feat: add async execution support to agent executor

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

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

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

@@ -242,17 +242,17 @@ def get_llm_response(
     """Call the LLM and return the response, handling any invalid responses.
 
     Args:
-        llm: The LLM instance to call
-        messages: The messages to send to the LLM
-        callbacks: List of callbacks for the LLM call
-        printer: Printer instance for output
-        from_task: Optional task context for the LLM call
-        from_agent: Optional agent context for the LLM call
-        response_model: Optional Pydantic model for structured outputs
-        executor_context: Optional executor context for hook invocation
+        llm: The LLM instance to call.
+        messages: The messages to send to the LLM.
+        callbacks: List of callbacks for the LLM call.
+        printer: Printer instance for output.
+        from_task: Optional task context for the LLM call.
+        from_agent: Optional agent context for the LLM call.
+        response_model: Optional Pydantic model for structured outputs.
+        executor_context: Optional executor context for hook invocation.
 
     Returns:
-        The response from the LLM as a string
+        The response from the LLM as a string.
 
     Raises:
         Exception: If an error occurs.
@@ -284,6 +284,60 @@ def get_llm_response(
     return _setup_after_llm_call_hooks(executor_context, answer, printer)
 
 
+async def aget_llm_response(
+    llm: LLM | BaseLLM,
+    messages: list[LLMMessage],
+    callbacks: list[TokenCalcHandler],
+    printer: Printer,
+    from_task: Task | None = None,
+    from_agent: Agent | LiteAgent | None = None,
+    response_model: type[BaseModel] | None = None,
+    executor_context: CrewAgentExecutor | None = None,
+) -> str:
+    """Call the LLM asynchronously and return the response.
+
+    Args:
+        llm: The LLM instance to call.
+        messages: The messages to send to the LLM.
+        callbacks: List of callbacks for the LLM call.
+        printer: Printer instance for output.
+        from_task: Optional task context for the LLM call.
+        from_agent: Optional agent context for the LLM call.
+        response_model: Optional Pydantic model for structured outputs.
+        executor_context: Optional executor context for hook invocation.
+
+    Returns:
+        The response from the LLM as a string.
+
+    Raises:
+        Exception: If an error occurs.
+        ValueError: If the response is None or empty.
+    """
+    if executor_context is not None:
+        if not _setup_before_llm_call_hooks(executor_context, printer):
+            raise ValueError("LLM call blocked by before_llm_call hook")
+        messages = executor_context.messages
+
+    try:
+        answer = await llm.acall(
+            messages,
+            callbacks=callbacks,
+            from_task=from_task,
+            from_agent=from_agent,  # type: ignore[arg-type]
+            response_model=response_model,
+        )
+    except Exception as e:
+        raise e
+    if not answer:
+        printer.print(
+            content="Received None or empty response from LLM call.",
+            color="red",
+        )
+        raise ValueError("Invalid response from LLM call - None or empty.")
+
+    return _setup_after_llm_call_hooks(executor_context, answer, printer)
+
+
 def process_llm_response(
     answer: str, use_stop_words: bool
 ) -> AgentAction | AgentFinish: