Gracefully handle empty outputs

Author: steven10a

src/guardrails/checks/text/llm_base.py

@@ -386,8 +386,10 @@ async def run_llm(
 
         result = response.choices[0].message.content
         if not result:
+            # Use base LLMOutput for empty responses to avoid validation errors
+            # with extended models that have required fields (e.g., LLMReasoningOutput)
             return (
-                output_model(
+                LLMOutput(
                     flagged=False,
                     confidence=0.0,
                 ),

tests/unit/checks/test_llm_base.py

@@ -301,3 +301,40 @@ async def fake_run_llm(
     assert "reason" not in result.info  # noqa: S101
     assert result.info["flagged"] is True  # noqa: S101
     assert result.info["confidence"] == 0.8  # noqa: S101
+
+
+@pytest.mark.asyncio
+async def test_run_llm_handles_empty_response_with_reasoning_output(monkeypatch: pytest.MonkeyPatch) -> None:
+    """When response content is empty, should return base LLMOutput even if output_model is LLMReasoningOutput."""
+    from types import SimpleNamespace
+
+    from guardrails.checks.text.llm_base import LLMReasoningOutput, run_llm
+
+    # Mock response with empty content
+    mock_response = SimpleNamespace(
+        choices=[SimpleNamespace(message=SimpleNamespace(content=""))],
+        usage=SimpleNamespace(prompt_tokens=10, completion_tokens=0, total_tokens=10),
+    )
+
+    async def fake_request_chat_completion(**kwargs: Any) -> Any:  # noqa: ARG001
+        return mock_response
+
+    monkeypatch.setattr(llm_base, "_request_chat_completion", fake_request_chat_completion)
+
+    # Call run_llm with LLMReasoningOutput (which requires a reason field)
+    result, token_usage = await run_llm(
+        text="test input",
+        system_prompt="test prompt",
+        client=SimpleNamespace(),  # type: ignore[arg-type]
+        model="gpt-test",
+        output_model=LLMReasoningOutput,
+    )
+
+    # Should return LLMOutput (not LLMReasoningOutput) to avoid validation error
+    assert isinstance(result, LLMOutput)  # noqa: S101
+    assert result.flagged is False  # noqa: S101
+    assert result.confidence == 0.0  # noqa: S101
+    # Should NOT have a reason field since we returned base LLMOutput
+    assert not hasattr(result, "reason") or not hasattr(result, "__dict__") or "reason" not in result.__dict__  # noqa: S101
+    assert token_usage.prompt_tokens == 10  # noqa: S101
+    assert token_usage.completion_tokens == 0  # noqa: S101