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

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]

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/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/crew.py

@@ -327,7 +327,7 @@ 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 +348,12 @@ 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
         )
 
@@ -948,6 +948,342 @@ class Crew(FlowTrackable, BaseModel):
         self._task_output_handler.reset()
         return list(results)
 
+    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:
+            for agent in self.agents:
+                if agent.llm is not None:
+                    agent.llm.stream = True
+
+            result_holder: list[CrewOutput] = []
+            current_task_info: TaskInfo = {
+                "index": 0,
+                "name": "",
+                "id": "",
+                "agent_role": "",
+                "agent_id": "",
+            }
+
+            state = create_streaming_state(
+                current_task_info, result_holder, use_async=True
+            )
+            output_holder: list[CrewStreamingOutput | FlowStreamingOutput] = []
+
+            async def run_crew() -> None:
+                try:
+                    self.stream = False
+                    result = await self.akickoff(inputs)
+                    if isinstance(result, CrewOutput):
+                        result_holder.append(result)
+                except Exception as e:
+                    signal_error(state, e, is_async=True)
+                finally:
+                    self.stream = True
+                    signal_end(state, is_async=True)
+
+            streaming_output = CrewStreamingOutput(
+                async_iterator=create_async_chunk_generator(
+                    state, run_crew, output_holder
+                )
+            )
+            output_holder.append(streaming_output)
+
+            return streaming_output
+
+        ctx = baggage.set_baggage(
+            "crew_context", CrewContext(id=str(self.id), key=self.key)
+        )
+        token = attach(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),
+            )
+
+            self._task_output_handler.reset()
+            self._logging_color = "bold_purple"
+
+            if inputs is not None:
+                self._inputs = inputs
+                self._interpolate_inputs(inputs)
+            self._set_tasks_callbacks()
+            self._set_allow_crewai_trigger_context_for_first_task()
+
+            for agent in self.agents:
+                agent.crew = self
+                agent.set_knowledge(crew_embedder=self.embedder)
+                if not agent.function_calling_llm:  # type: ignore[attr-defined]
+                    agent.function_calling_llm = self.function_calling_llm  # type: ignore[attr-defined]
+
+                if not agent.step_callback:  # type: ignore[attr-defined]
+                    agent.step_callback = self.step_callback  # type: ignore[attr-defined]
+
+                agent.create_agent_executor()
+
+            if self.planning:
+                self._handle_crew_planning()
+
+            if self.process == Process.sequential:
+                result = await self._arun_sequential_process()
+            elif self.process == Process.hierarchical:
+                result = await self._arun_hierarchical_process()
+            else:
+                raise NotImplementedError(
+                    f"The process '{self.process}' is not implemented yet."
+                )
+
+            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.
+        """
+        crew_copies = [self.copy() for _ in inputs]
+
+        if self.stream:
+            result_holder: list[list[CrewOutput]] = [[]]
+            current_task_info: TaskInfo = {
+                "index": 0,
+                "name": "",
+                "id": "",
+                "agent_role": "",
+                "agent_id": "",
+            }
+
+            state = create_streaming_state(
+                current_task_info, result_holder, use_async=True
+            )
+            output_holder: list[CrewStreamingOutput | FlowStreamingOutput] = []
+
+            async def run_all_crews() -> None:
+                try:
+                    streaming_outputs: list[CrewStreamingOutput] = []
+                    for i, crew in enumerate(crew_copies):
+                        streaming = await crew.akickoff(inputs=inputs[i])
+                        if isinstance(streaming, CrewStreamingOutput):
+                            streaming_outputs.append(streaming)
+
+                    async def consume_stream(
+                        stream_output: CrewStreamingOutput,
+                    ) -> CrewOutput:
+                        async for chunk in stream_output:
+                            if state.async_queue is not None and state.loop is not None:
+                                state.loop.call_soon_threadsafe(
+                                    state.async_queue.put_nowait, chunk
+                                )
+                        return stream_output.result
+
+                    crew_results = await asyncio.gather(
+                        *[consume_stream(s) for s in streaming_outputs]
+                    )
+                    result_holder[0] = list(crew_results)
+                except Exception as e:
+                    signal_error(state, e, is_async=True)
+                finally:
+                    signal_end(state, is_async=True)
+
+            streaming_output = CrewStreamingOutput(
+                async_iterator=create_async_chunk_generator(
+                    state, run_all_crews, output_holder
+                )
+            )
+
+            def set_results_wrapper(result: Any) -> None:
+                streaming_output._set_results(result)
+
+            streaming_output._set_result = set_results_wrapper  # type: ignore[method-assign]
+            output_holder.append(streaming_output)
+
+            return streaming_output
+
+        tasks = [
+            asyncio.create_task(crew_copy.akickoff(inputs=input_data))
+            for crew_copy, input_data in zip(crew_copies, inputs, strict=True)
+        ]
+
+        results = await asyncio.gather(*tasks)
+
+        total_usage_metrics = UsageMetrics()
+        for crew_copy in crew_copies:
+            if crew_copy.usage_metrics:
+                total_usage_metrics.add_usage_metrics(crew_copy.usage_metrics)
+        self.usage_metrics = total_usage_metrics
+
+        self._task_output_handler.reset()
+        return list(results)
+
+    async def _arun_sequential_process(self) -> CrewOutput:
+        """Executes tasks sequentially using native async and returns the final output."""
+        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):
+            if start_index is not None and task_index < start_index:
+                if task.output:
+                    if task.async_execution:
+                        task_outputs.append(task.output)
+                    else:
+                        task_outputs = [task.output]
+                        last_sync_output = task.output
+                continue
+
+            agent_to_use = self._get_agent_to_use(task)
+            if agent_to_use is None:
+                raise ValueError(
+                    f"No agent available for task: {task.description}. "
+                    f"Ensure that either the task has an assigned agent "
+                    f"or a manager agent is provided."
+                )
+
+            tools_for_task = task.tools or agent_to_use.tools or []
+            tools_for_task = self._prepare_tools(
+                agent_to_use,
+                task,
+                tools_for_task,
+            )
+
+            self._log_task_start(task, agent_to_use.role)
+
+            if isinstance(task, ConditionalTask):
+                skipped_task_output = await self._ahandle_conditional_task(
+                    task, task_outputs, pending_tasks, task_index, was_replayed
+                )
+                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=agent_to_use,
+                        context=context,
+                        tools=tools_for_task,
+                    )
+                )
+                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=agent_to_use,
+                    context=context,
+                    tools=tools_for_task,
+                )
+                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()
+
+        previous_output = task_outputs[-1] if task_outputs else None
+        if previous_output is not None and not task.should_execute(previous_output):
+            self._logger.log(
+                "debug",
+                f"Skipping conditional task: {task.description}",
+                color="yellow",
+            )
+            skipped_task_output = task.get_skipped_task_output()
+
+            if not was_replayed:
+                self._store_execution_log(task, skipped_task_output, task_index)
+            return skipped_task_output
+        return None
+
+    async def _aprocess_async_tasks(
+        self,
+        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."""
         self._logger.log("info", "Planning the crew execution")
@@ -1431,6 +1767,16 @@ 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.

