Lorenze/enh decouple executor from crew (#4209)

* wip restrcuturing agent executor and liteagent

* fix: handle None task in AgentExecutor to prevent errors

Added a check to ensure that if the task is None, the method returns early without attempting to access task properties. This change improves the robustness of the AgentExecutor by preventing potential errors when the task is not set.

* refactor: streamline AgentExecutor initialization by removing redundant parameters

Updated the Agent class to simplify the initialization of the AgentExecutor by removing unnecessary task and crew parameters in standalone mode. This change enhances code clarity and maintains backward compatibility by ensuring that the executor is correctly configured without redundant assignments.

* ensure executors work inside a flow due to flow in flow async structure

* refactor: enhance agent kickoff preparation by separating common logic

Updated the Agent class to introduce a new private method  that consolidates the common setup logic for both synchronous and asynchronous kickoff executions. This change improves code clarity and maintainability by reducing redundancy in the kickoff process, while ensuring that the agent can still execute effectively within both standalone and flow contexts.

* linting and tests

* fix test

* refactor: improve test for Agent kickoff parameters

Updated the test for the Agent class to ensure that the kickoff method correctly preserves parameters. The test now verifies the configuration of the agent after kickoff, enhancing clarity and maintainability. Additionally, the test for asynchronous kickoff within a flow context has been updated to reflect the Agent class instead of LiteAgent.

* refactor: update test task guardrail process output for improved validation

Refactored the test for task guardrail process output to enhance the validation of the output against the OpenAPI schema. The changes include a more structured request body and updated response handling to ensure compliance with the guardrail requirements. This update aims to improve the clarity and reliability of the test cases, ensuring that task outputs are correctly validated and feedback is appropriately provided.

* test fix cassette

* test fix cassette

* working

* working cassette

* refactor: streamline agent execution and enhance flow compatibility

Refactored the Agent class to simplify the execution method by removing the event loop check and clarifying the behavior when called from synchronous and asynchronous contexts. The changes ensure that the method operates seamlessly within flow methods, improving clarity in the documentation. Additionally, updated the AgentExecutor to set the response model to None, enhancing flexibility. New test cassettes were added to validate the functionality of agents within flow contexts, ensuring robust testing for both synchronous and asynchronous operations.

* fixed cassette

* Enhance Flow Execution Logic

- Introduced conditional execution for start methods in the Flow class.
- Unconditional start methods are prioritized during kickoff, while conditional starts are executed only if no unconditional starts are present.
- Improved handling of cyclic flows by allowing re-execution of conditional start methods triggered by routers.
- Added checks to continue execution chains for completed conditional starts.

These changes improve the flexibility and control of flow execution, ensuring that the correct methods are triggered based on the defined conditions.

* Enhance Agent and Flow Execution Logic

- Updated the Agent class to automatically detect the event loop and return a coroutine when called within a Flow, simplifying async handling for users.
- Modified Flow class to execute listeners sequentially, preventing race conditions on shared state during listener execution.
- Improved handling of coroutine results from synchronous methods, ensuring proper execution flow and state management.

These changes enhance the overall execution logic and user experience when working with agents and flows in CrewAI.

* Enhance Flow Listener Logic and Agent Imports

- Updated the Flow class to track fired OR listeners, ensuring that multi-source OR listeners only trigger once during execution. This prevents redundant executions and improves flow efficiency.
- Cleared fired OR listeners during cyclic flow resets to allow re-execution in new cycles.
- Modified the Agent class imports to include Coroutine from collections.abc, enhancing type handling for asynchronous operations.

These changes improve the control and performance of flow execution in CrewAI, ensuring more predictable behavior in complex scenarios.

* adjusted test due to new cassette

* ensure we dont finalize batch on just a liteagent finishing

* feat: cancellable parallelized flow methods

* feat: allow methods to be cancelled & run parallelized

* feat: ensure state is thread safe through proxy

* fix: check for proxy state

* fix: mimic BaseModel method

* chore: update final attr checks; test

* better description

* fix test

* chore: update test assumptions

* extra

---------

Co-authored-by: Greyson LaLonde <greyson.r.lalonde@gmail.com>

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

@@ -1,7 +1,7 @@
 from __future__ import annotations
 
 import asyncio
-from collections.abc import Callable, Sequence
+from collections.abc import Callable, Coroutine, Sequence
 import shutil
 import subprocess
 import time
@@ -34,6 +34,11 @@ from crewai.agents.agent_builder.base_agent import BaseAgent
 from crewai.agents.cache.cache_handler import CacheHandler
 from crewai.agents.crew_agent_executor import CrewAgentExecutor
 from crewai.events.event_bus import crewai_event_bus
+from crewai.events.types.agent_events import (
+    LiteAgentExecutionCompletedEvent,
+    LiteAgentExecutionErrorEvent,
+    LiteAgentExecutionStartedEvent,
+)
 from crewai.events.types.knowledge_events import (
     KnowledgeQueryCompletedEvent,
     KnowledgeQueryFailedEvent,
@@ -43,10 +48,10 @@ from crewai.events.types.memory_events import (
     MemoryRetrievalCompletedEvent,
     MemoryRetrievalStartedEvent,
 )
-from crewai.experimental.crew_agent_executor_flow import CrewAgentExecutorFlow
+from crewai.experimental.agent_executor import AgentExecutor
 from crewai.knowledge.knowledge import Knowledge
 from crewai.knowledge.source.base_knowledge_source import BaseKnowledgeSource
-from crewai.lite_agent import LiteAgent
+from crewai.lite_agent_output import LiteAgentOutput
 from crewai.llms.base_llm import BaseLLM
 from crewai.mcp import (
     MCPClient,
@@ -64,15 +69,18 @@ from crewai.security.fingerprint import Fingerprint
 from crewai.tools.agent_tools.agent_tools import AgentTools
 from crewai.utilities.agent_utils import (
     get_tool_names,
+    is_inside_event_loop,
     load_agent_from_repository,
     parse_tools,
     render_text_description_and_args,
 )
 from crewai.utilities.constants import TRAINED_AGENTS_DATA_FILE, TRAINING_DATA_FILE
-from crewai.utilities.converter import Converter
+from crewai.utilities.converter import Converter, ConverterError
+from crewai.utilities.guardrail import process_guardrail
 from crewai.utilities.guardrail_types import GuardrailType
 from crewai.utilities.llm_utils import create_llm
 from crewai.utilities.prompts import Prompts, StandardPromptResult, SystemPromptResult
+from crewai.utilities.pydantic_schema_utils import generate_model_description
 from crewai.utilities.token_counter_callback import TokenCalcHandler
 from crewai.utilities.training_handler import CrewTrainingHandler
 
@@ -89,9 +97,9 @@ if TYPE_CHECKING:
     from crewai_tools import CodeInterpreterTool
 
     from crewai.agents.agent_builder.base_agent import PlatformAppOrAction
-    from crewai.lite_agent_output import LiteAgentOutput
     from crewai.task import Task
     from crewai.tools.base_tool import BaseTool
+    from crewai.tools.structured_tool import CrewStructuredTool
     from crewai.utilities.types import LLMMessage
 
 
@@ -113,7 +121,7 @@ class Agent(BaseAgent):
     The agent can also have memory, can operate in verbose mode, and can delegate tasks to other agents.
 
     Attributes:
-            agent_executor: An instance of the CrewAgentExecutor or CrewAgentExecutorFlow class.
+            agent_executor: An instance of the CrewAgentExecutor or AgentExecutor class.
             role: The role of the agent.
             goal: The objective of the agent.
             backstory: The backstory of the agent.
@@ -238,9 +246,9 @@ class Agent(BaseAgent):
         Can be a single A2AConfig/A2AClientConfig/A2AServerConfig, or a list of any number of A2AConfig/A2AClientConfig with a single A2AServerConfig.
         """,
     )
-    executor_class: type[CrewAgentExecutor] | type[CrewAgentExecutorFlow] = Field(
+    executor_class: type[CrewAgentExecutor] | type[AgentExecutor] = Field(
         default=CrewAgentExecutor,
-        description="Class to use for the agent executor. Defaults to CrewAgentExecutor, can optionally use CrewAgentExecutorFlow.",
+        description="Class to use for the agent executor. Defaults to CrewAgentExecutor, can optionally use AgentExecutor.",
     )
 
     @model_validator(mode="before")
@@ -1583,26 +1591,25 @@ class Agent(BaseAgent):
             )
             return None
 
-    def kickoff(
+    def _prepare_kickoff(
         self,
         messages: str | list[LLMMessage],
         response_format: type[Any] | None = None,
-    ) -> LiteAgentOutput:
-        """
-        Execute the agent with the given messages using a LiteAgent instance.
+    ) -> tuple[AgentExecutor, dict[str, str], dict[str, Any], list[CrewStructuredTool]]:
+        """Prepare common setup for kickoff execution.
 
-        This method is useful when you want to use the Agent configuration but
-        with the simpler and more direct execution flow of LiteAgent.
+        This method handles all the common preparation logic shared between
+        kickoff() and kickoff_async(), including tool processing, prompt building,
+        executor creation, and input formatting.
 
         Args:
             messages: Either a string query or a list of message dictionaries.
-                     If a string is provided, it will be converted to a user message.
-                     If a list is provided, each dict should have 'role' and 'content' keys.
             response_format: Optional Pydantic model for structured output.
 
