Agent框架战后格局：九强定位与选型终局

graph TB
    subgraph "云厂商绑定阵营"
        OAI["OpenAI Agents SDK<br/>Handoff原生"]
        GADK["Google ADK 2.0<br/>Gemini绑定+A2A"]
        CAS["Claude Agent SDK<br/>Claude Code引擎"]
        MAF["Microsoft Agent Framework 1.0<br/>AutoGen+SK合并"]
    end

    subgraph "中立开源阵营"
        LG["LangGraph 1.2<br/>状态图基础设施"]
        CA["CrewAI 1.9<br/>角色编排"]
        AG["Agno 2.6<br/>高性能运行时"]
    end

    subgraph "垂直差异化阵营"
        MA["Mastra 1.0<br/>TypeScript原生"]
        SA["Smolagents<br/>极简主义"]
        DSPY["DSPy<br/>声明式编程"]
    end

    OAI -.->|"模型绑定"| LG
    GADK -.->|"A2A协议"| CA
    MAF -.->|.NET生态.| LG
    AG -->|"性能竞速"| LG

from langgraph.graph import StateGraph, END

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

def researcher(state):
    # 调用LLM进行研究
    result = llm.invoke(state["messages"])
    return {"messages": result, "next_action": "write"}

def writer(state):
    draft = llm.invoke(f"基于研究写文章: {state['messages']}")
    return {"messages": draft, "next_action": "review"}

graph = StateGraph(AgentState)
graph.add_node("researcher", researcher)
graph.add_node("writer", writer)
graph.add_edge("researcher", "writer")
graph.add_conditional_edges("writer", route, {"revise": "researcher", "done": END})

from crewai import Agent, Task, Crew, Process

researcher = Agent(
    role="高级研究分析师",
    goal="发现AI领域的突破性技术",
    backstory="你是一位有10年经验的技术分析师",
    tools=[search_tool, scrape_tool]
)

writer = Agent(
    role="技术内容策略师", 
    goal="将复杂技术转化为引人入胜的叙述",
    backstory="你擅长将技术深度与商业洞察结合"
)

research_task = Task(description="调研2026年Agent框架趋势", agent=researcher)
write_task = Task(description="撰写深度分析文章", agent=writer)

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

result = crew.kickoff()

from agents import Agent, Runner, handoff, GuardrailFunctionOutput

triage_agent = Agent(
    name="分流Agent",
    instructions="根据用户问题类型，移交给合适的专家",
    handoffs=[
        handoff(research_agent, 
                tool_name_override="transfer_to_research"),
        handoff(coding_agent,
                tool_name_override="transfer_to_coding")
    ]
)

# Guardrail: 检查输出安全性
def safety_check(ctx, agent, output):
    if contains_pii(output):
        return GuardrailFunctionOutput(
            output_info={"reason": "PII detected"},
            tripwire_triggered=True
        )
    return GuardrailFunctionOutput(output_info={}, tripwire_triggered=False)

result = Runner.run_sync(triage_agent, "帮我分析这个代码的性能问题")

// C# 示例：MAF的Agent编排
var agentBuilder = new AgentBuilder();
var researcher = await agentBuilder
    .WithPlugin(new WebSearchPlugin())
    .WithPlugin(new FilePlugin())
    .WithName("ResearchAgent")
    .BuildAsync();

var writer = await agentBuilder
    .WithPlugin(new DocumentPlugin())
    .WithName("WriterAgent") 
    .BuildAsync();

var orchestrator = new OrchestratorAgent()
    .AddAgent(researcher)
    .AddAgent(writer)
    .WithPattern(OrchestrationPattern.Sequential);

var result = await orchestrator.InvokeAsync("分析2026年AI趋势并写报告");

from google.adk import Agent, Workflow, LlmAgent

research_agent = LlmAgent(
    name="researcher",
    model="gemini-2.5-pro",
    instruction="分析技术趋势并提供数据支持",
    tools=[google_search, url_fetch]
)

writer_agent = LlmAgent(
    name="writer",
    model="gemini-2.5-flash",
    instruction="基于研究撰写分析报告"
)

workflow = Workflow()
workflow.add_agent(research_agent)
workflow.add_agent(writer_agent)
workflow.add_edge("researcher", "writer")

# 部署到Gemini Enterprise Agent Platform
result = await workflow.run("分析Agent框架趋势")

import { Agent } from "@anthropic-ai/claude-agent-sdk";

const agent = new Agent({
  model: "claude-sonnet-4-6",
  tools: [
    {
      name: "web_search",
      description: "搜索网络信息",
      execute: async (query) => searchWeb(query)
    },
    {
      name: "analyze_code",
      description: "分析代码质量",
      execute: async (path) => analyzeFile(path)
    }
  ],
  hooks: {
    beforeToolCall: async (tool, args) => {
      if (tool.name === "delete_file") return { deny: true, reason: "禁止删除" };
      return { allow: true };
    }
  }
});

const result = await agent.run("分析这个项目的架构并提出改进建议");

from agno.agent import Agent
from agno.models.openai import OpenAIChat
from agno.tools.duckduckgo import DuckDuckGoTools
from agno.memory.db.postgres import PgMemoryDb

web_agent = Agent(
    name="Web Analyst",
    model=OpenAIChat(id="gpt-4o"),
    tools=[DuckDuckGoTools()],
    memory=PgMemoryDb(table_name="agent_memories"),
    instructions=["总是引用来源", "用中文回复"],
    show_tool_calls=True,
    markdown=True
)

web_agent.print_response("分析2026年Agent框架趋势", stream=True)

import { Agent, Mastra } from "@mastra/core";
import { createWorkflow, createStep } from "@mastra/core/workflows";
import { z } from "zod";

const researcher = new Agent({
  name: "researcher",
  instructions: "你是一位技术调研专家",
  model: { modelId: "gpt-4o", provider: "openai" }
});

const researchStep = createStep({
  id: "research",
  inputSchema: z.object({ topic: z.string() }),
  execute: async ({ context }) => {
    const result = await researcher.generate(
      `深度调研: ${context.machineContext?.topic}`
    );
    return { research: result.text };
  }
});

const writeStep = createStep({
  id: "write",
  inputSchema: z.object({ research: z.string() }),
  execute: async ({ context }) => {
    // 写作逻辑
    return { article: "生成的文章内容" };
  }
});

const workflow = createWorkflow({
  steps: [researchStep, writeStep],
  id: "research-and-write"
});

const mastra = new Mastra({ 
  workflows: { "research-and-write": workflow }
});

from smolagents import CodeAgent, DuckDuckGoSearchTool, HfApiModel

agent = CodeAgent(
    tools=[DuckDuckGoSearchTool()],
    model=HfApiModel("Qwen/Qwen2.5-72B-Instruct"),
    additional_authorized_imports=["pandas", "numpy"]
)

result = agent.run(
    "搜索2026年Agent框架数据，用pandas整理成表格，找出增长最快的"
)

import dspy

class ResearchSignature(dspy.Signature):
    """分析技术趋势"""
    topic: str = dspy.InputField()
    findings: str = dspy.OutputField()
    confidence: float = dspy.OutputField()

optimizer = dspy.MIPROv2(metric=answer_quality_metric)
compiled_researcher = optimizer.compile(
    dspy.ReAct(ResearchSignature, tools=[search, fetch]),
    trainset=training_examples
)

graph TD
    START["选择Agent框架"] --> Q1{"你的主要语言?"}
    Q1 -->|"TypeScript"| Q2{"需要可视化编辑?"}
    Q1 -->|"Python"| Q3{"需要精细状态控制?"}
    Q1 -->|"C#"| MAF["Microsoft Agent Framework"]

    Q2 -->|"是"| MA["Mastra"]
    Q2 -->|"否"| CAS["Claude Agent SDK<br/>或 OpenAI Agents SDK"]

    Q3 -->|"是"| LG["LangGraph"]
    Q3 -->|"否"| Q4{"需要多Agent协作?"}

    Q4 -->|"是"| Q5{"角色编排 vs 性能优先?"}
    Q4 -->|"否"| Q6{"极简 vs 自动优化?"}

    Q5 -->|"角色编排"| CA["CrewAI"]
    Q5 -->|"性能优先"| AG["Agno"]

    Q6 -->|"极简"| SA["Smolagents"]
    Q6 -->|"自动优化"| DSPY["DSPy"]