example: realtime with transcripts

2025-02-26 16:29:07 +08:00
85 changed files with 433 additions and 3356 deletions
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -1,8 +1,7 @@
 repos:
-  - repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: v0.9.7
+  - repo: local
    hooks:
-      - id: ruff
-        language_version: python3
-        args: [ --select,  I, ]
-      - id: ruff-format
+      - id: ruff-format-hook
+        name: Check ruff formatting
+        entry: sh scripts/pre-commit.sh
+        language: system
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -9,65 +9,6 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0

 ### Added

- Added new base class `BaseObject` which is now the base class of
-  `FrameProcessor`, `PipelineRunner`, `PipelineTask` and `BaseTransport`. The
-  new `BaseObject` adds supports for event handlers.
-
- Added support for a unified format for specifying function calling across all
-  LLM services.
-
-```python
-  weather_function = FunctionSchema(
-      name="get_current_weather",
-      description="Get the current weather",
-      properties={
-          "location": {
-              "type": "string",
-              "description": "The city and state, e.g. San Francisco, CA",
-          },
-          "format": {
-              "type": "string",
-              "enum": ["celsius", "fahrenheit"],
-              "description": "The temperature unit to use. Infer this from the user's location.",
-          },
-      },
-      required=["location"],
-  )
-  tools = ToolsSchema(standard_tools=[weather_function])
-```
-
- Added `speech_threshold` parameter to `GladiaSTTService`.
-
- Allow passing user (`user_kwargs`) and assistant (`assistant_kwargs`) context
-  aggregator parameters when using `create_context_aggregator()`. The values are
-  passed as a mapping that will then be converted to arguments.
-
- Added `speed` as an `InputParam` for both `ElevenLabsTTSService` and
-  `ElevenLabsHttpTTSService`.
-
- Added new `LLMFullResponseAggregator` to aggregate full LLM completions. At
-  every completion the `on_completion` event handler is triggered.
-
- Added a new frame, `RTVIServerMessageFrame`, and RTVI message
-  `RTVIServerMessage` which provides a generic mechanism for sending custom
-  messages from server to client. The `RTVIServerMessageFrame` is processed by
-  the `RTVIObserver` and will be delivered to the client's `onServerMessage`
-  callback or `ServerMessage` event.
-
- Added `GoogleLLMOpenAIBetaService` for Google LLM integration with an
-  OpenAI-compatible interface. Added foundational example
-  `14o-function-calling-gemini-openai-format.py`.
-
- Added `AzureRealtimeBetaLLMService` to support Azure's OpeanAI Realtime API. Added
-  foundational example `19a-azure-realtime-beta.py`.
-
-## [0.0.58] - 2025-02-26
-
-### Added
-
- Added track-specific audio event `on_track_audio_data` to
-  `AudioBufferProcessor` for accessing separate input and output audio tracks.
-
 - Pipecat version will now be logged on every application startup. This will
  help us identify what version we are running in case of any issues.

@@ -104,10 +45,6 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 - ⚠️ `PipelineTask` now requires keyword arguments (except for the first one for
  the pipeline).

- Updated `PlayHTHttpTTSService` to take a `voice_engine` and `protocol` input
-  in the constructor. The previous method of providing a `voice_engine` input
-  that contains the engine and protocol is deprecated by PlayHT.
-
 - The base `TTSService` class now strips leading newlines before sending text
  to the TTS provider. This change is to solve issues where some TTS providers,
  like Azure, would not output text due to newlines.
@@ -141,9 +78,6 @@ stt = DeepgramSTTService(..., live_options=LiveOptions(model="nova-2-general"))

 ### Fixed

- Fixed a `GoogleLLMService` that was causing an exception when sending inline
-  audio in some cases.
-
 - Fixed an `AudioContextWordTTSService` issue that would cause an `EndFrame` to
  disconnect from the TTS service before audio from all the contexts was
  received. This affected services like Cartesia and Rime.
@@ -190,9 +124,6 @@ stt = DeepgramSTTService(..., live_options=LiveOptions(model="nova-2-general"))

 - Added Gemini support to `examples/phone-chatbot`.

- Added foundational example `34-audio-recording.py` showing how to use the
-  AudioBufferProcessor callbacks to save merged and track recordings.
-
 ## [0.0.57] - 2025-02-14

 ### Added
--- a/examples/canonical-metrics/bot.py
+++ b/examples/canonical-metrics/bot.py
@@ -113,8 +113,8 @@ async def main():
                llm,
                tts,
                transport.output(),
-                canonical,  # uploads audio buffer to Canonical AI for metrics
                audio_buffer_processor,  # captures audio into a buffer
+                canonical,  # uploads audio buffer to Canonical AI for metrics
                context_aggregator.assistant(),
            ]
        )
--- a/examples/foundational/07d-interruptible-elevenlabs-http.py
+++ b/examples/foundational/07d-interruptible-elevenlabs-http.py
@@ -1,103 +0,0 @@
-#
-# Copyright (c) 2024–2025, Daily
-#
-# SPDX-License-Identifier: BSD 2-Clause License
-#
-
-import asyncio
-import os
-import sys
-
-import aiohttp
-from dotenv import load_dotenv
-from loguru import logger
-from runner import configure
-
-from pipecat.audio.vad.silero import SileroVADAnalyzer
-from pipecat.pipeline.pipeline import Pipeline
-from pipecat.pipeline.runner import PipelineRunner
-from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
-from pipecat.services.elevenlabs import ElevenLabsHttpTTSService
-from pipecat.services.openai import OpenAILLMService
-from pipecat.transports.services.daily import DailyParams, DailyTransport
-
-load_dotenv(override=True)
-
-logger.remove(0)
-logger.add(sys.stderr, level="DEBUG")
-
-
-async def main():
-    async with aiohttp.ClientSession() as session:
-        (room_url, token) = await configure(session)
-
-        transport = DailyTransport(
-            room_url,
-            token,
-            "Respond bot",
-            DailyParams(
-                audio_out_enabled=True,
-                transcription_enabled=True,
-                vad_enabled=True,
-                vad_analyzer=SileroVADAnalyzer(),
-            ),
-        )
-
-        tts = ElevenLabsHttpTTSService(
-            api_key=os.getenv("ELEVENLABS_API_KEY", ""),
-            voice_id=os.getenv("ELEVENLABS_VOICE_ID", ""),
-            aiohttp_session=session,
-        )
-
-        llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
-
-        messages = [
-            {
-                "role": "system",
-                "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
-            },
-        ]
-
-        context = OpenAILLMContext(messages)
-        context_aggregator = llm.create_context_aggregator(context)
-
-        pipeline = Pipeline(
-            [
-                transport.input(),  # Transport user input
-                context_aggregator.user(),  # User responses
-                llm,  # LLM
-                tts,  # TTS
-                transport.output(),  # Transport bot output
-                context_aggregator.assistant(),  # Assistant spoken responses
-            ]
-        )
-
-        task = PipelineTask(
-            pipeline,
-            params=PipelineParams(
-                allow_interruptions=True,
-                enable_metrics=True,
-                enable_usage_metrics=True,
-                report_only_initial_ttfb=True,
-            ),
-        )
-
-        @transport.event_handler("on_first_participant_joined")
-        async def on_first_participant_joined(transport, participant):
-            await transport.capture_participant_transcription(participant["id"])
-            # Kick off the conversation.
-            messages.append({"role": "system", "content": "Please introduce yourself to the user."})
-            await task.queue_frames([context_aggregator.user().get_context_frame()])
-
-        @transport.event_handler("on_participant_left")
-        async def on_participant_left(transport, participant, reason):
-            await task.cancel()
-
-        runner = PipelineRunner()
-
-        await runner.run(task)
-
-
-if __name__ == "__main__":
-    asyncio.run(main())
--- a/examples/foundational/07q-interruptible-rime-http.py
+++ b/examples/foundational/07q-interruptible-rime-http.py
@@ -1,103 +0,0 @@
-#
-# Copyright (c) 2024–2025, Daily
-#
-# SPDX-License-Identifier: BSD 2-Clause License
-#
-
-import asyncio
-import os
-import sys
-
-import aiohttp
-from dotenv import load_dotenv
-from loguru import logger
-from runner import configure
-
-from pipecat.audio.vad.silero import SileroVADAnalyzer
-from pipecat.pipeline.pipeline import Pipeline
-from pipecat.pipeline.runner import PipelineRunner
-from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
-from pipecat.services.openai import OpenAILLMService
-from pipecat.services.rime import RimeHttpTTSService
-from pipecat.transports.services.daily import DailyParams, DailyTransport
-
-load_dotenv(override=True)
-
-logger.remove(0)
-logger.add(sys.stderr, level="DEBUG")
-
-
-async def main():
-    async with aiohttp.ClientSession() as session:
-        (room_url, token) = await configure(session)
-
-        transport = DailyTransport(
-            room_url,
-            token,
-            "Respond bot",
-            DailyParams(
-                audio_out_enabled=True,
-                transcription_enabled=True,
-                vad_enabled=True,
-                vad_analyzer=SileroVADAnalyzer(),
-            ),
-        )
-
-        tts = RimeHttpTTSService(
-            api_key=os.getenv("RIME_API_KEY", ""),
-            voice_id="rex",
-            aiohttp_session=session,
-        )
-
-        llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
-
-        messages = [
-            {
-                "role": "system",
-                "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
-            },
-        ]
-
-        context = OpenAILLMContext(messages)
-        context_aggregator = llm.create_context_aggregator(context)
-
-        pipeline = Pipeline(
-            [
-                transport.input(),  # Transport user input
-                context_aggregator.user(),  # User responses
-                llm,  # LLM
-                tts,  # TTS
-                transport.output(),  # Transport bot output
-                context_aggregator.assistant(),  # Assistant spoken responses
-            ]
-        )
-
-        task = PipelineTask(
-            pipeline,
-            params=PipelineParams(
-                allow_interruptions=True,
-                enable_metrics=True,
-                enable_usage_metrics=True,
-                report_only_initial_ttfb=True,
-            ),
-        )
-
-        @transport.event_handler("on_first_participant_joined")
-        async def on_first_participant_joined(transport, participant):
-            await transport.capture_participant_transcription(participant["id"])
-            # Kick off the conversation.
-            messages.append({"role": "system", "content": "Please introduce yourself to the user."})
-            await task.queue_frames([context_aggregator.user().get_context_frame()])
-
-        @transport.event_handler("on_participant_left")
-        async def on_participant_left(transport, participant, reason):
-            await task.cancel()
-
-        runner = PipelineRunner()
-
-        await runner.run(task)
-
-
-if __name__ == "__main__":
-    asyncio.run(main())
--- a/examples/foundational/14o-function-calling-gemini-openai-format.py
+++ b/examples/foundational/14o-function-calling-gemini-openai-format.py
@@ -1,137 +0,0 @@
-#
-# Copyright (c) 2024–2025, Daily
-#
-# SPDX-License-Identifier: BSD 2-Clause License
-#
-
-import asyncio
-import os
-import sys
-
-import aiohttp
-from dotenv import load_dotenv
-from loguru import logger
-from openai.types.chat import ChatCompletionToolParam
-from runner import configure
-
-from pipecat.audio.vad.silero import SileroVADAnalyzer
-from pipecat.frames.frames import TTSSpeakFrame
-from pipecat.pipeline.pipeline import Pipeline
-from pipecat.pipeline.runner import PipelineRunner
-from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.services.elevenlabs import ElevenLabsTTSService
-from pipecat.services.google import GoogleLLMOpenAIBetaService
-from pipecat.services.openai import OpenAILLMContext
-from pipecat.transports.services.daily import DailyParams, DailyTransport
-
-load_dotenv(override=True)
-
-logger.remove(0)
-logger.add(sys.stderr, level="DEBUG")
-
-
-async def start_fetch_weather(function_name, llm, context):
-    """Push a frame to the LLM; this is handy when the LLM response might take a while."""
-    await llm.push_frame(TTSSpeakFrame("Let me check on that."))
-    logger.debug(f"Starting fetch_weather_from_api with function_name: {function_name}")
-
-
-async def fetch_weather_from_api(function_name, tool_call_id, args, llm, context, result_callback):
-    await result_callback({"conditions": "nice", "temperature": "75"})
-
-
-async def main():
-    async with aiohttp.ClientSession() as session:
-        (room_url, token) = await configure(session)
-
-        transport = DailyTransport(
-            room_url,
-            token,
-            "Respond bot",
-            DailyParams(
-                audio_out_enabled=True,
-                transcription_enabled=True,
-                vad_enabled=True,
-                vad_analyzer=SileroVADAnalyzer(),
-            ),
-        )
-
-        tts = ElevenLabsTTSService(
-            api_key=os.getenv("ELEVENLABS_API_KEY", ""),
-            voice_id=os.getenv("ELEVENLABS_VOICE_ID", ""),
-        )
-
-        llm = GoogleLLMOpenAIBetaService(api_key=os.getenv("GEMINI_API_KEY"))
-        # Register a function_name of None to get all functions
-        # sent to the same callback with an additional function_name parameter.
-        llm.register_function(
-            "get_current_weather", fetch_weather_from_api, start_callback=start_fetch_weather
-        )
-
-        tools = [
-            ChatCompletionToolParam(
-                type="function",
-                function={
-                    "name": "get_current_weather",
-                    "description": "Get the current weather",
-                    "parameters": {
-                        "type": "object",
-                        "properties": {
-                            "location": {
-                                "type": "string",
-                                "description": "The city and state, e.g. San Francisco, CA",
-                            },
-                            "format": {
-                                "type": "string",
-                                "enum": ["celsius", "fahrenheit"],
-                                "description": "The temperature unit to use. Infer this from the users location.",
-                            },
-                        },
-                        "required": ["location", "format"],
-                    },
-                },
-            )
-        ]
-        messages = [
-            {
-                "role": "user",
-                "content": "Start a conversation with 'Hey there' to get the current weather.",
-            },
-        ]
-
-        context = OpenAILLMContext(messages, tools)
-        context_aggregator = llm.create_context_aggregator(context)
-
-        pipeline = Pipeline(
-            [
-                transport.input(),
-                context_aggregator.user(),
-                llm,
-                tts,
-                transport.output(),
-                context_aggregator.assistant(),
-            ]
-        )
-
-        task = PipelineTask(
-            pipeline,
-            params=PipelineParams(
-                allow_interruptions=True,
-                enable_metrics=True,
-                enable_usage_metrics=True,
-            ),
-        )
-
-        @transport.event_handler("on_first_participant_joined")
-        async def on_first_participant_joined(transport, participant):
-            await transport.capture_participant_transcription(participant["id"])
-            # Kick off the conversation.
-            await task.queue_frames([context_aggregator.user().get_context_frame()])
-
-        runner = PipelineRunner()
-
-        await runner.run(task)
-
-
-if __name__ == "__main__":
-    asyncio.run(main())
--- a/examples/foundational/19-openai-realtime-beta.py
+++ b/examples/foundational/19-openai-realtime-beta.py
@@ -16,10 +16,13 @@ from runner import configure

 from pipecat.audio.vad.silero import SileroVADAnalyzer
 from pipecat.audio.vad.vad_analyzer import VADParams
+from pipecat.frames.frames import TranscriptionMessage, TranscriptionUpdateFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
 from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
+from pipecat.processors.transcript_processor import TranscriptProcessor
+from pipecat.services.deepgram import DeepgramSTTService
 from pipecat.services.openai_realtime_beta import (
    InputAudioTranscription,
    OpenAIRealtimeBetaLLMService,
@@ -140,14 +143,22 @@ Remember, your responses should be short. Just one or two sentences, usually."""
            tools,
        )

+        stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
+
+        # Create transcript processor and handler
+        transcript = TranscriptProcessor()
+
        context_aggregator = llm.create_context_aggregator(context)

        pipeline = Pipeline(
            [
                transport.input(),  # Transport user input
+                stt,
+                transcript.user(),  # User transcripts
                context_aggregator.user(),
                llm,  # LLM
                context_aggregator.assistant(),
+                transcript.assistant(),  # Assistant transcripts
                transport.output(),  # Transport bot output
            ]
        )
@@ -162,9 +173,16 @@ Remember, your responses should be short. Just one or two sentences, usually."""
            ),
        )

+        # Register event handler for transcript updates
+        @transcript.event_handler("on_transcript_update")
+        async def on_transcript_update(processor, frame):
+            logger.debug(f"Received transcript update with {len(frame.messages)} new messages")
+            for msg in frame.messages:
+                logger.debug(msg)
+
        @transport.event_handler("on_first_participant_joined")
        async def on_first_participant_joined(transport, participant):
-            await transport.capture_participant_transcription(participant["id"])
+            # await transport.capture_participant_transcription(participant["id"])
            # Kick off the conversation.
            await task.queue_frames([context_aggregator.user().get_context_frame()])

--- a/examples/foundational/19a-azure-realtime-beta.py
+++ b/examples/foundational/19a-azure-realtime-beta.py
@@ -1,179 +0,0 @@
-#
-# Copyright (c) 2024–2025, Daily
-#
-# SPDX-License-Identifier: BSD 2-Clause License
-#
-
-import asyncio
-import os
-import sys
-from datetime import datetime
-
-import aiohttp
-import websockets
-from dotenv import load_dotenv
-from loguru import logger
-from runner import configure
-
-from pipecat.audio.vad.silero import SileroVADAnalyzer
-from pipecat.audio.vad.vad_analyzer import VADParams
-from pipecat.pipeline.pipeline import Pipeline
-from pipecat.pipeline.runner import PipelineRunner
-from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
-from pipecat.services.openai_realtime_beta import (
-    AzureRealtimeBetaLLMService,
-    InputAudioTranscription,
-    SessionProperties,
-    TurnDetection,
-)
-from pipecat.transports.services.daily import DailyParams, DailyTransport
-
-load_dotenv(override=True)
-
-logger.remove(0)
-logger.add(sys.stderr, level="DEBUG")
-
-
-async def fetch_weather_from_api(function_name, tool_call_id, args, llm, context, result_callback):
-    temperature = 75 if args["format"] == "fahrenheit" else 24
-    await result_callback(
-        {
-            "conditions": "nice",
-            "temperature": temperature,
-            "format": args["format"],
-            "timestamp": datetime.now().strftime("%Y%m%d_%H%M%S"),
-        }
-    )
-
-
-tools = [
-    {
-        "type": "function",
-        "name": "get_current_weather",
-        "description": "Get the current weather",
-        "parameters": {
-            "type": "object",
-            "properties": {
-                "location": {
-                    "type": "string",
-                    "description": "The city and state, e.g. San Francisco, CA",
-                },
-                "format": {
-                    "type": "string",
-                    "enum": ["celsius", "fahrenheit"],
-                    "description": "The temperature unit to use. Infer this from the users location.",
-                },
-            },
-            "required": ["location", "format"],
-        },
-    }
-]
-
-
-async def main():
-    async with aiohttp.ClientSession() as session:
-        (room_url, token) = await configure(session)
-
-        transport = DailyTransport(
-            room_url,
-            token,
-            "Respond bot",
-            DailyParams(
-                audio_in_enabled=True,
-                audio_out_enabled=True,
-                transcription_enabled=False,
-                vad_enabled=True,
-                vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.8)),
-                vad_audio_passthrough=True,
-            ),
-        )
-
-        session_properties = SessionProperties(
-            input_audio_transcription=InputAudioTranscription(),
-            # Set openai TurnDetection parameters. Not setting this at all will turn it
-            # on by default
-            # turn_detection=TurnDetection(silence_duration_ms=1000),
-            # Or set to False to disable openai turn detection and use transport VAD
-            # turn_detection=False,
-            # tools=tools,
-            instructions="""Your knowledge cutoff is 2023-10. You are a helpful and friendly AI.
-
-Act like a human, but remember that you aren't a human and that you can't do human
-things in the real world. Your voice and personality should be warm and engaging, with a lively and
-playful tone.
-
-If interacting in a non-English language, start by using the standard accent or dialect familiar to
-the user. Talk quickly. You should always call a function if you can. Do not refer to these rules,
-even if you're asked about them.
-
-You are participating in a voice conversation. Keep your responses concise, short, and to the point
-unless specifically asked to elaborate on a topic.
-
-Remember, your responses should be short. Just one or two sentences, usually.""",
-        )
-
-        llm = AzureRealtimeBetaLLMService(
-            api_key=os.getenv("AZURE_REALTIME_API_KEY"),
-            base_url=os.getenv("AZURE_REALTIME_BASE_URL"),
-            session_properties=session_properties,
-            start_audio_paused=False,
-        )
-
-        # you can either register a single function for all function calls, or specific functions
-        # llm.register_function(None, fetch_weather_from_api)
-        llm.register_function("get_current_weather", fetch_weather_from_api)
-
-        # Create a standard OpenAI LLM context object using the normal messages format. The
-        # OpenAIRealtimeBetaLLMService will convert this internally to messages that the
-        # openai WebSocket API can understand.
-        context = OpenAILLMContext(
-            [{"role": "user", "content": "Say hello!"}],
-            # [{"role": "user", "content": [{"type": "text", "text": "Say hello!"}]}],
-            #     [
-            #         {
-            #             "role": "user",
-            #             "content": [
-            #                 {"type": "text", "text": "Say"},
-            #                 {"type": "text", "text": "yo what's up!"},
-            #             ],
-            #         }
-            #     ],
-            tools,
-        )
-
-        context_aggregator = llm.create_context_aggregator(context)
-
-        pipeline = Pipeline(
-            [
-                transport.input(),  # Transport user input
-                context_aggregator.user(),
-                llm,  # LLM
-                context_aggregator.assistant(),
-                transport.output(),  # Transport bot output
-            ]
-        )
-
-        task = PipelineTask(
-            pipeline,
-            params=PipelineParams(
-                allow_interruptions=True,
-                enable_metrics=True,
-                enable_usage_metrics=True,
-                # report_only_initial_ttfb=True,
-            ),
-        )
-
-        @transport.event_handler("on_first_participant_joined")
-        async def on_first_participant_joined(transport, participant):
-            await transport.capture_participant_transcription(participant["id"])
-            # Kick off the conversation.
-            await task.queue_frames([context_aggregator.user().get_context_frame()])
-
-        runner = PipelineRunner()
-
-        await runner.run(task)
-
-
-if __name__ == "__main__":
-    asyncio.run(main())
--- a/examples/foundational/22d-natural-conversation-gemini-audio.py
+++ b/examples/foundational/22d-natural-conversation-gemini-audio.py
@@ -23,6 +23,7 @@ from pipecat.frames.frames import (
    FunctionCallInProgressFrame,
    FunctionCallResultFrame,
    InputAudioRawFrame,
+    LLMFullResponseEndFrame,
    LLMFullResponseStartFrame,
    StartFrame,
    StartInterruptionFrame,
@@ -36,7 +37,7 @@ from pipecat.pipeline.parallel_pipeline import ParallelPipeline
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.aggregators.llm_response import LLMAssistantResponseAggregator
+from pipecat.processors.aggregators.llm_response import LLMResponseAggregator
 from pipecat.processors.aggregators.openai_llm_context import (
    OpenAILLMContext,
    OpenAILLMContextFrame,
@@ -388,7 +389,7 @@ class AudioAccumulator(FrameProcessor):
            )
            self._user_speaking = False
            context = GoogleLLMContext()
-            context.add_audio_frames_message(audio_frames=self._audio_frames)
+            context.add_audio_frames_message(text="Audio follows", audio_frames=self._audio_frames)
            await self.push_frame(OpenAILLMContextFrame(context=context))
        elif isinstance(frame, InputAudioRawFrame):
            # Append the audio frame to our buffer. Treat the buffer as a ring buffer, dropping the oldest
@@ -431,11 +432,7 @@ class CompletenessCheck(FrameProcessor):
    async def process_frame(self, frame: Frame, direction: FrameDirection):
        await super().process_frame(frame, direction)