         Returns:
-            LiteAgentOutput: The result of the agent execution.
+            Tuple of (executor, inputs, agent_info, parsed_tools) ready for execution.
         """
+        # Process platform apps and MCP tools
         if self.apps:
             platform_tools = self.get_platform_tools(self.apps)
             if platform_tools and self.tools is not None:
@@ -1612,25 +1619,359 @@ class Agent(BaseAgent):
             if mcps and self.tools is not None:
                 self.tools.extend(mcps)
 
-        lite_agent = LiteAgent(
-            id=self.id,
-            role=self.role,
-            goal=self.goal,
-            backstory=self.backstory,
-            llm=self.llm,
-            tools=self.tools or [],
-            max_iterations=self.max_iter,
-            max_execution_time=self.max_execution_time,
-            respect_context_window=self.respect_context_window,
-            verbose=self.verbose,
-            response_format=response_format,
+        # Prepare tools
+        raw_tools: list[BaseTool] = self.tools or []
+        parsed_tools = parse_tools(raw_tools)
+
+        # Build agent_info for backward-compatible event emission
+        agent_info = {
+            "id": self.id,
+            "role": self.role,
+            "goal": self.goal,
+            "backstory": self.backstory,
+            "tools": raw_tools,
+            "verbose": self.verbose,
+        }
+
+        # Build prompt for standalone execution
+        prompt = Prompts(
+            agent=self,
+            has_tools=len(raw_tools) > 0,
             i18n=self.i18n,
-            original_agent=self,
-            guardrail=self.guardrail,
-            guardrail_max_retries=self.guardrail_max_retries,
+            use_system_prompt=self.use_system_prompt,
+            system_template=self.system_template,
+            prompt_template=self.prompt_template,
+            response_template=self.response_template,
+        ).task_execution()
+
+        # Prepare stop words
+        stop_words = [self.i18n.slice("observation")]
+        if self.response_template:
+            stop_words.append(
+                self.response_template.split("{{ .Response }}")[1].strip()
+            )
+
+        # Get RPM limit function
+        rpm_limit_fn = (
+            self._rpm_controller.check_or_wait if self._rpm_controller else None
         )
 
-        return lite_agent.kickoff(messages)
+        # Create the executor for standalone mode (no crew, no task)
+        executor = AgentExecutor(
+            task=None,
+            crew=None,
+            llm=cast(BaseLLM, self.llm),
+            agent=self,
+            prompt=prompt,
+            max_iter=self.max_iter,
+            tools=parsed_tools,
+            tools_names=get_tool_names(parsed_tools),
+            stop_words=stop_words,
+            tools_description=render_text_description_and_args(parsed_tools),
+            tools_handler=self.tools_handler,
+            original_tools=raw_tools,
+            step_callback=self.step_callback,
+            function_calling_llm=self.function_calling_llm,
+            respect_context_window=self.respect_context_window,
+            request_within_rpm_limit=rpm_limit_fn,
+            callbacks=[TokenCalcHandler(self._token_process)],
+            response_model=response_format,
+            i18n=self.i18n,
+        )
+
+        # Format messages
+        if isinstance(messages, str):
+            formatted_messages = messages
+        else:
+            formatted_messages = "\n".join(
+                str(msg.get("content", "")) for msg in messages if msg.get("content")
+            )
+
+        # Build the input dict for the executor
+        inputs = {
+            "input": formatted_messages,
+            "tool_names": get_tool_names(parsed_tools),
+            "tools": render_text_description_and_args(parsed_tools),
+        }
+
+        return executor, inputs, agent_info, parsed_tools
+
+    def kickoff(
+        self,
+        messages: str | list[LLMMessage],
+        response_format: type[Any] | None = None,
+    ) -> LiteAgentOutput | Coroutine[Any, Any, LiteAgentOutput]:
+        """
+        Execute the agent with the given messages using the AgentExecutor.
+
+        This method provides standalone agent execution without requiring a Crew.
+        It supports tools, response formatting, and guardrails.
+
+        When called from within a Flow (sync or async method), this automatically
+        detects the event loop and returns a coroutine that the Flow framework
+        awaits. Users don't need to handle async explicitly.
+
+        Args:
+            messages: Either a string query or a list of message dictionaries.
+                     If a string is provided, it will be converted to a user message.
+                     If a list is provided, each dict should have 'role' and 'content' keys.
+            response_format: Optional Pydantic model for structured output.
+
+        Returns:
+            LiteAgentOutput: The result of the agent execution.
+            When inside a Flow, returns a coroutine that resolves to LiteAgentOutput.
+
+        Note:
+            For explicit async usage outside of Flow, use kickoff_async() directly.
+        """
+        # Magic auto-async: if inside event loop (e.g., inside a Flow),
+        # return coroutine for Flow to await
+        if is_inside_event_loop():
+            return self.kickoff_async(messages, response_format)
+
+        executor, inputs, agent_info, parsed_tools = self._prepare_kickoff(
+            messages, response_format
+        )
+
+        try:
+            crewai_event_bus.emit(
+                self,
+                event=LiteAgentExecutionStartedEvent(
+                    agent_info=agent_info,
+                    tools=parsed_tools,
+                    messages=messages,
+                ),
+            )
+
+            output = self._execute_and_build_output(executor, inputs, response_format)
+
+            if self.guardrail is not None:
+                output = self._process_kickoff_guardrail(
+                    output=output,
+                    executor=executor,
+                    inputs=inputs,
+                    response_format=response_format,
+                )
+
+            crewai_event_bus.emit(
+                self,
+                event=LiteAgentExecutionCompletedEvent(
+                    agent_info=agent_info,
+                    output=output.raw,
+                ),
+            )
+
+            return output
+
+        except Exception as e:
+            crewai_event_bus.emit(
+                self,
+                event=LiteAgentExecutionErrorEvent(
+                    agent_info=agent_info,
+                    error=str(e),
+                ),
+            )
+            raise
+
+    def _execute_and_build_output(
+        self,
+        executor: AgentExecutor,
+        inputs: dict[str, str],
+        response_format: type[Any] | None = None,
+    ) -> LiteAgentOutput:
+        """Execute the agent and build the output object.
+
+        Args:
+            executor: The executor instance.
+            inputs: Input dictionary for execution.
+            response_format: Optional response format.
+
+        Returns:
+            LiteAgentOutput with raw output, formatted result, and metrics.
+        """
+        import json
+
+        # Execute the agent (this is called from sync path, so invoke returns dict)
+        result = cast(dict[str, Any], executor.invoke(inputs))
+        raw_output = result.get("output", "")
+
+        # Handle response format conversion
+        formatted_result: BaseModel | None = None
+        if response_format:
+            try:
+                model_schema = generate_model_description(response_format)
+                schema = json.dumps(model_schema, indent=2)
+                instructions = self.i18n.slice("formatted_task_instructions").format(
+                    output_format=schema
+                )
+
+                converter = Converter(
+                    llm=self.llm,
+                    text=raw_output,
+                    model=response_format,
+                    instructions=instructions,
+                )
+
+                conversion_result = converter.to_pydantic()
+                if isinstance(conversion_result, BaseModel):
+                    formatted_result = conversion_result
+            except ConverterError:
+                pass  # Keep raw output if conversion fails
+
+        # Get token usage metrics
+        if isinstance(self.llm, BaseLLM):
+            usage_metrics = self.llm.get_token_usage_summary()
+        else:
+            usage_metrics = self._token_process.get_summary()
+
+        return LiteAgentOutput(
+            raw=raw_output,
+            pydantic=formatted_result,
+            agent_role=self.role,
+            usage_metrics=usage_metrics.model_dump() if usage_metrics else None,
+            messages=executor.messages,
+        )
+
+    async def _execute_and_build_output_async(
+        self,
+        executor: AgentExecutor,
+        inputs: dict[str, str],
+        response_format: type[Any] | None = None,
+    ) -> LiteAgentOutput:
+        """Execute the agent asynchronously and build the output object.
+
+        This is the async version of _execute_and_build_output that uses
+        invoke_async() for native async execution within event loops.
+
+        Args:
+            executor: The executor instance.
+            inputs: Input dictionary for execution.
+            response_format: Optional response format.
+
+        Returns:
+            LiteAgentOutput with raw output, formatted result, and metrics.
+        """
+        import json
+
+        # Execute the agent asynchronously
+        result = await executor.invoke_async(inputs)
+        raw_output = result.get("output", "")
+
+        # Handle response format conversion
+        formatted_result: BaseModel | None = None
+        if response_format:
+            try:
+                model_schema = generate_model_description(response_format)
+                schema = json.dumps(model_schema, indent=2)
+                instructions = self.i18n.slice("formatted_task_instructions").format(
+                    output_format=schema
+                )
+
+                converter = Converter(
+                    llm=self.llm,
+                    text=raw_output,
+                    model=response_format,
+                    instructions=instructions,
+                )
+
+                conversion_result = converter.to_pydantic()
+                if isinstance(conversion_result, BaseModel):
+                    formatted_result = conversion_result
+            except ConverterError:
+                pass  # Keep raw output if conversion fails
+
+        # Get token usage metrics
+        if isinstance(self.llm, BaseLLM):
+            usage_metrics = self.llm.get_token_usage_summary()
+        else:
+            usage_metrics = self._token_process.get_summary()
+
+        return LiteAgentOutput(
+            raw=raw_output,
+            pydantic=formatted_result,
+            agent_role=self.role,
+            usage_metrics=usage_metrics.model_dump() if usage_metrics else None,
+            messages=executor.messages,
+        )
+
+    def _process_kickoff_guardrail(
+        self,
+        output: LiteAgentOutput,
+        executor: AgentExecutor,
+        inputs: dict[str, str],
+        response_format: type[Any] | None = None,
+        retry_count: int = 0,
+    ) -> LiteAgentOutput:
+        """Process guardrail for kickoff execution with retry logic.
+
+        Args:
+            output: Current agent output.
+            executor: The executor instance.
+            inputs: Input dictionary for re-execution.
+            response_format: Optional response format.
+            retry_count: Current retry count.
+
+        Returns:
+            Validated/updated output.
+        """
+        from crewai.utilities.guardrail_types import GuardrailCallable
+
+        # Ensure guardrail is callable
+        guardrail_callable: GuardrailCallable
+        if isinstance(self.guardrail, str):
+            from crewai.tasks.llm_guardrail import LLMGuardrail
+
+            guardrail_callable = cast(
+                GuardrailCallable,
+                LLMGuardrail(description=self.guardrail, llm=cast(BaseLLM, self.llm)),
+            )
+        elif callable(self.guardrail):
+            guardrail_callable = self.guardrail
+        else:
+            # Should not happen if called from kickoff with guardrail check
+            return output
+
+        guardrail_result = process_guardrail(
+            output=output,
+            guardrail=guardrail_callable,
+            retry_count=retry_count,
+            event_source=self,
+            from_agent=self,
+        )
+
+        if not guardrail_result.success:
+            if retry_count >= self.guardrail_max_retries:
+                raise ValueError(
+                    f"Agent's guardrail failed validation after {self.guardrail_max_retries} retries. "
+                    f"Last error: {guardrail_result.error}"
+                )
+
+            # Add feedback and re-execute
+            executor._append_message_to_state(
+                guardrail_result.error or "Guardrail validation failed",
+                role="user",
+            )
+
+            # Re-execute and build new output
+            output = self._execute_and_build_output(executor, inputs, response_format)
+
+            # Recursively retry guardrail
+            return self._process_kickoff_guardrail(
+                output=output,
+                executor=executor,
+                inputs=inputs,
+                response_format=response_format,
+                retry_count=retry_count + 1,
+            )
+
+        # Apply guardrail result if available
+        if guardrail_result.result is not None:
+            if isinstance(guardrail_result.result, str):
+                output.raw = guardrail_result.result
+            elif isinstance(guardrail_result.result, BaseModel):
+                output.pydantic = guardrail_result.result
+
+        return output
 
     async def kickoff_async(
         self,
@@ -1638,9 +1979,11 @@ class Agent(BaseAgent):
         response_format: type[Any] | None = None,
     ) -> LiteAgentOutput:
         """
-        Execute the agent asynchronously with the given messages using a LiteAgent instance.
+        Execute the agent asynchronously with the given messages.
 
-        This is the async version of the kickoff method.
+        This is the async version of the kickoff method that uses native async
+        execution. It is designed for use within async contexts, such as when
+        called from within an async Flow method.
 
         Args:
             messages: Either a string query or a list of message dictionaries.
@@ -1651,21 +1994,48 @@ class Agent(BaseAgent):
         Returns:
             LiteAgentOutput: The result of the agent execution.
         """
-        lite_agent = LiteAgent(
-            role=self.role,
-            goal=self.goal,
-            backstory=self.backstory,
-            llm=self.llm,
-            tools=self.tools or [],
-            max_iterations=self.max_iter,
-            max_execution_time=self.max_execution_time,
-            respect_context_window=self.respect_context_window,
-            verbose=self.verbose,
-            response_format=response_format,
-            i18n=self.i18n,
-            original_agent=self,
-            guardrail=self.guardrail,
-            guardrail_max_retries=self.guardrail_max_retries,
+        executor, inputs, agent_info, parsed_tools = self._prepare_kickoff(
+            messages, response_format
         )
 
-        return await lite_agent.kickoff_async(messages)
+        try:
+            crewai_event_bus.emit(
+                self,
+                event=LiteAgentExecutionStartedEvent(
+                    agent_info=agent_info,
+                    tools=parsed_tools,
+                    messages=messages,
+                ),
+            )
+
+            output = await self._execute_and_build_output_async(
+                executor, inputs, response_format
+            )
+
+            if self.guardrail is not None:
+                output = self._process_kickoff_guardrail(
+                    output=output,
+                    executor=executor,
+                    inputs=inputs,
+                    response_format=response_format,
+                )
+
+            crewai_event_bus.emit(
+                self,
+                event=LiteAgentExecutionCompletedEvent(
+                    agent_info=agent_info,
+                    output=output.raw,
+                ),
+            )
+
+            return output
+
+        except Exception as e:
+            crewai_event_bus.emit(
+                self,
+                event=LiteAgentExecutionErrorEvent(
+                    agent_info=agent_info,
+                    error=str(e),
+                ),
+            )
+            raise

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

@@ -21,9 +21,9 @@ if TYPE_CHECKING:
 
 
 class CrewAgentExecutorMixin:
-    crew: Crew
+    crew: Crew | None
     agent: Agent
-    task: Task
+    task: Task | None
     iterations: int
     max_iter: int
     messages: list[LLMMessage]

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

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

lib/crewai/src/crewai/experimental/agent_executor.py

@@ -1,6 +1,6 @@
 from __future__ import annotations
 
-from collections.abc import Callable
+from collections.abc import Callable, Coroutine
 import threading
 from typing import TYPE_CHECKING, Any, Literal, cast
 from uuid import uuid4
@@ -37,6 +37,7 @@ from crewai.utilities.agent_utils import (
     handle_unknown_error,
     has_reached_max_iterations,
     is_context_length_exceeded,
+    is_inside_event_loop,
     process_llm_response,
 )
 from crewai.utilities.constants import TRAINING_DATA_FILE
@@ -73,13 +74,17 @@ class AgentReActState(BaseModel):
     ask_for_human_input: bool = Field(default=False)
 
 
-class CrewAgentExecutorFlow(Flow[AgentReActState], CrewAgentExecutorMixin):
-    """Flow-based executor matching CrewAgentExecutor interface.
+class AgentExecutor(Flow[AgentReActState], CrewAgentExecutorMixin):
+    """Agent Executor for both standalone agents and crew-bound agents.
 
     Inherits from:
     - Flow[AgentReActState]: Provides flow orchestration capabilities
     - CrewAgentExecutorMixin: Provides memory methods (short/long/external term)
 
+    This executor can operate in two modes:
+    - Standalone mode: When crew and task are None (used by Agent.kickoff())
+    - Crew mode: When crew and task are provided (used by Agent.execute_task())
+
     Note: Multiple instances may be created during agent initialization
     (cache setup, RPM controller setup, etc.) but only the final instance
     should execute tasks via invoke().
@@ -88,8 +93,6 @@ class CrewAgentExecutorFlow(Flow[AgentReActState], CrewAgentExecutorMixin):
     def __init__(
         self,
         llm: BaseLLM,
-        task: Task,
-        crew: Crew,
         agent: Agent,
         prompt: SystemPromptResult | StandardPromptResult,
         max_iter: int,
@@ -98,6 +101,8 @@ class CrewAgentExecutorFlow(Flow[AgentReActState], CrewAgentExecutorMixin):
         stop_words: list[str],
         tools_description: str,
         tools_handler: ToolsHandler,
+        task: Task | None = None,
+        crew: Crew | None = None,
         step_callback: Any = None,
         original_tools: list[BaseTool] | None = None,
         function_calling_llm: BaseLLM | Any | None = None,
@@ -111,8 +116,6 @@ class CrewAgentExecutorFlow(Flow[AgentReActState], CrewAgentExecutorMixin):
 
         Args:
             llm: Language model instance.
-            task: Task to execute.
-            crew: Crew instance.
             agent: Agent to execute.
             prompt: Prompt templates.
             max_iter: Maximum iterations.
@@ -121,6 +124,8 @@ class CrewAgentExecutorFlow(Flow[AgentReActState], CrewAgentExecutorMixin):
             stop_words: Stop word list.
             tools_description: Tool descriptions.
             tools_handler: Tool handler instance.
+            task: Optional task to execute (None for standalone agent execution).
+            crew: Optional crew instance (None for standalone agent execution).
             step_callback: Optional step callback.
             original_tools: Original tool list.
             function_calling_llm: Optional function calling LLM.
@@ -131,9 +136,9 @@ class CrewAgentExecutorFlow(Flow[AgentReActState], CrewAgentExecutorMixin):
         """
         self._i18n: I18N = i18n or get_i18n()
         self.llm = llm
-        self.task = task
+        self.task: Task | None = task
         self.agent = agent
-        self.crew = crew
+        self.crew: Crew | None = crew
         self.prompt = prompt
         self.tools = tools
         self.tools_names = tools_names
@@ -178,7 +183,6 @@ class CrewAgentExecutorFlow(Flow[AgentReActState], CrewAgentExecutorMixin):
                     else self.stop
                 )
             )
-
         self._state = AgentReActState()
 
     def _ensure_flow_initialized(self) -> None:
@@ -264,7 +268,7 @@ class CrewAgentExecutorFlow(Flow[AgentReActState], CrewAgentExecutorMixin):
                 printer=self._printer,
                 from_task=self.task,
                 from_agent=self.agent,
-                response_model=self.response_model,
+                response_model=None,
                 executor_context=self,
             )
 
@@ -449,9 +453,99 @@ class CrewAgentExecutorFlow(Flow[AgentReActState], CrewAgentExecutorMixin):
 
         return "initialized"
 
-    def invoke(self, inputs: dict[str, Any]) -> dict[str, Any]:
+    def invoke(
+        self, inputs: dict[str, Any]
+    ) -> dict[str, Any] | Coroutine[Any, Any, dict[str, Any]]:
         """Execute agent with given inputs.
 
