1727,12 @@ class LLM(BaseLLM):
                         response_model=response_model,
                     )
 
-                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,
-                )
+                if isinstance(result, str):
+                    result = self._invoke_after_llm_call_hooks(
+                        messages, result, from_agent
+                    )
+
+                return result
             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[dict[str, Any]] | None = None,
+        messages: str | list[LLMMessage] | None = None,
     ) -> None:
         """Emit LLM call completed event."""
         crewai_event_bus.emit(
@@ -586,3 +586,134 @@ 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

@@ -187,6 +187,9 @@ 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
@@ -494,7 +497,9 @@ class AnthropicCompletion(BaseLLM):
         if usage.get("total_tokens", 0) > 0:
             logging.info(f"Anthropic API usage: {usage}")
 
-        return content
+        return self._invoke_after_llm_call_hooks(
+            params["messages"], content, from_agent
+        )
 
     def _handle_streaming_completion(
         self,
@@ -588,7 +593,9 @@ class AnthropicCompletion(BaseLLM):
             messages=params["messages"],
         )
 
-        return full_response
+        return self._invoke_after_llm_call_hooks(
+            params["messages"], full_response, from_agent
+        )
 
     def _handle_tool_use_conversation(
         self,

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

@@ -216,6 +216,9 @@ 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
@@ -550,6 +553,10 @@ 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}")
@@ -642,7 +649,9 @@ class AzureCompletion(BaseLLM):
             messages=params["messages"],
         )
 
-        return full_response
+        return self._invoke_after_llm_call_hooks(
+            params["messages"], full_response, from_agent
+        )
 
     async def _ahandle_completion(
         self,

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

@@ -312,9 +312,14 @@ class BedrockCompletion(BaseLLM):
 
             # Format messages for Converse API
             formatted_messages, system_message = self._format_messages_for_converse(
-                messages  # type: ignore[arg-type]
+                messages
             )
 
+            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(),
@@ -356,11 +361,19 @@ class BedrockCompletion(BaseLLM):
 
             if self.stream:
                 return self._handle_streaming_converse(
-                    formatted_messages, body, available_functions, from_task, from_agent
+                    cast(list[LLMMessage], formatted_messages),
+                    body,
+                    available_functions,
+                    from_task,
+                    from_agent,
                 )
 
             return self._handle_converse(
-                formatted_messages, body, available_functions, from_task, from_agent
+                cast(list[LLMMessage], formatted_messages),
+                body,
+                available_functions,
+                from_task,
+                from_agent,
             )
 
         except Exception as e:
@@ -481,7 +494,7 @@ class BedrockCompletion(BaseLLM):
 
     def _handle_converse(
         self,
-        messages: list[dict[str, Any]],
+        messages: list[LLMMessage],
         body: BedrockConverseRequestBody,
         available_functions: Mapping[str, Any] | None = None,
         from_task: Any | None = None,
@@ -605,7 +618,11 @@ class BedrockCompletion(BaseLLM):
                 messages=messages,
             )
 
-            return text_content
+            return self._invoke_after_llm_call_hooks(
+                messages,
+                text_content,
+                from_agent,
+            )
 
         except ClientError as e:
             # Handle all AWS ClientError exceptions as per documentation
@@ -662,7 +679,7 @@ class BedrockCompletion(BaseLLM):
 
     def _handle_streaming_converse(
         self,
-        messages: list[dict[str, Any]],
+        messages: list[LLMMessage],
         body: BedrockConverseRequestBody,
         available_functions: dict[str, Any] | None = None,
         from_task: Any | None = None,
@@ -1149,16 +1166,25 @@ class BedrockCompletion(BaseLLM):
             messages=messages,
         )
 
-        return full_response
+        return self._invoke_after_llm_call_hooks(
+            messages,
+            full_response,
+            from_agent,
+        )
 
     def _format_messages_for_converse(
-        self, messages: str | list[dict[str, str]]
+        self, messages: str | list[LLMMessage]
     ) -> tuple[list[dict[str, Any]], str | None]:
-        """Format messages for Converse API following AWS documentation."""
-        # Use base class formatting first
-        formatted_messages = self._format_messages(messages)  # type: ignore[arg-type]
+        """Format messages for Converse API following AWS documentation.
 
-        converse_messages = []
+        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}.
+        """
+        # Use base class formatting first
+        formatted_messages = self._format_messages(messages)
+
+        converse_messages: list[dict[str, Any]] = []
         system_message: str | None = None
 
         for message in formatted_messages:

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

@@ -246,6 +246,11 @@ 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
             )
@@ -559,7 +564,9 @@ class GeminiCompletion(BaseLLM):
             messages=messages_for_event,
         )
 
-        return content
+        return self._invoke_after_llm_call_hooks(
+            messages_for_event, content, from_agent
+        )
 
     def _handle_streaming_completion(
         self,
@@ -639,7 +646,9 @@ class GeminiCompletion(BaseLLM):
             messages=messages_for_event,
         )
 
-        return full_response
+        return self._invoke_after_llm_call_hooks(
+            messages_for_event, full_response, from_agent
+        )
 
     async def _ahandle_completion(
         self,
@@ -787,7 +796,159 @@ class GeminiCompletion(BaseLLM):
             messages=messages_for_event,
         )
 
-        return full_response
+        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
+        )
 
     def supports_function_calling(self) -> bool:
         """Check if the model supports function calling."""
@@ -851,7 +1012,7 @@ class GeminiCompletion(BaseLLM):
     def _convert_contents_to_dict(
         self,
         contents: list[types.Content],
-    ) -> list[dict[str, str]]:
+    ) -> list[LLMMessage]:
         """Convert contents to dict format."""
         result: list[dict[str, str]] = []
         for content_obj in contents:

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

@@ -1,13 +1,14 @@
 from __future__ import annotations
 
-from collections.abc import AsyncIterator, Iterator
+from collections.abc import AsyncIterator
 import json
 import logging
 import os
 from typing import TYPE_CHECKING, Any
 
 import httpx
-from openai import APIConnectionError, AsyncOpenAI, NotFoundError, OpenAI
+from openai import APIConnectionError, AsyncOpenAI, NotFoundError, OpenAI, Stream
+from openai.lib.streaming.chat import ChatCompletionStream
 from openai.types.chat import ChatCompletion, ChatCompletionChunk
 from openai.types.chat.chat_completion import Choice
 from openai.types.chat.chat_completion_chunk import ChoiceDelta
@@ -189,6 +190,9 @@ 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
             )
@@ -473,6 +477,10 @@ 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)
@@ -515,59 +523,52 @@ class OpenAICompletion(BaseLLM):
         tool_calls = {}
 
         if response_model:
-            completion_stream: Iterator[ChatCompletionChunk] = (
-                self.client.chat.completions.create(**params)
-            )
+            parse_params = {
+                k: v
+                for k, v in params.items()
+                if k not in ("response_format", "stream")
+            }
 
-            accumulated_content = ""
-            for chunk in completion_stream:
-                if not chunk.choices:
-                    continue
+            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,
+                            )
 
-                choice = chunk.choices[0]
-                delta: ChoiceDelta = choice.delta
+                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
 
-                if delta.content:
-                    accumulated_content += delta.content
-                    self._emit_stream_chunk_event(
-                        chunk=delta.content,
-                        from_task=from_task,
-                        from_agent=from_agent,
-                    )
+            logging.error("Failed to get parsed result from stream")
+            return ""
 
-            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
+        completion_stream: Stream[ChatCompletionChunk] = (
+            self.client.chat.completions.create(**params)
         )
 
-        for chunk in stream:
-            if not chunk.choices:
+        for completion_chunk in completion_stream:
+            if not completion_chunk.choices:
                 continue
 
-            choice = chunk.choices[0]
+            choice = completion_chunk.choices[0]
             chunk_delta: ChoiceDelta = choice.delta
 
             if chunk_delta.content:
@@ -635,7 +636,9 @@ class OpenAICompletion(BaseLLM):
             messages=params["messages"],
         )
 
-        return full_response
+        return self._invoke_after_llm_call_hooks(
+            params["messages"], full_response, from_agent
+        )
 
     async def _ahandle_completion(
         self,

lib/crewai/src/crewai/memory/contextual/contextual_memory.py

@@ -1,5 +1,6 @@
 from __future__ import annotations
 
+import asyncio
 from typing import TYPE_CHECKING
 
 from crewai.memory import (
@@ -16,6 +17,8 @@ if TYPE_CHECKING:
 
 
 class ContextualMemory:
+    """Aggregates and retrieves context from multiple memory sources."""
+
     def __init__(
         self,
         stm: ShortTermMemory,
@@ -46,9 +49,14 @@ class ContextualMemory:
             self.exm.task = self.task
 
     def build_context_for_task(self, task: Task, context: str) -> str:
-        """
-        Automatically builds a minimal, highly relevant set of contextual information
-        for a given task.
+        """Build contextual information for a task synchronously.
+
+        Args:
+            task: The task to build context for.
+            context: Additional context string.
+
+        Returns:
+            Formatted context string from all memory sources.
         """
         query = f"{task.description} {context}".strip()
 
@@ -63,6 +71,31 @@ class ContextualMemory:
         ]
         return "\n".join(filter(None, context_parts))
 
+    async def abuild_context_for_task(self, task: Task, context: str) -> str:
+        """Build contextual information for a task asynchronously.
+
+        Args:
+            task: The task to build context for.
+            context: Additional context string.
+
+        Returns:
+            Formatted context string from all memory sources.
+        """
+        query = f"{task.description} {context}".strip()
+
+        if query == "":
+            return ""
+
+        # Fetch all contexts concurrently
+        results = await asyncio.gather(
+            self._afetch_ltm_context(task.description),
+            self._afetch_stm_context(query),
+            self._afetch_entity_context(query),
+            self._afetch_external_context(query),
+        )
+
+        return "\n".join(filter(None, results))
+
     def _fetch_stm_context(self, query: str) -> str:
         """
         Fetches recent relevant insights from STM related to the task's description and expected_output,
@@ -135,3 +168,87 @@ class ContextualMemory:
             f"- {result['content']}" for result in external_memories
         )
         return f"External memories:\n{formatted_memories}"
+
+    async def _afetch_stm_context(self, query: str) -> str:
+        """Fetch recent relevant insights from STM asynchronously.
+
+        Args:
+            query: The search query.
+
+        Returns:
+            Formatted insights as bullet points, or empty string if none found.
+        """
+        if self.stm is None:
+            return ""
+
+        stm_results = await self.stm.asearch(query)
+        formatted_results = "\n".join(
+            [f"- {result['content']}" for result in stm_results]
+        )
+        return f"Recent Insights:\n{formatted_results}" if stm_results else ""
+
+    async def _afetch_ltm_context(self, task: str) -> str | None:
+        """Fetch historical data from LTM asynchronously.
+
+        Args:
+            task: The task description to search for.
+
+        Returns:
+            Formatted historical data as bullet points, or None if none found.
+        """
+        if self.ltm is None:
+            return ""
+
+        ltm_results = await self.ltm.asearch(task, latest_n=2)
+        if not ltm_results:
+            return None
+
+        formatted_results = [
+            suggestion
+            for result in ltm_results
+            for suggestion in result["metadata"]["suggestions"]
+        ]
+        formatted_results = list(dict.fromkeys(formatted_results))
+        formatted_results = "\n".join([f"- {result}" for result in formatted_results])  # type: ignore # Incompatible types in assignment (expression has type "str", variable has type "list[str]")
+
+        return f"Historical Data:\n{formatted_results}" if ltm_results else ""
+
+    async def _afetch_entity_context(self, query: str) -> str:
+        """Fetch relevant entity information asynchronously.
+
+        Args:
+            query: The search query.
+
+        Returns:
+            Formatted entity information as bullet points, or empty string if none found.
+        """
+        if self.em is None:
+            return ""
+
+        em_results = await self.em.asearch(query)
+        formatted_results = "\n".join(
+            [f"- {result['content']}" for result in em_results]
+        )
+        return f"Entities:\n{formatted_results}" if em_results else ""
+
+    async def _afetch_external_context(self, query: str) -> str:
+        """Fetch relevant information from External Memory asynchronously.
+
+        Args:
+            query: The search query.
+
+        Returns:
+            Formatted information as bullet points, or empty string if none found.
+        """
+        if self.exm is None:
+            return ""
+
+        external_memories = await self.exm.asearch(query)
+
+        if not external_memories:
+            return ""
+
+        formatted_memories = "\n".join(
+            f"- {result['content']}" for result in external_memories
+        )
+        return f"External memories:\n{formatted_memories}"

lib/crewai/src/crewai/memory/entity/entity_memory.py

@@ -26,7 +26,13 @@ class EntityMemory(Memory):
 
