Fetch full git history for setuptools_scm version detection

.github/workflows/build.yaml

@@ -21,20 +21,21 @@ jobs:
     runs-on: ubuntu-latest
     steps:
       - uses: actions/checkout@v4
-      
+        with:
+          fetch-depth: 0 # Fetch all history for setuptools_scm
       - name: Install uv
         uses: astral-sh/setup-uv@v3
         with:
           version: "latest"
-      
+
       - name: Set up Python
         run: uv python install 3.10
-      
+
       - name: Install development dependencies
         run: uv sync --group dev
-      
+
       - name: Build project
         run: uv build
-      
+
       - name: Install project in editable mode
-        run: uv pip install --editable .
+        run: uv pip install --editable .

CHANGELOG.md

@@ -7,32 +7,15 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 
 ## [Unreleased]
 
-### Changed
-
-- `FunctionFilter` now has a `filter_system_frames` arg, which controls whether
-  or not SystemFrames are filtered.
-
-- Upgraded `aws_sdk_bedrock_runtime` to v0.1.1 to resolve potential CPU issues
-  when running `AWSNovaSonicLLMService`.
-
-### Fixed
-
-- Fixed an issue in the runner where starting a DailyTransport room via
-  `/start` didn't support using the `DAILY_SAMPLE_ROOM_URL` env var.
-
-- Fixed an issue in `ServiceSwitcher` where the `STTService`s would result in
-  all STT services producing `TranscriptionFrame`s.
-
-## [0.0.91] - 2025-10-21
-
 ### Added
 
-- It is now possible to start a bot from the `/start` endpoint when using the
-  runner Daily's transport. This follows the Pipecat Cloud format with
-  `createDailyRoom` and `body` fields in the POST request body.
+- Added the [Pipecat CLI](https://github.com/pipecat-ai/pipecat-cli) to the
+  required dependencies, enabling you to scaffold a new project directly from
+  `pipecat-ai`. Get started with:
 
-- Added an ellipsis character (`…`) to the end of sentence detection in the
-  string utils.
+  ```bash
+  uv run pipecat init
+  ```
 
 - Expanded support for universal `LLMContext` to `AWSNovaSonicLLMService`.
   As a reminder, the context-setup pattern when using `LLMContext` is:
@@ -63,6 +46,9 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
   # or
   context: OpenAILLMContext
 
+  # Reading messages from context
+  messages = context.messages
+
   ## AFTER:
 
   # Context aggregator type
@@ -73,6 +59,9 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 
   # Context type
   context: LLMContext
+
+  # Reading messages from context
+  messages = context.get_messages()
   ```
 
 - Added support for `bulbul:v3` model in `SarvamTTSService` and
@@ -104,9 +93,6 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 
 ### Changed
 
-- `RunnerArguments` now include the `body` field, so there's no need to add it
-  to subclasses. Also, all `RunnerArguments` fields are now keyword-only.
-
 - `CartesiaSTTService` now inherits from `WebsocketSTTService`.
 
 - Package upgrades:
@@ -128,17 +114,8 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 
 ### Fixed
 
-- Fixed an issue where the `RTVIProcessor` was sending duplicate
-  `UserStartedSpeakingFrame` and `UserStoppedSpeakingFrame` messages.
-
-- Fixed an issue in `AWSBedrockLLMService` where both `temperature` and `top_p`
-  were always sent together, causing conflicts with models like Claude Sonnet 4.5
-  that don't allow both parameters simultaneously. The service now only includes
-  inference parameters that are explicitly set, and `InputParams` defaults have
-  been changed to `None` to rely on AWS Bedrock's built-in model defaults.
-
 - Fixed an issue in `RivaSegmentedSTTService` where a runtime error occurred due
-  to a mismatch in the `_handle_transcription` method's signature.
+  to a mismatch in the \_handle_transcription method's signature.
 
 - Fixed multiple pipeline task cancellation issues. `asyncio.CancelledError` is
   now handled properly in `PipelineTask` making it possible to cancel an asyncio

README.md

@@ -44,7 +44,7 @@ Looking to build structured conversations? Check out [Pipecat Flows](https://git
 
 Want to build beautiful and engaging experiences? Checkout the [Voice UI Kit](https://github.com/pipecat-ai/voice-ui-kit), a collection of components, hooks and templates for building voice AI applications quickly.
 
-### 🛠️ Create and deploy projects
+### 🛠️ CLI
 
 Create a new project in under a minute with the [Pipecat CLI](https://github.com/pipecat-ai/pipecat-cli). Then use the CLI to monitor and deploy your agent to production.
 

env.example

@@ -4,9 +4,6 @@ AICOUSTICS_LICENSE_KEY=...
 # Anthropic
 ANTHROPIC_API_KEY=...
 
-# Assembly AI
-ASSEMBLYAI_API_KEY=...
-
 # Async
 ASYNCAI_API_KEY=...
 ASYNCAI_VOICE_ID=...
@@ -24,19 +21,12 @@ AZURE_CHATGPT_API_KEY=...
 AZURE_CHATGPT_ENDPOINT=https://...
 AZURE_CHATGPT_MODEL=...
 
-AZURE_REALTIME_API_KEY=...
-AZURE_REALTIME_BASE_URL=...
-
 AZURE_DALLE_API_KEY=...
 AZURE_DALLE_ENDPOINT=https://...
 AZURE_DALLE_MODEL=...
 
 # Cartesia
 CARTESIA_API_KEY=...
-CARTESIA_VOICE_ID=...
-
-# Cerebras
-CEREBRAS_API_KEY=...
 
 # Daily
 DAILY_API_KEY=...
@@ -45,75 +35,42 @@ DAILY_SAMPLE_ROOM_URL=https://...
 # Deepgram
 DEEPGRAM_API_KEY=...
 
-# DeepSeek
-DEEPSEEK_API_KEY=...
-
 # ElevenLabs
 ELEVENLABS_API_KEY=...
 ELEVENLABS_VOICE_ID=...
 
+# Neuphonic
+NEUPHONIC_API_KEY=...
+
 # Fal
 FAL_KEY=...
 
 # Fireworks
 FIREWORKS_API_KEY=...
 
-# Fish Audio
-FISH_API_KEY=...
-
 # Gladia
 GLADIA_API_KEY=...
 GLADIA_REGION=...
 
 # Google
 GOOGLE_API_KEY=...
-GOOGLE_VERTEX_TEST_CREDENTIALS=...
 GOOGLE_CLOUD_PROJECT_ID=...
-GOOGLE_CLOUD_LOCATION=...
 GOOGLE_TEST_CREDENTIALS=...
-
-# Grok
-GROK_API_KEY=...
-
-# Groq
-GROQ_API_KEY=...
-
-# Heygen
-HEYGEN_API_KEY=...
+GOOGLE_VERTEX_TEST_CREDENTIALS=...
 
 # Hume
 HUME_API_KEY=...
-HUME_VOICE_ID=...
-
-# Inworld
-INWORLD_API_KEY=...
-
-# Krisp
-KRISP_MODEL_PATH=...
-
-# Krisp Viva
-KRISP_VIVA_MODEL_PATH=...
-
-# LiveKit
-LIVEKIT_API_KEY=...
-LIVEKIT_API_SECRET=...
 
 # LMNT
 LMNT_API_KEY=...
 LMNT_VOICE_ID=...
 
-# MiniMax
-MINIMAX_API_KEY=...
-MINIMAX_GROUP_ID=...
+# Perplexity
+PERPLEXITY_API_KEY=...
 
-# Mistral
-MISTRAL_API_KEY=...
-
-# Neuphonic
-NEUPHONIC_API_KEY=...
-
-# NVIDIA
-NVIDIA_API_KEY=...
+# PlayHT
+PLAYHT_USER_ID=...
+PLAYHT_API_KEY=...
 
 # OpenAI
 OPENAI_API_KEY=...
@@ -121,73 +78,92 @@ OPENAI_API_KEY=...
 # OpenPipe
 OPENPIPE_API_KEY=...
 
-# OpenRouter
-OPENROUTER_API_KEY=...
-
-# Perplexity
-PERPLEXITY_API_KEY=...
-
-# Picovoice Koala
-KOALA_ACCESS_KEY=...
-
-# Piper
-PIPER_BASE_URL=...
-
-# PlayHT
-PLAYHT_USER_ID=...
-PLAYHT_API_KEY=...
-
-# Plivo
-PLIVO_AUTH_ID=...
-PLIVO_AUTH_TOKEN=...
-
-# Qwen
-QWEN_API_KEY=...
-
-# Rime
-RIME_API_KEY=...
-RIME_VOICE_ID=...
-
-# SambaNova
-SAMBANOVA_API_KEY=...
-
-# Sarvam AI
-SARVAM_API_KEY=...
-
-# Sentry
-SENTRY_DSN=...
+# Tavus
+TAVUS_API_KEY=...
+TAVUS_REPLICA_ID=...
+TAVUS_PERSONA_ID=...
 
 # Simli
 SIMLI_API_KEY=...
 SIMLI_FACE_ID=...
 
-# Smart turn
-LOCAL_SMART_TURN_MODEL_PATH=...
-FAL_SMART_TURN_API_KEY=...
+# Krisp
+KRISP_MODEL_PATH=...
 
-# Soniox
-SONIOX_API_KEY=...
+# Krisp Viva
+KRISP_VIVA_MODEL_PATH=...
 
-# Speechmatics
-SPEECHMATICS_API_KEY=...
+# DeepSeek
+DEEPSEEK_API_KEY=...
 
-# Tavus
-TAVUS_API_KEY=...
-TAVUS_REPLICA_ID=...
+# Groq
+GROQ_API_KEY=...
 
-# Telnyx
-TELNYX_API_KEY=...
-TELNYX_ACCOUNT_SID=...
+# Grok
+GROK_API_KEY=...
+
+# Inworld
+INWORLD_API_KEY=...
 
 # Together.ai
 TOGETHER_API_KEY=...
 
+# Cerebras
+CEREBRAS_API_KEY=...
+
+# Fish Audio
+FISH_API_KEY=...
+
+# Assembly AI
+ASSEMBLYAI_API_KEY=...
+
+# OpenRouter
+OPENROUTER_API_KEY=...
+
+# Piper
+PIPER_BASE_URL=...
+
+# Smart turn
+LOCAL_SMART_TURN_MODEL_PATH=...
+FAL_SMART_TURN_API_KEY=...
+
 # Twilio
 TWILIO_ACCOUNT_SID=...
 TWILIO_AUTH_TOKEN=...
 
+# MiniMax
+MINIMAX_API_KEY=...
+MINIMAX_GROUP_ID=...
+
+# Sarvam AI
+SARVAM_API_KEY=...
+
+# Soniox
+SONIOX_API_KEY=
+
+# Speechmatics
+SPEECHMATICS_API_KEY=...
+
+# SambaNova
+SAMBANOVA_API_KEY=...
+
+# Sentry
+SENTRY_DSN=...
+
+# Heygen
+HEYGEN_API_KEY=...
+
+# Mistral
+MISTRAL_API_KEY=...
+
+# NVIDIA
+NVIDIA_API_KEY=...
+
+# Qwen
+QWEN_API_KEY=...
+
 # WhatsApp
-WHATSAPP_TOKEN=...
-WHATSAPP_WEBHOOK_VERIFICATION_TOKEN=...
-WHATSAPP_PHONE_NUMBER_ID=...
-WHATSAPP_APP_SECRET=...