+        When called from within an existing event loop (e.g., inside a Flow),
+        this method returns a coroutine that should be awaited. The Flow
+        framework handles this automatically.
+
+        Args:
+            inputs: Input dictionary containing prompt variables.
+
+        Returns:
+            Dictionary with agent output, or a coroutine if inside an event loop.
+        """
+        # Magic auto-async: if inside event loop, return coroutine for Flow to await
+        if is_inside_event_loop():
+            return self.invoke_async(inputs)
+
+        self._ensure_flow_initialized()
+
+        with self._execution_lock:
+            if self._is_executing:
+                raise RuntimeError(
+                    "Executor is already running. "
+                    "Cannot invoke the same executor instance concurrently."
+                )
+            self._is_executing = True
+            self._has_been_invoked = True
+
+        try:
+            # Reset state for fresh execution
+            self.state.messages.clear()
+            self.state.iterations = 0
+            self.state.current_answer = None
+            self.state.is_finished = False
+
+            if "system" in self.prompt:
+                prompt = cast("SystemPromptResult", self.prompt)
+                system_prompt = self._format_prompt(prompt["system"], inputs)
+                user_prompt = self._format_prompt(prompt["user"], inputs)
+                self.state.messages.append(
+                    format_message_for_llm(system_prompt, role="system")
+                )
+                self.state.messages.append(format_message_for_llm(user_prompt))
+            else:
+                user_prompt = self._format_prompt(self.prompt["prompt"], inputs)
+                self.state.messages.append(format_message_for_llm(user_prompt))
+
+            self.state.ask_for_human_input = bool(
+                inputs.get("ask_for_human_input", False)
+            )
+
+            self.kickoff()
+
+            formatted_answer = self.state.current_answer
+
+            if not isinstance(formatted_answer, AgentFinish):
+                raise RuntimeError(
+                    "Agent execution ended without reaching a final answer."
+                )
+
+            if self.state.ask_for_human_input:
+                formatted_answer = self._handle_human_feedback(formatted_answer)
+
+            self._create_short_term_memory(formatted_answer)
+            self._create_long_term_memory(formatted_answer)
+            self._create_external_memory(formatted_answer)
+
+            return {"output": formatted_answer.output}
+
+        except AssertionError:
+            fail_text = Text()
+            fail_text.append("❌ ", style="red bold")
+            fail_text.append(
+                "Agent failed to reach a final answer. This is likely a bug - please report it.",
+                style="red",
+            )
+            self._console.print(fail_text)
+            raise
+        except Exception as e:
+            handle_unknown_error(self._printer, e)
+            raise
+        finally:
+            self._is_executing = False
+
+    async def invoke_async(self, inputs: dict[str, Any]) -> dict[str, Any]:
+        """Execute agent asynchronously with given inputs.
+
+        This method is designed for use within async contexts, such as when
+        the agent is called from within an async Flow method. It uses
+        kickoff_async() directly instead of running in a separate thread.
+
         Args:
             inputs: Input dictionary containing prompt variables.
 
@@ -492,7 +586,8 @@ class CrewAgentExecutorFlow(Flow[AgentReActState], CrewAgentExecutorMixin):
                 inputs.get("ask_for_human_input", False)
             )
 
-            self.kickoff()
+            # Use async kickoff directly since we're already in an async context
+            await self.kickoff_async()
 
             formatted_answer = self.state.current_answer
 
@@ -583,11 +678,14 @@ class CrewAgentExecutorFlow(Flow[AgentReActState], CrewAgentExecutorMixin):
         if self.agent is None:
             raise ValueError("Agent cannot be None")
 
+        if self.task is None:
+            return
+
         crewai_event_bus.emit(
             self.agent,
             AgentLogsStartedEvent(
                 agent_role=self.agent.role,
-                task_description=(self.task.description if self.task else "Not Found"),
+                task_description=self.task.description,
                 verbose=self.agent.verbose
                 or (hasattr(self, "crew") and getattr(self.crew, "verbose", False)),
             ),
@@ -621,10 +719,12 @@ class CrewAgentExecutorFlow(Flow[AgentReActState], CrewAgentExecutorMixin):
             result: Agent's final output.
             human_feedback: Optional feedback from human.
         """
+        # Early return if no crew (standalone mode)
+        if self.crew is None:
+            return
+
         agent_id = str(self.agent.id)
-        train_iteration = (
-            getattr(self.crew, "_train_iteration", None) if self.crew else None
-        )
+        train_iteration = getattr(self.crew, "_train_iteration", None)
 
         if train_iteration is None or not isinstance(train_iteration, int):
             train_error = Text()
@@ -806,3 +906,7 @@ class CrewAgentExecutorFlow(Flow[AgentReActState], CrewAgentExecutorMixin):
         requiring arbitrary_types_allowed=True.
         """
         return core_schema.any_schema()
+
+
+# Backward compatibility alias (deprecated)
+CrewAgentExecutorFlow = AgentExecutor

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

@@ -12,6 +12,7 @@ from concurrent.futures import Future
 import copy
 import inspect
 import logging
+import threading
 from typing import (
     TYPE_CHECKING,
     Any,
@@ -64,6 +65,7 @@ from crewai.flow.persistence.base import FlowPersistence
 from crewai.flow.types import FlowExecutionData, FlowMethodName, PendingListenerKey
 from crewai.flow.utils import (
     _extract_all_methods,
+    _extract_all_methods_recursive,
     _normalize_condition,
     get_possible_return_constants,
     is_flow_condition_dict,
@@ -73,6 +75,7 @@ from crewai.flow.utils import (
     is_simple_flow_condition,
 )
 
+
 if TYPE_CHECKING:
     from crewai.flow.async_feedback.types import PendingFeedbackContext
     from crewai.flow.human_feedback import HumanFeedbackResult
@@ -396,6 +399,62 @@ def and_(*conditions: str | FlowCondition | Callable[..., Any]) -> FlowCondition
     return {"type": AND_CONDITION, "conditions": processed_conditions}
 
 
+class StateProxy(Generic[T]):
+    """Proxy that provides thread-safe access to flow state.
+
+    Wraps state objects (dict or BaseModel) and uses a lock for all write
+    operations to prevent race conditions when parallel listeners modify state.
+    """
+
+    __slots__ = ("_proxy_lock", "_proxy_state")
+
+    def __init__(self, state: T, lock: threading.Lock) -> None:
+        object.__setattr__(self, "_proxy_state", state)
+        object.__setattr__(self, "_proxy_lock", lock)
+
+    def __getattr__(self, name: str) -> Any:
+        return getattr(object.__getattribute__(self, "_proxy_state"), name)
+
+    def __setattr__(self, name: str, value: Any) -> None:
+        if name in ("_proxy_state", "_proxy_lock"):
+            object.__setattr__(self, name, value)
+        else:
+            with object.__getattribute__(self, "_proxy_lock"):
+                setattr(object.__getattribute__(self, "_proxy_state"), name, value)
+
+    def __getitem__(self, key: str) -> Any:
+        return object.__getattribute__(self, "_proxy_state")[key]
+
+    def __setitem__(self, key: str, value: Any) -> None:
+        with object.__getattribute__(self, "_proxy_lock"):
+            object.__getattribute__(self, "_proxy_state")[key] = value
+
+    def __delitem__(self, key: str) -> None:
+        with object.__getattribute__(self, "_proxy_lock"):
+            del object.__getattribute__(self, "_proxy_state")[key]
+
+    def __contains__(self, key: str) -> bool:
+        return key in object.__getattribute__(self, "_proxy_state")
+
+    def __repr__(self) -> str:
+        return repr(object.__getattribute__(self, "_proxy_state"))
+
+    def _unwrap(self) -> T:
+        """Return the underlying state object."""
+        return cast(T, object.__getattribute__(self, "_proxy_state"))
+
+    def model_dump(self) -> dict[str, Any]:
+        """Return state as a dictionary.
+
+        Works for both dict and BaseModel underlying states.
+        """
+        state = object.__getattribute__(self, "_proxy_state")
+        if isinstance(state, dict):
+            return state
+        result: dict[str, Any] = state.model_dump()
+        return result
+
+
 class FlowMeta(type):
     def __new__(
         mcs,
@@ -519,7 +578,12 @@ class Flow(Generic[T], metaclass=FlowMeta):
         self._methods: dict[FlowMethodName, FlowMethod[Any, Any]] = {}
         self._method_execution_counts: dict[FlowMethodName, int] = {}
         self._pending_and_listeners: dict[PendingListenerKey, set[FlowMethodName]] = {}
+        self._fired_or_listeners: set[FlowMethodName] = (
+            set()
+        )  # Track OR listeners that already fired
         self._method_outputs: list[Any] = []  # list to store all method outputs
+        self._state_lock = threading.Lock()
+        self._or_listeners_lock = threading.Lock()
         self._completed_methods: set[FlowMethodName] = (
             set()
         )  # Track completed methods for reload
@@ -564,13 +628,182 @@ class Flow(Generic[T], metaclass=FlowMeta):
                         method = method.__get__(self, self.__class__)
                     self._methods[method.__name__] = method
 
