docs: add a demo showing how to track usage

CHANGELOG.md

@@ -7,170 +7,17 @@ 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 an ellipsis character (`…`) to the end of sentence detection in the
-  string utils.
-
-- Expanded support for universal `LLMContext` to `AWSNovaSonicLLMService`.
-  As a reminder, the context-setup pattern when using `LLMContext` is:
-
-  ```python
-  context = LLMContext(messages, tools)
-  context_aggregator = LLMContextAggregatorPair(context)
-  ```
-
-  (Note that even though `AWSNovaSonicLLMService` now supports the universal
-  `LLMContext`, it is not meant to be swapped out for another LLM service at
-  runtime.)
-
-  Worth noting: whether or not you use the new context-setup pattern with
-  `AWSNovaSonicLLMService`, some types have changed under the hood:
-
-  ```python
-  ## BEFORE:
-
-  # Context aggregator type
-  context_aggregator: AWSNovaSonicContextAggregatorPair
-
-  # Context frame type
-  frame: OpenAILLMContextFrame
-
-  # Context type
-  context: AWSNovaSonicLLMContext
-  # or
-  context: OpenAILLMContext
-
-  ## AFTER:
-
-  # Context aggregator type
-  context_aggregator: LLMContextAggregatorPair
-
-  # Context frame type
-  frame: LLMContextFrame
-
-  # Context type
-  context: LLMContext
-  ```
-
-- Added support for `bulbul:v3` model in `SarvamTTSService` and
-  `SarvamHttpTTSService`.
-
-- Added `keyterms_prompt` parameter to `AssemblyAIConnectionParams`.
-
-- Added `speech_model` parameter to `AssemblyAIConnectionParams` to access the
-  multilingual model.
-
-- Added support for trickle ICE to the `SmallWebRTCTransport`.
-
-- Added support for updating `OpenAITTSService` settings (`instructions` and
-  `speed`) at runtime via `TTSUpdateSettingsFrame`.
-
-- Added `--whatsapp` flag to runner to better surface WhatsApp transport logs.
-
-- Added `on_connected` and `on_disconnected` events to TTS and STT
-  websocket-based services.
-
-- Added an `aggregate_sentences` arg in `ElevenLabsHttpTTSService`, where the
-  default value is True.
-
-- Added a `room_properties` arg to the Daily runner's `configure()` method,
-  allowing `DailyRoomProperties` to be provided.
-
 - The runner `--folder` argument now supports downloading files from
   subdirectories.
 
-### 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:
-
-  - `daily-python` upgraded to 0.20.0.
-  - `openai` upgraded to support up to 2.x.x.
-  - `openpipe` upgraded to support up to 5.x.x.
-
-- `SpeechmaticsSTTService` updated dependencies for `speechmatics-rt>=0.5.0`.
-
-### Deprecated
-
-- The `send_transcription_frames` argument to `AWSNovaSonicLLMService` is
-  deprecated. Transcription frames are now always sent. They go upstream, to be
-  handled by the user context aggregator. See "Added" section for details.
-
-- Types in `pipecat.services.aws.nova_sonic.context` have been deprecated due
-  to changes to support `LLMContext`. See "Changed" section for details.
-
 ### 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.
-
-- Fixed multiple pipeline task cancellation issues. `asyncio.CancelledError` is
-  now handled properly in `PipelineTask` making it possible to cancel an asyncio
-  task that it's executing a `PipelineRunner` cleanly. Also,
-  `PipelineTask.cancel()` does not block anymore waiting for the `CancelFrame`
-  to reach the end of the pipeline (going back to the behavior in < 0.0.83).
-
-- Fixed an issue in `ElevenLabsTTSService` and `ElevenLabsHttpTTSService` where
-  the Flash models would split words, resulting in a space being inserted
-  between words.
-
-- Fixed an issue where audio filters' `stop()` would not be called when using
-  `CancelFrame`.
-
-- Fixed an issue in `ElevenLabsHttpTTSService`, where
-  `apply_text_normalization` was incorrectly set as a query parameter. It's now
-  being added as a request parameter.
-
 - Fixed an issue where `RimeHttpTTSService` and `PiperTTSService` could generate
   incorrectly 16-bit aligned audio frames, potentially leading to internal
   errors or static audio.
 
-- Fixed an issue in `SpeechmaticsSTTService` where `AdditionalVocabEntry` items
-  needed to have `sounds_like` for the session to start.
-
-### Other
-
-- Added foundational example `47-sentry-metrics.py`, demonstrating how to use the
-  `SentryMetrics` processor.
-
-- Added foundational example `14x-function-calling-openpipe.py`.
-
 ## [0.0.90] - 2025-10-10
 
 ### Added

README.md

@@ -44,10 +44,6 @@ 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
-
-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.
-
 ### 🔍 Debugging
 
 Looking for help debugging your pipeline and processors? Check out [Whisker](https://github.com/pipecat-ai/whisker), a real-time Pipecat debugger.
@@ -67,24 +63,24 @@ Catch new features, interviews, and how-tos on our [Pipecat TV](https://www.yout
     <a href="https://github.com/pipecat-ai/pipecat-examples/tree/main/storytelling-chatbot"><img src="https://raw.githubusercontent.com/pipecat-ai/pipecat-examples/main/storytelling-chatbot/image.png" width="400" /></a>
     <br/>
     <a href="https://github.com/pipecat-ai/pipecat-examples/tree/main/translation-chatbot"><img src="https://raw.githubusercontent.com/pipecat-ai/pipecat-examples/main/translation-chatbot/image.png" width="400" /></a>&nbsp;
-    <a href="https://github.com/pipecat-ai/pipecat/blob/main/examples/foundational/12-describe-video.py"><img src="https://github.com/pipecat-ai/pipecat/blob/main/examples/foundational/assets/moondream.png" width="400" /></a>
+    <a href="https://github.com/pipecat-ai/pipecat-examples/tree/main/moondream-chatbot"><img src="https://raw.githubusercontent.com/pipecat-ai/pipecat-examples/main/moondream-chatbot/image.png" width="400" /></a>
 </p>
 
 ## 🧩 Available services
 
-| Category            | Services                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        |
+| Category            | Services                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              |
-| ------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| ------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| Speech-to-Text      | [AssemblyAI](https://docs.pipecat.ai/server/services/stt/assemblyai), [AWS](https://docs.pipecat.ai/server/services/stt/aws), [Azure](https://docs.pipecat.ai/server/services/stt/azure), [Cartesia](https://docs.pipecat.ai/server/services/stt/cartesia), [Deepgram](https://docs.pipecat.ai/server/services/stt/deepgram), [ElevenLabs](https://docs.pipecat.ai/server/services/stt/elevenlabs), [Fal Wizper](https://docs.pipecat.ai/server/services/stt/fal), [Gladia](https://docs.pipecat.ai/server/services/stt/gladia), [Google](https://docs.pipecat.ai/server/services/stt/google), [Groq (Whisper)](https://docs.pipecat.ai/server/services/stt/groq), [NVIDIA Riva](https://docs.pipecat.ai/server/services/stt/riva), [OpenAI (Whisper)](https://docs.pipecat.ai/server/services/stt/openai), [SambaNova (Whisper)](https://docs.pipecat.ai/server/services/stt/sambanova), [Soniox](https://docs.pipecat.ai/server/services/stt/soniox), [Speechmatics](https://docs.pipecat.ai/server/services/stt/speechmatics), [Ultravox](https://docs.pipecat.ai/server/services/stt/ultravox), [Whisper](https://docs.pipecat.ai/server/services/stt/whisper)                                                                                                                                                                              |
+| Speech-to-Text      | [AssemblyAI](https://docs.pipecat.ai/server/services/stt/assemblyai), [AWS](https://docs.pipecat.ai/server/services/stt/aws), [Azure](https://docs.pipecat.ai/server/services/stt/azure), [Cartesia](https://docs.pipecat.ai/server/services/stt/cartesia), [Deepgram](https://docs.pipecat.ai/server/services/stt/deepgram), [ElevenLabs](https://docs.pipecat.ai/server/services/stt/elevenlabs), [Fal Wizper](https://docs.pipecat.ai/server/services/stt/fal), [Gladia](https://docs.pipecat.ai/server/services/stt/gladia), [Google](https://docs.pipecat.ai/server/services/stt/google), [Groq (Whisper)](https://docs.pipecat.ai/server/services/stt/groq), [NVIDIA Riva](https://docs.pipecat.ai/server/services/stt/riva), [OpenAI (Whisper)](https://docs.pipecat.ai/server/services/stt/openai), [SambaNova (Whisper)](https://docs.pipecat.ai/server/services/stt/sambanova), [Soniox](https://docs.pipecat.ai/server/services/stt/soniox), [Speechmatics](https://docs.pipecat.ai/server/services/stt/speechmatics), [Ultravox](https://docs.pipecat.ai/server/services/stt/ultravox), [Whisper](https://docs.pipecat.ai/server/services/stt/whisper)                                                                                                                    |
-| LLMs                | [Anthropic](https://docs.pipecat.ai/server/services/llm/anthropic), [AWS](https://docs.pipecat.ai/server/services/llm/aws), [Azure](https://docs.pipecat.ai/server/services/llm/azure), [Cerebras](https://docs.pipecat.ai/server/services/llm/cerebras), [DeepSeek](https://docs.pipecat.ai/server/services/llm/deepseek), [Fireworks AI](https://docs.pipecat.ai/server/services/llm/fireworks), [Gemini](https://docs.pipecat.ai/server/services/llm/gemini), [Grok](https://docs.pipecat.ai/server/services/llm/grok), [Groq](https://docs.pipecat.ai/server/services/llm/groq), [Mistral](https://docs.pipecat.ai/server/services/llm/mistral), [NVIDIA NIM](https://docs.pipecat.ai/server/services/llm/nim), [Ollama](https://docs.pipecat.ai/server/services/llm/ollama), [OpenAI](https://docs.pipecat.ai/server/services/llm/openai), [OpenRouter](https://docs.pipecat.ai/server/services/llm/openrouter), [Perplexity](https://docs.pipecat.ai/server/services/llm/perplexity), [Qwen](https://docs.pipecat.ai/server/services/llm/qwen), [SambaNova](https://docs.pipecat.ai/server/services/llm/sambanova) [Together AI](https://docs.pipecat.ai/server/services/llm/together)                                                                                                                                                    |
+| LLMs                | [Anthropic](https://docs.pipecat.ai/server/services/llm/anthropic), [AWS](https://docs.pipecat.ai/server/services/llm/aws), [Azure](https://docs.pipecat.ai/server/services/llm/azure), [Cerebras](https://docs.pipecat.ai/server/services/llm/cerebras), [DeepSeek](https://docs.pipecat.ai/server/services/llm/deepseek), [Fireworks AI](https://docs.pipecat.ai/server/services/llm/fireworks), [Gemini](https://docs.pipecat.ai/server/services/llm/gemini), [Grok](https://docs.pipecat.ai/server/services/llm/grok), [Groq](https://docs.pipecat.ai/server/services/llm/groq), [Mistral](https://docs.pipecat.ai/server/services/llm/mistral), [NVIDIA NIM](https://docs.pipecat.ai/server/services/llm/nim), [Ollama](https://docs.pipecat.ai/server/services/llm/ollama), [OpenAI](https://docs.pipecat.ai/server/services/llm/openai), [OpenRouter](https://docs.pipecat.ai/server/services/llm/openrouter), [Perplexity](https://docs.pipecat.ai/server/services/llm/perplexity), [Qwen](https://docs.pipecat.ai/server/services/llm/qwen), [SambaNova](https://docs.pipecat.ai/server/services/llm/sambanova) [Together AI](https://docs.pipecat.ai/server/services/llm/together)                                                                                          |
 | Text-to-Speech      | [Async](https://docs.pipecat.ai/server/services/tts/asyncai), [AWS](https://docs.pipecat.ai/server/services/tts/aws), [Azure](https://docs.pipecat.ai/server/services/tts/azure), [Cartesia](https://docs.pipecat.ai/server/services/tts/cartesia), [Deepgram](https://docs.pipecat.ai/server/services/tts/deepgram), [ElevenLabs](https://docs.pipecat.ai/server/services/tts/elevenlabs), [Fish](https://docs.pipecat.ai/server/services/tts/fish), [Google](https://docs.pipecat.ai/server/services/tts/google), [Groq](https://docs.pipecat.ai/server/services/tts/groq), [Hume](https://docs.pipecat.ai/server/services/tts/hume), [Inworld](https://docs.pipecat.ai/server/services/tts/inworld), [LMNT](https://docs.pipecat.ai/server/services/tts/lmnt), [MiniMax](https://docs.pipecat.ai/server/services/tts/minimax), [Neuphonic](https://docs.pipecat.ai/server/services/tts/neuphonic), [NVIDIA Riva](https://docs.pipecat.ai/server/services/tts/riva), [OpenAI](https://docs.pipecat.ai/server/services/tts/openai), [Piper](https://docs.pipecat.ai/server/services/tts/piper), [PlayHT](https://docs.pipecat.ai/server/services/tts/playht), [Rime](https://docs.pipecat.ai/server/services/tts/rime), [Sarvam](https://docs.pipecat.ai/server/services/tts/sarvam), [XTTS](https://docs.pipecat.ai/server/services/tts/xtts) |
-| Speech-to-Speech    | [AWS Nova Sonic](https://docs.pipecat.ai/server/services/s2s/aws), [Gemini Multimodal Live](https://docs.pipecat.ai/server/services/s2s/gemini), [OpenAI Realtime](https://docs.pipecat.ai/server/services/s2s/openai)                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          |
+| Speech-to-Speech    | [AWS Nova Sonic](https://docs.pipecat.ai/server/services/s2s/aws), [Gemini Multimodal Live](https://docs.pipecat.ai/server/services/s2s/gemini), [OpenAI Realtime](https://docs.pipecat.ai/server/services/s2s/openai)                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                |
-| Transport           | [Daily (WebRTC)](https://docs.pipecat.ai/server/services/transport/daily), [FastAPI Websocket](https://docs.pipecat.ai/server/services/transport/fastapi-websocket), [SmallWebRTCTransport](https://docs.pipecat.ai/server/services/transport/small-webrtc), [WebSocket Server](https://docs.pipecat.ai/server/services/transport/websocket-server), Local                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      |
+| Transport           | [Daily (WebRTC)](https://docs.pipecat.ai/server/services/transport/daily), [FastAPI Websocket](https://docs.pipecat.ai/server/services/transport/fastapi-websocket), [SmallWebRTCTransport](https://docs.pipecat.ai/server/services/transport/small-webrtc), [WebSocket Server](https://docs.pipecat.ai/server/services/transport/websocket-server), Local                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            |
-| Serializers         | [Plivo](https://docs.pipecat.ai/server/utilities/serializers/plivo), [Twilio](https://docs.pipecat.ai/server/utilities/serializers/twilio), [Telnyx](https://docs.pipecat.ai/server/utilities/serializers/telnyx)                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                               |
+| Serializers         | [Plivo](https://docs.pipecat.ai/server/utilities/serializers/plivo), [Twilio](https://docs.pipecat.ai/server/utilities/serializers/twilio), [Telnyx](https://docs.pipecat.ai/server/utilities/serializers/telnyx)                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                     |
-| Video               | [HeyGen](https://docs.pipecat.ai/server/services/video/heygen), [Tavus](https://docs.pipecat.ai/server/services/video/tavus), [Simli](https://docs.pipecat.ai/server/services/video/simli)                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      |
+| Video               | [HeyGen](https://docs.pipecat.ai/server/services/video/heygen), [Tavus](https://docs.pipecat.ai/server/services/video/tavus), [Simli](https://docs.pipecat.ai/server/services/video/simli)                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            |
-| Memory              | [mem0](https://docs.pipecat.ai/server/services/memory/mem0)                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                     |
+| Memory              | [mem0](https://docs.pipecat.ai/server/services/memory/mem0)                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                           |
-| Vision & Image      | [fal](https://docs.pipecat.ai/server/services/image-generation/fal), [Google Imagen](https://docs.pipecat.ai/server/services/image-generation/fal), [Moondream](https://docs.pipecat.ai/server/services/vision/moondream)                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                       |
+| Vision & Image      | [fal](https://docs.pipecat.ai/server/services/image-generation/fal), [Google Imagen](https://docs.pipecat.ai/server/services/image-generation/fal), [Moondream](https://docs.pipecat.ai/server/services/vision/moondream)                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                             |
-| Audio Processing    | [Silero VAD](https://docs.pipecat.ai/server/utilities/audio/silero-vad-analyzer), [Krisp](https://docs.pipecat.ai/server/utilities/audio/krisp-filter), [Koala](https://docs.pipecat.ai/server/utilities/audio/koala-filter), [ai-coustics](https://docs.pipecat.ai/server/utilities/audio/aic-filter)                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          |
+| Audio Processing    | [Silero VAD](https://docs.pipecat.ai/server/utilities/audio/silero-vad-analyzer), [Krisp](https://docs.pipecat.ai/server/utilities/audio/krisp-filter), [Koala](https://docs.pipecat.ai/server/utilities/audio/koala-filter), [ai-coustics](https://docs.pipecat.ai/server/utilities/audio/aic-filter)                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                |
-| Analytics & Metrics | [OpenTelemetry](https://docs.pipecat.ai/server/utilities/opentelemetry), [Sentry](https://docs.pipecat.ai/server/services/analytics/sentry)                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                     |
+| Analytics & Metrics | [OpenTelemetry](https://docs.pipecat.ai/server/utilities/opentelemetry), [Sentry](https://docs.pipecat.ai/server/services/analytics/sentry)                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                           |
 
 📚 [View full services documentation →](https://docs.pipecat.ai/server/services/supported-services)
 

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=...
-
+GOOGLE_VERTEX_TEST_CREDENTIALS=...
-# Grok
-GROK_API_KEY=...
-
-# Groq
-GROQ_API_KEY=...
-
-# Heygen
-HEYGEN_API_KEY=...
 
 # 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
+# Perplexity
-MINIMAX_API_KEY=...
+PERPLEXITY_API_KEY=...
-MINIMAX_GROUP_ID=...
 
-# Mistral
+# PlayHT
-MISTRAL_API_KEY=...
+PLAYHT_USER_ID=...
-
+PLAYHT_API_KEY=...
-# Neuphonic
-NEUPHONIC_API_KEY=...
-
-# NVIDIA
-NVIDIA_API_KEY=...
 
 # OpenAI
 OPENAI_API_KEY=...
@@ -121,73 +78,92 @@ OPENAI_API_KEY=...
 # OpenPipe
 OPENPIPE_API_KEY=...
 
-# OpenRouter
+# Tavus
-OPENROUTER_API_KEY=...
+TAVUS_API_KEY=...
-
+TAVUS_REPLICA_ID=...
-# Perplexity
+TAVUS_PERSONA_ID=...
-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=...
 