-        if isinstance(frame, (EndFrame, CancelFrame)):
-            if self._idle_task:
-                await self.cancel_task(self._idle_task)
-                self._idle_task = None
-        elif isinstance(frame, UserStartedSpeakingFrame):
+        if isinstance(frame, UserStartedSpeakingFrame):
            if self._idle_task:
                await self.cancel_task(self._idle_task)
        elif isinstance(frame, TextFrame) and frame.text.startswith("YES"):
@@ -477,11 +474,19 @@ class CompletenessCheck(FrameProcessor):
            self._idle_task = None


-class LLMAggregatorBuffer(LLMAssistantResponseAggregator):
+class UserAggregatorBuffer(LLMResponseAggregator):
    """Buffers the output of the transcription LLM. Used by the bot output gate."""

    def __init__(self, **kwargs):
-        super().__init__(expect_stripped_words=False)
+        super().__init__(
+            messages=None,
+            role=None,
+            start_frame=LLMFullResponseStartFrame,
+            end_frame=LLMFullResponseEndFrame,
+            accumulator_frame=TextFrame,
+            handle_interruptions=True,
+            expect_stripped_words=False,
+        )
        self._transcription = ""

    async def process_frame(self, frame: Frame, direction: FrameDirection):
@@ -539,7 +544,7 @@ class OutputGate(FrameProcessor):
        self,
        notifier: BaseNotifier,
        context: OpenAILLMContext,
-        llm_transcription_buffer: LLMAggregatorBuffer,
+        user_transcription_buffer: "UserAggregatorBuffer",
        **kwargs,
    ):
        super().__init__(**kwargs)
@@ -547,7 +552,7 @@ class OutputGate(FrameProcessor):
        self._frames_buffer = []
        self._notifier = notifier
        self._context = context
-        self._transcription_buffer = llm_transcription_buffer
+        self._transcription_buffer = user_transcription_buffer
        self._gate_task = None

    def close_gate(self):
@@ -694,10 +699,10 @@ async def main():

        conversation_audio_context_assembler = ConversationAudioContextAssembler(context=context)

-        llm_aggregator_buffer = LLMAggregatorBuffer()
+        user_aggregator_buffer = UserAggregatorBuffer()

        bot_output_gate = OutputGate(
-            notifier=notifier, context=context, llm_transcription_buffer=llm_aggregator_buffer
+            notifier=notifier, context=context, user_transcription_buffer=user_aggregator_buffer
        )

        pipeline = Pipeline(
@@ -718,7 +723,7 @@ async def main():
                            ],
                            [
                                tx_llm,
-                                llm_aggregator_buffer,
+                                user_aggregator_buffer,
                            ],
                        )
                    ],
--- a/examples/foundational/34-audio-recording.py
+++ b/examples/foundational/34-audio-recording.py
@@ -1,186 +0,0 @@
-#
-# Copyright (c) 2024–2025, Daily
-#
-# SPDX-License-Identifier: BSD 2-Clause License
-#
-
-"""Audio Recording Example with Pipecat.
-
-This example demonstrates how to record audio from a conversation between a user and an AI assistant,
-saving both merged and individual audio tracks. It showcases the AudioBufferProcessor's capabilities
-to handle both combined and separate audio streams.
-
-The example:
-    1. Sets up a basic conversation with an AI assistant
-    2. Records the entire conversation
-    3. Saves three separate WAV files:
-        - A merged recording of both participants
-        - Individual recording of user audio
-        - Individual recording of assistant audio
-
-Example usage (run from pipecat root directory):
-    $ pip install "pipecat-ai[daily,openai,cartesia,silero]"
-    $ pip install -r dev-requirements.txt
-    $ python examples/foundational/34-audio-recording.py
-
-Requirements:
-    - OpenAI API key (for GPT-4)
-    - Cartesia API key (for text-to-speech)
-    - Daily API key (for video/audio transport)
-
-    Environment variables (.env file):
-        OPENAI_API_KEY=your_openai_key
-        CARTESIA_API_KEY=your_cartesia_key
-        DAILY_API_KEY=your_daily_key
-
-The recordings will be saved in a 'recordings' directory with timestamps:
-    recordings/
-        merged_20240315_123456.wav  (Combined audio)
-        user_20240315_123456.wav    (User audio only)
-        bot_20240315_123456.wav     (Bot audio only)
-
-Note:
-    This example requires the AudioBufferProcessor with track-specific audio support,
-    which provides both 'on_audio_data' and 'on_track_audio_data' events for
-    handling merged and separate audio tracks respectively.
-"""
-
-import asyncio
-import datetime
-import io
-import os
-import sys
-import wave
-
-import aiofiles
-import aiohttp
-from dotenv import load_dotenv
-from loguru import logger
-from runner import configure
-
-from pipecat.audio.vad.silero import SileroVADAnalyzer
-from pipecat.pipeline.pipeline import Pipeline
-from pipecat.pipeline.runner import PipelineRunner
-from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
-from pipecat.processors.audio.audio_buffer_processor import AudioBufferProcessor
-from pipecat.services.cartesia import CartesiaTTSService
-from pipecat.services.openai import OpenAILLMService
-from pipecat.transports.services.daily import DailyParams, DailyTransport
-
-load_dotenv(override=True)
-logger.remove(0)
-logger.add(sys.stderr, level="DEBUG")
-
-
-async def save_audio_file(audio: bytes, filename: str, sample_rate: int, num_channels: int):
-    """Save audio data to a WAV file."""
-    if len(audio) > 0:
-        with io.BytesIO() as buffer:
-            with wave.open(buffer, "wb") as wf:
-                wf.setsampwidth(2)
-                wf.setnchannels(num_channels)
-                wf.setframerate(sample_rate)
-                wf.writeframes(audio)
-            async with aiofiles.open(filename, "wb") as file:
-                await file.write(buffer.getvalue())
-        logger.info(f"Audio saved to {filename}")
-
-
-async def main():
-    async with aiohttp.ClientSession() as session:
-        (room_url, token) = await configure(session)
-
-        transport = DailyTransport(
-            room_url,
-            token,
-            "Recording bot",
-            DailyParams(
-                # audio_in_enabled=True,
-                audio_out_enabled=True,
-                transcription_enabled=True,
-                vad_enabled=True,
-                vad_analyzer=SileroVADAnalyzer(),
-                vad_audio_passthrough=True,  # Enable audio passthrough for recording
-            ),
-        )
-
-        tts = CartesiaTTSService(
-            api_key=os.getenv("CARTESIA_API_KEY"),
-            voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22",
-        )
-
-        llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4")
-
-        # Create audio buffer processor
-        audiobuffer = AudioBufferProcessor()
-
-        messages = [
-            {
-                "role": "system",
-                "content": "You are a helpful assistant demonstrating audio recording capabilities. Keep your responses brief and clear.",
-            },
-        ]
-
-        context = OpenAILLMContext(messages)
-        context_aggregator = llm.create_context_aggregator(context)
-
-        pipeline = Pipeline(
-            [
-                transport.input(),
-                context_aggregator.user(),
-                llm,
-                tts,
-                transport.output(),
-                audiobuffer,  # Add audio buffer to pipeline
-                context_aggregator.assistant(),
-            ]
-        )
-
-        task = PipelineTask(pipeline, params=PipelineParams(allow_interruptions=True))
-
-        @transport.event_handler("on_first_participant_joined")
-        async def on_first_participant_joined(transport, participant):
-            await transport.capture_participant_transcription(participant["id"])
-            await audiobuffer.start_recording()
-            messages.append(
-                {
-                    "role": "system",
-                    "content": "Greet the user and explain that this conversation will be recorded.",
-                }
-            )
-            await task.queue_frames([context_aggregator.user().get_context_frame()])
-
-        @transport.event_handler("on_participant_left")
-        async def on_participant_left(transport, participant, reason):
-            await audiobuffer.stop_recording()
-            await task.cancel()
-
-        # Handler for merged audio
-        @audiobuffer.event_handler("on_audio_data")
-        async def on_audio_data(buffer, audio, sample_rate, num_channels):
-            timestamp = datetime.datetime.now().strftime("%Y%m%d_%H%M%S")
-            filename = f"recordings/merged_{timestamp}.wav"
-            os.makedirs("recordings", exist_ok=True)
-            await save_audio_file(audio, filename, sample_rate, num_channels)
-
-        # Handler for separate tracks
-        @audiobuffer.event_handler("on_track_audio_data")
-        async def on_track_audio_data(buffer, user_audio, bot_audio, sample_rate, num_channels):
-            timestamp = datetime.datetime.now().strftime("%Y%m%d_%H%M%S")
-            os.makedirs("recordings", exist_ok=True)
-
-            # Save user audio
-            user_filename = f"recordings/user_{timestamp}.wav"
-            await save_audio_file(user_audio, user_filename, sample_rate, 1)
-
-            # Save bot audio
-            bot_filename = f"recordings/bot_{timestamp}.wav"
-            await save_audio_file(bot_audio, bot_filename, sample_rate, 1)
-
-        runner = PipelineRunner()
-        await runner.run(task)
-
-
-if __name__ == "__main__":
-    asyncio.run(main())
--- a/examples/phone-chatbot/bot_daily_gemini.py
+++ b/examples/phone-chatbot/bot_daily_gemini.py
@@ -7,17 +7,20 @@ import argparse
 import asyncio
 import os
 import sys
+from dataclasses import dataclass
 from typing import Optional

+import google.ai.generativelanguage as glm
 from dotenv import load_dotenv
 from loguru import logger

 from pipecat.audio.vad.silero import SileroVADAnalyzer
 from pipecat.frames.frames import (
-    EndFrame,
+    BotStoppedSpeakingFrame,
    EndTaskFrame,
+    Frame,
    InputAudioRawFrame,
-    StopTaskFrame,
+    SystemFrame,
    TranscriptionFrame,
    UserStartedSpeakingFrame,
    UserStoppedSpeakingFrame,
@@ -25,17 +28,12 @@ from pipecat.frames.frames import (
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
+from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContextFrame
 from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
 from pipecat.services.ai_services import LLMService
-from pipecat.services.deepgram import DeepgramSTTService
 from pipecat.services.elevenlabs import ElevenLabsTTSService
-from pipecat.services.google import GoogleLLMService
-from pipecat.services.google.google import GoogleLLMContext
-from pipecat.transports.services.daily import (
-    DailyDialinSettings,
-    DailyParams,
-    DailyTransport,
-)
+from pipecat.services.google import GoogleLLMContext, GoogleLLMService
+from pipecat.transports.services.daily import DailyDialinSettings, DailyParams, DailyTransport

 load_dotenv(override=True)

@@ -46,8 +44,6 @@ logger.add(sys.stderr, level="DEBUG")
 daily_api_key = os.getenv("DAILY_API_KEY", "")
 daily_api_url = os.getenv("DAILY_API_URL", "https://api.daily.co/v1")

-system_message = None
-

 class UserAudioCollector(FrameProcessor):
    """This FrameProcessor collects audio frames in a buffer, then adds them to the
@@ -121,13 +117,7 @@ class FunctionHandlers:
        self.context_switcher = context_switcher

    async def voicemail_response(
-        self,
-        function_name,
-        tool_call_id,
-        args,
-        llm: LLMService,
-        context,
-        result_callback,
+        self, function_name, tool_call_id, args, llm, context, result_callback
    ):
        """Function the bot can call to leave a voicemail message."""
        message = """You are Chatbot leaving a voicemail message. Say EXACTLY this message and nothing else:
@@ -137,48 +127,62 @@ class FunctionHandlers:
                    After saying this message, call the terminate_call function."""

        await self.context_switcher.switch_context(system_instruction=message)
+
        await result_callback("Leaving a voicemail message")

    async def human_conversation(
-        self,
-        function_name,
-        tool_call_id,
-        args,
-        llm: LLMService,
-        context,
-        result_callback,
+        self, function_name, tool_call_id, args, llm, context, result_callback
    ):
        """Function the bot can when it detects it's talking to a human."""
-        await llm.push_frame(StopTaskFrame(), FrameDirection.UPSTREAM)
+        message = """You are Chatbot talking to a human. Be friendly and helpful.
+
+                    Start with: "Hello! I'm a friendly chatbot. How can I help you today?"
+
+                    Keep your responses brief and to the point. Listen to what the person says.
+
+                    When the person indicates they're done with the conversation by saying something like:
+                    - "Goodbye"
+                    - "That's all"
+                    - "I'm done"
+                    - "Thank you, that's all I needed"
+
+                    THEN say: "Thank you for chatting. Goodbye!" and call the terminate_call function."""
+
+        await self.context_switcher.switch_context(system_instruction=message)
+
+        await result_callback("Talking to the customer")


 async def terminate_call(
-    function_name,
-    tool_call_id,
-    args,
-    llm: LLMService,
-    context,
-    result_callback,
-    call_state=None,
+    function_name, tool_call_id, args, llm: LLMService, context, result_callback
 ):
    """Function the bot can call to terminate the call upon completion of the call."""
-    if call_state:
-        call_state.bot_terminated_call = True
-    await llm.push_frame(EndTaskFrame(), FrameDirection.UPSTREAM)
+
+    await llm.queue_frame(EndTaskFrame(), FrameDirection.UPSTREAM)


 async def main(
    room_url: str,
    token: str,
-    callId: Optional[str],
-    callDomain: Optional[str],
+    callId: str,
+    callDomain: str,
    detect_voicemail: bool,
    dialout_number: Optional[str],
 ):
+    # dialin_settings are only needed if Daily's SIP URI is used
+    # If you are handling this via Twilio, Telnyx, set this to None
+    # and handle call-forwarding when on_dialin_ready fires.
+
+    # We don't want to specify dial-in settings if we're not dialing in
    dialin_settings = None
    if callId and callDomain:
        dialin_settings = DailyDialinSettings(call_id=callId, call_domain=callDomain)
-        transport_params = DailyParams(
+
+    transport = DailyTransport(
+        room_url,
+        token,
+        "Chatbot",
+        DailyParams(
            api_url=daily_api_url,
            api_key=daily_api_key,
            dialin_settings=dialin_settings,
@@ -188,30 +192,8 @@ async def main(
            vad_enabled=True,
            vad_analyzer=SileroVADAnalyzer(),
            vad_audio_passthrough=True,
-        )
-    else:
-        transport_params = DailyParams(
-            api_url=daily_api_url,
-            api_key=daily_api_key,
-            audio_in_enabled=True,
-            audio_out_enabled=True,
-            camera_out_enabled=False,
-            vad_enabled=True,
-            vad_analyzer=SileroVADAnalyzer(),
-            vad_audio_passthrough=True,
-        )
-
-    class CallState:
-        participant_left_early = False
-        bot_terminated_call = False
-
-    call_state = CallState()
-
-    transport = DailyTransport(
-        room_url,
-        token,
-        "Chatbot",
-        transport_params,
+            # transcription_enabled=True,
+        ),
    )

    tts = ElevenLabsTTSService(
@@ -219,10 +201,6 @@ async def main(
        voice_id=os.getenv("ELEVENLABS_VOICE_ID", ""),
    )

-    stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
-
-    ### VOICEMAIL PIPELINE
-
    tools = [
        {
            "function_declarations": [
@@ -244,67 +222,55 @@ async def main(

    system_instruction = """You are Chatbot trying to determine if this is a voicemail system or a human.

-    If you hear any of these phrases (or very similar ones):
-    - "Please leave a message after the beep"
-    - "No one is available to take your call"
-    - "Record your message after the tone"
-    - "You have reached voicemail for..."
-    - "You have reached [phone number]"
-    - "[phone number] is unavailable"
-    - "The person you are trying to reach..."
-    - "The number you have dialed..."
-    - "Your call has been forwarded to an automated voice messaging system"
+If you hear any of these phrases (or very similar ones):
+- "Please leave a message after the beep"
+- "No one is available to take your call"
+- "Record your message after the tone"
+- "You have reached voicemail for..."
+- "You have reached [phone number]"
+- "[phone number] is unavailable"
+- "The person you are trying to reach..."
+- "The number you have dialed..."
+- "Your call has been forwarded to an automated voice messaging system"

-    Then call the function switch_to_voicemail_response.
+Then call the function switch_to_voicemail_response.

-    If it sounds like a human (saying hello, asking questions, etc.), call the function switch_to_human_conversation.
+If it sounds like a human (saying hello, asking questions, etc.), call the function switch_to_human_conversation.

-    DO NOT say anything until you've determined if this is a voicemail or human."""
+DO NOT say anything until you've determined if this is a voicemail or human."""

-    voicemail_detection_llm = GoogleLLMService(
-        model="models/gemini-2.0-flash-lite",
+    llm = GoogleLLMService(
+        model="models/gemini-2.0-flash-lite-preview-02-05",
        api_key=os.getenv("GOOGLE_API_KEY"),
        system_instruction=system_instruction,
        tools=tools,
    )

-    voicemail_detection_context = GoogleLLMContext()
-    voicemail_detection_context_aggregator = voicemail_detection_llm.create_context_aggregator(
-        voicemail_detection_context
-    )
-    context_switcher = ContextSwitcher(
-        voicemail_detection_llm, voicemail_detection_context_aggregator.user()
-    )
+    context = GoogleLLMContext()
+    context_aggregator = llm.create_context_aggregator(context)
+    audio_collector = UserAudioCollector(context, context_aggregator.user())
+
+    context_switcher = ContextSwitcher(llm, context_aggregator.user())
    handlers = FunctionHandlers(context_switcher)

-    voicemail_detection_llm.register_function(
-        "switch_to_voicemail_response", handlers.voicemail_response
-    )
-    voicemail_detection_llm.register_function(
-        "switch_to_human_conversation", handlers.human_conversation
-    )
-    voicemail_detection_llm.register_function(
-        "terminate_call",
-        lambda *args, **kwargs: terminate_call(*args, **kwargs, call_state=call_state),
-    )
+    llm.register_function("switch_to_voicemail_response", handlers.voicemail_response)
+    llm.register_function("switch_to_human_conversation", handlers.human_conversation)
+    llm.register_function("terminate_call", terminate_call)

-    voicemail_detection_audio_collector = UserAudioCollector(
-        voicemail_detection_context, voicemail_detection_context_aggregator.user()
-    )
-
-    voicemail_detection_pipeline = Pipeline(
+    pipeline = Pipeline(
        [
            transport.input(),  # Transport user input
-            voicemail_detection_audio_collector,  # Collect audio frames
-            voicemail_detection_context_aggregator.user(),  # User responses
-            voicemail_detection_llm,  # LLM
+            audio_collector,  # Collect audio frames
+            context_aggregator.user(),  # User responses
+            llm,  # LLM
            tts,  # TTS
            transport.output(),  # Transport bot output
-            voicemail_detection_context_aggregator.assistant(),  # Assistant spoken responses
+            context_aggregator.assistant(),  # Assistant spoken responses
        ]
    )
-    voicemail_detection_pipeline_task = PipelineTask(
-        voicemail_detection_pipeline,
+
+    task = PipelineTask(
+        pipeline,
        params=PipelineParams(allow_interruptions=True),
    )

@@ -339,116 +305,25 @@ async def main(
        # machine to say something like 'Leave a message after the beep', or for the user to say 'Hello?'.
        @transport.event_handler("on_first_participant_joined")
        async def on_first_participant_joined(transport, participant):
-            logger.debug("Detect voicemail; capturing participant transcription")
            await transport.capture_participant_transcription(participant["id"])
    else:
-        logger.debug("+++++ No dialout number; assuming dialin")
+        logger.debug("no dialout number; assuming dialin")

        # Different handlers for dialin
        @transport.event_handler("on_first_participant_joined")
        async def on_first_participant_joined(transport, participant):
-            # This event is not firing for some reason
            await transport.capture_participant_transcription(participant["id"])
-            dialin_instructions = """Always call the function switch_to_human_conversation"""
-            messages = [
-                {
-                    "role": "system",
-                    "content": dialin_instructions,
-                }
-            ]
-            voicemail_detection_context_aggregator.user().set_messages(messages)
-            await voicemail_detection_pipeline_task.queue_frames(
-                [voicemail_detection_context_aggregator.user().get_context_frame()]
-            )
+            # For the dialin case, we want the bot to answer the phone and greet the user. We
+            # can prompt the bot to speak by putting the context into the pipeline.
+            await task.queue_frames([context_aggregator.user().get_context_frame()])
+
+    @transport.event_handler("on_participant_left")
+    async def on_participant_left(transport, participant, reason):
+        await task.cancel()

    runner = PipelineRunner()

