Fix lint

CHANGELOG.md

@@ -5,72 +5,6 @@ All notable changes to **Pipecat** will be documented in this file.
 The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/),
 and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html).
 
-## [Unreleased]
-
-### Added
-
-- Added support for Smart Turn Detection via the `turn_analyzer` transport
-  parameter. You can now choose between `SmartTurnAnalyzer()` for remote 
-  inference or `LocalCoreMLSmartTurnAnalyzer()` for on-device inference using 
-  Core ML.
-
-- `DeepgramTTSService` accepts `base_url` argument again, allowing you to
-  connect to an on-prem service.
-
-- Added `LLMUserAggregatorParams` and `LLMAssistantAggregatorParams` which allow
-  you to control aggregator settings. You can now pass these arguments when
-  creating aggregator pairs with `create_context_aggregator()`.
-
-- Added `previous_text` context support to ElevenLabsHttpTTSService, improving
-  speech consistency across sentences within an LLM response.
-
-- Added word/timestamp pairs to `ElevenLabsHttpTTSService`.
-
-- It is now possible to disable `SoundfileMixer` when created. You can then use
-  `MixerEnableFrame` to dynamically enable it when necessary.
-
-- Added `on_client_connected` and `on_client_disconnected` event handlers to
-  the `DailyTransport` class. These handlers map to the same underlying Daily
-  events as `on_participant_joined` and `on_participant_left`, respectively.
-  This makes it easier to write a single bot pipeline that can also use other
-  transports like `SmallWebRTCTransport` and `FastAPIWebsocketTransport`.
-
-### Changed
-
-- Daily's REST helpers now include an `eject_at_token_exp` param, which ejects
-  the user when their token expires. This new parameter defaults to False.
-  Also, the default value for `enable_prejoin_ui` changed to False and
-  `eject_at_room_exp` changed to False.
-
-- `OpenAILLMService` and `OpenPipeLLMService` now use `gpt-4.1` as their
-  default model.
-
-- `SoundfileMixer` constructor arguments need to be keywords.
-
-### Deprecated
-
-- `DeepgramSTTService` parameter `url` is now deprecated, use `base_url`
-  instead.
-
-### Removed
-
-- Parameters `user_kwargs` and `assistant_kwargs` when creating a context
-  aggregator pair using `create_context_aggregator()` have been removed. Use
-  `user_params` and `assistant_params` instead.
-
-### Fixed
-
-- Fixed an issue that would cause TTS websocket-based services to not cleanup
-  resources properly when disconnecting.
-
-- Fixed a `TavusVideoService` issue that was causing audio choppiness.
-
-- Fixed an issue in `SmallWebRTCTransport` where an error was thrown if the
-  client did not create a video transceiver.
-
-- Fixed an issue where LLM input parameters were not working and applied correctly in `GoogleVertexLLMService`, causing
-  unexpected behavior during inference.
-
 ## [0.0.63] - 2025-04-11
 
 ### Added

dot-env.template

@@ -92,8 +92,4 @@ ASSEMBLYAI_API_KEY=...
 OPENROUTER_API_KEY=...
 
 # Piper
-PIPER_BASE_URL=...
-
-# Smart turn
-LOCAL_SMART_TURN_MODEL_PATH=
-REMOTE_SMART_TURN_URL=
+PIPER_BASE_URL=...

examples/canonical-metrics/bot.py

@@ -72,7 +72,7 @@ async def main():
             # voice_id="gD1IexrzCvsXPHUuT0s3",
         )
 
-        llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
+        llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
 
         messages = [
             {

examples/chatbot-audio-recording/bot.py

@@ -95,7 +95,7 @@ async def main():
             # voice_id="gD1IexrzCvsXPHUuT0s3",
         )
 
-        llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
+        llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
 
         messages = [
             {

examples/deployment/flyio-example/bot.py

@@ -53,7 +53,7 @@ async def main(room_url: str, token: str):
         voice_id=os.getenv("ELEVENLABS_VOICE_ID", ""),
     )
 
-    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
+    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
 
     messages = [
         {

examples/deployment/modal-example/bot.py

@@ -43,7 +43,7 @@ async def main(room_url: str, token: str):
         api_key=os.getenv("CARTESIA_API_KEY", ""), voice_id="71a7ad14-091c-4e8e-a314-022ece01c121"
     )
 
-    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
+    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
 
     messages = [
         {

examples/deployment/pipecat-cloud-daily-pstn-server/fastapi-webhook-server/server.py

@@ -141,7 +141,6 @@ async def dial(request: RoomRequest, raw_request: Request):
             "display_name": request.From,
             "sip_mode": "dial-in",
             "num_endpoints": 2 if request.call_transfer is not None else 1,
-            "codecs": {"audio": ["OPUS"]},
         }
         daily_room_properties["sip"] = sip_config
 

examples/deployment/pipecat-cloud-daily-pstn-server/nextjs-webhook-server/pages/api/dial.js

@@ -103,7 +103,6 @@ export default async function handler(req, res) {
         display_name: From,
         sip_mode: 'dial-in',
         num_endpoints: call_transfer !== null ? 2 : 1,
-        codecs: {"audio": ["OPUS"]},
       };
       daily_room_properties.sip = sip_config;
     }
@@ -173,4 +172,4 @@ export const config = {
       sizeLimit: '1mb',
     },
   },
-};
+};

examples/deployment/pipecat-cloud-example/bot.py

@@ -61,7 +61,7 @@ async def main(room_url: str, token: str):
         api_key=os.getenv("CARTESIA_API_KEY"), voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22"
     )
 
-    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
+    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
 
     messages = [
         {

examples/foundational/01-say-one-thing.py

@@ -9,11 +9,10 @@ import os
 from dotenv import load_dotenv
 from loguru import logger
 
-from pipecat.frames.frames import EndFrame, TranscriptionFrame, TTSSpeakFrame
+from pipecat.frames.frames import EndFrame, TTSSpeakFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineTask
-from pipecat.processors.frame_processor import FrameProcessor
 from pipecat.services.cartesia.tts import CartesiaTTSService
 from pipecat.transports.base_transport import TransportParams
 from pipecat.transports.network.small_webrtc import SmallWebRTCTransport

examples/foundational/02-llm-say-one-thing.py

@@ -38,7 +38,7 @@ async def run_bot(webrtc_connection: SmallWebRTCConnection):
         voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
     )
 
-    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
+    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
 
     messages = [
         {

examples/foundational/05-sync-speech-and-image.py

@@ -85,7 +85,7 @@ async def run_bot(webrtc_connection: SmallWebRTCConnection):
 
     # Create an HTTP session for API calls
     async with aiohttp.ClientSession() as session:
-        llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
+        llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
 
         tts = CartesiaHttpTTSService(
             api_key=os.getenv("CARTESIA_API_KEY"),

examples/foundational/05a-local-sync-speech-and-image.py

@@ -93,7 +93,7 @@ async def main():
                         self.frame = frame
                     await self.push_frame(frame, direction)
 
-            llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
+            llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
 
             tts = CartesiaHttpTTSService(
                 api_key=os.getenv("CARTESIA_API_KEY"),

examples/foundational/06-listen-and-respond.py

@@ -10,7 +10,7 @@ from dotenv import load_dotenv
 from loguru import logger
 
 from pipecat.audio.vad.silero import SileroVADAnalyzer
-from pipecat.frames.frames import Frame, MetricsFrame, TranscriptionFrame, TTSSpeakFrame
+from pipecat.frames.frames import Frame, MetricsFrame
 from pipecat.metrics.metrics import (
     LLMUsageMetricsData,
     ProcessingMetricsData,
@@ -32,30 +32,7 @@ from pipecat.transports.network.webrtc_connection import SmallWebRTCConnection
 load_dotenv(override=True)
 
 
-# Custom processor that prints a message if it receives a TranscriptionFrame that says "banana"
-class BananaProcessor(FrameProcessor):
-    """A custom processor that listens for transcription frames containing the word 'banana'."""
-
-    def __init__(self):
-        super().__init__()
-
-    async def process_frame(self, frame: Frame, direction: FrameDirection):
-        # Ensure the super method is called first
-        await super().process_frame(frame, direction)
-
-        if isinstance(frame, TranscriptionFrame):
-            logger.debug(f"Received transcription frame: {frame.text}")
-            if "banana" in frame.text.lower():
-                logger.info("---- Received 'banana' in transcription frame")
-
-        # Push the frame after processing
-        await self.push_frame(frame)
-
-
 class MetricsLogger(FrameProcessor):
-    def __init__(self):
-        super().__init__()
-
     async def process_frame(self, frame: Frame, direction: FrameDirection):
         await super().process_frame(frame, direction)
 
@@ -96,7 +73,7 @@ async def run_bot(webrtc_connection: SmallWebRTCConnection):
         voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
     )
 
-    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
+    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
 
     ml = MetricsLogger()
 
@@ -110,13 +87,10 @@ async def run_bot(webrtc_connection: SmallWebRTCConnection):
     context = OpenAILLMContext(messages)
     context_aggregator = llm.create_context_aggregator(context)
 
-    banana = BananaProcessor()
-
     pipeline = Pipeline(
         [
             transport.input(),
             stt,
-            banana,
             context_aggregator.user(),
             llm,
             tts,

examples/foundational/06a-image-sync.py

@@ -91,7 +91,7 @@ async def run_bot(webrtc_connection: SmallWebRTCConnection):
         voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
     )
 
-    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
+    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
 
     messages = [
         {

examples/foundational/07-interruptible.py

@@ -45,7 +45,7 @@ async def run_bot(webrtc_connection: SmallWebRTCConnection):
         voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
     )
 
-    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
+    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
 
     messages = [
         {

examples/foundational/07a-interruptible-vad.py

@@ -44,7 +44,7 @@ async def run_bot(webrtc_connection: SmallWebRTCConnection):
         voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
     )
 
-    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
+    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
 
     messages = [
         {

examples/foundational/07b-interruptible-langchain.py

@@ -74,7 +74,7 @@ async def run_bot(webrtc_connection: SmallWebRTCConnection):
             ("human", "{input}"),
         ]
     )
-    chain = prompt | ChatOpenAI(model="gpt-4.1", temperature=0.7)
+    chain = prompt | ChatOpenAI(model="gpt-4o", temperature=0.7)
     history_chain = RunnableWithMessageHistory(
         chain,
         get_session_history,

examples/foundational/07c-interruptible-deepgram-vad.py

@@ -48,7 +48,7 @@ async def run_bot(webrtc_connection: SmallWebRTCConnection):
 
     tts = DeepgramTTSService(api_key=os.getenv("DEEPGRAM_API_KEY"), voice="aura-helios-en")
 
