wip: 优化了control_node consciousness_node和supervisory_node

config/config.yml

@@ -1 +1,2 @@
-version: v0.1
+version: v0.1
+name:

docs/problem.md

@@ -2,7 +2,7 @@
 ---
 ## 问题栏
 #### 🔴 核心缺陷与修复 (Bug Fixes & Stability)
-- [ ] /pretor/core/individual每个template进行优化
+- [x] /pretor/core/individual每个template进行优化
 - [ ] /pretor/worker_individual待完善复合子个体和基础子个体
 
 #### 🛡️ 安全与合规 (Security & Auth)
@@ -13,10 +13,11 @@
 - [ ] 优化import
 
 #### 🏗️ 架构演进 (Architecture & Refactoring)
-- [ ] 使用fastapi-users完善用户系统
+- ~~[ ] 使用fastapi-users完善用户系统~~(2026/4/19 fastapi-users会严重摧毁代码的优雅性)
+- [ ] 升级auth功能
 - [x] /pretor/api的接口函数进行重构
 - [ ] /dockerfile待完善
-- - [ ] 完善沙箱功能
+- [ ] 完善沙箱功能
 - [ ] 完善爬虫功能
 - [ ] 对接更多的provider
 
@@ -24,7 +25,7 @@
 ## 日志
 #### 2026/4/12
 - [x] /pretor/api的接口函数进行重构
-- [ ] /pretor/core/individual每个template进行优化
+- [x] /pretor/core/individual每个template进行优化
 - [ ] /pretor/worker_individual待完善复合子个体和基础子个体
 - [ ] /pretor/api待完善
 - [ ] /dockerfile待完善
@@ -32,10 +33,11 @@
 #### 2026/4/16
 - [ ] 发布v0.1.0正式版
 - [ ] 增加对应全workflow的情况追踪，使得在任务运行中人机交互更加自然方便
-- [ ] 使用fastapi-users完善用户系统
+- ~~[ ] 使用fastapi-users完善用户系统~~
 
 #### 2026/4/19
 - [ ] 完善沙箱功能
 - [ ] 完善爬虫功能
 - [ ] 对接更多的provider
-- [ ] 优化import
+- [ ] 优化import
+- [ ] 升级auth功能

pretor/api/agent.py

@@ -11,6 +11,8 @@
 #  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 #  See the License for the specific language governing permissions and
 #  limitations under the License.
+
+
 from typing import Union
 from pretor.utils.ray_hook import ray_actor_hook
 from fastapi import APIRouter, Request, Depends

pretor/api/auth.py

@@ -42,5 +42,4 @@ async def login_user(user_login: UserLogin):
     if user.user_name != user_login.user_name:
         pass
     token = await run_in_threadpool(Accessor.login_hashed_password, user, user_login.password)
-    return {"message":"success", "token":token}
+    return {"message":"success", "token":token}
-

pretor/api/cluster.py

@@ -0,0 +1,40 @@
+#  Copyright 2026 zhaoxi826
+#
+#  Licensed under the Apache License, Version 2.0 (the "License");
+#  you may not use this file except in compliance with the License.
+#  You may obtain a copy of the License at
+#
+#       http://www.apache.org/licenses/LICENSE-2.0
+#
+#  Unless required by applicable law or agreed to in writing, software
+#  distributed under the License is distributed on an "AS IS" BASIS,
+#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+#  See the License for the specific language governing permissions and
+#  limitations under the License.
+
+from fastapi import APIRouter, WebSocket, WebSocketDisconnect
+import ray
+import asyncio
+
+cluster_router = APIRouter(prefix="/api/v1/cluster", tags=["cluster"])
+
+@cluster_router.websocket("/ws/state")
+async def update_cluster_state(websocket: WebSocket):
+    await websocket.accept()
+    try:
+        while True:
+            nodes = ray.nodes()
+            payload = [
+                {
+                    "node_id": node["NodeID"],
+                    "node_name": node["NodeName"],
+                    "alive": node["Alive"],
+                    "resources": node["Resources"],
+                    "remaining": node["RemainingResources"]
+                }
+                for node in nodes
+            ]
+            await websocket.send_json(payload)
+            await asyncio.sleep(10)
+    except WebSocketDisconnect:
+        pass

pretor/api/platform/frontend.py

@@ -12,7 +12,7 @@
 #  See the License for the specific language governing permissions and
 #  limitations under the License.
 