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/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/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/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/utilities/agent_utils.py

@@ -242,17 +242,17 @@ def get_llm_response(
     """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:

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_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/crew/test_async_crew.py

@@ -0,0 +1,384 @@
+"""Tests for async crew execution."""
+
+import pytest
+from unittest.mock import AsyncMock, MagicMock, patch
+
+from crewai.agent import Agent
+from crewai.crew import Crew
+from crewai.task import Task
+from crewai.crews.crew_output import CrewOutput
+from crewai.tasks.task_output import TaskOutput
+
+
+@pytest.fixture
+def test_agent() -> Agent:
+    """Create a test agent."""
+    return Agent(
+        role="Test Agent",
+        goal="Test goal",
+        backstory="Test backstory",
+        llm="gpt-4o-mini",
+        verbose=False,
+    )
+
+
+@pytest.fixture
+def test_task(test_agent: Agent) -> Task:
+    """Create a test task."""
+    return Task(
+        description="Test task description",
+        expected_output="Test expected output",
+        agent=test_agent,
+    )
+
+
+@pytest.fixture
+def test_crew(test_agent: Agent, test_task: Task) -> Crew:
+    """Create a test crew."""
+    return Crew(
+        agents=[test_agent],
+        tasks=[test_task],
+        verbose=False,
+    )
+
+
+class TestAsyncCrewKickoff:
+    """Tests for async crew kickoff methods."""
+
+    @pytest.mark.asyncio
+    @patch("crewai.task.Task.aexecute_sync", new_callable=AsyncMock)
+    async def test_akickoff_basic(
+        self, mock_execute: AsyncMock, test_crew: Crew
+    ) -> None:
+        """Test basic async crew kickoff."""
+        mock_output = TaskOutput(
+            description="Test task description",
+            raw="Task result",
+            agent="Test Agent",
+        )
+        mock_execute.return_value = mock_output
+
+        result = await test_crew.akickoff()
+
+        assert result is not None
+        assert isinstance(result, CrewOutput)
+        assert result.raw == "Task result"
+        mock_execute.assert_called_once()
+
+    @pytest.mark.asyncio
+    @patch("crewai.task.Task.aexecute_sync", new_callable=AsyncMock)
+    async def test_akickoff_with_inputs(
+        self, mock_execute: AsyncMock, test_agent: Agent
+    ) -> None:
+        """Test async crew kickoff with inputs."""
+        task = Task(
+            description="Test task for {topic}",
+            expected_output="Expected output for {topic}",
+            agent=test_agent,
+        )
+        crew = Crew(
+            agents=[test_agent],
+            tasks=[task],
+            verbose=False,
+        )
+
+        mock_output = TaskOutput(
+            description="Test task for AI",
+            raw="Task result about AI",
+            agent="Test Agent",
+        )
+        mock_execute.return_value = mock_output
+
+        result = await crew.akickoff(inputs={"topic": "AI"})
+
+        assert result is not None
+        assert isinstance(result, CrewOutput)
+        mock_execute.assert_called_once()
+
+    @pytest.mark.asyncio
+    @patch("crewai.task.Task.aexecute_sync", new_callable=AsyncMock)
+    async def test_akickoff_multiple_tasks(
+        self, mock_execute: AsyncMock, test_agent: Agent
+    ) -> None:
+        """Test async crew kickoff with multiple tasks."""
+        task1 = Task(
+            description="First task",
+            expected_output="First output",
+            agent=test_agent,
+        )
+        task2 = Task(
+            description="Second task",
+            expected_output="Second output",
+            agent=test_agent,
+        )
+        crew = Crew(
+            agents=[test_agent],
+            tasks=[task1, task2],
+            verbose=False,
+        )
+
+        mock_output1 = TaskOutput(
+            description="First task",
+            raw="First result",
+            agent="Test Agent",
+        )
+        mock_output2 = TaskOutput(
+            description="Second task",
+            raw="Second result",
+            agent="Test Agent",
+        )
+        mock_execute.side_effect = [mock_output1, mock_output2]
+
+        result = await crew.akickoff()
+
+        assert result is not None
+        assert isinstance(result, CrewOutput)
+        assert result.raw == "Second result"
+        assert mock_execute.call_count == 2
+
+    @pytest.mark.asyncio
+    @patch("crewai.task.Task.aexecute_sync", new_callable=AsyncMock)
+    async def test_akickoff_handles_exception(
+        self, mock_execute: AsyncMock, test_crew: Crew
+    ) -> None:
+        """Test that async kickoff handles exceptions properly."""
+        mock_execute.side_effect = RuntimeError("Test error")
+
+        with pytest.raises(RuntimeError) as exc_info:
+            await test_crew.akickoff()
+
+        assert "Test error" in str(exc_info.value)
+
+    @pytest.mark.asyncio
+    @patch("crewai.task.Task.aexecute_sync", new_callable=AsyncMock)
+    async def test_akickoff_calls_before_callbacks(
+        self, mock_execute: AsyncMock, test_agent: Agent
+    ) -> None:
+        """Test that async kickoff calls before_kickoff_callbacks."""
+        callback_called = False
+
+        def before_callback(inputs: dict | None) -> dict:
+            nonlocal callback_called
+            callback_called = True
+            return inputs or {}
+
+        task = Task(
+            description="Test task",
+            expected_output="Test output",
+            agent=test_agent,
+        )
+        crew = Crew(
+            agents=[test_agent],
+            tasks=[task],
+            verbose=False,
+            before_kickoff_callbacks=[before_callback],
+        )
+
+        mock_output = TaskOutput(
+            description="Test task",
+            raw="Task result",
+            agent="Test Agent",
+        )
+        mock_execute.return_value = mock_output
+
+        await crew.akickoff()
+
+        assert callback_called
+
+    @pytest.mark.asyncio
+    @patch("crewai.task.Task.aexecute_sync", new_callable=AsyncMock)
+    async def test_akickoff_calls_after_callbacks(
+        self, mock_execute: AsyncMock, test_agent: Agent
+    ) -> None:
+        """Test that async kickoff calls after_kickoff_callbacks."""
+        callback_called = False
+
+        def after_callback(result: CrewOutput) -> CrewOutput:
+            nonlocal callback_called
+            callback_called = True
+            return result
+
+        task = Task(
+            description="Test task",
+            expected_output="Test output",
+            agent=test_agent,
+        )
+        crew = Crew(
+            agents=[test_agent],
+            tasks=[task],
+            verbose=False,
+            after_kickoff_callbacks=[after_callback],
+        )
+
+        mock_output = TaskOutput(
+            description="Test task",
+            raw="Task result",
+            agent="Test Agent",
+        )
+        mock_execute.return_value = mock_output
+
+        await crew.akickoff()
+
+        assert callback_called
+
+
+class TestAsyncCrewKickoffForEach:
+    """Tests for async crew kickoff_for_each methods."""
+
+    @pytest.mark.asyncio
+    @patch("crewai.task.Task.aexecute_sync", new_callable=AsyncMock)
+    async def test_akickoff_for_each_basic(
+        self, mock_execute: AsyncMock, test_agent: Agent
+    ) -> None:
+        """Test basic async kickoff_for_each."""
+        task = Task(
+            description="Test task for {topic}",
+            expected_output="Expected output",
+            agent=test_agent,
+        )
+        crew = Crew(
+            agents=[test_agent],
+            tasks=[task],
+            verbose=False,
+        )
+
+        mock_output1 = TaskOutput(
+            description="Test task for AI",
+            raw="Result about AI",
+            agent="Test Agent",
+        )
+        mock_output2 = TaskOutput(
+            description="Test task for ML",
+            raw="Result about ML",
+            agent="Test Agent",
+        )
+        mock_execute.side_effect = [mock_output1, mock_output2]
+
+        inputs = [{"topic": "AI"}, {"topic": "ML"}]
+        results = await crew.akickoff_for_each(inputs)
+
+        assert len(results) == 2
+        assert all(isinstance(r, CrewOutput) for r in results)
+
+    @pytest.mark.asyncio
+    @patch("crewai.task.Task.aexecute_sync", new_callable=AsyncMock)
+    async def test_akickoff_for_each_concurrent(
+        self, mock_execute: AsyncMock, test_agent: Agent
+    ) -> None:
+        """Test that async kickoff_for_each runs concurrently."""
+        task = Task(
+            description="Test task for {topic}",
+            expected_output="Expected output",
+            agent=test_agent,
+        )
+        crew = Crew(
+            agents=[test_agent],
+            tasks=[task],
+            verbose=False,
+        )
+
+        mock_output = TaskOutput(
+            description="Test task",
+            raw="Result",
+            agent="Test Agent",
+        )
+        mock_execute.return_value = mock_output
+
+        inputs = [{"topic": f"topic_{i}"} for i in range(3)]
+        results = await crew.akickoff_for_each(inputs)
+
+        assert len(results) == 3
+
+
+class TestAsyncTaskExecution:
+    """Tests for async task execution within crew."""
+
+    @pytest.mark.asyncio
+    @patch("crewai.task.Task.aexecute_sync", new_callable=AsyncMock)
+    async def test_aexecute_tasks_sequential(
+        self, mock_execute: AsyncMock, test_agent: Agent
+    ) -> None:
+        """Test async sequential task execution."""
+        task1 = Task(
+            description="First task",
+            expected_output="First output",
+            agent=test_agent,
+        )
+        task2 = Task(
+            description="Second task",
+            expected_output="Second output",
+            agent=test_agent,
+        )
+        crew = Crew(
+            agents=[test_agent],
+            tasks=[task1, task2],
+            verbose=False,
+        )
+
+        mock_output1 = TaskOutput(
+            description="First task",
+            raw="First result",
+            agent="Test Agent",
+        )
+        mock_output2 = TaskOutput(
+            description="Second task",
+            raw="Second result",
+            agent="Test Agent",
+        )
+        mock_execute.side_effect = [mock_output1, mock_output2]
+
+        result = await crew._aexecute_tasks(crew.tasks)
+
+        assert result is not None
+        assert result.raw == "Second result"
+        assert len(result.tasks_output) == 2
+
+    @pytest.mark.asyncio
+    @patch("crewai.task.Task.aexecute_sync", new_callable=AsyncMock)
+    async def test_aexecute_tasks_with_async_task(
+        self, mock_execute: AsyncMock, test_agent: Agent
+    ) -> None:
+        """Test async execution with async_execution task flag."""
+        task1 = Task(
+            description="Async task",
+            expected_output="Async output",
+            agent=test_agent,
+            async_execution=True,
+        )
+        task2 = Task(
+            description="Sync task",
+            expected_output="Sync output",
+            agent=test_agent,
+        )
+        crew = Crew(
+            agents=[test_agent],
+            tasks=[task1, task2],
+            verbose=False,
+        )
+
+        mock_output1 = TaskOutput(
+            description="Async task",
+            raw="Async result",
+            agent="Test Agent",
+        )
+        mock_output2 = TaskOutput(
+            description="Sync task",
+            raw="Sync result",
+            agent="Test Agent",
+        )
+        mock_execute.side_effect = [mock_output1, mock_output2]
+
+        result = await crew._aexecute_tasks(crew.tasks)
+
+        assert result is not None
+        assert mock_execute.call_count == 2
+
+
+class TestAsyncProcessAsyncTasks:
+    """Tests for _aprocess_async_tasks method."""
+
+    @pytest.mark.asyncio
+    async def test_aprocess_async_tasks_empty(self, test_crew: Crew) -> None:
+        """Test processing empty list of async tasks."""
+        result = await test_crew._aprocess_async_tasks([])
+        assert result == []

