Function _default_runner_factory

src/tool_system/tools/workflow.py:90–128

(registry: Any, provider: Any)

def _default_runner_factory(registry: Any, provider: Any) -> Callable[[ToolContext, str], Any]:
    def factory(context: ToolContext, run_id: str) -> Any:
        from src.workflow.runner import LiveAgentRunner

        def resolve(agent_type: str) -> Any:
            from src.agent.agent_definitions import GENERAL_PURPOSE_AGENT
            try:
                from src.agent.agent_definitions import find_agent_by_type
                from src.agent.load_agents_dir import get_agent_definitions_with_overrides

                agents = get_agent_definitions_with_overrides(str(context.cwd or "."))
                found = find_agent_by_type(agents, agent_type)
                return found or GENERAL_PURPOSE_AGENT
            except Exception:
                logger.exception("agent resolution failed; falling back to general-purpose")
                return GENERAL_PURPOSE_AGENT

        # Cap per-agent turns. The subagent default (30) lets an aimless model
        # (e.g. deepseek-flash) loop WebSearch/WebFetch ~30 times without ever
        # finalizing via StructuredOutput — observed as agents "stuck" for
        # minutes and burning ~30x the tokens. Workflow agents do focused tasks
        # (search a few sources → emit; verify → emit; synthesize → write), so a
        # tighter bound forces progress. Disciplined models finish well under it.
        # Env-tunable for workflows that genuinely need deeper agents.
        import os

        max_turns = int(os.environ.get("CLAWCODEX_WORKFLOW_MAX_TURNS", "18"))

        return LiveAgentRunner(
            provider=provider,
            tool_registry=registry,
            parent_context=context,
            base_tools=list(registry.list_tools()),
            resolve_agent=resolve,
            run_id=run_id,
            max_turns=max_turns,
        )

    return factory


def make_workflow_tool(