-from fastapi import APIRouter, Request, Depends, HTTPException, status, WebSocket, WebSocketDisconnect
+from fastapi import APIRouter, Depends, HTTPException, status, WebSocket, WebSocketDisconnect
 from pydantic import BaseModel
 from pretor.utils.access import Accessor, TokenData
 from pretor.api.platform.event import PretorEvent
@@ -46,14 +46,3 @@ async def create_message(message: Message,
     else:
         return {"message": message}
 
-@client_router.websocket("/ws/{event_id}")
-async def websocket_endpoint(websocket: WebSocket, event_id: str):
-    await websocket.accept()
-    global_state_machine = websocket.app.state.global_state_machine
-    try:
-        while True:
-            await websocket.send_text(await global_state_machine.get_pending(event_id))
-            response = await websocket.receive_text()
-            await global_state_machine.put_received(event_id, response)
-    except WebSocketDisconnect:
-        pass

pretor/api/workflow.py

@@ -0,0 +1,34 @@
+#  Copyright 2026 zhaoxi826
+#
+#  Licensed under the Apache License, Version 2.0 (the "License");
+#  you may not use this file except in compliance with the License.
+#  You may obtain a copy of the License at
+#
+#       http://www.apache.org/licenses/LICENSE-2.0
+#
+#  Unless required by applicable law or agreed to in writing, software
+#  distributed under the License is distributed on an "AS IS" BASIS,
+#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+#  See the License for the specific language governing permissions and
+#  limitations under the License.
+
+
+from pretor.utils.ray_hook import ray_actor_hook
+from fastapi import APIRouter, WebSocket, WebSocketDisconnect
+
+
+workflow_router = APIRouter(prefix="/api/v1/workflow", tags=["workflow"])
+
+@workflow_router.websocket("/ws/{event_id}")
+async def get_workflow(websocket: WebSocket, event_id: str):
+    await websocket.accept()
+    global_state_machine = ray_actor_hook("global_state_machine")
+    try:
+        while True:
+            await websocket.send(await global_state_machine.get_workflow.remote(event_id))
+            await websocket.send_text(await global_state_machine.get_pending.remote(event_id))
+            response = await websocket.receive_text()
+            await global_state_machine.put_received(event_id, response)
+    except WebSocketDisconnect:
+        pass
+

pretor/core/api/__init__.py

@@ -28,6 +28,8 @@ from pretor.api.platform.frontend import client_router
 from pretor.api.auth import auth_router
 from pretor.api.provider import provider_router
 from pretor.api.resource import resource_router
+from pretor.api.cluster import cluster_router
+from pretor.api.agent import agent_router
 
 @ray.remote
 class PretorGateway:
@@ -41,11 +43,15 @@ class PretorGateway:
         self.app = FastAPI()
         self.gateway = {}
 
-        self.app.include_router(client_router)
+        self.app.include_router(client_router)#客户端路径
-        self.app.include_router(auth_router)
-        self.app.include_router(provider_router)
-        self.app.include_router(resource_router)
 
