GuardrailsAI: use validatedOutput to allow usage of "fix" guards. Previously "fix" guards had no effect in llmOutput mode. (#12891)

docs/my-website/docs/proxy/guardrails/guardrails_ai.md

@@ -25,9 +25,10 @@ guardrails:
   - guardrail_name: "guardrails_ai-guard"
     litellm_params:
       guardrail: guardrails_ai
-      guard_name: "gibberish_guard" # 👈 Guardrail AI guard name
-      mode: "post_call"
-      api_base: os.environ/GUARDRAILS_AI_API_BASE # 👈 Guardrails AI API Base. Defaults to "http://0.0.0.0:8000"
+      guard_name: "detect-secrets-guard"            # 👈 Guardrail AI guard name
+      mode: "pre_call"
+      guardrails_ai_api_input_format: "llmOutput"   # 👈 This is the only option that currently works (and it is a default), use it for both pre_call and post_call hooks
+      api_base: os.environ/GUARDRAILS_AI_API_BASE   # 👈 Guardrails AI API Base. Defaults to "http://0.0.0.0:8000"
 ```
 
 2. Start LiteLLM Gateway 

litellm/proxy/guardrails/guardrail_hooks/guardrails_ai/guardrails_ai.py

@@ -121,6 +121,10 @@ class GuardrailsAI(CustomGuardrail):
     ) -> str:
         from httpx import URL
 
+        # This branch of code does not work with current version of GuardrailsAI API (as of July 2025), and it is unclear if it ever worked. 
+        # Use guardrails_ai_api_input_format: "llmOutput" config line for all guardrails (which is the default anyway)
+        # We can still use the "pre_call" mode to validate the inputs even if the API input format is technicallt "llmOutput"
+
         data = {
             "inputs": [
                 {
@@ -180,7 +184,7 @@ class GuardrailsAI(CustomGuardrail):
                 _result = await self.make_guardrails_ai_api_request(
                     llm_output=text, request_data=data
                 )
-                updated_text = _result.get("rawLlmOutput") or text
+                updated_text = _result.get("validatedOutput") or _result.get("rawLlmOutput") or text
             data["messages"] = set_last_user_message(data["messages"], updated_text)
 
         return data

tests/test_litellm/proxy/guardrails/guardrail_hooks/guardrails_ai/test_guardrails_ai.py

@@ -133,3 +133,62 @@ async def test_guardrails_ai_process_input():
         assert result["messages"][1]["content"] == "First question"
         assert result["messages"][2]["content"] == "First answer"
         assert result["messages"][3]["content"] == "sanitized message"
+
+    # Test case 7: Test validatedOutput preference over rawLlmOutput
+    with patch.object(
+        guardrails_ai_guardrail,
+        "make_guardrails_ai_api_request",
+        return_value=GuardrailsAIResponse(
+            rawLlmOutput="Somtimes I hav spelling errors in my vriting",
+            validatedOutput="Sometimes I have spelling errors in my writing",
+            validationPassed=True,
+            callId="test-123",
+        ),
+    ) as mock_api_request:
+
+        data = {
+            "messages": [
+                {"role": "user", "content": "Somtimes I hav spelling errors in my vriting"}
+            ]
+        }
+
+        result = await guardrails_ai_guardrail.process_input(data, "completion")
+
+        mock_api_request.assert_called_once_with(
+            llm_output="Somtimes I hav spelling errors in my vriting", request_data=data
+        )
+
+        # Should use validatedOutput when available
+        assert result["messages"][0]["content"] == "Sometimes I have spelling errors in my writing"
+
+    # Test case 8: Test fallback to rawLlmOutput when validatedOutput is not present
+    with patch.object(
+        guardrails_ai_guardrail,
+        "make_guardrails_ai_api_request",
+        return_value=GuardrailsAIResponse(
+            rawLlmOutput="fallback text",
+            validatedOutput="",  # Empty validatedOutput
+            validationPassed=True,
+            callId="test-456",
+        ),
+    ) as mock_api_request:
+
+        data = {"messages": [{"role": "user", "content": "Test message"}]}
+
+        result = await guardrails_ai_guardrail.process_input(data, "completion")
+
+        assert result["messages"][0]["content"] == "fallback text"
+
+    # Test case 9: Test fallback to original text when neither validatedOutput nor rawLlmOutput is present
+    with patch.object(
+        guardrails_ai_guardrail,
+        "make_guardrails_ai_api_request",
+        return_value={},  # Empty response
+    ) as mock_api_request:
+
+        data = {"messages": [{"role": "user", "content": "Original message"}]}
+
+        result = await guardrails_ai_guardrail.process_input(data, "completion")
+
+        # Should keep original content when no output fields are present
+        assert result["messages"][0]["content"] == "Original message"