Update comment

Change prompt to make it about vacuums and tvs
2025-02-20 15:12:12 +08:00 · 2025-02-20 15:07:09 +08:00
251 changed files with 3592 additions and 10100 deletions
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -1,8 +1,7 @@
 repos:
-  - repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: v0.9.7
+  - repo: local
    hooks:
-      - id: ruff
-        language_version: python3
-        args: [ --select,  I, ]
-      - id: ruff-format
+      - id: ruff-format-hook
+        name: Check ruff formatting
+        entry: sh scripts/pre-commit.sh
+        language: system
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -5,367 +5,10 @@ All notable changes to **Pipecat** will be documented in this file.
 The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/),
 and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html).

-## [Unreleased]
+## Unreleased

 ### Added

- ElevenLabs TTS services now support a sample rate of 8000.
-
-### Fixed
-
- Fixed an issue where the `RTVIObserver` would report two bot started and
-  stopped speaking events for each bot turn.
-
- Fixed an issue in `UltravoxSTTService` that caused improper audio processing
-  and incorrect LLM frame output.
-
-### Other
-
- Added `examples/foundational/07x-interruptible-local.py` to show how a local
-  transport can be used.
-
-## [0.0.60] - 2025-03-20
-
-### Added
-
- Added `default_headers` parameter to `BaseOpenAILLMService` constructor.
-
-### Changed
-
- Rollback to `deepgram-sdk` 3.8.0 since 3.10.1 was causing connections issues.
-
- Changed the default `InputAudioTranscription` model to `gpt-4o-transcribe`
-  for `OpenAIRealtimeBetaLLMService`.
-
-### Other
-
- Update the `19-openai-realtime-beta.py` and `19a-azure-realtime-beta.py`
-  examples to use the FunctionSchema format.
-
-## [0.0.59] - 2025-03-20
-
-### Added
-
- When registering a function call it is now possible to indicate if you want
-  the function call to be cancelled if there's a user interruption via
-  `cancel_on_interruption` (defaults to False). This is now possible because
-  function calls are executed concurrently.
-
- Added support for detecting idle pipelines. By default, if no activity has
-  been detected during 5 minutes, the `PipelineTask` will be automatically
-  cancelled. It is possible to override this behavior by passing
-  `cancel_on_idle_timeout=False`. It is also possible to change the default
-  timeout with `idle_timeout_secs` or the frames that prevent the pipeline from
-  being idle with `idle_timeout_frames`. Finally, an `on_idle_timeout` event
-  handler will be triggered if the idle timeout is reached (whether the pipeline
-  task is cancelled or not).
-
- Added `FalSTTService`, which provides STT for Fal's Wizper API.
-
- Added a `reconnect_on_error` parameter to websocket-based TTS services as well
-  as a `on_connection_error` event handler. The `reconnect_on_error` indicates
-  whether the TTS service should reconnect on error. The `on_connection_error`
-  will always get called if there's any error no matter the value of
-  `reconnect_on_error`. This allows, for example, to fallback to a different TTS
-  provider if something goes wrong with the current one.
-
- Added new `SkipTagsAggregator` that extends `BaseTextAggregator` to aggregate
-  text and skips end of sentence matching if aggregated text is between
-  start/end tags.
-
- Added new `PatternPairAggregator` that extends `BaseTextAggregator` to
-  identify content between matching pattern pairs in streamed text. This allows
-  for detection and processing of structured content like XML-style tags that
-  may span across multiple text chunks or sentence boundaries.
-
- Added new `BaseTextAggregator`. Text aggregators are used by the TTS service
-  to aggregate LLM tokens and decide when the aggregated text should be pushed
-  to the TTS service. They also allow for the text to be manipulated while it's
-  being aggregated. A text aggregator can be passed via `text_aggregator` to the
-  TTS service.
-
- Added new `sample_rate` constructor parameter to `TavusVideoService` to allow
-  changing the output sample rate.
-
- Added new `NeuphonicTTSService`.
-  (see https://neuphonic.com)
-
- Added new `UltravoxSTTService`.
-  (see https://github.com/fixie-ai/ultravox)
-
- Added `on_frame_reached_upstream` and `on_frame_reached_downstream` event
-  handlers to `PipelineTask`. Those events will be called when a frame reaches
-  the beginning or end of the pipeline respectively. Note that by default, the
-  event handlers will not be called unless a filter is set with
-  `PipelineTask.set_reached_upstream_filter()` or
-  `PipelineTask.set_reached_downstream_filter()`.
-
- Added support for Chirp voices in `GoogleTTSService`.
-
- Added a `flush_audio()` method to `FishTTSService` and `LmntTTSService`.
-
- Added a `set_language` convenience method for `GoogleSTTService`, allowing
-  you to set a single language. This is in addition to the `set_languages`
-  method which allows you to set a list of languages.
-
- Added `on_user_turn_audio_data` and `on_bot_turn_audio_data` to
-  `AudioBufferProcessor`. This gives the ability to grab the audio of only that
-  turn for both the user and the bot.
-
- Added new base class `BaseObject` which is now the base class of
-  `FrameProcessor`, `PipelineRunner`, `PipelineTask` and `BaseTransport`. The
-  new `BaseObject` adds supports for event handlers.
-
- Added support for a unified format for specifying function calling across all
-  LLM services.
-
-```python
-  weather_function = FunctionSchema(
-      name="get_current_weather",
-      description="Get the current weather",
-      properties={
-          "location": {
-              "type": "string",
-              "description": "The city and state, e.g. San Francisco, CA",
-          },
-          "format": {
-              "type": "string",
-              "enum": ["celsius", "fahrenheit"],
-              "description": "The temperature unit to use. Infer this from the user's location.",
-          },
-      },
-      required=["location"],
-  )
-  tools = ToolsSchema(standard_tools=[weather_function])
-```
-
- Added `speech_threshold` parameter to `GladiaSTTService`.
-
- Allow passing user (`user_kwargs`) and assistant (`assistant_kwargs`) context
-  aggregator parameters when using `create_context_aggregator()`. The values are
-  passed as a mapping that will then be converted to arguments.
-
- Added `speed` as an `InputParam` for both `ElevenLabsTTSService` and
-  `ElevenLabsHttpTTSService`.
-
- Added new `LLMFullResponseAggregator` to aggregate full LLM completions. At
-  every completion the `on_completion` event handler is triggered.
-
- Added a new frame, `RTVIServerMessageFrame`, and RTVI message
-  `RTVIServerMessage` which provides a generic mechanism for sending custom
-  messages from server to client. The `RTVIServerMessageFrame` is processed by
-  the `RTVIObserver` and will be delivered to the client's `onServerMessage`
-  callback or `ServerMessage` event.
-
- Added `GoogleLLMOpenAIBetaService` for Google LLM integration with an
-  OpenAI-compatible interface. Added foundational example
-  `14o-function-calling-gemini-openai-format.py`.
-
- Added `AzureRealtimeBetaLLMService` to support Azure's OpeanAI Realtime API. Added
-  foundational example `19a-azure-realtime-beta.py`.
-
- Introduced `GoogleVertexLLMService`, a new class for integrating with Vertex AI
-  Gemini models. Added foundational example
-  `14p-function-calling-gemini-vertex-ai.py`.
-
- Added support in `OpenAIRealtimeBetaLLMService` for a slate of new features:
-
-  - The `'gpt-4o-transcribe'` input audio transcription model, along
-    with new `language` and `prompt` options specific to that model.
-  - The `input_audio_noise_reduction` session property.
-
-    ```python
-    session_properties = SessionProperties(
-      # ...
-      input_audio_noise_reduction=InputAudioNoiseReduction(
-        type="near_field" # also supported: "far_field"
-      )
-      # ...
-    )
-    ```
-
-  - The `'semantic_vad'` `turn_detection` session property value, a more
-    sophisticated model for detecting when the user has stopped speaking.
-  - `on_conversation_item_created` and `on_conversation_item_updated`
-    events to `OpenAIRealtimeBetaLLMService`.
-
-    ```python
-    @llm.event_handler("on_conversation_item_created")
-    async def on_conversation_item_created(llm, item_id, item):
-      # ...
-
-    @llm.event_handler("on_conversation_item_updated")
-    async def on_conversation_item_updated(llm, item_id, item):
-      # `item` may not always be available here
-      # ...
-    ```
-
-  - The `retrieve_conversation_item(item_id)` method for introspecting a
-    conversation item on the server.
-
-    ```python
-    item = await llm.retrieve_conversation_item(item_id)
-    ```
-
-### Changed
-
- Updated `OpenAISTTService` to use `gpt-4o-transcribe` as the default
-  transcription model.
-
- Updated `OpenAITTSService` to use `gpt-4o-mini-tts` as the default TTS model.
-
- Function calls are now executed in tasks. This means that the pipeline will
-  not be blocked while the function call is being executed.
-
- ⚠️ `PipelineTask` will now be automatically cancelled if no bot activity is
-  happening in the pipeline. There are a few settings to configure this
-  behavior, see `PipelineTask` documentation for more details.
-
- All event handlers are now executed in separate tasks in order to prevent
-  blocking the pipeline. It is possible that event handlers take some time to
-  execute in which case the pipeline would be blocked waiting for the event
-  handler to complete.
-
- Updated `TranscriptProcessor` to support text output from
-  `OpenAIRealtimeBetaLLMService`.
-
- `OpenAIRealtimeBetaLLMService` and `GeminiMultimodalLiveLLMService` now push
-  a `TTSTextFrame`.
-
- Updated the default mode for `CartesiaTTSService` and
-  `CartesiaHttpTTSService` to `sonic-2`.
-
-### Deprecated
-
- Passing a `start_callback` to `LLMService.register_function()` is now
-  deprecated, simply move the code from the start callback to the function call.
-
- `TTSService` parameter `text_filter` is now deprecated, use `text_filters`
-  instead which is now a list. This allows passing multiple filters that will be
-  executed in order.
-
-### Removed
-
- Removed deprecated `audio.resample_audio()`, use `create_default_resampler()`
-  instead.
-
- Removed deprecated`stt_service` parameter from `STTMuteFilter`.
-
- Removed deprecated RTVI processors, use an `RTVIObserver` instead.
-
- Removed deprecated `AWSTTSService`, use `PollyTTSService` instead.
-
- Removed deprecated field `tier` from `DailyTranscriptionSettings`, use `model`
-  instead.
-
- Removed deprecated `pipecat.vad` package, use `pipecat.audio.vad` instead.
-
-### Fixed
-
- Fixed an assistant aggregator issue that could cause assistant text to be
-  split into multiple chunks during function calls.
-
- Fixed an assistant aggregator issue that was causing assistant text to not be
-  added to the context during function calls. This could lead to duplications.
-
- Fixed a `SegmentedSTTService` issue that was causing audio to be sent
-  prematurely to the STT service. Instead of analyzing the volume in this
-  service we rely on VAD events which use both VAD and volume.
-
- Fixed a `GeminiMultimodalLiveLLMService` issue that was causing messages to be
-  duplicated in the context when pushing `LLMMessagesAppendFrame` frames.
-
- Fixed an issue with `SegmentedSTTService` based services
-  (e.g. `GroqSTTService`) that was not allow audio to pass-through downstream.
-
- Fixed a `CartesiaTTSService` and `RimeTTSService` issue that would consider
-  text between spelling out tags end of sentence.
-
- Fixed a `match_endofsentence` issue that would result in floating point
-  numbers to be considered an end of sentence.
-
- Fixed a `match_endofsentence` issue that would result in emails to be
-  considered an end of sentence.
-
- Fixed an issue where the RTVI message `disconnect-bot` was pushing an
-  `EndFrame`, resulting in the pipeline not shutting down. It now pushes an
-  `EndTaskFrame` upstream to shutdown the pipeline.
-
- Fixed an issue with the `GoogleSTTService` where stream timeouts during
-  periods of inactivity were causing connection failures. The service now
-  properly detects timeout errors and handles reconnection gracefully,
-  ensuring continuous operation even after periods of silence or when using an
-  `STTMuteFilter`.
-
- Fixed an issue in `RimeTTSService` where the last line of text sent didn't
-  result in an audio output being generated.
-
- Fixed `OpenAIRealtimeBetaLLMService` by adding proper handling for:
-  - The `conversation.item.input_audio_transcription.delta` server message,
-    which was added server-side at some point and not handled client-side.
-  - Errors reported by the `response.done` server message.
-
-### Other
-
- Add foundational example `07w-interruptible-fal.py`, showing `FalSTTService`.
-
- Added a new Ultravox example
-  `examples/foundational/07u-interruptible-ultravox.py`.
-
- Added new Neuphonic examples
-  `examples/foundational/07v-interruptible-neuphonic.py` and
-  `examples/foundational/07v-interruptible-neuphonic-http.py`.
-
- Added a new example `examples/foundational/36-user-email-gathering.py` to show
-  how to gather user emails. The example uses's Cartesia's `<spell></spell>`
-  tags and Rime `spell()` function to spell out the emails for confirmation.
-
- Update the `34-audio-recording.py` example to include an STT processor.
-
- Added foundational example `35-voice-switching.py` showing how to use the new
-  `PatternPairAggregator`. This example shows how to encode information for the
-  LLM to instruct TTS voice changes, but this can be used to encode any
-  information into the LLM response, which you want to parse and use in other
-  parts of your application.
-
- Added a Pipecat Cloud deployment example to the `examples` directory.
-
- Removed foundational examples 28b and 28c as the TranscriptProcessor no
-  longer has an LLM depedency. Renamed foundational example 28a to
-  `28-transcript-processor.py`.
-
-## [0.0.58] - 2025-02-26
-
-### Added
-
- Added track-specific audio event `on_track_audio_data` to
-  `AudioBufferProcessor` for accessing separate input and output audio tracks.
-
- Pipecat version will now be logged on every application startup. This will
-  help us identify what version we are running in case of any issues.
-
- Added a new `StopFrame` which can be used to stop a pipeline task while
-  keeping the frame processors running. The frame processors could then be used
-  in a different pipeline. The difference between a `StopFrame` and a
-  `StopTaskFrame` is that, as with `EndFrame` and `EndTaskFrame`, the
-  `StopFrame` is pushed from the task and the `StopTaskFrame` is pushed upstream
-  inside the pipeline by any processor.
-
- Added a new `PipelineTask` parameter `observers` that replaces the previous
-  `PipelineParams.observers`.
-
- Added a new `PipelineTask` parameter `check_dangling_tasks` to enable or
-  disable checking for frame processors' dangling tasks when the Pipeline
-  finishes running.
-
- Added new `on_completion_timeout` event for LLM services (all OpenAI-based
-  services, Anthropic and Google). Note that this event will only get triggered
-  if LLM timeouts are setup and if the timeout was reached. It can be useful to
-  retrigger another completion and see if the timeout was just a blip.
-
- Added new log observers `LLMLogObserver` and `TranscriptionLogObserver` that
-  can be useful for debugging your pipelines.
-
 - Added `room_url` property to `DailyTransport`.

 - Added `addons` argument to `DeepgramSTTService`.
@@ -374,27 +17,6 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0

 ### Changed

- ⚠️ `PipelineTask` now requires keyword arguments (except for the first one for
-  the pipeline).
-
- Updated `PlayHTHttpTTSService` to take a `voice_engine` and `protocol` input
-  in the constructor. The previous method of providing a `voice_engine` input
-  that contains the engine and protocol is deprecated by PlayHT.
-
- The base `TTSService` class now strips leading newlines before sending text
-  to the TTS provider. This change is to solve issues where some TTS providers,
-  like Azure, would not output text due to newlines.
-
- `GrokLLMSService` now uses `grok-2` as the default model.
-
- `AnthropicLLMService` now uses `claude-3-7-sonnet-20250219` as the default
-  model.
-
- `RimeHttpTTSService` needs an `aiohttp.ClientSession` to be passed to the
-  constructor as all the other HTTP-based services.
-
- `RimeHttpTTSService` doesn't use a default voice anymore.
-
 - `DeepgramSTTService` now uses the new `nova-3` model by default. If you want
  to use the previous model you can pass `LiveOptions(model="nova-2-general")`.
  (see https://deepgram.com/learn/introducing-nova-3-speech-to-text-api)
@@ -403,52 +25,8 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 stt = DeepgramSTTService(..., live_options=LiveOptions(model="nova-2-general"))
 ```

-### Deprecated
-
- `PipelineParams.observers` is now deprecated, you the new `PipelineTask`
-  parameter `observers`.
-
-### Removed
-
- Remove `TransportParams.audio_out_is_live` since it was not being used at all.
-
 ### Fixed

- Fixed an issue that would cause undesired interruptions via
-  `EmulateUserStartedSpeakingFrame`.
-
- Fixed a `GoogleLLMService` that was causing an exception when sending inline
-  audio in some cases.
-
- Fixed an `AudioContextWordTTSService` issue that would cause an `EndFrame` to
-  disconnect from the TTS service before audio from all the contexts was
-  received. This affected services like Cartesia and Rime.
-
- Fixed an issue that was not allowing to pass an `OpenAILLMContext` to create
-  `GoogleLLMService`'s context aggregators.
-
- Fixed a `ElevenLabsTTSService`, `FishAudioTTSService`, `LMNTTTSService` and
-  `PlayHTTTSService` issue that was resulting in audio requested before an
-  interruption being played after an interruption.
-
- Fixed `match_endofsentence` support for ellipses.
-
- Fixed an issue where `EndTaskFrame` was not triggering
-  `on_client_disconnected` or closing the WebSocket in FastAPI.
-
- Fixed an issue in `DeepgramSTTService` where the `sample_rate` passed to the
-  `LiveOptions` was not being used, causing the service to use the default
-  sample rate of pipeline.
-
- Fixed a context aggregator issue that would not append the LLM text response
-  to the context if a function call happened in the same LLM turn.
-
- Fixed an issue that was causing HTTP TTS services to push `TTSStoppedFrame`
-  more than once.
-
- Fixed a `FishAudioTTSService` issue where `TTSStoppedFrame` was not being
-  pushed.
-
 - Fixed an issue that `start_callback` was not invoked for some LLM services.

