修复三个问题：1. 子图执行后的无限循环 2. llm_call没有输出 3. 思考打印两次

- 子图执行后直接进入finalize，避免回到react_reason循环
- llm_call节点检查是否已有final_result，避免重复调用LLM
- 直接在react_reason_node中通过adispatch_custom_event发送推理事件，避免通过state传递导致重复

backend/app/main_graph/nodes/llm_call.py

@@ -37,11 +37,11 @@ def create_llm_call_node(llm, tools: list):
     async def call_llm(state: MainGraphState, config: RunnableConfig) -> Dict[str, Any]:
         """
         LLM 调用节点（异步方法）
-
+        
         Args:
             state: 当前对话状态
             config: LangChain/LangGraph 自动注入的配置，包含 callbacks 等信息
-
+        
         Returns:
             更新后的状态字典
         """
@@ -49,6 +49,19 @@ def create_llm_call_node(llm, tools: list):
 
         memory_context = getattr(state, "memory_context", "暂无用户信息")
         start_time = time.time()
+        
+        # 关键修复：如果 state.final_result 已经存在（比如子图执行完），直接返回
+        if state.final_result:
+            info(f"[llm_call] 检测到已有最终结果，直接返回: {state.final_result[:100]}...")
+            elapsed_time = time.time() - start_time
+            return {
+                "final_result": state.final_result,
+                "success": True,
+                "current_phase": "done",
+                "llm_calls": getattr(state, 'llm_calls', 0) + 1,
+                "last_elapsed_time": elapsed_time,
+                "turns_since_last_summary": getattr(state, 'turns_since_last_summary', 0) + 1,
+            }
 
         try:
             # 添加 RAG 上下文到消息

backend/app/main_graph/nodes/react_nodes.py

@@ -16,6 +16,7 @@ from datetime import datetime
 # 导入我们的 intent.py
 from app.core.intent import (
     react_reason,
+    react_reason_async,
     get_route_by_reasoning,
     ReasoningAction,
     ReasoningResult
@@ -31,31 +32,17 @@ from app.logger import info
 
 # ========== 1. React 推理节点 ==========
 
-def react_reason_node(state: MainGraphState, config: Optional[Dict[str, Any]] = None) -> MainGraphState:
+async def react_reason_node(state: MainGraphState, config: Optional[Dict[str, Any]] = None) -> MainGraphState:
     """
-    React 模式推理节点：判断下一步做什么
+    React 模式推理节点：判断下一步做什么（异步版本）
     
     Returns: 更新后的状态
     """
     state.current_phase = "react_reasoning"
     state.reasoning_step += 1
     
-    # 发送推理开始事件
-    if config:
-        try:
-            from langchain_core.runnables.config import RunnableConfig
-            # 尝试获取回调管理器并发送自定义事件
-            callbacks = config.get("callbacks")
-            if callbacks:
-                from langchain_core.callbacks.manager import adispatch_custom_event
-                # 注意：这是异步操作，我们需要特殊处理
-                # 这里我们使用自定义流式写入器（如果存在）
-                pass
-        except Exception as e:
-            info(f"[react_reason] 无法发送回调事件: {e}")
-    
     info(f"[react_reason] 第 {state.reasoning_step} 次推理开始")
-
+    
     # 检查是否超过最大步数
     if state.reasoning_step > state.max_steps:
         state.current_phase = "max_steps_exceeded"
@@ -66,7 +53,7 @@ def react_reason_node(state: MainGraphState, config: Optional[Dict[str, Any]] =
         )
         state.success = False
         return state
-
+    
     # 准备上下文
     context = {
         "retrieved_docs": state.rag_docs,
@@ -74,15 +61,35 @@ def react_reason_node(state: MainGraphState, config: Optional[Dict[str, Any]] =
         "messages": state.messages,
         "errors": state.errors
     }
-
-    # 使用 intent.py 进行推理
-    # 注意：这里使用同步版本，内部会根据情况处理
-    result: ReasoningResult = react_reason(state.user_query, context)
+    
+    # 使用 intent.py 进行推理（现在直接用异步版本）
+    result: ReasoningResult = await react_reason_async(state.user_query, context)
     
     info(f"[react_reason] 推理结果: action={result.action.name}, confidence={result.confidence}")
     if result.reasoning:
         info(f"[react_reason] 推理过程: {result.reasoning}")
-
+    
+    # 关键修复：直接发送自定义事件给 agent_service，而不是通过 state
+    if config:
+        try:
+            from langchain_core.callbacks.manager import adispatch_custom_event
+            
+            callbacks = config.get("callbacks")
+            if callbacks:
+                info(f"[react_reason] 直接发送推理事件 #{state.reasoning_step}")
+                await adispatch_custom_event(
+                    "react_reasoning",
+                    {
+                        "step": state.reasoning_step,
+                        "action": result.action.name,
+                        "confidence": result.confidence,
+                        "reasoning": result.reasoning
+                    },
+                    callbacks=callbacks
+                )
+        except Exception as e:
+            info(f"[react_reason] 无法发送自定义事件: {e}")
+    
     # 记录推理历史
     state.reasoning_history.append({
         "step": state.reasoning_step,
@@ -91,30 +98,24 @@ def react_reason_node(state: MainGraphState, config: Optional[Dict[str, Any]] =
         "reasoning": result.reasoning,
         "timestamp": datetime.now().isoformat()
     })
-
+    
     # 更新状态
     state.debug_info["last_reasoning"] = {
         "action": result.action.name,
         "confidence": result.confidence,
         "reasoning": result.reasoning
     }
-
+    
     # 保存推理结果到状态
     state.debug_info["reasoning_result"] = result
-
+    
     # 确定下一步动作
     state.last_action = result.action.name
     