+    def _mark_or_listener_fired(self, listener_name: FlowMethodName) -> bool:
+        """Mark an OR listener as fired atomically.
+
+        Args:
+            listener_name: The name of the OR listener to mark.
+
+        Returns:
+            True if this call was the first to fire the listener.
+            False if the listener was already fired.
+        """
+        with self._or_listeners_lock:
+            if listener_name in self._fired_or_listeners:
+                return False
+            self._fired_or_listeners.add(listener_name)
+            return True
+
+    def _clear_or_listeners(self) -> None:
+        """Clear fired OR listeners for cyclic flows."""
+        with self._or_listeners_lock:
+            self._fired_or_listeners.clear()
+
+    def _discard_or_listener(self, listener_name: FlowMethodName) -> None:
+        """Discard a single OR listener from the fired set."""
+        with self._or_listeners_lock:
+            self._fired_or_listeners.discard(listener_name)
+
+    def _build_racing_groups(self) -> dict[frozenset[FlowMethodName], FlowMethodName]:
+        """Identify groups of methods that race for the same OR listener.
+
+        Analyzes the flow graph to find listeners with OR conditions that have
+        multiple trigger methods. These trigger methods form a "racing group"
+        where only the first to complete should trigger the OR listener.
+
+        Only methods that are EXCLUSIVELY sources for the OR listener are included
+        in the racing group. Methods that are also triggers for other listeners
+        (e.g., AND conditions) are not cancelled when another racing source wins.
+
+        Returns:
+            Dictionary mapping frozensets of racing method names to their
+            shared OR listener name.
+
+        Example:
+            If we have `@listen(or_(method_a, method_b))` on `handler`,
+            and method_a/method_b aren't used elsewhere,
+            this returns: {frozenset({'method_a', 'method_b'}): 'handler'}
+        """
+        racing_groups: dict[frozenset[FlowMethodName], FlowMethodName] = {}
+
+        method_to_listeners: dict[FlowMethodName, set[FlowMethodName]] = {}
+        for listener_name, condition_data in self._listeners.items():
+            if is_simple_flow_condition(condition_data):
+                _, methods = condition_data
+                for m in methods:
+                    method_to_listeners.setdefault(m, set()).add(listener_name)
+            elif is_flow_condition_dict(condition_data):
+                all_methods = _extract_all_methods_recursive(condition_data)
+                for m in all_methods:
+                    method_name = FlowMethodName(m) if isinstance(m, str) else m
+                    method_to_listeners.setdefault(method_name, set()).add(
+                        listener_name
+                    )
+
+        for listener_name, condition_data in self._listeners.items():
+            if listener_name in self._routers:
+                continue
+
+            trigger_methods: set[FlowMethodName] = set()
+
+            if is_simple_flow_condition(condition_data):
+                condition_type, methods = condition_data
+                if condition_type == OR_CONDITION and len(methods) > 1:
+                    trigger_methods = set(methods)
+
+            elif is_flow_condition_dict(condition_data):
+                top_level_type = condition_data.get("type", OR_CONDITION)
+                if top_level_type == OR_CONDITION:
+                    all_methods = _extract_all_methods_recursive(condition_data)
+                    if len(all_methods) > 1:
+                        trigger_methods = set(
+                            FlowMethodName(m) if isinstance(m, str) else m
+                            for m in all_methods
+                        )
+
+            if trigger_methods:
+                exclusive_methods = {
+                    m
+                    for m in trigger_methods
+                    if method_to_listeners.get(m, set()) == {listener_name}
+                }
+                if len(exclusive_methods) > 1:
+                    racing_groups[frozenset(exclusive_methods)] = listener_name
+
+        return racing_groups
+
+    def _get_racing_group_for_listeners(
+        self,
+        listener_names: list[FlowMethodName],
+    ) -> tuple[frozenset[FlowMethodName], FlowMethodName] | None:
+        """Check if the given listeners form a racing group.
+
+        Args:
+            listener_names: List of listener method names being executed.
+
+        Returns:
+            Tuple of (racing_members, or_listener_name) if these listeners race,
+            None otherwise.
+        """
+        if not hasattr(self, "_racing_groups_cache"):
+            self._racing_groups_cache = self._build_racing_groups()
+
+        listener_set = set(listener_names)
+
+        for racing_members, or_listener in self._racing_groups_cache.items():
+            if racing_members & listener_set:
+                racing_subset = racing_members & listener_set
+                if len(racing_subset) > 1:
+                    return (frozenset(racing_subset), or_listener)
+
+        return None
+
+    async def _execute_racing_listeners(
+        self,
+        racing_listeners: frozenset[FlowMethodName],
+        other_listeners: list[FlowMethodName],
+        result: Any,
+    ) -> None:
+        """Execute racing listeners with first-wins semantics.
+
+        Racing listeners are executed in parallel, but once the first one
+        completes, the others are cancelled. Non-racing listeners in the
+        same batch are executed normally in parallel.
+
+        Args:
+            racing_listeners: Set of listener names that race for an OR condition.
+            other_listeners: Other listeners to execute in parallel (not racing).
+            result: The result from the triggering method.
+        """
+        racing_tasks = [
+            asyncio.create_task(
+                self._execute_single_listener(name, result),
+                name=str(name),
+            )
+            for name in racing_listeners
+        ]
+
+        other_tasks = [
+            asyncio.create_task(
+                self._execute_single_listener(name, result),
+                name=str(name),
+            )
+            for name in other_listeners
+        ]
+
+        if racing_tasks:
+            for coro in asyncio.as_completed(racing_tasks):
+                try:
+                    await coro
+                except Exception as e:
+                    logger.debug(f"Racing listener failed: {e}")
+                    continue
+                break
+
+            for task in racing_tasks:
+                if not task.done():
+                    task.cancel()
+
+        if other_tasks:
+            await asyncio.gather(*other_tasks, return_exceptions=True)
+
     @classmethod
     def from_pending(
         cls,
         flow_id: str,
         persistence: FlowPersistence | None = None,
         **kwargs: Any,
-    ) -> "Flow[Any]":
+    ) -> Flow[Any]:
         """Create a Flow instance from a pending feedback state.
 
         This classmethod is used to restore a flow that was paused waiting
@@ -631,7 +864,7 @@ class Flow(Generic[T], metaclass=FlowMeta):
         return instance
 
     @property
-    def pending_feedback(self) -> "PendingFeedbackContext | None":
+    def pending_feedback(self) -> PendingFeedbackContext | None:
         """Get the pending feedback context if this flow is waiting for feedback.
 
         Returns:
@@ -716,9 +949,10 @@ class Flow(Generic[T], metaclass=FlowMeta):
         Raises:
             ValueError: If no pending feedback context exists
         """
-        from crewai.flow.human_feedback import HumanFeedbackResult
         from datetime import datetime
 
+        from crewai.flow.human_feedback import HumanFeedbackResult
+
         if self._pending_feedback_context is None:
             raise ValueError(
                 "No pending feedback context. Use from_pending() to restore a paused flow."
@@ -740,12 +974,14 @@ class Flow(Generic[T], metaclass=FlowMeta):
                 # No default and no feedback - use first outcome
                 collapsed_outcome = emit[0]
         elif emit:
-            # Collapse feedback to outcome using LLM
-            collapsed_outcome = self._collapse_to_outcome(
-                feedback=feedback,
-                outcomes=emit,
-                llm=llm,
-            )
+            if llm is not None:
+                collapsed_outcome = self._collapse_to_outcome(
+                    feedback=feedback,
+                    outcomes=emit,
+                    llm=llm,
+                )
+            else:
+                collapsed_outcome = emit[0]
 
         # Create result
         result = HumanFeedbackResult(
@@ -784,21 +1020,16 @@ class Flow(Generic[T], metaclass=FlowMeta):
         # This allows methods to re-execute in loops (e.g., implement_changes → suggest_changes → implement_changes)
         self._is_execution_resuming = False
 
-        # Determine what to pass to listeners
+        final_result: Any = result
         try:
             if emit and collapsed_outcome:
-                # Router behavior - the outcome itself triggers listeners
-                # First, add the outcome to method outputs as a router would
                 self._method_outputs.append(collapsed_outcome)
-
-                # Then trigger listeners for the outcome (e.g., "approved" triggers @listen("approved"))
-                final_result = await self._execute_listeners(
-                    FlowMethodName(collapsed_outcome),  # Use outcome as trigger
-                    result,  # Pass HumanFeedbackResult to listeners
+                await self._execute_listeners(
+                    FlowMethodName(collapsed_outcome),
+                    result,
                 )
             else:
-                # Normal behavior - pass the HumanFeedbackResult
-                final_result = await self._execute_listeners(
+                await self._execute_listeners(
                     FlowMethodName(context.method_name),
                     result,
                 )
@@ -894,18 +1125,17 @@ class Flow(Generic[T], metaclass=FlowMeta):
 
         # Handle case where initial_state is a type (class)
         if isinstance(self.initial_state, type):
-            if issubclass(self.initial_state, FlowState):
-                return self.initial_state()  # Uses model defaults
-            if issubclass(self.initial_state, BaseModel):
-                # Validate that the model has an id field
-                model_fields = getattr(self.initial_state, "model_fields", None)
+            state_class: type[T] = self.initial_state
+            if issubclass(state_class, FlowState):
+                return state_class()
+            if issubclass(state_class, BaseModel):
+                model_fields = getattr(state_class, "model_fields", None)
                 if not model_fields or "id" not in model_fields:
                     raise ValueError("Flow state model must have an 'id' field")
-                instance = self.initial_state()
-                # Ensure id is set - generate UUID if empty
-                if not getattr(instance, "id", None):
-                    object.__setattr__(instance, "id", str(uuid4()))
-                return instance
+                model_instance = state_class()
+                if not getattr(model_instance, "id", None):
+                    object.__setattr__(model_instance, "id", str(uuid4()))
+                return model_instance
             if self.initial_state is dict:
                 return cast(T, {"id": str(uuid4())})
 
@@ -970,7 +1200,7 @@ class Flow(Generic[T], metaclass=FlowMeta):
 
     @property
     def state(self) -> T:
-        return self._state
+        return StateProxy(self._state, self._state_lock)  # type: ignore[return-value]
 
     @property
     def method_outputs(self) -> list[Any]:
@@ -1295,6 +1525,7 @@ class Flow(Generic[T], metaclass=FlowMeta):
                 self._completed_methods.clear()
                 self._method_outputs.clear()
                 self._pending_and_listeners.clear()
+                self._clear_or_listeners()
             else:
                 # We're restoring from persistence, set the flag
                 self._is_execution_resuming = True
@@ -1346,9 +1577,26 @@ class Flow(Generic[T], metaclass=FlowMeta):
                 self._initialize_state(inputs)
 
             try:
+                # Determine which start methods to execute at kickoff
+                # Conditional start methods (with __trigger_methods__) are only triggered by their conditions
+                # UNLESS there are no unconditional starts (then all starts run as entry points)
+                unconditional_starts = [
+                    start_method
+                    for start_method in self._start_methods
+                    if not getattr(
+                        self._methods.get(start_method), "__trigger_methods__", None
+                    )
+                ]
+                # If there are unconditional starts, only run those at kickoff
+                # If there are NO unconditional starts, run all starts (including conditional ones)
+                starts_to_execute = (
+                    unconditional_starts
+                    if unconditional_starts
+                    else self._start_methods
+                )
                 tasks = [
                     self._execute_start_method(start_method)
-                    for start_method in self._start_methods
+                    for start_method in starts_to_execute
                 ]
                 await asyncio.gather(*tasks)
             except Exception as e:
@@ -1431,13 +1679,14 @@ class Flow(Generic[T], metaclass=FlowMeta):
                 )
                 self._event_futures.clear()
 
-            trace_listener = TraceCollectionListener()
-            if trace_listener.batch_manager.batch_owner_type == "flow":
-                if trace_listener.first_time_handler.is_first_time:
-                    trace_listener.first_time_handler.mark_events_collected()
-                    trace_listener.first_time_handler.handle_execution_completion()
-                else:
-                    trace_listener.batch_manager.finalize_batch()
+            if not self.suppress_flow_events:
+                trace_listener = TraceCollectionListener()
+                if trace_listener.batch_manager.batch_owner_type == "flow":
+                    if trace_listener.first_time_handler.is_first_time:
+                        trace_listener.first_time_handler.mark_events_collected()
+                        trace_listener.first_time_handler.handle_execution_completion()
+                    else:
+                        trace_listener.batch_manager.finalize_batch()
 
             return final_output
         finally:
@@ -1481,6 +1730,8 @@ class Flow(Generic[T], metaclass=FlowMeta):
                 return
             # For cyclic flows, clear from completed to allow re-execution
             self._completed_methods.discard(start_method_name)
+            # Also clear fired OR listeners to allow them to fire again in new cycle
+            self._clear_or_listeners()
 
         method = self._methods[start_method_name]
         enhanced_method = self._inject_trigger_payload_for_start_method(method)
@@ -1503,11 +1754,25 @@ class Flow(Generic[T], metaclass=FlowMeta):
                     if self.last_human_feedback is not None
                     else result
                 )
-                tasks = [
-                    self._execute_single_listener(listener_name, listener_result)
-                    for listener_name in listeners_for_result
-                ]
-                await asyncio.gather(*tasks)
+                racing_group = self._get_racing_group_for_listeners(
+                    listeners_for_result
+                )
+                if racing_group:
+                    racing_members, _ = racing_group
+                    other_listeners = [
+                        name
+                        for name in listeners_for_result
+                        if name not in racing_members
+                    ]
+                    await self._execute_racing_listeners(
+                        racing_members, other_listeners, listener_result
+                    )
+                else:
+                    tasks = [
+                        self._execute_single_listener(listener_name, listener_result)
+                        for listener_name in listeners_for_result
+                    ]
+                    await asyncio.gather(*tasks)
         else:
             await self._execute_listeners(start_method_name, result)
 
@@ -1573,11 +1838,19 @@ class Flow(Generic[T], metaclass=FlowMeta):
                 if future:
                     self._event_futures.append(future)
 
-            result = (
-                await method(*args, **kwargs)
-                if asyncio.iscoroutinefunction(method)
-                else method(*args, **kwargs)
-            )
+            if asyncio.iscoroutinefunction(method):
+                result = await method(*args, **kwargs)
+            else:
+                # Run sync methods in thread pool for isolation
+                # This allows Agent.kickoff() to work synchronously inside Flow methods
+                import contextvars
+
+                ctx = contextvars.copy_context()
+                result = await asyncio.to_thread(ctx.run, method, *args, **kwargs)
+
+            # Auto-await coroutines returned from sync methods (enables AgentExecutor pattern)
+            if asyncio.iscoroutine(result):
+                result = await result
 
             self._method_outputs.append(result)
             self._method_execution_counts[method_name] = (
@@ -1724,11 +1997,27 @@ class Flow(Generic[T], metaclass=FlowMeta):
                     listener_result = router_result_to_feedback.get(
                         str(current_trigger), result
                     )
-                    tasks = [
-                        self._execute_single_listener(listener_name, listener_result)
-                        for listener_name in listeners_triggered
-                    ]
-                    await asyncio.gather(*tasks)
+                    racing_group = self._get_racing_group_for_listeners(
+                        listeners_triggered
+                    )
+                    if racing_group:
+                        racing_members, _ = racing_group
+                        other_listeners = [
+                            name
+                            for name in listeners_triggered
+                            if name not in racing_members
+                        ]
+                        await self._execute_racing_listeners(
+                            racing_members, other_listeners, listener_result
+                        )
+                    else:
+                        tasks = [
+                            self._execute_single_listener(
+                                listener_name, listener_result
+                            )
+                            for listener_name in listeners_triggered
+                        ]
+                        await asyncio.gather(*tasks)
 
                 if current_trigger in router_results:
                     # Find start methods triggered by this router result
@@ -1745,14 +2034,16 @@ class Flow(Generic[T], metaclass=FlowMeta):
                                 should_trigger = current_trigger in all_methods
 
                             if should_trigger:
-                                # Only execute if this is a cycle (method was already completed)
+                                # Execute conditional start method triggered by router result
                                 if method_name in self._completed_methods:
-                                    # For router-triggered start methods in cycles, temporarily clear resumption flag
-                                    # to allow cyclic execution
+                                    # For cyclic re-execution, temporarily clear resumption flag
                                     was_resuming = self._is_execution_resuming
                                     self._is_execution_resuming = False
                                     await self._execute_start_method(method_name)
                                     self._is_execution_resuming = was_resuming
+                                else:
+                                    # First-time execution of conditional start
+                                    await self._execute_start_method(method_name)
 
     def _evaluate_condition(
         self,
@@ -1850,8 +2141,21 @@ class Flow(Generic[T], metaclass=FlowMeta):
                 condition_type, methods = condition_data
 
                 if condition_type == OR_CONDITION:
-                    if trigger_method in methods:
-                        triggered.append(listener_name)
+                    # Only trigger multi-source OR listeners (or_(A, B, C)) once - skip if already fired
+                    # Simple single-method listeners fire every time their trigger occurs
+                    # Routers also fire every time - they're decision points
+                    has_multiple_triggers = len(methods) > 1
+                    should_check_fired = has_multiple_triggers and not is_router
+
+                    if (
+                        not should_check_fired
+                        or listener_name not in self._fired_or_listeners
+                    ):
+                        if trigger_method in methods:
+                            triggered.append(listener_name)
+                            # Only track multi-source OR listeners (not single-method or routers)
+                            if should_check_fired:
+                                self._fired_or_listeners.add(listener_name)
                 elif condition_type == AND_CONDITION:
                     pending_key = PendingListenerKey(listener_name)
                     if pending_key not in self._pending_and_listeners:
@@ -1864,10 +2168,26 @@ class Flow(Generic[T], metaclass=FlowMeta):
                         self._pending_and_listeners.pop(pending_key, None)
 
             elif is_flow_condition_dict(condition_data):
+                # For complex conditions, check if top-level is OR and track accordingly
+                top_level_type = condition_data.get("type", OR_CONDITION)
+                is_or_based = top_level_type == OR_CONDITION
+
+                # Only track multi-source OR conditions (multiple sub-conditions), not routers
+                sub_conditions = condition_data.get("conditions", [])
+                has_multiple_triggers = is_or_based and len(sub_conditions) > 1
+                should_check_fired = has_multiple_triggers and not is_router
+
+                # Skip compound OR-based listeners that have already fired
+                if should_check_fired and listener_name in self._fired_or_listeners:
+                    continue
+
                 if self._evaluate_condition(
                     condition_data, trigger_method, listener_name
                 ):
                     triggered.append(listener_name)
+                    # Track compound OR-based listeners so they only fire once
+                    if should_check_fired:
+                        self._fired_or_listeners.add(listener_name)
 
         return triggered
 
@@ -1896,9 +2216,22 @@ class Flow(Generic[T], metaclass=FlowMeta):
             if self._is_execution_resuming:
                 # During resumption, skip execution but continue listeners
                 await self._execute_listeners(listener_name, None)
+
+                # For routers, also check if any conditional starts they triggered are completed
+                # If so, continue their chains
+                if listener_name in self._routers:
+                    for start_method_name in self._start_methods:
+                        if (
+                            start_method_name in self._listeners
+                            and start_method_name in self._completed_methods
+                        ):
+                            # This conditional start was executed, continue its chain
+                            await self._execute_start_method(start_method_name)
                 return
             # For cyclic flows, clear from completed to allow re-execution
             self._completed_methods.discard(listener_name)
+            # Also clear from fired OR listeners for cyclic flows
+            self._discard_or_listener(listener_name)
 
         try:
             method = self._methods[listener_name]
@@ -1931,11 +2264,25 @@ class Flow(Generic[T], metaclass=FlowMeta):
                         if self.last_human_feedback is not None
                         else listener_result
                     )
-                    tasks = [
-                        self._execute_single_listener(name, feedback_result)
-                        for name in listeners_for_result
-                    ]
-                    await asyncio.gather(*tasks)
+                    racing_group = self._get_racing_group_for_listeners(
+                        listeners_for_result
+                    )
+                    if racing_group:
+                        racing_members, _ = racing_group
+                        other_listeners = [
+                            name
+                            for name in listeners_for_result
+                            if name not in racing_members
+                        ]
+                        await self._execute_racing_listeners(
+                            racing_members, other_listeners, feedback_result
+                        )
+                    else:
+                        tasks = [
+                            self._execute_single_listener(name, feedback_result)
+                            for name in listeners_for_result
+                        ]
+                        await asyncio.gather(*tasks)
 
         except Exception as e:
             # Don't log HumanFeedbackPending as an error - it's expected control flow
@@ -2049,7 +2396,7 @@ class Flow(Generic[T], metaclass=FlowMeta):
         from crewai.llms.base_llm import BaseLLM as BaseLLMClass
         from crewai.utilities.i18n import get_i18n
 
-        # Get or create LLM instance
+        llm_instance: BaseLLMClass
         if isinstance(llm, str):
             llm_instance = LLM(model=llm)
         elif isinstance(llm, BaseLLMClass):
@@ -2084,26 +2431,23 @@ class Flow(Generic[T], metaclass=FlowMeta):
                 response_model=FeedbackOutcome,
             )
 
-            # Parse the response - LLM returns JSON string when using response_model
             if isinstance(response, str):
                 import json
 
                 try:
                     parsed = json.loads(response)
-                    return parsed.get("outcome", outcomes[0])
+                    return str(parsed.get("outcome", outcomes[0]))
                 except json.JSONDecodeError:
-                    # Not valid JSON, might be raw outcome string
                     response_clean = response.strip()
                     for outcome in outcomes:
                         if outcome.lower() == response_clean.lower():
                             return outcome
                     return outcomes[0]
             elif isinstance(response, FeedbackOutcome):
-                return response.outcome
+                return str(response.outcome)
             elif hasattr(response, "outcome"):
-                return response.outcome
+                return str(response.outcome)
             else:
-                # Unexpected type, fall back to first outcome
                 logger.warning(f"Unexpected response type: {type(response)}")
                 return outcomes[0]
 

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

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

lib/crewai/src/crewai/lite_agent.py

@@ -10,6 +10,7 @@ from typing import (
     get_origin,
 )
 import uuid
+import warnings
 
 from pydantic import (
     UUID4,
@@ -80,6 +81,11 @@ class LiteAgent(FlowTrackable, BaseModel):
     """
     A lightweight agent that can process messages and use tools.
 
+    .. deprecated::
+        LiteAgent is deprecated and will be removed in a future version.
+        Use ``Agent().kickoff(messages)`` instead, which provides the same
+        functionality with additional features like memory and knowledge support.
+
     This agent is simpler than the full Agent class, focusing on direct execution
     rather than task delegation. It's designed to be used for simple interactions
     where a full crew is not needed.
@@ -164,6 +170,18 @@ class LiteAgent(FlowTrackable, BaseModel):
         default_factory=get_after_llm_call_hooks
     )
 
+    @model_validator(mode="after")
+    def emit_deprecation_warning(self) -> Self:
+        """Emit deprecation warning for LiteAgent usage."""
+        warnings.warn(
+            "LiteAgent is deprecated and will be removed in a future version. "
+            "Use Agent().kickoff(messages) instead, which provides the same "
+            "functionality with additional features like memory and knowledge support.",
+            DeprecationWarning,
+            stacklevel=2,
+        )
+        return self
+
     @model_validator(mode="after")
     def setup_llm(self) -> Self:
         """Set up the LLM and other components after initialization."""

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

@@ -1,5 +1,6 @@
 from __future__ import annotations
 
+import asyncio
 from collections.abc import Callable, Sequence
 import json
 import re
@@ -54,6 +55,23 @@ console = Console()
 _MULTIPLE_NEWLINES: Final[re.Pattern[str]] = re.compile(r"\n+")
 
 
+def is_inside_event_loop() -> bool:
+    """Check if code is currently running inside an asyncio event loop.
+
+    This is used to detect when code is being called from within an async context
+    (e.g., inside a Flow). In such cases, callers should return a coroutine
+    instead of executing synchronously to avoid nested event loop errors.
+
+    Returns:
+        True if inside a running event loop, False otherwise.
+    """
+    try:
+        asyncio.get_running_loop()
+        return True
+    except RuntimeError:
+        return False
+
+
 def parse_tools(tools: list[BaseTool]) -> list[CrewStructuredTool]:
     """Parse tools to be used for the task.
 

lib/crewai/tests/agents/test_agent_executor.py

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

lib/crewai/tests/agents/test_lite_agent.py

@@ -72,62 +72,53 @@ class ResearchResult(BaseModel):
 
 @pytest.mark.vcr()
 @pytest.mark.parametrize("verbose", [True, False])
-def test_lite_agent_created_with_correct_parameters(monkeypatch, verbose):
-    """Test that LiteAgent is created with the correct parameters when Agent.kickoff() is called."""
+def test_agent_kickoff_preserves_parameters(verbose):
+    """Test that Agent.kickoff() uses the correct parameters from the Agent."""
     # Create a test agent with specific parameters
-    llm = LLM(model="gpt-4o-mini")
+    mock_llm = Mock(spec=LLM)
+    mock_llm.call.return_value = "Final Answer: Test response"
+    mock_llm.stop = []
+
+    from crewai.types.usage_metrics import UsageMetrics
+
+    mock_usage_metrics = UsageMetrics(
+        total_tokens=100,
+        prompt_tokens=50,
+        completion_tokens=50,
+        cached_prompt_tokens=0,
+        successful_requests=1,
+    )
+    mock_llm.get_token_usage_summary.return_value = mock_usage_metrics
+
     custom_tools = [WebSearchTool(), CalculatorTool()]
     max_iter = 10
-    max_execution_time = 300
 
     agent = Agent(
         role="Test Agent",
         goal="Test Goal",
         backstory="Test Backstory",
-        llm=llm,
+        llm=mock_llm,
         tools=custom_tools,
         max_iter=max_iter,
-        max_execution_time=max_execution_time,
         verbose=verbose,
     )
 
-    # Create a mock to capture the created LiteAgent
-    created_lite_agent = None
-    original_lite_agent = LiteAgent
+    # Call kickoff and verify it works
+    result = agent.kickoff("Test query")
 
-    # Define a mock LiteAgent class that captures its arguments
-    class MockLiteAgent(original_lite_agent):
-        def __init__(self, **kwargs):
-            nonlocal created_lite_agent
-            created_lite_agent = kwargs
-            super().__init__(**kwargs)
+    # Verify the agent was configured correctly
+    assert agent.role == "Test Agent"
+    assert agent.goal == "Test Goal"
+    assert agent.backstory == "Test Backstory"
+    assert len(agent.tools) == 2
+    assert isinstance(agent.tools[0], WebSearchTool)
+    assert isinstance(agent.tools[1], CalculatorTool)
+    assert agent.max_iter == max_iter
+    assert agent.verbose == verbose
 
-    # Patch the LiteAgent class
-    monkeypatch.setattr("crewai.agent.core.LiteAgent", MockLiteAgent)
-
-    # Call kickoff to create the LiteAgent
-    agent.kickoff("Test query")
-
-    # Verify all parameters were passed correctly
-    assert created_lite_agent is not None
-    assert created_lite_agent["role"] == "Test Agent"
-    assert created_lite_agent["goal"] == "Test Goal"
-    assert created_lite_agent["backstory"] == "Test Backstory"
-    assert created_lite_agent["llm"] == llm
-    assert len(created_lite_agent["tools"]) == 2
-    assert isinstance(created_lite_agent["tools"][0], WebSearchTool)
-    assert isinstance(created_lite_agent["tools"][1], CalculatorTool)
-    assert created_lite_agent["max_iterations"] == max_iter
-    assert created_lite_agent["max_execution_time"] == max_execution_time
-    assert created_lite_agent["verbose"] == verbose
-    assert created_lite_agent["response_format"] is None
-
-    # Test with a response_format
-    class TestResponse(BaseModel):
-        test_field: str
-
-    agent.kickoff("Test query", response_format=TestResponse)
-    assert created_lite_agent["response_format"] == TestResponse
+    # Verify kickoff returned a result
+    assert result is not None
+    assert result.raw is not None
 
 
 @pytest.mark.vcr()
@@ -310,7 +301,8 @@ def verify_agent_parent_flow(result, agent, flow):
 
 
 def test_sets_parent_flow_when_inside_flow():
-    captured_agent = None
+    """Test that an Agent can be created and executed inside a Flow context."""
+    captured_event = None
 
     mock_llm = Mock(spec=LLM)
     mock_llm.call.return_value = "Test response"
@@ -343,15 +335,17 @@ def test_sets_parent_flow_when_inside_flow():
     event_received = threading.Event()
 
     @crewai_event_bus.on(LiteAgentExecutionStartedEvent)
-    def capture_agent(source, event):
-        nonlocal captured_agent
-        captured_agent = source
+    def capture_event(source, event):
+        nonlocal captured_event
+        captured_event = event
         event_received.set()
 
-    flow.kickoff()
+    result = flow.kickoff()
 
     assert event_received.wait(timeout=5), "Timeout waiting for agent execution event"
-    assert captured_agent.parent_flow is flow
+    assert captured_event is not None
+    assert captured_event.agent_info["role"] == "Test Agent"
+    assert result is not None
 
 
 @pytest.mark.vcr()
@@ -373,16 +367,14 @@ def test_guardrail_is_called_using_string():
 
     @crewai_event_bus.on(LLMGuardrailStartedEvent)
     def capture_guardrail_started(source, event):
-        assert isinstance(source, LiteAgent)
-        assert source.original_agent == agent
+        assert isinstance(source, Agent)
         with condition:
             guardrail_events["started"].append(event)
             condition.notify()
 
     @crewai_event_bus.on(LLMGuardrailCompletedEvent)
     def capture_guardrail_completed(source, event):
-        assert isinstance(source, LiteAgent)
-        assert source.original_agent == agent
+        assert isinstance(source, Agent)
         with condition:
             guardrail_events["completed"].append(event)
             condition.notify()
@@ -683,3 +675,151 @@ def test_agent_kickoff_with_mcp_tools(mock_get_mcp_tools):
 