 - Fixed an issue that would cause `DeepgramSTTService` to stop working after an
@@ -462,9 +40,6 @@ stt = DeepgramSTTService(..., live_options=LiveOptions(model="nova-2-general"))

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

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

 ### Added
@@ -2097,7 +1672,7 @@ async def on_connected(processor):
  completed. If a task is never ran `has_finished()` will return False.

 - `PipelineRunner` now supports SIGTERM. If received, the runner will be
-  cancelled.
+  canceled.

 ### Fixed

--- a/README.md
+++ b/README.md
@@ -57,13 +57,13 @@ pip install "pipecat-ai[option,...]"

 | Category            | Services                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                | Install Command Example                 |
 | ------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | --------------------------------------- |
-| Speech-to-Text      | [AssemblyAI](https://docs.pipecat.ai/server/services/stt/assemblyai), [Azure](https://docs.pipecat.ai/server/services/stt/azure), [Deepgram](https://docs.pipecat.ai/server/services/stt/deepgram), [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), [OpenAI (Whisper)](https://docs.pipecat.ai/server/services/stt/openai), [Parakeet (NVIDIA)](https://docs.pipecat.ai/server/services/stt/parakeet), [Ultravox](https://docs.pipecat.ai/server/services/stt/ultravox), [Whisper](https://docs.pipecat.ai/server/services/stt/whisper)                                                                                                                                                                                  | `pip install "pipecat-ai[deepgram]"`    |
+| Speech-to-Text      | [AssemblyAI](https://docs.pipecat.ai/server/services/stt/assemblyai), [Azure](https://docs.pipecat.ai/server/services/stt/azure), [Deepgram](https://docs.pipecat.ai/server/services/stt/deepgram), [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), [OpenAI (Whisper)](https://docs.pipecat.ai/server/services/stt/openai), [Whisper](https://docs.pipecat.ai/server/services/stt/whisper)                                                                                                                                                                                                                                                                                                                                                                                              | `pip install "pipecat-ai[deepgram]"`    |
 | LLMs                | [Anthropic](https://docs.pipecat.ai/server/services/llm/anthropic), [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), [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), [Together AI](https://docs.pipecat.ai/server/services/llm/together) | `pip install "pipecat-ai[openai]"`      |
-| Text-to-Speech      | [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), [FastPitch (NVIDIA)](https://docs.pipecat.ai/server/services/tts/fastpitch), [Fish](https://docs.pipecat.ai/server/services/tts/fish), [Google](https://docs.pipecat.ai/server/services/tts/google), [LMNT](https://docs.pipecat.ai/server/services/tts/lmnt), [Neuphonic](https://docs.pipecat.ai/server/services/tts/neuphonic), [OpenAI](https://docs.pipecat.ai/server/services/tts/openai), [PlayHT](https://docs.pipecat.ai/server/services/tts/playht), [Rime](https://docs.pipecat.ai/server/services/tts/rime), [XTTS](https://docs.pipecat.ai/server/services/tts/xtts)                         | `pip install "pipecat-ai[cartesia]"`    |
+| Text-to-Speech      | [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), [LMNT](https://docs.pipecat.ai/server/services/tts/lmnt), [OpenAI](https://docs.pipecat.ai/server/services/tts/openai), [PlayHT](https://docs.pipecat.ai/server/services/tts/playht), [Rime](https://docs.pipecat.ai/server/services/tts/rime), [XTTS](https://docs.pipecat.ai/server/services/tts/xtts)                                                                                                                                                                          | `pip install "pipecat-ai[cartesia]"`    |
 | Speech-to-Speech    | [Gemini Multimodal Live](https://docs.pipecat.ai/server/services/s2s/gemini), [OpenAI Realtime](https://docs.pipecat.ai/server/services/s2s/openai)                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                     | `pip install "pipecat-ai[google]"`      |
 | Transport           | [Daily (WebRTC)](https://docs.pipecat.ai/server/services/transport/daily), [FastAPI Websocket](https://docs.pipecat.ai/server/services/transport/fastapi-websocket), [WebSocket Server](https://docs.pipecat.ai/server/services/transport/websocket-server), Local                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      | `pip install "pipecat-ai[daily]"`       |
 | Video               | [Tavus](https://docs.pipecat.ai/server/services/video/tavus), [Simli](https://docs.pipecat.ai/server/services/video/simli)                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              | `pip install "pipecat-ai[tavus,simli]"` |
-| 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)                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                               | `pip install "pipecat-ai[moondream]"`   |
+| Vision & Image      | [Moondream](https://docs.pipecat.ai/server/services/vision/moondream), [fal](https://docs.pipecat.ai/server/services/image-generation/fal)                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              | `pip install "pipecat-ai[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), [Noisereduce](https://docs.pipecat.ai/server/utilities/audio/noisereduce-filter)                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          | `pip install "pipecat-ai[silero]"`      |
 | Analytics & Metrics | [Canonical AI](https://docs.pipecat.ai/server/services/analytics/canonical), [Sentry](https://docs.pipecat.ai/server/services/analytics/sentry)                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                         | `pip install "pipecat-ai[canonical]"`   |

--- a/dev-requirements.txt
+++ b/dev-requirements.txt
@@ -3,10 +3,10 @@ coverage~=7.6.12
 grpcio-tools~=1.67.1
 pip-tools~=7.4.1
 pre-commit~=4.0.1
-pyright~=1.1.397
+pyright~=1.1.393
 pytest~=8.3.4
-pytest-asyncio~=0.25.3
-ruff~=0.11.1
+pytest-asyncio~=0.25.2
+ruff~=0.9.5
 setuptools~=70.0.0
 setuptools_scm~=8.1.0
 python-dotenv~=1.0.1
--- a/dot-env.template
+++ b/dot-env.template
@@ -18,9 +18,6 @@ AZURE_DALLE_API_KEY=...
 AZURE_DALLE_ENDPOINT=https://...
 AZURE_DALLE_MODEL=...

-# Cartesia
-CARTESIA_API_KEY=...
-
 # Daily
 DAILY_API_KEY=...
 DAILY_SAMPLE_ROOM_URL=https://...
@@ -29,9 +26,6 @@ DAILY_SAMPLE_ROOM_URL=https://...
 ELEVENLABS_API_KEY=...
 ELEVENLABS_VOICE_ID=...

-# Neuphonic
-NEUPHONIC_API_KEY=...
-
 # Fal
 FAL_KEY=...

--- a/examples/bot-ready-signalling/server/signalling_bot.py
+++ b/examples/bot-ready-signalling/server/signalling_bot.py
@@ -17,7 +17,7 @@ from runner import configure
 from pipecat.frames.frames import AudioRawFrame, EndFrame, OutputAudioRawFrame, TTSSpeakFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
-from pipecat.pipeline.task import PipelineTask
+from pipecat.pipeline.task import PipelineParams, PipelineTask
 from pipecat.services.cartesia import CartesiaTTSService
 from pipecat.transports.services.daily import DailyParams, DailyTransport

@@ -64,7 +64,7 @@ async def main():

        tts = CartesiaTTSService(
            api_key=os.getenv("CARTESIA_API_KEY"),
-            voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
+            voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22",  # British Lady
        )

        runner = PipelineRunner()
--- a/examples/canonical-metrics/bot.py
+++ b/examples/canonical-metrics/bot.py
@@ -113,13 +113,13 @@ async def main():
                llm,
                tts,
                transport.output(),
-                canonical,  # uploads audio buffer to Canonical AI for metrics
                audio_buffer_processor,  # captures audio into a buffer
+                canonical,  # uploads audio buffer to Canonical AI for metrics
                context_aggregator.assistant(),
            ]
        )

-        task = PipelineTask(pipeline, params=PipelineParams(allow_interruptions=True))
+        task = PipelineTask(pipeline, PipelineParams(allow_interruptions=True))

        @transport.event_handler("on_first_participant_joined")
        async def on_first_participant_joined(transport, participant):
--- a/examples/chatbot-audio-recording/bot.py
+++ b/examples/chatbot-audio-recording/bot.py
@@ -32,16 +32,10 @@ load_dotenv(override=True)
 logger.remove(0)
 logger.add(sys.stderr, level="DEBUG")

-# Create the recordings directory if it doesn't exist
-os.makedirs("recordings", exist_ok=True)

-
-async def save_audio(audio: bytes, sample_rate: int, num_channels: int, name: str):
+async def save_audio(audio: bytes, sample_rate: int, num_channels: int):
    if len(audio) > 0:
-        filename = os.path.join(
-            "recordings",
-            f"{name}_conversation_recording{datetime.datetime.now().strftime('%Y%m%d_%H%M%S')}.wav",
-        )
+        filename = f"conversation_recording{datetime.datetime.now().strftime('%Y%m%d_%H%M%S')}.wav"
        with io.BytesIO() as buffer:
            with wave.open(buffer, "wb") as wf:
                wf.setsampwidth(2)
@@ -116,7 +110,7 @@ async def main():

        # NOTE: Watch out! This will save all the conversation in memory. You
        # can pass `buffer_size` to get periodic callbacks.
-        audiobuffer = AudioBufferProcessor(enable_turn_audio=True)
+        audiobuffer = AudioBufferProcessor()

        pipeline = Pipeline(
            [
@@ -130,19 +124,11 @@ async def main():
            ]
        )

-        task = PipelineTask(pipeline, params=PipelineParams(allow_interruptions=True))
+        task = PipelineTask(pipeline, PipelineParams(allow_interruptions=True))

        @audiobuffer.event_handler("on_audio_data")
        async def on_audio_data(buffer, audio, sample_rate, num_channels):
-            await save_audio(audio, sample_rate, num_channels, "full")
-
-        @audiobuffer.event_handler("on_user_turn_audio_data")
-        async def on_user_turn_audio_data(buffer, audio, sample_rate, num_channels):
-            await save_audio(audio, sample_rate, num_channels, "user")
-
-        @audiobuffer.event_handler("on_bot_turn_audio_data")
-        async def on_bot_turn_audio_data(buffer, audio, sample_rate, num_channels):
-            await save_audio(audio, sample_rate, num_channels, "bot")
+            await save_audio(audio, sample_rate, num_channels)

        @transport.event_handler("on_first_participant_joined")
        async def on_first_participant_joined(transport, participant):
--- a/examples/deployment/flyio-example/bot.py
+++ b/examples/deployment/flyio-example/bot.py
@@ -70,7 +70,7 @@ async def main(room_url: str, token: str):
        ]
    )

-    task = PipelineTask(pipeline, params=PipelineParams(allow_interruptions=True))
+    task = PipelineTask(pipeline, PipelineParams(allow_interruptions=True))

    @transport.event_handler("on_first_participant_joined")
    async def on_first_participant_joined(transport, participant):
--- a/examples/deployment/modal-example/bot.py
+++ b/examples/deployment/modal-example/bot.py
@@ -34,7 +34,7 @@ async def main(room_url: str, token: str):
    )

    tts = CartesiaTTSService(
-        api_key=os.getenv("CARTESIA_API_KEY", ""), voice_id="71a7ad14-091c-4e8e-a314-022ece01c121"
+        api_key=os.getenv("CARTESIA_API_KEY", ""), voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22"
    )

    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
@@ -62,7 +62,7 @@ async def main(room_url: str, token: str):

    task = PipelineTask(
        pipeline,
-        params=PipelineParams(
+        PipelineParams(
            allow_interruptions=True,
            enable_metrics=True,
            enable_usage_metrics=True,
--- a/examples/deployment/pipecat-cloud-example/.gitignore
+++ b/examples/deployment/pipecat-cloud-example/.gitignore
@@ -1,94 +0,0 @@
-# Python
-__pycache__/
-*.py[cod]
-*$py.class
-*.so
-.Python
-build/
-dist/
-*.egg-info/
-*.egg
-.installed.cfg
-.eggs/
-downloads/
-lib/
-lib64/
-parts/
-sdist/
-var/
-wheels/
-share/python-wheels/
-MANIFEST
-
-# Virtual Environments
-venv/
-env/
-.env
-.venv/
-ENV/
-env.bak/
-venv.bak/
-
-# IDE
-.idea/
-.vscode/
-.spyderproject
-.spyproject
-.ropeproject
-
-# Testing and Coverage
-.coverage
-.coverage.*
-htmlcov/
-.pytest_cache/
-.tox/
-.nox/
-.cache
-nosetests.xml
-coverage.xml
-*.cover
-.hypothesis/
-cover/
-
-# Logs and Databases
-*.log
-*.db
-db.sqlite3
-db.sqlite3-journal
-pip-log.txt
-
-# System Files
-.DS_Store
-Thumbs.db
-desktop.ini
-*.swp
-*.swo
-*.bak
-*.tmp
-*~
-
-# Build and Documentation
-docs/_build/
-.pybuilder/
-target/
-instance/
-.webassets-cache
-.pdm.toml
-.pdm-python
-.pdm-build/
-__pypackages__/
-
-# Other
-*.mo
-*.pot
-*.sage.py
-.mypy_cache/
-.dmypy.json
-dmypy.json
-.pyre/
-.pytype/
-cython_debug/
-.ipynb_checkpoints
-
-# Pipecat cloud
-.pcc-deploy.toml
--- a/examples/deployment/pipecat-cloud-example/Dockerfile
+++ b/examples/deployment/pipecat-cloud-example/Dockerfile
@@ -1,7 +0,0 @@
-FROM dailyco/pipecat-base:latest
-
-COPY ./requirements.txt requirements.txt
-
-RUN pip install --no-cache-dir --upgrade -r requirements.txt
-
-COPY ./bot.py bot.py
--- a/examples/deployment/pipecat-cloud-example/README.md
+++ b/examples/deployment/pipecat-cloud-example/README.md
@@ -1,196 +0,0 @@
-# Pipecat Cloud Starter Project
-
-[![Docs](https://img.shields.io/badge/Documentation-blue)](https://docs.pipecat.daily.co) [![Discord](https://img.shields.io/discord/1217145424381743145)](https://discord.gg/dailyco)
-
-A template voice agent for [Pipecat Cloud](https://www.daily.co/products/pipecat-cloud/) that demonstrates building and deploying a conversational AI agent.
-
-> **For a detailed step-by-step guide, see our [Quickstart Documentation](https://docs.pipecat.daily.co/quickstart).**
-
-## Prerequisites
-
- Python 3.10+
- Linux, MacOS, or Windows Subsystem for Linux (WSL)
- [Docker](https://www.docker.com) and a Docker repository (e.g., [Docker Hub](https://hub.docker.com))
- A Docker Hub account (or other container registry account)
- [Pipecat Cloud](https://pipecat.daily.co) account
-
-> **Note**: If you haven't installed Docker yet, follow the official installation guides for your platform ([Linux](https://docs.docker.com/engine/install/), [Mac](https://docs.docker.com/desktop/setup/install/mac-install/), [Windows](https://docs.docker.com/desktop/setup/install/windows-install/)). For Docker Hub, [create a free account](https://hub.docker.com/signup) and log in via terminal with `docker login`.
-
-## Get Started
-
-### 1. Get the starter project
-
-Clone the starter project from GitHub:
-
-```bash
-git clone https://github.com/daily-co/pipecat-cloud-starter
-cd pipecat-cloud-starter
-```
-
-### 2. Set up your Python environment
-
-We recommend using a virtual environment to manage your Python dependencies.
-
-```bash
-# Create a virtual environment
-python -m venv .venv
-
-# Activate it
-source .venv/bin/activate  # On Windows: .venv\Scripts\activate
-
-# Install the Pipecat Cloud CLI
-pip install pipecatcloud
-```
-
-### 3. Authenticate with Pipecat Cloud
-
-```bash
-pcc auth login
-```
-
-### 4. Acquire required API keys
-
-This starter requires the following API keys:
-
- **OpenAI API Key**: Get from [platform.openai.com/api-keys](https://platform.openai.com/api-keys)
- **Cartesia API Key**: Get from [play.cartesia.ai/keys](https://play.cartesia.ai/keys)
- **Daily API Key**: Automatically provided through your Pipecat Cloud account
-
-### 5. Configure to run locally (optional)
-
-You can test your agent locally before deploying to Pipecat Cloud:
-
-```bash
-# Set environment variables with your API keys
-export CARTESIA_API_KEY="your_cartesia_key"
-export DAILY_API_KEY="your_daily_key"
-export OPENAI_API_KEY="your_openai_key"
-```
-
-> Your `DAILY_API_KEY` can be found at [https://pipecat.daily.co](https://pipecat.daily.co) under the `Settings` in the `Daily (WebRTC)` tab.
-
-First install requirements:
-
-```bash
-pip install -r requirements.txt
-```
-
-Then, launch the bot.py script locally:
-
-```bash
-LOCAL_RUN=1 python bot.py
-```
-
-## Deploy & Run
-
-### 1. Build and push your Docker image
-
-```bash
-# Build the image (targeting ARM architecture for cloud deployment)
-docker build --platform=linux/arm64 -t my-first-agent:latest .
-
-# Tag with your Docker username and version
-docker tag my-first-agent:latest your-username/my-first-agent:0.1
-
-# Push to Docker Hub
-docker push your-username/my-first-agent:0.1
-```
-
-### 2. Create a secret set for your API keys
-
-The starter project requires API keys for OpenAI and Cartesia:
-
-```bash
-# Copy the example env file
-cp env.example .env
-
-# Edit .env to add your API keys:
-# CARTESIA_API_KEY=your_cartesia_key
-# OPENAI_API_KEY=your_openai_key
-
-# Create a secret set from your .env file
-pcc secrets set my-first-agent-secrets --file .env
-```
-
-Alternatively, you can create secrets directly via CLI:
-
-```bash
-pcc secrets set my-first-agent-secrets \
-  CARTESIA_API_KEY=your_cartesia_key \
-  OPENAI_API_KEY=your_openai_key
-```
-
-### 3. Deploy to Pipecat Cloud
-
-```bash
-pcc deploy my-first-agent your-username/my-first-agent:0.1 --secrets my-first-agent-secrets
-```
-
-> **Note (Optional)**: For a more maintainable approach, you can use the included `pcc-deploy.toml` file:
->
-> ```toml
-> agent_name = "my-first-agent"
-> image = "your-username/my-first-agent:0.1"
-> secret_set = "my-first-agent-secrets"
->
-> [scaling]
->     min_instances = 0
-> ```
->
-> Then simply run `pcc deploy` without additional arguments.