 # Simli
 SIMLI_API_KEY=...
 SIMLI_FACE_ID=...
 
-# Smart turn
+# Krisp
-LOCAL_SMART_TURN_MODEL_PATH=...
+KRISP_MODEL_PATH=...
-FAL_SMART_TURN_API_KEY=...
 
-# Soniox
+# Krisp Viva
-SONIOX_API_KEY=...
+KRISP_VIVA_MODEL_PATH=...
 
-# Speechmatics
+# DeepSeek
-SPEECHMATICS_API_KEY=...
+DEEPSEEK_API_KEY=...
 
-# Tavus
+# Groq
-TAVUS_API_KEY=...
+GROQ_API_KEY=...
-TAVUS_REPLICA_ID=...
 
-# Telnyx
+# Grok
-TELNYX_API_KEY=...
+GROK_API_KEY=...
-TELNYX_ACCOUNT_SID=...
+
+# 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_TOKEN=
-WHATSAPP_WEBHOOK_VERIFICATION_TOKEN=...
+WHATSAPP_WEBHOOK_VERIFICATION_TOKEN=
-WHATSAPP_PHONE_NUMBER_ID=...
+WHATSAPP_PHONE_NUMBER_ID=
-WHATSAPP_APP_SECRET=...
+WHATSAPP_APP_SECRET=

examples/foundational/07-interruptible.py

@@ -21,8 +21,8 @@ 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.openai.llm import OpenAILLMService
 from pipecat.transports.base_transport import BaseTransport, TransportParams
 from pipecat.transports.daily.transport import DailyParams
@@ -58,7 +58,7 @@ transport_params = {
 async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
     logger.info(f"Starting bot")
 
-    stt = CartesiaSTTService(api_key=os.getenv("CARTESIA_API_KEY"))
+    stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
 
     tts = CartesiaTTSService(
         api_key=os.getenv("CARTESIA_API_KEY"),

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",
+        model="us.anthropic.claude-3-5-haiku-20241022-v1:0",
-        params=AWSBedrockLLMService.InputParams(temperature=0.8),
+        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/13f-cartesia-transcription.py

@@ -48,7 +48,10 @@ transport_params = {
 async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
     logger.info(f"Starting bot")
 
-    stt = CartesiaSTTService(api_key=os.getenv("CARTESIA_API_KEY"))
+    stt = CartesiaSTTService(
+        api_key=os.getenv("CARTESIA_API_KEY"),
+        base_url=os.getenv("CARTESIA_BASE_URL"),
+    )
 
     tl = TranscriptionLogger()
 

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",
+        model="us.anthropic.claude-3-5-haiku-20241022-v1:0",
-        params=AWSBedrockLLMService.InputParams(temperature=0.8),
+        params=AWSBedrockLLMService.InputParams(temperature=0.8, latency="optimized"),
     )
 
     # You can also register a function_name of None to get all functions

examples/foundational/14x-function-calling-openpipe.py

@@ -1,182 +0,0 @@
-#
-# Copyright (c) 2024–2025, Daily
-#
-# SPDX-License-Identifier: BSD 2-Clause License
-#
-
-import os
-import time
-
-from dotenv import load_dotenv
-from loguru import logger
-
-from pipecat.adapters.schemas.function_schema import FunctionSchema
-from pipecat.adapters.schemas.tools_schema import ToolsSchema
-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, TTSSpeakFrame
-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.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.llm_service import FunctionCallParams
-from pipecat.services.openpipe.llm import OpenPipeLLMService
-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)
-
-
-async def fetch_weather_from_api(params: FunctionCallParams):
-    await params.result_callback({"conditions": "nice", "temperature": "75"})
-
-
-async def fetch_restaurant_recommendation(params: FunctionCallParams):
-    await params.result_callback({"name": "The Golden Dragon"})
-
-
-# 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 = 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
-    )
-
-    timestamp = int(time.time())
-    llm = OpenPipeLLMService(
-        api_key=os.getenv("OPENAI_API_KEY"),
-        openpipe_api_key=os.getenv("OPENPIPE_API_KEY"),
-        tags={"conversation_id": f"pipecat-{timestamp}"},
-    )
-
-    # You can also register a function_name of None to get all functions
-    # sent to the same callback with an additional function_name parameter.
-    llm.register_function("get_current_weather", fetch_weather_from_api)
-    llm.register_function("get_restaurant_recommendation", fetch_restaurant_recommendation)
-
-    @llm.event_handler("on_function_calls_started")
-    async def on_function_calls_started(service, function_calls):
-        await tts.queue_frame(TTSSpeakFrame("Let me check on that."))
-
-    weather_function = FunctionSchema(
-        name="get_current_weather",
-        description="Get the current weather",
-        properties={
-            "location": {
-                "type": "string",
-                "description": "The city and state, e.g. San Francisco, CA",
-            },
-            "format": {
-                "type": "string",
-                "enum": ["celsius", "fahrenheit"],
-                "description": "The temperature unit to use. Infer this from the user's location.",
-            },
-        },
-        required=["location", "format"],
-    )
-    restaurant_function = FunctionSchema(
-        name="get_restaurant_recommendation",
-        description="Get a restaurant recommendation",
-        properties={
-            "location": {
-                "type": "string",
-                "description": "The city and state, e.g. San Francisco, CA",
-            },
-        },
-        required=["location"],
-    )
-    tools = ToolsSchema(standard_tools=[weather_function, restaurant_function])
-
-    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, tools)
-    context_aggregator = LLMContextAggregatorPair(context)
-
-    pipeline = Pipeline(
-        [
-            transport.input(),
-            stt,
-            context_aggregator.user(),
-            llm,
-            tts,
-            transport.output(),
-            context_aggregator.assistant(),
-        ]
-    )
-
-    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.
-        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/18-openai-realtime-usage.py

@@ -0,0 +1,156 @@
+#
+# Copyright (c) 2024–2025, Daily
+#
+# SPDX-License-Identifier: BSD 2-Clause License
+#
+
+"""Example: Print OpenAI Realtime API Token Usage Statistics
+
+This example demonstrates how to access and print token usage statistics
+from the OpenAI Realtime API, including detailed breakdowns of input/output
+tokens, cached tokens, and audio/text token usage.
+"""
+
+import os
+
+from dotenv import load_dotenv
+from loguru import logger
+
+from pipecat.audio.vad.silero import SileroVADAnalyzer
+from pipecat.audio.vad.vad_analyzer import VADParams
+from pipecat.pipeline.pipeline import Pipeline
+from pipecat.pipeline.runner import PipelineRunner
+from pipecat.pipeline.task import PipelineParams, PipelineTask
+from pipecat.runner.types import RunnerArguments
+from pipecat.runner.utils import create_transport
+from pipecat.services.openai.realtime.llm import OpenAIRealtimeLLMService
+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 don't get instantiated until 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)),
+    ),
+    "twilio": lambda: FastAPIWebsocketParams(
+        audio_in_enabled=True,
+        audio_out_enabled=True,
+        vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
+    ),
+    "webrtc": lambda: TransportParams(
+        audio_in_enabled=True,
+        audio_out_enabled=True,
+        vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
+    ),
+}
+
+
+async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
+    """Main function demonstrating usage statistics tracking."""
+    logger.info(f"Starting bot")
+
+    # Initialize the OpenAI Realtime service
+    llm = OpenAIRealtimeLLMService(
+        api_key=os.getenv("OPENAI_API_KEY") or "",
+        model="gpt-4o-realtime-preview-2024-12-17",
+    )
+
+    # To access usage statistics, we wrap the internal response handler
+    # This is the cleanest way to intercept usage data from the realtime API
+    original_handler = llm._handle_evt_response_done
+
+    async def custom_response_done_handler(evt):
+        """Custom handler that prints usage stats before calling original handler."""
+        # Print usage statistics if available
+        if evt.response.usage:
+            usage = evt.response.usage
+
+            logger.info("\n" + "=" * 50)
+            logger.info("📊 TOKEN USAGE STATISTICS")
+            logger.info("=" * 50)
+            logger.info(f"Total tokens: {usage.total_tokens}")
+            logger.info(f"Input tokens: {usage.input_tokens}")
+            logger.info(f"Output tokens: {usage.output_tokens}")
+
+            # Input token details
+            if usage.input_token_details:
+                logger.info(f"\n📥 Input token breakdown:")
+                logger.info(f"  • Cached tokens: {usage.input_token_details.cached_tokens}")
+                logger.info(f"  • Text tokens: {usage.input_token_details.text_tokens}")
+                logger.info(f"  • Audio tokens: {usage.input_token_details.audio_tokens}")
+
+                # Cached token details if available
+                if usage.input_token_details.cached_tokens_details:
+                    logger.info(
+                        f"  • Cached text tokens: {usage.input_token_details.cached_tokens_details.text_tokens}"
+                    )
+                    logger.info(
+                        f"  • Cached audio tokens: {usage.input_token_details.cached_tokens_details.audio_tokens}"
+                    )
+
+            # Output token details
+            if usage.output_token_details:
+                logger.info(f"\n📤 Output token breakdown:")
+                logger.info(f"  • Text tokens: {usage.output_token_details.text_tokens}")
+                logger.info(f"  • Audio tokens: {usage.output_token_details.audio_tokens}")
+
+            logger.info("=" * 50 + "\n")
+
+        # Call the original handler to maintain normal functionality
+        await original_handler(evt)
+
+    # Replace the handler with our custom one
+    llm._handle_evt_response_done = custom_response_done_handler
+
+    # Create pipeline
+    pipeline = Pipeline(
+        [
+            transport.input(),
+            llm,
+            transport.output(),
+        ]
+    )
+
+    # Create task
+    task = PipelineTask(
+        pipeline,
+        params=PipelineParams(
+            allow_interruptions=True,
+            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("Client connected")
+        logger.info("🎤 Speak into your microphone to interact with the assistant")
+        logger.info("📊 Usage statistics will be printed after each response")
+
+    @transport.event_handler("on_client_disconnected")
+    async def on_client_disconnected(transport, client):
+        logger.info("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/20c-persistent-context-anthropic.py

@@ -72,6 +72,7 @@ async def save_conversation(params: FunctionCallParams):
     )
     try:
         with open(filename, "w") as file:
+            # todo: extract 'system' into the first message in the list
             messages = params.context.get_messages()
             # remove the last message, which is the instruction we just gave to save the conversation
             messages.pop()

examples/foundational/20d-persistent-context-gemini.py

@@ -90,6 +90,7 @@ async def save_conversation(params: FunctionCallParams):
     )
     try:
         with open(filename, "w") as file:
+            # todo: extract 'system' into the first message in the list
             messages = params.context.get_messages()
             # remove the last message (the instruction to save the context)
             messages.pop()

examples/foundational/20e-persistent-context-aws-nova-sonic.py

@@ -20,8 +20,6 @@ from pipecat.frames.frames import LLMRunFrame
 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.aggregators.openai_llm_context import OpenAILLMContext
 from pipecat.runner.types import RunnerArguments
 from pipecat.runner.utils import create_transport
@@ -77,7 +75,7 @@ async def save_conversation(params: FunctionCallParams):
     filename = f"{BASE_FILENAME}{timestamp}.json"
     try:
         with open(filename, "w") as file:
-            messages = params.context.get_messages()
+            messages = params.context.get_messages_for_persistent_storage()
             # remove the last few messages. in reverse order, they are:
             # - the in progress save tool call
             # - the invocation of the save tool call
@@ -225,13 +223,13 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
     llm.register_function("get_saved_conversation_filenames", get_saved_conversation_filenames)
     llm.register_function("load_conversation", load_conversation)
 
-    context = LLMContext(
+    context = OpenAILLMContext(
         messages=[
             {"role": "system", "content": f"{system_instruction}"},
         ],
         tools=tools,
     )
-    context_aggregator = LLMContextAggregatorPair(context)
+    context_aggregator = llm.create_context_aggregator(context)
 
     pipeline = Pipeline(
         [

examples/foundational/40-aws-nova-sonic.py

@@ -18,8 +18,7 @@ from pipecat.frames.frames import LLMRunFrame
 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.openai_llm_context import OpenAILLMContext
-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.aws.nova_sonic.llm import AWSNovaSonicLLMService
@@ -120,7 +119,9 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
     llm.register_function("get_current_weather", fetch_weather_from_api)
 
     # Set up context and context management.
-    context = LLMContext(
+    # AWSNovaSonicService will adapt OpenAI LLM context objects with standard message format to
+    # what's expected by Nova Sonic.
+    context = OpenAILLMContext(
         messages=[
             {"role": "system", "content": f"{system_instruction}"},
             {
@@ -130,7 +131,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         ],
         tools=tools,
     )
-    context_aggregator = LLMContextAggregatorPair(context)
+    context_aggregator = llm.create_context_aggregator(context)
 
     # Build the pipeline
     pipeline = Pipeline(

examples/foundational/47-sentry-metrics.py

@@ -1,142 +0,0 @@
-#
-# Copyright (c) 2024–2025, Daily
-#
-# SPDX-License-Identifier: BSD 2-Clause License
-#
-
-import os
-
-import sentry_sdk
-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
-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.metrics.sentry import SentryMetrics
-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
-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")
-
-    # Initialize Sentry
-    sentry_sdk.init(
-        dsn=os.getenv("SENTRY_DSN"),
-        traces_sample_rate=1.0,
-    )
-
-    stt = DeepgramSTTService(
-        api_key=os.getenv("DEEPGRAM_API_KEY"),
-        metrics=SentryMetrics(),
-    )
-
-    tts = CartesiaTTSService(
-        api_key=os.getenv("CARTESIA_API_KEY"),
-        voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
-        metrics=SentryMetrics(),
-    )
-
-    llm = OpenAILLMService(
-        api_key=os.getenv("OPENAI_API_KEY"),
-        metrics=SentryMetrics(),
-    )
-
-    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,
-            context_aggregator.user(),  # User responses
-            llm,  # LLM
-            tts,  # TTS
-            transport.output(),  # Transport bot output
-            context_aggregator.assistant(),  # Assistant spoken responses
-        ]
-    )
-
-    task = PipelineTask(
-        pipeline,
-        params=PipelineParams(
-            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()])
-
-    @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/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[webrtc,daily,silero,deepgram,openai,cartesia,local-smart-turn-v3,runner]>=0.0.86",
-    "pipecat-ai-cli"
+    "pipecatcloud>=0.2.4"
 ]
 
 [dependency-groups]
 dev = [
-    "pyright>=1.1.404,<2",
+    "ruff~=0.12.1",
-    "ruff>=0.12.11,<1",
 ]
 
 [tool.ruff]

pyproject.toml

@@ -34,7 +34,7 @@ dependencies = [
     "pyloudnorm~=0.1.1",
     "resampy~=0.4.3",
     "soxr~=0.5.0",
-    "openai>=1.74.0,<3",
+    "openai>=1.74.0,<=1.99.1",
     # Pinning numba to resolve package dependencies
     "numba==0.61.2",
     "wait_for2>=0.4.1; python_version<'3.12'",
@@ -50,12 +50,12 @@ 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 = []
 deepseek = []
-daily = [ "daily-python~=0.20.0" ]
+daily = [ "daily-python~=0.19.9" ]
 deepgram = [ "deepgram-sdk~=4.7.0" ]
 elevenlabs = [ "pipecat-ai[websockets-base]" ]
 fal = [ "fal-client~=0.5.9" ]
@@ -84,7 +84,7 @@ nim = []
 neuphonic = [ "pipecat-ai[websockets-base]" ]
 noisereduce = [ "noisereduce~=3.0.3" ]
 openai = [ "pipecat-ai[websockets-base]" ]
-openpipe = [ "openpipe>=4.50.0,<6" ]
+openpipe = [ "openpipe~=4.50.0" ]
 openrouter = []
 perplexity = []
 playht = [ "pipecat-ai[websockets-base]" ]
@@ -102,7 +102,7 @@ silero = [ "onnxruntime>=1.20.1,<2" ]
 simli = [ "simli-ai~=0.1.10"]
 soniox = [ "pipecat-ai[websockets-base]" ]
 soundfile = [ "soundfile~=0.13.0" ]
-speechmatics = [ "speechmatics-rt>=0.5.0" ]
+speechmatics = [ "speechmatics-rt>=0.4.0" ]
 strands = [ "strands-agents>=1.9.1,<2" ]
 tavus=[]
 together = []

scripts/evals/run-release-evals.py

@@ -136,7 +136,6 @@ TESTS_14 = [
     ("14r-function-calling-aws.py", PROMPT_WEATHER, EVAL_WEATHER, BOT_SPEAKS_FIRST),
     ("14v-function-calling-openai.py", PROMPT_WEATHER, EVAL_WEATHER, BOT_SPEAKS_FIRST),
     ("14w-function-calling-mistral.py", PROMPT_WEATHER, EVAL_WEATHER, BOT_SPEAKS_FIRST),
-    ("14x-function-calling-openpipe.py", PROMPT_WEATHER, EVAL_WEATHER, BOT_SPEAKS_FIRST),
     # Currently not working.
     # ("14c-function-calling-together.py", PROMPT_WEATHER, EVAL_WEATHER, BOT_SPEAKS_FIRST),
     # ("14l-function-calling-deepseek.py", PROMPT_WEATHER, EVAL_WEATHER, BOT_SPEAKS_FIRST),

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/aws_nova_sonic_adapter.py

@@ -6,47 +6,13 @@
 
 """AWS Nova Sonic LLM adapter for Pipecat."""
 
