diff --git a/src/pipecat/services/google/gemini_live/llm.py b/src/pipecat/services/google/gemini_live/llm.py
index 13b5fb18c..f61c9826c 100644
--- a/src/pipecat/services/google/gemini_live/llm.py
+++ b/src/pipecat/services/google/gemini_live/llm.py
@@ -1710,11 +1710,26 @@ class GeminiLiveLLMService(LLMService):
             await self.push_frame(TTSStartedFrame())
             await self.push_frame(LLMFullResponseStartFrame())
 
-        frame = TTSTextFrame(text=text, aggregated_by=AggregationType.SENTENCE)
-        # Gemini Live text already includes any necessary inter-chunk spaces
-        frame.includes_inter_frame_spaces = True
+        await self._push_output_transcription_text_frames(text)
 
-        await self.push_frame(frame)
+    async def _push_output_transcription_text_frames(self, text: str):
+        # In a typical "cascade" LLM + TTS setup, LLMTextFrames would not
+        # proceed beyond the TTS service. Therefore, since a speech-to-speech
+        # service like Gemini Live combines both LLM and TTS functionality, you
+        # might think we wouldn't need to push LLMTextFrames at all. However,
+        # RTVI relies on LLMTextFrames being pushed to trigger its
+        # "bot-llm-text" event. So here we push an LLMTextFrame, too, but avoid
+        # appending it to context to avoid context message duplication.
+
+        # Push LLMTextFrame
+        llm_text_frame = LLMTextFrame(text)
+        llm_text_frame.append_to_context = False
+        await self.push_frame(llm_text_frame)
+
+        # Push TTSTextFrame
+        tts_text_frame = TTSTextFrame(text, aggregated_by=AggregationType.SENTENCE)
+        tts_text_frame.includes_inter_frame_spaces = True
+        await self.push_frame(tts_text_frame)
 
     async def _handle_msg_grounding_metadata(self, message: LiveServerMessage):
         """Handle dedicated grounding metadata messages."""