初步实现Route Agent

src/mini_agent/agent/base_agent.py

@@ -0,0 +1,69 @@
+from abc import abstractmethod
+from collections.abc import Iterator
+import json
+from typing import Any
+
+from openai.types.chat import ChatCompletionMessage, ChatCompletionMessageToolCallUnion
+from openai.types.chat.chat_completion_message_param import ChatCompletionMessageParam
+from pydantic import BaseModel, TypeAdapter
+
+from mini_agent.agent.context import ConversationContext
+from mini_agent.agent.message import Message
+
+
+class BaseAgent:
+    @property
+    @abstractmethod
+    def id(self) -> str:
+        pass
+
+    @property
+    @abstractmethod
+    def name(self) -> str:
+        pass
+
+    @property
+    @abstractmethod
+    def description(self) -> str:
+        pass
+
+    @property
+    @abstractmethod
+    def compatibility(self) -> list[str]:
+        pass
+
+    @property
+    @abstractmethod
+    def system_prompt(self) -> str:
+        pass
+
+    def serialize_json(self, data: Any) -> str:
+        if isinstance(data, BaseModel):
+            return data.model_dump_json(ensure_ascii=False)
+        return json.dumps(data, ensure_ascii=False, default=str)
+
+    def _initialize_context(self, context: ConversationContext, question: str) -> None:
+        """
+        初始化会话context
+        """
+        if len(context.messages) == 0:
+            context.messages.append({"role": "system", "content": self.system_prompt})
+        context.messages.append({"role": "user", "content": question})
+
+    def _build_message_param(self, message: ChatCompletionMessage) -> ChatCompletionMessageParam:
+        """
+        将ChatCompletionMessage转换成ChatCompletionMessageParam
+        """
+        message_param: ChatCompletionMessageParam = {
+            "role": message.role,
+            "content": message.content,
+        }
+
+        if message.tool_calls:
+            adapter = TypeAdapter(list[ChatCompletionMessageToolCallUnion])
+            message_param["tool_calls"] = adapter.dump_python(message.tool_calls, exclude_none=True)
+        return message_param
+
+    @abstractmethod
+    def run(self, question: str, context: ConversationContext) -> Iterator[Message]:
+        pass

src/mini_agent/agent/base_tool_agent.py

@@ -0,0 +1,58 @@
+from abc import abstractmethod
+import json
+from typing import Any, override
+
+from openai.types.chat import ChatCompletionMessageToolCallUnion
+
+from mini_agent.agent.base_agent import BaseAgent
+from mini_agent.agent.context import ConversationContext
+from mini_agent.tools.tool_repository import ToolRepository
+
+
+class BaseToolAgent(BaseAgent):
+    @property
+    @abstractmethod
+    def tool_repository(self) -> ToolRepository:
+        pass
+
+    @override
+    def _initialize_context(self, context: ConversationContext, question: str) -> None:
+        super()._initialize_context(context, question)
+        if len(context.discovered_tools) == 0 and self.tool_repository:
+            context.add_tool(self.tool_repository)
+
+    def _parse_tool_call(
+        self, tool_call: ChatCompletionMessageToolCallUnion
+    ) -> tuple[str, dict[str, Any]]:
+        """
+        解析Tool call,返回(tool_id, arguments)
+        """
+        if tool_call.type != "function" and tool_call.type != "custom":
+            raise ValueError(f"不支持的工具调用类型{tool_call.type}")
+        if tool_call.type == "function":
+            tool_id = tool_call.function.name
+            raw_arguments = tool_call.function.arguments
+        else:
+            tool_id = tool_call.custom.name
+            raw_arguments = tool_call.custom.input
+        arguments = json.loads(raw_arguments)
+        if not isinstance(arguments, dict):
+            raise ValueError("工具参数必须是 JSON 对象")
+        return (tool_id, arguments)
+
+    def _execute_tool_call(
+        self, tool_id: str, arguments: dict[str, Any], context: ConversationContext
+    ) -> Any:
+        """
+        执行tool call
+        """
+        observation = self.tool_repository.call_tool(tool_id, arguments)
+        # 更新当前可用工具
+        if tool_id == self.tool_repository.id:
+            available_tools = []
+            for item in observation:
+                if item.name not in context.discovered_tool_names:
+                    context.add_tool(item)
+                available_tools.append(item.to_openai())
+            observation = available_tools
+        return observation

src/mini_agent/agent/context.py

@@ -6,6 +6,7 @@ from mini_agent.tools.tool import Tool
 
 class ConversationContext(BaseModel):
     conversation_id: str = Field(description="会话ID")
+    run_id: str = Field(description="当前会话轮id")
     messages: list[ChatCompletionMessageParam] = Field(description="消息历史", default_factory=list)
 
     _discovered_tools: list[Tool] = PrivateAttr(default_factory=list)
@@ -17,6 +18,6 @@ class ConversationContext(BaseModel):
         return self._discovered_tools
 
     def add_tool(self, tool: Tool):
-        if tool.name not in self.discovered_tool_names:
-            self.discovered_tool_names.append(tool.name)
+        if tool.id not in self.discovered_tool_names:
+            self.discovered_tool_names.append(tool.id)
             self.discovered_tools.append(tool)

src/mini_agent/agent/llm_client.py

@@ -0,0 +1,48 @@
+import os
+
+from openai import OpenAI
+from openai.types.chat import (
+    ChatCompletion,
+    ChatCompletionMessageParam,
+    ChatCompletionToolChoiceOptionParam,
+    ChatCompletionToolParam,
+)
+
+
+class LLMClient:
+    def __init__(
+        self,
+        model: str | None = None,
+        api_key: str | None = None,
+        base_url: str | None = None,
+        timeout: int | None = None,
+    ) -> None:
+        self.model = model or os.getenv("LLM_MODEL_ID")
+        self.base_url = base_url or os.getenv("LLM_BASE_URL")
+        self.api_key = api_key or os.getenv("LLM_API_KEY")
+        self.timeout = timeout if timeout is not None else int(os.getenv("LLM_TIMEOUT", "60"))
+
+        if not all([self.model, self.base_url, self.api_key]):
+            raise ValueError("模型ID、API密钥和服务地址必须被提供或在.env文件中定义。")
+
+        self.client = OpenAI(
+            api_key=self.api_key,
+            base_url=self.base_url,
+            timeout=self.timeout,
+        )
+
+    def think(
+        self,
+        messages: list[ChatCompletionMessageParam],
+        tools: list[ChatCompletionToolParam],
+        tool_choice: ChatCompletionToolChoiceOptionParam = "auto",
+        **kwargs,
+    ) -> ChatCompletion:
+        assert self.model is not None
+        return self.client.chat.completions.create(
+            model=self.model,
+            messages=messages,
+            tools=tools,
+            tool_choice=tool_choice,
+            **kwargs
+        )

src/mini_agent/agent/message.py

@@ -1,47 +1,91 @@
 from datetime import datetime
 from enum import StrEnum
-from typing import Any
+from typing import Any, Literal
 from uuid import uuid4
 
 from pydantic import BaseModel, Field
 
 
+class TextContent(BaseModel):
+    type: Literal["text"] = "text"
+    text: str
+
+
+class ThinkContent(BaseModel):
+    type: Literal["think"] = "think"
+    text: str
+
+
+class ImageContent(BaseModel):
+    type: Literal["image"] = "image"
+    url: str
+    mime_type: str | None = None
+
+
+class JsonContent(BaseModel):
+    type: Literal["json"] = "json"
+    data: Any
+
+
+class ToolCallContent(BaseModel):
+    type: Literal["tool_call"] = "tool_call"
+    id: str
+    name: str
+    tool_call_id:str
+    arguments: dict[str, Any]
+
+
+class ToolResultContent(BaseModel):
+    type: Literal["tool_result"] = "tool_result"
+    tool_call_id: str
+    result: Any = None
+    error: str | None = None
+
+
+class ErrorContent(BaseModel):
+    type: Literal["error"] = "error"
+    message: str
+    code: str | None = None
+
+
+class AudioContent(BaseModel):
+    type: Literal["audio"] = "audio"
+    url: str
+    mime_type: str | None = None
+
+
+MessageContent = (
+    TextContent
+    | ThinkContent
+    | ImageContent
+    | JsonContent
+    | ToolCallContent
+    | ToolResultContent
+    | ErrorContent
+)
+
+
 class MessageRole(StrEnum):
     USER = "user"
     ASSISTANT = "assistant"
     TOOL = "tool"
 
 
-class MessageType(StrEnum):
-    TEXT = "text"
-    THINK = "think"
-    TOOL_CALL = "tool_call"
-    TOOL_RESULT = "tool_result"
-
-
-class MessageStatus(StrEnum):
+class RunStatus(StrEnum):
     RUNNING = "running"
     COMPLETED = "completed"
     FAILED = "failed"
+    CANCELLED = "cancelled"
 
 
 class Message(BaseModel):
-    id: str = Field(default_factory=lambda: str(uuid4()))
-    conversation_id: str
-    run_id: str | None = None
+    id: str = Field(description="消息ID", default_factory=lambda: str(uuid4()))
+    conversation_id: str = Field(description="会话ID")
+    run_id: str | None = Field(description="当前轮的ID", default=None)
+    run_status: RunStatus = Field(description="当前轮的状态")
 
-    role: MessageRole
-    type: MessageType
+    role: MessageRole = Field(description="当前角色")
 
-    content: Any = None
-    status: MessageStatus = MessageStatus.COMPLETED
-
-    tool_call_id: str | None = None
-
-    tool_name: str | None = None
-
-    arguments: dict[str, Any] | None = None
-
-    tool_call_result:Any |None = None
+    content: list[MessageContent] = Field(description="消息内容", default_factory=list)
 
     created_at: datetime = Field(default_factory=lambda: datetime.now())

src/mini_agent/agent/react_agent.py

@@ -1,154 +1,93 @@
 from collections.abc import Iterator
-import json
 import os
-from typing import Any
+from typing import override
 from uuid import uuid7
 
-from openai import OpenAI
-from openai.types.chat import ChatCompletionMessage, ChatCompletionMessageToolCallUnion
-from openai.types.chat.chat_completion_message_param import ChatCompletionMessageParam
-from pydantic import BaseModel, TypeAdapter
-
+from mini_agent.agent.base_tool_agent import BaseToolAgent
 from mini_agent.agent.context import ConversationContext
-from mini_agent.agent.message import Message, MessageRole, MessageStatus, MessageType
+from mini_agent.agent.llm_client import LLMClient
+from mini_agent.agent.message import (
+    ErrorContent,
+    Message,
+    MessageRole,
+    RunStatus,
+    TextContent,
+    ThinkContent,
+    ToolCallContent,
+    ToolResultContent,
+)
 from mini_agent.tools.tavily_search import TavilySearch
 from mini_agent.tools.tool_repository import ToolRepository
 
 
-class ReActAgent:
-    SYSTEM_PROMPT = """
-        你是一个能够调用外部工具的智能助手。
-
-        工作规则：
-
-        1. 首先理解用户的问题，判断是否需要外部工具。
-        2. 如果需要工具，调用工具搜索接口搜索合适的工具。
-        3. 根据搜索结果选择合适的工具。
-        4. 工具执行后，根据返回结果判断下一步行动。
-        5. 当已经获得足够的信息，能够完整回答用户问题时，直接给出最终答案。
-
-        注意事项：
-
-        - 不允许编造工具执行结果。
-        - 工具搜索没有结果时，可以尝试调整关键词重新搜索。
-        - 如果工具无法完成任务，明确告知用户。
-        - 不要为了调用工具而调用工具。
-        - 只有在任务已经完成或明确无法继续时才结束回答。
-        """
-
+class ReActAgent(BaseToolAgent):
     def __init__(
         self,
         tool_repository: ToolRepository,
-        model: str | None = None,
-        api_key: str | None = None,
-        base_url: str | None = None,
-        timeout: int | None = None,
+        llm_client: LLMClient,
         max_steps: int = 10,
     ) -> None:
 