-    @transport.event_handler("on_participant_left")
-    async def on_participant_left(transport, participant, reason):
-        call_state.participant_left_early = True
-        await voicemail_detection_pipeline_task.queue_frame(EndFrame())
-
-    print("!!! starting voicemail detection pipeline")
-    await runner.run(voicemail_detection_pipeline_task)
-    print("!!! Done with voicemail detection pipeline")
-
-    if call_state.participant_left_early or call_state.bot_terminated_call:
-        if call_state.participant_left_early:
-            print("!!! Participant left early; terminating call")
-        elif call_state.bot_terminated_call:
-            print("!!! Bot terminated call; not proceeding to human conversation")
-        return
-
-    ### HUMAN CONVERSATION PIPELINE
-
-    human_conversation_system_instruction = """You are Chatbot talking to a human. Be friendly and helpful.
-
-    Start with: "Hello! I'm a friendly chatbot. How can I help you today?"
-
-    Keep your responses brief and to the point. Listen to what the person says.
-
-    When the person indicates they're done with the conversation by saying something like:
-    - "Goodbye"
-    - "That's all"
-    - "I'm done"
-    - "Thank you, that's all I needed"
-
-    THEN say: "Thank you for chatting. Goodbye!" and call the terminate_call function."""
-
-    human_conversation_llm = GoogleLLMService(
-        model="models/gemini-2.0-flash-001",
-        api_key=os.getenv("GOOGLE_API_KEY"),
-        system_instruction=human_conversation_system_instruction,
-        tools=tools,
-    )
-    human_conversation_context = GoogleLLMContext()
-
-    human_conversation_context_aggregator = human_conversation_llm.create_context_aggregator(
-        human_conversation_context
-    )
-
-    human_conversation_llm.register_function(
-        "terminate_call",
-        lambda *args, **kwargs: terminate_call(*args, **kwargs, call_state=call_state),
-    )
-
-    human_conversation_pipeline = Pipeline(
-        [
-            transport.input(),  # Transport user input
-            stt,
-            human_conversation_context_aggregator.user(),  # User responses
-            human_conversation_llm,  # LLM
-            tts,  # TTS
-            transport.output(),  # Transport bot output
-            human_conversation_context_aggregator.assistant(),  # Assistant spoken responses
-        ]
-    )
-
-    human_conversation_pipeline_task = PipelineTask(
-        human_conversation_pipeline,
-        params=PipelineParams(allow_interruptions=True),
-    )
-
-    @transport.event_handler("on_participant_left")
-    async def on_participant_left(transport, participant, reason):
-        await voicemail_detection_pipeline_task.queue_frame(EndFrame())
-        await human_conversation_pipeline_task.queue_frame(EndFrame())
-
-    print("!!! starting human conversation pipeline")
-    human_conversation_context_aggregator.user().set_messages(
-        [
-            {
-                "role": "system",
-                "content": human_conversation_system_instruction,
-            }
-        ]
-    )
-    await human_conversation_pipeline_task.queue_frames(
-        [human_conversation_context_aggregator.user().get_context_frame()]
-    )
-    await runner.run(human_conversation_pipeline_task)
-
-    print("!!! Done with human conversation pipeline")
+    await runner.run(task)


 if __name__ == "__main__":
--- a/examples/unified-format-function-calling/base_function_calling.py
+++ b/examples/unified-format-function-calling/base_function_calling.py
@@ -1,134 +0,0 @@
-#
-# Copyright (c) 2024–2025, Daily
-#
-# SPDX-License-Identifier: BSD 2-Clause License
-#
-
-import os
-import sys
-
-import aiohttp
-from dotenv import load_dotenv
-from loguru import logger
-from runner import configure
-
-from pipecat.adapters.schemas.function_schema import FunctionSchema
-from pipecat.adapters.schemas.tools_schema import ToolsSchema
-from pipecat.audio.vad.silero import SileroVADAnalyzer
-from pipecat.frames.frames import TTSSpeakFrame
-from pipecat.pipeline.pipeline import Pipeline
-from pipecat.pipeline.runner import PipelineRunner
-from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
-from pipecat.services.ai_services import LLMService
-from pipecat.services.cartesia import CartesiaTTSService
-from pipecat.transports.services.daily import DailyParams, DailyTransport
-
-logger.remove(0)
-logger.add(sys.stderr, level="DEBUG")
-
-load_dotenv(override=True)
-
-
-async def start_fetch_weather(function_name, llm, context):
-    """Push a frame to the LLM; this is handy when the LLM response might take a while."""
-    await llm.push_frame(TTSSpeakFrame("Let me check on that."))
-    logger.debug(f"Starting fetch_weather_from_api with function_name: {function_name}")
-
-
-async def fetch_weather_from_api(function_name, tool_call_id, args, llm, context, result_callback):
-    await result_callback({"conditions": "nice", "temperature": "75"})
-
-
-class WeatherBot:
-    """Generic base class for setting up and running an LLM-powered bot."""
-
-    def __init__(self, llm: LLMService):
-        """Initialize the base handler with a specific LLM."""
-        self.llm = llm
-
-    async def run(self):
-        """Set up and start the processing pipeline."""
-        async with aiohttp.ClientSession() as session:
-            (room_url, token) = await configure(session)
-
-            transport = DailyTransport(
-                room_url,
-                token,
-                "Respond bot",
-                DailyParams(
-                    audio_out_enabled=True,
-                    transcription_enabled=True,
-                    vad_enabled=True,
-                    vad_analyzer=SileroVADAnalyzer(),
-                ),
-            )
-
-            tts = CartesiaTTSService(
-                api_key=os.getenv("CARTESIA_API_KEY"),
-                voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22",  # British Lady
-            )
-
-            # Register a function_name of None to get all functions
-            # sent to the same callback with an additional function_name parameter.
-            self.llm.register_function(
-                None, fetch_weather_from_api, start_callback=start_fetch_weather
-            )
-
-            weather_function = FunctionSchema(
-                name="get_current_weather",
-                description="Get the current weather",
-                properties={
-                    "location": {
-                        "type": "string",
-                        "description": "The city and state, e.g. San Francisco, CA",
-                    },
-                    "format": {
-                        "type": "string",
-                        "enum": ["celsius", "fahrenheit"],
-                        "description": "The temperature unit to use. Infer this from the user's location.",
-                    },
-                },
-                required=["location"],
-            )
-            tools = ToolsSchema(standard_tools=[weather_function])
-
-            messages = [
-                {
-                    "role": "system",
-                    "content": "You are a helpful assistant who can report the weather in any location in the universe. Respond concisely. Your response will be turned into speech so use only simple words and punctuation.",
-                },
-                {"role": "user", "content": " Start the conversation by introducing yourself."},
-            ]
-
-            context = OpenAILLMContext(messages, tools)
-            context_aggregator = self.llm.create_context_aggregator(context)
-
-            pipeline = Pipeline(
-                [
-                    transport.input(),
-                    context_aggregator.user(),
-                    self.llm,
-                    tts,
-                    transport.output(),
-                    context_aggregator.assistant(),
-                ]
-            )
-
-            task = PipelineTask(
-                pipeline,
-                params=PipelineParams(
-                    allow_interruptions=True,
-                    enable_metrics=True,
-                    enable_usage_metrics=True,
-                    report_only_initial_ttfb=True,
-                ),
-            )
-
-            @transport.event_handler("on_first_participant_joined")
-            async def on_first_participant_joined(transport, participant):
-                await transport.capture_participant_transcription(participant["id"])
-                await task.queue_frames([context_aggregator.user().get_context_frame()])
-
-            runner = PipelineRunner()
-            await runner.run(task)
--- a/examples/unified-format-function-calling/multimodal_base_function_calling.py
+++ b/examples/unified-format-function-calling/multimodal_base_function_calling.py
@@ -1,126 +0,0 @@
-#
-# Copyright (c) 2024–2025, Daily
-#
-# SPDX-License-Identifier: BSD 2-Clause License
-#
-
-import sys
-from typing import List
-
-import aiohttp
-from dotenv import load_dotenv
-from loguru import logger
-from runner import configure
-
-from pipecat.adapters.schemas.function_schema import FunctionSchema
-from pipecat.adapters.schemas.tools_schema import ToolsSchema
-from pipecat.audio.vad.silero import SileroVADAnalyzer
-from pipecat.frames.frames import TTSSpeakFrame
-from pipecat.pipeline.pipeline import Pipeline
-from pipecat.pipeline.runner import PipelineRunner
-from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
-from pipecat.services.ai_services import LLMService
-from pipecat.transports.services.daily import DailyParams, DailyTransport
-
-logger.remove(0)
-logger.add(sys.stderr, level="DEBUG")
-
-load_dotenv(override=True)
-
-
-async def start_fetch_weather(function_name, llm, context):
-    """Push a frame to the LLM; this is handy when the LLM response might take a while."""
-    await llm.push_frame(TTSSpeakFrame("Let me check on that."))
-    logger.debug(f"Starting fetch_weather_from_api with function_name: {function_name}")
-
-
-async def fetch_weather_from_api(function_name, tool_call_id, args, llm, context, result_callback):
-    await result_callback({"conditions": "nice", "temperature": "75"})
-
-
-class MultimodalWeatherBot:
-    """Generic base class for setting up and running an LLM-powered bot."""
-
-    def __init__(self, llm: LLMService):
-        """Initialize the base handler with a specific LLM."""
-        self.llm = llm
-
-    @staticmethod
-    def tools() -> ToolsSchema:
-        weather_function = FunctionSchema(
-            name="get_current_weather",
-            description="Get the current weather",
-            properties={
-                "location": {
-                    "type": "string",
-                    "description": "The city and state, e.g. San Francisco, CA",
-                },
-                "format": {
-                    "type": "string",
-                    "enum": ["celsius", "fahrenheit"],
-                    "description": "The temperature unit to use. Infer this from the user's location.",
-                },
-            },
-            required=["location"],
-        )
-        return ToolsSchema(standard_tools=[weather_function])
-
-    async def run(self):
-        """Set up and start the processing pipeline."""
-        async with aiohttp.ClientSession() as session:
-            (room_url, token) = await configure(session)
-
-            transport = DailyTransport(
-                room_url,
-                token,
-                "Respond bot",
-                DailyParams(
-                    audio_out_enabled=True,
-                    vad_enabled=True,
-                    vad_analyzer=SileroVADAnalyzer(),
-                    vad_audio_passthrough=True,
-                ),
-            )
-
-            # Register a function_name of None to get all functions
-            # sent to the same callback with an additional function_name parameter.
-            self.llm.register_function(
-                None, fetch_weather_from_api, start_callback=start_fetch_weather
-            )
-
-            messages = [
-                {
-                    "role": "system",
-                    "content": "You are a helpful assistant who can report the weather in any location in the universe. Respond concisely. Your response will be turned into speech so use only simple words and punctuation.",
-                },
-                {"role": "user", "content": " Start the conversation by introducing yourself."},
-            ]
-
-            context = OpenAILLMContext(messages, MultimodalWeatherBot.tools())
-            context_aggregator = self.llm.create_context_aggregator(context)
-
-            pipeline = Pipeline(
-                [
-                    transport.input(),
-                    context_aggregator.user(),
-                    self.llm,
-                    transport.output(),
-                    context_aggregator.assistant(),
-                ]
-            )
-
-            task = PipelineTask(
-                pipeline,
-                params=PipelineParams(
-                    allow_interruptions=True,
-                ),
-            )
-
-            @transport.event_handler("on_first_participant_joined")
-            async def on_first_participant_joined(transport, participant):
-                await transport.capture_participant_transcription(participant["id"])
-                await task.queue_frames([context_aggregator.user().get_context_frame()])
-
-            runner = PipelineRunner()
-            await runner.run(task)
--- a/examples/unified-format-function-calling/runner.py
+++ b/examples/unified-format-function-calling/runner.py
@@ -1,64 +0,0 @@
-#
-# Copyright (c) 2024–2025, Daily
-#
-# SPDX-License-Identifier: BSD 2-Clause License
-#
-
-import argparse
-import os
-from typing import Optional
-
-import aiohttp
-
-from pipecat.transports.services.helpers.daily_rest import DailyRESTHelper
-
-
-async def configure(aiohttp_session: aiohttp.ClientSession):
-    (url, token, _) = await configure_with_args(aiohttp_session)
-    return (url, token)
-
-
-async def configure_with_args(
-    aiohttp_session: aiohttp.ClientSession, parser: Optional[argparse.ArgumentParser] = None
-):
-    if not parser:
-        parser = argparse.ArgumentParser(description="Daily AI SDK Bot Sample")
-    parser.add_argument(
-        "-u", "--url", type=str, required=False, help="URL of the Daily room to join"
-    )
-    parser.add_argument(
-        "-k",
-        "--apikey",
-        type=str,
-        required=False,
-        help="Daily API Key (needed to create an owner token for the room)",
-    )
-
-    args, unknown = parser.parse_known_args()
-
-    url = args.url or os.getenv("DAILY_SAMPLE_ROOM_URL")
-    key = args.apikey or os.getenv("DAILY_API_KEY")
-
-    if not url:
-        raise Exception(
-            "No Daily room specified. use the -u/--url option from the command line, or set DAILY_SAMPLE_ROOM_URL in your environment to specify a Daily room URL."
-        )
-
-    if not key:
-        raise Exception(
-            "No Daily API key specified. use the -k/--apikey option from the command line, or set DAILY_API_KEY in your environment to specify a Daily API key, available from https://dashboard.daily.co/developers."
-        )
-
-    daily_rest_helper = DailyRESTHelper(
-        daily_api_key=key,
-        daily_api_url=os.getenv("DAILY_API_URL", "https://api.daily.co/v1"),
-        aiohttp_session=aiohttp_session,
-    )
-
-    # Create a meeting token for the given room with an expiration 1 hour in
-    # the future.
-    expiry_time: float = 60 * 60
-
-    token = await daily_rest_helper.get_token(url, expiry_time)
-
-    return (url, token, args)
--- a/examples/unified-format-function-calling/standard-function-calling-anthropic.py
+++ b/examples/unified-format-function-calling/standard-function-calling-anthropic.py
@@ -1,29 +0,0 @@
-#
-# Copyright (c) 2024–2025, Daily
-#
-# SPDX-License-Identifier: BSD 2-Clause License
-#
-
-import asyncio
-import os
-
-from base_function_calling import WeatherBot
-from dotenv import load_dotenv
-
-from pipecat.services.anthropic import AnthropicLLMService
-
-load_dotenv(override=True)
-
-
-class AnthropicWeatherBot(WeatherBot):
-    """Main class defining the LLM and passing it to the base handler."""
-
-    def __init__(self):
-        llm = AnthropicLLMService(
-            api_key=os.getenv("ANTHROPIC_API_KEY"), model="claude-3-5-sonnet-20240620"
-        )
-        super().__init__(llm)
-
-
-if __name__ == "__main__":
-    asyncio.run(AnthropicWeatherBot().run())
--- a/examples/unified-format-function-calling/standard-function-calling-azure.py
+++ b/examples/unified-format-function-calling/standard-function-calling-azure.py
@@ -1,31 +0,0 @@
-#
-# Copyright (c) 2024–2025, Daily
-#
-# SPDX-License-Identifier: BSD 2-Clause License
-#
-
-import asyncio
-import os
-
-from base_function_calling import WeatherBot
-from dotenv import load_dotenv
-
-from pipecat.services.azure import AzureLLMService
-
-load_dotenv(override=True)
-
-
-class AzureWeatherBot(WeatherBot):
-    """Main class defining the LLM and passing it to the base handler."""
-
-    def __init__(self):
-        llm = AzureLLMService(
-            api_key=os.getenv("AZURE_CHATGPT_API_KEY"),
-            endpoint=os.getenv("AZURE_CHATGPT_ENDPOINT"),
-            model=os.getenv("AZURE_CHATGPT_MODEL"),
-        )
-        super().__init__(llm)
-
-
-if __name__ == "__main__":
-    asyncio.run(AzureWeatherBot().run())
--- a/examples/unified-format-function-calling/standard-function-calling-cerebras.py
+++ b/examples/unified-format-function-calling/standard-function-calling-cerebras.py
@@ -1,27 +0,0 @@
-#
-# Copyright (c) 2024–2025, Daily
-#
-# SPDX-License-Identifier: BSD 2-Clause License
-#
-
-import asyncio
-import os
-
-from base_function_calling import WeatherBot
-from dotenv import load_dotenv
-
-from pipecat.services.cerebras import CerebrasLLMService
-
-load_dotenv(override=True)
-
-
-class CerebrasWeatherBot(WeatherBot):
-    """Main class defining the LLM and passing it to the base handler."""
-
-    def __init__(self):
-        llm = CerebrasLLMService(api_key=os.getenv("CEREBRAS_API_KEY"), model="llama-3.3-70b")
-        super().__init__(llm)
-
-
-if __name__ == "__main__":
-    asyncio.run(CerebrasWeatherBot().run())
--- a/examples/unified-format-function-calling/standard-function-calling-deepseek.py
+++ b/examples/unified-format-function-calling/standard-function-calling-deepseek.py
@@ -1,27 +0,0 @@
-#
-# Copyright (c) 2024–2025, Daily
-#
-# SPDX-License-Identifier: BSD 2-Clause License
-#
-
-import asyncio
-import os
-
-from base_function_calling import WeatherBot
-from dotenv import load_dotenv
-
-from pipecat.services.deepseek import DeepSeekLLMService
-
-load_dotenv(override=True)
-
-
-class DeepSeekWeatherBot(WeatherBot):
-    """Main class defining the LLM and passing it to the base handler."""
-
-    def __init__(self):
-        llm = DeepSeekLLMService(api_key=os.getenv("DEEPSEEK_API_KEY"), model="deepseek-chat")
-        super().__init__(llm)
-
-
-if __name__ == "__main__":
-    asyncio.run(DeepSeekWeatherBot().run())
--- a/examples/unified-format-function-calling/standard-function-calling-fireworks.py
+++ b/examples/unified-format-function-calling/standard-function-calling-fireworks.py
@@ -1,29 +0,0 @@
-#
-# Copyright (c) 2024–2025, Daily
-#
-# SPDX-License-Identifier: BSD 2-Clause License
-#
-
-import asyncio
-import os
-
-from base_function_calling import WeatherBot
-from dotenv import load_dotenv
-
-from pipecat.services.fireworks import FireworksLLMService
-
-load_dotenv(override=True)
-
-
-class FireworksWeatherBot(WeatherBot):
-    """Main class defining the LLM and passing it to the base handler."""
-
-    def __init__(self):
-        llm = FireworksLLMService(
-            api_key=os.getenv("FIREWORKS_API_KEY"),
-        )
-        super().__init__(llm)
-
-
-if __name__ == "__main__":
-    asyncio.run(FireworksWeatherBot().run())
--- a/examples/unified-format-function-calling/standard-function-calling-gemini-multimodal.py
+++ b/examples/unified-format-function-calling/standard-function-calling-gemini-multimodal.py
@@ -1,38 +0,0 @@
-#
-# Copyright (c) 2024–2025, Daily
-#
-# SPDX-License-Identifier: BSD 2-Clause License
-#
-
-import asyncio
-import os
-
-from dotenv import load_dotenv
-from multimodal_base_function_calling import MultimodalWeatherBot
-
-from pipecat.adapters.schemas.tools_schema import AdapterType
-from pipecat.services.gemini_multimodal_live import GeminiMultimodalLiveLLMService
-
-load_dotenv(override=True)
-
-
-class GeminiMultimodalWeatherBot(MultimodalWeatherBot):
-    """Main class defining the LLM and passing it to the base handler."""
-
-    def __init__(self):
-        search_tool = {"google_search": {}}
-        tools_def = MultimodalWeatherBot.tools()
-        tools_def.custom_tools = {AdapterType.GEMINI: [search_tool]}
-
-        llm = GeminiMultimodalLiveLLMService(
-            api_key=os.getenv("GOOGLE_API_KEY"),
-            voice_id="Puck",
-            transcribe_user_audio=True,
-            transcribe_model_audio=True,
-            tools=tools_def,
-        )
-        super().__init__(llm)
-
-
-if __name__ == "__main__":
-    asyncio.run(GeminiMultimodalWeatherBot().run())
--- a/examples/unified-format-function-calling/standard-function-calling-gemini.py
+++ b/examples/unified-format-function-calling/standard-function-calling-gemini.py
@@ -1,27 +0,0 @@
-#
-# Copyright (c) 2024–2025, Daily
-#
-# SPDX-License-Identifier: BSD 2-Clause License
-#
-
-import asyncio
-import os
-
-from base_function_calling import WeatherBot
-from dotenv import load_dotenv
-
-from pipecat.services.google import GoogleLLMService
-
-load_dotenv(override=True)
-
-
-class GeminiWeatherBot(WeatherBot):
-    """Main class defining the LLM and passing it to the base handler."""
-
-    def __init__(self):
-        llm = GoogleLLMService(api_key=os.getenv("GOOGLE_API_KEY"), model="gemini-2.0-flash-001")
-        super().__init__(llm)
-
-
-if __name__ == "__main__":
-    asyncio.run(GeminiWeatherBot().run())
--- a/examples/unified-format-function-calling/standard-function-calling-grok.py
+++ b/examples/unified-format-function-calling/standard-function-calling-grok.py
@@ -1,27 +0,0 @@
-#
-# Copyright (c) 2024–2025, Daily
-#
-# SPDX-License-Identifier: BSD 2-Clause License
-#
-
-import asyncio
-import os
-
-from base_function_calling import WeatherBot
-from dotenv import load_dotenv
-
-from pipecat.services.grok import GrokLLMService
-
-load_dotenv(override=True)
-
-
-class GrokWeatherBot(WeatherBot):
-    """Main class defining the LLM and passing it to the base handler."""
-
-    def __init__(self):
-        llm = GrokLLMService(api_key=os.getenv("GROK_API_KEY"))
-        super().__init__(llm)
-
-
-if __name__ == "__main__":
-    asyncio.run(GrokWeatherBot().run())
--- a/examples/unified-format-function-calling/standard-function-calling-groq.py
+++ b/examples/unified-format-function-calling/standard-function-calling-groq.py
@@ -1,27 +0,0 @@
-#
-# Copyright (c) 2024–2025, Daily
-#
-# SPDX-License-Identifier: BSD 2-Clause License
-#
-
-import asyncio
-import os
-
-from base_function_calling import WeatherBot
-from dotenv import load_dotenv
-
-from pipecat.services.groq import GroqLLMService
-
-load_dotenv(override=True)
-
-
-class GroqWeatherBot(WeatherBot):
-    """Main class defining the LLM and passing it to the base handler."""
-
-    def __init__(self):
-        llm = GroqLLMService(api_key=os.getenv("GROQ_API_KEY"), model="llama-3.3-70b-versatile")
-        super().__init__(llm)
-
-
-if __name__ == "__main__":
-    asyncio.run(GroqWeatherBot().run())
--- a/examples/unified-format-function-calling/standard-function-calling-nim.py
+++ b/examples/unified-format-function-calling/standard-function-calling-nim.py
@@ -1,29 +0,0 @@
-#
-# Copyright (c) 2024–2025, Daily
-#
-# SPDX-License-Identifier: BSD 2-Clause License
-#
-
-import asyncio
-import os
-
-from base_function_calling import WeatherBot
-from dotenv import load_dotenv
-
-from pipecat.services.nim import NimLLMService
-
-load_dotenv(override=True)
-
-
-class NimWeatherBot(WeatherBot):
-    """Main class defining the LLM and passing it to the base handler."""
-
-    def __init__(self):
-        llm = NimLLMService(
-            api_key=os.getenv("NVIDIA_API_KEY"), model="meta/llama-3.3-70b-instruct"
-        )
-        super().__init__(llm)
-
-
-if __name__ == "__main__":
-    asyncio.run(NimWeatherBot().run())
--- a/examples/unified-format-function-calling/standard-function-calling-openai-realtime.py
+++ b/examples/unified-format-function-calling/standard-function-calling-openai-realtime.py
@@ -1,43 +0,0 @@
-#
-# Copyright (c) 2024–2025, Daily
-#
-# SPDX-License-Identifier: BSD 2-Clause License
-#
-
-import asyncio
-import os
-
-from dotenv import load_dotenv
-from multimodal_base_function_calling import MultimodalWeatherBot
-
-from pipecat.services.openai_realtime_beta import (
-    InputAudioTranscription,
-    OpenAIRealtimeBetaLLMService,
-    SessionProperties,
-    TurnDetection,
-)
-
-load_dotenv(override=True)
-
-
-class OpenAiRealTimeWeatherBot(MultimodalWeatherBot):
-    """Main class defining the LLM and passing it to the base handler."""
-
-    def __init__(self):
-        session_properties = SessionProperties(
-            input_audio_transcription=InputAudioTranscription(),
-            # Set openai TurnDetection parameters. Not setting this at all will turn it
-            # on by default
-            turn_detection=TurnDetection(silence_duration_ms=1000),
-        )
-
-        llm = OpenAIRealtimeBetaLLMService(
-            api_key=os.getenv("OPENAI_API_KEY"),
-            session_properties=session_properties,
-            start_audio_paused=False,
-        )
-        super().__init__(llm)
-
-
-if __name__ == "__main__":
-    asyncio.run(OpenAiRealTimeWeatherBot().run())
--- a/examples/unified-format-function-calling/standard-function-calling-openai.py
+++ b/examples/unified-format-function-calling/standard-function-calling-openai.py
@@ -1,27 +0,0 @@
-#
-# Copyright (c) 2024–2025, Daily
-#
-# SPDX-License-Identifier: BSD 2-Clause License
-#
-
-import asyncio
-import os
-
-from base_function_calling import WeatherBot
-from dotenv import load_dotenv
-
-from pipecat.services.openai import OpenAILLMService
-
-load_dotenv(override=True)
-
-
-class OpenAiWeatherBot(WeatherBot):
-    """Main class defining the LLM and passing it to the base handler."""
-
-    def __init__(self):
-        llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
-        super().__init__(llm)
-
-
-if __name__ == "__main__":
-    asyncio.run(OpenAiWeatherBot().run())
--- a/examples/unified-format-function-calling/standard-function-calling-openrouter.py
+++ b/examples/unified-format-function-calling/standard-function-calling-openrouter.py
@@ -1,29 +0,0 @@
-#
-# Copyright (c) 2024–2025, Daily
-#
-# SPDX-License-Identifier: BSD 2-Clause License
-#
-
-import asyncio
-import os
-
-from base_function_calling import WeatherBot
-from dotenv import load_dotenv
-
-from pipecat.services.openrouter import OpenRouterLLMService
-
-load_dotenv(override=True)
-
-
-class OpenRouterWeatherBot(WeatherBot):
-    """Main class defining the LLM and passing it to the base handler."""
-
-    def __init__(self):
-        llm = OpenRouterLLMService(
-            api_key=os.getenv("OPENROUTER_API_KEY"), model="openai/gpt-4o-2024-11-20"
-        )
-        super().__init__(llm)
-
-
-if __name__ == "__main__":
-    asyncio.run(OpenRouterWeatherBot().run())
--- a/examples/unified-format-function-calling/standard-function-calling-together.py
+++ b/examples/unified-format-function-calling/standard-function-calling-together.py
@@ -1,30 +0,0 @@
-#
-# Copyright (c) 2024–2025, Daily
-#
-# SPDX-License-Identifier: BSD 2-Clause License
-#
-
-import asyncio
-import os
-
-from base_function_calling import WeatherBot
-from dotenv import load_dotenv
-
-from pipecat.services.together import TogetherLLMService
-
-load_dotenv(override=True)
-
-
-class TogetherWeatherBot(WeatherBot):
-    """Main class defining the LLM and passing it to the base handler."""
-
-    def __init__(self):
-        llm = TogetherLLMService(
-            api_key=os.getenv("TOGETHER_API_KEY"),
-            model="meta-llama/Meta-Llama-3.1-70B-Instruct-Turbo",
-        )
-        super().__init__(llm)
-
-
-if __name__ == "__main__":
-    asyncio.run(TogetherWeatherBot().run())
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -20,14 +20,17 @@ classifiers = [
    "Topic :: Scientific/Engineering :: Artificial Intelligence"
 ]
 dependencies = [
-    "aiohttp~=3.11.13",
+    "aiohttp~=3.11.11",
    "audioop-lts~=0.2.1; python_version>='3.13'",
+    # We need an older version of `httpx` that doesn't remove the deprecated
+    # `proxies` argument. This is necessary for Azure and Anthropic clients.
+    "httpx~=0.27.2",
    "loguru~=0.7.3",
    "Markdown~=3.7",
    "numpy~=1.26.4",
    "Pillow~=11.1.0",
    "protobuf~=5.29.3",
-    "pydantic~=2.10.6",
+    "pydantic~=2.10.5",
    "pyloudnorm~=0.1.1",
    "resampy~=0.4.3",
    "soxr~=0.5.0",
@@ -39,7 +42,7 @@ Source = "https://github.com/pipecat-ai/pipecat"
 Website = "https://pipecat.ai"

