fix(llm): normalise Mistral structured content in llm_call_async

llm_call_async returned raw list content for Mistral thinking models,
breaking callers that expect a str (e.g. auto-title). Match the sync
and streaming parsers by running list content through
_normalize_mistral_content.

Fixes #5435

src/llm_core.py

@@ -2109,7 +2109,17 @@ async def llm_call_async(
                     response = _parse_ollama_response(data)
                 else:
                     msg = data["choices"][0]["message"]
-                    response = msg.get("content") or msg.get("reasoning_content") or ""
+                    content = msg.get("content")
+                    if isinstance(content, list):
+                        # Mistral structured content — extract thinking + text
+                        # (same contract as llm_call / stream_llm; see #5435).
+                        text_part, thinking_part = _normalize_mistral_content(content)
+                        if thinking_part:
+                            response = thinking_part + "\n\n" + (text_part or "")
+                        else:
+                            response = text_part or msg.get("reasoning_content") or ""
+                    else:
+                        response = content or msg.get("reasoning_content") or ""
                 _set_cached_response(cache_key, response)
                 return response
             except Exception:

tests/test_llm_core_async_mistral_content.py

@@ -0,0 +1,71 @@
+"""Integration regression test for #5435.
+
+llm_call_async must normalise Mistral structured content to a plain string,
+matching llm_call (sync) and stream_llm. Before the fix, the async
+non-streaming parser returned the raw list when Mistral reasoning was enabled,
+violating its -> str contract, leaking a non-string into callers such as
+auto-title generation and memory extraction, and poisoning _response_cache
+with a non-string value.
+"""
+import asyncio
+
+import src.llm_core as llm_core
+
+
+class _FakeResponse:
+    is_success = True
+    status_code = 200
+    text = ""
+
+    def __init__(self, payload):
+        self._payload = payload
+
+    def json(self):
+        return self._payload
+
+
+def _payload(content):
+    return {"choices": [{"message": {"role": "assistant", "content": content}}]}
+
+
+def _call(monkeypatch, content):
+    async def fake_post(client, url, headers, **kwargs):
+        return _FakeResponse(_payload(content))
+
+    monkeypatch.setattr(llm_core, "httpx_post_kimi_aware_async", fake_post)
+    llm_core._response_cache.clear()
+    return asyncio.run(llm_core.llm_call_async(
+        "http://mistral.test/v1/chat/completions",
+        "mistral-medium",
+        [{"role": "user", "content": "q"}],
+    ))
+
+
+def test_llm_call_async_normalizes_mistral_structured_content(monkeypatch):
+    out = _call(monkeypatch, [
+        {"type": "thinking",
+         "thinking": [{"type": "text", "text": "Let me work through this..."}],
+         "closed": True},
+        {"type": "text", "text": "The answer is 42."},
+    ])
+    assert isinstance(out, str), f"expected str, got {type(out).__name__}"
+    assert "The answer is 42." in out
+    assert "Let me work through this..." in out
+    # The cache must hold the normalised string, not the raw list,
+    # otherwise repeat calls serve the poisoned value.
+    assert all(isinstance(v, str) for v in llm_core._response_cache.values())
+
+
+def test_llm_call_async_thinking_only_still_returns_str(monkeypatch):
+    out = _call(monkeypatch, [
+        {"type": "thinking",
+         "thinking": [{"type": "text", "text": "still thinking"}],
+         "closed": True},
+    ])
+    assert isinstance(out, str)
+    assert "still thinking" in out
+
+
+def test_llm_call_async_plain_string_passthrough(monkeypatch):
+    out = _call(monkeypatch, "plain answer")
+    assert out == "plain answer"