refactor: unify skills+cli runtime and streamline ag-ui flow

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

@@ -5,7 +5,6 @@ import contextlib
 from dataclasses import dataclass
 from datetime import datetime, timezone
 from typing import TYPE_CHECKING, Any, Awaitable, Callable
-from uuid import UUID
 
 from ag_ui.core.types import RunAgentInput
 from agentscope.formatter import OpenAIChatFormatter
@@ -18,7 +17,6 @@ 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, resolve_tool_function_names
 from core.agentscope.tools.toolkit import build_toolkit
 from core.agentscope.utils import (
     finalize_json_response,
@@ -38,8 +36,8 @@ from schemas.agent.forwarded_props import (
 from schemas.agent.runtime_models import (
     RouterAgentOutput,
     WorkerAgentOutputLite,
-    resolve_worker_output_model,
 )
+from schemas.agent.skill_config import SkillName
 from schemas.agent.system_agent import (
     AgentType,
     SystemAgentLLMConfig,
@@ -83,7 +81,6 @@ class AgentScopeRunner:
         work_memory: WorkProfileContent | None = None,
         cancel_checker: Callable[[], Awaitable[bool]] | None = None,
     ) -> dict[str, Any]:
-        owner_id = UUID(user_context.id)
         runtime_client_time = self._resolve_runtime_client_time(run_input=run_input)
         runtime_mode = self._resolve_runtime_mode(run_input=run_input)
         stop_cancel_watch = asyncio.Event()
@@ -110,9 +107,7 @@ class AgentScopeRunner:
                     agent_type=AgentType.WORKER,
                 )
                 worker_toolkit = self._build_toolkit(
-                    session=session,
-                    owner_id=owner_id,
-                    enabled_tools=runtime_config.enabled_tools,
+                    enabled_skills=runtime_config.enabled_skills
                 )
 
                 router_output = await self._execute_router_step(
@@ -178,19 +173,11 @@ class AgentScopeRunner:
     def _build_toolkit(
         self,
         *,
-        session: AsyncSession,
-        owner_id: UUID,
-        enabled_tools: list[AgentTool],
+        enabled_skills: list[SkillName],
     ) -> Any:
-        tool_names = (
-            sorted(resolve_tool_function_names(set(enabled_tools)))
-            if enabled_tools
-            else []
-        )
+        enabled_skill_names = {str(skill.value) for skill in enabled_skills}
         return build_toolkit(
-            session=session,
-            owner_id=owner_id,
-            enabled_tool_names=set(tool_names) if tool_names else None,
+            enabled_skill_names=enabled_skill_names if enabled_skill_names else None
         )
 
     async def _load_stage_config(
@@ -279,7 +266,6 @@ class AgentScopeRunner:
         runtime_mode: RuntimeMode,
         work_memory: WorkProfileContent | None,
     ) -> WorkerAgentOutputLite:
-        worker_output_model = resolve_worker_output_model(router_output.execution_mode)
         await self._emit_step_event(
             pipeline=pipeline,
             run_input=run_input,
@@ -295,13 +281,14 @@ class AgentScopeRunner:
             toolkit=toolkit,
             run_input=run_input,
             stage_config=stage_config,
-            worker_output_model=worker_output_model,
+            worker_output_model=WorkerAgentOutputLite,
             pipeline=pipeline,
             runtime_client_time=runtime_client_time,
             runtime_mode=runtime_mode,
             work_memory=work_memory,
+            requires_tool_evidence=router_output.requires_tool_evidence,
         )
-        worker_output = worker_output_model.model_validate(worker_result.payload)
+        worker_output = WorkerAgentOutputLite.model_validate(worker_result.payload)
         await self._emit_step_event(
             pipeline=pipeline,
             run_input=run_input,
@@ -338,7 +325,6 @@ class AgentScopeRunner:
                         user_context=user_context,
                         now_utc=datetime.now(timezone.utc),
                         runtime_client_time=runtime_client_time,
-                        tools=None,
                         user_memory=user_memory,
                     ),
                     "system",
@@ -400,6 +386,7 @@ class AgentScopeRunner:
         runtime_client_time: ClientTimeContext | None,
         runtime_mode: RuntimeMode,
         work_memory: WorkProfileContent | None,
+        requires_tool_evidence: bool = False,
     ) -> StageExecutionResult:
         tracking_model = self._build_model(stage_config=stage_config)
         emitter = PipelineStageEmitter(
@@ -420,12 +407,12 @@ class AgentScopeRunner:
                 now_utc=datetime.now(timezone.utc),
                 runtime_client_time=runtime_client_time,
                 extra_context=stage_config.extra_context,
-                tools=None,
                 work_memory=work_memory,
             ),
             toolkit=toolkit,
             model=tracking_model,
             emitter=emitter,
+            force_tool_on_first_reasoning=requires_tool_evidence,
         )
         async with self._active_agent_lock:
             self._active_agent = agent
@@ -494,6 +481,7 @@ class AgentScopeRunner:
         toolkit: Any,
         model: TrackingChatModel,
         emitter: PipelineStageEmitter | None = None,
+        force_tool_on_first_reasoning: bool = False,
     ) -> JsonReActAgent:
         return JsonReActAgent(
             name=agent_name,
@@ -503,6 +491,7 @@ class AgentScopeRunner:
             toolkit=toolkit,
             memory=InMemoryMemory(),
             emitter=emitter,
+            force_tool_on_first_reasoning=force_tool_on_first_reasoning,
         )
 
     async def _emit_step_event(