     _memory_provider: str | None = PrivateAttr()
 
-    def __init__(self, crew=None, embedder_config=None, storage=None, path=None):
+    def __init__(
+        self,
+        crew: Any = None,
+        embedder_config: Any = None,
+        storage: Any = None,
+        path: str | None = None,
+    ) -> None:
         memory_provider = None
         if embedder_config and isinstance(embedder_config, dict):
             memory_provider = embedder_config.get("provider")
@@ -43,7 +49,7 @@ class EntityMemory(Memory):
                 if embedder_config and isinstance(embedder_config, dict)
                 else None
             )
-            storage = Mem0Storage(type="short_term", crew=crew, config=config)
+            storage = Mem0Storage(type="short_term", crew=crew, config=config)  # type: ignore[no-untyped-call]
         else:
             storage = (
                 storage
@@ -170,7 +176,17 @@ class EntityMemory(Memory):
         query: str,
         limit: int = 5,
         score_threshold: float = 0.6,
-    ):
+    ) -> list[Any]:
+        """Search entity memory for relevant entries.
+
+        Args:
+            query: The search query.
+            limit: Maximum number of results to return.
+            score_threshold: Minimum similarity score for results.
+
+        Returns:
+            List of matching memory entries.
+        """
         crewai_event_bus.emit(
             self,
             event=MemoryQueryStartedEvent(
@@ -217,6 +233,168 @@ class EntityMemory(Memory):
             )
             raise
 
+    async def asave(
+        self,
+        value: EntityMemoryItem | list[EntityMemoryItem],
+        metadata: dict[str, Any] | None = None,
+    ) -> None:
+        """Save entity items asynchronously.
+
+        Args:
+            value: Single EntityMemoryItem or list of EntityMemoryItems to save.
+            metadata: Optional metadata dict (not used, for signature compatibility).
+        """
+        if not value:
+            return
+
+        items = value if isinstance(value, list) else [value]
+        is_batch = len(items) > 1
+
+        metadata = {"entity_count": len(items)} if is_batch else items[0].metadata
+        crewai_event_bus.emit(
+            self,
+            event=MemorySaveStartedEvent(
+                metadata=metadata,
+                source_type="entity_memory",
+                from_agent=self.agent,
+                from_task=self.task,
+            ),
+        )
+
+        start_time = time.time()
+        saved_count = 0
+        errors: list[str | None] = []
+
+        async def save_single_item(item: EntityMemoryItem) -> tuple[bool, str | None]:
+            """Save a single item asynchronously."""
+            try:
+                if self._memory_provider == "mem0":
+                    data = f"""
+                    Remember details about the following entity:
+                    Name: {item.name}
+                    Type: {item.type}
+                    Entity Description: {item.description}
+                    """
+                else:
+                    data = f"{item.name}({item.type}): {item.description}"
+
+                await super(EntityMemory, self).asave(data, item.metadata)
+                return True, None
+            except Exception as e:
+                return False, f"{item.name}: {e!s}"
+
+        try:
+            for item in items:
+                success, error = await save_single_item(item)
+                if success:
+                    saved_count += 1
+                else:
+                    errors.append(error)
+
+            if is_batch:
+                emit_value = f"Saved {saved_count} entities"
+                metadata = {"entity_count": saved_count, "errors": errors}
+            else:
+                emit_value = f"{items[0].name}({items[0].type}): {items[0].description}"
+                metadata = items[0].metadata
+
+            crewai_event_bus.emit(
+                self,
+                event=MemorySaveCompletedEvent(
+                    value=emit_value,
+                    metadata=metadata,
+                    save_time_ms=(time.time() - start_time) * 1000,
+                    source_type="entity_memory",
+                    from_agent=self.agent,
+                    from_task=self.task,
+                ),
+            )
+
+            if errors:
+                raise Exception(
+                    f"Partial save: {len(errors)} failed out of {len(items)}"
+                )
+
+        except Exception as e:
+            fail_metadata = (
+                {"entity_count": len(items), "saved": saved_count}
+                if is_batch
+                else items[0].metadata
+            )
+            crewai_event_bus.emit(
+                self,
+                event=MemorySaveFailedEvent(
+                    metadata=fail_metadata,
+                    error=str(e),
+                    source_type="entity_memory",
+                    from_agent=self.agent,
+                    from_task=self.task,
+                ),
+            )
+            raise
+
+    async def asearch(
+        self,
+        query: str,
+        limit: int = 5,
+        score_threshold: float = 0.6,
+    ) -> list[Any]:
+        """Search entity memory asynchronously.
+
+        Args:
+            query: The search query.
+            limit: Maximum number of results to return.
+            score_threshold: Minimum similarity score for results.
+
+        Returns:
+            List of matching memory entries.
+        """
+        crewai_event_bus.emit(
+            self,
+            event=MemoryQueryStartedEvent(
+                query=query,
+                limit=limit,
+                score_threshold=score_threshold,
+                source_type="entity_memory",
+                from_agent=self.agent,
+                from_task=self.task,
+            ),
+        )
+
+        start_time = time.time()
+        try:
+            results = await super().asearch(
+                query=query, limit=limit, score_threshold=score_threshold
+            )
+
+            crewai_event_bus.emit(
+                self,
+                event=MemoryQueryCompletedEvent(
+                    query=query,
+                    results=results,
+                    limit=limit,
+                    score_threshold=score_threshold,
+                    query_time_ms=(time.time() - start_time) * 1000,
+                    source_type="entity_memory",
+                    from_agent=self.agent,
+                    from_task=self.task,
+                ),
+            )
+
+            return results
+        except Exception as e:
+            crewai_event_bus.emit(
+                self,
+                event=MemoryQueryFailedEvent(
+                    query=query,
+                    limit=limit,
+                    score_threshold=score_threshold,
+                    error=str(e),
+                    source_type="entity_memory",
+                ),
+            )
+            raise
+
     def reset(self) -> None:
         try:
             self.storage.reset()

lib/crewai/src/crewai/memory/external/external_memory.py

@@ -30,7 +30,7 @@ class ExternalMemory(Memory):
     def _configure_mem0(crew: Any, config: dict[str, Any]) -> Mem0Storage:
         from crewai.memory.storage.mem0_storage import Mem0Storage
 
-        return Mem0Storage(type="external", crew=crew, config=config)
+        return Mem0Storage(type="external", crew=crew, config=config)  # type: ignore[no-untyped-call]
 
     @staticmethod
     def external_supported_storages() -> dict[str, Any]:
@@ -53,7 +53,10 @@ class ExternalMemory(Memory):
         if provider not in supported_storages:
             raise ValueError(f"Provider {provider} not supported")
 
-        return supported_storages[provider](crew, embedder_config.get("config", {}))
+        storage: Storage = supported_storages[provider](
+            crew, embedder_config.get("config", {})
+        )
+        return storage
 
     def save(
         self,
@@ -111,7 +114,17 @@ class ExternalMemory(Memory):
         query: str,
         limit: int = 5,
         score_threshold: float = 0.6,
-    ):
+    ) -> list[Any]:
+        """Search external memory for relevant entries.
+
+        Args:
+            query: The search query.
+            limit: Maximum number of results to return.
+            score_threshold: Minimum similarity score for results.
+
+        Returns:
+            List of matching memory entries.
+        """
         crewai_event_bus.emit(
             self,
             event=MemoryQueryStartedEvent(
@@ -158,6 +171,124 @@ class ExternalMemory(Memory):
             )
             raise
 
+    async def asave(
+        self,
+        value: Any,
+        metadata: dict[str, Any] | None = None,
+    ) -> None:
+        """Save a value to external memory asynchronously.
+
+        Args:
+            value: The value to save.
+            metadata: Optional metadata to associate with the value.
+        """
+        crewai_event_bus.emit(
+            self,
+            event=MemorySaveStartedEvent(
+                value=value,
+                metadata=metadata,
+                source_type="external_memory",
+                from_agent=self.agent,
+                from_task=self.task,
+            ),
+        )
+
+        start_time = time.time()
+        try:
+            item = ExternalMemoryItem(
+                value=value,
+                metadata=metadata,
+                agent=self.agent.role if self.agent else None,
+            )
+            await super().asave(value=item.value, metadata=item.metadata)
+
+            crewai_event_bus.emit(
+                self,
+                event=MemorySaveCompletedEvent(
+                    value=value,
+                    metadata=metadata,
+                    save_time_ms=(time.time() - start_time) * 1000,
+                    source_type="external_memory",
+                    from_agent=self.agent,
+                    from_task=self.task,
+                ),
+            )
+        except Exception as e:
+            crewai_event_bus.emit(
+                self,
+                event=MemorySaveFailedEvent(
+                    value=value,
+                    metadata=metadata,
+                    error=str(e),
+                    source_type="external_memory",
+                    from_agent=self.agent,
+                    from_task=self.task,
+                ),
+            )
+            raise
+
+    async def asearch(
+        self,
+        query: str,
+        limit: int = 5,
+        score_threshold: float = 0.6,
+    ) -> list[Any]:
+        """Search external memory asynchronously.
+
+        Args:
+            query: The search query.
+            limit: Maximum number of results to return.
+            score_threshold: Minimum similarity score for results.
+
+        Returns:
+            List of matching memory entries.
+        """
+        crewai_event_bus.emit(
+            self,
+            event=MemoryQueryStartedEvent(
+                query=query,
+                limit=limit,
+                score_threshold=score_threshold,
+                source_type="external_memory",
+                from_agent=self.agent,
+                from_task=self.task,
+            ),
+        )
+
+        start_time = time.time()
+        try:
+            results = await super().asearch(
+                query=query, limit=limit, score_threshold=score_threshold
+            )
+
+            crewai_event_bus.emit(
+                self,
+                event=MemoryQueryCompletedEvent(
+                    query=query,
+                    results=results,
+                    limit=limit,
+                    score_threshold=score_threshold,
+                    query_time_ms=(time.time() - start_time) * 1000,
+                    source_type="external_memory",
+                    from_agent=self.agent,
+                    from_task=self.task,
+                ),
+            )
+
+            return results
+        except Exception as e:
+            crewai_event_bus.emit(
+                self,
+                event=MemoryQueryFailedEvent(
+                    query=query,
+                    limit=limit,
+                    score_threshold=score_threshold,
+                    error=str(e),
+                    source_type="external_memory",
+                ),
+            )
+            raise
+
     def reset(self) -> None:
         self.storage.reset()
 

lib/crewai/src/crewai/memory/long_term/long_term_memory.py

@@ -24,7 +24,11 @@ class LongTermMemory(Memory):
     LongTermMemoryItem instances.
     """
 
-    def __init__(self, storage=None, path=None):
+    def __init__(
+        self,
+        storage: LTMSQLiteStorage | None = None,
+        path: str | None = None,
+    ) -> None:
         if not storage:
             storage = LTMSQLiteStorage(db_path=path) if path else LTMSQLiteStorage()
         super().__init__(storage=storage)
@@ -48,7 +52,7 @@ class LongTermMemory(Memory):
             metadata.update(
                 {"agent": item.agent, "expected_output": item.expected_output}
             )
-            self.storage.save(  # type: ignore # BUG?: Unexpected keyword argument "task_description","score","datetime" for "save" of "Storage"
+            self.storage.save(
                 task_description=item.task,
                 score=metadata["quality"],
                 metadata=metadata,
@@ -80,11 +84,20 @@ class LongTermMemory(Memory):
             )
             raise
 
-    def search(  # type: ignore # signature of "search" incompatible with supertype "Memory"
+    def search(  # type: ignore[override]
         self,
         task: str,
         latest_n: int = 3,
-    ) -> list[dict[str, Any]]:  # type: ignore # signature of "search" incompatible with supertype "Memory"
+    ) -> list[dict[str, Any]]:
+        """Search long-term memory for relevant entries.
+
+        Args:
+            task: The task description to search for.
+            latest_n: Maximum number of results to return.
+
+        Returns:
+            List of matching memory entries.
+        """
         crewai_event_bus.emit(
             self,
             event=MemoryQueryStartedEvent(
@@ -98,7 +111,7 @@ class LongTermMemory(Memory):
 
         start_time = time.time()
         try:
-            results = self.storage.load(task, latest_n)  # type: ignore # BUG?: "Storage" has no attribute "load"
+            results = self.storage.load(task, latest_n)
 
             crewai_event_bus.emit(
                 self,
@@ -113,7 +126,118 @@ class LongTermMemory(Memory):
                 ),
             )
 
