多Agent编排六代跃迁：从Pipeline到涌现的架构选型栈

# LangGraph Pipeline实现
from langgraph.graph import StateGraph, START, END

def researcher(state):
    docs = retrieve(state["query"])
    return {"research": summarize(docs)}

def writer(state):
    return {"draft": write_article(state["research"])}

def reviewer(state):
    return {"final": review(state["draft"])}

graph = StateGraph(AgentState)
graph.add_node("researcher", researcher)
graph.add_node("writer", writer)
graph.add_node("reviewer", reviewer)
graph.add_edge(START, "researcher")
graph.add_edge("researcher", "writer")
graph.add_edge("writer", "reviewer")
graph.add_edge("reviewer", END)

# LangGraph Supervisor模式
from langgraph.graph import StateGraph, END
from typing import Literal

def supervisor(state) -> Literal["coder", "reviewer", "searcher", "end"]:
    messages = state["messages"]
    decision = llm.invoke(f"Based on: {messages[-1]}\n"
                          f"Next action? coder/reviewer/searcher/end")
    return decision

def coder(state):
    return {"messages": [code_agent.invoke(state["messages"])]}

def reviewer(state):
    return {"messages": [review_agent.invoke(state["messages"])]}

graph = StateGraph(AgentState)
graph.add_node("supervisor", supervisor)
graph.add_node("coder", coder)
graph.add_node("reviewer", reviewer)
graph.add_conditional_edges("supervisor", supervisor,
    {"coder": "coder", "reviewer": "reviewer",
     "searcher": "searcher", "end": END})
for agent in ["coder", "reviewer", "searcher"]:
    graph.add_edge(agent, "supervisor")

{
  "name": "code-reviewer",
  "description": "Reviews code for security vulnerabilities",
  "capabilities": ["static-analysis", "dependency-audit"],
  "inputSchema": {"type": "object", "properties": {
    "code": {"type": "string"},
    "language": {"type": "string"}
  }},
  "endpoint": "https://agents.example.com/code-reviewer"
}

# CrewAI层级编排
from crewai import Agent, Task, Crew, Process

researcher = Agent(role="Senior Researcher",
                   goal="Research {topic}",
                   backstory="Expert at deep research",
                   allow_delegation=True)

writer = Agent(role="Tech Writer",
               goal="Write article based on research",
               backstory="Skilled at clear technical writing",
               allow_delegation=False)

manager = Agent(role="Project Manager",
                goal="Coordinate research and writing",
                backstory="Experienced PM",
                allow_delegation=True)

crew = Crew(agents=[researcher, writer],
            tasks=[...],
            process=Process.hierarchical,
            manager_agent=manager,
            manager_llm="gpt-4o",
            delegation_depth=3)

# Google ADK事件驱动模式
from google.adk import Agent, EventBus

event_bus = EventBus()

# 订单创建Agent
order_agent = Agent(
    name="order-processor",
    instructions="Process incoming orders and emit events",
    event_bus=event_bus
)

# 库存Agent订阅订单事件
@event_bus.subscribe("order.created")
async def check_inventory(event):
    result = await inventory_agent.invoke(event.data)
    if result.in_stock:
        event_bus.publish("inventory.confirmed", result)
    else:
        event_bus.publish("inventory.depleted", result)

# 物流Agent订阅库存确认事件
@event_bus.subscribe("inventory.confirmed")
async def arrange_shipping(event):
    shipping = await shipping_agent.invoke(event.data)
    event_bus.publish("shipping.arranged", shipping)

# OpenAI Agents SDK Swarm模式
from openai import Agent, handoff

triage_agent = Agent(
    name="Triage Agent",
    instructions="Determine the nature of the user's request",
    handoffs=[
        handoff(agent="code_agent", 
                description="Transfer to code agent for programming tasks"),
        handoff(agent="research_agent",
                description="Transfer to research agent for information queries")
    ]
)

code_agent = Agent(
    name="Code Agent",
    instructions="Write and debug code",
    handoffs=[
        handoff(agent="review_agent",
                description="Transfer for code review after implementation")
    ]
)

review_agent = Agent(
    name="Review Agent",
    instructions="Review code quality and suggest improvements",
    handoffs=[
        handoff(agent="triage_agent",
                description="Transfer back if task scope changes")
    ]
)

