feat: Phase 3 - parallel execution, progress reporting, result caching + AgentChat bug fixes

Phase 3 能力:
- DAG 并行执行 (workflow_engine): asyncio.gather 并行执行就绪节点
- Debate 并行 (orchestrator): for 循环改为 asyncio.gather
- 粒度进度上报 (workflow_engine + tasks + websocket): Redis 推送 + DB 降级
- 工具结果缓存 (tool_manager): 确定性工具默认开启缓存
- LLM 响应缓存 (core): messages[-4:] + model 哈希，5min TTL

AgentChat bug 修复 (Gitea #1-#5):
- #1 SSE 降级重复空消息: fallback POST 前移除占位消息
- #2 streamTimeout 泄漏: while 正常退出后 clearTimeout
- #3 loading 闪烁: final/error 事件中提前设 loading=false
- #4 SSE 事件类型对齐: 确认匹配，未知类型加 console.warn
- #5 retryMessage 流式残留: 重试时清理占位消息

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

backend/app/agent_runtime/tool_manager.py

@@ -3,6 +3,8 @@ Agent 工具管理器：包装已有 ToolRegistry，提供 Agent 需要的工具
 """
 from __future__ import annotations
 
+import hashlib
+import json
 import logging
 from typing import Any, Dict, List, Optional
 
@@ -10,6 +12,12 @@ from app.services.tool_registry import tool_registry
 
 logger = logging.getLogger(__name__)
 
+# 默认确定性工具（结果可缓存）
+_DETERMINISTIC_TOOLS = {
+    "file_read", "math_calculate", "database_query",
+    "json", "text", "csv", "excel", "pdf", "image",
+}
+
 
 class AgentToolManager:
     """
@@ -17,12 +25,54 @@ class AgentToolManager:
     - 将 ToolRegistry 的工具 schema 转为 OpenAI Function Calling 格式
     - 按 Agent 配置过滤（白名单/黑名单）
     - 执行工具调用并返回结果字符串
+    - 工具结果缓存（Redis / 内存 fallback）
     """
 
     def __init__(self, include_tools: Optional[List[str]] = None,
-                 exclude_tools: Optional[List[str]] = None):
+                 exclude_tools: Optional[List[str]] = None,
+                 cache_enabled: bool = True,
+                 cache_tool_whitelist: Optional[List[str]] = None,
+                 cache_ttl_ms: int = 3600000):
         self._include_tools: set = set(include_tools or [])
         self._exclude_tools: set = set(exclude_tools or [])
+        self._cache_enabled = cache_enabled
+        self._cache_whitelist: set = set(cache_tool_whitelist or [])
+        self._cache_ttl_s = max(1, int(cache_ttl_ms / 1000))
+        self._cache_store: Dict[str, str] = {}  # 内存 fallback
+
+    def _is_cacheable(self, tool_name: str) -> bool:
+        """判断工具结果是否可缓存。"""
+        if not self._cache_enabled:
+            return False
+        if self._cache_whitelist:
+            return tool_name in self._cache_whitelist
+        return tool_name in _DETERMINISTIC_TOOLS
+
+    @staticmethod
+    def _cache_key(name: str, args: Dict[str, Any]) -> str:
+        raw = json.dumps([name, args], sort_keys=True, ensure_ascii=False)
+        return f"tool:{name}:{hashlib.sha256(raw.encode()).hexdigest()[:16]}"
+
+    async def _cache_get(self, key: str) -> Optional[str]:
+        try:
+            from app.core.redis_client import get_redis_client
+            redis = get_redis_client()
+            if redis:
+                return await redis.get(key)
+        except Exception:
+            pass
+        return self._cache_store.get(key)
+
+    async def _cache_set(self, key: str, value: str):
+        try:
+            from app.core.redis_client import get_redis_client
+            redis = get_redis_client()
+            if redis:
+                await redis.setex(key, self._cache_ttl_s, value)
+                return
+        except Exception:
+            pass
+        self._cache_store[key] = value
 
     def get_tool_schemas(self) -> List[Dict[str, Any]]:
         """获取 Agent 可用的工具定义列表（OpenAI Function Calling 格式）。"""
@@ -55,7 +105,7 @@ class AgentToolManager:
 
     async def execute(self, name: str, args: Dict[str, Any]) -> str:
         """
-        执行工具调用。
+        执行工具调用（带缓存）。
 
         优先查找内置工具，其次查找数据库自定义工具（HTTP / Code）。
 
@@ -66,8 +116,24 @@ class AgentToolManager:
         Returns:
             工具执行结果的字符串表示
         """
+        # 缓存检查
+        if self._is_cacheable(name):
+            ck = self._cache_key(name, args)
+            cached = await self._cache_get(ck)
+            if cached is not None:
+                logger.info("Agent 工具命中缓存: %s", name)
+                return cached
+
         logger.info("Agent 执行工具: %s", name)
-        return await tool_registry.execute_tool(name, args)
+        result = await tool_registry.execute_tool(name, args)
+
+        # 缓存写入
+        if self._is_cacheable(name):
+            ck = self._cache_key(name, args)
+            await self._cache_set(ck, result)
+            logger.debug("Agent 工具结果已缓存: %s", name)
+
+        return result
 
     @staticmethod
     def _extract_tool_name(schema: Dict[str, Any]) -> Optional[str]: