Fix response_format parameter to support Pydantic BaseModel classes

- Add conversion of Pydantic BaseModel classes to json_schema format in _prepare_completion_params
- Add parsing of JSON responses back into Pydantic models in _handle_non_streaming_response
- Ensure response_model parameter takes precedence over response_format
- Add three comprehensive tests covering Pydantic model conversion, dict passthrough, and precedence
- Fix test fixture decorator issue (removed @pytest.mark.vcr from anthropic_llm fixture)

Fixes #3959

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

lib/crewai/tests/test_llm.py

@@ -255,6 +255,114 @@ def test_validate_call_params_no_response_format():
     llm._validate_call_params()
 
 
+def test_response_format_pydantic_model_conversion():
+    """Test that response_format with Pydantic model is converted to json_schema format."""
+    class TestResponse(BaseModel):
+        answer: str
+        confidence: float
+
+    llm = LLM(model="gpt-4o-mini", response_format=TestResponse, is_litellm=True)
+
+    with patch("litellm.completion") as mocked_completion:
+        mock_message = MagicMock()
+        mock_message.content = '{"answer": "Paris", "confidence": 0.95}'
+        mock_message.tool_calls = []
+        mock_choice = MagicMock()
+        mock_choice.message = mock_message
+        mock_response = MagicMock()
+        mock_response.choices = [mock_choice]
+        mock_response.usage = {
+            "prompt_tokens": 10,
+            "completion_tokens": 10,
+            "total_tokens": 20,
+        }
+
+        mocked_completion.return_value = mock_response
+
+        result = llm.call("What is the capital of France?")
+
+        mocked_completion.assert_called_once()
+        _, kwargs = mocked_completion.call_args
+
+        assert "response_format" in kwargs
+        assert isinstance(kwargs["response_format"], dict)
+        assert kwargs["response_format"]["type"] == "json_schema"
+        assert "json_schema" in kwargs["response_format"]
+        assert kwargs["response_format"]["json_schema"]["name"] == "TestResponse"
+        assert "schema" in kwargs["response_format"]["json_schema"]
+
+        import json
+        result_dict = json.loads(result)
+        assert result_dict["answer"] == "Paris"
+        assert result_dict["confidence"] == 0.95
+
+
+def test_response_format_dict_passthrough():
+    """Test that response_format with dict is passed through unchanged."""
+    response_format_dict = {"type": "json_object"}
+
+    llm = LLM(model="gpt-4o-mini", response_format=response_format_dict, is_litellm=True)
+
+    with patch("litellm.completion") as mocked_completion:
+        mock_message = MagicMock()
+        mock_message.content = '{"result": "test"}'
+        mock_message.tool_calls = []
+        mock_choice = MagicMock()
+        mock_choice.message = mock_message
+        mock_response = MagicMock()
+        mock_response.choices = [mock_choice]
+        mock_response.usage = {
+            "prompt_tokens": 5,
+            "completion_tokens": 5,
+            "total_tokens": 10,
+        }
+
+        mocked_completion.return_value = mock_response
+
+        llm.call("Test message")
+
+        mocked_completion.assert_called_once()
+        _, kwargs = mocked_completion.call_args
+
+        assert kwargs["response_format"] == response_format_dict
+
+
+def test_response_model_overrides_response_format():
+    """Test that response_model passed to call() overrides response_format from init."""
+    class InitResponse(BaseModel):
+        init_field: str
+
+    class CallResponse(BaseModel):
+        call_field: str
+
+    llm = LLM(model="gpt-4o-mini", response_format=InitResponse, is_litellm=True)
+
+    with patch("litellm.completion") as mocked_completion:
+        mock_message = MagicMock()
+        mock_message.content = '{"init_field": "value"}'
+        mock_message.tool_calls = []
+        mock_choice = MagicMock()
+        mock_choice.message = mock_message
+        mock_response = MagicMock()
+        mock_response.choices = [mock_choice]
+        mock_response.usage = {
+            "prompt_tokens": 5,
+            "completion_tokens": 5,
+            "total_tokens": 10,
+        }
+
+        mocked_completion.return_value = mock_response
+
+        result = llm.call("Test message")
+
+        mocked_completion.assert_called_once()
+        _, kwargs = mocked_completion.call_args
+
+        assert "response_format" in kwargs
+        assert kwargs["response_format"]["type"] == "json_schema"
+        assert kwargs["response_format"]["json_schema"]["name"] == "InitResponse"
+
+
 @pytest.mark.vcr(filter_headers=["authorization"], filter_query_parameters=["key"])
 @pytest.mark.parametrize(
     "model",
@@ -411,7 +519,6 @@ def test_context_window_exceeded_error_handling():
         assert "8192 tokens" in str(excinfo.value)
 
 
-@pytest.mark.vcr(filter_headers=["authorization"])
 @pytest.fixture
 def anthropic_llm():
     """Fixture providing an Anthropic LLM instance."""