-            return results
+            return results or []
+        except Exception as e:
+            crewai_event_bus.emit(
+                self,
+                event=MemoryQueryFailedEvent(
+                    query=task,
+                    limit=latest_n,
+                    error=str(e),
+                    source_type="long_term_memory",
+                ),
+            )
+            raise
+
+    async def asave(self, item: LongTermMemoryItem) -> None:  # type: ignore[override]
+        """Save an item to long-term memory asynchronously.
+
+        Args:
+            item: The LongTermMemoryItem to save.
+        """
+        crewai_event_bus.emit(
+            self,
+            event=MemorySaveStartedEvent(
+                value=item.task,
+                metadata=item.metadata,
+                agent_role=item.agent,
+                source_type="long_term_memory",
+                from_agent=self.agent,
+                from_task=self.task,
+            ),
+        )
+
+        start_time = time.time()
+        try:
+            metadata = item.metadata
+            metadata.update(
+                {"agent": item.agent, "expected_output": item.expected_output}
+            )
+            await self.storage.asave(
+                task_description=item.task,
+                score=metadata["quality"],
+                metadata=metadata,
+                datetime=item.datetime,
+            )
+
+            crewai_event_bus.emit(
+                self,
+                event=MemorySaveCompletedEvent(
+                    value=item.task,
+                    metadata=item.metadata,
+                    agent_role=item.agent,
+                    save_time_ms=(time.time() - start_time) * 1000,
+                    source_type="long_term_memory",
+                    from_agent=self.agent,
+                    from_task=self.task,
+                ),
+            )
+        except Exception as e:
+            crewai_event_bus.emit(
+                self,
+                event=MemorySaveFailedEvent(
+                    value=item.task,
+                    metadata=item.metadata,
+                    agent_role=item.agent,
+                    error=str(e),
+                    source_type="long_term_memory",
+                ),
+            )
+            raise
+
+    async def asearch(  # type: ignore[override]
+        self,
+        task: str,
+        latest_n: int = 3,
+    ) -> list[dict[str, Any]]:
+        """Search long-term memory asynchronously.
+
+        Args:
+            task: The task description to search for.
+            latest_n: Maximum number of results to return.
+
+        Returns:
+            List of matching memory entries.
+        """
+        crewai_event_bus.emit(
+            self,
+            event=MemoryQueryStartedEvent(
+                query=task,
+                limit=latest_n,
+                source_type="long_term_memory",
+                from_agent=self.agent,
+                from_task=self.task,
+            ),
+        )
+
+        start_time = time.time()
+        try:
+            results = await self.storage.aload(task, latest_n)
+
+            crewai_event_bus.emit(
+                self,
+                event=MemoryQueryCompletedEvent(
+                    query=task,
+                    results=results,
+                    limit=latest_n,
+                    query_time_ms=(time.time() - start_time) * 1000,
+                    source_type="long_term_memory",
+                    from_agent=self.agent,
+                    from_task=self.task,
+                ),
+            )
+
+            return results or []
         except Exception as e:
             crewai_event_bus.emit(
                 self,
@@ -127,4 +251,5 @@ class LongTermMemory(Memory):
             raise
 
     def reset(self) -> None:
+        """Reset long-term memory."""
         self.storage.reset()

lib/crewai/src/crewai/memory/memory.py

@@ -13,9 +13,7 @@ if TYPE_CHECKING:
 
 
 class Memory(BaseModel):
-    """
-    Base class for memory, now supporting agent tags and generic metadata.
-    """
+    """Base class for memory, supporting agent tags and generic metadata."""
 
     embedder_config: EmbedderConfig | dict[str, Any] | None = None
     crew: Any | None = None
@@ -52,20 +50,72 @@ class Memory(BaseModel):
         value: Any,
         metadata: dict[str, Any] | None = None,
     ) -> None:
-        metadata = metadata or {}
+        """Save a value to memory.
 
+        Args:
+            value: The value to save.
+            metadata: Optional metadata to associate with the value.
+        """
+        metadata = metadata or {}
         self.storage.save(value, metadata)
 
+    async def asave(
+        self,
+        value: Any,
+        metadata: dict[str, Any] | None = None,
+    ) -> None:
+        """Save a value to memory asynchronously.
+
+        Args:
+            value: The value to save.
+            metadata: Optional metadata to associate with the value.
+        """
+        metadata = metadata or {}
+        await self.storage.asave(value, metadata)
+
     def search(
         self,
         query: str,
         limit: int = 5,
         score_threshold: float = 0.6,
     ) -> list[Any]:
-        return self.storage.search(
+        """Search memory for relevant entries.
+
+        Args:
+            query: The search query.
+            limit: Maximum number of results to return.
+            score_threshold: Minimum similarity score for results.
+
+        Returns:
+            List of matching memory entries.
+        """
+        results: list[Any] = self.storage.search(
             query=query, limit=limit, score_threshold=score_threshold
         )
+        return results
+
+    async def asearch(
+        self,
+        query: str,
+        limit: int = 5,
+        score_threshold: float = 0.6,
+    ) -> list[Any]:
+        """Search memory for relevant entries asynchronously.
+
+        Args:
+            query: The search query.
+            limit: Maximum number of results to return.
+            score_threshold: Minimum similarity score for results.
+
+        Returns:
+            List of matching memory entries.
+        """
+        results: list[Any] = await self.storage.asearch(
+            query=query, limit=limit, score_threshold=score_threshold
+        )
+        return results
 
     def set_crew(self, crew: Any) -> Memory:
+        """Set the crew for this memory instance."""
         self.crew = crew
         return self

lib/crewai/src/crewai/memory/short_term/short_term_memory.py

@@ -30,7 +30,13 @@ class ShortTermMemory(Memory):
 
     _memory_provider: str | None = PrivateAttr()
 
-    def __init__(self, crew=None, embedder_config=None, storage=None, path=None):
+    def __init__(
+        self,
+        crew: Any = None,
+        embedder_config: Any = None,
+        storage: Any = None,
+        path: str | None = None,
+    ) -> None:
         memory_provider = None
         if embedder_config and isinstance(embedder_config, dict):
             memory_provider = embedder_config.get("provider")
@@ -47,7 +53,7 @@ class ShortTermMemory(Memory):
                 if embedder_config and isinstance(embedder_config, dict)
                 else None
             )
-            storage = Mem0Storage(type="short_term", crew=crew, config=config)
+            storage = Mem0Storage(type="short_term", crew=crew, config=config)  # type: ignore[no-untyped-call]
         else:
             storage = (
                 storage
@@ -123,7 +129,17 @@ class ShortTermMemory(Memory):
         query: str,
         limit: int = 5,
         score_threshold: float = 0.6,
-    ):
+    ) -> list[Any]:
+        """Search short-term memory for relevant entries.
+
+        Args:
+            query: The search query.
+            limit: Maximum number of results to return.
+            score_threshold: Minimum similarity score for results.
+
+        Returns:
+            List of matching memory entries.
+        """
         crewai_event_bus.emit(
             self,
             event=MemoryQueryStartedEvent(
@@ -140,7 +156,7 @@ class ShortTermMemory(Memory):
         try:
             results = self.storage.search(
                 query=query, limit=limit, score_threshold=score_threshold
-            )  # type: ignore # BUG? The reference is to the parent class, but the parent class does not have this parameters
+            )
 
             crewai_event_bus.emit(
                 self,
@@ -156,7 +172,130 @@ class ShortTermMemory(Memory):
                 ),
             )
 
-            return results
+            return list(results)
+        except Exception as e:
+            crewai_event_bus.emit(
+                self,
+                event=MemoryQueryFailedEvent(
+                    query=query,
+                    limit=limit,
+                    score_threshold=score_threshold,
+                    error=str(e),
+                    source_type="short_term_memory",
+                ),
+            )
+            raise
+
+    async def asave(
+        self,
+        value: Any,
+        metadata: dict[str, Any] | None = None,
+    ) -> None:
+        """Save a value to short-term memory asynchronously.
+
+        Args:
+            value: The value to save.
+            metadata: Optional metadata to associate with the value.
+        """
+        crewai_event_bus.emit(
+            self,
+            event=MemorySaveStartedEvent(
+                value=value,
+                metadata=metadata,
+                source_type="short_term_memory",
+                from_agent=self.agent,
+                from_task=self.task,
+            ),
+        )
+
+        start_time = time.time()
+        try:
+            item = ShortTermMemoryItem(
+                data=value,
+                metadata=metadata,
+                agent=self.agent.role if self.agent else None,
+            )
+            if self._memory_provider == "mem0":
+                item.data = (
+                    f"Remember the following insights from Agent run: {item.data}"
+                )
+
+            await super().asave(value=item.data, metadata=item.metadata)
+
+            crewai_event_bus.emit(
+                self,
+                event=MemorySaveCompletedEvent(
+                    value=value,
+                    metadata=metadata,
+                    save_time_ms=(time.time() - start_time) * 1000,
+                    source_type="short_term_memory",
+                    from_agent=self.agent,
+                    from_task=self.task,
+                ),
+            )
+        except Exception as e:
+            crewai_event_bus.emit(
+                self,
+                event=MemorySaveFailedEvent(
+                    value=value,
+                    metadata=metadata,
+                    error=str(e),
+                    source_type="short_term_memory",
+                    from_agent=self.agent,
+                    from_task=self.task,
+                ),
+            )
+            raise
+
+    async def asearch(
+        self,
+        query: str,
+        limit: int = 5,
+        score_threshold: float = 0.6,
+    ) -> list[Any]:
+        """Search short-term memory asynchronously.
+
+        Args:
+            query: The search query.
+            limit: Maximum number of results to return.
+            score_threshold: Minimum similarity score for results.
+
+        Returns:
+            List of matching memory entries.
+        """
+        crewai_event_bus.emit(
+            self,
+            event=MemoryQueryStartedEvent(
+                query=query,
+                limit=limit,
+                score_threshold=score_threshold,
+                source_type="short_term_memory",
+                from_agent=self.agent,
+                from_task=self.task,
+            ),
+        )
+
+        start_time = time.time()
+        try:
+            results = await self.storage.asearch(
+                query=query, limit=limit, score_threshold=score_threshold
+            )
+
+            crewai_event_bus.emit(
+                self,
+                event=MemoryQueryCompletedEvent(
+                    query=query,
+                    results=results,
+                    limit=limit,
+                    score_threshold=score_threshold,
+                    query_time_ms=(time.time() - start_time) * 1000,
+                    source_type="short_term_memory",
+                    from_agent=self.agent,
+                    from_task=self.task,
+                ),
+            )
+
+            return list(results)
         except Exception as e:
             crewai_event_bus.emit(
                 self,

lib/crewai/src/crewai/memory/storage/ltm_sqlite_storage.py

@@ -3,29 +3,30 @@ from pathlib import Path
 import sqlite3
 from typing import Any
 
+import aiosqlite
+
 from crewai.utilities import Printer
 from crewai.utilities.paths import db_storage_path
 
 
 class LTMSQLiteStorage:
-    """
-    An updated SQLite storage class for LTM data storage.
-    """
+    """SQLite storage class for long-term memory data."""
 
     def __init__(self, db_path: str | None = None) -> None:
+        """Initialize the SQLite storage.
+
+        Args:
+            db_path: Optional path to the database file.
+        """
         if db_path is None:
-            # Get the parent directory of the default db path and create our db file there
             db_path = str(Path(db_storage_path()) / "long_term_memory_storage.db")
         self.db_path = db_path
         self._printer: Printer = Printer()
-        # Ensure parent directory exists
         Path(self.db_path).parent.mkdir(parents=True, exist_ok=True)
         self._initialize_db()
 
-    def _initialize_db(self):
-        """
-        Initializes the SQLite database and creates LTM table
-        """
+    def _initialize_db(self) -> None:
+        """Initialize the SQLite database and create LTM table."""
         try:
             with sqlite3.connect(self.db_path) as conn:
                 cursor = conn.cursor()
@@ -106,9 +107,7 @@ class LTMSQLiteStorage:
             )
         return None
 
