六大Multi-Agent任务拆解引擎架构对比

用户请求 → [Router LLM] → 意图分类 → 分发到Agent A/B/C
                ↑                          ↓
                ←──── 结果汇聚 ←───────────┘

from langgraph_supervisor import create_supervisor
from langgraph.prebuilt import create_react_agent

# 定义专职Agent
research_agent = create_react_agent(
    model="openai:gpt-4.1-mini",
    tools=[web_search, arxiv_search],
    prompt="You are a research specialist..."
)
writer_agent = create_react_agent(
    model="openai:gpt-4.1",
    tools=[write_file],
    prompt="You are a content writer..."
)
reviewer_agent = create_react_agent(
    model="anthropic:claude-sonnet-4-20250514",
    tools=[read_file, grammar_check],
    prompt="You are a quality reviewer..."
)

# Supervisor自动进行意图分类与分发
supervisor = create_supervisor(
    agents=[research_agent, writer_agent, reviewer_agent],
    model="openai:gpt-4.1",  # Router模型
    prompt="Route tasks to the best-suited agent."
).compile()

from langgraph.graph import StateGraph, START, END

class PipelineState(TypedDict):
    topic: str
    research_data: str
    draft: str
    final_article: str

graph = StateGraph(PipelineState)

# 固定串行编排
graph.add_node("research", research_agent)
graph.add_node("write", writer_agent)
graph.add_node("review", reviewer_agent)

graph.add_edge(START, "research")
graph.add_edge("research", "write")
graph.add_edge("write", "review")
graph.add_edge("review", END)

pipeline = graph.compile()

// Microsoft Agent Framework - Fan-out Pattern
var orchestrator = new OrchestratorAgent();

// 并行分发给多个专职Agent
var tasks = new List<Task<AgentResponse>>();
foreach (var agent in specializedAgents)
{
    tasks.Add(agent.ExecuteAsync(subTask));
}

var results = await Task.WhenAll(tasks);

// Fan-in: 聚合结果
var finalResult = await aggregatorAgent.SynthesizeAsync(
    results.Select(r => r.Output).ToList()
);

                [CEO Agent]
               /           \
        [VP Research]  [VP Content]
        /          \        \
   [Search]  [Analysis]  [Writer]

from crewai import Crew, Agent, Task, Process

# 定义层级角色
manager = Agent(
    role="Project Manager",
    goal="Decompose complex tasks and delegate to specialists",
    llm="gpt-4.1",
    allow_delegation=True
)

researcher = Agent(
    role="Senior Researcher",
    goal="Find and analyze relevant information",
    tools=[search_tool, scrape_tool],
    llm="gpt-4.1-mini"
)

analyst = Agent(
    role="Data Analyst",
    goal="Extract insights from research data",
    tools=[python_repl, pandas_tool],
    llm="gpt-4.1"
)

crew = Crew(
    agents=[researcher, analyst],
    tasks=tasks,
    process=Process.hierarchical,
    manager_agent=manager,
    verbose=True
)

from agents import Agent, Runner, handoff

# 定义专职Agent，每个Agent声明自己能处理的范围
billing_agent = Agent(
    name="Billing Specialist",
    instructions="Handle billing inquiries, payment issues, refunds.",
    model="gpt-4.1-mini"
)

technical_agent = Agent(
    name="Technical Support",
    instructions="Handle technical issues, debugging, API questions.",
    model="gpt-4.1"
)

# Triage Agent通过handoff主动转交
triage_agent = Agent(
    name="Triage Agent",
    instructions="""You are the first point of contact.
    Analyze the user's request and hand off to the appropriate specialist.
    You can handle simple greetings and FAQs yourself.""",
    model="gpt-4.1",
    handoffs=[
        handoff(agent=billing_agent),
        handoff(agent=technical_agent),
    ]
)

result = await Runner.run(triage_agent, "I was charged twice for my subscription")
# Triage自动判断 → handoff to billing_agent

     [Agent A] ←→ [Agent B]
        ↕  ↘        ↕  ↙
     [Agent C] ←→ [Agent D]
        ↕           ↕
     [Agent E] ←→ [Agent F]

     没有中心节点
     每对Agent独立协商
     任务分工通过涌现产生

graph TB
    subgraph "任务拆解引擎分类"
        direction TB
        A[用户复杂请求] --> B{拆解策略选择}

        B -->|边界清晰| C[Supervisor集中式]
        B -->|步骤固定| D[Pipeline流水线]
        B -->|子任务独立| E[Fan-out并行]
        B -->|复杂度高| F[Hierarchical层级]
        B -->|会话驱动| G[Handoff接力]
        B -->|大规模去中心| H[AgentNet涌现]
    end

    subgraph "Supervisor"
        C --> C1[Router LLM意图分类]
        C1 --> C2[分发到专职Agent]
        C2 --> C3[结果汇聚]
    end

    subgraph "Fan-out/Fan-in"
        E --> E1[Dispatcher广播]
        E1 --> E2[Agent1]
        E1 --> E3[Agent2]
        E1 --> E4[AgentN]
        E2 --> E5[Reduce聚合]
        E3 --> E5
        E4 --> E5
    end

    subgraph "Handoff"
        G --> G1[Triage Agent]
        G1 -->|判断转交| G2[Specialist A]
        G1 -->|判断转交| G3[Specialist B]
        G2 -->|需要协助| G3
        G3 -->|完成返回| G1
    end

    subgraph "AgentNet"
        H --> H1[Agent竞标]
        H1 <--> H2[进化协调]
        H1 <--> H3[自发分工]
        H2 <--> H3
    end

    C3 --> I[最终输出]
    E5 --> I
    G1 --> I
    H3 --> I

# NVIDIA LLM Router - Intent-based + Complexity-based双路由
router_prompt = """Classify this request:
1. Which agent should handle it? [research/writer/coder/reviewer]
2. What complexity level? [simple/medium/complex]
3. Does it need multi-agent collaboration? [yes/no]

Request: {user_input}
"""

# 混合架构：外层Supervisor + 内层Fan-out + Handoff容错
class HybridOrchestrator:
    def __init__(self):
        self.router = EmbeddingRouter()  # 粗筛：Embedding路由
        self.llm_router = LLMRouter()    # 精排：LLM路由
        self.agents = {
            "research": ResearchTeam(fan_out=True),   # 研究团队内部并行
            "writing": WritingPipeline(),              # 写作团队内部串行
            "review": ReviewSwarm(),                   # 审核团队Swarm模式
        }

    async def run(self, task):
        # Step 1: Embedding粗筛
        candidates = self.router.route(task, top_k=3)
        # Step 2: LLM精排
        best_agent = self.llm_router.refine(task, candidates)
        # Step 3: 分发执行
        result = await self.agents[best_agent].execute(task)
        # Step 4: 如果失败，Handoff到备选Agent
        if not result.success:
            for fallback in candidates[1:]:
                result = await self.agents[fallback].execute(task)
                if result.success:
                    break
        return result