多智能体协作系统：2026年架构模式与协议生态深度解析

# Google ADK 监督者模式示例
from google.adk import Agent, Runner

supervisor = Agent(
    name="task_supervisor",
    model="gemini-2.5-pro",
    instruction="分析用户请求，决定分配给哪个专家Agent处理",
    sub_agents=[
        research_agent,    # 负责信息检索
        analysis_agent,    # 负责数据分析
        writing_agent,     # 负责内容生成
    ],
    output_key="final_response"
)

# 监督者根据任务类型自动路由
runner = Runner(agent=supervisor)
result = await runner.run("分析2026年Q1新能源车市场趋势并生成报告")

┌─────────────────────────────────────────┐
│              Blackboard                  │
│  ┌──────┐ ┌──────┐ ┌──────┐ ┌──────┐   │
│  │事实层│ │假设层│ │推论层│ │决策层│   │
│  └──┬───┘ └──┬───┘ └──┬───┘ └──┬───┘   │
│     │        │        │        │        │
├─────┴────────┴────────┴────────┴────────┤
│              Control Module              │
│    (条件触发、优先级调度、冲突消解)       │
├─────┬────────┬────────┬────────┬────────┤
│     │        │        │        │        │
│  Agent-A  Agent-B  Agent-C  Agent-D     │
│  (检索)   (分析)   (验证)   (生成)       │
└─────────────────────────────────────────┘

# 基于黑板模式的多Agent协调（概念示例）
class Blackboard:
    def __init__(self):
        self.layers = {
            "facts": [],      # 事实层：原始数据
            "hypotheses": [], # 假设层：Agent提出的假设
            "inferences": [], # 推论层：经过验证的推论
            "decisions": []   # 决策层：最终决策
        }
        self.control = ControlModule()

    async def post(self, layer: str, entry: Entry, agent_id: str):
        """Agent向黑板写入条目"""
        self.layers[layer].append(entry)
        # 触发控制模块评估是否激活其他Agent
        await self.control.evaluate(self.layers, agent_id)

class ControlModule:
    async def evaluate(self, layers, triggering_agent):
        """根据黑板状态决定下一步激活哪些Agent"""
        if self._has_sufficient_facts(layers["facts"]):
            await self.activate("hypothesis_agents")
        if self._has_consensus(layers["hypotheses"]):
            await self.activate("verification_agents")

# 事件驱动多Agent示例（基于消息总线）
import asyncio
from datetime import datetime

class EventBus:
    def __init__(self):
        self.subscribers = {}

    async def publish(self, event_type: str, payload: dict):
        for handler in self.subscribers.get(event_type, []):
            await handler(payload)

    def subscribe(self, event_type: str, handler):
        self.subscribers.setdefault(event_type, []).append(handler)

bus = EventBus()

# 审计Agent订阅所有决策事件
async def audit_handler(payload):
    await log_to_compliance(payload["decision"], payload["agent_id"])

bus.subscribe("decision_made", audit_handler)

# 风控Agent订阅交易事件并发布风险评估
async def risk_handler(payload):
    risk_score = await assess_risk(payload["transaction"])
    await bus.publish("risk_assessed", {
        "transaction_id": payload["transaction"]["id"],
        "risk_score": risk_score
    })

bus.subscribe("transaction_received", risk_handler)

# 分层递阶配置示例
hierarchy:
  strategic:
    - ceo_agent:
        goal: "最大化季度营收"
        delegates_to: [vp_sales, vp_product]

  tactical:
    - vp_sales:
        goal: "制定销售策略"
        delegates_to: [regional_manager_east, regional_manager_west]
    - vp_product:
        goal: "优化产品路线图"
        delegates_to: [feature_team, bugfix_team]

  operational:
    - regional_manager_east:
        goal: "华东区销售执行"
        tools: [crm_api, email_sender, calendar]

# OpenAI Swarm 风格的对等协作
from swarm import Agent, Swarm

researcher = Agent(
    name="Researcher",
    instructions="搜索和分析信息。完成后将对话转交给Writer。",
    functions=[transfer_to_writer]
)

writer = Agent(
    name="Writer",
    instructions="基于Researcher提供的信息撰写文章。需要更多数据时转回给Researcher。",
    functions=[transfer_to_researcher]
)

# Agent之间通过handoff直接交接
def transfer_to_writer():
    return writer

def transfer_to_researcher():
    return researcher

client = Swarm()
response = client.run(
    agent=researcher,
    messages=[{"role": "user", "content": "写一篇关于量子计算的深度报道"}]
)