-    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
+    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
 
     messages = [
         {

examples/foundational/07c-interruptible-deepgram.py

@@ -42,7 +42,7 @@ async def run_bot(webrtc_connection: SmallWebRTCConnection):
 
     tts = DeepgramTTSService(api_key=os.getenv("DEEPGRAM_API_KEY"), voice="aura-helios-en")
 
-    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
+    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
 
     messages = [
         {

examples/foundational/07d-interruptible-elevenlabs-http.py

@@ -49,7 +49,7 @@ async def run_bot(webrtc_connection: SmallWebRTCConnection):
             aiohttp_session=session,
         )
 
-        llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
+        llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
 
         messages = [
             {

examples/foundational/07d-interruptible-elevenlabs.py

@@ -45,7 +45,7 @@ async def run_bot(webrtc_connection: SmallWebRTCConnection):
         voice_id=os.getenv("ELEVENLABS_VOICE_ID", ""),
     )
 
-    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
+    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
 
     messages = [
         {

examples/foundational/07e-interruptible-playht-http.py

@@ -46,7 +46,7 @@ async def run_bot(webrtc_connection: SmallWebRTCConnection):
         voice_url="s3://voice-cloning-zero-shot/d9ff78ba-d016-47f6-b0ef-dd630f59414e/female-cs/manifest.json",
     )
 
-    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
+    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
 
     messages = [
         {

examples/foundational/07e-interruptible-playht.py

@@ -48,7 +48,7 @@ async def run_bot(webrtc_connection: SmallWebRTCConnection):
         params=PlayHTTTSService.InputParams(language=Language.EN),
     )
 
-    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
+    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
 
     messages = [
         {

examples/foundational/07g-interruptible-openai.py

@@ -46,7 +46,7 @@ async def run_bot(webrtc_connection: SmallWebRTCConnection):
 
     tts = OpenAITTSService(api_key=os.getenv("OPENAI_API_KEY"), voice="ballad")
 
-    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
+    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
 
     messages = [
         {

examples/foundational/07h-interruptible-openpipe.py

@@ -50,6 +50,7 @@ async def run_bot(webrtc_connection: SmallWebRTCConnection):
     llm = OpenPipeLLMService(
         api_key=os.getenv("OPENAI_API_KEY"),
         openpipe_api_key=os.getenv("OPENPIPE_API_KEY"),
+        model="gpt-4o",
         tags={"conversation_id": f"pipecat-{timestamp}"},
     )
 

examples/foundational/07i-interruptible-xtts.py

@@ -49,7 +49,7 @@ async def run_bot(webrtc_connection: SmallWebRTCConnection):
             base_url="http://localhost:8000",
         )
 
-        llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
+        llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
 
         messages = [
             {

examples/foundational/07j-interruptible-gladia.py

@@ -54,7 +54,7 @@ async def run_bot(webrtc_connection: SmallWebRTCConnection):
         voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
     )
 
-    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY", ""))
+    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY", ""), model="gpt-4o")
 
     messages = [
         {

examples/foundational/07k-interruptible-lmnt.py

@@ -42,7 +42,7 @@ async def run_bot(webrtc_connection: SmallWebRTCConnection):
 
     tts = LmntTTSService(api_key=os.getenv("LMNT_API_KEY"), voice_id="morgan")
 
-    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
+    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
 
     messages = [
         {

examples/foundational/07m-interruptible-polly.py

@@ -48,7 +48,7 @@ async def run_bot(webrtc_connection: SmallWebRTCConnection):
         params=PollyTTSService.InputParams(engine="neural", language="en-GB", rate="1.05"),
     )
 
-    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
+    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
 
     messages = [
         {

examples/foundational/07o-interruptible-assemblyai.py

@@ -47,7 +47,7 @@ async def run_bot(webrtc_connection: SmallWebRTCConnection):
         voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
     )
 
-    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
+    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
 
     messages = [
         {

examples/foundational/07p-interruptible-krisp.py

@@ -44,7 +44,7 @@ async def run_bot(webrtc_connection: SmallWebRTCConnection):
 
     tts = DeepgramTTSService(api_key=os.getenv("DEEPGRAM_API_KEY"), voice="aura-helios-en")
 
-    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
+    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
 
     messages = [
         {

examples/foundational/07q-interruptible-rime-http.py

@@ -49,7 +49,7 @@ async def run_bot(webrtc_connection: SmallWebRTCConnection):
             aiohttp_session=session,
         )
 
-        llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
+        llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
 
         messages = [
             {

examples/foundational/07q-interruptible-rime.py

@@ -45,7 +45,7 @@ async def run_bot(webrtc_connection: SmallWebRTCConnection):
         voice_id="rex",
     )
 
-    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
+    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
 
     messages = [
         {

examples/foundational/07t-interruptible-fish.py

@@ -45,7 +45,7 @@ async def run_bot(webrtc_connection: SmallWebRTCConnection):
         model="4ce7e917cedd4bc2bb2e6ff3a46acaa1",  # Barack Obama
     )
 
-    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
+    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
 
     messages = [
         {

examples/foundational/07v-interruptible-neuphonic-http.py

@@ -45,7 +45,7 @@ async def run_bot(webrtc_connection: SmallWebRTCConnection):
         voice_id="fc854436-2dac-4d21-aa69-ae17b54e98eb",  # Emily
     )
 
-    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
+    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
 
     messages = [
         {

examples/foundational/07v-interruptible-neuphonic.py

@@ -45,7 +45,7 @@ async def run_bot(webrtc_connection: SmallWebRTCConnection):
         voice_id="fc854436-2dac-4d21-aa69-ae17b54e98eb",  # Emily
     )
 
-    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
+    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
 
     messages = [
         {

examples/foundational/07w-interruptible-fal.py

@@ -47,7 +47,7 @@ async def run_bot(webrtc_connection: SmallWebRTCConnection):
         voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
     )
 
-    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
+    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
 
     messages = [
         {

examples/foundational/07x-interruptible-local.py

@@ -45,7 +45,7 @@ async def main():
         voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
     )
 
-    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
+    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
 
     messages = [
         {

examples/foundational/10-wake-phrase.py

@@ -47,7 +47,7 @@ async def run_bot(webrtc_connection: SmallWebRTCConnection):
         voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
     )
 
-    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
+    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
 
     messages = [
         {

examples/foundational/11-sound-effects.py

@@ -93,7 +93,7 @@ async def run_bot(webrtc_connection: SmallWebRTCConnection):
 
     stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
 
-    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
+    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
 
     tts = CartesiaTTSService(
         api_key=os.getenv("CARTESIA_API_KEY"),

examples/foundational/12b-describe-video-gpt-4o.py

@@ -74,7 +74,7 @@ async def run_bot(webrtc_connection: SmallWebRTCConnection):
     stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
 
     # OpenAI GPT-4o for vision analysis
-    openai = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
+    openai = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
 
     tts = CartesiaTTSService(
         api_key=os.getenv("CARTESIA_API_KEY"),

examples/foundational/14-function-calling.py

@@ -53,7 +53,7 @@ async def run_bot(webrtc_connection: SmallWebRTCConnection):
         voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
     )
 
-    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
+    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
 
     # You can also register a function_name of None to get all functions
     # sent to the same callback with an additional function_name parameter.

examples/foundational/14d-function-calling-video.py

@@ -82,7 +82,7 @@ async def run_bot(webrtc_connection: SmallWebRTCConnection):
         voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
     )
 
-    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
+    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
     llm.register_function("get_weather", get_weather)
     llm.register_function("get_image", get_image)
 

examples/foundational/15-switch-voices.py

@@ -83,7 +83,7 @@ async def run_bot(webrtc_connection: SmallWebRTCConnection):
         voice_id="a0e99841-438c-4a64-b679-ae501e7d6091",  # Barbershop Man
     )
 
-    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
+    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
     llm.register_function("switch_voice", switch_voice)
 
     tools = [

examples/foundational/15a-switch-languages.py

@@ -73,7 +73,7 @@ async def run_bot(webrtc_connection: SmallWebRTCConnection):
         voice_id="d4db5fb9-f44b-4bd1-85fa-192e0f0d75f9",  # Spanish-speaking Lady
     )
 
-    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
+    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
     llm.register_function("switch_language", switch_language)
 
     tools = [

examples/foundational/16-gpu-container-local-bot.py

@@ -6,6 +6,7 @@
 
 import os
 
+import aiohttp
 from dotenv import load_dotenv
 from loguru import logger
 
@@ -39,101 +40,105 @@ async def run_bot(webrtc_connection: SmallWebRTCConnection):
         ),
     )
 
-    stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
+    # Create an HTTP session
+    async with aiohttp.ClientSession() as session:
+        stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
 
-    tts = DeepgramTTSService(
-        api_key=os.getenv("DEEPGRAM_API_KEY"),
-        voice="aura-asteria-en",
-        base_url="http://0.0.0.0:8080",
-    )
+        tts = DeepgramTTSService(
+            aiohttp_session=session,
+            api_key=os.getenv("DEEPGRAM_API_KEY"),
+            voice="aura-asteria-en",
+            base_url="http://0.0.0.0:8080/v1/speak",
+        )
 
-    llm = OpenAILLMService(
-        # To use OpenAI
-        # api_key=os.getenv("OPENAI_API_KEY"),
-        # Or, to use a local vLLM (or similar) api server
-        model="meta-llama/Meta-Llama-3-8B-Instruct",
-        base_url="http://0.0.0.0:8000/v1",
-    )
+        llm = OpenAILLMService(
+            # To use OpenAI
+            # api_key=os.getenv("OPENAI_API_KEY"),
+            # model="gpt-4o"
+            # Or, to use a local vLLM (or similar) api server
+            model="meta-llama/Meta-Llama-3-8B-Instruct",
+            base_url="http://0.0.0.0:8000/v1",
+        )
 
-    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
-            stt,  # STT
-            context_aggregator.user(),
-            llm,  # LLM
-            tts,  # TTS
-            transport.output(),  # Transport bot output
-            context_aggregator.assistant(),
+        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.",
+            },
         ]
-    )
 