 [project.optional-dependencies]
-anthropic = [ "anthropic~=0.47.2" ]
+anthropic = [ "anthropic~=0.45.2" ]
 assemblyai = [ "assemblyai~=0.36.0" ]
 aws = [ "boto3~=1.35.99" ]
 azure = [ "azure-cognitiveservices-speech~=1.42.0"]
@@ -47,13 +50,13 @@ canonical = [ "aiofiles~=24.1.0" ]
 cartesia = [ "cartesia~=1.3.1", "websockets~=13.1" ]
 cerebras = []
 deepseek = []
-daily = [ "daily-python~=0.15.0" ]
+daily = [ "daily-python~=0.14.2" ]
 deepgram = [ "deepgram-sdk~=3.8.0" ]
 elevenlabs = [ "websockets~=13.1" ]
 fal = [ "fal-client~=0.5.6" ]
 fish = [ "ormsgpack~=1.7.0", "websockets~=13.1" ]
 gladia = [ "websockets~=13.1" ]
-google = [ "google-cloud-speech~=2.31.0", "google-cloud-texttospeech~=2.25.0", "google-genai~=1.3.0", "google-generativeai~=0.8.4" ]
+google = [ "google-cloud-speech~=2.31.0", "google-cloud-texttospeech~=2.25.0", "google-genai~=1.2.0", "google-generativeai~=0.8.4" ]
 grok = []
 groq = []
 gstreamer = [ "pygobject~=3.50.0" ]
@@ -70,7 +73,7 @@ noisereduce = [ "noisereduce~=3.0.3" ]
 openai = [ "websockets~=13.1" ]
 openpipe = [ "openpipe~=4.45.0" ]
 perplexity = []
-playht = [ "pyht~=0.1.12", "websockets~=13.1" ]
+playht = [ "pyht~=0.1.6", "websockets~=13.1" ]
 rime = [ "websockets~=13.1" ]
 riva = [ "nvidia-riva-client~=2.18.0" ]
 sentry = [ "sentry-sdk~=2.20.0" ]
--- a/scripts/fix-ruff.sh
+++ b/scripts/fix-ruff.sh
@@ -1,4 +0,0 @@
-ruff format src
-ruff format examples
-ruff format tests
-ruff check --select I --fix
--- a/src/pipecat/adapters/init.py
+++ b/src/pipecat/adapters/init.py
--- a/src/pipecat/adapters/base_llm_adapter.py
+++ b/src/pipecat/adapters/base_llm_adapter.py
@@ -1,22 +0,0 @@
-from abc import ABC, abstractmethod
-from typing import Any, List, Union, cast
-
-from loguru import logger
-
-from pipecat.adapters.schemas.tools_schema import ToolsSchema
-
-
-class BaseLLMAdapter(ABC):
-    @abstractmethod
-    def to_provider_tools_format(self, tools_schema: ToolsSchema) -> List[Any]:
-        """Converts tools to the provider's format."""
-        pass
-
-    def from_standard_tools(self, tools: Any) -> List[Any]:
-        if isinstance(tools, ToolsSchema):
-            logger.debug(f"Retrieving the tools using the adapter: {type(self)}")
-            return self.to_provider_tools_format(tools)
-        # Fallback to return the same tools in case they are not in a standard format
-        return tools
-
-    # TODO: we can move the logic to also handle the Messages here
--- a/src/pipecat/adapters/schemas/init.py
+++ b/src/pipecat/adapters/schemas/init.py
--- a/src/pipecat/adapters/schemas/function_schema.py
+++ b/src/pipecat/adapters/schemas/function_schema.py
@@ -1,55 +0,0 @@
-#
-# Copyright (c) 2024–2025, Daily
-#
-# SPDX-License-Identifier: BSD 2-Clause License
-#
-
-from typing import Any, Dict, List
-
-
-class FunctionSchema:
-    def __init__(
-        self, name: str, description: str, properties: Dict[str, Any], required: List[str]
-    ) -> None:
-        """Standardized function schema representation.
-
-        :param name: Name of the function.
-        :param description: Description of the function.
-        :param properties: Dictionary defining properties types and descriptions.
-        :param required: List of required parameters.
-        """
-        self._name = name
-        self._description = description
-        self._properties = properties
-        self._required = required
-
-    def to_default_dict(self) -> Dict[str, Any]:
-        """Converts the function schema to a dictionary.
-
-        :return: Dictionary representation of the function schema.
-        """
-        return {
-            "name": self._name,
-            "description": self._description,
-            "parameters": {
-                "type": "object",
-                "properties": self._properties,
-                "required": self._required,
-            },
-        }
-
-    @property
-    def name(self) -> str:
-        return self._name
-
-    @property
-    def description(self) -> str:
-        return self._description
-
-    @property
-    def properties(self) -> Dict[str, Any]:
-        return self._properties
-
-    @property
-    def required(self) -> List[str]:
-        return self._required
--- a/src/pipecat/adapters/schemas/tools_schema.py
+++ b/src/pipecat/adapters/schemas/tools_schema.py
@@ -1,43 +0,0 @@
-#
-# Copyright (c) 2024–2025, Daily
-#
-# SPDX-License-Identifier: BSD 2-Clause License
-#
-
-from enum import Enum
-from typing import Any, Dict, List
-
-from pipecat.adapters.schemas.function_schema import FunctionSchema
-
-
-class AdapterType(Enum):
-    GEMINI = "gemini"  # that is the only service where we are able to add custom tools for now
-
-
-class ToolsSchema:
-    def __init__(
-        self,
-        standard_tools: List[FunctionSchema],
-        custom_tools: Dict[AdapterType, List[Dict[str, Any]]] = None,
-    ) -> None:
-        """
-        A schema for tools that includes both standardized function schemas
-        and custom tools that do not follow the FunctionSchema format.
-
-        :param standard_tools: List of tools following FunctionSchema.
-        :param custom_tools: List of tools in a custom format (e.g., search_tool).
-        """
-        self._standard_tools = standard_tools
-        self._custom_tools = custom_tools
-
-    @property
-    def standard_tools(self) -> List[FunctionSchema]:
-        return self._standard_tools
-
-    @property
-    def custom_tools(self) -> Dict[AdapterType, List[Dict[str, Any]]]:
-        return self._custom_tools
-
-    @custom_tools.setter
-    def custom_tools(self, value: Dict[AdapterType, List[Dict[str, Any]]]) -> None:
-        self._custom_tools = value
--- a/src/pipecat/adapters/services/init.py
+++ b/src/pipecat/adapters/services/init.py
--- a/src/pipecat/adapters/services/anthropic_adapter.py
+++ b/src/pipecat/adapters/services/anthropic_adapter.py
@@ -1,34 +0,0 @@
-#
-# Copyright (c) 2024–2025, Daily
-#
-# SPDX-License-Identifier: BSD 2-Clause License
-#
-
-from typing import Any, Dict, List, Union
-
-from pipecat.adapters.base_llm_adapter import BaseLLMAdapter
-from pipecat.adapters.schemas.function_schema import FunctionSchema
-from pipecat.adapters.schemas.tools_schema import ToolsSchema
-
-
-class AnthropicLLMAdapter(BaseLLMAdapter):
-    @staticmethod
-    def _to_anthropic_function_format(function: FunctionSchema) -> Dict[str, Any]:
-        return {
-            "name": function.name,
-            "description": function.description,
-            "input_schema": {
-                "type": "object",
-                "properties": function.properties,
-                "required": function.required,
-            },
-        }
-
-    def to_provider_tools_format(self, tools_schema: ToolsSchema) -> List[Dict[str, Any]]:
-        """Converts function schemas to Anthropic's function-calling format.
-
-        :return: Anthropic formatted function call definition.
-        """
-
-        functions_schema = tools_schema.standard_tools
-        return [self._to_anthropic_function_format(func) for func in functions_schema]
--- a/src/pipecat/adapters/services/gemini_adapter.py
+++ b/src/pipecat/adapters/services/gemini_adapter.py
@@ -1,28 +0,0 @@
-#
-# Copyright (c) 2024–2025, Daily
-#
-# SPDX-License-Identifier: BSD 2-Clause License
-#
-
-from typing import Any, Dict, List, Union
-
-from pipecat.adapters.base_llm_adapter import BaseLLMAdapter
-from pipecat.adapters.schemas.tools_schema import AdapterType, ToolsSchema
-
-
-class GeminiLLMAdapter(BaseLLMAdapter):
-    def to_provider_tools_format(self, tools_schema: ToolsSchema) -> List[Dict[str, Any]]:
-        """Converts function schemas to Gemini's function-calling format.
-
-        :return: Gemini formatted function call definition.
-        """
-
-        functions_schema = tools_schema.standard_tools
-        formatted_standard_tools = [
-            {"function_declarations": [func.to_default_dict() for func in functions_schema]}
-        ]
-        custom_gemini_tools = []
-        if tools_schema.custom_tools:
-            custom_gemini_tools = tools_schema.custom_tools.get(AdapterType.GEMINI, [])
-
-        return formatted_standard_tools + custom_gemini_tools
--- a/src/pipecat/adapters/services/open_ai_adapter.py
+++ b/src/pipecat/adapters/services/open_ai_adapter.py
@@ -1,24 +0,0 @@
-#
-# Copyright (c) 2024–2025, Daily
-#
-# SPDX-License-Identifier: BSD 2-Clause License
-#
-from typing import List
-
-from openai.types.chat import ChatCompletionToolParam
-
-from pipecat.adapters.base_llm_adapter import BaseLLMAdapter
-from pipecat.adapters.schemas.tools_schema import ToolsSchema
-
-
-class OpenAILLMAdapter(BaseLLMAdapter):
-    def to_provider_tools_format(self, tools_schema: ToolsSchema) -> List[ChatCompletionToolParam]:
-        """Converts function schemas to OpenAI's function-calling format.
-
-        :return: OpenAI formatted function call definition.
-        """
-        functions_schema = tools_schema.standard_tools
-        return [
-            ChatCompletionToolParam(type="function", function=func.to_default_dict())
-            for func in functions_schema
-        ]
--- a/src/pipecat/adapters/services/open_ai_realtime_adapter.py
+++ b/src/pipecat/adapters/services/open_ai_realtime_adapter.py
@@ -1,34 +0,0 @@
-#
-# Copyright (c) 2024–2025, Daily
-#
-# SPDX-License-Identifier: BSD 2-Clause License
-#
-from typing import Any, Dict, List, Union
-
-from pipecat.adapters.base_llm_adapter import BaseLLMAdapter
-from pipecat.adapters.schemas.function_schema import FunctionSchema
-from pipecat.adapters.schemas.tools_schema import ToolsSchema
-
-
-class OpenAIRealtimeLLMAdapter(BaseLLMAdapter):
-    @staticmethod
-    def _to_openai_realtime_function_format(function: FunctionSchema) -> Dict[str, Any]:
-        return {
-            "type": "function",
-            "name": function.name,
-            "description": function.description,
-            "parameters": {
-                "type": "object",
-                "properties": function.properties,
-                "required": function.required,
-            },
-        }
-
-    def to_provider_tools_format(self, tools_schema: ToolsSchema) -> List[Dict[str, Any]]:
-        """Converts function schemas to Openai Realtime function-calling format.
-
-        :return: Openai Realtime formatted function call definition.
-        """
-
-        functions_schema = tools_schema.standard_tools
-        return [self._to_openai_realtime_function_format(func) for func in functions_schema]
--- a/src/pipecat/audio/turn/init.py
+++ b/src/pipecat/audio/turn/init.py
--- a/src/pipecat/audio/turn/base_turn_analyzer.py
+++ b/src/pipecat/audio/turn/base_turn_analyzer.py
@@ -1,32 +0,0 @@
-#
-# Copyright (c) 2024–2025, Daily
-#
-# SPDX-License-Identifier: BSD 2-Clause License
-#
-
-
-from abc import ABC, abstractmethod
-from enum import Enum
-from typing import Optional
-
-
-class EndOfTurnState(Enum):
-    COMPLETE = 1
-    INCOMPLETE = 2
-
-
-class BaseEndOfTurnAnalyzer(ABC):
-    def __init__(self, *, sample_rate: Optional[int] = None):
-        self._init_sample_rate = sample_rate
-        self._sample_rate = 0
-
-    @property
-    def sample_rate(self) -> int:
-        return self._sample_rate
-
-    def set_sample_rate(self, sample_rate: int):
-        self._sample_rate = self._init_sample_rate or sample_rate
-
-    @abstractmethod
-    def analyze_audio(self, buffer: bytes) -> EndOfTurnState:
-        pass
--- a/src/pipecat/audio/turn/smart_turn.py
+++ b/src/pipecat/audio/turn/smart_turn.py
@@ -1,83 +0,0 @@
-#
-# Copyright (c) 2024–2025, Daily
-#
-# SPDX-License-Identifier: BSD 2-Clause License
-#
-
-
-import numpy as np
-import torch
-from loguru import logger
-from transformers import AutoFeatureExtractor, Wav2Vec2BertForSequenceClassification
-
-from pipecat.audio.turn.base_turn_analyzer import BaseEndOfTurnAnalyzer, EndOfTurnState
-
-# MODEL_PATH = "model-v1"
-MODEL_PATH = "pipecat-ai/smart-turn"
-
-
-class SmartTurnAnalyzer(BaseEndOfTurnAnalyzer):
-    def __init__(self):
-        super().__init__()
-        self._audio_buffer = bytearray()
-
-        logger.debug("Loading Smart Turn model...")
-
-        # Load model and processor
-        model = Wav2Vec2BertForSequenceClassification.from_pretrained(MODEL_PATH)
-        self._processor = AutoFeatureExtractor.from_pretrained(MODEL_PATH)
-
-        # Set model to evaluation mode and move to GPU if available
-        self._device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-        self._model = model.to(self._device)
-        self._model.eval()
-
-        logger.debug("Loaded Smart Turn")
-
-    def analyze_audio(self, buffer: bytes) -> EndOfTurnState:
-        self._audio_buffer += buffer
-        if len(self._audio_buffer) < 16000 * 2 * 6:
-            return EndOfTurnState.INCOMPLETE
-
-        audio_int16 = np.frombuffer(self._audio_buffer, dtype=np.int16)
-
-        # Divide by 32768 because we have signed 16-bit data.
-        audio_float32 = np.frombuffer(audio_int16, dtype=np.int16).astype(np.float32) / 32768.0
-        print(audio_float32)
-
-        # Process audio
-        inputs = self._processor(
-            audio_float32,
-            sampling_rate=16000,
-            padding="max_length",
-            truncation=True,
-            max_length=800,  # Maximum length as specified in training
-            return_attention_mask=True,
-            return_tensors="pt",
-        )
-
-        # Move inputs to device
-        inputs = {k: v.to(self._device) for k, v in inputs.items()}
-
-        # Run inference
-        with torch.no_grad():
-            outputs = self._model(**inputs)
-            logits = outputs.logits
-
-            # Get probabilities using softmax
-            probabilities = torch.nn.functional.softmax(logits, dim=1)
-            completion_prob = probabilities[0, 1].item()  # Probability of class 1 (Complete)
-
-            # Make prediction (1 for Complete, 0 for Incomplete)
-            prediction = 1 if completion_prob > 0.5 else 0
-
-        state = EndOfTurnState.COMPLETE if prediction == 1 else EndOfTurnState.INCOMPLETE
-
-        if state == EndOfTurnState.COMPLETE:
-            self._audio_buffer = bytearray()
-        else:
-            self._audio_buffer = self._audio_buffer[len(buffer) :]
-
-        print("AAAAAAAAAAAA", state)
-
-        return state
--- a/src/pipecat/audio/vad/vad_analyzer.py
+++ b/src/pipecat/audio/vad/vad_analyzer.py
@@ -4,7 +4,7 @@
 # SPDX-License-Identifier: BSD 2-Clause License
 #

-from abc import ABC, abstractmethod
+from abc import abstractmethod
 from enum import Enum
 from typing import Optional

@@ -33,7 +33,7 @@ class VADParams(BaseModel):
    min_volume: float = VAD_MIN_VOLUME


-class VADAnalyzer(ABC):
+class VADAnalyzer:
    def __init__(self, *, sample_rate: Optional[int] = None, params: VADParams):
        self._init_sample_rate = sample_rate
        self._sample_rate = 0
--- a/src/pipecat/frames/frames.py
+++ b/src/pipecat/frames/frames.py
@@ -568,8 +568,7 @@ class UserStoppedSpeakingFrame(SystemFrame):
@dataclass
 class EmulateUserStartedSpeakingFrame(SystemFrame):
    """Emitted by internal processors upstream to emulate VAD behavior when a
-    user starts speaking.
-    """
+    user starts speaking."""

    pass

@@ -577,20 +576,7 @@ class EmulateUserStartedSpeakingFrame(SystemFrame):
@dataclass
 class EmulateUserStoppedSpeakingFrame(SystemFrame):
    """Emitted by internal processors upstream to emulate VAD behavior when a
-    user stops speaking.
-    """
-
-    pass
-
-
-@dataclass
-class UserEndOfTurnFrame(SystemFrame):
-    """Emitted by VAD to indicate that a user has started speaking. This can be
-    used for interruptions or other times when detecting that someone is
-    speaking is more important than knowing what they're saying (as you will
-    with a TranscriptionFrame)
-
-    """
+    user stops speaking."""

    pass

--- a/src/pipecat/pipeline/base_task.py
+++ b/src/pipecat/pipeline/base_task.py
@@ -5,14 +5,25 @@
 #

 import asyncio
-from abc import abstractmethod
+from abc import ABC, abstractmethod
 from typing import AsyncIterable, Iterable

 from pipecat.frames.frames import Frame
-from pipecat.utils.base_object import BaseObject


-class BaseTask(BaseObject):
+class BaseTask(ABC):
+    @property
+    @abstractmethod
+    def id(self) -> int:
+        """Returns the unique indetifier for this task."""
+        pass
+
+    @property
+    @abstractmethod
+    def name(self) -> str:
+        """Returns the name of this task."""
+        pass
+
    @abstractmethod
    def set_event_loop(self, loop: asyncio.AbstractEventLoop):
        """Sets the event loop that this task will run on."""
--- a/src/pipecat/pipeline/runner.py
+++ b/src/pipecat/pipeline/runner.py
@@ -12,10 +12,10 @@ from typing import Optional
 from loguru import logger

 from pipecat.pipeline.task import PipelineTask
-from pipecat.utils.base_object import BaseObject
+from pipecat.utils.utils import obj_count, obj_id


-class PipelineRunner(BaseObject):
+class PipelineRunner:
    def __init__(
        self,
        *,
@@ -24,7 +24,8 @@ class PipelineRunner(BaseObject):
        force_gc: bool = False,
        loop: Optional[asyncio.AbstractEventLoop] = None,
    ):
-        super().__init__(name=name)
+        self.id: int = obj_id()
+        self.name: str = name or f"{self.__class__.__name__}#{obj_count(self)}"

        self._tasks = {}
        self._sig_task = None
@@ -73,3 +74,6 @@ class PipelineRunner(BaseObject):
        collected = gc.collect()
        logger.debug(f"Garbage collector: collected {collected} objects.")
        logger.debug(f"Garbage collector: uncollectable objects {gc.garbage}")
+
+    def __str__(self):
+        return self.name
--- a/src/pipecat/pipeline/task.py
+++ b/src/pipecat/pipeline/task.py
@@ -32,6 +32,7 @@ from pipecat.pipeline.base_task import BaseTask
 from pipecat.pipeline.task_observer import TaskObserver
 from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
 from pipecat.utils.asyncio import BaseTaskManager, TaskManager
+from pipecat.utils.utils import obj_count, obj_id

 HEARTBEAT_SECONDS = 1.0
 HEARTBEAT_MONITOR_SECONDS = HEARTBEAT_SECONDS * 5
@@ -137,7 +138,9 @@ class PipelineTask(BaseTask):
        task_manager: Optional[BaseTaskManager] = None,
        check_dangling_tasks: bool = True,
    ):
-        super().__init__()
+        self._id: int = obj_id()
+        self._name: str = f"{self.__class__.__name__}#{obj_count(self)}"
+
        self._pipeline = pipeline
        self._clock = clock
        self._params = params
@@ -177,6 +180,16 @@ class PipelineTask(BaseTask):

        self._observer = TaskObserver(observers=observers, task_manager=self._task_manager)

+    @property
+    def id(self) -> int:
+        """Returns the unique indetifier for this task."""
+        return self._id
+
+    @property
+    def name(self) -> str:
+        """Returns the name of this task."""
+        return self._name
+
    @property
    def params(self) -> PipelineParams:
        """Returns the pipeline parameters of this task."""
@@ -421,3 +434,6 @@ class PipelineTask(BaseTask):
        tasks = [t.get_name() for t in self._task_manager.current_tasks()]
        if tasks:
            logger.warning(f"Dangling tasks detected: {tasks}")
+
+    def __str__(self):
+        return self.name
--- a/src/pipecat/pipeline/task_observer.py
+++ b/src/pipecat/pipeline/task_observer.py
@@ -13,6 +13,7 @@ from pipecat.frames.frames import Frame
 from pipecat.observers.base_observer import BaseObserver
 from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
 from pipecat.utils.asyncio import BaseTaskManager
+from pipecat.utils.utils import obj_count, obj_id


@dataclass
@@ -55,10 +56,20 @@ class TaskObserver(BaseObserver):
    """

    def __init__(self, *, observers: List[BaseObserver] = [], task_manager: BaseTaskManager):
+        self._id: int = obj_id()
+        self._name: str = f"{self.__class__.__name__}#{obj_count(self)}"
        self._observers = observers
        self._task_manager = task_manager
        self._proxies: List[Proxy] = []

+    @property
+    def id(self) -> int:
+        return self._id
+
+    @property
+    def name(self) -> str:
+        return self._name
+
    async def start(self):
        """Starts all proxy observer tasks."""
        self._proxies = self._create_proxies(self._observers)
@@ -89,7 +100,7 @@ class TaskObserver(BaseObserver):
            queue = asyncio.Queue()
            task = self._task_manager.create_task(
                self._proxy_task_handler(queue, observer),
-                f"TaskObserver::{observer.__class__.__name__}::_proxy_task_handler",
+                f"{self}::{observer.__class__.__name__}::_proxy_task_handler",
            )
            proxy = Proxy(queue=queue, task=task, observer=observer)
            proxies.append(proxy)
@@ -101,3 +112,6 @@ class TaskObserver(BaseObserver):
            await observer.on_push_frame(
                data.src, data.dst, data.frame, data.direction, data.timestamp
            )
+
+    def __str__(self):
+        return self.name
--- a/src/pipecat/processors/aggregators/llm_response.py
+++ b/src/pipecat/processors/aggregators/llm_response.py
@@ -22,7 +22,6 @@ from pipecat.frames.frames import (
    LLMMessagesFrame,
    LLMMessagesUpdateFrame,
    LLMSetToolsFrame,
-    LLMTextFrame,
    StartFrame,
    StartInterruptionFrame,
    TextFrame,
@@ -37,59 +36,6 @@ from pipecat.processors.aggregators.openai_llm_context import (
 from pipecat.processors.frame_processor import FrameDirection, FrameProcessor


-class LLMFullResponseAggregator(FrameProcessor):
-    """This is an LLM aggregator that aggregates a full LLM completion. It
-    aggregates LLM text frames (tokens) received between
-    `LLMFullResponseStartFrame` and `LLMFullResponseEndFrame`. Every full
-    completion is returned via the "on_completion" event handler:
-
-       @aggregator.event_handler("on_completion")
-       async def on_completion(
-           aggregator: LLMFullResponseAggregator,
-           completion: str,
-           completed: bool,
-       )
-
-    """
-
-    def __init__(self, **kwargs):
-        super().__init__(**kwargs)
-
-        self._aggregation = ""
-        self._started = False
-
-        self._register_event_handler("on_completion")
-
-    async def process_frame(self, frame: Frame, direction: FrameDirection):
-        await super().process_frame(frame, direction)
-
-        if isinstance(frame, StartInterruptionFrame):
-            await self._call_event_handler("on_completion", self._aggregation, False)
-            self._aggregation = ""
-            self._started = False
-        elif isinstance(frame, LLMFullResponseStartFrame):
-            await self._handle_llm_start(frame)
-        elif isinstance(frame, LLMFullResponseEndFrame):
-            await self._handle_llm_end(frame)
-        elif isinstance(frame, LLMTextFrame):
-            await self._handle_llm_text(frame)
-
-        await self.push_frame(frame, direction)
-
-    async def _handle_llm_start(self, _: LLMFullResponseStartFrame):
-        self._started = True
-
-    async def _handle_llm_end(self, _: LLMFullResponseEndFrame):
-        await self._call_event_handler("on_completion", self._aggregation, True)
-        self._started = False
-        self._aggregation = ""
-
-    async def _handle_llm_text(self, frame: TextFrame):
-        if not self._started:
-            return
-        self._aggregation += frame.text
-
-
 class BaseLLMResponseAggregator(FrameProcessor):
    """This is the base class for all LLM response aggregators. These
    aggregators process incoming frames and aggregate content until they are
--- a/src/pipecat/processors/aggregators/openai_llm_context.py
+++ b/src/pipecat/processors/aggregators/openai_llm_context.py
@@ -20,8 +20,6 @@ from openai.types.chat import (
 )
 from PIL import Image

-from pipecat.adapters.base_llm_adapter import BaseLLMAdapter
-from pipecat.adapters.schemas.tools_schema import ToolsSchema
 from pipecat.frames.frames import (
    AudioRawFrame,
    Frame,
@@ -46,20 +44,13 @@ class OpenAILLMContext:
    def __init__(
        self,
        messages: Optional[List[ChatCompletionMessageParam]] = None,
-        tools: List[ChatCompletionToolParam] | NotGiven | ToolsSchema = NOT_GIVEN,
+        tools: List[ChatCompletionToolParam] | NotGiven = NOT_GIVEN,
        tool_choice: ChatCompletionToolChoiceOptionParam | NotGiven = NOT_GIVEN,
    ):
        self._messages: List[ChatCompletionMessageParam] = messages if messages else []
        self._tool_choice: ChatCompletionToolChoiceOptionParam | NotGiven = tool_choice
-        self._tools: List[ChatCompletionToolParam] | NotGiven | ToolsSchema = tools
+        self._tools: List[ChatCompletionToolParam] | NotGiven = tools
        self._user_image_request_context = {}
-        self._llm_adapter: Optional[BaseLLMAdapter] = None
-
-    def get_llm_adapter(self) -> Optional[BaseLLMAdapter]:
-        return self._llm_adapter
-
-    def set_llm_adapter(self, llm_adapter: BaseLLMAdapter):
-        self._llm_adapter = llm_adapter

    @staticmethod
    def from_messages(messages: List[dict]) -> "OpenAILLMContext":
@@ -76,9 +67,7 @@ class OpenAILLMContext:
        return self._messages

    @property
-    def tools(self) -> List[ChatCompletionToolParam] | NotGiven | List[Any]:
-        if self._llm_adapter:
-            return self._llm_adapter.from_standard_tools(self._tools)
+    def tools(self) -> List[ChatCompletionToolParam] | NotGiven:
        return self._tools

    @property
@@ -163,7 +152,7 @@ class OpenAILLMContext:
    def set_tool_choice(self, tool_choice: ChatCompletionToolChoiceOptionParam | NotGiven):
        self._tool_choice = tool_choice

-    def set_tools(self, tools: List[ChatCompletionToolParam] | NotGiven | ToolsSchema = NOT_GIVEN):
+    def set_tools(self, tools: List[ChatCompletionToolParam] | NotGiven = NOT_GIVEN):
        if tools != NOT_GIVEN and len(tools) == 0:
            tools = NOT_GIVEN
        self._tools = tools
--- a/src/pipecat/processors/audio/audio_buffer_processor.py
+++ b/src/pipecat/processors/audio/audio_buffer_processor.py
@@ -21,32 +21,20 @@ from pipecat.processors.frame_processor import FrameDirection, FrameProcessor


 class AudioBufferProcessor(FrameProcessor):
-    """Processes and buffers audio frames from both input (user) and output (bot) sources.
+    """This processor buffers audio raw frames (input and output). The mixed
+    audio can be obtained by registering an "on_audio_data" event handler.
+    The event handler will be called every time `buffer_size` is reached.

-    This processor manages audio buffering and synchronization, providing both merged and
-    track-specific audio access through event handlers. It supports various audio configurations
-    including sample rate conversion and mono/stereo output.
+    You can provide the desired output `sample_rate` and incoming audio frames
+    will resampled to match it. Also, you can provide the number of channels, 1
+    for mono and 2 for stereo. With mono audio user and bot audio will be mixed,
+    in the case of stereo the left channel will be used for the user's audio and
+    the right channel for the bot.

-    Events:
-        on_audio_data: Triggered when buffer_size is reached, providing merged audio
-        on_track_audio_data: Triggered when buffer_size is reached, providing separate tracks
+    Most of the time, user audio will be a continuous stream but it's possible
+    that in some cases only the spoken audio is sent. To accomodate for those
+    cases make sure to set `user_continuous_stream` accordingly.

-    Args:
-        sample_rate (Optional[int]): Desired output sample rate. If None, uses source rate
-        num_channels (int): Number of channels (1 for mono, 2 for stereo). Defaults to 1
-        buffer_size (int): Size of buffer before triggering events. 0 for no buffering
-        user_continuous_stream (bool): Whether user audio is continuous or speech-only
-
-    Audio handling:
-        - Mono output (num_channels=1): User and bot audio are mixed
-        - Stereo output (num_channels=2): User audio on left, bot audio on right
-        - Automatic resampling of incoming audio to match desired sample_rate
-        - Silence insertion for non-continuous audio streams
-        - Buffer synchronization between user and bot audio
-
-    Note:
-        When user_continuous_stream is False, the processor expects only speech
-        segments and will handle silence insertion between segments automatically.
    """

    def __init__(
@@ -77,45 +65,21 @@ class AudioBufferProcessor(FrameProcessor):
        self._resampler = create_default_resampler()

        self._register_event_handler("on_audio_data")
-        self._register_event_handler("on_track_audio_data")

    @property
    def sample_rate(self) -> int:
-        """Current sample rate of the audio processor.
-
-        Returns:
-            int: The sample rate in Hz
-        """
        return self._sample_rate

    @property
    def num_channels(self) -> int:
-        """Number of channels in the audio output.
-
-        Returns:
-            int: Number of channels (1 for mono, 2 for stereo)
-        """
        return self._num_channels

    def has_audio(self) -> bool:
-        """Check if both user and bot audio buffers contain data.
-
-        Returns:
-            bool: True if both buffers contain audio data
-        """
        return self._buffer_has_audio(self._user_audio_buffer) and self._buffer_has_audio(
            self._bot_audio_buffer
        )

    def merge_audio_buffers(self) -> bytes:
-        """Merge user and bot audio buffers into a single audio stream.
-
-        For mono output, audio is mixed. For stereo output, user audio is placed
-        on the left channel and bot audio on the right channel.
-
-        Returns:
-            bytes: Mixed audio data
-        """
        if self._num_channels == 1:
            return mix_audio(bytes(self._user_audio_buffer), bytes(self._bot_audio_buffer))
        elif self._num_channels == 2:
@@ -126,23 +90,14 @@ class AudioBufferProcessor(FrameProcessor):
            return b""

    async def start_recording(self):
-        """Start recording audio from both user and bot.
-
-        Initializes recording state and resets audio buffers.
-        """
        self._recording = True
        self._reset_recording()

    async def stop_recording(self):
-        """Stop recording and trigger final audio data handlers.
-
-        Calls audio handlers with any remaining buffered audio before stopping.
-        """
        await self._call_on_audio_data_handler()
        self._recording = False

    async def process_frame(self, frame: Frame, direction: FrameDirection):
-        """Process incoming audio frames and manage audio buffers."""
        await super().process_frame(frame, direction)

        # Update output sample rate if necessary.
@@ -205,21 +160,10 @@ class AudioBufferProcessor(FrameProcessor):
        if not self.has_audio() or not self._recording:
            return

-        # Call original handler with merged audio
        merged_audio = self.merge_audio_buffers()
        await self._call_event_handler(
            "on_audio_data", merged_audio, self._sample_rate, self._num_channels
        )
-
-        # Call new handler with separate tracks
-        await self._call_event_handler(
-            "on_track_audio_data",
-            bytes(self._user_audio_buffer),
-            bytes(self._bot_audio_buffer),
-            self._sample_rate,
-            self._num_channels,
-        )
-
        self._reset_audio_buffers()

    def _buffer_has_audio(self, buffer: bytearray) -> bool:
--- a/src/pipecat/processors/frame_processor.py
+++ b/src/pipecat/processors/frame_processor.py
@@ -5,6 +5,7 @@
 #

 import asyncio
+import inspect
 from enum import Enum
 from typing import Awaitable, Callable, Coroutine, Optional

@@ -23,7 +24,7 @@ from pipecat.frames.frames import (
 from pipecat.metrics.metrics import LLMTokenUsage, MetricsData
 from pipecat.processors.metrics.frame_processor_metrics import FrameProcessorMetrics
 from pipecat.utils.asyncio import BaseTaskManager
-from pipecat.utils.base_object import BaseObject
+from pipecat.utils.utils import obj_count, obj_id


 class FrameDirection(Enum):
@@ -31,7 +32,7 @@ class FrameDirection(Enum):
    UPSTREAM = 2


-class FrameProcessor(BaseObject):
+class FrameProcessor:
    def __init__(
        self,
        *,
@@ -39,11 +40,14 @@ class FrameProcessor(BaseObject):
        metrics: Optional[FrameProcessorMetrics] = None,
        **kwargs,
    ):
-        super().__init__(name=name)
+        self._id: int = obj_id()
+        self._name = name or f"{self.__class__.__name__}#{obj_count(self)}"
        self._parent: Optional["FrameProcessor"] = None
        self._prev: Optional["FrameProcessor"] = None
        self._next: Optional["FrameProcessor"] = None

+        self._event_handlers: dict = {}
+
        # Clock
        self._clock: Optional[BaseClock] = None

@@ -250,6 +254,23 @@ class FrameProcessor(BaseObject):
        else:
            await self.__push_queue.put((frame, direction))

+    def event_handler(self, event_name: str):
+        def decorator(handler):
+            self.add_event_handler(event_name, handler)
+            return handler
+
+        return decorator
+
+    def add_event_handler(self, event_name: str, handler):
+        if event_name not in self._event_handlers:
+            raise Exception(f"Event handler {event_name} not registered")
+        self._event_handlers[event_name].append(handler)
+
+    def _register_event_handler(self, event_name: str):
+        if event_name in self._event_handlers:
+            raise Exception(f"Event handler {event_name} already registered")
+        self._event_handlers[event_name] = []
+
    async def __start(self, frame: StartFrame):
        self.__create_input_task()
        self.__create_push_task()
@@ -364,3 +385,16 @@ class FrameProcessor(BaseObject):
            (frame, direction) = await self.__push_queue.get()
            await self.__internal_push_frame(frame, direction)
            self.__push_queue.task_done()
+
+    async def _call_event_handler(self, event_name: str, *args, **kwargs):
+        try:
+            for handler in self._event_handlers[event_name]:
+                if inspect.iscoroutinefunction(handler):
+                    await handler(self, *args, **kwargs)
+                else:
+                    handler(self, *args, **kwargs)
+        except Exception as e:
+            logger.exception(f"Exception in event handler {event_name}: {e}")
+
+    def __str__(self):
+        return self.name
--- a/src/pipecat/processors/frameworks/rtvi.py
+++ b/src/pipecat/processors/frameworks/rtvi.py
@@ -375,22 +375,6 @@ class RTVIMetricsMessage(BaseModel):
    data: Mapping[str, Any]


-class RTVIServerMessage(BaseModel):
-    label: RTVIMessageLiteral = RTVI_MESSAGE_LABEL
-    type: Literal["server-message"] = "server-message"
-    data: Any
-
-
-@dataclass
-class RTVIServerMessageFrame(SystemFrame):
-    """A frame for sending server messages to the client."""
-
-    data: Any
-
-    def __str__(self):
-        return f"{self.name}(data: {self.data})"
-
-
 class RTVIFrameProcessor(FrameProcessor):
    def __init__(self, direction: FrameDirection = FrameDirection.DOWNSTREAM, **kwargs):
        super().__init__(**kwargs)
@@ -726,9 +710,6 @@ class RTVIObserver(BaseObserver):
                mark_as_seen = False
        elif isinstance(frame, MetricsFrame):
            await self._handle_metrics(frame)
-        elif isinstance(frame, RTVIServerMessageFrame):
-            message = RTVIServerMessage(data=frame.data)
-            await self.push_transport_message_urgent(message)

        if mark_as_seen:
            self._frames_seen.add(frame.id)
--- a/src/pipecat/services/ai_services.py
+++ b/src/pipecat/services/ai_services.py
@@ -8,12 +8,10 @@ import asyncio
 import io
 import wave
 from abc import abstractmethod
-from typing import Any, AsyncGenerator, Dict, List, Mapping, Optional, Tuple, Type
+from typing import Any, AsyncGenerator, Dict, List, Mapping, Optional, Tuple

 from loguru import logger

-from pipecat.adapters.base_llm_adapter import BaseLLMAdapter
-from pipecat.adapters.services.open_ai_adapter import OpenAILLMAdapter
 from pipecat.audio.utils import calculate_audio_volume, exp_smoothing
 from pipecat.frames.frames import (
    AudioRawFrame,
@@ -139,23 +137,10 @@ class AIService(FrameProcessor):
 class LLMService(AIService):
    """This class is a no-op but serves as a base class for LLM services."""