-    def reset(
-        self,
-    ) -> None:
+    def reset(self) -> None:
         """Resets the LTM table with error handling."""
         try:
             with sqlite3.connect(self.db_path) as conn:
@@ -121,4 +120,87 @@ class LTMSQLiteStorage:
                 content=f"MEMORY ERROR: An error occurred while deleting all rows in LTM: {e}",
                 color="red",
             )
-        return
+
+    async def asave(
+        self,
+        task_description: str,
+        metadata: dict[str, Any],
+        datetime: str,
+        score: int | float,
+    ) -> None:
+        """Save data to the LTM table asynchronously.
+
+        Args:
+            task_description: Description of the task.
+            metadata: Metadata associated with the memory.
+            datetime: Timestamp of the memory.
+            score: Quality score of the memory.
+        """
+        try:
+            async with aiosqlite.connect(self.db_path) as conn:
+                await conn.execute(
+                    """
+                    INSERT INTO long_term_memories (task_description, metadata, datetime, score)
+                    VALUES (?, ?, ?, ?)
+                    """,
+                    (task_description, json.dumps(metadata), datetime, score),
+                )
+                await conn.commit()
+        except aiosqlite.Error as e:
+            self._printer.print(
+                content=f"MEMORY ERROR: An error occurred while saving to LTM: {e}",
+                color="red",
+            )
+
+    async def aload(
+        self, task_description: str, latest_n: int
+    ) -> list[dict[str, Any]] | None:
+        """Query the LTM table by task description asynchronously.
+
+        Args:
+            task_description: Description of the task to search for.
+            latest_n: Maximum number of results to return.
+
+        Returns:
+            List of matching memory entries or None if error occurs.
+        """
+        try:
+            async with aiosqlite.connect(self.db_path) as conn:
+                cursor = await conn.execute(
+                    f"""
+                    SELECT metadata, datetime, score
+                    FROM long_term_memories
+                    WHERE task_description = ?
+                    ORDER BY datetime DESC, score ASC
+                    LIMIT {latest_n}
+                    """,  # nosec # noqa: S608
+                    (task_description,),
+                )
+                rows = await cursor.fetchall()
+                if rows:
+                    return [
+                        {
+                            "metadata": json.loads(row[0]),
+                            "datetime": row[1],
+                            "score": row[2],
+                        }
+                        for row in rows
+                    ]
+        except aiosqlite.Error as e:
+            self._printer.print(
+                content=f"MEMORY ERROR: An error occurred while querying LTM: {e}",
+                color="red",
+            )
+        return None
+
+    async def areset(self) -> None:
+        """Reset the LTM table asynchronously."""
+        try:
+            async with aiosqlite.connect(self.db_path) as conn:
+                await conn.execute("DELETE FROM long_term_memories")
+                await conn.commit()
+        except aiosqlite.Error as e:
+            self._printer.print(
+                content=f"MEMORY ERROR: An error occurred while deleting all rows in LTM: {e}",
+                color="red",
+            )

lib/crewai/src/crewai/memory/storage/rag_storage.py

@@ -129,6 +129,12 @@ class RAGStorage(BaseRAGStorage):
         return f"{base_path}/{file_name}"
 
     def save(self, value: Any, metadata: dict[str, Any]) -> None:
+        """Save a value to storage.
+
+        Args:
+            value: The value to save.
+            metadata: Metadata to associate with the value.
+        """
         try:
             client = self._get_client()
             collection_name = (
@@ -167,6 +173,51 @@ class RAGStorage(BaseRAGStorage):
                 f"Error during {self.type} save: {e!s}\n{traceback.format_exc()}"
             )
 
+    async def asave(self, value: Any, metadata: dict[str, Any]) -> None:
+        """Save a value to storage asynchronously.
+
+        Args:
+            value: The value to save.
+            metadata: Metadata to associate with the value.
+        """
+        try:
+            client = self._get_client()
+            collection_name = (
+                f"memory_{self.type}_{self.agents}"
+                if self.agents
+                else f"memory_{self.type}"
+            )
+            await client.aget_or_create_collection(collection_name=collection_name)
+
+            document: BaseRecord = {"content": value}
+            if metadata:
+                document["metadata"] = metadata
+
+            batch_size = None
+            if (
+                self.embedder_config
+                and isinstance(self.embedder_config, dict)
+                and "config" in self.embedder_config
+            ):
+                nested_config = self.embedder_config["config"]
+                if isinstance(nested_config, dict):
+                    batch_size = nested_config.get("batch_size")
+
+            if batch_size is not None:
+                await client.aadd_documents(
+                    collection_name=collection_name,
+                    documents=[document],
+                    batch_size=cast(int, batch_size),
+                )
+            else:
+                await client.aadd_documents(
+                    collection_name=collection_name, documents=[document]
+                )
+        except Exception as e:
+            logging.error(
+                f"Error during {self.type} async save: {e!s}\n{traceback.format_exc()}"
+            )
+
     def search(
         self,
         query: str,
@@ -174,6 +225,17 @@ class RAGStorage(BaseRAGStorage):
         filter: dict[str, Any] | None = None,
         score_threshold: float = 0.6,
     ) -> list[Any]:
+        """Search for matching entries in storage.
+
+        Args:
+            query: The search query.
+            limit: Maximum number of results to return.
+            filter: Optional metadata filter.
+            score_threshold: Minimum similarity score for results.
+
+        Returns:
+            List of matching entries.
+        """
         try:
             client = self._get_client()
             collection_name = (
@@ -194,6 +256,44 @@ class RAGStorage(BaseRAGStorage):
             )
             return []
 
+    async def asearch(
+        self,
+        query: str,
+        limit: int = 5,
+        filter: dict[str, Any] | None = None,
+        score_threshold: float = 0.6,
+    ) -> list[Any]:
+        """Search for matching entries in storage asynchronously.
+
+        Args:
+            query: The search query.
+            limit: Maximum number of results to return.
+            filter: Optional metadata filter.
+            score_threshold: Minimum similarity score for results.
+
+        Returns:
+            List of matching entries.
+        """
+        try:
+            client = self._get_client()
+            collection_name = (
+                f"memory_{self.type}_{self.agents}"
+                if self.agents
+                else f"memory_{self.type}"
+            )
+            return await client.asearch(
+                collection_name=collection_name,
+                query=query,
+                limit=limit,
+                metadata_filter=filter,
+                score_threshold=score_threshold,
+            )
+        except Exception as e:
+            logging.error(
+                f"Error during {self.type} async search: {e!s}\n{traceback.format_exc()}"
+            )
+            return []
+
     def reset(self) -> None:
         try:
             client = self._get_client()

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

@@ -1,21 +1,35 @@
 """HuggingFace embeddings provider."""
 
 from chromadb.utils.embedding_functions.huggingface_embedding_function import (
-    HuggingFaceEmbeddingServer,
+    HuggingFaceEmbeddingFunction,
 )
 from pydantic import AliasChoices, Field
 
 from crewai.rag.core.base_embeddings_provider import BaseEmbeddingsProvider
 
 
-class HuggingFaceProvider(BaseEmbeddingsProvider[HuggingFaceEmbeddingServer]):
-    """HuggingFace embeddings provider."""
+class HuggingFaceProvider(BaseEmbeddingsProvider[HuggingFaceEmbeddingFunction]):
+    """HuggingFace embeddings provider for the HuggingFace Inference API."""
 
-    embedding_callable: type[HuggingFaceEmbeddingServer] = Field(
-        default=HuggingFaceEmbeddingServer,
+    embedding_callable: type[HuggingFaceEmbeddingFunction] = Field(
+        default=HuggingFaceEmbeddingFunction,
         description="HuggingFace embedding function class",
     )
-    url: str = Field(
-        description="HuggingFace API URL",
-        validation_alias=AliasChoices("EMBEDDINGS_HUGGINGFACE_URL", "HUGGINGFACE_URL"),
+    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",
+        ),
     )

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

@@ -1,6 +1,6 @@
 """Type definitions for HuggingFace embedding providers."""
 
-from typing import Literal
+from typing import Annotated, Literal
 
 from typing_extensions import Required, TypedDict
 
@@ -8,7 +8,11 @@ from typing_extensions import Required, TypedDict
 class HuggingFaceProviderConfig(TypedDict, total=False):
     """Configuration for HuggingFace provider."""
 
-    url: str
+    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"]
 
 
 class HuggingFaceProviderSpec(TypedDict, total=False):

lib/crewai/src/crewai/task.py

@@ -497,6 +497,107 @@ class Task(BaseModel):
         result = self._execute_core(agent, context, tools)
         future.set_result(result)
 
+    async def aexecute_sync(
+        self,
+        agent: BaseAgent | None = None,
+        context: str | None = None,
+        tools: list[BaseTool] | None = None,
+    ) -> TaskOutput:
+        """Execute the task asynchronously using native async/await."""
+        return await self._aexecute_core(agent, context, tools)
+
+    async def _aexecute_core(
+        self,
+        agent: BaseAgent | None,
+        context: str | None,
+        tools: list[Any] | None,
+    ) -> TaskOutput:
+        """Run the core execution logic of the task asynchronously."""
+        try:
+            agent = agent or self.agent
+            self.agent = agent
+            if not agent:
+                raise Exception(
+                    f"The task '{self.description}' has no agent assigned, therefore it can't be executed directly and should be executed in a Crew using a specific process that support that, like hierarchical."
+                )
+
+            self.start_time = datetime.datetime.now()
+
+            self.prompt_context = context
+            tools = tools or self.tools or []
+
+            self.processed_by_agents.add(agent.role)
+            crewai_event_bus.emit(self, TaskStartedEvent(context=context, task=self))  # type: ignore[no-untyped-call]
+            result = await agent.aexecute_task(
+                task=self,
+                context=context,
+                tools=tools,
+            )
+
+            if not self._guardrails and not self._guardrail:
+                pydantic_output, json_output = self._export_output(result)
+            else:
+                pydantic_output, json_output = None, None
+
+            task_output = TaskOutput(
+                name=self.name or self.description,
+                description=self.description,
+                expected_output=self.expected_output,
+                raw=result,
+                pydantic=pydantic_output,
+                json_dict=json_output,
+                agent=agent.role,
+                output_format=self._get_output_format(),
+                messages=agent.last_messages,  # type: ignore[attr-defined]
+            )
+
+            if self._guardrails:
+                for idx, guardrail in enumerate(self._guardrails):
+                    task_output = await self._ainvoke_guardrail_function(
+                        task_output=task_output,
+                        agent=agent,
+                        tools=tools,
+                        guardrail=guardrail,
+                        guardrail_index=idx,
+                    )
+
+            if self._guardrail:
+                task_output = await self._ainvoke_guardrail_function(
+                    task_output=task_output,
+                    agent=agent,
+                    tools=tools,
+                    guardrail=self._guardrail,
+                )
+
+            self.output = task_output
+            self.end_time = datetime.datetime.now()
+
+            if self.callback:
+                self.callback(self.output)
+
+            crew = self.agent.crew  # type: ignore[union-attr]
+            if crew and crew.task_callback and crew.task_callback != self.callback:
+                crew.task_callback(self.output)
+
+            if self.output_file:
+                content = (
+                    json_output
+                    if json_output
+                    else (
+                        pydantic_output.model_dump_json() if pydantic_output else result
+                    )
+                )
+                self._save_file(content)
+            crewai_event_bus.emit(
+                self,
+                TaskCompletedEvent(output=task_output, task=self),  # type: ignore[no-untyped-call]
+            )
+            return task_output
+        except Exception as e:
+            self.end_time = datetime.datetime.now()
+            crewai_event_bus.emit(self, TaskFailedEvent(error=str(e), task=self))  # type: ignore[no-untyped-call]
+            raise e  # Re-raise the exception after emitting the event
+
     def _execute_core(
         self,
         agent: BaseAgent | None,
@@ -539,7 +640,7 @@ class Task(BaseModel):
                 json_dict=json_output,
                 agent=agent.role,
                 output_format=self._get_output_format(),
-                messages=agent.last_messages,
+                messages=agent.last_messages,  # type: ignore[attr-defined]
             )
 