-
-> **Note**: If your repository is private, you'll need to add credentials:
->
-> ```bash
-> # Create pull secret (you’ll be prompted for credentials)
-> pcc secrets image-pull-secret pull-secret https://index.docker.io/v1/
->
-> # Deploy with credentials
-> pcc deploy my-first-agent your-username/my-first-agent:0.1 --credentials pull-secret
-> ```
-
-### 4. Check deployment and scaling (optional)
-
-By default, your agent will use "scale-to-zero" configuration, which means it may have a cold start of around 10 seconds when first used. By default, idle instances are maintained for 5 minutes before being terminated when using scale-to-zero.
-
-For more responsive testing, you can scale your deployment to keep a minimum of one instance warm:
-
-```bash
-# Ensure at least one warm instance is always available
-pcc deploy my-first-agent your-username/my-first-agent:0.1 --min-instances 1
-
-# Check the status of your deployment
-pcc agent status my-first-agent
-```
-
-By default, idle instances are maintained for 5 minutes before being terminated when using scale-to-zero.
-
-### 5. Create an API key
-
-```bash
-# Create a public API key for accessing your agent
-pcc organizations keys create
-
-# Set it as the default key to use with your agent
-pcc organizations keys use
-```
-
-### 6. Start your agent
-
-```bash
-# Start a session with your agent in a Daily room
-pcc agent start my-first-agent --use-daily
-```
-
-This will return a URL, which you can use to connect to your running agent.
-
-## Documentation
-
-For more details on Pipecat Cloud and its capabilities:
-
- [Pipecat Cloud Documentation](https://docs.pipecat.daily.co)
- [Pipecat Project Documentation](https://docs.pipecat.ai)
-
-## Support
-
-Join our [Discord community](https://discord.gg/dailyco) for help and discussions.
--- a/examples/deployment/pipecat-cloud-example/bot.py
+++ b/examples/deployment/pipecat-cloud-example/bot.py
@@ -1,161 +0,0 @@
-#
-# Copyright (c) 2025, Daily
-#
-# SPDX-License-Identifier: BSD 2-Clause License
-#
-
-import os
-
-import aiohttp
-from dotenv import load_dotenv
-from loguru import logger
-from pipecatcloud.agent import DailySessionArguments
-
-from pipecat.audio.vad.silero import SileroVADAnalyzer
-from pipecat.frames.frames import LLMMessagesFrame
-from pipecat.pipeline.pipeline import Pipeline
-from pipecat.pipeline.runner import PipelineRunner
-from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
-from pipecat.services.cartesia import CartesiaTTSService
-from pipecat.services.openai import OpenAILLMService
-from pipecat.transports.services.daily import DailyParams, DailyTransport
-
-# Check if we're in local development mode
-LOCAL_RUN = os.getenv("LOCAL_RUN")
-if LOCAL_RUN:
-    import asyncio
-    import webbrowser
-
-    try:
-        from local_runner import configure
-    except ImportError:
-        logger.error("Could not import local_runner module. Local development mode may not work.")
-
-# Load environment variables
-load_dotenv(override=True)
-
-
-async def main(room_url: str, token: str):
-    """Main pipeline setup and execution function.
-
-    Args:
-        room_url: The Daily room URL
-        token: The Daily room token
-    """
-    logger.debug("Starting bot in room: {}", room_url)
-
-    transport = DailyTransport(
-        room_url,
-        token,
-        "bot",
-        DailyParams(
-            audio_out_enabled=True,
-            transcription_enabled=True,
-            vad_enabled=True,
-            vad_analyzer=SileroVADAnalyzer(),
-        ),
-    )
-
-    tts = CartesiaTTSService(
-        api_key=os.getenv("CARTESIA_API_KEY"), voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22"
-    )
-
-    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
-
-    messages = [
-        {
-            "role": "system",
-            "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
-        },
-    ]
-
-    context = OpenAILLMContext(messages)
-    context_aggregator = llm.create_context_aggregator(context)
-
-    pipeline = Pipeline(
-        [
-            transport.input(),
-            context_aggregator.user(),
-            llm,
-            tts,
-            transport.output(),
-            context_aggregator.assistant(),
-        ]
-    )
-
-    task = PipelineTask(
-        pipeline,
-        params=PipelineParams(
-            allow_interruptions=True,
-            enable_metrics=True,
-            enable_usage_metrics=True,
-            report_only_initial_ttfb=True,
-        ),
-    )
-
-    @transport.event_handler("on_first_participant_joined")
-    async def on_first_participant_joined(transport, participant):
-        logger.info("First participant joined: {}", participant["id"])
-        await transport.capture_participant_transcription(participant["id"])
-        # Kick off the conversation.
-        messages.append(
-            {
-                "role": "system",
-                "content": "Please start with 'Hello World' and introduce yourself to the user.",
-            }
-        )
-        await task.queue_frames([LLMMessagesFrame(messages)])
-
-    @transport.event_handler("on_participant_left")
-    async def on_participant_left(transport, participant, reason):
-        logger.info("Participant left: {}", participant)
-        await task.cancel()
-
-    runner = PipelineRunner()
-
-    await runner.run(task)
-
-
-async def bot(args: DailySessionArguments):
-    """Main bot entry point compatible with the FastAPI route handler.
-
-    Args:
-        room_url: The Daily room URL
-        token: The Daily room token
-        body: The configuration object from the request body
-        session_id: The session ID for logging
-    """
-    logger.info(f"Bot process initialized {args.room_url} {args.token}")
-
-    try:
-        await main(args.room_url, args.token)
-        logger.info("Bot process completed")
-    except Exception as e:
-        logger.exception(f"Error in bot process: {str(e)}")
-        raise
-
-
-# Local development functions
-async def local_main():
-    """Function for local development testing."""
-    try:
-        async with aiohttp.ClientSession() as session:
-            (room_url, token) = await configure(session)
-            logger.warning("_")
-            logger.warning("_")
-            logger.warning(f"Talk to your voice agent here: {room_url}")
-            logger.warning("_")
-            logger.warning("_")
-            webbrowser.open(room_url)
-            await main(room_url, token)
-    except Exception as e:
-        logger.exception(f"Error in local development mode: {e}")
-
-
-# Local development entry point
-if LOCAL_RUN and __name__ == "__main__":
-    try:
-        asyncio.run(local_main())
-    except Exception as e:
-        logger.exception(f"Failed to run in local mode: {e}")
--- a/examples/deployment/pipecat-cloud-example/env.example
+++ b/examples/deployment/pipecat-cloud-example/env.example
@@ -1,2 +0,0 @@
-CARTESIA_API_KEY=
-OPENAI_API_KEY=
--- a/examples/deployment/pipecat-cloud-example/local_runner.py
+++ b/examples/deployment/pipecat-cloud-example/local_runner.py
@@ -1,46 +0,0 @@
-#
-# Copyright (c) 2024–2025, Daily
-#
-# SPDX-License-Identifier: BSD 2-Clause License
-#
-
-import os
-
-import aiohttp
-
-from pipecat.transports.services.helpers.daily_rest import DailyRESTHelper, DailyRoomParams
-
-
-async def configure(aiohttp_session: aiohttp.ClientSession):
-    (url, token) = await configure_with_args(aiohttp_session)
-    return (url, token)
-
-
-async def configure_with_args(aiohttp_session: aiohttp.ClientSession = None):
-    key = os.getenv("DAILY_API_KEY")
-    if not key:
-        raise Exception(
-            "No Daily API key specified. set DAILY_API_KEY in your environment to specify a Daily API key, available from https://dashboard.daily.co/developers."
-        )
-
-    daily_rest_helper = DailyRESTHelper(
-        daily_api_key=key,
-        daily_api_url=os.getenv("DAILY_API_URL", "https://api.daily.co/v1"),
-        aiohttp_session=aiohttp_session,
-    )
-
-    room = await daily_rest_helper.create_room(
-        DailyRoomParams(properties={"enable_prejoin_ui": False})
-    )
-    if not room.url:
-        raise HTTPException(status_code=500, detail="Failed to create room")
-
-    url = room.url
-
-    # Create a meeting token for the given room with an expiration 1 hour in
-    # the future.
-    expiry_time: float = 60 * 60
-
-    token = await daily_rest_helper.get_token(url, expiry_time)
-
-    return (url, token)
--- a/examples/deployment/pipecat-cloud-example/pcc-deploy.toml
+++ b/examples/deployment/pipecat-cloud-example/pcc-deploy.toml
@@ -1,6 +0,0 @@
-agent_name = "my-first-agent"
-image = "your-username/my-first-agent:0.1"
-secret_set = "my-first-agent-secrets"
-
-[scaling]
-	min_instances = 0
--- a/examples/deployment/pipecat-cloud-example/requirements.txt
+++ b/examples/deployment/pipecat-cloud-example/requirements.txt
@@ -1,3 +0,0 @@
-pipecatcloud
-pipecat-ai[cartesia,daily,openai,silero]>=0.0.58
-python-dotenv~=1.0.1
--- a/examples/foundational/01-say-one-thing.py
+++ b/examples/foundational/01-say-one-thing.py
@@ -36,7 +36,7 @@ async def main():

        tts = CartesiaTTSService(
            api_key=os.getenv("CARTESIA_API_KEY"),
-            voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
+            voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22",  # British Lady
        )

        runner = PipelineRunner()
--- a/examples/foundational/01a-local-audio.py
+++ b/examples/foundational/01a-local-audio.py
@@ -29,7 +29,7 @@ async def main():

    tts = CartesiaTTSService(
        api_key=os.getenv("CARTESIA_API_KEY"),
-        voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
+        voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22",  # British Lady
    )

    pipeline = Pipeline([tts, transport.output()])
--- a/examples/foundational/01b-livekit-audio.py
+++ b/examples/foundational/01b-livekit-audio.py
@@ -83,7 +83,7 @@ async def main():

        tts = CartesiaTTSService(
            api_key=os.getenv("CARTESIA_API_KEY"),
-            voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
+            voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22",  # British Lady
        )

        runner = PipelineRunner()
--- a/examples/foundational/02-llm-say-one-thing.py
+++ b/examples/foundational/02-llm-say-one-thing.py
@@ -37,7 +37,7 @@ async def main():

        tts = CartesiaTTSService(
            api_key=os.getenv("CARTESIA_API_KEY"),
-            voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
+            voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22",  # British Lady
        )

        llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
--- a/examples/foundational/03a-local-still-frame.py
+++ b/examples/foundational/03a-local-still-frame.py
@@ -18,7 +18,8 @@ from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineTask
 from pipecat.services.fal import FalImageGenService
-from pipecat.transports.local.tk import TkLocalTransport, TkTransportParams
+from pipecat.transports.base_transport import TransportParams
+from pipecat.transports.local.tk import TkLocalTransport

 load_dotenv(override=True)

@@ -33,9 +34,7 @@ async def main():

        transport = TkLocalTransport(
            tk_root,
-            TkTransportParams(
-                camera_out_enabled=True, camera_out_width=1024, camera_out_height=1024
-            ),
+            TransportParams(camera_out_enabled=True, camera_out_width=1024, camera_out_height=1024),
        )

        imagegen = FalImageGenService(
--- a/examples/foundational/03b-still-frame-imagen.py
+++ b/examples/foundational/03b-still-frame-imagen.py
@@ -44,8 +44,7 @@ async def main():
        runner = PipelineRunner()

        task = PipelineTask(
-            Pipeline([imagegen, transport.output()]),
-            params=PipelineParams(enable_metrics=True),
+            Pipeline([imagegen, transport.output()]), PipelineParams(enable_metrics=True)
        )

        @transport.event_handler("on_first_participant_joined")
--- a/examples/foundational/05-sync-speech-and-image.py
+++ b/examples/foundational/05-sync-speech-and-image.py
@@ -87,7 +87,7 @@ async def main():

        tts = CartesiaHttpTTSService(
            api_key=os.getenv("CARTESIA_API_KEY"),
-            voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
+            voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22",  # British Lady
        )

        imagegen = FalImageGenService(
--- a/examples/foundational/05a-local-sync-speech-and-image.py
+++ b/examples/foundational/05a-local-sync-speech-and-image.py
@@ -30,7 +30,8 @@ from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
 from pipecat.services.cartesia import CartesiaHttpTTSService
 from pipecat.services.fal import FalImageGenService
 from pipecat.services.openai import OpenAILLMService
-from pipecat.transports.local.tk import TkLocalTransport, TkTransportParams
+from pipecat.transports.base_transport import TransportParams
+from pipecat.transports.local.tk import TkLocalTransport, TkOutputTransport

 load_dotenv(override=True)

@@ -97,7 +98,7 @@ async def main():

            tts = CartesiaHttpTTSService(
                api_key=os.getenv("CARTESIA_API_KEY"),
-                voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
+                voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22",  # British Lady
            )

            imagegen = FalImageGenService(
@@ -151,7 +152,7 @@ async def main():

        transport = TkLocalTransport(
            tk_root,
-            TkTransportParams(
+            TransportParams(
                audio_out_enabled=True,
                camera_out_enabled=True,
                camera_out_width=1024,
--- a/examples/foundational/06-listen-and-respond.py
+++ b/examples/foundational/06-listen-and-respond.py
@@ -74,7 +74,7 @@ async def main():

        tts = CartesiaTTSService(
            api_key=os.getenv("CARTESIA_API_KEY"),
-            voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
+            voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22",  # British Lady
        )

        llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
@@ -105,10 +105,7 @@ async def main():

        task = PipelineTask(
            pipeline,
-            params=PipelineParams(
-                enable_metrics=True,
-                enable_usage_metrics=True,
-            ),
+            PipelineParams(enable_metrics=True, enable_usage_metrics=True),
        )

        @transport.event_handler("on_first_participant_joined")
--- a/examples/foundational/06a-image-sync.py
+++ b/examples/foundational/06a-image-sync.py
@@ -93,7 +93,7 @@ async def main():

        tts = CartesiaTTSService(
            api_key=os.getenv("CARTESIA_API_KEY"),
-            voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
+            voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22",  # British Lady
        )

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

        task = PipelineTask(
            pipeline,
-            params=PipelineParams(
+            PipelineParams(
                allow_interruptions=True,
                enable_metrics=True,
                enable_usage_metrics=True,
--- a/examples/foundational/07-interruptible-vad.py
+++ b/examples/foundational/07-interruptible-vad.py
@@ -47,7 +47,7 @@ async def main():

        tts = CartesiaTTSService(
            api_key=os.getenv("CARTESIA_API_KEY"),
-            voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
+            voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22",  # British Lady
        )

        llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
@@ -76,7 +76,7 @@ async def main():

        task = PipelineTask(
            pipeline,
-            params=PipelineParams(
+            PipelineParams(
                allow_interruptions=True,
                enable_metrics=True,
                enable_usage_metrics=True,
--- a/examples/foundational/07-interruptible.py
+++ b/examples/foundational/07-interruptible.py
@@ -46,7 +46,7 @@ async def main():

        tts = CartesiaTTSService(
            api_key=os.getenv("CARTESIA_API_KEY"),
-            voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
+            voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22",  # British Lady
        )

        llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
@@ -74,7 +74,7 @@ async def main():

        task = PipelineTask(
            pipeline,
-            params=PipelineParams(
+            PipelineParams(
                allow_interruptions=True,
                enable_metrics=True,
                enable_usage_metrics=True,
--- a/examples/foundational/07a-interruptible-anthropic.py
+++ b/examples/foundational/07a-interruptible-anthropic.py
@@ -46,7 +46,7 @@ async def main():

        tts = CartesiaTTSService(
            api_key=os.getenv("CARTESIA_API_KEY"),
-            voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
+            voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22",  # British Lady
        )

        llm = AnthropicLLMService(
@@ -79,7 +79,7 @@ async def main():

        task = PipelineTask(
            pipeline,
-            params=PipelineParams(
+            PipelineParams(
                allow_interruptions=True,
                enable_metrics=True,
                enable_usage_metrics=True,
--- a/examples/foundational/07b-interruptible-langchain.py
+++ b/examples/foundational/07b-interruptible-langchain.py
@@ -64,7 +64,7 @@ async def main():

        tts = CartesiaTTSService(
            api_key=os.getenv("CARTESIA_API_KEY"),
-            voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
+            voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22",  # British Lady
        )

        prompt = ChatPromptTemplate.from_messages(
@@ -103,7 +103,7 @@ async def main():

        task = PipelineTask(
            pipeline,
-            params=PipelineParams(
+            PipelineParams(
                allow_interruptions=True,
                enable_metrics=True,
                enable_usage_metrics=True,
--- a/examples/foundational/07c-interruptible-deepgram-vad.py
+++ b/examples/foundational/07c-interruptible-deepgram-vad.py
@@ -81,7 +81,7 @@ async def main():

        task = PipelineTask(
            pipeline,
-            params=PipelineParams(
+            PipelineParams(
                allow_interruptions=True,
                enable_metrics=True,
                enable_usage_metrics=True,
--- a/examples/foundational/07c-interruptible-deepgram.py
+++ b/examples/foundational/07c-interruptible-deepgram.py
@@ -74,7 +74,7 @@ async def main():

        task = PipelineTask(
            pipeline,
-            params=PipelineParams(
+            PipelineParams(
                allow_interruptions=True,
                enable_metrics=True,
                enable_usage_metrics=True,
--- a/examples/foundational/07d-interruptible-elevenlabs-http.py
+++ b/examples/foundational/07d-interruptible-elevenlabs-http.py
@@ -1,103 +0,0 @@
-#
-# Copyright (c) 2024–2025, Daily
-#
-# SPDX-License-Identifier: BSD 2-Clause License
-#
-
-import asyncio
-import os
-import sys
-
-import aiohttp
-from dotenv import load_dotenv
-from loguru import logger
-from runner import configure
-
-from pipecat.audio.vad.silero import SileroVADAnalyzer
-from pipecat.pipeline.pipeline import Pipeline
-from pipecat.pipeline.runner import PipelineRunner
-from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
-from pipecat.services.elevenlabs import ElevenLabsHttpTTSService
-from pipecat.services.openai import OpenAILLMService
-from pipecat.transports.services.daily import DailyParams, DailyTransport
-
-load_dotenv(override=True)
-
-logger.remove(0)
-logger.add(sys.stderr, level="DEBUG")
-
-
-async def main():
-    async with aiohttp.ClientSession() as session:
-        (room_url, token) = await configure(session)
-
-        transport = DailyTransport(
-            room_url,
-            token,
-            "Respond bot",
-            DailyParams(
-                audio_out_enabled=True,
-                transcription_enabled=True,
-                vad_enabled=True,
-                vad_analyzer=SileroVADAnalyzer(),
-            ),
-        )
-
-        tts = ElevenLabsHttpTTSService(
-            api_key=os.getenv("ELEVENLABS_API_KEY", ""),
-            voice_id=os.getenv("ELEVENLABS_VOICE_ID", ""),
-            aiohttp_session=session,
-        )
-
-        llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
-
-        messages = [
-            {
-                "role": "system",
-                "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
-            },
-        ]
-
-        context = OpenAILLMContext(messages)
-        context_aggregator = llm.create_context_aggregator(context)
-
-        pipeline = Pipeline(
-            [
-                transport.input(),  # Transport user input
-                context_aggregator.user(),  # User responses
-                llm,  # LLM
-                tts,  # TTS
-                transport.output(),  # Transport bot output
-                context_aggregator.assistant(),  # Assistant spoken responses
-            ]
-        )
-
-        task = PipelineTask(
-            pipeline,
-            params=PipelineParams(
-                allow_interruptions=True,
-                enable_metrics=True,
-                enable_usage_metrics=True,
-                report_only_initial_ttfb=True,
-            ),
-        )
-
-        @transport.event_handler("on_first_participant_joined")
-        async def on_first_participant_joined(transport, participant):
-            await transport.capture_participant_transcription(participant["id"])
-            # Kick off the conversation.
