fix: 修复了get_tool调用缓存协程对象的bug

pretor/api/resource.py

@@ -82,5 +82,8 @@ async def delete_skill(skill_name: str, _: TokenData = Depends(RoleChecker(allow
 @resource_router.get("/tool")
 async def get_tools(_: TokenData = Depends(RoleChecker(allowed_roles=UserAuthority.USER))):
     global_state_machine = ray_actor_hook("global_state_machine").global_state_machine
-    tools = await global_state_machine.get_tool_list.remote("default")
-    return {"tools": list(tools.keys())}
+    tool_mapper = await global_state_machine.get_tool_mapper.remote()
+    all_tool_names = set()
+    for scope_tools in tool_mapper.values():
+        all_tool_names.update(scope_tools.keys())
+    return {"tools": list(all_tool_names)}

pretor/core/individual/consciousness_node/consciousness_node.py

@@ -134,7 +134,7 @@ class ConsciousnessNode:
     async def _run(self, payload: Union[ForSupervisoryInput, ForWorkflowInput, ForWorkflowEngineInput]) -> Union[ForSupervisoryNode, ForWorkflow, ForWorkflowEngine]:
         try:
             self.agent.retries = 3
-            tool = get_tool("control_node")
+            tool = await get_tool("control_node")
             if isinstance(payload, ForWorkflowEngineInput):
                 deps = ConsciousnessNodeDeps(
                     original_command=payload.original_command,

pretor/core/individual/control_node/control_node.py

@@ -89,7 +89,7 @@ class ControlNode:
             )
             self.logger.debug(f"ControlNode: 开始执行工作流节点 [{payload.workflow_step.name}] (原生重试开启)")
 
-            tool = get_tool("control_node")
+            tool = await get_tool("control_node")
 
             result = await self.agent.run(
                 f"请根据提供的 workflow_step 上下文，执行此步骤并输出结果。\n详细指令或附加数据：{payload.workflow_step.model_dump_json()}",

pretor/core/individual/supervisory_node/supervisory_node.py

@@ -172,7 +172,7 @@ class SupervisoryNode:
             if isinstance(payload, TerminationMessage):
                 prompt_message = f"【工作流执行结束报告】\n请将以下技术报告转化为对用户的友好回复：\n{message}"
             self.agent.retries = 3
-            tool = get_tool("supervisory_node")
+            tool = await get_tool("supervisory_node")
             result = await self.agent.run(prompt_message,
                                           deps=deps,
                                           tools=tool)

pretor/plugin/tool_plugin/file_reader/file_reader.py

@@ -17,6 +17,7 @@ from pretor.plugin.tool_plugin.base_tool import BaseToolData
 import os
 
 class FileReaderData(BaseToolData):
+    is_system: bool = True
     name: str = "file_reader"
     description: str = "读取本地文件的内容"
 

pretor/utils/get_tool.py

@@ -15,12 +15,12 @@
 import importlib
 from typing import Callable, Dict, List
 import pathlib
-from functools import lru_cache
 from pretor.utils.ray_hook import ray_actor_hook
 
 from pretor.utils.logger import get_logger
 logger = get_logger('get_tool')
 _tool_cache: Dict[str, Callable] = {}
+_agent_tool_result_cache: Dict[str, List[Callable]] = {}
 
 
 def _get_tool_func(tool_name: str) -> Callable | None:
@@ -48,10 +48,13 @@ def del_tool_cache(tool_name: str) -> None:
         del _tool_cache[tool_name]
     refresh_agent_tools()
 
-@lru_cache(maxsize=1)
+
 async def get_tool(agent_name: str) -> List[Callable]:
+    cached = _agent_tool_result_cache.get(agent_name)
+    if cached is not None:
+        return cached
     global_state_machine = ray_actor_hook("global_state_machine")
-    _tool_list = await global_state_machine.get_tool_list.remote( agent_name)
+    _tool_list = await global_state_machine.get_tool_list.remote(agent_name)
     tool_list = []
     for tool_name in _tool_list.keys():
         tool_func = _get_tool_func(tool_name)
@@ -59,9 +62,8 @@ async def get_tool(agent_name: str) -> List[Callable]:
             tool_list.append(tool_func)
         else:
             continue
+    _agent_tool_result_cache[agent_name] = tool_list
     return tool_list
 
 def refresh_agent_tools() -> None:
-    get_tool.cache_clear()
-
-
+    _agent_tool_result_cache.clear()