-import copy
 import json
-from dataclasses import dataclass
+from typing import Any, Dict, List, TypedDict
-from enum import Enum
-from typing import Any, Dict, List, Optional, TypedDict
-
-from loguru import logger
 
 from pipecat.adapters.base_llm_adapter import BaseLLMAdapter
 from pipecat.adapters.schemas.function_schema import FunctionSchema
 from pipecat.adapters.schemas.tools_schema import ToolsSchema
-from pipecat.processors.aggregators.llm_context import LLMContext, LLMContextMessage
+from pipecat.processors.aggregators.llm_context import LLMContext
-
-
-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
 
 
 class AWSNovaSonicLLMInvocationParams(TypedDict):
@@ -55,9 +21,7 @@ class AWSNovaSonicLLMInvocationParams(TypedDict):
     This is a placeholder until support for universal LLMContext machinery is added for AWS Nova Sonic.
     """
 
-    system_instruction: Optional[str]
+    pass
-    messages: List[AWSNovaSonicConversationHistoryMessage]
-    tools: List[Dict[str, Any]]
 
 
 class AWSNovaSonicLLMAdapter(BaseLLMAdapter[AWSNovaSonicLLMInvocationParams]):
@@ -70,7 +34,7 @@ class AWSNovaSonicLLMAdapter(BaseLLMAdapter[AWSNovaSonicLLMInvocationParams]):
     @property
     def id_for_llm_specific_messages(self) -> str:
         """Get the identifier used in LLMSpecificMessage instances for AWS Nova Sonic."""
-        return "aws-nova-sonic"
+        raise NotImplementedError("Universal LLMContext is not yet supported for AWS Nova Sonic.")
 
     def get_llm_invocation_params(self, context: LLMContext) -> AWSNovaSonicLLMInvocationParams:
         """Get AWS Nova Sonic-specific LLM invocation parameters from a universal LLM context.
@@ -83,13 +47,7 @@ class AWSNovaSonicLLMAdapter(BaseLLMAdapter[AWSNovaSonicLLMInvocationParams]):
         Returns:
             Dictionary of parameters for invoking AWS Nova Sonic's LLM API.
         """
-        messages = self._from_universal_context_messages(self.get_messages(context))
+        raise NotImplementedError("Universal LLMContext is not yet supported for AWS Nova Sonic.")
-        return {
-            "system_instruction": messages.system_instruction,
-            "messages": messages.messages,
-            # NOTE: LLMContext's tools are guaranteed to be a ToolsSchema (or NOT_GIVEN)
-            "tools": self.from_standard_tools(context.tools) or [],
-        }
 
     def get_messages_for_logging(self, context) -> List[Dict[str, Any]]:
         """Get messages from a universal LLM context in a format ready for logging about AWS Nova Sonic.
@@ -104,75 +62,7 @@ class AWSNovaSonicLLMAdapter(BaseLLMAdapter[AWSNovaSonicLLMInvocationParams]):
         Returns:
             List of messages in a format ready for logging about AWS Nova Sonic.
         """
-        return self._from_universal_context_messages(self.get_messages(context)).messages
+        raise NotImplementedError("Universal LLMContext is not yet supported for AWS Nova Sonic.")
-
-    @dataclass
-    class ConvertedMessages:
-        """Container for Google-formatted messages converted from universal context."""
-
-        messages: List[AWSNovaSonicConversationHistoryMessage]
-        system_instruction: Optional[str] = None
-
-    def _from_universal_context_messages(
-        self, universal_context_messages: List[LLMContextMessage]
-    ) -> ConvertedMessages:
-        system_instruction = None
-        messages = []
-
-        # Bail if there are no messages
-        if not universal_context_messages:
-            return self.ConvertedMessages()
-
-        universal_context_messages = copy.deepcopy(universal_context_messages)
-
-        # If we have a "system" message as our first message, let's pull that out into "instruction"
-        if universal_context_messages[0].get("role") == "system":
-            system = universal_context_messages.pop(0)
-            content = system.get("content")
-            if isinstance(content, str):
-                system_instruction = content
-            elif isinstance(content, list):
-                system_instruction = content[0].get("text")
-            if system_instruction:
-                self._system_instruction = system_instruction
-
-        # Process remaining messages to fill out conversation history.
-        # Nova Sonic supports "user" and "assistant" messages in history.
-        for universal_context_message in universal_context_messages:
-            message = self._from_universal_context_message(universal_context_message)
-            if message:
-                messages.append(message)
-
-        return self.ConvertedMessages(messages=messages, system_instruction=system_instruction)
-
-    def _from_universal_context_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
 
     @staticmethod
     def _to_aws_nova_sonic_function_format(function: FunctionSchema) -> Dict[str, Any]:

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 dataclasses import dataclass
-from typing import Any, Dict, List, Optional, Tuple, TypedDict
+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(
+            if isinstance(message, LLMSpecificMessage):
-                message,
+                # Assume that LLMSpecificMessage wraps a message in Google format
-                params=self.MessageConversionParams(
+                messages.append(message.message)
-                    already_have_system_instruction=bool(system_instruction),
+                continue
-                    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
 
-            # Merge tool call ID to name mapping
+            # Convert standard format to Google format
-            if result.tool_call_id_to_name_mapping:
+            converted = self._from_standard_message(
-                tool_call_id_to_name_mapping.update(result.tool_call_id_to_name_mapping)
+                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
+        self, message: LLMStandardMessage, already_have_system_instruction: bool
-    ) -> MessageConversionResult:
+    ) -> 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
+            Content object with role and parts, or a plain string for system
-            system instruction string.
+            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(
+                    return " ".join(part["text"] for part in content if part.get("type") == "text")
-                        part["text"] for part in content if part.get("type") == "text"
-                    )
-                if system_instruction:
-                    return self.MessageConversionResult(system_instruction=system_instruction)
         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(
+                Part(
-                    name=function_name,
+                    function_response=FunctionResponse(
-                    response=response_dict,
+                        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(
+        return Content(role=role, parts=parts)
-            content=Content(role=role, parts=parts),
-            tool_call_id_to_name_mapping=tool_call_id_to_name_mapping,
-        )

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

src/pipecat/pipeline/runner.py

@@ -70,15 +70,11 @@ class PipelineRunner(BaseObject):
         """
         logger.debug(f"Runner {self} started running {task}")
         self._tasks[task.name] = task
-
+        params = PipelineTaskParams(loop=self._loop)
-        # PipelineTask handles asyncio.CancelledError to shutdown the pipeline
-        # properly and re-raises it in case there's more cleanup to do.
         try:
-            params = PipelineTaskParams(loop=self._loop)
             await task.run(params)
         except asyncio.CancelledError:
-            pass
+            await self._cancel()
-
         del self._tasks[task.name]
 
         # Cleanup base object.

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.
+    """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.
-    """
 
     def __init__(self, services: List[FrameProcessor]):
-        """Initialize the service switcher strategy with a list of services.
+        """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.
-        """
         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.
+        """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.
-        """
         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.
+        """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.
-        """
         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.
+            """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
 
             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.py

@@ -269,9 +269,6 @@ class PipelineTask(BasePipelineTask):
         # StopFrame) has been received at the end of the pipeline.
         self._pipeline_end_event = asyncio.Event()
 
-        # This event is set when the pipeline truly finishes.
-        self._pipeline_finished_event = asyncio.Event()
-
         # This is the final pipeline. It is composed of a source processor,
         # followed by the user pipeline, and ending with a sink processor. The
         # source allows us to receive and react to upstream frames, and the sink
@@ -404,7 +401,11 @@ class PipelineTask(BasePipelineTask):
         await self.queue_frame(EndFrame())
 
     async def cancel(self):
-        """Request the running pipeline to cancel."""
+        """Immediately stop the running pipeline.
+
+        Cancels all running tasks and stops frame processing without
+        waiting for completion.
+        """
         if not self._finished:
             await self._cancel()
 
@@ -416,38 +417,51 @@ class PipelineTask(BasePipelineTask):
         """
         if self.has_finished():
             return
-
+        cleanup_pipeline = True
-        # Setup processors.
-        await self._setup(params)
-
-        # Create all main tasks and wait for the main push task. This is the
-        # task that pushes frames to the very beginning of our pipeline (i.e. to
-        # our controlled source processor).
-        await self._create_tasks()
-
         try:
-            # Wait for pipeline to finish.
+            # Setup processors.
-            await self._wait_for_pipeline_finished()
+            await self._setup(params)
+
+            # Create all main tasks and wait of the main push task. This is the
+            # task that pushes frames to the very beginning of our pipeline (our
+            # controlled source processor).
+            push_task = await self._create_tasks()
+            await push_task
+
+            # We have already cleaned up the pipeline inside the task.
+            cleanup_pipeline = False
+
+            # Pipeline has finished nicely.
+            self._finished = True
         except asyncio.CancelledError:
-            logger.debug(f"Pipeline task {self} got cancelled from outside...")
+            # Raise exception back to the pipeline runner so it can cancel this
-            # We have been cancelled from outside, let's just cancel everything.
+            # task properly.
-            await self._cancel()
-            # Wait again for pipeline to finish. This time we have really
-            # cancelled, so it should really finish.
-            await self._wait_for_pipeline_finished()
-            # Re-raise in case there's more cleanup to do.
             raise
         finally:
             # We can reach this point for different reasons:
             #
-            # 1. The pipeline task has finished (try case).
+            # 1. The task has finished properly (e.g. `EndFrame`).
-            # 2. By an asyncio task cancellation (except case).
+            # 2. By calling `PipelineTask.cancel()`.
-            logger.debug(f"Pipeline task {self} is finishing...")
+            # 3. By asyncio task cancellation.
-            await self._cancel_tasks()
+            #
-            if self._check_dangling_tasks:
+            # Case (1) will execute the code below without issues because
-                self._print_dangling_tasks()
+            # `self._finished` is true.
-            self._finished = True
+            #
-            logger.debug(f"Pipeline task {self} has finished")
+            # Case (2) will execute the code below without issues because
+            # `self._cancelled` is true.
+            #
+            # Case (3) will raise the exception above (because we are cancelling
+            # the asyncio task). This will be then captured by the
+            # `PipelineRunner` which will call `PipelineTask.cancel()` and
+            # therefore becoming case (2).
+            if self._finished or self._cancelled:
+                logger.debug(f"Pipeline task {self} is finishing cleanup...")
+                await self._cancel_tasks()
+                await self._cleanup(cleanup_pipeline)
+                if self._check_dangling_tasks:
+                    self._print_dangling_tasks()
+                self._finished = True
+                logger.debug(f"Pipeline task {self} has finished")
 
     async def queue_frame(self, frame: Frame):
         """Queue a single frame to be pushed down the pipeline.
@@ -475,7 +489,19 @@ class PipelineTask(BasePipelineTask):
         if not self._cancelled:
             logger.debug(f"Cancelling pipeline task {self}")
             self._cancelled = True
-            await self.queue_frame(CancelFrame())
+            cancel_frame = CancelFrame()
+            # Make sure everything is cleaned up downstream. This is sent
+            # out-of-band from the main streaming task which is what we want since
+            # we want to cancel right away.
+            await self._pipeline.queue_frame(cancel_frame)
+            # Wait for CancelFrame to make it through the pipeline.
+            await self._wait_for_pipeline_end(cancel_frame)
+            # Only cancel the push task, we don't want to be able to process any
+            # other frame after cancel. Everything else will be cancelled in
+            # run().
+            if self._process_push_task:
+                await self._task_manager.cancel_task(self._process_push_task)
+                self._process_push_task = None
 
     async def _create_tasks(self):
         """Create and start all pipeline processing tasks."""
@@ -577,17 +603,6 @@ class PipelineTask(BasePipelineTask):
 
         self._pipeline_end_event.clear()
 
-        # We are really done.
-        self._pipeline_finished_event.set()
-
-    async def _wait_for_pipeline_finished(self):
-        await self._pipeline_finished_event.wait()
-        self._pipeline_finished_event.clear()
-        # Make sure we wait for the main task to complete.
-        if self._process_push_task:
-            await self._process_push_task
-            self._process_push_task = None
-
     async def _setup(self, params: PipelineTaskParams):
         """Set up the pipeline task and all processors."""
         mgr_params = TaskManagerParams(loop=params.loop)

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

@@ -15,10 +15,9 @@ service-specific adapter.
 """
 
 import base64
-import copy
 import io
 from dataclasses import dataclass
-from typing import TYPE_CHECKING, Any, List, Optional, TypeAlias, Union
+from typing import Any, List, Optional, TypeAlias, Union
 
 from loguru import logger
 from openai._types import NOT_GIVEN as OPEN_AI_NOT_GIVEN
@@ -32,9 +31,6 @@ from PIL import Image
 from pipecat.adapters.schemas.tools_schema import ToolsSchema
 from pipecat.frames.frames import AudioRawFrame
 
-if TYPE_CHECKING:
-    from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
-
 # "Re-export" types from OpenAI that we're using as universal context types.
 # NOTE: if universal message types need to someday diverge from OpenAI's, we
 # should consider managing our own definitions. But we should do so carefully,
@@ -69,26 +65,6 @@ class LLMContext:
     and content formatting.
     """
 
-    @staticmethod
-    def from_openai_context(openai_context: "OpenAILLMContext") -> "LLMContext":
-        """Create a universal LLM context from an OpenAI-specific context.
-
-        NOTE: this should only be used internally, for facilitating migration
-        from OpenAILLMContext to LLMContext. New user code should use
-        LLMContext directly.
-
-        Args:
-            openai_context: The OpenAI LLM context to convert.
-
-        Returns:
-            New LLMContext instance with converted messages and settings.
-        """
-        return LLMContext(
-            messages=openai_context.get_messages(),
-            tools=openai_context.tools,
-            tool_choice=openai_context.tool_choice,
-        )
-
     def __init__(
         self,
         messages: Optional[List[LLMContextMessage]] = None,
@@ -106,19 +82,6 @@ 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 +89,7 @@ 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
+                logged.
-                incompatible LLM-specific messages.
 
         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
+        # Ignore system frames, end frames and frames that are not following the
-        if direction != self._direction:
+        # direction of this gate
-            return True
+        return isinstance(frame, (SystemFrame, EndFrame)) or direction != self._direction
-
-        # 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
 
     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

@@ -76,14 +76,12 @@ 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,
     sip_enable_video: Optional[bool] = False,
     sip_num_endpoints: Optional[int] = 1,
     sip_codecs: Optional[Dict[str, List[str]]] = None,
-    room_properties: Optional[DailyRoomProperties] = None,
 ) -> DailyRoomConfig:
     """Configure Daily room URL and token with optional SIP capabilities.
 
@@ -93,7 +91,6 @@ 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.
@@ -102,10 +99,6 @@ async def configure(
         sip_num_endpoints: Number of allowed SIP endpoints.
         sip_codecs: Codecs to support for audio and video. If None, uses Daily defaults.
             Example: {"audio": ["OPUS"], "video": ["H264"]}
-        room_properties: Optional DailyRoomProperties to use instead of building from
-            individual parameters. When provided, this overrides room_exp_duration and
-            SIP-related parameters. If not provided, properties are built from the
-            individual parameters as before.
 
     Returns:
         DailyRoomConfig: Object with room_url, token, and optional sip_endpoint.
@@ -122,48 +115,18 @@ async def configure(
         # SIP-enabled room
         sip_config = await configure(session, sip_caller_phone="+15551234567")
         print(f"SIP endpoint: {sip_config.sip_endpoint}")
-
-        # Custom room properties with recording enabled
-        custom_props = DailyRoomProperties(
-            enable_recording="cloud",
-            max_participants=2,
-        )
-        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. "
             "Get your API key from https://dashboard.daily.co/developers"
         )
 
-    # Warn if both room_properties and individual parameters are provided
-    if room_properties is not None:
-        individual_params_provided = any(
-            [
-                room_exp_duration != 2.0,
-                token_exp_duration != 2.0,
-                sip_caller_phone is not None,
-                sip_enable_video is not False,
-                sip_num_endpoints != 1,
-                sip_codecs is not None,
-            ]
-        )
-        if individual_params_provided:
-            logger.warning(
-                "Both room_properties and individual parameters (room_exp_duration, token_exp_duration, "
-                "sip_*) were provided. The room_properties will be used and individual parameters "
-                "will be ignored."
-            )
-
     # Determine if SIP mode is enabled
     sip_enabled = sip_caller_phone is not None
 
-    # If room_properties is provided, check if it has SIP configuration
-    if room_properties and room_properties.sip:
-        sip_enabled = True
-
     daily_rest_helper = DailyRESTHelper(
         daily_api_key=api_key,
         daily_api_url=os.getenv("DAILY_API_URL", "https://api.daily.co/v1"),
@@ -187,29 +150,27 @@ async def configure(
     room_name = f"{room_prefix}-{uuid.uuid4().hex[:8]}"
     logger.info(f"Creating new Daily room: {room_name}")
 
-    # Use provided room_properties or build from parameters
+    # Calculate expiration time
-    if room_properties is None:
+    expiration_time = time.time() + (room_exp_duration * 60 * 60)
-        # Calculate expiration time
-        expiration_time = time.time() + (room_exp_duration * 60 * 60)
 
-        # Create room properties
+    # Create room properties
-        room_properties = DailyRoomProperties(
+    room_properties = DailyRoomProperties(
-            exp=expiration_time,
+        exp=expiration_time,
-            eject_at_room_exp=True,
+        eject_at_room_exp=True,
+    )
+
+    # Add SIP configuration if enabled
+    if sip_enabled:
+        sip_params = DailyRoomSipParams(
+            display_name=sip_caller_phone,
+            video=sip_enable_video,
+            sip_mode="dial-in",
+            num_endpoints=sip_num_endpoints,
+            codecs=sip_codecs,
         )
-
+        room_properties.sip = sip_params
-        # Add SIP configuration if enabled
+        room_properties.enable_dialout = True  # Enable outbound calls if needed
-        if sip_enabled:
+        room_properties.start_video_off = not sip_enable_video  # Voice-only by default
-            sip_params = DailyRoomSipParams(
-                display_name=sip_caller_phone,
-                video=sip_enable_video,
-                sip_mode="dial-in",
-                num_endpoints=sip_num_endpoints,
-                codecs=sip_codecs,
-            )
-            room_properties.sip = sip_params
-            room_properties.enable_dialout = True  # Enable outbound calls if needed
-            room_properties.start_video_off = not sip_enable_video  # Voice-only by default
 
     # Create room parameters
     room_params = DailyRoomParams(name=room_name, properties=room_properties)

src/pipecat/runner/run.py

@@ -70,19 +70,16 @@ import asyncio
 import mimetypes
 import os
 import sys
-import uuid
 from contextlib import asynccontextmanager
-from http import HTTPMethod
 from pathlib import Path
-from typing import Any, Dict, List, Optional, TypedDict
+from typing import Optional
 
 import aiohttp
-from fastapi.responses import FileResponse, Response
+from fastapi.responses import FileResponse
 from loguru import logger
 
 from pipecat.runner.types import (
     DailyRunnerArguments,
-    RunnerArguments,
     SmallWebRTCRunnerArguments,
     WebSocketRunnerArguments,
 )
@@ -169,7 +166,6 @@ def _create_server_app(
     host: str = "localhost",
     proxy: str,
     esp32_mode: bool = False,
-    whatsapp_enabled: bool = False,
     folder: Optional[str] = None,
 ):
     """Create FastAPI app with transport-specific routes."""
@@ -186,8 +182,7 @@ def _create_server_app(
     # Set up transport-specific routes
     if transport_type == "webrtc":
         _setup_webrtc_routes(app, esp32_mode=esp32_mode, host=host, folder=folder)
-        if whatsapp_enabled:
+        _setup_whatsapp_routes(app)
-            _setup_whatsapp_routes(app)
     elif transport_type == "daily":
         _setup_daily_routes(app)
     elif transport_type in TELEPHONY_TRANSPORTS:
@@ -205,10 +200,8 @@ def _setup_webrtc_routes(
     try:
         from pipecat_ai_small_webrtc_prebuilt.frontend import SmallWebRTCPrebuiltUI
 