lib/crewai/tests/knowledge/test_async_knowledge.py

@@ -0,0 +1,212 @@
+"""Tests for async knowledge operations."""
+
+from unittest.mock import AsyncMock, MagicMock, patch
+
+import pytest
+
+from crewai.knowledge.knowledge import Knowledge
+from crewai.knowledge.source.string_knowledge_source import StringKnowledgeSource
+from crewai.knowledge.storage.knowledge_storage import KnowledgeStorage
+
+
+class TestAsyncKnowledgeStorage:
+    """Tests for async KnowledgeStorage operations."""
+
+    @pytest.mark.asyncio
+    async def test_asearch_returns_results(self):
+        """Test that asearch returns search results."""
+        mock_client = MagicMock()
+        mock_client.asearch = AsyncMock(
+            return_value=[{"content": "test result", "score": 0.9}]
+        )
+
+        storage = KnowledgeStorage(collection_name="test_collection")
+        storage._client = mock_client
+
+        results = await storage.asearch(["test query"])
+
+        assert len(results) == 1
+        assert results[0]["content"] == "test result"
+        mock_client.asearch.assert_called_once()
+
+    @pytest.mark.asyncio
+    async def test_asearch_empty_query_raises_error(self):
+        """Test that asearch handles empty query."""
+        storage = KnowledgeStorage(collection_name="test_collection")
+
+        # Empty query should not raise but return empty results due to error handling
+        results = await storage.asearch([])
+        assert results == []
+
+    @pytest.mark.asyncio
+    async def test_asave_calls_client_methods(self):
+        """Test that asave calls the correct client methods."""
+        mock_client = MagicMock()
+        mock_client.aget_or_create_collection = AsyncMock()
+        mock_client.aadd_documents = AsyncMock()
+
+        storage = KnowledgeStorage(collection_name="test_collection")
+        storage._client = mock_client
+
+        await storage.asave(["document 1", "document 2"])
+
+        mock_client.aget_or_create_collection.assert_called_once_with(
+            collection_name="knowledge_test_collection"
+        )
+        mock_client.aadd_documents.assert_called_once()
+
+    @pytest.mark.asyncio
+    async def test_areset_calls_client_delete(self):
+        """Test that areset calls delete_collection on the client."""
+        mock_client = MagicMock()
+        mock_client.adelete_collection = AsyncMock()
+
+        storage = KnowledgeStorage(collection_name="test_collection")
+        storage._client = mock_client
+
+        await storage.areset()
+
+        mock_client.adelete_collection.assert_called_once_with(
+            collection_name="knowledge_test_collection"
+        )
+
+
+class TestAsyncKnowledge:
+    """Tests for async Knowledge operations."""
+
+    @pytest.mark.asyncio
+    async def test_aquery_calls_storage_asearch(self):
+        """Test that aquery calls storage.asearch."""
+        mock_storage = MagicMock(spec=KnowledgeStorage)
+        mock_storage.asearch = AsyncMock(
+            return_value=[{"content": "result", "score": 0.8}]
+        )
+
+        knowledge = Knowledge(
+            collection_name="test",
+            sources=[],
+            storage=mock_storage,
+        )
+
+        results = await knowledge.aquery(["test query"])
+
+        assert len(results) == 1
+        mock_storage.asearch.assert_called_once_with(
+            ["test query"],
+            limit=5,
+            score_threshold=0.6,
+        )
+
+    @pytest.mark.asyncio
+    async def test_aquery_raises_when_storage_not_initialized(self):
+        """Test that aquery raises ValueError when storage is None."""
+        knowledge = Knowledge(
+            collection_name="test",
+            sources=[],
+            storage=MagicMock(spec=KnowledgeStorage),
+        )
+        knowledge.storage = None
+
+        with pytest.raises(ValueError, match="Storage is not initialized"):
+            await knowledge.aquery(["test query"])
+
+    @pytest.mark.asyncio
+    async def test_aadd_sources_calls_source_aadd(self):
+        """Test that aadd_sources calls aadd on each source."""
+        mock_storage = MagicMock(spec=KnowledgeStorage)
+        mock_source = MagicMock()
+        mock_source.aadd = AsyncMock()
+
+        knowledge = Knowledge(
+            collection_name="test",
+            sources=[mock_source],
+            storage=mock_storage,
+        )
+
+        await knowledge.aadd_sources()
+
+        mock_source.aadd.assert_called_once()
+        assert mock_source.storage == mock_storage
+
+    @pytest.mark.asyncio
+    async def test_areset_calls_storage_areset(self):
+        """Test that areset calls storage.areset."""
+        mock_storage = MagicMock(spec=KnowledgeStorage)
+        mock_storage.areset = AsyncMock()
+
+        knowledge = Knowledge(
+            collection_name="test",
+            sources=[],
+            storage=mock_storage,
+        )
+
+        await knowledge.areset()
+
+        mock_storage.areset.assert_called_once()
+
+    @pytest.mark.asyncio
+    async def test_areset_raises_when_storage_not_initialized(self):
+        """Test that areset raises ValueError when storage is None."""
+        knowledge = Knowledge(
+            collection_name="test",
+            sources=[],
+            storage=MagicMock(spec=KnowledgeStorage),
+        )
+        knowledge.storage = None
+
+        with pytest.raises(ValueError, match="Storage is not initialized"):
+            await knowledge.areset()
+
+
+class TestAsyncStringKnowledgeSource:
+    """Tests for async StringKnowledgeSource operations."""
+
+    @pytest.mark.asyncio
+    async def test_aadd_saves_documents_asynchronously(self):
+        """Test that aadd chunks and saves documents asynchronously."""
+        mock_storage = MagicMock(spec=KnowledgeStorage)
+        mock_storage.asave = AsyncMock()
+
+        source = StringKnowledgeSource(content="Test content for async processing")
+        source.storage = mock_storage
+
+        await source.aadd()
+
+        mock_storage.asave.assert_called_once()
+        assert len(source.chunks) > 0
+
+    @pytest.mark.asyncio
+    async def test_aadd_raises_without_storage(self):
+        """Test that aadd raises ValueError when storage is not set."""
+        source = StringKnowledgeSource(content="Test content")
+        source.storage = None
+
+        with pytest.raises(ValueError, match="No storage found"):
+            await source.aadd()
+
+
+class TestAsyncBaseKnowledgeSource:
+    """Tests for async _asave_documents method."""
+
+    @pytest.mark.asyncio
+    async def test_asave_documents_calls_storage_asave(self):
+        """Test that _asave_documents calls storage.asave."""
+        mock_storage = MagicMock(spec=KnowledgeStorage)
+        mock_storage.asave = AsyncMock()
+
+        source = StringKnowledgeSource(content="Test")
+        source.storage = mock_storage
+        source.chunks = ["chunk1", "chunk2"]
+
+        await source._asave_documents()
+
+        mock_storage.asave.assert_called_once_with(["chunk1", "chunk2"])
+
+    @pytest.mark.asyncio
+    async def test_asave_documents_raises_without_storage(self):
+        """Test that _asave_documents raises ValueError when storage is None."""
+        source = StringKnowledgeSource(content="Test")
+        source.storage = None
+
+        with pytest.raises(ValueError, match="No storage found"):
+            await source._asave_documents()