+WHATSAPP_TOKEN=
+WHATSAPP_WEBHOOK_VERIFICATION_TOKEN=
+WHATSAPP_PHONE_NUMBER_ID=
+WHATSAPP_APP_SECRET=

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

@@ -67,8 +67,8 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
 
     llm = AWSBedrockLLMService(
         aws_region="us-west-2",
-        model="us.anthropic.claude-haiku-4-5-20251001-v1:0",
-        params=AWSBedrockLLMService.InputParams(temperature=0.8),
+        model="us.anthropic.claude-3-5-haiku-20241022-v1:0",
+        params=AWSBedrockLLMService.InputParams(temperature=0.8, latency="optimized"),
     )
 
     messages = [

examples/foundational/08-bots-arguing.py

@@ -0,0 +1,147 @@
+import asyncio
+import logging
+import os
+from typing import Tuple
+
+import aiohttp
+from dotenv import load_dotenv
+
+from pipecat.frames.frames import AudioFrame, EndFrame, ImageFrame, LLMContextFrame, TextFrame
+from pipecat.pipeline.pipeline import Pipeline
+from pipecat.processors.aggregators import SentenceAggregator
+from pipecat.processors.aggregators.llm_context import LLMContext
+from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
+from pipecat.runner.daily import configure
+from pipecat.services.azure import AzureLLMService, AzureTTSService
+from pipecat.services.elevenlabs import ElevenLabsTTSService
+from pipecat.services.fal import FalImageGenService
+from pipecat.transports.daily.transport import DailyTransport
+
+load_dotenv(override=True)
+
+logging.basicConfig(format=f"%(levelno)s %(asctime)s %(message)s")
+logger = logging.getLogger("pipecat")
+logger.setLevel(logging.DEBUG)
+
+
+async def main():
+    async with aiohttp.ClientSession() as session:
+        (room_url, _) = await configure(session)
+
+        transport = DailyTransport(
+            room_url,
+            None,
+            "Respond bot",
+            duration_minutes=10,
+            mic_enabled=True,
+            mic_sample_rate=16000,
+            camera_enabled=True,
+            camera_width=1024,
+            camera_height=1024,
+        )
+
+        llm = AzureLLMService(
+            api_key=os.getenv("AZURE_CHATGPT_API_KEY"),
+            endpoint=os.getenv("AZURE_CHATGPT_ENDPOINT"),
+            model=os.getenv("AZURE_CHATGPT_MODEL"),
+        )
+        tts1 = AzureTTSService(
+            api_key=os.getenv("AZURE_SPEECH_API_KEY"),
+            region=os.getenv("AZURE_SPEECH_REGION"),
+        )
+        tts2 = ElevenLabsTTSService(
+            api_key=os.getenv("ELEVENLABS_API_KEY"),
+            voice_id="jBpfuIE2acCO8z3wKNLl",
+        )
+        dalle = FalImageGenService(
+            params=FalImageGenService.InputParams(image_size="1024x1024"),
+            aiohttp_session=session,
+            key=os.getenv("FAL_KEY"),
+        )
+
+        bot1_messages = [
+            {
+                "role": "system",
+                "content": "You are a stern librarian. You strongly believe that a hot dog is a sandwich. Start by stating this fact in a few sentences, then be prepared to debate this with the user. You shouldn't ever compromise on the fundamental truth that a hot dog is a sandwich. Your responses should only be a few sentences long.",
+            },
+        ]
+        bot2_messages = [
+            {
+                "role": "system",
+                "content": "You are a silly cat, and you strongly believe that a hot dog is not a sandwich. Debate this with the user, only responding with a few sentences. Don't ever accept that a hot dog is a sandwich.",
+            },
+        ]
+
+        async def get_text_and_audio(messages) -> Tuple[str, bytearray]:
+            """This function streams text from the LLM and uses the TTS service to convert
+            that text to speech as it's received.
+            """
+            source_queue = asyncio.Queue()
+            sink_queue = asyncio.Queue()
+            sentence_aggregator = SentenceAggregator()
+            pipeline = Pipeline([llm, sentence_aggregator, tts1], source_queue, sink_queue)
+
+            await source_queue.put(LLMContextFrame(LLMContext(messages)))
+            await source_queue.put(EndFrame())
+            await pipeline.run_pipeline()
+
+            message = ""
+            all_audio = bytearray()
+            while sink_queue.qsize():
+                frame = sink_queue.get_nowait()
+                if isinstance(frame, TextFrame):
+                    message += frame.text
+                elif isinstance(frame, AudioFrame):
+                    all_audio.extend(frame.audio)
+
+            return (message, all_audio)
+
+        async def get_bot1_statement():
+            message, audio = await get_text_and_audio(bot1_messages)
+
+            bot1_messages.append({"role": "assistant", "content": message})
+            bot2_messages.append({"role": "user", "content": message})
+
+            return audio
+
+        async def get_bot2_statement():
+            message, audio = await get_text_and_audio(bot2_messages)
+
+            bot2_messages.append({"role": "assistant", "content": message})
+            bot1_messages.append({"role": "user", "content": message})
+
+            return audio
+
+        async def argue():
+            for i in range(100):
+                print(f"In iteration {i}")
+
+                bot1_description = "A woman conservatively dressed as a librarian in a library surrounded by books, cartoon, serious, highly detailed"
+
+                (audio1, image_data1) = await asyncio.gather(
+                    get_bot1_statement(), dalle.run_image_gen(bot1_description)
+                )
+                await transport.send_queue.put(
+                    [
+                        ImageFrame(image_data1[1], image_data1[2]),
+                        AudioFrame(audio1),
+                    ]
+                )
+
+                bot2_description = "A cat dressed in a hot dog costume, cartoon, bright colors, funny, highly detailed"
+
+                (audio2, image_data2) = await asyncio.gather(
+                    get_bot2_statement(), dalle.run_image_gen(bot2_description)
+                )
+                await transport.send_queue.put(
+                    [
+                        ImageFrame(image_data2[1], image_data2[2]),
+                        AudioFrame(audio2),
+                    ]
+                )
+
+        await asyncio.gather(transport.run(), argue())
+
+
+if __name__ == "__main__":
+    asyncio.run(main())

examples/foundational/08-custom-frame-processor.py

@@ -1,170 +0,0 @@
-#
-# Copyright (c) 2024–2025, Daily
-#
-# SPDX-License-Identifier: BSD 2-Clause License
-#
-
-import io
-import os
-import re
-
-from dotenv import load_dotenv
-from loguru import logger
-
-from pipecat.audio.turn.smart_turn.base_smart_turn import SmartTurnParams
-from pipecat.audio.turn.smart_turn.local_smart_turn_v3 import LocalSmartTurnAnalyzerV3
-from pipecat.audio.vad.silero import SileroVADAnalyzer
-from pipecat.audio.vad.vad_analyzer import VADParams
-from pipecat.frames.frames import (
-    Frame,
-    LLMRunFrame,
-    MetricsFrame,
-)
-from pipecat.pipeline.pipeline import Pipeline
-from pipecat.pipeline.runner import PipelineRunner
-from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.aggregators.llm_context import LLMContext
-from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
-from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
-from pipecat.runner.types import RunnerArguments
-from pipecat.runner.utils import create_transport
-from pipecat.services.cartesia.tts import CartesiaTTSService
-from pipecat.services.deepgram.stt import DeepgramSTTService
-from pipecat.services.openai.llm import OpenAILLMService
-from pipecat.transports.base_transport import BaseTransport, TransportParams
-from pipecat.transports.daily.transport import DailyParams
-
-load_dotenv(override=True)
-
-
-def format_metrics(metrics, indent=0):
-    lines = []
-    tab = "\t" * indent
-
-    for metric in metrics:
-        lines.append(tab + type(metric).__name__)
-        for field, value in vars(metric).items():
-            if hasattr(value, "__dict__") and not isinstance(
-                value, (str, int, float, bool, type(None))
-            ):
-                lines.append(f"{tab}\t{field}={type(value).__name__}")
-                for k, v in vars(value).items():
-                    lines.append(f"{tab}\t\t{k}={repr(v)}")
-            else:
-                lines.append(f"{tab}\t{field}={repr(value)}")
-
-    return "\n".join(lines)
-
-
-class MetricsFrameLogger(FrameProcessor):
-    """MetricsFrameLogger formats and logs all MetericsFrames"""
-
-    def __init__(self, **kwargs):
-        super().__init__(**kwargs)
-
-    async def process_frame(self, frame: Frame, direction: FrameDirection):
-        await super().process_frame(frame, direction)
-
-        if isinstance(frame, MetricsFrame):
-            logger.info(f"{frame.name}\n    {format_metrics(frame.data)}")
-            await self.push_frame(frame, direction)
-
-        # ALWAYS push all frames
-        else:
-            # SUPER IMPORTANT: always push every frame!
-            await self.push_frame(frame, direction)
-
-
-# We store functions so objects (e.g. SileroVADAnalyzer) don't get
-# instantiated. The function will be called when the desired transport gets
-# selected.
-transport_params = {
-    "daily": lambda: DailyParams(
-        audio_in_enabled=True,
-        audio_out_enabled=True,
-        vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
-        turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
-    ),
-    "webrtc": lambda: TransportParams(
-        audio_in_enabled=True,
-        audio_out_enabled=True,
-        video_out_enabled=True,
-        vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
-        turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
-    ),
-}
-
-
-async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
-    logger.info(f"Starting bot")
-
-    stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
-
-    tts = CartesiaTTSService(
-        api_key=os.getenv("CARTESIA_API_KEY"),
-        voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
-    )
-
-    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
-
-    messages = [
-        {
-            "role": "system",
-            "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
-        },
-    ]
-
-    context = LLMContext(messages)
-    context_aggregator = LLMContextAggregatorPair(context)
-
-    metrics_frame_processor = MetricsFrameLogger()
-
-    pipeline = Pipeline(
-        [
-            transport.input(),
-            stt,
-            context_aggregator.user(),
-            llm,
-            tts,
-            transport.output(),
-            context_aggregator.assistant(),
-            metrics_frame_processor,  # pretty print metrics frames
-        ]
-    )
-
-    task = PipelineTask(
-        pipeline,
-        params=PipelineParams(
-            enable_metrics=True,
-            enable_usage_metrics=True,
-        ),
-        idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
-    )
-
-    @transport.event_handler("on_client_connected")
-    async def on_client_connected(transport, client):
-        logger.info(f"Client connected: {client}")
-        # Kick off the conversation.
-        messages.append({"role": "system", "content": "Please introduce yourself to the user."})
-        await task.queue_frames([LLMRunFrame()])
-
-    @transport.event_handler("on_client_disconnected")
-    async def on_client_disconnected(transport, client):
-        logger.info(f"Client disconnected")
-        await task.cancel()
-
-    runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
-
-    await runner.run(task)
-
-
-async def bot(runner_args: RunnerArguments):
-    """Main bot entry point compatible with Pipecat Cloud."""
-    transport = await create_transport(runner_args, transport_params)
-    await run_bot(transport, runner_args)
-
-
-if __name__ == "__main__":
-    from pipecat.runner.run import main
-
-    main()

examples/foundational/14r-function-calling-aws.py

@@ -79,8 +79,8 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
 
     llm = AWSBedrockLLMService(
         aws_region="us-west-2",
-        model="us.anthropic.claude-haiku-4-5-20251001-v1:0",
-        params=AWSBedrockLLMService.InputParams(temperature=0.8),
+        model="us.anthropic.claude-3-5-haiku-20241022-v1:0",
+        params=AWSBedrockLLMService.InputParams(temperature=0.8, latency="optimized"),
     )
 
     # You can also register a function_name of None to get all functions

examples/foundational/48-service-switcher.py

@@ -1,153 +0,0 @@
-#
-# Copyright (c) 2024–2025, Daily
-#
-# SPDX-License-Identifier: BSD 2-Clause License
-#
-
-import asyncio
-import os
-
-from dotenv import load_dotenv
-from loguru import logger
-
-from pipecat.audio.turn.smart_turn.base_smart_turn import SmartTurnParams
-from pipecat.audio.turn.smart_turn.local_smart_turn_v3 import LocalSmartTurnAnalyzerV3
-from pipecat.audio.vad.silero import SileroVADAnalyzer
-from pipecat.audio.vad.vad_analyzer import VADParams
-from pipecat.frames.frames import LLMRunFrame, ManuallySwitchServiceFrame
-from pipecat.pipeline.pipeline import Pipeline
-from pipecat.pipeline.runner import PipelineRunner
-from pipecat.pipeline.service_switcher import ServiceSwitcher, ServiceSwitcherStrategyManual
-from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.aggregators.llm_context import LLMContext
-from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
-from pipecat.runner.types import RunnerArguments
-from pipecat.runner.utils import create_transport
-from pipecat.services.cartesia.stt import CartesiaSTTService
-from pipecat.services.cartesia.tts import CartesiaTTSService
-from pipecat.services.deepgram.stt import DeepgramSTTService
-from pipecat.services.deepgram.tts import DeepgramTTSService
-from pipecat.services.google.llm import GoogleLLMService
-from pipecat.services.openai.llm import OpenAILLMService
-from pipecat.transports.base_transport import BaseTransport, TransportParams
-from pipecat.transports.daily.transport import DailyParams
-from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
-
-load_dotenv(override=True)
-
-# We store functions so objects (e.g. SileroVADAnalyzer) don't get
-# instantiated. The function will be called when the desired transport gets
-# selected.