-    # OpenAILLMAdapter is used as the default adapter since it aligns with most LLM implementations.
-    # However, subclasses should override this with a more specific adapter when necessary.
-    adapter_class: Type[BaseLLMAdapter] = OpenAILLMAdapter
-
    def __init__(self, **kwargs):
        super().__init__(**kwargs)
        self._callbacks = {}
        self._start_callbacks = {}
-        self._adapter = self.adapter_class()
-
-    def get_llm_adapter(self) -> BaseLLMAdapter:
-        return self._adapter
-
-    def create_context_aggregator(
-        self, context: OpenAILLMContext, *, assistant_expect_stripped_words: bool = True
-    ) -> Any:
-        pass

        self._register_event_handler("on_completion_timeout")

--- a/src/pipecat/services/anthropic.py
+++ b/src/pipecat/services/anthropic.py
@@ -11,14 +11,13 @@ import io
 import json
 import re
 from dataclasses import dataclass
-from typing import Any, Dict, List, Mapping, Optional, Union
+from typing import Any, Dict, List, Optional, Union

 import httpx
 from loguru import logger
 from PIL import Image
 from pydantic import BaseModel, Field

-from pipecat.adapters.services.anthropic_adapter import AnthropicLLMAdapter
 from pipecat.frames.frames import (
    Frame,
    FunctionCallInProgressFrame,
@@ -86,9 +85,6 @@ class AnthropicLLMService(LLMService):
    use `AsyncAnthropicBedrock` and `AsyncAnthropicVertex` clients
    """

-    # Overriding the default adapter to use the Anthropic one.
-    adapter_class = AnthropicLLMAdapter
-
    class InputParams(BaseModel):
        enable_prompt_caching_beta: Optional[bool] = False
        max_tokens: Optional[int] = Field(default_factory=lambda: 4096, ge=1)
@@ -127,38 +123,16 @@ class AnthropicLLMService(LLMService):
    def enable_prompt_caching_beta(self) -> bool:
        return self._enable_prompt_caching_beta

+    @staticmethod
    def create_context_aggregator(
-        self,
-        context: OpenAILLMContext,
-        *,
-        user_kwargs: Mapping[str, Any] = {},
-        assistant_kwargs: Mapping[str, Any] = {},
+        context: OpenAILLMContext, *, assistant_expect_stripped_words: bool = True
    ) -> AnthropicContextAggregatorPair:
-        """Create an instance of AnthropicContextAggregatorPair from an
-        OpenAILLMContext. Constructor keyword arguments for both the user and
-        assistant aggregators can be provided.
-
-        Args:
-            context (OpenAILLMContext): The LLM context.
-            user_kwargs (Mapping[str, Any], optional): Additional keyword
-                arguments for the user context aggregator constructor. Defaults
-                to an empty mapping.
-            assistant_kwargs (Mapping[str, Any], optional): Additional keyword
-                arguments for the assistant context aggregator
-                constructor. Defaults to an empty mapping.
-
-        Returns:
-            AnthropicContextAggregatorPair: A pair of context aggregators, one
-            for the user and one for the assistant, encapsulated in an
-            AnthropicContextAggregatorPair.
-
-        """
-        context.set_llm_adapter(self.get_llm_adapter())
-
        if isinstance(context, OpenAILLMContext):
            context = AnthropicLLMContext.from_openai_context(context)
-        user = AnthropicUserContextAggregator(context, **user_kwargs)
-        assistant = AnthropicAssistantContextAggregator(context, **assistant_kwargs)
+        user = AnthropicUserContextAggregator(context)
+        assistant = AnthropicAssistantContextAggregator(
+            context, expect_stripped_words=assistant_expect_stripped_words
+        )
        return AnthropicContextAggregatorPair(_user=user, _assistant=assistant)

    async def _process_context(self, context: OpenAILLMContext):
@@ -178,7 +152,7 @@ class AnthropicLLMService(LLMService):
            await self.start_processing_metrics()

            logger.debug(
-                f"{self}: Generating chat [{context.system}] | [{context.get_messages_for_logging()}]"
+                f"Generating chat: {context.system} | {context.get_messages_for_logging()}"
            )

            messages = context.messages
@@ -388,7 +362,6 @@ class AnthropicLLMContext(OpenAILLMContext):
            tools=openai_context.tools,
            tool_choice=openai_context.tool_choice,
        )
-        self.set_llm_adapter(openai_context.get_llm_adapter())
        self._restructure_from_openai_messages()
        return self

--- a/src/pipecat/services/aws.py
+++ b/src/pipecat/services/aws.py
@@ -197,7 +197,7 @@ class PollyTTSService(TTSService):
                return audio_data
            return None

-        logger.debug(f"{self}: Generating TTS [{text}]")
+        logger.debug(f"Generating TTS: [{text}]")

        try:
            await self.start_ttfb_metrics()
--- a/src/pipecat/services/azure.py
+++ b/src/pipecat/services/azure.py
@@ -578,7 +578,7 @@ class AzureTTSService(AzureBaseTTSService):
        self._audio_queue.put_nowait(None)

    async def run_tts(self, text: str) -> AsyncGenerator[Frame, None]:
-        logger.debug(f"{self}: Generating TTS [{text}]")
+        logger.debug(f"Generating TTS: [{text}]")

        try:
            if self._speech_synthesizer is None:
@@ -645,7 +645,7 @@ class AzureHttpTTSService(AzureBaseTTSService):
        )

    async def run_tts(self, text: str) -> AsyncGenerator[Frame, None]:
-        logger.debug(f"{self}: Generating TTS [{text}]")
+        logger.debug(f"Generating TTS: [{text}]")

        await self.start_ttfb_metrics()

--- a/src/pipecat/services/canonical.py
+++ b/src/pipecat/services/canonical.py
@@ -62,21 +62,17 @@ class CanonicalMetricsService(AIService):
        self,
        *,
        aiohttp_session: aiohttp.ClientSession,
+        audio_buffer_processor: AudioBufferProcessor,
        call_id: str,
        assistant: str,
        api_key: str,
        api_url: str = "https://voiceapp.canonical.chat/api/v1",
        assistant_speaks_first: bool = True,
        output_dir: str = "recordings",
-        audio_buffer_processor: Optional[AudioBufferProcessor] = None,
        context: Optional[OpenAILLMContext] = None,
        **kwargs,
    ):
        super().__init__(**kwargs)
-        # Validate that at least one of audio_buffer_processor or context is provided
-        if audio_buffer_processor is None and context is None:
-            raise ValueError("At least one of audio_buffer_processor or context must be specified")
-
        self._aiohttp_session = aiohttp_session
        self._audio_buffer_processor = audio_buffer_processor
        self._api_key = api_key
@@ -89,36 +85,16 @@ class CanonicalMetricsService(AIService):

    async def stop(self, frame: EndFrame):
        await super().stop(frame)
-        await self._process_completion()
+        await self._process_audio()

    async def cancel(self, frame: CancelFrame):
        await super().cancel(frame)
-        await self._process_completion()
+        await self._process_audio()

    async def process_frame(self, frame: Frame, direction: FrameDirection):
        await super().process_frame(frame, direction)
        await self.push_frame(frame, direction)

-    async def _process_completion(self):
-        if self._audio_buffer_processor is not None:
-            await self._process_audio()
-        elif self._context is not None:
-            await self._process_transcript()
-
-    async def _process_transcript(self):
-        params = {
-            "callId": self._call_id,
-            "assistant": {"id": self._assistant, "speaksFirst": self._assistant_speaks_first},
-            "transcript": self._context.messages,
-        }
-        response = await self._aiohttp_session.post(
-            f"{self._api_url}/call",
-            headers=self._request_headers(),
-            json=params,
-        )
-        if not response.ok:
-            logger.error(f"Failed to process transcript: {await response.text()}")
-
    async def _process_audio(self):
        audio_buffer_processor = self._audio_buffer_processor

--- a/src/pipecat/services/cartesia.py
+++ b/src/pipecat/services/cartesia.py
@@ -272,7 +272,7 @@ class CartesiaTTSService(AudioContextWordTTSService):
                logger.error(f"{self} error, unknown message type: {msg}")

    async def run_tts(self, text: str) -> AsyncGenerator[Frame, None]:
-        logger.debug(f"{self}: Generating TTS [{text}]")
+        logger.debug(f"Generating TTS: [{text}]")

        try:
            if not self._websocket:
@@ -358,7 +358,7 @@ class CartesiaHttpTTSService(TTSService):
        await self._client.close()

    async def run_tts(self, text: str) -> AsyncGenerator[Frame, None]:
-        logger.debug(f"{self}: Generating TTS [{text}]")
+        logger.debug(f"Generating TTS: [{text}]")

        try:
            voice_controls = None
--- a/src/pipecat/services/deepgram.py
+++ b/src/pipecat/services/deepgram.py
@@ -70,7 +70,7 @@ class DeepgramTTSService(TTSService):
        return True

    async def run_tts(self, text: str) -> AsyncGenerator[Frame, None]:
-        logger.debug(f"{self}: Generating TTS [{text}]")
+        logger.debug(f"Generating TTS: [{text}]")

        options = SpeakOptions(
            model=self._voice_id,
--- a/src/pipecat/services/elevenlabs.py
+++ b/src/pipecat/services/elevenlabs.py
@@ -116,44 +116,6 @@ def output_format_from_sample_rate(sample_rate: int) -> str:
    return "pcm_16000"


-def build_elevenlabs_voice_settings(
-    settings: Dict[str, Any],
-) -> Optional[Dict[str, Union[float, bool]]]:
-    """Build voice settings dictionary for ElevenLabs based on provided settings.
-
-    Args:
-        settings: Dictionary containing voice settings parameters
-
-    Returns:
-        Dictionary of voice settings or None if required parameters are missing
-    """
-    voice_settings = {}
-    if settings["stability"] is not None and settings["similarity_boost"] is not None:
-        voice_settings["stability"] = settings["stability"]
-        voice_settings["similarity_boost"] = settings["similarity_boost"]
-        if settings["style"] is not None:
-            voice_settings["style"] = settings["style"]
-        if settings["use_speaker_boost"] is not None:
-            voice_settings["use_speaker_boost"] = settings["use_speaker_boost"]
-        if settings["speed"] is not None:
-            voice_settings["speed"] = settings["speed"]
-    else:
-        if settings["style"] is not None:
-            logger.warning(
-                "'style' is set but will not be applied because 'stability' and 'similarity_boost' are not both set."
-            )
-        if settings["use_speaker_boost"] is not None:
-            logger.warning(
-                "'use_speaker_boost' is set but will not be applied because 'stability' and 'similarity_boost' are not both set."
-            )
-        if settings["speed"] is not None:
-            logger.warning(
-                "'speed' is set but will not be applied because 'stability' and 'similarity_boost' are not both set."
-            )
-
-    return voice_settings or None
-
-
 def calculate_word_times(
    alignment_info: Mapping[str, Any], cumulative_time: float
 ) -> List[Tuple[str, float]]:
@@ -183,7 +145,6 @@ class ElevenLabsTTSService(InterruptibleWordTTSService):
        similarity_boost: Optional[float] = None
        style: Optional[float] = None
        use_speaker_boost: Optional[bool] = None
-        speed: Optional[float] = None
        auto_mode: Optional[bool] = True

        @model_validator(mode="after")
@@ -241,7 +202,6 @@ class ElevenLabsTTSService(InterruptibleWordTTSService):
            "similarity_boost": params.similarity_boost,
            "style": params.style,
            "use_speaker_boost": params.use_speaker_boost,
-            "speed": params.speed,
            "auto_mode": str(params.auto_mode).lower(),
        }
        self.set_model_name(model)
@@ -264,7 +224,28 @@ class ElevenLabsTTSService(InterruptibleWordTTSService):
        return language_to_elevenlabs_language(language)

    def _set_voice_settings(self):
-        return build_elevenlabs_voice_settings(self._settings)
+        voice_settings = {}
+        if (
+            self._settings["stability"] is not None
+            and self._settings["similarity_boost"] is not None
+        ):
+            voice_settings["stability"] = self._settings["stability"]
+            voice_settings["similarity_boost"] = self._settings["similarity_boost"]
+            if self._settings["style"] is not None:
+                voice_settings["style"] = self._settings["style"]
+            if self._settings["use_speaker_boost"] is not None:
+                voice_settings["use_speaker_boost"] = self._settings["use_speaker_boost"]
+        else:
+            if self._settings["style"] is not None:
+                logger.warning(
+                    "'style' is set but will not be applied because 'stability' and 'similarity_boost' are not both set."
+                )
+            if self._settings["use_speaker_boost"] is not None:
+                logger.warning(
+                    "'use_speaker_boost' is set but will not be applied because 'stability' and 'similarity_boost' are not both set."
+                )
+
+        return voice_settings or None

    async def set_model(self, model: str):
        await super().set_model(model)
@@ -414,7 +395,7 @@ class ElevenLabsTTSService(InterruptibleWordTTSService):
            await self._websocket.send(json.dumps(msg))

    async def run_tts(self, text: str) -> AsyncGenerator[Frame, None]:
-        logger.debug(f"{self}: Generating TTS [{text}]")
+        logger.debug(f"Generating TTS: [{text}]")

        try:
            if not self._websocket:
@@ -460,7 +441,6 @@ class ElevenLabsHttpTTSService(TTSService):
        similarity_boost: Optional[float] = None
        style: Optional[float] = None
        use_speaker_boost: Optional[bool] = None
-        speed: Optional[float] = None

    def __init__(
        self,
@@ -490,7 +470,6 @@ class ElevenLabsHttpTTSService(TTSService):
            "similarity_boost": params.similarity_boost,
            "style": params.style,
            "use_speaker_boost": params.use_speaker_boost,
-            "speed": params.speed,
        }
        self.set_model_name(model)
        self.set_voice(voice_id)
@@ -500,8 +479,34 @@ class ElevenLabsHttpTTSService(TTSService):
    def can_generate_metrics(self) -> bool:
        return True

-    def _set_voice_settings(self):
-        return build_elevenlabs_voice_settings(self._settings)
+    def _set_voice_settings(self) -> Optional[Dict[str, Union[float, bool]]]:
+        """Configure voice settings if stability and similarity_boost are provided.
+
+        Returns:
+            Dictionary of voice settings or None if required parameters are missing.
+        """
+        voice_settings: Dict[str, Union[float, bool]] = {}
+        if (
+            self._settings["stability"] is not None
+            and self._settings["similarity_boost"] is not None
+        ):
+            voice_settings["stability"] = float(self._settings["stability"])
+            voice_settings["similarity_boost"] = float(self._settings["similarity_boost"])
+            if self._settings["style"] is not None:
+                voice_settings["style"] = float(self._settings["style"])
+            if self._settings["use_speaker_boost"] is not None:
+                voice_settings["use_speaker_boost"] = bool(self._settings["use_speaker_boost"])
+        else:
+            if self._settings["style"] is not None:
+                logger.warning(
+                    "'style' is set but will not be applied because 'stability' and 'similarity_boost' are not both set."
+                )
+            if self._settings["use_speaker_boost"] is not None:
+                logger.warning(
+                    "'use_speaker_boost' is set but will not be applied because 'stability' and 'similarity_boost' are not both set."
+                )
+
+        return voice_settings or None

    async def start(self, frame: StartFrame):
        await super().start(frame)
@@ -516,7 +521,7 @@ class ElevenLabsHttpTTSService(TTSService):
        Yields:
            Frames containing audio data and status information
        """
-        logger.debug(f"{self}: Generating TTS [{text}]")
+        logger.debug(f"Generating TTS: [{text}]")

        url = f"{self._base_url}/v1/text-to-speech/{self._voice_id}/stream"

@@ -565,12 +570,10 @@ class ElevenLabsHttpTTSService(TTSService):

                await self.start_tts_usage_metrics(text)

-                # Process the streaming response
-                CHUNK_SIZE = 1024
-
                yield TTSStartedFrame()
-                async for chunk in response.content.iter_chunked(CHUNK_SIZE):
-                    if len(chunk) > 0:
+
+                async for chunk in response.content:
+                    if chunk:
                        await self.stop_ttfb_metrics()
                        yield TTSAudioRawFrame(chunk, self.sample_rate, 1)
        except Exception as e:
--- a/src/pipecat/services/fish.py
+++ b/src/pipecat/services/fish.py
@@ -178,7 +178,7 @@ class FishAudioTTSService(InterruptibleTTSService):
                logger.error(f"Error processing message: {e}")

    async def run_tts(self, text: str) -> AsyncGenerator[Frame, None]:
-        logger.debug(f"{self}: Generating Fish TTS: [{text}]")
+        logger.debug(f"Generating Fish TTS: [{text}]")
        try:
            if not self._websocket or self._websocket.closed:
                await self._connect()
--- a/src/pipecat/services/gemini_multimodal_live/gemini.py
+++ b/src/pipecat/services/gemini_multimodal_live/gemini.py
@@ -9,14 +9,12 @@ import base64
 import json
 from dataclasses import dataclass
 from enum import Enum
-from typing import Any, Dict, List, Mapping, Optional, Union
+from typing import Any, Dict, List, Optional

 import websockets
 from loguru import logger
 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 (
    BotStartedSpeakingFrame,
    BotStoppedSpeakingFrame,
@@ -154,9 +152,6 @@ class InputParams(BaseModel):


 class GeminiMultimodalLiveLLMService(LLMService):
-    # Overriding the default adapter to use the Gemini one.
-    adapter_class = GeminiLLMAdapter
-
    def __init__(
        self,
        *,
@@ -167,7 +162,7 @@ class GeminiMultimodalLiveLLMService(LLMService):
        start_audio_paused: bool = False,
        start_video_paused: bool = False,
        system_instruction: Optional[str] = None,
-        tools: Optional[Union[List[dict], ToolsSchema]] = None,
+        tools: Optional[List[dict]] = None,
        transcribe_user_audio: bool = False,
        transcribe_model_audio: bool = False,
        params: InputParams = InputParams(),
@@ -440,7 +435,7 @@ class GeminiMultimodalLiveLLMService(LLMService):
                )
            if self._tools:
                logger.debug(f"Gemini is configuring to use tools{self._tools}")
-                config.setup.tools = self.get_llm_adapter().from_standard_tools(self._tools)
+                config.setup.tools = self._tools
            await self.send_client_event(config)

        except Exception as e:
@@ -706,39 +701,11 @@ class GeminiMultimodalLiveLLMService(LLMService):
        await self.push_frame(TTSStoppedFrame())

    def create_context_aggregator(
-        self,
-        context: OpenAILLMContext,
-        *,
-        user_kwargs: Mapping[str, Any] = {},
-        assistant_kwargs: Mapping[str, Any] = {},
+        self, context: OpenAILLMContext, *, assistant_expect_stripped_words: bool = False
    ) -> GeminiMultimodalLiveContextAggregatorPair:
-        """Create an instance of GeminiMultimodalLiveContextAggregatorPair from
-        an OpenAILLMContext. Constructor keyword arguments for both the user and
-        assistant aggregators can be provided.
-
-        Args:
-            context (OpenAILLMContext): The LLM context.
-            user_kwargs (Mapping[str, Any], optional): Additional keyword
-                arguments for the user context aggregator constructor. Defaults
-                to an empty mapping.
-            assistant_kwargs (Mapping[str, Any], optional): Additional keyword
-                arguments for the assistant context aggregator
-                constructor. Defaults to an empty mapping.
-
-        Returns:
-            GeminiMultimodalLiveContextAggregatorPair: A pair of context
-            aggregators, one for the user and one for the assistant,
-            encapsulated in an GeminiMultimodalLiveContextAggregatorPair.
-
-        """
-        context.set_llm_adapter(self.get_llm_adapter())
-
        GeminiMultimodalLiveContext.upgrade(context)
-        user = GeminiMultimodalLiveUserContextAggregator(context, **user_kwargs)
-
-        default_assistant_kwargs = {"expect_stripped_words": False}
-        default_assistant_kwargs.update(assistant_kwargs)
+        user = GeminiMultimodalLiveUserContextAggregator(context)
        assistant = GeminiMultimodalLiveAssistantContextAggregator(
-            context, **default_assistant_kwargs
+            context, expect_stripped_words=assistant_expect_stripped_words
        )
        return GeminiMultimodalLiveContextAggregatorPair(_user=user, _assistant=assistant)
--- a/src/pipecat/services/gladia.py
+++ b/src/pipecat/services/gladia.py
@@ -136,7 +136,6 @@ class GladiaSTTService(STTService):
        maximum_duration_without_endpointing: Optional[int] = 10
        audio_enhancer: Optional[bool] = None
        words_accurate_timestamps: Optional[bool] = None
-        speech_threshold: Optional[float] = 0.99

    def __init__(
        self,
@@ -149,6 +148,7 @@ class GladiaSTTService(STTService):
        **kwargs,
    ):
        super().__init__(sample_rate=sample_rate, **kwargs)
+
        self._api_key = api_key
        self._url = url
        self._settings = {
@@ -166,7 +166,6 @@ class GladiaSTTService(STTService):
            "maximum_duration_without_endpointing": params.maximum_duration_without_endpointing,
            "pre_processing": {
                "audio_enhancer": params.audio_enhancer,
-                "speech_threshold": params.speech_threshold,
            },
            "realtime_processing": {
                "words_accurate_timestamps": params.words_accurate_timestamps,
--- a/src/pipecat/services/google/google.py
+++ b/src/pipecat/services/google/google.py
@@ -12,16 +12,12 @@ import os
 import time

 from google.api_core.exceptions import DeadlineExceeded
-from openai import AsyncStream
-from openai.types.chat import ChatCompletionChunk
-
-from pipecat.adapters.services.gemini_adapter import GeminiLLMAdapter

 # Suppress gRPC fork warnings
 os.environ["GRPC_ENABLE_FORK_SUPPORT"] = "false"

 from dataclasses import dataclass
-from typing import Any, AsyncGenerator, Dict, List, Literal, Mapping, Optional, Union
+from typing import Any, AsyncGenerator, Dict, List, Literal, Optional, Union

 from loguru import logger
 from PIL import Image
@@ -58,10 +54,7 @@ from pipecat.processors.frame_processor import FrameDirection
 from pipecat.services.ai_services import ImageGenService, LLMService, STTService, TTSService
 from pipecat.services.google.frames import LLMSearchResponseFrame
 from pipecat.services.openai import (
-    BaseOpenAILLMService,
    OpenAIAssistantContextAggregator,
-    OpenAILLMService,
-    OpenAIUnhandledFunctionException,
    OpenAIUserContextAggregator,
 )
 from pipecat.transcriptions.language import Language
@@ -729,9 +722,7 @@ class GoogleLLMContext(OpenAILLMContext):

        self.add_message(glm.Content(role="user", parts=parts))

-    def add_audio_frames_message(
-        self, *, audio_frames: list[AudioRawFrame], text: str = "Audio follows"
-    ):
+    def add_audio_frames_message(self, *, audio_frames: list[AudioRawFrame], text: str = None):
        if not audio_frames:
            return

@@ -740,9 +731,8 @@ class GoogleLLMContext(OpenAILLMContext):

        parts = []
        data = b"".join(frame.audio for frame in audio_frames)
-        # NOTE(aleix): According to the docs only text or inline_data should be needed.
-        # (see https://cloud.google.com/vertex-ai/generative-ai/docs/model-reference/inference)
-        parts.append(glm.Part(text=text))
+        if text:
+            parts.append(glm.Part(text=text))
        parts.append(
            glm.Part(
                inline_data=glm.Blob(
@@ -952,9 +942,6 @@ class GoogleLLMService(LLMService):
    franca for all LLM services, so that it is easy to switch between different LLMs.
    """

-    # Overriding the default adapter to use the Gemini one.
-    adapter_class = GeminiLLMAdapter
-
    class InputParams(BaseModel):
        max_tokens: Optional[int] = Field(default=4096, ge=1)
        temperature: Optional[float] = Field(default=None, ge=0.0, le=2.0)
@@ -1008,8 +995,8 @@ class GoogleLLMService(LLMService):

        try:
            logger.debug(
-                # f"{self}: Generating chat [{self._system_instruction}] | [{context.get_messages_for_logging()}]"
-                f"{self}: Generating chat [{context.get_messages_for_logging()}]"
+                # f"Generating chat: {self._system_instruction} | {context.get_messages_for_logging()}"
+                f"Generating chat: {context.get_messages_for_logging()}"
            )

            messages = context.messages
@@ -1185,153 +1172,17 @@ class GoogleLLMService(LLMService):
        if context:
            await self._process_context(context)

+    @staticmethod
    def create_context_aggregator(
-        self,
-        context: OpenAILLMContext,
-        *,
-        user_kwargs: Mapping[str, Any] = {},
-        assistant_kwargs: Mapping[str, Any] = {},
+        context: OpenAILLMContext, *, assistant_expect_stripped_words: bool = True
    ) -> GoogleContextAggregatorPair:
-        """Create an instance of GoogleContextAggregatorPair from an
-        OpenAILLMContext. Constructor keyword arguments for both the user and
-        assistant aggregators can be provided.
-
-        Args:
-            context (OpenAILLMContext): The LLM context.
-            user_kwargs (Mapping[str, Any], optional): Additional keyword
-                arguments for the user context aggregator constructor. Defaults
-                to an empty mapping.
-            assistant_kwargs (Mapping[str, Any], optional): Additional keyword
-                arguments for the assistant context aggregator
-                constructor. Defaults to an empty mapping.
-
-        Returns:
-            GoogleContextAggregatorPair: A pair of context aggregators, one for
-            the user and one for the assistant, encapsulated in an
-            GoogleContextAggregatorPair.
-
-        """
-        context.set_llm_adapter(self.get_llm_adapter())
-
        if isinstance(context, OpenAILLMContext):
            context = GoogleLLMContext.upgrade_to_google(context)
-        user = GoogleUserContextAggregator(context, **user_kwargs)
-        assistant = GoogleAssistantContextAggregator(context, **assistant_kwargs)
-        return GoogleContextAggregatorPair(_user=user, _assistant=assistant)
-
-
-class GoogleLLMOpenAIBetaService(OpenAILLMService):
-    """This class implements inference with Google's AI LLM models using the OpenAI format.
-    Ref - https://ai.google.dev/gemini-api/docs/openai
-    """
-
-    def __init__(
-        self,
-        *,
-        api_key: str,
-        base_url: str = "https://generativelanguage.googleapis.com/v1beta/openai/",
-        model: str = "gemini-2.0-flash",
-        **kwargs,
-    ):
-        super().__init__(api_key=api_key, base_url=base_url, model=model, **kwargs)
-
-    async def _process_context(self, context: OpenAILLMContext):
-        functions_list = []
-        arguments_list = []
-        tool_id_list = []
-        func_idx = 0
-        function_name = ""
-        arguments = ""
-        tool_call_id = ""
-
-        await self.start_ttfb_metrics()
-
-        chunk_stream: AsyncStream[ChatCompletionChunk] = await self._stream_chat_completions(
-            context
+        user = GoogleUserContextAggregator(context)
+        assistant = GoogleAssistantContextAggregator(
+            context, expect_stripped_words=assistant_expect_stripped_words
        )
-
-        async for chunk in chunk_stream:
-            if chunk.usage:
-                tokens = LLMTokenUsage(
-                    prompt_tokens=chunk.usage.prompt_tokens,
-                    completion_tokens=chunk.usage.completion_tokens,
-                    total_tokens=chunk.usage.total_tokens,
-                )
-                await self.start_llm_usage_metrics(tokens)
-
-            if chunk.choices is None or len(chunk.choices) == 0:
-                continue
-
-            await self.stop_ttfb_metrics()
-
-            if not chunk.choices[0].delta:
-                continue
-
-            if chunk.choices[0].delta.tool_calls:
-                # We're streaming the LLM response to enable the fastest response times.
-                # For text, we just yield each chunk as we receive it and count on consumers
-                # to do whatever coalescing they need (eg. to pass full sentences to TTS)
-                #
-                # If the LLM is a function call, we'll do some coalescing here.
-                # If the response contains a function name, we'll yield a frame to tell consumers
-                # that they can start preparing to call the function with that name.
-                # We accumulate all the arguments for the rest of the streamed response, then when
-                # the response is done, we package up all the arguments and the function name and
-                # yield a frame containing the function name and the arguments.
-                logger.debug(f"Tool call: {chunk.choices[0].delta.tool_calls}")
-                tool_call = chunk.choices[0].delta.tool_calls[0]
-                if tool_call.index != func_idx:
-                    functions_list.append(function_name)
-                    arguments_list.append(arguments)
-                    tool_id_list.append(tool_call_id)
-                    function_name = ""
-                    arguments = ""
-                    tool_call_id = ""
-                    func_idx += 1
-                if tool_call.function and tool_call.function.name:
-                    function_name += tool_call.function.name
-                    tool_call_id = tool_call.id
-                if tool_call.function and tool_call.function.arguments:
-                    # Keep iterating through the response to collect all the argument fragments
-                    arguments += tool_call.function.arguments
-            elif chunk.choices[0].delta.content:
-                await self.push_frame(LLMTextFrame(chunk.choices[0].delta.content))
-
-        # if we got a function name and arguments, check to see if it's a function with
-        # a registered handler. If so, run the registered callback, save the result to
-        # the context, and re-prompt to get a chat answer. If we don't have a registered
-        # handler, raise an exception.
-        if function_name and arguments:
-            # added to the list as last function name and arguments not added to the list
-            functions_list.append(function_name)
-            arguments_list.append(arguments)
-            tool_id_list.append(tool_call_id)
-
-            logger.debug(
-                f"Function list: {functions_list}, Arguments list: {arguments_list}, Tool ID list: {tool_id_list}"
-            )
-            for index, (function_name, arguments, tool_id) in enumerate(
-                zip(functions_list, arguments_list, tool_id_list), start=1
-            ):
-                if function_name == "":
-                    # TODO: Remove the _process_context method once Google resolves the bug
-                    # where the index is incorrectly set to None instead of returning the actual index,
-                    # which currently results in an empty function name('').
-                    continue
-                if self.has_function(function_name):
-                    run_llm = False
-                    arguments = json.loads(arguments)
-                    await self.call_function(
-                        context=context,
-                        function_name=function_name,
-                        arguments=arguments,
-                        tool_call_id=tool_id,
-                        run_llm=run_llm,
-                    )
-                else:
-                    raise OpenAIUnhandledFunctionException(
-                        f"The LLM tried to call a function named '{function_name}', but there isn't a callback registered for that function."
-                    )
+        return GoogleContextAggregatorPair(_user=user, _assistant=assistant)


 class GoogleTTSService(TTSService):
@@ -1443,7 +1294,7 @@ class GoogleTTSService(TTSService):
        return ssml

    async def run_tts(self, text: str) -> AsyncGenerator[Frame, None]:
-        logger.debug(f"{self}: Generating TTS [{text}]")
+        logger.debug(f"Generating TTS: [{text}]")

        try:
            await self.start_ttfb_metrics()
--- a/src/pipecat/services/grok.py
+++ b/src/pipecat/services/grok.py
@@ -7,7 +7,7 @@

 import json
 from dataclasses import dataclass
-from typing import Any, Mapping, Optional
+from typing import Optional

 from loguru import logger

@@ -206,34 +206,12 @@ class GrokLLMService(OpenAILLMService):
        if tokens.completion_tokens > self._completion_tokens:
            self._completion_tokens = tokens.completion_tokens

+    @staticmethod
    def create_context_aggregator(
-        self,
-        context: OpenAILLMContext,
-        *,
-        user_kwargs: Mapping[str, Any] = {},
-        assistant_kwargs: Mapping[str, Any] = {},
+        context: OpenAILLMContext, *, assistant_expect_stripped_words: bool = True
    ) -> GrokContextAggregatorPair:
-        """Create an instance of GrokContextAggregatorPair from an
-        OpenAILLMContext. Constructor keyword arguments for both the user and
-        assistant aggregators can be provided.
-
-        Args:
-            context (OpenAILLMContext): The LLM context.
-            user_kwargs (Mapping[str, Any], optional): Additional keyword
-                arguments for the user context aggregator constructor. Defaults
-                to an empty mapping.
-            assistant_kwargs (Mapping[str, Any], optional): Additional keyword
-                arguments for the assistant context aggregator
-                constructor. Defaults to an empty mapping.
-
-        Returns:
-            GrokContextAggregatorPair: A pair of context aggregators, one for
-            the user and one for the assistant, encapsulated in an
-            GrokContextAggregatorPair.
-
-        """
-        context.set_llm_adapter(self.get_llm_adapter())
-
-        user = OpenAIUserContextAggregator(context, **user_kwargs)
-        assistant = GrokAssistantContextAggregator(context, **assistant_kwargs)
+        user = OpenAIUserContextAggregator(context)
+        assistant = GrokAssistantContextAggregator(
+            context, expect_stripped_words=assistant_expect_stripped_words
+        )
        return GrokContextAggregatorPair(_user=user, _assistant=assistant)
--- a/src/pipecat/services/lmnt.py
+++ b/src/pipecat/services/lmnt.py
@@ -196,7 +196,7 @@ class LmntTTSService(InterruptibleTTSService):

    async def run_tts(self, text: str) -> AsyncGenerator[Frame, None]:
        """Generate TTS audio from text."""
-        logger.debug(f"{self}: Generating TTS [{text}]")
+        logger.debug(f"Generating TTS: [{text}]")

        try:
            if not self._websocket:
--- a/src/pipecat/services/openai.py
+++ b/src/pipecat/services/openai.py
@@ -8,7 +8,7 @@ import base64
 import io
 import json
 from dataclasses import dataclass
-from typing import Any, AsyncGenerator, Dict, List, Literal, Mapping, Optional
+from typing import Any, AsyncGenerator, Dict, List, Literal, Optional

 import aiohttp
 import httpx
@@ -178,7 +178,7 @@ class BaseOpenAILLMService(LLMService):
    async def _stream_chat_completions(
        self, context: OpenAILLMContext
    ) -> AsyncStream[ChatCompletionChunk]:
-        logger.debug(f"{self}: Generating chat [{context.get_messages_for_logging()}]")
+        logger.debug(f"Generating chat: {context.get_messages_for_logging()}")

        messages: List[ChatCompletionMessageParam] = context.get_messages()

@@ -343,35 +343,14 @@ class OpenAILLMService(BaseOpenAILLMService):
    ):
        super().__init__(model=model, params=params, **kwargs)

+    @staticmethod
    def create_context_aggregator(
-        self,
-        context: OpenAILLMContext,
-        *,
-        user_kwargs: Mapping[str, Any] = {},
-        assistant_kwargs: Mapping[str, Any] = {},
+        context: OpenAILLMContext, *, assistant_expect_stripped_words: bool = True
    ) -> OpenAIContextAggregatorPair:
-        """Create an instance of OpenAIContextAggregatorPair from an
-        OpenAILLMContext. Constructor keyword arguments for both the user and
-        assistant aggregators can be provided.
-
-        Args:
-            context (OpenAILLMContext): The LLM context.
-            user_kwargs (Mapping[str, Any], optional): Additional keyword
-                arguments for the user context aggregator constructor. Defaults
-                to an empty mapping.
-            assistant_kwargs (Mapping[str, Any], optional): Additional keyword
-                arguments for the assistant context aggregator
-                constructor. Defaults to an empty mapping.
-
-        Returns:
-            OpenAIContextAggregatorPair: A pair of context aggregators, one for
-            the user and one for the assistant, encapsulated in an
-            OpenAIContextAggregatorPair.
-
-        """
-        context.set_llm_adapter(self.get_llm_adapter())
-        user = OpenAIUserContextAggregator(context, **user_kwargs)
-        assistant = OpenAIAssistantContextAggregator(context, **assistant_kwargs)
+        user = OpenAIUserContextAggregator(context)
+        assistant = OpenAIAssistantContextAggregator(
+            context, expect_stripped_words=assistant_expect_stripped_words
+        )
        return OpenAIContextAggregatorPair(_user=user, _assistant=assistant)


@@ -529,7 +508,7 @@ class OpenAITTSService(TTSService):
            )

    async def run_tts(self, text: str) -> AsyncGenerator[Frame, None]:
-        logger.debug(f"{self}: Generating TTS [{text}]")
+        logger.debug(f"Generating TTS: [{text}]")
        try:
            await self.start_ttfb_metrics()

@@ -551,10 +530,8 @@ class OpenAITTSService(TTSService):

                await self.start_tts_usage_metrics(text)

-                CHUNK_SIZE = 1024
-
                yield TTSStartedFrame()
-                async for chunk in r.iter_bytes(CHUNK_SIZE):
+                async for chunk in r.iter_bytes(8192):
                    if len(chunk) > 0:
                        await self.stop_ttfb_metrics()
                        frame = TTSAudioRawFrame(chunk, self.sample_rate, 1)
--- a/src/pipecat/services/openai_realtime_beta/init.py
+++ b/src/pipecat/services/openai_realtime_beta/init.py
@@ -1,3 +1,2 @@
-from .azure import AzureRealtimeBetaLLMService
 from .events import InputAudioTranscription, SessionProperties, TurnDetection
 from .openai import OpenAIRealtimeBetaLLMService
--- a/src/pipecat/services/openai_realtime_beta/azure.py
+++ b/src/pipecat/services/openai_realtime_beta/azure.py
@@ -1,64 +0,0 @@
-#
-# Copyright (c) 2024–2025, Daily
-#
-# SPDX-License-Identifier: BSD 2-Clause License
-#
-
-import os
-
-from loguru import logger
-
-from .openai import OpenAIRealtimeBetaLLMService
-
-try:
-    import websockets
-except ModuleNotFoundError as e:
-    logger.error(f"Exception: {e}")
-    logger.error(
-        "In order to use OpenAI, you need to `pip install pipecat-ai[openai]`. Also, set `OPENAI_API_KEY` environment variable."
-    )
-    raise Exception(f"Missing module: {e}")
-
-
-class AzureRealtimeBetaLLMService(OpenAIRealtimeBetaLLMService):
-    """Subclass of OpenAI Realtime API Service with adjustments for Azure's wss connection."""
-
-    def __init__(
-        self,
-        *,
-        api_key: str,
-        base_url: str,
-        **kwargs,
-    ):
-        """Constructor takes the same arguments as the parent class, OpenAIRealtimeBetaLLMService.
-
-        Note that the following are required arguments:
-            api_key: The API key for the Azure OpenAI service.
-            base_url: The base URL for the Azure OpenAI service.
-
-        base_url should be set to the full Azure endpoint URL including the api-version and the deployment name. For example,
-
-        wss://my-project.openai.azure.com/openai/realtime?api-version=2024-10-01-preview&deployment=my-realtime-deployment
-        """
-        super().__init__(base_url=base_url, api_key=api_key, **kwargs)
-        self.api_key = api_key
-        self.base_url = base_url
-
-    async def _connect(self):
-        try:
-            if self._websocket:
-                # Here we assume that if we have a websocket, we are connected. We
-                # handle disconnections in the send/recv code paths.
-                return
-
-            logger.info(f"Connecting to {self.base_url}, api key: {self.api_key}")
-            self._websocket = await websockets.connect(
-                uri=self.base_url,
-                extra_headers={
-                    "api-key": self.api_key,
-                },
-            )
-            self._receive_task = self.create_task(self._receive_task_handler())
-        except Exception as e:
-            logger.error(f"{self} initialization error: {e}")
-            self._websocket = None
--- a/src/pipecat/services/openai_realtime_beta/openai.py
+++ b/src/pipecat/services/openai_realtime_beta/openai.py
@@ -4,16 +4,14 @@
 # SPDX-License-Identifier: BSD 2-Clause License
 #

+import asyncio
 import base64
 import json
 import time
 from dataclasses import dataclass
-from typing import Any, Mapping

 from loguru import logger

-from pipecat.adapters.services.open_ai_realtime_adapter import OpenAIRealtimeLLMAdapter
-
 try:
    import websockets
 except ModuleNotFoundError as e:
@@ -78,9 +76,6 @@ class OpenAIUnhandledFunctionException(Exception):


 class OpenAIRealtimeBetaLLMService(LLMService):
-    # Overriding the default adapter to use the OpenAIRealtimeLLMAdapter one.
-    adapter_class = OpenAIRealtimeLLMAdapter
-
    def __init__(
        self,
        *,
@@ -576,37 +571,11 @@ class OpenAIRealtimeBetaLLMService(LLMService):
        await self.send_client_event(events.InputAudioBufferAppendEvent(audio=payload))

    def create_context_aggregator(
-        self,
-        context: OpenAILLMContext,
-        *,
-        user_kwargs: Mapping[str, Any] = {},
-        assistant_kwargs: Mapping[str, Any] = {},
+        self, context: OpenAILLMContext, *, assistant_expect_stripped_words: bool = False
    ) -> OpenAIContextAggregatorPair:
-        """Create an instance of OpenAIContextAggregatorPair from an
-        OpenAILLMContext. Constructor keyword arguments for both the user and
-        assistant aggregators can be provided.
-
-        Args:
-            context (OpenAILLMContext): The LLM context.
-            user_kwargs (Mapping[str, Any], optional): Additional keyword
-                arguments for the user context aggregator constructor. Defaults
-                to an empty mapping.
-            assistant_kwargs (Mapping[str, Any], optional): Additional keyword
-                arguments for the assistant context aggregator
-                constructor. Defaults to an empty mapping.
-
-        Returns:
-            OpenAIContextAggregatorPair: A pair of context aggregators, one for
-            the user and one for the assistant, encapsulated in an
-            OpenAIContextAggregatorPair.
-
-        """
-        context.set_llm_adapter(self.get_llm_adapter())
-
        OpenAIRealtimeLLMContext.upgrade_to_realtime(context)
-        user = OpenAIRealtimeUserContextAggregator(context, **user_kwargs)
-
-        default_assistant_kwargs = {"expect_stripped_words": False}
-        default_assistant_kwargs.update(assistant_kwargs)
-        assistant = OpenAIRealtimeAssistantContextAggregator(context, **default_assistant_kwargs)
+        user = OpenAIRealtimeUserContextAggregator(context)
+        assistant = OpenAIRealtimeAssistantContextAggregator(
+            context, expect_stripped_words=assistant_expect_stripped_words
+        )
        return OpenAIContextAggregatorPair(_user=user, _assistant=assistant)
--- a/src/pipecat/services/playht.py
+++ b/src/pipecat/services/playht.py
@@ -269,7 +269,7 @@ class PlayHTTTSService(InterruptibleTTSService):
                    logger.error(f"Invalid JSON message: {message}")

    async def run_tts(self, text: str) -> AsyncGenerator[Frame, None]:
-        logger.debug(f"{self}: Generating TTS [{text}]")
+        logger.debug(f"Generating TTS: [{text}]")

        try:
            # Reconnect if the websocket is closed
@@ -323,8 +323,7 @@ class PlayHTHttpTTSService(TTSService):
        api_key: str,
        user_id: str,
        voice_url: str,
-        voice_engine: str = "Play3.0-mini",
-        protocol: str = "http",  # Options: http, ws
+        voice_engine: str = "Play3.0-mini-http",  # Options: Play3.0-mini-http, Play3.0-mini-ws
        sample_rate: Optional[int] = None,
        params: InputParams = InputParams(),
        **kwargs,
@@ -338,24 +337,12 @@ class PlayHTHttpTTSService(TTSService):
            user_id=self._user_id,
            api_key=self._api_key,
        )
-
-        # Check if voice_engine contains protocol information (backward compatibility)
-        if "-http" in voice_engine:
-            # Extract the base engine name
-            voice_engine = voice_engine.replace("-http", "")
-            protocol = "http"
-        elif "-ws" in voice_engine:
-            # Extract the base engine name
-            voice_engine = voice_engine.replace("-ws", "")
-            protocol = "ws"
-
        self._settings = {
            "language": self.language_to_service_language(params.language)
            if params.language
            else "english",
            "format": Format.FORMAT_WAV,
            "voice_engine": voice_engine,
-            "protocol": protocol,
            "speed": params.speed,
            "seed": params.seed,
        }
@@ -392,26 +379,23 @@ class PlayHTHttpTTSService(TTSService):
        return language_to_playht_language(language)

    async def run_tts(self, text: str) -> AsyncGenerator[Frame, None]:
-        logger.debug(f"{self}: Generating TTS [{text}]")
+        logger.debug(f"Generating TTS: [{text}]")

        try:
            options = self._create_options()
+            b = bytearray()
+            in_header = True

            await self.start_ttfb_metrics()

            playht_gen = self._client.tts(
-                text,
-                voice_engine=self._settings["voice_engine"],
-                protocol=self._settings["protocol"],
-                options=options,
+                text, voice_engine=self._settings["voice_engine"], options=options
            )

            await self.start_tts_usage_metrics(text)

            yield TTSStartedFrame()

-            b = bytearray()
-            in_header = True
            async for chunk in playht_gen:
                # skip the RIFF header.
                if in_header:
@@ -426,10 +410,11 @@ class PlayHTHttpTTSService(TTSService):
                            fh.read(size)
                            (data, size) = struct.unpack("<4sI", fh.read(8))
                        in_header = False
-                elif len(chunk) > 0:
-                    await self.stop_ttfb_metrics()
-                    frame = TTSAudioRawFrame(chunk, self.sample_rate, 1)
-                    yield frame
+                else:
+                    if len(chunk):
+                        await self.stop_ttfb_metrics()
+                        frame = TTSAudioRawFrame(chunk, self.sample_rate, 1)
+                        yield frame
        except Exception as e:
            logger.error(f"{self} error generating TTS: {e}")
        finally:
--- a/src/pipecat/services/rime.py
+++ b/src/pipecat/services/rime.py
@@ -309,7 +309,7 @@ class RimeTTSService(AudioContextWordTTSService):
        Yields:
            Frames containing audio data and timing information.
        """
-        logger.debug(f"{self}: Generating TTS [{text}]")
+        logger.debug(f"Generating TTS: [{text}]")
        try:
            if not self._websocket:
                await self._connect()
@@ -376,7 +376,7 @@ class RimeHttpTTSService(TTSService):
        return True

    async def run_tts(self, text: str) -> AsyncGenerator[Frame, None]:
-        logger.debug(f"{self}: Generating TTS [{text}]")
+        logger.debug(f"Generating TTS: [{text}]")

        headers = {
            "Accept": "audio/pcm",
@@ -407,10 +407,10 @@ class RimeHttpTTSService(TTSService):
                yield TTSStartedFrame()

                # Process the streaming response
-                CHUNK_SIZE = 1024
+                chunk_size = 8192

-                async for chunk in response.content.iter_chunked(CHUNK_SIZE):
-                    if len(chunk) > 0:
+                async for chunk in response.content.iter_chunked(chunk_size):
+                    if chunk:
                        await self.stop_ttfb_metrics()
                        frame = TTSAudioRawFrame(chunk, self.sample_rate, 1)
                        yield frame
--- a/src/pipecat/services/riva.py
+++ b/src/pipecat/services/riva.py
@@ -101,7 +101,7 @@ class FastPitchTTSService(TTSService):
        await self.start_ttfb_metrics()
        yield TTSStartedFrame()

-        logger.debug(f"{self}: Generating TTS [{text}]")
+        logger.debug(f"Generating TTS: [{text}]")

        try:
            queue = asyncio.Queue()
--- a/src/pipecat/services/xtts.py
+++ b/src/pipecat/services/xtts.py
@@ -118,7 +118,7 @@ class XTTSService(TTSService):
            self._studio_speakers = await r.json()

    async def run_tts(self, text: str) -> AsyncGenerator[Frame, None]:
-        logger.debug(f"{self}: Generating TTS [{text}]")
+        logger.debug(f"Generating TTS: [{text}]")

        if not self._studio_speakers:
            logger.error(f"{self} no studio speakers available")
@@ -150,10 +150,8 @@ class XTTSService(TTSService):

            yield TTSStartedFrame()

-            CHUNK_SIZE = 1024
-
            buffer = bytearray()
-            async for chunk in r.content.iter_chunked(CHUNK_SIZE):
+            async for chunk in r.content.iter_chunked(1024):
                if len(chunk) > 0:
                    await self.stop_ttfb_metrics()
                    # Append new chunk to the buffer.
--- a/src/pipecat/transports/base_input.py
+++ b/src/pipecat/transports/base_input.py
@@ -10,7 +10,6 @@ from typing import Optional

 from loguru import logger

-from pipecat.audio.turn.base_turn_analyzer import BaseEndOfTurnAnalyzer, EndOfTurnState
 from pipecat.audio.vad.vad_analyzer import VADAnalyzer, VADState
 from pipecat.frames.frames import (
    BotInterruptionFrame,
@@ -25,7 +24,6 @@ from pipecat.frames.frames import (
    StartInterruptionFrame,
    StopInterruptionFrame,
    SystemFrame,
-    UserEndOfTurnFrame,
    UserStartedSpeakingFrame,
    UserStoppedSpeakingFrame,
    VADParamsUpdateFrame,
@@ -66,19 +64,12 @@ class BaseInputTransport(FrameProcessor):
    def vad_analyzer(self) -> Optional[VADAnalyzer]:
        return self._params.vad_analyzer

-    @property
-    def end_of_turn_analyzer(self) -> Optional[BaseEndOfTurnAnalyzer]:
-        return self._params.end_of_turn_analyzer
-
    async def start(self, frame: StartFrame):
        self._sample_rate = self._params.audio_in_sample_rate or frame.audio_in_sample_rate

        # Configure VAD analyzer.
        if self._params.vad_enabled and self._params.vad_analyzer:
            self._params.vad_analyzer.set_sample_rate(self._sample_rate)
-        # Configure End of turn analyzer.
-        if self._params.end_of_turn_analyzer:
-            self._params.end_of_turn_analyzer.set_sample_rate(self._sample_rate)
        # Start audio filter.
        if self._params.audio_in_filter:
            await self._params.audio_in_filter.start(self._sample_rate)
@@ -207,25 +198,8 @@ class BaseInputTransport(FrameProcessor):
            vad_state = new_vad_state
        return vad_state

-    async def _end_of_turn_analyze(self, audio_frame: InputAudioRawFrame) -> EndOfTurnState:
-        state = EndOfTurnState.INCOMPLETE
-        if self.end_of_turn_analyzer:
-            state = await self.get_event_loop().run_in_executor(
-                self._executor, self.end_of_turn_analyzer.analyze_audio, audio_frame.audio
-            )
-        return state
-
-    async def _handle_end_of_turn(
-        self, audio_frame: InputAudioRawFrame, end_of_turn_state: EndOfTurnState
-    ):
-        new_eot_state = await self._end_of_turn_analyze(audio_frame)
-        if new_eot_state != end_of_turn_state:
-            await self.push_frame(UserEndOfTurnFrame())
-        return new_eot_state
-
    async def _audio_task_handler(self):
        vad_state: VADState = VADState.QUIET
-        end_of_turn_state: EndOfTurnState = EndOfTurnState.INCOMPLETE
        while True:
            frame: InputAudioRawFrame = await self._audio_in_queue.get()

@@ -241,9 +215,6 @@ class BaseInputTransport(FrameProcessor):
                vad_state = await self._handle_vad(frame, vad_state)
                audio_passthrough = self._params.vad_audio_passthrough

-            if self._params.end_of_turn_analyzer:
-                end_of_turn_state = await self._handle_end_of_turn(frame, end_of_turn_state)
-
            # Push audio downstream if passthrough.
            if audio_passthrough:
                await self.push_frame(frame)
--- a/src/pipecat/transports/base_output.py
+++ b/src/pipecat/transports/base_output.py
@@ -232,9 +232,6 @@ class BaseOutputTransport(FrameProcessor):
            await self.push_frame(BotStoppedSpeakingFrame())
            await self.push_frame(BotStoppedSpeakingFrame(), FrameDirection.UPSTREAM)
            self._bot_speaking = False
-            # Clean audio buffer (there could be tiny left overs if not multiple
-            # to our output chunk size).
-            self._audio_buffer = bytearray()

    #
    # Sink tasks
--- a/src/pipecat/transports/base_transport.py
+++ b/src/pipecat/transports/base_transport.py
@@ -4,17 +4,18 @@
 # SPDX-License-Identifier: BSD 2-Clause License
 #

-from abc import abstractmethod
+import inspect
+from abc import ABC, abstractmethod
 from typing import Optional

+from loguru import logger
 from pydantic import BaseModel, ConfigDict

 from pipecat.audio.filters.base_audio_filter import BaseAudioFilter
 from pipecat.audio.mixers.base_audio_mixer import BaseAudioMixer
-from pipecat.audio.turn.base_turn_analyzer import BaseEndOfTurnAnalyzer
 from pipecat.audio.vad.vad_analyzer import VADAnalyzer
 from pipecat.processors.frame_processor import FrameProcessor
-from pipecat.utils.base_object import BaseObject
+from pipecat.utils.utils import obj_count, obj_id


 class TransportParams(BaseModel):
@@ -40,10 +41,9 @@ class TransportParams(BaseModel):
    vad_enabled: bool = False
    vad_audio_passthrough: bool = False
    vad_analyzer: Optional[VADAnalyzer] = None
-    end_of_turn_analyzer: Optional[BaseEndOfTurnAnalyzer] = None


-class BaseTransport(BaseObject):
+class BaseTransport(ABC):
    def __init__(
        self,
        *,
@@ -51,14 +51,54 @@ class BaseTransport(BaseObject):
        input_name: Optional[str] = None,
        output_name: Optional[str] = None,
    ):
-        super().__init__(name=name)
+        self._id: int = obj_id()
+        self._name = name or f"{self.__class__.__name__}#{obj_count(self)}"
        self._input_name = input_name
        self._output_name = output_name
+        self._event_handlers: dict = {}
+
+    @property
+    def id(self) -> int:
+        return self._id
+
+    @property
+    def name(self) -> str:
+        return self._name

    @abstractmethod
    def input(self) -> FrameProcessor:
-        pass
+        raise NotImplementedError

    @abstractmethod
    def output(self) -> FrameProcessor:
-        pass
+        raise NotImplementedError
+
+    def event_handler(self, event_name: str):
+        def decorator(handler):
+            self.add_event_handler(event_name, handler)
+            return handler
+
+        return decorator
+
+    def add_event_handler(self, event_name: str, handler):
+        if event_name not in self._event_handlers:
+            raise Exception(f"Event handler {event_name} not registered")
+        self._event_handlers[event_name].append(handler)
+
+    def _register_event_handler(self, event_name: str):
+        if event_name in self._event_handlers:
+            raise Exception(f"Event handler {event_name} already registered")
+        self._event_handlers[event_name] = []
+
+    async def _call_event_handler(self, event_name: str, *args, **kwargs):
+        try:
+            for handler in self._event_handlers[event_name]:
+                if inspect.iscoroutinefunction(handler):
+                    await handler(self, *args, **kwargs)
+                else:
+                    handler(self, *args, **kwargs)
+        except Exception as e:
+            logger.exception(f"Exception in event handler {event_name}: {e}")
+
+    def __str__(self):
+        return self.name
--- a/src/pipecat/transports/services/helpers/daily_rest.py
+++ b/src/pipecat/transports/services/helpers/daily_rest.py
@@ -195,10 +195,6 @@ class DailyMeetingTokenProperties(BaseModel):
        default=None,
        description="Start cloud recording when the user joins the room. This can be used to always record and archive meetings, for example in a customer support context.",
    )
-    permissions: Optional[dict] = Field(
-        default=None,
-        description="Specifies the initial default permissions for a non-meeting-owner participant joining a call.",
-    )


 class DailyMeetingTokenParams(BaseModel):
--- a/src/pipecat/utils/base_object.py
+++ b/src/pipecat/utils/base_object.py
@@ -1,58 +0,0 @@
-#
-# Copyright (c) 2024–2025, Daily
-#
-# SPDX-License-Identifier: BSD 2-Clause License
-#
-
-import inspect
-from abc import ABC
-from typing import Optional
-
-from loguru import logger
-
-from pipecat.utils.utils import obj_count, obj_id
-
-
-class BaseObject(ABC):
-    def __init__(self, *, name: Optional[str] = None):
-        self._id: int = obj_id()
-        self._name = name or f"{self.__class__.__name__}#{obj_count(self)}"
-        self._event_handlers: dict = {}
-
-    @property
-    def id(self) -> int:
-        return self._id
-
-    @property
-    def name(self) -> str:
-        return self._name
-
-    def event_handler(self, event_name: str):
-        def decorator(handler):
-            self.add_event_handler(event_name, handler)
-            return handler
-
-        return decorator
-
-    def add_event_handler(self, event_name: str, handler):
-        if event_name not in self._event_handlers:
-            raise Exception(f"Event handler {event_name} not registered")
-        self._event_handlers[event_name].append(handler)
-
-    def _register_event_handler(self, event_name: str):
-        if event_name in self._event_handlers:
-            raise Exception(f"Event handler {event_name} already registered")
-        self._event_handlers[event_name] = []
-
-    async def _call_event_handler(self, event_name: str, *args, **kwargs):
-        try:
-            for handler in self._event_handlers[event_name]:
-                if inspect.iscoroutinefunction(handler):
-                    await handler(self, *args, **kwargs)
-                else:
-                    handler(self, *args, **kwargs)
-        except Exception as e:
-            logger.exception(f"Exception in event handler {event_name}: {e}")
-
-    def __str__(self):
-        return self.name
--- a/src/pipecat/utils/test_frame_processor.py
+++ b/src/pipecat/utils/test_frame_processor.py
@@ -8,8 +8,6 @@ class TestException(Exception):


 class TestFrameProcessor(FrameProcessor):
-    __test__ = False  # Prevents pytest from collecting this class as a test
-
    def __init__(self, test_frames):
        self.test_frames = test_frames
        self._list_counter = 0
--- a/tests/integration/test_integration_unified_function_calling.py
+++ b/tests/integration/test_integration_unified_function_calling.py
@@ -1,96 +0,0 @@
-import os
-from unittest.mock import AsyncMock
-
-import pytest
-from dotenv import load_dotenv
-
-from pipecat.adapters.schemas.function_schema import FunctionSchema
-from pipecat.adapters.schemas.tools_schema import ToolsSchema
-from pipecat.frames.frames import (
-    LLMFullResponseEndFrame,
-    LLMFullResponseStartFrame,
-    LLMTextFrame,
-)
-from pipecat.processors.frame_processor import FrameDirection
-from pipecat.services.ai_services import LLMService
-from pipecat.services.anthropic import AnthropicLLMService
-from pipecat.services.google import GoogleLLMService
-from pipecat.services.openai import OpenAILLMContext, OpenAILLMContextFrame, OpenAILLMService
-from pipecat.utils.test_frame_processor import TestFrameProcessor
-
-load_dotenv(override=True)
-
-
-def standard_tools() -> ToolsSchema:
-    weather_function = FunctionSchema(
-        name="get_current_weather",
-        description="Get the current weather",
-        properties={
-            "location": {
-                "type": "string",
-                "description": "The city and state, e.g. San Francisco, CA",
-            },
-            "format": {
-                "type": "string",
-                "enum": ["celsius", "fahrenheit"],
-                "description": "The temperature unit to use. Infer this from the user's location.",
-            },
-        },
-        required=["location"],
-    )
-    tools_def = ToolsSchema(standard_tools=[weather_function])
-    return tools_def
-
-
-async def _test_llm_function_calling(llm: LLMService):
-    # Create an AsyncMock for the function
-    mock_fetch_weather = AsyncMock()
-
-    llm.register_function(None, mock_fetch_weather)
-    t = TestFrameProcessor([LLMFullResponseStartFrame, LLMTextFrame, LLMFullResponseEndFrame])
-    llm.link(t)
-
-    messages = [
-        {
-            "role": "system",
-            "content": "You are a helpful assistant who can report the weather in any location in the universe. Respond concisely. Your response will be turned into speech so use only simple words and punctuation.",
-        },
-        {"role": "user", "content": " How is the weather today in San Francisco, California?"},
-    ]
-    context = OpenAILLMContext(messages, standard_tools())
-    # This is done by default inside the create_context_aggregator
-    context.set_llm_adapter(llm.get_llm_adapter())
-
-    frame = OpenAILLMContextFrame(context)
-
-    # This will fail if an exception is raised
-    await llm.process_frame(frame, FrameDirection.DOWNSTREAM)
-
-    # Assert that the mock function was called
-    mock_fetch_weather.assert_called_once()
-
-
-@pytest.mark.skipif(os.getenv("OPENAI_API_KEY") is None, reason="OPENAI_API_KEY is not set")
-@pytest.mark.asyncio
-async def test_unified_function_calling_openai():
-    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
-    # This will fail if an exception is raised
-    await _test_llm_function_calling(llm)
-
-
-@pytest.mark.skipif(os.getenv("GOOGLE_API_KEY") is None, reason="GOOGLE_API_KEY is not set")
-@pytest.mark.asyncio
-async def test_unified_function_calling_gemini():
-    llm = GoogleLLMService(api_key=os.getenv("GOOGLE_API_KEY"), model="gemini-2.0-flash-001")
-    # This will fail if an exception is raised
-    await _test_llm_function_calling(llm)
-
-
-@pytest.mark.skipif(os.getenv("ANTHROPIC_API_KEY") is None, reason="ANTHROPIC_API_KEY is not set")
-@pytest.mark.asyncio
-async def test_unified_function_calling_anthropic():
-    llm = AnthropicLLMService(
-        api_key=os.getenv("ANTHROPIC_API_KEY"), model="claude-3-5-sonnet-20240620"
-    )
-    # This will fail if an exception is raised
-    await _test_llm_function_calling(llm)
--- a/tests/test_function_calling_adapters.py
+++ b/tests/test_function_calling_adapters.py
@@ -1,176 +0,0 @@
-#
-# Copyright (c) 2024–2025, Daily
-#
-# SPDX-License-Identifier: BSD 2-Clause License
-#
-
-import unittest
-
-from openai.types.chat import ChatCompletionToolParam
-
-from pipecat.adapters.schemas.function_schema import FunctionSchema
-from pipecat.adapters.schemas.tools_schema import AdapterType, ToolsSchema
-from pipecat.adapters.services.anthropic_adapter import AnthropicLLMAdapter
-from pipecat.adapters.services.gemini_adapter import GeminiLLMAdapter
-from pipecat.adapters.services.open_ai_adapter import OpenAILLMAdapter
-from pipecat.adapters.services.open_ai_realtime_adapter import OpenAIRealtimeLLMAdapter
-
-
-class TestFunctionAdapters(unittest.TestCase):
-    def setUp(self) -> None:
-        """Sets up a common tools schema for all tests."""
-        function_def = FunctionSchema(
-            name="get_weather",
-            description="Get the weather in a given location",
-            properties={
-                "location": {"type": "string", "description": "The city, e.g. San Francisco"},
-                "format": {
-                    "type": "string",
-                    "enum": ["celsius", "fahrenheit"],
-                    "description": "The temperature unit to use.",
-                },
-            },
-            required=["location", "format"],
-        )
-        self.tools_def = ToolsSchema(standard_tools=[function_def])
-
-    def test_openai_adapter(self):
-        """Test OpenAI adapter format transformation."""
-        expected = [
-            ChatCompletionToolParam(
-                type="function",
-                function={
-                    "name": "get_weather",
-                    "description": "Get the weather in a given location",
-                    "parameters": {
-                        "type": "object",
-                        "properties": {
-                            "location": {
-                                "type": "string",
-                                "description": "The city, e.g. San Francisco",
-                            },
-                            "format": {
-                                "type": "string",
-                                "enum": ["celsius", "fahrenheit"],
-                                "description": "The temperature unit to use.",
-                            },
-                        },
-                        "required": ["location", "format"],
-                    },
-                },
-            )
-        ]
-        assert OpenAILLMAdapter().to_provider_tools_format(self.tools_def) == expected
-
-    def test_anthropic_adapter(self):
-        """Test Anthropic adapter format transformation."""
-        expected = [
-            {
-                "name": "get_weather",
-                "description": "Get the weather in a given location",
-                "input_schema": {
-                    "type": "object",
-                    "properties": {
-                        "location": {
-                            "type": "string",
-                            "description": "The city, e.g. San Francisco",
-                        },
-                        "format": {
-                            "type": "string",
-                            "enum": ["celsius", "fahrenheit"],
-                            "description": "The temperature unit to use.",
-                        },
-                    },
-                    "required": ["location", "format"],
-                },
-            }
-        ]
-        assert AnthropicLLMAdapter().to_provider_tools_format(self.tools_def) == expected
-
-    def test_gemini_adapter(self):
-        """Test Gemini adapter format transformation."""
-        expected = [
-            {
-                "function_declarations": [
-                    {
-                        "name": "get_weather",
-                        "description": "Get the weather in a given location",
-                        "parameters": {
-                            "type": "object",
-                            "properties": {
-                                "location": {
-                                    "type": "string",
-                                    "description": "The city, e.g. San Francisco",
-                                },
-                                "format": {
-                                    "type": "string",
-                                    "enum": ["celsius", "fahrenheit"],
-                                    "description": "The temperature unit to use.",
-                                },
-                            },
-                            "required": ["location", "format"],
-                        },
-                    }
-                ]
-            }
-        ]
-        assert GeminiLLMAdapter().to_provider_tools_format(self.tools_def) == expected
-
-    def test_openai_realtime_adapter(self):
-        """Test Anthropic adapter format transformation."""
-        expected = [
-            {
-                "type": "function",
-                "name": "get_weather",
-                "description": "Get the weather in a given location",
-                "parameters": {
-                    "type": "object",
-                    "properties": {
-                        "location": {
-                            "type": "string",
-                            "description": "The city, e.g. San Francisco",
-                        },
-                        "format": {
-                            "type": "string",
-                            "enum": ["celsius", "fahrenheit"],
-                            "description": "The temperature unit to use.",
-                        },
-                    },
-                    "required": ["location", "format"],
-                },
-            }
-        ]
-        assert OpenAIRealtimeLLMAdapter().to_provider_tools_format(self.tools_def) == expected
-
-    def test_gemini_adapter_with_custom_tools(self):
-        """Test Gemini adapter format transformation."""
-        search_tool = {"google_search": {}}
-        expected = [
-            {
-                "function_declarations": [
-                    {
-                        "name": "get_weather",
-                        "description": "Get the weather in a given location",
-                        "parameters": {
-                            "type": "object",
-                            "properties": {
-                                "location": {
-                                    "type": "string",
-                                    "description": "The city, e.g. San Francisco",
-                                },
-                                "format": {
-                                    "type": "string",
-                                    "enum": ["celsius", "fahrenheit"],
-                                    "description": "The temperature unit to use.",
-                                },
-                            },
-                            "required": ["location", "format"],
-                        },
-                    }
-                ]
-            },
-            search_tool,
-        ]
-        tools_def = self.tools_def
-        tools_def.custom_tools = {AdapterType.GEMINI: [search_tool]}
-        assert GeminiLLMAdapter().to_provider_tools_format(tools_def) == expected
--- a/tests/test_llm_response.py
+++ b/tests/test_llm_response.py
@@ -1,136 +0,0 @@
-#
-# Copyright (c) 2024-2025 Daily
-#
-# SPDX-License-Identifier: BSD 2-Clause License
-#
-
-import unittest
-
-from pipecat.frames.frames import (
-    LLMFullResponseEndFrame,
-    LLMFullResponseStartFrame,
-    LLMTextFrame,
-    StartInterruptionFrame,
-)
-from pipecat.processors.aggregators.llm_response import LLMFullResponseAggregator
-from pipecat.tests.utils import SleepFrame, run_test
-
-
-class TestLLMFullResponseAggregator(unittest.IsolatedAsyncioTestCase):
-    async def test_empty(self):
-        completion_ok = False
-
-        aggregator = LLMFullResponseAggregator()
-
-        @aggregator.event_handler("on_completion")
-        async def on_completion(aggregator, completion, completed):
-            nonlocal completion_ok
-            completion_ok = completion == "" and completed
-
-        frames_to_send = [LLMFullResponseStartFrame(), LLMFullResponseEndFrame()]
-        expected_down_frames = [LLMFullResponseStartFrame, LLMFullResponseEndFrame]
-        await run_test(
-            aggregator,
-            frames_to_send=frames_to_send,
-            expected_down_frames=expected_down_frames,
-        )
-        assert completion_ok
-
-    async def test_simple(self):
-        completion_ok = False
-
-        aggregator = LLMFullResponseAggregator()
-
-        @aggregator.event_handler("on_completion")
-        async def on_completion(aggregator, completion, completed):
-            nonlocal completion_ok
-            completion_ok = completion == "Hello from Pipecat!" and completed
-
-        frames_to_send = [
-            LLMFullResponseStartFrame(),
-            LLMTextFrame("Hello from Pipecat!"),
-            LLMFullResponseEndFrame(),
-        ]
-        expected_down_frames = [LLMFullResponseStartFrame, LLMTextFrame, LLMFullResponseEndFrame]
-        await run_test(
-            aggregator,
-            frames_to_send=frames_to_send,
-            expected_down_frames=expected_down_frames,
-        )
-        assert completion_ok
-
-    async def test_multiple(self):
-        completion_ok = False
-
-        aggregator = LLMFullResponseAggregator()
-
-        @aggregator.event_handler("on_completion")
-        async def on_completion(aggregator, completion, completed):
-            nonlocal completion_ok
-            completion_ok = completion == "Hello from Pipecat!" and completed
-
-        frames_to_send = [
-            LLMFullResponseStartFrame(),
-            LLMTextFrame("Hello "),
-            LLMTextFrame("from "),
-            LLMTextFrame("Pipecat!"),
-            LLMFullResponseEndFrame(),
-        ]
-        expected_down_frames = [
-            LLMFullResponseStartFrame,
-            LLMTextFrame,
-            LLMTextFrame,
-            LLMTextFrame,
-            LLMFullResponseEndFrame,
-        ]
-        await run_test(
-            aggregator,
-            frames_to_send=frames_to_send,
-            expected_down_frames=expected_down_frames,
-        )
-        assert completion_ok
-
-    async def test_interruption(self):
-        completion_ok = True
-
-        completion_result = [("Hello ", False), ("Hello there!", True)]
-        completion_index = 0
-
-        aggregator = LLMFullResponseAggregator()
-
-        @aggregator.event_handler("on_completion")
-        async def on_completion(aggregator, completion, completed):
-            nonlocal completion_result, completion_index, completion_ok
-            (completion_expected, completion_completed) = completion_result[completion_index]
-            completion_ok = (
-                completion_ok
-                and completion == completion_expected
-                and completed == completion_completed
-            )
-            completion_index += 1
-
-        frames_to_send = [
-            LLMFullResponseStartFrame(),
-            LLMTextFrame("Hello "),
-            SleepFrame(),
-            StartInterruptionFrame(),
-            LLMFullResponseStartFrame(),
-            LLMTextFrame("Hello "),
-            LLMTextFrame("there!"),
-            LLMFullResponseEndFrame(),
-        ]
-        expected_down_frames = [
-            LLMFullResponseStartFrame,
-            LLMTextFrame,
-            StartInterruptionFrame,
-            LLMFullResponseStartFrame,
-            LLMTextFrame,
-            LLMTextFrame,
-            LLMFullResponseEndFrame,
-        ]
-        await run_test(
-            aggregator,
-            frames_to_send=frames_to_send,
-            expected_down_frames=expected_down_frames,
-        )
-        assert completion_ok