+        self.app.include_router(auth_router)#用户路径
+        self.app.include_router(provider_router)#供应商路径
+        self.app.include_router(resource_router)#资源路径
+        self.app.include_router(cluster_router)#集群信息路径
+        self.app.include_router(agent_router)#agent路径
+
+        
         frontend_dir = os.path.join(os.path.dirname(os.path.dirname(os.path.dirname(os.path.dirname(__file__)))), "frontend", "dist")
 
         if os.path.exists(frontend_dir):

pretor/core/global_state_machine/global_state_machine.py

@@ -20,7 +20,7 @@ from pretor.core.global_state_machine.model_provider import Provider, ProviderAr
 import httpx
 import json
 from loguru import logger
-from typing import Dict, Literal
+from typing import Dict, Literal, List
 from pretor.core.database.postgres import PostgresDatabase
 from pretor.api.platform.event import PretorEvent
 import asyncio
@@ -64,6 +64,9 @@ class GlobalStateMachine:
     def update_workflow(self, event_id: str, workflow: PretorWorkflow) -> None:
         self.event_dict[event_id].workflow = workflow
 
+    def get_workflow(self, event_id: str) -> PretorWorkflow:
+        return self.event_dict[event_id].workflow
+
     async def put_pending(self, event_id, item) -> None:
         await self.event_dict[event_id].pending_queue.put(item)
 
@@ -161,6 +164,9 @@ class GlobalStateMachine:
     def workflow_template_generate(self, workflow_template: WorkflowTemplate) -> None:
         self.global_workflow_template_manager.generate_workflow_template(workflow_template)
 
+    def get_workflow_template_list(self) -> List[Dict[str, str]]:
+        return self.global_workflow_template_manager.workflow_templates_registry
+
     ###以下为skill_manager方法
     def add_skill(self, skill_name: str):
         skill_plugin_dir = pathlib.Path(__file__).parent.parent.parent / "plugin" / "skill_plugin" / skill_name

pretor/core/individual/consciousness_node/consciousness_node.py

@@ -21,6 +21,8 @@ from pydantic_ai import Agent, RunContext
 from pretor.core.global_state_machine.global_state_machine import GlobalStateMachine
 from pretor.core.global_state_machine.model_provider.base_provider import Provider
 from pretor.adapter.model_adapter.agent_factory import AgentFactory
+from loguru import logger
+
 
 @ray.remote
 class ConsciousnessNode:
@@ -41,7 +43,15 @@ class ConsciousnessNode:
         Returns:
             无返回
         """
-        system_prompt: str = ""
+        system_prompt: str = (
+            "你叫Pretor，是一个多智能体AI助手系统中的【意识节点 (Consciousness Node)】。\n"
+            "你是系统的'高级规划师'和'架构师'，负责处理监控节点分配过来的复杂任务。\n"
+            "你的主要工作场景包括：\n"
+            "1. 拆解任务 (Workflow Generation)：结合用户的原始命令和提供的模板，生成严谨、可执行的工作流 (PretorWorkflow)，并将其输出为 ForWorkflowEngine 格式。拆解时步骤应清晰连贯。\n"
+            "2. 中途指导 (Workflow Execution)：在工作流执行中，如果某一步骤指派给你，你需要对控制节点的结果进行分析或提供下一步的指导，输出 ForWorkflow 格式。\n"
+            "3. 总结报告 (Supervisory Report)：在整个工作流执行完毕后，你需要对整体流程、各个控制节点的执行情况进行审查，并生成一份技术性的总结报告，输出 ForSupervisoryNode 格式。\n"
+            "请确保所有的思考和生成过程符合逻辑，严密且高质量。"
+        )
         output_type = Union[ForSupervisoryNode, ForWorkflow, ForWorkflowEngine]
         provider: Provider = global_state_machine.get_provider.remote(provider_title)
         agent_factory = AgentFactory()
@@ -54,22 +64,30 @@ class ConsciousnessNode:
 