-            messages.append({"role": "system", "content": "Please introduce yourself to the user."})
-            await task.queue_frames([context_aggregator.user().get_context_frame()])
-
-        @transport.event_handler("on_participant_left")
-        async def on_participant_left(transport, participant, reason):
-            await task.cancel()
-
-        runner = PipelineRunner()
-
-        await runner.run(task)
-
-
-if __name__ == "__main__":
-    asyncio.run(main())
--- a/examples/foundational/07d-interruptible-elevenlabs.py
+++ b/examples/foundational/07d-interruptible-elevenlabs.py
@@ -74,7 +74,7 @@ async def main():

        task = PipelineTask(
            pipeline,
-            params=PipelineParams(
+            PipelineParams(
                allow_interruptions=True,
                enable_metrics=True,
                enable_usage_metrics=True,
--- a/examples/foundational/07e-interruptible-playht-http.py
+++ b/examples/foundational/07e-interruptible-playht-http.py
@@ -75,7 +75,7 @@ async def main():

        task = PipelineTask(
            pipeline,
-            params=PipelineParams(
+            PipelineParams(
                allow_interruptions=True,
                enable_metrics=True,
                enable_usage_metrics=True,
--- a/examples/foundational/07e-interruptible-playht.py
+++ b/examples/foundational/07e-interruptible-playht.py
@@ -77,7 +77,7 @@ async def main():

        task = PipelineTask(
            pipeline,
-            params=PipelineParams(
+            PipelineParams(
                allow_interruptions=True,
                enable_metrics=True,
                enable_usage_metrics=True,
--- a/examples/foundational/07f-interruptible-azure.py
+++ b/examples/foundational/07f-interruptible-azure.py
@@ -83,7 +83,7 @@ async def main():

        task = PipelineTask(
            pipeline,
-            params=PipelineParams(
+            PipelineParams(
                allow_interruptions=True,
                enable_metrics=True,
                enable_usage_metrics=True,
--- a/examples/foundational/07g-interruptible-openai.py
+++ b/examples/foundational/07g-interruptible-openai.py
@@ -51,20 +51,16 @@ async def main():
        #     api_key="gsk_***",
        #     model="whisper-large-v3",
        # )
-        stt = OpenAISTTService(
-            api_key=os.getenv("OPENAI_API_KEY"),
-            model="gpt-4o-transcribe-latest",
-            prompt="Expect words related to dogs, such as breed names.",
-        )
+        stt = OpenAISTTService(api_key=os.getenv("OPENAI_API_KEY"), model="whisper-1")

-        tts = OpenAITTSService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o-mini-tts-latest")
+        tts = OpenAITTSService(api_key=os.getenv("OPENAI_API_KEY"), voice="alloy")

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

        messages = [
            {
                "role": "system",
-                "content": "You are very knowledgable about dogs. 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.",
+                "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.",
            },
        ]

@@ -85,7 +81,7 @@ async def main():

        task = PipelineTask(
            pipeline,
-            params=PipelineParams(
+            PipelineParams(
                allow_interruptions=True,
                enable_metrics=True,
                enable_usage_metrics=True,
--- a/examples/foundational/07h-interruptible-openpipe.py
+++ b/examples/foundational/07h-interruptible-openpipe.py
@@ -47,7 +47,7 @@ async def main():

        tts = CartesiaTTSService(
            api_key=os.getenv("CARTESIA_API_KEY"),
-            voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
+            voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22",  # British Lady
        )

        timestamp = int(time.time())
@@ -81,7 +81,7 @@ async def main():

        task = PipelineTask(
            pipeline,
-            params=PipelineParams(
+            PipelineParams(
                allow_interruptions=True,
                enable_metrics=True,
                enable_usage_metrics=True,
--- a/examples/foundational/07i-interruptible-xtts.py
+++ b/examples/foundational/07i-interruptible-xtts.py
@@ -75,7 +75,7 @@ async def main():

        task = PipelineTask(
            pipeline,
-            params=PipelineParams(
+            PipelineParams(
                allow_interruptions=True,
                enable_metrics=True,
                enable_usage_metrics=True,
--- a/examples/foundational/07j-interruptible-gladia.py
+++ b/examples/foundational/07j-interruptible-gladia.py
@@ -51,7 +51,7 @@ async def main():

        tts = CartesiaTTSService(
            api_key=os.getenv("CARTESIA_API_KEY"),
-            voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
+            voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22",  # British Lady
        )

        llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
@@ -80,7 +80,7 @@ async def main():

        task = PipelineTask(
            pipeline,
-            params=PipelineParams(
+            PipelineParams(
                allow_interruptions=True,
                enable_metrics=True,
                enable_usage_metrics=True,
--- a/examples/foundational/07k-interruptible-lmnt.py
+++ b/examples/foundational/07k-interruptible-lmnt.py
@@ -71,7 +71,7 @@ async def main():

        task = PipelineTask(
            pipeline,
-            params=PipelineParams(
+            PipelineParams(
                allow_interruptions=True,
                enable_metrics=True,
                enable_usage_metrics=True,
--- a/examples/foundational/07l-interruptible-together.py
+++ b/examples/foundational/07l-interruptible-together.py
@@ -46,7 +46,7 @@ async def main():

        tts = CartesiaTTSService(
            api_key=os.getenv("CARTESIA_API_KEY"),
-            voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
+            voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22",  # British Lady
        )

        llm = TogetherLLMService(
@@ -88,7 +88,7 @@ async def main():

        task = PipelineTask(
            pipeline,
-            params=PipelineParams(
+            PipelineParams(
                allow_interruptions=True,
                enable_metrics=True,
                enable_usage_metrics=True,
--- a/examples/foundational/07m-interruptible-polly.py
+++ b/examples/foundational/07m-interruptible-polly.py
@@ -81,7 +81,7 @@ async def main():

        task = PipelineTask(
            pipeline,
-            params=PipelineParams(
+            PipelineParams(
                allow_interruptions=True,
                enable_metrics=True,
                enable_usage_metrics=True,
--- a/examples/foundational/07n-interruptible-google.py
+++ b/examples/foundational/07n-interruptible-google.py
@@ -79,7 +79,7 @@ async def main():

        task = PipelineTask(
            pipeline,
-            params=PipelineParams(
+            PipelineParams(
                allow_interruptions=True,
                enable_metrics=True,
                enable_usage_metrics=True,
--- a/examples/foundational/07o-interruptible-assemblyai.py
+++ b/examples/foundational/07o-interruptible-assemblyai.py
@@ -51,7 +51,7 @@ async def main():

        tts = CartesiaTTSService(
            api_key=os.getenv("CARTESIA_API_KEY"),
-            voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
+            voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22",  # British Lady
        )

        llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
@@ -80,7 +80,7 @@ async def main():

        task = PipelineTask(
            pipeline,
-            params=PipelineParams(
+            PipelineParams(
                allow_interruptions=True,
                enable_metrics=True,
                enable_usage_metrics=True,
--- a/examples/foundational/07p-interruptible-krisp.py
+++ b/examples/foundational/07p-interruptible-krisp.py
@@ -76,7 +76,7 @@ async def main():

        task = PipelineTask(
            pipeline,
-            params=PipelineParams(
+            PipelineParams(
                allow_interruptions=True,
                enable_metrics=True,
                enable_usage_metrics=True,
--- a/examples/foundational/07q-interruptible-rime-http.py
+++ b/examples/foundational/07q-interruptible-rime-http.py
@@ -1,103 +0,0 @@
-#
-# Copyright (c) 2024–2025, Daily
-#
-# SPDX-License-Identifier: BSD 2-Clause License
-#
-
-import asyncio
-import os
-import sys
-
-import aiohttp
-from dotenv import load_dotenv
-from loguru import logger
-from runner import configure
-
-from pipecat.audio.vad.silero import SileroVADAnalyzer
-from pipecat.pipeline.pipeline import Pipeline
-from pipecat.pipeline.runner import PipelineRunner
-from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
-from pipecat.services.openai import OpenAILLMService
-from pipecat.services.rime import RimeHttpTTSService
-from pipecat.transports.services.daily import DailyParams, DailyTransport
-
-load_dotenv(override=True)
-
-logger.remove(0)
-logger.add(sys.stderr, level="DEBUG")
-
-
-async def main():
-    async with aiohttp.ClientSession() as session:
-        (room_url, token) = await configure(session)
-
-        transport = DailyTransport(
-            room_url,
-            token,
-            "Respond bot",
-            DailyParams(
-                audio_out_enabled=True,
-                transcription_enabled=True,
-                vad_enabled=True,
-                vad_analyzer=SileroVADAnalyzer(),
-            ),
-        )
-
-        tts = RimeHttpTTSService(
-            api_key=os.getenv("RIME_API_KEY", ""),
-            voice_id="rex",
-            aiohttp_session=session,
-        )
-
-        llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
-
-        messages = [
-            {
-                "role": "system",
-                "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
-            },
-        ]
-
-        context = OpenAILLMContext(messages)
-        context_aggregator = llm.create_context_aggregator(context)
-
-        pipeline = Pipeline(
-            [
-                transport.input(),  # Transport user input
-                context_aggregator.user(),  # User responses
-                llm,  # LLM
-                tts,  # TTS
-                transport.output(),  # Transport bot output
-                context_aggregator.assistant(),  # Assistant spoken responses
-            ]
-        )
-
-        task = PipelineTask(
-            pipeline,
-            params=PipelineParams(
-                allow_interruptions=True,
-                enable_metrics=True,
-                enable_usage_metrics=True,
-                report_only_initial_ttfb=True,
-            ),
-        )
-
-        @transport.event_handler("on_first_participant_joined")
-        async def on_first_participant_joined(transport, participant):
-            await transport.capture_participant_transcription(participant["id"])
-            # Kick off the conversation.
-            messages.append({"role": "system", "content": "Please introduce yourself to the user."})
-            await task.queue_frames([context_aggregator.user().get_context_frame()])
-
-        @transport.event_handler("on_participant_left")
-        async def on_participant_left(transport, participant, reason):
-            await task.cancel()
-
-        runner = PipelineRunner()
-
-        await runner.run(task)
-
-
-if __name__ == "__main__":
-    asyncio.run(main())
--- a/examples/foundational/07q-interruptible-rime.py
+++ b/examples/foundational/07q-interruptible-rime.py
@@ -74,7 +74,7 @@ async def main():

        task = PipelineTask(
            pipeline,
-            params=PipelineParams(
+            PipelineParams(
                allow_interruptions=True,
                enable_metrics=True,
                enable_usage_metrics=True,
--- a/examples/foundational/07r-interruptible-riva-nim.py
+++ b/examples/foundational/07r-interruptible-riva-nim.py
@@ -74,7 +74,7 @@ async def main():
            ]
        )

-        task = PipelineTask(pipeline, params=PipelineParams(allow_interruptions=True))
+        task = PipelineTask(pipeline, PipelineParams(allow_interruptions=True))

        @transport.event_handler("on_first_participant_joined")
        async def on_first_participant_joined(transport, participant):
--- a/examples/foundational/07s-interruptible-google-audio-in.py
+++ b/examples/foundational/07s-interruptible-google-audio-in.py
@@ -213,7 +213,7 @@ async def main():

        tts = CartesiaTTSService(
            api_key=os.getenv("CARTESIA_API_KEY"),
-            voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
+            voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22",  # British Lady
        )

        llm = GoogleLLMService(api_key=os.getenv("GOOGLE_API_KEY"), model="gemini-2.0-flash-001")
@@ -251,7 +251,7 @@ async def main():

        task = PipelineTask(
            pipeline,
-            params=PipelineParams(
+            PipelineParams(
                allow_interruptions=True,
                enable_metrics=True,
                enable_usage_metrics=True,
--- a/examples/foundational/07t-interruptible-fish.py
+++ b/examples/foundational/07t-interruptible-fish.py
@@ -74,7 +74,7 @@ async def main():

        task = PipelineTask(
            pipeline,
-            params=PipelineParams(
+            PipelineParams(
                allow_interruptions=True,
                enable_metrics=True,
                enable_usage_metrics=True,
--- a/examples/foundational/07u-interruptible-ultravox.py
+++ b/examples/foundational/07u-interruptible-ultravox.py
@@ -1,91 +0,0 @@
-#
-# Copyright (c) 2024–2025, Daily
-#
-# SPDX-License-Identifier: BSD 2-Clause License
-#
-
-import asyncio
-import os
-import sys
-
-import aiohttp
-from dotenv import load_dotenv
-from loguru import logger
-from runner import configure
-
-from pipecat.audio.vad.silero import SileroVADAnalyzer
-from pipecat.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.services.cartesia import CartesiaTTSService
-from pipecat.services.ultravox import UltravoxSTTService
-from pipecat.transports.services.daily import DailyParams, DailyTransport
-
-load_dotenv(override=True)
-
-# NOTE: This example requires GPU resources to run efficiently.
-# The Ultravox model is compute-intensive and performs best with GPU acceleration.
-# This can be deployed on cloud GPU providers like Cerebrium.ai for optimal performance.
-
-logger.remove(0)
-logger.add(sys.stderr, level="DEBUG")
-
-# Want to initialize the ultravox processor since it takes time to load the model and dont
-# want to load it every time the pipeline is run
-ultravox_processor = UltravoxSTTService(
-    model_size="fixie-ai/ultravox-v0_4_1-llama-3_1-8b",
-    hf_token=os.getenv("HF_TOKEN"),
-)
-
-
-async def main():
-    async with aiohttp.ClientSession() as session:
-        (room_url, token) = await configure(session)
-
-        transport = DailyTransport(
-            room_url,
-            token,
-            "Respond bot",
-            DailyParams(
-                audio_out_enabled=True,
-                transcription_enabled=False,
-                vad_enabled=True,
-                vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
-                vad_audio_passthrough=True,
-            ),
-        )
-
-        tts = CartesiaTTSService(
-            api_key=os.environ.get("CARTESIA_API_KEY"),
-            voice_id="97f4b8fb-f2fe-444b-bb9a-c109783a857a",
-        )
-
-        pipeline = Pipeline(
-            [
-                transport.input(),  # Transport user input
-                ultravox_processor,
-                tts,  # TTS
-                transport.output(),  # Transport bot output
-            ]
-        )
-
-        task = PipelineTask(
-            pipeline,
-            params=PipelineParams(
-                allow_interruptions=True,
-                enable_metrics=True,
-            ),
-        )
-
-        @transport.event_handler("on_participant_left")
-        async def on_participant_left(transport, participant, reason):
-            await task.cancel()
-
-        runner = PipelineRunner()
-
-        await runner.run(task)
-
-
-if __name__ == "__main__":
-    asyncio.run(main())
--- a/examples/foundational/07v-interruptible-neuphonic-http.py
+++ b/examples/foundational/07v-interruptible-neuphonic-http.py
@@ -1,102 +0,0 @@
-#
-# Copyright (c) 2024–2025, Daily
-#
-# SPDX-License-Identifier: BSD 2-Clause License
-#
-
-import asyncio
-import os
-import sys
-
-import aiohttp
-from dotenv import load_dotenv
-from loguru import logger
-from runner import configure
-
-from pipecat.audio.vad.silero import SileroVADAnalyzer
-from pipecat.pipeline.pipeline import Pipeline
-from pipecat.pipeline.runner import PipelineRunner
-from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
-from pipecat.services.neuphonic import NeuphonicHttpTTSService
-from pipecat.services.openai import OpenAILLMService
-from pipecat.transports.services.daily import DailyParams, DailyTransport
-
-load_dotenv(override=True)
-
-logger.remove(0)
-logger.add(sys.stderr, level="DEBUG")
-
-
-async def main():
-    async with aiohttp.ClientSession() as session:
-        (room_url, token) = await configure(session)
-
-        transport = DailyTransport(
-            room_url,
-            token,
-            "Respond bot",
-            DailyParams(
-                audio_out_enabled=True,
-                transcription_enabled=True,
-                vad_enabled=True,
-                vad_analyzer=SileroVADAnalyzer(),
-            ),
-        )
-
-        tts = NeuphonicHttpTTSService(
-            api_key=os.getenv("NEUPHONIC_API_KEY"),
-            voice_id="fc854436-2dac-4d21-aa69-ae17b54e98eb",  # Emily
-        )
-
-        llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
-
-        messages = [
-            {
-                "role": "system",
-                "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
-            },
-        ]
-
-        context = OpenAILLMContext(messages)
-        context_aggregator = llm.create_context_aggregator(context)
-
-        pipeline = Pipeline(
-            [
-                transport.input(),  # Transport user input
-                context_aggregator.user(),  # User responses
-                llm,  # LLM
-                tts,  # TTS
-                transport.output(),  # Transport bot output
-                context_aggregator.assistant(),  # Assistant spoken responses
-            ]
-        )
-
-        task = PipelineTask(
-            pipeline,
-            params=PipelineParams(
-                allow_interruptions=True,
-                enable_metrics=True,
-                enable_usage_metrics=True,
-                report_only_initial_ttfb=True,
-            ),
-        )
-
-        @transport.event_handler("on_first_participant_joined")
-        async def on_first_participant_joined(transport, participant):
-            await transport.capture_participant_transcription(participant["id"])
-            # Kick off the conversation.