-    task = PipelineTask(
-        pipeline,
-        params=PipelineParams(
-            allow_interruptions=True,
-            enable_metrics=True,
-        ),
-    )
+        context = OpenAILLMContext(messages)
+        context_aggregator = llm.create_context_aggregator(context)
 
-    # When the first participant joins, the bot should introduce itself.
-    @transport.event_handler("on_client_connected")
-    async def on_client_connected(transport, client):
-        logger.info(f"Client connected")
-        # 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()])
+        pipeline = Pipeline(
+            [
+                transport.input(),  # Transport user input
+                stt,  # STT
+                context_aggregator.user(),
+                llm,  # LLM
+                tts,  # TTS
+                transport.output(),  # Transport bot output
+                context_aggregator.assistant(),
+            ]
+        )
 
-    # Handle "latency-ping" messages. The client will send app messages that look like
-    # this:
-    #   { "latency-ping": { ts: <client-side timestamp> }}
-    #
-    # We want to send an immediate pong back to the client from this handler function.
-    # Also, we will push a frame into the top of the pipeline and send it after the
-    #
-    @transport.event_handler("on_app_message")
-    async def on_app_message(transport, message, sender):
-        try:
-            if "latency-ping" in message:
-                logger.debug(f"Received latency ping app message: {message}")
-                ts = message["latency-ping"]["ts"]
-                # Send immediately
-                transport.output().send_message(
-                    DailyTransportMessageFrame(
-                        message={"latency-pong-msg-handler": {"ts": ts}}, participant_id=sender
+        task = PipelineTask(
+            pipeline,
+            params=PipelineParams(
+                allow_interruptions=True,
+                enable_metrics=True,
+            ),
+        )
+
+        # When the first participant joins, the bot should introduce itself.
+        @transport.event_handler("on_client_connected")
+        async def on_client_connected(transport, client):
+            logger.info(f"Client connected")
+            # 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()])
+
+        # Handle "latency-ping" messages. The client will send app messages that look like
+        # this:
+        #   { "latency-ping": { ts: <client-side timestamp> }}
+        #
+        # We want to send an immediate pong back to the client from this handler function.
+        # Also, we will push a frame into the top of the pipeline and send it after the
+        #
+        @transport.event_handler("on_app_message")
+        async def on_app_message(transport, message, sender):
+            try:
+                if "latency-ping" in message:
+                    logger.debug(f"Received latency ping app message: {message}")
+                    ts = message["latency-ping"]["ts"]
+                    # Send immediately
+                    transport.output().send_message(
+                        DailyTransportMessageFrame(
+                            message={"latency-pong-msg-handler": {"ts": ts}}, participant_id=sender
+                        )
                     )
-                )
-                # And push to the pipeline for the Daily transport.output to send
-                await task.queue_frame(
-                    DailyTransportMessageFrame(
-                        message={"latency-pong-pipeline-delivery": {"ts": ts}},
-                        participant_id=sender,
+                    # And push to the pipeline for the Daily transport.output to send
+                    await task.queue_frame(
+                        DailyTransportMessageFrame(
+                            message={"latency-pong-pipeline-delivery": {"ts": ts}},
+                            participant_id=sender,
+                        )
                     )
-                )
-        except Exception as e:
-            logger.debug(f"message handling error: {e} - {message}")
+            except Exception as e:
+                logger.debug(f"message handling error: {e} - {message}")
 
-    @transport.event_handler("on_client_disconnected")
-    async def on_client_disconnected(transport, client):
-        logger.info(f"Client disconnected")
+        @transport.event_handler("on_client_disconnected")
+        async def on_client_disconnected(transport, client):
+            logger.info(f"Client disconnected")
 
-    @transport.event_handler("on_client_closed")
-    async def on_client_closed(transport, client):
-        logger.info(f"Client closed connection")
-        await task.cancel()
+        @transport.event_handler("on_client_closed")
+        async def on_client_closed(transport, client):
+            logger.info(f"Client closed connection")
+            await task.cancel()
 
-    runner = PipelineRunner(handle_sigint=False)
+        runner = PipelineRunner(handle_sigint=False)
 
-    await runner.run(task)
+        await runner.run(task)
 
 
 if __name__ == "__main__":

examples/foundational/17-detect-user-idle.py

@@ -47,7 +47,7 @@ async def run_bot(webrtc_connection: SmallWebRTCConnection):
         voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
     )
 
-    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
+    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
 
     messages = [
         {

examples/foundational/20a-persistent-context-openai.py

@@ -185,7 +185,7 @@ async def run_bot(webrtc_connection: SmallWebRTCConnection):
         voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
     )
 
-    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
+    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
 
     # you can either register a single function for all function calls, or specific functions
     # llm.register_function(None, fetch_weather_from_api)

examples/foundational/22-natural-conversation.py

@@ -56,7 +56,7 @@ async def run_bot(webrtc_connection: SmallWebRTCConnection):
     # statement. This doesn't really need to be an LLM, we could use NLP
     # libraries for that, but it was easier as an example because we
     # leverage the context aggregators.
-    statement_llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
+    statement_llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
 
     statement_messages = [
         {
@@ -69,7 +69,7 @@ async def run_bot(webrtc_connection: SmallWebRTCConnection):
     statement_context_aggregator = statement_llm.create_context_aggregator(statement_context)
 
     # This is the regular LLM.
-    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
+    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
 
     messages = [
         {

examples/foundational/22b-natural-conversation-proposal.py

@@ -224,10 +224,10 @@ async def run_bot(webrtc_connection: SmallWebRTCConnection):
     # This is the LLM that will be used to detect if the user has finished a
     # statement. This doesn't really need to be an LLM, we could use NLP
     # libraries for that, but we have the machinery to use an LLM, so we might as well!
-    statement_llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
+    statement_llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
 
     # This is the regular LLM.
-    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
+    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
     # You can also 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)

examples/foundational/22c-natural-conversation-mixed-llms.py

@@ -428,10 +428,16 @@ async def run_bot(webrtc_connection: SmallWebRTCConnection):
     # This is the LLM that will be used to detect if the user has finished a
     # statement. This doesn't really need to be an LLM, we could use NLP
     # libraries for that, but we have the machinery to use an LLM, so we might as well!
-    statement_llm = AnthropicLLMService(api_key=os.getenv("ANTHROPIC_API_KEY"))
+    statement_llm = AnthropicLLMService(
+        api_key=os.getenv("ANTHROPIC_API_KEY"),
+        model="claude-3-5-sonnet-20241022",
+    )
 
     # This is the regular LLM.
-    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
+    llm = OpenAILLMService(
+        api_key=os.getenv("OPENAI_API_KEY"),
+        model="gpt-4o",
+    )
     # 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)

examples/foundational/22d-natural-conversation-gemini-audio.py

@@ -33,10 +33,7 @@ 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 (
-    LLMAssistantAggregatorParams,
-    LLMAssistantResponseAggregator,
-)
+from pipecat.processors.aggregators.llm_response import LLMAssistantResponseAggregator
 from pipecat.processors.aggregators.openai_llm_context import (
     OpenAILLMContext,
     OpenAILLMContextFrame,
@@ -481,7 +478,7 @@ class LLMAggregatorBuffer(LLMAssistantResponseAggregator):
     """Buffers the output of the transcription LLM. Used by the bot output gate."""
 
     def __init__(self, **kwargs):
-        super().__init__(params=LLMAssistantAggregatorParams(expect_stripped_words=False))
+        super().__init__(expect_stripped_words=False)
         self._transcription = ""
 
     async def process_frame(self, frame: Frame, direction: FrameDirection):

examples/foundational/23-bot-background-sound-daily.py

@@ -62,7 +62,7 @@ async def main():
             voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
         )
 
-        llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
+        llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
 
         messages = [
             {

examples/foundational/23-bot-background-sound-p2p.py

@@ -4,13 +4,15 @@
 # SPDX-License-Identifier: BSD 2-Clause License
 #
 
-"""Usage
+"""
+Usage
 -----
 Set the path to your background audio file using the `INPUT_AUDIO_PATH` environment variable, then run the bot using:
 
     INPUT_AUDIO_PATH=path/to/your_audio.mp3 python 23-bot-background-sound.py
 
 Example:
+
     INPUT_AUDIO_PATH=my_audio.mp3 python 23-bot-background-sound.py
 """
 
@@ -69,7 +71,7 @@ async def run_bot(webrtc_connection: SmallWebRTCConnection):
         voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
     )
 
-    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
+    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
 
     messages = [
         {

examples/foundational/24-stt-mute-filter.py

@@ -64,7 +64,7 @@ async def run_bot(webrtc_connection: SmallWebRTCConnection):
 
     tts = DeepgramTTSService(api_key=os.getenv("DEEPGRAM_API_KEY"), voice="aura-helios-en")
 
-    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
+    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
     llm.register_function("get_current_weather", fetch_weather_from_api)
 
     weather_function = FunctionSchema(

examples/foundational/28-transcription-processor.py

@@ -109,7 +109,10 @@ async def run_bot(webrtc_connection: SmallWebRTCConnection):
         voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
     )
 
-    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
+    llm = OpenAILLMService(
+        api_key=os.getenv("OPENAI_API_KEY"),
+        model="gpt-4o",
+    )
 
     messages = [
         {

examples/foundational/29-livekit-audio-chat.py

@@ -127,7 +127,7 @@ async def main():
             ),
         )
 
-        llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
+        llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
 
         tts = CartesiaTTSService(
             api_key=os.getenv("CARTESIA_API_KEY"),

examples/foundational/30-observer.py

@@ -88,7 +88,7 @@ async def run_bot(webrtc_connection: SmallWebRTCConnection):
         voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
     )
 
-    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
+    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
 
     messages = [
         {

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

@@ -120,7 +120,7 @@ async def run_bot(webrtc_connection: SmallWebRTCConnection):
     )
 
     # Initialize LLM
-    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
+    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
 
     # System prompt for storytelling with voice switching
     system_prompt = """You are an engaging storyteller that uses different voices to bring stories to life.

examples/foundational/36-user-email-gathering.py

@@ -63,7 +63,7 @@ async def run_bot(webrtc_connection: SmallWebRTCConnection):
     #     aiohttp_session=session,
     # )
 
