output, color="green", verbose=self.verbose
            )
        final_output = output.return_values
        if self.return_intermediate_steps:
            final_output["intermediate_steps"] = intermediate_steps
        return final_output
    def _take_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[CallbackManagerForChainRun] = 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 = self.agent.plan(
                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)
                    text = str(e.llm_output)
                else:
                    observation = "Invalid or incomplete response"
            elif isinstance(self.handle_parsing_errors, str):
                observation = self.handle_parsing_errors
            elif callable(self.handle_parsing_errors):
                observation = self.handle_parsing_errors(e)