-            messages.append({"role": "system", "content": "Please introduce yourself to the user."})
-            await task.queue_frames([context_aggregator.user().get_context_frame()])
-
-        @transport.event_handler("on_participant_left")
-        async def on_participant_left(transport, participant, reason):
-            await task.cancel()
-
-        runner = PipelineRunner()
-
-        await runner.run(task)
-
-
-if __name__ == "__main__":
-    asyncio.run(main())
--- a/examples/foundational/07v-interruptible-neuphonic.py
+++ b/examples/foundational/07v-interruptible-neuphonic.py
@@ -1,102 +0,0 @@
-#
-# Copyright (c) 2024–2025, Daily
-#
-# SPDX-License-Identifier: BSD 2-Clause License
-#
-
-import asyncio
-import os
-import sys
-
-import aiohttp
-from dotenv import load_dotenv
-from loguru import logger
-from runner import configure
-
-from pipecat.audio.vad.silero import SileroVADAnalyzer
-from pipecat.pipeline.pipeline import Pipeline
-from pipecat.pipeline.runner import PipelineRunner
-from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
-from pipecat.services.neuphonic import NeuphonicTTSService
-from pipecat.services.openai import OpenAILLMService
-from pipecat.transports.services.daily import DailyParams, DailyTransport
-
-load_dotenv(override=True)
-
-logger.remove(0)
-logger.add(sys.stderr, level="DEBUG")
-
-
-async def main():
-    async with aiohttp.ClientSession() as session:
-        (room_url, token) = await configure(session)
-
-        transport = DailyTransport(
-            room_url,
-            token,
-            "Respond bot",
-            DailyParams(
-                audio_out_enabled=True,
-                transcription_enabled=True,
-                vad_enabled=True,
-                vad_analyzer=SileroVADAnalyzer(),
-            ),
-        )
-
-        tts = NeuphonicTTSService(
-            api_key=os.getenv("NEUPHONIC_API_KEY"),
-            voice_id="fc854436-2dac-4d21-aa69-ae17b54e98eb",  # Emily
-        )
-
-        llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
-
-        messages = [
-            {
-                "role": "system",
-                "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
-            },
-        ]
-
-        context = OpenAILLMContext(messages)
-        context_aggregator = llm.create_context_aggregator(context)
-
-        pipeline = Pipeline(
-            [
-                transport.input(),  # Transport user input
-                context_aggregator.user(),  # User responses
-                llm,  # LLM
-                tts,  # TTS
-                transport.output(),  # Transport bot output
-                context_aggregator.assistant(),  # Assistant spoken responses
-            ]
-        )
-
-        task = PipelineTask(
-            pipeline,
-            params=PipelineParams(
-                allow_interruptions=True,
-                enable_metrics=True,
-                enable_usage_metrics=True,
-                report_only_initial_ttfb=True,
-            ),
-        )
-
-        @transport.event_handler("on_first_participant_joined")
-        async def on_first_participant_joined(transport, participant):
-            await transport.capture_participant_transcription(participant["id"])
-            # Kick off the conversation.
-            messages.append({"role": "system", "content": "Please introduce yourself to the user."})
-            await task.queue_frames([context_aggregator.user().get_context_frame()])
-
-        @transport.event_handler("on_participant_left")
-        async def on_participant_left(transport, participant, reason):
-            await task.cancel()
-
-        runner = PipelineRunner()
-
-        await runner.run(task)
-
-
-if __name__ == "__main__":
-    asyncio.run(main())
--- a/examples/foundational/07w-interruptible-fal.py
+++ b/examples/foundational/07w-interruptible-fal.py
@@ -1,110 +0,0 @@
-#
-# Copyright (c) 2024–2025, Daily
-#
-# SPDX-License-Identifier: BSD 2-Clause License
-#
-
-import asyncio
-import os
-import sys
-
-import aiohttp
-from dotenv import load_dotenv
-from loguru import logger
-from runner import configure
-
-from pipecat.audio.vad.silero import SileroVADAnalyzer
-from pipecat.pipeline.pipeline import Pipeline
-from pipecat.pipeline.runner import PipelineRunner
-from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
-from pipecat.services.cartesia import CartesiaTTSService
-from pipecat.services.fal import FalSTTService
-from pipecat.services.gladia import GladiaSTTService
-from pipecat.services.openai import OpenAILLMService
-from pipecat.transports.services.daily import DailyParams, DailyTransport
-
-load_dotenv(override=True)
-
-logger.remove(0)
-logger.add(sys.stderr, level="DEBUG")
-
-
-async def main():
-    async with aiohttp.ClientSession() as session:
-        (room_url, token) = await configure(session)
-
-        transport = DailyTransport(
-            room_url,
-            token,
-            "Respond bot",
-            DailyParams(
-                audio_out_enabled=True,
-                vad_enabled=True,
-                vad_analyzer=SileroVADAnalyzer(),
-                vad_audio_passthrough=True,
-            ),
-        )
-
-        stt = FalSTTService(
-            api_key=os.getenv("FAL_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"), model="gpt-4o")
-
-        messages = [
-            {
-                "role": "system",
-                "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
-            },
-        ]
-
-        context = OpenAILLMContext(messages)
-        context_aggregator = llm.create_context_aggregator(context)
-
-        pipeline = Pipeline(
-            [
-                transport.input(),  # Transport user input
-                stt,  # STT
-                context_aggregator.user(),  # User responses
-                llm,  # LLM
-                tts,  # TTS
-                transport.output(),  # Transport bot output
-                context_aggregator.assistant(),  # Assistant spoken responses
-            ]
-        )
-
-        task = PipelineTask(
-            pipeline,
-            params=PipelineParams(
-                allow_interruptions=True,
-                enable_metrics=True,
-                enable_usage_metrics=True,
-                report_only_initial_ttfb=True,
-            ),
-        )
-
-        @transport.event_handler("on_first_participant_joined")
-        async def on_first_participant_joined(transport, participant):
-            await transport.capture_participant_transcription(participant["id"])
-            # Kick off the conversation.
-            messages.append({"role": "system", "content": "Please introduce yourself to the user."})
-            await task.queue_frames([context_aggregator.user().get_context_frame()])
-
-        # Register an event handler to exit the application when the user leaves.
-        @transport.event_handler("on_participant_left")
-        async def on_participant_left(transport, participant, reason):
-            await task.cancel()
-
-        runner = PipelineRunner()
-
-        await runner.run(task)
-
-
-if __name__ == "__main__":
-    asyncio.run(main())
--- a/examples/foundational/07x-interruptible-local.py
+++ b/examples/foundational/07x-interruptible-local.py
@@ -1,91 +0,0 @@
-#
-# Copyright (c) 2024–2025, Daily
-#
-# SPDX-License-Identifier: BSD 2-Clause License
-#
-
-import asyncio
-import os
-import sys
-
-from dotenv import load_dotenv
-from loguru import logger
-
-from pipecat.audio.vad.silero import SileroVADAnalyzer
-from pipecat.pipeline.pipeline import Pipeline
-from pipecat.pipeline.runner import PipelineRunner
-from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
-from pipecat.services.cartesia import CartesiaTTSService
-from pipecat.services.deepgram import DeepgramSTTService
-from pipecat.services.openai import OpenAILLMService
-from pipecat.transports.local.audio import LocalAudioTransport, LocalAudioTransportParams
-
-load_dotenv(override=True)
-
-logger.remove(0)
-logger.add(sys.stderr, level="DEBUG")
-
-
-async def main():
-    transport = LocalAudioTransport(
-        LocalAudioTransportParams(
-            audio_in_enabled=True,
-            audio_out_enabled=True,
-            vad_enabled=True,
-            vad_analyzer=SileroVADAnalyzer(),
-            vad_audio_passthrough=True,
-        )
-    )
-
-    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"), model="gpt-4o")
-
-    messages = [
-        {
-            "role": "system",
-            "content": "You are a helpful LLM. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
-        },
-    ]
-
-    context = OpenAILLMContext(messages)
-    context_aggregator = llm.create_context_aggregator(context)
-
-    pipeline = Pipeline(
-        [
-            transport.input(),  # Transport user input
-            stt,
-            context_aggregator.user(),  # User responses
-            llm,  # LLM
-            tts,  # TTS
-            transport.output(),  # Transport bot output
-            context_aggregator.assistant(),  # Assistant spoken responses
-        ]
-    )
-
-    task = PipelineTask(
-        pipeline,
-        params=PipelineParams(
-            allow_interruptions=True,
-            enable_metrics=True,
-            enable_usage_metrics=True,
-            report_only_initial_ttfb=True,
-        ),
-    )
-
-    messages.append({"role": "system", "content": "Please introduce yourself to the user."})
-    await task.queue_frames([context_aggregator.user().get_context_frame()])
-
-    runner = PipelineRunner()
-
-    await runner.run(task)
-
-
-if __name__ == "__main__":
-    asyncio.run(main())
--- a/examples/foundational/09-mirror.py
+++ b/examples/foundational/09-mirror.py
@@ -78,11 +78,7 @@ async def main():
        runner = PipelineRunner()

        task = PipelineTask(
-            pipeline,
-            params=PipelineParams(
-                audio_in_sample_rate=24000,
-                audio_out_sample_rate=24000,
-            ),
+            pipeline, PipelineParams(audio_in_sample_rate=24000, audio_out_sample_rate=24000)
        )

        await runner.run(task)
--- a/examples/foundational/09a-local-mirror.py
+++ b/examples/foundational/09a-local-mirror.py
@@ -24,7 +24,8 @@ from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
 from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
-from pipecat.transports.local.tk import TkLocalTransport, TkTransportParams
+from pipecat.transports.base_transport import TransportParams
+from pipecat.transports.local.tk import TkLocalTransport
 from pipecat.transports.services.daily import DailyParams, DailyTransport

 load_dotenv(override=True)
@@ -66,7 +67,7 @@ async def main():

        tk_transport = TkLocalTransport(
            tk_root,
-            TkTransportParams(
+            TransportParams(
                audio_out_enabled=True,
                camera_out_enabled=True,
                camera_out_is_live=True,
@@ -82,11 +83,7 @@ async def main():
        pipeline = Pipeline([daily_transport.input(), MirrorProcessor(), tk_transport.output()])

        task = PipelineTask(
-            pipeline,
-            params=PipelineParams(
-                audio_in_sample_rate=24000,
-                audio_out_sample_rate=24000,
-            ),
+            pipeline, PipelineParams(audio_in_sample_rate=24000, audio_out_sample_rate=24000)
        )

        async def run_tk():
--- a/examples/foundational/10-wake-phrase.py
+++ b/examples/foundational/10-wake-phrase.py
@@ -47,7 +47,7 @@ async def main():

        tts = CartesiaTTSService(
            api_key=os.getenv("CARTESIA_API_KEY"),
-            voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
+            voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22",  # British Lady
        )

        llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
@@ -76,7 +76,7 @@ async def main():
            ]
        )

-        task = PipelineTask(pipeline, params=PipelineParams(allow_interruptions=True))
+        task = PipelineTask(pipeline, PipelineParams(allow_interruptions=True))

        @transport.event_handler("on_first_participant_joined")
        async def on_first_participant_joined(transport, participant):
--- a/examples/foundational/11-sound-effects.py
+++ b/examples/foundational/11-sound-effects.py
@@ -100,7 +100,7 @@ async def main():

        tts = CartesiaTTSService(
            api_key=os.getenv("CARTESIA_API_KEY"),
-            voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
+            voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22",  # British Lady
        )

        messages = [
--- a/examples/foundational/12-describe-video.py
+++ b/examples/foundational/12-describe-video.py
@@ -77,7 +77,7 @@ async def main():

        tts = CartesiaTTSService(
            api_key=os.getenv("CARTESIA_API_KEY"),
-            voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
+            voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22",  # British Lady
        )

        @transport.event_handler("on_first_participant_joined")
--- a/examples/foundational/12a-describe-video-gemini-flash.py
+++ b/examples/foundational/12a-describe-video-gemini-flash.py
@@ -77,7 +77,7 @@ async def main():

        tts = CartesiaTTSService(
            api_key=os.getenv("CARTESIA_API_KEY"),
-            voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
+            voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22",  # British Lady
        )

        @transport.event_handler("on_first_participant_joined")
--- a/examples/foundational/12b-describe-video-gpt-4o.py
+++ b/examples/foundational/12b-describe-video-gpt-4o.py
@@ -76,7 +76,7 @@ async def main():

        tts = CartesiaTTSService(
            api_key=os.getenv("CARTESIA_API_KEY"),
-            voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
+            voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22",  # British Lady
        )

        @transport.event_handler("on_first_participant_joined")
--- a/examples/foundational/12c-describe-video-anthropic.py
+++ b/examples/foundational/12c-describe-video-anthropic.py
@@ -76,7 +76,7 @@ async def main():

        tts = CartesiaTTSService(
            api_key=os.getenv("CARTESIA_API_KEY"),
-            voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
+            voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22",  # British Lady
        )

        @transport.event_handler("on_first_participant_joined")
--- a/examples/foundational/14-function-calling.py
+++ b/examples/foundational/14-function-calling.py
@@ -11,10 +11,9 @@ import sys
 import aiohttp
 from dotenv import load_dotenv
 from loguru import logger
+from openai.types.chat import ChatCompletionToolParam
 from runner import configure

-from pipecat.adapters.schemas.function_schema import FunctionSchema
-from pipecat.adapters.schemas.tools_schema import ToolsSchema
 from pipecat.audio.vad.silero import SileroVADAnalyzer
 from pipecat.frames.frames import TTSSpeakFrame
 from pipecat.pipeline.pipeline import Pipeline
@@ -30,9 +29,22 @@ logger.remove(0)
 logger.add(sys.stderr, level="DEBUG")


-async def fetch_weather_from_api(function_name, tool_call_id, args, llm, context, result_callback):
-    await llm.push_frame(TTSSpeakFrame("Let me check on that."))
-    await result_callback({"conditions": "nice", "temperature": "75"})
+async def start_fetch_products(function_name, llm, context):
+    """Push a frame to the LLM; this is handy when the LLM response might take a while."""
+    await llm.push_frame(TTSSpeakFrame("I'll take a look!"))
+    logger.debug(f"Starting fetch_products_from_api with function_name: {function_name}")
+
+
+async def fetch_products_from_api(function_name, tool_call_id, args, llm, context, result_callback):
+    logger.debug(f"args for fetch_products_from_api: {args}")
+    # In the real world you'd fetch the products from an API. We're hardcoding them here.