-    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
+    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
     # You can aslo register a function_name of None to get all functions
     # sent to the same callback with an additional function_name parameter.
     llm.register_function("store_user_emails", store_user_emails)

examples/foundational/37-mem0.py

@@ -210,6 +210,10 @@ async def run_bot(webrtc_connection: SmallWebRTCConnection):
     @rtvi.event_handler("on_client_ready")
     async def on_client_ready(rtvi):
         await rtvi.set_bot_ready()
+
+    @transport.event_handler("on_client_connected")
+    async def on_client_connected(transport, client):
+        logger.info(f"Client connected")
         # Get personalized greeting based on user memories. Can pass agent_id and run_id as per requirement of the application to manage short term memory or agent specific memory.
         greeting = await get_initial_greeting(
             memory_client=memory.memory_client, user_id=USER_ID, agent_id=None, run_id=None
@@ -221,10 +225,6 @@ async def run_bot(webrtc_connection: SmallWebRTCConnection):
         # Queue the context frame to start the conversation
         await task.queue_frames([context_aggregator.user().get_context_frame()])
 
-    @transport.event_handler("on_client_connected")
-    async def on_client_connected(transport, client):
-        logger.info(f"Client connected")
-
     @transport.event_handler("on_client_disconnected")
     async def on_client_disconnected(transport, client):
         logger.info(f"Client disconnected")

examples/foundational/38-smart-turn.py

@@ -1,111 +0,0 @@
-#
-# Copyright (c) 2024–2025, Daily
-#
-# SPDX-License-Identifier: BSD 2-Clause License
-#
-
-import os
-
-from dotenv import load_dotenv
-from loguru import logger
-
-from pipecat.audio.turn.smart_turn import SmartTurnAnalyzer
-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.cartesia.tts import CartesiaTTSService
-from pipecat.services.deepgram.stt import DeepgramSTTService
-from pipecat.services.openai.llm import OpenAILLMService
-from pipecat.transports.base_transport import TransportParams
-from pipecat.transports.network.small_webrtc import SmallWebRTCTransport
-from pipecat.transports.network.webrtc_connection import SmallWebRTCConnection
-
-load_dotenv(override=True)
-
-
-async def run_bot(webrtc_connection: SmallWebRTCConnection):
-    logger.info(f"Starting bot")
-
-    remote_smart_turn_url = os.getenv("REMOTE_SMART_TURN_URL")
-
-    transport = SmallWebRTCTransport(
-        webrtc_connection=webrtc_connection,
-        params=TransportParams(
-            audio_in_enabled=True,
-            audio_out_enabled=True,
-            vad_enabled=True,
-            vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
-            vad_audio_passthrough=True,
-            turn_analyzer=SmartTurnAnalyzer(url=remote_smart_turn_url),
-        ),
-    )
-
-    stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
-
-    tts = CartesiaTTSService(
-        api_key=os.getenv("CARTESIA_API_KEY"),
-        voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
-    )
-
-    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
-
-    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
-            stt,
-            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_client_connected")
-    async def on_client_connected(transport, client):
-        logger.info(f"Client connected")
-        # 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_client_disconnected")
-    async def on_client_disconnected(transport, client):
-        logger.info(f"Client disconnected")
-
-    @transport.event_handler("on_client_closed")
-    async def on_client_closed(transport, client):
-        logger.info(f"Client closed connection")
-        await task.cancel()
-
-    runner = PipelineRunner(handle_sigint=False)
-
-    await runner.run(task)
-
-
-if __name__ == "__main__":
-    from run import main
-
-    main()

examples/foundational/38a-local-smart-turn.py

@@ -1,129 +0,0 @@
-#
-# Copyright (c) 2024–2025, Daily
-#
-# SPDX-License-Identifier: BSD 2-Clause License
-#
-
-import os
-
-from dotenv import load_dotenv
-from loguru import logger
-
-from pipecat.audio.turn.base_smart_turn import SmartTurnParams
-from pipecat.audio.turn.local_smart_turn import LocalCoreMLSmartTurnAnalyzer
-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.cartesia.tts import CartesiaTTSService
-from pipecat.services.deepgram.stt import DeepgramSTTService
-from pipecat.services.openai.llm import OpenAILLMService
-from pipecat.transports.base_transport import TransportParams
-from pipecat.transports.network.small_webrtc import SmallWebRTCTransport
-from pipecat.transports.network.webrtc_connection import SmallWebRTCConnection
-
-load_dotenv(override=True)
-
-
-async def run_bot(webrtc_connection: SmallWebRTCConnection):
-    logger.info(f"Starting bot")
-
-    # To use this locally, set the environment variable LOCAL_SMART_TURN_MODEL_PATH
-    # to the path where the smart-turn repo is cloned.
-    #
-    # Example setup:
-    #
-    #   # Git LFS (Large File Storage)
-    #   brew install git-lfs
-    #   # Hugging Face uses LFS to store large model files, including .mlpackage
-    #   git lfs install
-    #   # Clone the repo with the smart_turn_classifier.mlpackage
-    #   git clone https://huggingface.co/pipecat-ai/smart-turn
-    #
-    # Then set the env variable:
-    #   export LOCAL_SMART_TURN_MODEL_PATH=./smart-turn
-    # or add it to your .env file
-    smart_turn_model_path = os.getenv("LOCAL_SMART_TURN_MODEL_PATH")
-
-    transport = SmallWebRTCTransport(
-        webrtc_connection=webrtc_connection,
-        params=TransportParams(
-            audio_in_enabled=True,
-            audio_out_enabled=True,
-            vad_enabled=True,
-            vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
-            vad_audio_passthrough=True,
-            turn_analyzer=LocalCoreMLSmartTurnAnalyzer(
-                smart_turn_model_path=smart_turn_model_path, params=SmartTurnParams()
-            ),
-        ),
-    )
-
-    stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
-
-    tts = CartesiaTTSService(
-        api_key=os.getenv("CARTESIA_API_KEY"),
-        voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
-    )
-
-    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
-
-    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
-            stt,
-            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_client_connected")
-    async def on_client_connected(transport, client):
-        logger.info(f"Client connected")
-        # 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_client_disconnected")
-    async def on_client_disconnected(transport, client):
-        logger.info(f"Client disconnected")
-
-    @transport.event_handler("on_client_closed")
-    async def on_client_closed(transport, client):
-        logger.info(f"Client closed connection")
-        await task.cancel()
-
-    runner = PipelineRunner(handle_sigint=False)
-
-    await runner.run(task)
-
-
-if __name__ == "__main__":
-    from run import main
-
-    main()

examples/instant-voice/server/src/single_bot.py

@@ -98,16 +98,14 @@ async def main():
     @rtvi.event_handler("on_client_ready")
     async def on_client_ready(rtvi):
         await rtvi.set_bot_ready()
-        # Kick off the conversation
-        await task.queue_frames([context_aggregator.user().get_context_frame()])
 
     @daily_transport.event_handler("on_first_participant_joined")
     async def on_first_participant_joined(transport, participant):
-        logger.debug("First participant joined: {}", participant["id"])
+        await task.queue_frames([context_aggregator.user().get_context_frame()])
 
     @daily_transport.event_handler("on_participant_left")
     async def on_participant_left(transport, participant, reason):
-        logger.debug(f"Participant left: {participant}")
+        print(f"Participant left: {participant}")
         await task.cancel()
 
     runner = PipelineRunner(handle_sigint=False)

examples/moondream-chatbot/bot.py

@@ -156,7 +156,7 @@ async def main():
             voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
         )
 
-        llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
+        llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
 
         ta = TalkingAnimation()
 

examples/news-chatbot/server/news_bot.py

@@ -148,13 +148,10 @@ async def main():
         @rtvi.event_handler("on_client_ready")
         async def on_client_ready(rtvi):
             await rtvi.set_bot_ready()
-            # Kick off the conversation
-            await task.queue_frames([context_aggregator.user().get_context_frame()])
 
         @transport.event_handler("on_first_participant_joined")
         async def on_first_participant_joined(transport, participant):
-            logger.debug("First participant joined: {}", participant["id"])
-            await transport.capture_participant_transcription(participant["id"])
+            await task.queue_frames([context_aggregator.user().get_context_frame()])
 
         @transport.event_handler("on_participant_left")
         async def on_participant_left(transport, participant, reason):

examples/p2p-webrtc/daily-interop-bridge/README.md

@@ -1,61 +0,0 @@
-# SmallWebRTC and Daily
-
-A Pipecat example demonstrating how to interoperate audio and video between `SmallWebRTCTransport` and `DailyTransport`.
-
-## 🚀 Quick Start
-
-### 1️⃣ Start the Bot Server
-
-#### 🔧 Set Up the Environment
-1. Create and activate a virtual environment:
-   ```bash
-   python3 -m venv venv
-   source venv/bin/activate  # On Windows: venv\Scripts\activate
-   ```
-
-2. Install dependencies:
-   ```bash
-   pip install -r requirements.txt
-   ```
-
-3. Configure environment variables:
-   - Copy `env.example` to `.env`
-   ```bash
-   cp env.example .env
-   ```
-   - Add your API keys
-
-#### ▶️ Run the Server
-```bash
-python server.py
-```
-
-###  1️⃣ Connect the first client using Daily Prebuilt
-
-- Open your browser and navigate to the same URL that you configured inside your `.env` file:
-  - `DAILY_SAMPLE_ROOM_URL`
-
-### 2️⃣ Connect the second client using SmallWebRTC Prebuilt UI
-
-- Open your browser and navigate to:
-👉 http://localhost:7860
-  - (Or use your custom port, if configured)
-
-## ⚠️ Important Note
-Ensure the bot server is running before using any client implementations.
-
-## 📌 Requirements
-
-- Python **3.10+**
-- Node.js **16+** (for JavaScript components)
-- Google API Key
-- Modern web browser with WebRTC support
-
----
-
-### 💡 Notes
-- Ensure all dependencies are installed before running the server.
-- Check the `.env` file for missing configurations.
-- WebRTC requires a secure environment (HTTPS) for full functionality in production.
-
-Happy coding! 🎉

examples/p2p-webrtc/daily-interop-bridge/bot.py

@@ -1,128 +0,0 @@
-#
-# Copyright (c) 2025, Daily
-#
-# SPDX-License-Identifier: BSD 2-Clause License
-#
-import os
-import sys
-
-from dotenv import load_dotenv
-from loguru import logger
-
-from pipecat.frames.frames import (
-    InputAudioRawFrame,
-    InputImageRawFrame,
-    OutputAudioRawFrame,
-    OutputImageRawFrame,
-)
-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.frame_processor import Frame, FrameDirection, FrameProcessor
-from pipecat.transports.base_transport import TransportParams
-from pipecat.transports.network.small_webrtc import SmallWebRTCTransport
-from pipecat.transports.services.daily import DailyParams, DailyTransport
-
-load_dotenv(override=True)
-
-logger.remove(0)
-logger.add(sys.stderr, level="DEBUG")
-
-
-class MirrorProcessor(FrameProcessor):
-    async def process_frame(self, frame: Frame, direction: FrameDirection):
-        await super().process_frame(frame, direction)
-
-        if isinstance(frame, InputAudioRawFrame):
-            await self.push_frame(
-                OutputAudioRawFrame(
-                    audio=frame.audio,
-                    sample_rate=frame.sample_rate,
-                    num_channels=frame.num_channels,
-                )
-            )
-        elif isinstance(frame, InputImageRawFrame):
-            await self.push_frame(
-                OutputImageRawFrame(image=frame.image, size=frame.size, format=frame.format)
-            )
-        else:
-            await self.push_frame(frame, direction)
-
-
-async def run_bot(webrtc_connection):
-    pipecat_transport = SmallWebRTCTransport(
-        webrtc_connection=webrtc_connection,
-        params=TransportParams(
-            camera_in_enabled=True,
-            camera_out_enabled=True,
-            camera_out_is_live=True,
-            audio_in_enabled=True,
-            audio_out_enabled=True,
-            camera_out_width=1280,
-            camera_out_height=720,
-            vad_enabled=False,
-        ),
-    )
-
-    room_url = os.getenv("DAILY_SAMPLE_ROOM_URL", "")
-    daily_transport = DailyTransport(
-        room_url,
-        None,
-        "SmallWebRTC",
-        params=DailyParams(
-            camera_in_enabled=True,
-            camera_out_enabled=True,
-            camera_out_is_live=True,
-            audio_in_enabled=True,
-            audio_out_enabled=True,
-            camera_out_width=1280,
-            camera_out_height=720,
-            vad_enabled=False,
-        ),
-    )
-
-    pipeline = Pipeline(
-        [
-            ParallelPipeline(
-                [
-                    daily_transport.input(),
-                    MirrorProcessor(),
-                    pipecat_transport.output(),
-                ],
-                [
-                    pipecat_transport.input(),
-                    MirrorProcessor(),
-                    daily_transport.output(),
-                ],
-            )
-        ]
-    )
-
-    task = PipelineTask(
-        pipeline,
-        params=PipelineParams(
-            allow_interruptions=False,
-        ),
-    )
-
-    @daily_transport.event_handler("on_participant_joined")
-    async def on_participant_joined(transport, participant):
-        await transport.capture_participant_video(participant["id"])
-
-    @pipecat_transport.event_handler("on_client_connected")
-    async def on_client_connected(transport, client):
-        logger.info("Pipecat Client connected")
-
-    @pipecat_transport.event_handler("on_client_disconnected")
-    async def on_client_disconnected(transport, client):
-        logger.info("Pipecat Client disconnected")
-
-    @pipecat_transport.event_handler("on_client_closed")
-    async def on_client_closed(transport, client):
-        logger.info("Pipecat Client closed")
-        await task.cancel()
-
-    runner = PipelineRunner(handle_sigint=False)
-
-    await runner.run(task)

examples/p2p-webrtc/daily-interop-bridge/env.example

@@ -1,2 +0,0 @@
-DAILY_API_KEY=
-DAILY_SAMPLE_ROOM_URL=

examples/p2p-webrtc/daily-interop-bridge/requirements.txt

@@ -1,5 +0,0 @@
-python-dotenv
-fastapi[all]
-uvicorn
-aiortc
-pipecat-ai[silero, webrtc, daily]

examples/p2p-webrtc/daily-interop-bridge/server.py

@@ -1,89 +0,0 @@
-import argparse
-import asyncio
-import logging
-from contextlib import asynccontextmanager
-from typing import Dict
-
-import uvicorn
-from bot import run_bot
-from dotenv import load_dotenv
-from fastapi import BackgroundTasks, FastAPI
-from fastapi.responses import RedirectResponse
-from pipecat_ai_small_webrtc_prebuilt.frontend import SmallWebRTCPrebuiltUI
-
-from pipecat.transports.network.webrtc_connection import SmallWebRTCConnection
-
-# Load environment variables
-load_dotenv(override=True)
-
-logger = logging.getLogger("pc")
-
-app = FastAPI()
-
-# Store connections by pc_id
-pcs_map: Dict[str, SmallWebRTCConnection] = {}
-
-ice_servers = ["stun:stun.l.google.com:19302"]
-
-# Mount the frontend at /
-app.mount("/prebuilt", SmallWebRTCPrebuiltUI)
-
-
-@app.get("/", include_in_schema=False)
-async def root_redirect():
-    return RedirectResponse(url="/prebuilt/")
-
-
-@app.post("/api/offer")
-async def offer(request: dict, background_tasks: BackgroundTasks):
-    pc_id = request.get("pc_id")
-
-    if pc_id and pc_id in pcs_map:
-        pipecat_connection = pcs_map[pc_id]
-        logger.info(f"Reusing existing connection for pc_id: {pc_id}")
-        await pipecat_connection.renegotiate(
-            sdp=request["sdp"], type=request["type"], restart_pc=request.get("restart_pc", False)
-        )
-    else:
-        pipecat_connection = SmallWebRTCConnection(ice_servers)
-        await pipecat_connection.initialize(sdp=request["sdp"], type=request["type"])
-
-        @pipecat_connection.event_handler("closed")
-        async def handle_disconnected(webrtc_connection: SmallWebRTCConnection):
-            logger.info(f"Discarding peer connection for pc_id: {webrtc_connection.pc_id}")
-            pcs_map.pop(webrtc_connection.pc_id, None)
-
-        background_tasks.add_task(run_bot, pipecat_connection)
-
-    answer = pipecat_connection.get_answer()
-    # Updating the peer connection inside the map
-    pcs_map[answer["pc_id"]] = pipecat_connection
-
-    return answer
-
-
-@asynccontextmanager
-async def lifespan(app: FastAPI):
-    yield  # Run app
-    coros = [pc.close() for pc in pcs_map.values()]
-    await asyncio.gather(*coros)
-    pcs_map.clear()
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser(description="WebRTC demo")
-    parser.add_argument(
-        "--host", default="localhost", help="Host for HTTP server (default: localhost)"
-    )
-    parser.add_argument(
-        "--port", type=int, default=7860, help="Port for HTTP server (default: 7860)"
-    )
-    parser.add_argument("--verbose", "-v", action="count")
-    args = parser.parse_args()
-
-    if args.verbose:
-        logging.basicConfig(level=logging.DEBUG)
-    else:
-        logging.basicConfig(level=logging.INFO)
-
-    uvicorn.run(app, host=args.host, port=args.port)

examples/p2p-webrtc/video-transform/server/bot.py

@@ -135,12 +135,12 @@ async def run_bot(webrtc_connection):
     async def on_client_ready(rtvi):
         logger.info("Pipecat client ready.")
         await rtvi.set_bot_ready()
-        # Kick off the conversation.
-        await task.queue_frames([context_aggregator.user().get_context_frame()])
 
     @pipecat_transport.event_handler("on_client_connected")
     async def on_client_connected(transport, client):
         logger.info("Pipecat Client connected")
+        # Kick off the conversation.
+        await task.queue_frames([context_aggregator.user().get_context_frame()])
 
     @pipecat_transport.event_handler("on_client_disconnected")
     async def on_client_disconnected(transport, client):

examples/p2p-webrtc/voice-agent/index.html

@@ -40,9 +40,7 @@
         const createSmallWebRTCConnection = async (audioTrack) => {
             const pc = new RTCPeerConnection()
             pc.ontrack = e => audioEl.srcObject = e.streams[0]
-            // SmallWebRTCTransport expects to receive both transceivers
             pc.addTransceiver(audioTrack, { direction: 'sendrecv' })
-            pc.addTransceiver('video', { direction: 'sendrecv' })
             await pc.setLocalDescription(await pc.createOffer())
             //await waitForIceGatheringComplete(pc)
             const offer = pc.localDescription

examples/patient-intake/bot.py

@@ -324,7 +324,7 @@ async def main():
         #     voice_id="846d6cb0-2301-48b6-9683-48f5618ea2f6",  # Spanish-speaking Lady
         # )
 
-        llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
+        llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
 
         messages = []
         context = OpenAILLMContext(messages=messages)

examples/phone-chatbot/bot_twilio.py

@@ -60,7 +60,7 @@ async def main(room_url: str, token: str, callId: str, sipUri: str):
         voice_id=os.getenv("ELEVENLABS_VOICE_ID", ""),
     )
 
-    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
+    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
 
     messages = [
         {

examples/phone-chatbot/call_transfer.py

@@ -305,7 +305,7 @@ async def main(
     tools = ToolsSchema(standard_tools=[terminate_call_function, dial_operator_function])
 
     # Initialize LLM
-    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
+    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
 
     # Register functions with the LLM
     llm.register_function(

examples/phone-chatbot/simple_dialin.py

@@ -129,7 +129,7 @@ async def main(
     system_instruction = """You are Chatbot, a friendly, helpful robot. 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, but keep your responses brief. Start by introducing yourself. If the user ends the conversation, **IMMEDIATELY** call the `terminate_call` function. """
 
     # Initialize LLM
-    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
+    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
 
     # Register functions with the LLM
     llm.register_function("terminate_call", terminate_call)

examples/phone-chatbot/simple_dialout.py

@@ -101,7 +101,7 @@ async def main(
     system_instruction = """You are Chatbot, a friendly, helpful robot. 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, but keep your responses brief. Start by introducing yourself. If the user ends the conversation, **IMMEDIATELY** call the `terminate_call` function. """
 
     # Initialize LLM
-    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
+    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
 
     # Register functions with the LLM
     llm.register_function("terminate_call", terminate_call)

examples/sentry-metrics/bot.py

@@ -63,6 +63,7 @@ async def main():
 
         llm = OpenAILLMService(
             api_key=os.getenv("OPENAI_API_KEY"),
+            model="gpt-4o",
             metrics=SentryMetrics(),
         )
 

examples/simple-chatbot/client/react/src/components/DebugDisplay.tsx

@@ -1,13 +1,13 @@
-import { useRef, useCallback } from 'react';
+import { useRef, useCallback } from "react";
 import {
   Participant,
   RTVIEvent,
   TransportState,
   TranscriptData,
   BotLLMTextData,
-} from '@pipecat-ai/client-js';
-import { useRTVIClient, useRTVIClientEvent } from '@pipecat-ai/client-react';
-import './DebugDisplay.css';
+} from "@pipecat-ai/client-js";
+import { useRTVIClient, useRTVIClientEvent } from "@pipecat-ai/client-react";
+import "./DebugDisplay.css";
 
 export function DebugDisplay() {
   const debugLogRef = useRef<HTMLDivElement>(null);
@@ -16,14 +16,14 @@ export function DebugDisplay() {
   const log = useCallback((message: string) => {
     if (!debugLogRef.current) return;
 
-    const entry = document.createElement('div');
+    const entry = document.createElement("div");
     entry.textContent = `${new Date().toISOString()} - ${message}`;
 
     // Add styling based on message type
-    if (message.startsWith('User: ')) {
-      entry.style.color = '#2196F3'; // blue for user
-    } else if (message.startsWith('Bot: ')) {
-      entry.style.color = '#4CAF50'; // green for bot
+    if (message.startsWith("User: ")) {
+      entry.style.color = "#2196F3"; // blue for user
+    } else if (message.startsWith("Bot: ")) {
+      entry.style.color = "#4CAF50"; // green for bot
     }
 
     debugLogRef.current.appendChild(entry);
@@ -68,7 +68,7 @@ export function DebugDisplay() {
     useCallback(
       (track: MediaStreamTrack, participant?: Participant) => {
         log(
-          `Track started: ${track.kind} from ${participant?.name || 'unknown'}`
+          `Track started: ${track.kind} from ${participant?.name || "unknown"}`
         );
       },
       [log]
@@ -76,11 +76,11 @@ export function DebugDisplay() {
   );
 
   useRTVIClientEvent(
-    RTVIEvent.TrackStopped,
+    RTVIEvent.TrackedStopped,
     useCallback(
       (track: MediaStreamTrack, participant?: Participant) => {
         log(
-          `Track stopped: ${track.kind} from ${participant?.name || 'unknown'}`
+          `Track stopped: ${track.kind} from ${participant?.name || "unknown"}`
         );
       },
       [log]
@@ -135,6 +135,16 @@ export function DebugDisplay() {
     )
   );
 
+  useRTVIClientEvent(
+    RTVIEvent.ServerMessage,
+    useCallback(
+      (data: unknown) => {
+        log(`Server Message: ${data}`);
+      },
+      [log]
+    )
+  );
+
   return (
     <div className="debug-panel">
       <h3>Debug Info</h3>

examples/simple-chatbot/server/README.md

@@ -70,17 +70,3 @@ Run the server:
 ```bash
 python server.py
 ```
-
-## Troubleshooting
-
-If you encounred this error:
-
-```bash
-aiohttp.client_exceptions.ClientConnectorCertificateError: Cannot connect to host api.daily.co:443 ssl:True [SSLCertVerificationError: (1, '[SSL: CERTIFICATE_VERIFY_FAILED] certificate verify failed: unable to get local issuer certificate (_ssl.c:1000)')]
-```
-
-It's because Python cannot verify the SSL certificate from https://api.daily.co when making a POST request to create a room or token.
-
-This is a common issue when the system doesn't have the proper CA certificates.
-
-Install SSL Certificates (macOS): `/Applications/Python\ 3.12/Install\ Certificates.command`

examples/simple-chatbot/server/bot-gemini.py

@@ -183,12 +183,11 @@ async def main():
         @rtvi.event_handler("on_client_ready")
         async def on_client_ready(rtvi):
             await rtvi.set_bot_ready()
-            # Kick off the conversation
-            await task.queue_frames([context_aggregator.user().get_context_frame()])
 
         @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()])
 
         @transport.event_handler("on_participant_left")
         async def on_participant_left(transport, participant, reason):

examples/simple-chatbot/server/bot-openai.py

@@ -40,7 +40,12 @@ 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.frame_processor import FrameDirection, FrameProcessor
-from pipecat.processors.frameworks.rtvi import RTVIConfig, RTVIObserver, RTVIProcessor
+from pipecat.processors.frameworks.rtvi import (
+    RTVIConfig,
+    RTVIObserver,
+    RTVIProcessor,
+    RTVIServerMessageFrame,
+)
 from pipecat.services.elevenlabs.tts import ElevenLabsTTSService
 from pipecat.services.openai.llm import OpenAILLMService
 from pipecat.transports.services.daily import DailyParams, DailyTransport
@@ -90,15 +95,22 @@ class TalkingAnimation(FrameProcessor):
         """
         await super().process_frame(frame, direction)
 
+        # Send a custom message to client
+        animation_frame = RTVIServerMessageFrame(
+            data={"type": "animation", "payload": {"is_talking": self._is_talking}}
+        )
+
         # Switch to talking animation when bot starts speaking
         if isinstance(frame, BotStartedSpeakingFrame):
             if not self._is_talking:
                 await self.push_frame(talking_frame)
                 self._is_talking = True
+                await self.push_frame(animation_frame)
         # Return to static frame when bot stops speaking
         elif isinstance(frame, BotStoppedSpeakingFrame):
             await self.push_frame(quiet_frame)
             self._is_talking = False
+            await self.push_frame(animation_frame)
 
         await self.push_frame(frame, direction)
 
@@ -155,7 +167,7 @@ async def main():
         )
 
         # Initialize LLM service
-        llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
+        llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
 
         messages = [
             {
@@ -210,12 +222,11 @@ async def main():
         @rtvi.event_handler("on_client_ready")
         async def on_client_ready(rtvi):
             await rtvi.set_bot_ready()
-            # Kick off the conversation
-            await task.queue_frames([context_aggregator.user().get_context_frame()])
 
         @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()])
 
         @transport.event_handler("on_participant_left")
         async def on_participant_left(transport, participant, reason):

examples/telnyx-chatbot/bot.py

@@ -48,7 +48,7 @@ async def run_bot(
         ),
     )
 
-    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
+    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
 
     stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
 

examples/translation-chatbot/bot.py

@@ -150,7 +150,7 @@ async def main():
         in_language = "English"
         out_language = "Spanish"
 
-        llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
+        llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
         context = OpenAILLMContext()
         context_aggregator = llm.create_context_aggregator(context)
 

examples/twilio-chatbot/bot.py

@@ -68,7 +68,7 @@ async def run_bot(websocket_client: WebSocket, stream_sid: str, testing: bool):
         ),
     )
 
-    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
+    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
 
     stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"), audio_passthrough=True)
 

examples/twilio-chatbot/client.py

@@ -98,7 +98,7 @@ async def run_client(client_name: str, server_url: str, duration_secs: int):
         ),
     )
 
-    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
+    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
 
     # We let the audio passthrough so we can record the conversation.
     stt = DeepgramSTTService(

examples/websocket-server/bot.py

@@ -91,7 +91,7 @@ async def main():
         )
     )
 
-    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
+    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
 
     stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
 

pyproject.toml

@@ -54,7 +54,7 @@ fal = [ "fal-client~=0.5.9" ]
 fireworks = []
 fish = [ "ormsgpack~=1.7.0", "websockets~=13.1" ]
 gladia = [ "websockets~=13.1" ]
-google = [ "google-cloud-speech~=2.31.1", "google-cloud-texttospeech~=2.25.1", "google-genai~=1.7.0", "google-generativeai~=0.8.4", "websockets~=13.1" ]
+google = [ "google-cloud-speech~=2.31.1", "google-cloud-texttospeech~=2.25.1", "google-genai~=1.7.0", "google-generativeai~=0.8.4" ]
 grok = []
 groq = [ "groq~=0.20.0" ]
 gstreamer = [ "pygobject~=3.50.0" ]
@@ -79,8 +79,6 @@ qwen = []
 rime = [ "websockets~=13.1" ]
 riva = [ "nvidia-riva-client~=2.19.0" ]
 sentry = [ "sentry-sdk~=2.23.1" ]
-local-smart-turn = [ "coremltools>=8.0", "transformers", "torch==2.5.0", "torchaudio==2.5.0" ]
-remote-smart-turn = []
 silero = [ "onnxruntime~=1.20.1" ]
 simli = [ "simli-ai~=0.1.10"]
 soundfile = [ "soundfile~=0.13.0" ]

src/pipecat/audio/mixers/soundfile_mixer.py

@@ -38,11 +38,9 @@ class SoundfileMixer(BaseAudioMixer):
 
     def __init__(
         self,
-        *,
         sound_files: Mapping[str, str],
         default_sound: str,
         volume: float = 0.4,
-        mixing: bool = True,
         loop: bool = True,
         **kwargs,
     ):
@@ -54,7 +52,7 @@ class SoundfileMixer(BaseAudioMixer):
         self._sound_pos = 0
         self._sounds: Dict[str, Any] = {}
         self._current_sound = default_sound
-        self._mixing = mixing
+        self._mixing = True
         self._loop = loop
 
     async def start(self, sample_rate: int):

src/pipecat/audio/turn/base_smart_turn.py

@@ -1,168 +0,0 @@
-#
-# Copyright (c) 2024–2025, Daily
-#
-# SPDX-License-Identifier: BSD 2-Clause License
-#
-
-import time
-from abc import abstractmethod
-from typing import Dict, Optional
-
-import numpy as np
-from loguru import logger
-from pydantic import BaseModel
-
-from pipecat.audio.turn.base_turn_analyzer import BaseTurnAnalyzer, EndOfTurnState
-
-# Default timing parameters
-STOP_SECS = 3
-PRE_SPEECH_MS = 0
-MAX_DURATION_SECONDS = 8  # Max allowed segment duration
-USE_ONLY_LAST_VAD_SEGMENT = True
-
-
-class SmartTurnParams(BaseModel):
-    stop_secs: float = STOP_SECS
-    pre_speech_ms: float = PRE_SPEECH_MS
-    max_duration_secs: float = MAX_DURATION_SECONDS
-    # not exposing this for now yet until the model can handle it.
-    # use_only_last_vad_segment: bool = USE_ONLY_LAST_VAD_SEGMENT
-
-
-class BaseSmartTurn(BaseTurnAnalyzer):
-    def __init__(
-        self, *, sample_rate: Optional[int] = None, params: SmartTurnParams = SmartTurnParams()
-    ):
-        super().__init__(sample_rate=sample_rate)
-        self._params = params
-        # Configuration
-        self._stop_ms = self._params.stop_secs * 1000  # silence threshold in ms
-        # Inference state
-        self._audio_buffer = []
-        self._speech_triggered = False
-        self._silence_ms = 0
-        self._speech_start_time = None
-
-    @property
-    def speech_triggered(self) -> bool:
-        return self._speech_triggered
-
-    def append_audio(self, buffer: bytes, is_speech: bool) -> EndOfTurnState:
-        # Convert raw audio to float32 format and append to the buffer
-        audio_int16 = np.frombuffer(buffer, dtype=np.int16)
-        audio_float32 = np.frombuffer(audio_int16, dtype=np.int16).astype(np.float32) / 32768.0
-        self._audio_buffer.append((time.time(), audio_float32))
-
-        state = EndOfTurnState.INCOMPLETE
-
-        if is_speech:
-            # Reset silence tracking on speech
-            self._silence_ms = 0
-            self._speech_triggered = True
-            if self._speech_start_time is None:
-                self._speech_start_time = time.time()
-                logger.debug(f"Speech started at {self._speech_start_time}")
-        else:
-            if self._speech_triggered:
-                chunk_duration_ms = len(audio_int16) / (self._sample_rate / 1000)
-                self._silence_ms += chunk_duration_ms
-                # If silence exceeds threshold, mark end of turn
-                if self._silence_ms >= self._stop_ms:
-                    logger.debug(
-                        f"End of Turn complete due to stop_secs. Silence in ms: {self._silence_ms}"
-                    )
-                    state = EndOfTurnState.COMPLETE
-                    self._clear(state)
-            else:
-                # Trim buffer to prevent unbounded growth before speech
-                max_buffer_time = (
-                    (self._params.pre_speech_ms / 1000)
-                    + self._params.stop_secs
-                    + self._params.max_duration_secs
-                )
-                while (
-                    self._audio_buffer and self._audio_buffer[0][0] < time.time() - max_buffer_time
-                ):
-                    self._audio_buffer.pop(0)
-
-        return state
-
-    def analyze_end_of_turn(self) -> EndOfTurnState:
-        logger.debug("Analyzing End of Turn...")
-        state = self._process_speech_segment(self._audio_buffer)
-        if state == EndOfTurnState.COMPLETE or USE_ONLY_LAST_VAD_SEGMENT:
-            self._clear(state)
-        logger.debug(f"End of Turn result: {state}")
-        return state
-
-    def _clear(self, turn_state: EndOfTurnState):
-        # Reset internal state for next turn
-        logger.debug("Clearing audio buffer...")
-        # If the state is still incomplete, keep the _speech_triggered as True
-        self._speech_triggered = turn_state == EndOfTurnState.INCOMPLETE
-        self._audio_buffer = []
-        self._speech_start_time = None
-        self._silence_ms = 0
-
-    def _process_speech_segment(self, audio_buffer) -> EndOfTurnState:
-        state = EndOfTurnState.INCOMPLETE
-
-        if not audio_buffer:
-            return state
-
-        # Extract recent audio segment for prediction
-        start_time = self._speech_start_time - (self._params.pre_speech_ms / 1000)
-        start_index = 0
-        for i, (t, _) in enumerate(audio_buffer):
-            if t >= start_time:
-                start_index = i
-                break
-
-        end_index = len(audio_buffer) - 1
-
-        # Extract the audio segment
-        segment_audio_chunks = [chunk for _, chunk in audio_buffer[start_index : end_index + 1]]
-        segment_audio = np.concatenate(segment_audio_chunks)
-
-        logger.debug(f"Segment audio chunks after start index: {len(segment_audio)}")
-
-        # Limit maximum duration
-        max_samples = int(self._params.max_duration_secs * self.sample_rate)
-        if len(segment_audio) > max_samples:
-            # slices the array to keep the last max_samples samples, discarding the earlier part.
-            segment_audio = segment_audio[-max_samples:]
-
-        logger.debug(f"Segment audio chunks after limiting duration: {len(segment_audio)}")
-
-        if len(segment_audio) > 0:
-            start_time = time.perf_counter()
-            result = self._predict_endpoint(segment_audio)
-            state = (
-                EndOfTurnState.COMPLETE if result["prediction"] == 1 else EndOfTurnState.INCOMPLETE
-            )
-            end_time = time.perf_counter()
-
-            logger.debug("--------")
-            logger.debug(f"Prediction: {'Complete' if result['prediction'] == 1 else 'Incomplete'}")
-            logger.debug(f"Probability of complete: {result['probability']:.4f}")
-            logger.debug(f"Prediction took {(end_time - start_time) * 1000:.2f}ms seconds")
-        else:
-            logger.debug(f"params: {self._params}, stop_ms: {self._stop_ms}")
-            logger.debug("Captured empty audio segment, skipping prediction.")
-
-        return state
-
-    @abstractmethod
-    def _predict_endpoint(self, buffer: np.ndarray) -> Dict[str, any]:
-        """
-        Abstract method to predict if a turn has ended based on audio.
-
-        Args:
-            buffer: Float32 numpy array of audio samples at 16kHz.
-
-        Returns:
-            Dictionary with:
-              - prediction: 1 if turn is complete, else 0
-              - probability: Confidence of the prediction
-        """
-        pass

src/pipecat/audio/turn/base_turn_analyzer.py

@@ -1,81 +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 BaseTurnAnalyzer(ABC):
-    """
-    Abstract base class for analyzing user end of turn.
-    """
-
-    def __init__(self, *, sample_rate: Optional[int] = None):
-        self._init_sample_rate = sample_rate
-        self._sample_rate = 0
-
-    @property
-    def sample_rate(self) -> int:
-        """
-        Returns the current sample rate.
-
-        Returns:
-            int: The effective sample rate for audio processing.
-        """
-        return self._sample_rate
-
-    def set_sample_rate(self, sample_rate: int):
-        """
-        Sets the sample rate for audio processing.
-
-        If the initial sample rate was provided, it will use that; otherwise, it sets to
-        the provided sample rate.
-
-        Args:
-            sample_rate (int): The sample rate to set.
-        """
-        self._sample_rate = self._init_sample_rate or sample_rate
-
-    @property
-    @abstractmethod
-    def speech_triggered(self) -> bool:
-        """
-        Determines if speech has been detected.
-
-        Returns:
-            bool: True if speech is triggered, otherwise False.
-        """
-        pass
-
-    @abstractmethod
-    def append_audio(self, buffer: bytes, is_speech: bool) -> EndOfTurnState:
-        """
-        Appends audio data for analysis.
-
-        Args:
-            buffer (bytes): The audio data to append.
-            is_speech (bool): Indicates whether the appended audio is speech or not.
-
-        Returns:
-            EndOfTurnState: The resulting state after appending the audio.
-        """
-        pass
-
-    @abstractmethod
-    def analyze_end_of_turn(self) -> EndOfTurnState:
-        """
-        Analyzes if an end of turn has occurred based on the audio input.
-
-        Returns:
-            EndOfTurnState: The result of the end of turn analysis.
-        """
-        pass

src/pipecat/audio/turn/local_smart_turn.py

@@ -1,65 +0,0 @@
-#
-# Copyright (c) 2024–2025, Daily
-#
-# SPDX-License-Identifier: BSD 2-Clause License
-#
-
-
-import os
-from typing import Dict
-
-import numpy as np
-import torch
-from loguru import logger
-
-from pipecat.audio.turn.base_smart_turn import BaseSmartTurn
-
-try:
-    import coremltools as ct
-    from transformers import AutoFeatureExtractor
-except ModuleNotFoundError as e:
-    logger.error(f"Exception: {e}")
-    logger.error(
-        "In order to use the LocalSmartTurnAnalyzer, you need to `pip install pipecat-ai[local-smart-turn]`."
-    )
-    raise Exception(f"Missing module: {e}")
-
-
-class LocalCoreMLSmartTurnAnalyzer(BaseSmartTurn):
-    def __init__(self, smart_turn_model_path: str, **kwargs):
-        super().__init__(**kwargs)
-
-        if not smart_turn_model_path:
-            logger.error("smart_turn_model_path is not set.")
-            raise Exception("smart_turn_model_path must be provided.")
-
-        core_ml_model_path = f"{smart_turn_model_path}/coreml/smart_turn_classifier.mlpackage"
-
-        logger.debug("Loading Local Smart Turn model...")
-        # Only load the processor, not the torch model
-        self._turn_processor = AutoFeatureExtractor.from_pretrained(smart_turn_model_path)
-        self._turn_model = ct.models.MLModel(core_ml_model_path)
-        logger.debug("Loaded Local Smart Turn")
-
-    def _predict_endpoint(self, audio_array: np.ndarray) -> Dict[str, any]:
-        inputs = self._turn_processor(
-            audio_array,
-            sampling_rate=16000,
-            padding="max_length",
-            truncation=True,
-            max_length=800,  # Maximum length as specified in training
-            return_attention_mask=True,
-            return_tensors="pt",
-        )
-
-        output = self._turn_model.predict(dict(inputs))
-        logits = output["logits"]  # Core ML returns numpy array
-        logits_tensor = torch.tensor(logits)
-        probabilities = torch.nn.functional.softmax(logits_tensor, dim=1)
-        completion_prob = probabilities[0, 1].item()  # Probability of class 1 (Complete)
-        prediction = 1 if completion_prob > 0.5 else 0
-
-        return {
-            "prediction": prediction,
-            "probability": completion_prob,
-        }

