DeepResearchTool

def __init__(
    self,
    llm_config: dict[str, Any],
    max_web_steps: int = 30,
):
    """Initialize the DeepResearchTool.

    Args:
        llm_config (dict[str, Any]): The LLM configuration.
        max_web_steps (int, optional): The maximum number of web steps. Defaults to 30.
    """
    self.llm_config = llm_config

    self.summarizer_agent = ConversableAgent(
        name="SummarizerAgent",
        system_message=(
            "You are an agent with a task of answering the question provided by the user."
            "First you need to split the question into subquestions by calling the 'split_question_and_answer_subquestions' method."
            "Then you need to sintesize the answers the original question by combining the answers to the subquestions."
        ),
        is_termination_msg=lambda x: x.get("content", "")
        and x.get("content", "").startswith(self.ANSWER_CONFIRMED_PREFIX),
        llm_config=llm_config,
        human_input_mode="NEVER",
    )

    self.critic_agent = ConversableAgent(
        name="CriticAgent",
        system_message=(
            "You are a critic agent responsible for evaluating the answer provided by the summarizer agent.\n"
            "Your task is to assess the quality of the answer based on its coherence, relevance, and completeness.\n"
            "Provide constructive feedback on how the answer can be improved.\n"
            "If the answer is satisfactory, call the 'confirm_answer' method to end the task.\n"
        ),
        is_termination_msg=lambda x: x.get("content", "")
        and x.get("content", "").startswith(self.ANSWER_CONFIRMED_PREFIX),
        llm_config=llm_config,
        human_input_mode="NEVER",
    )

    def delegate_research_task(
        task: Annotated[str, "The task to perform a research on."],
        llm_config: Annotated[dict[str, Any], Depends(on(llm_config))],
        max_web_steps: Annotated[int, Depends(on(max_web_steps))],
    ) -> str:
        """Delegate a research task to the agent.

        Args:
            task (str): The task to perform a research on.
            llm_config (dict[str, Any]): The LLM configuration.
            max_web_steps (int): The maximum number of web steps.

        Returns:
            str: The answer to the research task.
        """

        @self.summarizer_agent.register_for_execution()
        @self.critic_agent.register_for_llm(description="Call this method to confirm the final answer.")
        def confirm_summary(answer: str, reasoning: str) -> str:
            return f"{self.ANSWER_CONFIRMED_PREFIX}" + answer + "\nReasoning: " + reasoning

        split_question_and_answer_subquestions = DeepResearchTool._get_split_question_and_answer_subquestions(
            llm_config=llm_config,
            max_web_steps=max_web_steps,
        )

        self.summarizer_agent.register_for_llm(description="Split the question into subquestions and get answers.")(
            split_question_and_answer_subquestions
        )
        self.critic_agent.register_for_execution()(split_question_and_answer_subquestions)

        result = self.critic_agent.initiate_chat(
            self.summarizer_agent,
            message="Please answer the following question: " + task,
            # This outer chat should preserve the history of the conversation
            clear_history=False,
        )

        return result.summary

    super().__init__(
        name=delegate_research_task.__name__,
        description="Delegate a research task to the deep research agent.",
        func_or_tool=delegate_research_task,
    )