     # Verify MCP tools were retrieved
     mock_get_mcp_tools.assert_called_once_with("https://mcp.exa.ai/mcp?api_key=test_exa_key&profile=research")
+
+
+# ============================================================================
+# Tests for LiteAgent inside Flow (magic auto-async pattern)
+# ============================================================================
+
+from crewai.flow.flow import listen
+
+
+@pytest.mark.vcr()
+def test_lite_agent_inside_flow_sync():
+    """Test that LiteAgent.kickoff() works magically inside a Flow.
+
+    This tests the "magic auto-async" pattern where calling agent.kickoff()
+    from within a Flow automatically detects the event loop and returns a
+    coroutine that the Flow framework awaits. Users don't need to use async/await.
+    """
+    # Track execution
+    execution_log = []
+
+    class TestFlow(Flow):
+        @start()
+        def run_agent(self):
+            execution_log.append("flow_started")
+            agent = Agent(
+                role="Test Agent",
+                goal="Answer questions",
+                backstory="A helpful test assistant",
+                llm=LLM(model="gpt-4o-mini"),
+                verbose=False,
+            )
+            # Magic: just call kickoff() normally - it auto-detects Flow context
+            result = agent.kickoff(messages="What is 2+2? Reply with just the number.")
+            execution_log.append("agent_completed")
+            return result
+
+    flow = TestFlow()
+    result = flow.kickoff()
+
+    # Verify the flow executed successfully
+    assert "flow_started" in execution_log
+    assert "agent_completed" in execution_log
+    assert result is not None
+    assert isinstance(result, LiteAgentOutput)
+
+
+@pytest.mark.vcr()
+def test_lite_agent_inside_flow_with_tools():
+    """Test that LiteAgent with tools works correctly inside a Flow."""
+    class TestFlow(Flow):
+        @start()
+        def run_agent_with_tools(self):
+            agent = Agent(
+                role="Calculator Agent",
+                goal="Perform calculations",
+                backstory="A math expert",
+                llm=LLM(model="gpt-4o-mini"),
+                tools=[CalculatorTool()],
+                verbose=False,
+            )
+            result = agent.kickoff(messages="Calculate 10 * 5")
+            return result
+
+    flow = TestFlow()
+    result = flow.kickoff()
+
+    assert result is not None
+    assert isinstance(result, LiteAgentOutput)
+    assert result.raw is not None
+
+
+@pytest.mark.vcr()
+def test_multiple_agents_in_same_flow():
+    """Test that multiple LiteAgents can run sequentially in the same Flow."""
+    class MultiAgentFlow(Flow):
+        @start()
+        def first_step(self):
+            agent1 = Agent(
+                role="First Agent",
+                goal="Greet users",
+                backstory="A friendly greeter",
+                llm=LLM(model="gpt-4o-mini"),
+                verbose=False,
+            )
+            return agent1.kickoff(messages="Say hello")
+
+        @listen(first_step)
+        def second_step(self, first_result):
+            agent2 = Agent(
+                role="Second Agent",
+                goal="Say goodbye",
+                backstory="A polite farewell agent",
+                llm=LLM(model="gpt-4o-mini"),
+                verbose=False,
+            )
+            return agent2.kickoff(messages="Say goodbye")
+
+    flow = MultiAgentFlow()
+    result = flow.kickoff()
+
+    assert result is not None
+    assert isinstance(result, LiteAgentOutput)
+
+
+@pytest.mark.vcr()
+def test_lite_agent_kickoff_async_inside_flow():
+    """Test that Agent.kickoff_async() works correctly from async Flow methods."""
+    class AsyncAgentFlow(Flow):
+        @start()
+        async def async_agent_step(self):
+            agent = Agent(
+                role="Async Test Agent",
+                goal="Answer questions asynchronously",
+                backstory="An async helper",
+                llm=LLM(model="gpt-4o-mini"),
+                verbose=False,
+            )
+            result = await agent.kickoff_async(messages="What is 3+3?")
+            return result
+
+    flow = AsyncAgentFlow()
+    result = flow.kickoff()
+
+    assert result is not None
+    assert isinstance(result, LiteAgentOutput)
+
+
+@pytest.mark.vcr()
+def test_lite_agent_standalone_still_works():
+    """Test that LiteAgent.kickoff() still works normally outside of a Flow.
+
+    This verifies that the magic auto-async pattern doesn't break standalone usage
+    where there's no event loop running.
+    """
+    agent = Agent(
+        role="Standalone Agent",
+        goal="Answer questions",
+        backstory="A helpful assistant",
+        llm=LLM(model="gpt-4o-mini"),
+        verbose=False,
+    )
+
+    # This should work normally - no Flow, no event loop
+    result = agent.kickoff(messages="What is 5+5? Reply with just the number.")
+
+    assert result is not None
+    assert isinstance(result, LiteAgentOutput)
+    assert result.raw is not None

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

@@ -0,0 +1,119 @@
+interactions:
+- request:
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+      test assistant\nYour personal goal is: Answer questions\nTo give my best complete
+      final answer to the task respond using the exact following format:\n\nThought:
+      I now can give a great answer\nFinal Answer: Your final answer must be the great
+      and the most complete as possible, it must be outcome described.\n\nI MUST use
+      these formats, my job depends on it!"},{"role":"user","content":"\nCurrent Task:
+      What is 2+2? Reply with just the number.\n\nBegin! This is VERY important to
+      you, use the tools available and give your best Final Answer, your job depends
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lib/crewai/tests/cassettes/agents/test_lite_agent_inside_flow_with_tools.yaml

@@ -0,0 +1,255 @@
+interactions:
+- request:
+    body: '{"messages":[{"role":"system","content":"You are Calculator Agent. A math
+      expert\nYour personal goal is: Perform calculations\nYou ONLY have access to
+      the following tools, and should NEVER make up tools that are not listed here:\n\nTool
+      Name: calculate\nTool Arguments: {\n  \"properties\": {\n    \"expression\":
+      {\n      \"title\": \"Expression\",\n      \"type\": \"string\"\n    }\n  },\n  \"required\":
+      [\n    \"expression\"\n  ],\n  \"title\": \"CalculatorToolSchema\",\n  \"type\":
+      \"object\",\n  \"additionalProperties\": false\n}\nTool Description: Calculate
+      the result of a mathematical expression.\n\nIMPORTANT: Use the following format
+      in your response:\n\n```\nThought: you should always think about what to do\nAction:
+      the action to take, only one name of [calculate], just the name, exactly as
+      it''s written.\nAction Input: the input to the action, just a simple JSON object,
+      enclosed in curly braces, using \" to wrap keys and values.\nObservation: the
+      result of the action\n```\n\nOnce all necessary information is gathered, return
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@@ -1202,7 +1202,8 @@ def test_complex_and_or_branching():
     )
     assert execution_order.index("branch_2b") > min_branch_1_index
 
-    # Final should be last and after both 2a and 2b
+
+    # Final should be after both 2a and 2b
     assert execution_order[-1] == "final"
     assert execution_order.index("final") > execution_order.index("branch_2a")
     assert execution_order.index("final") > execution_order.index("branch_2b")
@@ -1255,10 +1256,11 @@ def test_conditional_router_paths_exclusivity():
 
 
 def test_state_consistency_across_parallel_branches():
-    """Test that state remains consistent when branches execute sequentially.
+    """Test that state remains consistent when branches execute in parallel.
 
-    Note: Branches triggered by the same parent execute sequentially, not in parallel.
-    This ensures predictable state mutations and prevents race conditions.
+    Note: Branches triggered by the same parent execute in parallel for efficiency.
+    Thread-safe state access via StateProxy ensures no race conditions.
+    We check the execution order to ensure the branches execute in parallel.
     """
     execution_order = []
 
@@ -1295,12 +1297,14 @@ def test_state_consistency_across_parallel_branches():
     flow = StateConsistencyFlow()
     flow.kickoff()
 
-    # Branches execute sequentially, so branch_a runs first, then branch_b
-    assert flow.state["branch_a_value"] == 10  # Sees initial value
-    assert flow.state["branch_b_value"] == 11  # Sees value after branch_a increment
+    assert "branch_a" in execution_order
+    assert "branch_b" in execution_order
+    assert "verify_state" in execution_order
 
-    # Final counter should reflect both increments sequentially
-    assert flow.state["counter"] == 16  # 10 + 1 + 5
+    assert flow.state["branch_a_value"] is not None
+    assert flow.state["branch_b_value"] is not None
+
+    assert flow.state["counter"] == 16
 
 
 def test_deeply_nested_conditions():

lib/crewai/tests/test_flow_persistence.py

@@ -247,4 +247,4 @@ def test_persistence_with_base_model(tmp_path):
     assert message.role == "user"
     assert message.type == "text"
     assert message.content == "Hello, World!"
-    assert isinstance(flow.state, State)
+    assert isinstance(flow.state._unwrap(), State)

lib/crewai/tests/test_task_guardrails.py

@@ -185,8 +185,8 @@ def test_task_guardrail_process_output(task_output):
 
     result = guardrail(task_output)
     assert result[0] is False
-
-    assert result[1] == "The task result contains more than 10 words, violating the guardrail. The text provided contains about 21 words."
+    # Check that feedback is provided (wording varies by LLM)
+    assert result[1] == "The task output exceeds the word limit of 10 words by containing 22 words."
 
     guardrail = LLMGuardrail(
         description="Ensure the result has less than 500 words", llm=LLM(model="gpt-4o")

lib/crewai/tests/utilities/test_events.py

@@ -348,11 +348,11 @@ def test_agent_emits_execution_error_event(base_agent, base_task):
 
     error_message = "Error happening while sending prompt to model."
     base_agent.max_retry_limit = 0
-    with patch.object(
-        CrewAgentExecutor, "invoke", wraps=base_agent.agent_executor.invoke
-    ) as invoke_mock:
-        invoke_mock.side_effect = Exception(error_message)
 
+    # Patch at the class level since agent_executor is created lazily
+    with patch.object(
+        CrewAgentExecutor, "invoke", side_effect=Exception(error_message)
+    ):
         with pytest.raises(Exception):  # noqa: B017
             base_agent.execute_task(
                 task=base_task,