-        from pipecat.transports.smallwebrtc.connection import IceServer, SmallWebRTCConnection
+        from pipecat.transports.smallwebrtc.connection import SmallWebRTCConnection
         from pipecat.transports.smallwebrtc.request_handler import (
-            IceCandidate,
-            SmallWebRTCPatchRequest,
             SmallWebRTCRequest,
             SmallWebRTCRequestHandler,
         )
@@ -216,16 +209,6 @@ def _setup_webrtc_routes(
         logger.error(f"WebRTC transport dependencies not installed: {e}")
         return
 
-    class IceConfig(TypedDict):
-        iceServers: List[IceServer]
-
-    class StartBotResult(TypedDict, total=False):
-        sessionId: str
-        iceConfig: Optional[IceConfig]
-
-    # In-memory store of active sessions: session_id -> session info
-    active_sessions: Dict[str, Dict[str, Any]] = {}
-
     # Mount the frontend
     app.mount("/client", SmallWebRTCPrebuiltUI)
 
@@ -271,74 +254,6 @@ def _setup_webrtc_routes(
         )
         return answer
 
-    @app.patch("/api/offer")
-    async def ice_candidate(request: SmallWebRTCPatchRequest):
-        """Handle WebRTC new ice candidate requests."""
-        logger.debug(f"Received patch request: {request}")
-        await small_webrtc_handler.handle_patch_request(request)
-        return {"status": "success"}
-
-    @app.post("/start")
-    async def rtvi_start(request: Request):
-        """Mimic Pipecat Cloud's /start endpoint."""
-        # Parse the request body
-        try:
-            request_data = await request.json()
-            logger.debug(f"Received request: {request_data}")
-        except Exception as e:
-            logger.error(f"Failed to parse request body: {e}")
-            request_data = {}
-
-        # Store session info immediately in memory, replicate the behavior expected on Pipecat Cloud
-        session_id = str(uuid.uuid4())
-        active_sessions[session_id] = request_data
-
-        result: StartBotResult = {"sessionId": session_id}
-        if request_data.get("enableDefaultIceServers"):
-            result["iceConfig"] = IceConfig(
-                iceServers=[IceServer(urls="stun:stun.l.google.com:19302")]
-            )
-
-        return result
-
-    @app.api_route(
-        "/sessions/{session_id}/{path:path}",
-        methods=["GET", "POST", "PUT", "PATCH", "DELETE"],
-    )
-    async def proxy_request(
-        session_id: str, path: str, request: Request, background_tasks: BackgroundTasks
-    ):
-        """Mimic Pipecat Cloud's proxy."""
-        active_session = active_sessions.get(session_id)
-        if active_session is None:
-            return Response(content="Invalid or not-yet-ready session_id", status_code=404)
-
-        if path.endswith("api/offer"):
-            # Parse the request body and convert to SmallWebRTCRequest
-            try:
-                request_data = await request.json()
-                if request.method == HTTPMethod.POST.value:
-                    webrtc_request = SmallWebRTCRequest(
-                        sdp=request_data["sdp"],
-                        type=request_data["type"],
-                        pc_id=request_data.get("pc_id"),
-                        restart_pc=request_data.get("restart_pc"),
-                        request_data=request_data,
-                    )
-                    return await offer(webrtc_request, background_tasks)
-                elif request.method == HTTPMethod.PATCH.value:
-                    patch_request = SmallWebRTCPatchRequest(
-                        pc_id=request_data["pc_id"],
-                        candidates=[IceCandidate(**c) for c in request_data.get("candidates", [])],
-                    )
-                    return await ice_candidate(patch_request)
-            except Exception as e:
-                logger.error(f"Failed to parse WebRTC request: {e}")
-                return Response(content="Invalid WebRTC request", status_code=400)
-
-        logger.info(f"Received request for path: {path}")
-        return Response(status_code=200)
-
     @asynccontextmanager
     async def smallwebrtc_lifespan(app: FastAPI):
         """Manage FastAPI application lifecycle and cleanup connections."""
@@ -374,29 +289,6 @@ def _add_lifespan_to_app(app: FastAPI, new_lifespan):
 
 def _setup_whatsapp_routes(app: FastAPI):
     """Set up WebRTC-specific routes."""
-    WHATSAPP_APP_SECRET = os.getenv("WHATSAPP_APP_SECRET")
-    WHATSAPP_PHONE_NUMBER_ID = os.getenv("WHATSAPP_PHONE_NUMBER_ID")
-    WHATSAPP_TOKEN = os.getenv("WHATSAPP_TOKEN")
-    WHATSAPP_WEBHOOK_VERIFICATION_TOKEN = os.getenv("WHATSAPP_WEBHOOK_VERIFICATION_TOKEN")
-
-    if not all(
-        [
-            WHATSAPP_APP_SECRET,
-            WHATSAPP_PHONE_NUMBER_ID,
-            WHATSAPP_TOKEN,
-            WHATSAPP_WEBHOOK_VERIFICATION_TOKEN,
-        ]
-    ):
-        logger.error(
-            """Missing required environment variables for WhatsApp transport:
-    WHATSAPP_APP_SECRET
-    WHATSAPP_PHONE_NUMBER_ID
-    WHATSAPP_TOKEN
-    WHATSAPP_WEBHOOK_VERIFICATION_TOKEN
-            """
-        )
-        return
-
     try:
         from pipecat_ai_small_webrtc_prebuilt.frontend import SmallWebRTCPrebuiltUI
 
@@ -408,7 +300,24 @@ def _setup_whatsapp_routes(app: FastAPI):
         from pipecat.transports.whatsapp.api import WhatsAppWebhookRequest
         from pipecat.transports.whatsapp.client import WhatsAppClient
     except ImportError as e:
-        logger.error(f"WhatsApp transport dependencies not installed: {e}")
+        logger.error(f"WebRTC transport dependencies not installed: {e}")
+        return
+
+    WHATSAPP_TOKEN = os.getenv("WHATSAPP_TOKEN")
+    WHATSAPP_PHONE_NUMBER_ID = os.getenv("WHATSAPP_PHONE_NUMBER_ID")
+    WHATSAPP_WEBHOOK_VERIFICATION_TOKEN = os.getenv("WHATSAPP_WEBHOOK_VERIFICATION_TOKEN")
+    WHATSAPP_APP_SECRET = os.getenv("WHATSAPP_APP_SECRET")
+
+    if not all(
+        [
+            WHATSAPP_TOKEN,
+            WHATSAPP_PHONE_NUMBER_ID,
+            WHATSAPP_WEBHOOK_VERIFICATION_TOKEN,
+        ]
+    ):
+        logger.debug(
+            "Missing required environment variables for WhatsApp transport. Keeping it disabled."
+        )
         return
 
     # Global WhatsApp client instance
@@ -530,9 +439,9 @@ 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 +456,11 @@ def _setup_daily_routes(app: FastAPI):
             asyncio.create_task(bot_module.bot(runner_args))
             return RedirectResponse(room_url)
 
-    @app.post("/start")
+    async def _handle_rtvi_request(request: Request):
-    async def start_agent(request: Request):
+        """Common handler for both /start and /connect endpoints.
-        """Handler for /start endpoints.
 
         Expects POST body like::
+
             {
                 "createDailyRoom": true,
                 "dailyRoomProperties": { "start_video_off": true },
@@ -568,38 +477,47 @@ 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)
+        # Extract the body data that should be passed to the bot
-        body = request_data.get("body", {})
+        # This mimics Pipecat Cloud's behavior
+        bot_body = request_data.get("body", {})
 
-        bot_module = _get_bot_module()
+        # Log the extracted body data for debugging
-
+        if bot_body:
-        existing_room_url = os.getenv("DAILY_SAMPLE_ROOM_URL")
+            logger.info(f"Extracted body data for bot: {bot_body}")
-
-        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()),
-                }
         else:
-            runner_args = RunnerArguments(body=body)
+            logger.debug("No body data provided in request")
 
-        # Start the bot in the background
+        import aiohttp
-        asyncio.create_task(bot_module.bot(runner_args))
 
-        return result
+        from pipecat.runner.daily import configure
+
+        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):
@@ -658,6 +576,8 @@ def _setup_telephony_routes(app: FastAPI, *, transport_type: str, proxy: str):
 async def _run_daily_direct():
     """Run Daily bot with direct connection (no FastAPI server)."""
     try:
+        import aiohttp
+
         from pipecat.runner.daily import configure
     except ImportError as e:
         logger.error("Daily transport dependencies not installed.")
@@ -769,12 +689,6 @@ def main():
     parser.add_argument(
         "--verbose", "-v", action="count", default=0, help="Increase logging verbosity"
     )
-    parser.add_argument(
-        "--whatsapp",
-        action="store_true",
-        default=False,
-        help="Ensure requried WhatsApp environment variables are present",
-    )
 
     args = parser.parse_args()
 
@@ -817,11 +731,10 @@ def main():
         print()
         if args.esp32:
             print(f"🚀 Bot ready! (ESP32 mode)")
-        elif args.whatsapp:
+            print(f"   → Open http://{args.host}:{args.port}/client in your browser")
-            print(f"🚀 Bot ready! (WhatsApp)")
         else:
             print(f"🚀 Bot ready!")
-        print(f"   → Open http://{args.host}:{args.port}/client in your browser")
+            print(f"   → Open http://{args.host}:{args.port}/client in your browser")
         print()
     elif args.transport == "daily":
         print()
@@ -839,7 +752,6 @@ def main():
         host=args.host,
         proxy=args.proxy,
         esp32_mode=args.esp32,
-        whatsapp_enabled=args.whatsapp,
         folder=args.folder,
     )
 

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)
-    handle_sigint: bool = field(init=False, kw_only=True)
+    handle_sigterm: bool = field(init=False)
-    handle_sigterm: bool = field(init=False, kw_only=True)
+    pipeline_idle_timeout_secs: int = field(init=False)
-    pipeline_idle_timeout_secs: int = field(init=False, kw_only=True)
-    body: Optional[Any] = field(default_factory=dict, kw_only=True)
 
     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/assemblyai/models.py

@@ -108,8 +108,6 @@ class AssemblyAIConnectionParams(BaseModel):
         end_of_turn_confidence_threshold: Confidence threshold for end-of-turn detection.
         min_end_of_turn_silence_when_confident: Minimum silence duration when confident about end-of-turn.
         max_turn_silence: Maximum silence duration before forcing end-of-turn.
-        keyterms_prompt: List of key terms to guide transcription. Will be JSON serialized before sending.
-        speech_model: Select between English and multilingual models. Defaults to "universal-streaming-english".
     """
 
     sample_rate: int = 16000
@@ -119,7 +117,3 @@ class AssemblyAIConnectionParams(BaseModel):
     end_of_turn_confidence_threshold: Optional[float] = None
     min_end_of_turn_silence_when_confident: Optional[int] = None
     max_turn_silence: Optional[int] = None
-    keyterms_prompt: Optional[List[str]] = None
-    speech_model: Literal["universal-streaming-english", "universal-streaming-multilingual"] = (
-        "universal-streaming-english"
-    )

src/pipecat/services/assemblyai/stt.py

@@ -174,16 +174,11 @@ class AssemblyAISTTService(STTService):
 
     def _build_ws_url(self) -> str:
         """Build WebSocket URL with query parameters using urllib.parse.urlencode."""
-        params = {}
+        params = {
-        for k, v in self._connection_params.model_dump().items():
+            k: str(v).lower() if isinstance(v, bool) else v
-            if v is not None:
+            for k, v in self._connection_params.model_dump().items()
-                if k == "keyterms_prompt":
+            if v is not None
-                    params[k] = json.dumps(v)
+        }
-                elif isinstance(v, bool):
-                    params[k] = str(v).lower()
-                else:
-                    params[k] = v
-
         if params:
             query_string = urlencode(params)
             return f"{self._api_endpoint_base_url}?{query_string}"
@@ -202,8 +197,6 @@ class AssemblyAISTTService(STTService):
             )
             self._connected = True
             self._receive_task = self.create_task(self._receive_task_handler())
-
-            await self._call_event_handler("on_connected")
         except Exception as e:
             logger.error(f"Failed to connect to AssemblyAI: {e}")
             self._connected = False
@@ -245,7 +238,6 @@ class AssemblyAISTTService(STTService):
             self._websocket = None
             self._connected = False
             self._receive_task = None
-            await self._call_event_handler("on_disconnected")
 
     async def _receive_task_handler(self):
         """Handle incoming WebSocket messages."""

src/pipecat/services/asyncai/tts.py

@@ -235,8 +235,6 @@ class AsyncAITTSService(InterruptibleTTSService):
             }
 
             await self._get_websocket().send(json.dumps(init_msg))
-
-            await self._call_event_handler("on_connected")
         except Exception as e:
             logger.error(f"{self} initialization error: {e}")
             self._websocket = None
@@ -254,7 +252,6 @@ class AsyncAITTSService(InterruptibleTTSService):
         finally:
             self._websocket = None
             self._started = False
-            await self._call_event_handler("on_disconnected")
 
     def _get_websocket(self):
         if self._websocket:

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)
+        max_tokens: Optional[int] = Field(default_factory=lambda: 4096, ge=1)
-        temperature: Optional[float] = Field(default=None, ge=0.0, le=1.0)
+        temperature: Optional[float] = Field(default_factory=lambda: 0.7, ge=0.0, le=1.0)
-        top_p: Optional[float] = Field(default=None, 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
+            # Set up inference config
-            inference_config = self._build_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

@@ -8,77 +8,8 @@
 
 This module provides specialized context aggregators and message handling for AWS Nova Sonic,
 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`.
-    Using the new patterns should allow you to not need types from this module.
-
-    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
-    ```
-
-    AFTER:
-    ```
-    # Setup
-    context = LLMContext(messages, tools)
-    context_aggregator = LLMContextAggregatorPair(context)
-
-    # Context frame type
-    frame: LLMContextFrame
-
-    # Context type
-    context: LLMContext
-    ```
 """
 
-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"
-        "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"
-        "```\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"
-        "```",
-        DeprecationWarning,
-        stacklevel=2,
-    )
-
 import copy
 from dataclasses import dataclass, field
 from enum import Enum

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

@@ -25,7 +25,7 @@ from loguru import logger
 from pydantic import BaseModel, Field
 
 from pipecat.adapters.schemas.tools_schema import ToolsSchema
-from pipecat.adapters.services.aws_nova_sonic_adapter import AWSNovaSonicLLMAdapter, Role
+from pipecat.adapters.services.aws_nova_sonic_adapter import AWSNovaSonicLLMAdapter
 from pipecat.frames.frames import (
     BotStoppedSpeakingFrame,
     CancelFrame,
@@ -33,30 +33,35 @@ from pipecat.frames.frames import (
     Frame,
     FunctionCallFromLLM,
     InputAudioRawFrame,
-    InterruptionFrame,
+    InterimTranscriptionFrame,
     LLMContextFrame,
     LLMFullResponseEndFrame,
     LLMFullResponseStartFrame,
+    LLMTextFrame,
     StartFrame,
     TranscriptionFrame,
     TTSAudioRawFrame,
     TTSStartedFrame,
     TTSStoppedFrame,
     TTSTextFrame,
-    UserStartedSpeakingFrame,
-    UserStoppedSpeakingFrame,
 )
-from pipecat.processors.aggregators.llm_context import LLMContext
 from pipecat.processors.aggregators.llm_response import (
     LLMAssistantAggregatorParams,
     LLMUserAggregatorParams,
 )
-from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
 from pipecat.processors.aggregators.openai_llm_context import (
     OpenAILLMContext,
     OpenAILLMContextFrame,
 )
 from pipecat.processors.frame_processor import FrameDirection
+from pipecat.services.aws.nova_sonic.context import (
+    AWSNovaSonicAssistantContextAggregator,
+    AWSNovaSonicContextAggregatorPair,
+    AWSNovaSonicLLMContext,
+    AWSNovaSonicUserContextAggregator,
+    Role,
+)
+from pipecat.services.aws.nova_sonic.frames import AWSNovaSonicFunctionCallResultFrame
 from pipecat.services.llm_service import LLMService
 from pipecat.utils.time import time_now_iso8601
 
@@ -212,11 +217,6 @@ class AWSNovaSonicLLMService(LLMService):
             system_instruction: System-level instruction for the model.
             tools: Available tools/functions for the model to use.
             send_transcription_frames: Whether to emit transcription frames.
-
-                .. deprecated:: 0.0.91
-                    This parameter is deprecated and will be removed in a future version.
-                    Transcription frames are always sent.
-
             **kwargs: Additional arguments passed to the parent LLMService.
         """
         super().__init__(**kwargs)
@@ -230,20 +230,8 @@ class AWSNovaSonicLLMService(LLMService):
         self._params = params or Params()
         self._system_instruction = system_instruction
         self._tools = tools
-
+        self._send_transcription_frames = send_transcription_frames
-        if not send_transcription_frames:
+        self._context: Optional[AWSNovaSonicLLMContext] = None
-            import warnings
-
-            with warnings.catch_warnings():
-                warnings.simplefilter("always")
-                warnings.warn(
-                    "`send_transcription_frames` is deprecated and will be removed in a future version. "
-                    "Transcription frames are always sent.",
-                    DeprecationWarning,
-                    stacklevel=2,
-                )
-
-        self._context: Optional[LLMContext] = None
         self._stream: Optional[
             DuplexEventStream[
                 InvokeModelWithBidirectionalStreamInput,
@@ -256,17 +244,12 @@ class AWSNovaSonicLLMService(LLMService):
         self._input_audio_content_name: Optional[str] = None
         self._content_being_received: Optional[CurrentContent] = None
         self._assistant_is_responding = False
-        self._may_need_repush_assistant_text = False
         self._ready_to_send_context = False
         self._handling_bot_stopped_speaking = False
         self._triggering_assistant_response = False
-        self._waiting_for_trigger_transcription = False
         self._disconnecting = False
         self._connected_time: Optional[float] = None
         self._wants_connection = False
-        self._user_text_buffer = ""
-        self._assistant_text_buffer = ""
-        self._completed_tool_calls = set()
 
         file_path = files("pipecat.services.aws.nova_sonic").joinpath("ready.wav")
         with wave.open(file_path.open("rb"), "rb") as wav_file:
@@ -319,12 +302,12 @@ class AWSNovaSonicLLMService(LLMService):
         logger.debug("Resetting conversation")
         await self._handle_bot_stopped_speaking(delay_to_catch_trailing_assistant_text=False)
 
-        # Grab context to carry through disconnect/reconnect
+        # Carry over previous context through disconnect
         context = self._context
-
         await self._disconnect()
+        self._context = context
+
         await self._start_connecting()
-        await self._handle_context(context)
 
     #
     # frame processing
@@ -339,35 +322,28 @@ class AWSNovaSonicLLMService(LLMService):
         """
         await super().process_frame(frame, direction)
 
-        if isinstance(frame, (LLMContextFrame, OpenAILLMContextFrame)):
+        if isinstance(frame, OpenAILLMContextFrame):
-            context = (
+            await self._handle_context(frame.context)
-                frame.context
+        elif isinstance(frame, LLMContextFrame):
-                if isinstance(frame, LLMContextFrame)
+            raise NotImplementedError(
-                else LLMContext.from_openai_context(frame.context)
+                "Universal LLMContext is not yet supported for AWS Nova Sonic."
             )
-            await self._handle_context(context)
         elif isinstance(frame, InputAudioRawFrame):
             await self._handle_input_audio_frame(frame)
         elif isinstance(frame, BotStoppedSpeakingFrame):
             await self._handle_bot_stopped_speaking(delay_to_catch_trailing_assistant_text=True)
-        elif isinstance(frame, InterruptionFrame):
+        elif isinstance(frame, AWSNovaSonicFunctionCallResultFrame):
-            await self._handle_interruption_frame()
+            await self._handle_function_call_result(frame)
 
         await self.push_frame(frame, direction)
 
-    async def _handle_context(self, context: LLMContext):
+    async def _handle_context(self, context: OpenAILLMContext):
-        if self._disconnecting:
-            return
-
         if not self._context:
-            # We got our initial context
+            # We got our initial context - try to finish connecting
-            # Try to finish connecting
+            self._context = AWSNovaSonicLLMContext.upgrade_to_nova_sonic(
-            self._context = context
+                context, self._system_instruction
+            )
             await self._finish_connecting_if_context_available()
-        else:
-            # We got an updated context
-            # Send results for any newly-completed function calls
-            await self._process_completed_function_calls(send_new_results=True)
 
     async def _handle_input_audio_frame(self, frame: InputAudioRawFrame):
         # Wait until we're done sending the assistant response trigger audio before sending audio
@@ -417,9 +393,9 @@ class AWSNovaSonicLLMService(LLMService):
         else:
             await finalize_assistant_response()
 
-    async def _handle_interruption_frame(self):
+    async def _handle_function_call_result(self, frame: AWSNovaSonicFunctionCallResultFrame):
-        if self._assistant_is_responding:
+        result = frame.result_frame
-            self._may_need_repush_assistant_text = True
+        await self._send_tool_result(tool_call_id=result.tool_call_id, result=result.result)
 
     #
     # LLM communication: lifecycle
@@ -455,17 +431,6 @@ class AWSNovaSonicLLMService(LLMService):
             logger.error(f"{self} initialization error: {e}")
             await self._disconnect()
 
-    async def _process_completed_function_calls(self, send_new_results: bool):
-        # Check for set of completed function calls in the context
-        for message in self._context.get_messages():
-            if message.get("role") and message.get("content") != "IN_PROGRESS":
-                tool_call_id = message.get("tool_call_id")
-                if tool_call_id and tool_call_id not in self._completed_tool_calls:
-                    # Found a newly-completed function call - send the result to the service
-                    if send_new_results:
-                        await self._send_tool_result(tool_call_id, message.get("content"))
-                    self._completed_tool_calls.add(tool_call_id)
-
     async def _finish_connecting_if_context_available(self):
         # We can only finish connecting once we've gotten our initial context and we're ready to
         # send it
@@ -474,38 +439,30 @@ class AWSNovaSonicLLMService(LLMService):
 
         logger.info("Finishing connecting (setting up session)...")
 
-        # Initialize our bookkeeping of already-completed tool calls in the
-        # context
-        await self._process_completed_function_calls(send_new_results=False)
-
         # Read context
-        adapter: AWSNovaSonicLLMAdapter = self.get_llm_adapter()
+        history = self._context.get_messages_for_initializing_history()
-        llm_connection_params = adapter.get_llm_invocation_params(self._context)
 
         # Send prompt start event, specifying tools.
         # Tools from context take priority over self._tools.
         tools = (
-            llm_connection_params["tools"]
+            self._context.tools
-            if llm_connection_params["tools"]
+            if self._context.tools
-            else adapter.from_standard_tools(self._tools)
+            else self.get_llm_adapter().from_standard_tools(self._tools)
         )
         logger.debug(f"Using tools: {tools}")
         await self._send_prompt_start_event(tools)
 
         # Send system instruction.
         # Instruction from context takes priority over self._system_instruction.
-        system_instruction = (
+        # (NOTE: this prioritizing occurred automatically behind the scenes: the context was
-            llm_connection_params["system_instruction"]
+        # initialized with self._system_instruction and then updated itself from its messages when
-            if llm_connection_params["system_instruction"]
+        # get_messages_for_initializing_history() was called).
-            else self._system_instruction
+        logger.debug(f"Using system instruction: {history.system_instruction}")
-        )
+        if history.system_instruction:
-        logger.debug(f"Using system instruction: {system_instruction}")
+            await self._send_text_event(text=history.system_instruction, role=Role.SYSTEM)
-        if system_instruction:
-            await self._send_text_event(text=system_instruction, role=Role.SYSTEM)
 
         # Send conversation history
-        for message in llm_connection_params["messages"]:
+        for message in history.messages:
-            # logger.debug(f"Seeding conversation history with message: {message}")
             await self._send_text_event(text=message.text, role=message.role)
 
         # Start audio input
@@ -535,12 +492,9 @@ class AWSNovaSonicLLMService(LLMService):
                 await self._send_session_end_events()
                 self._client = None
 
-            # Clean up context
-            self._context = None
-
             # Clean up stream
             if self._stream:
-                await self._stream.close()
+                await self._stream.input_stream.close()
                 self._stream = None
 
             # NOTE: see explanation of HACK, below
@@ -556,23 +510,15 @@ class AWSNovaSonicLLMService(LLMService):
                 self._receive_task = None
 
             # Reset remaining connection-specific state
-            # Should be all private state except:
-            # - _wants_connection
-            # - _assistant_response_trigger_audio
             self._prompt_name = None
             self._input_audio_content_name = None
             self._content_being_received = None
             self._assistant_is_responding = False
-            self._may_need_repush_assistant_text = False
             self._ready_to_send_context = False
             self._handling_bot_stopped_speaking = False
             self._triggering_assistant_response = False
-            self._waiting_for_trigger_transcription = False
             self._disconnecting = False
             self._connected_time = None
-            self._user_text_buffer = ""
-            self._assistant_text_buffer = ""
-            self._completed_tool_calls = set()
 
             logger.info("Finished disconnecting")
         except Exception as e:
@@ -880,10 +826,6 @@ class AWSNovaSonicLLMService(LLMService):
                             # Handle the LLM completion ending
                             await self._handle_completion_end_event(event_json)
         except Exception as e:
-            if self._disconnecting:
-                # Errors are kind of expected while disconnecting, so just
-                # ignore them and do nothing
-                return
             logger.error(f"{self} error processing responses: {e}")
             if self._wants_connection:
                 await self.reset_conversation()
@@ -1014,7 +956,7 @@ class AWSNovaSonicLLMService(LLMService):
     async def _report_assistant_response_started(self):
         logger.debug("Assistant response started")
 
-        # Report the start of the assistant response.
+        # Report that the assistant has started their response.
         await self.push_frame(LLMFullResponseStartFrame())
 
         # Report that equivalent of TTS (this is a speech-to-speech model) started
@@ -1026,16 +968,23 @@ class AWSNovaSonicLLMService(LLMService):
 
         logger.debug(f"Assistant response text added: {text}")
 
-        # Report the text of the assistant response.
+        # Report some text added to the ongoing assistant response
+        await self.push_frame(LLMTextFrame(text))
+
+        # Report some text added to the *equivalent* of TTS (this is a speech-to-speech model)
         await self.push_frame(TTSTextFrame(text))
 
-        # HACK: here we're also buffering the assistant text ourselves as a
+        # TODO: this is a (hopefully temporary) HACK. Here we directly manipulate the context rather
-        # backup rather than relying solely on the assistant context aggregator
+        # than relying on the frames pushed to the assistant context aggregator. The pattern of
-        # to do it, because the text arrives from Nova Sonic only after all the
+        # receiving full-sentence text after the assistant has spoken does not easily fit with the
-        # assistant audio frames have been pushed, meaning that if an
+        # Pipecat expectation of chunks of text streaming in while the assistant is speaking.
-        # interruption frame were to arrive we would lose all of it (the text
+        # Interruption handling was especially challenging. Rather than spend days trying to fit a
-        # frames sitting in the queue would be wiped).
+        # square peg in a round hole, I decided on this hack for the time being. We can most cleanly
-        self._assistant_text_buffer += text
+        # abandon this hack if/when AWS Nova Sonic implements streaming smaller text chunks
+        # interspersed with audio. Note that when we move away from this hack, we need to make sure
+        # that on an interruption we avoid sending LLMFullResponseEndFrame, which gets the
+        # LLMAssistantContextAggregator into a bad state.
+        self._context.buffer_assistant_text(text)
 
     async def _report_assistant_response_ended(self):
         if not self._context:  # should never happen
@@ -1043,34 +992,14 @@ class AWSNovaSonicLLMService(LLMService):
 
         logger.debug("Assistant response ended")
 
-        # If an interruption frame arrived while the assistant was responding
+        # Report that the assistant has finished their response.
-        # we may have lost all of the assistant text (see HACK, above), so
-        # re-push it downstream to the aggregator now.
-        if self._may_need_repush_assistant_text:
-            # Just in case, check that assistant text hasn't already made it
-            # into the context (sometimes it does, despite the interruption).
-            messages = self._context.get_messages()
-            last_message = messages[-1] if messages else None
-            if (
-                not last_message
-                or last_message.get("role") != "assistant"
-                or last_message.get("content") != self._assistant_text_buffer
-            ):
-                # We also need to re-push the LLMFullResponseStartFrame since the
-                # TTSTextFrame would be ignored otherwise (the interruption frame
-                # would have cleared the assistant aggregator state).
-                await self.push_frame(LLMFullResponseStartFrame())
-                await self.push_frame(TTSTextFrame(self._assistant_text_buffer))
-            self._may_need_repush_assistant_text = False
-
-        # Report the end of the assistant response.
         await self.push_frame(LLMFullResponseEndFrame())
 
         # Report that equivalent of TTS (this is a speech-to-speech model) stopped.
         await self.push_frame(TTSStoppedFrame())
 
-        # Clear out the buffered assistant text
+        # For an explanation of this hack, see _report_assistant_response_text_added.
-        self._assistant_text_buffer = ""
+        self._context.flush_aggregated_assistant_text()
 
     #
     # user transcription reporting
@@ -1087,67 +1016,33 @@ class AWSNovaSonicLLMService(LLMService):
 
         logger.debug(f"User transcription text added: {text}")
 
-        # HACK: here we're buffering the user text ourselves rather than
+        # Manually add new user transcription text to context.
-        # relying on the upstream user context aggregator to do it, because the
+        # We can't rely on the user context aggregator to do this since it's upstream from the LLM.
-        # text arrives in fairly large chunks spaced fairly far apart in time.
+        self._context.buffer_user_text(text)
-        # That means the user text would be split between different messages in
+
-        # context. Even if we sent placeholder InterimTranscriptionFrames in
+        # Report that some new user transcription text is available.
-        # between each TranscriptionFrame to tell the aggregator to hold off on
+        if self._send_transcription_frames:
-        # finalizing the user message, the aggregator would likely get the last
+            await self.push_frame(
-        # chunk too late.
+                InterimTranscriptionFrame(text=text, user_id="", timestamp=time_now_iso8601())
-        self._user_text_buffer += f" {text}" if self._user_text_buffer else text
+            )
 
     async def _report_user_transcription_ended(self):
         if not self._context:  # should never happen
             return
 
+        # Manually add user transcription to context (if any has been buffered).
+        # We can't rely on the user context aggregator to do this since it's upstream from the LLM.
+        transcription = self._context.flush_aggregated_user_text()
+
+        if not transcription:
+            return
+
         logger.debug(f"User transcription ended")
 
-        # Report to the upstream user context aggregator that some new user
+        if self._send_transcription_frames:
-        # transcription text is available.
+            await self.push_frame(
-
+                TranscriptionFrame(text=transcription, user_id="", timestamp=time_now_iso8601())
-        # HACK: Check if this transcription was triggered by our own
-        # assistant response trigger. If so, we need to wrap it with
-        # UserStarted/StoppedSpeakingFrames; otherwise the user aggregator
-        # would fire an EmulatedUserStartedSpeakingFrame, which would
-        # trigger an interruption, which would prevent us from writing the
-        # assistant response to context.
-        #
-        # Sending an EmulateUserStartedSpeakingFrame ourselves doesn't
-        # work: it just causes the interruption we're trying to avoid.
-        #
-        # Setting enable_emulated_vad_interruptions also doesn't work: at
-        # the time the user aggregator receives the TranscriptionFrame, it
-        # doesn't yet know the assistant has started responding, so it
-        # doesn't know that emulating the user starting to speak would
-        # cause an interruption.
-        should_wrap_in_user_started_stopped_speaking_frames = (
-            self._waiting_for_trigger_transcription
-            and self._user_text_buffer.strip().lower() == "ready"
-        )
-
-        # Start wrapping the upstream transcription in UserStarted/StoppedSpeakingFrames if needed
-        if should_wrap_in_user_started_stopped_speaking_frames:
-            logger.debug(
-                "Wrapping assistant response trigger transcription with upstream UserStarted/StoppedSpeakingFrames"
             )
-            await self.push_frame(UserStartedSpeakingFrame(), direction=FrameDirection.UPSTREAM)
-
-        # Send the transcription upstream for the user context aggregator
-        frame = TranscriptionFrame(
-            text=self._user_text_buffer, user_id="", timestamp=time_now_iso8601()
-        )
-        await self.push_frame(frame, direction=FrameDirection.UPSTREAM)
-
-        # Finish wrapping the upstream transcription in UserStarted/StoppedSpeakingFrames if needed
-        if should_wrap_in_user_started_stopped_speaking_frames:
-            await self.push_frame(UserStoppedSpeakingFrame(), direction=FrameDirection.UPSTREAM)
-
-        # Clear out the buffered user text
-        self._user_text_buffer = ""
-
-        # We're no longer waiting for a trigger transcription
-        self._waiting_for_trigger_transcription = False
 
     #
     # context
@@ -1159,26 +1054,23 @@ class AWSNovaSonicLLMService(LLMService):
         *,
         user_params: LLMUserAggregatorParams = LLMUserAggregatorParams(),
         assistant_params: LLMAssistantAggregatorParams = LLMAssistantAggregatorParams(),
-    ) -> LLMContextAggregatorPair:
+    ) -> AWSNovaSonicContextAggregatorPair:
         """Create context aggregator pair for managing conversation context.
 
-        NOTE: this method exists only for backward compatibility. New code
-        should instead do:
-            context = LLMContext(...)
-            context_aggregator = LLMContextAggregatorPair(context)
-
         Args:
-            context: The OpenAI LLM context.
+            context: The OpenAI LLM context to upgrade.
             user_params: Parameters for the user context aggregator.
             assistant_params: Parameters for the assistant context aggregator.
 
         Returns:
             A pair of user and assistant context aggregators.
         """
-        context = LLMContext.from_openai_context(context)
+        context.set_llm_adapter(self.get_llm_adapter())
-        return LLMContextAggregatorPair(
+
-            context, user_params=user_params, assistant_params=assistant_params
+        user = AWSNovaSonicUserContextAggregator(context=context, params=user_params)
-        )
+        assistant = AWSNovaSonicAssistantContextAggregator(context=context, params=assistant_params)
+
+        return AWSNovaSonicContextAggregatorPair(user, assistant)
 
     #
     # assistant response trigger (HACK)
@@ -1216,8 +1108,6 @@ class AWSNovaSonicLLMService(LLMService):
         try:
             logger.debug("Sending assistant response trigger...")
 
-            self._waiting_for_trigger_transcription = True
-
             chunk_duration = 0.02  # what we might get from InputAudioRawFrame
             chunk_size = int(
                 chunk_duration

src/pipecat/services/aws/stt.py

@@ -286,7 +286,6 @@ class AWSTranscribeSTTService(STTService):
 
                 logger.info(f"{self} Successfully connected to AWS Transcribe")
 
-                await self._call_event_handler("on_connected")
             except Exception as e:
                 logger.error(f"{self} Failed to connect to AWS Transcribe: {e}")
                 await self._disconnect()
@@ -311,7 +310,6 @@ class AWSTranscribeSTTService(STTService):
             logger.warning(f"{self} Error closing WebSocket connection: {e}")
         finally:
             self._ws_client = None
-            await self._call_event_handler("on_disconnected")
 
     def language_to_service_language(self, language: Language) -> str | None:
         """Convert internal language enum to AWS Transcribe language code.

src/pipecat/services/aws_nova_sonic/context.py

@@ -8,14 +8,18 @@
 
 This module provides specialized context aggregators and message handling for AWS Nova Sonic,
 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`.
-    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.
 """
 
+import warnings
+
 from pipecat.services.aws.nova_sonic.context import *
+
+with warnings.catch_warnings():
+    warnings.simplefilter("always")
+    warnings.warn(
+        "Types in pipecat.services.aws_nova_sonic.context are deprecated. "
+        "Please use the equivalent types from "
+        "pipecat.services.aws.nova_sonic.context instead.",
+        DeprecationWarning,
+        stacklevel=2,
+    )

src/pipecat/services/cartesia/stt.py

@@ -28,12 +28,13 @@ from pipecat.frames.frames import (
     UserStoppedSpeakingFrame,
 )
 from pipecat.processors.frame_processor import FrameDirection
-from pipecat.services.stt_service import WebsocketSTTService
+from pipecat.services.stt_service import STTService
 from pipecat.transcriptions.language import Language
 from pipecat.utils.time import time_now_iso8601
 from pipecat.utils.tracing.service_decorators import traced_stt
 
 try:
+    import websockets
     from websockets.asyncio.client import connect as websocket_connect
     from websockets.protocol import State
 except ModuleNotFoundError as e:
@@ -123,7 +124,7 @@ class CartesiaLiveOptions:
         return cls(**json.loads(json_str))
 
 
-class CartesiaSTTService(WebsocketSTTService):
+class CartesiaSTTService(STTService):
     """Speech-to-text service using Cartesia Live API.
 
     Provides real-time speech transcription through WebSocket connection
@@ -175,7 +176,8 @@ class CartesiaSTTService(WebsocketSTTService):
         self.set_model_name(merged_options.model)
         self._api_key = api_key
         self._base_url = base_url or "api.cartesia.ai"
-        self._receive_task = None
+        self._connection = None
+        self._receiver_task = None
 
     def can_generate_metrics(self) -> bool:
         """Check if the service can generate processing metrics.
@@ -212,27 +214,6 @@ class CartesiaSTTService(WebsocketSTTService):
         await super().cancel(frame)
         await self._disconnect()
 
-    async def start_metrics(self):
-        """Start performance metrics collection for transcription processing."""
-        await self.start_ttfb_metrics()
-        await self.start_processing_metrics()
-
-    async def process_frame(self, frame: Frame, direction: FrameDirection):
-        """Process incoming frames and handle speech events.
-
-        Args:
-            frame: The frame to process.
-            direction: Direction of frame flow in the pipeline.
-        """
-        await super().process_frame(frame, direction)
-
-        if isinstance(frame, UserStartedSpeakingFrame):
-            await self.start_metrics()
-        elif isinstance(frame, UserStoppedSpeakingFrame):
-            # Send finalize command to flush the transcription session
-            if self._websocket and self._websocket.state is State.OPEN:
-                await self._websocket.send("finalize")
-
     async def run_stt(self, audio: bytes) -> AsyncGenerator[Frame, None]:
         """Process audio data for speech-to-text transcription.
 
@@ -243,71 +224,45 @@ class CartesiaSTTService(WebsocketSTTService):
             None - transcription results are handled via WebSocket responses.
         """
         # If the connection is closed, due to timeout, we need to reconnect when the user starts speaking again
-        if not self._websocket or self._websocket.state is State.CLOSED:
+        if not self._connection or self._connection.state is State.CLOSED:
             await self._connect()
 
-        await self._websocket.send(audio)
+        await self._connection.send(audio)
         yield None
 
     async def _connect(self):
-        await self._connect_websocket()
+        params = self._settings.to_dict()
+        ws_url = f"wss://{self._base_url}/stt/websocket?{urllib.parse.urlencode(params)}"
+        logger.debug(f"Connecting to Cartesia: {ws_url}")
+        headers = {"Cartesia-Version": "2025-04-16", "X-API-Key": self._api_key}
 
-        if self._websocket and not self._receive_task:
-            self._receive_task = asyncio.create_task(self._receive_task_handler(self._report_error))
-
-    async def _disconnect(self):
-        if self._receive_task:
-            await self.cancel_task(self._receive_task)
-            self._receive_task = None
-
-        await self._disconnect_websocket()
-
-    async def _connect_websocket(self):
         try:
-            if self._websocket and self._websocket.state is State.OPEN:
+            self._connection = await websocket_connect(ws_url, additional_headers=headers)
-                return
+            # Setup the receiver task to handle the incoming messages from the Cartesia server
-            logger.debug("Connecting to Cartesia STT")
+            if self._receiver_task is None or self._receiver_task.done():
-
+                self._receiver_task = asyncio.create_task(self._receive_messages())
-            params = self._settings.to_dict()
+            logger.debug(f"Connected to Cartesia")
-            ws_url = f"wss://{self._base_url}/stt/websocket?{urllib.parse.urlencode(params)}"
-            headers = {"Cartesia-Version": "2025-04-16", "X-API-Key": self._api_key}
-
-            self._websocket = await websocket_connect(ws_url, additional_headers=headers)
-            await self._call_event_handler("on_connected")
         except Exception as e:
             logger.error(f"{self}: unable to connect to Cartesia: {e}")
 
-    async def _disconnect_websocket(self):
-        try:
-            if self._websocket and self._websocket.state is State.OPEN:
-                logger.debug("Disconnecting from Cartesia STT")
-                await self._websocket.close()
-        except Exception as e:
-            logger.error(f"{self} error closing websocket: {e}")
-        finally:
-            self._websocket = None
-            await self._call_event_handler("on_disconnected")
-
-    def _get_websocket(self):
-        if self._websocket:
-            return self._websocket
-        raise Exception("Websocket not connected")
-
-    async def _process_messages(self):
-        async for message in self._get_websocket():
-            try:
-                data = json.loads(message)
-                await self._process_response(data)
-            except json.JSONDecodeError:
-                logger.warning(f"Received non-JSON message: {message}")
-
     async def _receive_messages(self):
-        while True:
+        try:
-            await self._process_messages()
+            while True:
-            # Cartesia times out after 5 minutes of innactivity (no keepalive
+                if not self._connection or self._connection.state is State.CLOSED:
-            # mechanism is available). So, we try to reconnect.
+                    break
-            logger.debug(f"{self} Cartesia connection was disconnected (timeout?), reconnecting")
+
-            await self._connect_websocket()
+                message = await self._connection.recv()
+                try:
+                    data = json.loads(message)
+                    await self._process_response(data)
+                except json.JSONDecodeError:
+                    logger.warning(f"Received non-JSON message: {message}")
+        except asyncio.CancelledError:
+            pass
+        except websockets.exceptions.ConnectionClosed as e:
+            logger.debug(f"WebSocket connection closed: {e}")
+        except Exception as e:
+            logger.error(f"Error in message receiver: {e}")
 
     async def _process_response(self, data):
         if "type" in data:
@@ -361,3 +316,41 @@ class CartesiaSTTService(WebsocketSTTService):
                         language,
                     )
                 )
+
+    async def _disconnect(self):
+        if self._receiver_task:
+            self._receiver_task.cancel()
+            try:
+                await self._receiver_task
+            except asyncio.CancelledError:
+                pass
+            except Exception as e:
+                logger.exception(f"Unexpected exception while cancelling task: {e}")
+            self._receiver_task = None
+
+        if self._connection and self._connection.state is State.OPEN:
+            logger.debug("Disconnecting from Cartesia")
+
+            await self._connection.close()
+            self._connection = None
+
+    async def start_metrics(self):
+        """Start performance metrics collection for transcription processing."""
+        await self.start_ttfb_metrics()
+        await self.start_processing_metrics()
+
+    async def process_frame(self, frame: Frame, direction: FrameDirection):
+        """Process incoming frames and handle speech events.
+
+        Args:
+            frame: The frame to process.
+            direction: Direction of frame flow in the pipeline.
+        """
+        await super().process_frame(frame, direction)
+
+        if isinstance(frame, UserStartedSpeakingFrame):
+            await self.start_metrics()
+        elif isinstance(frame, UserStoppedSpeakingFrame):
+            # Send finalize command to flush the transcription session
+            if self._connection and self._connection.state is State.OPEN:
+                await self._connection.send("finalize")

src/pipecat/services/cartesia/tts.py

@@ -344,11 +344,10 @@ class CartesiaTTSService(AudioContextWordTTSService):
         try:
             if self._websocket and self._websocket.state is State.OPEN:
                 return
-            logger.debug("Connecting to Cartesia TTS")
+            logger.debug("Connecting to Cartesia")
             self._websocket = await websocket_connect(
                 f"{self._url}?api_key={self._api_key}&cartesia_version={self._cartesia_version}"
             )
-            await self._call_event_handler("on_connected")
         except Exception as e:
             logger.error(f"{self} initialization error: {e}")
             self._websocket = None
@@ -366,7 +365,6 @@ class CartesiaTTSService(AudioContextWordTTSService):
         finally:
             self._context_id = None
             self._websocket = None
-            await self._call_event_handler("on_disconnected")
 
     def _get_websocket(self):
         if self._websocket:

src/pipecat/services/deepgram/flux/stt.py

@@ -205,7 +205,6 @@ class DeepgramFluxSTTService(WebsocketSTTService):
                 additional_headers={"Authorization": f"Token {self._api_key}"},
             )
             logger.debug("Connected to Deepgram Flux Websocket")
-            await self._call_event_handler("on_connected")
         except Exception as e:
             logger.error(f"{self} initialization error: {e}")
             self._websocket = None
@@ -226,9 +225,6 @@ class DeepgramFluxSTTService(WebsocketSTTService):
                 await self._websocket.close()
         except Exception as e:
             logger.error(f"{self} error closing websocket: {e}")
-        finally:
-            self._websocket = None
-            await self._call_event_handler("on_disconnected")
 
     async def _send_close_stream(self) -> None:
         """Sends a CloseStream control message to the Deepgram Flux WebSocket API.

src/pipecat/services/elevenlabs/tts.py

@@ -168,24 +168,16 @@ def build_elevenlabs_voice_settings(
 
 
 def calculate_word_times(
-    alignment_info: Mapping[str, Any],
+    alignment_info: Mapping[str, Any], cumulative_time: float
-    cumulative_time: float,
+) -> List[Tuple[str, float]]:
-    partial_word: str = "",
-    partial_word_start_time: float = 0.0,
-) -> tuple[List[Tuple[str, float]], str, float]:
     """Calculate word timestamps from character alignment information.
 
     Args:
         alignment_info: Character alignment data from ElevenLabs API.
         cumulative_time: Base time offset for this chunk.
-        partial_word: Partial word carried over from previous chunk.
-        partial_word_start_time: Start time of the partial word.
 
     Returns:
-        Tuple of (word_times, new_partial_word, new_partial_word_start_time):
+        List of (word, timestamp) tuples.
-        - word_times: List of (word, timestamp) tuples for complete words
-        - new_partial_word: Incomplete word at end of chunk (empty if chunk ends with space)
-        - new_partial_word_start_time: Start time of the incomplete word
     """
     chars = alignment_info["chars"]
     char_start_times_ms = alignment_info["charStartTimesMs"]
@@ -194,37 +186,41 @@ def calculate_word_times(
         logger.error(
             f"calculate_word_times: length mismatch - chars={len(chars)}, times={len(char_start_times_ms)}"
         )
-        return ([], partial_word, partial_word_start_time)
+        return []
 
     # Build words and track their start positions
     words = []
-    word_start_times = []
+    word_start_indices = []
-    current_word = partial_word  # Start with any partial word from previous chunk
+    current_word = ""
-    word_start_time = partial_word_start_time if partial_word else None
+    word_start_index = None
 
     for i, char in enumerate(chars):
         if char == " ":
             # End of current word
             if current_word:  # Only add non-empty words
                 words.append(current_word)
-                word_start_times.append(word_start_time)
+                word_start_indices.append(word_start_index)
                 current_word = ""
-                word_start_time = None
+                word_start_index = None
         else:
             # Building a word
-            if word_start_time is None:  # First character of new word
+            if word_start_index is None:  # First character of new word
-                # Convert from milliseconds to seconds and add cumulative offset
+                word_start_index = i
-                word_start_time = cumulative_time + (char_start_times_ms[i] / 1000.0)
             current_word += char
 
-    # Build result for complete words
+    # Handle the last word if there's no trailing space
-    word_times = list(zip(words, word_start_times))
+    if current_word and word_start_index is not None:
+        words.append(current_word)
+        word_start_indices.append(word_start_index)
 
-    # Return any incomplete word at the end of this chunk
+    # Calculate timestamps for each word
-    new_partial_word = current_word if current_word else ""
+    word_times = []
-    new_partial_word_start_time = word_start_time if word_start_time is not None else 0.0
+    for word, start_idx in zip(words, word_start_indices):
+        # Convert from milliseconds to seconds and add cumulative offset
+        start_time_seconds = cumulative_time + (char_start_times_ms[start_idx] / 1000.0)
+        word_times.append((word, start_time_seconds))
 
-    return (word_times, new_partial_word, new_partial_word_start_time)
+    return word_times
 
 
 class ElevenLabsTTSService(AudioContextWordTTSService):
@@ -336,9 +332,6 @@ class ElevenLabsTTSService(AudioContextWordTTSService):
         # there's an interruption or TTSStoppedFrame.
         self._started = False
         self._cumulative_time = 0
-        # Track partial words that span across alignment chunks
-        self._partial_word = ""
-        self._partial_word_start_time = 0.0
 
         # Context management for v1 multi API
         self._context_id = None
@@ -528,7 +521,6 @@ class ElevenLabsTTSService(AudioContextWordTTSService):
                 url, max_size=16 * 1024 * 1024, additional_headers={"xi-api-key": self._api_key}
             )
 
-            await self._call_event_handler("on_connected")
         except Exception as e:
             logger.error(f"{self} initialization error: {e}")
             self._websocket = None
@@ -551,7 +543,6 @@ class ElevenLabsTTSService(AudioContextWordTTSService):
             self._started = False
             self._context_id = None
             self._websocket = None
-            await self._call_event_handler("on_disconnected")
 
     def _get_websocket(self):
         if self._websocket:
@@ -579,8 +570,6 @@ class ElevenLabsTTSService(AudioContextWordTTSService):
                 logger.error(f"Error closing context on interruption: {e}")
             self._context_id = None
             self._started = False
-            self._partial_word = ""
-            self._partial_word_start_time = 0.0
 
     async def _receive_messages(self):
         """Handle incoming WebSocket messages from ElevenLabs."""
@@ -620,14 +609,7 @@ class ElevenLabsTTSService(AudioContextWordTTSService):
 
             if msg.get("alignment"):
                 alignment = msg["alignment"]
-                word_times, self._partial_word, self._partial_word_start_time = (
+                word_times = calculate_word_times(alignment, self._cumulative_time)
-                    calculate_word_times(
-                        alignment,
-                        self._cumulative_time,
-                        self._partial_word,
-                        self._partial_word_start_time,
-                    )
-                )
 
                 if word_times:
                     await self.add_word_timestamps(word_times)
@@ -701,8 +683,6 @@ class ElevenLabsTTSService(AudioContextWordTTSService):
                     yield TTSStartedFrame()
                     self._started = True
                     self._cumulative_time = 0
-                    self._partial_word = ""
-                    self._partial_word_start_time = 0.0
                     # If a context ID does not exist, create a new one and
                     # register it. If an ID exists, that means the Pipeline is
                     # configured for allow_interruptions=False, so continue
@@ -776,7 +756,6 @@ class ElevenLabsHttpTTSService(WordTTSService):
         base_url: str = "https://api.elevenlabs.io",
         sample_rate: Optional[int] = None,
         params: Optional[InputParams] = None,
-        aggregate_sentences: Optional[bool] = True,
         **kwargs,
     ):
         """Initialize the ElevenLabs HTTP TTS service.
@@ -789,11 +768,10 @@ class ElevenLabsHttpTTSService(WordTTSService):
             base_url: Base URL for ElevenLabs HTTP API.
             sample_rate: Audio sample rate. If None, uses default.
             params: Additional input parameters for voice customization.
-            aggregate_sentences: Whether to aggregate sentences within the TTSService.
             **kwargs: Additional arguments passed to the parent service.
         """
         super().__init__(
-            aggregate_sentences=aggregate_sentences,
+            aggregate_sentences=True,
             push_text_frames=False,
             push_stop_frames=True,
             sample_rate=sample_rate,
@@ -831,10 +809,6 @@ class ElevenLabsHttpTTSService(WordTTSService):
         # Store previous text for context within a turn
         self._previous_text = ""
 
-        # Track partial words that span across alignment chunks
-        self._partial_word = ""
-        self._partial_word_start_time = 0.0
-
     def language_to_service_language(self, language: Language) -> Optional[str]:
         """Convert pipecat Language to ElevenLabs language code.
 
@@ -862,8 +836,6 @@ class ElevenLabsHttpTTSService(WordTTSService):
         self._cumulative_time = 0
         self._started = False
         self._previous_text = ""
-        self._partial_word = ""
-        self._partial_word_start_time = 0.0
         logger.debug(f"{self}: Reset internal state")
 
     async def start(self, frame: StartFrame):
@@ -898,13 +870,11 @@ class ElevenLabsHttpTTSService(WordTTSService):
     def calculate_word_times(self, alignment_info: Mapping[str, Any]) -> List[Tuple[str, float]]:
