fix: handle empty ChatGPT responses output

app/agent/llm/helper.py

@@ -405,6 +405,7 @@ def _patch_openai_responses_instructions_support():
         return
 
     _patch_openai_interleaved_reasoning_content_support()
+    _patch_openai_responses_empty_output_support()
 
     if getattr(ChatOpenAI, "_moviepilot_responses_instructions_patched", False):
         return
@@ -464,6 +465,64 @@ def _patch_openai_responses_instructions_support():
     logger.debug("已修补 langchain-openai responses API 的 instructions 兼容性")
 
 
+def _patch_openai_responses_empty_output_support():
+    """
+    修补 langchain-openai Responses API 流式完成事件 output 为空的兼容性。
+
+    ChatGPT Codex 后端有时会在 `response.completed` chunk 里返回
+    `response.output = None`，但前面的 delta chunk 已经包含实际文本。
+    langchain-openai 在收尾阶段遍历 output 会抛出 TypeError，这里将缺失
+    output 规整为空列表，让收尾 chunk 只承载 usage/metadata。
+    """
+    try:
+        import langchain_openai.chat_models.base as _openai_base
+    except Exception as err:
+        logger.debug(f"跳过 langchain-openai responses output 修补：{err}")
+        return
+
+    if getattr(_openai_base, "_moviepilot_responses_empty_output_patched", False):
+        return
+
+    original_construct = getattr(
+        _openai_base, "_construct_lc_result_from_responses_api", None
+    )
+    if not callable(original_construct):
+        logger.warning("langchain-openai 缺少 Responses API 结果构造函数，无法修补 output")
+        return
+
+    def _clone_response_with_empty_output(response):
+        """
+        复制 Responses 对象，把缺失 output 规整为空列表。
+        """
+        model_copy = getattr(response, "model_copy", None)
+        if callable(model_copy):
+            try:
+                return model_copy(update={"output": []})
+            except Exception as err:
+                logger.debug(f"复制 Responses 对象失败，回退原地修补 output：{err}")
+
+        try:
+            setattr(response, "output", [])
+        except Exception as err:
+            logger.debug(f"原地修补 Responses output 失败：{err}")
+        return response
+
+    @wraps(original_construct)
+    def _patched_construct_lc_result_from_responses_api(response, *args, **kwargs):
+        """
+        在 Responses API 收尾 chunk 缺少 output 时跳过空内容遍历。
+        """
+        if hasattr(response, "output") and getattr(response, "output", None) is None:
+            response = _clone_response_with_empty_output(response)
+        return original_construct(response, *args, **kwargs)
+
+    _openai_base._construct_lc_result_from_responses_api = (
+        _patched_construct_lc_result_from_responses_api
+    )
+    _openai_base._moviepilot_responses_empty_output_patched = True
+    logger.debug("已修补 langchain-openai responses API 空 output 兼容性")
+
+
 class LLMHelper:
     """LLM模型相关辅助功能"""
 

tests/test_llm_helper_testcall.py

@@ -108,8 +108,17 @@ def _build_fake_openai_modules(chat_openai_cls=_FakeChatOpenAIForPatch):
     def _convert_delta_to_message_chunk(delta, default_class):
         return AIMessageChunk(content=delta.get("content") or "")
 
+    def _construct_lc_result_from_responses_api(response, *args, **kwargs):
+        """模拟旧版 langchain-openai 直接遍历 response.output 的行为。"""
+        for _item in response.output:
+            pass
+        return SimpleNamespace(args=args, kwargs=kwargs, response=response)
+
     base_module._convert_dict_to_message = _convert_dict_to_message
     base_module._convert_delta_to_message_chunk = _convert_delta_to_message_chunk
+    base_module._construct_lc_result_from_responses_api = (
+        _construct_lc_result_from_responses_api
+    )
 
     return {
         "langchain_openai": openai_module,
@@ -262,6 +271,39 @@ class LlmHelperTestCallTest(unittest.TestCase):
             "先调用工具",
         )
 
+    def test_openai_responses_patch_handles_completed_chunk_without_output(self):
+        """校验 Responses API 流式完成事件 output 为空时不再崩溃。"""
+
+        class _FakeResponse:
+            """模拟 OpenAI Responses API 完成事件里的 Response 对象。"""
+
+            def __init__(self, output):
+                """保存 output 字段用于复现空输出场景。"""
+                self.output = output
+
+            def model_copy(self, update=None):
+                """模拟 Pydantic v2 model_copy(update=...) 行为。"""
+                copied = _FakeResponse(self.output)
+                for key, value in (update or {}).items():
+                    setattr(copied, key, value)
+                return copied
+
+        fake_modules, openai_base = _build_fake_openai_modules()
+        with patch.dict(sys.modules, fake_modules):
+            with self.assertRaises(TypeError):
+                openai_base._construct_lc_result_from_responses_api(
+                    _FakeResponse(None)
+                )
+
+            llm_module._patch_openai_responses_instructions_support()
+            result = openai_base._construct_lc_result_from_responses_api(
+                _FakeResponse(None),
+                schema=object,
+            )
+
+        self.assertEqual(result.response.output, [])
+        self.assertEqual(result.kwargs.get("schema"), object)
+
     def test_openai_compatible_patch_injects_xiaomi_reasoning_content(self):
         fake_modules, _ = _build_fake_openai_modules()
         with patch.dict(sys.modules, fake_modules):