         @self.agent.system_prompt
         async def dynamic_prompt(ctx: RunContext[ConsciousnessNodeDeps]):
-            return f"Context: original_command: {ctx.deps.original_command}, workflow_template: {ctx.deps.workflow_template}"
+            prompt = system_prompt + "\n\n"
+            prompt += (
+                f"=== 当前任务上下文 ===\n"
+                f"- 当前指令 (Command): {ctx.deps.command}\n"
+                f"- 原始用户命令 (Original Command): {ctx.deps.original_command}\n"
+            )
+            if ctx.deps.workflow_template:
+                prompt += f"- 选定工作流模板 (Workflow Template): {ctx.deps.workflow_template}\n"
 
-    async def working(self, payload: Union[ForWorkflowEngineInput, ForWorkflowInput, ForSupervisoryInput]) -> str:
+            return prompt
-        result: Union[ForWorkflowEngine, ForWorkflow, ForSupervisoryNode] = await self._run(payload)
-        if isinstance(result, ForWorkflowEngine):
-            return result
 
-        elif isinstance(result, ForWorkflow):
+    async def working(self, payload: Union[ForWorkflowEngineInput, ForWorkflowInput, ForSupervisoryInput]) -> Union[ForWorkflowEngine, ForWorkflow, ForSupervisoryNode, None]:
-            return result
+        try:
-
+            result = await self._run(payload)
-        elif isinstance(result, ForSupervisoryInput):
+            if isinstance(result, (ForWorkflowEngine, ForWorkflow, ForSupervisoryNode)):
-            return result
+                return result
-        
+            else:
-        else:
+                logger.error(f"ConsciousnessNode: 未知或不匹配的返回类型: {type(result)}")
+                return None
+        except Exception as e:
+            logger.exception("ConsciousnessNode在执行working时发生严重错误")
             return None
 
+
     @overload
     async def _run(self, payload: ForWorkflowEngineInput) -> ForWorkflowEngine:
         """
@@ -109,29 +127,41 @@ class ConsciousnessNode:
         """
         pass
 
-    async def _run(self, payload: Union[ForSupervisoryInput, ForWorkflowInput, ForWorkflowEngineInput])\
+    async def _run(self, payload: Union[ForSupervisoryInput, ForWorkflowInput, ForWorkflowEngineInput]) -> Union[ForSupervisoryNode, ForWorkflow, ForWorkflowEngine]:
-            -> Union[ForSupervisoryNode, ForWorkflow, ForWorkflowEngine]:
+        try:
+            self.agent.retries = 3
+            if isinstance(payload, ForWorkflowEngineInput):
+                deps = ConsciousnessNodeDeps(
+                    original_command=payload.original_command,
+                    workflow_template=payload.workflow_template,
+                    command="拆解原始命令变成一个工作流"
+                )
+                logger.debug("ConsciousnessNode: 开始生成工作流 (原生重试开启)")
+                result = await self.agent.run(
+                    "根据original_command制定严密的可执行workflow，可以学习并参考workflow_template的设计理念",
+                    deps=deps)
+                return result.output
 