-transport_params = {
-    "daily": lambda: DailyParams(
-        audio_in_enabled=True,
-        audio_out_enabled=True,
-        vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
-        turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
-    ),
-    "twilio": lambda: FastAPIWebsocketParams(
-        audio_in_enabled=True,
-        audio_out_enabled=True,
-        vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
-        turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
-    ),
-    "webrtc": lambda: TransportParams(
-        audio_in_enabled=True,
-        audio_out_enabled=True,
-        vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
-        turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
-    ),
-}
-
-
-async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
-    logger.info(f"Starting bot")
-
-    stt_cartesia = CartesiaSTTService(api_key=os.getenv("CARTESIA_API_KEY"))
-    stt_deepgram = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
-    stt_switcher = ServiceSwitcher(
-        services=[stt_cartesia, stt_deepgram], strategy_type=ServiceSwitcherStrategyManual
-    )
-
-    tts_cartesia = CartesiaTTSService(
-        api_key=os.getenv("CARTESIA_API_KEY"),
-        voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",
-    )
-    tts_deepgram = DeepgramTTSService(api_key=os.getenv("DEEPGRAM_API_KEY"))
-    tts_switcher = ServiceSwitcher(
-        services=[tts_cartesia, tts_deepgram], strategy_type=ServiceSwitcherStrategyManual
-    )
-
-    llm_openai = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
-    llm_google = GoogleLLMService(api_key=os.getenv("GOOGLE_API_KEY"))
-    llm_switcher = ServiceSwitcher(
-        services=[llm_openai, llm_google], strategy_type=ServiceSwitcherStrategyManual
-    )
-
-    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 = LLMContext(messages)
-    context_aggregator = LLMContextAggregatorPair(context)
-
-    pipeline = Pipeline(
-        [
-            transport.input(),  # Transport user input
-            stt_switcher,
-            context_aggregator.user(),  # User responses
-            llm_switcher,  # LLM
-            tts_switcher,  # TTS
-            transport.output(),  # Transport bot output
-            context_aggregator.assistant(),  # Assistant spoken responses
-        ]
-    )
-
-    task = PipelineTask(
-        pipeline,
-        params=PipelineParams(
-            enable_metrics=True,
-            enable_usage_metrics=True,
-        ),
-        idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
-    )
-
-    @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([LLMRunFrame()])
-        await asyncio.sleep(15)
-        print(f"Switching to {stt_deepgram}")
-        await task.queue_frames([ManuallySwitchServiceFrame(service=stt_deepgram)])
-        await asyncio.sleep(15)
-        print(f"Switching to {llm_google}")
-        await task.queue_frames([ManuallySwitchServiceFrame(service=llm_google)])
-        await asyncio.sleep(15)
-        print(f"Switching to {tts_deepgram}")
-        await task.queue_frames([ManuallySwitchServiceFrame(service=tts_deepgram)])
-
-    @transport.event_handler("on_client_disconnected")
-    async def on_client_disconnected(transport, client):
-        logger.info(f"Client disconnected")
-        await task.cancel()
-
-    runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
-
-    await runner.run(task)
-
-
-async def bot(runner_args: RunnerArguments):
-    """Main bot entry point compatible with Pipecat Cloud."""
-    transport = await create_transport(runner_args, transport_params)
-    await run_bot(transport, runner_args)
-
-
-if __name__ == "__main__":
-    from pipecat.runner.run import main
-
-    main()

examples/quickstart/README.md

@@ -73,13 +73,13 @@ Transform your local bot into a production-ready service. Pipecat Cloud handles
 
 1. [Sign up for Pipecat Cloud](https://pipecat.daily.co/sign-up).
 
-2. Install the Pipecat CLI:
+2. Install the Pipecat Cloud CLI:
 
    ```bash
-   uv tool install pipecat-ai-cli
+   uv add pipecatcloud
    ```
 
-> 💡 Tip: You can run the `pipecat` CLI using the `pc` alias.
+> 💡 Tip: You can run the `pipecatcloud` CLI using the `pcc` alias.
 
 3. Set up Docker for building your bot image:
 
@@ -113,22 +113,12 @@ secret_set = "quickstart-secrets"
 
 > 💡 Tip: [Set up `image_credentials`](https://docs.pipecat.ai/deployment/pipecat-cloud/fundamentals/secrets#image-pull-secrets) in your TOML file for authenticated image pulls
 
-### Log in to Pipecat Cloud
-
-To start using the CLI, authenticate to Pipecat Cloud:
-
-```bash
-pipecat cloud auth login
-```
-
-You'll be presented with a link that you can click to authenticate your client.
-
 ### Configure secrets
 
 Upload your API keys to Pipecat Cloud's secure storage:
 
 ```bash
-pipecat cloud secrets set quickstart-secrets --file .env
+uv run pcc secrets set quickstart-secrets --file .env
 ```
 
 This creates a secret set called `quickstart-secrets` (matching your TOML file) and uploads all your API keys from `.env`.
@@ -138,13 +128,13 @@ This creates a secret set called `quickstart-secrets` (matching your TOML file)
 Build your Docker image and push to Docker Hub:
 
 ```bash
-pipecat cloud docker build-push
+uv run pcc docker build-push
 ```
 
 Deploy to Pipecat Cloud:
 
 ```bash
-pipecat cloud deploy
+uv run pcc deploy
 ```
 
 ### Connect to your agent

examples/quickstart/pcc-deploy.toml

@@ -1,11 +1,6 @@
 agent_name = "quickstart"
 image = "your_username/quickstart:0.1"
 secret_set = "quickstart-secrets"
-agent_profile = "agent-1x"
-
-# RECOMMENDED: Set an image pull secret:
-# https://docs.pipecat.ai/deployment/pipecat-cloud/fundamentals/secrets#image-pull-secrets
-# image_credentials = "your_image_pull_secret"
 
 [scaling]
 	min_agents = 1

examples/quickstart/pyproject.toml

@@ -4,14 +4,13 @@ version = "0.1.0"
 description = "Quickstart example for building voice AI bots with Pipecat"
 requires-python = ">=3.10"
 dependencies = [
-    "pipecat-ai[webrtc,daily,silero,deepgram,openai,cartesia,local-smart-turn-v3,runner]",
-    "pipecat-ai-cli"
+    "pipecat-ai[webrtc,daily,silero,deepgram,openai,cartesia,local-smart-turn-v3,runner]>=0.0.86",
+    "pipecatcloud>=0.2.4"
 ]
 
 [dependency-groups]
 dev = [
-    "pyright>=1.1.404,<2",
-    "ruff>=0.12.11,<1",
+    "ruff~=0.12.1",
 ]
 
 [tool.ruff]

pyproject.toml

@@ -38,6 +38,7 @@ dependencies = [
     # Pinning numba to resolve package dependencies
     "numba==0.61.2",
     "wait_for2>=0.4.1; python_version<'3.12'",
+    "pipecat-ai-cli"
 ]
 
 [project.urls]
@@ -50,7 +51,7 @@ anthropic = [ "anthropic~=0.49.0" ]
 assemblyai = [ "pipecat-ai[websockets-base]" ]
 asyncai = [ "pipecat-ai[websockets-base]" ]
 aws = [ "aioboto3~=15.0.0", "pipecat-ai[websockets-base]" ]
-aws-nova-sonic = [ "aws_sdk_bedrock_runtime~=0.1.1; python_version>='3.12'" ]
+aws-nova-sonic = [ "aws_sdk_bedrock_runtime~=0.1.0; python_version>='3.12'" ]
 azure = [ "azure-cognitiveservices-speech~=1.42.0"]
 cartesia = [ "cartesia~=2.0.3", "pipecat-ai[websockets-base]" ]
 cerebras = []

src/pipecat/adapters/services/anthropic_adapter.py

@@ -110,7 +110,7 @@ class AnthropicLLMAdapter(BaseLLMAdapter[AnthropicLLMInvocationParams]):
         system = NOT_GIVEN
         messages = []
 
-        # First, map messages using self._from_universal_context_message(m)
+        # first, map messages using self._from_universal_context_message(m)
         try:
             messages = [self._from_universal_context_message(m) for m in universal_context_messages]
         except Exception as e:

src/pipecat/adapters/services/bedrock_adapter.py

@@ -107,7 +107,7 @@ class AWSBedrockLLMAdapter(BaseLLMAdapter[AWSBedrockLLMInvocationParams]):
         system = None
         messages = []
 
-        # First, map messages using self._from_universal_context_message(m)
+        # first, map messages using self._from_universal_context_message(m)
         try:
             messages = [self._from_universal_context_message(m) for m in universal_context_messages]
         except Exception as e:

src/pipecat/adapters/services/gemini_adapter.py

@@ -8,8 +8,8 @@
 
 import base64
 import json
-from dataclasses import dataclass, field
-from typing import Any, Dict, List, Optional, Tuple, TypedDict
+from dataclasses import dataclass
+from typing import Any, Dict, List, Optional, TypedDict
 
 from loguru import logger
 from openai import NotGiven
@@ -133,28 +133,6 @@ class GeminiLLMAdapter(BaseLLMAdapter[GeminiLLMInvocationParams]):
         messages: List[Content]
         system_instruction: Optional[str] = None
 
-    @dataclass
-    class MessageConversionResult:
-        """Result of converting a single universal context message to Google format.
-
-        Either content (a Google Content object) or a system instruction string
-        is guaranteed to be set.
-
-        Also returns a tool call ID to name mapping for any tool calls
-        discovered in the message.
-        """
-
-        content: Optional[Content] = None
-        system_instruction: Optional[str] = None
-        tool_call_id_to_name_mapping: Dict[str, str] = field(default_factory=dict)
-
-    @dataclass
-    class MessageConversionParams:
-        """Parameters for converting a single universal context message to Google format."""
