# Microsoft Agent Framework 1.0 — Python 示例
from microsoft.agents import Agent, AgentTeam, MCPToolProvider

# 定义工具（通过MCP协议连接）
tools = MCPToolProvider.from_config("mcp_config.json")

# 创建Agent
researcher = Agent(
    name="researcher",
    instructions="You are a research analyst. Find and summarize relevant information.",
    tools=tools,
    model="gpt-4o"
)

writer = Agent(
    name="writer",
    instructions="You are a technical writer. Transform research into clear articles.",
    model="gpt-4o"
)

# 使用AgentTeam编排
team = AgentTeam(agents=[researcher, writer], mode="sequential")
result = await team.run("Write a market analysis on AI agent frameworks")

from langgraph.graph import StateGraph, END
from typing import TypedDict, Annotated
import operator

class AgentState(TypedDict):
    messages: Annotated[list, operator.add]
    next_action: str

def think(state: AgentState) -> AgentState:
    response = llm.invoke(state["messages"])
    return {"messages": [response], "next_action": response.tool_calls[0]["name"] if response.tool_calls else "respond"}

def act(state: AgentState) -> AgentState:
    tool_result = execute_tool(state["messages"][-1].tool_calls[0])
    return {"messages": [tool_result], "next_action": "think"}

def should_continue(state: AgentState) -> str:
    return state["next_action"] if state["next_action"] != "respond" else END

graph = StateGraph(AgentState)
graph.add_node("think", think)
graph.add_node("act", act)
graph.add_conditional_edges("think", should_continue, {"act": "act", END: END})
graph.add_edge("act", "think")
graph.set_entry_point("think")

app = graph.compile(checkpointer=SqliteSaver(conn))  # 持久化状态

from crewai import Agent, Task, Crew, Process
from crewai_tools import SerperDevTool, ScrapeWebsiteTool

search_tool = SerperDevTool()
scrape_tool = ScrapeWebsiteTool()

researcher = Agent(
    role="Senior Research Analyst",
    goal="Uncover cutting-edge developments in AI agent technology",
    backstory="You work at a leading tech think tank. Your expertise lies in identifying emerging trends.",
    tools=[search_tool, scrape_tool],
    llm="deepseek/deepseek-chat",  # v1.12: 原生支持非OpenAI模型
    memory=True,  # v1.12: 层级记忆隔离
    verbose=True
)

writer = Agent(
    role="Tech Content Strategist",
    goal="Craft compelling content on tech advancements",
    backstory="You are a renowned content strategist, known for turning complex concepts into compelling narratives.",
    llm="gpt-4o",
    memory=True
)

research_task = Task(
    description="Research the latest developments in AI agent frameworks and provide a comprehensive analysis.",
    expected_output="A detailed report with key findings and insights.",
    agent=researcher
)

write_task = Task(
    description="Using the research findings, write a compelling blog post.",
    expected_output="A polished blog post formatted as markdown.",
    agent=writer
)

crew = Crew(
    agents=[researcher, writer],
    tasks=[research_task, write_task],
    process=Process.sequential,
    memory=True  # v1.12: Qdrant Edge 后端
)

result = crew.kickoff()

from agents import Agent, RunContext, function_tool, Runner
from agents import guardrail

@function_tool
def lookup_order(order_id: str) -> str:
    """Look up an order by ID."""
    return f"Order {order_id}: Shipped, arrives in 2 days."

@guardrail
async def check_escalation(ctx: RunContext, input_text: str) -> bool:
    """If user is angry or asks for manager, escalate."""
    trigger_words = ["manager", "supervisor", "angry", "unacceptable"]
    return any(w in input_text.lower() for w in trigger_words)

triage_agent = Agent(
    name="Triage Agent",
    instructions="Help customers with their orders. Escalate if needed.",
    tools=[lookup_order],
    guardrails=[check_escalation],
)

specialist_agent = Agent(
    name="Specialist Agent",
    instructions="You handle complex customer issues that have been escalated.",
)

# Handoff: triage → specialist
result = await Runner.run(
    triage_agent,
    messages=[{"role": "user", "content": "I want to speak to a manager about order #123"}],
    handoffs={"escalation": specialist_agent}  # 护栏触发后自动handoff
)

# Google ADK 2.0 — AgentTeam 示例
from google.adk import Agent, AgentTeam, A2AClient
from google.adk.tools import google_search

researcher = Agent(
    name="researcher",
    model="gemini-2.5-pro",
    instruction="Research the given topic thoroughly",
    tools=[google_search],
)

analyzer = Agent(
    name="analyzer",
    model="gemini-2.5-pro",
    instruction="Analyze the research and identify key patterns",
)

