fix(searcher): prevent silent termination when MAX_ITERATIONS reached

When tool rounds hit MAX_ITERATIONS, the router was jumping directly to
END without giving the LLM a chance to produce a final text reply.
Added a `force-summary` node that invokes the LLM without tools,
ensuring a coherent Chinese summary is always generated after all
search results are collected.

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

langgraph/src/agent/searcher/index.ts

@@ -3,12 +3,13 @@ import { AIMessage } from "@langchain/core/messages";
 import { SearcherAnnotation, SearcherState } from "./types.js";
 import { agentNode } from "./nodes/agent.js";
 import { toolExecutorNode } from "./nodes/tool-executor.js";
+import { forceSummaryNode } from "./nodes/force-summary.js";
 
 const MAX_ITERATIONS = 3;
 
 function routeAfterAgent(
   state: SearcherState,
-): "tool-executor" | typeof END {
+): "tool-executor" | "force-summary" | typeof END {
   const lastMsg = state.messages[state.messages.length - 1];
 
   const aiMsg = lastMsg as AIMessage | undefined;
@@ -20,7 +21,8 @@ function routeAfterAgent(
     ).length;
 
     if (toolRounds >= MAX_ITERATIONS) {
-      return END;
+      // 达到上限时，强制生成最终总结而非直接结束
+      return "force-summary";
     }
     return "tool-executor";
   }
@@ -31,9 +33,11 @@ function routeAfterAgent(
 const builder = new StateGraph(SearcherAnnotation)
   .addNode("agent", agentNode)
   .addNode("tool-executor", toolExecutorNode)
+  .addNode("force-summary", forceSummaryNode)
   .addEdge(START, "agent")
-  .addConditionalEdges("agent", routeAfterAgent, ["tool-executor", END])
-  .addEdge("tool-executor", "agent");
+  .addConditionalEdges("agent", routeAfterAgent, ["tool-executor", "force-summary", END])
+  .addEdge("tool-executor", "agent")
+  .addEdge("force-summary", END);
 
 export const searcherGraph = builder.compile();
 searcherGraph.name = "Searcher";

langgraph/src/agent/searcher/nodes/force-summary.ts

@@ -0,0 +1,45 @@
+/**
+ * Force summary node: called when MAX_ITERATIONS is reached.
+ * Invokes LLM without tools to produce a final text summary based on all collected search results.
+ */
+import { createLlm, type ModelMode } from "@/agent/utils/create-llm";
+import { truncateMessages } from "@/agent/utils/truncate-messages";
+import { LangGraphRunnableConfig } from "@langchain/langgraph";
+import { HumanMessage } from "@langchain/core/messages";
+import { SearcherState, SearcherUpdate } from "../types.js";
+
+const FORCE_SUMMARY_SYSTEM = `你是深度搜索助手。你已完成所有搜索步骤，现在必须基于已收集的搜索结果生成一份完整的最终回答。
+
+## 要求
+- 综合所有已获取的搜索结果和网页内容，直接给出完整回答
+- 结论先行：1-2 句话直接回答核心问题
+- 列出关键数据和重要信息
+- 回答末尾列出参考来源（格式：[编号] 标题 - URL）
+- 用中文回复
+- 不要说"我需要更多信息"或建议继续搜索——直接基于已有内容回答
+- 搜索结果已通过卡片展示给用户，文字回复侧重分析和总结，不复述搜索摘要`;
+
+export async function forceSummaryNode(
+  state: SearcherState,
+  config: LangGraphRunnableConfig,
+): Promise<SearcherUpdate> {
+  const modelMode = ((config.configurable?.modelMode as string) ?? "auto") as ModelMode;
+  const llm = createLlm({ modelMode });
+
+  const truncated = truncateMessages(state.messages);
+
+  // Append an explicit instruction to stop tool usage and summarize
+  const summaryInstruction = new HumanMessage(
+    "已完成所有搜索。请立即基于上面收集到的所有搜索结果，生成完整的最终回答。不要再调用任何工具。",
+  );
+
+  const messagesWithSystem = [
+    { role: "system" as const, content: FORCE_SUMMARY_SYSTEM },
+    ...truncated,
+    summaryInstruction,
+  ];
+
+  // Invoke without tools to guarantee a text response
+  const message = await llm.invoke(messagesWithSystem);
+  return { messages: [message], timestamp: Date.now() };
+}