Fix: Ensure EndFrame propagates through AIService before stop()

This fix addresses a critical bug where EndFrame (and potentially other
system frames) would trigger the stop() method in AIService but never
be pushed downstream to subsequent processors, causing pipelines to hang.

The issue occurred because AIService.process_frame() would call stop(frame)
for EndFrame without first pushing it downstream. This meant that downstream
processors never received the shutdown signal, leaving the pipeline in a
waiting state.

The fix ensures EndFrame is pushed downstream BEFORE calling stop(), following
the same pattern used by RTVIProcessor and properly-implemented processors.
This guarantees that:
1. Downstream processors receive the EndFrame for proper cleanup
2. The stop() method can then safely perform service-specific cleanup
3. The ordering prevents race conditions during shutdown

This bug affected all AI services inheriting from AIService that didn't
override process_frame() to explicitly handle EndFrame, including scenarios
with TTS services, LLM services, and other AI service implementations.

Fixes pipeline hangs during graceful shutdown when EndFrame is sent.

CHANGELOG.md

@@ -16,82 +16,6 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
   services that subclass `TTSService` can indicate whether the text in the
   `TTSTextFrame`s they push already contain any necessary inter-frame spaces.
 
-- Introduced new `AggregatedTextFrame` type to support representing a best effort of
-  the perceived llm output whether or not it is processed by the TTS. This new frame
-  type includes the field `aggregated_by` to represent the conceptual format by which
-  the given text is aggregated. `TTSTextFrame`s now inherit from `AggregatedTextFrame`.
-  With this inheritance, an observer can watch for `AggregatedTextFrame`s to accumlate
-  the perceived output and determine whether or not the text was spoken based on if that
-  frame is also a `TTSTextFrame`. (See bullet below on new `bot-output` which takes
-  advantage of this)
-
-- Introduced `LLMTextProcessor`: A new processor meant to allow customization for how
-  LLMTextFrames should be aggregated and considered. It's purpose is to turn
-  `LLMTextFrame`s into `AggregatedTextFrame`s. By default, a TTSService will still
-  aggregate `LLMTextFrame`s by sentence for the service to consume. However, if you
-  wish to override how the llm text is aggregated, you should no longer override the
-  TTS's internal aggregator, but instead, insert this processor between your LLM and
-  TTS in the pipeline.
-
-- New `bot-output` RTVI message to represent what the bot actually "says".
-  - The `RTVIObserver` now emits `bot-output` messages based off the new `AggregatedTextFrame`s
-    (`bot-tts-text` and `bot-llm-text` are still supported and generated, but `bot-transcript` is
-    now deprecated in lieu of this new, more thorough, message).
-  - The new `RTVIBotOutputMessage` includes the fields:
-    - `spoken`: A boolean indicating whether the text was spoken by TTS
-    - `aggregated_by`: A string representing how the text was aggregated ("sentence", "word",
-      "my custom aggregation")
-  - Introduced new fields to `RTVIObserver` to support the new `bot-output` messaging:
-    - `bot_output_enabled`: Defaults to True. Set to false to disable bot-output messages.
-    - `skip_aggregator_types`: Defaults to `None`. Set to a list of strings that match
-        aggregation types that should not be included in bot-output messages. (Ex. `credit_card`)
-  - Introduced new methods, `add_text_transformer()` and `remove_text_transformer()`, to `RTVIObserver` to support providing (and subsequently removing)
-    callbacks for various types of aggregations (or all aggregations with `*`) that can modify the
-    text before being sent as a `bot-output` or `tts-text` message. (Think obscuring the credit card
-    or inserting extra detail the client might want that the context doesn't need.)
-
-- Updated the base aggregator type:
-  - Introduced a new `Aggregation` dataclass to represent both the aggregated `text` and
-    a string identifying the `type` of aggregation (ex. "sentence", "word", "my custom
-    aggregation")
-  - **BREAKING**: `BaseTextAggregator.text` now returns an `Aggregation` (instead of `str`).
-    To update: `aggregated_text = myAggregator.text` -> `aggregated_text = myAggregator.text.text`
-  - **BREAKING**: `BaseTextAggregator.aggregate()` now returns `Optional[Aggregation]`
-    (instead of `Optional[str]`). To update:
-      ```
-      aggregation = myAggregator.aggregate(text)
-      if (aggregation):
-        print(f"successfully aggregated text: {aggregation.text}") // instead of {aggregation}
-      ```
-  - `SimpleTextAggregator`, `SkipTagsAggregator`, `PatternPairAggregator` updated to
-    produce/consume `Aggregation` objects.
-
-- Augmented the `PatternPairAggregator`:
-  - Introduced a new, preferred version of `add_pattern` to support a new option for treating a
-    match as a separate aggregation returned from `aggregate()`. This replaces the now
-    deprecated `add_pattern_pair` method and you provide a `MatchAction` in lieu of the `remove_match` field.
-    - `MatchAction` enum: `REMOVE`, `KEEP`, `AGGREGATE`, allowing customization for how
-      a match should be handled.
-      - `REMOVE`: The text along with its delimiters will be removed from the streaming text.
-                  Sentence aggregation will continue on as if this text did not exist.
-      - `KEEP`: The delimiters will be removed, but the content between them will be kept.
-                Sentence aggregation will continue on with the internal text included.
-      - `AGGREGATE`: The delimiters will be removed and the content between will be treated
-                as a separate aggregation. Any text before the start of the pattern will be
-                returned early, whether or not a complete sentence was found. Then the pattern
-                will be returned. Then the aggregation will continue on sentence matching after
-                the closing delimiter is found. The content between the delimiters is not
-                aggregated by sentence. It is aggregated as one single block of text.
-      - `PatternMatch` now extends `Aggregation` and provides richer info to handlers.
-  - **BREAKING**: The `PatternMatch` type returned to handlers registered via `on_pattern_match`
-     has been updated to subclass from the new `Aggregation` type, which means that `content`
-     has been replaced with `text` and `pattern_id` has been replaced with `type`:
-       ```
-       async dev on_match_tag(match: PatternMatch):
-          pattern = match.type # instead of match.pattern_id
-          text = match.text # instead of match.content
-       ```
-
 ### Changed
 
 - Updated all STT and TTS services to use consistent error handling pattern with
@@ -109,42 +33,11 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 - Updated language mappings for the Google and Gemini TTS services to match
   official documentation.
 
-- `TextFrame` new field `append_to_context` used to indicate if the encompassing
-  text should be added to the LLM context (by the LLM assistant aggregator). It
-  defaults to `True`.
-
-- TTS flow respects aggregation metadata
-  - `TTSService` accepts a new `skip_aggregator_types` to avoid speaking certain aggregation types
-    (now determined/returned by the aggregator)
-  - TTS services push `AggregatedTextFrame` in addition to `TTSTextFrame`s when either an
-    aggregation occurs that should not be spoken or when the TTS service supports word-by-word
-    timestamping. In the latter case, the `TTSService` preliminarily generates an
-    `AggregatedTextFrame`, aggregated by sentence to generate the full sentence content as early
-    as possible.
-  - Introduced a new methods, `add_text_transformer()` and `remove_text_transformer()`:
-    These functions introduce the ability to provide (and subsequently remove) callbacks to the TTS to transform text based on
-    its aggregated type prior to sending the text to the underlying TTS service. This makes it
-    possible to do things like introduce TTS-specific tags for spelling or emotion or change the
-    pronunciation of something on the fly.
-
 ### Deprecated
 
 - The `api_key` parameter in `GeminiTTSService` is deprecated. Use
   `credentials` or `credentials_path` instead for Google Cloud authentication.
 
-- The RTVI `bot-transcription` event is deprecated in favor of the new `bot-output`
-  message which is the canonical representation of bot output (spoken or not). The code
-  still emits a transcription message for backwards compatibility while transition occurs.
-
-- The TTS constructor field, `text_aggregator` is deprecated in favor of the new
-  `LLMTextProcessor`. TTSServices still have an internal aggregator for support of default
-  behavior, but if you want to override the aggregation behavior, you should use the new
-  processor.
-
-- Deprecated `add_pattern_pair` in the `PatternPairAggregator` which takes a `pattern_id`
-  and `remove_match` field in favor of the new `add_pattern` method which takes a `type` and an
-  `action`
-
 ### Fixed
 
 - Fixed subtle issue of assistant context messages ending up with double spaces

examples/foundational/35-pattern-pair-voice-switching.py

@@ -62,11 +62,7 @@ from pipecat.services.openai.llm import OpenAILLMService
 from pipecat.transports.base_transport import BaseTransport, TransportParams
 from pipecat.transports.daily.transport import DailyParams
 from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
-from pipecat.utils.text.pattern_pair_aggregator import (
-    MatchAction,
-    PatternMatch,
-    PatternPairAggregator,
-)
+from pipecat.utils.text.pattern_pair_aggregator import PatternMatch, PatternPairAggregator
 
 load_dotenv(override=True)
 
@@ -110,16 +106,16 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
     pattern_aggregator = PatternPairAggregator()
 
     # Add pattern for voice switching
-    pattern_aggregator.add_pattern(
-        type="voice",
+    pattern_aggregator.add_pattern_pair(
+        pattern_id="voice_tag",
         start_pattern="<voice>",
         end_pattern="</voice>",
-        action=MatchAction.REMOVE,  # Remove tags from final text
+        remove_match=True,
     )
 
     # Register handler for voice switching
     async def on_voice_tag(match: PatternMatch):
-        voice_name = match.text.strip().lower()
+        voice_name = match.content.strip().lower()
         if voice_name in VOICE_IDS:
             # First flush any existing audio to finish the current context
             await tts.flush_audio()
@@ -129,7 +125,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         else:
             logger.warning(f"Unknown voice: {voice_name}")
 
-    pattern_aggregator.on_pattern_match("voice", on_voice_tag)
+    pattern_aggregator.on_pattern_match("voice_tag", on_voice_tag)
 
     stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
 

src/pipecat/extensions/ivr/ivr_navigator.py

@@ -31,11 +31,7 @@ from pipecat.pipeline.pipeline import Pipeline
 from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContextFrame
 from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
 from pipecat.services.llm_service import LLMService
-from pipecat.utils.text.pattern_pair_aggregator import (
-    MatchAction,
-    PatternMatch,
-    PatternPairAggregator,
-)
+from pipecat.utils.text.pattern_pair_aggregator import PatternMatch, PatternPairAggregator
 
 
 class IVRStatus(Enum):
@@ -118,15 +114,15 @@ class IVRProcessor(FrameProcessor):
     def _setup_xml_patterns(self):
         """Set up XML pattern detection and handlers."""
         # Register DTMF pattern
-        self._aggregator.add_pattern("dtmf", "<dtmf>", "</dtmf>", action=MatchAction.REMOVE)
+        self._aggregator.add_pattern_pair("dtmf", "<dtmf>", "</dtmf>", remove_match=True)
         self._aggregator.on_pattern_match("dtmf", self._handle_dtmf_action)
 
         # Register mode pattern
-        self._aggregator.add_pattern("mode", "<mode>", "</mode>", action=MatchAction.REMOVE)
+        self._aggregator.add_pattern_pair("mode", "<mode>", "</mode>", remove_match=True)
         self._aggregator.on_pattern_match("mode", self._handle_mode_action)
 
         # Register IVR pattern
-        self._aggregator.add_pattern("ivr", "<ivr>", "</ivr>", action=MatchAction.REMOVE)
+        self._aggregator.add_pattern_pair("ivr", "<ivr>", "</ivr>", remove_match=True)
         self._aggregator.on_pattern_match("ivr", self._handle_ivr_action)
 
     async def process_frame(self, frame: Frame, direction: FrameDirection):
@@ -163,7 +159,7 @@ class IVRProcessor(FrameProcessor):
         Args:
             match: The pattern match containing DTMF content.
         """
-        value = match.text
+        value = match.content
         logger.debug(f"DTMF detected: {value}")
 
         try:
@@ -184,7 +180,7 @@ class IVRProcessor(FrameProcessor):
         Args:
             match: The pattern match containing IVR status content.
         """
-        status = match.text
+        status = match.content
         logger.trace(f"IVR status detected: {status}")
 