-        if isinstance(payload, ForWorkflowEngineInput):
+            elif isinstance(payload, ForWorkflowInput):
-            deps = ConsciousnessNodeDeps(original_command=payload.original_command,
+                deps = ConsciousnessNodeDeps(
-                                         workflow_template=payload.workflow_template,
+                    original_command=payload.original_command,
-                                         command="拆解原始命令变成一个工作流")
+                    command="完成workflow step中分配给意识节点的特定任务或指导"
-            result = await self.agent.run(f"根据original_command制定workflow，可以学习workflow_template",
+                )
-                                          deps=deps,
+                logger.debug("ConsciousnessNode: 开始处理工作流节点任务 (原生重试开启)")
-                                          output_type=ForWorkflowEngine,)
+                result = await self.agent.run(f"处理此工作流步骤信息：\n{payload.workflow_step.model_dump_json()}",
-            return result.output
+                                              deps=deps)
-        elif isinstance(payload, ForWorkflowInput):
+                return result.output
-            deps = ConsciousnessNodeDeps(original_command=payload.original_command,
+
-                                         command="完成workflowstep的任务")
+            elif isinstance(payload, ForSupervisoryInput):
-            result = await self.agent.run(payload.workflow_step.model_dump_json(),
+                deps = ConsciousnessNodeDeps(
-                                         deps=deps,
+                    original_command=payload.original_command,
-                                         output_type=ForWorkflow)
+                    command="对于工作流整体执行结果进行检查，并且生成一份专业的技术性总结报告"
-            return result.output
+                )
-        elif isinstance(payload, ForSupervisoryInput):
+                logger.debug("ConsciousnessNode: 开始生成技术总结报告 (原生重试开启)")
-            deps = ConsciousnessNodeDeps(original_command=payload.original_command,
+                result = await self.agent.run(
-                                         command="对于结果进行检查，并且生成一份技术性的总结报告")
+                    f"基于以下工作流的执行记录，生成技术报告：\n{payload.workflow.model_dump_json()}",
-            result = await self.agent.run(payload.workflow.model_dump_json(),
+                    deps=deps)
-                                          deps=deps,
+                return result.output
-                                          output_type=ForSupervisoryNode)
+        except Exception as e:
-            return result.output
+            logger.exception(f"ConsciousnessNode 模型生成最终失败: {str(e)}")
-        return None
+            raise RuntimeError(f"ConsciousnessNode 无法完成任务: {str(e)}") from e

pretor/core/individual/consciousness_node/template.py

@@ -15,6 +15,7 @@
 
 from pretor.core.workflow.workflow import PretorWorkflow, WorkStep
 from pretor.utils.agent_model import ResponseModel, DepsModel, InputModel
+from pydantic import Field
 
 
 #意识节点回复类
@@ -25,17 +26,18 @@ class ConsciousnessNodeResponse(ResponseModel):
 
 class ForWorkflowEngine(ConsciousnessNodeResponse):
     """生成workflow并放入WorkflowEngine"""
-    workflow: PretorWorkflow
+    workflow: PretorWorkflow = Field(..., description="生成好的符合规范的完整工作流对象。")
+    reasoning: str = Field(..., description="生成此工作流的原因和思路简述。")
 
 
 class ForWorkflow(ConsciousnessNodeResponse):
     """处理workflow中需要ConsciousnessNode的工作"""
-    output: str
+    output: str = Field(..., description="对当前工作流步骤的具体处理结果或指导意见。")
 
 
 class ForSupervisoryNode(ConsciousnessNodeResponse):
     """工作流完成后进行校验并返回给SupervisoryNode"""
-    output: str
+    output: str = Field(..., description="为监控节点提供的全工作流执行情况的技术性总结报告。")
 
 
 class ConsciousnessNodeDeps(DepsModel):
@@ -60,4 +62,4 @@ class ForWorkflowInput(ConsciousnessNodeInput):
 
 class ForSupervisoryInput(ConsciousnessNodeInput):
     workflow: PretorWorkflow
-    original_command: str
+    original_command: str

pretor/core/individual/control_node/control_node.py

@@ -13,7 +13,8 @@
 #  limitations under the License.
 
 import ray
-from pydantic_ai import Agent
+from loguru import logger
+from pydantic_ai import Agent, RunContext
 from pretor.core.global_state_machine.global_state_machine import GlobalStateMachine
 from pretor.core.global_state_machine.model_provider.base_provider import Provider
 from pretor.adapter.model_adapter.agent_factory import AgentFactory
@@ -38,7 +39,15 @@ class ControlNode:
         Returns:
             无返回
         """
-        system_prompt: str = ""
+        system_prompt: str = (
+            "你叫Pretor，是一个多智能体AI助手系统中的【控制节点 (Control Node)】。\n"
+            "你是系统的'执行者'和'车间主任'，专门负责执行工作流中分配给你的具体子任务。\n"
+            "你的工作职责是：\n"
+            "1. 仔细分析分配给你的工作流步骤 (workflow_step) 的目标和要求。\n"
+            "2. 运用你被分配的工具 (如有) 或者依靠自身的知识和推理能力，精准、高效地完成该任务。\n"
+            "3. 将执行的结果、产生的数据或者具体的输出，严格按照 ForWorkflow 格式返回。\n"
+            "请注意：你的输出应当具体、实用，直接提供任务所要求的结果，不要做过多无关的寒暄。"
+        )
         output_type = ForWorkflow
         provider: Provider = global_state_machine.get_provider.remote(provider_title)
         agent_factory = AgentFactory()
@@ -48,13 +57,39 @@ class ControlNode:
                                                 system_prompt=system_prompt,
                                                 deps_type=ControlNodeDeps,
                                                 agent_name="control_node")
+        @self.agent.system_prompt
+        async def dynamic_prompt(ctx: RunContext[ControlNodeDeps]):
+            prompt = system_prompt + "\n\n"
+            prompt += (
+                f"=== 当前任务步骤上下文 ===\n"
+                f"- 步骤名称 (Name): {ctx.deps.workflow_step.name}\n"
+                f"- 步骤目标/描述 (Description): {ctx.deps.workflow_step.description}\n"
+                f"- 前置步骤结果参考 (Previous Results): {ctx.deps.workflow_step.precondition}\n"
+                f"- 可用工具 (Available Tools): {ctx.deps.current_tools}\n"
+            )
+            return prompt
 
     async def working(self, payload: ForWorkflowInput) -> str:
-        result: ForWorkflow = await self._run(payload)
+        try:
-        return result
+            result: ForWorkflow = await self._run(payload)
+            return result
+        except Exception as e:
+            logger.exception("ControlNode在执行working时发生严重错误")
+            return None
 
     async def _run(self, payload: ForWorkflowInput) -> ForWorkflow:
-        deps = ControlNodeDeps(workflow_step=payload.workflow_step)
+        try:
-        result = await self.agent.run(f"根据workflow_step分配任务",
+            self.agent.retries = 3
-                                      deps=deps)
+            deps = ControlNodeDeps(
-        return result.output
+                workflow_step=payload.workflow_step
+            )
+            logger.debug(f"ControlNode: 开始执行工作流节点 [{payload.workflow_step.name}] (原生重试开启)")
+
+            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()}",
+                deps=deps
+            )
+            return result.output
+        except Exception as e:
+            logger.exception(f"ControlNode 在执行步骤 [{payload.workflow_step.name}] 时最终失败: {str(e)}")
+            raise RuntimeError(f"ControlNode 执行步骤失败: {str(e)}") from e

pretor/core/individual/control_node/template.py

@@ -13,6 +13,7 @@
 #  limitations under the License.
 
 
+from pydantic import Field
 from pretor.core.workflow.workflow import WorkStep
 from pretor.utils.agent_model import ResponseModel, InputModel, DepsModel
 
@@ -24,13 +25,15 @@ class ControlNodeResponse(ResponseModel):
 class ControlNodeInput(InputModel):
     pass
 
+
 class ControlNodeDeps(DepsModel):
     workflow_step: WorkStep
+    # In the future, this can be dynamically populated with tools specific to the current task execution
 
 
 class ForWorkflow(ControlNodeResponse):
-    output: str
+    output: str = Field(..., description="控制节点执行特定工作流步骤的结果。包含执行细节和输出数据。")
 
 
 class ForWorkflowInput(ControlNodeInput):
-    workflow_step: WorkStep
+    workflow_step: WorkStep

pretor/core/individual/supervisory_node/supervisory_node.py

@@ -21,6 +21,8 @@ from pretor.core.global_state_machine.global_state_machine import GlobalStateMac
 from pretor.core.global_state_machine.model_provider import Provider
 from pretor.core.individual.supervisory_node.template import ForConsciousnessNode, ForUser, SupervisoryNodeDeps, TerminationMessage
 from pydantic_ai import RunContext, Agent
+from loguru import logger
+from pretor.utils.ray_hook import ray_actor_hook
 
 @ray.remote
 class SupervisoryNode:
@@ -40,7 +42,16 @@ class SupervisoryNode:
         Returns:
             无返回
         """
-        system_prompt: str = ""
+        system_prompt: str = (
+            "你叫Pretor，是一个多智能体AI助手系统中的【监控节点 (Supervisory Node)】。\n"
+            "你是系统的'前台接待'和'大脑皮层'，负责接收用户的初始请求或工作流的最终报告。\n"
+            "你的核心职责是进行【意图识别与路由】。请仔细阅读用户的请求：\n"
+            "1. 如果用户只是进行简单的问候、闲聊或查询非常基础的信息，请直接生成友好的回复，使用 ForUser 格式。\n"
+            "2. 如果用户提出的是复杂任务（如需要编写代码、多步骤规划、数据处理等），请务必将其判定为需要工作流处理的任务，"
+            "   并使用 ForConsciousnessNode 格式，同时从提供的【可用模板列表】中选择最合适的工作流模板移交给意识节点。\n"
+            "3. 如果你收到的是 TerminationMessage（代表工作流已完成并生成了报告），请将报告内容转化为友好的面向用户的回复，使用 ForUser 格式。\n"
+            "请保持冷静、专业，并严格遵循上述路由规则。"
+        )
         output_type = Union[ForConsciousnessNode, ForUser]
         provider: Provider = await global_state_machine.get_provider.remote(provider_title)
         agent_factory = AgentFactory()
@@ -53,7 +64,21 @@ class SupervisoryNode:
 
         @self.agent.system_prompt
         async def dynamic_prompt(ctx: RunContext[SupervisoryNodeDeps]):
-            return f"Context: Platform={ctx.deps.platform}, User={ctx.deps.user_name}, Time={ctx.deps.time}"
+            prompt = system_prompt + "\n\n"
+            prompt += (
+                f"=== 当前上下文 ===\n"
+                f"- 平台 (Platform): {ctx.deps.platform}\n"
+                f"- 用户名 (User): {ctx.deps.user_name}\n"
+                f"- 当前时间 (Time): {ctx.deps.time}\n"
+                f"- 可用工作流模板 (Available Templates): {ctx.deps.available_templates}\n"
+            )
+            if ctx.deps.error_history:
+                prompt += (
+                    f"\n=== 错误重试指示 ===\n"
+                    f"警告：前一次尝试失败，错误信息如下：\n{ctx.deps.error_history}\n"
+                    f"请务必修正该错误并按照要求的 Pydantic 格式输出。"
+                )
+            return prompt
 
     ###工作函数
     async def working(self, payload: Union[PretorEvent, TerminationMessage]) -> str:
@@ -65,16 +90,20 @@ class SupervisoryNode:
         Returns:
             str,监控节点对于用户的回复
         """
-        result = await self._run(payload)
+        try:
-        if isinstance(result, ForConsciousnessNode):
+            result = await self._run(payload)
-
+            if isinstance(result, ForConsciousnessNode):
-            return "任务已创建"
+                logger.info(f"SupervisoryNode: 任务已分配给意识节点，选用模板 [{result.workflow_template}]")
-
+                return f"任务已创建，准备创建工作流。原因：{result.reasoning}"
-        elif isinstance(result, ForUser):
+            elif isinstance(result, ForUser):
-            return result.content
+                logger.info("SupervisoryNode: 直接向用户返回简单回复。")
-
+                return result.context
-        else:
+            else:
-            return "未知响应类型"
+                logger.error(f"SupervisoryNode: 未知响应类型: {type(result)}")
+                return "抱歉，系统内部遇到未知错误，无法正确处理您的请求。"
+        except Exception as e:
+            logger.exception("SupervisoryNode在处理请求时发生未捕获的严重错误")
+            return "抱歉，监控节点处理请求时发生严重错误，请联系管理员。"
 
     @overload
     async def _run(self, payload: PretorEvent) -> Union[ForConsciousnessNode, ForUser]:
@@ -87,7 +116,7 @@ class SupervisoryNode:
             ForUser对象，监控节点对于用户进行的简单回答
             ForConsciousnessNode对象，监控节点将用户的请求判断为复杂任务，将PretorEvent传递给意识节点，并且给选择好的工作流模板
         """
-        pass
+        ...
 
     @overload
     async def _run(self, payload: TerminationMessage) -> ForUser:
@@ -99,7 +128,7 @@ class SupervisoryNode:
         Returns:
             ForUser对象，工作流结束后给用户的返回
         """
-        pass
+        ...
 
     async def _run(self, payload: Union[PretorEvent, TerminationMessage]) -> Union[ForConsciousnessNode, ForUser]:
         """
@@ -111,14 +140,28 @@ class SupervisoryNode:
             ForConsciousnessNode对象，对意识节点发送的消息
             ForUser对象，对用户发送到消息
         """
-        if isinstance(payload, PretorEvent):
+        platform = payload.platform
-            deps = SupervisoryNodeDeps(platform=payload.platform,
+        user_name = payload.user_name
-                                       user_name=payload.user_name,
+        message = payload.message
-                                       time=datetime.datetime.now())
+        time_str = datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S")
-            result = await self.agent.run(payload.message, deps=deps)
+        try:
-        else:
+            global_state_machine = ray_actor_hook("global_state_machine")
-            deps = SupervisoryNodeDeps(platform=payload.platform,
+            workflow_template_dict = await global_state_machine.get_workflow_template_list.remote()
-                                       user_name=payload.user_name,
+            available_templates_str = "\n".join([f"- 名称: {k}, 描述/内容: {v}" for k, v in
-                                       time=datetime.datetime.now())
+                                                 workflow_template_dict.items()]) if workflow_template_dict else "暂无注册的工作流模板"
-            result = await self.agent.run(payload.message, deps=deps)
+            deps = SupervisoryNodeDeps(
-        return result.output
+                platform=platform,
+                user_name=user_name,
+                time=time_str,
+                available_templates=available_templates_str
+            )
+            logger.debug("SupervisoryNode 开始生成 (启用原生 Pydantic-AI 重试)")
+            prompt_message = message
+            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
+            result = await self.agent.run(prompt_message, deps=deps)
+            return result.output
+        except Exception as e:
+            logger.exception(f"SupervisoryNode 模型生成或解析最终失败: {str(e)}")
+            return ForUser(context="系统当前负载过高或遇到复杂内部错误，请稍后再试。")

pretor/core/individual/supervisory_node/template.py

@@ -20,10 +20,11 @@ class SupervisoryNodeResponse(ResponseModel):
     pass
 
 class ForUser(SupervisoryNodeResponse):
-    context: str = Field(...,description="对用户的回复，应当使用和蔼的语气进行回复")
+    context: str = Field(..., description="对用户的回复，应当使用和蔼的语气进行回复。用于直接解答简单问题或返回最终报告。")
 
 class ForConsciousnessNode(SupervisoryNodeResponse):
-    workflow_template: str = Field(..., description="选择的工作流模板，应当为对应模板的name字段")
+    orkflow_template: str = Field(..., description="选择的工作流模板的名称，用于处理复杂任务。")
+    reasoning: str = Field(..., description="选择将任务移交意识节点并选用该模板的简短原因。")
 
 class TerminationMessage(BaseModel):
     platform: str
@@ -33,4 +34,7 @@ class TerminationMessage(BaseModel):
 class SupervisoryNodeDeps(DepsModel):
     platform: str
     user_name: str
-    time: str
+    time: str
+    retry_count: int = 0
+    error_history: str = ""
+    available_templates: str = "默认工作流 (default_workflow)"