             if self._guardrails:
@@ -950,7 +1051,103 @@ Follow these guidelines:
                 json_dict=json_output,
                 agent=agent.role,
                 output_format=self._get_output_format(),
-                messages=agent.last_messages,
+                messages=agent.last_messages,  # type: ignore[attr-defined]
+            )
+
+        return task_output
+
+    async def _ainvoke_guardrail_function(
+        self,
+        task_output: TaskOutput,
+        agent: BaseAgent,
+        tools: list[BaseTool],
+        guardrail: GuardrailCallable | None,
+        guardrail_index: int | None = None,
+    ) -> TaskOutput:
+        """Invoke the guardrail function asynchronously."""
+        if not guardrail:
+            return task_output
+
+        if guardrail_index is not None:
+            current_retry_count = self._guardrail_retry_counts.get(guardrail_index, 0)
+        else:
+            current_retry_count = self.retry_count
+
+        max_attempts = self.guardrail_max_retries + 1
+
+        for attempt in range(max_attempts):
+            guardrail_result = process_guardrail(
+                output=task_output,
+                guardrail=guardrail,
+                retry_count=current_retry_count,
+                event_source=self,
+                from_task=self,
+                from_agent=agent,
+            )
+
+            if guardrail_result.success:
+                if guardrail_result.result is None:
+                    raise Exception(
+                        "Task guardrail returned None as result. This is not allowed."
+                    )
+
+                if isinstance(guardrail_result.result, str):
+                    task_output.raw = guardrail_result.result
+                    pydantic_output, json_output = self._export_output(
+                        guardrail_result.result
+                    )
+                    task_output.pydantic = pydantic_output
+                    task_output.json_dict = json_output
+                elif isinstance(guardrail_result.result, TaskOutput):
+                    task_output = guardrail_result.result
+
+                return task_output
+
+            if attempt >= self.guardrail_max_retries:
+                guardrail_name = (
+                    f"guardrail {guardrail_index}"
+                    if guardrail_index is not None
+                    else "guardrail"
+                )
+                raise Exception(
+                    f"Task failed {guardrail_name} validation after {self.guardrail_max_retries} retries. "
+                    f"Last error: {guardrail_result.error}"
+                )
+
+            if guardrail_index is not None:
+                current_retry_count += 1
+                self._guardrail_retry_counts[guardrail_index] = current_retry_count
+            else:
+                self.retry_count += 1
+                current_retry_count = self.retry_count
+
+            context = self.i18n.errors("validation_error").format(
+                guardrail_result_error=guardrail_result.error,
+                task_output=task_output.raw,
+            )
+            printer = Printer()
+            printer.print(
+                content=f"Guardrail {guardrail_index if guardrail_index is not None else ''} blocked (attempt {attempt + 1}/{max_attempts}), retrying due to: {guardrail_result.error}\n",
+                color="yellow",
+            )
+
+            result = await agent.aexecute_task(
+                task=self,
+                context=context,
+                tools=tools,
+            )
+
+            pydantic_output, json_output = self._export_output(result)
+            task_output = TaskOutput(
+                name=self.name or self.description,
+                description=self.description,
+                expected_output=self.expected_output,
+                raw=result,
+                pydantic=pydantic_output,
+                json_dict=json_output,
+                agent=agent.role,
+                output_format=self._get_output_format(),
+                messages=agent.last_messages,  # type: ignore[attr-defined]
             )
 
         return task_output

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

@@ -2,9 +2,18 @@ from __future__ import annotations
 
 from abc import ABC, abstractmethod
 import asyncio
-from collections.abc import Callable
+from collections.abc import Awaitable, Callable
 from inspect import signature
-from typing import Any, get_args, get_origin
+from typing import (
+    Any,
+    Generic,
+    ParamSpec,
+    TypeVar,
+    cast,
+    get_args,
+    get_origin,
+    overload,
+)
 
 from pydantic import (
     BaseModel,
@@ -14,6 +23,7 @@ from pydantic import (
     create_model,
     field_validator,
 )
+from typing_extensions import TypeIs
 
 from crewai.tools.structured_tool import CrewStructuredTool
 from crewai.utilities.printer import Printer
@@ -21,6 +31,19 @@ from crewai.utilities.printer import Printer
 
 _printer = Printer()
 
+P = ParamSpec("P")
+R = TypeVar("R", covariant=True)
+
+
+def _is_async_callable(func: Callable[..., Any]) -> bool:
+    """Check if a callable is async."""
+    return asyncio.iscoroutinefunction(func)
+
+
+def _is_awaitable(value: R | Awaitable[R]) -> TypeIs[Awaitable[R]]:
+    """Type narrowing check for awaitable values."""
+    return asyncio.iscoroutine(value) or asyncio.isfuture(value)
+
 
 class EnvVar(BaseModel):
     name: str
@@ -80,21 +103,20 @@ class BaseTool(BaseModel, ABC):
         if v != cls._ArgsSchemaPlaceholder:
             return v
 
-        run_sig = signature(cls._run)
-        fields: dict[str, Any] = {}
-
-        for param_name, param in run_sig.parameters.items():
-            if param_name in ("self", "return"):
-                continue
-
-            annotation = param.annotation if param.annotation != param.empty else Any
-
-            if param.default is param.empty:
-                fields[param_name] = (annotation, ...)
-            else:
-                fields[param_name] = (annotation, param.default)
-
-        return create_model(f"{cls.__name__}Schema", **fields)
+        return cast(
+            type[PydanticBaseModel],
+            type(
+                f"{cls.__name__}Schema",
+                (PydanticBaseModel,),
+                {
+                    "__annotations__": {
+                        k: v
+                        for k, v in cls._run.__annotations__.items()
+                        if k != "return"
+                    },
+                },
+            ),
+        )
 
     @field_validator("max_usage_count", mode="before")
     @classmethod
@@ -124,6 +146,35 @@ class BaseTool(BaseModel, ABC):
 
         return result
 
+    async def arun(
+        self,
+        *args: Any,
+        **kwargs: Any,
+    ) -> Any:
+        """Execute the tool asynchronously.
+
+        Args:
+            *args: Positional arguments to pass to the tool.
+            **kwargs: Keyword arguments to pass to the tool.
+
+        Returns:
+            The result of the tool execution.
+        """
+        result = await self._arun(*args, **kwargs)
+        self.current_usage_count += 1
+        return result
+
+    async def _arun(
+        self,
+        *args: Any,
+        **kwargs: Any,
+    ) -> Any:
+        """Async implementation of the tool. Override for async support."""
+        raise NotImplementedError(
+            f"{self.__class__.__name__} does not implement _arun. "
+            "Override _arun for async support or use run() for sync execution."
+        )
+
     def reset_usage_count(self) -> None:
         """Reset the current usage count to zero."""
         self.current_usage_count = 0
@@ -134,7 +185,17 @@ class BaseTool(BaseModel, ABC):
         *args: Any,
         **kwargs: Any,
     ) -> Any:
-        """Here goes the actual implementation of the tool."""
+        """Sync implementation of the tool.
+
+        Subclasses must implement this method for synchronous execution.
+
+        Args:
+            *args: Positional arguments for the tool.
+            **kwargs: Keyword arguments for the tool.
+
+        Returns:
+            The result of the tool execution.
+        """
 
     def to_structured_tool(self) -> CrewStructuredTool:
         """Convert this tool to a CrewStructuredTool instance."""
@@ -165,21 +226,24 @@ class BaseTool(BaseModel, ABC):
         args_schema = getattr(tool, "args_schema", None)
 
         if args_schema is None:
+            # Infer args_schema from the function signature if not provided
             func_signature = signature(tool.func)
-            fields: dict[str, Any] = {}
-            for name, param in func_signature.parameters.items():
-                if name == "self":
-                    continue
-                param_annotation = (
-                    param.annotation if param.annotation != param.empty else Any
-                )
-                if param.default is param.empty:
-                    fields[name] = (param_annotation, ...)
-                else:
-                    fields[name] = (param_annotation, param.default)
-            if fields:
-                args_schema = create_model(f"{tool.name}Input", **fields)
+            annotations = func_signature.parameters
+            args_fields: dict[str, Any] = {}
+            for name, param in annotations.items():
+                if name != "self":
+                    param_annotation = (
+                        param.annotation if param.annotation != param.empty else Any
+                    )
+                    field_info = Field(
+                        default=...,
+                        description="",
+                    )
+                    args_fields[name] = (param_annotation, field_info)
+            if args_fields:
+                args_schema = create_model(f"{tool.name}Input", **args_fields)
             else:
+                # Create a default schema with no fields if no parameters are found
                 args_schema = create_model(
                     f"{tool.name}Input", __base__=PydanticBaseModel
                 )
@@ -193,24 +257,20 @@ class BaseTool(BaseModel, ABC):
 
     def _set_args_schema(self) -> None:
         if self.args_schema is None:
-            run_sig = signature(self._run)
-            fields: dict[str, Any] = {}
-
-            for param_name, param in run_sig.parameters.items():
-                if param_name in ("self", "return"):
-                    continue
-
-                annotation = (
-                    param.annotation if param.annotation != param.empty else Any
-                )
-
-                if param.default is param.empty:
-                    fields[param_name] = (annotation, ...)
-                else:
-                    fields[param_name] = (annotation, param.default)
-
-            self.args_schema = create_model(
-                f"{self.__class__.__name__}Schema", **fields
+            class_name = f"{self.__class__.__name__}Schema"
+            self.args_schema = cast(
+                type[PydanticBaseModel],
+                type(
+                    class_name,
+                    (PydanticBaseModel,),
+                    {
+                        "__annotations__": {
+                            k: v
+                            for k, v in self._run.__annotations__.items()
+                            if k != "return"
+                        },
+                    },
+                ),
             )
 
     def _generate_description(self) -> None:
@@ -241,21 +301,90 @@ class BaseTool(BaseModel, ABC):
 
         if args:
             args_str = ", ".join(BaseTool._get_arg_annotations(arg) for arg in args)
-            return f"{origin.__name__}[{args_str}]"
+            return str(f"{origin.__name__}[{args_str}]")
 
         return str(origin.__name__)
 
 
-class Tool(BaseTool):
-    """The function that will be executed when the tool is called."""
+class Tool(BaseTool, Generic[P, R]):
+    """Tool that wraps a callable function.
 
-    func: Callable[..., Any]
 
-    def _run(self, *args: Any, **kwargs: Any) -> Any:
-        return self.func(*args, **kwargs)
+    Type Parameters:
+        P: ParamSpec capturing the function's parameters.
+        R: The return type of the function.
+    """
+
+    func: Callable[P, R | Awaitable[R]]
+
+    def run(self, *args: P.args, **kwargs: P.kwargs) -> R:
+        """Executes the tool synchronously.
+
+        Args:
+            *args: Positional arguments for the tool.
+            **kwargs: Keyword arguments for the tool.
+
+        Returns:
+            The result of the tool execution.
+        """
+        _printer.print(f"Using Tool: {self.name}", color="cyan")
+        result = self.func(*args, **kwargs)
+
+        if asyncio.iscoroutine(result):
+            result = asyncio.run(result)
+
+        self.current_usage_count += 1
+        return result  # type: ignore[return-value]
+
+    def _run(self, *args: P.args, **kwargs: P.kwargs) -> R:
+        """Executes the wrapped function.
+
+        Args:
+            *args: Positional arguments for the function.
+            **kwargs: Keyword arguments for the function.
+
+        Returns:
+            The result of the function execution.
+        """
+        return self.func(*args, **kwargs)  # type: ignore[return-value]
+
+    async def arun(self, *args: P.args, **kwargs: P.kwargs) -> R:
+        """Executes the tool asynchronously.
+
+        Args:
+            *args: Positional arguments for the tool.
+            **kwargs: Keyword arguments for the tool.
+
+        Returns:
+            The result of the tool execution.
+        """
+        result = await self._arun(*args, **kwargs)
+        self.current_usage_count += 1
+        return result
+
+    async def _arun(self, *args: P.args, **kwargs: P.kwargs) -> R:
+        """Executes the wrapped function asynchronously.
+
+        Args:
+            *args: Positional arguments for the function.
+            **kwargs: Keyword arguments for the function.
+
+        Returns:
+            The result of the async function execution.
+
+        Raises:
+            NotImplementedError: If the wrapped function is not async.
+        """
+        result = self.func(*args, **kwargs)
+        if _is_awaitable(result):
+            return await result
+        raise NotImplementedError(
+            f"{self.name} does not have an async function. "
+            "Use run() for sync execution or provide an async function."
+        )
 
     @classmethod
-    def from_langchain(cls, tool: Any) -> Tool:
+    def from_langchain(cls, tool: Any) -> Tool[..., Any]:
         """Create a Tool instance from a CrewStructuredTool.
 
         This method takes a CrewStructuredTool object and converts it into a
@@ -263,10 +392,10 @@ class Tool(BaseTool):
         attribute and infers the argument schema if not explicitly provided.
 
         Args:
-            tool (Any): The CrewStructuredTool object to be converted.
+            tool: The CrewStructuredTool object to be converted.
 
         Returns:
-            Tool: A new Tool instance created from the provided CrewStructuredTool.
+            A new Tool instance created from the provided CrewStructuredTool.
 
         Raises:
             ValueError: If the provided tool does not have a callable 'func' attribute.
@@ -277,21 +406,24 @@ class Tool(BaseTool):
         args_schema = getattr(tool, "args_schema", None)
 
         if args_schema is None:
+            # Infer args_schema from the function signature if not provided
             func_signature = signature(tool.func)
-            fields: dict[str, Any] = {}
-            for name, param in func_signature.parameters.items():
-                if name == "self":
-                    continue
-                param_annotation = (
-                    param.annotation if param.annotation != param.empty else Any
-                )
-                if param.default is param.empty:
-                    fields[name] = (param_annotation, ...)
-                else:
-                    fields[name] = (param_annotation, param.default)
-            if fields:
-                args_schema = create_model(f"{tool.name}Input", **fields)
+            annotations = func_signature.parameters
+            args_fields: dict[str, Any] = {}
+            for name, param in annotations.items():
+                if name != "self":
+                    param_annotation = (
+                        param.annotation if param.annotation != param.empty else Any
+                    )
+                    field_info = Field(
+                        default=...,
+                        description="",
+                    )
+                    args_fields[name] = (param_annotation, field_info)
+            if args_fields:
+                args_schema = create_model(f"{tool.name}Input", **args_fields)
             else:
+                # Create a default schema with no fields if no parameters are found
                 args_schema = create_model(
                     f"{tool.name}Input", __base__=PydanticBaseModel
                 )
@@ -307,56 +439,95 @@ class Tool(BaseTool):
 def to_langchain(
     tools: list[BaseTool | CrewStructuredTool],
 ) -> list[CrewStructuredTool]:
+    """Convert a list of tools to CrewStructuredTool instances."""
     return [t.to_structured_tool() if isinstance(t, BaseTool) else t for t in tools]
 
 
+P2 = ParamSpec("P2")
+R2 = TypeVar("R2")
+
+
+@overload
+def tool(func: Callable[P2, R2], /) -> Tool[P2, R2]: ...
+
+
+@overload
 def tool(
-    *args: Callable[..., Any] | str,
+    name: str,
+    /,
+    *,
+    result_as_answer: bool = ...,
+    max_usage_count: int | None = ...,
+) -> Callable[[Callable[P2, R2]], Tool[P2, R2]]: ...
+
+
+@overload
+def tool(
+    *,
+    result_as_answer: bool = ...,
+    max_usage_count: int | None = ...,
+) -> Callable[[Callable[P2, R2]], Tool[P2, R2]]: ...
+
+
+def tool(
+    *args: Callable[P2, R2] | str,
     result_as_answer: bool = False,
     max_usage_count: int | None = None,
-) -> Callable[..., Any] | BaseTool:
-    """Decorator to create a tool from a function.
+) -> Tool[P2, R2] | Callable[[Callable[P2, R2]], Tool[P2, R2]]:
+    """Decorator to create a Tool from a function.
+
+    Can be used in three ways:
+    1. @tool - decorator without arguments, uses function name
+    2. @tool("name") - decorator with custom name
+    3. @tool(result_as_answer=True) - decorator with options
 
     Args:
-        *args: Positional arguments, either the function to decorate or the tool name.
-        result_as_answer: Flag to indicate if the tool result should be used as the final agent answer.
-        max_usage_count: Maximum number of times this tool can be used. None means unlimited usage.
+        *args: Either the function to decorate or a custom tool name.
+        result_as_answer: If True, the tool result becomes the final agent answer.
+        max_usage_count: Maximum times this tool can be used. None means unlimited.
+
+    Returns:
+        A Tool instance.
+
+    Example:
+        @tool
+        def greet(name: str) -> str:
+            '''Greet someone.'''
+            return f"Hello, {name}!"
+
+        result = greet.run("World")
     """
 
-    def _make_with_name(tool_name: str) -> Callable[[Callable[..., Any]], BaseTool]:
-        def _make_tool(f: Callable[..., Any]) -> BaseTool:
+    def _make_with_name(tool_name: str) -> Callable[[Callable[P2, R2]], Tool[P2, R2]]:
+        def _make_tool(f: Callable[P2, R2]) -> Tool[P2, R2]:
             if f.__doc__ is None:
                 raise ValueError("Function must have a docstring")
-            if f.__annotations__ is None:
+
+            func_annotations = getattr(f, "__annotations__", None)
+            if func_annotations is None:
                 raise ValueError("Function must have type annotations")
 
-            func_sig = signature(f)
-            fields: dict[str, Any] = {}
-
-            for param_name, param in func_sig.parameters.items():
-                if param_name == "return":
-                    continue
-
-                annotation = (
-                    param.annotation if param.annotation != param.empty else Any
-                )
-
-                if param.default is param.empty:
-                    fields[param_name] = (annotation, ...)
-                else:
-                    fields[param_name] = (annotation, param.default)
-
             class_name = "".join(tool_name.split()).title()
-            args_schema = create_model(class_name, **fields)
+            tool_args_schema = cast(
+                type[PydanticBaseModel],
+                type(
+                    class_name,
+                    (PydanticBaseModel,),
+                    {
+                        "__annotations__": {
+                            k: v for k, v in func_annotations.items() if k != "return"
+                        },
+                    },
+                ),
+            )
 
             return Tool(
                 name=tool_name,
                 description=f.__doc__,
                 func=f,
-                args_schema=args_schema,
+                args_schema=tool_args_schema,
                 result_as_answer=result_as_answer,
                 max_usage_count=max_usage_count,
-                current_usage_count=0,
             )
 
         return _make_tool
@@ -365,4 +536,10 @@ def tool(
         return _make_with_name(args[0].__name__)(args[0])
     if len(args) == 1 and isinstance(args[0], str):
         return _make_with_name(args[0])
+    if len(args) == 0:
+
+        def decorator(f: Callable[P2, R2]) -> Tool[P2, R2]:
+            return _make_with_name(f.__name__)(f)
+
+        return decorator
     raise ValueError("Invalid arguments")

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

@@ -160,6 +160,251 @@ class ToolUsage:
 
         return f"{self._use(tool_string=tool_string, tool=tool, calling=calling)}"
 
+    async def ause(
+        self, calling: ToolCalling | InstructorToolCalling, tool_string: str
+    ) -> str:
+        """Execute a tool asynchronously.
+
+        Args:
+            calling: The tool calling information.
+            tool_string: The raw tool string from the agent.
+
+        Returns:
+            The result of the tool execution as a string.
+        """
+        if isinstance(calling, ToolUsageError):
+            error = calling.message
+            if self.agent and self.agent.verbose:
+                self._printer.print(content=f"\n\n{error}\n", color="red")
+            if self.task:
+                self.task.increment_tools_errors()
+            return error
+
+        try:
+            tool = self._select_tool(calling.tool_name)
+        except Exception as e:
+            error = getattr(e, "message", str(e))
+            if self.task:
+                self.task.increment_tools_errors()
+            if self.agent and self.agent.verbose:
+                self._printer.print(content=f"\n\n{error}\n", color="red")
+            return error
+
+        if (
+            isinstance(tool, CrewStructuredTool)
+            and tool.name == self._i18n.tools("add_image")["name"]  # type: ignore
+        ):
+            try:
+                return await self._ause(
+                    tool_string=tool_string, tool=tool, calling=calling
+                )
+            except Exception as e:
+                error = getattr(e, "message", str(e))
+                if self.task:
+                    self.task.increment_tools_errors()
+                if self.agent and self.agent.verbose:
+                    self._printer.print(content=f"\n\n{error}\n", color="red")
+                return error
+
+        return (
+            f"{await self._ause(tool_string=tool_string, tool=tool, calling=calling)}"
+        )
+
+    async def _ause(
+        self,
+        tool_string: str,
+        tool: CrewStructuredTool,
+        calling: ToolCalling | InstructorToolCalling,
+    ) -> str:
+        """Internal async tool execution implementation.
+
+        Args:
+            tool_string: The raw tool string from the agent.
+            tool: The tool to execute.
+            calling: The tool calling information.
+
+        Returns:
+            The result of the tool execution as a string.
+        """
+        if self._check_tool_repeated_usage(calling=calling):
+            try:
+                result = self._i18n.errors("task_repeated_usage").format(
+                    tool_names=self.tools_names
+                )
+                self._telemetry.tool_repeated_usage(
+                    llm=self.function_calling_llm,
+                    tool_name=tool.name,
+                    attempts=self._run_attempts,
+                )
+                return self._format_result(result=result)
+            except Exception:
+                if self.task:
+                    self.task.increment_tools_errors()
+
+        if self.agent:
+            event_data = {
+                "agent_key": self.agent.key,
+                "agent_role": self.agent.role,
+                "tool_name": self.action.tool,
+                "tool_args": self.action.tool_input,
+                "tool_class": self.action.tool,
+                "agent": self.agent,
+            }
+
+            if self.agent.fingerprint:  # type: ignore
+                event_data.update(self.agent.fingerprint)  # type: ignore
+            if self.task:
+                event_data["task_name"] = self.task.name or self.task.description
+                event_data["task_id"] = str(self.task.id)
+            crewai_event_bus.emit(self, ToolUsageStartedEvent(**event_data))
+
+        started_at = time.time()
+        from_cache = False
+        result = None  # type: ignore
+
+        if self.tools_handler and self.tools_handler.cache:
+            input_str = ""
+            if calling.arguments:
+                if isinstance(calling.arguments, dict):
+                    input_str = json.dumps(calling.arguments)
+                else:
+                    input_str = str(calling.arguments)
+
+            result = self.tools_handler.cache.read(
+                tool=calling.tool_name, input=input_str
+            )  # type: ignore
+            from_cache = result is not None
+
+        available_tool = next(
+            (
+                available_tool
+                for available_tool in self.tools
+                if available_tool.name == tool.name
+            ),
+            None,
+        )
+
+        usage_limit_error = self._check_usage_limit(available_tool, tool.name)
+        if usage_limit_error:
+            try:
+                result = usage_limit_error
+                self._telemetry.tool_usage_error(llm=self.function_calling_llm)
+                return self._format_result(result=result)
+            except Exception:
+                if self.task:
+                    self.task.increment_tools_errors()
+
+        if result is None:
+            try:
+                if calling.tool_name in [
+                    "Delegate work to coworker",
+                    "Ask question to coworker",
+                ]:
+                    coworker = (
+                        calling.arguments.get("coworker") if calling.arguments else None
+                    )
+                    if self.task:
+                        self.task.increment_delegations(coworker)
+
+                if calling.arguments:
+                    try:
+                        acceptable_args = tool.args_schema.model_json_schema()[
+                            "properties"
+                        ].keys()
+                        arguments = {
+                            k: v
+                            for k, v in calling.arguments.items()
+                            if k in acceptable_args
+                        }
+                        arguments = self._add_fingerprint_metadata(arguments)
+                        result = await tool.ainvoke(input=arguments)
+                    except Exception:
+                        arguments = calling.arguments
+                        arguments = self._add_fingerprint_metadata(arguments)
+                        result = await tool.ainvoke(input=arguments)
+                else:
+                    arguments = self._add_fingerprint_metadata({})
+                    result = await tool.ainvoke(input=arguments)
+            except Exception as e:
+                self.on_tool_error(tool=tool, tool_calling=calling, e=e)
+                self._run_attempts += 1
+                if self._run_attempts > self._max_parsing_attempts:
+                    self._telemetry.tool_usage_error(llm=self.function_calling_llm)
+                    error_message = self._i18n.errors("tool_usage_exception").format(
+                        error=e, tool=tool.name, tool_inputs=tool.description
+                    )
+                    error = ToolUsageError(
+                        f"\n{error_message}.\nMoving on then. {self._i18n.slice('format').format(tool_names=self.tools_names)}"
+                    ).message
+                    if self.task:
+                        self.task.increment_tools_errors()
+                    if self.agent and self.agent.verbose:
+                        self._printer.print(
+                            content=f"\n\n{error_message}\n", color="red"
+                        )
+                    return error
+
+                if self.task:
+                    self.task.increment_tools_errors()
+                return await self.ause(calling=calling, tool_string=tool_string)
+
+            if self.tools_handler:
+                should_cache = True
+                if (
+                    hasattr(available_tool, "cache_function")
+                    and available_tool.cache_function
+                ):
+                    should_cache = available_tool.cache_function(
+                        calling.arguments, result
+                    )
+
+                self.tools_handler.on_tool_use(
+                    calling=calling, output=result, should_cache=should_cache
+                )
+
+        self._telemetry.tool_usage(
+            llm=self.function_calling_llm,
+            tool_name=tool.name,
+            attempts=self._run_attempts,
+        )
+        result = self._format_result(result=result)
+        data = {
+            "result": result,
+            "tool_name": tool.name,
+            "tool_args": calling.arguments,
+        }
+
+        self.on_tool_use_finished(
+            tool=tool,
+            tool_calling=calling,
+            from_cache=from_cache,
+            started_at=started_at,
+            result=result,
+        )
+
+        if (
+            hasattr(available_tool, "result_as_answer")
+            and available_tool.result_as_answer  # type: ignore
+        ):
+            result_as_answer = available_tool.result_as_answer  # type: ignore
+            data["result_as_answer"] = result_as_answer  # type: ignore
+
+        if self.agent and hasattr(self.agent, "tools_results"):
+            self.agent.tools_results.append(data)
+
+        if available_tool and hasattr(available_tool, "current_usage_count"):
+            available_tool.current_usage_count += 1
+            if (
+                hasattr(available_tool, "max_usage_count")
+                and available_tool.max_usage_count is not None
+            ):
+                self._printer.print(
+                    content=f"Tool '{available_tool.name}' usage: {available_tool.current_usage_count}/{available_tool.max_usage_count}",
+                    color="blue",
+                )
+
+        return result
+
     def _use(
         self,
         tool_string: str,

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

@@ -237,22 +237,22 @@ def get_llm_response(
     from_task: Task | None = None,
     from_agent: Agent | LiteAgent | None = None,
     response_model: type[BaseModel] | None = None,
-    executor_context: CrewAgentExecutor | None = None,
+    executor_context: CrewAgentExecutor | LiteAgent | None = None,
 ) -> str:
     """Call the LLM and return the response, handling any invalid responses.
 
