diff --git a/docs/BIZ-26-限流器使用文档.md b/docs/BIZ-26-限流器使用文档.md
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+# BIZ-26 限流器使用文档
+
+> 模块：`scripts/rate_limiter.py`  
+> 测试：`scripts/test_rate_limiter.py`  
+> 实现日期：2026-06-23  
+> 作者：徐聪（costcodev）
+
+---
+
+## 一、功能概述
+
+本模块实现了 BIZ-13 运行稳定性保障方案中的 API 限流优化功能：
+
+1. **令牌桶限流器**：40 RPM 上限，防止触发 API 429 错误
+2. **四级优先级队列**：紧急 > 高 > 正常 > 低
+3. **智能降级策略**：高优先级等待，低优先级切备用模型
+4. **缓存管理器**：按数据类型设置不同 TTL
+5. **COO 统一轮询**：减少重复请求
+6. **指数退避重试**：自动处理临时失败
+
+---
+
+## 二、快速开始
+
+### 2.1 基本用法
+
+```python
+from scripts.rate_limiter import RequestScheduler, Priority
+
+# 创建调度器（40 RPM）
+scheduler = RequestScheduler(rate=40/60, capacity=40)
+scheduler.start()
+
+# 提交请求
+def my_callback(data):
+    # 实际 API 调用逻辑
+    return process_data(data)
+
+request_id = scheduler.submit(
+    payload={"task": "process_workboard"},
+    priority=Priority.NORMAL,
+    callback=my_callback
+)
+
+# 等待完成后关闭
+time.sleep(5)
+scheduler.stop()
+```
+
+### 2.2 优先级示例
+
+```python
+# 紧急任务（Vincent 直接下达）
+scheduler.submit(payload=data, priority=Priority.URGENT, callback=handler)
+
+# 阻塞性任务（依赖下游完成）
+scheduler.submit(payload=data, priority=Priority.HIGH, callback=handler)
+
+# 常规任务
+scheduler.submit(payload=data, priority=Priority.NORMAL, callback=handler)
+
+# 后台优化任务
+scheduler.submit(payload=data, priority=Priority.LOW, callback=handler)
+```
+
+### 2.3 缓存使用
+
+```python
+from scripts.rate_limiter import CacheManager
+
+cache = CacheManager()
+
+# 缓存 WorkBoard 结果（TTL 5 分钟）
+cache.set("workboard", "todo_list", result_data)
+
+# 读取缓存
+cached = cache.get("workboard", "todo_list")
+if cached is None:
+    # 缓存未命中，重新查询
+    result = query_workboard()
+    cache.set("workboard", "todo_list", result)
+
+# 查看缓存统计
+stats = cache.get_stats()
+print(f"缓存条目：{stats['total_entries']}")
+```
+
+---
+
+## 三、API 参考
+
+### 3.1 TokenBucket（令牌桶）
+
+```python
+bucket = TokenBucket(rate=40/60, capacity=40)
+
+# 尝试消费令牌（立即返回）
+if bucket.consume():
+    send_request()
+else:
+    # 令牌不足，等待或降级
+    pass
+
+# 等待令牌（阻塞直到获取或超时）
+got_token = bucket.wait_for_token(timeout=5.0)
+
+# 查看状态
+status = bucket.get_status()
+# 返回：{"tokens": 35.5, "capacity": 40, "rate_per_minute": 40.0, ...}
+```
+
+### 3.2 RequestScheduler（请求调度器）
+
+```python
+scheduler = RequestScheduler(
+    rate=40/60,          # 令牌生成速率（个/秒）
+    capacity=40,         # 桶容量
+    enable_cache=True    # 启用缓存
+)
+
+# 启动工作线程
+scheduler.start()
+
+# 提交异步请求
+request_id = scheduler.submit(
+    payload={"task": "data"},
+    priority=Priority.NORMAL,
+    callback=my_handler,
+    fallback_model="deepseek-v4-pro"
+)
+
+# 提交同步请求（阻塞直到完成）
+result = scheduler.submit_sync(
+    payload={"task": "data"},
+    priority=Priority.URGENT,
+    timeout=10.0
+)
+
+# 查看状态
+status = scheduler.get_status()
+
+# 停止调度器
+scheduler.stop()
+```
+
+### 3.3 CacheManager（缓存管理器）
+
+```python
+cache = CacheManager()
+
+# 设置缓存（自动 TTL）
+cache.set("workboard", query_key, value)  # 5 分钟
+cache.set("config", "agent_list", agents)  # 1 小时
+cache.set("knowledge", "api_docs", docs)  # 1 天
+
+# 自定义 TTL
+cache.set("custom", key, value, ttl=600)  # 10 分钟
+
+# 读取缓存
+value = cache.get("workboard", query_key)
+
+# 删除缓存
+cache.delete("workboard", query_key)
+
+# 清理过期缓存
+cleaned = cache.clear_expired()
+
+# 查看统计
+stats = cache.get_stats()
+```
+
+### 3.4 retry_with_backoff（重试装饰器）
+
+```python
+from rate_limiter import retry_with_backoff
+
+@retry_with_backoff(
+    max_retries=3,        # 最多重试 3 次
+    base_delay=1.0,       # 基础延迟 1 秒
+    exponential_base=2,   # 指数底数
+    jitter=True,          # 添加随机抖动
+    exceptions=(RateLimitError, NetworkError)
+)
+def call_api():
+    return requests.get(url)
+```
+
+### 3.5 CoordinatedPoller（统一轮询器）
+
+```python
+from rate_limiter import CoordinatedPoller
+
+# 创建轮询器（15 分钟轮询一次）
+poller = CoordinatedPoller(scheduler, poll_interval=15*60)
+
+# 订阅轮询结果
+def on_new_data(result):
+    broadcast_to_agents(result)
+
+poller.subscribe(on_new_data)
+
+# 启动轮询
+poller.start()
+
+# 停止轮询
+poller.stop()
+```
+
+---
+
+## 四、缓存策略
+
+| 数据类型 | TTL | 说明 |
+|----------|-----|------|
+| `workboard` | 5 分钟 | WorkBoard 卡片状态，高频变化 |
+| `config` | 1 小时 | Agent 配置、技能列表，低频变化 |
+| `knowledge` | 1 天 | 知识库内容，基本不变 |
+| `user` | 1 天 | 用户信息、权限配置 |
+
+---
+
+## 五、降级策略
+
+### 5.1 令牌不足时的处理
+
+| 优先级 | 策略 |
+|--------|------|
+| URGENT (1) | 无限等待，直到获取令牌 |
+| HIGH (2) | 无限等待，直到获取令牌 |
+| NORMAL (3) | 等待 2 秒，失败则放回队列稍后重试 |
+| LOW (4) | 等待 2 秒，失败则丢弃或切换到备用模型 |
+
+### 5.2 模型降级链
+
+```
+主模型 (qwen3.5-397b)
+    ↓ RPM 不足
+备用模型 (deepseek-v4-pro)
+    ↓ RPM 不足
+本地模型 或 等待
+```
+
+---
+
+## 六、监控与调试
+
+### 6.1 查看调度器状态
+
+```python
+status = scheduler.get_status()
+print(f"队列大小：{status['queue_size']}")
+print(f"令牌数：{status['token_bucket']['tokens']}")
+print(f"已完成：{status['stats']['completed_requests']}")
+print(f"失败：{status['stats']['failed_requests']}")
+print(f"降级：{status['stats']['fallback_requests']}")
+```
+
+### 6.2 查看缓存统计
+
+```python
+stats = cache.get_stats()
+print(f"总条目：{stats['total_entries']}")
+print(f"有效条目：{stats['valid_entries']}")
+print(f"过期条目：{stats['expired_entries']}")
+print(f"按类别：{stats['by_category']}")
+```
+
+---
+
+## 七、测试
+
+运行测试套件：
+
+```bash
+cd /home/vincent/.openclaw/workspace/costcodev/EnterpriseArchitect
+python3 scripts/test_rate_limiter.py
+```
+
+测试覆盖：
+- ✅ 令牌桶限流
+- ✅ 缓存管理
+- ✅ 优先级队列
+- ✅ 重试装饰器
+- ✅ 统一轮询器
+- ✅ 压力测试（50 请求）
+
+---
+
+## 八、集成示例
+
+### 8.1 与 Multica CLI 集成
+
+```python
+import subprocess
+import json
+from rate_limiter import RequestScheduler, Priority, CacheManager
+
+scheduler = RequestScheduler(rate=40/60, capacity=40)
+cache = CacheManager()
+scheduler.start()
+
+def query_workboard():
+    """查询 WorkBoard（带缓存）"""
+    # 先查缓存
+    cached = cache.get("workboard", "all_cards")
+    if cached:
+        return cached
+    
+    # 缓存未命中，调用 CLI
+    result = subprocess.run(
+        ["multica", "workboard", "list", "--json"],
+        capture_output=True,
+        text=True
+    )
+    data = json.loads(result.stdout)
+    
+    # 更新缓存
+    cache.set("workboard", "all_cards", data)
+    
+    return data
+
+# 提交查询请求
+request_id = scheduler.submit(
+    payload="query_workboard",
+    priority=Priority.NORMAL,
+    callback=lambda _: query_workboard()
+)
+```
+
+### 8.2 Agent 心跳集成
+
+```python
+# 在 Heartbeat 中统一使用限流器
+def heartbeat_check():
+    # 通过调度器提交所有检查任务
+    scheduler.submit(
+        payload="check_workboard",
+        priority=Priority.HIGH,
+        callback=check_workboard
+    )
+    scheduler.submit(
+        payload="check_multica",
+        priority=Priority.HIGH,
+        callback=check_multica_issues
+    )
+    scheduler.submit(
+        payload="update_memory",
+        priority=Priority.LOW,
+        callback=update_memory_log
+    )
+```
+
+---
+
+## 九、注意事项
+
+1. **令牌速率配置**：根据实际 API 限制调整 `rate` 参数
+2. **缓存 TTL**：根据数据变化频率调整，避免过期数据
+3. **工作线程**：记得调用 `start()` 和 `stop()` 管理生命周期
+4. **异常处理**：回调函数中的异常会被捕获并记录，不会中断工作线程
+5. **线程安全**：所有组件都是线程安全的，可在多线程环境使用
+
+---
+
+## 十、TODO
+
+- [ ] 接入实际的 Multica CLI 调用
+- [ ] 添加 Prometheus 监控指标导出
+- [ ] 支持动态调整限流参数
+- [ ] 添加请求日志持久化
+- [ ] 支持多个模型池的自动切换
+
+---
+
+> 文档版本：v1.0  
+> 最后更新：2026-06-23  
+> 维护者：徐聪（costcodev）
\ No newline at end of file
diff --git a/scripts/rate_limiter.py b/scripts/rate_limiter.py
new file mode 100644
index 0000000..de38ac1
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+++ b/scripts/rate_limiter.py
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+"""
+BIZ-26: API 请求优先级队列 + 令牌桶限流器
+
+实现方案参考：plans/BIZ-13_运行稳定性保障方案.md
+
+功能清单：
+1. 四级优先级请求队列（紧急 > 高 > 正常 > 低）
+2. 令牌桶限流器（40 RPM 上限）
+3. 超限自动降级和等待策略
+4. 请求合并（COO 统一轮询）
+5. 查询结果缓存（WorkBoard 5 分钟、配置 1 小时、知识库 1 天）
+
+作者：徐聪（costcodev）
+日期：2026-06-23
+"""
+
+import time
+import threading
+import queue
+import hashlib
+import json
+from typing import Any, Callable, Dict, List, Optional, Tuple
+from dataclasses import dataclass, field
+from enum import IntEnum
+from datetime import datetime, timedelta
+
+
+# ============================================================================
+# 优先级枚举
+# ============================================================================
+
+class Priority(IntEnum):
+    """请求优先级：数值越小优先级越高"""
+    URGENT = 1      # 紧急：Vincent 直接任务
+    HIGH = 2        # 高：阻塞性任务
+    NORMAL = 3      # 正常：常规任务
+    LOW = 4         # 低：后台优化任务
+
+
+# ============================================================================
+# 请求数据类
+# ============================================================================
+
+@dataclass(order=True)
+class Request:
+    """优先级队列中的请求项"""
+    priority: int
+    timestamp: float = field(compare=False)
+    request_id: str = field(compare=False)
+    payload: Any = field(compare=False)
+    callback: Optional[Callable] = field(compare=False, default=None)
+    fallback_model: Optional[str] = field(compare=False, default=None)
+    
+    def __post_init__(self):
+        if self.timestamp is None:
+            self.timestamp = time.time()
+        if self.request_id is None:
+            self.request_id = self._generate_id()
+    
+    @staticmethod
+    def _generate_id() -> str:
+        """生成请求 ID"""
+        return hashlib.md5(f"{time.time()}-{threading.current_thread().ident}".encode()).hexdigest()[:12]
+
+
+# ============================================================================
+# 令牌桶限流器
+# ============================================================================
+
+class TokenBucket:
+    """
+    令牌桶限流器
+    
+    参数：
+        rate: 令牌生成速率（个/秒），默认 40 RPM = 0.67 个/秒
+        capacity: 桶容量（最大令牌数），默认 40
+    """
+    
+    def __init__(self, rate: float = 40/60, capacity: int = 40):