lib/crewai/tests/memory/test_async_memory.py

@@ -0,0 +1,496 @@
+"""Tests for async memory operations."""
+
+import threading
+from collections import defaultdict
+from unittest.mock import ANY, AsyncMock, MagicMock, patch
+
+import pytest
+
+from crewai.agent import Agent
+from crewai.crew import Crew
+from crewai.events.event_bus import crewai_event_bus
+from crewai.events.types.memory_events import (
+    MemoryQueryCompletedEvent,
+    MemoryQueryStartedEvent,
+    MemorySaveCompletedEvent,
+    MemorySaveStartedEvent,
+)
+from crewai.memory.contextual.contextual_memory import ContextualMemory
+from crewai.memory.entity.entity_memory import EntityMemory
+from crewai.memory.entity.entity_memory_item import EntityMemoryItem
+from crewai.memory.external.external_memory import ExternalMemory
+from crewai.memory.long_term.long_term_memory import LongTermMemory
+from crewai.memory.long_term.long_term_memory_item import LongTermMemoryItem
+from crewai.memory.short_term.short_term_memory import ShortTermMemory
+from crewai.task import Task
+
+
+@pytest.fixture
+def mock_agent():
+    """Fixture to create a mock agent."""
+    return Agent(
+        role="Researcher",
+        goal="Search relevant data and provide results",
+        backstory="You are a researcher at a leading tech think tank.",
+        tools=[],
+        verbose=True,
+    )
+
+
+@pytest.fixture
+def mock_task(mock_agent):
+    """Fixture to create a mock task."""
+    return Task(
+        description="Perform a search on specific topics.",
+        expected_output="A list of relevant URLs based on the search query.",
+        agent=mock_agent,
+    )
+
+
+@pytest.fixture
+def short_term_memory(mock_agent, mock_task):
+    """Fixture to create a ShortTermMemory instance."""
+    return ShortTermMemory(crew=Crew(agents=[mock_agent], tasks=[mock_task]))
+
+
+@pytest.fixture
+def long_term_memory(tmp_path):
+    """Fixture to create a LongTermMemory instance."""
+    db_path = str(tmp_path / "test_ltm.db")
+    return LongTermMemory(path=db_path)
+
+
+@pytest.fixture
+def entity_memory(tmp_path, mock_agent, mock_task):
+    """Fixture to create an EntityMemory instance."""
+    return EntityMemory(
+        crew=Crew(agents=[mock_agent], tasks=[mock_task]),
+        path=str(tmp_path / "test_entities"),
+    )
+
+
+class TestAsyncShortTermMemory:
+    """Tests for async ShortTermMemory operations."""
+
+    @pytest.mark.asyncio
+    async def test_asave_emits_events(self, short_term_memory):
+        """Test that asave emits the correct events."""
+        events: dict[str, list] = defaultdict(list)
+        condition = threading.Condition()
+
+        @crewai_event_bus.on(MemorySaveStartedEvent)
+        def on_save_started(source, event):
+            with condition:
+                events["MemorySaveStartedEvent"].append(event)
+                condition.notify()
+
+        @crewai_event_bus.on(MemorySaveCompletedEvent)
+        def on_save_completed(source, event):
+            with condition:
+                events["MemorySaveCompletedEvent"].append(event)
+                condition.notify()
+
+        await short_term_memory.asave(
+            value="async test value",
+            metadata={"task": "async_test_task"},
+        )
+
+        with condition:
+            success = condition.wait_for(
+                lambda: len(events["MemorySaveStartedEvent"]) >= 1
+                and len(events["MemorySaveCompletedEvent"]) >= 1,
+                timeout=5,
+            )
+        assert success, "Timeout waiting for async save events"
+
+        assert len(events["MemorySaveStartedEvent"]) >= 1
+        assert len(events["MemorySaveCompletedEvent"]) >= 1
+        assert events["MemorySaveStartedEvent"][-1].value == "async test value"
+        assert events["MemorySaveStartedEvent"][-1].source_type == "short_term_memory"
+
+    @pytest.mark.asyncio
+    async def test_asearch_emits_events(self, short_term_memory):
+        """Test that asearch emits the correct events."""
+        events: dict[str, list] = defaultdict(list)
+        search_started = threading.Event()
+        search_completed = threading.Event()
+
+        with patch.object(short_term_memory.storage, "asearch", new_callable=AsyncMock, return_value=[]):
+
+            @crewai_event_bus.on(MemoryQueryStartedEvent)
+            def on_search_started(source, event):
+                events["MemoryQueryStartedEvent"].append(event)
+                search_started.set()
+
+            @crewai_event_bus.on(MemoryQueryCompletedEvent)
+            def on_search_completed(source, event):
+                events["MemoryQueryCompletedEvent"].append(event)
+                search_completed.set()
+
+            await short_term_memory.asearch(
+                query="async test query",
+                limit=3,
+                score_threshold=0.35,
+            )
+
+            assert search_started.wait(timeout=2), "Timeout waiting for search started event"
+            assert search_completed.wait(timeout=2), "Timeout waiting for search completed event"
+
+        assert len(events["MemoryQueryStartedEvent"]) >= 1
+        assert len(events["MemoryQueryCompletedEvent"]) >= 1
+        assert events["MemoryQueryStartedEvent"][-1].query == "async test query"
+        assert events["MemoryQueryStartedEvent"][-1].source_type == "short_term_memory"
+
+
+class TestAsyncLongTermMemory:
+    """Tests for async LongTermMemory operations."""
+
+    @pytest.mark.asyncio
+    async def test_asave_emits_events(self, long_term_memory):
+        """Test that asave emits the correct events."""
+        events: dict[str, list] = defaultdict(list)
+        condition = threading.Condition()
+
+        @crewai_event_bus.on(MemorySaveStartedEvent)
+        def on_save_started(source, event):
+            with condition:
+                events["MemorySaveStartedEvent"].append(event)
+                condition.notify()
+
+        @crewai_event_bus.on(MemorySaveCompletedEvent)
+        def on_save_completed(source, event):
+            with condition:
+                events["MemorySaveCompletedEvent"].append(event)
+                condition.notify()
+
+        item = LongTermMemoryItem(
+            task="async test task",
+            agent="test_agent",
+            expected_output="test output",
+            datetime="2024-01-01T00:00:00",
+            quality=0.9,
+            metadata={"task": "async test task", "quality": 0.9},
+        )
+
+        await long_term_memory.asave(item)
+
+        with condition:
+            success = condition.wait_for(
+                lambda: len(events["MemorySaveStartedEvent"]) >= 1
+                and len(events["MemorySaveCompletedEvent"]) >= 1,
+                timeout=5,
+            )
+        assert success, "Timeout waiting for async save events"
+
+        assert len(events["MemorySaveStartedEvent"]) >= 1
+        assert len(events["MemorySaveCompletedEvent"]) >= 1
+        assert events["MemorySaveStartedEvent"][-1].source_type == "long_term_memory"
+
+    @pytest.mark.asyncio
+    async def test_asearch_emits_events(self, long_term_memory):
+        """Test that asearch emits the correct events."""
+        events: dict[str, list] = defaultdict(list)
+        search_started = threading.Event()
+        search_completed = threading.Event()
+
+        @crewai_event_bus.on(MemoryQueryStartedEvent)
+        def on_search_started(source, event):
+            events["MemoryQueryStartedEvent"].append(event)
+            search_started.set()
+
+        @crewai_event_bus.on(MemoryQueryCompletedEvent)
+        def on_search_completed(source, event):
+            events["MemoryQueryCompletedEvent"].append(event)
+            search_completed.set()
+
+        await long_term_memory.asearch(task="async test task", latest_n=3)
+
+        assert search_started.wait(timeout=2), "Timeout waiting for search started event"
+        assert search_completed.wait(timeout=2), "Timeout waiting for search completed event"
+
+        assert len(events["MemoryQueryStartedEvent"]) >= 1
+        assert len(events["MemoryQueryCompletedEvent"]) >= 1
+        assert events["MemoryQueryStartedEvent"][-1].source_type == "long_term_memory"
+
+    @pytest.mark.asyncio
+    async def test_asave_and_asearch_integration(self, long_term_memory):
+        """Test that asave followed by asearch works correctly."""
+        item = LongTermMemoryItem(
+            task="integration test task",
+            agent="test_agent",
+            expected_output="test output",
+            datetime="2024-01-01T00:00:00",
+            quality=0.9,
+            metadata={"task": "integration test task", "quality": 0.9},
+        )
+
+        await long_term_memory.asave(item)
+        results = await long_term_memory.asearch(task="integration test task", latest_n=1)
+
+        assert results is not None
+        assert len(results) == 1
+        assert results[0]["metadata"]["agent"] == "test_agent"
+
+
+class TestAsyncEntityMemory:
+    """Tests for async EntityMemory operations."""
+
+    @pytest.mark.asyncio
+    async def test_asave_single_item_emits_events(self, entity_memory):
+        """Test that asave with a single item emits the correct events."""
+        events: dict[str, list] = defaultdict(list)
+        condition = threading.Condition()
+
+        @crewai_event_bus.on(MemorySaveStartedEvent)
+        def on_save_started(source, event):
+            with condition:
+                events["MemorySaveStartedEvent"].append(event)
+                condition.notify()
+
+        @crewai_event_bus.on(MemorySaveCompletedEvent)
+        def on_save_completed(source, event):
+            with condition:
+                events["MemorySaveCompletedEvent"].append(event)
+                condition.notify()
+
+        item = EntityMemoryItem(
+            name="TestEntity",
+            type="Person",
+            description="A test entity for async operations",
+            relationships="Related to other test entities",
+        )
+
+        await entity_memory.asave(item)
+
+        with condition:
+            success = condition.wait_for(
+                lambda: len(events["MemorySaveStartedEvent"]) >= 1
+                and len(events["MemorySaveCompletedEvent"]) >= 1,
+                timeout=5,
+            )
+        assert success, "Timeout waiting for async save events"
+
+        assert len(events["MemorySaveStartedEvent"]) >= 1
+        assert len(events["MemorySaveCompletedEvent"]) >= 1
+        assert events["MemorySaveStartedEvent"][-1].source_type == "entity_memory"
+
+    @pytest.mark.asyncio
+    async def test_asearch_emits_events(self, entity_memory):
+        """Test that asearch emits the correct events."""
+        events: dict[str, list] = defaultdict(list)
+        search_started = threading.Event()
+        search_completed = threading.Event()
+
+        @crewai_event_bus.on(MemoryQueryStartedEvent)
+        def on_search_started(source, event):
+            events["MemoryQueryStartedEvent"].append(event)
+            search_started.set()
+
+        @crewai_event_bus.on(MemoryQueryCompletedEvent)
+        def on_search_completed(source, event):
+            events["MemoryQueryCompletedEvent"].append(event)
+            search_completed.set()
+
+        await entity_memory.asearch(query="TestEntity", limit=5, score_threshold=0.6)
+
+        assert search_started.wait(timeout=2), "Timeout waiting for search started event"
+        assert search_completed.wait(timeout=2), "Timeout waiting for search completed event"
+
+        assert len(events["MemoryQueryStartedEvent"]) >= 1
+        assert len(events["MemoryQueryCompletedEvent"]) >= 1
+        assert events["MemoryQueryStartedEvent"][-1].source_type == "entity_memory"
+
+
+class TestAsyncContextualMemory:
+    """Tests for async ContextualMemory operations."""
+
+    @pytest.mark.asyncio
+    async def test_abuild_context_for_task_with_empty_query(self, mock_task):
+        """Test that abuild_context_for_task returns empty string for empty query."""
+        mock_task.description = ""
+        contextual_memory = ContextualMemory(
+            stm=None,
+            ltm=None,
+            em=None,
+            exm=None,
+        )
+
+        result = await contextual_memory.abuild_context_for_task(mock_task, "")
+        assert result == ""
+
+    @pytest.mark.asyncio
+    async def test_abuild_context_for_task_with_none_memories(self, mock_task):
+        """Test that abuild_context_for_task handles None memory sources."""
+        contextual_memory = ContextualMemory(
+            stm=None,
+            ltm=None,
+            em=None,
+            exm=None,
+        )
+
+        result = await contextual_memory.abuild_context_for_task(mock_task, "some context")
+        assert result == ""
+
+    @pytest.mark.asyncio
+    async def test_abuild_context_for_task_aggregates_results(self, mock_agent, mock_task):
+        """Test that abuild_context_for_task aggregates results from all memory sources."""
+        mock_stm = MagicMock(spec=ShortTermMemory)
+        mock_stm.asearch = AsyncMock(return_value=[{"content": "STM insight"}])
+
+        mock_ltm = MagicMock(spec=LongTermMemory)
+        mock_ltm.asearch = AsyncMock(
+            return_value=[{"metadata": {"suggestions": ["LTM suggestion"]}}]
+        )
+
+        mock_em = MagicMock(spec=EntityMemory)
+        mock_em.asearch = AsyncMock(return_value=[{"content": "Entity info"}])
+
+        mock_exm = MagicMock(spec=ExternalMemory)
+        mock_exm.asearch = AsyncMock(return_value=[{"content": "External memory"}])
+
+        contextual_memory = ContextualMemory(
+            stm=mock_stm,
+            ltm=mock_ltm,
+            em=mock_em,
+            exm=mock_exm,
+            agent=mock_agent,
+            task=mock_task,
+        )
+
+        result = await contextual_memory.abuild_context_for_task(mock_task, "additional context")
+
+        assert "Recent Insights:" in result
+        assert "STM insight" in result
+        assert "Historical Data:" in result
+        assert "LTM suggestion" in result
+        assert "Entities:" in result
+        assert "Entity info" in result
+        assert "External memories:" in result
+        assert "External memory" in result
+
+    @pytest.mark.asyncio
+    async def test_afetch_stm_context_returns_formatted_results(self, mock_agent, mock_task):
+        """Test that _afetch_stm_context returns properly formatted results."""
+        mock_stm = MagicMock(spec=ShortTermMemory)
+        mock_stm.asearch = AsyncMock(
+            return_value=[
+                {"content": "First insight"},
+                {"content": "Second insight"},
+            ]
+        )
+
+        contextual_memory = ContextualMemory(
+            stm=mock_stm,
+            ltm=None,
+            em=None,
+            exm=None,
+        )
+
+        result = await contextual_memory._afetch_stm_context("test query")
+
+        assert "Recent Insights:" in result
+        assert "- First insight" in result
+        assert "- Second insight" in result
+
+    @pytest.mark.asyncio
+    async def test_afetch_ltm_context_returns_formatted_results(self, mock_agent, mock_task):
+        """Test that _afetch_ltm_context returns properly formatted results."""
+        mock_ltm = MagicMock(spec=LongTermMemory)
+        mock_ltm.asearch = AsyncMock(
+            return_value=[
+                {"metadata": {"suggestions": ["Suggestion 1", "Suggestion 2"]}},
+            ]
+        )
+
+        contextual_memory = ContextualMemory(
+            stm=None,
+            ltm=mock_ltm,
+            em=None,
+            exm=None,
+        )
+
+        result = await contextual_memory._afetch_ltm_context("test task")
+
+        assert "Historical Data:" in result
+        assert "- Suggestion 1" in result
+        assert "- Suggestion 2" in result
+
+    @pytest.mark.asyncio
+    async def test_afetch_entity_context_returns_formatted_results(self, mock_agent, mock_task):
+        """Test that _afetch_entity_context returns properly formatted results."""
+        mock_em = MagicMock(spec=EntityMemory)
+        mock_em.asearch = AsyncMock(
+            return_value=[
+                {"content": "Entity A details"},
+                {"content": "Entity B details"},
+            ]
+        )
+
+        contextual_memory = ContextualMemory(
+            stm=None,
+            ltm=None,
+            em=mock_em,
+            exm=None,
+        )
+
+        result = await contextual_memory._afetch_entity_context("test query")
+
+        assert "Entities:" in result
+        assert "- Entity A details" in result
+        assert "- Entity B details" in result
+
+    @pytest.mark.asyncio
+    async def test_afetch_external_context_returns_formatted_results(self):
+        """Test that _afetch_external_context returns properly formatted results."""
+        mock_exm = MagicMock(spec=ExternalMemory)
+        mock_exm.asearch = AsyncMock(
+            return_value=[
+                {"content": "External data 1"},
+                {"content": "External data 2"},
+            ]
+        )
+
+        contextual_memory = ContextualMemory(
+            stm=None,
+            ltm=None,
+            em=None,
+            exm=mock_exm,
+        )
+
+        result = await contextual_memory._afetch_external_context("test query")
+
+        assert "External memories:" in result
+        assert "- External data 1" in result
+        assert "- External data 2" in result
+
+    @pytest.mark.asyncio
+    async def test_afetch_methods_return_empty_for_empty_results(self):
+        """Test that async fetch methods return empty string for no results."""
+        mock_stm = MagicMock(spec=ShortTermMemory)
+        mock_stm.asearch = AsyncMock(return_value=[])
+
+        mock_ltm = MagicMock(spec=LongTermMemory)
+        mock_ltm.asearch = AsyncMock(return_value=[])
+
+        mock_em = MagicMock(spec=EntityMemory)
+        mock_em.asearch = AsyncMock(return_value=[])
+
+        mock_exm = MagicMock(spec=ExternalMemory)
+        mock_exm.asearch = AsyncMock(return_value=[])
+
+        contextual_memory = ContextualMemory(
+            stm=mock_stm,
+            ltm=mock_ltm,
+            em=mock_em,
+            exm=mock_exm,
+        )
+
+        stm_result = await contextual_memory._afetch_stm_context("query")
+        ltm_result = await contextual_memory._afetch_ltm_context("task")
+        em_result = await contextual_memory._afetch_entity_context("query")
+        exm_result = await contextual_memory._afetch_external_context("query")
+
+        assert stm_result == ""
+        assert ltm_result is None
+        assert em_result == ""
+        assert exm_result == ""