+    product = args["product"]
+    if product == "vacuums":
+        await result_callback({"vacuums": ["Dyson V11", "Roomba i7"]})
+    elif product == "tvs":
+        await result_callback({"tvs": ["Samsung 65 inch", "LG 55 inch"]})
+    else:
+        await result_callback({"error": "Unknown product"})


 async def main():
@@ -53,37 +65,38 @@ async def main():

        tts = CartesiaTTSService(
            api_key=os.getenv("CARTESIA_API_KEY"),
-            voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
+            voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22",  # British Lady
        )

        llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
-
-        # You can also register a function_name of None to get all functions
+        # 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(None, fetch_products_from_api, start_callback=start_fetch_products)

-        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",
+        tools = [
+            ChatCompletionToolParam(
+                type="function",
+                function={
+                    "name": "get_products",
+                    "description": "Get the list of products available.",
+                    "parameters": {
+                        "type": "object",
+                        "properties": {
+                            "product": {
+                                "type": "string",
+                                "enum": ["vacuums", "tvs"],
+                                "description": "The type of product to show.",
+                            }
+                        },
+                        "required": ["product"],
+                    },
                },
-                "format": {
-                    "type": "string",
-                    "enum": ["celsius", "fahrenheit"],
-                    "description": "The temperature unit to use. Infer this from the user's location.",
-                },
-            },
-            required=["location", "format"],
-        )
-        tools = ToolsSchema(standard_tools=[weather_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.",
+                "content": "You are a helpful customer service agent named Hailey in a video call. Your goal is to sell vacuums or tvs. 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.",
            },
        ]

@@ -103,7 +116,7 @@ async def main():

        task = PipelineTask(
            pipeline,
-            params=PipelineParams(
+            PipelineParams(
                allow_interruptions=True,
                enable_metrics=True,
                enable_usage_metrics=True,
--- a/examples/foundational/14a-function-calling-anthropic.py
+++ b/examples/foundational/14a-function-calling-anthropic.py
@@ -13,8 +13,6 @@ from dotenv import load_dotenv
 from loguru import logger
 from runner import configure

-from pipecat.adapters.schemas.function_schema import FunctionSchema
-from pipecat.adapters.schemas.tools_schema import ToolsSchema
 from pipecat.audio.vad.silero import SileroVADAnalyzer
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
@@ -53,26 +51,30 @@ async def main():

        tts = CartesiaTTSService(
            api_key=os.getenv("CARTESIA_API_KEY"),
-            voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
+            voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22",  # British Lady
        )

        llm = AnthropicLLMService(
-            api_key=os.getenv("ANTHROPIC_API_KEY"), model="claude-3-7-sonnet-latest"
+            api_key=os.getenv("ANTHROPIC_API_KEY"), model="claude-3-5-sonnet-20240620"
        )
        llm.register_function("get_weather", get_weather)

-        weather_function = FunctionSchema(
-            name="get_weather",
-            description="Get the current weather",
-            properties={
-                "location": {
-                    "type": "string",
-                    "description": "The city and state, e.g. San Francisco, CA",
+        tools = [
+            {
+                "name": "get_weather",
+                "description": "Get the current weather in a given location",
+                "input_schema": {
+                    "type": "object",
+                    "properties": {
+                        "location": {
+                            "type": "string",
+                            "description": "The city and state, e.g. San Francisco, CA",
+                        }
+                    },
+                    "required": ["location"],
                },
-            },
-            required=["location"],
-        )
-        tools = ToolsSchema(standard_tools=[weather_function])
+            }
+        ]

        # todo: test with very short initial user message

@@ -97,13 +99,7 @@ async def main():
            ]
        )

-        task = PipelineTask(
-            pipeline,
-            params=PipelineParams(
-                allow_interruptions=True,
-                enable_metrics=True,
-            ),
-        )
+        task = PipelineTask(pipeline, PipelineParams(allow_interruptions=True, enable_metrics=True))

        @transport.event_handler("on_first_participant_joined")
        async def on_first_participant_joined(transport, participant):
--- a/examples/foundational/14b-function-calling-anthropic-video.py
+++ b/examples/foundational/14b-function-calling-anthropic-video.py
@@ -13,8 +13,6 @@ from dotenv import load_dotenv
 from loguru import logger
 from runner import configure

-from pipecat.adapters.schemas.function_schema import FunctionSchema
-from pipecat.adapters.schemas.tools_schema import ToolsSchema
 from pipecat.audio.vad.silero import SileroVADAnalyzer
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
@@ -39,12 +37,7 @@ async def get_weather(function_name, tool_call_id, arguments, llm, context, resu

 async def get_image(function_name, tool_call_id, arguments, llm, context, result_callback):
    question = arguments["question"]
-    await llm.request_image_frame(
-        user_id=video_participant_id,
-        function_name=function_name,
-        tool_call_id=tool_call_id,
-        text_content=question,
-    )
+    await llm.request_image_frame(user_id=video_participant_id, text_content=question)


 async def main():
@@ -67,40 +60,48 @@ async def main():

        tts = CartesiaTTSService(
            api_key=os.getenv("CARTESIA_API_KEY"),
-            voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
+            voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22",  # British Lady
        )

        llm = AnthropicLLMService(
            api_key=os.getenv("ANTHROPIC_API_KEY"),
-            model="claude-3-7-sonnet-latest",
+            # model="claude-3-5-sonnet-20240620",
+            model="claude-3-5-sonnet-latest",
            enable_prompt_caching_beta=True,
        )
        llm.register_function("get_weather", get_weather)
        llm.register_function("get_image", get_image)

-        weather_function = FunctionSchema(
-            name="get_weather",
-            description="Get the current weather",
-            properties={
-                "location": {
-                    "type": "string",
-                    "description": "The city and state, e.g. San Francisco, CA",
+        tools = [
+            {
+                "name": "get_weather",
+                "description": "Get the current weather in a given location",
+                "input_schema": {
+                    "type": "object",
+                    "properties": {
+                        "location": {
+                            "type": "string",
+                            "description": "The city and state, e.g. San Francisco, CA",
+                        }
+                    },
+                    "required": ["location"],
                },
            },
-            required=["location"],
-        )
-        get_image_function = FunctionSchema(
-            name="get_image",
-            description="Get an image from the video stream.",
-            properties={
-                "question": {
-                    "type": "string",
-                    "description": "The question that the user is asking about the image.",
-                }
+            {
+                "name": "get_image",
+                "description": "Get an image from the video stream.",
+                "input_schema": {
+                    "type": "object",
+                    "properties": {
+                        "question": {
+                            "type": "string",
+                            "description": "The question that the user is asking about the image.",
+                        }
+                    },
+                    "required": ["question"],
+                },
            },
-            required=["question"],
-        )
-        tools = ToolsSchema(standard_tools=[weather_function, get_image_function])
+        ]

        # todo: test with very short initial user message

@@ -152,13 +153,7 @@ If you need to use a tool, simply use the tool. Do not tell the user the tool yo
            ]
        )

-        task = PipelineTask(
-            pipeline,
-            params=PipelineParams(
-                allow_interruptions=True,
-                enable_metrics=True,
-            ),
-        )
+        task = PipelineTask(pipeline, PipelineParams(allow_interruptions=True, enable_metrics=True))

        @transport.event_handler("on_first_participant_joined")
        async def on_first_participant_joined(transport, participant):
--- a/examples/foundational/14c-function-calling-together.py
+++ b/examples/foundational/14c-function-calling-together.py
@@ -11,10 +11,9 @@ import sys
 import aiohttp
 from dotenv import load_dotenv
 from loguru import logger
+from openai.types.chat import ChatCompletionToolParam
 from runner import configure

-from pipecat.adapters.schemas.function_schema import FunctionSchema
-from pipecat.adapters.schemas.tools_schema import ToolsSchema
 from pipecat.audio.vad.silero import SileroVADAnalyzer
 from pipecat.frames.frames import TTSSpeakFrame
 from pipecat.pipeline.pipeline import Pipeline
@@ -31,8 +30,13 @@ logger.remove(0)
 logger.add(sys.stderr, level="DEBUG")


-async def fetch_weather_from_api(function_name, tool_call_id, args, llm, context, result_callback):
+async def start_fetch_weather(function_name, llm, context):
+    """Push a frame to the LLM; this is handy when the LLM response might take a while."""
    await llm.push_frame(TTSSpeakFrame("Let me check on that."))
+    logger.debug(f"Starting fetch_weather_from_api with function_name: {function_name}")
+
+
+async def fetch_weather_from_api(function_name, tool_call_id, args, llm, context, result_callback):
    await result_callback({"conditions": "nice", "temperature": "75"})


@@ -54,34 +58,41 @@ async def main():

        tts = CartesiaTTSService(
            api_key=os.getenv("CARTESIA_API_KEY"),
-            voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
+            voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22",  # British Lady
        )

        llm = TogetherLLMService(
            api_key=os.getenv("TOGETHER_API_KEY"),
            model="meta-llama/Meta-Llama-3.1-70B-Instruct-Turbo",
        )
-        # You can also register a function_name of None to get all functions
+        # 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(None, fetch_weather_from_api, start_callback=start_fetch_weather)

-        weather_function = FunctionSchema(
-            name="get_current_weather",
-            description="Get the current weather",
-            properties={
-                "location": {
-                    "type": "string",
-                    "description": "The city and state, e.g. San Francisco, CA",
+        tools = [
+            ChatCompletionToolParam(
+                type="function",
+                function={
+                    "name": "get_current_weather",
+                    "description": "Get the current weather",
+                    "parameters": {
+                        "type": "object",
+                        "properties": {
+                            "location": {
+                                "type": "string",
+                                "description": "The city and state, e.g. San Francisco, CA",
+                            },
+                            "format": {
+                                "type": "string",
+                                "enum": ["celsius", "fahrenheit"],
+                                "description": "The temperature unit to use. Infer this from the users location.",
+                            },
+                        },
+                        "required": ["location", "format"],
+                    },
                },
-                "format": {
-                    "type": "string",
-                    "enum": ["celsius", "fahrenheit"],
-                    "description": "The temperature unit to use. Infer this from the user's location.",
-                },
-            },
-            required=["location", "format"],
-        )
-        tools = ToolsSchema(standard_tools=[weather_function])
+            )
+        ]
        messages = [
            {
                "role": "system",
--- a/examples/foundational/14d-function-calling-video.py
+++ b/examples/foundational/14d-function-calling-video.py
@@ -11,10 +11,9 @@ import sys
 import aiohttp
 from dotenv import load_dotenv
 from loguru import logger
+from openai.types.chat import ChatCompletionToolParam
 from runner import configure

-from pipecat.adapters.schemas.function_schema import FunctionSchema
-from pipecat.adapters.schemas.tools_schema import ToolsSchema
 from pipecat.audio.vad.silero import SileroVADAnalyzer
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
@@ -39,12 +38,7 @@ async def get_weather(function_name, tool_call_id, arguments, llm, context, resu
 async def get_image(function_name, tool_call_id, arguments, llm, context, result_callback):
    logger.debug(f"!!! IN get_image {video_participant_id}, {arguments}")
    question = arguments["question"]
-    await llm.request_image_frame(
-        user_id=video_participant_id,
-        function_name=function_name,
-        tool_call_id=tool_call_id,
-        text_content=question,
-    )
+    await llm.request_image_frame(user_id=video_participant_id, text_content=question)


 async def main():
@@ -65,41 +59,54 @@ async def main():

        tts = CartesiaTTSService(
            api_key=os.getenv("CARTESIA_API_KEY"),
-            voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
+            voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22",  # British Lady
        )

        llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
        llm.register_function("get_weather", get_weather)
        llm.register_function("get_image", get_image)

-        weather_function = FunctionSchema(
-            name="get_weather",
-            description="Get the current weather",
-            properties={
-                "location": {
-                    "type": "string",
-                    "description": "The city and state, e.g. San Francisco, CA",
+        tools = [
+            ChatCompletionToolParam(
+                type="function",
+                function={
+                    "name": "get_weather",
+                    "description": "Get the current weather",
+                    "parameters": {
+                        "type": "object",
+                        "properties": {
+                            "location": {
+                                "type": "string",
+                                "description": "The city and state, e.g. San Francisco, CA",
+                            },
+                            "format": {
+                                "type": "string",
+                                "enum": ["celsius", "fahrenheit"],
+                                "description": "The temperature unit to use. Infer this from the users location.",
+                            },
+                        },
+                        "required": ["location", "format"],
+                    },
                },
-                "format": {
-                    "type": "string",
-                    "enum": ["celsius", "fahrenheit"],
-                    "description": "The temperature unit to use. Infer this from the user's location.",
+            ),
+            ChatCompletionToolParam(
+                type="function",
+                function={
+                    "name": "get_image",
+                    "description": "Get an image from the video stream.",
+                    "parameters": {
+                        "type": "object",
+                        "properties": {
+                            "question": {
+                                "type": "string",
+                                "description": "The question to ask the AI to generate an image of",
+                            },
+                        },
+                        "required": ["question"],
+                    },
                },
-            },
-            required=["location"],
-        )
-        get_image_function = FunctionSchema(
-            name="get_image",
-            description="Get an image from the video stream.",
-            properties={
-                "question": {
-                    "type": "string",
-                    "description": "The question that the user is asking about the image.",
-                }
-            },
-            required=["question"],
-        )
-        tools = ToolsSchema(standard_tools=[weather_function, get_image_function])
+            ),
+        ]

        system_prompt = """\
 You are a helpful assistant who converses with a user and answers questions. Respond concisely to general questions.
@@ -146,7 +153,7 @@ indicate you should use the get_image tool are:
            await transport.capture_participant_transcription(participant["id"])
            await transport.capture_participant_video(video_participant_id, framerate=0)
            # Kick off the conversation.
-            await task.queue_frames([context_aggregator.user().get_context_frame()])
+            await tts.say("Hi! Ask me about the weather in San Francisco.")

        runner = PipelineRunner()

--- a/examples/foundational/14e-function-calling-gemini.py
+++ b/examples/foundational/14e-function-calling-gemini.py
@@ -13,8 +13,6 @@ from dotenv import load_dotenv
 from loguru import logger
 from runner import configure

-from pipecat.adapters.schemas.function_schema import FunctionSchema
-from pipecat.adapters.schemas.tools_schema import ToolsSchema
 from pipecat.audio.vad.silero import SileroVADAnalyzer
 from pipecat.frames.frames import TTSSpeakFrame
 from pipecat.pipeline.pipeline import Pipeline
@@ -33,8 +31,13 @@ logger.add(sys.stderr, level="DEBUG")
 video_participant_id = None


-async def get_weather(function_name, tool_call_id, arguments, llm, context, result_callback):
+async def start_fetch_weather(function_name, llm, context):
+    """Push a frame to the LLM; this is handy when the LLM response might take a while."""
    await llm.push_frame(TTSSpeakFrame("Let me check on that."))
+    logger.debug(f"Starting fetch_weather_from_api with function_name: {function_name}")
+
+
+async def get_weather(function_name, tool_call_id, arguments, llm, context, result_callback):
    location = arguments["location"]
    await result_callback(f"The weather in {location} is currently 72 degrees and sunny.")

@@ -42,12 +45,7 @@ async def get_weather(function_name, tool_call_id, arguments, llm, context, resu
 async def get_image(function_name, tool_call_id, arguments, llm, context, result_callback):
    logger.debug(f"!!! IN get_image {video_participant_id}, {arguments}")
    question = arguments["question"]
-    await llm.request_image_frame(
-        user_id=video_participant_id,
-        function_name=function_name,
-        tool_call_id=tool_call_id,
-        text_content=question,
-    )
+    await llm.request_image_frame(user_id=video_participant_id, text_content=question)


 async def main():
@@ -68,41 +66,52 @@ async def main():

        tts = CartesiaTTSService(
            api_key=os.getenv("CARTESIA_API_KEY"),
-            voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
+            voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22",  # British Lady
        )

        llm = GoogleLLMService(api_key=os.getenv("GOOGLE_API_KEY"), model="gemini-2.0-flash-001")
-        llm.register_function("get_weather", get_weather)
+        llm.register_function("get_weather", get_weather, start_fetch_weather)
        llm.register_function("get_image", get_image)

-        weather_function = FunctionSchema(
-            name="get_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"],
-        )
-        get_image_function = FunctionSchema(
-            name="get_image",
-            description="Get an image from the video stream.",
-            properties={
-                "question": {
-                    "type": "string",
-                    "description": "The question that the user is asking about the image.",
-                }
-            },
-            required=["question"],
-        )
-        tools = ToolsSchema(standard_tools=[weather_function, get_image_function])
+        tools = [
+            {
+                "function_declarations": [
+                    {
+                        "name": "get_weather",
+                        "description": "Get the current weather",
+                        "parameters": {
+                            "type": "object",
+                            "properties": {
+                                "location": {
+                                    "type": "string",
+                                    "description": "The city and state, e.g. San Francisco, CA",
+                                },
+                                "format": {
+                                    "type": "string",
+                                    "enum": ["celsius", "fahrenheit"],
+                                    "description": "The temperature unit to use. Infer this from the users location.",
+                                },
+                            },
+                            "required": ["location", "format"],
+                        },
+                    },
+                    {
+                        "name": "get_image",
+                        "description": "Get and image from the camera or video stream.",
+                        "parameters": {
+                            "type": "object",
+                            "properties": {
+                                "question": {
+                                    "type": "string",
+                                    "description": "The question to to use when running inference on the acquired image.",
+                                },
+                            },
+                            "required": ["question"],
+                        },
+                    },
+                ]
+            }
+        ]

        system_prompt = """\
 You are a helpful assistant who converses with a user and answers questions. Respond concisely to general questions.
@@ -143,7 +152,7 @@ indicate you should use the get_image tool are:

        task = PipelineTask(
            pipeline,
-            params=PipelineParams(
+            PipelineParams(
                allow_interruptions=True,
                enable_metrics=True,
                enable_usage_metrics=True,
--- a/examples/foundational/14f-function-calling-groq.py
+++ b/examples/foundational/14f-function-calling-groq.py
@@ -11,10 +11,9 @@ import sys
 import aiohttp
 from dotenv import load_dotenv
 from loguru import logger
+from openai.types.chat import ChatCompletionToolParam
 from runner import configure

-from pipecat.adapters.schemas.function_schema import FunctionSchema
-from pipecat.adapters.schemas.tools_schema import ToolsSchema
 from pipecat.audio.vad.silero import SileroVADAnalyzer
 from pipecat.frames.frames import TTSSpeakFrame
 from pipecat.pipeline.pipeline import Pipeline
@@ -31,8 +30,13 @@ logger.remove(0)
 logger.add(sys.stderr, level="DEBUG")


-async def fetch_weather_from_api(function_name, tool_call_id, args, llm, context, result_callback):
+async def start_fetch_weather(function_name, llm, context):
+    """Push a frame to the LLM; this is handy when the LLM response might take a while."""
    await llm.push_frame(TTSSpeakFrame("Let me check on that."))
+    logger.debug(f"Starting fetch_weather_from_api with function_name: {function_name}")
+
+
+async def fetch_weather_from_api(function_name, tool_call_id, args, llm, context, result_callback):
    await result_callback({"conditions": "nice", "temperature": "75"})


@@ -56,31 +60,38 @@ async def main():

        tts = CartesiaTTSService(
            api_key=os.getenv("CARTESIA_API_KEY"),
-            voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
+            voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22",  # British Lady
        )

        llm = GroqLLMService(api_key=os.getenv("GROQ_API_KEY"), model="llama-3.3-70b-versatile")
-        # You can also register a function_name of None to get all functions
+        # 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(None, fetch_weather_from_api, start_callback=start_fetch_weather)

-        weather_function = FunctionSchema(
-            name="get_current_weather",
-            description="Get the current weather",
-            properties={
-                "location": {
-                    "type": "string",
-                    "description": "The city and state, e.g. San Francisco, CA",
+        tools = [
+            ChatCompletionToolParam(
+                type="function",
+                function={
+                    "name": "get_current_weather",
+                    "description": "Get the current weather",
+                    "parameters": {
+                        "type": "object",
+                        "properties": {
+                            "location": {
+                                "type": "string",
+                                "description": "The city and state, e.g. San Francisco, CA",
+                            },
+                            "unit": {
+                                "type": "string",
+                                "enum": ["celsius", "fahrenheit"],
+                                "description": "The temperature unit to use. Infer this from the users location.",
+                            },
+                        },
+                        "required": ["location"],
+                    },
                },
-                "format": {
-                    "type": "string",
-                    "enum": ["celsius", "fahrenheit"],
-                    "description": "The temperature unit to use. Infer this from the user's location.",
-                },
-            },
-            required=["location"],
-        )
-        tools = ToolsSchema(standard_tools=[weather_function])
+            )
+        ]
        messages = [
            {
                "role": "system",
@@ -105,7 +116,7 @@ async def main():

        task = PipelineTask(
            pipeline,
-            params=PipelineParams(
+            PipelineParams(
                allow_interruptions=True,
                enable_metrics=True,
                enable_usage_metrics=True,
--- a/examples/foundational/14g-function-calling-grok.py
+++ b/examples/foundational/14g-function-calling-grok.py
@@ -11,11 +11,11 @@ import sys
 import aiohttp
 from dotenv import load_dotenv
 from loguru import logger
+from openai.types.chat import ChatCompletionToolParam
 from runner import configure

-from pipecat.adapters.schemas.function_schema import FunctionSchema
-from pipecat.adapters.schemas.tools_schema import ToolsSchema
 from pipecat.audio.vad.silero import SileroVADAnalyzer
+from pipecat.frames.frames import TTSSpeakFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
@@ -30,6 +30,12 @@ logger.remove(0)
 logger.add(sys.stderr, level="DEBUG")


+async def start_fetch_weather(function_name, llm, context):
+    """Push a frame to the LLM; this is handy when the LLM response might take a while."""
