Cursor Router实测：请求级智能路由如何砍掉一半AI编程成本

from enum import Enum
from dataclasses import dataclass

class ComplexityLevel(Enum):
    SIMPLE = "simple"
    MODERATE = "moderate"
    COMPLEX = "complex"
    CRITICAL = "critical"

class ModelTier(Enum):
    LITE = "haiku-tier"       # 轻量级模型，成本最低
    STANDARD = "sonnet-tier"  # 标准模型，成本中等
    FRONTIER = "opus-tier"    # 前沿模型，成本最高

@dataclass
class ProgrammingRequest:
    request_type: str          # completion, generation, fix, refactor
    context_length: int        # 上下文token数
    code_depth: int            # 代码嵌套深度
    conversation_turns: int    # 对话轮次
    language: str              # 编程语言

class CursorRouter:
    def __init__(self):
        # 复杂度权重配置
        self.type_weights = {
            "completion": 0.2,
            "generation": 0.5,
            "fix": 0.6,
            "refactor": 0.8,
        }
        # 模型路由映射
        self.routing_map = {
            ComplexityLevel.SIMPLE: ModelTier.LITE,
            ComplexityLevel.MODERATE: ModelTier.STANDARD,
            ComplexityLevel.COMPLEX: ModelTier.FRONTIER,
            ComplexityLevel.CRITICAL: ModelTier.FRONTIER,
        }

    def evaluate_complexity(self, req: ProgrammingRequest) -> ComplexityLevel:
        """评估请求复杂度"""
        # 基础分 = 请求类型权重
        score = self.type_weights.get(req.request_type, 0.5)

        # 上下文长度加分（非线性）
        if req.context_length > 8000:
            score += 0.3
        elif req.context_length > 2000:
            score += 0.15

        # 代码结构复杂度加分
        if req.code_depth > 5:
            score += 0.2
        elif req.code_depth > 3:
            score += 0.1

        # 对话轮次加分
        if req.conversation_turns > 5:
            score += 0.15

        # 映射到复杂度等级
        if score < 0.35:
            return ComplexityLevel.SIMPLE
        elif score < 0.65:
            return ComplexityLevel.MODERATE
        elif score < 0.85:
            return ComplexityLevel.COMPLEX
        else:
            return ComplexityLevel.CRITICAL

    def route(self, req: ProgrammingRequest) -> ModelTier:
        """路由决策"""
        complexity = self.evaluate_complexity(req)
        model_tier = self.routing_map[complexity]
        print(f"请求类型: {req.request_type}, "
              f"复杂度: {complexity.value}, "
              f"路由到: {model_tier.value}")
        return model_tier


# 使用示例
router = CursorRouter()

# 简单的代码补全请求
simple_req = ProgrammingRequest(
    request_type="completion",
    context_length=500,
    code_depth=2,
    conversation_turns=1,
    language="python"
)
router.route(simple_req)
# 输出: 请求类型: completion, 复杂度: simple, 路由到: haiku-tier

# 复杂的架构重构请求
complex_req = ProgrammingRequest(
    request_type="refactor",
    context_length=12000,
    code_depth=7,
    conversation_turns=8,
    language="typescript"
)
router.route(complex_req)
# 输出: 请求类型: refactor, 复杂度: critical, 路由到: opus-tier

# OpenWorker安装与配置示例（伪代码展示配置逻辑）

# Step 1: 安装OpenWorker
# pip install openworker

from openworker import OpenWorker

# Step 2: 初始化Worker
worker = OpenWorker(
    model_provider="anthropic",     # 或 "local" 使用本地模型
    model_name="claude-sonnet",
    require_approval=True,          # 重要操作需要人工批准
    workspace="./my_workspace"
)

# Step 3: 连接应用
worker.connect("slack", token="xoxb-your-slack-token")
worker.connect("gmail", credentials="./gmail_creds.json")
worker.connect("calendar", account="user@example.com")
worker.connect("filesystem", root="./project")

# Step 4: 查看已连接的应用
connected = worker.list_connections()
print(f"已连接 {len(connected)} 个应用:")
for app in connected:
    print(f"  - {app.name}: {app.status}")

# Step 5: 执行任务
result = worker.execute(
    "查看我今天的日历安排，如果有会议则提前在Slack上通知相关同事，"
    "并将会议议程整理成文档保存到项目目录"
)

# OpenWorker会在执行重要操作前请求人工批准
# 例如：发送Slack消息前会弹出确认

# Muse Glimmer本地部署示例

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

# 使用4位量化加载30B模型
model_name = "meta/muse-glimmer-30b"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    device_map="auto",
    torch_dtype=torch.float16,
    load_in_4bit=True,           # 4位量化，显存占用约16-18GB
    trust_remote_code=True
)