-        self.model = model or os.getenv("LLM_MODEL_ID")
-        self.base_url = base_url or os.getenv("LLM_BASE_URL")
-        self.api_key = api_key or os.getenv("LLM_API_KEY")
-        self.timeout = timeout if timeout is not None else int(os.getenv("LLM_TIMEOUT", "60"))
+        super().__init__()
 
-        if not all([self.model, self.base_url, self.api_key]):
-            raise ValueError("模型ID、API密钥和服务地址必须被提供或在.env文件中定义。")
-
-        self.client = OpenAI(
-            api_key=self.api_key,
-            base_url=self.base_url,
-            timeout=self.timeout,
-        )
-
-        self.tool_repos = tool_repository
+        self.client = llm_client
         self.max_steps = max_steps
         web_search_tool = TavilySearch()
         web_search_tool.config(api_key=os.getenv("TAVILY_KEY", ""))
-        self.tool_repos.register(web_search_tool)
+        self._tool_repository = tool_repository
+        self.tool_repository.register(web_search_tool)
 
-    def serialize_json(self, data: Any) -> str:
-        if isinstance(data, BaseModel):
-            return data.model_dump_json()
-        return json.dumps(data, ensure_ascii=False, default=str)
+    @property
+    def id(self) -> str:
+        return "react_agent"
 
-    def _initialize_context(self, context: ConversationContext, question: str) -> None:
-        """
-        初始化会话context
-        """
-        if len(context.messages) == 0:
-            context.messages.append({"role": "system", "content": self.SYSTEM_PROMPT})
-        if len(context.discovered_tools) == 0:
-            context.add_tool(self.tool_repos)
-        context.messages.append({"role": "user", "content": question})
+    @property
+    def name(self) -> str:
+        return "ReAct Agent"
 
-    def _build_message_param(self, message: ChatCompletionMessage) -> ChatCompletionMessageParam:
-        """
-        将ChatCompletionMessage转换成ChatCompletionMessageParam
-        """
-        message_param: ChatCompletionMessageParam = {
-            "role": message.role,
-            "content": message.content,
-        }
+    @property
+    def description(self) -> str:
+        return "思考-回答-观察-再思考的模式来解决问题"
 
-        if message.tool_calls:
-            adapter = TypeAdapter(list[ChatCompletionMessageToolCallUnion])
-            message_param["tool_calls"] = adapter.dump_python(message.tool_calls, exclude_none=True)
-        return message_param
+    @property
+    def compatibility(self) -> list[str]:
+        return ["通用问题回答", "资料网络搜索"]
 
-    def _parse_tool_call(
-        self, tool_call: ChatCompletionMessageToolCallUnion
-    ) -> tuple[str, dict[str, Any]]:
-        """
-        解析Tool call,返回(tool_name, arguments)
-        """
-        if tool_call.type != "function" and tool_call.type != "custom":
-            raise ValueError(f"不支持的工具调用类型{tool_call.type}")
-        if tool_call.type == "function":
-            tool_name = tool_call.function.name
-            raw_arguments = tool_call.function.arguments
-        else:
-            tool_name = tool_call.custom.name
-            raw_arguments = tool_call.custom.input
-        arguments = json.loads(raw_arguments)
-        if not isinstance(arguments, dict):
-            raise ValueError("工具参数必须是 JSON 对象")
-        return (tool_name, arguments)
+    @property
+    def tool_repository(self) -> ToolRepository:
+        return self._tool_repository
 