lib/crewai/tests/task/test_async_task.py

@@ -0,0 +1,386 @@
+"""Tests for async task execution."""
+
+import pytest
+from unittest.mock import AsyncMock, MagicMock, patch
+
+from crewai.agent import Agent
+from crewai.task import Task
+from crewai.tasks.task_output import TaskOutput
+from crewai.tasks.output_format import OutputFormat
+
+
+@pytest.fixture
+def test_agent() -> Agent:
+    """Create a test agent."""
+    return Agent(
+        role="Test Agent",
+        goal="Test goal",
+        backstory="Test backstory",
+        llm="gpt-4o-mini",
+        verbose=False,
+    )
+
+
+class TestAsyncTaskExecution:
+    """Tests for async task execution methods."""
+
+    @pytest.mark.asyncio
+    @patch("crewai.Agent.aexecute_task", new_callable=AsyncMock)
+    async def test_aexecute_sync_basic(
+        self, mock_execute: AsyncMock, test_agent: Agent
+    ) -> None:
+        """Test basic async task execution."""
+        mock_execute.return_value = "Async task result"
+        task = Task(
+            description="Test task description",
+            expected_output="Test expected output",
+            agent=test_agent,
+        )
+
+        result = await task.aexecute_sync()
+
+        assert result is not None
+        assert isinstance(result, TaskOutput)
+        assert result.raw == "Async task result"
+        assert result.agent == "Test Agent"
+        mock_execute.assert_called_once()
+
+    @pytest.mark.asyncio
+    @patch("crewai.Agent.aexecute_task", new_callable=AsyncMock)
+    async def test_aexecute_sync_with_context(
+        self, mock_execute: AsyncMock, test_agent: Agent
+    ) -> None:
+        """Test async task execution with context."""
+        mock_execute.return_value = "Async result"
+        task = Task(
+            description="Test task description",
+            expected_output="Test expected output",
+            agent=test_agent,
+        )
+
+        context = "Additional context for the task"
+        result = await task.aexecute_sync(context=context)
+
+        assert result is not None
+        assert task.prompt_context == context
+        mock_execute.assert_called_once()
+        call_kwargs = mock_execute.call_args[1]
+        assert call_kwargs["context"] == context
+
+    @pytest.mark.asyncio
+    @patch("crewai.Agent.aexecute_task", new_callable=AsyncMock)
+    async def test_aexecute_sync_with_tools(
+        self, mock_execute: AsyncMock, test_agent: Agent
+    ) -> None:
+        """Test async task execution with custom tools."""
+        mock_execute.return_value = "Async result"
+        task = Task(
+            description="Test task description",
+            expected_output="Test expected output",
+            agent=test_agent,
+        )
+
+        mock_tool = MagicMock()
+        mock_tool.name = "test_tool"
+
+        result = await task.aexecute_sync(tools=[mock_tool])
+
+        assert result is not None
+        mock_execute.assert_called_once()
+        call_kwargs = mock_execute.call_args[1]
+        assert mock_tool in call_kwargs["tools"]
+
+    @pytest.mark.asyncio
+    @patch("crewai.Agent.aexecute_task", new_callable=AsyncMock)
+    async def test_aexecute_sync_sets_start_and_end_time(
+        self, mock_execute: AsyncMock, test_agent: Agent
+    ) -> None:
+        """Test that async execution sets start and end times."""
+        mock_execute.return_value = "Async result"
+        task = Task(
+            description="Test task description",
+            expected_output="Test expected output",
+            agent=test_agent,
+        )
+
+        assert task.start_time is None
+        assert task.end_time is None
+
+        await task.aexecute_sync()
+
+        assert task.start_time is not None
+        assert task.end_time is not None
+        assert task.end_time >= task.start_time
+
+    @pytest.mark.asyncio
+    @patch("crewai.Agent.aexecute_task", new_callable=AsyncMock)
+    async def test_aexecute_sync_stores_output(
+        self, mock_execute: AsyncMock, test_agent: Agent
+    ) -> None:
+        """Test that async execution stores the output."""
+        mock_execute.return_value = "Async task result"
+        task = Task(
+            description="Test task description",
+            expected_output="Test expected output",
+            agent=test_agent,
+        )
+
+        assert task.output is None
+
+        await task.aexecute_sync()
+
+        assert task.output is not None
+        assert task.output.raw == "Async task result"
+
+    @pytest.mark.asyncio
+    @patch("crewai.Agent.aexecute_task", new_callable=AsyncMock)
+    async def test_aexecute_sync_adds_agent_to_processed_by(
+        self, mock_execute: AsyncMock, test_agent: Agent
+    ) -> None:
+        """Test that async execution adds agent to processed_by_agents."""
+        mock_execute.return_value = "Async result"
+        task = Task(
+            description="Test task description",
+            expected_output="Test expected output",
+            agent=test_agent,
+        )
+
+        assert len(task.processed_by_agents) == 0
+
+        await task.aexecute_sync()
+
+        assert "Test Agent" in task.processed_by_agents
+
+    @pytest.mark.asyncio
+    @patch("crewai.Agent.aexecute_task", new_callable=AsyncMock)
+    async def test_aexecute_sync_calls_callback(
+        self, mock_execute: AsyncMock, test_agent: Agent
+    ) -> None:
+        """Test that async execution calls the callback."""
+        mock_execute.return_value = "Async result"
+        callback = MagicMock()
+        task = Task(
+            description="Test task description",
+            expected_output="Test expected output",
+            agent=test_agent,
+            callback=callback,
+        )
+
+        await task.aexecute_sync()
+
+        callback.assert_called_once()
+        assert isinstance(callback.call_args[0][0], TaskOutput)
+
+    @pytest.mark.asyncio
+    async def test_aexecute_sync_without_agent_raises(self) -> None:
+        """Test that async execution without agent raises exception."""
+        task = Task(
+            description="Test task",
+            expected_output="Test output",
+        )
+
+        with pytest.raises(Exception) as exc_info:
+            await task.aexecute_sync()
+
+        assert "has no agent assigned" in str(exc_info.value)
+
+    @pytest.mark.asyncio
+    @patch("crewai.Agent.aexecute_task", new_callable=AsyncMock)
+    async def test_aexecute_sync_with_different_agent(
+        self, mock_execute: AsyncMock, test_agent: Agent
+    ) -> None:
+        """Test async execution with a different agent than assigned."""
