2026八大AI Agent框架深度横评：谁是真正的生产级王者？

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

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

def supervisor(state: AgentState) -> AgentState:
    """路由决策节点 - 根据任务类型分发给专业Agent"""
    last_msg = state["messages"][-1]
    if "数据分析" in last_msg:
        return {**state, "next_agent": "analyst"}
    elif "报告生成" in last_msg:
        return {**state, "next_agent": "writer"}
    return {**state, "next_agent": "general"}

def analyst_agent(state: AgentState) -> AgentState:
    result = "数据分析完成: 发现3个关键趋势"
    return {**state, "messages": [result], "task_result": result}

def writer_agent(state: AgentState) -> AgentState:
    result = f"报告已生成: 基于{state['task_result']}"
    return {**state, "messages": [result]}

graph = StateGraph(AgentState)
graph.add_node("supervisor", supervisor)
graph.add_node("analyst", analyst_agent)
graph.add_node("writer", writer_agent)
graph.add_edge(START, "supervisor")
graph.add_conditional_edges("supervisor", 
    lambda s: s["next_agent"],
    {"analyst": "analyst", "writer": "writer", "general": END})
graph.add_edge("analyst", END)
graph.add_edge("writer", END)

app = graph.compile()

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

search_tool = SerperDevTool()
scrape_tool = ScrapeWebsiteTool()

researcher = Agent(
    role="高级技术研究员",
    goal="深度分析AI Agent领域最新技术趋势",
    backstory="你是一位拥有10年经验的技术分析师，"
              "擅长从海量信息中提炼关键洞察",
    tools=[search_tool, scrape_tool],
    verbose=True,
    allow_delegation=False
)

writer = Agent(
    role="技术内容总监",
    goal="将研究结果转化为高质量技术文章",
    backstory="你是一位顶级科技媒体编辑，"
              "擅长将复杂技术概念转化为清晰易懂的文章",
    verbose=True,
    allow_delegation=True
)

research_task = Task(
    description="调研2026年最值得关注的5个AI Agent框架，"
                "分析各自的技术架构和适用场景",
    expected_output="包含架构对比表格的详细调研报告",
    agent=researcher
)

write_task = Task(
    description="基于调研报告撰写一篇3000字的深度分析文章",
    expected_output="格式完整的Markdown文章，包含代码示例",
    agent=writer
)

crew = Crew(
    agents=[researcher, writer],
    tasks=[research_task, write_task],
    process=Process.sequential,
    verbose=True
)

result = crew.kickoff()

from agents import Agent, Runner, function_tool, guardrail
from pydantic import BaseModel

class MathResult(BaseModel):
    answer: float
    explanation: str

@function_tool
def calculate(expression: str) -> str:
    """安全计算数学表达式"""
    try:
        result = eval(expression, {"__builtins__": {}}, {})
        return f"计算结果: {result}"
    except Exception as e:
        return f"计算错误: {e}"

triage_agent = Agent(
    name="路由助手",
    instructions="判断用户需求并转接到专业Agent。"
                "数学问题交给数学Agent，创意写作交给写作Agent。",
)

math_agent = Agent(
    name="数学专家",
    instructions="你是一位数学专家。使用calculate工具解决计算问题，"
                "并给出详细的解题步骤。",
    tools=[calculate],
)

writer_agent = Agent(
    name="创意写作助手",
    instructions="你是一位创意写作专家。帮助用户进行文案创作、"
                "故事编写和内容优化。",
)

# 定义Handoff：路由Agent可以将控制权移交给专业Agent
triage_agent.handoffs = [math_agent, writer_agent]

# 定义Guardrail：防止处理危险请求
@guardrail
def safety_check(agent, input_text):
    dangerous_patterns = ["黑客", "攻击", "破解"]
    if any(p in input_text for p in dangerous_patterns):
        return False, "请求被安全策略拦截"
    return True, input_text

result = Runner.run_sync(
    starting_agent=triage_agent,
    input="请帮我计算 (235 + 780) * 0.15 的结果",
    guardrails=[safety_check]
)
print(result.final_output)

from microsoft.agent_framework import Agent, Team, Message
from microsoft.agent_framework.tools import code_interpreter