-
-        already_have_system_instruction: bool
-        tool_call_id_to_name_mapping: Dict[str, str]
-
     def _from_universal_context_messages(
         self, universal_context_messages: List[LLMContextMessage]
     ) -> ConvertedMessages:
@@ -178,26 +156,24 @@ class GeminiLLMAdapter(BaseLLMAdapter[GeminiLLMInvocationParams]):
         """
         system_instruction = None
         messages = []
-        tool_call_id_to_name_mapping = {}
 
         # Process each message, preserving Google-formatted messages and converting others
         for message in universal_context_messages:
-            result = self._from_universal_context_message(
-                message,
-                params=self.MessageConversionParams(
-                    already_have_system_instruction=bool(system_instruction),
-                    tool_call_id_to_name_mapping=tool_call_id_to_name_mapping,
-                ),
-            )
-            # Each result is either a Content or a system instruction
-            if result.content:
-                messages.append(result.content)
-            elif result.system_instruction:
-                system_instruction = result.system_instruction
+            if isinstance(message, LLMSpecificMessage):
+                # Assume that LLMSpecificMessage wraps a message in Google format
+                messages.append(message.message)
+                continue
 
-            # Merge tool call ID to name mapping
-            if result.tool_call_id_to_name_mapping:
-                tool_call_id_to_name_mapping.update(result.tool_call_id_to_name_mapping)
+            # Convert standard format to Google format
+            converted = self._from_standard_message(
+                message, already_have_system_instruction=bool(system_instruction)
+            )
+            if isinstance(converted, Content):
+                # Regular (non-system) message
+                messages.append(converted)
+            else:
+                # System instruction
+                system_instruction = converted
 
         # Check if we only have function-related messages (no regular text)
         has_regular_messages = any(
@@ -217,16 +193,9 @@ class GeminiLLMAdapter(BaseLLMAdapter[GeminiLLMInvocationParams]):
 
         return self.ConvertedMessages(messages=messages, system_instruction=system_instruction)
 
-    def _from_universal_context_message(
-        self, message: LLMContextMessage, *, params: MessageConversionParams
-    ) -> MessageConversionResult:
-        if isinstance(message, LLMSpecificMessage):
-            return self.MessageConversionResult(content=message.message)
-        return self._from_standard_message(message, params=params)
-
     def _from_standard_message(
-        self, message: LLMStandardMessage, *, params: MessageConversionParams
-    ) -> MessageConversionResult:
+        self, message: LLMStandardMessage, already_have_system_instruction: bool
+    ) -> Content | str:
         """Convert standard universal context message to Google Content object.
 
         Handles conversion of text, images, and function calls to Google's
@@ -236,11 +205,10 @@ class GeminiLLMAdapter(BaseLLMAdapter[GeminiLLMInvocationParams]):
         Args:
             message: Message in standard universal context format.
             already_have_system_instruction: Whether we already have a system instruction
-            params: Parameters for conversion.
 
         Returns:
-            MessageConversionResult containing either a Content object or a
-            system instruction string.
+            Content object with role and parts, or a plain string for system
+            messages.
 
         Examples:
             Standard text message::
@@ -274,48 +242,38 @@ class GeminiLLMAdapter(BaseLLMAdapter[GeminiLLMInvocationParams]):
             Converts to Google Content with::
 
                 Content(
-                    role="user",
+                    role="model",
                     parts=[Part(function_call=FunctionCall(name="search", args={"query": "test"}))]
                 )
         """
         role = message["role"]
         content = message.get("content", [])
-
         if role == "system":
-            if params.already_have_system_instruction:
+            if already_have_system_instruction:
                 role = "user"  # Convert system message to user role if we already have a system instruction
             else:
-                system_instruction: str = None
+                # System instructions are returned as plain text
                 if isinstance(content, str):
-                    system_instruction = content
+                    return content
                 elif isinstance(content, list):
                     # If content is a list, we assume it's a list of text parts, per the standard
-                    system_instruction = " ".join(
-                        part["text"] for part in content if part.get("type") == "text"
-                    )
-                if system_instruction:
-                    return self.MessageConversionResult(system_instruction=system_instruction)
+                    return " ".join(part["text"] for part in content if part.get("type") == "text")
         elif role == "assistant":
             role = "model"
 
         parts = []
-        tool_call_id_to_name_mapping = {}
-
         if message.get("tool_calls"):
             for tc in message["tool_calls"]:
-                id = tc["id"]
-                name = tc["function"]["name"]
-                tool_call_id_to_name_mapping[id] = name
                 parts.append(
                     Part(
                         function_call=FunctionCall(
-                            name=name,
+                            name=tc["function"]["name"],
                             args=json.loads(tc["function"]["arguments"]),
                         )
                     )
                 )
         elif role == "tool":
-            role = "user"
+            role = "model"
             try:
                 response = json.loads(message["content"])
                 if isinstance(response, dict):
@@ -326,17 +284,12 @@ class GeminiLLMAdapter(BaseLLMAdapter[GeminiLLMInvocationParams]):
                 # Response might not be JSON-deserializable.
                 # This occurs with a UserImageFrame, for example, where we get a plain "COMPLETED" string.
                 response_dict = {"value": message["content"]}
-
-            # Get function name from mapping using tool_call_id, or fallback
-            tool_call_id = message.get("tool_call_id")
-            function_name = "tool_call_result"  # Default fallback
-            if tool_call_id and tool_call_id in params.tool_call_id_to_name_mapping:
-                function_name = params.tool_call_id_to_name_mapping[tool_call_id]
-
             parts.append(
-                Part.from_function_response(
-                    name=function_name,
-                    response=response_dict,
+                Part(
+                    function_response=FunctionResponse(
+                        name="tool_call_result",  # seems to work to hard-code the same name every time
+                        response=response_dict,
+                    )
                 )
             )
         elif isinstance(content, str):
@@ -359,7 +312,4 @@ class GeminiLLMAdapter(BaseLLMAdapter[GeminiLLMInvocationParams]):
                     audio_bytes = base64.b64decode(input_audio["data"])
                     parts.append(Part(inline_data=Blob(mime_type="audio/wav", data=audio_bytes)))
 
-        return self.MessageConversionResult(
-            content=Content(role=role, parts=parts),
-            tool_call_id_to_name_mapping=tool_call_id_to_name_mapping,
-        )
+        return Content(role=role, parts=parts)

src/pipecat/pipeline/llm_switcher.py

@@ -14,41 +14,20 @@ from pipecat.services.llm_service import LLMService
 
 
 class LLMSwitcher(ServiceSwitcher[StrategyType]):
-    """A pipeline that switches between different LLMs at runtime.
-
-    Example::
-
-        llm_switcher = LLMSwitcher(
-            llms=[openai_llm, anthropic_llm],
-            strategy_type=ServiceSwitcherStrategyManual
-        )
-    """
+    """A pipeline that switches between different LLMs at runtime."""
 
     def __init__(self, llms: List[LLMService], strategy_type: Type[StrategyType]):
-        """Initialize the service switcher with a list of LLMs and a switching strategy.
-
-        Args:
-            llms: List of LLM services to switch between.
-            strategy_type: The strategy class to use for switching between LLMs.
-        """
+        """Initialize the service switcher with a list of LLMs and a switching strategy."""
         super().__init__(llms, strategy_type)
 
     @property
     def llms(self) -> List[LLMService]:
-        """Get the list of LLMs managed by this switcher.
-
-        Returns:
-            List of LLM services managed by this switcher.
-        """
+        """Get the list of LLMs managed by this switcher."""
         return self.services
 
     @property
     def active_llm(self) -> Optional[LLMService]:
-        """Get the currently active LLM.
-
-        Returns:
-            The currently active LLM service, or None if no LLM is active.
-        """
+        """Get the currently active LLM, if any."""
         return self.strategy.active_service
 
     async def run_inference(self, context: LLMContext) -> Optional[str]:

src/pipecat/pipeline/service_switcher.py

@@ -21,22 +21,10 @@ from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
 
 
 class ServiceSwitcherStrategy:
-    """Base class for service switching strategies.
-
-    Note:
-        Strategy classes are instantiated internally by ServiceSwitcher.
-        Developers should pass the strategy class (not an instance) to ServiceSwitcher.
-    """
+    """Base class for service switching strategies."""
 
     def __init__(self, services: List[FrameProcessor]):
-        """Initialize the service switcher strategy with a list of services.
-
-        Note:
-            This is called internally by ServiceSwitcher. Do not instantiate directly.
-
-        Args:
-            services: List of frame processors to switch between.
-        """
+        """Initialize the service switcher strategy with a list of services."""
         self.services = services
         self.active_service: Optional[FrameProcessor] = None
 
@@ -58,24 +46,10 @@ class ServiceSwitcherStrategyManual(ServiceSwitcherStrategy):
 
     This strategy allows the user to manually select which service is active.
     The initial active service is the first one in the list.
-
-    Example::
-
-        stt_switcher = ServiceSwitcher(
-            services=[stt_1, stt_2],
-            strategy_type=ServiceSwitcherStrategyManual
-        )
     """
 
     def __init__(self, services: List[FrameProcessor]):
-        """Initialize the manual service switcher strategy with a list of services.
-
-        Note:
-            This is called internally by ServiceSwitcher. Do not instantiate directly.
-
-        Args:
-            services: List of frame processors to switch between.
-        """
+        """Initialize the manual service switcher strategy with a list of services."""
         super().__init__(services)
         self.active_service = services[0] if services else None
 
@@ -111,12 +85,7 @@ class ServiceSwitcher(ParallelPipeline, Generic[StrategyType]):
     """A pipeline that switches between different services at runtime."""
 
     def __init__(self, services: List[FrameProcessor], strategy_type: Type[StrategyType]):
-        """Initialize the service switcher with a list of services and a switching strategy.
-
-        Args:
-            services: List of frame processors to switch between.
-            strategy_type: The strategy class to use for switching between services.
-        """
+        """Initialize the service switcher with a list of services and a switching strategy."""
         strategy = strategy_type(services)
         super().__init__(*self._make_pipeline_definitions(services, strategy))
         self.services = services
@@ -131,20 +100,14 @@ class ServiceSwitcher(ParallelPipeline, Generic[StrategyType]):
             active_service: FrameProcessor,
             direction: FrameDirection,
         ):
-            """Initialize the service switcher filter with a strategy and direction.
-
-            Args:
-                wrapped_service: The service that this filter wraps.
-                active_service: The currently active service.
-                direction: The direction of frame flow to filter.
-            """
-            self._wrapped_service = wrapped_service
-            self._active_service = active_service
+            """Initialize the service switcher filter with a strategy and direction."""
 
             async def filter(_: Frame) -> bool:
                 return self._wrapped_service == self._active_service
 
-            super().__init__(filter, direction, filter_system_frames=True)
+            super().__init__(filter, direction)
+            self._wrapped_service = wrapped_service
+            self._active_service = active_service
 
         async def process_frame(self, frame, direction):
             """Process a frame through the filter, handling special internal filter-updating frames."""

src/pipecat/pipeline/task_observer.py

@@ -189,7 +189,7 @@ class TaskObserver(BaseObserver):
             if isinstance(data, FramePushed):
                 if on_push_frame_deprecated:
                     await observer.on_push_frame(
-                        data.source, data.destination, data.frame, data.direction, data.timestamp
+                        data.src, data.dst, data.frame, data.direction, data.timestamp
                     )
                 else:
                     await observer.on_push_frame(data)

src/pipecat/processors/aggregators/llm_context.py

@@ -106,19 +106,6 @@ class LLMContext:
         self._tools: ToolsSchema | NotGiven = LLMContext._normalize_and_validate_tools(tools)
         self._tool_choice: LLMContextToolChoice | NotGiven = tool_choice
 
-    @property
-    def messages(self) -> List[LLMContextMessage]:
-        """Get the current messages list.
-
-        NOTE: This is equivalent to calling `get_messages()` with no filter. If
-        you want to filter out LLM-specific messages that don't pertain to your
-        LLM, use `get_messages()` directly.
-
-        Returns:
-            List of conversation messages.
-        """
-        return self.get_messages()
-
     def get_messages(self, llm_specific_filter: Optional[str] = None) -> List[LLMContextMessage]:
         """Get the current messages list.
 
@@ -126,8 +113,7 @@ class LLMContext:
             llm_specific_filter: Optional filter to return LLM-specific
                 messages for the given LLM, in addition to the standard
                 messages. If messages end up being filtered, an error will be
-                logged; this is intended to catch accidental use of
-                incompatible LLM-specific messages.
+                logged.
 
         Returns:
             List of conversation messages.

src/pipecat/processors/filters/function_filter.py

@@ -12,7 +12,7 @@ allowing for flexible frame filtering logic in processing pipelines.
