refactor: 梳理规则体系并统一记忆与部署流程

backend/src/core/agentscope/runtime/runner.py

@@ -16,7 +16,7 @@ from core.agentscope.schemas.agui_input import extract_latest_user_payload
 from core.agentscope.runtime.json_react_agent import JsonReActAgent
 from core.agentscope.runtime.model_tracking import TrackingChatModel
 from core.agentscope.runtime.stage_emitter import PipelineStageEmitter
-from core.agentscope.tools.tool_config import AgentTool
+from core.agentscope.tools.tool_config import AgentTool, resolve_tool_function_names
 from core.agentscope.tools.toolkit import build_toolkit
 from core.agentscope.utils import (
     finalize_json_response,
@@ -123,7 +123,11 @@ class AgentScopeRunner:
         owner_id: UUID,
         enabled_tools: list[AgentTool],
     ) -> Any:
-        tool_names = [t.value for t in enabled_tools] if enabled_tools else []
+        tool_names = (
+            sorted(resolve_tool_function_names(set(enabled_tools)))
+            if enabled_tools
+            else []
+        )
         return build_toolkit(
             session=session,
             owner_id=owner_id,
@@ -189,6 +193,14 @@ class AgentScopeRunner:
             run_input=run_input,
             step_name=AgentType.ROUTER.value,
             event_type="STEP_FINISHED",
+            extra_event={
+                "_router_persist": {
+                    "router_output": router_output.model_dump(
+                        mode="json", exclude_none=True
+                    ),
+                    "response_metadata": router_result.response_metadata,
+                }
+            },
         )
         return router_output
 
@@ -382,11 +394,13 @@ class AgentScopeRunner:
         self, *, stage_config: SystemAgentRuntimeConfig
     ) -> TrackingChatModel:
         generate_kwargs: dict[str, Any] = {
-            "temperature": stage_config.llm_config.temperature,
-            "max_tokens": stage_config.llm_config.max_tokens,
             "timeout": stage_config.llm_config.timeout_seconds,
             "extra_body": {"enable_thinking": False},
         }
+        if stage_config.llm_config.temperature is not None:
+            generate_kwargs["temperature"] = stage_config.llm_config.temperature
+        if stage_config.llm_config.max_tokens is not None:
+            generate_kwargs["max_tokens"] = stage_config.llm_config.max_tokens
 
         model = OpenAIChatModel(
             model_name=stage_config.model_code,
@@ -423,15 +437,19 @@ class AgentScopeRunner:
         run_input: RunAgentInput,
         step_name: str,
         event_type: str,
+        extra_event: dict[str, Any] | None = None,
     ) -> None:
+        payload: dict[str, Any] = {
+            "type": event_type,
+            "threadId": run_input.thread_id,
+            "runId": run_input.run_id,
+            "stepName": step_name,
+        }
+        if extra_event:
+            payload.update(extra_event)
         await pipeline.emit(
             session_id=run_input.thread_id,
-            event={
-                "type": event_type,
-                "threadId": run_input.thread_id,
-                "runId": run_input.run_id,
-                "stepName": step_name,
-            },
+            event=payload,
         )
 
     def _resolve_runtime_client_time(