Method _inner_loop

src/harness/agentic_loop/loop.py:902–1101

The core while(True) tool-calling loop.

(self)

            self._orchestrator_tools = saved_orch

    def _inner_loop(self) -> str:
        """The core while(True) tool-calling loop."""
        context_limit = get_model_context_limit(self.config.model)
        wrapping_up = False
        empty_response_count = 0

        while self.session.turn_count < self.config.max_turns:
            self.session.turn_count += 1

            # ── Proactive context monitoring ──────────────────────────────
            if not wrapping_up:
                current_tokens = count_message_tokens(
                    self.session.messages, self.config.model
                )
                if current_tokens > 0:
                    utilization = current_tokens / context_limit
                    self.logger.event(
                        "agent_iteration",
                        {
                            "number": self.session.turn_count,
                            "max": self.config.max_turns,
                            "tokens": current_tokens,
                            "limit": context_limit,
                            "utilization": round(utilization * 100, 1),
                        },
                    )
                    if utilization >= _CONTEXT_WRAP_UP_THRESHOLD:
                        self.session.add_user_message(
                            "You are approaching the context limit. "
                            "Please wrap up your current work and provide a final "
                            "response now. Do not start any new tool calls."
                        )
                        self.logger.event(
                            "warning",
                            {
                                "message": (
                                    f"Context at {utilization:.0%} "
                                    f"({current_tokens:,}/{context_limit:,} tokens). "
                                    "Wrap-up message injected."
                                ),
                            },
                        )
                        wrapping_up = True

            # ── Call LLM ─────────────────────────────────────────────────
            try:
                response = self.llm_client.completion(
                    model=self.config.model,
                    messages=self.session.messages,
                    tools=self._all_tools if self._all_tools else None,
                    temperature=self.config.temperature,
                    **self.config.model_kwargs,
                )
            except litellm.NotFoundError as e:
                self.logger.event(
                    "error",
                    {
                        "message": f"Model not found: '{self.config.model}'. Check the model name and try again.\n{e}",