 
 from typing import Awaitable, Callable
 
-from pipecat.frames.frames import CancelFrame, EndFrame, Frame, StartFrame, SystemFrame
+from pipecat.frames.frames import EndFrame, Frame, SystemFrame
 from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
 
 
@@ -28,7 +28,6 @@ class FunctionFilter(FrameProcessor):
         self,
         filter: Callable[[Frame], Awaitable[bool]],
         direction: FrameDirection = FrameDirection.DOWNSTREAM,
-        filter_system_frames: bool = False,
     ):
         """Initialize the function filter.
 
@@ -37,32 +36,22 @@ class FunctionFilter(FrameProcessor):
                 frame should pass through, False otherwise.
             direction: The direction to apply filtering. Only frames moving in
                 this direction will be filtered. Defaults to DOWNSTREAM.
-            filter_system_frames: Whether to filter system frames. Defaults to False.
         """
         super().__init__()
         self._filter = filter
         self._direction = direction
-        self._filter_system_frames = filter_system_frames
 
     #
     # Frame processor
     #
 
+    # Ignore system frames, end frames and frames that are not following the
+    # direction of this gate
     def _should_passthrough_frame(self, frame, direction):
         """Check if a frame should pass through without filtering."""
-        # Always passthrough frames in the wrong direction
-        if direction != self._direction:
-            return True
-
-        # Always passthrough lifecycle frames
-        if isinstance(frame, (StartFrame, EndFrame, CancelFrame)):
-            return True
-
-        # If not filtering system frames, passthrough all other system frames
-        if not self._filter_system_frames and isinstance(frame, SystemFrame):
-            return True
-
-        return False
+        # Ignore system frames, end frames and frames that are not following the
+        # direction of this gate
+        return isinstance(frame, (SystemFrame, EndFrame)) or direction != self._direction
 
     async def process_frame(self, frame: Frame, direction: FrameDirection):
         """Process a frame through the filter.

src/pipecat/processors/frameworks/rtvi.py

@@ -1018,7 +1018,6 @@ class RTVIObserver(BaseObserver):
 
         if (
             isinstance(frame, (UserStartedSpeakingFrame, UserStoppedSpeakingFrame))
-            and (direction == FrameDirection.DOWNSTREAM)
             and self._params.user_speaking_enabled
         ):
             await self._handle_interruptions(frame)

src/pipecat/runner/daily.py

@@ -76,7 +76,6 @@ class DailyRoomConfig(BaseModel):
 async def configure(
     aiohttp_session: aiohttp.ClientSession,
     *,
-    api_key: Optional[str] = None,
     room_exp_duration: Optional[float] = 2.0,
     token_exp_duration: Optional[float] = 2.0,
     sip_caller_phone: Optional[str] = None,
@@ -93,7 +92,6 @@ async def configure(
 
     Args:
         aiohttp_session: HTTP session for making API requests.
-        api_key: Daily API key.
         room_exp_duration: Room expiration time in hours.
         token_exp_duration: Token expiration time in hours.
         sip_caller_phone: Phone number or identifier for SIP display name.
@@ -131,7 +129,7 @@ async def configure(
         config = await configure(session, room_properties=custom_props)
     """
     # Check for required API key
-    api_key = api_key or os.getenv("DAILY_API_KEY")
+    api_key = os.getenv("DAILY_API_KEY")
     if not api_key:
         raise Exception(
             "DAILY_API_KEY environment variable is required. "

src/pipecat/runner/run.py

@@ -82,7 +82,6 @@ from loguru import logger
 
 from pipecat.runner.types import (
     DailyRunnerArguments,
-    RunnerArguments,
     SmallWebRTCRunnerArguments,
     WebSocketRunnerArguments,
 )
@@ -310,7 +309,7 @@ def _setup_webrtc_routes(
     ):
         """Mimic Pipecat Cloud's proxy."""
         active_session = active_sessions.get(session_id)
-        if active_session is None:
+        if not active_session:
             return Response(content="Invalid or not-yet-ready session_id", status_code=404)
 
         if path.endswith("api/offer"):
@@ -530,9 +529,9 @@ def _setup_daily_routes(app: FastAPI):
     """Set up Daily-specific routes."""
 
     @app.get("/")
-    async def create_room_and_start_agent():
+    async def start_agent():
         """Launch a Daily bot and redirect to room."""
-        print("Starting bot with Daily transport and redirecting to Daily room")
+        print("Starting bot with Daily transport")
 
         import aiohttp
 
@@ -547,11 +546,11 @@ def _setup_daily_routes(app: FastAPI):
             asyncio.create_task(bot_module.bot(runner_args))
             return RedirectResponse(room_url)
 
-    @app.post("/start")
-    async def start_agent(request: Request):
-        """Handler for /start endpoints.
+    async def _handle_rtvi_request(request: Request):
+        """Common handler for both /start and /connect endpoints.
 
         Expects POST body like::
+
             {
                 "createDailyRoom": true,
                 "dailyRoomProperties": { "start_video_off": true },
@@ -568,38 +567,45 @@ def _setup_daily_routes(app: FastAPI):
             logger.error(f"Failed to parse request body: {e}")
             request_data = {}
 
-        create_daily_room = request_data.get("createDailyRoom", False)
-        body = request_data.get("body", {})
+        # Extract the body data that should be passed to the bot
+        # This mimics Pipecat Cloud's behavior
+        bot_body = request_data.get("body", {})
 
-        bot_module = _get_bot_module()
-
-        existing_room_url = os.getenv("DAILY_SAMPLE_ROOM_URL")
-
-        result = None
-
-        # Configure room if:
-        # 1. Explicitly requested via createDailyRoom in payload
-        # 2. Using pre-configured room from DAILY_SAMPLE_ROOM_URL env var
-        if create_daily_room or existing_room_url:
-            import aiohttp
-
-            from pipecat.runner.daily import configure
-
-            async with aiohttp.ClientSession() as session:
-                room_url, token = await configure(session)
-                runner_args = DailyRunnerArguments(room_url=room_url, token=token, body=body)
-                result = {
-                    "dailyRoom": room_url,
-                    "dailyToken": token,
-                    "sessionId": str(uuid.uuid4()),
-                }
+        # Log the extracted body data for debugging
+        if bot_body:
+            logger.info(f"Extracted body data for bot: {bot_body}")
         else:
-            runner_args = RunnerArguments(body=body)
+            logger.debug("No body data provided in request")
 
-        # Start the bot in the background
-        asyncio.create_task(bot_module.bot(runner_args))
+        from pipecat.runner.daily import configure
 
-        return result
+        async with aiohttp.ClientSession() as session:
+            room_url, token = await configure(session)
+
+            # Start the bot in the background with extracted body data
+            bot_module = _get_bot_module()
+            runner_args = DailyRunnerArguments(room_url=room_url, token=token, body=bot_body)
+            asyncio.create_task(bot_module.bot(runner_args))
+            # Match PCC /start endpoint response format:
+            return {"dailyRoom": room_url, "dailyToken": token}
+
+    @app.post("/start")
+    async def rtvi_start(request: Request):
+        """Launch a Daily bot and return connection info for RTVI clients."""
+        return await _handle_rtvi_request(request)
+
+    @app.post("/connect")
+    async def rtvi_connect(request: Request):
+        """Launch a Daily bot and return connection info for RTVI clients.
+
+        .. deprecated:: 0.0.78
+            Use /start instead. This endpoint will be removed in a future version.
+        """
+        logger.warning(
+            "DEPRECATED: /connect endpoint is deprecated. Please use /start instead. "
+            "This endpoint will be removed in a future version."
+        )
+        return await _handle_rtvi_request(request)
 
 
 def _setup_telephony_routes(app: FastAPI, *, transport_type: str, proxy: str):

src/pipecat/runner/types.py

@@ -20,11 +20,9 @@ from fastapi import WebSocket
 class RunnerArguments:
     """Base class for runner session arguments."""
 
-    # Use kw_only so subclasses don't need to worry about ordering.
-    handle_sigint: bool = field(init=False, kw_only=True)
-    handle_sigterm: bool = field(init=False, kw_only=True)
-    pipeline_idle_timeout_secs: int = field(init=False, kw_only=True)
-    body: Optional[Any] = field(default_factory=dict, kw_only=True)
+    handle_sigint: bool = field(init=False)
+    handle_sigterm: bool = field(init=False)
+    pipeline_idle_timeout_secs: int = field(init=False)
 
     def __post_init__(self):
         self.handle_sigint = False
@@ -44,6 +42,7 @@ class DailyRunnerArguments(RunnerArguments):
 
     room_url: str
     token: Optional[str] = None
+    body: Optional[Any] = field(default_factory=dict)
 
 
 @dataclass
@@ -56,6 +55,7 @@ class WebSocketRunnerArguments(RunnerArguments):
     """
 
     websocket: WebSocket
+    body: Optional[Any] = field(default_factory=dict)
 
 
 @dataclass

src/pipecat/services/aws/llm.py

@@ -720,11 +720,11 @@ class AWSBedrockLLMService(LLMService):
             additional_model_request_fields: Additional model-specific parameters.
         """
 
-        max_tokens: Optional[int] = Field(default=None, ge=1)
-        temperature: Optional[float] = Field(default=None, ge=0.0, le=1.0)
-        top_p: Optional[float] = Field(default=None, ge=0.0, le=1.0)
+        max_tokens: Optional[int] = Field(default_factory=lambda: 4096, ge=1)
+        temperature: Optional[float] = Field(default_factory=lambda: 0.7, ge=0.0, le=1.0)
+        top_p: Optional[float] = Field(default_factory=lambda: 0.999, ge=0.0, le=1.0)
         stop_sequences: Optional[List[str]] = Field(default_factory=lambda: [])
-        latency: Optional[str] = Field(default=None)
+        latency: Optional[str] = Field(default_factory=lambda: "standard")
         additional_model_request_fields: Optional[Dict[str, Any]] = Field(default_factory=dict)
 
     def __init__(
@@ -801,24 +801,6 @@ class AWSBedrockLLMService(LLMService):
         """
         return True
 
-    def _build_inference_config(self) -> Dict[str, Any]:
-        """Build inference config with only the parameters that are set.
-
-        This prevents conflicts with models (e.g., Claude Sonnet 4.5) that don't
-        allow certain parameter combinations like temperature and top_p together.
-
-        Returns:
-            Dictionary containing only the inference parameters that are not None.
-        """
-        inference_config = {}
-        if self._settings["max_tokens"] is not None:
-            inference_config["maxTokens"] = self._settings["max_tokens"]
-        if self._settings["temperature"] is not None:
-            inference_config["temperature"] = self._settings["temperature"]
-        if self._settings["top_p"] is not None:
-            inference_config["topP"] = self._settings["top_p"]
-        return inference_config
-
     async def run_inference(self, context: LLMContext | OpenAILLMContext) -> Optional[str]:
         """Run a one-shot, out-of-band (i.e. out-of-pipeline) inference with the given LLM context.
 
@@ -844,16 +826,16 @@ class AWSBedrockLLMService(LLMService):
         model_id = self.model_name
 
         # Prepare request parameters
-        inference_config = self._build_inference_config()
-
         request_params = {
             "modelId": model_id,
             "messages": messages,
+            "inferenceConfig": {
+                "maxTokens": 8192,
+                "temperature": 0.7,
+                "topP": 0.9,
+            },
         }
 
-        if inference_config:
-            request_params["inferenceConfig"] = inference_config
-
         if system:
             request_params["system"] = system
 
@@ -992,20 +974,21 @@ class AWSBedrockLLMService(LLMService):
             tools = params_from_context["tools"]
             tool_choice = params_from_context["tool_choice"]
 
-            # Set up inference config - only include parameters that are set
-            inference_config = self._build_inference_config()
+            # Set up inference config
+            inference_config = {
+                "maxTokens": self._settings["max_tokens"],
+                "temperature": self._settings["temperature"],
+                "topP": self._settings["top_p"],
+            }
 
             # Prepare request parameters
             request_params = {
                 "modelId": self.model_name,
                 "messages": messages,
+                "inferenceConfig": inference_config,
                 "additionalModelRequestFields": self._settings["additional_model_request_fields"],
             }
 
-            # Only add inference config if it has parameters
-            if inference_config:
-                request_params["inferenceConfig"] = inference_config
-
             # Add system message
             if system:
                 request_params["system"] = system

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

@@ -10,8 +10,7 @@ This module provides specialized context aggregators and message handling for AW
 including conversation history management and role-specific message processing.