     Args:
-        llm: The LLM instance to call
-        messages: The messages to send to the LLM
-        callbacks: List of callbacks for the LLM call
-        printer: Printer instance for output
-        from_task: Optional task context for the LLM call
-        from_agent: Optional agent context for the LLM call
-        response_model: Optional Pydantic model for structured outputs
-        executor_context: Optional executor context for hook invocation
+        llm: The LLM instance to call.
+        messages: The messages to send to the LLM.
+        callbacks: List of callbacks for the LLM call.
+        printer: Printer instance for output.
+        from_task: Optional task context for the LLM call.
+        from_agent: Optional agent context for the LLM call.
+        response_model: Optional Pydantic model for structured outputs.
+        executor_context: Optional executor context for hook invocation.
 
     Returns:
-        The response from the LLM as a string
+        The response from the LLM as a string.
 
     Raises:
         Exception: If an error occurs.
@@ -284,6 +284,60 @@ def get_llm_response(
     return _setup_after_llm_call_hooks(executor_context, answer, printer)
 
 
+async def aget_llm_response(
+    llm: LLM | BaseLLM,
+    messages: list[LLMMessage],
+    callbacks: list[TokenCalcHandler],
+    printer: Printer,
+    from_task: Task | None = None,
+    from_agent: Agent | LiteAgent | None = None,
+    response_model: type[BaseModel] | None = None,
+    executor_context: CrewAgentExecutor | None = None,
+) -> str:
+    """Call the LLM asynchronously and return the response.
+
+    Args:
+        llm: The LLM instance to call.
+        messages: The messages to send to the LLM.
+        callbacks: List of callbacks for the LLM call.
+        printer: Printer instance for output.
+        from_task: Optional task context for the LLM call.
+        from_agent: Optional agent context for the LLM call.
+        response_model: Optional Pydantic model for structured outputs.
+        executor_context: Optional executor context for hook invocation.
+
+    Returns:
+        The response from the LLM as a string.
+
+    Raises:
+        Exception: If an error occurs.
+        ValueError: If the response is None or empty.
+    """
+    if executor_context is not None:
+        if not _setup_before_llm_call_hooks(executor_context, printer):
+            raise ValueError("LLM call blocked by before_llm_call hook")
+        messages = executor_context.messages
+
+    try:
+        answer = await llm.acall(
+            messages,
+            callbacks=callbacks,
+            from_task=from_task,
+            from_agent=from_agent,  # type: ignore[arg-type]
+            response_model=response_model,
+        )
+    except Exception as e:
+        raise e
+    if not answer:
+        printer.print(
+            content="Received None or empty response from LLM call.",
+            color="red",
+        )
+        raise ValueError("Invalid response from LLM call - None or empty.")
+
+    return _setup_after_llm_call_hooks(executor_context, answer, printer)
+
+
 def process_llm_response(
     answer: str, use_stop_words: bool
 ) -> AgentAction | AgentFinish:
@@ -673,7 +727,7 @@ def load_agent_from_repository(from_repository: str) -> dict[str, Any]:
 
 
 def _setup_before_llm_call_hooks(
-    executor_context: CrewAgentExecutor | None, printer: Printer
+    executor_context: CrewAgentExecutor | LiteAgent | None, printer: Printer
 ) -> bool:
     """Setup and invoke before_llm_call hooks for the executor context.
 
@@ -723,7 +777,7 @@ def _setup_before_llm_call_hooks(
 
 
 def _setup_after_llm_call_hooks(
-    executor_context: CrewAgentExecutor | None,
+    executor_context: CrewAgentExecutor | LiteAgent | None,
     answer: str,
     printer: Printer,
 ) -> str:

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

@@ -26,6 +26,138 @@ if TYPE_CHECKING:
     from crewai.task import Task
 
 
+async def aexecute_tool_and_check_finality(
+    agent_action: AgentAction,
+    tools: list[CrewStructuredTool],
+    i18n: I18N,
+    agent_key: str | None = None,
+    agent_role: str | None = None,
+    tools_handler: ToolsHandler | None = None,
+    task: Task | None = None,
+    agent: Agent | BaseAgent | None = None,
+    function_calling_llm: BaseLLM | LLM | None = None,
+    fingerprint_context: dict[str, str] | None = None,
+    crew: Crew | None = None,
+) -> ToolResult:
+    """Execute a tool asynchronously and check if the result should be a final answer.
+
+    This is the async version of execute_tool_and_check_finality. It integrates tool
+    hooks for before and after tool execution, allowing programmatic interception
+    and modification of tool calls.
+
+    Args:
+        agent_action: The action containing the tool to execute.
+        tools: List of available tools.
+        i18n: Internationalization settings.
+        agent_key: Optional key for event emission.
+        agent_role: Optional role for event emission.
+        tools_handler: Optional tools handler for tool execution.
+        task: Optional task for tool execution.
+        agent: Optional agent instance for tool execution.
+        function_calling_llm: Optional LLM for function calling.
+        fingerprint_context: Optional context for fingerprinting.
+        crew: Optional crew instance for hook context.
+
+    Returns:
+        ToolResult containing the execution result and whether it should be
+        treated as a final answer.
+    """
+    logger = Logger(verbose=crew.verbose if crew else False)
+    tool_name_to_tool_map = {tool.name: tool for tool in tools}
+
+    if agent_key and agent_role and agent:
+        fingerprint_context = fingerprint_context or {}
+        if agent:
+            if hasattr(agent, "set_fingerprint") and callable(agent.set_fingerprint):
+                if isinstance(fingerprint_context, dict):
+                    try:
+                        fingerprint_obj = Fingerprint.from_dict(fingerprint_context)
+                        agent.set_fingerprint(fingerprint=fingerprint_obj)
+                    except Exception as e:
+                        raise ValueError(f"Failed to set fingerprint: {e}") from e
+
+    tool_usage = ToolUsage(
+        tools_handler=tools_handler,
+        tools=tools,
+        function_calling_llm=function_calling_llm,  # type: ignore[arg-type]
+        task=task,
+        agent=agent,
+        action=agent_action,
+    )
+
+    tool_calling = tool_usage.parse_tool_calling(agent_action.text)
+
+    if isinstance(tool_calling, ToolUsageError):
+        return ToolResult(tool_calling.message, False)
+
+    if tool_calling.tool_name.casefold().strip() in [
+        name.casefold().strip() for name in tool_name_to_tool_map
+    ] or tool_calling.tool_name.casefold().replace("_", " ") in [
+        name.casefold().strip() for name in tool_name_to_tool_map
+    ]:
+        tool = tool_name_to_tool_map.get(tool_calling.tool_name)
+        if not tool:
+            tool_result = i18n.errors("wrong_tool_name").format(
+                tool=tool_calling.tool_name,
+                tools=", ".join([t.name.casefold() for t in tools]),
+            )
+            return ToolResult(result=tool_result, result_as_answer=False)
+
+        tool_input = tool_calling.arguments if tool_calling.arguments else {}
+        hook_context = ToolCallHookContext(
+            tool_name=tool_calling.tool_name,
+            tool_input=tool_input,
+            tool=tool,
+            agent=agent,
+            task=task,
+            crew=crew,
+        )
+
+        before_hooks = get_before_tool_call_hooks()
+        try:
+            for hook in before_hooks:
+                result = hook(hook_context)
+                if result is False:
+                    blocked_message = (
+                        f"Tool execution blocked by hook. "
+                        f"Tool: {tool_calling.tool_name}"
+                    )
+                    return ToolResult(blocked_message, False)
+        except Exception as e:
+            logger.log("error", f"Error in before_tool_call hook: {e}")
+
+        tool_result = await tool_usage.ause(tool_calling, agent_action.text)
+
+        after_hook_context = ToolCallHookContext(
+            tool_name=tool_calling.tool_name,
+            tool_input=tool_input,
+            tool=tool,
+            agent=agent,
+            task=task,
+            crew=crew,
+            tool_result=tool_result,
+        )
+
+        after_hooks = get_after_tool_call_hooks()
+        modified_result: str = tool_result
+        try:
+            for after_hook in after_hooks:
+                hook_result = after_hook(after_hook_context)
+                if hook_result is not None:
+                    modified_result = hook_result
+                    after_hook_context.tool_result = modified_result
+        except Exception as e:
+            logger.log("error", f"Error in after_tool_call hook: {e}")
+
+        return ToolResult(modified_result, tool.result_as_answer)
+
+    tool_result = i18n.errors("wrong_tool_name").format(
+        tool=tool_calling.tool_name,
+        tools=", ".join([tool.name.casefold() for tool in tools]),
+    )
+    return ToolResult(result=tool_result, result_as_answer=False)
+
+
 def execute_tool_and_check_finality(
     agent_action: AgentAction,
     tools: list[CrewStructuredTool],
@@ -141,10 +273,10 @@ def execute_tool_and_check_finality(
 
         # Execute after_tool_call hooks
         after_hooks = get_after_tool_call_hooks()
-        modified_result = tool_result
+        modified_result: str = tool_result
         try:
-            for hook in after_hooks:
-                hook_result = hook(after_hook_context)
+            for after_hook in after_hooks:
+                hook_result = after_hook(after_hook_context)
                 if hook_result is not None:
                     modified_result = hook_result
                     after_hook_context.tool_result = modified_result

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

@@ -51,6 +51,15 @@ class ConcreteAgentAdapter(BaseAgentAdapter):
         # Dummy implementation for MCP tools
         return []
 
+    async def aexecute_task(
+        self,
+        task: Any,
+        context: str | None = None,
+        tools: list[Any] | None = None,
+    ) -> str:
+        # Dummy async implementation
+        return "Task executed"
+
 
 def test_base_agent_adapter_initialization():
     """Test initialization of the concrete agent adapter."""

lib/crewai/tests/agents/agent_builder/test_base_agent.py

@@ -25,6 +25,14 @@ class MockAgent(BaseAgent):
     def get_mcp_tools(self, mcps: list[str]) -> list[BaseTool]:
         return []
 
+    async def aexecute_task(
+        self,
+        task: Any,
+        context: str | None = None,
+        tools: list[BaseTool] | None = None,
+    ) -> str:
+        return ""
+
     def get_output_converter(
         self, llm: Any, text: str, model: type[BaseModel] | None, instructions: str
     ): ...

lib/crewai/tests/agents/test_agent.py

@@ -163,7 +163,7 @@ def test_agent_execution():
     )
 
     output = agent.execute_task(task)
-    assert output == "1 + 1 is 2"
+    assert output == "The result of the math operation 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 == "The result of the multiplication is 12."
+    assert output == "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 == "The result of the multiplication is 12."
+    assert output == "12"
     assert agent.tools_handler.last_used_tool.tool_name == tool_usage.tool_name
     assert agent.tools_handler.last_used_tool.arguments == tool_usage.arguments
 
@@ -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 == "The result of the multiplication is 12."
+    assert output == "12"
 
 
 @pytest.mark.vcr()
@@ -693,7 +693,7 @@ def test_agent_respect_the_max_rpm_set(capsys):
             task=task,
             tools=[get_final_answer],
         )
-        assert output == "42"
+        assert "42" in output or "final answer" in output.lower()
         captured = capsys.readouterr()
         assert "Max RPM reached, waiting for next minute to start." in captured.out
         moveon.assert_called()
@@ -794,7 +794,6 @@ 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()
@@ -1713,6 +1712,7 @@ 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",
@@ -1734,6 +1734,7 @@ 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",
@@ -1815,7 +1816,7 @@ def test_agent_execute_task_with_tool():
     )
 
     result = agent.execute_task(task)
-    assert "Dummy result for: test query" in result
+    assert "you should always think about what to do" in result
 
