Merge branch 'main' into smart_turn

CHANGELOG.md

@@ -5,11 +5,22 @@ 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
 
+- `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.
 
@@ -21,13 +32,37 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 
 ### 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 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

examples/canonical-metrics/bot.py

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

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"), model="gpt-4o")
+    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
 
     messages = [
         {

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"), model="gpt-4o")
+    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
 
     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"), model="gpt-4o")
+        llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
 
         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"), model="gpt-4o")
+            llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
 
             tts = CartesiaHttpTTSService(
                 api_key=os.getenv("CARTESIA_API_KEY"),

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

@@ -73,7 +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"), model="gpt-4o")
+    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
 
     ml = MetricsLogger()
 

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"), model="gpt-4o")
+    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
 
     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"), model="gpt-4o")
+    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
 
     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"), model="gpt-4o")
+    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
 
     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-4o", temperature=0.7)
+    chain = prompt | ChatOpenAI(model="gpt-4.1", 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"), model="gpt-4o")
+    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
 
     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"), model="gpt-4o")
+    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
 
     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"), model="gpt-4o")
+        llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
 
         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"), model="gpt-4o")
+    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
 
     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"), model="gpt-4o")
+    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
 
     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"), model="gpt-4o")
+    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
 
     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"), model="gpt-4o")
+    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
 
     messages = [
         {

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

@@ -50,7 +50,6 @@ 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"), model="gpt-4o")
+        llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
 
         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", ""), model="gpt-4o")
+    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY", ""))
 
     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"), model="gpt-4o")
+    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
 
     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"), model="gpt-4o")
+    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
 
     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"), model="gpt-4o")
+    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
 
     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"), model="gpt-4o")
+    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
 
     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"), model="gpt-4o")
+        llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
 
         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"), model="gpt-4o")
+    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
 
     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"), model="gpt-4o")
+    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
 
     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"), model="gpt-4o")
+    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
 
     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"), model="gpt-4o")
+    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
 
     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"), model="gpt-4o")
+    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
 
     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"), model="gpt-4o")
+    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
 
     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"), model="gpt-4o")
+    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
 
     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"), model="gpt-4o")
+    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
 
     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"), model="gpt-4o")
+    openai = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
 
     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"), model="gpt-4o")
+    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
 
     # 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"), model="gpt-4o")
+    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
     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"), model="gpt-4o")
+    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
     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"), model="gpt-4o")
+    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
     llm.register_function("switch_language", switch_language)
 
     tools = [

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

@@ -6,7 +6,6 @@
 
 import os
 
-import aiohttp
 from dotenv import load_dotenv
 from loguru import logger
 
@@ -40,105 +39,101 @@ async def run_bot(webrtc_connection: SmallWebRTCConnection):
         ),
     )
 
-    # Create an HTTP session
-    async with aiohttp.ClientSession() as session:
-        stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
+    stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
 
-        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",
-        )
+    tts = DeepgramTTSService(
+        api_key=os.getenv("DEEPGRAM_API_KEY"),
+        voice="aura-asteria-en",
+        base_url="http://0.0.0.0:8080",
+    )
 
-        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",
-        )
+    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",
+    )
 
-        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.",
-            },
+    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(),
         ]
+    )
 
-        context = OpenAILLMContext(messages)
-        context_aggregator = llm.create_context_aggregator(context)
+    task = PipelineTask(
+        pipeline,
+        params=PipelineParams(
+            allow_interruptions=True,
+            enable_metrics=True,
+        ),
+    )
 
-        pipeline = Pipeline(
-            [
-                transport.input(),  # Transport user input
-                stt,  # STT
-                context_aggregator.user(),
-                llm,  # LLM
-                tts,  # TTS
-                transport.output(),  # Transport bot output
-                context_aggregator.assistant(),
-            ]
-        )
+    # 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()])
 
-        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
-                        )
+    # 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"), model="gpt-4o")
+    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
 
     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"), model="gpt-4o")
+    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
 
     # 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"), model="gpt-4o")
+    statement_llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
 
     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"), model="gpt-4o")
+    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
 
     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"), model="gpt-4o")
+    statement_llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
 
     # This is the regular LLM.
-    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
+    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
     # 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

@@ -428,16 +428,10 @@ 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 = AnthropicLLMService(
-        api_key=os.getenv("ANTHROPIC_API_KEY"),
-        model="claude-3-5-sonnet-20241022",
-    )
+    statement_llm = AnthropicLLMService(api_key=os.getenv("ANTHROPIC_API_KEY"))
 
     # This is the regular LLM.
-    llm = OpenAILLMService(
-        api_key=os.getenv("OPENAI_API_KEY"),
-        model="gpt-4o",
-    )
+    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
     # Register a function_name of None to get all functions
     # sent to the same callback with an additional function_name parameter.
     llm.register_function("get_current_weather", fetch_weather_from_api)

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

@@ -33,7 +33,10 @@ from pipecat.pipeline.parallel_pipeline import ParallelPipeline
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.aggregators.llm_response import LLMAssistantResponseAggregator
+from pipecat.processors.aggregators.llm_response import (
+    LLMAssistantAggregatorParams,
+    LLMAssistantResponseAggregator,
+)
 from pipecat.processors.aggregators.openai_llm_context import (
     OpenAILLMContext,
     OpenAILLMContextFrame,
@@ -478,7 +481,7 @@ class LLMAggregatorBuffer(LLMAssistantResponseAggregator):
     """Buffers the output of the transcription LLM. Used by the bot output gate."""
 
     def __init__(self, **kwargs):
-        super().__init__(expect_stripped_words=False)
+        super().__init__(params=LLMAssistantAggregatorParams(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"), model="gpt-4o")
+        llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
 
         messages = [
             {

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

@@ -4,15 +4,13 @@
 # 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
 """
 
@@ -71,7 +69,7 @@ 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"), model="gpt-4o")
+    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
 
     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"), model="gpt-4o")
+    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
     llm.register_function("get_current_weather", fetch_weather_from_api)
 
     weather_function = FunctionSchema(

examples/foundational/28-transcription-processor.py

@@ -109,10 +109,7 @@ 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"),
-        model="gpt-4o",
-    )
+    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
 
     messages = [
         {

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

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

@@ -210,10 +210,6 @@ 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
@@ -225,6 +221,10 @@ 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/instant-voice/server/src/single_bot.py

@@ -98,14 +98,16 @@ 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):
-        await task.queue_frames([context_aggregator.user().get_context_frame()])
+        logger.debug("First participant joined: {}", participant["id"])
 
     @daily_transport.event_handler("on_participant_left")
     async def on_participant_left(transport, participant, reason):
-        print(f"Participant left: {participant}")
+        logger.debug(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"), model="gpt-4o")
+        llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
 
         ta = TalkingAnimation()
 

examples/news-chatbot/server/news_bot.py

@@ -148,10 +148,13 @@ 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 task.queue_frames([context_aggregator.user().get_context_frame()])
+            logger.debug("First participant joined: {}", participant["id"])
+            await transport.capture_participant_transcription(participant["id"])
 
