fix(chat): always persist reasoning_content on assistant event_payload

Non-thinking profiles previously wrote model reasoning only as separate
event_type=reasoning rows whose event_payload held chunk_index metadata, so
JSON inspection showed tool payloads but no reasoning_content key.

Store the full merged reasoning blob on the primary assistant row whenever
present (same shape as thinking mode). Update instruction-leak filtering to
scan event_payload.reasoning_content as well as legacy reasoning rows.

Co-authored-by: Cursor <cursoragent@cursor.com>

interfaces/admin/chat_api.py

@@ -238,12 +238,20 @@ def _filter_internal_instruction_user_messages(msgs: list[Any]) -> list[Any]:
     instruction_texts: set[str] = set()
     for m in msgs:
         role = str(getattr(m, "role", "") or "")
-        event_type = str(getattr(m, "event_type", "") or "")
-        if role != "assistant" or event_type != "reasoning":
+        if role != "assistant":
             continue
-        instr = _extract_manager_instruction_text(str(getattr(m, "content", "") or ""))
-        if instr:
-            instruction_texts.add(instr)
+        event_type = str(getattr(m, "event_type", "") or "")
+        candidates: list[str] = []
+        if event_type == "reasoning":
+            candidates.append(str(getattr(m, "content", "") or ""))
+        ep = _safe_json_object(getattr(m, "event_payload", None))
+        rc = str(ep.get("reasoning_content") or "").strip()
+        if rc:
+            candidates.append(rc)
+        for text in candidates:
+            instr = _extract_manager_instruction_text(text)
+            if instr:
+                instruction_texts.add(instr)
     if not instruction_texts:
         return msgs
     filtered: list[Any] = []

runtime/direct_loop.py

@@ -1008,7 +1008,6 @@ def _persist_assistant_step(
     assistant_text: str,
     reasoning_text: str,
     llm_tool_calls: list[Any],
-    thinking_mode_enabled: bool = False,
 ) -> _LoopStepResult:
     stored_tool_calls = []
     for tc in llm_tool_calls:
@@ -1025,16 +1024,11 @@ def _persist_assistant_step(
     reasoning_full = "\n".join([str(x or "").strip() for x in reasoning_chunks if str(x or "").strip()]).strip()
     # Keep empty body as-is when model returns nothing and there are no tool calls.
     # The UI should treat this as an invisible intermediate/final empty response.
-    if not thinking_mode_enabled:
-        for idx, chunk in enumerate(reasoning_chunks):
-            store.add_message(
-                session_id=session_id,
-                role="assistant",
-                content=chunk,
-                turn_uuid=turn_uuid,
-                event_type="reasoning",
-                event_payload={"chunk_index": int(idx), "chunk_count": len(reasoning_chunks)},
-            )
+    #
+    # Persist full reasoning on the primary assistant row's ``event_payload.reasoning_content`` for
+    # both thinking and non-thinking profiles. Previously non-thinking used separate
+    # ``event_type=reasoning`` rows with only chunk_index in payload, so JSON inspection looked
+    # like "no reasoning_content anywhere" while tools carried rich ``event_payload``.
     assistant_row = store.add_message(
         session_id=session_id,
         role="assistant",
@@ -1042,7 +1036,7 @@ def _persist_assistant_step(
         tool_calls=stored_tool_calls or None,
         turn_uuid=turn_uuid,
         event_type="tool_call" if stored_tool_calls else "assistant_text",
-        event_payload=({"reasoning_content": reasoning_full} if (thinking_mode_enabled and reasoning_full) else None),
+        event_payload=({"reasoning_content": reasoning_full} if reasoning_full else None),
     )
     return _LoopStepResult(
         assistant_text=assistant_body,
@@ -1453,7 +1447,6 @@ def run_oclaw_direct_loop(
                 assistant_text=assistant_text,
                 reasoning_text=reasoning_text,
                 llm_tool_calls=llm_tool_calls,
-                thinking_mode_enabled=bool(getattr(model, "thinking_mode_enabled", False)),
             )
         final_text = step.assistant_text
         if not step.llm_tool_calls:
@@ -1559,7 +1552,6 @@ def run_oclaw_direct_loop(
             assistant_text=str(getattr(resp, "content", "") or ""),
             reasoning_text=str(getattr(resp, "reasoning_content", "") or ""),
             llm_tool_calls=[],
-            thinking_mode_enabled=bool(getattr(model, "thinking_mode_enabled", False)),
         )
         final_text = step.assistant_text
 

tests/test_chat_api_message_filter.py

@@ -21,6 +21,22 @@ def test_filter_internal_instruction_user_messages_hides_polluted_user_row() ->
     assert [str(getattr(x, "event_type", "")) for x in out] == ["reasoning", "assistant_text"]
 
 
+def test_filter_internal_instruction_user_messages_hides_from_assistant_event_payload() -> None:
+    polluted_text = "请检查并修复网关启动失败。"
+    rows = [
+        SimpleNamespace(role="user", event_type="user_text", content=polluted_text),
+        SimpleNamespace(
+            role="assistant",
+            event_type="assistant_text",
+            content="已修复",
+            event_payload={"reasoning_content": f"任务分配\nspecialist=ops\ninstruction:\n{polluted_text}"},
+        ),
+    ]
+    out = _filter_internal_instruction_user_messages(rows)
+    assert len(out) == 1
+    assert str(getattr(out[0], "event_type", "")) == "assistant_text"
+
+
 def test_filter_internal_instruction_user_messages_keeps_normal_user_rows() -> None:
     rows = [
         SimpleNamespace(role="user", event_type="user_text", content="你好"),