         # Convert string to enum, with validation
@@ -215,7 +211,7 @@ class IVRProcessor(FrameProcessor):
         Args:
             match: The pattern match containing mode content.
         """
-        mode = match.text
+        mode = match.content
         logger.debug(f"Mode detected: {mode}")
         if mode == "conversation":
             await self._handle_conversation()

src/pipecat/frames/frames.py

@@ -12,7 +12,6 @@ and LLM processing.
 """
 
 from dataclasses import dataclass, field
-from enum import Enum
 from typing import (
     TYPE_CHECKING,
     Any,
@@ -338,14 +337,11 @@ class TextFrame(DataFrame):
     # 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
 
     def __str__(self):
         pts = format_pts(self.pts)
@@ -359,32 +355,8 @@ class LLMTextFrame(TextFrame):
     pass
 
 
-class AggregationType(str, Enum):
-    """Built-in aggregation strings."""
-
-    SENTENCE = "sentence"
-    WORD = "word"
-
-    def __str__(self):
-        return self.value
-
-
 @dataclass
-class AggregatedTextFrame(TextFrame):
-    """Text frame representing an aggregation of TextFrames.
-
-    This frame contains multiple TextFrames aggregated together for processing
-    or output along with a field to indicate how they are aggregated.
-
-    Parameters:
-        aggregated_by: Method used to aggregate the text frames.
-    """
-
-    aggregated_by: AggregationType | str
-
-
-@dataclass
-class TTSTextFrame(AggregatedTextFrame):
+class TTSTextFrame(TextFrame):
     """Text frame generated by Text-to-Speech services."""
 
     pass

src/pipecat/processors/aggregators/llm_response.py

@@ -1001,7 +1001,7 @@ class LLMAssistantContextAggregator(LLMContextResponseAggregator):
         await self.push_aggregation()
 
     async def _handle_text(self, frame: TextFrame):
-        if not self._started or not frame.append_to_context:
+        if not self._started:
             return
 
         if self._params.expect_stripped_words:

src/pipecat/processors/aggregators/llm_response_universal.py

@@ -814,7 +814,7 @@ class LLMAssistantAggregator(LLMContextAggregator):
         await self.push_aggregation()
 
     async def _handle_text(self, frame: TextFrame):
-        if not self._started or not frame.append_to_context:
+        if not self._started:
             return
 
         # Make sure we really have text (spaces count, too!)

src/pipecat/processors/aggregators/llm_text_processor.py

@@ -1,106 +0,0 @@
-#
-# Copyright (c) 2024–2025, Daily
-#
-# SPDX-License-Identifier: BSD 2-Clause License
-#
-
-"""LLM text processor module for processing and aggregating raw LLM output text.
-
-This processor will convert LLMTextFrames into AggregatedTextFrames based on the
-configured text aggregator. Using the customizable aggregator, it provides
-functionality to handle or manipulate LLM text frames before they are sent to other
-components such as TTS services or context aggregators. It can be used to pre-aggregate
-and categorize, modify, or filter direct output tokens from the LLM.
-"""
-
-from typing import Optional
-
-from pipecat.frames.frames import (
-    AggregatedTextFrame,
-    EndFrame,
-    Frame,
-    InterruptionFrame,
-    LLMFullResponseEndFrame,
-    LLMTextFrame,
-)
-from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
-from pipecat.utils.text.base_text_aggregator import BaseTextAggregator
-from pipecat.utils.text.simple_text_aggregator import SimpleTextAggregator
-
-
-class LLMTextProcessor(FrameProcessor):
-    """A processor for handling or manipulating LLM text frames before they are processed further.
-
-    This processor will convert LLMTextFrames into AggregatedTextFrames based on the configured
-    text aggregator. Using the customizable aggregator, it provides functionality to handle or
-    manipulate LLM text frames before they are sent to other components such as TTS services or
-    context aggregators. It can be used to pre-aggregate and categorize, modify, or filter direct
-    output tokens from the LLM.
-    """
-
-    def __init__(self, *, text_aggregator: Optional[BaseTextAggregator] = None, **kwargs):
-        """Initialize the LLM text processor.
-
-        Args:
-            text_aggregator: An optional text aggregator to use for processing LLM text frames. By
-                default, a SimpleTextAggregator aggregating by sentence will be used.
-            **kwargs: Additional arguments passed to parent class.
-
-        TODO: Allow transformations per aggregation type or all (and deprecate the TTS filters).
-        """
-        super().__init__(**kwargs)
-        self._text_aggregator: BaseTextAggregator = text_aggregator or SimpleTextAggregator()
-
-    async def process_frame(self, frame: Frame, direction: FrameDirection):
-        """Process an LLMTextFrames using the aggregator to generate AggregatedTextFrames.
-
-        Args:
-            frame: The frame to process.
-            direction: The direction of frame flow in the pipeline.
-        """
-        await super().process_frame(frame, direction)
-
-        if isinstance(frame, InterruptionFrame):
-            await self._handle_interruption(frame)
-            await self.push_frame(frame, direction)
-        elif isinstance(frame, LLMTextFrame):
-            await self._handle_llm_text(frame)
-        elif isinstance(frame, LLMFullResponseEndFrame):
-            await self._handle_llm_end(frame.skip_tts)
-            await self.push_frame(frame, direction)
-        elif isinstance(frame, EndFrame):
-            await self._handle_llm_end()
-            await self.push_frame(frame, direction)
-        else:
-            await self.push_frame(frame, direction)
-
-    async def _handle_interruption(self, _):
-        """Handle interruptions by resetting the text aggregator."""
-        await self._text_aggregator.handle_interruption()
-
-    async def reset(self):
-        """Reset the internal state of the text processor and its aggregator."""
-        await self._text_aggregator.reset()
-
-    async def _handle_llm_text(self, in_frame: LLMTextFrame):
-        aggregation = await self._text_aggregator.aggregate(in_frame.text)
-        if aggregation:
-            out_frame = AggregatedTextFrame(
-                text=aggregation.text,
-                aggregated_by=aggregation.type,
-            )
-            out_frame.skip_tts = in_frame.skip_tts
-            await self.push_frame(out_frame)
-
-    async def _handle_llm_end(self, skip_tts: bool = False):
-        # Flush any remaining aggregated text at the end of the LLM response
-        aggregation = self._text_aggregator.text
-        await self._text_aggregator.reset()
-        text = aggregation.text.strip()
-        if text:
-            out_frame = AggregatedTextFrame(
-                text=text,
-                aggregated_by=aggregation.type,
-            )
-            out_frame.skip_tts = skip_tts
-            await self.push_frame(out_frame)

src/pipecat/processors/frameworks/rtvi.py

@@ -24,7 +24,6 @@ from typing import (
     Literal,
     Mapping,
     Optional,
-    Tuple,
     Union,
 )
 
@@ -33,8 +32,6 @@ from pydantic import BaseModel, Field, PrivateAttr, ValidationError
 
 from pipecat.audio.utils import calculate_audio_volume
 from pipecat.frames.frames import (
-    AggregatedTextFrame,
-    AggregationType,
     BotStartedSpeakingFrame,
     BotStoppedSpeakingFrame,
     CancelFrame,
@@ -707,29 +704,6 @@ class RTVITextMessageData(BaseModel):
     text: str
 
 
-class RTVIBotOutputMessageData(RTVITextMessageData):
-    """Data for bot output RTVI messages.
-
-    Extends RTVITextMessageData to include metadata about the output.
-    """
-
-    spoken: bool = False  # Indicates if the text has been spoken by TTS
-    aggregated_by: AggregationType | str
-    # Indicates what form the text is in (e.g., by word, sentence, etc.)
-
-
-class RTVIBotOutputMessage(BaseModel):
-    """Message containing bot output text.
-
-    An event meant to holistically represent what the bot is outputting,
-    along with metadata about the output and if it has been spoken.
-    """
-
-    label: RTVIMessageLiteral = RTVI_MESSAGE_LABEL
-    type: Literal["bot-output"] = "bot-output"
-    data: RTVIBotOutputMessageData
-
-
 class RTVIBotTranscriptionMessage(BaseModel):
     """Message containing bot transcription text.
 
@@ -922,7 +896,6 @@ class RTVIObserverParams:
         Parameter `errors_enabled` is deprecated. Error messages are always enabled.
 
     Parameters:
-        bot_output_enabled: Indicates if bot output messages should be sent.
         bot_llm_enabled: Indicates if the bot's LLM messages should be sent.
         bot_tts_enabled: Indicates if the bot's TTS messages should be sent.
         bot_speaking_enabled: Indicates if the bot's started/stopped speaking messages should be sent.
@@ -934,17 +907,9 @@ class RTVIObserverParams:
         metrics_enabled: Indicates if metrics messages should be sent.
         system_logs_enabled: Indicates if system logs should be sent.
         errors_enabled: [Deprecated] Indicates if errors messages should be sent.
-        skip_aggregator_types: List of aggregation types to skip sending as tts/output messages.
-          Note: if using this to avoid sending secure information, be sure to also disable
-                bot_llm_enabled to avoid leaking through LLM messages.
-        bot_output_transforms: A list of callables to transform text before just before sending it
-            to TTS. Each callable takes the aggregated text and its type, and returns the
-            transformed text. To register, provide a list of tuples of
-            (aggregation_type | '*', transform_function).
         audio_level_period_secs: How often audio levels should be sent if enabled.
     """
 
-    bot_output_enabled: bool = True
     bot_llm_enabled: bool = True
     bot_tts_enabled: bool = True
     bot_speaking_enabled: bool = True
@@ -956,15 +921,6 @@ class RTVIObserverParams:
     metrics_enabled: bool = True
     system_logs_enabled: bool = False
     errors_enabled: Optional[bool] = None
-    skip_aggregator_types: Optional[List[AggregationType | str]] = None
-    bot_output_transforms: Optional[
-        List[
-            Tuple[
-                AggregationType | str,
-                Callable[[str, AggregationType | str], Awaitable[str]],
-            ]
-        ]
-    ] = None
     audio_level_period_secs: float = 0.15
 
 
@@ -1017,45 +973,8 @@ class RTVIObserver(BaseObserver):
                     DeprecationWarning,
                 )
 
-        self._aggregation_transforms: List[
-            Tuple[AggregationType | str, Callable[[str, AggregationType | str], Awaitable[str]]]
-        ] = self._params.bot_output_transforms or []
-
-    def add_bot_output_transformer(
-        self,
-        transform_function: Callable[[str, AggregationType | str], Awaitable[str]],
-        aggregation_type: AggregationType | str = "*",
-    ):
-        """Transform text for a specific aggregation type before sending as Bot Output or TTS.
-
-        Args:
-            transform_function: The function to apply for transformation. This function should take
-                the text and aggregation type as input and return the transformed text.
-                Ex.: async def my_transform(text: str, aggregation_type: str) -> str:
-            aggregation_type: The type of aggregation to transform. This value defaults to "*" to
-                handle all text before sending to the client.
-        """
-        self._aggregation_transforms.append((aggregation_type, transform_function))
-
-    def remove_bot_output_transformer(
-        self,
-        transform_function: Callable[[str, AggregationType | str], Awaitable[str]],
-        aggregation_type: AggregationType | str = "*",
-    ):
-        """Remove a text transformer for a specific aggregation type.
-
-        Args:
-            transform_function: The function to remove.
-            aggregation_type: The type of aggregation to remove the transformer for.
-        """
-        self._aggregation_transforms = [
-            (agg_type, func)
-            for agg_type, func in self._aggregation_transforms
-            if not (agg_type == aggregation_type and func == transform_function)
-        ]
-
     async def _logger_sink(self, message):
-        """Logger sink so we can send system logs to RTVI clients."""
+        """Logger sink so we cna send system logs to RTVI clients."""
         message = RTVISystemLogMessage(data=RTVITextMessageData(text=message))
         await self.send_rtvi_message(message)
 
@@ -1129,15 +1048,12 @@ class RTVIObserver(BaseObserver):
             await self.send_rtvi_message(RTVIBotTTSStartedMessage())
         elif isinstance(frame, TTSStoppedFrame) and self._params.bot_tts_enabled:
             await self.send_rtvi_message(RTVIBotTTSStoppedMessage())
-        elif isinstance(frame, AggregatedTextFrame) and (
-            self._params.bot_output_enabled or self._params.bot_tts_enabled
-        ):
-            if isinstance(frame, TTSTextFrame) and not isinstance(src, BaseOutputTransport):
-                # This check is to make sure we handle the frame when it has gone
-                # through the transport and has correct timing.
-                mark_as_seen = False
+        elif isinstance(frame, TTSTextFrame) and self._params.bot_tts_enabled:
+            if isinstance(src, BaseOutputTransport):
+                message = RTVIBotTTSTextMessage(data=RTVITextMessageData(text=frame.text))
+                await self.send_rtvi_message(message)
             else:
-                await self._handle_aggregated_llm_text(frame)
+                mark_as_seen = False
         elif isinstance(frame, MetricsFrame) and self._params.metrics_enabled:
             await self._handle_metrics(frame)
         elif isinstance(frame, RTVIServerMessageFrame):
@@ -1168,6 +1084,15 @@ class RTVIObserver(BaseObserver):
         if mark_as_seen:
             self._frames_seen.add(frame.id)
 
+    async def _push_bot_transcription(self):
+        """Push accumulated bot transcription as a message."""
+        if len(self._bot_transcription) > 0:
+            message = RTVIBotTranscriptionMessage(
+                data=RTVITextMessageData(text=self._bot_transcription)
+            )
+            await self.send_rtvi_message(message)
+            self._bot_transcription = ""
+
     async def _handle_interruptions(self, frame: Frame):
         """Handle user speaking interruption frames."""
         message = None
@@ -1190,45 +1115,14 @@ class RTVIObserver(BaseObserver):
         if message:
             await self.send_rtvi_message(message)
 
-    async def _handle_aggregated_llm_text(self, frame: AggregatedTextFrame):
-        """Handle aggregated LLM text output frames."""
-        # Skip certain aggregator types if configured to do so.
-        if (
-            self._params.skip_aggregator_types
-            and frame.aggregated_by in self._params.skip_aggregator_types
-        ):
-            return
-
-        text = frame.text
-        type = frame.aggregated_by
-        for aggregation_type, transform in self._aggregation_transforms:
-            if aggregation_type == type or aggregation_type == "*":
-                text = await transform(text, type)
-
-        isTTS = isinstance(frame, TTSTextFrame)
-        if self._params.bot_output_enabled:
-            message = RTVIBotOutputMessage(
-                data=RTVIBotOutputMessageData(text=text, spoken=isTTS, aggregated_by=type)
-            )
-            await self.send_rtvi_message(message)
-
-        if isTTS and self._params.bot_tts_enabled:
-            tts_message = RTVIBotTTSTextMessage(data=RTVITextMessageData(text=text))
-            await self.send_rtvi_message(tts_message)
-
     async def _handle_llm_text_frame(self, frame: LLMTextFrame):
         """Handle LLM text output frames."""
         message = RTVIBotLLMTextMessage(data=RTVITextMessageData(text=frame.text))
         await self.send_rtvi_message(message)
 
-        # TODO (mrkb): Remove all this logic when we fully deprecate bot-transcription messages.
         self._bot_transcription += frame.text
-
-        if match_endofsentence(self._bot_transcription) and len(self._bot_transcription) > 0:
-            await self.send_rtvi_message(
-                RTVIBotTranscriptionMessage(data=RTVITextMessageData(text=self._bot_transcription))
-            )
-            self._bot_transcription = ""
+        if match_endofsentence(self._bot_transcription):
+            await self._push_bot_transcription()
 
     async def _handle_user_transcriptions(self, frame: Frame):
         """Handle user transcription frames."""
@@ -1354,7 +1248,7 @@ class RTVIProcessor(FrameProcessor):
         # Default to 0.3.0 which is the last version before actually having a
         # "client-version".
         self._client_version = [0, 3, 0]
-        self._llm_skip_tts: bool = False  # Keep in sync with llm_service.py's configuration.
+        self._skip_tts: bool = False  # Keep in sync with llm_service.py
 
         self._registered_actions: Dict[str, RTVIAction] = {}
         self._registered_services: Dict[str, RTVIService] = {}
@@ -1547,7 +1441,7 @@ class RTVIProcessor(FrameProcessor):
         elif isinstance(frame, RTVIActionFrame):
             await self._action_queue.put(frame)
         elif isinstance(frame, LLMConfigureOutputFrame):
-            self._llm_skip_tts = frame.skip_tts
+            self._skip_tts = frame.skip_tts
             await self.push_frame(frame, direction)
         # Other frames
         else:
@@ -1803,9 +1697,9 @@ class RTVIProcessor(FrameProcessor):
         opts = data.options if data.options is not None else RTVISendTextOptions()
         if opts.run_immediately:
             await self.interrupt_bot()
-        cur_llm_skip_tts = self._llm_skip_tts
+        cur_skip_tts = self._skip_tts
         should_skip_tts = not opts.audio_response
-        toggle_skip_tts = cur_llm_skip_tts != should_skip_tts
+        toggle_skip_tts = cur_skip_tts != should_skip_tts
         if toggle_skip_tts:
             output_frame = LLMConfigureOutputFrame(skip_tts=should_skip_tts)
             await self.push_frame(output_frame)
@@ -1815,7 +1709,7 @@ class RTVIProcessor(FrameProcessor):
         )
         await self.push_frame(text_frame)
         if toggle_skip_tts:
-            output_frame = LLMConfigureOutputFrame(skip_tts=cur_llm_skip_tts)
+            output_frame = LLMConfigureOutputFrame(skip_tts=cur_skip_tts)
             await self.push_frame(output_frame)
 
     async def _handle_update_context(self, data: RTVIAppendToContextData):

src/pipecat/services/ai_service.py

@@ -152,6 +152,9 @@ class AIService(FrameProcessor):
         elif isinstance(frame, CancelFrame):
             await self.cancel(frame)
         elif isinstance(frame, EndFrame):
+            # Push EndFrame before stop(), because stop() may wait on tasks to
+            # finish and downstream processors need to receive the EndFrame.
+            await self.push_frame(frame, direction)
             await self.stop(frame)
 
     async def process_generator(self, generator: AsyncGenerator[Frame | None, None]):

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

@@ -27,7 +27,6 @@ from pydantic import BaseModel, Field
 from pipecat.adapters.schemas.tools_schema import ToolsSchema
 from pipecat.adapters.services.aws_nova_sonic_adapter import AWSNovaSonicLLMAdapter, Role
 from pipecat.frames.frames import (
-    AggregationType,
     BotStoppedSpeakingFrame,
     CancelFrame,
     EndFrame,
@@ -1028,7 +1027,7 @@ class AWSNovaSonicLLMService(LLMService):
         logger.debug(f"Assistant response text added: {text}")
 
         # Report the text of the assistant response.
-        frame = TTSTextFrame(text, aggregated_by=AggregationType.SENTENCE)
+        frame = TTSTextFrame(text)
         frame.includes_inter_frame_spaces = True
         await self.push_frame(frame)
 
@@ -1063,9 +1062,7 @@ class AWSNovaSonicLLMService(LLMService):
                 # TTSTextFrame would be ignored otherwise (the interruption frame
                 # would have cleared the assistant aggregator state).
                 await self.push_frame(LLMFullResponseStartFrame())
-                frame = TTSTextFrame(
-                    self._assistant_text_buffer, aggregated_by=AggregationType.SENTENCE
-                )
+                frame = TTSTextFrame(self._assistant_text_buffer)
                 frame.includes_inter_frame_spaces = True
                 await self.push_frame(frame)
             self._may_need_repush_assistant_text = False

src/pipecat/services/cartesia/tts.py

@@ -10,8 +10,7 @@ import base64
 import json
 import uuid
 import warnings
-from enum import Enum
-from typing import AsyncGenerator, List, Literal, Optional
+from typing import AsyncGenerator, List, Literal, Optional, Union
 
 from loguru import logger
 from pydantic import BaseModel, Field
@@ -126,72 +125,6 @@ def language_to_cartesia_language(language: Language) -> Optional[str]:
     return resolve_language(language, LANGUAGE_MAP, use_base_code=True)
 
 
-class CartesiaEmotion(str, Enum):
-    """Predefined Emotions supported by Cartesia."""
-
-    # Primary emotions supported by Cartesia
-    NEUTRAL = "neutral"
-    ANGRY = "angry"
-    EXCITED = "excited"
-    CONTENT = "content"
-    SAD = "sad"
-    SCARED = "scared"
-    # Additional emotions supported by Cartesia
-    HAPPY = "happy"
-    ENTHUSIASTIC = "enthusiastic"
-    ELATED = "elated"
-    EUPHORIC = "euphoric"
-    TRIUMPHANT = "triumphant"
-    AMAZED = "amazed"
-    SURPRISED = "surprised"
-    FLIRTATIOUS = "flirtatious"
-    JOKING_COMEDIC = "joking/comedic"
-    CURIOUS = "curious"
-    PEACEFUL = "peaceful"
-    SERENE = "serene"
-    CALM = "calm"
-    GRATEFUL = "grateful"
-    AFFECTIONATE = "affectionate"
-    TRUST = "trust"
-    SYMPATHETIC = "sympathetic"
-    ANTICIPATION = "anticipation"
-    MYSTERIOUS = "mysterious"
-    MAD = "mad"
-    OUTRAGED = "outraged"
-    FRUSTRATED = "frustrated"
-    AGITATED = "agitated"
-    THREATENED = "threatened"
-    DISGUSTED = "disgusted"
-    CONTEMPT = "contempt"
-    ENVIOUS = "envious"
-    SARCASTIC = "sarcastic"
-    IRONIC = "ironic"
-    DEJECTED = "dejected"
-    MELANCHOLIC = "melancholic"
-    DISAPPOINTED = "disappointed"
-    HURT = "hurt"
-    GUILTY = "guilty"
-    BORED = "bored"
-    TIRED = "tired"
-    REJECTED = "rejected"
-    NOSTALGIC = "nostalgic"
-    WISTFUL = "wistful"
-    APOLOGETIC = "apologetic"
-    HESITANT = "hesitant"
-    INSECURE = "insecure"
-    CONFUSED = "confused"
-    RESIGNED = "resigned"
-    ANXIOUS = "anxious"
-    PANICKED = "panicked"
-    ALARMED = "alarmed"
-    PROUD = "proud"
-    CONFIDENT = "confident"
-    DISTANT = "distant"
-    SKEPTICAL = "skeptical"
-    CONTEMPLATIVE = "contemplative"
-    DETERMINED = "determined"
-
-
 class CartesiaTTSService(AudioContextWordTTSService):
     """Cartesia TTS service with WebSocket streaming and word timestamps.
 
@@ -249,10 +182,6 @@ class CartesiaTTSService(AudioContextWordTTSService):
             container: Audio container format.
             params: Additional input parameters for voice customization.
             text_aggregator: Custom text aggregator for processing input text.
-
-                .. deprecated:: 0.0.95
-                    Use an LLMTextProcessor before the TTSService for custom text aggregation.
-
             aggregate_sentences: Whether to aggregate sentences within the TTSService.
             **kwargs: Additional arguments passed to the parent service.
         """
@@ -271,18 +200,10 @@ class CartesiaTTSService(AudioContextWordTTSService):
             push_text_frames=False,
             pause_frame_processing=True,
             sample_rate=sample_rate,
-            text_aggregator=text_aggregator,
+            text_aggregator=text_aggregator or SkipTagsAggregator([("<spell>", "</spell>")]),
             **kwargs,
         )
 
-        if not text_aggregator:
-            # Always skip tags added for spelled-out text
-            # Note: This is primarily to support backwards compatibility.
-            #    The preferred way of taking advantage of Cartesia SSML Tags is
-            #    to use an LLMTextProcessor and/or a text_transformer to identify
-            #    and insert these tags for the purpose of the TTS service alone.
-            self._text_aggregator = SkipTagsAggregator([("<spell>", "</spell>")])
-
         params = params or CartesiaTTSService.InputParams()
 
         self._api_key = api_key
@@ -336,27 +257,6 @@ class CartesiaTTSService(AudioContextWordTTSService):
         """
         return language_to_cartesia_language(language)
 
-    # A set of Cartesia-specific helpers for text transformations
-    def SPELL(text: str) -> str:
-        """Wrap text in Cartesia spell tag."""
-        return f"<spell>{text}</spell>"
-
-    def EMOTION_TAG(emotion: CartesiaEmotion) -> str:
-        """Convenience method to create an emotion tag."""
-        return f'<emotion value="{emotion}" />'
-
-    def PAUSE_TAG(seconds: float) -> str:
-        """Convenience method to create a pause tag."""
-        return f'<break time="{seconds}s" />'
-
-    def VOLUME_TAG(volume: float) -> str:
-        """Convenience method to create a volume tag."""
-        return f'<volume ratio="{volume}" />'
-
-    def SPEED_TAG(speed: float) -> str:
-        """Convenience method to create a speed tag."""
-        return f'<speed ratio="{speed}" />'
-
     def _is_cjk_language(self, language: str) -> bool:
         """Check if the given language is CJK (Chinese, Japanese, Korean).
 

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

@@ -27,7 +27,6 @@ from pydantic import BaseModel, Field
 from pipecat.adapters.schemas.tools_schema import ToolsSchema
 from pipecat.adapters.services.gemini_adapter import GeminiLLMAdapter
 from pipecat.frames.frames import (
-    AggregationType,
     BotStartedSpeakingFrame,
     BotStoppedSpeakingFrame,
     CancelFrame,
@@ -1647,7 +1646,7 @@ class GeminiLiveLLMService(LLMService):
             await self.push_frame(TTSStartedFrame())
             await self.push_frame(LLMFullResponseStartFrame())
 
-        frame = TTSTextFrame(text=text, aggregated_by=AggregationType.SENTENCE)
+        frame = TTSTextFrame(text=text)
         # Gemini Live text already includes any necessary inter-chunk spaces
         frame.includes_inter_frame_spaces = True
 

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

@@ -19,7 +19,6 @@ from pipecat.adapters.services.open_ai_realtime_adapter import (
     OpenAIRealtimeLLMAdapter,
 )
 from pipecat.frames.frames import (
-    AggregationType,
     BotStoppedSpeakingFrame,
     CancelFrame,
     EndFrame,
@@ -687,7 +686,7 @@ class OpenAIRealtimeLLMService(LLMService):
         # We receive audio transcript deltas (as opposed to text deltas) when
         # the output modality is "audio" (the default)
         if evt.delta:
-            frame = TTSTextFrame(evt.delta, aggregated_by=AggregationType.SENTENCE)
+            frame = TTSTextFrame(evt.delta)
             # OpenAI Realtime text already includes any necessary inter-chunk spaces
             frame.includes_inter_frame_spaces = True
             await self.push_frame(frame)

src/pipecat/services/openai_realtime_beta/openai.py

@@ -17,7 +17,6 @@ from loguru import logger
 
 from pipecat.adapters.services.open_ai_realtime_adapter import OpenAIRealtimeLLMAdapter
 from pipecat.frames.frames import (
-    AggregationType,
     BotStoppedSpeakingFrame,
     CancelFrame,
     EndFrame,
@@ -653,7 +652,7 @@ class OpenAIRealtimeBetaLLMService(LLMService):
     async def _handle_evt_audio_transcript_delta(self, evt):
         if evt.delta:
             await self.push_frame(LLMTextFrame(evt.delta))
-            await self.push_frame(TTSTextFrame(evt.delta, aggregated_by=AggregationType.SENTENCE))
+            await self.push_frame(TTSTextFrame(evt.delta))
 
     async def _handle_evt_speech_started(self, evt):
         await self._truncate_current_audio_response()

src/pipecat/services/rime/tts.py

@@ -113,10 +113,6 @@ class RimeTTSService(AudioContextWordTTSService):
             sample_rate: Audio sample rate in Hz.
             params: Additional configuration parameters.
             text_aggregator: Custom text aggregator for processing input text.
-
-                .. deprecated:: 0.0.95
-                    Use an LLMTextProcessor before the TTSService for custom text aggregation.
-
             aggregate_sentences: Whether to aggregate sentences within the TTSService.
             **kwargs: Additional arguments passed to parent class.
         """
@@ -127,17 +123,10 @@ class RimeTTSService(AudioContextWordTTSService):
             push_stop_frames=True,
             pause_frame_processing=True,
             sample_rate=sample_rate,
+            text_aggregator=text_aggregator or SkipTagsAggregator([("spell(", ")")]),
             **kwargs,
         )
 
-        if not text_aggregator:
-            # Always skip tags added for spelled-out text
-            # Note: This is primarily to support backwards compatibility.
-            #    The preferred way of taking advantage of Rime spelling is
-            #    to use an LLMTextProcessor and/or a text_transformer to identify
-            #    and insert these tags for the purpose of the TTS service alone.
-            self._text_aggregator = SkipTagsAggregator([("spell(", ")")])
-
         params = params or RimeTTSService.InputParams()
 
         # Store service configuration
@@ -163,7 +152,6 @@ class RimeTTSService(AudioContextWordTTSService):
         self._context_id = None  # Tracks current turn
         self._receive_task = None
         self._cumulative_time = 0  # Accumulates time across messages
-        self._extra_msg_fields = {}  # Extra fields for next message
 
     def can_generate_metrics(self) -> bool:
         """Check if this service can generate processing metrics.
@@ -193,31 +181,6 @@ class RimeTTSService(AudioContextWordTTSService):
         self._model = model
         await super().set_model(model)
 
-    # A set of Rime-specific helpers for text transformations
-    def SPELL(text: str) -> str:
-        """Wrap text in Rime spell function."""
-        return f"spell({text})"
-
-    def PAUSE_TAG(seconds: float) -> str:
-        """Convenience method to create a pause tag."""
-        return f"<{seconds * 1000}>"
-
-    def PRONOUNCE(self, text: str, word: str, phoneme: str) -> str:
-        """Convenience method to support Rime's custom pronunciations feature.
-
-        https://docs.rime.ai/api-reference/custom-pronunciation
-        """
-        self._extra_msg_fields["phonemizeBetweenBrackets"] = True
-        return text.replace(word, f"{phoneme}")
-
-    def INLINE_SPEED(self, text: str, speed: float) -> str:
-        """Convenience method to support inline speeds."""
-        if not self._extra_msg_fields:
-            self._extra_msg_fields = {}
-        speed_vals = self._extra_msg_fields.get("inlineSpeedAlpha", "").split(",")
-        self._extra_msg_fields["inlineSpeedAlpha"] = ",".join(speed_vals + [str(speed)])
-        return f"[{text}]"
-
     async def _update_settings(self, settings: Mapping[str, Any]):
         """Update service settings and reconnect if voice changed."""
         prev_voice = self._voice_id
@@ -230,11 +193,7 @@ class RimeTTSService(AudioContextWordTTSService):
 
     def _build_msg(self, text: str = "") -> dict:
         """Build JSON message for Rime API."""
-        msg = {"text": text, "contextId": self._context_id}
-        if self._extra_msg_fields:
-            msg |= self._extra_msg_fields
-            self._extra_msg_fields = {}
-        return msg
+        return {"text": text, "contextId": self._context_id}
 
     def _build_clear_msg(self) -> dict:
         """Build clear operation message."""

src/pipecat/services/tts_service.py

@@ -12,8 +12,6 @@ from typing import (
     Any,
     AsyncGenerator,
     AsyncIterator,
-    Awaitable,
-    Callable,
     Dict,
     List,
     Mapping,
@@ -25,8 +23,6 @@ from typing import (
 from loguru import logger
 
 from pipecat.frames.frames import (
-    AggregatedTextFrame,
-    AggregationType,
     BotStartedSpeakingFrame,
     BotStoppedSpeakingFrame,
     CancelFrame,
@@ -105,16 +101,6 @@ class TTSService(AIService):
         sample_rate: Optional[int] = None,
         # Text aggregator to aggregate incoming tokens and decide when to push to the TTS.
         text_aggregator: Optional[BaseTextAggregator] = None,
-        # Types of text aggregations that should not be spoken.
-        skip_aggregator_types: Optional[List[str]] = [],
-        # A list of callables to transform text before just before sending it to TTS.
-        # Each callable takes the aggregated text and its type, and returns the transformed text.
-        # To register, provide a list of tuples of (aggregation_type | '*', transform_function).
-        text_transforms: Optional[
-            List[
-                Tuple[AggregationType | str, Callable[[str, str | AggregationType], Awaitable[str]]]
-            ]
-        ] = None,
         # Text filter executed after text has been aggregated.
         text_filters: Optional[Sequence[BaseTextFilter]] = None,
         text_filter: Optional[BaseTextFilter] = None,
@@ -134,16 +120,6 @@ class TTSService(AIService):
             pause_frame_processing: Whether to pause frame processing during audio generation.
             sample_rate: Output sample rate for generated audio.
             text_aggregator: Custom text aggregator for processing incoming text.
-
-                .. deprecated:: 0.0.95
-                    Use an LLMTextProcessor before the TTSService for custom text aggregation.
-
-            skip_aggregator_types: List of aggregation types that should not be spoken.
-            text_transforms: A list of callables to transform text before just before sending it
-                to TTS. Each callable takes the aggregated text and its type, and returns the
-                transformed text. To register, provide a list of tuples of
-                (aggregation_type | '*', transform_function).
-
             text_filters: Sequence of text filters to apply after aggregation.
             text_filter: Single text filter (deprecated, use text_filters).
 
@@ -166,21 +142,6 @@ class TTSService(AIService):
         self._voice_id: str = ""
         self._settings: Dict[str, Any] = {}
         self._text_aggregator: BaseTextAggregator = text_aggregator or SimpleTextAggregator()
-        if text_aggregator:
-            import warnings
-
-            with warnings.catch_warnings():
-                warnings.simplefilter("always")
-                warnings.warn(
-                    "Parameter 'text_aggregator' is deprecated. Use an LLMTextProcessor before the TTSService for custom text aggregation.",
-                    DeprecationWarning,
-                )
-
-        self._skip_aggregator_types: List[str] = skip_aggregator_types or []
-        self._text_transforms: List[
-            Tuple[AggregationType | str, Callable[[str, AggregationType | str], Awaitable[str]]]
-        ] = text_transforms or []
-        # TODO: Deprecate _text_filters when added to LLMTextProcessor
         self._text_filters: Sequence[BaseTextFilter] = text_filters or []
         self._transport_destination: Optional[str] = transport_destination
         self._tracing_enabled: bool = False
@@ -337,39 +298,6 @@ class TTSService(AIService):
             await self.cancel_task(self._stop_frame_task)
             self._stop_frame_task = None
 
-    def add_text_transformer(
-        self,
-        transform_function: Callable[[str, AggregationType | str], Awaitable[str]],
-        aggregation_type: AggregationType | str = "*",
-    ):
-        """Transform text for a specific aggregation type.
-
-        Args:
-            transform_function: The function to apply for transformation. This function should take
-                the text and aggregation type as input and return the transformed text.
-                Ex.: async def my_transform(text: str, aggregation_type: str) -> str:
-            aggregation_type: The type of aggregation to transform. This value defaults to "*" indicating
-                the function should handle all text before sending to TTS.
-        """
-        self._text_transforms.append((aggregation_type, transform_function))
-
-    def remove_text_transformer(
-        self,
-        transform_function: Callable[[str, AggregationType | str], Awaitable[str]],
-        aggregation_type: AggregationType | str = "*",
-    ):
-        """Remove a text transformer for a specific aggregation type.
-
-        Args:
-            transform_function: The function to remove.
-            aggregation_type: The type of aggregation to remove the transformer for.
-        """
-        self._text_transforms = [
-            (agg_type, func)
-            for agg_type, func in self._text_transforms
-            if not (agg_type == aggregation_type and func == transform_function)
-        ]
-
     async def _update_settings(self, settings: Mapping[str, Any]):
         for key, value in settings.items():
             if key in self._settings:
@@ -425,8 +353,6 @@ class TTSService(AIService):
             and frame.skip_tts
         ):
             await self.push_frame(frame, direction)
-        elif isinstance(frame, AggregatedTextFrame):
-            await self._push_tts_frames(frame)
         elif (
             isinstance(frame, TextFrame)
             and not isinstance(frame, InterimTranscriptionFrame)
@@ -442,10 +368,10 @@ class TTSService(AIService):
             # pause to avoid audio overlapping.
             await self._maybe_pause_frame_processing()
 
-            aggregate = self._text_aggregator.text
+            sentence = self._text_aggregator.text
             await self._text_aggregator.reset()
             self._processing_text = False
-            await self._push_tts_frames(AggregatedTextFrame(aggregate.text, aggregate.type))
+            await self._push_tts_frames(sentence)
             if isinstance(frame, LLMFullResponseEndFrame):
                 if self._push_text_frames:
                     await self.push_frame(frame, direction)
@@ -454,7 +380,7 @@ class TTSService(AIService):
         elif isinstance(frame, TTSSpeakFrame):
             # Store if we were processing text or not so we can set it back.
             processing_text = self._processing_text
-            await self._push_tts_frames(AggregatedTextFrame(frame.text, AggregationType.SENTENCE))
+            await self._push_tts_frames(frame.text)
             # We pause processing incoming frames because we are sending data to
             # the TTS. We pause to avoid audio overlapping.
             await self._maybe_pause_frame_processing()
@@ -546,24 +472,13 @@ class TTSService(AIService):
         text: Optional[str] = None
         if not self._aggregate_sentences:
             text = frame.text
-            aggregated_by = "token"
         else:
-            aggregate = await self._text_aggregator.aggregate(frame.text)
-            if aggregate:
-                text = aggregate.text
-                aggregated_by = aggregate.type
+            text = await self._text_aggregator.aggregate(frame.text)
 
         if text:
-            logger.trace(f"Pushing TTS frames for text: {text}, {aggregated_by}")
-            await self._push_tts_frames(AggregatedTextFrame(text, aggregated_by))
-
-    async def _push_tts_frames(self, src_frame: AggregatedTextFrame):
-        type = src_frame.aggregated_by
-        text = src_frame.text
-        if type in self._skip_aggregator_types:
-            await self.push_frame(src_frame)
-            return
+            await self._push_tts_frames(text)
 
+    async def _push_tts_frames(self, text: str):
         # Remove leading newlines only
         text = text.lstrip("\n")
 
@@ -584,39 +499,15 @@ class TTSService(AIService):
             await filter.reset_interruption()
             text = await filter.filter(text)
 
-        if not text.strip():
-            await self.stop_processing_metrics()
-            return
-
-        # To support use cases that may want to know the text before it's spoken, we
-        # push the AggregatedTextFrame version before transforming and sending to TTS.
-        # However, we do not want to add this text to the assistant context until it
-        # is spoken, so we set append_to_context to False.
-        src_frame.append_to_context = False
-        await self.push_frame(src_frame)
-
-        # Note: Text transformations are meant to only affect the text sent to the TTS for
-        # TTS-specific purposes. This allows for explicit TTS modifications (e.g., inserting
-        # TTS supported tags for spelling or emotion or replacing an @ with "at"). For TTS
-        # services that support word-level timestamps, this CAN affect the resulting context
-        # since the TTSTextFrames are generated from the TTS output stream
-        transformed_text = text
-        for aggregation_type, transform in self._text_transforms:
-            if aggregation_type == type or aggregation_type == "*":
-                transformed_text = await transform(transformed_text, type)
-        await self.process_generator(self.run_tts(transformed_text))
+        if text:
+            await self.process_generator(self.run_tts(text))
 
         await self.stop_processing_metrics()
 
         if self._push_text_frames:
-            # In TTS services that support word timestamps, the TTSTextFrames
-            # are pushed as words are spoken. However, in the case where the TTS service
-            # does not support word timestamps (i.e. _push_text_frames is True), we send
-            # the original (non-transformed) text after the TTS generation has completed.
-            # This way, if we are interrupted, the text is not added to the assistant
-            # context and the context that IS added does not include TTS-specific tags
-            # or transformations.
-            frame = TTSTextFrame(text, aggregated_by=type)
+            # We send the original text after the audio. This way, if we are
+            # interrupted, the text is not added to the assistant context.
+            frame = TTSTextFrame(text)
             frame.includes_inter_frame_spaces = self.includes_inter_frame_spaces
             await self.push_frame(frame)
 
@@ -744,7 +635,7 @@ class WordTTSService(TTSService):
                 frame = TTSStoppedFrame()
                 frame.pts = last_pts
             else:
-                frame = TTSTextFrame(word, aggregated_by=AggregationType.WORD)
+                frame = TTSTextFrame(word)
                 frame.pts = self._initial_word_timestamp + timestamp
             if frame:
                 last_pts = frame.pts

src/pipecat/tests/utils.py

@@ -203,16 +203,8 @@ async def run_test(
             if not isinstance(frame, EndFrame) or not send_end_frame:
                 received_down_frames.append(frame)
 
-        down_frames_printed = "["
-        for frame in received_down_frames:
-            down_frames_printed += f"{frame.__class__.__name__}, "
-        down_frames_printed += "]"
-        expected_frames_printed = "["
-        for frame in expected_down_frames:
-            expected_frames_printed += f"{frame.__name__}, "
-        expected_frames_printed += "]"
-        print("received DOWN frames =", down_frames_printed)
-        print("expected DOWN frames =", expected_frames_printed)
+        print("received DOWN frames =", received_down_frames)
+        print("expected DOWN frames =", expected_down_frames)
 
         assert len(received_down_frames) == len(expected_down_frames)
 

src/pipecat/utils/text/base_text_aggregator.py

@@ -12,46 +12,9 @@ aggregated text should be sent for speech synthesis.
 """
 
 from abc import ABC, abstractmethod
-from dataclasses import dataclass
-from enum import Enum
 from typing import Optional
 
 
-class AggregationType(str, Enum):
-    """Built-in aggregation strings."""
-
-    SENTENCE = "sentence"
-    WORD = "word"
-
-    def __str__(self):
-        return self.value
-
-
-@dataclass
-class Aggregation:
-    """Data class representing aggregated text and its type.
-
-    An Aggregation object is created whenever a stream of text is aggregated by
-    a text aggregator. It contains the aggregated text and a type indicating
-    the nature of the aggregation.
-
-    Parameters:
-        text: The aggregated text content.
-        type: The type of aggregation the text represents (e.g., 'sentence', 'word', 'token', 'my_custom_aggregation').
-    """
-
-    text: str
-    type: str
-
-    def __str__(self) -> str:
-        """Return a string representation of the aggregation.
-
-        Returns:
-            A descriptive string showing the type and text of the aggregation.
-        """
-        return f"Aggregation by {self.type}: {self.text}"
-
-
 class BaseTextAggregator(ABC):
     """Base class for text aggregators in the Pipecat framework.
 
@@ -67,7 +30,7 @@ class BaseTextAggregator(ABC):
 
     @property
     @abstractmethod
-    def text(self) -> Aggregation:
+    def text(self) -> str:
         """Get the currently aggregated text.
 
         Subclasses must implement this property to return the text that has
@@ -79,13 +42,12 @@ class BaseTextAggregator(ABC):
         pass
 
     @abstractmethod
-    async def aggregate(self, text: str) -> Optional[Aggregation]:
+    async def aggregate(self, text: str) -> Optional[str]:
         """Aggregate the specified text with the currently accumulated text.
 
         This method should be implemented to define how the new text contributes
-        to the aggregation process. It returns the aggregated text and a string
-        describing how it was aggregated if it's ready to be processed,
-        or None otherwise.
+        to the aggregation process. It returns the updated aggregated text if
+        it's ready to be processed, or None otherwise.
 
         Subclasses should implement their specific logic for:
 

src/pipecat/utils/text/pattern_pair_aggregator.py

@@ -8,41 +8,19 @@
 
 This module provides an aggregator that identifies and processes content between
 pattern pairs (like XML tags or custom delimiters) in streaming text, with
-support for custom handlers and configurable actions for when a pattern is found.
+support for custom handlers and configurable pattern removal.
 """
 
 import re
-from enum import Enum
-from typing import Awaitable, Callable, List, Optional, Tuple
+from typing import Awaitable, Callable, Optional, Tuple
 
 from loguru import logger
 
 from pipecat.utils.string import match_endofsentence
-from pipecat.utils.text.base_text_aggregator import Aggregation, AggregationType, BaseTextAggregator
+from pipecat.utils.text.base_text_aggregator import BaseTextAggregator
 
 
-class MatchAction(Enum):
-    """Actions to take when a pattern pair is matched.
-
-    Parameters:
-        REMOVE: The text along with its delimiters will be removed from the streaming text.
-              Sentence aggregation will continue on as if this text did not exist.
-        KEEP: The delimiters will be removed, but the content between them will be kept.
-              Sentence aggregation will continue on with the internal text included.
-        AGGREGATE: The delimiters will be removed and the content between will be treated
-              as a separate aggregation. Any text before the start of the pattern will be
-              returned early, whether or not a complete sentence was found. Then the pattern
-              will be returned. Then the aggregation will continue on sentence matching after
-              the closing delimiter is found. The content between the delimiters is not
-              aggregated by sentence. It is aggregated as one single block of text.
-    """
-
-    REMOVE = "remove"
-    KEEP = "keep"
-    AGGREGATE = "aggregate"
-
-
-class PatternMatch(Aggregation):
+class PatternMatch:
     """Represents a matched pattern pair with its content.
 
     A PatternMatch object is created when a complete pattern pair is found
@@ -51,25 +29,25 @@ class PatternMatch(Aggregation):
     content between the patterns.
     """
 
-    def __init__(self, content: str, type: str, full_match: str):
+    def __init__(self, pattern_id: str, full_match: str, content: str):
         """Initialize a pattern match.
 
         Args:
-            type: The type of the matched pattern pair. It should be representative
-                   of the content type (e.g., 'sentence', 'code', 'speaker', 'custom').
+            pattern_id: The identifier of the matched pattern pair.
             full_match: The complete text including start and end patterns.
             content: The text content between the start and end patterns.
         """
-        super().__init__(text=content, type=type)
+        self.pattern_id = pattern_id
         self.full_match = full_match
+        self.content = content
 
     def __str__(self) -> str:
         """Return a string representation of the pattern match.
 
         Returns:
-            A descriptive string showing the pattern type and content.
+            A descriptive string showing the pattern ID and content.
         """
-        return f"PatternMatch(type={self.type}, text={self.text}, full_match={self.full_match})"
+        return f"PatternMatch(id={self.pattern_id}, content={self.content})"
 
 
 class PatternPairAggregator(BaseTextAggregator):
@@ -77,21 +55,16 @@ class PatternPairAggregator(BaseTextAggregator):
 
     This aggregator buffers text until it can identify complete pattern pairs
     (defined by start and end patterns), processes the content between these
-    patterns using registered handlers. By default, its aggregation method
-    returns text at sentence boundaries, and remove the content found between
-    any matched patterns. However, matched patterns can also be configured to
-    returned as a separate aggregation object containing the content between
-    their start and end patterns or left in, so that only the delimiters are
-    removed and a callback can be triggered.
-
-    This aggregator is particularly useful for processing structured content in
-    streaming text, such as XML tags, markdown formatting, or custom delimiters.
+    patterns using registered handlers, and returns text at sentence boundaries.
+    It's particularly useful for processing structured content in streaming text,
+    such as XML tags, markdown formatting, or custom delimiters.
 
     The aggregator ensures that patterns spanning multiple text chunks are
-    correctly identified.
+    correctly identified and handles cases where patterns contain sentence
+    boundaries.
     """
 
-    def __init__(self, **kwargs):
+    def __init__(self):
         """Initialize the pattern pair aggregator.
 
         Creates an empty aggregator with no patterns or handlers registered.
@@ -102,23 +75,16 @@ class PatternPairAggregator(BaseTextAggregator):
         self._handlers = {}
 
     @property
-    def text(self) -> Aggregation:
-        """Get the currently aggregated text.
+    def text(self) -> str:
+        """Get the currently buffered text.
 
         Returns:
-            The text that has been accumulated in the buffer.
+            The current text buffer content that hasn't been processed yet.
         """
-        pattern_start = self._match_start_of_pattern(self._text)
-        if pattern_start:
-            return Aggregation(self._text, pattern_start[1].get("type", AggregationType.SENTENCE))
-        return Aggregation(self._text, AggregationType.SENTENCE)
+        return self._text
 
-    def add_pattern(
-        self,
-        type: str,
-        start_pattern: str,
-        end_pattern: str,
-        action: MatchAction = MatchAction.REMOVE,
+    def add_pattern_pair(
+        self, pattern_id: str, start_pattern: str, end_pattern: str, remove_match: bool = True
     ) -> "PatternPairAggregator":
         """Add a pattern pair to detect in the text.
 
@@ -127,94 +93,41 @@ class PatternPairAggregator(BaseTextAggregator):
         the end pattern, and treat the content between them as a match.
 
         Args:
-            type: Identifier for this pattern pair. Should be unique and ideally descriptive.
-                  (e.g., 'code', 'speaker', 'custom'). type can not be 'sentence' or 'word' as
-                  those are reserved for the default behavior.
+            pattern_id: Unique identifier for this pattern pair.
             start_pattern: Pattern that marks the beginning of content.
             end_pattern: Pattern that marks the end of content.
-            action: What to do when a complete pattern is matched:
-                    - MatchAction.REMOVE: Remove the matched pattern from the text.
-                    - MatchAction.KEEP: Keep the matched pattern in the text and treat it as
-                                        normal text. This allows you to register handlers for
-                                        the pattern without affecting the aggregation logic.
-                    - MatchAction.AGGREGATE: Return the matched pattern as a separate
-                                             aggregation object.
+            remove_match: Whether to remove the matched content from the text.
 
         Returns:
             Self for method chaining.
         """
-        if type in [AggregationType.SENTENCE, AggregationType.WORD]:
-            raise ValueError(
-                f"The aggregation type '{type}' is reserved for default behavior and can not be used for custom patterns."
-            )
-        self._patterns[type] = {
+        self._patterns[pattern_id] = {
             "start": start_pattern,
             "end": end_pattern,
-            "type": type,
-            "action": action,
+            "remove_match": remove_match,
         }
         return self
 
-    def add_pattern_pair(
-        self, pattern_id: str, start_pattern: str, end_pattern: str, remove_match: bool = True
-    ):
-        """Add a pattern pair to detect in the text.
-
-        .. deprecated:: 0.0.95
-            This function is deprecated and will be removed in a future version.
-            Use `add_pattern` with a type and MatchAction instead.
-
-            This method calls `add_pattern` setting type with the provided pattern_id and action
-            to either MatchAction.REMOVE or MatchAction.KEEP based on `remove_match`.
-
-        Args:
-            pattern_id: Identifier for this pattern pair. Should be unique and ideally descriptive.
-                        (e.g., 'code', 'speaker', 'custom'). pattern_id can not be 'sentence' or 'word'
-                        as those arereserved for the default behavior.
-            start_pattern: Pattern that marks the beginning of content.
-            end_pattern: Pattern that marks the end of content.
-            remove_match: If True, the matched pattern will be removed from the text. (Same as MatchAction.REMOVE)
-                          If False, it will be kept and treated as normal text. (Same as MatchAction.KEEP)
-        """
-        import warnings
-
-        with warnings.catch_warnings():
-            warnings.simplefilter("once")
-            warnings.warn(
-                "add_pattern_pair with a pattern_id or remove_match is deprecated and will be"
-                " removed in a future version. Use add_pattern with a type and MatchAction instead",
-                DeprecationWarning,
-                stacklevel=2,
-            )
-
-        action = MatchAction.REMOVE if remove_match else MatchAction.KEEP
-        return self.add_pattern(
-            type=pattern_id,
-            start_pattern=start_pattern,
-            end_pattern=end_pattern,
-            action=action,
-        )
-
     def on_pattern_match(
-        self, type: str, handler: Callable[[PatternMatch], Awaitable[None]]
+        self, pattern_id: str, handler: Callable[[PatternMatch], Awaitable[None]]
     ) -> "PatternPairAggregator":
         """Register a handler for when a pattern pair is matched.
 
         The handler will be called whenever a complete match for the
-        specified type is found in the text.
+        specified pattern ID is found in the text.
 
         Args:
-            type: The type of the pattern pair to trigger the handler.
+            pattern_id: ID of the pattern pair to match.
             handler: Async function to call when pattern is matched.
                      The function should accept a PatternMatch object.
 
         Returns:
             Self for method chaining.
         """
-        self._handlers[type] = handler
+        self._handlers[pattern_id] = handler
         return self
 
-    async def _process_complete_patterns(self, text: str) -> Tuple[List[PatternMatch], str]:
+    async def _process_complete_patterns(self, text: str) -> Tuple[str, bool]:
         """Process all complete pattern pairs in the text.
 
         Searches for all complete pattern pairs in the text, calls the
@@ -224,19 +137,19 @@ class PatternPairAggregator(BaseTextAggregator):
             text: The text to process for pattern matches.
 
         Returns:
-            Tuple of (all_matches, processed_text) where:
+            Tuple of (processed_text, was_modified) where:
 
-            - all_matches is a list of all pattern matches found. Note: There really should only ever be 1.
-            - processed_text is the text after processing patterns. If no patterns are found, it will be the same as input text.
+            - processed_text is the text after processing patterns
+            - was_modified indicates whether any changes were made
         """
-        all_matches = []
         processed_text = text
+        modified = False
 
-        for type, pattern_info in self._patterns.items():
+        for pattern_id, pattern_info in self._patterns.items():
             # Escape special regex characters in the patterns
             start = re.escape(pattern_info["start"])
             end = re.escape(pattern_info["end"])
-            action = pattern_info["action"]
+            remove_match = pattern_info["remove_match"]
 
             # Create regex to match from start pattern to end pattern
             # The .*? is non-greedy to handle nested patterns
@@ -251,24 +164,25 @@ class PatternPairAggregator(BaseTextAggregator):
                 full_match = match.group(0)  # Full match including patterns
 
                 # Create pattern match object
-                pattern_match = PatternMatch(content=content, type=type, full_match=full_match)
+                pattern_match = PatternMatch(
+                    pattern_id=pattern_id, full_match=full_match, content=content
+                )
 
                 # Call the appropriate handler if registered
-                if type in self._handlers:
+                if pattern_id in self._handlers:
                     try:
-                        await self._handlers[type](pattern_match)
+                        await self._handlers[pattern_id](pattern_match)
                     except Exception as e:
-                        logger.error(f"Error in pattern handler for {type}: {e}")
+                        logger.error(f"Error in pattern handler for {pattern_id}: {e}")
 
                 # Remove the pattern from the text if configured
-                if action == MatchAction.REMOVE:
+                if remove_match:
                     processed_text = processed_text.replace(full_match, "", 1)
-                else:
-                    all_matches.append(pattern_match)
+                    modified = True
 
-        return all_matches, processed_text
+        return processed_text, modified
 
-    def _match_start_of_pattern(self, text: str) -> Optional[Tuple[int, dict]]:
+    def _has_incomplete_patterns(self, text: str) -> bool:
         """Check if text contains incomplete pattern pairs.
 
         Determines whether the text contains any start patterns without
@@ -278,10 +192,9 @@ class PatternPairAggregator(BaseTextAggregator):
             text: The text to check for incomplete patterns.
 
         Returns:
-            A tuple of (start_index, pattern_info) if an incomplete pattern is found,
-            or None if no patterns are found or all patterns are complete.
+            True if there are incomplete patterns, False otherwise.
         """
-        for type, pattern_info in self._patterns.items():
+        for pattern_id, pattern_info in self._patterns.items():
             start = pattern_info["start"]
             end = pattern_info["end"]
 
@@ -290,16 +203,12 @@ class PatternPairAggregator(BaseTextAggregator):
             end_count = text.count(end)
 
             # If there are more starts than ends, we have incomplete patterns
-            # Again, this is written generically but there only ever should
-            # be one pattern active at a time, so the counts should be 0 or 1.
-            # Which is why we base the return on the first found.
             if start_count > end_count:
-                start_index = text.find(start)
-                return [start_index, pattern_info]
+                return True
 
-        return None
+        return False
 
-    async def aggregate(self, text: str) -> Optional[PatternMatch]:
+    async def aggregate(self, text: str) -> Optional[str]:
         """Aggregate text and process pattern pairs.
 
         This method adds the new text to the buffer, processes any complete pattern
@@ -318,34 +227,16 @@ class PatternPairAggregator(BaseTextAggregator):
         self._text += text
 
         # Process any complete patterns in the buffer
-        patterns, processed_text = await self._process_complete_patterns(self._text)
+        processed_text, modified = await self._process_complete_patterns(self._text)
 
-        self._text = processed_text
-
-        if len(patterns) > 0:
-            if len(patterns) > 1:
-                logger.warning(
-                    f"Multiple patterns matched: {[p.type for p in patterns]}. Only the first pattern will be returned."
-                )
-            # If the pattern found is set to be aggregated, return it
-            action = self._patterns[patterns[0].type].get("action", MatchAction.REMOVE)
-            if action == MatchAction.AGGREGATE:
-                self._text = ""
-                return patterns[0]
+        # Only update the buffer if modifications were made
+        if modified:
+            self._text = processed_text
 
         # Check if we have incomplete patterns
-        pattern_start = self._match_start_of_pattern(self._text)
-        if pattern_start is not None:
-            # If the start pattern is at the beginning or should not be separately aggregated, return None
-            if (
-                pattern_start[0] == 0
-                or pattern_start[1].get("action", MatchAction.REMOVE) != MatchAction.AGGREGATE
-            ):
-                return None
-            # Otherwise, strip the text up to the start pattern and return it
-            result = self._text[: pattern_start[0]]
-            self._text = self._text[pattern_start[0] :]
-            return PatternMatch(content=result, type=AggregationType.SENTENCE, full_match=result)
+        if self._has_incomplete_patterns(self._text):
+            # Still waiting for complete patterns
+            return None
 
         # Find sentence boundary if no incomplete patterns
         eos_marker = match_endofsentence(self._text)
@@ -353,7 +244,7 @@ class PatternPairAggregator(BaseTextAggregator):
             # Extract text up to the sentence boundary
             result = self._text[:eos_marker]
             self._text = self._text[eos_marker:]
-            return PatternMatch(content=result, type=AggregationType.SENTENCE, full_match=result)
+            return result
 
         # No complete sentence found yet
         return None

src/pipecat/utils/text/simple_text_aggregator.py

@@ -14,7 +14,7 @@ text processing scenarios.
 from typing import Optional
 
 from pipecat.utils.string import match_endofsentence
-from pipecat.utils.text.base_text_aggregator import Aggregation, AggregationType, BaseTextAggregator
+from pipecat.utils.text.base_text_aggregator import BaseTextAggregator
 
 
 class SimpleTextAggregator(BaseTextAggregator):
@@ -33,15 +33,15 @@ class SimpleTextAggregator(BaseTextAggregator):
         self._text = ""
 
     @property
-    def text(self) -> Aggregation:
+    def text(self) -> str:
         """Get the currently aggregated text.
 
         Returns:
             The text that has been accumulated in the buffer.
         """
-        return Aggregation(self._text, AggregationType.SENTENCE)
+        return self._text
 
-    async def aggregate(self, text: str) -> Optional[Aggregation]:
+    async def aggregate(self, text: str) -> Optional[str]:
         """Aggregate text and return completed sentences.
 