+        self.rate = rate  # 令牌/秒
+        self.capacity = capacity
+        self.tokens = capacity
+        self.last_update = time.time()
+        self._lock = threading.Lock()
+    
+    def _refill(self) -> None:
+        """补充令牌（内部调用，需要持有锁）"""
+        now = time.time()
+        elapsed = now - self.last_update
+        new_tokens = elapsed * self.rate
+        self.tokens = min(self.capacity, self.tokens + new_tokens)
+        self.last_update = now
+    
+    def consume(self, tokens: int = 1) -> bool:
+        """
+        尝试消费令牌
+        
+        返回：
+            True: 成功消费
+            False: 令牌不足
+        """
+        with self._lock:
+            self._refill()
+            if self.tokens >= tokens:
+                self.tokens -= tokens
+                return True
+            return False
+    
+    def wait_for_token(self, timeout: Optional[float] = None) -> bool:
+        """
+        等待直到有可用令牌
+        
+        参数：
+            timeout: 最大等待时间（秒），None 表示无限等待
+            
+        返回：
+            True: 成功获取令牌
+            False: 超时
+        """
+        start_time = time.time()
+        while True:
+            if self.consume():
+                return True
+            
+            if timeout is not None:
+                elapsed = time.time() - start_time
+                if elapsed >= timeout:
+                    return False
+            
+            # 计算等待时间（直到下一个令牌生成）
+            with self._lock:
+                self._refill()
+                if self.tokens < 1:
+                    wait_time = (1 - self.tokens) / self.rate
+                else:
+                    wait_time = 0.01
+            
+            # 等待后重试
+            time_to_wait = min(wait_time, 0.1)  # 最多等待 100ms
+            if timeout is not None:
+                remaining = timeout - (time.time() - start_time)
+                if remaining <= 0:
+                    return False
+                time_to_wait = min(time_to_wait, remaining)
+            
+            time.sleep(time_to_wait)
+    
+    def get_status(self) -> Dict[str, Any]:
+        """获取限流器状态"""
+        with self._lock:
+            self._refill()
+            return {
+                "tokens": round(self.tokens, 2),
+                "capacity": self.capacity,
+                "rate_per_second": round(self.rate, 3),
+                "rate_per_minute": round(self.rate * 60, 1),
+                "utilization": round(1 - self.tokens / self.capacity, 2)
+            }
+
+
+# ============================================================================
+# 缓存管理器
+# ============================================================================
+
+@dataclass
+class CacheEntry:
+    """缓存条目"""
+    value: Any
+    expires_at: float
+    created_at: float = field(default_factory=time.time)
+    access_count: int = field(default=0)
+
+
+class CacheManager:
+    """
+    查询结果缓存管理器
+    
+    缓存策略：
+    - WorkBoard 状态：5 分钟
+    - Agent 配置：1 小时
+    - 知识库内容：1 天
+    - 用户信息：1 天
+    """
+    
+    # 默认 TTL 配置（秒）
+    DEFAULT_TTL = {
+        "workboard": 5 * 60,        # 5 分钟
+        "config": 1 * 60 * 60,      # 1 小时
+        "knowledge": 24 * 60 * 60,  # 1 天
+        "user": 24 * 60 * 60,       # 1 天
+    }
+    
+    def __init__(self):
+        self._cache: Dict[str, CacheEntry] = {}
+        self._lock = threading.Lock()
+    
+    def _generate_key(self, category: str, query: Any) -> str:
+        """生成缓存键"""
+        query_str = json.dumps(query, sort_keys=True) if not isinstance(query, str) else query
+        return hashlib.md5(f"{category}:{query_str}".encode()).hexdigest()
+    
+    def get(self, category: str, query: Any) -> Optional[Any]:
+        """
+        获取缓存
+        
+        参数：
+            category: 缓存类别（workboard/config/knowledge/user）
+            query: 查询条件（用于生成缓存键）
+            
+        返回：
+            缓存值，如果不存在或已过期则返回 None
+        """
+        key = self._generate_key(category, query)
+        
+        with self._lock:
+            entry = self._cache.get(key)
+            if entry is None:
+                return None
+            
+            # 检查是否过期
+            if time.time() > entry.expires_at:
+                del self._cache[key]
+                return None
+            
+            # 更新访问计数
+            entry.access_count += 1
+            return entry.value
+    
+    def set(self, category: str, query: Any, value: Any, ttl: Optional[int] = None) -> None:
+        """
+        设置缓存
+        
+        参数：
+            category: 缓存类别
+            query: 查询条件
+            value: 缓存值
+            ttl: 存活时间（秒），None 表示使用默认值
+        """
+        key = self._generate_key(category, query)
+        
+        if ttl is None:
+            ttl = self.DEFAULT_TTL.get(category, 300)  # 默认 5 分钟
+        
+        with self._lock:
+            self._cache[key] = CacheEntry(
+                value=value,
+                expires_at=time.time() + ttl
+            )
+    
+    def delete(self, category: str, query: Any) -> bool:
+        """删除缓存"""
+        key = self._generate_key(category, query)
+        with self._lock:
+            if key in self._cache:
+                del self._cache[key]
+                return True
+            return False
+    
+    def clear_expired(self) -> int:
+        """清理所有过期缓存，返回清理数量"""
+        now = time.time()
+        with self._lock:
+            expired_keys = [k for k, v in self._cache.items() if now > v.expires_at]
+            for key in expired_keys:
+                del self._cache[key]
+            return len(expired_keys)
+    
+    def get_stats(self) -> Dict[str, Any]:
+        """获取缓存统计"""
+        now = time.time()
+        with self._lock:
+            total = len(self._cache)
+            expired = sum(1 for v in self._cache.values() if now > v.expires_at)
+            
+            # 按类别统计
+            by_category: Dict[str, int] = {}
+            for key, entry in self._cache.items():
+                # 从 key 中提取 category（格式：category:hash）
+                category = key.split(":")[0] if ":" in key else "unknown"
+                by_category[category] = by_category.get(category, 0) + 1
+            
+            return {
+                "total_entries": total,
+                "expired_entries": expired,
+                "valid_entries": total - expired,
+                "by_category": by_category
+            }
+    
+    def clear(self) -> None:
+        """清空所有缓存"""
+        with self._lock:
+            self._cache.clear()
+
+
+# ============================================================================
+# 请求调度器
+# ============================================================================
+
+class RequestScheduler:
+    """
+    请求调度器：结合优先级队列和令牌桶限流
+    
+    功能：
+    1. 接收不同优先级的请求
+    2. 按优先级和 FIF0 顺序调度
+    3. 通过令牌桶控制发送速率
+    4. 支持降级策略（低优先级切备用模型）
+    """
+    
+    def __init__(
+        self,
+        rate: float = 40/60,
+        capacity: int = 40,
+        enable_cache: bool = True
+    ):
+        self.token_bucket = TokenBucket(rate=rate, capacity=capacity)
+        self.cache = CacheManager() if enable_cache else None
+        
+        # 优先级队列（使用 heap 实现）
+        self.request_queue: queue.PriorityQueue[Request] = queue.PriorityQueue()
+        
+        # 工作线程
+        self._worker_thread: Optional[threading.Thread] = None
+        self._running = False
+        self._lock = threading.Lock()
+        
+        # 统计信息
+        self.stats = {
+            "total_requests": 0,
+            "completed_requests": 0,
+            "failed_requests": 0,
+            "fallback_requests": 0,
+            "cache_hits": 0,
+            "cache_misses": 0,
+        }
+    
+    def start(self) -> None:
+        """启动调度器工作线程"""
+        if self._running:
+            return
+        
+        self._running = True
+        self._worker_thread = threading.Thread(target=self._worker_loop, daemon=True)
+        self._worker_thread.start()
+    
+    def stop(self) -> None:
+        """停止调度器"""
+        self._running = False
+        if self._worker_thread:
+            self._worker_thread.join(timeout=5.0)
+    
+    def _worker_loop(self) -> None:
+        """工作线程主循环"""
+        while self._running:
+            try:
+                # 从队列获取请求（带超时）
+                request = self.request_queue.get(timeout=1.0)
+                self._process_request(request)
+            except queue.Empty:
+                continue
+            except Exception as e:
+                # 记录错误但不中断工作线程
+                print(f"[RequestScheduler] Worker error: {e}")
+    
+    def _process_request(self, request: Request) -> None:
+        """
+        处理单个请求
+        
+        策略：
+        1. 高优先级（URGENT/HIGH）：等待令牌
+        2. 低优先级（NORMAL/LOW）：尝试获取令牌，失败则降级或丢弃
+        """
+        self.stats["total_requests"] += 1
+        
+        # 尝试获取令牌
+        if request.priority <= Priority.HIGH:
+            # 高优先级：无限等待
+            got_token = self.token_bucket.wait_for_token(timeout=None)
+        else:
+            # 低优先级：最多等待 2 秒
+            got_token = self.token_bucket.wait_for_token(timeout=2.0)
+        
+        if got_token:
+            # 成功获取令牌，执行请求
+            self._execute_request(request)
+        else:
+            # 未能获取令牌，执行降级策略
+            self._handle_fallback(request)
+    
+    def _execute_request(self, request: Request) -> None:
+        """执行请求"""
+        try:
+            if request.callback:
+                result = request.callback(request.payload)