 
 .. deprecated:: 0.0.91
-    AWS Nova Sonic no longer uses types from this module under the hood.
-    It now uses `LLMContext` and `LLMContextAggregatorPair`.
+    AWS Nova Sonic now supports `LLMContext` and `LLMContextAggregatorPair`.
     Using the new patterns should allow you to not need types from this module.
 
     BEFORE:
@@ -27,6 +26,9 @@ including conversation history management and role-specific message processing.
     context: AWSNovaSonicLLMContext
     # or
     context: OpenAILLMContext
+
+    # Reading messages from context
+    messages = context.messages
     ```
 
     AFTER:
@@ -40,6 +42,9 @@ including conversation history management and role-specific message processing.
 
     # Context type
     context: LLMContext
+
+    # Reading messages from context
+    messages = context.get_messages()
     ```
 """
 
@@ -48,10 +53,8 @@ import warnings
 with warnings.catch_warnings():
     warnings.simplefilter("always")
     warnings.warn(
-        "Types in pipecat.services.aws.nova_sonic.context (or "
-        "pipecat.services.aws_nova_sonic.context) are deprecated. \n"
-        "AWS Nova Sonic no longer uses types from this module under the hood. \n"
-        "It now uses `LLMContext` and `LLMContextAggregatorPair`. \n"
+        "Types in pipecat.services.aws.nova_sonic.context are deprecated. \n"
+        "AWS Nova Sonic now supports `LLMContext` and `LLMContextAggregatorPair`. \n"
         "Using the new patterns should allow you to not need types from this module.\n\n"
         "BEFORE:\n"
         "```\n"
@@ -64,6 +67,8 @@ with warnings.catch_warnings():
         "context: AWSNovaSonicLLMContext\n"
         "# or\n"
         "context: OpenAILLMContext\n\n"
+        "# Reading messages from context\n"
+        "messages = context.messages\n"
         "```\n\n"
         "AFTER:\n"
         "```\n"
@@ -74,363 +79,9 @@ with warnings.catch_warnings():
         "frame: LLMContextFrame\n\n"
         "# Context type\n"
         "context: LLMContext\n\n"
+        "# Reading messages from context\n"
+        "messages = context.messages\n"
         "```",
         DeprecationWarning,
         stacklevel=2,
     )
-
-import copy
-from dataclasses import dataclass, field
-from enum import Enum
-
-from loguru import logger
-
-from pipecat.frames.frames import (
-    BotStoppedSpeakingFrame,
-    DataFrame,
-    Frame,
-    FunctionCallResultFrame,
-    InterruptionFrame,
-    LLMFullResponseEndFrame,
-    LLMFullResponseStartFrame,
-    LLMMessagesAppendFrame,
-    LLMMessagesUpdateFrame,
-    LLMSetToolChoiceFrame,
-    LLMSetToolsFrame,
-    TextFrame,
-    UserImageRawFrame,
-)
-from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
-from pipecat.processors.frame_processor import FrameDirection
-from pipecat.services.aws.nova_sonic.frames import AWSNovaSonicFunctionCallResultFrame
-from pipecat.services.openai.llm import (
-    OpenAIAssistantContextAggregator,
-    OpenAIUserContextAggregator,
-)
-
-
-class Role(Enum):
-    """Roles supported in AWS Nova Sonic conversations.
-
-    Parameters:
-        SYSTEM: System-level messages (not used in conversation history).
-        USER: Messages sent by the user.
-        ASSISTANT: Messages sent by the assistant.
-        TOOL: Messages sent by tools (not used in conversation history).
-    """
-
-    SYSTEM = "SYSTEM"
-    USER = "USER"
-    ASSISTANT = "ASSISTANT"
-    TOOL = "TOOL"
-
-
-@dataclass
-class AWSNovaSonicConversationHistoryMessage:
-    """A single message in AWS Nova Sonic conversation history.
-
-    Parameters:
-        role: The role of the message sender (USER or ASSISTANT only).
-        text: The text content of the message.
-    """
-
-    role: Role  # only USER and ASSISTANT
-    text: str
-
-
-@dataclass
-class AWSNovaSonicConversationHistory:
-    """Complete conversation history for AWS Nova Sonic initialization.
-
-    Parameters:
-        system_instruction: System-level instruction for the conversation.
-        messages: List of conversation messages between user and assistant.
-    """
-
-    system_instruction: str = None
-    messages: list[AWSNovaSonicConversationHistoryMessage] = field(default_factory=list)
-
-
-class AWSNovaSonicLLMContext(OpenAILLMContext):
-    """Specialized LLM context for AWS Nova Sonic service.
-
-    Extends OpenAI context with Nova Sonic-specific message handling,
-    conversation history management, and text buffering capabilities.
-    """
-
-    def __init__(self, messages=None, tools=None, **kwargs):
-        """Initialize AWS Nova Sonic LLM context.
-
-        Args:
-            messages: Initial messages for the context.
-            tools: Available tools for the context.
-            **kwargs: Additional arguments passed to parent class.
-        """
-        super().__init__(messages=messages, tools=tools, **kwargs)
-        self.__setup_local()
-
-    def __setup_local(self, system_instruction: str = ""):
-        self._assistant_text = ""
-        self._user_text = ""
-        self._system_instruction = system_instruction
-
-    @staticmethod
-    def upgrade_to_nova_sonic(
-        obj: OpenAILLMContext, system_instruction: str
-    ) -> "AWSNovaSonicLLMContext":
-        """Upgrade an OpenAI context to AWS Nova Sonic context.
-
-        Args:
-            obj: The OpenAI context to upgrade.
-            system_instruction: System instruction for the context.
-
-        Returns:
-            The upgraded AWS Nova Sonic context.
-        """
-        if isinstance(obj, OpenAILLMContext) and not isinstance(obj, AWSNovaSonicLLMContext):
-            obj.__class__ = AWSNovaSonicLLMContext
-            obj.__setup_local(system_instruction)
-        return obj
-
-    # NOTE: this method has the side-effect of updating _system_instruction from messages
-    def get_messages_for_initializing_history(self) -> AWSNovaSonicConversationHistory:
-        """Get conversation history for initializing AWS Nova Sonic session.
-
-        Processes stored messages and extracts system instruction and conversation
-        history in the format expected by AWS Nova Sonic.
-
-        Returns:
-            Formatted conversation history with system instruction and messages.
-        """
-        history = AWSNovaSonicConversationHistory(system_instruction=self._system_instruction)
-
-        # Bail if there are no messages
-        if not self.messages:
-            return history
-
-        messages = copy.deepcopy(self.messages)
-
-        # If we have a "system" message as our first message, let's pull that out into "instruction"
-        if messages[0].get("role") == "system":
-            system = messages.pop(0)
-            content = system.get("content")
-            if isinstance(content, str):
-                history.system_instruction = content
-            elif isinstance(content, list):
-                history.system_instruction = content[0].get("text")
-            if history.system_instruction:
-                self._system_instruction = history.system_instruction
-
-        # Process remaining messages to fill out conversation history.
-        # Nova Sonic supports "user" and "assistant" messages in history.
-        for message in messages:
-            history_message = self.from_standard_message(message)
-            if history_message:
-                history.messages.append(history_message)
-
-        return history
-
-    def get_messages_for_persistent_storage(self):
-        """Get messages formatted for persistent storage.
-
-        Returns:
-            List of messages including system instruction if present.
-        """
-        messages = super().get_messages_for_persistent_storage()
-        # If we have a system instruction and messages doesn't already contain it, add it
-        if self._system_instruction and not (messages and messages[0].get("role") == "system"):
-            messages.insert(0, {"role": "system", "content": self._system_instruction})
-        return messages
-
-    def from_standard_message(self, message) -> AWSNovaSonicConversationHistoryMessage:
-        """Convert standard message format to Nova Sonic format.
-
-        Args:
-            message: Standard message dictionary to convert.
-
-        Returns:
-            Nova Sonic conversation history message, or None if not convertible.
-        """
-        role = message.get("role")
-        if message.get("role") == "user" or message.get("role") == "assistant":
-            content = message.get("content")
-            if isinstance(message.get("content"), list):
-                content = ""
-                for c in message.get("content"):
-                    if c.get("type") == "text":
-                        content += " " + c.get("text")
-                    else:
-                        logger.error(
-                            f"Unhandled content type in context message: {c.get('type')} - {message}"
-                        )
-            # There won't be content if this is an assistant tool call entry.
-            # We're ignoring those since they can't be loaded into AWS Nova Sonic conversation
-            # history
-            if content:
-                return AWSNovaSonicConversationHistoryMessage(role=Role[role.upper()], text=content)
-        # NOTE: we're ignoring messages with role "tool" since they can't be loaded into AWS Nova
-        # Sonic conversation history
-
-    def buffer_user_text(self, text):
-        """Buffer user text for later flushing to context.
-
-        Args:
-            text: User text to buffer.
-        """
-        self._user_text += f" {text}" if self._user_text else text
-        # logger.debug(f"User text buffered: {self._user_text}")
-
-    def flush_aggregated_user_text(self) -> str:
-        """Flush buffered user text to context as a complete message.
-
-        Returns:
-            The flushed user text, or empty string if no text was buffered.
-        """
-        if not self._user_text:
-            return ""
-        user_text = self._user_text
-        message = {
-            "role": "user",
-            "content": [{"type": "text", "text": user_text}],
-        }
-        self._user_text = ""
-        self.add_message(message)
-        # logger.debug(f"Context updated (user): {self.get_messages_for_logging()}")
-        return user_text
-
-    def buffer_assistant_text(self, text):
-        """Buffer assistant text for later flushing to context.
-
-        Args:
-            text: Assistant text to buffer.
-        """
-        self._assistant_text += text
-        # logger.debug(f"Assistant text buffered: {self._assistant_text}")
-
-    def flush_aggregated_assistant_text(self):
-        """Flush buffered assistant text to context as a complete message."""
-        if not self._assistant_text:
-            return
-        message = {
-            "role": "assistant",
-            "content": [{"type": "text", "text": self._assistant_text}],
-        }
-        self._assistant_text = ""
-        self.add_message(message)
-        # logger.debug(f"Context updated (assistant): {self.get_messages_for_logging()}")
-
-
-@dataclass
-class AWSNovaSonicMessagesUpdateFrame(DataFrame):
-    """Frame containing updated AWS Nova Sonic context.
-
-    Parameters:
-        context: The updated AWS Nova Sonic LLM context.
-    """
-
-    context: AWSNovaSonicLLMContext
-
-
-class AWSNovaSonicUserContextAggregator(OpenAIUserContextAggregator):
-    """Context aggregator for user messages in AWS Nova Sonic conversations.
-
-    Extends the OpenAI user context aggregator to emit Nova Sonic-specific
-    context update frames.
-    """
-
-    async def process_frame(
-        self, frame: Frame, direction: FrameDirection = FrameDirection.DOWNSTREAM
-    ):
-        """Process frames and emit Nova Sonic-specific context updates.
-
-        Args:
-            frame: The frame to process.
-            direction: The direction the frame is traveling.
-        """
-        await super().process_frame(frame, direction)
-
-        # Parent does not push LLMMessagesUpdateFrame
-        if isinstance(frame, LLMMessagesUpdateFrame):
-            await self.push_frame(AWSNovaSonicMessagesUpdateFrame(context=self._context))
-
-
-class AWSNovaSonicAssistantContextAggregator(OpenAIAssistantContextAggregator):
-    """Context aggregator for assistant messages in AWS Nova Sonic conversations.
-
-    Provides specialized handling for assistant responses and function calls
-    in AWS Nova Sonic context, with custom frame processing logic.
-    """
-
-    async def process_frame(self, frame: Frame, direction: FrameDirection):
-        """Process frames with Nova Sonic-specific logic.
-
-        Args:
-            frame: The frame to process.
-            direction: The direction the frame is traveling.
-        """
-        # HACK: For now, disable the context aggregator by making it just pass through all frames
-        # that the parent handles (except the function call stuff, which we still need).
-        # For an explanation of this hack, see
-        # AWSNovaSonicLLMService._report_assistant_response_text_added.
-        if isinstance(
-            frame,
-            (
-                InterruptionFrame,
-                LLMFullResponseStartFrame,
-                LLMFullResponseEndFrame,
-                TextFrame,
-                LLMMessagesAppendFrame,
-                LLMMessagesUpdateFrame,
-                LLMSetToolsFrame,
-                LLMSetToolChoiceFrame,
-                UserImageRawFrame,
-                BotStoppedSpeakingFrame,
-            ),
-        ):
-            await self.push_frame(frame, direction)
-        else:
-            await super().process_frame(frame, direction)
-
-    async def handle_function_call_result(self, frame: FunctionCallResultFrame):
-        """Handle function call results for AWS Nova Sonic.
-
-        Args:
-            frame: The function call result frame to handle.
-        """
-        await super().handle_function_call_result(frame)
-
-        # The standard function callback code path pushes the FunctionCallResultFrame from the LLM
-        # itself, so we didn't have a chance to add the result to the AWS Nova Sonic server-side
-        # context. Let's push a special frame to do that.
-        await self.push_frame(
-            AWSNovaSonicFunctionCallResultFrame(result_frame=frame), FrameDirection.UPSTREAM
-        )
-
-
-@dataclass
-class AWSNovaSonicContextAggregatorPair:
-    """Pair of user and assistant context aggregators for AWS Nova Sonic.
-
-    Parameters:
-        _user: The user context aggregator.
-        _assistant: The assistant context aggregator.
-    """
-
-    _user: AWSNovaSonicUserContextAggregator
-    _assistant: AWSNovaSonicAssistantContextAggregator
-
-    def user(self) -> AWSNovaSonicUserContextAggregator:
-        """Get the user context aggregator.
-
-        Returns:
-            The user context aggregator instance.
-        """
-        return self._user
-
-    def assistant(self) -> AWSNovaSonicAssistantContextAggregator:
-        """Get the assistant context aggregator.
-
-        Returns:
-            The assistant context aggregator instance.
-        """
-        return self._assistant

src/pipecat/services/aws_nova_sonic/context.py

@@ -10,12 +10,78 @@ This module provides specialized context aggregators and message handling for AW
 including conversation history management and role-specific message processing.
 
 .. deprecated:: 0.0.91
-    AWS Nova Sonic no longer uses types from this module under the hood.
-    It now uses `LLMContext` and `LLMContextAggregatorPair`.
+    AWS Nova Sonic now supports `LLMContext` and `LLMContextAggregatorPair`.
     Using the new patterns should allow you to not need types from this module.
 
-    See deprecation warning in pipecat.services.aws.nova_sonic.context for more
-    details.
+    BEFORE:
+    ```
+    # Setup
+    context = OpenAILLMContext(messages, tools)
+    context_aggregator = llm.create_context_aggregator(context)
+
+    # Context frame type
+    frame: OpenAILLMContextFrame
+
+    # Context type
+    context: AWSNovaSonicLLMContext
+    # or
+    context: OpenAILLMContext
+
+    # Reading messages from context
+    messages = context.messages
+    ```
+
+    AFTER:
+    ```
+    # Setup
+    context = LLMContext(messages, tools)
+    context_aggregator = LLMContextAggregatorPair(context)
+
+    # Context frame type
+    frame: LLMContextFrame
+
+    # Context type
+    context: LLMContext
+
+    # Reading messages from context
+    messages = context.get_messages()
+    ```
 """
 
-from pipecat.services.aws.nova_sonic.context import *
+import warnings
+
+with warnings.catch_warnings():
+    warnings.simplefilter("always")
+    warnings.warn(
+        "Types in pipecat.services.aws_nova_sonic.context are deprecated. \n"
+        "AWS Nova Sonic now supports `LLMContext` and `LLMContextAggregatorPair`. \n"
+        "Using the new patterns should allow you to not need types from this module.\n\n"
+        "BEFORE:\n"
+        "```\n"
+        "# Setup\n"
+        "context = OpenAILLMContext(messages, tools)\n"
+        "context_aggregator = llm.create_context_aggregator(context)\n\n"
+        "# Context frame type\n"
+        "frame: OpenAILLMContextFrame\n\n"
+        "# Context type\n"
+        "context: AWSNovaSonicLLMContext\n"
+        "# or\n"
+        "context: OpenAILLMContext\n\n"
+        "# Reading messages from context\n"
+        "messages = context.messages\n"
+        "```\n\n"
+        "AFTER:\n"
+        "```\n"
+        "# Setup\n"
+        "context = LLMContext(messages, tools)\n"
+        "context_aggregator = LLMContextAggregatorPair(context)\n\n"
+        "# Context frame type\n"
+        "frame: LLMContextFrame\n\n"
+        "# Context type\n"
+        "context: LLMContext\n\n"
+        "# Reading messages from context\n"
+        "messages = context.messages\n"
+        "```",
+        DeprecationWarning,
+        stacklevel=2,
+    )

src/pipecat/services/google/llm.py

@@ -1034,23 +1034,6 @@ class GoogleLLMService(LLMService):
         if context:
             await self._process_context(context)
 
-    async def stop(self, frame):
-        """Override stop to gracefully close the client."""
-        await super().stop(frame)
-        await self._close_client()
-
-    async def cancel(self, frame):
-        """Override cancel to gracefully close the client."""
-        await super().cancel(frame)
-        await self._close_client()
-
-    async def _close_client(self):
-        try:
-            await self._client.aio.aclose()
-        except Exception:
-            # Do nothing - we're shutting down anyway
-            pass
-
     def create_context_aggregator(
         self,
         context: OpenAILLMContext,

src/pipecat/tests/__init__.py

@@ -1,12 +0,0 @@
-#
-# Copyright (c) 2024-2025 Daily
-#
-# SPDX-License-Identifier: BSD 2-Clause License
-#
-
-"""Public testing API for Pipecat frame processors."""
-
-from .serialization import dict_to_frame, frame_to_dict, load_frames_from_json
-from .test_runner import run_test_from_file
-
-__all__ = ["dict_to_frame", "frame_to_dict", "load_frames_from_json", "run_test_from_file"]

src/pipecat/tests/serialization.py

@@ -1,150 +0,0 @@
-#
-# Copyright (c) 2024-2025 Daily
-#
-# SPDX-License-Identifier: BSD 2-Clause License
-#
-
-"""Frame serialization and deserialization for testing."""
-
-import base64
-import inspect
-import json
-from pathlib import Path
-from typing import Any, Dict, List
-
-from pipecat.frames import frames
-
-
-def _get_frame_class(frame_type: str):
-    """Get a frame class by name from the frames module.
-    
-    Args:
-        frame_type: The name of the frame class (e.g., "TextFrame")
-        
-    Returns:
-        The frame class object
-        
-    Raises:
-        ValueError: If the frame type is not found
-    """
-    if not hasattr(frames, frame_type):
-        raise ValueError(f"Unknown frame type: {frame_type}")
-    
-    cls = getattr(frames, frame_type)
-    if not inspect.isclass(cls) or not issubclass(cls, frames.Frame):
-        raise ValueError(f"{frame_type} is not a valid Frame class")
-    
-    return cls
-
-
-def dict_to_frame(data: Dict[str, Any]) -> frames.Frame:
-    """Convert a dictionary to a Frame object.
-    
-    Args:
-        data: Dictionary containing frame data with a "type" key
-        
-    Returns:
-        A Frame instance
-        
-    Raises:
-        ValueError: If frame type is missing or invalid
-        
-    Example:
-        >>> dict_to_frame({"type": "TextFrame", "text": "hello"})
-        TextFrame(text="hello")
-    """
-    if "type" not in data:
-        raise ValueError("Frame dictionary must contain a 'type' field")
-    
-    frame_type = data["type"]
-    frame_cls = _get_frame_class(frame_type)
-    
-    # Build kwargs from data, excluding 'type'
-    kwargs = {k: v for k, v in data.items() if k != "type"}
-    
-    # Special handling for audio frames with base64 encoded audio
-    if "audio" in kwargs and isinstance(kwargs["audio"], str):
-        kwargs["audio"] = base64.b64decode(kwargs["audio"])
-    
-    # Special handling for image frames with base64 encoded images
-    if "image" in kwargs and isinstance(kwargs["image"], str):
-        kwargs["image"] = base64.b64decode(kwargs["image"])
-    
-    try:
-        return frame_cls(**kwargs)
-    except TypeError as e:
-        raise ValueError(f"Failed to create {frame_type}: {e}")
-
-
-def load_frames_from_json(filepath: str) -> List[frames.Frame]:
-    """Load frames from a JSON file.
-    
-    Args:
-        filepath: Path to JSON file containing frame data
-        
-    Returns:
-        List of Frame objects
-        
-    Raises:
-        FileNotFoundError: If the file doesn't exist
-        ValueError: If JSON is invalid or frames cannot be deserialized
-        
-    Example JSON format:
-        {
-          "input_frames": [
-            {"type": "TextFrame", "text": "hello"},
-            {"type": "EndFrame"}
-          ]
-        }
-    """
-    path = Path(filepath)
-    if not path.exists():
-        raise FileNotFoundError(f"Frame file not found: {filepath}")
-    
-    with open(path, "r") as f:
-        data = json.load(f)
-    
-    if not isinstance(data, dict):
-        raise ValueError("JSON must contain a dictionary")
-    
-    if "input_frames" not in data:
-        raise ValueError("JSON must contain an 'input_frames' key")
-    
-    frame_dicts = data["input_frames"]
-    if not isinstance(frame_dicts, list):
-        raise ValueError("'input_frames' must be a list")
-    
-    return [dict_to_frame(frame_dict) for frame_dict in frame_dicts]
-
-
-def frame_to_dict(frame: frames.Frame) -> Dict[str, Any]:
-    """Convert a Frame object to a dictionary.
-    
-    Args:
-        frame: Frame object to serialize
-        
-    Returns:
-        Dictionary representation of the frame
-        
-    Example:
-        >>> frame_to_dict(TextFrame(text="hello"))
-        {"type": "TextFrame", "text": "hello"}
-    """
-    result = {"type": frame.__class__.__name__}
-    
-    # Get all fields from the dataclass
-    if hasattr(frame, "__dataclass_fields__"):
-        for field_name in frame.__dataclass_fields__:
-            # Skip internal fields from base Frame class
-            if field_name in ("id", "name", "pts", "metadata", "transport_source", "transport_destination"):
-                continue
-            
-            value = getattr(frame, field_name, None)
-            if value is not None:
-                # Special handling for bytes (audio/image data)
-                if isinstance(value, bytes):
-                    result[field_name] = base64.b64encode(value).decode("utf-8")
-                else:
-                    result[field_name] = value
-    
-    return result

src/pipecat/tests/test_runner.py

@@ -1,169 +0,0 @@
-#
-# Copyright (c) 2024-2025 Daily
-#
-# SPDX-License-Identifier: BSD 2-Clause License
-#
-
-"""Test runner for frame processors from JSON test files."""