src/pipecat/audio/turn/smart_turn.py

@@ -1,75 +0,0 @@
-#
-# Copyright (c) 2024–2025, Daily
-#
-# SPDX-License-Identifier: BSD 2-Clause License
-#
-
-
-import io
-import os
-from typing import Dict
-
-import numpy as np
-import requests
-from loguru import logger
-
-from pipecat.audio.turn.base_smart_turn import BaseSmartTurn
-
-
-class SmartTurnAnalyzer(BaseSmartTurn):
-    def __init__(self, url: str, **kwargs):
-        super().__init__(**kwargs)
-        self.remote_smart_turn_url = url
-
-        if not self.remote_smart_turn_url:
-            logger.error("remote_smart_turn_url is not set.")
-            raise Exception("remote_smart_turn_url must be provided.")
-
-        # Use a session to reuse connections (keep-alive)
-        self.session = requests.Session()
-        self.session.headers.update({"Connection": "keep-alive"})
-
-    def _serialize_array(self, audio_array: np.ndarray) -> bytes:
-        logger.trace("Serializing NumPy array to bytes...")
-        buffer = io.BytesIO()
-        np.save(buffer, audio_array)
-        serialized_bytes = buffer.getvalue()
-        logger.trace(f"Serialized size: {len(serialized_bytes)} bytes")
-        return serialized_bytes
-
-    def _send_raw_request(self, data_bytes: bytes):
-        headers = {"Content-Type": "application/octet-stream"}
-        logger.trace(
-            f"Sending {len(data_bytes)} bytes as raw body to {self.remote_smart_turn_url}..."
-        )
-        try:
-            response = self.session.post(
-                self.remote_smart_turn_url,
-                data=data_bytes,
-                headers=headers,
-                timeout=60,
-            )
-
-            logger.trace("\n--- Response ---")
-            logger.trace(f"Status Code: {response.status_code}")
-
-            if response.ok:
-                try:
-                    logger.trace("Response JSON:")
-                    logger.trace(response.json())
-                    return response.json()
-                except requests.exceptions.JSONDecodeError:
-                    logger.trace("Response Content (non-JSON):")
-                    logger.trace(response.text)
-            else:
-                logger.trace("Response Content (Error):")
-                logger.trace(response.text)
-                response.raise_for_status()
-
-        except requests.exceptions.RequestException as e:
-            logger.error(f"Failed to send raw request to Daily Smart Turn: {e}")
-            raise Exception("Failed to send raw request to Daily Smart Turn.")
-
-    def _predict_endpoint(self, audio_array: np.ndarray) -> Dict[str, any]:
-        serialized_array = self._serialize_array(audio_array)
-        return self._send_raw_request(serialized_array)

src/pipecat/processors/aggregators/llm_response.py

@@ -6,7 +6,6 @@
 
 import asyncio
 from abc import abstractmethod
-from dataclasses import dataclass
 from typing import Dict, List, Literal, Set
 
 from loguru import logger
@@ -47,16 +46,6 @@ from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
 from pipecat.utils.time import time_now_iso8601
 
 
-@dataclass
-class LLMUserAggregatorParams:
-    aggregation_timeout: float = 1.0
-
-
-@dataclass
-class LLMAssistantAggregatorParams:
-    expect_stripped_words: bool = True
-
-
 class LLMFullResponseAggregator(FrameProcessor):
     """This is an LLM aggregator that aggregates a full LLM completion. It
     aggregates LLM text frames (tokens) received between
@@ -241,23 +230,11 @@ class LLMUserContextAggregator(LLMContextResponseAggregator):
     def __init__(
         self,
         context: OpenAILLMContext,
-        *,
-        params: LLMUserAggregatorParams = LLMUserAggregatorParams(),
+        aggregation_timeout: float = 1.0,
         **kwargs,
     ):
         super().__init__(context=context, role="user", **kwargs)
-        self._params = params
-        if "aggregation_timeout" in kwargs:
-            import warnings
-
-            with warnings.catch_warnings():
-                warnings.simplefilter("always")
-                warnings.warn(
-                    "Parameter 'aggregation_timeout' is deprecated, use 'params' instead.",
-                    DeprecationWarning,
-                )
-
-            self._params.aggregation_timeout = kwargs["aggregation_timeout"]
+        self._aggregation_timeout = aggregation_timeout
 
         self._seen_interim_results = False
         self._user_speaking = False
@@ -380,9 +357,7 @@ class LLMUserContextAggregator(LLMContextResponseAggregator):
     async def _aggregation_task_handler(self):
         while True:
             try:
-                await asyncio.wait_for(
-                    self._aggregation_event.wait(), self._params.aggregation_timeout
-                )
+                await asyncio.wait_for(self._aggregation_event.wait(), self._aggregation_timeout)
                 await self._maybe_push_bot_interruption()
             except asyncio.TimeoutError:
                 if not self._user_speaking:
@@ -419,27 +394,9 @@ class LLMAssistantContextAggregator(LLMContextResponseAggregator):
 
     """
 
-    def __init__(
-        self,
-        context: OpenAILLMContext,
-        *,
-        params: LLMAssistantAggregatorParams = LLMAssistantAggregatorParams(),
-        **kwargs,
-    ):
+    def __init__(self, context: OpenAILLMContext, *, expect_stripped_words: bool = True, **kwargs):
         super().__init__(context=context, role="assistant", **kwargs)
-        self._params = params
-
-        if "expect_stripped_words" in kwargs:
-            import warnings
-
-            with warnings.catch_warnings():
-                warnings.simplefilter("always")
-                warnings.warn(
-                    "Parameter 'expect_stripped_words' is deprecated, use 'params' instead.",
-                    DeprecationWarning,
-                )
-
-            self._params.expect_stripped_words = kwargs["expect_stripped_words"]
+        self._expect_stripped_words = expect_stripped_words
 
         self._started = 0
         self._function_calls_in_progress: Dict[str, FunctionCallInProgressFrame] = {}
@@ -601,7 +558,7 @@ class LLMAssistantContextAggregator(LLMContextResponseAggregator):
         if not self._started:
             return
 
-        if self._params.expect_stripped_words:
+        if self._expect_stripped_words:
             self._aggregation += f" {frame.text}" if self._aggregation else frame.text
         else:
             self._aggregation += frame.text
@@ -615,14 +572,8 @@ class LLMAssistantContextAggregator(LLMContextResponseAggregator):
 
 
 class LLMUserResponseAggregator(LLMUserContextAggregator):
-    def __init__(
-        self,
-        messages: List[dict] = [],
-        *,
-        params: LLMUserAggregatorParams = LLMUserAggregatorParams(),
-        **kwargs,
-    ):
-        super().__init__(context=OpenAILLMContext(messages), params=params, **kwargs)
+    def __init__(self, messages: List[dict] = [], **kwargs):
+        super().__init__(context=OpenAILLMContext(messages), **kwargs)
 
     async def push_aggregation(self):
         if len(self._aggregation) > 0:
@@ -637,14 +588,8 @@ class LLMUserResponseAggregator(LLMUserContextAggregator):
 
 
 class LLMAssistantResponseAggregator(LLMAssistantContextAggregator):
-    def __init__(
-        self,
-        messages: List[dict] = [],
-        *,
-        params: LLMAssistantAggregatorParams = LLMAssistantAggregatorParams(),
-        **kwargs,
-    ):
-        super().__init__(context=OpenAILLMContext(messages), params=params, **kwargs)
+    def __init__(self, messages: List[dict] = [], **kwargs):
+        super().__init__(context=OpenAILLMContext(messages), **kwargs)
 
     async def push_aggregation(self):
         if len(self._aggregation) > 0:

src/pipecat/py.typed

@@ -1 +0,0 @@
- 

src/pipecat/services/anthropic/llm.py

@@ -11,7 +11,7 @@ import io
 import json
 import re
 from dataclasses import dataclass
-from typing import Any, Dict, List, Optional, Union
+from typing import Any, Dict, List, Mapping, Optional, Union
 
 import httpx
 from loguru import logger
@@ -35,9 +35,7 @@ from pipecat.frames.frames import (
 )
 from pipecat.metrics.metrics import LLMTokenUsage
 from pipecat.processors.aggregators.llm_response import (
-    LLMAssistantAggregatorParams,
     LLMAssistantContextAggregator,
-    LLMUserAggregatorParams,
     LLMUserContextAggregator,
 )
 from pipecat.processors.aggregators.openai_llm_context import (
@@ -51,7 +49,10 @@ try:
     from anthropic import NOT_GIVEN, AsyncAnthropic, NotGiven
 except ModuleNotFoundError as e:
     logger.error(f"Exception: {e}")
-    logger.error("In order to use Anthropic, you need to `pip install pipecat-ai[anthropic]`.")
+    logger.error(
+        "In order to use Anthropic, you need to `pip install pipecat-ai[anthropic]`. "
+        + "Also, set `ANTHROPIC_API_KEY` environment variable."
+    )
     raise Exception(f"Missing module: {e}")
 
 
@@ -119,8 +120,8 @@ class AnthropicLLMService(LLMService):
         self,
         context: OpenAILLMContext,
         *,
-        user_params: LLMUserAggregatorParams = LLMUserAggregatorParams(),
-        assistant_params: LLMAssistantAggregatorParams = LLMAssistantAggregatorParams(),
+        user_kwargs: Mapping[str, Any] = {},
+        assistant_kwargs: Mapping[str, Any] = {},
     ) -> AnthropicContextAggregatorPair:
         """Create an instance of AnthropicContextAggregatorPair from an
         OpenAILLMContext. Constructor keyword arguments for both the user and
@@ -128,10 +129,12 @@ class AnthropicLLMService(LLMService):
 
         Args:
             context (OpenAILLMContext): The LLM context.
-            user_params (LLMUserAggregatorParams, optional): User aggregator
-                parameters.
-            assistant_params (LLMAssistantAggregatorParams, optional): User
-                aggregator parameters.
+            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
@@ -143,8 +146,8 @@ class AnthropicLLMService(LLMService):
 
         if isinstance(context, OpenAILLMContext):
             context = AnthropicLLMContext.from_openai_context(context)
-        user = AnthropicUserContextAggregator(context, params=user_params)
-        assistant = AnthropicAssistantContextAggregator(context, params=assistant_params)
+        user = AnthropicUserContextAggregator(context, **user_kwargs)
+        assistant = AnthropicAssistantContextAggregator(context, **assistant_kwargs)
         return AnthropicContextAggregatorPair(_user=user, _assistant=assistant)
 
     async def _process_context(self, context: OpenAILLMContext):

src/pipecat/services/aws/tts.py

@@ -231,9 +231,9 @@ class PollyTTSService(TTSService):
 
             yield TTSStartedFrame()
 
-            CHUNK_SIZE = 1024
-            for i in range(0, len(audio_data), CHUNK_SIZE):
-                chunk = audio_data[i : i + CHUNK_SIZE]
+            chunk_size = 8192
+            for i in range(0, len(audio_data), chunk_size):
+                chunk = audio_data[i : i + chunk_size]
                 if len(chunk) > 0:
                     await self.stop_ttfb_metrics()
                     frame = TTSAudioRawFrame(chunk, self.sample_rate, 1)