feat(advisor): wire MessagesInterceptor registry into anthropic_messages handler

litellm/llms/anthropic/experimental_pass_through/messages/handler.py

@@ -26,6 +26,7 @@ from litellm.utils import ProviderConfigManager, client
 
 from ..adapters.handler import LiteLLMMessagesToCompletionTransformationHandler
 from ..responses_adapters.handler import LiteLLMMessagesToResponsesAPIHandler
+from .interceptors import get_messages_interceptors
 from .utils import AnthropicMessagesRequestUtils, mock_response
 
 # Providers that are routed directly to the OpenAI Responses API instead of
@@ -236,6 +237,23 @@ async def anthropic_messages(
     if short_circuit_response is not None:
         return short_circuit_response
 
+    # Run registered MessagesInterceptors (e.g. advisor orchestration loop).
+    # api_key and api_base are explicit params (not in **kwargs) so pass them
+    # explicitly so interceptor sub-calls can route to the same backend.
+    for interceptor in get_messages_interceptors():
+        if interceptor.can_handle(tools, custom_llm_provider):
+            return await interceptor.handle(
+                model=model,
+                messages=messages,
+                tools=tools,
+                stream=original_stream,
+                max_tokens=max_tokens,
+                custom_llm_provider=custom_llm_provider,
+                api_key=api_key,
+                api_base=api_base,
+                **kwargs,
+            )
+
     loop = asyncio.get_event_loop()
     kwargs["is_async"] = True