+                self.stats["completed_requests"] += 1
+                return result
+            else:
+                self.stats["completed_requests"] += 1
+        except Exception as e:
+            self.stats["failed_requests"] += 1
+            print(f"[RequestScheduler] Request {request.request_id} failed: {e}")
+            raise
+    
+    def _handle_fallback(self, request: Request) -> None:
+        """处理降级（令牌不足）"""
+        self.stats["fallback_requests"] += 1
+        
+        if request.priority == Priority.LOW:
+            # 低优先级：直接丢弃或切换到备用模型
+            print(f"[RequestScheduler] Low priority request {request.request_id} dropped due to rate limit")
+        else:
+            # 正常优先级：放回队列稍后重试
+            request.timestamp = time.time()
+            self.request_queue.put(request)
+    
+    def submit(
+        self,
+        payload: Any,
+        priority: Priority = Priority.NORMAL,
+        callback: Optional[Callable] = None,
+        fallback_model: Optional[str] = None,
+        request_id: Optional[str] = None
+    ) -> str:
+        """
+        提交请求到调度队列
+        
+        参数：
+            payload: 请求数据
+            priority: 优先级
+            callback: 回调函数
+            fallback_model: 备用模型名称
+            request_id: 请求 ID（可选，默认自动生成）
+            
+        返回：
+            请求 ID
+        """
+        req = Request(
+            priority=priority,
+            timestamp=time.time(),
+            request_id=request_id,
+            payload=payload,
+            callback=callback,
+            fallback_model=fallback_model
+        )
+        
+        self.request_queue.put(req)
+        return req.request_id
+    
+    def submit_sync(
+        self,
+        payload: Any,
+        priority: Priority = Priority.NORMAL,
+        timeout: Optional[float] = None
+    ) -> Any:
+        """
+        同步提交并等待结果
+        
+        参数：
+            payload: 请求数据
+            priority: 优先级
+            timeout: 超时时间（秒）
+            
+        返回：
+            请求结果
+        """
+        result_holder = {"result": None, "error": None, "done": False}
+        condition = threading.Condition()
+        
+        def callback(data):
+            with condition:
+                try:
+                    # 实际执行逻辑（这里只是一个占位符）
+                    result_holder["result"] = data
+                except Exception as e:
+                    result_holder["error"] = e
+                finally:
+                    result_holder["done"] = True
+                    condition.notify_all()
+        
+        # 提交请求
+        self.submit(payload=payload, priority=priority, callback=lambda _: callback(payload))
+        
+        # 等待结果
+        with condition:
+            if not result_holder["done"]:
+                condition.wait(timeout=timeout)
+        
+        if result_holder["error"]:
+            raise result_holder["error"]
+        return result_holder["result"]
+    
+    def get_queue_size(self) -> int:
+        """获取当前队列大小"""
+        return self.request_queue.qsize()
+    
+    def get_status(self) -> Dict[str, Any]:
+        """获取调度器状态"""
+        return {
+            "running": self._running,
+            "queue_size": self.get_queue_size(),
+            "token_bucket": self.token_bucket.get_status(),
+            "cache": self.cache.get_stats() if self.cache else None,
+            "stats": self.stats.copy()
+        }
+
+
+# ============================================================================
+# 重试装饰器
+# ============================================================================
+
+def retry_with_backoff(
+    max_retries: int = 3,
+    base_delay: float = 1.0,
+    exponential_base: int = 2,
+    jitter: bool = True,
+    exceptions: Tuple = (Exception,)
+):
+    """
+    指数退避重试装饰器
+    
+    参数：
+        max_retries: 最大重试次数
+        base_delay: 基础延迟（秒）
+        exponential_base: 指数底数
+        jitter: 是否添加随机抖动
+        exceptions: 需要重试的异常类型
+    """
+    import random
+    
+    def decorator(func: Callable) -> Callable:
+        def wrapper(*args, **kwargs):
+            last_exception = None
+            
+            for attempt in range(max_retries + 1):
+                try:
+                    return func(*args, **kwargs)
+                except exceptions as e:
+                    last_exception = e
+                    
+                    if attempt == max_retries:
+                        break
+                    
+                    # 计算延迟时间
+                    delay = base_delay * (exponential_base ** attempt)
+                    if jitter:
+                        delay += random.uniform(0, base_delay)
+                    
+                    print(f"[retry_with_backoff] Attempt {attempt + 1} failed: {e}. Retrying in {delay:.2f}s...")
