resp.update(
            {
                "action": "on_agent_action",
                "tool": action.tool,
                "tool_input": action.tool_input,
                "log": action.log,
            }
        )
        resp.update(self.metrics)
        self.mlflg.metrics(self.metrics, step=self.metrics["step"])
        self.records["on_agent_action_records"].append(resp)
        self.records["action_records"].append(resp)
        self.mlflg.jsonf(resp, f"agent_action_{tool_starts}")
    def _create_session_analysis_df(self) -> Any:
        """Create a dataframe with all the information from the session."""
        pd = import_pandas()
        on_llm_start_records_df = pd.DataFrame(self.records["on_llm_start_records"])
        on_llm_end_records_df = pd.DataFrame(self.records["on_llm_end_records"])
        llm_input_columns = ["step", "prompt"]
        if "name" in on_llm_start_records_df.columns:
            llm_input_columns.append("name")
        elif "id" in on_llm_start_records_df.columns:
            # id is llm class's full import path. For example:
            # ["langchain", "llms", "openai", "AzureOpenAI"]
            on_llm_start_records_df["name"] = on_llm_start_records_df["id"].apply(
                lambda id_: id_[-1]
            )
            llm_input_columns.append("name")
        llm_input_prompts_df = (
            on_llm_start_records_df[llm_input_columns]
            .dropna(axis=1)
            .rename({"step": "prompt_step"}, axis=1)
        )
        complexity_metrics_columns = []
        visualizations_columns = []
        complexity_metrics_columns = [