f"{observation} and said {response_text}",
                    self.memory.now_key: now,
                },
            )
            return True, f"{self.name} said {response_text}"
        else:
            return False, result
    ######################################################
    # Agent stateful' summary methods.                   #
    # Each dialog or response prompt includes a header   #
    # summarizing the agent's self-description. This is  #
    # updated periodically through probing its memories  #
    ######################################################
    def _compute_agent_summary(self) -> str:
        """"""
        prompt = PromptTemplate.from_template(
            "How would you summarize {name}'s core characteristics given the"
            + " following statements:\n"
            + "{relevant_memories}"
            + "Do not embellish."
            + "\n\nSummary: "
        )
        # The agent seeks to think about their core characteristics.
        return (
            self.chain(prompt)
            .run(name=self.name, queries=[f"{self.name}'s core characteristics"])
            .strip()
        )
[docs]    def get_summary(
        self, force_refresh: bool = False, now: Optional[datetime] = None
    ) -> str:
        """Return a descriptive summary of the agent."""
        current_time = datetime.now() if now is None else now
        since_refresh = (current_time - self.last_refreshed).seconds
        if (
            not self.summary
            or since_refresh >= self.summary_refresh_seconds
            or force_refresh
        ):
            self.summary = self._compute_agent_summary()
            self.last_refreshed = current_time
        age = self.age if self.age is not None else "N/A"
        return (
            f"Name: {self.name} (age: {age})"