378 lines
13 KiB
Python
378 lines
13 KiB
Python
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"""
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Agent Orchestrator — 多 Agent 编排引擎。
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支持三种协作模式：
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- route: Router Agent 分析问题 → 分发到最合适的 Specialist Agent
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- sequential: Agent 流水线执行，前者输出作为后者输入
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- debate: 多个 Agent 独立回答 → Aggregator 汇总为最终答案
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"""
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from __future__ import annotations
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import json
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import logging
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import uuid
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from typing import Any, Callable, Dict, List, Optional
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from pydantic import BaseModel, Field
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from app.agent_runtime import (
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AgentRuntime,
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AgentConfig,
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AgentLLMConfig,
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AgentToolConfig,
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AgentResult,
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)
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from app.agent_runtime.core import _LLMClient
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logger = logging.getLogger(__name__)
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class OrchestratorAgentConfig(BaseModel):
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"""编排中单个 Agent 的配置"""
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id: str = Field(..., description="Agent 标识")
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name: str = Field(default="Agent", description="显示名称")
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system_prompt: str = Field(default="你是一个有用的AI助手。")
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model: str = Field(default="deepseek-v4-flash")
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provider: str = Field(default="deepseek")
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temperature: float = 0.7
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max_iterations: int = 10
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tools: List[str] = Field(default_factory=list, description="工具白名单，空=全部")
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description: str = Field(default="", description="Agent 专长描述（路由模式用）")
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class OrchestratorStep(BaseModel):
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"""编排中的单步执行记录"""
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agent_id: str
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agent_name: str
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input: str = ""
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output: str = ""
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iterations_used: int = 0
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tool_calls_made: int = 0
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error: Optional[str] = None
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class OrchestratorResult(BaseModel):
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"""编排执行结果"""
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mode: str
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final_answer: str
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steps: List[OrchestratorStep] = Field(default_factory=list)
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agent_results: List[Dict[str, Any]] = Field(default_factory=list)
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_ROUTER_SYSTEM_PROMPT = """你是一个路由调度员。你的任务是从以下 Specialist Agent 中选择一个最适合处理用户问题的 Agent。
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可用的 Specialist Agent：
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{agent_list}
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请返回 JSON 格式（不要 markdown 包裹），包含：
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1. "selected_agent": 选中的 Agent ID
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2. "reason": 选择理由（一句话）
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规则：
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- 选择与问题最匹配的 Agent
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- 如果问题涉及多个领域，选择最相关的那个
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- 必须从上述列表中选择，不能编造 Agent ID"""
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_AGGREGATOR_SYSTEM_PROMPT = """你是一个回答汇总员。多个 AI Agent 对同一个问题给出了不同的回答。
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请分析所有回答，输出一份综合的最终答案。
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- 如果各 Agent 回答一致，合并要点
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- 如果有分歧，指出不同观点并给出你的判断
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- 以专业、清晰的格式输出最终答案"""
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class AgentOrchestrator:
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"""
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多 Agent 编排器。
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用法：
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orch = AgentOrchestrator()
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result = await orch.run("route", question, [agent1, agent2, agent3])
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"""
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def __init__(self, default_llm_config: Optional[AgentLLMConfig] = None):
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self._default_llm = default_llm_config or AgentLLMConfig(
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model="deepseek-v4-flash",
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temperature=0.3,
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)
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async def run(
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self,
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mode: str,
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question: str,
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agents: List[OrchestratorAgentConfig],
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on_llm_call: Optional[Callable[[Dict[str, Any]], Any]] = None,
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) -> OrchestratorResult:
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"""执行多 Agent 编排。"""
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mode = mode.lower()
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if mode == "route":
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return await self._route(question, agents, on_llm_call)
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elif mode == "sequential":
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return await self._sequential(question, agents, on_llm_call)
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elif mode == "debate":
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return await self._debate(question, agents, on_llm_call)
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else:
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raise ValueError(f"不支持的编排模式: {mode}，可选: route, sequential, debate")
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async def _route(
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self, question: str, agents: List[OrchestratorAgentConfig],
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on_llm_call: Optional[Callable] = None,
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) -> OrchestratorResult:
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"""路由模式：Router → Specialist。"""
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# 构建 Agent 列表描述
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agent_lines = []
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for a in agents:
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desc = a.description or a.name