code_agent = Agent(
    name="Python开发者",
    description="编写和执行Python代码解决技术问题",
    tools=[code_interpreter()],
    instructions="你是一位资深Python开发者。"
                "编写清晰、高效的代码，并提供详细注释。"
)

review_agent = Agent(
    name="代码审查员",
    description="审查代码质量、安全性和性能",
    instructions="你是一位严格的代码审查专家。"
                "从安全性、性能、可读性三个维度评审代码。"
)

team = Team(
    name="开发团队",
    agents=[code_agent, review_agent],
    mode="round_robin"  # 轮流交互模式
)

result = await team.run(
    "编写一个高效的文件去重工具，要求："
    "1) 支持大文件 2) 使用哈希校验 3) 提供进度显示"
)

from google.adk import Agent, Tool, Runner
from google.adk.a2a import A2AServer, AgentCard

@Tool
def search_database(query: str) -> str:
    """搜索企业知识库"""
    # 连接BigQuery/Cloud SQL
    return f"查询'{query}'的结果: [相关文档...]"

@Tool
def generate_report(data: str) -> str:
    """生成分析报告"""
    return f"报告已生成，基于数据: {data[:100]}..."

analyst = Agent(
    name="数据分析师",
    model="gemini-2.5-pro",
    instruction="你是企业数据分析专家。先搜索数据，再生成报告。",
    tools=[search_database, generate_report],
)

# 将Agent暴露为A2A服务，供其他框架的Agent调用
card = AgentCard(
    name="enterprise-analyst",
    description="企业数据分析Agent，支持知识库搜索和报告生成",
    skills=["search", "report_generation"],
    endpoint="https://my-agent.a2a.dev"
)

server = A2AServer(agent=analyst, card=card)
runner = Runner(agent=analyst)
result = runner.run("分析Q1季度销售趋势")

# MCP Server示例：暴露企业内部API作为Agent工具
from mcp.server import Server
from mcp.types import Tool, TextContent

server = Server("enterprise-tools")

@server.list_tools()
async def list_tools():
    return [
        Tool(
            name="query_orders",
            description="查询订单系统中的订单信息",
            inputSchema={
                "type": "object",
                "properties": {
                    "order_id": {"type": "string", "description": "订单号"},
                    "date_range": {"type": "string", "description": "日期范围"}
                },
                "required": ["order_id"]
            }
        ),
        Tool(
            name="create_ticket",
            description="在工单系统中创建支持工单",
            inputSchema={
                "type": "object",
                "properties": {
                    "title": {"type": "string"},
                    "description": {"type": "string"},
                    "priority": {"type": "string", "enum": ["low","medium","high"]}
                },
                "required": ["title", "description"]
            }
        )
    ]

@server.call_tool()
async def call_tool(name, arguments):
    if name == "query_orders":
        result = f"订单{arguments['order_id']}: 状态-已发货, 金额-¥2,980"
        return [TextContent(type="text", text=result)]
    elif name == "create_ticket":
        return [TextContent(type="text", text=f"工单已创建: #{hash(arguments['title']) % 10000}")]

# 启动MCP Server（stdio模式，供Claude Desktop等客户端连接）
server.run()

from smolagents import CodeAgent, HfApiModel, tool

@tool
def get_weather(city: str) -> str:
    """获取指定城市的天气信息"""
    # 实际应用中调用天气API
    return f"{city}: 晴, 25°C, 湿度45%"

@tool
def calculate_distance(city1: str, city2: str) -> str:
    """计算两个城市之间的距离"""
    distances = {"北京-上海": "1213km", "北京-广州": "2129km"}
    key = f"{city1}-{city2}"
    return distances.get(key, f"{city1}到{city2}约1000km")

agent = CodeAgent(
    model=HfApiModel("Qwen/Qwen2.5-72B-Instruct"),  # 使用开源模型
    tools=[get_weather, calculate_distance],
    max_steps=5,
    additional_authorized_imports=["math", "datetime"]
)

result = agent.run(
    "北京和上海哪个城市今天更热？两地之间的距离是多少？"
)
print(result)