+    await llm.push_frame(TTSSpeakFrame("Let me check on that."))
+    logger.debug(f"Starting fetch_weather_from_api with function_name: {function_name}")
+
+
 async def fetch_weather_from_api(function_name, tool_call_id, args, llm, context, result_callback):
    await result_callback({"conditions": "nice", "temperature": "75"})

@@ -52,31 +58,38 @@ async def main():

        tts = CartesiaTTSService(
            api_key=os.getenv("CARTESIA_API_KEY"),
-            voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
+            voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22",  # British Lady
        )

        llm = GrokLLMService(api_key=os.getenv("GROK_API_KEY"))
-        # You can also register a function_name of None to get all functions
+        # 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(None, fetch_weather_from_api, start_callback=start_fetch_weather)

-        weather_function = FunctionSchema(
-            name="get_current_weather",
-            description="Get the current weather",
-            properties={
-                "location": {
-                    "type": "string",
-                    "description": "The city and state, e.g. San Francisco, CA",
+        tools = [
+            ChatCompletionToolParam(
+                type="function",
+                function={
+                    "name": "get_current_weather",
+                    "description": "Get the current weather",
+                    "parameters": {
+                        "type": "object",
+                        "properties": {
+                            "location": {
+                                "type": "string",
+                                "description": "The city and state, e.g. San Francisco, CA",
+                            },
+                            "format": {
+                                "type": "string",
+                                "enum": ["celsius", "fahrenheit"],
+                                "description": "The temperature unit to use. Infer this from the users location.",
+                            },
+                        },
+                        "required": ["location", "format"],
+                    },
                },
-                "format": {
-                    "type": "string",
-                    "enum": ["celsius", "fahrenheit"],
-                    "description": "The temperature unit to use. Infer this from the user's location.",
-                },
-            },
-            required=["location", "format"],
-        )
-        tools = ToolsSchema(standard_tools=[weather_function])
+            )
+        ]
        messages = [
            {
                "role": "system",
@@ -100,7 +113,7 @@ async def main():

        task = PipelineTask(
            pipeline,
-            params=PipelineParams(
+            PipelineParams(
                allow_interruptions=True,
                enable_metrics=True,
                enable_usage_metrics=True,
--- a/examples/foundational/14h-function-calling-azure.py
+++ b/examples/foundational/14h-function-calling-azure.py
@@ -11,10 +11,9 @@ import sys
 import aiohttp
 from dotenv import load_dotenv
 from loguru import logger
+from openai.types.chat import ChatCompletionToolParam
 from runner import configure

-from pipecat.adapters.schemas.function_schema import FunctionSchema
-from pipecat.adapters.schemas.tools_schema import ToolsSchema
 from pipecat.audio.vad.silero import SileroVADAnalyzer
 from pipecat.frames.frames import TTSSpeakFrame
 from pipecat.pipeline.pipeline import Pipeline
@@ -31,8 +30,13 @@ logger.remove(0)
 logger.add(sys.stderr, level="DEBUG")


-async def fetch_weather_from_api(function_name, tool_call_id, args, llm, context, result_callback):
+async def start_fetch_weather(function_name, llm, context):
+    """Push a frame to the LLM; this is handy when the LLM response might take a while."""
    await llm.push_frame(TTSSpeakFrame("Let me check on that."))
+    logger.debug(f"Starting fetch_weather_from_api with function_name: {function_name}")
+
+
+async def fetch_weather_from_api(function_name, tool_call_id, args, llm, context, result_callback):
    await result_callback({"conditions": "nice", "temperature": "75"})


@@ -54,7 +58,7 @@ async def main():

        tts = CartesiaTTSService(
            api_key=os.getenv("CARTESIA_API_KEY"),
-            voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
+            voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22",  # British Lady
        )

        llm = AzureLLMService(
@@ -62,27 +66,34 @@ async def main():
            endpoint=os.getenv("AZURE_CHATGPT_ENDPOINT"),
            model=os.getenv("AZURE_CHATGPT_MODEL"),
        )
-        # You can also register a function_name of None to get all functions
+        # 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(None, fetch_weather_from_api, start_callback=start_fetch_weather)

-        weather_function = FunctionSchema(
-            name="get_current_weather",
-            description="Get the current weather",
-            properties={
-                "location": {
-                    "type": "string",
-                    "description": "The city and state, e.g. San Francisco, CA",
+        tools = [
+            ChatCompletionToolParam(
+                type="function",
+                function={
+                    "name": "get_current_weather",
+                    "description": "Get the current weather",
+                    "parameters": {
+                        "type": "object",
+                        "properties": {
+                            "location": {
+                                "type": "string",
+                                "description": "The city and state, e.g. San Francisco, CA",
+                            },
+                            "format": {
+                                "type": "string",
+                                "enum": ["celsius", "fahrenheit"],
+                                "description": "The temperature unit to use. Infer this from the users location.",
+                            },
+                        },
+                        "required": ["location", "format"],
+                    },
                },
-                "format": {
-                    "type": "string",
-                    "enum": ["celsius", "fahrenheit"],
-                    "description": "The temperature unit to use. Infer this from the user's location.",
-                },
-            },
-            required=["location", "format"],
-        )
-        tools = ToolsSchema(standard_tools=[weather_function])
+            )
+        ]
        messages = [
            {
                "role": "system",
@@ -106,7 +117,7 @@ async def main():

        task = PipelineTask(
            pipeline,
-            params=PipelineParams(
+            PipelineParams(
                allow_interruptions=True,
                enable_metrics=True,
                enable_usage_metrics=True,
--- a/examples/foundational/14i-function-calling-fireworks.py
+++ b/examples/foundational/14i-function-calling-fireworks.py
@@ -11,10 +11,9 @@ import sys
 import aiohttp
 from dotenv import load_dotenv
 from loguru import logger
+from openai.types.chat import ChatCompletionToolParam
 from runner import configure

-from pipecat.adapters.schemas.function_schema import FunctionSchema
-from pipecat.adapters.schemas.tools_schema import ToolsSchema
 from pipecat.audio.vad.silero import SileroVADAnalyzer
 from pipecat.frames.frames import TTSSpeakFrame
 from pipecat.pipeline.pipeline import Pipeline
@@ -31,8 +30,13 @@ logger.remove(0)
 logger.add(sys.stderr, level="DEBUG")


-async def fetch_weather_from_api(function_name, tool_call_id, args, llm, context, result_callback):
+async def start_fetch_weather(function_name, llm, context):
+    """Push a frame to the LLM; this is handy when the LLM response might take a while."""
    await llm.push_frame(TTSSpeakFrame("Let me check on that."))
+    logger.debug(f"Starting fetch_weather_from_api with function_name: {function_name}")
+
+
+async def fetch_weather_from_api(function_name, tool_call_id, args, llm, context, result_callback):
    await result_callback({"conditions": "nice", "temperature": "75"})


@@ -54,34 +58,41 @@ async def main():

        tts = CartesiaTTSService(
            api_key=os.getenv("CARTESIA_API_KEY"),
-            voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
+            voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22",  # British Lady
        )

        llm = FireworksLLMService(
            api_key=os.getenv("FIREWORKS_API_KEY"),
-            model="accounts/fireworks/models/llama-v3p1-405b-instruct",
+            model="accounts/fireworks/models/firefunction-v2",
        )
-        # You can also register a function_name of None to get all functions
+        # 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(None, fetch_weather_from_api, start_callback=start_fetch_weather)

-        weather_function = FunctionSchema(
-            name="get_current_weather",
-            description="Get the current weather",
-            properties={
-                "location": {
-                    "type": "string",
-                    "description": "The city and state, e.g. San Francisco, CA",
+        tools = [
+            ChatCompletionToolParam(
+                type="function",
+                function={
+                    "name": "get_current_weather",
+                    "description": "Get the current weather",
+                    "parameters": {
+                        "type": "object",
+                        "properties": {
+                            "location": {
+                                "type": "string",
+                                "description": "The city and state, e.g. San Francisco, CA",
+                            },
+                            "format": {
+                                "type": "string",
+                                "enum": ["celsius", "fahrenheit"],
+                                "description": "The temperature unit to use. Infer this from the users location.",
+                            },
+                        },
+                        "required": ["location", "format"],
+                    },
                },
-                "format": {
-                    "type": "string",
-                    "enum": ["celsius", "fahrenheit"],
-                    "description": "The temperature unit to use. Infer this from the user's location.",
-                },
-            },
-            required=["location", "format"],
-        )
-        tools = ToolsSchema(standard_tools=[weather_function])
+            )
+        ]
        messages = [
            {
                "role": "system",
@@ -105,7 +116,7 @@ async def main():

        task = PipelineTask(
            pipeline,
-            params=PipelineParams(
+            PipelineParams(
                allow_interruptions=True,
                enable_metrics=True,
                enable_usage_metrics=True,
--- a/examples/foundational/14j-function-calling-nim.py
+++ b/examples/foundational/14j-function-calling-nim.py
@@ -11,10 +11,9 @@ import sys
 import aiohttp
 from dotenv import load_dotenv
 from loguru import logger
+from openai.types.chat import ChatCompletionToolParam
 from runner import configure

-from pipecat.adapters.schemas.function_schema import FunctionSchema
-from pipecat.adapters.schemas.tools_schema import ToolsSchema
 from pipecat.audio.vad.silero import SileroVADAnalyzer
 from pipecat.frames.frames import TTSSpeakFrame
 from pipecat.pipeline.pipeline import Pipeline
@@ -31,8 +30,13 @@ logger.remove(0)
 logger.add(sys.stderr, level="DEBUG")


-async def fetch_weather_from_api(function_name, tool_call_id, args, llm, context, result_callback):
+async def start_fetch_weather(function_name, llm, context):
+    """Push a frame to the LLM; this is handy when the LLM response might take a while."""
    await llm.push_frame(TTSSpeakFrame("Let me check on that."))
+    logger.debug(f"Starting fetch_weather_from_api with function_name: {function_name}")
+
+
+async def fetch_weather_from_api(function_name, tool_call_id, args, llm, context, result_callback):
    await result_callback({"conditions": "nice", "temperature": "75"})


@@ -54,34 +58,41 @@ async def main():

        tts = CartesiaTTSService(
            api_key=os.getenv("CARTESIA_API_KEY"),
-            voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
-            # text_filters=[MarkdownTextFilter()],
+            voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22",  # British Lady
+            # text_filter=MarkdownTextFilter(),
        )

        llm = NimLLMService(
            api_key=os.getenv("NVIDIA_API_KEY"), model="meta/llama-3.3-70b-instruct"
        )
-        # You can also register a function_name of None to get all functions
+        # 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(None, fetch_weather_from_api, start_callback=start_fetch_weather)

-        weather_function = FunctionSchema(
-            name="get_current_weather",
-            description="Get the current weather",
-            properties={
-                "location": {
-                    "type": "string",
-                    "description": "The city and state, e.g. San Francisco, CA",
+        tools = [
+            ChatCompletionToolParam(
+                type="function",
+                function={
+                    "name": "get_current_weather",
+                    "description": "Returns the current weather at a location, if one is specified, and defaults to the user's location.",
+                    "parameters": {
+                        "type": "object",
+                        "properties": {
+                            "location": {
+                                "type": "string",
+                                "description": "The location to find the weather of, or if not provided, it's the default location.",
+                            },
+                            "format": {
+                                "type": "string",
+                                "enum": ["celsius", "fahrenheit"],
+                                "description": "Whether to use SI or USCS units (celsius or fahrenheit).",
+                            },
+                        },
+                        "required": ["location", "format"],
+                    },
                },
-                "format": {
-                    "type": "string",
-                    "enum": ["celsius", "fahrenheit"],
-                    "description": "The temperature unit to use. Infer this from the user's location.",
-                },
-            },
-            required=["location", "format"],
-        )
-        tools = ToolsSchema(standard_tools=[weather_function])
+            )
+        ]
        messages = [
            {
                "role": "system",
@@ -105,7 +116,7 @@ async def main():

        task = PipelineTask(
            pipeline,
-            params=PipelineParams(
+            PipelineParams(
                allow_interruptions=True,
                enable_metrics=True,
                enable_usage_metrics=True,
--- a/examples/foundational/14k-function-calling-cerebras.py
+++ b/examples/foundational/14k-function-calling-cerebras.py
@@ -11,10 +11,9 @@ import sys
 import aiohttp
 from dotenv import load_dotenv
 from loguru import logger
+from openai.types.chat import ChatCompletionToolParam
 from runner import configure

-from pipecat.adapters.schemas.function_schema import FunctionSchema
-from pipecat.adapters.schemas.tools_schema import ToolsSchema
 from pipecat.audio.vad.silero import SileroVADAnalyzer
 from pipecat.frames.frames import TTSSpeakFrame
 from pipecat.pipeline.pipeline import Pipeline
@@ -31,8 +30,13 @@ logger.remove(0)
 logger.add(sys.stderr, level="DEBUG")


-async def fetch_weather_from_api(function_name, tool_call_id, args, llm, context, result_callback):
+async def start_fetch_weather(function_name, llm, context):
+    """Push a frame to the LLM; this is handy when the LLM response might take a while."""
    await llm.push_frame(TTSSpeakFrame("Let me check on that."))
+    logger.debug(f"Starting fetch_weather_from_api with function_name: {function_name}")
+
+
+async def fetch_weather_from_api(function_name, tool_call_id, args, llm, context, result_callback):
    await result_callback({"conditions": "nice", "temperature": "75"})


@@ -54,31 +58,38 @@ async def main():

        tts = CartesiaTTSService(
            api_key=os.getenv("CARTESIA_API_KEY"),
-            voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
+            voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22",  # British Lady
        )

        llm = CerebrasLLMService(api_key=os.getenv("CEREBRAS_API_KEY"), model="llama-3.3-70b")
-        # You can also register a function_name of None to get all functions
+        # 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(None, fetch_weather_from_api, start_callback=start_fetch_weather)

-        weather_function = FunctionSchema(
-            name="get_current_weather",
-            description="Get the current weather",
-            properties={
-                "location": {
-                    "type": "string",
-                    "description": "The city and state, e.g. San Francisco, CA",
+        tools = [
+            ChatCompletionToolParam(
+                type="function",
+                function={
+                    "name": "get_current_weather",
+                    "description": "Get the current weather for a specific location. You MUST use this function whenever asked about weather.",
+                    "parameters": {
+                        "type": "object",
+                        "properties": {
+                            "location": {
+                                "type": "string",
+                                "description": "The city and state, e.g. San Francisco, CA",
+                            },
+                            "format": {
+                                "type": "string",
+                                "enum": ["celsius", "fahrenheit"],
+                                "description": "The temperature unit to use. Use fahrenheit for US locations, celsius for others.",
+                            },
+                        },
+                        "required": ["location", "format"],
+                    },
                },
-                "format": {
-                    "type": "string",
-                    "enum": ["celsius", "fahrenheit"],
-                    "description": "The temperature unit to use. Infer this from the user's location.",
-                },
-            },
-            required=["location", "format"],
-        )
-        tools = ToolsSchema(standard_tools=[weather_function])
+            )
+        ]
        messages = [
            {
                "role": "system",
@@ -112,7 +123,7 @@ Start by asking me for my location. Then, use 'get_weather_current' to give me a

        task = PipelineTask(
            pipeline,
-            params=PipelineParams(
+            PipelineParams(
                allow_interruptions=True,
                enable_metrics=True,
                enable_usage_metrics=True,
--- a/examples/foundational/14l-function-calling-deepseek.py
+++ b/examples/foundational/14l-function-calling-deepseek.py
@@ -11,10 +11,9 @@ import sys
 import aiohttp
 from dotenv import load_dotenv
 from loguru import logger
+from openai.types.chat import ChatCompletionToolParam
 from runner import configure

-from pipecat.adapters.schemas.function_schema import FunctionSchema
-from pipecat.adapters.schemas.tools_schema import ToolsSchema
 from pipecat.audio.vad.silero import SileroVADAnalyzer
 from pipecat.frames.frames import TTSSpeakFrame
 from pipecat.pipeline.pipeline import Pipeline
@@ -31,8 +30,13 @@ logger.remove(0)
 logger.add(sys.stderr, level="DEBUG")


-async def fetch_weather_from_api(function_name, tool_call_id, args, llm, context, result_callback):
+async def start_fetch_weather(function_name, llm, context):
+    """Push a frame to the LLM; this is handy when the LLM response might take a while."""
    await llm.push_frame(TTSSpeakFrame("Let me check on that."))
