主流智能体平台终极对比：2026 选型全景图

from agents import Agent, Runner, tool

@tool
def search_web(query: str) -> str:
    """搜索网络"""
    # 工具实现
    return search_api(query)

agent = Agent(
    name="研究助手",
    instructions="你是一个研究助手，使用工具搜索信息并回答。",
    tools=[search_web],
    model="gpt-4o",
)

result = Runner.run_sync(agent, "2026年AI Agent领域有哪些新进展？")
print(result.final_output)

import anthropic

client = anthropic.Anthropic()

response = client.messages.create(
    model="claude-sonnet-4-20250514",
    max_tokens=4096,
    tools=[{
        "name": "execute_code",
        "description": "执行 Python 代码",
        "input_schema": {
            "type": "object",
            "properties": {"code": {"type": "string"}},
            "required": ["code"]
        }
    }],
    messages=[{"role": "user", "content": "计算斐波那契数列前20项"}],
)

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

class State(TypedDict):
    messages: Annotated[list, operator.add]
    next: str

def research_node(state: State):
    # 研究节点
    return {"messages": [research_result]}

def write_node(state: State):
    # 写作节点
    return {"messages": [article]}

workflow = StateGraph(State)
workflow.add_node("research", research_node)
workflow.add_node("write", write_node)
workflow.set_entry_point("research")
workflow.add_edge("research", "write")
workflow.add_edge("write", END)

app = workflow.compile()
result = app.invoke({"messages": [HumanMessage(content="写一篇关于AI的报道")]})

from crewai import Agent, Task, Crew

researcher = Agent(
    role="研究员",
    goal="收集和分析信息",
    backstory="资深研究员，擅长快速信息检索",
    tools=[search_tool],
)

writer = Agent(
    role="作家",
    goal="将研究成果转化为可读文章",
    backstory="资深科技作家",
)

research_task = Task(
    description="研究量子计算的最新进展",
    agent=researcher,
    expected_output="研究摘要",
)

write_task = Task(
    description="基于研究摘要写一篇科普文章",
    agent=writer,
    expected_output="2000字科普文章",
    context=[research_task],
)

crew = Crew(agents=[researcher, writer], tasks=[research_task, write_task])
result = crew.kickoff()

from autogen import AssistantAgent, UserProxyAgent, GroupChat

coder = AssistantAgent(
    name="coder",
    system_message="你是程序员，负责写代码。",
    llm_config={"model": "gpt-4o"},
)

reviewer = AssistantAgent(
    name="reviewer",
    system_message="你是代码审查员。",
    llm_config={"model": "gpt-4o"},
)

user = UserProxyAgent(
    name="user",
    human_input_mode="NEVER",
    code_execution_config={"work_dir": "workspace"},
)

group_chat = GroupChat(
    agents=[user, coder, reviewer],
    messages=[],
    max_round=10,
)

manager = AssistantAgent(
    name="manager",
    system_message="管理对话流程。",
    llm_config={"model": "gpt-4o"},
    is_termination_msg=lambda x: x.get("content", "").find("TERMINATE") >= 0,
)

user.initiate_chat(manager, message="写一个排序算法并审查", group_chat=group_chat)

你的需求是什么？
│
├─ 非技术人员，想快速搭 Bot
│   ├─ 要发布到飞书/微信/抖音 → Coze
│   └─ 要可视化 RAG + API 发布 → Dify
│
├─ 开发者，想用代码构建 Agent
│   ├─ 只用 OpenAI → OpenAI Agents SDK
│   ├─ 只用 Claude → Anthropic Tool Use
│   ├─ 需要多模型/复杂编排 → LangGraph
│   ├─ 多角色协作 → CrewAI
│   └─ 对话式研究 → AutoGen
│
├─ 个人用户，想要常驻 AI 助手
│   └─ → OpenClaw
│
└─ 企业级生产部署
    ├─ 低代码 + 团队协作 → Dify
    ├─ 代码 + 全控制 → LangGraph
    └─ 个人效率 → OpenClaw