# 在24GB显存的GPU上运行（如RTX 4090）
prompt = """请用Python实现一个线程安全的生产者-消费者队列，
支持超时等待和优雅关闭。"""

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(
    **inputs,
    max_new_tokens=1024,
    temperature=0.7,
    top_p=0.9
)

response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)

# 混合路由策略示例

class HybridRouter:
    def __init__(self):
        self.cloud_model = "claude-opus-5"
        self.local_model = "muse-glimmer-30b"
        self.sensitive_keywords = ["密码", "密钥", "token", "secret", "credential"]

    def route(self, request, is_sensitive=False):
        # 敏感数据强制走本地
        if is_sensitive or self._contains_sensitive_data(request):
            return self.local_model

        # 复杂度评估
        complexity = self._evaluate(request)

        if complexity == "simple":
            return self.local_model     # 简单任务用本地模型
        elif complexity == "moderate":
            return "claude-sonnet"      # 中等任务用中端云模型
        else:
            return self.cloud_model     # 复杂任务用前沿云模型

    def _contains_sensitive_data(self, request):
        return any(kw in request.lower() for kw in self.sensitive_keywords)

    def _evaluate(self, request):
        # 复杂度评估逻辑（简化版）
        if len(request) < 200:
            return "simple"
        elif len(request) < 1000:
            return "moderate"
        else:
            return "complex"


# 成本计算
router = HybridRouter()

# 模拟月度成本
monthly_requests = 50000  # 每月5万次请求
cost_breakdown = {
    "cloud_frontier": {"ratio": 0.15, "cost_per_req": 0.15},  # Opus 5
    "cloud_standard": {"ratio": 0.25, "cost_per_req": 0.06},  # Sonnet
    "local_model":    {"ratio": 0.60, "cost_per_req": 0.001}, # Muse Glimmer（电费+折旧）
}

total_cost = 0
for tier, info in cost_breakdown.items():
    count = monthly_requests * info["ratio"]
    cost = count * info["cost_per_req"]
    total_cost += cost
    print(f"{tier}: {count:.0f}次 × ${info['cost_per_req']} = ${cost:.2f}")

print(f"
月度总成本: ${total_cost:.2f}")
print(f"对比全前沿模型: ${monthly_requests * 0.15:.2f}")
print(f"节约: ${(monthly_requests * 0.15 - total_cost) / (monthly_requests * 0.15) * 100:.1f}%")

# AI编程成本监控仪表板示例

import json
from datetime import datetime, timedelta

class CostMonitor:
    def __init__(self, budget_monthly=5000):
        self.budget = budget_monthly
        self.records = []

    def log_request(self, model_tier, tokens_in, tokens_out, cost):
        self.records.append({
            "timestamp": datetime.now().isoformat(),
            "model_tier": model_tier,
            "tokens_in": tokens_in,
            "tokens_out": tokens_out,
            "cost": cost
        })

    def get_summary(self):
        total_cost = sum(r["cost"] for r in self.records)
        by_tier = {}
        for r in self.records:
            tier = r["model_tier"]
            if tier not in by_tier:
                by_tier[tier] = {"count": 0, "cost": 0}
            by_tier[tier]["count"] += 1
            by_tier[tier]["cost"] += r["cost"]

        usage_rate = (total_cost / self.budget) * 100

        print("=" * 50)
        print("AI编程成本监控报告")
        print("=" * 50)
        print(f"月度预算: ${self.budget:.2f}")
        print(f"已使用: ${total_cost:.2f} ({usage_rate:.1f}%)")
        print(f"剩余: ${self.budget - total_cost:.2f}")
        print("
按模型层级分解:")
        for tier, data in by_tier.items():
            print(f"  {tier}: {data['count']}次, ${data['cost']:.2f}")

        if usage_rate > 80:
            print("
[警告] 预算使用超过80%，建议检查路由配置")
        elif usage_rate > 50:
            day_of_month = datetime.now().day
            if usage_rate > (day_of_month / 30) * 100:
                print("
[注意] 成本增长速度高于时间进度，建议优化")

monitor = CostMonitor(budget_monthly=5000)

# 模拟记录
monitor.log_request("haiku-tier", 500, 100, 0.02)
monitor.log_request("sonnet-tier", 2000, 500, 0.06)
monitor.log_request("opus-tier", 8000, 2000, 0.15)
monitor.log_request("haiku-tier", 300, 80, 0.015)
monitor.log_request("sonnet-tier", 1500, 400, 0.05)

monitor.get_summary()