Fixed an issue in GoogleLLMContext where it would inject the system_message as a "user" message into cases where it was not meant to; it was only meant to do that when there were no "regular" (non-function-call) messages in the context, to ensure that inference would run properly.

CHANGELOG.md

@@ -17,6 +17,11 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 - Fixed an issue where, in some edge cases, the `EmulateUserStartedSpeakingFrame`
   could be created even if we didn't have a transcription.
 
+- Fixed an issue in `GoogleLLMContext` where it would inject the
+  `system_message` as a "user" message into cases where it was not meant to;
+  it was only meant to do that when there were no "regular" (non-function-call)
+  messages in the context, to ensure that inference would run properly.
+
 ## [0.0.76] - 2025-07-11
 
 ### Added

src/pipecat/services/google/llm.py

@@ -627,9 +627,9 @@ class GoogleLLMContext(OpenAILLMContext):
         # Check if we only have function-related messages (no regular text)
         has_regular_messages = any(
             len(msg.parts) == 1
-            and not getattr(msg.parts[0], "text", None)
-            and getattr(msg.parts[0], "function_call", None)
-            and getattr(msg.parts[0], "function_response", None)
+            and getattr(msg.parts[0], "text", None)
+            and not getattr(msg.parts[0], "function_call", None)
+            and not getattr(msg.parts[0], "function_response", None)
             for msg in self._messages
         )