         """Calculate word timing from character alignment data.
 
-        This method handles partial words that may span across multiple alignment chunks.
-
         Args:
             alignment_info: Character timing data from ElevenLabs.
 
         Returns:
-            List of (word, timestamp) pairs for complete words in this chunk.
+            List of (word, timestamp) pairs.
 
         Example input data::
 
@@ -930,28 +900,30 @@ class ElevenLabsHttpTTSService(WordTTSService):
         # Build the words and find their start times
         words = []
         word_start_times = []
-        # Start with any partial word from previous chunk
+        current_word = ""
-        current_word = self._partial_word
+        first_char_idx = -1
-        word_start_time = self._partial_word_start_time if self._partial_word else None
 
         for i, char in enumerate(chars):
             if char == " ":
                 if current_word:  # Only add non-empty words
                     words.append(current_word)
-                    word_start_times.append(word_start_time)
-                    current_word = ""
-                    word_start_time = None
-            else:
-                if word_start_time is None:  # First character of a new word
                     # Use time of the first character of the word, offset by cumulative time
-                    word_start_time = self._cumulative_time + char_start_times[i]
+                    word_start_times.append(
+                        self._cumulative_time + char_start_times[first_char_idx]
+                    )
+                    current_word = ""
+                    first_char_idx = -1
+            else:
+                if not current_word:  # This is the first character of a new word
+                    first_char_idx = i
                 current_word += char
 
-        # Store any incomplete word at the end of this chunk
+        # Don't forget the last word if there's no trailing space
-        self._partial_word = current_word if current_word else ""
+        if current_word and first_char_idx >= 0:
-        self._partial_word_start_time = word_start_time if word_start_time is not None else 0.0
+            words.append(current_word)
+            word_start_times.append(self._cumulative_time + char_start_times[first_char_idx])
 
-        # Create word-time pairs for complete words only
+        # Create word-time pairs
         word_times = list(zip(words, word_start_times))
 
         return word_times
@@ -987,9 +959,6 @@ class ElevenLabsHttpTTSService(WordTTSService):
         if self._voice_settings:
             payload["voice_settings"] = self._voice_settings
 
-        if self._settings["apply_text_normalization"] is not None:
-            payload["apply_text_normalization"] = self._settings["apply_text_normalization"]
-
         language = self._settings["language"]
         if self._model_name in ELEVENLABS_MULTILINGUAL_MODELS and language:
             payload["language_code"] = language
@@ -1010,6 +979,8 @@ class ElevenLabsHttpTTSService(WordTTSService):
         }
         if self._settings["optimize_streaming_latency"] is not None:
             params["optimize_streaming_latency"] = self._settings["optimize_streaming_latency"]
+        if self._settings["apply_text_normalization"] is not None:
+            params["apply_text_normalization"] = self._settings["apply_text_normalization"]
 
         try:
             await self.start_ttfb_metrics()
@@ -1070,14 +1041,6 @@ class ElevenLabsHttpTTSService(WordTTSService):
                         logger.error(f"Error processing response: {e}", exc_info=True)
                         continue
 
-                # After processing all chunks, emit any remaining partial word
-                # since this is the end of the utterance
-                if self._partial_word:
-                    final_word_time = [(self._partial_word, self._partial_word_start_time)]
-                    await self.add_word_timestamps(final_word_time)
-                    self._partial_word = ""
-                    self._partial_word_start_time = 0.0
-
                 # After processing all chunks, add the total utterance duration
                 # to the cumulative time to ensure next utterance starts after this one
                 if utterance_duration > 0:

src/pipecat/services/fish/tts.py

@@ -225,8 +225,6 @@ class FishAudioTTSService(InterruptibleTTSService):
             start_message = {"event": "start", "request": {"text": "", **self._settings}}
             await self._websocket.send(ormsgpack.packb(start_message))
             logger.debug("Sent start message to Fish Audio")
-
-            await self._call_event_handler("on_connected")
         except Exception as e:
             logger.error(f"Fish Audio initialization error: {e}")
             self._websocket = None
@@ -247,7 +245,6 @@ class FishAudioTTSService(InterruptibleTTSService):
             self._request_id = None
             self._started = False
             self._websocket = None
-            await self._call_event_handler("on_disconnected")
 
     async def flush_audio(self):
         """Flush any buffered audio by sending a flush event to Fish Audio."""

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/services/google/stt.py

@@ -730,8 +730,6 @@ class GoogleSTTService(STTService):
         self._request_queue = asyncio.Queue()
         self._streaming_task = self.create_task(self._stream_audio())
 
-        await self._call_event_handler("on_connected")
-
     async def _disconnect(self):
         """Clean up streaming recognition resources."""
         if self._streaming_task:
@@ -739,8 +737,6 @@ class GoogleSTTService(STTService):
             await self.cancel_task(self._streaming_task)
             self._streaming_task = None
 
-        await self._call_event_handler("on_disconnected")
-
     async def _request_generator(self):
         """Generates requests for the streaming recognize method."""
         recognizer_path = f"projects/{self._project_id}/locations/{self._location}/recognizers/_"

src/pipecat/services/lmnt/tts.py

@@ -222,7 +222,6 @@ class LmntTTSService(InterruptibleTTSService):
             # Send initialization message
             await self._websocket.send(json.dumps(init_msg))
 
-            await self._call_event_handler("on_connected")
         except Exception as e:
             logger.error(f"{self} initialization error: {e}")
             self._websocket = None
@@ -244,7 +243,6 @@ class LmntTTSService(InterruptibleTTSService):
         finally:
             self._started = False
             self._websocket = None
-            await self._call_event_handler("on_disconnected")
 
     def _get_websocket(self):
         """Get the WebSocket connection if available."""

src/pipecat/services/neuphonic/tts.py

@@ -293,8 +293,6 @@ class NeuphonicTTSService(InterruptibleTTSService):
             headers = {"x-api-key": self._api_key}
 
             self._websocket = await websocket_connect(url, additional_headers=headers)
-
-            await self._call_event_handler("on_connected")
         except Exception as e:
             logger.error(f"{self} initialization error: {e}")
             self._websocket = None
@@ -313,7 +311,6 @@ class NeuphonicTTSService(InterruptibleTTSService):
         finally:
             self._started = False
             self._websocket = None
-            await self._call_event_handler("on_disconnected")
 
     async def _receive_messages(self):
         """Receive and process messages from Neuphonic WebSocket."""

src/pipecat/services/openai/tts.py

@@ -14,7 +14,6 @@ from typing import AsyncGenerator, Dict, Literal, Optional
 
 from loguru import logger
 from openai import AsyncOpenAI, BadRequestError
-from pydantic import BaseModel
 
 from pipecat.frames.frames import (
     ErrorFrame,
@@ -56,17 +55,6 @@ class OpenAITTSService(TTSService):
 
     OPENAI_SAMPLE_RATE = 24000  # OpenAI TTS always outputs at 24kHz
 
-    class InputParams(BaseModel):
-        """Input parameters for OpenAI TTS configuration.
-
-        Parameters:
-            instructions: Instructions to guide voice synthesis behavior.
-            speed: Voice speed control (0.25 to 4.0, default 1.0).
-        """
-
-        instructions: Optional[str] = None
-        speed: Optional[float] = None
-
     def __init__(
         self,
         *,
@@ -77,7 +65,6 @@ class OpenAITTSService(TTSService):
         sample_rate: Optional[int] = None,
         instructions: Optional[str] = None,
         speed: Optional[float] = None,
-        params: Optional[InputParams] = None,
         **kwargs,
     ):
         """Initialize OpenAI TTS service.
@@ -90,11 +77,7 @@ class OpenAITTSService(TTSService):
             sample_rate: Output audio sample rate in Hz. If None, uses OpenAI's default 24kHz.
             instructions: Optional instructions to guide voice synthesis behavior.
             speed: Voice speed control (0.25 to 4.0, default 1.0).
-            params: Optional synthesis controls (acting instructions, speed, ...).
             **kwargs: Additional keyword arguments passed to TTSService.
-
-                .. deprecated:: 0.0.91
-                        The `instructions` and `speed` parameters are deprecated, use `InputParams` instead.
         """
         if sample_rate and sample_rate != self.OPENAI_SAMPLE_RATE:
             logger.warning(
@@ -103,26 +86,12 @@ class OpenAITTSService(TTSService):
             )
         super().__init__(sample_rate=sample_rate, **kwargs)
 
+        self._speed = speed
         self.set_model_name(model)
         self.set_voice(voice)
+        self._instructions = instructions
         self._client = AsyncOpenAI(api_key=api_key, base_url=base_url)
 
-        if instructions or speed:
-            import warnings
-
-            with warnings.catch_warnings():
-                warnings.simplefilter("always")
-                warnings.warn(
-                    "The `instructions` and `speed` parameters are deprecated, use `InputParams` instead.",
-                    DeprecationWarning,
-                    stacklevel=2,
-                )
-
-        self._settings = {
-            "instructions": params.instructions if params else instructions,
-            "speed": params.speed if params else speed,
-        }
-
     def can_generate_metrics(self) -> bool:
         """Check if this service can generate processing metrics.
 
@@ -175,11 +144,11 @@ class OpenAITTSService(TTSService):
                 "response_format": "pcm",
             }
 
-            if self._settings["instructions"]:
+            if self._instructions:
-                create_params["instructions"] = self._settings["instructions"]
+                create_params["instructions"] = self._instructions
 
-            if self._settings["speed"]:
+            if self._speed:
-                create_params["speed"] = self._settings["speed"]
+                create_params["speed"] = self._speed
 
             async with self._client.audio.speech.with_streaming_response.create(
                 **create_params

src/pipecat/services/playht/tts.py

@@ -269,8 +269,6 @@ class PlayHTTTSService(InterruptibleTTSService):
                 raise ValueError("WebSocket URL is not a string")
 
             self._websocket = await websocket_connect(self._websocket_url)
-
-            await self._call_event_handler("on_connected")
         except ValueError as e:
             logger.error(f"{self} initialization error: {e}")
             self._websocket = None
@@ -293,7 +291,6 @@ class PlayHTTTSService(InterruptibleTTSService):
         finally:
             self._request_id = None
             self._websocket = None
-            await self._call_event_handler("on_disconnected")
 
     async def _get_websocket_url(self):
         """Retrieve WebSocket URL from PlayHT API."""

src/pipecat/services/rime/tts.py

@@ -255,8 +255,6 @@ class RimeTTSService(AudioContextWordTTSService):
             url = f"{self._url}?{params}"
             headers = {"Authorization": f"Bearer {self._api_key}"}
             self._websocket = await websocket_connect(url, additional_headers=headers)
-
-            await self._call_event_handler("on_connected")
         except Exception as e:
             logger.error(f"{self} initialization error: {e}")
             self._websocket = None
@@ -274,7 +272,6 @@ class RimeTTSService(AudioContextWordTTSService):
         finally:
             self._context_id = None
             self._websocket = None
-            await self._call_event_handler("on_disconnected")
 
     def _get_websocket(self):
         """Get active websocket connection or raise exception."""

src/pipecat/services/riva/stt.py

@@ -583,9 +583,7 @@ class RivaSegmentedSTTService(SegmentedSTTService):
             self._config.language_code = self._language
 
     @traced_stt
-    async def _handle_transcription(
+    async def _handle_transcription(self, transcript: str, language: Optional[Language] = None):
-        self, transcript: str, is_final: bool, language: Optional[Language] = None
-    ):
         """Handle a transcription result with tracing."""
         pass
 

src/pipecat/services/sarvam/tts.py

@@ -76,29 +76,17 @@ class SarvamHttpTTSService(TTSService):
 
     Example::
 
-        tts = SarvamHttpTTSService(
+        tts = SarvamTTSService(
             api_key="your-api-key",
             voice_id="anushka",
             model="bulbul:v2",
             aiohttp_session=session,
-            params=SarvamHttpTTSService.InputParams(
+            params=SarvamTTSService.InputParams(
                 language=Language.HI,
                 pitch=0.1,
                 pace=1.2
             )
         )
-
-        # For bulbul v3 beta with any speaker:
-        tts_v3 = SarvamHttpTTSService(
-            api_key="your-api-key",
-            voice_id="speaker_name",
-            model="bulbul:v3,
-            aiohttp_session=session,
-            params=SarvamHttpTTSService.InputParams(
-                language=Language.HI,
-                temperature=0.8
-            )
-        )
     """
 
     class InputParams(BaseModel):
@@ -117,14 +105,6 @@ class SarvamHttpTTSService(TTSService):
         pace: Optional[float] = Field(default=1.0, ge=0.3, le=3.0)
         loudness: Optional[float] = Field(default=1.0, ge=0.1, le=3.0)
         enable_preprocessing: Optional[bool] = False
-        temperature: Optional[float] = Field(
-            default=0.6,
-            ge=0.01,
-            le=1.0,
-            description="Controls the randomness of the output for bulbul v3 beta. "
-            "Lower values make the output more focused and deterministic, while "
-            "higher values make it more random. Range: 0.01 to 1.0. Default: 0.6.",
-        )
 
     def __init__(
         self,
@@ -144,7 +124,7 @@ class SarvamHttpTTSService(TTSService):
             api_key: Sarvam AI API subscription key.
             aiohttp_session: Shared aiohttp session for making requests.
             voice_id: Speaker voice ID (e.g., "anushka", "meera"). Defaults to "anushka".
-            model: TTS model to use ("bulbul:v2" or "bulbul:v3-beta" or "bulbul:v3"). Defaults to "bulbul:v2".
+            model: TTS model to use ("bulbul:v1" or "bulbul:v2"). Defaults to "bulbul:v2".
             base_url: Sarvam AI API base URL. Defaults to "https://api.sarvam.ai".
             sample_rate: Audio sample rate in Hz (8000, 16000, 22050, 24000). If None, uses default.
             params: Additional voice and preprocessing parameters. If None, uses defaults.
@@ -158,32 +138,16 @@ class SarvamHttpTTSService(TTSService):
         self._base_url = base_url
         self._session = aiohttp_session
 
-        # Build base settings common to all models
         self._settings = {
             "language": (
                 self.language_to_service_language(params.language) if params.language else "en-IN"
             ),
+            "pitch": params.pitch,
+            "pace": params.pace,
+            "loudness": params.loudness,
             "enable_preprocessing": params.enable_preprocessing,
         }
 
-        # Add model-specific parameters
-        if model in ("bulbul:v3-beta", "bulbul:v3"):
-            self._settings.update(
-                {
-                    "temperature": getattr(params, "temperature", 0.6),
-                    "model": model,
-                }
-            )
-        else:
-            self._settings.update(
-                {
-                    "pitch": params.pitch,
-                    "pace": params.pace,
-                    "loudness": params.loudness,
-                    "model": model,
-                }
-            )
-
         self.set_model_name(model)
         self.set_voice(voice_id)
 
@@ -311,18 +275,6 @@ class SarvamTTSService(InterruptibleTTSService):
                 pace=1.2
             )
         )
-
-        # For bulbul v3 beta with any speaker and temperature:
-        # Note: pace and loudness are not supported for bulbul v3 and bulbul v3 beta
-        tts_v3 = SarvamTTSService(
-            api_key="your-api-key",
-            voice_id="speaker_name",
-            model="bulbul:v3",
-            params=SarvamTTSService.InputParams(
-                language=Language.HI,
-                temperature=0.8
-            )
-        )
     """
 
     class InputParams(BaseModel):
@@ -358,14 +310,6 @@ class SarvamTTSService(InterruptibleTTSService):
         output_audio_codec: Optional[str] = "linear16"
         output_audio_bitrate: Optional[str] = "128k"
         language: Optional[Language] = Language.EN
-        temperature: Optional[float] = Field(
-            default=0.6,
-            ge=0.01,
-            le=1.0,
-            description="Controls the randomness of the output for bulbul v3 beta. "
-            "Lower values make the output more focused and deterministic, while "
-            "higher values make it more random. Range: 0.01 to 1.0. Default: 0.6.",
-        )
 
     def __init__(
         self,
@@ -385,7 +329,6 @@ class SarvamTTSService(InterruptibleTTSService):
         Args:
             api_key: Sarvam API key for authenticating TTS requests.
             model: Identifier of the Sarvam speech model (default "bulbul:v2").
-                Supports "bulbul:v2", "bulbul:v3-beta" and "bulbul:v3".
             voice_id: Voice identifier for synthesis (default "anushka").
             url: WebSocket URL for connecting to the TTS backend (default production URL).
             aiohttp_session: Optional shared aiohttp session. To maintain backward compatibility.
@@ -428,12 +371,15 @@ class SarvamTTSService(InterruptibleTTSService):
         self._api_key = api_key
         self.set_model_name(model)
         self.set_voice(voice_id)
-        # Build base settings common to all models
+        # Configuration parameters
         self._settings = {
             "target_language_code": (
                 self.language_to_service_language(params.language) if params.language else "en-IN"
             ),
+            "pitch": params.pitch,
+            "pace": params.pace,
             "speaker": voice_id,
+            "loudness": params.loudness,
             "speech_sample_rate": 0,
             "enable_preprocessing": params.enable_preprocessing,
             "min_buffer_size": params.min_buffer_size,
@@ -441,24 +387,6 @@ class SarvamTTSService(InterruptibleTTSService):
             "output_audio_codec": params.output_audio_codec,
             "output_audio_bitrate": params.output_audio_bitrate,
         }
-
-        # Add model-specific parameters
-        if model in ("bulbul:v3-beta", "bulbul:v3"):
-            self._settings.update(
-                {
-                    "temperature": getattr(params, "temperature", 0.6),
-                    "model": model,
-                }
-            )
-        else:
-            self._settings.update(
-                {
-                    "pitch": params.pitch,
-                    "pace": params.pace,
-                    "loudness": params.loudness,
-                    "model": model,
-                }
-            )
         self._started = False
 
         self._receive_task = None
@@ -597,7 +525,6 @@ class SarvamTTSService(InterruptibleTTSService):
             logger.debug("Connected to Sarvam TTS Websocket")
             await self._send_config()
 
-            await self._call_event_handler("on_connected")
         except Exception as e:
             logger.error(f"{self} initialization error: {e}")
             self._websocket = None
@@ -629,10 +556,6 @@ class SarvamTTSService(InterruptibleTTSService):
                 await self._websocket.close()
         except Exception as e:
             logger.error(f"{self} error closing websocket: {e}")
-        finally:
-            self._started = False
-            self._websocket = None
-            await self._call_event_handler("on_disconnected")
 
     def _get_websocket(self):
         if self._websocket:

src/pipecat/services/speechmatics/stt.py

@@ -577,7 +577,6 @@ class SpeechmaticsSTTService(STTService):
                 ),
             )
             logger.debug(f"{self} Connected to Speechmatics STT service")
-            await self._call_event_handler("on_connected")
         except Exception as e:
             logger.error(f"{self} Error connecting to Speechmatics: {e}")
             self._client = None
@@ -596,7 +595,6 @@ class SpeechmaticsSTTService(STTService):
             logger.error(f"{self} Error closing Speechmatics client: {e}")
         finally:
             self._client = None
-            await self._call_event_handler("on_disconnected")
 
     def _process_config(self) -> None:
         """Create a formatted STT transcription config.
@@ -620,7 +618,7 @@ class SpeechmaticsSTTService(STTService):
             transcription_config.additional_vocab = [
                 {
                     "content": e.content,
-                    **({"sounds_like": e.sounds_like} if e.sounds_like else {}),
+                    "sounds_like": e.sounds_like,
                 }
                 for e in self._params.additional_vocab
             ]

src/pipecat/services/stt_service.py

@@ -35,25 +35,6 @@ class STTService(AIService):
     Provides common functionality for STT services including audio passthrough,
     muting, settings management, and audio processing. Subclasses must implement
     the run_stt method to provide actual speech recognition.
-
-    Event handlers:
-        on_connected: Called when connected to the STT service.
-        on_connected: Called when disconnected from the STT service.
-        on_connection_error: Called when a connection to the STT service error occurs.
