2026六大AI Agent框架深度横评：谁才是开发者的终极选择？

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

class AgentState(TypedDict):
    messages: list
    next_action: str

def researcher(state: AgentState):
    # 研究节点：检索相关信息
    docs = retriever.invoke(state["messages"][-1])
    return {"messages": state["messages"] + [docs]}

def writer(state: AgentState):
    # 写作节点：基于研究内容生成文章
    draft = llm.invoke(f"Write based on: {state['messages'][-1]}")
    return {"messages": state["messages"] + [draft]}

def reviewer(state: AgentState):
    # 审核节点：决定是否需要修改
    review = llm.invoke(f"Review: {state['messages'][-1]}")
    if "approved" in review:
        return {"next_action": "done"}
    return {"next_action": "rewrite"}

# 构建状态图
graph = StateGraph(AgentState)
graph.add_node("researcher", researcher)
graph.add_node("writer", writer)
graph.add_node("reviewer", reviewer)
graph.add_conditional_edges("reviewer", 
    lambda s: "writer" if s["next_action"] == "rewrite" else END)
graph.add_edge("researcher", "writer")
graph.add_edge("writer", "reviewer")
app = graph.compile(checkpointer=SqliteSaver())

from crewai import Agent, Task, Crew, Process

researcher = Agent(
    role="市场研究员",
    goal="发现2026年AI市场趋势",
    backstory="你是一位有10年经验的市场分析师",
    llm="gpt-4o",
    tools=[search_tool, web_scraper]
)

writer = Agent(
    role="技术作家", 
    goal="将研究发现转化为易读报告",
    backstory="你擅长将复杂技术概念通俗化",
    llm="claude-sonnet-4-6"
)

research_task = Task(
    description="调研2026年AI Agent市场的Top 10趋势",
    agent=researcher,
    expected_output="包含数据支撑的趋势报告"
)

write_task = Task(
    description="将研究报告转化为3000字的深度文章",
    agent=writer,
    expected_output="结构清晰的公众号文章"
)

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

result = crew.kickoff()

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

# 定义Agent
planner = AssistantAgent(
    name="planner",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    system_message="你是任务规划专家，将复杂任务分解为可执行的子任务"
)

coder = AssistantAgent(
    name="coder", 
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    system_message="你是高级Python开发者，编写高质量代码"
)

reviewer = AssistantAgent(
    name="reviewer",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    system_message="你是代码审核专家，确保代码质量和安全性"
)

# 创建团队：轮询式协作
team = RoundRobinGroupChat(
    agents=[planner, coder, reviewer],
    max_turns=6  # 最多6轮对话
)

# 执行任务
result = await team.run(task="实现一个支持并发请求的Web爬虫")

from agents import Agent, Runner, handoff, guardrail

# 定义工具
def search_web(query: str) -> str:
    """搜索网络信息"""
    return web_search_tool(query)

def calculate(expression: str) -> float:
    """安全计算数学表达式"""
    return safe_eval(expression)

# 创建专家Agent
research_agent = Agent(
    name="研究专家",
    instructions="你负责搜索和整理信息。只处理研究类问题。",
    tools=[search_web],
)

math_agent = Agent(
    name="数学专家", 
    instructions="你负责数学计算。只处理数学问题。",
    tools=[calculate],
)

# 创建主Agent并配置Handoff
triage_agent = Agent(
    name="调度员",
    instructions="判断用户问题的类型，转交给合适的专家。",
    handoffs=[
        handoff(research_agent, "处理需要搜索信息的问题"),
        handoff(math_agent, "处理数学计算问题"),
    ]
)

# 配置Guardrail
@guardrail
def check_input(ctx):
    """防止注入攻击"""
    if detect_injection(ctx.input):
        return GuardrailResult(tripwire_triggered=True)
    return GuardrailResult(tripwire_triggered=False)

# 运行
result = Runner.run_sync(triage_agent, "2026年AI市场有多大？", 
                         input_guardrails=[check_input])

from claude_agent_sdk import Agent, Tool

# 定义自定义工具
@Tool
def query_database(sql: str) -> str:
    """查询业务数据库"""
    return db.execute(sql).fetchall()

@Tool  
def send_notification(user_id: str, message: str) -> bool:
    """给用户发送通知"""
    return notification_service.send(user_id, message)

# 创建Agent
agent = Agent(
    model="claude-sonnet-4-6",
    tools=[query_database, send_notification],
    system_prompt="""你是一个客户服务Agent。
    1. 先查询数据库了解用户情况
    2. 根据情况给出解决方案
    3. 需要时主动发送通知给用户""",
    max_turns=10,  # 最多10轮工具调用
)

# 执行Agent
result = agent.run("用户#1234投诉订单未到货，请帮我处理")
print(result.final_output)
print(f"使用了 {result.tool_calls_count} 次工具调用")
print(f"消耗 {result.total_tokens} tokens")

from google.adk import Agent, Tool, Runner
from google.adk.sessions import InMemorySessionService

@Tool
def search_knowledge_base(query: str) -> str:
    """搜索内部知识库"""
    return vector_store.search(query, top_k=5)

@Tool
def create_ticket(title: str, priority: str) -> str:
    """创建工单"""
    return ticket_system.create(title=title, priority=priority)

# 定义Agent
support_agent = Agent(
    name="技术支持Agent",
    model="gemini-2.5-pro",
    tools=[search_knowledge_base, create_ticket],
    instruction="""你是技术支持Agent。
    先搜索知识库找答案，找不到就创建工单升级处理。""",
)

# A2A原生：暴露Agent能力给其他框架
from google.adk.a2a import expose_agent
a2a_endpoint = expose_agent(support_agent, port=8080)

# 运行
runner = Runner(agent=support_agent, 
                session_service=InMemorySessionService())
result = runner.run("服务器503错误怎么排查？")