# LangGraph自适应编排（实验性）
from langgraph.graph import DynamicGraph

class AdaptiveOrchestrator:
    def __init__(self):
        self.graph = DynamicGraph()
        self.pattern_metrics = {
            "pipeline": {"latency": [], "success": [], "tokens": []},
            "supervisor": {"latency": [], "success": [], "tokens": []},
            "swarm": {"latency": [], "success": [], "tokens": []}
        }

    def select_pattern(self, task):
        complexity = self.estimate_complexity(task)
        if complexity < 0.3:
            return "pipeline"
        elif complexity < 0.7:
            return "supervisor"
        else:
            return "swarm"

    async def execute(self, task):
        pattern = self.select_pattern(task)
        graph = self.build_graph(pattern)
        result = await graph.ainvoke(task)
        self.record_metrics(pattern, result)
        # 动态切换：如果失败率超阈值，尝试下一种模式
        if not result.success and self.should_escalate(pattern):
            next_pattern = self.next_pattern(pattern)
            graph = self.build_graph(next_pattern)
            result = await graph.ainvoke(task)
        return result

graph TB
    subgraph "编排模式层（六代）"
        P[Pipeline] --> S[Supervisor]
        S --> H[Hierarchical]
        H --> E[Event-Driven]
        E --> SW[Swarm]
        SW --> A[Adaptive]
    end

    subgraph "协议层"
        A2A[A2A Protocol<br/>Agent-to-Agent通信]
        MCP[MCP Protocol<br/>Agent-to-Tool通信]
    end

    A --> A2A
    A2A --> MCP

    style P fill:#4CAF50,color:#fff
    style S fill:#2196F3,color:#fff
    style H fill:#FF9800,color:#fff
    style E fill:#9C27B0,color:#fff
    style SW fill:#F44336,color:#fff
    style A fill:#E91E63,color:#fff
    style A2A fill:#607D8B,color:#fff
    style MCP fill:#795548,color:#fff

graph TD
    START[需要多Agent协作] --> Q1{任务流程是否固定?}
    Q1 -->|是| PIPE[Pipeline模式<br/>推荐: LangGraph/CrewAI]
    Q1 -->|否| Q2{是否需要中央决策?}

    Q2 -->|是| Q3{决策层级 > 2?}
    Q3 -->|否| SUP[Supervisor模式<br/>推荐: LangGraph]
    Q3 -->|是| HIER[Hierarchical模式<br/>推荐: CrewAI]

    Q2 -->|否| Q4{Agent间需要异步通信?}
    Q4 -->|是| EVT[Event-Driven模式<br/>推荐: Google ADK + Temporal]
    Q4 -->|否| Q5{Agent数量 > 5?}

    Q5 -->|是| SWARM[Swarm模式<br/>推荐: OpenAI Agents SDK]
    Q5 -->|否| PIPE2[Pipeline模式<br/>简单优先]

    SWARM --> Q6{需要运行时自适应?}
    Q6 -->|是| ADAPTIVE[Adaptive模式<br/>推荐: LangGraph实验特性<br/>⚠️ 生产就绪度2/10]
    Q6 -->|否| SWARM

    style PIPE fill:#4CAF50,color:#fff
    style SUP fill:#2196F3,color:#fff
    style HIER fill:#FF9800,color:#fff
    style EVT fill:#9C27B0,color:#fff
    style SWARM fill:#F44336,color:#fff
    style ADAPTIVE fill:#E91E63,color:#fff

stateDiagram-v2
    [*] --> Planning: 接收任务
    Planning --> Policy: 生成执行计划
    Policy --> Execution: 注入治理约束

    state Execution {
        [*] --> Dispatching
        Dispatching --> Running: 分配给Agent
        Running --> Validating: Agent返回结果
        Validating --> Dispatching: 需要更多步骤
        Validating --> Checkpointing: 步骤完成
        Checkpointing --> Dispatching: 还有待办任务
    }

    Execution --> Quality_Ops: 全部Agent完成
    Quality_Ops --> Planning: 质量不达标→重新规划
    Quality_Ops --> [*]: 质量达标→输出结果

    note right of Planning
        Pipeline: 固定计划
        Supervisor: 动态计划
        Hierarchical: 递归计划
        Adaptive: 自适应计划
    end note

    note right of Execution
        Event-Driven: 异步分发
        Swarm: 对等交接
    end note