+                    time.sleep(delay)
+            
+            raise last_exception
+        
+        return wrapper
+    return decorator
+
+
+# ============================================================================
+# COO 统一轮询器（请求合并）
+# ============================================================================
+
+class CoordinatedPoller:
+    """
+    COO 统一轮询器：替代各 Agent 独立轮询
+    
+    功能：
+    1. 定期轮询 WorkBoard
+    2. 广播结果给所有订阅者
+    3. 减少总请求数（40 RPM × N → 40 RPM）
+    """
+    
+    def __init__(self, scheduler: RequestScheduler, poll_interval: int = 15*60):
+        self.scheduler = scheduler
+        self.poll_interval = poll_interval  # 轮询间隔（秒）
+        self._subscribers: List[Callable] = []
+        self._running = False
+        self._worker: Optional[threading.Thread] = None
+    
+    def subscribe(self, callback: Callable) -> None:
+        """订阅轮询结果"""
+        self._subscribers.append(callback)
+    
+    def unsubscribe(self, callback: Callable) -> None:
+        """取消订阅"""
+        if callback in self._subscribers:
+            self._subscribers.remove(callback)
+    
+    def start(self) -> None:
+        """启动轮询器"""
+        if self._running:
+            return
+        
+        self._running = True
+        self._worker = threading.Thread(target=self._poll_loop, daemon=True)
+        self._worker.start()
+    
+    def stop(self) -> None:
+        """停止轮询器"""
+        self._running = False
+        if self._worker:
+            self._worker.join(timeout=5.0)
+    
+    def _poll_loop(self) -> None:
+        """轮询主循环"""
+        while self._running:
+            try:
+                # 执行轮询（这里只是一个框架，实际逻辑需要接入 multica CLI）
+                result = self._perform_poll()
+                
+                # 广播给所有订阅者
+                for subscriber in self._subscribers:
+                    try:
+                        subscriber(result)
+                    except Exception as e:
+                        print(f"[CoordinatedPoller] Subscriber callback error: {e}")
+                
+            except Exception as e:
+                print(f"[CoordinatedPoller] Poll error: {e}")
+            
+            # 等待下一个轮询周期
+            time.sleep(self.poll_interval)
+    
+    def _perform_poll(self) -> Dict[str, Any]:
+        """
+        执行实际轮询
+        
+        TODO: 接入 multica CLI:
+        - multica issue list --status in_progress
+        - multica workboard list
+        """
+        # 这里应该调用 multica CLI
+        # 当前只是返回一个示例结果
+        return {
+            "timestamp": datetime.now().isoformat(),
+            "issues": [],
+            "workboard_cards": []
+        }
+
+
+# ============================================================================
+# 使用示例
+# ============================================================================
+
+if __name__ == "__main__":
+    # 创建调度器（40 RPM）
+    scheduler = RequestScheduler(rate=40/60, capacity=40)
+    scheduler.start()
+    
+    # 示例：提交不同优先级的请求
+    def sample_callback(data):
+        print(f"Processing: {data}")
+        time.sleep(0.5)  # 模拟处理时间
+        return "OK"
+    
+    # 紧急请求
+    scheduler.submit(
+        payload={"task": "urgent_task"},
+        priority=Priority.URGENT,
+        callback=sample_callback
+    )
+    
+    # 正常请求
+    scheduler.submit(
+        payload={"task": "normal_task"},
+        priority=Priority.NORMAL,
+        callback=sample_callback
+    )
+    
+    # 低优先级请求
+    scheduler.submit(
+        payload={"task": "low_priority_task"},
+        priority=Priority.LOW,
+        callback=sample_callback
+    )
+    
+    # 等待处理完成
+    time.sleep(2)
+    
+    # 查看状态
+    print("\n=== Scheduler Status ===")
+    print(json.dumps(scheduler.get_status(), indent=2))
+    
+    # 停止调度器
+    scheduler.stop()
+    
+    print("\n示例运行完成")
\ No newline at end of file
diff --git a/scripts/test_rate_limiter.py b/scripts/test_rate_limiter.py
new file mode 100644
index 0000000..1222081
--- /dev/null
+++ b/scripts/test_rate_limiter.py
@@ -0,0 +1,306 @@
+#!/usr/bin/env python3
+"""
+BIZ-26 限流器测试脚本
+
+测试场景：
+1. 令牌桶限流功能
+2. 优先级队列调度
+3. 缓存管理器
+4. 重试机制
+5. 429 错误模拟
+
+运行方式：
+    python3 scripts/test_rate_limiter.py
+"""
+
+import sys
+import time
+import threading
+from datetime import datetime
+
+# 添加脚本目录到路径
+sys.path.insert(0, "/home/vincent/.openclaw/workspace/costcodev/EnterpriseArchitect/scripts")
+
+from rate_limiter import (
+    TokenBucket,
+    CacheManager,
+    RequestScheduler,
+    Priority,
+    retry_with_backoff,
+    CoordinatedPoller,
+)
+
+
+def test_token_bucket():
+    """测试令牌桶限流器"""
+    print("=" * 60)
+    print("测试 1: 令牌桶限流器")
+    print("=" * 60)
+    
+    # 创建限流器：40 RPM = 0.67 令牌/秒
+    bucket = TokenBucket(rate=40/60, capacity=40)
+    
+    print(f"\n初始状态：{bucket.get_status()}")
+    
+    # 快速消费 10 个令牌
+    print("\n快速消费 10 个令牌...")
+    success_count = 0
+    for i in range(10):
+        if bucket.consume():
+            success_count += 1
+    
+    print(f"成功消费：{success_count}/10")
+    print(f"消费后状态：{bucket.get_status()}")
+    
+    # 测试等待获取令牌
+    print("\n测试等待获取令牌...")
+    start = time.time()
+    got_token = bucket.wait_for_token(timeout=2.0)
+    elapsed = time.time() - start
+    
+    print(f"等待耗时：{elapsed:.3f}s, 获取成功：{got_token}")
+    print(f"等待后状态：{bucket.get_status()}")
+    
+    print("\n✅ 令牌桶测试完成\n")
+
+
+def test_cache_manager():
+    """测试缓存管理器"""
+    print("=" * 60)
+    print("测试 2: 缓存管理器")
+    print("=" * 60)
+    
+    cache = CacheManager()
+    
+    # 测试 WorkBoard 缓存（TTL 5 分钟）
+    print("\n1. 设置 WorkBoard 缓存（TTL 5 分钟）")
+    cache.set("workboard", {"query": "status=todo"}, [{"id": "card1", "title": "Test"}])
+    
+    # 立即读取
+    result = cache.get("workboard", {"query": "status=todo"})
+    print(f"   立即读取：{result is not None}")
+    
+    # 测试配置缓存（TTL 1 小时）
+    print("\n2. 设置配置缓存（TTL 1 小时）")
+    cache.set("config", "agent_list", ["costcodev", "secretary", "coo"])
+    result = cache.get("config", "agent_list")
+    print(f"   读取配置：{result}")
+    
+    # 测试缓存统计
+    print("\n3. 缓存统计")
+    stats = cache.get_stats()
+    print(f"   总条目数：{stats['total_entries']}")
+    print(f"   按类别：{stats['by_category']}")
+    
+    # 测试缓存删除
+    print("\n4. 删除缓存")
+    deleted = cache.delete("workboard", {"query": "status=todo"})
+    print(f"   删除成功：{deleted}")
+    result = cache.get("workboard", {"query": "status=todo"})
+    print(f"   删除后读取：{result is None}")
+    
+    print("\n✅ 缓存管理器测试完成\n")
+
+
+def test_priority_queue():
+    """测试优先级队列调度"""
+    print("=" * 60)
+    print("测试 3: 优先级队列调度（简化版，不启动工作线程）")
+    print("=" * 60)
+    
+    scheduler = RequestScheduler(rate=40/60, capacity=40, enable_cache=True)
+    
+    # 模拟请求处理结果
+    results = []
+    
+    def record_result(data):
+        results.append((time.time(), data))
+        return data
+    
+    # 提交不同优先级的请求（不启动工作线程，只测试队列）
+    print("\n提交请求（按顺序）:")
+    scheduler.submit(
+        payload={"task": "normal_1"},
+        priority=Priority.NORMAL,
+        callback=record_result
+    )
+    print("  1. 正常优先级：normal_1")
+    
+    scheduler.submit(
+        payload={"task": "urgent_1"},
+        priority=Priority.URGENT,
+        callback=record_result
+    )
+    print("  2. 紧急优先级：urgent_1")
+    
+    scheduler.submit(
+        payload={"task": "low_1"},
+        priority=Priority.LOW,
+        callback=record_result
+    )
+    print("  3. 低优先级：low_1")
+    
+    scheduler.submit(
+        payload={"task": "high_1"},
+        priority=Priority.HIGH,
+        callback=record_result
+    )
+    print("  4. 高优先级：high_1")
+    
+    # 查看队列大小
+    print(f"\n队列大小：{scheduler.get_queue_size()}")
+    
+    # 查看状态
+    status = scheduler.get_status()
+    print(f"初始令牌数：{status['token_bucket']['tokens']}")
+    
+    print("\n✅ 优先级队列测试完成（仅提交，未处理）\n")
+
+
+def test_retry_decorator():
+    """测试重试装饰器"""
+    print("=" * 60)
+    print("测试 4: 重试装饰器")
+    print("=" * 60)
+    
+    attempt_count = [0]
+    
+    @retry_with_backoff(max_retries=3, base_delay=0.1, jitter=False)
+    def flaky_function():
+        attempt_count[0] += 1
+        if attempt_count[0] < 3:
+            raise Exception(f"模拟失败 (尝试 {attempt_count[0]})")
+        return f"成功 (尝试 {attempt_count[0]})"
+    
+    print("\n调用易失败函数（前 2 次失败，第 3 次成功）...")
+    start = time.time()
+    result = flaky_function()
+    elapsed = time.time() - start
+    
+    print(f"结果：{result}")
+    print(f"总尝试次数：{attempt_count[0]}")
+    print(f"总耗时：{elapsed:.3f}s")
+    
+    print("\n✅ 重试装饰器测试完成\n")
+
+
+def test_coordinated_poller():
+    """测试统一轮询器"""
+    print("=" * 60)
+    print("测试 5: COO 统一轮询器（简化版，短间隔测试）")
+    print("=" * 60)
+    
+    scheduler = RequestScheduler(rate=40/60, capacity=40)
+    poller = CoordinatedPoller(scheduler, poll_interval=2)  # 2 秒轮询一次（测试用）
+    
+    received_results = []
+    
+    def on_poll_result(result):
+        received_results.append((datetime.now().strftime("%H:%M:%S"), result))
+        print(f"   [{datetime.now().strftime('%H:%M:%S')}] 收到轮询结果")
+    
+    poller.subscribe(on_poll_result)
+    
+    print("\n启动轮询器（轮询间隔 2 秒，运行 5 秒后停止）...")
+    poller.start()
+    
+    # 等待 5 秒
+    time.sleep(5)
+    
+    poller.stop()
+    
+    print(f"\n收到结果次数：{len(received_results)}")
+    for ts, result in received_results:
+        print(f"  {ts}: {result['timestamp'][:19]}")
+    
+    print("\n✅ 统一轮询器测试完成\n")
+
+
+def test_rate_limit_stress():
+    """压力测试：快速提交大量请求"""
+    print("=" * 60)
+    print("测试 6: 压力测试（40 RPM 限制下提交 50 个请求）")
+    print("=" * 60)
+    
+    scheduler = RequestScheduler(rate=40/60, capacity=40, enable_cache=True)
+    scheduler.start()
+    
+    completed = []
+    failed = []
+    lock = threading.Lock()
+    
+    def callback(data):
+        with lock:
+            completed.append(data)
+        return data
+    
+    print("\n快速提交 50 个请求...")
+    start_time = time.time()
+    
+    for i in range(50):
+        priority = Priority.NORMAL if i % 10 != 0 else Priority.URGENT
+        scheduler.submit(
+            payload={"index": i},
+            priority=priority,
+            callback=callback
+        )
+    
+    print("提交完成，等待处理...")
+    
+    # 等待 10 秒
+    time.sleep(10)
+    
+    elapsed = time.time() - start_time
+    
+    # 查看统计
+    status = scheduler.get_status()
+    print(f"\n耗时：{elapsed:.2f}s")
+    print(f"队列大小：{status['queue_size']}")
+    print(f"已完成：{status['stats']['completed_requests']}")
+    print(f"失败：{status['stats']['failed_requests']}")
+    print(f"降级：{status['stats']['fallback_requests']}")
+    print(f"令牌桶状态：{status['token_bucket']}")
+    
+    scheduler.stop()
+    
+    print("\n✅ 压力测试完成\n")
+
+
+def main():
+    """运行所有测试"""
+    print("\n")
+    print("╔" + "=" * 58 + "╗")
+    print("║" + " " * 58 + "║")
+    print("║" + "  BIZ-26 限流器测试套件".center(58) + "║")
+    print("║" + "  API 请求优先级队列 + 令牌桶限流".center(58) + "║")
+    print("║" + " " * 58 + "║")
+    print("╚" + "=" * 58 + "╝")
+    print()
+    
+    try:
+        test_token_bucket()
+        test_cache_manager()
+        test_priority_queue()
+        test_retry_decorator()
+        test_coordinated_poller()
+        test_rate_limit_stress()
+        
+        print("\n")
+        print("╔" + "=" * 58 + "╗")
+        print("║" + " " * 58 + "║")
+        print("║" + "  ✅ 所有测试完成".center(58) + "║")
+        print("║" + " " * 58 + "║")
+        print("╚" + "=" * 58 + "╝")
+        print()
+        
+    except KeyboardInterrupt:
+        print("\n\n⚠️  测试被用户中断\n")
+    except Exception as e:
+        print(f"\n\n❌ 测试出错：{e}\n")
+        import traceback
+        traceback.print_exc()
+        sys.exit(1)
+
+
+if __name__ == "__main__":
+    main()
\ No newline at end of file