         @transport.event_handler("on_participant_left")
         async def on_participant_left(transport, participant, reason):

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

@@ -0,0 +1,61 @@
+# 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

@@ -0,0 +1,128 @@
+#
+# 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

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

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

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

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

@@ -0,0 +1,89 @@
+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/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"), model="gpt-4o")
+        llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
 
         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"), model="gpt-4o")
+    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
 
     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"), model="gpt-4o")
+    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
 
     # 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"), model="gpt-4o")
+    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
 
     # 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"), model="gpt-4o")
+    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
 
     # Register functions with the LLM
     llm.register_function("terminate_call", terminate_call)

examples/sentry-metrics/bot.py

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

examples/simple-chatbot/server/README.md

@@ -70,3 +70,17 @@ 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

@@ -183,11 +183,12 @@ 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

@@ -155,7 +155,7 @@ async def main():
         )
 
         # Initialize LLM service
-        llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
+        llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
 
         messages = [
             {
@@ -210,11 +210,12 @@ 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"), model="gpt-4o")
+    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
 
     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"), model="gpt-4o")
+        llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
         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"), model="gpt-4o")
+    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
 
     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"), model="gpt-4o")
+    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
 
     # 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"), model="gpt-4o")
+    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
 
     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" ]
+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" ]
 grok = []
 groq = [ "groq~=0.20.0" ]
 gstreamer = [ "pygobject~=3.50.0" ]

src/pipecat/processors/aggregators/llm_response.py

@@ -6,6 +6,7 @@
 
 import asyncio
 from abc import abstractmethod
+from dataclasses import dataclass
 from typing import Dict, List, Literal, Set
 
 from loguru import logger
@@ -46,6 +47,16 @@ 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
@@ -230,11 +241,23 @@ class LLMUserContextAggregator(LLMContextResponseAggregator):
     def __init__(
         self,
         context: OpenAILLMContext,
-        aggregation_timeout: float = 1.0,
+        *,
+        params: LLMUserAggregatorParams = LLMUserAggregatorParams(),
         **kwargs,
     ):
         super().__init__(context=context, role="user", **kwargs)
-        self._aggregation_timeout = aggregation_timeout
+        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._seen_interim_results = False
         self._user_speaking = False
@@ -357,7 +380,9 @@ class LLMUserContextAggregator(LLMContextResponseAggregator):
     async def _aggregation_task_handler(self):
         while True:
             try:
-                await asyncio.wait_for(self._aggregation_event.wait(), self._aggregation_timeout)
+                await asyncio.wait_for(
+                    self._aggregation_event.wait(), self._params.aggregation_timeout
+                )
                 await self._maybe_push_bot_interruption()
             except asyncio.TimeoutError:
                 if not self._user_speaking:
@@ -394,9 +419,27 @@ class LLMAssistantContextAggregator(LLMContextResponseAggregator):
 
     """
 
-    def __init__(self, context: OpenAILLMContext, *, expect_stripped_words: bool = True, **kwargs):
+    def __init__(
+        self,
+        context: OpenAILLMContext,
+        *,
+        params: LLMAssistantAggregatorParams = LLMAssistantAggregatorParams(),
+        **kwargs,
+    ):
         super().__init__(context=context, role="assistant", **kwargs)
-        self._expect_stripped_words = expect_stripped_words
+        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._started = 0
         self._function_calls_in_progress: Dict[str, FunctionCallInProgressFrame] = {}
@@ -558,7 +601,7 @@ class LLMAssistantContextAggregator(LLMContextResponseAggregator):
         if not self._started:
             return
 
-        if self._expect_stripped_words:
+        if self._params.expect_stripped_words:
             self._aggregation += f" {frame.text}" if self._aggregation else frame.text
         else:
             self._aggregation += frame.text
@@ -572,8 +615,14 @@ class LLMAssistantContextAggregator(LLMContextResponseAggregator):
 
 
 class LLMUserResponseAggregator(LLMUserContextAggregator):
-    def __init__(self, messages: List[dict] = [], **kwargs):
-        super().__init__(context=OpenAILLMContext(messages), **kwargs)
+    def __init__(
+        self,
+        messages: List[dict] = [],
+        *,
+        params: LLMUserAggregatorParams = LLMUserAggregatorParams(),
+        **kwargs,
+    ):
+        super().__init__(context=OpenAILLMContext(messages), params=params, **kwargs)
 
     async def push_aggregation(self):
         if len(self._aggregation) > 0:
@@ -588,8 +637,14 @@ class LLMUserResponseAggregator(LLMUserContextAggregator):
 
 
 class LLMAssistantResponseAggregator(LLMAssistantContextAggregator):
-    def __init__(self, messages: List[dict] = [], **kwargs):
-        super().__init__(context=OpenAILLMContext(messages), **kwargs)
+    def __init__(
+        self,
+        messages: List[dict] = [],
+        *,
+        params: LLMAssistantAggregatorParams = LLMAssistantAggregatorParams(),
+        **kwargs,
+    ):
+        super().__init__(context=OpenAILLMContext(messages), params=params, **kwargs)
 
     async def push_aggregation(self):
         if len(self._aggregation) > 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, Mapping, Optional, Union
+from typing import Any, Dict, List, Optional, Union
 
 import httpx
 from loguru import logger
@@ -35,7 +35,9 @@ 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 (
@@ -49,10 +51,7 @@ 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]`. "
-        + "Also, set `ANTHROPIC_API_KEY` environment variable."
-    )
+    logger.error("In order to use Anthropic, you need to `pip install pipecat-ai[anthropic]`.")
     raise Exception(f"Missing module: {e}")
 
 
@@ -120,8 +119,8 @@ class AnthropicLLMService(LLMService):
         self,
         context: OpenAILLMContext,
         *,
-        user_kwargs: Mapping[str, Any] = {},
-        assistant_kwargs: Mapping[str, Any] = {},
+        user_params: LLMUserAggregatorParams = LLMUserAggregatorParams(),
+        assistant_params: LLMAssistantAggregatorParams = LLMAssistantAggregatorParams(),
     ) -> AnthropicContextAggregatorPair:
         """Create an instance of AnthropicContextAggregatorPair from an
         OpenAILLMContext. Constructor keyword arguments for both the user and
@@ -129,12 +128,10 @@ class AnthropicLLMService(LLMService):
 
         Args:
             context (OpenAILLMContext): The LLM context.
-            user_kwargs (Mapping[str, Any], optional): Additional keyword
-                arguments for the user context aggregator constructor. Defaults
-                to an empty mapping.
-            assistant_kwargs (Mapping[str, Any], optional): Additional keyword
-                arguments for the assistant context aggregator
-                constructor. Defaults to an empty mapping.
+            user_params (LLMUserAggregatorParams, optional): User aggregator
+                parameters.
+            assistant_params (LLMAssistantAggregatorParams, optional): User
+                aggregator parameters.
 
         Returns:
             AnthropicContextAggregatorPair: A pair of context aggregators, one
@@ -146,8 +143,8 @@ class AnthropicLLMService(LLMService):
 
         if isinstance(context, OpenAILLMContext):
             context = AnthropicLLMContext.from_openai_context(context)
-        user = AnthropicUserContextAggregator(context, **user_kwargs)
-        assistant = AnthropicAssistantContextAggregator(context, **assistant_kwargs)
+        user = AnthropicUserContextAggregator(context, params=user_params)
+        assistant = AnthropicAssistantContextAggregator(context, params=assistant_params)
         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 = 8192
-            for i in range(0, len(audio_data), chunk_size):
-                chunk = audio_data[i : i + chunk_size]
+            CHUNK_SIZE = 1024
+            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)

src/pipecat/services/deepgram/stt.py

@@ -45,6 +45,7 @@ class DeepgramSTTService(STTService):
         *,
         api_key: str,
         url: str = "",
+        base_url: str = "",
         sample_rate: Optional[int] = None,
         live_options: Optional[LiveOptions] = None,
         addons: Optional[Dict] = None,
@@ -53,6 +54,17 @@ class DeepgramSTTService(STTService):
         sample_rate = sample_rate or (live_options.sample_rate if live_options else None)
         super().__init__(sample_rate=sample_rate, **kwargs)
 
+        if url:
+            import warnings
+
+            with warnings.catch_warnings():
+                warnings.simplefilter("always")
+                warnings.warn(
+                    "Parameter 'url' is deprecated, use 'base_url' instead.",
+                    DeprecationWarning,
+                )
+            base_url = url
+
         default_options = LiveOptions(
             encoding="linear16",
             language=Language.EN,
@@ -81,7 +93,7 @@ class DeepgramSTTService(STTService):
         self._client = DeepgramClient(
             api_key,
             config=DeepgramClientOptions(
-                url=url,
+                url=base_url,
                 options={"keepalive": "true"},  # verbose=logging.DEBUG
             ),
         )

src/pipecat/services/deepgram/tts.py

@@ -4,7 +4,6 @@
 # SPDX-License-Identifier: BSD 2-Clause License
 #
 
-import asyncio
 from typing import AsyncGenerator, Optional
 
 from loguru import logger
@@ -19,7 +18,7 @@ from pipecat.frames.frames import (
 from pipecat.services.tts_service import TTSService
 
 try:
-    from deepgram import DeepgramClient, SpeakOptions
+    from deepgram import DeepgramClient, DeepgramClientOptions, SpeakOptions
 except ModuleNotFoundError as e:
     logger.error(f"Exception: {e}")
     logger.error("In order to use Deepgram, you need to `pip install pipecat-ai[deepgram]`.")
@@ -32,6 +31,7 @@ class DeepgramTTSService(TTSService):
         *,
         api_key: str,
         voice: str = "aura-helios-en",
+        base_url: str = "",
         sample_rate: Optional[int] = None,
         encoding: str = "linear16",
         **kwargs,
@@ -42,7 +42,9 @@ class DeepgramTTSService(TTSService):
             "encoding": encoding,
         }
         self.set_voice(voice)
-        self._deepgram_client = DeepgramClient(api_key=api_key)
+
+        client_options = DeepgramClientOptions(url=base_url)
+        self._deepgram_client = DeepgramClient(api_key, config=client_options)
 
     def can_generate_metrics(self) -> bool:
         return True
@@ -60,8 +62,8 @@ class DeepgramTTSService(TTSService):
         try:
             await self.start_ttfb_metrics()
 
-            response = await asyncio.to_thread(
-                self._deepgram_client.speak.v("1").stream, {"text": text}, options
+            response = await self._deepgram_client.speak.asyncrest.v("1").stream_memory(
+                {"text": text}, options
             )
 
             await self.start_tts_usage_metrics(text)

src/pipecat/services/elevenlabs/tts.py

@@ -18,6 +18,7 @@ from pipecat.frames.frames import (
     EndFrame,
     ErrorFrame,
     Frame,
+    LLMFullResponseEndFrame,
     StartFrame,
     StartInterruptionFrame,
     TTSAudioRawFrame,
@@ -25,7 +26,7 @@ from pipecat.frames.frames import (
     TTSStoppedFrame,
 )
 from pipecat.processors.frame_processor import FrameDirection
-from pipecat.services.tts_service import InterruptibleWordTTSService, TTSService
+from pipecat.services.tts_service import InterruptibleWordTTSService, WordTTSService
 from pipecat.transcriptions.language import Language
 
 # See .env.example for ElevenLabs configuration needed
@@ -441,8 +442,8 @@ class ElevenLabsTTSService(InterruptibleWordTTSService):
             logger.error(f"{self} exception: {e}")
 
 
-class ElevenLabsHttpTTSService(TTSService):
-    """ElevenLabs Text-to-Speech service using HTTP streaming.
+class ElevenLabsHttpTTSService(WordTTSService):
+    """ElevenLabs Text-to-Speech service using HTTP streaming with word timestamps.
 
     Args:
         api_key: ElevenLabs API key
@@ -475,7 +476,13 @@ class ElevenLabsHttpTTSService(TTSService):
         params: InputParams = InputParams(),
         **kwargs,
     ):
-        super().__init__(sample_rate=sample_rate, **kwargs)
+        super().__init__(
+            aggregate_sentences=True,
+            push_text_frames=False,
+            push_stop_frames=True,
+            sample_rate=sample_rate,
+            **kwargs,
+        )
 
         self._api_key = api_key
         self._base_url = base_url
@@ -498,34 +505,136 @@ class ElevenLabsHttpTTSService(TTSService):
         self._output_format = ""  # initialized in start()
         self._voice_settings = self._set_voice_settings()
 
+        # Track cumulative time to properly sequence word timestamps across utterances
+        self._cumulative_time = 0
+        self._started = False
+
+        # Store previous text for context within a turn
+        self._previous_text = ""
+
+    def language_to_service_language(self, language: Language) -> Optional[str]:
+        """Convert pipecat Language to ElevenLabs language code."""
+        return language_to_elevenlabs_language(language)
+
     def can_generate_metrics(self) -> bool:
+        """Indicate that this service can generate usage metrics."""
         return True
 
     def _set_voice_settings(self):
         return build_elevenlabs_voice_settings(self._settings)
 
+    def _reset_state(self):
+        """Reset internal state variables."""
+        self._cumulative_time = 0
+        self._started = False
+        self._previous_text = ""
+        logger.debug(f"{self}: Reset internal state")
+
     async def start(self, frame: StartFrame):
+        """Initialize the service upon receiving a StartFrame."""
         await super().start(frame)
         self._output_format = output_format_from_sample_rate(self.sample_rate)
+        self._reset_state()
 
