OpenClaw 多模型路由与智能切换

世界上有一种选择困难叫做"用哪个模型"——Claude逻辑强但容易拒答，GPT理解好但偶尔胡说八道，DeepSeek便宜但有时候真的便宜到没法用。于是我想：让Agent自己根据任务选模型怎么样？写代码找Claude，画图找GPT，省钱找DeepSeek——像点外卖一样，看菜下单。

为什么需要多模型路由

• 任务适配：不同模型有不同特长，一个模型打天下不现实
• 成本优化：简单任务用轻量模型，高端模型留给复杂任务
• 风险分散：一个模型挂了，切换到备用模型继续运行
• 合规需求：某些行业要求使用本地部署模型
• 性能调优：根据负载自动切换，保持响应速度

OpenClaw 路由配置

基础配置

# openclaw.config.yaml
models:
  # 默认模型
  default: claude-sonnet-4-20250514

  # 可用模型列表
  available:
    - id: claude-sonnet
      provider: anthropic
      model: claude-sonnet-4-20250514
      capabilities: [reasoning, code, creative]
      maxTokens: 64000
      costPer1K: 0.003

    - id: gpt-4-turbo
      provider: openai
      model: gpt-4-turbo-2024-04-09
      capabilities: [vision, creative, general]
      maxTokens: 128000
      costPer1K: 0.01

    - id: deepseek-coder
      provider: deepseek
      model: deepseek-coder
      capabilities: [code]
      maxTokens: 32000
      costPer1K: 0.0001

路由规则

# 路由规则配置
routing:
  # 基于任务类型
  by_task:
    coding:
      model: claude-sonnet
      reason: "Claude代码理解最强"
    creative_writing:
      model: gpt-4-turbo
      reason: "GPT创意写作更自然"
    data_analysis:
      model: gpt-4-turbo
      reason: "GPT结构化输出更稳定"
    simple_tasks:
      model: deepseek-coder
      reason: "简单任务用便宜的模型"

  # 基于上下文大小
  by_context:
    small:  # < 4K
      model: gpt-3.5-turbo
    medium: # 4K - 32K
      model: claude-sonnet
    large:  # > 32K
      model: claude-opus

  # 基于用户级别
  by_user:
    free:
      model: gpt-3.5-turbo
      fallback: deepseek-coder
    pro:
      model: claude-sonnet
      fallback: gpt-4-turbo
    enterprise:
      model: claude-opus
      priority: highest

智能路由逻辑

任务分类器

// 任务分类路由
function selectModel(task) {
  const taskType = classifyTask(task);

  switch (taskType) {
    case 'code_generation':
    case 'code_review':
    case 'debugging':
      return 'claude-sonnet';

    case 'image_analysis':
    case 'document_analysis':
      return 'gpt-4-turbo';

    case 'creative_writing':
    case 'brainstorming':
      return 'gpt-4-turbo';

    case 'data_extraction':
    case 'formatting':
    case 'translation':
      return 'deepseek-coder';  // 省钱

    default:
      return config.defaultModel;
  }
}

// 任务分类函数
function classifyTask(task) {
  const keywords = {
    code_generation: ['写代码', '实现', '编码', '函数', 'class'],
    debugging: ['调试', 'bug', '错误', '修复', '不工作'],
    creative_writing: ['写文章', '创作', '故事', '文案', '标题']
  };

  for (const [type, words] of Object.entries(keywords)) {
    if (words.some(w => task.includes(w))) return type;
  }
  return 'general';
}

负载均衡

// 轮询式负载均衡
class ModelLoadBalancer {
  constructor(models) {
    this.models = models;
    this.currentIndex = 0;
    this.modelStats = {};
  }

  // 健康检查权重
  selectModel() {
    const healthy = this.models.filter(m =>
      this.getModelHealth(m.id) > 0.5
    );

    if (healthy.length === 0) {
      throw new Error('所有模型不可用');
    }

    // 加权轮询（考虑健康度和成本）
    const weights = healthy.map(m => ({
      model: m,
      weight: this.calculateWeight(m)
    }));

    return this.weightedSelect(weights);
  }

  calculateWeight(model) {
    const health = this.getModelHealth(model.id);
    const costFactor = 1 / model.costPer1K;
    return health * 0.7 + costFactor * 0.3;
  }
}

故障转移机制

模型心跳检测

// 模型健康检查
async function checkModelHealth(modelId) {
  try {
    const testPrompt = "回复OK";
    const result = await callModel(modelId, testPrompt, { timeout: 5000 });
    return {
      healthy: result.success,
      latency: result.latency,
      lastChecked: Date.now()
    };
  } catch (err) {
    return {
      healthy: false,
      error: err.message,
      lastChecked: Date.now()
    };
  }
}

// 定期健康检查
setInterval(() => {
  for (const model of config.available) {
    checkModelHealth(model.id).then(health => {
      modelHealth[model.id] = health;
    });
  }
}, 60000);  // 每分钟检查一次

故障转移策略

// 成本预算控制
const costControl = {
  // 用户级别预算
  budgets: {
    free: { daily: 0.5, perRequest: 0.02 },
    pro: { daily: 10, perRequest: 0.50 },
    enterprise: { daily: Infinity, perRequest: Infinity }
  },

  // 动态模型选择
  selectByBudget(userLevel, estimatedCost) {
    const budget = this.budgets[userLevel];

    if (estimatedCost > budget.perRequest) {
      // 降级到便宜的模型
      return 'deepseek-coder';
    }
    return null;  // 使用默认模型
  }
};

// 成本监控报告
{
  "date": "2026-04-24",
  "total": {
    "requests": 1234,
    "tokens": 567890,
    "cost": "$12.34"
  },
  "byModel": {
    "claude-sonnet": { requests: 456, cost: "$8.50" },
    "gpt-4-turbo": { requests: 234, cost: "$3.20" },
    "deepseek-coder": { requests: 544, cost: "$0.64" }
  },
  "savings": {
    "routingEnabled": true,
    "savedRequests": 544,
    "savedCost": "$5.44",
    "savingsRate": "30%"
  }
}