-    def _execute_tool_call(
-        self, tool_name: str, arguments: dict[str, Any], context: ConversationContext
-    ) -> Any:
-        """
-        执行tool call
-        """
-        observation = self.tool_repos.call_tool(tool_name, arguments)
-        # 更新当前可用工具
-        if tool_name == self.tool_repos.name:
-            available_tools = []
-            for item in observation:
-                if item.name not in context.discovered_tool_names:
-                    context.add_tool(item)
-                available_tools.append(self.tool_repos.tool_to_openai(item))
-            observation = available_tools
-        return observation
+    @property
+    def system_prompt(self) -> str:
+        return """
+            你是一个能够调用外部工具的智能助手。
 
+            工作规则：
+
+            1. 首先理解用户的问题，判断是否需要外部工具。
+            2. 如果需要工具，可以从工具库中搜索你所需要的工具。
+            3. 从工具搜索结果中选择合适的工具并调用执行。
+            4. 工具执行后，根据返回结果判断下一步行动。
+            5. 当已经获得足够的信息，能够完整回答用户问题时，直接给出最终答案。
+
+            注意事项：
+
+            - 不允许编造工具执行结果。
+            - 工具搜索没有结果时，可以尝试调整关键词重新搜索。
+            - 如果工具无法完成任务，明确告知用户。
+            - 不要为了调用工具而调用工具。
+            - 只有在任务已经完成或明确无法继续时才结束回答。
+            """
+
+    @override
     def run(self, question: str, context: ConversationContext) -> Iterator[Message]:
         self._initialize_context(context, question)
         run_id = str(uuid7())
 
-        yield Message(
-            conversation_id=context.conversation_id,
-            role=MessageRole.USER,
-            run_id=run_id,
-            status=MessageStatus.RUNNING,
-            type=MessageType.TEXT,
-            content=question,
-        )
         for _ in range(self.max_steps):
-            tools = [self.tool_repos.tool_to_openai(tool) for tool in context.discovered_tools]
+            tools = [tool.to_openai() for tool in context.discovered_tools]
 
-            assert self.model is not None
-            response = self.client.chat.completions.create(
-                model=self.model,
+            response = self.client.think(
                 messages=context.messages,
                 tools=tools,
                 tool_choice="auto",
@@ -166,54 +105,58 @@ class ReActAgent:
                     yield Message(
                         conversation_id=context.conversation_id,
                         run_id=run_id,
+                        run_status=RunStatus.COMPLETED,
                         role=MessageRole.ASSISTANT,
-                        content=message.content,
-                        status=MessageStatus.COMPLETED,
-                        type=MessageType.TEXT,
+                        content=[TextContent(text=message.content)],
                     )
                     return
                 elif reasoning_content := getattr(message, "reasoning_content", None):
                     yield Message(
                         conversation_id=context.conversation_id,
                         run_id=run_id,
+                        run_status=RunStatus.RUNNING,
                         role=MessageRole.ASSISTANT,
-                        type=MessageType.THINK,
-                        content=reasoning_content,
-                        status=MessageStatus.RUNNING,
+                        content=[ThinkContent(text=reasoning_content)],
                     )
                 else:
                     yield Message(
                         conversation_id=context.conversation_id,
                         run_id=run_id,
+                        run_status=RunStatus.FAILED,
                         role=MessageRole.ASSISTANT,
-                        type=MessageType.TEXT,
-                        status=MessageStatus.FAILED,
-                        content="模型没有返回有效内容",
+                        content=[ErrorContent(message="模型没有返回有效内容")],
                     )
                     return
 
