Revert

- Add skip_tts as an init parameter for TextFrame, LLMFullResponseStartFrame,
  and LLMFullResponseEndFrame instead of setting it post-init
- Update all LLM services to pass skip_tts when creating frames:
  - Anthropic, AWS (Bedrock, Nova Sonic, AgentCore), Google (Gemini, Gemini Live)
  - OpenAI (base, realtime), OpenAI Realtime Beta, SambaNova
- Add _get_skip_tts() helper method in LLMService base class
- Remove push_frame override that was setting skip_tts after frame creation

src/pipecat/frames/frames.py

@@ -327,23 +327,19 @@ class TextFrame(DataFrame):
 
     Parameters:
         text: The text content.
+        skip_tts: Whether this text should skip TTS processing.
     """
 
     text: str
-    skip_tts: bool = field(init=False)
+    skip_tts: bool = field(default=False, kw_only=True)
     # Whether any necessary inter-frame (leading/trailing) spaces are already
     # included in the text.
-    # NOTE: Ideally this would be available at init time with a default value,
-    # but that would impact how subclasses can be initialized (it would require
-    # mandatory fields of theirs to have defaults to preserve
-    # non-default-before-default argument order)
     includes_inter_frame_spaces: bool = field(init=False)
     # Whether this text frame should be appended to the LLM context.
     append_to_context: bool = field(init=False)
 
     def __post_init__(self):
         super().__post_init__()
-        self.skip_tts = False
         self.includes_inter_frame_spaces = False
         self.append_to_context = True
 
@@ -1630,24 +1626,23 @@ class LLMFullResponseStartFrame(ControlFrame):
 
     Used to indicate the beginning of an LLM response. Followed by one or
     more TextFrames and a final LLMFullResponseEndFrame.
+
+    Parameters:
+        skip_tts: Whether LLM output should skip TTS processing.
     """
 
-    skip_tts: bool = field(init=False)
-
-    def __post_init__(self):
-        super().__post_init__()
-        self.skip_tts = False
+    skip_tts: bool = field(default=False, kw_only=True)
 
 
 @dataclass
 class LLMFullResponseEndFrame(ControlFrame):
-    """Frame indicating the end of an LLM response."""
+    """Frame indicating the end of an LLM response.
 
-    skip_tts: bool = field(init=False)
+    Parameters:
+        skip_tts: Whether LLM output should skip TTS processing.
+    """
 
-    def __post_init__(self):
-        super().__post_init__()
-        self.skip_tts = False
+    skip_tts: bool = field(default=False, kw_only=True)
 
 
 @dataclass

src/pipecat/services/anthropic/llm.py

@@ -327,7 +327,7 @@ class AnthropicLLMService(LLMService):
         cache_read_input_tokens = 0
 
         try:
-            await self.push_frame(LLMFullResponseStartFrame())
+            await self.push_frame(LLMFullResponseStartFrame(skip_tts=self._get_skip_tts()))
             await self.start_processing_metrics()
 
             params_from_context = self._get_llm_invocation_params(context)
@@ -373,7 +373,9 @@ class AnthropicLLMService(LLMService):
 
                 if event.type == "content_block_delta":
                     if hasattr(event.delta, "text"):
-                        await self.push_frame(LLMTextFrame(event.delta.text))
+                        await self.push_frame(
+                            LLMTextFrame(event.delta.text, skip_tts=self._get_skip_tts())
+                        )
                         completion_tokens_estimate += self._estimate_tokens(event.delta.text)
                     elif hasattr(event.delta, "partial_json") and tool_use_block:
                         json_accumulator += event.delta.partial_json
@@ -461,7 +463,7 @@ class AnthropicLLMService(LLMService):
             await self.push_error(error_msg=f"Unknown error occurred: {e}", exception=e)
         finally:
             await self.stop_processing_metrics()
-            await self.push_frame(LLMFullResponseEndFrame())
+            await self.push_frame(LLMFullResponseEndFrame(skip_tts=self._get_skip_tts()))
             comp_tokens = (
                 completion_tokens
                 if not use_completion_tokens_estimate

src/pipecat/services/aws/agent_core.py

@@ -172,7 +172,7 @@ class AWSAgentCoreProcessor(FrameProcessor):
         await asyncio.sleep(self._output_response_timeout)
         if self._output_response_open:
             self._output_response_open = False
-            await self.push_frame(LLMFullResponseEndFrame())
+            await self.push_frame(LLMFullResponseEndFrame(skip_tts=self._get_skip_tts()))
 
     async def _push_text_frame(self, text: str):
         """Push a text frame, managing output response bookends."""
@@ -182,11 +182,11 @@ class AWSAgentCoreProcessor(FrameProcessor):
 
         # Open output response if needed
         if not self._output_response_open:
-            await self.push_frame(LLMFullResponseStartFrame())
+            await self.push_frame(LLMFullResponseStartFrame(skip_tts=self._get_skip_tts()))
             self._output_response_open = True
 
         # Push the text frame
-        await self.push_frame(LLMTextFrame(text))
+        await self.push_frame(LLMTextFrame(text, skip_tts=self._get_skip_tts()))
         self._last_text_frame_time = asyncio.get_event_loop().time()
 
         # Schedule closing the output response after timeout
@@ -253,6 +253,6 @@ class AWSAgentCoreProcessor(FrameProcessor):
                     if self._close_task and not self._close_task.done():
                         await self.cancel_task(self._close_task)
                     self._output_response_open = False
-                    await self.push_frame(LLMFullResponseEndFrame())
+                    await self.push_frame(LLMFullResponseEndFrame(skip_tts=self._get_skip_tts()))
         else:
             await self.push_frame(frame, direction)

src/pipecat/services/aws/llm.py

@@ -981,7 +981,7 @@ class AWSBedrockLLMService(LLMService):
         using_noop_tool = False
 
         try:
-            await self.push_frame(LLMFullResponseStartFrame())
+            await self.push_frame(LLMFullResponseStartFrame(skip_tts=self._get_skip_tts()))
             await self.start_processing_metrics()
 
             await self.start_ttfb_metrics()
@@ -1078,7 +1078,9 @@ class AWSBedrockLLMService(LLMService):
                     if "contentBlockDelta" in event:
                         delta = event["contentBlockDelta"]["delta"]
                         if "text" in delta:
-                            await self.push_frame(LLMTextFrame(delta["text"]))
+                            await self.push_frame(
+                                LLMTextFrame(delta["text"], skip_tts=self._get_skip_tts())
+                            )
                             completion_tokens_estimate += self._estimate_tokens(delta["text"])
                         elif "toolUse" in delta and "input" in delta["toolUse"]:
                             # Handle partial JSON for tool use
@@ -1139,7 +1141,7 @@ class AWSBedrockLLMService(LLMService):
             await self.push_error(error_msg=f"Unknown error occurred: {e}", exception=e)
         finally:
             await self.stop_processing_metrics()
-            await self.push_frame(LLMFullResponseEndFrame())
+            await self.push_frame(LLMFullResponseEndFrame(skip_tts=self._get_skip_tts()))
             comp_tokens = (
                 completion_tokens
                 if not use_completion_tokens_estimate

src/pipecat/services/aws/nova_sonic/llm.py

@@ -1016,7 +1016,7 @@ class AWSNovaSonicLLMService(LLMService):
         logger.debug("Assistant response started")
 
         # Report the start of the assistant response.
-        await self.push_frame(LLMFullResponseStartFrame())
+        await self.push_frame(LLMFullResponseStartFrame(skip_tts=self._get_skip_tts()))
 
         # Report that equivalent of TTS (this is a speech-to-speech model) started
         await self.push_frame(TTSStartedFrame())
@@ -1062,7 +1062,7 @@ class AWSNovaSonicLLMService(LLMService):
                 # We also need to re-push the LLMFullResponseStartFrame since the
                 # TTSTextFrame would be ignored otherwise (the interruption frame
                 # would have cleared the assistant aggregator state).
-                await self.push_frame(LLMFullResponseStartFrame())
+                await self.push_frame(LLMFullResponseStartFrame(skip_tts=self._get_skip_tts()))
                 frame = TTSTextFrame(
                     self._assistant_text_buffer, aggregated_by=AggregationType.SENTENCE
                 )
@@ -1071,7 +1071,7 @@ class AWSNovaSonicLLMService(LLMService):
             self._may_need_repush_assistant_text = False
 
         # Report the end of the assistant response.
-        await self.push_frame(LLMFullResponseEndFrame())
+        await self.push_frame(LLMFullResponseEndFrame(skip_tts=self._get_skip_tts()))
 
         # Report that equivalent of TTS (this is a speech-to-speech model) stopped.
         await self.push_frame(TTSStoppedFrame())

src/pipecat/services/google/gemini_live/llm.py

@@ -1448,11 +1448,11 @@ class GeminiLiveLLMService(LLMService):
                 # Update bot responding state and send service start frame
                 # (AUDIO modality case)
                 await self._set_bot_is_responding(True)
-                await self.push_frame(LLMFullResponseStartFrame())
+                await self.push_frame(LLMFullResponseStartFrame(skip_tts=self._get_skip_tts()))
 
             self._bot_text_buffer += text
             self._search_result_buffer += text  # Also accumulate for grounding
-            frame = LLMTextFrame(text=text)
+            frame = LLMTextFrame(text=text, skip_tts=self._get_skip_tts())
             await self.push_frame(frame)
 
         # Check for grounding metadata in server content
@@ -1491,7 +1491,7 @@ class GeminiLiveLLMService(LLMService):
         if not self._bot_is_responding:
             await self._set_bot_is_responding(True)
             await self.push_frame(TTSStartedFrame())
-            await self.push_frame(LLMFullResponseStartFrame())
+            await self.push_frame(LLMFullResponseStartFrame(skip_tts=self._get_skip_tts()))
 
         self._bot_audio_buffer.extend(audio)
         frame = TTSAudioRawFrame(
@@ -1552,10 +1552,10 @@ class GeminiLiveLLMService(LLMService):
             if not text:
                 # AUDIO modality case
                 await self.push_frame(TTSStoppedFrame())
-                await self.push_frame(LLMFullResponseEndFrame())
+                await self.push_frame(LLMFullResponseEndFrame(skip_tts=self._get_skip_tts()))
             else:
                 # TEXT modality case
-                await self.push_frame(LLMFullResponseEndFrame())
+                await self.push_frame(LLMFullResponseEndFrame(skip_tts=self._get_skip_tts()))
 
     @traced_stt
     async def _handle_user_transcription(
@@ -1643,7 +1643,7 @@ class GeminiLiveLLMService(LLMService):
         if not self._bot_is_responding:
             await self._set_bot_is_responding(True)
             await self.push_frame(TTSStartedFrame())
-            await self.push_frame(LLMFullResponseStartFrame())
+            await self.push_frame(LLMFullResponseStartFrame(skip_tts=self._get_skip_tts()))
 
         frame = TTSTextFrame(text=text, aggregated_by=AggregationType.SENTENCE)
         # Gemini Live text already includes any necessary inter-chunk spaces

src/pipecat/services/google/llm.py

@@ -876,7 +876,7 @@ class GoogleLLMService(LLMService):
 
     @traced_llm
     async def _process_context(self, context: OpenAILLMContext | LLMContext):
-        await self.push_frame(LLMFullResponseStartFrame())
+        await self.push_frame(LLMFullResponseStartFrame(skip_tts=self._get_skip_tts()))
 
         prompt_tokens = 0
         completion_tokens = 0
@@ -920,7 +920,9 @@ class GoogleLLMService(LLMService):
                         for part in candidate.content.parts:
                             if not part.thought and part.text:
                                 search_result += part.text
-                                await self.push_frame(LLMTextFrame(part.text))
+                                await self.push_frame(
+                                    LLMTextFrame(part.text, skip_tts=self._get_skip_tts())
+                                )
                             elif part.function_call:
                                 function_call = part.function_call
                                 id = function_call.id or str(uuid.uuid4())
@@ -1002,7 +1004,7 @@ class GoogleLLMService(LLMService):
                     reasoning_tokens=reasoning_tokens,
                 )
             )
-            await self.push_frame(LLMFullResponseEndFrame())
+            await self.push_frame(LLMFullResponseEndFrame(skip_tts=self._get_skip_tts()))
 
     async def process_frame(self, frame: Frame, direction: FrameDirection):
         """Process incoming frames and handle different frame types.

src/pipecat/services/google/llm_openai.py

@@ -136,7 +136,9 @@ class GoogleLLMOpenAIBetaService(OpenAILLMService):
                     # Keep iterating through the response to collect all the argument fragments
                     arguments += tool_call.function.arguments
             elif chunk.choices[0].delta.content:
-                await self.push_frame(LLMTextFrame(chunk.choices[0].delta.content))
+                await self.push_frame(
+                    LLMTextFrame(chunk.choices[0].delta.content, skip_tts=self._get_skip_tts())
+                )
 
         # if we got a function name and arguments, check to see if it's a function with
         # a registered handler. If so, run the registered callback, save the result to

src/pipecat/services/llm_service.py

@@ -9,17 +9,7 @@
 import asyncio
 import inspect
 from dataclasses import dataclass
-from typing import (
-    Any,
-    Awaitable,
-    Callable,
-    Dict,
-    Mapping,
-    Optional,
-    Protocol,
-    Sequence,
-    Type,
-)
+from typing import Any, Awaitable, Callable, Dict, Mapping, Optional, Protocol, Sequence, Type
 
 from loguru import logger
 
@@ -285,17 +275,13 @@ class LLMService(AIService):
         elif isinstance(frame, LLMConfigureOutputFrame):
             self._skip_tts = frame.skip_tts
 
-    async def push_frame(self, frame: Frame, direction: FrameDirection = FrameDirection.DOWNSTREAM):
-        """Pushes a frame.
+    def _get_skip_tts(self) -> bool:
+        """Get the current skip_tts configuration.
 
-        Args:
-            frame: The frame to push.
-            direction: The direction of frame pushing.
+        Returns:
+            The current skip_tts setting for frames generated by this LLM.
         """
-        if isinstance(frame, (LLMTextFrame, LLMFullResponseStartFrame, LLMFullResponseEndFrame)):
-            frame.skip_tts = self._skip_tts
-
-        await super().push_frame(frame, direction)
+        return self._skip_tts
 
     async def _handle_interruptions(self, _: InterruptionFrame):
         for function_name, entry in self._functions.items():

src/pipecat/services/openai/base_llm.py

@@ -13,13 +13,7 @@ from typing import Any, Dict, List, Mapping, Optional
 
 import httpx
 from loguru import logger
-from openai import (
-    NOT_GIVEN,
-    APITimeoutError,
-    AsyncOpenAI,
-    AsyncStream,
-    DefaultAsyncHttpxClient,
-)
+from openai import NOT_GIVEN, APITimeoutError, AsyncOpenAI, AsyncStream, DefaultAsyncHttpxClient
 from openai.types.chat import ChatCompletionChunk, ChatCompletionMessageParam
 from pydantic import BaseModel, Field
 
@@ -396,14 +390,20 @@ class BaseOpenAILLMService(LLMService):
                     # Keep iterating through the response to collect all the argument fragments
                     arguments += tool_call.function.arguments
             elif chunk.choices[0].delta.content:
-                await self.push_frame(LLMTextFrame(chunk.choices[0].delta.content))
+                await self.push_frame(
+                    LLMTextFrame(chunk.choices[0].delta.content, skip_tts=self._get_skip_tts())
+                )
 
             # When gpt-4o-audio / gpt-4o-mini-audio is used for llm or stt+llm
             # we need to get LLMTextFrame for the transcript
             elif hasattr(chunk.choices[0].delta, "audio") and chunk.choices[0].delta.audio.get(
                 "transcript"
             ):
-                await self.push_frame(LLMTextFrame(chunk.choices[0].delta.audio["transcript"]))
+                await self.push_frame(
+                    LLMTextFrame(
+                        chunk.choices[0].delta.audio["transcript"], skip_tts=self._get_skip_tts()
+                    )
+                )
 
         # if we got a function name and arguments, check to see if it's a function with
         # a registered handler. If so, run the registered callback, save the result to
@@ -463,11 +463,11 @@ class BaseOpenAILLMService(LLMService):
 
         if context:
             try:
-                await self.push_frame(LLMFullResponseStartFrame())
+                await self.push_frame(LLMFullResponseStartFrame(skip_tts=self._get_skip_tts()))
                 await self.start_processing_metrics()
                 await self._process_context(context)
             except httpx.TimeoutException:
                 await self._call_event_handler("on_completion_timeout")
             finally:
                 await self.stop_processing_metrics()
-                await self.push_frame(LLMFullResponseEndFrame())
+                await self.push_frame(LLMFullResponseEndFrame(skip_tts=self._get_skip_tts()))

src/pipecat/services/openai/realtime/llm.py

@@ -15,9 +15,7 @@ from typing import Optional
 from loguru import logger
 
 from pipecat.adapters.schemas.tools_schema import ToolsSchema
-from pipecat.adapters.services.open_ai_realtime_adapter import (
-    OpenAIRealtimeLLMAdapter,
-)
+from pipecat.adapters.services.open_ai_realtime_adapter import OpenAIRealtimeLLMAdapter
 from pipecat.frames.frames import (
     AggregationType,
     BotStoppedSpeakingFrame,
@@ -284,7 +282,7 @@ class OpenAIRealtimeLLMService(LLMService):
         await self._truncate_current_audio_response()
         await self.stop_all_metrics()
         if self._current_assistant_response:
-            await self.push_frame(LLMFullResponseEndFrame())
+            await self.push_frame(LLMFullResponseEndFrame(skip_tts=self._get_skip_tts()))
             # Only push TTSStoppedFrame if audio modality is enabled
             if self._is_modality_enabled("audio"):
                 await self.push_frame(TTSStoppedFrame())
@@ -608,7 +606,7 @@ class OpenAIRealtimeLLMService(LLMService):
 
         if evt.item.role == "assistant":
             self._current_assistant_response = evt.item
-            await self.push_frame(LLMFullResponseStartFrame())
+            await self.push_frame(LLMFullResponseStartFrame(skip_tts=self._get_skip_tts()))
 
     async def _handle_evt_conversation_item_done(self, evt):
         """Handle conversation.item.done event - item is fully completed."""
@@ -669,7 +667,7 @@ class OpenAIRealtimeLLMService(LLMService):
         )
         await self.start_llm_usage_metrics(tokens)
         await self.stop_processing_metrics()
-        await self.push_frame(LLMFullResponseEndFrame())
+        await self.push_frame(LLMFullResponseEndFrame(skip_tts=self._get_skip_tts()))
         self._current_assistant_response = None
         # error handling
         if evt.response.status == "failed":
@@ -683,7 +681,7 @@ class OpenAIRealtimeLLMService(LLMService):
         # We receive text deltas (as opposed to audio transcript deltas) when
         # the output modality is "text"
         if evt.delta:
-            frame = LLMTextFrame(evt.delta)
+            frame = LLMTextFrame(evt.delta, skip_tts=self._get_skip_tts())
             await self.push_frame(frame)
 
     async def _handle_evt_audio_transcript_delta(self, evt):
@@ -817,7 +815,7 @@ class OpenAIRealtimeLLMService(LLMService):
 
         logger.debug("Creating response")
 
-        await self.push_frame(LLMFullResponseStartFrame())
+        await self.push_frame(LLMFullResponseStartFrame(skip_tts=self._get_skip_tts()))
         await self.start_processing_metrics()
         await self.start_ttfb_metrics()
         await self.send_client_event(

