fix: fall back to reasoning_content for reasoning models

src/memu/llm/backends/base.py

@@ -3,6 +3,19 @@
 from typing import Any
 
 
+def _extract_content_from_dict(data: dict[str, Any]) -> str:
+    """Extract text content from a raw API response dict.
+
+    Falls back to ``reasoning_content`` for reasoning models (e.g. MiniMax-M2.7,
+    DeepSeek-R1) that put their output there instead of ``content``.
+    """
+    msg = data["choices"][0]["message"]
+    content = msg.get("content")
+    if not content:
+        content = msg.get("reasoning_content")
+    return content or ""
+
+
 class LLMBackend:
     """Defines how to talk to a specific HTTP LLM provider."""
 

src/memu/llm/backends/doubao.py

@@ -1,8 +1,8 @@
 from __future__ import annotations
 
-from typing import Any, cast
+from typing import Any
 
-from memu.llm.backends.base import LLMBackend
+from memu.llm.backends.base import LLMBackend, _extract_content_from_dict
 
 
 class DoubaoLLMBackend(LLMBackend):
@@ -29,7 +29,7 @@ def build_summary_payload(
         return payload
 
     def parse_summary_response(self, data: dict[str, Any]) -> str:
-        return cast(str, data["choices"][0]["message"]["content"])
+        return _extract_content_from_dict(data)
 
     def build_vision_payload(
         self,

src/memu/llm/backends/openai.py

@@ -1,8 +1,8 @@
 from __future__ import annotations
 
-from typing import Any, cast
+from typing import Any
 
-from memu.llm.backends.base import LLMBackend
+from memu.llm.backends.base import LLMBackend, _extract_content_from_dict
 
 
 class OpenAILLMBackend(LLMBackend):
@@ -26,7 +26,7 @@ def build_summary_payload(
         }
 
     def parse_summary_response(self, data: dict[str, Any]) -> str:
-        return cast(str, data["choices"][0]["message"]["content"])
+        return _extract_content_from_dict(data)
 
     def build_vision_payload(
         self,

src/memu/llm/backends/openrouter.py

@@ -1,8 +1,8 @@
 from __future__ import annotations
 
-from typing import Any, cast
+from typing import Any
 
-from memu.llm.backends.base import LLMBackend
+from memu.llm.backends.base import LLMBackend, _extract_content_from_dict
 
 
 class OpenRouterLLMBackend(LLMBackend):
@@ -30,7 +30,7 @@ def build_summary_payload(
 
     def parse_summary_response(self, data: dict[str, Any]) -> str:
         """Parse OpenRouter response (OpenAI-compatible format)."""
-        return cast(str, data["choices"][0]["message"]["content"])
+        return _extract_content_from_dict(data)
 
     def build_vision_payload(
         self,

src/memu/llm/openai_sdk.py

@@ -17,6 +17,19 @@
 logger = logging.getLogger(__name__)
 
 
+def _extract_content(response: ChatCompletion) -> str:
+    """Extract text content from a chat completion, with fallback for reasoning models.
+
+    Some reasoning models (e.g. MiniMax-M2.7, DeepSeek-R1) return their output in
+    ``reasoning_content`` instead of ``content``.  This helper checks both fields.
+    """
+    msg = response.choices[0].message
+    content = msg.content
+    if not content:
+        content = getattr(msg, "reasoning_content", None)
+    return content or ""
+
+
 class OpenAISDKClient:
     """OpenAI LLM client that relies on the official Python SDK."""
 
@@ -59,9 +72,9 @@ async def chat(
             temperature=temperature,
             max_tokens=max_tokens,
         )
-        content = response.choices[0].message.content
+        content = _extract_content(response)
         logger.debug("OpenAI chat response: %s", response)
-        return content or "", response
+        return content, response
 
     async def summarize(
         self,
@@ -82,9 +95,9 @@ async def summarize(
             temperature=1,
             max_tokens=max_tokens,
         )
-        content = response.choices[0].message.content
+        content = _extract_content(response)
         logger.debug("OpenAI summarize response: %s", response)
-        return content or "", response
+        return content, response
 
     async def vision(
         self,
@@ -148,9 +161,9 @@ async def vision(
             temperature=1,
             max_tokens=max_tokens,
         )
-        content = response.choices[0].message.content
+        content = _extract_content(response)
         logger.debug("OpenAI vision response: %s", response)
-        return content or "", response
+        return content, response
 
     async def embed(self, inputs: list[str]) -> tuple[list[list[float]], CreateEmbeddingResponse | None]:
         """Create text embeddings via the official SDK."""

tests/llm/test_extract_content.py

@@ -0,0 +1,87 @@
+"""Tests for reasoning_content fallback in content extraction helpers."""
+
+from __future__ import annotations
+
+from types import SimpleNamespace
+from unittest.mock import MagicMock
+
+from memu.llm.backends.base import _extract_content_from_dict
+from memu.llm.openai_sdk import _extract_content
+
+
+# -- _extract_content (SDK path, ChatCompletion objects) --
+
+
+def _fake_completion(content=None, reasoning_content=None):
+    """Build a minimal ChatCompletion-like object."""
+    msg = MagicMock()
+    msg.content = content
+    # reasoning_content is an extra attr on some providers
+    if reasoning_content is not None:
+        msg.reasoning_content = reasoning_content
+    else:
+        # simulate the attr not existing at all
+        del msg.reasoning_content
+    choice = SimpleNamespace(message=msg)
+    return SimpleNamespace(choices=[choice])
+
+
+class TestExtractContent:
+    def test_normal_content(self):
+        resp = _fake_completion(content="hello world")
+        assert _extract_content(resp) == "hello world"
+
+    def test_reasoning_content_fallback(self):
+        resp = _fake_completion(content=None, reasoning_content="thought result")
+        assert _extract_content(resp) == "thought result"
+
+    def test_empty_string_content_falls_back(self):
+        resp = _fake_completion(content="", reasoning_content="fallback")
+        assert _extract_content(resp) == "fallback"
+
+    def test_both_none_returns_empty(self):
+        resp = _fake_completion(content=None)
+        assert _extract_content(resp) == ""
+
+    def test_content_preferred_over_reasoning(self):
+        resp = _fake_completion(content="real answer", reasoning_content="thinking")
+        assert _extract_content(resp) == "real answer"
+
+
+# -- _extract_content_from_dict (HTTP path, raw dicts) --
+
+
+def _fake_dict_response(content=None, reasoning_content=None):
+    """Build a minimal raw API response dict."""
+    msg = {}
+    if content is not None:
+        msg["content"] = content
+    if reasoning_content is not None:
+        msg["reasoning_content"] = reasoning_content
+    return {"choices": [{"message": msg}]}
+
+
+class TestExtractContentFromDict:
+    def test_normal_content(self):
+        data = _fake_dict_response(content="hello")
+        assert _extract_content_from_dict(data) == "hello"
+
+    def test_reasoning_content_fallback(self):
+        data = _fake_dict_response(reasoning_content="thought")
+        assert _extract_content_from_dict(data) == "thought"
+
+    def test_empty_string_content_falls_back(self):
+        data = _fake_dict_response(content="", reasoning_content="fb")
+        assert _extract_content_from_dict(data) == "fb"
+
+    def test_both_missing_returns_empty(self):
+        data = _fake_dict_response()
+        assert _extract_content_from_dict(data) == ""
+
+    def test_content_preferred_over_reasoning(self):
+        data = _fake_dict_response(content="answer", reasoning_content="thinking")
+        assert _extract_content_from_dict(data) == "answer"
+
+    def test_none_content_with_reasoning(self):
+        data = _fake_dict_response(content=None, reasoning_content="result")
+        assert _extract_content_from_dict(data) == "result"