"on_llm_end_records": [],
            "on_chain_start_records": [],
            "on_chain_end_records": [],
            "on_tool_start_records": [],
            "on_tool_end_records": [],
            "on_text_records": [],
            "on_agent_finish_records": [],
            "on_agent_action_records": [],
            "action_records": [],
        }
    def _reset(self) -> None:
        for k, v in self.metrics.items():
            self.metrics[k] = 0
        for k, v in self.records.items():
            self.records[k] = []
[docs]    def on_llm_start(
        self, serialized: Dict[str, Any], prompts: List[str], **kwargs: Any
    ) -> None:
        """Run when LLM starts."""
        self.metrics["step"] += 1
        self.metrics["llm_starts"] += 1
        self.metrics["starts"] += 1
        llm_starts = self.metrics["llm_starts"]
        resp: Dict[str, Any] = {}
        resp.update({"action": "on_llm_start"})
        resp.update(flatten_dict(serialized))
        resp.update(self.metrics)
        self.mlflg.metrics(self.metrics, step=self.metrics["step"])
        for idx, prompt in enumerate(prompts):
            prompt_resp = deepcopy(resp)
            prompt_resp["prompt"] = prompt
            self.records["on_llm_start_records"].append(prompt_resp)
            self.records["action_records"].append(prompt_resp)
            self.mlflg.jsonf(prompt_resp, f"llm_start_{llm_starts}_prompt_{idx}")
[docs]    def on_llm_new_token(self, token: str, **kwargs: Any) -> None:
        """Run when LLM generates a new token."""