fix(llm): reject empty responses output

Author: Mouseww

RELEASE_NOTES.md

@@ -1,16 +1,16 @@
-# Anything Analyzer v3.6.30
+# Anything Analyzer v3.6.31
 
 ## 修复
 
-- **Anthropic/MiniMax 非流式空文本诊断** — 避免兼容服务只返回工具块或非文本块时被误判为空成功结果
-  - 非流式 Anthropic 兼容响应现在要求至少包含一个文本内容块
-  - MiniMax 回归测试覆盖无 `text` 内容的响应，确保给出明确格式错误
+- **Responses API 非流式空文本诊断** — 避免 `completed` 响应缺少 `output_text` 文本时被误判为空成功结果
+  - 非流式 Responses 响应现在要求顶层 `output_text` 或 `output[].content[]` 至少提取到一个文本块
+  - 工具调用最终文本轮次复用同一校验，避免无文本结果静默通过
 
 ## 下载
 
 | 平台 | 文件 |
 |------|------|
-| Windows | Anything-Analyzer-Setup-3.6.30.exe |
-| macOS (Apple Silicon) | Anything-Analyzer-3.6.30-arm64.dmg |
-| macOS (Intel) | Anything-Analyzer-3.6.30-x64.dmg |
-| Linux | Anything-Analyzer-3.6.30.AppImage |
+| Windows | Anything-Analyzer-Setup-3.6.31.exe |
+| macOS (Apple Silicon) | Anything-Analyzer-3.6.31-arm64.dmg |
+| macOS (Intel) | Anything-Analyzer-3.6.31-x64.dmg |
+| Linux | Anything-Analyzer-3.6.31.AppImage |

package.json

@@ -1,6 +1,6 @@
 {
   "name": "anything-analyzer",
-  "version": "3.6.30",
+  "version": "3.6.31",
   "description": "Universal web protocol analyzer with embedded browser and AI-powered analysis",
   "packageManager": "pnpm@10.24.0",
   "main": "./out/main/index.js",

src/main/ai/llm-router.ts

@@ -43,18 +43,41 @@ type AnthropicContentBlock = AnthropicTextBlock | AnthropicToolUseBlock;
 
 const DEFAULT_TIMEOUT = 600000; // 10 minutes — LLM relay servers can be slow; user can cancel manually
 
-function extractResponsesOutputText(output: Array<{ type: string; content?: Array<{ type: string; text: string }> }>): string {
+interface ResponsesOutputItem {
+  type: string;
+  content?: Array<{ type: string; text?: unknown }>;
+}
+
+function extractResponsesOutputText(output: ResponsesOutputItem[]): string {
   let content = "";
   for (const item of output) {
     if (item.type === "message" && Array.isArray(item.content)) {
       content += item.content
-        .filter((c) => c.type === "output_text")
-        .map((c) => c.text)
+        .filter((c) => c.type === "output_text" && typeof c.text === "string")
+        .map((c) => c.text as string)
         .join("");
     }
   }
   return content;
 }
+
+function readResponsesOutputText(data: {
+  output_text?: unknown;
+  output?: ResponsesOutputItem[];
+}): string {
+  const content =
+    typeof data.output_text === "string" && data.output_text.length > 0
+      ? data.output_text
+      : Array.isArray(data.output)
+        ? extractResponsesOutputText(data.output)
+        : "";
+
+  if (content.length === 0) {
+    throw new Error(`LLM 响应格式异常: 缺少 output_text 字段 — ${JSON.stringify(data).slice(0, 200)}`);
+  }
+
+  return content;
+}
 
 /**
  * Sanitize string content in LLM request body to remove control characters
@@ -475,16 +498,14 @@ export class LLMRouter {
         status?: string;
         incomplete_details?: ResponsesIncompleteDetails;
         error?: { message?: string };
-        output: Array<{
-          type: string;
+        output: Array<ResponsesOutputItem & {
           id?: string;
           name?: string;
           arguments?: string;
-          content?: Array<{ type: string; text: string }>;
-        }>;
-        output_text?: string;
-        usage?: { input_tokens: number; output_tokens: number };
-      }>(response);
+        }>;
+        output_text?: string;
+        usage?: { input_tokens: number; output_tokens: number };
+      }>(response);
 
       totalPromptTokens += data.usage?.input_tokens || 0;
       totalCompletionTokens += data.usage?.output_tokens || 0;
@@ -527,19 +548,14 @@ export class LLMRouter {
           });
         }
         continue;
-      }
-
-      // No function calls → extract text
-      let content = extractResponsesOutputText(data.output);
-
-      // Fallback: check output_text at top level
-      if (!content && typeof data.output_text === "string") {
-        content = data.output_text;
-      }
-
-      if (onChunk && content) onChunk(content);
-      return {
-        content,
+      }
+
+      // No function calls → extract text
+      const content = readResponsesOutputText(data);
+
+      if (onChunk && content) onChunk(content);
+      return {
+        content,
         promptTokens: totalPromptTokens,
         completionTokens: totalCompletionTokens,
       };
@@ -627,10 +643,7 @@ export class LLMRouter {
       incomplete_details?: ResponsesIncompleteDetails;
       error?: { message?: string };
       output_text?: string;
-      output?: Array<{
-        type: string;
-        content?: Array<{ type: string; text: string }>;
-      }>;
+      output?: ResponsesOutputItem[];
       usage?: { input_tokens: number; output_tokens: number };
     }>(response);
     if (data.status === "incomplete") {
@@ -642,7 +655,7 @@ export class LLMRouter {
     if (typeof data.output_text !== "string" && !Array.isArray(data.output)) {
       throw new Error(`LLM 响应格式异常: 缺少 output 字段 — ${JSON.stringify(data).slice(0, 200)}`);
     }
-    const content = data.output_text || (Array.isArray(data.output) ? extractResponsesOutputText(data.output) : "");
+    const content = readResponsesOutputText(data);
     return {
       content,
       promptTokens: data.usage?.input_tokens || 0,

tests/main/ai/llm-router.test.ts

@@ -427,6 +427,22 @@ describe("LLMRouter", () => {
       ).rejects.toThrow("LLM 响应格式异常: 缺少 output 字段");
     });
 
+    it("should reject Responses API results without output text", async () => {
+      const config: LLMProviderConfig = { ...baseConfig, apiType: "responses" };
+      fetchSpy.mockResolvedValueOnce(
+        createJSONResponse({
+          status: "completed",
+          output: [],
+        }),
+      );
+
+      const router = new LLMRouter(config);
+
+      await expect(
+        router.complete([{ role: "user", content: "test" }]),
+      ).rejects.toThrow("LLM 响应格式异常: 缺少 output_text 字段");
+    });
+
     it("should reject incomplete Responses API results", async () => {
       const config: LLMProviderConfig = { ...baseConfig, apiType: "responses" };
       fetchSpy.mockResolvedValueOnce(