fix(llm): preserve tool_calls when response also contains text

lib/crewai/src/crewai/llm.py

@@ -1235,8 +1235,12 @@ class LLM(BaseLLM):
         # --- 4) Check for tool calls
         tool_calls = response_message.tool_calls or []
 
-        # --- 5) If no tool calls or no available functions, return the text response directly as long as there is a text response
-        if (not tool_calls or not available_functions) and text_response:
+        # --- 5) If there are tool calls but no available functions, return the tool calls
+        if tool_calls and not available_functions:
+            return tool_calls
+
+        # --- 6) If there are no tool calls to execute, return the text response directly
+        if not tool_calls and text_response:
             self._handle_emit_call_events(
                 response=text_response,
                 call_type=LLMCallType.LLM_CALL,
@@ -1247,11 +1251,6 @@ class LLM(BaseLLM):
             )
             return text_response
 
-        # --- 6) If there are tool calls but no available functions, return the tool calls
-        # This allows the caller (e.g., executor) to handle tool execution
-        if tool_calls and not available_functions:
-            return tool_calls
-
         # --- 7) Handle tool calls if present (execute when available_functions provided)
         if tool_calls and available_functions:
             tool_result = self._handle_tool_call(
@@ -1384,7 +1383,10 @@ class LLM(BaseLLM):
 
         tool_calls = response_message.tool_calls or []
 
-        if (not tool_calls or not available_functions) and text_response:
+        if tool_calls and not available_functions:
+            return tool_calls
+
+        if not tool_calls and text_response:
             self._handle_emit_call_events(
                 response=text_response,
                 call_type=LLMCallType.LLM_CALL,
@@ -1395,11 +1397,6 @@ class LLM(BaseLLM):
             )
             return text_response
 
-        # If there are tool calls but no available functions, return the tool calls
-        # This allows the caller (e.g., executor) to handle tool execution
-        if tool_calls and not available_functions:
-            return tool_calls
-
         # Handle tool calls if present (execute when available_functions provided)
         if tool_calls and available_functions:
             tool_result = self._handle_tool_call(

lib/crewai/tests/test_llm.py

@@ -177,6 +177,7 @@ def test_llm_passes_additional_params():
         # Create mocks for response structure
         mock_message = MagicMock()
         mock_message.content = "Test response"
+        mock_message.tool_calls = None
         mock_choice = MagicMock()
         mock_choice.message = mock_message
         mock_response = MagicMock()
@@ -1146,3 +1147,52 @@ async def test_usage_info_streaming_with_acall():
     assert llm._token_usage["total_tokens"] > 0
 
     assert len(result) > 0
+
+
+def _build_response_with_text_and_tool_calls():
+    """Mimic a litellm ModelResponse that contains both content and tool_calls."""
+    from litellm.types.utils import ChatCompletionMessageToolCall, Function
+
+    response_message = MagicMock()
+    response_message.content = "I will search for the given query."
+    response_message.tool_calls = [
+        ChatCompletionMessageToolCall(
+            id="call_123",
+            type="function",
+            function=Function(name="search", arguments='{"q": "x"}'),
+        )
+    ]
+    choice = MagicMock(message=response_message)
+    response = MagicMock(choices=[choice], model_extra=None)
+    return response
+
+
+def test_non_streaming_returns_tool_calls_when_text_also_present():
+    """A response with both text and tool_calls must not drop the tool_calls
+    when available_functions is None (executor-managed tool execution path).
+    """
+    llm = LLM(model="gpt-4o-mini", is_litellm=True)
+    response = _build_response_with_text_and_tool_calls()
+
+    with patch("crewai.llm.litellm.completion", return_value=response):
+        result = llm.call("anything", available_functions=None)
+
+    assert isinstance(result, list)
+    assert len(result) == 1
+    assert result[0].function.name == "search"
+
+
+@pytest.mark.asyncio
+async def test_non_streaming_async_returns_tool_calls_when_text_also_present():
+    llm = LLM(model="openai/gpt-4o-mini", is_litellm=True, stream=False)
+    response = _build_response_with_text_and_tool_calls()
+
+    async def _ret(*args, **kwargs):
+        return response
+
+    with patch("crewai.llm.litellm.acompletion", side_effect=_ret):
+        result = await llm.acall("anything", available_functions=None)
+
+    assert isinstance(result, list)
+    assert len(result) == 1
+    assert result[0].function.name == "search"