fix(middleware): handle list-type AIMessage.content in LoopDetectionMiddleware (#1823)

* fix: inject longTermBackground into memory prompt

The format_memory_for_injection function only processed recentMonths and
earlierContext from the history section, silently dropping longTermBackground.

The LLM writes longTermBackground correctly and it persists to memory.json,
but it was never injected into the system prompt — making the user's
long-term background invisible to the AI.

Add the missing field handling and a regression test.

* fix(middleware): handle list-type AIMessage.content in LoopDetectionMiddleware

LangChain AIMessage.content can be str | list. When using providers that
return structured content blocks (e.g. Anthropic thinking mode, certain
OpenAI-compatible gateways), content is a list of dicts like
[{"type": "text", "text": "..."}].

The hard_limit branch in _apply() concatenated content with a string via
(last_msg.content or "") + f"\n\n{_HARD_STOP_MSG}", which raises
TypeError when content is a non-empty list (list + str is invalid).

Add _append_text() static method that:
- Returns the text directly when content is None
- Appends a {"type": "text"} block when content is a list
- Falls back to string concatenation when content is a str

This is consistent with how other modules in the project already handle
list content (client.py._extract_text, memory_middleware, executor.py).

* test(middleware): add unit tests for _append_text and list content hard stop

Add regression tests to verify LoopDetectionMiddleware handles list-type
AIMessage.content correctly during hard stop:

- TestAppendText: unit tests for the new _append_text() static method
  covering None, str, list (including empty list) content types
- TestHardStopWithListContent: integration tests verifying hard stop
  works correctly with list content (Anthropic thinking mode), None
  content, and str content

Requested by reviewer in PR #1823.

* fix(middleware): improve _append_text robustness and test isolation

- Add explicit isinstance(content, str) check with fallback for
  unexpected types (coerce to str) to prevent TypeError on edge cases
- Deep-copy list content in _make_state() test helper to prevent
  shared mutable references across test iterations
- Add test_unexpected_type_coerced_to_str: verify fallback for
  non-str/list/None content types
- Add test_list_content_not_mutated_in_place: verify _append_text
  does not modify the original list

* style: fix ruff format whitespace in test file

---------

Co-authored-by: ppyt <14163465+ppyt@users.noreply.github.com>

backend/packages/harness/deerflow/agents/middlewares/loop_detection_middleware.py

@@ -182,6 +182,23 @@ class LoopDetectionMiddleware(AgentMiddleware[AgentState]):
 
         return None, False
 
+    @staticmethod
+    def _append_text(content: str | list | None, text: str) -> str | list:
+        """Append *text* to AIMessage content, handling str, list, and None.
+
+        When content is a list of content blocks (e.g. Anthropic thinking mode),
+        we append a new ``{"type": "text", ...}`` block instead of concatenating
+        a string to a list, which would raise ``TypeError``.
+        """
+        if content is None:
+            return text
+        if isinstance(content, list):
+            return [*content, {"type": "text", "text": f"\n\n{text}"}]
+        if isinstance(content, str):
+            return content + f"\n\n{text}"
+        # Fallback: coerce unexpected types to str to avoid TypeError
+        return str(content) + f"\n\n{text}"
+
     def _apply(self, state: AgentState, runtime: Runtime) -> dict | None:
         warning, hard_stop = self._track_and_check(state, runtime)
 
@@ -192,7 +209,7 @@ class LoopDetectionMiddleware(AgentMiddleware[AgentState]):
             stripped_msg = last_msg.model_copy(
                 update={
                     "tool_calls": [],
-                    "content": (last_msg.content or "") + f"\n\n{_HARD_STOP_MSG}",
+                    "content": self._append_text(last_msg.content, _HARD_STOP_MSG),
                 }
             )
             return {"messages": [stripped_msg]}

backend/tests/test_loop_detection_middleware.py

@@ -1,5 +1,6 @@
 """Tests for LoopDetectionMiddleware."""
 
+import copy
 from unittest.mock import MagicMock
 
 from langchain_core.messages import AIMessage, HumanMessage, SystemMessage
@@ -19,8 +20,13 @@ def _make_runtime(thread_id="test-thread"):
 
 
 def _make_state(tool_calls=None, content=""):
-    """Build a minimal AgentState dict with an AIMessage."""
-    msg = AIMessage(content=content, tool_calls=tool_calls or [])
+    """Build a minimal AgentState dict with an AIMessage.
+
+    Deep-copies *content* when it is mutable (e.g. list) so that
+    successive calls never share the same object reference.
+    """
+    safe_content = copy.deepcopy(content) if isinstance(content, list) else content
+    msg = AIMessage(content=safe_content, tool_calls=tool_calls or [])
     return {"messages": [msg]}
 
 
@@ -229,3 +235,114 @@ class TestLoopDetection:
 
         mw._apply(_make_state(tool_calls=call), runtime)
         assert "default" in mw._history
+
+
+class TestAppendText:
+    """Unit tests for LoopDetectionMiddleware._append_text."""
+
+    def test_none_content_returns_text(self):
+        result = LoopDetectionMiddleware._append_text(None, "hello")
+        assert result == "hello"
+
+    def test_str_content_concatenates(self):
+        result = LoopDetectionMiddleware._append_text("existing", "appended")
+        assert result == "existing\n\nappended"
+
+    def test_empty_str_content_concatenates(self):
+        result = LoopDetectionMiddleware._append_text("", "appended")
+        assert result == "\n\nappended"
+
+    def test_list_content_appends_text_block(self):
+        """List content (e.g. Anthropic thinking mode) should get a new text block."""
+        content = [
+            {"type": "thinking", "text": "Let me think..."},
+            {"type": "text", "text": "Here is my answer"},
+        ]
+        result = LoopDetectionMiddleware._append_text(content, "stop msg")
+        assert isinstance(result, list)
+        assert len(result) == 3
+        assert result[0] == content[0]
+        assert result[1] == content[1]
+        assert result[2] == {"type": "text", "text": "\n\nstop msg"}
+
+    def test_empty_list_content_appends_text_block(self):
+        result = LoopDetectionMiddleware._append_text([], "stop msg")
+        assert isinstance(result, list)
+        assert len(result) == 1
+        assert result[0] == {"type": "text", "text": "\n\nstop msg"}
+
+    def test_unexpected_type_coerced_to_str(self):
+        """Unexpected content types should be coerced to str as a fallback."""
+        result = LoopDetectionMiddleware._append_text(42, "stop msg")
+        assert isinstance(result, str)
+        assert result == "42\n\nstop msg"
+
+    def test_list_content_not_mutated_in_place(self):
+        """_append_text must not modify the original list."""
+        original = [{"type": "text", "text": "hello"}]
+        result = LoopDetectionMiddleware._append_text(original, "appended")
+        assert len(original) == 1  # original unchanged
+        assert len(result) == 2  # new list has the appended block
+
+
+class TestHardStopWithListContent:
+    """Regression tests: hard stop must not crash when AIMessage.content is a list."""
+
+    def test_hard_stop_with_list_content(self):
+        """Hard stop on list content should not raise TypeError (regression)."""
+        mw = LoopDetectionMiddleware(warn_threshold=2, hard_limit=4)
+        runtime = _make_runtime()
+        call = [_bash_call("ls")]
+
+        # Build state with list content (e.g. Anthropic thinking mode)
+        list_content = [
+            {"type": "thinking", "text": "Let me think..."},
+            {"type": "text", "text": "I'll run ls"},
+        ]
+
+        for _ in range(3):
+            mw._apply(_make_state(tool_calls=call, content=list_content), runtime)
+
+        # Fourth call triggers hard stop — must not raise TypeError
+        result = mw._apply(_make_state(tool_calls=call, content=list_content), runtime)
+        assert result is not None
+        msg = result["messages"][0]
+        assert isinstance(msg, AIMessage)
+        assert msg.tool_calls == []
+        # Content should remain a list with the stop message appended
+        assert isinstance(msg.content, list)
+        assert len(msg.content) == 3
+        assert msg.content[2]["type"] == "text"
+        assert _HARD_STOP_MSG in msg.content[2]["text"]
+
+    def test_hard_stop_with_none_content(self):
+        """Hard stop on None content should produce a plain string."""
+        mw = LoopDetectionMiddleware(warn_threshold=2, hard_limit=4)
+        runtime = _make_runtime()
+        call = [_bash_call("ls")]
+
+        for _ in range(3):
+            mw._apply(_make_state(tool_calls=call), runtime)
+
+        # Fourth call with default empty-string content
+        result = mw._apply(_make_state(tool_calls=call), runtime)
+        assert result is not None
+        msg = result["messages"][0]
+        assert isinstance(msg.content, str)
+        assert _HARD_STOP_MSG in msg.content
+
+    def test_hard_stop_with_str_content(self):
+        """Hard stop on str content should concatenate the stop message."""
+        mw = LoopDetectionMiddleware(warn_threshold=2, hard_limit=4)
+        runtime = _make_runtime()
+        call = [_bash_call("ls")]
+
+        for _ in range(3):
+            mw._apply(_make_state(tool_calls=call, content="thinking..."), runtime)
+
+        result = mw._apply(_make_state(tool_calls=call, content="thinking..."), runtime)
+        assert result is not None
+        msg = result["messages"][0]
+        assert isinstance(msg.content, str)
+        assert msg.content.startswith("thinking...")
+        assert _HARD_STOP_MSG in msg.content