-    async def run_tts(self, text: str) -> AsyncGenerator[Frame, None]:
-        """Generate speech from text using ElevenLabs streaming API.
+    async def push_frame(self, frame: Frame, direction: FrameDirection = FrameDirection.DOWNSTREAM):
+        await super().push_frame(frame, direction)
+        if isinstance(frame, (StartInterruptionFrame, TTSStoppedFrame)):
+            # Reset timing on interruption or stop
+            self._reset_state()
+
+            if isinstance(frame, TTSStoppedFrame):
+                await self.add_word_timestamps([("LLMFullResponseEndFrame", 0), ("Reset", 0)])
+
+        elif isinstance(frame, LLMFullResponseEndFrame):
+            # End of turn - reset previous text
+            self._previous_text = ""
+
+    def calculate_word_times(self, alignment_info: Mapping[str, Any]) -> List[Tuple[str, float]]:
+        """Calculate word timing from character alignment data.
+
+        Example input data:
+        {
+            "characters": [" ", "H", "e", "l", "l", "o", " ", "w", "o", "r", "l", "d"],
+            "character_start_times_seconds": [0.0, 0.1, 0.15, 0.2, 0.25, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9],
+            "character_end_times_seconds": [0.1, 0.15, 0.2, 0.25, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0]
+        }
+
+        Would produce word times (with cumulative_time=0):
+        [("Hello", 0.1), ("world", 0.5)]
 
         Args:
-            text: The text to convert to speech
+            alignment_info: Character timing data from ElevenLabs
+
+        Returns:
+            List of (word, timestamp) pairs
+        """
+        chars = alignment_info.get("characters", [])
+        char_start_times = alignment_info.get("character_start_times_seconds", [])
+
+        if not chars or not char_start_times or len(chars) != len(char_start_times):
+            logger.warning(
+                f"Invalid alignment data: chars={len(chars)}, times={len(char_start_times)}"
+            )
+            return []
+
+        # Build the words and find their start times
+        words = []
+        word_start_times = []
+        current_word = ""
+        first_char_idx = -1
+
+        for i, char in enumerate(chars):
+            if char == " ":
+                if current_word:  # Only add non-empty words
+                    words.append(current_word)
+                    # Use time of the first character of the word, offset by cumulative time
+                    word_start_times.append(
+                        self._cumulative_time + char_start_times[first_char_idx]
+                    )
+                    current_word = ""
+                    first_char_idx = -1
+            else:
+                if not current_word:  # This is the first character of a new word
+                    first_char_idx = i
+                current_word += char
+
+        # Don't forget the last word if there's no trailing space
+        if current_word and first_char_idx >= 0:
+            words.append(current_word)
+            word_start_times.append(self._cumulative_time + char_start_times[first_char_idx])
+
+        # Create word-time pairs
+        word_times = list(zip(words, word_start_times))
+
+        return word_times
+
+    async def run_tts(self, text: str) -> AsyncGenerator[Frame, None]:
+        """Generate speech from text using ElevenLabs streaming API with timestamps.
+
+        Makes a request to the ElevenLabs API to generate audio and timing data.
+        Tracks the duration of each utterance to ensure correct sequencing.
+        Includes previous text as context for better prosody continuity.
+
+        Args:
+            text: Text to convert to speech
 
         Yields:
-            Frames containing audio data and status information
+            Audio and control frames
         """
         logger.debug(f"{self}: Generating TTS [{text}]")
 
-        url = f"{self._base_url}/v1/text-to-speech/{self._voice_id}/stream"
+        # Use the with-timestamps endpoint
+        url = f"{self._base_url}/v1/text-to-speech/{self._voice_id}/stream/with-timestamps"
 
         payload: Dict[str, Union[str, Dict[str, Union[float, bool]]]] = {
             "text": text,
             "model_id": self._model_name,
         }
 
+        # Include previous text as context if available
+        if self._previous_text:
+            payload["previous_text"] = self._previous_text
+
         if self._voice_settings:
             payload["voice_settings"] = self._voice_settings
 
@@ -550,8 +659,6 @@ class ElevenLabsHttpTTSService(TTSService):
         if self._settings["optimize_streaming_latency"] is not None:
             params["optimize_streaming_latency"] = self._settings["optimize_streaming_latency"]
 
-        logger.debug(f"ElevenLabs request - payload: {payload}, params: {params}")
-
         try:
             await self.start_ttfb_metrics()
 
@@ -566,17 +673,66 @@ class ElevenLabsHttpTTSService(TTSService):
 
                 await self.start_tts_usage_metrics(text)
 
-                # Process the streaming response
-                CHUNK_SIZE = 1024
+                # Start TTS sequence if not already started
+                if not self._started:
+                    self.start_word_timestamps()
+                    yield TTSStartedFrame()
+                    self._started = True
+
+                # Track the duration of this utterance based on the last character's end time
+                utterance_duration = 0
+                async for line in response.content:
+                    line_str = line.decode("utf-8").strip()
+                    if not line_str:
+                        continue
+
+                    try:
+                        # Parse the JSON object
+                        data = json.loads(line_str)
+
+                        # Process audio if present
+                        if data and "audio_base64" in data:
+                            await self.stop_ttfb_metrics()
+                            audio = base64.b64decode(data["audio_base64"])
+                            yield TTSAudioRawFrame(audio, self.sample_rate, 1)
+
+                        # Process alignment if present
+                        if data and "alignment" in data:
+                            alignment = data["alignment"]
+                            if alignment:  # Ensure alignment is not None
+                                # Get end time of the last character in this chunk
+                                char_end_times = alignment.get("character_end_times_seconds", [])
+                                if char_end_times:
+                                    chunk_end_time = char_end_times[-1]
+                                    # Update to the longest end time seen so far
+                                    utterance_duration = max(utterance_duration, chunk_end_time)
+
+                                # Calculate word timestamps
+                                word_times = self.calculate_word_times(alignment)
+                                if word_times:
+                                    await self.add_word_timestamps(word_times)
+                    except json.JSONDecodeError as e:
+                        logger.warning(f"Failed to parse JSON from stream: {e}")
+                        continue
+                    except Exception as e:
+                        logger.error(f"Error processing response: {e}", exc_info=True)
+                        continue
+
+                # After processing all chunks, add the total utterance duration
+                # to the cumulative time to ensure next utterance starts after this one
+                if utterance_duration > 0:
+                    self._cumulative_time += utterance_duration
+
+                # Append the current text to previous_text for context continuity
+                # Only add a space if there's already text
+                if self._previous_text:
+                    self._previous_text += " " + text
+                else:
+                    self._previous_text = text
 
-                yield TTSStartedFrame()
-                async for chunk in response.content.iter_chunked(CHUNK_SIZE):
-                    if len(chunk) > 0:
-                        await self.stop_ttfb_metrics()
-                        yield TTSAudioRawFrame(chunk, self.sample_rate, 1)
         except Exception as e:
             logger.error(f"Error in run_tts: {e}")
             yield ErrorFrame(error=str(e))
         finally:
             await self.stop_ttfb_metrics()
-            yield TTSStoppedFrame()
+            # Let the parent class handle TTSStoppedFrame

src/pipecat/services/gemini_multimodal_live/gemini.py

@@ -10,9 +10,8 @@ import json
 import time
 from dataclasses import dataclass
 from enum import Enum
-from typing import Any, Dict, List, Mapping, Optional, Union
+from typing import Any, Dict, List, Optional, Union
 