# LangGraph 实现流水线模式
from langgraph.graph import StateGraph, END

class PipelineState(TypedDict):
    raw_input: str
    extracted_entities: list
    analysis_result: dict
    final_report: str

def extract(state):
    """实体提取Agent"""
    entities = entity_agent.run(state["raw_input"])
    return {"extracted_entities": entities}

def analyze(state):
    """分析Agent"""
    result = analysis_agent.run(state["extracted_entities"])
    return {"analysis_result": result}

def report(state):
    """报告生成Agent"""
    report = writer_agent.run(state["analysis_result"])
    return {"final_report": report}

graph = StateGraph(PipelineState)
graph.add_node("extract", extract)
graph.add_node("analyze", analyze)
graph.add_node("report", report)
graph.add_edge("extract", "analyze")
graph.add_edge("analyze", "report")
graph.add_edge("report", END)

// AgentCard — Agent的"名片"，用于自动发现
{
  "name": "financial-advisor-agent",
  "description": "专业金融分析Agent，擅长投资组合优化",
  "url": "https://agents.example.com/financial-advisor",
  "capabilities": {
    "streaming": true,
    "pushNotifications": true
  },
  "skills": [
    {
      "id": "portfolio-analysis",
      "name": "投资组合分析",
      "description": "分析风险收益比并提供再平衡建议"
    }
  ],
  "authentication": {
    "schemes": ["openid"]
  }
}

# A2A 任务生命周期
async def a2a_workflow():
    # 1. 发现远程Agent
    remote_agent = await discover_agent("financial-advisor-agent")

    # 2. 创建任务
    task = await remote_agent.create_task({
        "message": "分析我的科技股投资组合风险敞口"
    })

    # 3. 流式接收中间结果
    async for event in task.stream():
        if event.type == "status_update":
            print(f"进度: {event.state}")  # working → completed
        elif event.type == "artifact":
            print(f"产出: {event.artifact}")

    # 4. 获取最终结果
    result = await task.get_result()

# Ruflo 蜂群配置
swarm:
  topology: mesh  # mesh | star | hierarchy
  agents:
    - name: architect
      role: "系统架构设计"
      model: claude-opus-4-6
    - name: coder
      role: "代码实现"
      model: claude-sonnet-4-6
      instances: 3  # 水平扩展
    - name: reviewer
      role: "代码审查"
      model: claude-opus-4-6

  memory:
    type: shared    # 共享记忆空间
    backend: redis  # Redis持久化

  federation:
    enabled: true
    consensus: raft # Raft共识协议

// Bee Agent Framework 多Agent协作
import { Agent, Tool, BeeAgent } from "bee-agent-framework";

const researcher = new BeeAgent({
  llm: new ChatOpenAI({ model: "gpt-4o" }),
  tools: [new WebSearchTool(), new FileReaderTool()],
  memory: new TokenMemory(),
});

const analyst = new BeeAgent({
  llm: new ChatOpenAI({ model: "gpt-4o" }),
  tools: [new DataAnalysisTool(), new ChartGeneratorTool()],
  memory: new TokenMemory(),
});

// Agent间通过ACP协议通信
const pipeline = Agent.pipeline([researcher, analyst]);
const result = await pipeline.run("分析最新AI行业投资趋势");

# Temporal 持久化多Agent工作流
from temporalio import workflow

@workflow.defn
class MultiAgentResearchWorkflow:
    @workflow.run
    async def run(self, query: str) -> str:
        # 阶段1：并行搜索（Scatter）
        search_results = await asyncio.gather(
            workflow.execute_activity(
                search_web, query, start_to_close_timeout=timedelta(seconds=30)
            ),
            workflow.execute_activity(
                search_database, query, start_to_close_timeout=timedelta(seconds=60)
            ),
        )

        # 阶段2：综合分析
        analysis = await workflow.execute_activity(
            analyze_findings, search_results,
            retry_policy=RetryPolicy(maximum_attempts=3),
        )

        # 阶段3：生成报告（Gather）
        report = await workflow.execute_activity(
            generate_report, analysis,
            start_to_close_timeout=timedelta(seconds=120),
        )
        return report

from metagpt.software_company import generate_repo, ProjectRepo

# 一行需求 → 完整项目
repo: ProjectRepo = await generate_repo("构建一个支持多租户的在线教育平台")
# 自动生成：需求文档 → 竞品分析 → 系统设计 → API设计 → 代码 → 测试用例

Temporal Workflow（确定性重放）
  └── Activity: Agent推理（可重试、可超时、可取消）
  └── Activity: 工具调用（幂等性保证）
  └── Timer: 人机交互等待（可暂停数天）