fix(provider): strip think tags for MiniMax-M3 and other OpenAI-compatible models (#2330)

* fix(provider): strip think tags for MiniMax-M3 and other OpenAI-compatible models

MiniMax-M3 (and other OpenAI-compatible providers) emit chain-of-thought
reasoning directly in the content field wrapped in  tags, instead
of using a separate reasoning_content field or the legacy CRETIRE_REASONING
markers. The existing remove_think logic only handled CRETIRE_* tags, so
think blocks leaked into user-visible output even when remove_think was enabled.

- Add _ThinkStripState: a stateful filter that correctly handles  tags
  split across streaming chunk boundaries.
- Add _strip_think classmethod with regex patterns for both  and
  CRETIRE_* tags.
- Wire think_state into invoke_llm_stream so deltas are filtered before
  reaching the accumulator.
- Add remove_think safety net in _StreamAccumulator so the final message
  from tool-call rounds also gets stripped.
- Fix remove_think resolution to use defensive nested .get() so
  pipelines missing output.misc don't raise AttributeError.

* fix(litellmchat): add missing _CLOSE_TAG class attribute on _ThinkStripState

* fix(provider): handle think stripping across LiteLLM paths

---------

Co-authored-by: WangCham <651122857@qq.com>

src/langbot/pkg/provider/modelmgr/requesters/litellmchat.py

@@ -13,6 +13,151 @@ import langbot_plugin.api.entities.builtin.pipeline.query as pipeline_query
 import langbot_plugin.api.entities.builtin.provider.message as provider_message
 
 
+class _ThinkStripState:
+    """Stateful filter that drops think blocks across chunks."""
+
+    _THINK_OPEN = '<think>'
+    _THINK_CLOSE = '</think>'
+    _LEGACY_OPEN = 'CRETIRE_REASONING_BEGINk'
+    _LEGACY_CLOSE = 'CRETIRE_REASONING_ENDk'
+
+    def __init__(self) -> None:
+        self._pairs: tuple[tuple[str, str], ...] = (
+            (self._THINK_OPEN, self._THINK_CLOSE),
+            (self._LEGACY_OPEN, self._LEGACY_CLOSE),
+        )
+        self._open_tags = tuple(open_tag for open_tag, _close_tag in self._pairs)
+        self._buf = ''
+        self._close_tag: str | None = None
+        self._pending_initial = True
+
+    def feed(self, chunk: str) -> str:
+        """Feed a streaming delta and return user-visible content."""
+        if not chunk:
+            return chunk
+
+        text = self._buf + chunk
+        if self._close_tag is not None:
+            return self._consume_think_body(text)
+
+        return self._process_visible_text(text)
+
+    def flush(self) -> str:
+        """Release buffered visible content when the stream ends."""
+        if self._close_tag is not None:
+            self._buf = ''
+            self._close_tag = None
+            return ''
+
+        pending, self._buf = self._buf, ''
+        self._close_tag = None
+        return pending
+
+    def _consume_think_body(self, text: str) -> str:
+        close_tag = self._close_tag
+        if close_tag is None:
+            return text
+
+        close_idx = text.find(close_tag)
+        if close_idx != -1:
+            self._close_tag = None
+            self._buf = ''
+            self._pending_initial = False
+            return self._process_visible_text(text[close_idx + len(close_tag) :])
+
+        self._buf = self._close_prefix(text, close_tag)
+        return ''
+
+    def _process_visible_text(self, text: str) -> str:
+        out: list[str] = []
+        index = 0
+
+        while index < len(text):
+            if self._pending_initial:
+                open_idx, open_tag, close_tag = self._find_next_open(text, index)
+                orphan_close_idx, orphan_close_tag = self._find_next_close(text, index)
+
+                if orphan_close_idx != -1 and (open_idx == -1 or orphan_close_idx < open_idx):
+                    self._pending_initial = False
+                    index = orphan_close_idx + len(orphan_close_tag)
+                    continue
+
+                if open_idx == -1:
+                    self._buf = text[index:]
+                    return ''.join(out)
+
+                if open_idx > index:
+                    self._pending_initial = False
+                    out.append(text[index:open_idx])
+                    index = open_idx
+                    continue
+
+            open_idx, open_tag, close_tag = self._find_next_open(text, index)
+            if open_idx == -1:
+                emit_end = self._visible_emit_end(text, index)
+                out.append(text[index:emit_end])
+                if emit_end > index:
+                    self._pending_initial = False
+                self._buf = text[emit_end:]
+                return ''.join(out)
+
+            out.append(text[index:open_idx])
+            if open_idx > index:
+                self._pending_initial = False
+            body_start = open_idx + len(open_tag)
+            close_idx = text.find(close_tag, body_start)
+            if close_idx == -1:
+                self._close_tag = close_tag
+                self._buf = self._close_prefix(text[body_start:], close_tag)
+                return ''.join(out)
+
+            self._pending_initial = False
+            index = close_idx + len(close_tag)
+
+        self._buf = ''
+        return ''.join(out)
+
+    def _find_next_open(self, text: str, start: int) -> tuple[int, str, str]:
+        best_idx = -1
+        best_open = ''
+        best_close = ''
+        for open_tag, close_tag in self._pairs:
+            idx = text.find(open_tag, start)
+            if idx != -1 and (best_idx == -1 or idx < best_idx):
+                best_idx = idx
+                best_open = open_tag
+                best_close = close_tag
+        return best_idx, best_open, best_close
+
+    def _find_next_close(self, text: str, start: int) -> tuple[int, str]:
+        best_idx = -1
+        best_close = ''
+        for _open_tag, close_tag in self._pairs:
+            idx = text.find(close_tag, start)
+            if idx != -1 and (best_idx == -1 or idx < best_idx):
+                best_idx = idx
+                best_close = close_tag
+        return best_idx, best_close
+
+    def _visible_emit_end(self, text: str, start: int) -> int:
+        visible = text[start:]
+        limit = min(len(visible), max(len(open_tag) for open_tag in self._open_tags) - 1)
+        for keep in range(limit, 0, -1):
+            suffix = visible[-keep:]
+            if any(open_tag.startswith(suffix) for open_tag in self._open_tags):
+                return len(text) - keep
+        return len(text)
+
+    @staticmethod
+    def _close_prefix(text: str, close_tag: str) -> str:
+        limit = min(len(text), len(close_tag) - 1)
+        for keep in range(limit, 0, -1):
+            suffix = text[-keep:]
+            if close_tag.startswith(suffix):
+                return suffix
+        return ''
+
+
 class LiteLLMRequester(requester.ProviderAPIRequester):
     """LiteLLM unified API requester supporting chat, embedding, and rerank."""
 
@@ -237,6 +382,25 @@ class LiteLLMRequester(requester.ProviderAPIRequester):
 
         return req_messages
 
+    _THINK_PATTERNS: tuple[str, ...] = (
+        r'^\s*(?:(?!<think>).)*?</think>\s*',
+        r'^\s*(?:(?!CRETIRE_REASONING_BEGINk).)*?CRETIRE_REASONING_ENDk\s*',
+        r'<think>.*?</think>',
+        r'CRETIRE_REASONING_BEGINk.*?CRETIRE_REASONING_ENDk',
+    )
+
+    @classmethod
+    def _strip_think(cls, content: str) -> str:
+        """Strip chain-of-thought blocks from ``content``."""
+        if not content:
+            return content
+
+        import re
+
+        for pattern in cls._THINK_PATTERNS:
+            content = re.sub(pattern, '', content, flags=re.DOTALL)
+        return content.strip()
+
     def _process_thinking_content(self, content: str, reasoning_content: str | None, remove_think: bool) -> str:
         """Process thinking/reasoning content.
 
@@ -248,20 +412,12 @@ class LiteLLMRequester(requester.ProviderAPIRequester):
         Returns:
             Processed content string
         """
-        # Extract and handle thinking tags
+        if remove_think and content:
-        if content and 'CRETIRE_REASONING_BEGINk' in content and 'CRETIRE_REASONING_ENDk' in content:
+            content = self._strip_think(content)
-            import re
 
-            think_pattern = r'CRETIRE_REASONING_BEGINk(.*?)CRETIRE_REASONING_ENDk'
+        if reasoning_content and not remove_think:
+            content = f'<think>\n{reasoning_content}\n</think>\n{content or ""}'.strip()
 
-            if remove_think:
-                # Remove thinking tags and their content from output
-                content = re.sub(think_pattern, '', content, flags=re.DOTALL).strip()
-            # else: preserve thinking content as-is
-
-        # Handle separate reasoning_content field
-        # Currently we don't include reasoning_content in user-facing output regardless of remove_think
-        # because it's typically internal model reasoning, not user-visible thinking
         return content or ''
 
     @staticmethod
@@ -570,6 +726,7 @@ class LiteLLMRequester(requester.ProviderAPIRequester):
         chunk_idx = 0
         role = 'assistant'
         tool_call_state: dict[int, dict[str, typing.Any]] = {}
+        think_state = _ThinkStripState() if remove_think else None
 
         try:
             response = await acompletion(**args)
@@ -613,6 +770,12 @@ class LiteLLMRequester(requester.ProviderAPIRequester):
                         # Use reasoning_content as the displayed content
                         delta_content = reasoning_content
 
+                if think_state is not None and delta_content:
+                    delta_content = think_state.feed(delta_content)
+                    if not delta_content:
+                        chunk_idx += 1
+                        continue
+
                 tool_calls = self._normalize_stream_tool_calls(delta.get('tool_calls'), tool_call_state)
 
                 if chunk_idx == 0 and not delta_content and not tool_calls:
@@ -634,6 +797,15 @@ class LiteLLMRequester(requester.ProviderAPIRequester):
                 yield provider_message.MessageChunk(**chunk_data)
                 chunk_idx += 1
 
+            if think_state is not None:
+                pending_content = think_state.flush()
+                if pending_content:
+                    yield provider_message.MessageChunk(
+                        role=role,
+                        content=pending_content,
+                        is_final=True,
+                    )
+
         except Exception as e:
             self._handle_litellm_error(e)
 

src/langbot/pkg/provider/runners/localagent.py

@@ -49,12 +49,23 @@ def _model_has_ability(model: modelmgr_requester.RuntimeLLMModel, ability: str)
 class _StreamAccumulator:
     """Accumulate streamed content and fragmented OpenAI-style tool calls."""
 
-    def __init__(self, msg_sequence: int = 0, initial_content: str | None = None):
+    def __init__(
+        self,
+        msg_sequence: int = 0,
+        initial_content: str | None = None,
+        remove_think: bool = False,
+    ):
         self.tool_calls_map: dict[str, provider_message.ToolCall] = {}
         self.msg_idx = 0
         self.accumulated_content = initial_content or ''
         self.last_role = 'assistant'
         self.msg_sequence = msg_sequence
+        self.remove_think = remove_think
+        self._think_state = None
+        if remove_think:
+            from ..modelmgr.requesters.litellmchat import _ThinkStripState
+
+            self._think_state = _ThinkStripState()
 
     def add(self, msg: provider_message.MessageChunk) -> provider_message.MessageChunk | None:
         self.msg_idx += 1
@@ -63,7 +74,10 @@ class _StreamAccumulator:
             self.last_role = msg.role
 
         if msg.content:
-            self.accumulated_content += msg.content
+            content = msg.content
+            if self._think_state is not None:
+                content = self._think_state.feed(content)
+            self.accumulated_content += content
 
         if msg.tool_calls:
             for tool_call in msg.tool_calls:
@@ -79,11 +93,14 @@ class _StreamAccumulator:
                 if tool_call.function and tool_call.function.arguments:
                     self.tool_calls_map[tool_call.id].function.arguments += tool_call.function.arguments
 
+        if msg.is_final:
+            self._flush_think_state()
+
         if self.msg_idx % 8 == 0 or msg.is_final:
             self.msg_sequence += 1
             return provider_message.MessageChunk(
                 role=self.last_role,
-                content=self.accumulated_content,
+                content=self._maybe_strip_think(self.accumulated_content),
                 tool_calls=list(self.tool_calls_map.values()) if (self.tool_calls_map and msg.is_final) else None,
                 is_final=msg.is_final,
                 msg_sequence=self.msg_sequence,
@@ -92,13 +109,29 @@ class _StreamAccumulator:
         return None
 
     def final_message(self) -> provider_message.MessageChunk:
+        self._flush_think_state()
         return provider_message.MessageChunk(
             role=self.last_role,
-            content=self.accumulated_content,
+            content=self._maybe_strip_think(self.accumulated_content),
             tool_calls=list(self.tool_calls_map.values()) if self.tool_calls_map else None,
             msg_sequence=self.msg_sequence,
         )
 
+    def _maybe_strip_think(self, content: str) -> str:
+        if not self.remove_think or not content:
+            return content
+
+        from ..modelmgr.requesters.litellmchat import LiteLLMRequester
+
+        return LiteLLMRequester._strip_think(content)
+
+    def _flush_think_state(self) -> None:
+        if self._think_state is None:
+            return
+        pending = self._think_state.flush()
+        if pending:
+            self.accumulated_content += pending
+
 
 @runner.runner_class('local-agent')
 class LocalAgentRunner(runner.RequestRunner):
@@ -448,7 +481,7 @@ class LocalAgentRunner(runner.RequestRunner):
         except AttributeError:
             is_stream = False
 
-        remove_think = query.pipeline_config['output'].get('misc', '').get('remove-think')
+        remove_think = ((query.pipeline_config.get('output') or {}).get('misc') or {}).get('remove-think', False)
 
         # Build ordered candidate list (primary + fallbacks)
         candidates = await self._get_model_candidates(query)
@@ -472,7 +505,7 @@ class LocalAgentRunner(runner.RequestRunner):
             final_msg = msg
         else:
             # Streaming: invoke with fallback
-            stream_accumulator = _StreamAccumulator(msg_sequence=1)
+            stream_accumulator = _StreamAccumulator(msg_sequence=1, remove_think=remove_think)
 
             stream_src, use_llm_model = await self._invoke_stream_with_fallback(
                 query,
@@ -576,6 +609,7 @@ class LocalAgentRunner(runner.RequestRunner):
                 stream_accumulator = _StreamAccumulator(
                     msg_sequence=first_end_sequence,
                     initial_content=first_content,
+                    remove_think=remove_think,
                 )
 
                 tool_stream_src = use_llm_model.provider.invoke_llm_stream(

tests/unit_tests/provider/test_litellmchat.py

@@ -279,6 +279,122 @@ class TestInvokeLLMStreamUsage:
 
         assert query.variables['_stream_usage']['total_tokens'] == 12
 
+    @pytest.mark.asyncio
+    async def test_stream_removes_leading_think_across_chunks(self):
+        """A leading think block split across chunks must be removed."""
+        import langbot_plugin.api.entities.builtin.pipeline.query as pipeline_query
+        import langbot_plugin.api.entities.builtin.provider.message as provider_message
+
+        mock_ap = Mock()
+        mock_ap.tool_mgr = Mock()
+        mock_ap.tool_mgr.generate_tools_for_openai = AsyncMock(return_value=None)
+        requester = litellmchat.LiteLLMRequester(ap=mock_ap, config={})
+        model = MockRuntimeModel('minimax-m3', 'test-api-key')
+
+        chunks = [
+            self._make_chunk(content='<thi'),
+            self._make_chunk(content='nk>hidden'),
+            self._make_chunk(content=' reasoning</thi'),
+            self._make_chunk(content='nk>Visible answer', finish_reason='stop'),
+        ]
+
+        async def _aiter(*args, **kwargs):
+            for c in chunks:
+                yield c
+
+        query = Mock(spec=pipeline_query.Query)
+        query.variables = {}
+        messages = [provider_message.Message(role='user', content='Hi')]
+
+        with patch.object(litellmchat, 'acompletion', new=AsyncMock(side_effect=lambda **kw: _aiter())):
+            collected = [
+                chunk
+                async for chunk in requester.invoke_llm_stream(
+                    query=query,
+                    model=model,
+                    messages=messages,
+                    remove_think=True,
+                )
+            ]
+
+        assert ''.join(chunk.content or '' for chunk in collected) == 'Visible answer'
+
+    @pytest.mark.asyncio
+    async def test_stream_removes_initial_orphan_think_close(self):
+        """Initial reasoning content without an open tag is removed until </think>."""
+        import langbot_plugin.api.entities.builtin.pipeline.query as pipeline_query
+        import langbot_plugin.api.entities.builtin.provider.message as provider_message
+
+        mock_ap = Mock()
+        mock_ap.tool_mgr = Mock()
+        mock_ap.tool_mgr.generate_tools_for_openai = AsyncMock(return_value=None)
+        requester = litellmchat.LiteLLMRequester(ap=mock_ap, config={})
+        model = MockRuntimeModel('minimax-m3', 'test-api-key')
+
+        chunks = [
+            self._make_chunk(content='hidden reasoning'),
+            self._make_chunk(content=' still hidden</thi'),
+            self._make_chunk(content='nk>Visible answer', finish_reason='stop'),
+        ]
+
+        async def _aiter(*args, **kwargs):
+            for c in chunks:
+                yield c
+
+        query = Mock(spec=pipeline_query.Query)
+        query.variables = {}
+        messages = [provider_message.Message(role='user', content='Hi')]
+
+        with patch.object(litellmchat, 'acompletion', new=AsyncMock(side_effect=lambda **kw: _aiter())):
+            collected = [
+                chunk
+                async for chunk in requester.invoke_llm_stream(
+                    query=query,
+                    model=model,
+                    messages=messages,
+                    remove_think=True,
+                )
+            ]
+
+        assert ''.join(chunk.content or '' for chunk in collected) == 'Visible answer'
+
+    @pytest.mark.asyncio
+    async def test_stream_removes_non_leading_think_content(self):
+        """A think block in the answer body is removed with its content."""
+        import langbot_plugin.api.entities.builtin.pipeline.query as pipeline_query
+        import langbot_plugin.api.entities.builtin.provider.message as provider_message
+
+        mock_ap = Mock()
+        mock_ap.tool_mgr = Mock()
+        mock_ap.tool_mgr.generate_tools_for_openai = AsyncMock(return_value=None)
+        requester = litellmchat.LiteLLMRequester(ap=mock_ap, config={})
+        model = MockRuntimeModel('gpt-4o', 'test-api-key')
+
+        chunks = [
+            self._make_chunk(content='Use <think>x</think> as an XML-like example.', finish_reason='stop'),
+        ]
+
+        async def _aiter(*args, **kwargs):
+            for c in chunks:
+                yield c
+
+        query = Mock(spec=pipeline_query.Query)
+        query.variables = {}
+        messages = [provider_message.Message(role='user', content='Hi')]
+
+        with patch.object(litellmchat, 'acompletion', new=AsyncMock(side_effect=lambda **kw: _aiter())):
+            collected = [
+                chunk
+                async for chunk in requester.invoke_llm_stream(
+                    query=query,
+                    model=model,
+                    messages=messages,
+                    remove_think=True,
+                )
+            ]
+
+        assert ''.join(chunk.content or '' for chunk in collected) == 'Use  as an XML-like example.'
+
     @pytest.mark.asyncio
     async def test_stream_tool_call_delta_missing_id_and_name(self):
         """LiteLLM may stream tool-call argument deltas with id/name set to None."""
@@ -482,6 +598,38 @@ class TestProcessThinkingContent:
         result = requester._process_thinking_content(content, None, remove_think=True)
         assert result == 'The answer is 42.'
 
+    def test_remove_leading_think_tag(self):
+        """Test removing a leading <think> block when remove_think=True"""
+        requester = litellmchat.LiteLLMRequester(ap=Mock(), config={})
+
+        content = '<think>Let me think...</think> The answer is 42.'
+        result = requester._process_thinking_content(content, None, remove_think=True)
+        assert result == 'The answer is 42.'
+
+    def test_remove_non_leading_think_tag(self):
+        """Test removing <think> and its content in the answer body"""
+        requester = litellmchat.LiteLLMRequester(ap=Mock(), config={})
+
+        content = 'Use <think>example</think> in the document.'
+        result = requester._process_thinking_content(content, None, remove_think=True)
+        assert result == 'Use  in the document.'
+
+    def test_remove_initial_orphan_think_close(self):
+        """Test removing leading reasoning content when only </think> is visible"""
+        requester = litellmchat.LiteLLMRequester(ap=Mock(), config={})
+
+        content = 'hidden reasoning</think> Visible answer.'
+        result = requester._process_thinking_content(content, None, remove_think=True)
+        assert result == 'Visible answer.'
+
+    def test_remove_multiple_think_tags(self):
+        """Test removing multiple <think> blocks"""
+        requester = litellmchat.LiteLLMRequester(ap=Mock(), config={})
+
+        content = '<think>hidden</think> Keep <think>example</think>.'
+        result = requester._process_thinking_content(content, None, remove_think=True)
+        assert result == 'Keep .'
+
     def test_preserve_thinking_markers(self):
         """Test preserving thinking markers when remove_think=False"""
         requester = litellmchat.LiteLLMRequester(ap=Mock(), config={})
@@ -491,6 +639,20 @@ class TestProcessThinkingContent:
         assert 'CRETIRE_REASONING_BEGINk' in result
         assert 'The answer is 42.' in result
 
+    def test_preserve_reasoning_content_when_remove_think_false(self):
+        """Test showing separate reasoning_content when remove_think=False"""
+        requester = litellmchat.LiteLLMRequester(ap=Mock(), config={})
+
+        result = requester._process_thinking_content('The answer is 42.', 'Let me think...', remove_think=False)
+        assert result == '<think>\nLet me think...\n</think>\nThe answer is 42.'
+
+    def test_hide_reasoning_content_when_remove_think_true(self):
+        """Test hiding separate reasoning_content when remove_think=True"""
+        requester = litellmchat.LiteLLMRequester(ap=Mock(), config={})
+
+        result = requester._process_thinking_content('The answer is 42.', 'Let me think...', remove_think=True)
+        assert result == 'The answer is 42.'
+
     def test_empty_content(self):
         """Test empty content"""
         requester = litellmchat.LiteLLMRequester(ap=Mock(), config={})

tests/unit_tests/provider/test_localagent_sandbox_exec.py

@@ -163,6 +163,51 @@ def test_stream_accumulator_merges_fragmented_tool_call_arguments():
     assert final_msg.tool_calls[0].function.arguments == '{"command":"pwd"}'
 
 
+def test_stream_accumulator_strips_leading_think_from_tool_round_content():
+    accumulator = _StreamAccumulator(
+        msg_sequence=3,
+        initial_content='I will search for LangBot.',
+        remove_think=True,
+    )
+
+    assert accumulator.add(provider_message.MessageChunk(role='assistant', content='<thi')) is None
+    assert accumulator.add(provider_message.MessageChunk(role='assistant', content='nk>hidden')) is None
+    emitted = accumulator.add(
+        provider_message.MessageChunk(
+            role='assistant',
+            content=' reasoning</think>Here is the answer.',
+            is_final=True,
+        )
+    )
+
+    assert emitted is not None
+    assert emitted.content == 'I will search for LangBot.Here is the answer.'
+    assert '<think>' not in emitted.content
+    assert 'hidden reasoning' not in emitted.content
+
+
+def test_stream_accumulator_strips_initial_orphan_think_close_from_tool_round_content():
+    accumulator = _StreamAccumulator(
+        msg_sequence=3,
+        initial_content='I will search for LangBot.',
+        remove_think=True,
+    )
+
+    assert accumulator.add(provider_message.MessageChunk(role='assistant', content='hidden reasoning')) is None
+    emitted = accumulator.add(
+        provider_message.MessageChunk(
+            role='assistant',
+            content=' still hidden</think>Here is the answer.',
+            is_final=True,
+        )
+    )
+
+    assert emitted is not None
+    assert emitted.content == 'I will search for LangBot.Here is the answer.'
+    assert '</think>' not in emitted.content
+    assert 'hidden reasoning' not in emitted.content
+
+
 @pytest.mark.asyncio
 async def test_localagent_uses_exec_for_exact_calculation():
     provider = RecordingProvider()