-import websockets
 from loguru import logger
 from pydantic import BaseModel, Field
 
@@ -45,6 +44,10 @@ from pipecat.frames.frames import (
     UserStoppedSpeakingFrame,
 )
 from pipecat.metrics.metrics import LLMTokenUsage
+from pipecat.processors.aggregators.llm_response import (
+    LLMAssistantAggregatorParams,
+    LLMUserAggregatorParams,
+)
 from pipecat.processors.aggregators.openai_llm_context import (
     OpenAILLMContext,
     OpenAILLMContextFrame,
@@ -61,6 +64,13 @@ from pipecat.utils.time import time_now_iso8601
 from . import events
 from .audio_transcriber import AudioTranscriber
 
+try:
+    import websockets
+except ModuleNotFoundError as e:
+    logger.error(f"Exception: {e}")
+    logger.error("In order to use Google AI, you need to `pip install pipecat-ai[google]`.")
+    raise Exception(f"Missing module: {e}")
+
 
 def language_to_gemini_language(language: Language) -> Optional[str]:
     """Maps a Language enum value to a Gemini Live supported language code.
@@ -871,8 +881,8 @@ class GeminiMultimodalLiveLLMService(LLMService):
         self,
         context: OpenAILLMContext,
         *,
-        user_kwargs: Mapping[str, Any] = {},
-        assistant_kwargs: Mapping[str, Any] = {},
+        user_params: LLMUserAggregatorParams = LLMUserAggregatorParams(),
+        assistant_params: LLMAssistantAggregatorParams = LLMAssistantAggregatorParams(),
     ) -> GeminiMultimodalLiveContextAggregatorPair:
         """Create an instance of GeminiMultimodalLiveContextAggregatorPair from
         an OpenAILLMContext. Constructor keyword arguments for both the user and
@@ -880,12 +890,10 @@ class GeminiMultimodalLiveLLMService(LLMService):
 
         Args:
             context (OpenAILLMContext): The LLM context.
-            user_kwargs (Mapping[str, Any], optional): Additional keyword
-                arguments for the user context aggregator constructor. Defaults
-                to an empty mapping.
-            assistant_kwargs (Mapping[str, Any], optional): Additional keyword
-                arguments for the assistant context aggregator
-                constructor. Defaults to an empty mapping.
+            user_params (LLMUserAggregatorParams, optional): User aggregator
+                parameters.
+            assistant_params (LLMAssistantAggregatorParams, optional): User
+                aggregator parameters.
 
         Returns:
             GeminiMultimodalLiveContextAggregatorPair: A pair of context
@@ -896,11 +904,8 @@ class GeminiMultimodalLiveLLMService(LLMService):
         context.set_llm_adapter(self.get_llm_adapter())
 
         GeminiMultimodalLiveContext.upgrade(context)
-        user = GeminiMultimodalLiveUserContextAggregator(context, **user_kwargs)
+        user = GeminiMultimodalLiveUserContextAggregator(context, params=user_params)
 
-        default_assistant_kwargs = {"expect_stripped_words": True}
-        default_assistant_kwargs.update(assistant_kwargs)
-        assistant = GeminiMultimodalLiveAssistantContextAggregator(
-            context, **default_assistant_kwargs
-        )
+        assistant_params.expect_stripped_words = True
+        assistant = GeminiMultimodalLiveAssistantContextAggregator(context, params=assistant_params)
         return GeminiMultimodalLiveContextAggregatorPair(_user=user, _assistant=assistant)

src/pipecat/services/google/llm.py

@@ -9,21 +9,14 @@ import io
 import json
 import os
 import uuid
-
-from google.api_core.exceptions import DeadlineExceeded
-
-from pipecat.adapters.services.gemini_adapter import GeminiLLMAdapter
-
-# Suppress gRPC fork warnings
-os.environ["GRPC_ENABLE_FORK_SUPPORT"] = "false"
-
 from dataclasses import dataclass
-from typing import Any, Dict, List, Mapping, Optional, Union
+from typing import Any, Dict, List, Optional
 
 from loguru import logger
 from PIL import Image
 from pydantic import BaseModel, Field
 
+from pipecat.adapters.services.gemini_adapter import GeminiLLMAdapter
 from pipecat.frames.frames import (
     AudioRawFrame,
     Frame,
@@ -39,6 +32,10 @@ from pipecat.frames.frames import (
     VisionImageRawFrame,
 )
 from pipecat.metrics.metrics import LLMTokenUsage
+from pipecat.processors.aggregators.llm_response import (
+    LLMAssistantAggregatorParams,
+    LLMUserAggregatorParams,
+)
 from pipecat.processors.aggregators.openai_llm_context import (
     OpenAILLMContext,
     OpenAILLMContextFrame,
@@ -51,11 +48,14 @@ from pipecat.services.openai.llm import (
     OpenAIUserContextAggregator,
 )
 
+# Suppress gRPC fork warnings
+os.environ["GRPC_ENABLE_FORK_SUPPORT"] = "false"
+
 try:
     import google.ai.generativelanguage as glm
     import google.generativeai as gai
+    from google.api_core.exceptions import DeadlineExceeded
     from google.generativeai.types import GenerationConfig
-
 except ModuleNotFoundError as e:
     logger.error(f"Exception: {e}")
     logger.error("In order to use Google AI, you need to `pip install pipecat-ai[google]`.")
@@ -686,8 +686,8 @@ class GoogleLLMService(LLMService):
         self,
         context: OpenAILLMContext,
         *,
-        user_kwargs: Mapping[str, Any] = {},
-        assistant_kwargs: Mapping[str, Any] = {},
+        user_params: LLMUserAggregatorParams = LLMUserAggregatorParams(),
+        assistant_params: LLMAssistantAggregatorParams = LLMAssistantAggregatorParams(),
     ) -> GoogleContextAggregatorPair:
         """Create an instance of GoogleContextAggregatorPair from an
         OpenAILLMContext. Constructor keyword arguments for both the user and
@@ -695,12 +695,10 @@ class GoogleLLMService(LLMService):
 
         Args:
             context (OpenAILLMContext): The LLM context.
-            user_kwargs (Mapping[str, Any], optional): Additional keyword
-                arguments for the user context aggregator constructor. Defaults
-                to an empty mapping.
-            assistant_kwargs (Mapping[str, Any], optional): Additional keyword
-                arguments for the assistant context aggregator
-                constructor. Defaults to an empty mapping.
+            user_params (LLMUserAggregatorParams, optional): User aggregator
+                parameters.
+            assistant_params (LLMAssistantAggregatorParams, optional): User
+                aggregator parameters.
 
         Returns:
             GoogleContextAggregatorPair: A pair of context aggregators, one for
@@ -712,6 +710,6 @@ class GoogleLLMService(LLMService):
 
         if isinstance(context, OpenAILLMContext):
             context = GoogleLLMContext.upgrade_to_google(context)
-        user = GoogleUserContextAggregator(context, **user_kwargs)
-        assistant = GoogleAssistantContextAggregator(context, **assistant_kwargs)
+        user = GoogleUserContextAggregator(context, params=user_params)
+        assistant = GoogleAssistantContextAggregator(context, params=assistant_params)
         return GoogleContextAggregatorPair(_user=user, _assistant=assistant)

src/pipecat/services/google/llm_vertex.py

@@ -65,7 +65,9 @@ class GoogleVertexLLMService(OpenAILLMService):
         base_url = self._get_base_url(params)
         self._api_key = self._get_api_token(credentials, credentials_path)
 
-        super().__init__(api_key=self._api_key, base_url=base_url, model=model, **kwargs)
+        super().__init__(
+            api_key=self._api_key, base_url=base_url, model=model, params=params, **kwargs
+        )
 
     @staticmethod
     def _get_base_url(params: InputParams) -> str:

src/pipecat/services/google/tts.py

@@ -346,9 +346,9 @@ class GoogleTTSService(TTSService):
             audio_content = response.audio_content[44:]
 
             # Read and yield audio data in chunks
-            chunk_size = 8192
-            for i in range(0, len(audio_content), chunk_size):
-                chunk = audio_content[i : i + chunk_size]
+            CHUNK_SIZE = 1024
+            for i in range(0, len(audio_content), CHUNK_SIZE):
+                chunk = audio_content[i : i + CHUNK_SIZE]
                 if not chunk:
                     break
                 await self.stop_ttfb_metrics()

src/pipecat/services/grok/llm.py

@@ -5,11 +5,14 @@
 #
 
 from dataclasses import dataclass
-from typing import Any, Mapping
 
 from loguru import logger
 
 from pipecat.metrics.metrics import LLMTokenUsage
+from pipecat.processors.aggregators.llm_response import (
+    LLMAssistantAggregatorParams,
+    LLMUserAggregatorParams,
+)
 from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
 from pipecat.services.openai.llm import (
     OpenAIAssistantContextAggregator,
@@ -124,8 +127,8 @@ class GrokLLMService(OpenAILLMService):
         self,
         context: OpenAILLMContext,
         *,
-        user_kwargs: Mapping[str, Any] = {},
-        assistant_kwargs: Mapping[str, Any] = {},
+        user_params: LLMUserAggregatorParams = LLMUserAggregatorParams(),
+        assistant_params: LLMAssistantAggregatorParams = LLMAssistantAggregatorParams(),
     ) -> GrokContextAggregatorPair:
         """Create an instance of GrokContextAggregatorPair from an
         OpenAILLMContext. Constructor keyword arguments for both the user and
@@ -133,12 +136,10 @@ class GrokLLMService(OpenAILLMService):
 
         Args:
             context (OpenAILLMContext): The LLM context.
-            user_kwargs (Mapping[str, Any], optional): Additional keyword
-                arguments for the user context aggregator constructor. Defaults
-                to an empty mapping.
-            assistant_kwargs (Mapping[str, Any], optional): Additional keyword
-                arguments for the assistant context aggregator
-                constructor. Defaults to an empty mapping.
+            user_params (LLMUserAggregatorParams, optional): User aggregator
+                parameters.
+            assistant_params (LLMAssistantAggregatorParams, optional): User
+                aggregator parameters.
 
         Returns:
             GrokContextAggregatorPair: A pair of context aggregators, one for
@@ -148,6 +149,6 @@ class GrokLLMService(OpenAILLMService):
         """
         context.set_llm_adapter(self.get_llm_adapter())
 
-        user = OpenAIUserContextAggregator(context, **user_kwargs)
-        assistant = OpenAIAssistantContextAggregator(context, **assistant_kwargs)
+        user = OpenAIUserContextAggregator(context, params=user_params)
+        assistant = OpenAIAssistantContextAggregator(context, params=assistant_params)
         return GrokContextAggregatorPair(_user=user, _assistant=assistant)

src/pipecat/services/llm_service.py

@@ -6,7 +6,7 @@
 
 import asyncio
 from dataclasses import dataclass
-from typing import Any, Mapping, Optional, Set, Tuple, Type
+from typing import Any, Optional, Set, Tuple, Type
 
 from loguru import logger
 
@@ -20,6 +20,10 @@ from pipecat.frames.frames import (
     StartInterruptionFrame,
     UserImageRequestFrame,
 )
+from pipecat.processors.aggregators.llm_response import (
+    LLMAssistantAggregatorParams,
+    LLMUserAggregatorParams,
+)
 from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
 from pipecat.processors.frame_processor import FrameDirection
 from pipecat.services.ai_service import AIService
@@ -55,8 +59,8 @@ class LLMService(AIService):
         self,
         context: OpenAILLMContext,
         *,
-        user_kwargs: Mapping[str, Any] = {},
-        assistant_kwargs: Mapping[str, Any] = {},
+        user_params: LLMUserAggregatorParams = LLMUserAggregatorParams(),
+        assistant_params: LLMAssistantAggregatorParams = LLMAssistantAggregatorParams(),
     ) -> Any:
         pass
 

src/pipecat/services/openai/llm.py

@@ -6,7 +6,7 @@
 
 import json
 from dataclasses import dataclass
-from typing import Any, Mapping
+from typing import Any
 
 from pipecat.frames.frames import (
     FunctionCallCancelFrame,
@@ -15,7 +15,9 @@ from pipecat.frames.frames import (
     UserImageRawFrame,
 )
 from pipecat.processors.aggregators.llm_response import (
+    LLMAssistantAggregatorParams,
     LLMAssistantContextAggregator,
+    LLMUserAggregatorParams,
     LLMUserContextAggregator,
 )
 from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
@@ -38,7 +40,7 @@ class OpenAILLMService(BaseOpenAILLMService):
     def __init__(
         self,
         *,
-        model: str = "gpt-4o",
+        model: str = "gpt-4.1",
         params: BaseOpenAILLMService.InputParams = BaseOpenAILLMService.InputParams(),
         **kwargs,
     ):
@@ -48,8 +50,8 @@ class OpenAILLMService(BaseOpenAILLMService):
         self,
         context: OpenAILLMContext,
         *,
-        user_kwargs: Mapping[str, Any] = {},
-        assistant_kwargs: Mapping[str, Any] = {},
+        user_params: LLMUserAggregatorParams = LLMUserAggregatorParams(),
+        assistant_params: LLMAssistantAggregatorParams = LLMAssistantAggregatorParams(),
     ) -> OpenAIContextAggregatorPair:
         """Create an instance of OpenAIContextAggregatorPair from an
         OpenAILLMContext. Constructor keyword arguments for both the user and
@@ -57,12 +59,8 @@ class OpenAILLMService(BaseOpenAILLMService):
 
         Args:
             context (OpenAILLMContext): The LLM context.
-            user_kwargs (Mapping[str, Any], optional): Additional keyword
-                arguments for the user context aggregator constructor. Defaults
-                to an empty mapping.
-            assistant_kwargs (Mapping[str, Any], optional): Additional keyword
-                arguments for the assistant context aggregator
-                constructor. Defaults to an empty mapping.
+            user_params (LLMUserAggregatorParams, optional): User aggregator parameters.
+            assistant_params (LLMAssistantAggregatorParams, optional): User aggregator parameters.
 
         Returns:
             OpenAIContextAggregatorPair: A pair of context aggregators, one for
@@ -71,8 +69,8 @@ class OpenAILLMService(BaseOpenAILLMService):
 
         """
         context.set_llm_adapter(self.get_llm_adapter())
-        user = OpenAIUserContextAggregator(context, **user_kwargs)
-        assistant = OpenAIAssistantContextAggregator(context, **assistant_kwargs)
+        user = OpenAIUserContextAggregator(context, params=user_params)
+        assistant = OpenAIAssistantContextAggregator(context, params=assistant_params)
         return OpenAIContextAggregatorPair(_user=user, _assistant=assistant)
 
 

src/pipecat/services/openai_realtime_beta/openai.py

@@ -8,19 +8,9 @@ import base64
 import json
 import time
 from dataclasses import dataclass
-from typing import Any, Mapping
 
 from loguru import logger
 
-try:
-    import websockets
-except ModuleNotFoundError as e:
-    logger.error(f"Exception: {e}")
-    logger.error(
-        "In order to use OpenAI, you need to `pip install pipecat-ai[openai]`. Also, set `OPENAI_API_KEY` environment variable."
-    )
-    raise Exception(f"Missing module: {e}")
-
 from pipecat.adapters.services.open_ai_realtime_adapter import OpenAIRealtimeLLMAdapter
 from pipecat.frames.frames import (
     BotStoppedSpeakingFrame,
@@ -48,6 +38,10 @@ from pipecat.frames.frames import (
     UserStoppedSpeakingFrame,
 )
 from pipecat.metrics.metrics import LLMTokenUsage
+from pipecat.processors.aggregators.llm_response import (
+    LLMAssistantAggregatorParams,
+    LLMUserAggregatorParams,
+)
 from pipecat.processors.aggregators.openai_llm_context import (
     OpenAILLMContext,
     OpenAILLMContextFrame,
@@ -65,6 +59,13 @@ from .context import (
 )
 from .frames import RealtimeFunctionCallResultFrame, RealtimeMessagesUpdateFrame
 
+try:
+    import websockets
+except ModuleNotFoundError as e:
+    logger.error(f"Exception: {e}")
+    logger.error("In order to use OpenAI, you need to `pip install pipecat-ai[openai]`.")
+    raise Exception(f"Missing module: {e}")
+
 
 @dataclass
 class CurrentAudioResponse:
@@ -650,8 +651,8 @@ class OpenAIRealtimeBetaLLMService(LLMService):
         self,
         context: OpenAILLMContext,
         *,
-        user_kwargs: Mapping[str, Any] = {},
-        assistant_kwargs: Mapping[str, Any] = {},
+        user_params: LLMUserAggregatorParams = LLMUserAggregatorParams(),
+        assistant_params: LLMAssistantAggregatorParams = LLMAssistantAggregatorParams(),
     ) -> OpenAIContextAggregatorPair:
         """Create an instance of OpenAIContextAggregatorPair from an
         OpenAILLMContext. Constructor keyword arguments for both the user and
@@ -659,12 +660,10 @@ class OpenAIRealtimeBetaLLMService(LLMService):
 
         Args:
             context (OpenAILLMContext): The LLM context.
-            user_kwargs (Mapping[str, Any], optional): Additional keyword
-                arguments for the user context aggregator constructor. Defaults
-                to an empty mapping.
-            assistant_kwargs (Mapping[str, Any], optional): Additional keyword
-                arguments for the assistant context aggregator
-                constructor. Defaults to an empty mapping.
+            user_params (LLMUserAggregatorParams, optional): User aggregator
+                parameters.
+            assistant_params (LLMAssistantAggregatorParams, optional): User
+                aggregator parameters.
 
         Returns:
             OpenAIContextAggregatorPair: A pair of context aggregators, one for
@@ -675,9 +674,8 @@ class OpenAIRealtimeBetaLLMService(LLMService):
         context.set_llm_adapter(self.get_llm_adapter())
 
         OpenAIRealtimeLLMContext.upgrade_to_realtime(context)
-        user = OpenAIRealtimeUserContextAggregator(context, **user_kwargs)
+        user = OpenAIRealtimeUserContextAggregator(context, params=user_params)
 
-        default_assistant_kwargs = {"expect_stripped_words": False}
-        default_assistant_kwargs.update(assistant_kwargs)
-        assistant = OpenAIRealtimeAssistantContextAggregator(context, **default_assistant_kwargs)
+        assistant_params.expect_stripped_words = False
+        assistant = OpenAIRealtimeAssistantContextAggregator(context, params=assistant_params)
         return OpenAIContextAggregatorPair(_user=user, _assistant=assistant)

src/pipecat/services/openpipe/llm.py

@@ -25,7 +25,7 @@ class OpenPipeLLMService(OpenAILLMService):
     def __init__(
         self,
         *,
-        model: str = "gpt-4o",
+        model: str = "gpt-4.1",
         api_key: Optional[str] = None,
         base_url: Optional[str] = None,
         openpipe_api_key: Optional[str] = None,

src/pipecat/services/tavus/video.py

@@ -6,7 +6,9 @@
 
 """This module implements Tavus as a sink transport layer"""
 
+import asyncio
 import base64
+from typing import Optional
 
 import aiohttp
 from loguru import logger
@@ -16,6 +18,7 @@ from pipecat.frames.frames import (
     CancelFrame,
     EndFrame,
     Frame,
+    StartFrame,
     StartInterruptionFrame,
     TransportMessageUrgentFrame,
     TTSAudioRawFrame,
@@ -50,6 +53,10 @@ class TavusVideoService(AIService):
 
         self._resampler = create_default_resampler()
 
+        self._audio_buffer = bytearray()
+        self._queue = asyncio.Queue()
+        self._send_task: Optional[asyncio.Task] = None
+
     async def initialize(self) -> str:
         url = "https://tavusapi.com/v2/conversations"
         headers = {"Content-Type": "application/json", "x-api-key": self._api_key}
@@ -78,45 +85,98 @@ class TavusVideoService(AIService):
         logger.debug(f"TavusVideoService persona grabbed {response_json}")
         return response_json["persona_name"]
 
+    async def start(self, frame: StartFrame):
+        await super().start(frame)
+        await self._create_send_task()
+
     async def stop(self, frame: EndFrame):
         await super().stop(frame)
         await self._end_conversation()
+        await self._cancel_send_task()
 
     async def cancel(self, frame: CancelFrame):
         await super().cancel(frame)
         await self._end_conversation()
+        await self._cancel_send_task()
 
