)
            else:
                tool_run_kwargs = self.agent.tool_run_logging_kwargs()
                observation = InvalidTool().run(
                    {
                        "requested_tool_name": agent_action.tool,
                        "available_tool_names": list(name_to_tool_map.keys()),
                    },
                    verbose=self.verbose,
                    color=None,
                    callbacks=run_manager.get_child() if run_manager else None,
                    **tool_run_kwargs,
                )
            result.append((agent_action, observation))
        return result
    async def _atake_next_step(
        self,
        name_to_tool_map: Dict[str, BaseTool],
        color_mapping: Dict[str, str],
        inputs: Dict[str, str],
        intermediate_steps: List[Tuple[AgentAction, str]],
        run_manager: Optional[AsyncCallbackManagerForChainRun] = None,
    ) -> Union[AgentFinish, List[Tuple[AgentAction, str]]]:
        """Take a single step in the thought-action-observation loop.
        Override this to take control of how the agent makes and acts on choices.
        """
        try:
            intermediate_steps = self._prepare_intermediate_steps(intermediate_steps)
            # Call the LLM to see what to do.
            output = await self.agent.aplan(
                intermediate_steps,
                callbacks=run_manager.get_child() if run_manager else None,
                **inputs,
            )
        except OutputParserException as e:
            if isinstance(self.handle_parsing_errors, bool):
                raise_error = not self.handle_parsing_errors
            else:
                raise_error = False
            if raise_error:
                raise e
            text = str(e)
            if isinstance(self.handle_parsing_errors, bool):
                if e.send_to_llm:
                    observation = str(e.observation)