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agent_lines.append(f"- id: {a.id}, name: {a.name}, description: {desc}")
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agent_list_str = "\n".join(agent_lines)
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router_prompt = _ROUTER_SYSTEM_PROMPT.format(agent_list=agent_list_str)
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# 创建 Router Agent
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router_runtime = AgentRuntime(
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AgentConfig(
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name="router",
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system_prompt=router_prompt,
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llm=AgentLLMConfig(
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model=self._default_llm.model,
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temperature=0.1, # 低温度确保确定性
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),
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tools=AgentToolConfig(
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include_tools=[], # Router 不需要工具
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),
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),
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on_llm_call=on_llm_call,
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)
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router_result = await router_runtime.run(question)
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if not router_result.success:
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return OrchestratorResult(
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mode="route",
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final_answer=f"路由决策失败: {router_result.content}",
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steps=[],
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)
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# 解析 Router 的输出
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selected_agent_id = None
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try:
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parsed = json.loads(router_result.content.strip().removeprefix("```json").removesuffix("```").strip())
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selected_agent_id = parsed.get("selected_agent", "")
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except (json.JSONDecodeError, AttributeError):
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# 尝试从文本中提取
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for a in agents:
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if a.id in router_result.content:
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selected_agent_id = a.id
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break
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if not selected_agent_id:
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# 取第一个
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selected_agent_id = agents[0].id if agents else ""
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# 找到对应的 Specialist Agent
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specialist = next((a for a in agents if a.id == selected_agent_id), agents[0] if agents else None)
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if not specialist:
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return OrchestratorResult(
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mode="route",
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final_answer="没有可用的 Specialist Agent",
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steps=[],
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)
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# 运行 Specialist Agent
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specialist_runtime = AgentRuntime(
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AgentConfig(
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name=specialist.name,
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system_prompt=specialist.system_prompt,
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llm=AgentLLMConfig(
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model=specialist.model,
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provider=specialist.provider,
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temperature=specialist.temperature,
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max_iterations=specialist.max_iterations,
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),
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tools=AgentToolConfig(
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include_tools=specialist.tools,
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),
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),
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on_llm_call=on_llm_call,
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)
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specialist_result = await specialist_runtime.run(question)
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return OrchestratorResult(
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mode="route",
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final_answer=specialist_result.content,
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steps=[
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OrchestratorStep(
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agent_id="router",
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agent_name="Router",
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input=question,
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output=f"选择: {specialist.name} ({specialist.id})",
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),
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OrchestratorStep(
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agent_id=specialist.id,
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agent_name=specialist.name,
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input=question,
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output=specialist_result.content[:300],
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iterations_used=specialist_result.iterations_used,
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tool_calls_made=specialist_result.tool_calls_made,
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),
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],
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agent_results=[
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{"agent_id": specialist.id, "agent_name": specialist.name, "output": specialist_result.content},
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],
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)
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async def _sequential(
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self, question: str, agents: List[OrchestratorAgentConfig],
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on_llm_call: Optional[Callable] = None,
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) -> OrchestratorResult:
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"""顺序模式：Agent A 输出 → Agent B 输入。"""
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if not agents:
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return OrchestratorResult(mode="sequential", final_answer="无 Agent 可执行")
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steps: List[OrchestratorStep] = []
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current_input = question
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for i, agent_cfg in enumerate(agents):
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runtime = AgentRuntime(
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AgentConfig(
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name=agent_cfg.name,
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system_prompt=agent_cfg.system_prompt,
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llm=AgentLLMConfig(
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model=agent_cfg.model,
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provider=agent_cfg.provider,
