diff --git a/examples/experimental/litellm/src/switchyard_litellm/client.py b/examples/experimental/litellm/src/switchyard_litellm/client.py
index dd83b5b0a..277fbe6fa 100644
--- a/examples/experimental/litellm/src/switchyard_litellm/client.py
+++ b/examples/experimental/litellm/src/switchyard_litellm/client.py
@@ -300,7 +300,7 @@ def _response(response: ModelResponse) -> dict[str, object]:
             "name": tool_call.function.name,
             "arguments": arguments,
         })
-    if not content:
+    if not content and choice.finish_reason != "content_filter":
         raise ValueError("LiteLLM returned no text content")
     return {
         "id": response.id,
diff --git a/examples/experimental/litellm/tests/test_client.py b/examples/experimental/litellm/tests/test_client.py
index 157b35c05..0f7e6555a 100644
--- a/examples/experimental/litellm/tests/test_client.py
+++ b/examples/experimental/litellm/tests/test_client.py
@@ -15,6 +15,7 @@
 from litellm.exceptions import (
     ContextWindowExceededError as LiteLLMContextWindowExceededError,
 )
+from litellm.types.utils import Choices, Message, Usage
 from switchyard_litellm import LiteLLMSyClient
 
 from switchyard.libsy import ContextWindowExceededError
@@ -582,3 +583,46 @@ async def test_cached_token_count_preserves_explicit_zero() -> None:
         await client.aclose()
 
     assert response["usage"]["cached_input_tokens"] == 0
+
+
+def test_response_content_filter_empty_content() -> None:
+    """Empty content with a content_filter finish reason must be normalized."""
+    from switchyard_litellm.client import _response
+
+    response = ModelResponse(
+        id="chatcmpl-test",
+        model="openai/strong",
+        choices=[
+            Choices(
+                message=Message(content=None, tool_calls=None),
+                finish_reason="content_filter",
+            )
+        ],
+        usage=Usage(prompt_tokens=0, completion_tokens=0, total_tokens=0),
+    )
+    result = _response(response)
+    outputs = result["outputs"]
+    assert isinstance(outputs, list)
+    output = outputs[0]
+    assert isinstance(output, dict)
+    assert output["content"] == []
+    assert output["stop_reason"] == "content_filter"
+
+
+def test_response_empty_content_without_content_filter_raises() -> None:
+    """Empty content with any other finish reason must still raise."""
+    from switchyard_litellm.client import _response
+
+    response = ModelResponse(
+        id="chatcmpl-test",
+        model="openai/strong",
+        choices=[
+            Choices(
+                message=Message(content=None, tool_calls=None),
+                finish_reason="stop",
+            )
+        ],
+        usage=Usage(prompt_tokens=0, completion_tokens=0, total_tokens=0),
+    )
+    with pytest.raises(ValueError, match="LiteLLM returned no text content"):
+        _response(response)