from langgraph.graph import StateGraph, END

def router(state):
    if state["confidence"] < 0.7:
        return "human_review"
    return "execute"

graph = StateGraph(AgentState)
graph.add_node("planner", plan_step)
graph.add_node("executor", execute_step)
graph.add_node("human_review", human_review_step)
graph.add_conditional_edges("planner", router)
graph.add_edge("executor", END)

app = graph.compile(checkpointer=SqliteSaver(conn))

from crewai import Agent, Task, Crew, Process

researcher = Agent(
    role="Senior Research Analyst",
    goal="Uncover cutting-edge developments in AI",
    backstory="You are a seasoned analyst at a major tech consultancy.",
    tools=[search_tool, scrape_tool],
    verbose=True
)

writer = Agent(
    role="Tech Content Strategist", 
    goal="Craft compelling content on AI advancements",
    backstory="You are a former tech journalist turned content strategist.",
    verbose=True
)

crew = Crew(
    agents=[researcher, writer],
    tasks=[research_task, write_task],
    process=Process.sequential  # 或 Process.hierarchical
)
result = crew.kickoff(inputs={"topic": "AI agents in production"})

from openai import Agent, Runner, handoff, function_tool

@function_tool
def lookup_order(order_id: str) -> str:
    return f"Order {order_id}: shipped, ETA 2 days"

triage_agent = Agent(
    name="Triage Agent",
    instructions="Route to the right specialist.",
    handoffs=[handoff(sales_agent), handoff(support_agent)]
)

support_agent = Agent(
    name="Support Agent", 
    instructions="Help with order and product issues.",
    tools=[lookup_order],
    handoffs=[handoff(triage_agent)]  # 可回退
)

result = await Runner.run(triage_agent, messages=[...])

from autogen_agentchat.agents import AssistantAgent
from autogen_agentchat.teams import RoundRobinGroupChat
from autogen_ext.models.openai import OpenAIChatCompletionClient

model_client = OpenAIChatCompletionClient(model="gpt-4o")

analyst = AssistantAgent("analyst", model_client, 
    system_message="Analyze data and provide insights.")
critic = AssistantAgent("critic", model_client,
    system_message="Challenge assumptions and find flaws.")
writer = AssistantAgent("writer", model_client,
    system_message="Synthesize into a clear report.")

team = RoundRobinGroupChat([analyst, critic, writer], max_turns=6)
result = await team.run(task="Analyze Q3 revenue decline")

记忆架构四层模型
┌─────────────────────────────────────────┐
│  L4: 元认知记忆 (反思、策略优化)          │ ← 仅LangGraph/Claude
├─────────────────────────────────────────┤
│  L3: 语义记忆 (知识图谱、向量索引)         │ ← CrewAI Enterprise/
│                                          │   LangGraph Store
├─────────────────────────────────────────┤
│  L2: 情景记忆 (会话历史、交互摘要)         │ ← 全栈支持
├─────────────────────────────────────────┤
│  L1: 工作记忆 (上下文窗口内缓冲)           │ ← 全栈支持
└─────────────────────────────────────────┘

graph TB
    subgraph "用户意图层"
        U[用户请求] --> P[意图解析与路由]
    end

    subgraph "编排引擎层"
        P --> |简单任务| SA[单Agent + 工具]
        P --> |流水线任务| SEQ[顺序链式编排]
        P --> |分类路由| RT[路由分发器]
        P --> |批量处理| MAP[并行扇出]
        P --> |复杂决策| DBT[辩论共识]

        RT --> A1[专家Agent 1]
        RT --> A2[专家Agent 2]
        RT --> A3[专家Agent N]

        MAP --> W1[工作者 1]
        MAP --> W2[工作者 2]
        MAP --> AGG[聚合器]

        DBT --> D1[正方Agent]
        DBT --> D2[反方Agent]
        DBT --> D3[裁判Agent]
    end

    subgraph "记忆层"
        A1 & A2 & A3 --> WM[工作记忆<br/>上下文窗口]
        WM --> EM[情景记忆<br/>会话摘要]
        EM --> SM[语义记忆<br/>知识图谱/向量索引]
        SM --> MM[元认知记忆<br/>策略优化]
    end

    subgraph "工具层"
        A1 & A2 & A3 --> TL[工具注册表]
        TL --> API[REST/GraphQL API]
        TL --> DB[(数据库)]
        TL --> FS[文件系统]
        TL --> WEB[网页搜索]
    end

    subgraph "可观测性"
        SA & SEQ & RT --> OBS[LangSmith/Langfuse<br/>Tracing + Metrics]
        OBS --> LOG[结构化日志]
        OBS --> COST[Token成本追踪]
        OBS --> EVAL[质量评估]
    end

graph LR
    START[选型起点] --> Q1{需要多Agent协作?}
    Q1 --> |否| Q2{模型锁定可接受?}
    Q2 --> |是, OpenAI| OAI[OpenAI Agents SDK<br/>⏱ 15min上手]
    Q2 --> |是, Claude| CLA[Claude Agent SDK<br/>⏱ 30min上手]
    Q2 --> |否, 需灵活| LG1[LangGraph 单Agent<br/>⏱ 2h上手]

    Q1 --> |是| Q3{协作模式?}
    Q3 --> |角色分工| CREW[CrewAI<br/>⏱ 30min上手]
    Q3 --> |对话/辩论| AG[AutoGen<br/>⏱ 1h上手]
    Q3 --> |复杂状态流| LG2[LangGraph 多Agent<br/>⏱ 4h上手]
    Q3 --> |GCP生态| VTX[Vertex AI Builder<br/>⏱ 1.5h上手]

    OAI & CLA & LG1 --> PROD{需要生产部署?}
    CREW & AG & LG2 --> PROD
    PROD --> |是, 托管| MANAGED[LangGraph Cloud /<br/>CrewAI Enterprise /<br/>OpenAI Platform]
    PROD --> |是, 自托管| SELF[自托管 + LangSmith /<br/>Langfuse可观测]
    PROD --> |否, PoC| POOL[本地运行即可]