fix(agent): content 为空时回退 reasoning_content(修思考模型空响应)

qwen3.7-max 思考模型常返回空 message.content、答案在 reasoning_content。原代码只读
content → 拿到 "" → json.loads("") → "Expecting value: line 1 column 1 (char 0)" → 任务失败。
新增 _message_text():content → reasoning_content(attr 或 OpenAI SDK model_extra)回退,
_complete 两个返回点都用它。

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>

agent/task_executor.py

@@ -595,6 +595,21 @@ Return ONLY the JSON, no other text."""
         except Exception as exc:
             return f"[tool error: {exc}]"
 
+    @staticmethod
+    def _message_text(msg) -> str:
+        """Robustly extract the assistant's textual answer. qwen3.7-max (a THINKING model) can return
+        an empty `content` with the real answer in `reasoning_content`; reading only `content` then
+        yields "" → `json.loads("")` → "Expecting value: line 1 column 1 (char 0)" → task fails.
+        Fallback chain: content → reasoning_content (attribute or OpenAI-SDK model_extra)."""
+        text = (getattr(msg, "content", None) or "").strip()
+        if text:
+            return text
+        rc = getattr(msg, "reasoning_content", None)
+        if not rc:
+            extra = getattr(msg, "model_extra", None) or {}
+            rc = extra.get("reasoning_content") if isinstance(extra, dict) else None
+        return (rc or "").strip()
+
     async def _complete(self, prompt: str, max_tokens: int) -> str:
         """Call the LLM with optional Jina MCP tools; handles the tool-call loop."""
         extra_headers = self._model_attribution_headers()
@@ -620,7 +635,7 @@ Return ONLY the JSON, no other text."""
                 logger.info("[LLM·第%d轮] 思考/回复:\n%s", round_i + 1, msg.content.strip()[:1200])
 
             if not msg.tool_calls:
-                return msg.content or ""
+                return self._message_text(msg)
 
             # Execute each tool call and feed results back
             messages.append(msg.model_dump(exclude_unset=True))
@@ -646,7 +661,7 @@ Return ONLY the JSON, no other text."""
             extra_headers=extra_headers or None,
         )
         self._record_openai_usage(response)
-        return response.choices[0].message.content or ""
+        return self._message_text(response.choices[0].message)
 
     async def peer_reply(self, *, query: str, capabilities: list[str],
                          last_summary: Optional[str], max_tokens: Optional[int] = None) -> dict: