添加长期存储，流式检查

app/nodes/tool_call.py

@@ -7,6 +7,7 @@ import asyncio
 from typing import Any, Dict
 from langchain_core.messages import AIMessage, ToolMessage
 from langgraph.runtime import Runtime
+from langgraph.config import get_stream_writer
 
 # 本地模块
 from app.state import MessagesState, GraphContext
@@ -25,13 +26,15 @@ def create_tool_call_node(tools_by_name: Dict[str, Any]):
         异步节点函数
     """
     
-    async def call_tools(state: MessagesState, runtime: Runtime[GraphContext]) -> Dict[str, Any]:
+    from langchain_core.runnables.config import RunnableConfig
+    
+    async def call_tools(state: MessagesState, config: RunnableConfig) -> Dict[str, Any]:
         """
         工具执行节点（异步方法）
         
         Args:
             state: 当前对话状态
-            runtime: LangGraph 运行时上下文
+            config: 运行时配置
             
         Returns:
             包含 ToolMessage 的状态更新
@@ -62,6 +65,10 @@ def create_tool_call_node(tools_by_name: Dict[str, Any]):
                 results.append(ToolMessage(content=err_msg, tool_call_id=tool_id))
                 continue
 
+            # 获取流式写入器并发送工具调用开始事件
+            writer = get_stream_writer()
+            writer({"type": "custom", "data": {"type": "tool_start", "tool": tool_name}})
+            
             try:
                 # 修复闭包问题：将变量作为默认参数传入 lambda
                 # 如果工具支持异步 (ainvoke)，优先使用异步调用
@@ -77,9 +84,15 @@ def create_tool_call_node(tools_by_name: Dict[str, Any]):
                 result_preview = str(observation).replace("\n", " ")
                 debug(f"   └─ ✅ 结果: {result_preview}")
                 results.append(ToolMessage(content=str(observation), tool_call_id=tool_id))
+                
+                # 发送工具调用完成事件
+                writer({"type": "custom", "data": {"type": "tool_end", "tool": tool_name, "success": True}})
             except Exception as e:
                 debug(f"   └─ ❌ 异常: {e}")
                 results.append(ToolMessage(content=f"Error: {e}", tool_call_id=tool_id))
+                
+                # 发送工具调用失败事件
+                writer({"type": "custom", "data": {"type": "tool_end", "tool": tool_name, "success": False, "error": str(e)}})
 
         info(f"🛠️  [工具调用] 执行完成，返回 {len(results)} 条 ToolMessage")
         
@@ -87,4 +100,4 @@ def create_tool_call_node(tools_by_name: Dict[str, Any]):
         log_state_change("tool_node", {**state, **result}, "离开")
         return result
     
-    return call_tools
+    return call_tools