Method _run_evolution_loop

openspace/skill_engine/evolver.py:1084–1239

Run evolution as a token-driven agent loop. Modeled after ``GroundingAgent.process()`` — the loop continues until the LLM outputs an explicit completion/failure token, NOT based on whether tools were called. Termination signals (checked every iteration, regardless o

(
        self,
        prompt: str,
        ctx: EvolutionContext,
    )

        return None

    async def _run_evolution_loop(
        self,
        prompt: str,
        ctx: EvolutionContext,
    ) -> Optional[str]:
        """Run evolution as a token-driven agent loop.

        Modeled after ``GroundingAgent.process()`` — the loop continues
        until the LLM outputs an explicit completion/failure token, NOT
        based on whether tools were called.

        Termination signals (checked every iteration, regardless of tool use):
          - ``EVOLUTION_COMPLETE`` in assistant content → success, return edit.
          - ``EVOLUTION_FAILED``   in assistant content → failure, return None.

        Tool availability:
          - Iterations 1 … N-1: tools enabled (LLM may gather information).
          - Iteration N (final): tools disabled, LLM must output a decision.

        Each non-final iteration without a token gets a nudge message
        telling the LLM which iteration it is on and how many remain.

        Conversations are recorded to ``conversations.jsonl`` via
        ``RecordingManager`` (agent_name="SkillEvolver") so the full
        evolution dialogue is preserved for debugging and replay.
        """
        from openspace.recording import RecordingManager

        model = self._model or self._llm_client.model

        # Merge tools from context and instance-level
        evolution_tools: List["BaseTool"] = list(ctx.available_tools or [])
        if not evolution_tools:
            evolution_tools = list(self._available_tools)

        messages: List[Dict[str, Any]] = [
            {"role": "user", "content": prompt},
        ]

        # Record initial conversation setup
        await RecordingManager.record_conversation_setup(
            setup_messages=copy.deepcopy(messages),
            tools=evolution_tools if evolution_tools else None,
            agent_name="SkillEvolver",
            extra={
                "evolution_type": ctx.suggestion.evolution_type.value,
                "trigger": ctx.trigger.value,
                "target_skills": ctx.suggestion.target_skill_ids,
            },
        )

        for iteration in range(_MAX_EVOLUTION_ITERATIONS):
            is_last = iteration == _MAX_EVOLUTION_ITERATIONS - 1

            # Snapshot message count before any additions + LLM call
            msg_count_before = len(messages)

            # Final round: disable tools and force a decision