Method _create_summary

mini_agent/agent.py:262–319

Create summary for one execution round Args: messages: List of messages to summarize round_num: Round number Returns: Summary text

(self, messages: list[Message], round_num: int)

        print(f"{Colors.DIM}  Note: API token count will update on next LLM call{Colors.RESET}")

    async def _create_summary(self, messages: list[Message], round_num: int) -> str:
        """Create summary for one execution round

        Args:
            messages: List of messages to summarize
            round_num: Round number

        Returns:
            Summary text
        """
        if not messages:
            return ""

        # Build summary content
        summary_content = f"Round {round_num} execution process:\n\n"
        for msg in messages:
            if msg.role == "assistant":
                content_text = msg.content if isinstance(msg.content, str) else str(msg.content)
                summary_content += f"Assistant: {content_text}\n"
                if msg.tool_calls:
                    tool_names = [tc.function.name for tc in msg.tool_calls]
                    summary_content += f"  → Called tools: {', '.join(tool_names)}\n"
            elif msg.role == "tool":
                result_preview = msg.content if isinstance(msg.content, str) else str(msg.content)
                summary_content += f"  ← Tool returned: {result_preview}...\n"

        # Call LLM to generate concise summary
        try:
            summary_prompt = f"""Please provide a concise summary of the following Agent execution process:

{summary_content}

Requirements:
1. Focus on what tasks were completed and which tools were called
2. Keep key execution results and important findings
3. Be concise and clear, within 1000 words
4. Use English
5. Do not include "user" related content, only summarize the Agent's execution process"""

            summary_msg = Message(role="user", content=summary_prompt)
            response = await self.llm.generate(
                messages=[
                    Message(
                        role="system",
                        content="You are an assistant skilled at summarizing Agent execution processes.",
                    ),
                    summary_msg,
                ]
            )

            summary_text = response.content
            print(f"{Colors.BRIGHT_GREEN}✓ Summary for round {round_num} generated successfully{Colors.RESET}")
            return summary_text

        except Exception as e:
            print(f"{Colors.BRIGHT_RED}✗ Summary generation failed for round {round_num}: {e}{Colors.RESET}")
            # Use simple text summary on failure
            return summary_content