             if message.tool_calls:
                 for tool_call in message.tool_calls:
-                    tool_name = None
+                    tool_id = None
                     arguments = {}
-                    tool_call_status = MessageStatus.RUNNING
+                    tool_call_error: str | None = None
                     try:
-                        tool_name, arguments = self._parse_tool_call(tool_call)
+                        tool_id, arguments = self._parse_tool_call(tool_call)
+                        tool = self.tool_repository.get_tool(id=tool_id)
+                        if not tool:
+                            raise KeyError(f"{tool_id}工具不存在")
                         yield Message(
                             conversation_id=context.conversation_id,
                             run_id=run_id,
                             role=MessageRole.ASSISTANT,
-                            status=MessageStatus.RUNNING,
-                            type=MessageType.TOOL_CALL,
-                            content=f"call tool:{tool_name}({arguments})",
-                            tool_name=tool_name,
-                            arguments=arguments,
+                            run_status=RunStatus.RUNNING,
+                            content=[
+                                ToolCallContent(
+                                    tool_call_id=tool_call.id,
+                                    id=tool.id,
+                                    name=tool.name,
+                                    arguments=arguments,
+                                )
+                            ],
                         )
 
-                        observation = self._execute_tool_call(tool_name, arguments, context)
+                        observation = self._execute_tool_call(tool_id, arguments, context)
 
                     except Exception as e:
-                        tool_call_status = MessageStatus.FAILED
+                        tool_call_error = str(e)
                         observation = {"error": str(e)}
 
                     # 统一记录工具执行结果
@@ -228,20 +171,21 @@ class ReActAgent:
                     yield Message(
                         conversation_id=context.conversation_id,
                         run_id=run_id,
+                        run_status=RunStatus.RUNNING,
                         role=MessageRole.TOOL,
-                        type=MessageType.TOOL_RESULT,
-                        status=tool_call_status,
-                        content=f"call {tool_name or 'unknown'} result:{self.serialize_json(observation)}",
-                        tool_name=tool_name,
-                        arguments=arguments,
-                        tool_call_result=observation,
+                        content=[
+                            ToolResultContent(
+                                tool_call_id=tool_call.id,
+                                result="执行成功" if tool_call_error is None else None,
+                                error=tool_call_error,
+                            )
+                        ],
                     )
 
         yield Message(
             conversation_id=context.conversation_id,
             run_id=run_id,
+            run_status=RunStatus.FAILED,
             role=MessageRole.ASSISTANT,
-            status=MessageStatus.FAILED,
-            type=MessageType.TEXT,
-            content=f"已达到最大执行步数 ({self.max_steps})，任务未完成",
+            content=[ErrorContent(message=f"已达到最大执行步数 ({self.max_steps})，任务未完成")],
         )

src/mini_agent/agent/route_agent.py

@@ -0,0 +1,236 @@
+from collections.abc import Iterator
+from typing import Literal, override
+from uuid import uuid7
+
+from pydantic import BaseModel, Field
+
+from mini_agent.agent.base_agent import BaseAgent
+from mini_agent.agent.context import ConversationContext
+from mini_agent.agent.llm_client import LLMClient
+from mini_agent.agent.message import (
+    ErrorContent,
+    Message,
+    MessageRole,
+    RunStatus,
+    TextContent,
+    ThinkContent,
+)
+
+
+class RouteDecision(BaseModel):
+    action: Literal["route", "direct", "clarify", "reject"] = Field(description="路由动作")
+    agent_id: str | None = Field(description="Agent id", default=None)
+    reason: str | None = Field(description="路由理由", default=None)
+    message: str | None = Field(description="回复用户的消息", default=None)
+    task: str | None = Field(description="交给子Agent的任务描述")
+
+
+class RouteAgent(BaseAgent):
+    def __init__(self, llm_client: LLMClient, max_steps: int = 10) -> None:
+        super().__init__()
+        self.sub_agents: dict[str, BaseAgent] = {}
+        self.llm_client = llm_client
+        self.max_steps = max_steps
+
+    def register_agent(self, agent: BaseAgent):
+        if agent.id not in self.sub_agents:
+            self.sub_agents[agent.id] = agent
+        else:
+            raise ValueError(f"{agent.id}已经存在")
+
+    @property
+    def system_prompt(self) -> str:
+        template = """
+        # 角色
+
+        你是一个 AI Agent 路由调度器（Route Agent），负责理解用户请求，并将其分配给最合适的专业 Agent。
+
+        你不直接处理用户的业务问题，不执行工具，不编造业务数据。你的核心职责是识别用户意图、选择目标 Agent，并生成清晰的路由决策。
+
+        # 核心职责
+
+        1. 理解用户当前请求的真实意图。
+        2. 结合当前会话上下文，理解用户的追问、省略表达和上下文引用。
+        3. 从系统提供的 Agent 列表中选择最合适的 Agent。
+        4. 判断用户请求是否需要补充信息。
+        5. 判断当前请求是否可以直接回答，还是必须交给专业 Agent。
+        6. 输出符合指定结构的路由决策。
+
+        # 路由规则
+
+        ## 1. Agent 选择
+
+        - 必须从系统提供的可用 Agent 列表中选择目标 Agent。
+        - 优先选择职责与用户请求最匹配的 Agent。
+        - 不得虚构 Agent，不得调用未注册的 Agent。
+        - 如果一个请求涉及多个领域，优先选择能够完成主要任务的 Agent。
+        - 如果需要多个 Agent 协作，只有在系统明确支持多 Agent 调度时才能提出多个目标。
+
+        ## 2. 上下文理解
+
+        - 用户当前消息优先级最高，但不能脱离历史对话理解。
+        - 如果用户使用“继续”“刚才那个”“再查一下”等表达，应结合历史消息确定实际任务。
+        - 如果用户是在纠正上一轮结果，应将请求交给原任务对应的 Agent。
+        - 如果用户明确提出新的任务，应重新进行路由，不要机械沿用上一轮 Agent。
+        - 不要因为历史上使用过某个 Agent，就将无关的新问题继续分配给它。
+
+        ## 3. 信息不足
+
+        - 如果缺少执行任务所必需的信息，选择需要补充信息的处理方式。
+        - 只询问真正影响任务执行的必要信息。
+        - 不要为了补充非必要信息而阻断任务。
+        - 如果可以使用合理默认值，应优先继续执行。
+
+        ## 4. 直接回答
+
+        以下情况可以直接回答，不需要调用专业 Agent：
+
+        - 简单的常识性问题。
+        - 问候、感谢、告别等日常交流。
+        - 对当前 Agent 工作状态的简单询问。
+        - 不涉及外部数据查询的简单解释。
+        - 用户只是要求修改、解释或讨论当前对话中已经提供的内容。
+
+        直接回答时，不得伪装成调用了专业 Agent。
+
+        ## 5. 无法路由
+
+        出现以下情况时，选择无法路由：
+
+        - 用户意图无法理解。
+        - 系统没有合适的 Agent。
+        - 用户请求超出所有可用 Agent 的职责范围。
+        - 需要执行的操作不被系统支持。
+
+        应简要说明原因，不要虚构处理能力。
+
+        # 水利业务路由参考
+
+        系统提供以下 Agent，参考这些Agent的职责做出选择：
+
+        {}
+
+        以上只是路由参考，实际选择必须以系统提供的 Agent 定义为准。
+
+        # 输出要求
+
+        必须输出符合系统指定 JSON Schema 的结构化结果。
+
+        路由决策至少包含以下语义：
+
+        - action：决策类型，可选 route、direct、clarify、reject。
+        - agent_id：目标 Agent 标识。非 route 决策时为空。
+        - reason：路由原因。
+        - message：直接回复用户的内容，或者需要向用户询问的内容。
+        - task：交给目标 Agent 的任务描述，尽可能保留用户原始需求和必要上下文。
+
+        不要输出 JSON 以外的解释文字。
+
+        # 重要约束
+
+        1. 你只负责路由，不负责执行专业任务。
+        2. 不得调用业务工具。
+        3. 不得虚构系统中不存在的 Agent。
+        4. 不得擅自修改用户需求。
+        5. 不得丢失用户请求中的关键参数。
+        6. 不要将工具调用需求误判为普通聊天。
+        7. 对于明确的业务查询，优先路由给专业 Agent。
+        8. 输出必须符合指定 JSON Schema，不允许输出额外字段。
+        """
+        agent_descriptions = [
+            f"-{agent.id}: {','.join(agent.compatibility)}" for agent in self.sub_agents.values()
+        ]
+        return template.format("\n".join(agent_descriptions))
+
+    @override
+    def run(self, question: str, context: ConversationContext) -> Iterator[Message]:
+        self._initialize_context(context=context, question=question)
+        run_id = context.run_id
+        for _ in range(self.max_steps):
+            response = self.llm_client.think(
+                messages=context.messages,
+                tools = [],
+                tool_choice="auto",
+                response_format={
+                    "type": "json_schema",
+                    "json_schema": {
+                        "name": "agent_decision",
+                        "strict": True,
+                        "schema": RouteDecision.model_json_schema(),
+                    },
+                },
+            )
+
+            message = response.choices[0].message
+
+            # 保存模型返回消息
+            message_param = self._build_message_param(message)
+            context.messages.append(message_param)
+
+            if message.content:
+                decision = RouteDecision.model_validate_json(message.content)
+                if decision.action == "clarify" or decision.action == "direct":
+                    yield Message(
+                        conversation_id=context.conversation_id,
+                        run_id=run_id,
+                        run_status=RunStatus.COMPLETED,
+                        role=MessageRole.ASSISTANT,
+                        content=[TextContent(text=decision.message or "")],
+                    )
+                    if decision.action == "direct":
+                        return
+                elif decision.action == "route":
+                    agent = self.sub_agents.get(decision.agent_id or "")
+                    if not agent:
+                        context.messages.append(
+                            {"role": "user", "content": f"{decision.agent_id} Agent不存在"}
+                        )
+                    else:
+                        yield Message(
+                            conversation_id=context.conversation_id,
+                            run_id=context.run_id,
+                            run_status=RunStatus.RUNNING,
+                            role = MessageRole.ASSISTANT,
+                            content= [ThinkContent(text=decision.reason  or "正在路由请求")]
+                        )
+                        agent_context = ConversationContext(conversation_id=context.conversation_id,run_id=str(uuid7()))
+                        for agent_message in agent.run(
+                            context=agent_context, question=decision.task or question
+                        ):
+                            yield Message(
+                                conversation_id=context.conversation_id,
+                                run_id=run_id,
+                                run_status=agent_message.run_status,
+                                content=agent_message.content,
+                                role=agent_message.role,
+                            )
+                            if (
+                                agent_message.run_status == RunStatus.COMPLETED
+                                or agent_message.run_status == RunStatus.FAILED
+                            ):
+                                return
+
+            elif reasoning_content := getattr(message, "reasoning_content", None):
+                yield Message(
+                    conversation_id=context.conversation_id,
+                    run_id=run_id,
+                    run_status=RunStatus.RUNNING,
+                    role=MessageRole.ASSISTANT,
+                    content=[ThinkContent(text=reasoning_content)],
+                )
+            else:
+                yield Message(
+                    conversation_id=context.conversation_id,
+                    run_id=run_id,
+                    run_status=RunStatus.FAILED,
+                    role=MessageRole.ASSISTANT,
+                    content=[ErrorContent(message="模型没有返回有效内容")],
+                )
+                return
+        yield Message(
+            conversation_id=context.conversation_id,
+            run_id=run_id,
+            run_status=RunStatus.FAILED,
+            role=MessageRole.ASSISTANT,
+            content=[ErrorContent(message=f"已达到最大执行步数 ({self.max_steps})，任务未完成")],
+        )