-    # 发送推理完成事件（通过状态更新，agent_service 会处理）
-    # 我们在状态中保存推理内容，以便 agent_service 可以通过 state_update 事件发送
-    state.debug_info["latest_reasoning"] = {
-        "step": state.reasoning_step,
-        "action": result.action.name,
-        "confidence": result.confidence,
-        "reasoning": result.reasoning,
-        "sent": False  # 标记是否已发送到前端
-    }
-
+    # 关键修复：不再设置 latest_reasoning，避免 agent_service 重复读取
+    if "latest_reasoning" in state.debug_info:
+        del state.debug_info["latest_reasoning"]
+    
     return state
 
 
@@ -300,6 +301,16 @@ def route_by_reasoning(state: MainGraphState) -> str:
         if state.retry_action and "rag" in state.retry_action.lower():
             return "rag_retrieve"
         return "react_reason"
+    
+    # 关键修复：检查是否已经执行过子图，如果是，直接去 llm_call
+    previous_actions = [h.get("action") for h in state.reasoning_history]
+    if "subgraph_completed" in previous_actions or state.final_result:
+        return "llm_call"
+    
+    # 检查是否刚刚执行完 rag 或 web search，应该继续推理一次然后去 llm_call
+    # 但为了避免死循环，我们设置一个简单的规则
+    if len(previous_actions) > 3:
+        return "llm_call"
 
     # 获取推理结果
     reasoning_result: Optional[ReasoningResult] = state.debug_info.get("reasoning_result")

backend/app/main_graph/utils/main_graph_builder.py

@@ -61,15 +61,34 @@ def wrap_subgraph_for_error_handling(subgraph, name: str):
             result = subgraph.invoke(state)
             
             # 更新主图状态
+            subgraph_result = None
             if name == "contact":
                 state.contact_result = result
+                subgraph_result = result.get("final_result", "")
             elif name == "dictionary":
                 state.dictionary_result = result
+                subgraph_result = result.get("final_result", "")
             elif name == "news_analysis":
                 state.news_result = result
+                subgraph_result = result.get("final_result", "")
+            
+            # 关键：设置最终结果，这样就不需要再回到 react_reason 了
+            if subgraph_result:
+                state.final_result = subgraph_result
+            else:
+                state.final_result = "子图执行完成"
             
             # 标记成功
             state.success = True
+            state.current_phase = "done"
+            # 标记不再需要推理，避免循环
+            state.reasoning_history.append({
+                "step": state.reasoning_step,
+                "action": "subgraph_completed",
+                "confidence": 1.0,
+                "reasoning": f"{name}子图执行完成",
+                "timestamp": datetime.now().isoformat()
+            })
             return state
             
         except Exception as e:
@@ -189,18 +208,18 @@ def build_react_main_graph(llm=None, tools=None, mem0_client=None) -> StateGraph
     graph.add_node("finalize", finalize_node)
     
     # ========== 添加边 ==========
-    
+
     # 第一阶段：记忆检索
     if retrieve_memory_node:
         graph.add_edge(START, "retrieve_memory")
         graph.add_edge("retrieve_memory", "memory_trigger")
     else:
         graph.add_edge(START, "memory_trigger")
-    
+
     # 进入第二阶段
     graph.add_edge("memory_trigger", "init_state")
     graph.add_edge("init_state", "react_reason")
-    
+
     # 第二阶段：React 循环推理
     graph.add_conditional_edges(
         "react_reason",
@@ -215,15 +234,17 @@ def build_react_main_graph(llm=None, tools=None, mem0_client=None) -> StateGraph
             "llm_call": "llm_call"
         }
     )
-    
-    # 循环边
+
+    # 循环边（rag、web_search、error 回到 reason）
     graph.add_edge("rag_retrieve", "react_reason")
     graph.add_edge("web_search", "react_reason")
-    graph.add_edge("contact_subgraph", "react_reason")
-    graph.add_edge("dictionary_subgraph", "react_reason")
-    graph.add_edge("news_analysis_subgraph", "react_reason")
     graph.add_edge("handle_error", "react_reason")
     
+    # 关键修改：子图执行完后直接去 finalize（避免循环）
+    graph.add_edge("contact_subgraph", "finalize")
+    graph.add_edge("dictionary_subgraph", "finalize")
+    graph.add_edge("news_analysis_subgraph", "finalize")
+    
     # 第三阶段：llm_call 后进入完成处理
     if llm_node is not None:
         if summarize_node: