fix: use _extract_stop_reason to safely handle non-string stop_reason values

MagicMock objects (and other non-Anthropic responses) can return non-string
values for getattr(response, 'stop_reason', None). Add a typed extraction
helper that returns None unless the value is actually a string, preventing
Pydantic validation errors in LLMCallCompletedEvent.

Co-Authored-By: João <joao@crewai.com>

lib/crewai/src/crewai/events/types/llm_events.py

@@ -57,6 +57,7 @@ class LLMCallCompletedEvent(LLMEventBase):
     messages: str | list[dict[str, Any]] | None = None
     response: Any
     call_type: LLMCallType
+    stop_reason: str | None = None
 
 
 class LLMCallFailedEvent(LLMEventBase):

lib/crewai/src/crewai/llms/base_llm.py

@@ -412,6 +412,7 @@ class BaseLLM(ABC):
         from_task: Task | None = None,
         from_agent: Agent | None = None,
         messages: str | list[LLMMessage] | None = None,
+        stop_reason: str | None = None,
     ) -> None:
         """Emit LLM call completed event."""
         from crewai.utilities.serialization import to_serializable
@@ -426,6 +427,7 @@ class BaseLLM(ABC):
                 from_agent=from_agent,
                 model=self.model,
                 call_id=get_current_call_id(),
+                stop_reason=stop_reason,
             ),
         )
 

lib/crewai/src/crewai/llms/providers/anthropic/completion.py

@@ -676,6 +676,26 @@ class AnthropicCompletion(BaseLLM):
 
         return converted
 
+    @staticmethod
+    def _extract_stop_reason(response: Message | BetaMessage) -> str | None:
+        """Extract stop_reason from an Anthropic response, returning None if absent."""
+        raw = getattr(response, "stop_reason", None)
+        return raw if isinstance(raw, str) else None
+
+    def _warn_if_truncated(
+        self,
+        response: Message | BetaMessage,
+        from_agent: Any | None = None,
+    ) -> None:
+        """Log a warning if the response was truncated due to max_tokens."""
+        stop_reason = self._extract_stop_reason(response)
+        if stop_reason == "max_tokens":
+            agent_hint = f" [{from_agent.role}]" if from_agent else ""
+            logging.warning(
+                f"Truncated response{agent_hint}: stop_reason='max_tokens'. "
+                f"Consider increasing max_tokens (current: {self.max_tokens})."
+            )
+
     def _format_messages_for_anthropic(
         self, messages: str | list[LLMMessage]
     ) -> tuple[list[LLMMessage], str | None]:
@@ -858,6 +878,9 @@ class AnthropicCompletion(BaseLLM):
         usage = self._extract_anthropic_token_usage(response)
         self._track_token_usage_internal(usage)
 
+        stop_reason = self._extract_stop_reason(response)
+        self._warn_if_truncated(response, from_agent)
+
         if _is_pydantic_model_class(response_model) and response.content:
             if use_native_structured_output:
                 for block in response.content:
@@ -869,6 +892,7 @@ class AnthropicCompletion(BaseLLM):
                             from_task=from_task,
                             from_agent=from_agent,
                             messages=params["messages"],
+                            stop_reason=stop_reason,
                         )
                         return structured_data
             else:
@@ -884,6 +908,7 @@ class AnthropicCompletion(BaseLLM):
                             from_task=from_task,
                             from_agent=from_agent,
                             messages=params["messages"],
+                            stop_reason=stop_reason,
                         )
                         return structured_data
 
@@ -906,6 +931,7 @@ class AnthropicCompletion(BaseLLM):
                         from_task=from_task,
                         from_agent=from_agent,
                         messages=params["messages"],
+                        stop_reason=stop_reason,
                     )
                     return list(tool_uses)
 
@@ -937,6 +963,7 @@ class AnthropicCompletion(BaseLLM):
             from_task=from_task,
             from_agent=from_agent,
             messages=params["messages"],
+            stop_reason=stop_reason,
         )
 
         if usage.get("total_tokens", 0) > 0:
@@ -1077,6 +1104,9 @@ class AnthropicCompletion(BaseLLM):
         usage = self._extract_anthropic_token_usage(final_message)
         self._track_token_usage_internal(usage)
 
+        stop_reason = self._extract_stop_reason(final_message)
+        self._warn_if_truncated(final_message, from_agent)
+
         if _is_pydantic_model_class(response_model):
             if use_native_structured_output:
                 structured_data = response_model.model_validate_json(full_response)
@@ -1086,6 +1116,7 @@ class AnthropicCompletion(BaseLLM):
                     from_task=from_task,
                     from_agent=from_agent,
                     messages=params["messages"],
+                    stop_reason=stop_reason,
                 )
                 return structured_data
             for block in final_message.content:
@@ -1100,6 +1131,7 @@ class AnthropicCompletion(BaseLLM):
                         from_task=from_task,
                         from_agent=from_agent,
                         messages=params["messages"],
+                        stop_reason=stop_reason,
                     )
                     return structured_data
 
@@ -1129,6 +1161,7 @@ class AnthropicCompletion(BaseLLM):
             from_task=from_task,
             from_agent=from_agent,
             messages=params["messages"],
+            stop_reason=stop_reason,
         )
 
         return self._invoke_after_llm_call_hooks(
@@ -1275,6 +1308,9 @@ class AnthropicCompletion(BaseLLM):
             follow_up_usage = self._extract_anthropic_token_usage(final_response)
             self._track_token_usage_internal(follow_up_usage)
 
+            stop_reason = self._extract_stop_reason(final_response)
+            self._warn_if_truncated(final_response, from_agent)
+
             final_content = ""
             thinking_blocks: list[ThinkingBlock] = []
 
@@ -1299,6 +1335,7 @@ class AnthropicCompletion(BaseLLM):
                 from_task=from_task,
                 from_agent=from_agent,
                 messages=follow_up_params["messages"],
+                stop_reason=stop_reason,
             )
 
             # Log combined token usage
@@ -1379,6 +1416,9 @@ class AnthropicCompletion(BaseLLM):
         usage = self._extract_anthropic_token_usage(response)
         self._track_token_usage_internal(usage)
 
+        stop_reason = self._extract_stop_reason(response)
+        self._warn_if_truncated(response, from_agent)
+
         if _is_pydantic_model_class(response_model) and response.content:
             if use_native_structured_output:
                 for block in response.content:
@@ -1390,6 +1430,7 @@ class AnthropicCompletion(BaseLLM):
                             from_task=from_task,
                             from_agent=from_agent,
                             messages=params["messages"],
+                            stop_reason=stop_reason,
                         )
                         return structured_data
             else:
@@ -1405,6 +1446,7 @@ class AnthropicCompletion(BaseLLM):
                             from_task=from_task,
                             from_agent=from_agent,
                             messages=params["messages"],
+                            stop_reason=stop_reason,
                         )
                         return structured_data
 
@@ -1425,6 +1467,7 @@ class AnthropicCompletion(BaseLLM):
                         from_task=from_task,
                         from_agent=from_agent,
                         messages=params["messages"],
+                        stop_reason=stop_reason,
                     )
                     return list(tool_uses)
 
@@ -1448,6 +1491,7 @@ class AnthropicCompletion(BaseLLM):
             from_task=from_task,
             from_agent=from_agent,
             messages=params["messages"],
+            stop_reason=stop_reason,
         )
 
         if usage.get("total_tokens", 0) > 0:
@@ -1576,6 +1620,9 @@ class AnthropicCompletion(BaseLLM):
         usage = self._extract_anthropic_token_usage(final_message)
         self._track_token_usage_internal(usage)
 
+        stop_reason = self._extract_stop_reason(final_message)
+        self._warn_if_truncated(final_message, from_agent)
+
         if _is_pydantic_model_class(response_model):
             if use_native_structured_output:
                 structured_data = response_model.model_validate_json(full_response)
@@ -1585,6 +1632,7 @@ class AnthropicCompletion(BaseLLM):
                     from_task=from_task,
                     from_agent=from_agent,
                     messages=params["messages"],
+                    stop_reason=stop_reason,
                 )
                 return structured_data
             for block in final_message.content:
@@ -1599,6 +1647,7 @@ class AnthropicCompletion(BaseLLM):
                         from_task=from_task,
                         from_agent=from_agent,
                         messages=params["messages"],
+                        stop_reason=stop_reason,
                     )
                     return structured_data
 
@@ -1627,6 +1676,7 @@ class AnthropicCompletion(BaseLLM):
             from_task=from_task,
             from_agent=from_agent,
             messages=params["messages"],
+            stop_reason=stop_reason,
         )
 
         return full_response
@@ -1671,6 +1721,9 @@ class AnthropicCompletion(BaseLLM):
             follow_up_usage = self._extract_anthropic_token_usage(final_response)
             self._track_token_usage_internal(follow_up_usage)
 
+            stop_reason = self._extract_stop_reason(final_response)
+            self._warn_if_truncated(final_response, from_agent)
+
             final_content = ""
             if final_response.content:
                 for content_block in final_response.content:
@@ -1685,6 +1738,7 @@ class AnthropicCompletion(BaseLLM):
                 from_task=from_task,
                 from_agent=from_agent,
                 messages=follow_up_params["messages"],
+                stop_reason=stop_reason,
             )
 
             total_usage = {

lib/crewai/tests/llms/anthropic/test_anthropic.py

@@ -1463,3 +1463,217 @@ def test_tool_search_saves_input_tokens():
         f"Expected tool_search ({usage_search.prompt_tokens}) to use fewer input tokens "
         f"than no search ({usage_no_search.prompt_tokens})"
     )
+
+
+def test_anthropic_warns_on_max_tokens_truncation():
+    """Test that a warning is logged when Anthropic response has stop_reason='max_tokens'."""
+    import logging
+
+    llm = LLM(model="anthropic/claude-3-5-sonnet-20241022")
+
+    with patch.object(llm.client.messages, "create") as mock_create:
+        mock_response = MagicMock()
+        mock_response.content = [MagicMock(text="truncated response")]
+        mock_response.content[0].type = "text"
+        mock_response.stop_reason = "max_tokens"
+        mock_response.usage = MagicMock(
+            input_tokens=100, output_tokens=4096, cache_read_input_tokens=0
+        )
+        mock_create.return_value = mock_response
+
+        with patch("crewai.llms.providers.anthropic.completion.logging") as mock_logging:
+            result = llm.call("Tell me a very long story")
+
+            assert result == "truncated response"
+            mock_logging.warning.assert_called_once()
+            warning_msg = mock_logging.warning.call_args[0][0]
+            assert "stop_reason='max_tokens'" in warning_msg
+            assert "Consider increasing max_tokens" in warning_msg
+
+
+def test_anthropic_no_warning_on_end_turn():
+    """Test that no truncation warning is logged when stop_reason is 'end_turn'."""
+    llm = LLM(model="anthropic/claude-3-5-sonnet-20241022")
+
+    with patch.object(llm.client.messages, "create") as mock_create:
+        mock_response = MagicMock()
+        mock_response.content = [MagicMock(text="complete response")]
+        mock_response.content[0].type = "text"
+        mock_response.stop_reason = "end_turn"
+        mock_response.usage = MagicMock(
+            input_tokens=50, output_tokens=25, cache_read_input_tokens=0
+        )
+        mock_create.return_value = mock_response
+
+        with patch("crewai.llms.providers.anthropic.completion.logging") as mock_logging:
+            result = llm.call("Hello")
+
+            assert result == "complete response"
+            mock_logging.warning.assert_not_called()
+
+
+def test_anthropic_truncation_warning_includes_agent_role():
+    """Test that the truncation warning includes the agent role when available."""
+    llm = LLM(model="anthropic/claude-3-5-sonnet-20241022")
+
+    mock_agent = MagicMock()
+    mock_agent.role = "Research Analyst"
+
+    with patch.object(llm.client.messages, "create") as mock_create:
+        mock_response = MagicMock()
+        mock_response.content = [MagicMock(text="truncated")]
+        mock_response.content[0].type = "text"
+        mock_response.stop_reason = "max_tokens"
+        mock_response.usage = MagicMock(
+            input_tokens=100, output_tokens=4096, cache_read_input_tokens=0
+        )
+        mock_create.return_value = mock_response
+
+        with patch("crewai.llms.providers.anthropic.completion.logging") as mock_logging:
+            llm.call("Tell me everything", from_agent=mock_agent)
+
+            mock_logging.warning.assert_called_once()
+            warning_msg = mock_logging.warning.call_args[0][0]
+            assert "[Research Analyst]" in warning_msg
+            assert "stop_reason='max_tokens'" in warning_msg
+
+
+def test_anthropic_stop_reason_in_completed_event():
+    """Test that stop_reason is included in the LLMCallCompletedEvent."""
+    from crewai.events.types.llm_events import LLMCallCompletedEvent
+    from crewai.events import crewai_event_bus
+
+    llm = LLM(model="anthropic/claude-3-5-sonnet-20241022")
+
+    captured_events = []
+
+    def capture_event(source, event):
+        if isinstance(event, LLMCallCompletedEvent):
+            captured_events.append(event)
+
+    crewai_event_bus.register_handler(LLMCallCompletedEvent, capture_event)
+
+    try:
+        with patch.object(llm.client.messages, "create") as mock_create:
+            mock_response = MagicMock()
+            mock_response.content = [MagicMock(text="truncated response")]
+            mock_response.content[0].type = "text"
+            mock_response.stop_reason = "max_tokens"
+            mock_response.usage = MagicMock(
+                input_tokens=100, output_tokens=4096, cache_read_input_tokens=0
+            )
+            mock_create.return_value = mock_response
+
+            llm.call("Tell me a long story")
+
+            crewai_event_bus.flush(timeout=5.0)
+
+            assert len(captured_events) >= 1
+            event = captured_events[-1]
+            assert event.stop_reason == "max_tokens"
+    finally:
+        crewai_event_bus.off(LLMCallCompletedEvent, capture_event)
+
+
+def test_anthropic_stop_reason_none_when_normal_completion():
+    """Test that stop_reason is None in event when response completes normally."""
+    from crewai.events.types.llm_events import LLMCallCompletedEvent
+    from crewai.events import crewai_event_bus
+
+    llm = LLM(model="anthropic/claude-3-5-sonnet-20241022")
+
+    captured_events = []
+
+    def capture_event(source, event):
+        if isinstance(event, LLMCallCompletedEvent):
+            captured_events.append(event)
+
+    crewai_event_bus.register_handler(LLMCallCompletedEvent, capture_event)
+
+    try:
+        with patch.object(llm.client.messages, "create") as mock_create:
+            mock_response = MagicMock()
+            mock_response.content = [MagicMock(text="complete response")]
+            mock_response.content[0].type = "text"
+            mock_response.stop_reason = "end_turn"
+            mock_response.usage = MagicMock(
+                input_tokens=50, output_tokens=25, cache_read_input_tokens=0
+            )
+            mock_create.return_value = mock_response
+
+            llm.call("Hello")
+
+            crewai_event_bus.flush(timeout=5.0)
+
+            assert len(captured_events) >= 1
+            event = captured_events[-1]
+            assert event.stop_reason == "end_turn"
+    finally:
+        crewai_event_bus.off(LLMCallCompletedEvent, capture_event)
+
+
+def test_anthropic_tool_use_conversation_warns_on_truncation():
+    """Test that _handle_tool_use_conversation warns when the follow-up response is truncated."""
+    from anthropic.types import ToolUseBlock, TextBlock
+
+    llm = LLM(model="anthropic/claude-3-5-sonnet-20241022")
+
+    # Mock initial response with tool use
+    mock_initial_response = MagicMock()
+    mock_tool_block = MagicMock(spec=ToolUseBlock)
+    mock_tool_block.type = "tool_use"
+    mock_tool_block.id = "tool_123"
+    mock_tool_block.name = "get_data"
+    mock_tool_block.input = {"query": "test"}
+    mock_initial_response.content = [mock_tool_block]
+
+    # Mock final response with max_tokens truncation
+    mock_final_response = MagicMock()
+    mock_text_block = MagicMock(spec=TextBlock)
+    mock_text_block.text = "truncated tool result"
+    mock_text_block.type = "text"
+    mock_final_response.content = [mock_text_block]
+    mock_final_response.stop_reason = "max_tokens"
+    mock_final_response.usage = MagicMock(
+        input_tokens=200, output_tokens=4096, cache_read_input_tokens=0
+    )
+
+    with patch.object(llm.client.messages, "create", return_value=mock_final_response):
+        with patch("crewai.llms.providers.anthropic.completion.logging") as mock_logging:
+
+            def mock_tool_fn(**kwargs):
+                return "tool output"
+
+            result = llm._handle_tool_use_conversation(
+                initial_response=mock_initial_response,
+                tool_uses=[mock_tool_block],
+                params={"messages": [{"role": "user", "content": "test"}], "model": "claude-3-5-sonnet-20241022", "max_tokens": 4096},
+                available_functions={"get_data": mock_tool_fn},
+            )
+
+            assert result == "truncated tool result"
+            mock_logging.warning.assert_called_once()
+            warning_msg = mock_logging.warning.call_args[0][0]
+            assert "stop_reason='max_tokens'" in warning_msg
+
+
+def test_llm_call_completed_event_has_stop_reason_field():
+    """Test that LLMCallCompletedEvent has the stop_reason field with correct default."""
+    from crewai.events.types.llm_events import LLMCallCompletedEvent, LLMCallType
+
+    # Default stop_reason should be None
+    event = LLMCallCompletedEvent(
+        response="test",
+        call_type=LLMCallType.LLM_CALL,
+        call_id="test-id",
+    )
+    assert event.stop_reason is None
+
+    # stop_reason can be set
+    event_with_reason = LLMCallCompletedEvent(
+        response="test",
+        call_type=LLMCallType.LLM_CALL,
+        call_id="test-id",
+        stop_reason="max_tokens",
+    )
+    assert event_with_reason.stop_reason == "max_tokens"