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temperature=agent_cfg.temperature,
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max_iterations=agent_cfg.max_iterations,
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),
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tools=AgentToolConfig(
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include_tools=agent_cfg.tools,
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),
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),
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on_llm_call=on_llm_call,
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)
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# 第一个 Agent 接收原始问题，后续 Agent 接收前一个的输出
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agent_input = current_input
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if i > 0:
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agent_input = (
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f"这是前一个 Agent 的处理结果，请在此基础上继续处理。\n\n"
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f"原始问题: {question}\n\n"
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f"前序输出:\n{current_input}"
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)
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result = await runtime.run(agent_input)
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step = OrchestratorStep(
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agent_id=agent_cfg.id,
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agent_name=agent_cfg.name,
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input=agent_input[:200],
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output=result.content[:500],
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iterations_used=result.iterations_used,
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tool_calls_made=result.tool_calls_made,
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error=None if result.success else result.error,
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)
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steps.append(step)
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if not result.success:
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break
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current_input = result.content
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final_answer = steps[-1].output if steps else "无输出"
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return OrchestratorResult(
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mode="sequential",
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final_answer=final_answer,
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steps=steps,
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agent_results=[
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{"agent_id": s.agent_id, "agent_name": s.agent_name, "output": s.output}
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for s in steps
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],
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)
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async def _debate(
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self, question: str, agents: List[OrchestratorAgentConfig],
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on_llm_call: Optional[Callable] = None,
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) -> OrchestratorResult:
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"""辩论模式：多 Agent 独立回答 → Aggregator 汇总。"""
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if not agents:
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return OrchestratorResult(mode="debate", final_answer="无 Agent 可执行")
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steps: List[OrchestratorStep] = []
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agent_outputs: List[Dict[str, Any]] = []
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# 第一阶段：所有 Agent 独立回答
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for agent_cfg in agents:
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runtime = AgentRuntime(
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AgentConfig(
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name=agent_cfg.name,
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system_prompt=agent_cfg.system_prompt,
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llm=AgentLLMConfig(
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model=agent_cfg.model,
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provider=agent_cfg.provider,
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temperature=agent_cfg.temperature,
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max_iterations=agent_cfg.max_iterations,
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),
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tools=AgentToolConfig(
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include_tools=agent_cfg.tools,
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),
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),
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on_llm_call=on_llm_call,
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)
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result = await runtime.run(question)
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step = OrchestratorStep(
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agent_id=agent_cfg.id,
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agent_name=agent_cfg.name,
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input=question,
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output=result.content[:500],
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iterations_used=result.iterations_used,
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tool_calls_made=result.tool_calls_made,
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error=None if result.success else result.error,
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)
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steps.append(step)
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agent_outputs.append({
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"agent_id": agent_cfg.id,
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"agent_name": agent_cfg.name,
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"output": result.content,
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})
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# 第二阶段：Aggregator 汇总所有回答
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if len(agent_outputs) >= 2:
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outputs_text = "\n\n---\n\n".join(
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f"## {ao['agent_name']} 的回答\n{ao['output']}" for ao in agent_outputs
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)
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aggregator_prompt = (
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f"用户问题: {question}\n\n"
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f"以下是多个 AI Agent 对该问题的回答:\n\n{outputs_text}\n\n"
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"请综合所有回答，输出一份完整、准确的最终答案。"
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|||
|
|
)
|
|||
|
|
|
|||
|
|
aggregator_runtime = AgentRuntime(
|
|||
|
|
AgentConfig(
|
|||
|
|
name="aggregator",
|
|||
|
|
system_prompt=_AGGREGATOR_SYSTEM_PROMPT,
|
|||
|
|
llm=AgentLLMConfig(
|
|||
|
|
model=self._default_llm.model,
|
|||
|
|
temperature=0.3,
|
|||
|
|
),
|
|||
|
|
tools=AgentToolConfig(include_tools=[]),
|
|||
|
|
),
|
|||
|
|
on_llm_call=on_llm_call,
|
|||
|
|
)
|
|||
|
|
final_result = await aggregator_runtime.run(aggregator_prompt)
|
|||
|
|
|
|||
|
|
final_answer = final_result.content
|
|||
|
|
steps.append(OrchestratorStep(
|
|||
|
|
agent_id="aggregator",
|
|||
|
|
agent_name="Aggregator",
|
|||
|
|
input="汇总各 Agent 回答",
|
|||
|
|
output=final_answer[:500],
|
|||
|
|
))
|
|||
|
|
else:
|
|||
|
|
final_answer = agent_outputs[0]["output"] if agent_outputs else "无回答"
|
|||
|
|
|
|||
|
|
return OrchestratorResult(
|
|||
|
|
mode="debate",
|
|||
|
|
final_answer=final_answer,
|
|||
|
|
steps=steps,
|
|||
|
|
agent_results=agent_outputs,
|
|||
|
|
)
|