2026年AI Agent框架选型实战指南：LangGraph、CrewAI与MCP协议深度对比

from langgraph import StateGraph
from typing import TypedDict

# 定义Agent状态
class AgentState(TypedDict):
    messages: list
    tool_results: list
    current_step: str

# 创建状态图
graph = StateGraph(AgentState)

# 添加节点
graph.add_node("understand", understand_intent)
graph.add_node("plan", create_plan)
graph.add_node("execute", execute_tools)
graph.add_node("respond", generate_response)

# 定义边（决策路径）
graph.add_edge("understand", "plan")
graph.add_conditional_edges(
    "plan",
    # Neural Edge: 使用小模型决定路由
    lambda state: route_decision(state),
    {
        "need_tools": "execute",
        "direct_answer": "respond",
    }
)
graph.add_edge("execute", "respond")

# 编译并运行
app = graph.compile()
result = app.invoke({
    "messages": [{"role": "user", "content": "帮我分析这个CSV文件"}]
})

from crewai import Agent, Task, Crew

# 定义角色
researcher = Agent(
    role="研究员",
    goal="收集和分析相关信息",
    backstory="你是一位经验丰富的数据研究员",
    tools=[search_tool, analyze_tool],
)

writer = Agent(
    role="技术作家",
    goal="将研究结果整理成清晰的报告",
    backstory="你擅长将复杂技术内容转化为易懂的文章",
)

# 定义任务
research_task = Task(
    description="研究AI Agent框架的最新趋势",
    agent=researcher,
    expected_output="一份包含关键发现的调研报告",
)

writing_task = Task(
    description="基于调研结果撰写技术文章",
    agent=writer,
    expected_output="一篇结构清晰的技术文章",
    context=[research_task],  # 依赖前一个任务的输出
)

# 创建团队并执行
crew = Crew(
    agents=[researcher, writer],
    tasks=[research_task, writing_task],
    process="sequential",  # 顺序执行
)

result = crew.kickoff()

# MCP服务端示例：将工具暴露给任何兼容MCP的Agent
from mcp import MCPServer, Tool

server = MCPServer(name="data-tools")

@server.tool()
def query_database(sql: str) -> str:
    """执行SQL查询并返回结果"""
    return execute_query(sql)

@server.tool()
def generate_chart(data: str, chart_type: str) -> str:
    """生成图表并返回URL"""
    return create_visualization(data, chart_type)

# 任何支持MCP的Agent都可以自动发现并使用这些工具
# 无论你使用LangGraph、CrewAI还是OpenAI Agents SDK
server.run()

# 三层架构的实现示例
class ThreeTierAgentSystem:
    def __init__(self):
        # 第一层：基础能力
        self.foundation = FoundationTier(
            model="gpt-4",
            tools=[mcp_tool_1, mcp_tool_2],
            memory=PersistentMemory(),
        )
        
        # 第二层：流程编排
        self.workflow = WorkflowTier(
            foundation=self.foundation,
            state_manager=StateManager(),
            tracing=TracingConfig(enabled=True),
        )
        
        # 第三层：自主决策（需要护栏）
        self.autonomous = AutonomousTier(
            workflow=self.workflow,
            guardrails=[
                CostLimit(max_tokens=10000),
                SafetyFilter(level="strict"),
                HumanApproval(required_for=["delete", "send_email"]),
            ],
        )
    
    def execute(self, goal):
        complexity = self.assess_complexity(goal)
        if complexity == "simple":
            return self.foundation.handle(goal)
        elif complexity == "multi_step":
            return self.workflow.handle(goal)
        else:
            return self.autonomous.handle(goal)

from opentelemetry import trace

tracer = trace.get_tracer("agent-system")

class ObservableAgent:
    def __init__(self, agent, name):
        self.agent = agent
        self.name = name
    
    def run(self, input_data):
        with tracer.start_as_current_span(f"agent_{self.name}") as span:
            span.set_attribute("agent.name", self.name)
            span.set_attribute("agent.input_length", len(str(input_data)))
            
            try:
                result = self.agent.run(input_data)
                span.set_attribute("agent.status", "success")
                return result
            except Exception as e:
                span.set_attribute("agent.status", "error")
                span.record_exception(e)
                raise

class TokenBudget:
    def __init__(self, daily_limit=1_000_000):
        self.daily_limit = daily_limit
        self.used = 0
    
    def check_and_deduct(self, tokens):
        if self.used + tokens > self.daily_limit:
            raise BudgetExceeded(
                f"预算超限：已用{self.used}，限额{self.daily_limit}"
            )
        self.used += tokens
    
    def get_usage_report(self):
        return {
            "used": self.used,
            "limit": self.daily_limit,
            "remaining": self.daily_limit - self.used,
            "utilization": f"{self.used/self.daily_limit*100:.1f}%"
        }

class CheckpointManager:
    def __init__(self, storage):
        self.storage = storage
    
    def save_checkpoint(self, agent_id, state):
        self.storage.save(
            key=f"checkpoint:{agent_id}",
            value=state,
            ttl=3600  # 1小时过期
        )
    
    def restore_checkpoint(self, agent_id):
        return self.storage.load(f"checkpoint:{agent_id}")
    
    def run_with_recovery(self, agent, input_data):
        checkpoint = self.restore_checkpoint(agent.id)
        if checkpoint:
            agent.state = checkpoint
            print(f"从检查点恢复，继续执行...")
        return agent.run(input_data)