+        mock_execute.return_value = "Other agent result"
+        task = Task(
+            description="Test task description",
+            expected_output="Test expected output",
+            agent=test_agent,
+        )
+
+        other_agent = Agent(
+            role="Other Agent",
+            goal="Other goal",
+            backstory="Other backstory",
+            llm="gpt-4o-mini",
+            verbose=False,
+        )
+
+        result = await task.aexecute_sync(agent=other_agent)
+
+        assert result.raw == "Other agent result"
+        assert result.agent == "Other Agent"
+        mock_execute.assert_called_once()
+
+    @pytest.mark.asyncio
+    @patch("crewai.Agent.aexecute_task", new_callable=AsyncMock)
+    async def test_aexecute_sync_handles_exception(
+        self, mock_execute: AsyncMock, test_agent: Agent
+    ) -> None:
+        """Test that async execution handles exceptions properly."""
+        mock_execute.side_effect = RuntimeError("Test error")
+        task = Task(
+            description="Test task description",
+            expected_output="Test expected output",
+            agent=test_agent,
+        )
+
+        with pytest.raises(RuntimeError) as exc_info:
+            await task.aexecute_sync()
+
+        assert "Test error" in str(exc_info.value)
+        assert task.end_time is not None
+
+
+class TestAsyncGuardrails:
+    """Tests for async guardrail invocation."""
+
+    @pytest.mark.asyncio
+    @patch("crewai.Agent.aexecute_task", new_callable=AsyncMock)
+    async def test_ainvoke_guardrail_success(
+        self, mock_execute: AsyncMock, test_agent: Agent
+    ) -> None:
+        """Test async guardrail invocation with successful validation."""
+        mock_execute.return_value = "Async task result"
+
+        def guardrail_fn(output: TaskOutput) -> tuple[bool, str]:
+            return True, output.raw
+
+        task = Task(
+            description="Test task",
+            expected_output="Test output",
+            agent=test_agent,
+            guardrail=guardrail_fn,
+        )
+
+        result = await task.aexecute_sync()
+
+        assert result is not None
+        assert result.raw == "Async task result"
+
+    @pytest.mark.asyncio
+    @patch("crewai.Agent.aexecute_task", new_callable=AsyncMock)
+    async def test_ainvoke_guardrail_failure_then_success(
+        self, mock_execute: AsyncMock, test_agent: Agent
+    ) -> None:
+        """Test async guardrail that fails then succeeds on retry."""
+        mock_execute.side_effect = ["First result", "Second result"]
+        call_count = 0
+
+        def guardrail_fn(output: TaskOutput) -> tuple[bool, str]:
+            nonlocal call_count
+            call_count += 1
+            if call_count == 1:
+                return False, "First attempt failed"
+            return True, output.raw
+
+        task = Task(
+            description="Test task",
+            expected_output="Test output",
+            agent=test_agent,
+            guardrail=guardrail_fn,
+        )
+
+        result = await task.aexecute_sync()
+
+        assert result is not None
+        assert call_count == 2
+
+    @pytest.mark.asyncio
+    @patch("crewai.Agent.aexecute_task", new_callable=AsyncMock)
+    async def test_ainvoke_guardrail_max_retries_exceeded(
+        self, mock_execute: AsyncMock, test_agent: Agent
+    ) -> None:
+        """Test async guardrail that exceeds max retries."""
+        mock_execute.return_value = "Async result"
+
+        def guardrail_fn(output: TaskOutput) -> tuple[bool, str]:
+            return False, "Always fails"
+
+        task = Task(
+            description="Test task",
+            expected_output="Test output",
+            agent=test_agent,
+            guardrail=guardrail_fn,
+            guardrail_max_retries=2,
+        )
+
+        with pytest.raises(Exception) as exc_info:
+            await task.aexecute_sync()
+
+        assert "validation after" in str(exc_info.value)
+        assert "2 retries" in str(exc_info.value)
+
+    @pytest.mark.asyncio
+    @patch("crewai.Agent.aexecute_task", new_callable=AsyncMock)
+    async def test_ainvoke_multiple_guardrails(
+        self, mock_execute: AsyncMock, test_agent: Agent
+    ) -> None:
+        """Test async execution with multiple guardrails."""
+        mock_execute.return_value = "Async result"
+        guardrail1_called = False
+        guardrail2_called = False
+
+        def guardrail1(output: TaskOutput) -> tuple[bool, str]:
+            nonlocal guardrail1_called
+            guardrail1_called = True
+            return True, output.raw
+
+        def guardrail2(output: TaskOutput) -> tuple[bool, str]:
+            nonlocal guardrail2_called
+            guardrail2_called = True
+            return True, output.raw
+
+        task = Task(
+            description="Test task",
+            expected_output="Test output",
+            agent=test_agent,
+            guardrails=[guardrail1, guardrail2],
+        )
+
+        await task.aexecute_sync()
+
+        assert guardrail1_called
+        assert guardrail2_called
+
+
+class TestAsyncTaskOutput:
+    """Tests for async task output handling."""
+
+    @pytest.mark.asyncio
+    @patch("crewai.Agent.aexecute_task", new_callable=AsyncMock)
+    async def test_aexecute_sync_output_format_raw(
+        self, mock_execute: AsyncMock, test_agent: Agent
+    ) -> None:
+        """Test async execution with raw output format."""
+        mock_execute.return_value = '{"key": "value"}'
+        task = Task(
+            description="Test task",
+            expected_output="Test output",
+            agent=test_agent,
+        )
+
+        result = await task.aexecute_sync()
+
+        assert result.output_format == OutputFormat.RAW
+
+    @pytest.mark.asyncio
+    @patch("crewai.Agent.aexecute_task", new_callable=AsyncMock)
+    async def test_aexecute_sync_task_output_attributes(
+        self, mock_execute: AsyncMock, test_agent: Agent
+    ) -> None:
+        """Test that task output has correct attributes."""
+        mock_execute.return_value = "Test result"
+        task = Task(
+            description="Test description",
+            expected_output="Test expected",
+            agent=test_agent,
+            name="Test Task Name",
+        )
+
+        result = await task.aexecute_sync()
+
+        assert result.name == "Test Task Name"
+        assert result.description == "Test description"
+        assert result.expected_output == "Test expected"
+        assert result.raw == "Test result"
+        assert result.agent == "Test Agent"

lib/crewai/tests/test_flow.py

@@ -1492,3 +1492,144 @@ def test_flow_copy_state_with_dict_state():
 
     flow.state["test"] = "modified"
     assert copied_state["test"] == "value"
+
+
+class TestFlowAkickoff:
+    """Tests for the native async akickoff method."""
+
+    @pytest.mark.asyncio
+    async def test_akickoff_basic(self):
+        """Test basic akickoff execution."""
+        execution_order = []
+
+        class SimpleFlow(Flow):
+            @start()
+            def step_1(self):
+                execution_order.append("step_1")
+                return "step_1_result"
+
+            @listen(step_1)
+            def step_2(self, result):
+                execution_order.append("step_2")
+                return "final_result"
+
+        flow = SimpleFlow()
+        result = await flow.akickoff()
+
+        assert execution_order == ["step_1", "step_2"]
+        assert result == "final_result"
+
+    @pytest.mark.asyncio
+    async def test_akickoff_with_inputs(self):
+        """Test akickoff with inputs."""
+
+        class InputFlow(Flow):
+            @start()
+            def process_input(self):
+                return self.state.get("value", "default")
+
+        flow = InputFlow()
+        result = await flow.akickoff(inputs={"value": "custom_value"})
+
+        assert result == "custom_value"
+
+    @pytest.mark.asyncio
+    async def test_akickoff_with_async_methods(self):
+        """Test akickoff with async flow methods."""
+        execution_order = []
+
+        class AsyncMethodFlow(Flow):
+            @start()
+            async def async_step_1(self):
+                execution_order.append("async_step_1")
+                await asyncio.sleep(0.01)
+                return "async_result"
+
+            @listen(async_step_1)
+            async def async_step_2(self, result):
+                execution_order.append("async_step_2")
+                await asyncio.sleep(0.01)
+                return f"final_{result}"
+
+        flow = AsyncMethodFlow()
+        result = await flow.akickoff()
+
+        assert execution_order == ["async_step_1", "async_step_2"]
+        assert result == "final_async_result"
+
+    @pytest.mark.asyncio
+    async def test_akickoff_equivalent_to_kickoff_async(self):
+        """Test that akickoff produces the same results as kickoff_async."""
+        execution_order_akickoff = []
+        execution_order_kickoff_async = []
+
+        class TestFlow(Flow):
+            def __init__(self, execution_list):
+                super().__init__()
+                self._execution_list = execution_list
+
+            @start()
+            def step_1(self):
+                self._execution_list.append("step_1")
+                return "result_1"
+
+            @listen(step_1)
+            def step_2(self, result):
+                self._execution_list.append("step_2")
+                return "result_2"
+
+        flow1 = TestFlow(execution_order_akickoff)
+        result1 = await flow1.akickoff()
+
+        flow2 = TestFlow(execution_order_kickoff_async)
+        result2 = await flow2.kickoff_async()
+
+        assert execution_order_akickoff == execution_order_kickoff_async
+        assert result1 == result2
+
+    @pytest.mark.asyncio
+    async def test_akickoff_with_multiple_starts(self):
+        """Test akickoff with multiple start methods."""
+        execution_order = []
+
+        class MultiStartFlow(Flow):
+            @start()
+            def start_a(self):
+                execution_order.append("start_a")
+
+            @start()
+            def start_b(self):
+                execution_order.append("start_b")
+
+        flow = MultiStartFlow()
+        await flow.akickoff()
+
+        assert "start_a" in execution_order
+        assert "start_b" in execution_order
+
+    @pytest.mark.asyncio
+    async def test_akickoff_with_router(self):
+        """Test akickoff with router method."""
+        execution_order = []
+
+        class RouterFlow(Flow):
+            @start()
+            def begin(self):
+                execution_order.append("begin")
+                return "data"
+
+            @router(begin)
+            def route(self, data):
+                execution_order.append("route")
+                return "PATH_A"
+
+            @listen("PATH_A")
+            def handle_path_a(self):
+                execution_order.append("path_a")
+                return "path_a_result"
+
+        flow = RouterFlow()
+        result = await flow.akickoff()
+
+        assert execution_order == ["begin", "route", "path_a"]
+        assert result == "path_a_result"

uv.lock

@@ -247,6 +247,18 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/fb/76/641ae371508676492379f16e2fa48f4e2c11741bd63c48be4b12a6b09cba/aiosignal-1.4.0-py3-none-any.whl", hash = "sha256:053243f8b92b990551949e63930a839ff0cf0b0ebbe0597b0f3fb19e1a0fe82e", size = 7490, upload-time = "2025-07-03T22:54:42.156Z" },
 ]
 
+[[package]]
+name = "aiosqlite"
+version = "0.21.0"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "typing-extensions" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/13/7d/8bca2bf9a247c2c5dfeec1d7a5f40db6518f88d314b8bca9da29670d2671/aiosqlite-0.21.0.tar.gz", hash = "sha256:131bb8056daa3bc875608c631c678cda73922a2d4ba8aec373b19f18c17e7aa3", size = 13454, upload-time = "2025-02-03T07:30:16.235Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/f5/10/6c25ed6de94c49f88a91fa5018cb4c0f3625f31d5be9f771ebe5cc7cd506/aiosqlite-0.21.0-py3-none-any.whl", hash = "sha256:2549cf4057f95f53dcba16f2b64e8e2791d7e1adedb13197dd8ed77bb226d7d0", size = 15792, upload-time = "2025-02-03T07:30:13.6Z" },
+]
+
 [[package]]
 name = "annotated-doc"
 version = "0.0.4"
@@ -1100,6 +1112,7 @@ wheels = [
 name = "crewai"
 source = { editable = "lib/crewai" }
 dependencies = [
+    { name = "aiosqlite" },
     { name = "appdirs" },
     { name = "chromadb" },
     { name = "click" },
@@ -1183,6 +1196,7 @@ watson = [
 requires-dist = [
     { name = "a2a-sdk", marker = "extra == 'a2a'", specifier = "~=0.3.10" },
     { name = "aiobotocore", marker = "extra == 'aws'", specifier = "~=2.25.2" },
+    { name = "aiosqlite", specifier = "~=0.21.0" },
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