# AgentTeam: 多Agent协调
team = AgentTeam(
    agents=[researcher, analyzer],
    strategy="sequential",  # or "parallel", "debate"
)

# A2A: 跨框架Agent通信
external_agent = A2AClient.discover("https://other-company.com/agent/weather")
result = await team.run("Analyze AI agent market trends", external_agents=[external_agent])

# Strands Agents — 事件驱动模型
from strands import Agent, Tool
from strands.tools import mcp_client

@mcp_client("filesystem-mcp-server")
async def file_operations():
    pass

agent = Agent(
    model="anthropic.claude-3-5-sonnet-20241022-v2:0",  # Bedrock模型ID
    tools=[file_operations],
    system_prompt="You are a file management assistant.",
)

# 部署到AgentCore Runtime
# strands deploy --runtime agentcore --memory 2GB --timeout 300s

# AG2 — GroupChat with Docker工具
import ag2
from ag2 import AssistantAgent, UserProxyAgent, GroupChat, GroupChatManager

llm_config = {"model": "gpt-4o", "api_key": "your-key"}

coder = AssistantAgent(
    name="Coder",
    system_message="You write Python code to solve problems.",
    llm_config=llm_config,
)

reviewer = AssistantAgent(
    name="Reviewer",
    system_message="You review code for correctness and style.",
    llm_config=llm_config,
)

user_proxy = UserProxyAgent(
    name="User",
    human_input_mode="NEVER",
    code_execution_config={"use_docker": True},  # AG2: 原生Docker沙箱
)

group_chat = GroupChat(agents=[coder, reviewer, user_proxy], messages=[], max_round=10)
manager = GroupChatManager(groupchat=group_chat, llm_config=llm_config)

user_proxy.initiate_chat(manager, message="Write a function to check if a number is prime and test it.")

graph TB
    subgraph "厂商框架"
        MS["Microsoft Agent Framework 1.0<br/>SK基座 + AutoGen编排<br/>.NET + Python"]
        OAI["OpenAI Agents SDK<br/>Agent + Handoff + Guardrail<br/>仅OpenAI模型"]
        GOOG["Google ADK 2.0<br/>AgentTeam + A2A协议<br/>Python/Go/TS"]
        AWS["Amazon Strands<br/>事件驱动 + AgentCore Runtime<br/>Serverless"]
    end

    subgraph "独立框架"
        LG["LangGraph v1.1.3<br/>StateGraph + Checkpoint<br/>图驱动状态机"]
        CR["CrewAI v1.12<br/>角色 + 任务 + 技能<br/>角色扮演多Agent"]
    end

    subgraph "社区框架"
        AG2["AG2<br/>AutoGen社区fork<br/>AgentOS理念"]
    end

    subgraph "协议层"
        MCP["MCP 协议<br/>工具连接标准化"]
        A2A["A2A 协议<br/>Agent间通信标准化"]
    end

    MS -->|原生支持| MCP
    MS -->|原生支持| A2A
    GOOG -->|核心特性| A2A
    AWS -->|原生支持| MCP
    LG -->|通过LangChain| MCP
    CR -->|通过LiteLLM| MCP
    AG2 -->|社区插件| MCP

    style MS fill:#0078d4,color:#fff
    style OAI fill:#10a37f,color:#fff
    style GOOG fill:#4285f4,color:#fff
    style AWS fill:#ff9900,color:#fff
    style LG fill:#1c3c3c,color:#fff
    style CR fill:#6366f1,color:#fff
    style AG2 fill:#6b7280,color:#fff

flowchart TD
    START[选择Agent框架] --> Q1{是否绑定特定云厂商?}

    Q1 -->|已有Azure投资| MS[Microsoft Agent Framework 1.0]
    Q1 -->|已有AWS投资| AMZ[Amazon Strands + AgentCore]
    Q1 -->|已有GCP投资| GGL[Google ADK 2.0]
    Q1 -->|只用OpenAI| OAI[OpenAI Agents SDK]
    Q1 -->|需要模型自由| Q2{核心工作流类型?}

    Q2 -->|复杂状态图/审批流| LG[LangGraph]
    Q2 -->|角色扮演/团队协作| CR[CrewAI]
    Q2 -->|AutoGen存量迁移| Q3{愿意重写代码?}

    Q3 -->|是, 追求官方支持| MS2[Microsoft Agent Framework]
    Q3 -->|否, 最小改动| AG[AG2]

    MS --> PROD[生产部署]
    AMZ --> PROD
    GGL --> PROD
    OAI --> PROD
    LG --> PROD
    CR --> PROD
    AG --> RISK[⚠️ 社区维护风险]

    style START fill:#1a1a2e,color:#fff
    style RISK fill:#dc2626,color:#fff
    style PROD fill:#059669,color:#fff