Agent框架成熟度洗牌：六栈中局定档

from langgraph.graph import StateGraph, START, END

def router(state):
    if state["confidence"] < 0.7:
        return "human_review"
    return "auto_execute"

graph = StateGraph(AgentState)
graph.add_node("plan", plan_step)
graph.add_node("execute", execute_step)
graph.add_node("human_review", human_review_step)
graph.add_conditional_edges("plan", router)
graph.add_edge("execute", END)
app = graph.compile()

from crewai import Agent, Task, Crew

researcher = Agent(role="Researcher", goal="Find latest data",
                   backstory="Expert analyst", llm="gpt-4o")
writer = Agent(role="Writer", goal="Write report",
               backstory="Senior tech writer", llm="gpt-4o")

crew = Crew(agents=[researcher, writer],
            tasks=[Task(description="Research AI agents",
                        agent=researcher),
                   Task(description="Write summary",
                        agent=writer)])
result = crew.kickoff()

from openai import Agent, Runner, function_tool

@function_tool
def lookup_order(order_id: str) -> str:
    return f"Order {order_id}: shipped, ETA 2 days"

triage_agent = Agent(name="Triage", instructions="Route customer queries",
                     tools=[lookup_order])
refund_agent = Agent(name="Refund", instructions="Process refund requests")
triage_agent.handoffs = [refund_agent]

result = Runner.run_sync(triage_agent, "Where is my order #12345?")

from claude_agent import Agent, Tool

class SearchTool(Tool):
    name = "web_search"
    description = "Search the web for information"
    input_schema = {"query": {"type": "string"}}

    async def execute(self, query: str) -> str:
        # Type-safe tool execution
        results = await search_engine(query)
        return results

agent = Agent(
    model="claude-sonnet-4-6",
    tools=[SearchTool()],
    guardrails={"max_iterations": 10, "cost_limit": "$0.50"}
)
response = await agent.run("Analyze AI agent market trends")

from google.adk import Agent, Tool

agent = Agent(
    model="gemini-2.5-pro",
    instruction="You are a research assistant",
    tools=[
        Tool.google_search(),       # Built-in search grounding
        Tool.bigquery_query(),      # Direct BigQuery access
        Tool.cloud_function("my-fn") # Serverless integration
    ]
)

result = agent.run("What are the latest AI agent benchmarks?")
# Grounded response with Google Search citations

from pydantic_ai import Agent
from pydantic import BaseModel

class SearchResult(BaseModel):
    title: str
    url: str
    relevance: float

agent = Agent(
    'openai:gpt-4o',
    result_type=SearchResult,  # Structured output guaranteed
    system_prompt="Return structured search results"
)

result = agent.run_sync("AI agent frameworks 2026")
# result.data is typed as SearchResult — IDE autocomplete works

graph TB
    subgraph "2026 Agent框架格局"
        direction TB
        MCP["MCP 协议层<br/>工具发现·调用·认证"]
        A2A["A2A 协议层<br/>Agent间通信"]
    end

    subgraph "厂商SDK层"
        OAI["OpenAI Agents SDK<br/>锁定: OpenAI API"]
        CLA["Claude Agent SDK<br/>锁定: Anthropic API"]
        GADK["Google ADK<br/>锁定: Vertex AI"]
    end

    subgraph "社区框架层"
        LG["LangGraph<br/>图编排·状态持久化"]
        CR["CrewAI<br/>角色扮演·快速上手"]
        PA["PydanticAI<br/>类型安全·单Agent"]
    end

    subgraph "基础设施层"
        LLM["LLM 提供商<br/>OpenAI/Anthropic/Google"]
        TOOL["工具生态<br/>MCP Servers"]
        OBS["可观测性<br/>LangSmith/Arize"]
    end

    MCP --> OAI
    MCP --> CLA
    MCP --> GADK
    MCP --> LG
    MCP --> CR
    MCP --> PA
    A2A --> LG
    A2A --> CR

    OAI --> LLM
    CLA --> LLM
    GADK --> LLM
    LG --> LLM
    CR --> LLM
    PA --> LLM

    TOOL --> MCP
    OBS --> LG
    OBS --> CR

quadrantChart
    title Agent框架2026中局成熟度矩阵
    x-axis 生态锁定度 --> 
    y-axis 生产就绪度 -->
    quadrant-1 工业级+高锁定
    quadrant-2 工业级+低锁定
    quadrant-3 实验级+低锁定
    quadrant-4 实验级+高锁定

    LangGraph: [0.35, 0.92]
    CrewAI: [0.25, 0.72]
    OpenAI SDK: [0.82, 0.78]
    Claude SDK: [0.78, 0.65]
    Google ADK: [0.85, 0.55]
    PydanticAI: [0.15, 0.58]