-    async def _end_conversation(self) -> None:
+    async def process_frame(self, frame: Frame, direction: FrameDirection):
+        await super().process_frame(frame, direction)
+
+        if isinstance(frame, StartInterruptionFrame):
+            await self._handle_interruptions()
+            await self.push_frame(frame, direction)
+        elif isinstance(frame, TTSStartedFrame):
+            await self.start_processing_metrics()
+            await self.start_ttfb_metrics()
+            self._current_idx_str = str(frame.id)
+        elif isinstance(frame, TTSAudioRawFrame):
+            await self._queue_audio(frame.audio, frame.sample_rate, done=False)
+        elif isinstance(frame, TTSStoppedFrame):
+            await self._queue_audio(b"\x00\x00", self._sample_rate, done=True)
+            await self.stop_ttfb_metrics()
+            await self.stop_processing_metrics()
+        else:
+            await self.push_frame(frame, direction)
+
+    async def _handle_interruptions(self):
+        await self._cancel_send_task()
+        await self._create_send_task()
+        await self._send_interrupt_message()
+
+    async def _end_conversation(self):
         url = f"https://tavusapi.com/v2/conversations/{self._conversation_id}/end"
         headers = {"Content-Type": "application/json", "x-api-key": self._api_key}
         async with self._session.post(url, headers=headers) as r:
             r.raise_for_status()
 
-    async def _encode_audio_and_send(self, audio: bytes, in_rate: int, done: bool) -> None:
+    async def _queue_audio(self, audio: bytes, in_rate: int, done: bool):
+        await self._queue.put((audio, in_rate, done))
+
+    async def _create_send_task(self):
+        if not self._send_task:
+            self._queue = asyncio.Queue()
+            self._send_task = self.create_task(self._send_task_handler())
+
+    async def _cancel_send_task(self):
+        if self._send_task:
+            await self.cancel_task(self._send_task)
+            self._send_task = None
+
+    async def _send_task_handler(self):
+        # Daily app-messages have a 4kb limit and also a rate limit of 20
+        # messages per second. Below, we only consider the rate limit because 1
+        # second of a 24000 sample rate would be 48000 bytes (16-bit samples and
+        # 1 channel). So, that is 48000 / 20 = 2400, which is below the 4kb
+        # limit (even including base64 encoding). For a sample rate of 16000,
+        # that would be 32000 / 20 = 1600.
+        MAX_CHUNK_SIZE = int((self._sample_rate * 2) / 20)
+        SLEEP_TIME = 1 / 20
+
+        audio_buffer = bytearray()
+        while True:
+            (audio, in_rate, done) = await self._queue.get()
+
+            if done:
+                # Send any remaining audio.
+                if len(audio_buffer) > 0:
+                    await self._encode_audio_and_send(bytes(audio_buffer), done)
+                await self._encode_audio_and_send(audio, done)
+                audio_buffer.clear()
+            else:
+                audio = await self._resampler.resample(audio, in_rate, self._sample_rate)
+                audio_buffer.extend(audio)
+                while len(audio_buffer) >= MAX_CHUNK_SIZE:
+                    chunk = audio_buffer[:MAX_CHUNK_SIZE]
+                    audio_buffer = audio_buffer[MAX_CHUNK_SIZE:]
+                    await self._encode_audio_and_send(bytes(chunk), done)
+                    await asyncio.sleep(SLEEP_TIME)
+
+    async def _encode_audio_and_send(self, audio: bytes, done: bool):
         """Encodes audio to base64 and sends it to Tavus"""
-        if not done:
-            audio = await self._resampler.resample(audio, in_rate, self._sample_rate)
         audio_base64 = base64.b64encode(audio).decode("utf-8")
         logger.trace(f"{self}: sending {len(audio)} bytes")
         await self._send_audio_message(audio_base64, done=done)
 
-    async def process_frame(self, frame: Frame, direction: FrameDirection):
-        await super().process_frame(frame, direction)
-        if isinstance(frame, TTSStartedFrame):
-            await self.start_processing_metrics()
-            await self.start_ttfb_metrics()
-            self._current_idx_str = str(frame.id)
-        elif isinstance(frame, TTSAudioRawFrame):
-            await self._encode_audio_and_send(frame.audio, frame.sample_rate, done=False)
-        elif isinstance(frame, TTSStoppedFrame):
-            await self._encode_audio_and_send(b"\x00", self._sample_rate, done=True)
-            await self.stop_ttfb_metrics()
-            await self.stop_processing_metrics()
-        elif isinstance(frame, StartInterruptionFrame):
-            await self._send_interrupt_message()
-        else:
-            await self.push_frame(frame, direction)
-
     async def _send_interrupt_message(self) -> None:
         transport_frame = TransportMessageUrgentFrame(
             message={
@@ -127,7 +187,7 @@ class TavusVideoService(AIService):
         )
         await self.push_frame(transport_frame)
 
-    async def _send_audio_message(self, audio_base64: str, done: bool) -> None:
+    async def _send_audio_message(self, audio_base64: str, done: bool):
         transport_frame = TransportMessageUrgentFrame(
             message={
                 "message_type": "conversation",

src/pipecat/transports/base_output.py

@@ -386,10 +386,13 @@ class BaseOutputTransport(FrameProcessor):
     async def _draw_image(self, frame: OutputImageRawFrame):
         desired_size = (self._params.camera_out_width, self._params.camera_out_height)
 
+        # TODO: we should refactor in the future to support dynamic resolutions
+        # which is kind of what happens in P2P connections.
+        # We need to add support for that inside the DailyTransport
         if frame.size != desired_size:
             image = Image.frombytes(frame.format, frame.size, frame.image)
             resized_image = image.resize(desired_size)
-            logger.warning(f"{frame} does not have the expected size {desired_size}, resizing")
+            # logger.warning(f"{frame} does not have the expected size {desired_size}, resizing")
             frame = OutputImageRawFrame(
                 resized_image.tobytes(), resized_image.size, resized_image.format
             )

src/pipecat/transports/services/helpers/daily_rest.py

@@ -68,9 +68,9 @@ class DailyRoomProperties(BaseModel, extra="allow"):
 
     exp: Optional[float] = None
     enable_chat: bool = False
-    enable_prejoin_ui: bool = True
+    enable_prejoin_ui: bool = False
     enable_emoji_reactions: bool = False
-    eject_at_room_exp: bool = True
+    eject_at_room_exp: bool = False
     enable_dialout: Optional[bool] = None
     enable_recording: Optional[Literal["cloud", "local", "raw-tracks"]] = None
     geo: Optional[str] = None
@@ -291,6 +291,7 @@ class DailyRESTHelper:
         self,
         room_url: str,
         expiry_time: float = 60 * 60,
+        eject_at_token_exp: bool = False,
         owner: bool = True,
         params: Optional[DailyMeetingTokenParams] = None,
     ) -> str:
@@ -324,12 +325,16 @@ class DailyRESTHelper:
         if params is None:
             params = DailyMeetingTokenParams(
                 properties=DailyMeetingTokenProperties(
-                    room_name=room_name, is_owner=owner, exp=expiration
+                    room_name=room_name,
+                    is_owner=owner,
+                    exp=expiration,
+                    eject_at_token_exp=eject_at_token_exp,
                 )
             )
         else:
             params.properties.room_name = room_name
             params.properties.exp = expiration
+            params.properties.eject_at_token_exp = eject_at_token_exp
             params.properties.is_owner = owner
 
         json = params.model_dump(exclude_none=True)