-
-import json
-from pathlib import Path
-from typing import Any, Dict, List, Optional, Tuple
-
-from pipecat.frames.frames import Frame
-from pipecat.processors.frame_processor import FrameProcessor
-
-from .serialization import dict_to_frame, frame_to_dict, load_frames_from_json
-
-
-async def run_test_from_file(
-    processor: FrameProcessor,
-    test_file: str,
-) -> Tuple[List[Frame], Optional[List[Dict[str, Any]]], bool]:
-    """Run a processor test from a JSON test file.
-
-    Args:
-        processor: The frame processor to test
-        test_file: Path to JSON test file
-
-    Returns:
-        Tuple of (output_frames, expected_output, passed)
-        - output_frames: List of Frame objects that were output
-        - expected_output: List of expected frame dicts (None if not specified)
-        - passed: True if test passed, False if failed, None if no validation
-
-    Raises:
-        FileNotFoundError: If test file doesn't exist
-        ValueError: If test file is invalid
-
-    Example test file format:
-        {
-          "input_frames": [
-            {"type": "TextFrame", "text": "hello"}
-          ],
-          "expected_output": [
-            {"type": "TextFrame"},
-            {"type": "EndFrame"}
-          ]
-        }
-    """
-    path = Path(test_file)
-    if not path.exists():
-        raise FileNotFoundError(f"Test file not found: {test_file}")
-
-    with open(path, "r") as f:
-        test_data = json.load(f)
-
-    # Load input frames
-    if "input_frames" not in test_data:
-        raise ValueError("Test file must contain 'input_frames'")
-
-    input_frames = [dict_to_frame(frame_dict) for frame_dict in test_data["input_frames"]]
-
-    # Load expected output (optional)
-    expected_output = test_data.get("expected_output", None)
-
-    # Run the test
-    # Note: run_test() only collects frames if expected_down_frames is provided,
-    # so we need to manually collect from the pipeline ourselves
-    import asyncio
-    from pipecat.frames.frames import EndFrame
-    from pipecat.processors.frame_processor import FrameDirection
-    from pipecat.tests.utils import QueuedFrameProcessor
-    from pipecat.pipeline.pipeline import Pipeline
-    from pipecat.pipeline.task import PipelineTask, PipelineParams
-    from pipecat.pipeline.runner import PipelineRunner
-
-    # Set up the test pipeline manually
-    received_down = asyncio.Queue()
-    received_up = asyncio.Queue()
-    source = QueuedFrameProcessor(
-        queue=received_up,
-        queue_direction=FrameDirection.UPSTREAM,
-        ignore_start=True,
-    )
-    sink = QueuedFrameProcessor(
-        queue=received_down,
-        queue_direction=FrameDirection.DOWNSTREAM,
-        ignore_start=True,
-    )
-
-    pipeline = Pipeline([source, processor, sink])
-    task = PipelineTask(
-        pipeline,
-        params=PipelineParams(),
-        observers=[],
-        cancel_on_idle_timeout=False,
-    )
-
-    async def push_frames():
-        await asyncio.sleep(0.01)
-        for frame in input_frames:
-            await task.queue_frame(frame)
-        await task.queue_frame(EndFrame())
-
-    runner = PipelineRunner()
-    await asyncio.gather(runner.run(task), push_frames())
-
-    # Collect all frames from the downstream queue
-    downstream_frames = []
-    while not received_down.empty():
-        frame = await received_down.get()
-        if not isinstance(frame, EndFrame):
-            downstream_frames.append(frame)
-
-    # Validate if expected_output is provided
-    passed = None
-    if expected_output is not None:
-        passed = _validate_output(downstream_frames, expected_output)
-
-    return downstream_frames, expected_output, passed
-
-
-def _validate_output(actual_frames: List[Frame], expected_output: List[Dict[str, Any]]) -> bool:
-    """Validate actual output frames against expected output.
-
-    Args:
-        actual_frames: List of frames that were actually output
-        expected_output: List of expected frame specifications
-
-    Returns:
-        True if validation passed, False otherwise
-    """
-    if len(actual_frames) != len(expected_output):
-        return False
-
-    for actual, expected in zip(actual_frames, expected_output):
-        # Check frame type
-        if "type" not in expected:
-            return False
-
-        expected_type = expected["type"]
-        if actual.__class__.__name__ != expected_type:
-            return False
-
-        # Check specific fields if provided
-        for field_name, expected_value in expected.items():
-            if field_name == "type":
-                continue
-
-            if not hasattr(actual, field_name):
-                return False
-
-            actual_value = getattr(actual, field_name)
-
-            # Special handling for different types
-            if isinstance(expected_value, str) and isinstance(actual_value, str):
-                # For string fields, support partial matching with "contains"
-                if field_name.endswith("_contains"):
-                    base_field = field_name.replace("_contains", "")
-                    if hasattr(actual, base_field):
-                        actual_text = getattr(actual, base_field)
-                        if expected_value not in actual_text:
-                            return False
-                elif actual_value != expected_value:
-                    return False
-            elif actual_value != expected_value:
-                return False
-
-    return True

src/pipecat/utils/string.py

@@ -47,7 +47,6 @@ SENTENCE_ENDING_PUNCTUATION: FrozenSet[str] = frozenset(
         "!",
         "?",
         ";",
-        "…",
         # East Asian punctuation (Chinese (Traditional & Simplified), Japanese, Korean)
         "。",  # Ideographic full stop
         "？",  # Full-width question mark

tests/test_service_switcher.py

@@ -7,12 +7,10 @@
 """Unit tests for ServiceSwitcher and related components."""
 
 import unittest
-from dataclasses import dataclass
 
 from pipecat.frames.frames import (
     Frame,
     ManuallySwitchServiceFrame,
-    SystemFrame,
     TextFrame,
 )
 from pipecat.pipeline.pipeline import Pipeline
@@ -54,13 +52,6 @@ class MockFrameProcessor(FrameProcessor):
         self.frame_count = 0
 
 
-@dataclass
-class DummySystemFrame(SystemFrame):
-    """A dummy system frame for testing purposes."""
-
-    text: str = ""
-
-
 class TestServiceSwitcherStrategyManual(unittest.IsolatedAsyncioTestCase):
     """Test cases for ServiceSwitcherStrategyManual."""
 
@@ -149,22 +140,14 @@ class TestServiceSwitcher(unittest.IsolatedAsyncioTestCase):
         # Send some test frames
         frames_to_send = [
             TextFrame(text="Hello 1"),
-            DummySystemFrame(text="System Message 1"),
             TextFrame(text="Hello 2"),
-            DummySystemFrame(text="System Message 2"),
             TextFrame(text="Hello 3"),
         ]
 
         await run_test(
             switcher,
             frames_to_send=frames_to_send,
-            expected_down_frames=[
-                DummySystemFrame,
-                DummySystemFrame,
-                TextFrame,
-                TextFrame,
-                TextFrame,
-            ],
+            expected_down_frames=[TextFrame, TextFrame, TextFrame],
             expected_up_frames=[],  # Expect no error frames
         )
 
@@ -173,13 +156,7 @@ class TestServiceSwitcher(unittest.IsolatedAsyncioTestCase):
         text_frames = [f for f in self.service1.processed_frames if isinstance(f, TextFrame)]
         self.assertEqual(len(text_frames), 3)
 
-        # Only service1 should have processed the system frames
-        system_frames = [
-            f for f in self.service1.processed_frames if isinstance(f, DummySystemFrame)
-        ]
-        self.assertEqual(len(system_frames), 2)
-
-        # Check that other services don't receive text frames (they still get StartFrame/EndFrame)
+        # Check that other services don't receive text frames (they might get StartFrame/EndFrame)
         service2_text_frames = [
             f for f in self.service2.processed_frames if isinstance(f, TextFrame)
         ]
@@ -189,24 +166,10 @@ class TestServiceSwitcher(unittest.IsolatedAsyncioTestCase):
         self.assertEqual(len(service2_text_frames), 0)
         self.assertEqual(len(service3_text_frames), 0)
 
-        # Check that other services don't receive dummy system frames (they still get StartFrame/EndFrame)
-        service2_system_frames = [
-            f for f in self.service2.processed_frames if isinstance(f, DummySystemFrame)
-        ]
-        service3_system_frames = [
-            f for f in self.service3.processed_frames if isinstance(f, DummySystemFrame)
-        ]
-        self.assertEqual(len(service2_system_frames), 0)
-        self.assertEqual(len(service3_system_frames), 0)
-
         # Verify the actual text frames processed
         for i, frame in enumerate(text_frames):
             self.assertEqual(frame.text, f"Hello {i + 1}")
 
-        # Verify the actual system frames processed
-        for i, frame in enumerate(system_frames):
-            self.assertEqual(frame.text, f"System Message {i + 1}")
-
     async def test_service_switching(self):
         """Test that after service switching using ManuallySwitchServiceFrame, the new active service receives frames while others don't."""
         switcher = ServiceSwitcher(self.services, ServiceSwitcherStrategyManual)

uv.lock

@@ -410,16 +410,16 @@ wheels = [
 
 [[package]]
 name = "aws-sdk-bedrock-runtime"
-version = "0.1.1"
+version = "0.1.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "smithy-aws-core", extra = ["eventstream", "json"], marker = "python_full_version >= '3.12'" },
     { name = "smithy-core", marker = "python_full_version >= '3.12'" },
     { name = "smithy-http", extra = ["awscrt"], marker = "python_full_version >= '3.12'" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/1d/78/48574454b3cac869df67665e4a403ebfc3abfcfba2c2ff01ccfd67d55f8f/aws_sdk_bedrock_runtime-0.1.1.tar.gz", hash = "sha256:c896f99e675c3a1ab600633a07b785f3dc9fe8ab94f640b1f992b63da2dfc784", size = 82446, upload-time = "2025-10-21T20:25:25.845Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/84/e1/39971b907c83a7525bab112c9b395e1bb6d4bc23bc1712d6d7a050662217/aws_sdk_bedrock_runtime-0.1.0.tar.gz", hash = "sha256:bd062de5a48404f64e1dfe6fb8841fbbf68e8f1798c357d14eb427274cb96a2b", size = 85419, upload-time = "2025-09-29T19:40:01.855Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/83/07/62c0b70223d178c138f29124ac2f7973a6ba803abc7735b6a01a85217f3d/aws_sdk_bedrock_runtime-0.1.1-py3-none-any.whl", hash = "sha256:c0336b377b2112cf88197d3d44302fbeb3efb1101989fa49ae55e78f49cfe345", size = 74954, upload-time = "2025-10-21T20:25:24.973Z" },
+    { url = "https://files.pythonhosted.org/packages/3d/e1/5b36bffe85010cdcd44730d1c2d5244653d57c002f440141d7fc3b9f1347/aws_sdk_bedrock_runtime-0.1.0-py3-none-any.whl", hash = "sha256:aac6ff47069d456ca5e23083d96a01e3e0cbc215414e6753c289d7d9efef3335", size = 78853, upload-time = "2025-09-29T19:40:00.341Z" },
 ]
 
 [[package]]
@@ -433,31 +433,31 @@ wheels = [
 
 [[package]]
 name = "awscrt"
-version = "0.28.2"
+version = "0.28.1"
 source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/d4/1b/a885a699217967c3ff0e1c49ac5b1e2a050d1a8b87d1e85e958a56e3d3f5/awscrt-0.28.2.tar.gz", hash = "sha256:9715a888f2042e710dc8aeb355963a29b77e7a4cc25a14659cebd21a5fa476c1", size = 37894849, upload-time = "2025-10-14T19:06:16.867Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/a0/1c/5c9e6a7375c2a1355aadeb2d06c96c95934ec37ff29ebaab2919f59c3ff1/awscrt-0.28.1.tar.gz", hash = "sha256:70a28fd6ff3e0abb7854ea8a9133bc9e5de681a0d9bdbd8a599a23d13a448685", size = 37956730, upload-time = "2025-09-19T00:58:31.564Z" }
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