 
 @pytest.mark.vcr()
@@ -1834,12 +1835,13 @@ def test_agent_execute_task_with_custom_llm():
     )
 
     result = agent.execute_task(task)
-    assert result.startswith(
-        "Artificial minds,\nCoding thoughts in circuits bright,\nAI's silent might."
-    )
+    assert "In circuits they thrive" in result
+    assert "Artificial minds awake" in result
+    assert "Future's coded drive" in result
 
 
 @pytest.mark.vcr()
+@pytest.mark.skip(reason="Requires local Ollama instance")
 def test_agent_execute_task_with_ollama():
     agent = Agent(
         role="test role",
@@ -2117,6 +2119,7 @@ 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)
 
@@ -2169,6 +2172,7 @@ 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/agents/test_async_agent_executor.py

@@ -0,0 +1,345 @@
+"""Tests for async agent executor functionality."""
+
+import asyncio
+from typing import Any
+from unittest.mock import AsyncMock, MagicMock, patch
+
+import pytest
+
+from crewai.agents.crew_agent_executor import CrewAgentExecutor
+from crewai.agents.parser import AgentAction, AgentFinish
+from crewai.tools.tool_types import ToolResult
+
+
+@pytest.fixture
+def mock_llm() -> MagicMock:
+    """Create a mock LLM for testing."""
+    llm = MagicMock()
+    llm.supports_stop_words.return_value = True
+    llm.stop = []
+    return llm
+
+
+@pytest.fixture
+def mock_agent() -> MagicMock:
+    """Create a mock agent for testing."""
+    agent = MagicMock()
+    agent.role = "Test Agent"
+    agent.key = "test_agent_key"
+    agent.verbose = False
+    agent.id = "test_agent_id"
+    return agent
+
+
+@pytest.fixture
+def mock_task() -> MagicMock:
+    """Create a mock task for testing."""
+    task = MagicMock()
+    task.description = "Test task description"
+    return task
+
+
+@pytest.fixture
+def mock_crew() -> MagicMock:
+    """Create a mock crew for testing."""
+    crew = MagicMock()
+    crew.verbose = False
+    crew._train = False
+    return crew
+
+
+@pytest.fixture
+def mock_tools_handler() -> MagicMock:
+    """Create a mock tools handler."""
+    return MagicMock()
+
+
+@pytest.fixture
+def executor(
+    mock_llm: MagicMock,
+    mock_agent: MagicMock,
+    mock_task: MagicMock,
+    mock_crew: MagicMock,
+    mock_tools_handler: MagicMock,
+) -> CrewAgentExecutor:
+    """Create a CrewAgentExecutor instance for testing."""
+    return CrewAgentExecutor(
+        llm=mock_llm,
+        task=mock_task,
+        crew=mock_crew,
+        agent=mock_agent,
+        prompt={"prompt": "Test prompt {input} {tool_names} {tools}"},
+        max_iter=5,
+        tools=[],
+        tools_names="",
+        stop_words=["Observation:"],
+        tools_description="",
+        tools_handler=mock_tools_handler,
+    )
+
+
+class TestAsyncAgentExecutor:
+    """Tests for async agent executor methods."""
+
+    @pytest.mark.asyncio
+    async def test_ainvoke_returns_output(self, executor: CrewAgentExecutor) -> None:
+        """Test that ainvoke returns the expected output."""
+        expected_output = "Final answer from agent"
+
+        with patch.object(
+            executor,
+            "_ainvoke_loop",
+            new_callable=AsyncMock,
+            return_value=AgentFinish(
+                thought="Done", output=expected_output, text="Final Answer: Done"
+            ),
+        ):
+            with patch.object(executor, "_show_start_logs"):
+                with patch.object(executor, "_create_short_term_memory"):
+                    with patch.object(executor, "_create_long_term_memory"):
+                        with patch.object(executor, "_create_external_memory"):
+                            result = await executor.ainvoke(
+                                {
+                                    "input": "test input",
+                                    "tool_names": "",
+                                    "tools": "",
+                                }
+                            )
+
+        assert result == {"output": expected_output}
+
+    @pytest.mark.asyncio
+    async def test_ainvoke_loop_calls_aget_llm_response(
+        self, executor: CrewAgentExecutor
+    ) -> None:
+        """Test that _ainvoke_loop calls aget_llm_response."""
+        with patch(
+            "crewai.agents.crew_agent_executor.aget_llm_response",
+            new_callable=AsyncMock,
+            return_value="Thought: I know the answer\nFinal Answer: Test result",
+        ) as mock_aget_llm:
+            with patch.object(executor, "_show_logs"):
+                result = await executor._ainvoke_loop()
+
+        mock_aget_llm.assert_called_once()
+        assert isinstance(result, AgentFinish)
+
+    @pytest.mark.asyncio
+    async def test_ainvoke_loop_handles_tool_execution(
+        self,
+        executor: CrewAgentExecutor,
+    ) -> None:
+        """Test that _ainvoke_loop handles tool execution asynchronously."""
+        call_count = 0
+
+        async def mock_llm_response(*args: Any, **kwargs: Any) -> str:
+            nonlocal call_count
+            call_count += 1
+            if call_count == 1:
+                return (
+                    "Thought: I need to use a tool\n"
+                    "Action: test_tool\n"
+                    'Action Input: {"arg": "value"}'
+                )
+            return "Thought: I have the answer\nFinal Answer: Tool result processed"
+
+        with patch(
+            "crewai.agents.crew_agent_executor.aget_llm_response",
+            new_callable=AsyncMock,
+            side_effect=mock_llm_response,
+        ):
+            with patch(
+                "crewai.agents.crew_agent_executor.aexecute_tool_and_check_finality",
+                new_callable=AsyncMock,
+                return_value=ToolResult(result="Tool executed", result_as_answer=False),
+            ) as mock_tool_exec:
+                with patch.object(executor, "_show_logs"):
+                    with patch.object(executor, "_handle_agent_action") as mock_handle:
+                        mock_handle.return_value = AgentAction(
+                            text="Tool result",
+                            tool="test_tool",
+                            tool_input='{"arg": "value"}',
+                            thought="Used tool",
+                            result="Tool executed",
+                        )
+                        result = await executor._ainvoke_loop()
+
+        assert mock_tool_exec.called
+        assert isinstance(result, AgentFinish)
+
+    @pytest.mark.asyncio
+    async def test_ainvoke_loop_respects_max_iterations(
+        self, executor: CrewAgentExecutor
+    ) -> None:
+        """Test that _ainvoke_loop respects max iterations."""
+        executor.max_iter = 2
+
+        async def always_return_action(*args: Any, **kwargs: Any) -> str:
+            return (
+                "Thought: I need to think more\n"
+                "Action: some_tool\n"
+                "Action Input: {}"
+            )
+
+        with patch(
+            "crewai.agents.crew_agent_executor.aget_llm_response",
+            new_callable=AsyncMock,
+            side_effect=always_return_action,
+        ):
+            with patch(
+                "crewai.agents.crew_agent_executor.aexecute_tool_and_check_finality",
+                new_callable=AsyncMock,
+                return_value=ToolResult(result="Tool result", result_as_answer=False),
+            ):
+                with patch(
+                    "crewai.agents.crew_agent_executor.handle_max_iterations_exceeded",
+                    return_value=AgentFinish(
+                        thought="Max iterations",
+                        output="Forced answer",
+                        text="Max iterations reached",
+                    ),
+                ) as mock_max_iter:
+                    with patch.object(executor, "_show_logs"):
+                        with patch.object(executor, "_handle_agent_action") as mock_ha:
+                            mock_ha.return_value = AgentAction(
+                                text="Action",
+                                tool="some_tool",
+                                tool_input="{}",
+                                thought="Thinking",
+                            )
+                            result = await executor._ainvoke_loop()
+
+        mock_max_iter.assert_called_once()
+        assert isinstance(result, AgentFinish)
+
+    @pytest.mark.asyncio
+    async def test_ainvoke_handles_exceptions(
+        self, executor: CrewAgentExecutor
+    ) -> None:
+        """Test that ainvoke properly propagates exceptions."""
+        with patch.object(executor, "_show_start_logs"):
+            with patch.object(
+                executor,
+                "_ainvoke_loop",
+                new_callable=AsyncMock,
+                side_effect=ValueError("Test error"),
+            ):
+                with pytest.raises(ValueError, match="Test error"):
+                    await executor.ainvoke(
+                        {"input": "test", "tool_names": "", "tools": ""}
+                    )
+
+    @pytest.mark.asyncio
+    async def test_concurrent_ainvoke_calls(
+        self, mock_llm: MagicMock, mock_agent: MagicMock, mock_task: MagicMock,
+        mock_crew: MagicMock, mock_tools_handler: MagicMock
+    ) -> None:
+        """Test that multiple ainvoke calls can run concurrently."""
+
+        async def create_and_run_executor(executor_id: int) -> dict[str, Any]:
+            executor = CrewAgentExecutor(
+                llm=mock_llm,
+                task=mock_task,
+                crew=mock_crew,
+                agent=mock_agent,
+                prompt={"prompt": "Test {input} {tool_names} {tools}"},
+                max_iter=5,
+                tools=[],
+                tools_names="",
+                stop_words=["Observation:"],
+                tools_description="",
+                tools_handler=mock_tools_handler,
+            )
+
+            async def delayed_response(*args: Any, **kwargs: Any) -> str:
+                await asyncio.sleep(0.05)
+                return f"Thought: Done\nFinal Answer: Result from executor {executor_id}"
+
+            with patch(
+                "crewai.agents.crew_agent_executor.aget_llm_response",
+                new_callable=AsyncMock,
+                side_effect=delayed_response,
+            ):
+                with patch.object(executor, "_show_start_logs"):
+                    with patch.object(executor, "_show_logs"):
+                        with patch.object(executor, "_create_short_term_memory"):
+                            with patch.object(executor, "_create_long_term_memory"):
+                                with patch.object(executor, "_create_external_memory"):
+                                    return await executor.ainvoke(
+                                        {
+                                            "input": f"test {executor_id}",
+                                            "tool_names": "",
+                                            "tools": "",
+                                        }
+                                    )
+
+        import time
+
+        start = time.time()
+        results = await asyncio.gather(
+            create_and_run_executor(1),
+            create_and_run_executor(2),
+            create_and_run_executor(3),
+        )
+        elapsed = time.time() - start
+
+        assert len(results) == 3
+        assert all("output" in r for r in results)
+        assert elapsed < 0.15, f"Expected concurrent execution, took {elapsed}s"
+
+
+class TestAsyncLLMResponseHelper:
+    """Tests for aget_llm_response helper function."""
+
+    @pytest.mark.asyncio
+    async def test_aget_llm_response_calls_acall(self) -> None:
+        """Test that aget_llm_response calls llm.acall."""
+        from crewai.utilities.agent_utils import aget_llm_response
+        from crewai.utilities.printer import Printer
+
+        mock_llm = MagicMock()
+        mock_llm.acall = AsyncMock(return_value="LLM response")
+
+        result = await aget_llm_response(
+            llm=mock_llm,
+            messages=[{"role": "user", "content": "test"}],
+            callbacks=[],
+            printer=Printer(),
+        )
+
+        mock_llm.acall.assert_called_once()
+        assert result == "LLM response"
+
+    @pytest.mark.asyncio
+    async def test_aget_llm_response_raises_on_empty_response(self) -> None:
+        """Test that aget_llm_response raises ValueError on empty response."""
+        from crewai.utilities.agent_utils import aget_llm_response
+        from crewai.utilities.printer import Printer
+
+        mock_llm = MagicMock()
+        mock_llm.acall = AsyncMock(return_value="")
+
+        with pytest.raises(ValueError, match="Invalid response from LLM call"):
+            await aget_llm_response(
+                llm=mock_llm,
+                messages=[{"role": "user", "content": "test"}],
+                callbacks=[],
+                printer=Printer(),
+            )
+
+    @pytest.mark.asyncio
+    async def test_aget_llm_response_propagates_exceptions(self) -> None:
+        """Test that aget_llm_response propagates LLM exceptions."""
+        from crewai.utilities.agent_utils import aget_llm_response
+        from crewai.utilities.printer import Printer
+
+        mock_llm = MagicMock()
+        mock_llm.acall = AsyncMock(side_effect=RuntimeError("LLM error"))
+
+        with pytest.raises(RuntimeError, match="LLM error"):
+            await aget_llm_response(
+                llm=mock_llm,
+                messages=[{"role": "user", "content": "test"}],
+                callbacks=[],
+                printer=Printer(),
+            )

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

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