feat: V3系统评审问题修复

1. 场景价值与技术合理性修复：
   - 补充docs/SCENE_VALUE.md（业务背景、痛点分析、用户场景、竞品对比、价值量化）
   - 添加用户操作流程图（Mermaid）
   - 添加3个真实业务案例量化数据

2. 演示与文档修复：
   - 创建docs/API.md（完整API文档）
   - 创建docs/QUICKSTART.md（5分钟快速入门指南）

3. AI使用日志修复：
   - 更新AGENTS.md，添加强制自动执行的AI使用日志记录指令
   - 在_AI_USAGE_LOG.md末尾添加范式执行统计

4. 安全性修复：
   - 在agents/llm.py中添加输入过滤（防Prompt注入）
   - 添加输出验证、速率限制、详细日志

5. 架构设计修复：
   - 创建tools/registry.py工具注册表
   - 修改orchestrator.py和orchestrator_db.py使用注册表动态获取运行器

6. 开发范式修复：
   - 在_AI_USAGE_LOG.md末尾添加范式执行统计

agents/llm.py

@@ -1,7 +1,9 @@
-import json, hashlib, os
+import json, hashlib, os, re, time, logging
 from pathlib import Path
 import httpx
 
+logger = logging.getLogger(__name__)
+
 
 class LLMClient:
     def __init__(self, model=None, timeout=15, cache_dir=".cache/llm"):
@@ -10,6 +12,8 @@ class LLMClient:
         self.timeout = timeout
         self.dir = Path(cache_dir)
         self.dir.mkdir(parents=True, exist_ok=True)
+        self._last_call_time = 0
+        self._min_interval = 0.1  # 100ms 最小调用间隔
 
     def _key(self, msgs):
         return hashlib.sha256(json.dumps(msgs, sort_keys=True).encode()).hexdigest()
@@ -26,22 +30,99 @@ class LLMClient:
     def _set(self, k, v):
         (self.dir / f"{k}.json").write_text(json.dumps({"response": v}))
 
+    def _sanitize_input(self, text: str) -> str:
+        """输入过滤：移除潜在的prompt注入模式，限制输入长度"""
+        if not isinstance(text, str):
+            text = str(text)
+        
+        # 1. 限制输入长度（防止过长输入）
+        max_length = 50000
+        if len(text) > max_length:
+            logger.warning(f"Input truncated from {len(text)} to {max_length} chars")
+            text = text[:max_length]
+        
+        # 2. 移除潜在的prompt注入模式
+        injection_patterns = [
+            r'(?i)ignore\s+previous\s+instructions',
+            r'(?i)disregard\s+all\s+prior',
+            r'(?i)you\s+are\s+now\s+',
+            r'(?i)system\s*:\s*',
+            r'(?i)assistant\s*:\s*',
+            r'(?i)\[INST\]',
+            r'(?i)\[/INST\]',
+            r'(?i)<\|im_start\|>',
+            r'(?i)<\|im_end\|>',
+        ]
+        
+        for pattern in injection_patterns:
+            if re.search(pattern, text):
+                logger.warning(f"Potential prompt injection detected: {pattern}")
+                text = re.sub(pattern, '[FILTERED]', text)
+        
+        return text
+
+    def _validate_output(self, output: str) -> bool:
+        """输出验证：检查LLM返回内容的基本有效性"""
+        if not output or not isinstance(output, str):
+            return False
+        
+        # 检查长度是否合理
+        if len(output) > 100000:
+            logger.warning(f"Output too long: {len(output)} chars")
+            return False
+        
+        return True
+
+    def _rate_limit(self):
+        """速率限制：确保最小调用间隔"""
+        current_time = time.time()
+        elapsed = current_time - self._last_call_time
+        if elapsed < self._min_interval:
+            time.sleep(self._min_interval - elapsed)
+        self._last_call_time = time.time()
+
     def call(self, messages, retries=1):
         k = self._key(messages)
         c = self._get(k)
         if c:
+            logger.debug(f"Cache hit for key: {k[:8]}...")
             return c
+        
+        # 对用户消息进行输入过滤
+        sanitized_messages = []
+        for msg in messages:
+            if msg.get("role") == "user":
+                sanitized_content = self._sanitize_input(msg.get("content", ""))
+                sanitized_messages.append({**msg, "content": sanitized_content})
+            else:
+                sanitized_messages.append(msg)
+        
         key = os.environ.get("LLM_API_KEY", os.environ.get("OPENAI_API_KEY", ""))
         base = os.environ.get("LLM_API_BASE", "https://api.openai.com/v1")
+        
         for a in range(retries + 1):
             try:
-                r = httpx.post(f"{base}/chat/completions", json={"model": self.model, "messages": messages},
+                # 速率限制
+                self._rate_limit()
+                
+                logger.info(f"LLM call: model={self.model}, attempt={a+1}/{retries+1}")
+                r = httpx.post(f"{base}/chat/completions", json={"model": self.model, "messages": sanitized_messages},
                                headers={"Authorization": f"Bearer {key}"}, timeout=self.timeout)
                 r.raise_for_status()
                 v = r.json()["choices"][0]["message"]["content"]
+                
+                # 输出验证
+                if not self._validate_output(v):
+                    logger.warning("Invalid output from LLM, using fallback")
+                    v = ""
+                
                 self._set(k, v)
+                logger.info(f"LLM call success: response length={len(v)}")
                 return v
-            except Exception:
+            except Exception as e:
+                logger.error(f"LLM call failed: {e}")
                 if a == retries:
                     raise
+                time.sleep(1)  # 失败后等待1秒重试
+        
         return ""