-
-    Example::
-
-        @stt.event_handler("on_connected")
-        async def on_connected(stt: STTService):
-            logger.debug(f"STT connected")
-
-        @stt.event_handler("on_disconnected")
-        async def on_disconnected(stt: STTService):
-            logger.debug(f"STT disconnected")
-
-        @stt.event_handler("on_connection_error")
-        async def on_connection_error(stt: STTService, error: str):
-            logger.error(f"STT connection error: {error}")
     """
 
     def __init__(
@@ -81,10 +62,6 @@ class STTService(AIService):
         self._muted: bool = False
         self._user_id: str = ""
 
-        self._register_event_handler("on_connected")
-        self._register_event_handler("on_disconnected")
-        self._register_event_handler("on_connection_error")
-
     @property
     def is_muted(self) -> bool:
         """Check if the STT service is currently muted.
@@ -315,6 +292,15 @@ class WebsocketSTTService(STTService, WebsocketService):
 
     Combines STT functionality with websocket connectivity, providing automatic
     error handling and reconnection capabilities.
+
+    Event handlers:
+        on_connection_error: Called when a websocket connection error occurs.
+
+    Example::
+
+        @stt.event_handler("on_connection_error")
+        async def on_connection_error(stt: STTService, error: str):
+            logger.error(f"STT connection error: {error}")
     """
 
     def __init__(self, *, reconnect_on_error: bool = True, **kwargs):
@@ -326,6 +312,7 @@ class WebsocketSTTService(STTService, WebsocketService):
         """
         STTService.__init__(self, **kwargs)
         WebsocketService.__init__(self, reconnect_on_error=reconnect_on_error, **kwargs)
+        self._register_event_handler("on_connection_error")
 
     async def _report_error(self, error: ErrorFrame):
         await self._call_event_handler("on_connection_error", error.error)

src/pipecat/services/tts_service.py

@@ -59,25 +59,6 @@ class TTSService(AIService):
     Provides common functionality for TTS services including text aggregation,
     filtering, audio generation, and frame management. Supports configurable
     sentence aggregation, silence insertion, and frame processing control.
-
-    Event handlers:
-        on_connected: Called when connected to the STT service.
-        on_connected: Called when disconnected from the STT service.
-        on_connection_error: Called when a connection to the STT service error occurs.
-
-    Example::
-
-        @tts.event_handler("on_connected")
-        async def on_connected(tts: TTSService):
-            logger.debug(f"TTS connected")
-
-        @tts.event_handler("on_disconnected")
-        async def on_disconnected(tts: TTSService):
-            logger.debug(f"TTS disconnected")
-
-        @tts.event_handler("on_connection_error")
-        async def on_connection_error(stt: TTSService, error: str):
-            logger.error(f"TTS connection error: {error}")
     """
 
     def __init__(
@@ -162,10 +143,6 @@ class TTSService(AIService):
 
         self._processing_text: bool = False
 
-        self._register_event_handler("on_connected")
-        self._register_event_handler("on_disconnected")
-        self._register_event_handler("on_connection_error")
-
     @property
     def sample_rate(self) -> int:
         """Get the current sample rate for audio output.
@@ -649,6 +626,7 @@ class WebsocketTTSService(TTSService, WebsocketService):
         """
         TTSService.__init__(self, **kwargs)
         WebsocketService.__init__(self, reconnect_on_error=reconnect_on_error, **kwargs)
+        self._register_event_handler("on_connection_error")
 
     async def _report_error(self, error: ErrorFrame):
         await self._call_event_handler("on_connection_error", error.error)
@@ -700,6 +678,15 @@ class WebsocketWordTTSService(WordTTSService, WebsocketService):
     """Base class for websocket-based TTS services that support word timestamps.
 
     Combines word timestamp functionality with websocket connectivity.
+
+    Event handlers:
+        on_connection_error: Called when a websocket connection error occurs.
+
+    Example::
+
+        @tts.event_handler("on_connection_error")
+        async def on_connection_error(tts: TTSService, error: str):
+            logger.error(f"TTS connection error: {error}")
     """
 
     def __init__(self, *, reconnect_on_error: bool = True, **kwargs):
@@ -711,6 +698,7 @@ class WebsocketWordTTSService(WordTTSService, WebsocketService):
         """
         WordTTSService.__init__(self, **kwargs)
         WebsocketService.__init__(self, reconnect_on_error=reconnect_on_error, **kwargs)
+        self._register_event_handler("on_connection_error")
 
     async def _report_error(self, error: ErrorFrame):
         await self._call_event_handler("on_connection_error", error.error)

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/transports/base_input.py

@@ -232,9 +232,6 @@ class BaseInputTransport(FrameProcessor):
         """
         # Cancel and wait for the audio input task to finish.
         await self._cancel_audio_task()
-        # Stop audio filter.
-        if self._params.audio_in_filter:
-            await self._params.audio_in_filter.stop()
 
     async def set_transport_ready(self, frame: StartFrame):
         """Called when the transport is ready to stream.

src/pipecat/transports/base_output.py

@@ -293,15 +293,15 @@ class BaseOutputTransport(FrameProcessor):
         """
         await super().process_frame(frame, direction)
 
+        #
+        # System frames (like InterruptionFrame) are pushed immediately. Other
+        # frames require order so they are put in the sink queue.
+        #
         if isinstance(frame, StartFrame):
             # Push StartFrame before start(), because we want StartFrame to be
             # processed by every processor before any other frame is processed.
             await self.push_frame(frame, direction)
             await self.start(frame)
-        elif isinstance(frame, EndFrame):
-            await self.stop(frame)
-            # Keep pushing EndFrame down so all the pipeline stops nicely.
-            await self.push_frame(frame, direction)
         elif isinstance(frame, CancelFrame):
             await self.cancel(frame)
             await self.push_frame(frame, direction)
@@ -314,6 +314,21 @@ class BaseOutputTransport(FrameProcessor):
             await self.write_dtmf(frame)
         elif isinstance(frame, SystemFrame):
             await self.push_frame(frame, direction)
+        # Control frames.
+        elif isinstance(frame, EndFrame):
+            await self.stop(frame)
+            # Keep pushing EndFrame down so all the pipeline stops nicely.
+            await self.push_frame(frame, direction)
+        elif isinstance(frame, MixerControlFrame):
+            await self._handle_frame(frame)
+        # Other frames.
+        elif isinstance(frame, OutputAudioRawFrame):
+            await self._handle_frame(frame)
+        elif isinstance(frame, (OutputImageRawFrame, SpriteFrame)):
+            await self._handle_frame(frame)
+        # TODO(aleix): Images and audio should support presentation timestamps.
+        elif frame.pts:
+            await self._handle_frame(frame)
         elif direction == FrameDirection.UPSTREAM:
             await self.push_frame(frame, direction)
         else:
@@ -395,13 +410,6 @@ class BaseOutputTransport(FrameProcessor):
 
             # Indicates if the bot is currently speaking.
             self._bot_speaking = False
-            # Last time a BotSpeakingFrame was pushed.
-            self._bot_speaking_frame_time = 0
-            # How often a BotSpeakingFrame should be pushed (value should be
-            # lower than the audio chunks).
-            self._bot_speaking_frame_period = 0.2
-            # Last time the bot actually spoke.
-            self._bot_speech_last_time = 0
 
             self._audio_task: Optional[asyncio.Task] = None
             self._video_task: Optional[asyncio.Task] = None
@@ -593,71 +601,39 @@ class BaseOutputTransport(FrameProcessor):
 
         async def _bot_started_speaking(self):
             """Handle bot started speaking event."""
-            if self._bot_speaking:
+            if not self._bot_speaking:
-                return
+                logger.debug(
+                    f"Bot{f' [{self._destination}]' if self._destination else ''} started speaking"
+                )
 
-            logger.debug(
+                downstream_frame = BotStartedSpeakingFrame()
-                f"Bot{f' [{self._destination}]' if self._destination else ''} started speaking"
+                downstream_frame.transport_destination = self._destination
-            )
+                upstream_frame = BotStartedSpeakingFrame()
+                upstream_frame.transport_destination = self._destination
+                await self._transport.push_frame(downstream_frame)
+                await self._transport.push_frame(upstream_frame, FrameDirection.UPSTREAM)
 
-            downstream_frame = BotStartedSpeakingFrame()
+                self._bot_speaking = True
-            downstream_frame.transport_destination = self._destination
-            upstream_frame = BotStartedSpeakingFrame()
-            upstream_frame.transport_destination = self._destination
-            await self._transport.push_frame(downstream_frame)
-            await self._transport.push_frame(upstream_frame, FrameDirection.UPSTREAM)
-
-            self._bot_speaking = True
 
         async def _bot_stopped_speaking(self):
             """Handle bot stopped speaking event."""
-            if not self._bot_speaking:
+            if self._bot_speaking:
-                return
+                logger.debug(
+                    f"Bot{f' [{self._destination}]' if self._destination else ''} stopped speaking"
+                )
 
-            logger.debug(
+                downstream_frame = BotStoppedSpeakingFrame()
-                f"Bot{f' [{self._destination}]' if self._destination else ''} stopped speaking"
+                downstream_frame.transport_destination = self._destination
-            )
+                upstream_frame = BotStoppedSpeakingFrame()
+                upstream_frame.transport_destination = self._destination
+                await self._transport.push_frame(downstream_frame)
+                await self._transport.push_frame(upstream_frame, FrameDirection.UPSTREAM)
 
-            downstream_frame = BotStoppedSpeakingFrame()
+                self._bot_speaking = False
-            downstream_frame.transport_destination = self._destination
-            upstream_frame = BotStoppedSpeakingFrame()
-            upstream_frame.transport_destination = self._destination
-            await self._transport.push_frame(downstream_frame)
-            await self._transport.push_frame(upstream_frame, FrameDirection.UPSTREAM)
 
-            self._bot_speaking = False
+                # Clean audio buffer (there could be tiny left overs if not multiple
-
+                # to our output chunk size).
-            # Clean audio buffer (there could be tiny left overs if not multiple
+                self._audio_buffer = bytearray()
-            # to our output chunk size).
-            self._audio_buffer = bytearray()
-
-        async def _bot_currently_speaking(self):
-            """Handle bot speaking event."""
-            await self._bot_started_speaking()
-
-            diff_time = time.time() - self._bot_speaking_frame_time
-            if diff_time >= self._bot_speaking_frame_period:
-                await self._transport.push_frame(BotSpeakingFrame())
-                await self._transport.push_frame(BotSpeakingFrame(), FrameDirection.UPSTREAM)
-                self._bot_speaking_frame_time = time.time()
-
-            self._bot_speech_last_time = time.time()
-
-        async def _maybe_bot_currently_speaking(self, frame: SpeechOutputAudioRawFrame):
-            if not is_silence(frame.audio):
-                await self._bot_currently_speaking()
-            else:
-                silence_duration = time.time() - self._bot_speech_last_time
-                if silence_duration > BOT_VAD_STOP_SECS:
-                    await self._bot_stopped_speaking()
-
-        async def _handle_bot_speech(self, frame: Frame):
-            # TTS case.
-            if isinstance(frame, TTSAudioRawFrame):
-                await self._bot_currently_speaking()
-            # Speech stream case.
-            elif isinstance(frame, SpeechOutputAudioRawFrame):
-                await self._maybe_bot_currently_speaking(frame)
 
         async def _handle_frame(self, frame: Frame):
             """Handle various frame types with appropriate processing.
@@ -665,9 +641,7 @@ class BaseOutputTransport(FrameProcessor):
             Args:
                 frame: The frame to handle.
             """
-            if isinstance(frame, OutputAudioRawFrame):
+            if isinstance(frame, OutputImageRawFrame):
-                await self._handle_bot_speech(frame)
-            elif isinstance(frame, OutputImageRawFrame):
                 await self._set_video_image(frame)
             elif isinstance(frame, SpriteFrame):
                 await self._set_video_images(frame.images)
@@ -731,7 +705,39 @@ class BaseOutputTransport(FrameProcessor):
 
         async def _audio_task_handler(self):
             """Main audio processing task handler."""
+            # Push a BotSpeakingFrame every 200ms, we don't really need to push it
+            # at every audio chunk. If the audio chunk is bigger than 200ms, push at
+            # every audio chunk.
+            TOTAL_CHUNK_MS = self._params.audio_out_10ms_chunks * 10
+            BOT_SPEAKING_CHUNK_PERIOD = max(int(200 / TOTAL_CHUNK_MS), 1)
+            bot_speaking_counter = 0
+            speech_last_speaking_time = 0
+
             async for frame in self._next_frame():
+                # Notify the bot started speaking upstream if necessary and that
+                # it's actually speaking.
+                is_speaking = False
+                if isinstance(frame, TTSAudioRawFrame):
+                    is_speaking = True
+                elif isinstance(frame, SpeechOutputAudioRawFrame):
+                    if not is_silence(frame.audio):
+                        is_speaking = True
+                        speech_last_speaking_time = time.time()
+                    else:
+                        silence_duration = time.time() - speech_last_speaking_time
+                        if silence_duration > BOT_VAD_STOP_SECS:
+                            await self._bot_stopped_speaking()
+
+                if is_speaking:
+                    await self._bot_started_speaking()
+                    if bot_speaking_counter % BOT_SPEAKING_CHUNK_PERIOD == 0:
+                        await self._transport.push_frame(BotSpeakingFrame())
+                        await self._transport.push_frame(
+                            BotSpeakingFrame(), FrameDirection.UPSTREAM
+                        )
+                        bot_speaking_counter = 0
+                    bot_speaking_counter += 1
+
                 # No need to push EndFrame, it's pushed from process_frame().
                 if isinstance(frame, EndFrame):
                     break

src/pipecat/transports/smallwebrtc/connection.py

@@ -689,8 +689,3 @@ class SmallWebRTCConnection(BaseObject):
                 )()
                 if track:
                     track.set_enabled(signalling_message.enabled)
-
-    async def add_ice_candidate(self, candidate):
-        """Handle incoming ICE candidates."""
-        logger.debug(f"Adding remote candidate: {candidate}")
-        await self.pc.addIceCandidate(candidate)

src/pipecat/transports/smallwebrtc/request_handler.py

@@ -14,7 +14,6 @@ from dataclasses import dataclass
 from enum import Enum
 from typing import Any, Awaitable, Callable, Dict, List, Optional
 
-from aiortc.sdp import candidate_from_sdp
 from fastapi import HTTPException
 from loguru import logger
 
@@ -40,34 +39,6 @@ class SmallWebRTCRequest:
     request_data: Optional[Any] = None
 
 
-@dataclass
-class IceCandidate:
-    """The remote ice candidate object received from the peer connection.
-
-    Parameters:
-        candidate: The ice candidate patch SDP string (Session Description Protocol).
-        sdp_mid: The SDP mid for the candidate patch.
-        sdp_mline_index: The SDP mline index for the candidate patch.
-    """
-
-    candidate: str
-    sdp_mid: str
-    sdp_mline_index: int
-
-
-@dataclass
-class SmallWebRTCPatchRequest:
-    """Small WebRTC transport session arguments for the runner.
-
-    Parameters:
-        pc_id: Identifier for the peer connection.
-        candidates: A list of ICE candidate patches.
-    """
-
-    pc_id: str
-    candidates: List[IceCandidate]
-
-
 class ConnectionMode(Enum):
     """Enum defining the connection handling modes."""
 
@@ -226,19 +197,6 @@ class SmallWebRTCRequestHandler:
             logger.debug(f"SmallWebRTC request details: {request}")
             raise
 
-    async def handle_patch_request(self, request: SmallWebRTCPatchRequest):
-        """Handle a SmallWebRTC patch candidate request."""
-        peer_connection = self._pcs_map.get(request.pc_id)
-
-        if not peer_connection:
-            raise HTTPException(status_code=404, detail="Peer connection not found")
-
-        for c in request.candidates:
-            candidate = candidate_from_sdp(c.candidate)
-            candidate.sdpMid = c.sdp_mid
-            candidate.sdpMLineIndex = c.sdp_mline_index
-            await peer_connection.add_ice_candidate(candidate)
-
     async def close(self):
         """Clear the connection map."""
         coros = [pc.disconnect() for pc in self._pcs_map.values()]

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_pipeline.py

@@ -254,7 +254,7 @@ class TestPipelineTask(unittest.IsolatedAsyncioTestCase):
 
         try:
             await asyncio.wait_for(
-                task.run(PipelineTaskParams(loop=asyncio.get_event_loop())),
+                asyncio.shield(task.run(PipelineTaskParams(loop=asyncio.get_event_loop()))),
                 timeout=1.0,
             )
         except asyncio.TimeoutError:
@@ -290,7 +290,7 @@ class TestPipelineTask(unittest.IsolatedAsyncioTestCase):
         await task.queue_frame(TextFrame(text="Hello!"))
         try:
             await asyncio.wait_for(
-                task.run(PipelineTaskParams(loop=asyncio.get_event_loop())),
+                asyncio.shield(task.run(PipelineTaskParams(loop=asyncio.get_event_loop()))),
                 timeout=1.0,
             )
         except asyncio.TimeoutError:
@@ -301,8 +301,11 @@ class TestPipelineTask(unittest.IsolatedAsyncioTestCase):
         identity = IdentityFilter()
         pipeline = Pipeline([identity])
         task = PipelineTask(pipeline, idle_timeout_secs=0.2)
-        # This shouldn't freeze, so nothing to check really.
+        try:
-        await task.run(PipelineTaskParams(loop=asyncio.get_event_loop()))
+            await task.run(PipelineTaskParams(loop=asyncio.get_event_loop()))
+            assert False
+        except asyncio.CancelledError:
+            assert True
 
     async def test_no_idle_task(self):
         identity = IdentityFilter()
@@ -310,7 +313,7 @@ class TestPipelineTask(unittest.IsolatedAsyncioTestCase):
         task = PipelineTask(pipeline, idle_timeout_secs=0.2, cancel_on_idle_timeout=False)
         try:
             await asyncio.wait_for(
-                task.run(PipelineTaskParams(loop=asyncio.get_event_loop())),
+                asyncio.shield(task.run(PipelineTaskParams(loop=asyncio.get_event_loop()))),
                 timeout=0.3,
             )
         except asyncio.TimeoutError:
@@ -329,7 +332,11 @@ class TestPipelineTask(unittest.IsolatedAsyncioTestCase):
             ),
             idle_timeout_secs=0.3,
         )
-        await task.run(PipelineTaskParams(loop=asyncio.get_event_loop()))
+        try:
+            await task.run(PipelineTaskParams(loop=asyncio.get_event_loop()))
+            assert False
+        except asyncio.CancelledError:
+            assert True
 
     async def test_idle_task_event_handler_no_frames(self):
         identity = IdentityFilter()
@@ -344,8 +351,11 @@ class TestPipelineTask(unittest.IsolatedAsyncioTestCase):
             idle_timeout = True
             await task.cancel()
 
-        await task.run(PipelineTaskParams(loop=asyncio.get_event_loop()))
+        try:
-        assert idle_timeout
+            await task.run(PipelineTaskParams(loop=asyncio.get_event_loop()))
+            assert False
+        except asyncio.CancelledError:
+            assert idle_timeout
 
     async def test_idle_task_event_handler_quiet_user(self):
         identity = IdentityFilter()
@@ -406,15 +416,12 @@ class TestPipelineTask(unittest.IsolatedAsyncioTestCase):
             asyncio.create_task(delayed_frames()),
         ]
 
-        _, pending = await asyncio.wait(tasks, return_when=asyncio.FIRST_COMPLETED)
+        await asyncio.wait(tasks, return_when=asyncio.FIRST_COMPLETED)
 
         diff_time = time.time() - start_time
 
         self.assertGreater(diff_time, sleep_time_secs * 3)
 
-        # Wait for the pending tasks to complete.
-        await asyncio.gather(*pending)
-
     async def test_task_cancel_timeout(self):
         class CancelFilter(FrameProcessor):
             def __init__(self, **kwargs):

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=[
+            expected_down_frames=[TextFrame, TextFrame, TextFrame],
-                DummySystemFrame,
-                DummySystemFrame,
-                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
+        # Check that other services don't receive text frames (they might get StartFrame/EndFrame)
-        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)
         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 = [
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+    { 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" }
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     { name = "openai" },
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-    { name = "opentelemetry-sdk" },
-    { name = "opentelemetry-semantic-conventions-ai" },
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     { name = "partial-json-parser" },
     { name = "pillow" },
@@ -7487,6 +7560,7 @@ dependencies = [
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     { name = "pyyaml" },
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     { name = "pydantic" },
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