src/pipecat/services/openai_realtime_beta/openai.py

@@ -265,7 +265,7 @@ class OpenAIRealtimeBetaLLMService(LLMService):
         await self._truncate_current_audio_response()
         await self.stop_all_metrics()
         if self._current_assistant_response:
-            await self.push_frame(LLMFullResponseEndFrame())
+            await self.push_frame(LLMFullResponseEndFrame(skip_tts=self._get_skip_tts()))
             # Only push TTSStoppedFrame if audio modality is enabled
             if self._is_modality_enabled("audio"):
                 await self.push_frame(TTSStoppedFrame())
@@ -564,7 +564,7 @@ class OpenAIRealtimeBetaLLMService(LLMService):
             self._user_and_response_message_tuple = (evt.item, {"done": False, "output": []})
         elif evt.item.role == "assistant":
             self._current_assistant_response = evt.item
-            await self.push_frame(LLMFullResponseStartFrame())
+            await self.push_frame(LLMFullResponseStartFrame(skip_tts=self._get_skip_tts()))
 
     async def _handle_evt_input_audio_transcription_delta(self, evt):
         if self._send_transcription_frames:
@@ -623,7 +623,7 @@ class OpenAIRealtimeBetaLLMService(LLMService):
         )
         await self.start_llm_usage_metrics(tokens)
         await self.stop_processing_metrics()
-        await self.push_frame(LLMFullResponseEndFrame())
+        await self.push_frame(LLMFullResponseEndFrame(skip_tts=self._get_skip_tts()))
         self._current_assistant_response = None
         # error handling
         if evt.response.status == "failed":
@@ -647,11 +647,11 @@ class OpenAIRealtimeBetaLLMService(LLMService):
 
     async def _handle_evt_text_delta(self, evt):
         if evt.delta:
-            await self.push_frame(LLMTextFrame(evt.delta))
+            await self.push_frame(LLMTextFrame(evt.delta, skip_tts=self._get_skip_tts()))
 
     async def _handle_evt_audio_transcript_delta(self, evt):
         if evt.delta:
-            await self.push_frame(LLMTextFrame(evt.delta))
+            await self.push_frame(LLMTextFrame(evt.delta, skip_tts=self._get_skip_tts()))
             await self.push_frame(TTSTextFrame(evt.delta, aggregated_by=AggregationType.SENTENCE))
 
     async def _handle_evt_speech_started(self, evt):
@@ -747,7 +747,7 @@ class OpenAIRealtimeBetaLLMService(LLMService):
 
         logger.debug(f"Creating response: {self._context.get_messages_for_logging()}")
 
-        await self.push_frame(LLMFullResponseStartFrame())
+        await self.push_frame(LLMFullResponseStartFrame(skip_tts=self._get_skip_tts()))
         await self.start_processing_metrics()
         await self.start_ttfb_metrics()
         await self.send_client_event(

src/pipecat/services/sambanova/llm.py

@@ -14,9 +14,7 @@ from openai import AsyncStream
 from openai.types.chat import ChatCompletionChunk
 
 from pipecat.adapters.services.open_ai_adapter import OpenAILLMInvocationParams
-from pipecat.frames.frames import (
-    LLMTextFrame,
-)
+from pipecat.frames.frames import LLMTextFrame
 from pipecat.metrics.metrics import LLMTokenUsage
 from pipecat.processors.aggregators.llm_context import LLMContext
 from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
@@ -176,14 +174,20 @@ class SambaNovaLLMService(OpenAILLMService):  # type: ignore
                     # Keep iterating through the response to collect all the argument fragments
                     arguments += tool_call.function.arguments
             elif chunk.choices[0].delta.content:
-                await self.push_frame(LLMTextFrame(chunk.choices[0].delta.content))
+                await self.push_frame(
+                    LLMTextFrame(chunk.choices[0].delta.content, skip_tts=self._get_skip_tts())
+                )
 
             # When gpt-4o-audio / gpt-4o-mini-audio is used for llm or stt+llm
             # we need to get LLMTextFrame for the transcript
             elif hasattr(chunk.choices[0].delta, "audio") and chunk.choices[0].delta.audio.get(
                 "transcript"
             ):
-                await self.push_frame(LLMTextFrame(chunk.choices[0].delta.audio["transcript"]))
+                await self.push_frame(
+                    LLMTextFrame(
+                        chunk.choices[0].delta.audio["transcript"], skip_tts=self._get_skip_tts()
+                    )
+                )
 
         # if we got a function name and arguments, check to see if it's a function with
         # a registered handler. If so, run the registered callback, save the result to