         Adds the new text to the buffer and checks for end-of-sentence markers.
@@ -64,7 +64,7 @@ class SimpleTextAggregator(BaseTextAggregator):
             result = self._text[:eos_end_marker]
             self._text = self._text[eos_end_marker:]
 
-        return Aggregation(result, AggregationType.SENTENCE) if result else None
+        return result
 
     async def handle_interruption(self):
         """Handle interruptions by clearing the text buffer.

src/pipecat/utils/text/skip_tags_aggregator.py

@@ -14,7 +14,7 @@ as a unit regardless of internal punctuation.
 from typing import Optional, Sequence
 
 from pipecat.utils.string import StartEndTags, match_endofsentence, parse_start_end_tags
-from pipecat.utils.text.base_text_aggregator import Aggregation, AggregationType, BaseTextAggregator
+from pipecat.utils.text.base_text_aggregator import BaseTextAggregator
 
 
 class SkipTagsAggregator(BaseTextAggregator):
@@ -49,9 +49,9 @@ class SkipTagsAggregator(BaseTextAggregator):
         Returns:
             The current text buffer content that hasn't been processed yet.
         """
-        return Aggregation(self._text, AggregationType.SENTENCE)
+        return self._text
 
-    async def aggregate(self, text: str) -> Optional[Aggregation]:
+    async def aggregate(self, text: str) -> Optional[str]:
         """Aggregate text while respecting tag boundaries.
 
         This method adds the new text to the buffer, processes any complete
@@ -80,7 +80,7 @@ class SkipTagsAggregator(BaseTextAggregator):
                 # Extract text up to the sentence boundary
                 result = self._text[:eos_marker]
                 self._text = self._text[eos_marker:]
-                return Aggregation(result, AggregationType.SENTENCE)
+                return result
 
         # No complete sentence found yet
         return None

src/pipecat/utils/tracing/service_decorators.py

@@ -23,7 +23,7 @@ if TYPE_CHECKING:
     from opentelemetry import context as context_api
     from opentelemetry import trace
 
-from pipecat.processors.aggregators.llm_context import LLMContext
+from pipecat.processors.aggregators.llm_context import NOT_GIVEN, LLMContext
 from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
 from pipecat.utils.tracing.service_attributes import (
     add_gemini_live_span_attributes,
@@ -399,11 +399,6 @@ def traced_llm(func: Optional[Callable] = None, *, name: Optional[str] = None) -
                                 if hasattr(self, "get_llm_adapter"):
                                     adapter = self.get_llm_adapter()
                                     messages = adapter.get_messages_for_logging(context)
-                            elif hasattr(context, "get_messages"):
-                                # Fallback for unknown context types
-                                messages = context.get_messages()
-                            elif hasattr(context, "messages"):
-                                messages = context.messages
 
                             # Serialize messages if available
                             if messages:
@@ -424,15 +419,10 @@ def traced_llm(func: Optional[Callable] = None, *, name: Optional[str] = None) -
                                 if hasattr(self, "get_llm_adapter") and hasattr(context, "tools"):
                                     adapter = self.get_llm_adapter()
                                     tools = adapter.from_standard_tools(context.tools)
-                            elif hasattr(context, "tools"):
-                                # Fallback for unknown context types
-                                tools = context.tools
 
                             # Serialize and count tools if available
-                            # Check if tools is not None and not NOT_GIVEN (using attribute check as fallback)
-                            if tools is not None and not (
-                                hasattr(tools, "__name__") and tools.__name__ == "NOT_GIVEN"
-                            ):
+                            # Check if tools is not None and not NOT_GIVEN
+                            if tools is not None and tools is not NOT_GIVEN:
                                 serialized_tools = json.dumps(tools)
                                 tool_count = len(tools) if isinstance(tools, list) else 1
 

tests/test_pattern_pair_aggregator.py

@@ -7,42 +7,30 @@
 import unittest
 from unittest.mock import AsyncMock
 
-from pipecat.utils.text.pattern_pair_aggregator import (
-    MatchAction,
-    PatternMatch,
-    PatternPairAggregator,
-)
+from pipecat.utils.text.pattern_pair_aggregator import PatternMatch, PatternPairAggregator
 
 
 class TestPatternPairAggregator(unittest.IsolatedAsyncioTestCase):
     def setUp(self):
         self.aggregator = PatternPairAggregator()
         self.test_handler = AsyncMock()
-        self.code_handler = AsyncMock()
 
         # Add a test pattern
         self.aggregator.add_pattern_pair(
             pattern_id="test_pattern",
             start_pattern="<test>",
             end_pattern="</test>",
-        )
-        self.aggregator.add_pattern(
-            type="code_pattern",
-            start_pattern="<code>",
-            end_pattern="</code>",
-            action=MatchAction.AGGREGATE,
+            remove_match=True,
         )
 
         # Register the mock handler
         self.aggregator.on_pattern_match("test_pattern", self.test_handler)
-        self.aggregator.on_pattern_match("code_pattern", self.code_handler)
 
     async def test_pattern_match_and_removal(self):
         # First part doesn't complete the pattern
         result = await self.aggregator.aggregate("Hello <test>pattern")
         self.assertIsNone(result)
-        self.assertEqual(self.aggregator.text.text, "Hello <test>pattern")
-        self.assertEqual(self.aggregator.text.type, "test_pattern")
+        self.assertEqual(self.aggregator.text, "Hello <test>pattern")
 
         # Second part completes the pattern and includes an exclamation point
         result = await self.aggregator.aggregate(" content</test>!")
@@ -51,49 +39,20 @@ class TestPatternPairAggregator(unittest.IsolatedAsyncioTestCase):
         self.test_handler.assert_called_once()
         call_args = self.test_handler.call_args[0][0]
         self.assertIsInstance(call_args, PatternMatch)
-        self.assertEqual(call_args.type, "test_pattern")
+        self.assertEqual(call_args.pattern_id, "test_pattern")
         self.assertEqual(call_args.full_match, "<test>pattern content</test>")
-        self.assertEqual(call_args.text, "pattern content")
+        self.assertEqual(call_args.content, "pattern content")
 
         # The exclamation point should be treated as a sentence boundary,
         # so the result should include just text up to and including "!"
-        self.assertEqual(result.text, "Hello !")
-        self.assertEqual(result.type, "sentence")
+        self.assertEqual(result, "Hello !")
 
         # Next sentence should be processed separately
         result = await self.aggregator.aggregate(" This is another sentence.")
-        self.assertEqual(result.text, " This is another sentence.")
+        self.assertEqual(result, " This is another sentence.")
 
         # Buffer should be empty after returning a complete sentence
-        self.assertEqual(self.aggregator.text.text, "")
-
-    async def test_pattern_match_and_aggregate(self):
-        # First part doesn't complete the pattern
-        result = await self.aggregator.aggregate("Here is code <code>pattern")
-        self.assertEqual(result.text, "Here is code ")
-        self.assertEqual(self.aggregator.text.text, "<code>pattern")
-        self.assertEqual(self.aggregator.text.type, "code_pattern")
-
-        # Second part completes the pattern and includes an exclamation point
-        result = await self.aggregator.aggregate(" content</code>")
-
-        # Verify the handler was called with correct PatternMatch object
-        self.code_handler.assert_called_once()
-        call_args = self.code_handler.call_args[0][0]
-        self.assertIsInstance(call_args, PatternMatch)
-        self.assertEqual(call_args.type, "code_pattern")
-        self.assertEqual(call_args.full_match, "<code>pattern content</code>")
-        self.assertEqual(call_args.text, "pattern content")
-        self.assertEqual(result.text, "pattern content")
-        self.assertEqual(result.type, "code_pattern")
-
-        # Next sentence should be processed separately
-        result = await self.aggregator.aggregate(" This is another sentence.")
-        self.assertEqual(result.text, " This is another sentence.")
-        self.assertEqual(result.type, "sentence")
-
-        # Buffer should be empty after returning a complete sentence
-        self.assertEqual(self.aggregator.text.text, "")
+        self.assertEqual(self.aggregator.text, "")
 
     async def test_incomplete_pattern(self):
         # Add text with incomplete pattern
@@ -106,30 +65,26 @@ class TestPatternPairAggregator(unittest.IsolatedAsyncioTestCase):
         self.test_handler.assert_not_called()
 
         # Buffer should contain the incomplete text
-        self.assertEqual(self.aggregator.text.text, "Hello <test>pattern content")
-        self.assertEqual(self.aggregator.text.type, "test_pattern")
+        self.assertEqual(self.aggregator.text, "Hello <test>pattern content")
 
         # Reset and confirm buffer is cleared
         await self.aggregator.reset()
-        self.assertEqual(self.aggregator.text.text, "")
+        self.assertEqual(self.aggregator.text, "")
 
     async def test_multiple_patterns(self):
         # Set up multiple patterns and handlers
         voice_handler = AsyncMock()
         emphasis_handler = AsyncMock()
 
-        self.aggregator.add_pattern(
-            type="voice",
-            start_pattern="<voice>",
-            end_pattern="</voice>",
-            action=MatchAction.REMOVE,
+        self.aggregator.add_pattern_pair(
+            pattern_id="voice", start_pattern="<voice>", end_pattern="</voice>", remove_match=True
         )
 
-        self.aggregator.add_pattern(
-            type="emphasis",
+        self.aggregator.add_pattern_pair(
+            pattern_id="emphasis",
             start_pattern="<em>",
             end_pattern="</em>",
-            action=MatchAction.KEEP,  # Keep emphasis tags
+            remove_match=False,  # Keep emphasis tags
         )
 
         self.aggregator.on_pattern_match("voice", voice_handler)
@@ -142,19 +97,19 @@ class TestPatternPairAggregator(unittest.IsolatedAsyncioTestCase):
         # Both handlers should be called with correct data
         voice_handler.assert_called_once()
         voice_match = voice_handler.call_args[0][0]
-        self.assertEqual(voice_match.type, "voice")
-        self.assertEqual(voice_match.text, "female")
+        self.assertEqual(voice_match.pattern_id, "voice")
+        self.assertEqual(voice_match.content, "female")
 
         emphasis_handler.assert_called_once()
         emphasis_match = emphasis_handler.call_args[0][0]
-        self.assertEqual(emphasis_match.type, "emphasis")
-        self.assertEqual(emphasis_match.text, "very")
+        self.assertEqual(emphasis_match.pattern_id, "emphasis")
+        self.assertEqual(emphasis_match.content, "very")
 
         # Voice pattern should be removed, emphasis pattern should remain
-        self.assertEqual(result.text, "Hello  I am <em>very</em> excited to meet you!")
+        self.assertEqual(result, "Hello  I am <em>very</em> excited to meet you!")
 
         # Buffer should be empty
-        self.assertEqual(self.aggregator.text.text, "")
+        self.assertEqual(self.aggregator.text, "")
 
     async def test_handle_interruption(self):
         # Start with incomplete pattern
@@ -165,7 +120,7 @@ class TestPatternPairAggregator(unittest.IsolatedAsyncioTestCase):
         await self.aggregator.handle_interruption()
 
         # Buffer should be cleared
-        self.assertEqual(self.aggregator.text.text, "")
+        self.assertEqual(self.aggregator.text, "")
 
         # Handler should not have been called
         self.test_handler.assert_not_called()
@@ -183,10 +138,10 @@ class TestPatternPairAggregator(unittest.IsolatedAsyncioTestCase):
         # Handler should be called with entire content
         self.test_handler.assert_called_once()
         call_args = self.test_handler.call_args[0][0]
-        self.assertEqual(call_args.text, "This is sentence one. This is sentence two.")
+        self.assertEqual(call_args.content, "This is sentence one. This is sentence two.")
 
         # Pattern should be removed, resulting in text with sentences merged
-        self.assertEqual(result.text, "Hello  Final sentence.")
+        self.assertEqual(result, "Hello  Final sentence.")
 
         # Buffer should be empty
-        self.assertEqual(self.aggregator.text.text, "")
+        self.assertEqual(self.aggregator.text, "")

tests/test_piper_tts.py

@@ -13,7 +13,6 @@ import pytest
 from aiohttp import web
 
 from pipecat.frames.frames import (
-    AggregatedTextFrame,
     ErrorFrame,
     TTSAudioRawFrame,
     TTSSpeakFrame,
@@ -75,7 +74,6 @@ async def test_run_piper_tts_success(aiohttp_client):
         ]
 
         expected_returned_frames = [
-            AggregatedTextFrame,
             TTSStartedFrame,
             TTSAudioRawFrame,
             TTSAudioRawFrame,
@@ -123,7 +121,7 @@ async def test_run_piper_tts_error(aiohttp_client):
             TTSSpeakFrame(text="Error case."),
         ]
 
-        expected_down_frames = [AggregatedTextFrame, TTSStoppedFrame, TTSTextFrame]
+        expected_down_frames = [TTSStoppedFrame, TTSTextFrame]
 
         expected_up_frames = [ErrorFrame]
 

tests/test_simple_text_aggregator.py

@@ -15,20 +15,15 @@ class TestSimpleTextAggregator(unittest.IsolatedAsyncioTestCase):
 
     async def test_reset_aggregations(self):
         assert await self.aggregator.aggregate("Hello ") == None
-        assert self.aggregator.text.text == "Hello "
+        assert self.aggregator.text == "Hello "
         await self.aggregator.reset()
-        assert self.aggregator.text.text == ""
+        assert self.aggregator.text == ""
 
     async def test_simple_sentence(self):
         assert await self.aggregator.aggregate("Hello ") == None
-        aggregate = await self.aggregator.aggregate("Pipecat!")
-        assert aggregate.text == "Hello Pipecat!"
-        assert aggregate.type == "sentence"
-        assert self.aggregator.text.text == ""
+        assert await self.aggregator.aggregate("Pipecat!") == "Hello Pipecat!"
+        assert self.aggregator.text == ""
 
     async def test_multiple_sentences(self):
-        aggregate = await self.aggregator.aggregate("Hello Pipecat! How are ")
-        assert aggregate.text == "Hello Pipecat!"
-        assert self.aggregator.text.text == " How are "
-        aggregate = await self.aggregator.aggregate("you?")
-        assert aggregate.text == " How are you?"
+        assert await self.aggregator.aggregate("Hello Pipecat! How are ") == "Hello Pipecat!"
+        assert await self.aggregator.aggregate("you?") == " How are you?"

tests/test_skip_tags_aggregator.py

@@ -18,18 +18,16 @@ class TestSkipTagsAggregator(unittest.IsolatedAsyncioTestCase):
 
         # No tags involved, aggregate at end of sentence.
         result = await self.aggregator.aggregate("Hello Pipecat!")
-        self.assertEqual(result.text, "Hello Pipecat!")
-        self.assertEqual(result.type, "sentence")
-        self.assertEqual(self.aggregator.text.text, "")
+        self.assertEqual(result, "Hello Pipecat!")
+        self.assertEqual(self.aggregator.text, "")
 
     async def test_basic_tags(self):
         await self.aggregator.reset()
 
         # Tags involved, avoid aggregation during tags.
         result = await self.aggregator.aggregate("My email is <spell>foo@pipecat.ai</spell>.")
-        self.assertEqual(result.text, "My email is <spell>foo@pipecat.ai</spell>.")
-        self.assertEqual(result.type, "sentence")
-        self.assertEqual(self.aggregator.text.text, "")
+        self.assertEqual(result, "My email is <spell>foo@pipecat.ai</spell>.")
+        self.assertEqual(self.aggregator.text, "")
 
     async def test_streaming_tags(self):
         await self.aggregator.reset()
@@ -37,22 +35,20 @@ class TestSkipTagsAggregator(unittest.IsolatedAsyncioTestCase):
         # Tags involved, stream small chunk of texts.
         result = await self.aggregator.aggregate("My email is <sp")
         self.assertIsNone(result)
-        self.assertEqual(self.aggregator.text.text, "My email is <sp")
+        self.assertEqual(self.aggregator.text, "My email is <sp")
 
         result = await self.aggregator.aggregate("ell>foo.")
         self.assertIsNone(result)
-        self.assertEqual(self.aggregator.text.text, "My email is <spell>foo.")
+        self.assertEqual(self.aggregator.text, "My email is <spell>foo.")
 
         result = await self.aggregator.aggregate("bar@pipecat.")
         self.assertIsNone(result)
-        self.assertEqual(self.aggregator.text.text, "My email is <spell>foo.bar@pipecat.")
+        self.assertEqual(self.aggregator.text, "My email is <spell>foo.bar@pipecat.")
 
         result = await self.aggregator.aggregate("ai</spe")
         self.assertIsNone(result)
-        self.assertEqual(self.aggregator.text.text, "My email is <spell>foo.bar@pipecat.ai</spe")
-        self.assertEqual(self.aggregator.text.type, "sentence")
+        self.assertEqual(self.aggregator.text, "My email is <spell>foo.bar@pipecat.ai</spe")
 
         result = await self.aggregator.aggregate("ll>.")
-        self.assertEqual(result.text, "My email is <spell>foo.bar@pipecat.ai</spell>.")
-        self.assertEqual(self.aggregator.text.text, "")
-        self.assertEqual(self.aggregator.text.type, "sentence")
+        self.assertEqual(result, "My email is <spell>foo.bar@pipecat.ai</spell>.")
+        self.assertEqual(self.aggregator.text, "")

tests/test_transcript_processor.py

@@ -11,7 +11,6 @@ from datetime import datetime, timezone
 from typing import List, Tuple, cast
 
 from pipecat.frames.frames import (
-    AggregationType,
     BotStartedSpeakingFrame,
     BotStoppedSpeakingFrame,
     CancelFrame,
@@ -131,11 +130,11 @@ class TestUserTranscriptProcessor(unittest.IsolatedAsyncioTestCase):
         frames_to_send = [
             BotStartedSpeakingFrame(),
             SleepFrame(),  # Wait for StartedSpeaking to process
-            TTSTextFrame(text="Hello", aggregated_by=AggregationType.WORD),
-            TTSTextFrame(text="world!", aggregated_by=AggregationType.WORD),
-            TTSTextFrame(text="How", aggregated_by=AggregationType.WORD),
-            TTSTextFrame(text="are", aggregated_by=AggregationType.WORD),
-            TTSTextFrame(text="you?", aggregated_by=AggregationType.WORD),
+            TTSTextFrame(text="Hello"),
+            TTSTextFrame(text="world!"),
+            TTSTextFrame(text="How"),
+            TTSTextFrame(text="are"),
+            TTSTextFrame(text="you?"),
             SleepFrame(),  # Wait for text frames to queue
             BotStoppedSpeakingFrame(),
         ]
@@ -196,9 +195,9 @@ class TestUserTranscriptProcessor(unittest.IsolatedAsyncioTestCase):
         frames_to_send = [
             BotStartedSpeakingFrame(),
             SleepFrame(),
-            TTSTextFrame(text="", aggregated_by=AggregationType.WORD),  # Empty text
-            TTSTextFrame(text="   ", aggregated_by=AggregationType.WORD),  # Just whitespace
-            TTSTextFrame(text="\n", aggregated_by=AggregationType.WORD),  # Just newline
+            TTSTextFrame(text=""),  # Empty text
+            TTSTextFrame(text="   "),  # Just whitespace
+            TTSTextFrame(text="\n"),  # Just newline
             BotStoppedSpeakingFrame(),
             # Pipeline ends here; run_test will automatically send EndFrame
         ]
@@ -236,14 +235,14 @@ class TestUserTranscriptProcessor(unittest.IsolatedAsyncioTestCase):
         frames_to_send = [
             BotStartedSpeakingFrame(),
             SleepFrame(),
-            TTSTextFrame(text="Hello", aggregated_by=AggregationType.WORD),
-            TTSTextFrame(text="world!", aggregated_by=AggregationType.WORD),
+            TTSTextFrame(text="Hello"),
+            TTSTextFrame(text="world!"),
             SleepFrame(),
             InterruptionFrame(),  # User interrupts here
             SleepFrame(),
             BotStartedSpeakingFrame(),
-            TTSTextFrame(text="New", aggregated_by=AggregationType.WORD),
-            TTSTextFrame(text="response", aggregated_by=AggregationType.WORD),
+            TTSTextFrame(text="New"),
+            TTSTextFrame(text="response"),
             SleepFrame(),
             BotStoppedSpeakingFrame(),
         ]
@@ -300,8 +299,8 @@ class TestUserTranscriptProcessor(unittest.IsolatedAsyncioTestCase):
         frames_to_send = [
             BotStartedSpeakingFrame(),
             SleepFrame(),
-            TTSTextFrame(text="Hello", aggregated_by=AggregationType.WORD),
-            TTSTextFrame(text="world", aggregated_by=AggregationType.WORD),
+            TTSTextFrame(text="Hello"),
+            TTSTextFrame(text="world"),
             # Pipeline ends here; run_test will automatically send EndFrame
         ]
 
@@ -339,8 +338,8 @@ class TestUserTranscriptProcessor(unittest.IsolatedAsyncioTestCase):
         frames_to_send = [
             BotStartedSpeakingFrame(),
             SleepFrame(),
-            TTSTextFrame(text="Hello", aggregated_by=AggregationType.WORD),
-            TTSTextFrame(text="world", aggregated_by=AggregationType.WORD),
+            TTSTextFrame(text="Hello"),
+            TTSTextFrame(text="world"),
             SleepFrame(),  # Ensure messages are processed
             CancelFrame(),
         ]
@@ -402,8 +401,8 @@ class TestUserTranscriptProcessor(unittest.IsolatedAsyncioTestCase):
         frames_to_send = [
             BotStartedSpeakingFrame(),
             SleepFrame(),
-            TTSTextFrame(text="Assistant", aggregated_by=AggregationType.WORD),
-            TTSTextFrame(text="message", aggregated_by=AggregationType.WORD),
+            TTSTextFrame(text="Assistant"),
+            TTSTextFrame(text="message"),
             BotStoppedSpeakingFrame(),
         ]
 
@@ -440,7 +439,7 @@ class TestUserTranscriptProcessor(unittest.IsolatedAsyncioTestCase):
 
         # Test the specific pattern shared
         def make_tts_text_frame(text: str) -> TTSTextFrame:
-            frame = TTSTextFrame(text=text, aggregated_by=AggregationType.WORD)
+            frame = TTSTextFrame(text=text)
             frame.includes_inter_frame_spaces = True
             return frame
 

uv.lock

@@ -36,12 +36,12 @@ wheels = [
 
 [[package]]
 name = "aic-sdk"
-version = "1.0.2"
+version = "1.1.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "numpy" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/51/90/b02e853e863c303f8456c689b42ac24ad403b781adc9642d0a91ed4bed7e/aic_sdk-1.0.2.tar.gz", hash = "sha256:239097dd3aaa8a8a0fd7542b75d2510cb34144caec796370639b7c636acbc56e", size = 32059, upload-time = "2025-08-24T09:20:03.9Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/99/83/bf38b95d98c67b8ebc574fb4a4f23c07a3740b51992d7522976173d30b98/aic_sdk-1.1.0.tar.gz", hash = "sha256:04e08df695581c8cb4db8acca20e73815e9f449e7bd08e0162fd55518c727963", size = 34954, upload-time = "2025-11-11T20:45:24.25Z" }
 
 [[package]]
 name = "aioboto3"
@@ -4647,7 +4647,7 @@ docs = [
 [package.metadata]
 requires-dist = [
     { name = "accelerate", marker = "extra == 'moondream'", specifier = "~=1.10.0" },
-    { name = "aic-sdk", marker = "extra == 'aic'", specifier = "~=1.0.1" },
+    { name = "aic-sdk", marker = "extra == 'aic'", specifier = "~=1.1.0" },
     { name = "aioboto3", marker = "extra == 'aws'", specifier = "~=15.0.0" },
     { name = "aiofiles", specifier = ">=24.1.0,<25" },
     { name = "aiohttp", specifier = ">=3.11.12,<4" },