💰 OpenClaw Agent Context Budget 管理

OpenClaw Context Budget Token 优化 成本控制 Context Express

凌晨1点42分，我检查上个月的 API 账单——9,847 元。不是模型太贵，是我的 Agent 太不懂节制了。

什么是 Context Budget？

Context Budget 就是给 Agent 的「Token 零花钱」——每次对话能花多少 Token，得有个预算。没有预算管理的 Agent，就像一个月底拿到工资就梭哈的程序员，爽完就完了。

1️⃣ Token 预算分配策略

# context-budget-config.yaml
budget:
  # 总预算控制
  total:
    max_per_session: 128000
    max_per_hour: 1000000
    max_per_day: 5000000
  
  # 层级预算分配
  tiers:
    critical:
      - "user_input: 20%"
      - "system_prompt: 15%"
      - "execution_context: 25%"
    standard:
      - "tool_results: 20%"
      - "memory_retrieval: 10%"
      - "intermediate_thinking: 10%"
    optional:
      - "logging_debug: 0% (production)"
      - "historical_context: 0% (compress first)"
  
  # 动态调整
  dynamic_adjustment:
    enabled: true
    strategy: "task_complexity"
    scale_factor: 1.5  # 复杂任务预算乘数

2️⃣ Context Express 高效上下文

OpenClaw v2026.5 引入的 Context Express 功能，大幅降低冗余：

# 传统模式
message:
  role: "assistant"
  content: "根据您的问题，我搜索了相关资料，以下是搜索结果..."
  tokens: 450  # 废话太多！

# Context Express 模式
message:
  role: "assistant"
  content: "搜索结果: [{title: '...', url: '...', snippet: '...'}]"
  tokens: 120  # 直接报结果
  context_express:
    mode: "minimal"
    structural: true  # 结构化格式
    
# 全局配置
context_express:
  enabled: true
  modes:
    compact:
      description: "移除助手闲聊前缀"
      rules: ["no_greeting", "no_transition_phrases", "minimal_echo"]
    ultra:
      description: "极端压缩"
      enabled_for: ["batch_tasks", "monitoring"]

3️⃣ Context Window 分级管理

# context-window-tiers.yaml
tiers:
  short_term:
    max_tokens: 8000
    retention: "session"
    content: ["当前对话", "最新工具结果"]
    priority: "highest"
    
  medium_term:
    max_tokens: 32000
    retention: "1 hour"
    content: ["任务上下文", "部分历史", "常用数据"]
    priority: "high"
    
  long_term:
    max_tokens: 64000
    retention: "24 hours"
    content: ["向量压缩记忆"]
    priority: "low"
    
  archive:
    max_tokens: 0
    storage: "lancedb"
    compression: "auto"
    retrieval: "on_demand"

4️⃣ 成本优化实战：日处理百万 Token

以 miaoquai.com 的 SEO 内容生成为例：

# cost-optimization-pipeline.yaml
pipeline:
  name: "seo_bulk_generation"
  
  optimization_rules:
    - rule: "批量处理"
      method: "batch_requests"
      savings: "40%"
      
    - rule: "Result 缓存"
      method: "tool_result_caching"
      cache_duration: "24h"
      savings: "25%"
      cache_storage: "redis"
      
    - rule: "模型分层"
      method: "cheap_for_simple"
      routing:
        simple_tasks: "openclaw-light"
        complex_tasks: "claude-sonnet"
        critical_path: "claude-opus"
      savings: "60%"
      
    - rule: "Context Express"
      method: "minimal_context"
      savings: "35%"
      
    - rule: "失败快速重试"
      method: "cost_aware_retry"
      max_retries: 2
      retry_multiplier: 0.5  # 降级模型重试
      
  total_savings: "75-85%"

了解 OpenClaw Cost Optimization 完整指南。

5️⃣ Context Budget 监控与告警

# budget-monitoring.yaml
monitoring:
  metrics:
    - "budget.utilization"
    - "budget.waste_ratio"
    - "token_per_task"
  
  alerts:
    - name: "Budget Overrun"
      condition: "session_usage > budget * 0.9"
      action: "notify_owner"
    
    - name: "Abnormal Spending"
      condition: "token_per_task > avg * 3"
      action: "pause_new_tasks"
  
  reporting:
    daily: true
    format: "html"
    recipients: ["ops@miaoquai.com"]

📊 Context Budget 对比

📚 相关资源

• Context Window 管理
• Context Express 使用指南
• Context 压缩技术
• Token 分析 & 成本智能
• Cost-Aware Routing
• 成本追踪工具

💡 妙趣总结

相关阅读：Context Window 优化 • 成本优化总览 • Agent Cache

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