+    logger.debug(f"Starting fetch_weather_from_api with function_name: {function_name}")
+
+
+async def fetch_weather_from_api(function_name, tool_call_id, args, llm, context, result_callback):
    await result_callback({"conditions": "nice", "temperature": "75"})


@@ -54,31 +58,38 @@ async def main():

        tts = CartesiaTTSService(
            api_key=os.getenv("CARTESIA_API_KEY"),
-            voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
+            voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22",  # British Lady
        )

        llm = DeepSeekLLMService(api_key=os.getenv("DEEPSEEK_API_KEY"), model="deepseek-chat")
-        # You can also register a function_name of None to get all functions
+        # 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(None, fetch_weather_from_api, start_callback=start_fetch_weather)

-        weather_function = FunctionSchema(
-            name="get_current_weather",
-            description="Get the current weather",
-            properties={
-                "location": {
-                    "type": "string",
-                    "description": "The city and state, e.g. San Francisco, CA",
+        tools = [
+            ChatCompletionToolParam(
+                type="function",
+                function={
+                    "name": "get_current_weather",
+                    "description": "Get the current weather for a specific location. You MUST use this function whenever asked about weather.",
+                    "parameters": {
+                        "type": "object",
+                        "properties": {
+                            "location": {
+                                "type": "string",
+                                "description": "The city and state, e.g. San Francisco, CA",
+                            },
+                            "format": {
+                                "type": "string",
+                                "enum": ["celsius", "fahrenheit"],
+                                "description": "The temperature unit to use. Use fahrenheit for US locations, celsius for others.",
+                            },
+                        },
+                        "required": ["location", "format"],
+                    },
                },
-                "format": {
-                    "type": "string",
-                    "enum": ["celsius", "fahrenheit"],
-                    "description": "The temperature unit to use. Infer this from the user's location.",
-                },
-            },
-            required=["location", "format"],
-        )
-        tools = ToolsSchema(standard_tools=[weather_function])
+            )
+        ]
        messages = [
            {
                "role": "system",
@@ -112,7 +123,7 @@ Start by asking me for my location. Then, use 'get_weather_current' to give me a

        task = PipelineTask(
            pipeline,
-            params=PipelineParams(
+            PipelineParams(
                allow_interruptions=True,
                enable_metrics=True,
                enable_usage_metrics=True,
--- a/examples/foundational/14m-function-calling-openrouter.py
+++ b/examples/foundational/14m-function-calling-openrouter.py
@@ -11,10 +11,9 @@ import sys
 import aiohttp
 from dotenv import load_dotenv
 from loguru import logger
+from openai.types.chat import ChatCompletionToolParam
 from runner import configure

-from pipecat.adapters.schemas.function_schema import FunctionSchema
-from pipecat.adapters.schemas.tools_schema import ToolsSchema
 from pipecat.audio.vad.silero import SileroVADAnalyzer
 from pipecat.frames.frames import TTSSpeakFrame
 from pipecat.pipeline.pipeline import Pipeline
@@ -31,8 +30,13 @@ logger.remove(0)
 logger.add(sys.stderr, level="DEBUG")


-async def fetch_weather_from_api(function_name, tool_call_id, args, llm, context, result_callback):
+async def start_fetch_weather(function_name, llm, context):
+    """Push a frame to the LLM; this is handy when the LLM response might take a while."""
    await llm.push_frame(TTSSpeakFrame("Let me check on that."))
+    logger.debug(f"Starting fetch_weather_from_api with function_name: {function_name}")
+
+
+async def fetch_weather_from_api(function_name, tool_call_id, args, llm, context, result_callback):
    await result_callback({"conditions": "nice", "temperature": "75"})


@@ -62,27 +66,34 @@ async def main():
        llm = OpenRouterLLMService(
            api_key=os.getenv("OPENROUTER_API_KEY"), model="openai/gpt-4o-2024-11-20"
        )
-        # You can also register a function_name of None to get all functions
+        # 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(None, fetch_weather_from_api, start_callback=start_fetch_weather)

-        weather_function = FunctionSchema(
-            name="get_current_weather",
-            description="Get the current weather",
-            properties={
-                "location": {
-                    "type": "string",
-                    "description": "The city and state, e.g. San Francisco, CA",
+        tools = [
+            ChatCompletionToolParam(
+                type="function",
+                function={
+                    "name": "get_current_weather",
+                    "description": "Get the current weather",
+                    "parameters": {
+                        "type": "object",
+                        "properties": {
+                            "location": {
+                                "type": "string",
+                                "description": "The city and state, e.g. San Francisco, CA",
+                            },
+                            "format": {
+                                "type": "string",
+                                "enum": ["celsius", "fahrenheit"],
+                                "description": "The temperature unit to use. Infer this from the users location.",
+                            },
+                        },
+                        "required": ["location", "format"],
+                    },
                },
-                "format": {
-                    "type": "string",
-                    "enum": ["celsius", "fahrenheit"],
-                    "description": "The temperature unit to use. Infer this from the user's location.",
-                },
-            },
-            required=["location", "format"],
-        )
-        tools = ToolsSchema(standard_tools=[weather_function])
+            )
+        ]
        messages = [
            {
                "role": "system",
@@ -106,7 +117,7 @@ async def main():

        task = PipelineTask(
            pipeline,
-            params=PipelineParams(
+            PipelineParams(
                allow_interruptions=True,
                enable_metrics=True,
                enable_usage_metrics=True,
--- a/examples/foundational/14n-function-calling-perplexity.py
+++ b/examples/foundational/14n-function-calling-perplexity.py
@@ -55,7 +55,7 @@ async def main():

        tts = CartesiaTTSService(
            api_key=os.getenv("CARTESIA_API_KEY"),
-            voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
+            voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22",  # British Lady
        )

        llm = PerplexityLLMService(api_key=os.getenv("PERPLEXITY_API_KEY"), model="sonar")
@@ -83,7 +83,7 @@ async def main():

        task = PipelineTask(
            pipeline,
-            params=PipelineParams(
+            PipelineParams(
                allow_interruptions=True,
                enable_metrics=True,
                enable_usage_metrics=True,
--- a/examples/foundational/14o-function-calling-gemini-openai-format.py
+++ b/examples/foundational/14o-function-calling-gemini-openai-format.py
@@ -1,124 +0,0 @@
-#
-# Copyright (c) 2024–2025, Daily
-#
-# SPDX-License-Identifier: BSD 2-Clause License
-#
-
-import asyncio
-import os
-import sys
-
-import aiohttp
-from dotenv import load_dotenv
-from loguru import logger
-from runner import configure
-
-from pipecat.adapters.schemas.function_schema import FunctionSchema
-from pipecat.adapters.schemas.tools_schema import ToolsSchema
-from pipecat.audio.vad.silero import SileroVADAnalyzer
-from pipecat.frames.frames import TTSSpeakFrame
-from pipecat.pipeline.pipeline import Pipeline
-from pipecat.pipeline.runner import PipelineRunner
-from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.services.elevenlabs import ElevenLabsTTSService
-from pipecat.services.google import GoogleLLMOpenAIBetaService
-from pipecat.services.openai import OpenAILLMContext
-from pipecat.transports.services.daily import DailyParams, DailyTransport
-
-load_dotenv(override=True)
-
-logger.remove(0)
-logger.add(sys.stderr, level="DEBUG")
-
-
-async def fetch_weather_from_api(function_name, tool_call_id, args, llm, context, result_callback):
-    await llm.push_frame(TTSSpeakFrame("Let me check on that."))
-    await result_callback({"conditions": "nice", "temperature": "75"})
-
-
-async def main():
-    async with aiohttp.ClientSession() as session:
-        (room_url, token) = await configure(session)
-
-        transport = DailyTransport(
-            room_url,
-            token,
-            "Respond bot",
-            DailyParams(
-                audio_out_enabled=True,
-                transcription_enabled=True,
-                vad_enabled=True,
-                vad_analyzer=SileroVADAnalyzer(),
-            ),
-        )
-
-        tts = ElevenLabsTTSService(
-            api_key=os.getenv("ELEVENLABS_API_KEY", ""),
-            voice_id=os.getenv("ELEVENLABS_VOICE_ID", ""),
-        )
-
-        llm = GoogleLLMOpenAIBetaService(api_key=os.getenv("GEMINI_API_KEY"))
-        # You can aslo register a function_name of None to get all functions
-        # sent to the same callback with an additional function_name parameter.
-        llm.register_function("get_current_weather", fetch_weather_from_api)
-
-        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"],
-        )
-        tools = ToolsSchema(standard_tools=[weather_function])
-        messages = [
-            {
-                "role": "user",
-                "content": "Start a conversation with 'Hey there' to get the current weather.",
-            },
-        ]
-
-        context = OpenAILLMContext(messages, tools)
-        context_aggregator = llm.create_context_aggregator(context)
-
-        pipeline = Pipeline(
-            [
-                transport.input(),
-                context_aggregator.user(),
-                llm,
-                tts,
-                transport.output(),
-                context_aggregator.assistant(),
-            ]
-        )
-
-        task = PipelineTask(
-            pipeline,
-            params=PipelineParams(
-                allow_interruptions=True,
-                enable_metrics=True,
-                enable_usage_metrics=True,
-            ),
-        )
-
-        @transport.event_handler("on_first_participant_joined")
-        async def on_first_participant_joined(transport, participant):
-            await transport.capture_participant_transcription(participant["id"])
-            # Kick off the conversation.
-            await task.queue_frames([context_aggregator.user().get_context_frame()])
-
-        runner = PipelineRunner()
-
-        await runner.run(task)
-
-
-if __name__ == "__main__":
-    asyncio.run(main())
--- a/examples/foundational/14p-function-calling-gemini-vertex-ai.py
+++ b/examples/foundational/14p-function-calling-gemini-vertex-ai.py
@@ -1,130 +0,0 @@
-#
-# Copyright (c) 2024–2025, Daily
-#
-# SPDX-License-Identifier: BSD 2-Clause License
-#
-
-import asyncio
-import os
-import sys
-
-import aiohttp
-from dotenv import load_dotenv
-from loguru import logger
-from runner import configure
-
-from pipecat.adapters.schemas.function_schema import FunctionSchema
-from pipecat.adapters.schemas.tools_schema import ToolsSchema
-from pipecat.audio.vad.silero import SileroVADAnalyzer
-from pipecat.frames.frames import TTSSpeakFrame
-from pipecat.pipeline.pipeline import Pipeline
-from pipecat.pipeline.runner import PipelineRunner
-from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.services.elevenlabs import ElevenLabsTTSService
-from pipecat.services.google import GoogleVertexLLMService
-from pipecat.services.openai import OpenAILLMContext
-from pipecat.transports.services.daily import DailyParams, DailyTransport
-
-load_dotenv(override=True)
-
-logger.remove(0)
-logger.add(sys.stderr, level="DEBUG")
-
-
-async def fetch_weather_from_api(function_name, tool_call_id, args, llm, context, result_callback):
-    await llm.push_frame(TTSSpeakFrame("Let me check on that."))
-    await result_callback({"conditions": "nice", "temperature": "75"})
-
-
-async def main():
-    async with aiohttp.ClientSession() as session:
-        (room_url, token) = await configure(session)
-
-        transport = DailyTransport(
-            room_url,
-            token,
-            "Respond bot",
-            DailyParams(
-                audio_out_enabled=True,
-                transcription_enabled=True,
-                vad_enabled=True,
-                vad_analyzer=SileroVADAnalyzer(),
-            ),
-        )
-
-        tts = ElevenLabsTTSService(
-            api_key=os.getenv("ELEVENLABS_API_KEY", ""),
-            voice_id=os.getenv("ELEVENLABS_VOICE_ID", ""),
-        )
-
-        llm = GoogleVertexLLMService(
-            # credentials="<json-credentials>",
-            params=GoogleVertexLLMService.InputParams(
-                project_id="<google-project-id>",
-            )
-        )
-        # You can aslo register a function_name of None to get all functions
-        # sent to the same callback with an additional function_name parameter.
-        llm.register_function("get_current_weather", fetch_weather_from_api)
-
-        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"],
-        )
-        tools = ToolsSchema(standard_tools=[weather_function])
-
-        messages = [
-            {
-                "role": "user",
-                "content": "Start a conversation with 'Hey there' to get the current weather.",
-            },
-        ]
-
-        context = OpenAILLMContext(messages, tools)
-        context_aggregator = llm.create_context_aggregator(context)
-
-        pipeline = Pipeline(
-            [
-                transport.input(),
-                context_aggregator.user(),
-                llm,
-                tts,
-                transport.output(),
-                context_aggregator.assistant(),
-            ]
-        )
-
-        task = PipelineTask(
-            pipeline,
-            params=PipelineParams(
-                allow_interruptions=True,
-                enable_metrics=True,
-                enable_usage_metrics=True,
-            ),
-        )
-
-        @transport.event_handler("on_first_participant_joined")
-        async def on_first_participant_joined(transport, participant):
-            await transport.capture_participant_transcription(participant["id"])
-            # Kick off the conversation.
-            await task.queue_frames([context_aggregator.user().get_context_frame()])
-
-        runner = PipelineRunner()
-
-        await runner.run(task)
-
-
-if __name__ == "__main__":
-    asyncio.run(main())
--- a/examples/foundational/15-switch-voices.py
+++ b/examples/foundational/15-switch-voices.py
@@ -78,7 +78,7 @@ async def main():

        british_lady = CartesiaTTSService(
            api_key=os.getenv("CARTESIA_API_KEY"),
-            voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
+            voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22",  # British Lady
        )

        barbershop_man = CartesiaTTSService(
@@ -125,10 +125,7 @@ async def main():
                llm,  # LLM
                ParallelPipeline(  # TTS (one of the following vocies)
                    [FunctionFilter(news_lady_filter), news_lady],  # News Lady voice
-                    [
-                        FunctionFilter(british_lady_filter),
-                        british_lady,
-                    ],  # British Reading Lady voice
+                    [FunctionFilter(british_lady_filter), british_lady],  # British Lady voice
                    [FunctionFilter(barbershop_man_filter), barbershop_man],  # Barbershop Man voice
                ),
                transport.output(),  # Transport bot output
@@ -136,7 +133,7 @@ async def main():
            ]
        )

-        task = PipelineTask(pipeline, params=PipelineParams(allow_interruptions=True))
+        task = PipelineTask(pipeline, PipelineParams(allow_interruptions=True))

        @transport.event_handler("on_first_participant_joined")
        async def on_first_participant_joined(transport, participant):
--- a/examples/foundational/15a-switch-languages.py
+++ b/examples/foundational/15a-switch-languages.py
@@ -71,7 +71,7 @@ async def main():

        english_tts = CartesiaTTSService(
            api_key=os.getenv("CARTESIA_API_KEY"),
-            voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
+            voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22",  # British Lady
        )

        spanish_tts = CartesiaTTSService(
@@ -126,7 +126,7 @@ async def main():
            ]
        )

-        task = PipelineTask(pipeline, params=PipelineParams(allow_interruptions=True))
+        task = PipelineTask(pipeline, PipelineParams(allow_interruptions=True))

        @transport.event_handler("on_first_participant_joined")
        async def on_first_participant_joined(transport, participant):
--- a/examples/foundational/16-gpu-container-local-bot.py
+++ b/examples/foundational/16-gpu-container-local-bot.py
@@ -85,13 +85,7 @@ async def main():
            ]
        )

-        task = PipelineTask(
-            pipeline,
-            params=PipelineParams(
-                allow_interruptions=True,
-                enable_metrics=True,
-            ),
-        )
+        task = PipelineTask(pipeline, PipelineParams(allow_interruptions=True, enable_metrics=True))

        # When a participant joins, start transcription for that participant so the
        # bot can "hear" and respond to them.
--- a/examples/foundational/17-detect-user-idle.py
+++ b/examples/foundational/17-detect-user-idle.py
@@ -48,7 +48,7 @@ async def main():

        tts = CartesiaTTSService(
            api_key=os.getenv("CARTESIA_API_KEY"),
-            voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
+            voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22",  # British Lady
        )

        llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
@@ -108,7 +108,7 @@ async def main():

        task = PipelineTask(
            pipeline,
-            params=PipelineParams(
+            PipelineParams(
                allow_interruptions=True,
                enable_metrics=True,
                report_only_initial_ttfb=True,
--- a/examples/foundational/18-gstreamer-filesrc.py
+++ b/examples/foundational/18-gstreamer-filesrc.py
@@ -38,6 +38,7 @@ async def main():
            "GStreamer",
            DailyParams(
                audio_out_enabled=True,
+                audio_out_is_live=True,
                camera_out_enabled=True,
                camera_out_width=1280,
                camera_out_height=720,
--- a/examples/foundational/19-openai-realtime-beta.py
+++ b/examples/foundational/19-openai-realtime-beta.py
@@ -14,8 +14,6 @@ from dotenv import load_dotenv
 from loguru import logger
 from runner import configure

-from pipecat.adapters.schemas.function_schema import FunctionSchema
-from pipecat.adapters.schemas.tools_schema import ToolsSchema
 from pipecat.audio.vad.silero import SileroVADAnalyzer
 from pipecat.audio.vad.vad_analyzer import VADParams
 from pipecat.pipeline.pipeline import Pipeline
@@ -23,11 +21,10 @@ from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
 from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
 from pipecat.services.openai_realtime_beta import (
-    InputAudioNoiseReduction,
    InputAudioTranscription,
    OpenAIRealtimeBetaLLMService,
-    SemanticTurnDetection,
    SessionProperties,
+    TurnDetection,
 )
 from pipecat.transports.services.daily import DailyParams, DailyTransport

@@ -49,25 +46,28 @@ async def fetch_weather_from_api(function_name, tool_call_id, args, llm, context
    )


-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",
+tools = [
+    {
+        "type": "function",
+        "name": "get_current_weather",
+        "description": "Get the current weather",
+        "parameters": {
+            "type": "object",
+            "properties": {
+                "location": {
+                    "type": "string",
+                    "description": "The city and state, e.g. San Francisco, CA",
+                },
+                "format": {
+                    "type": "string",
+                    "enum": ["celsius", "fahrenheit"],
+                    "description": "The temperature unit to use. Infer this from the users location.",
+                },
+            },
+            "required": ["location", "format"],
        },
-        "format": {
-            "type": "string",
-            "enum": ["celsius", "fahrenheit"],
-            "description": "The temperature unit to use. Infer this from the users location.",
-        },
-    },
-    required=["location", "format"],
-)
-
-# Create tools schema
-tools = ToolsSchema(standard_tools=[weather_function])
+    }
+]


 async def main():
@@ -92,10 +92,9 @@ async def main():
            input_audio_transcription=InputAudioTranscription(),
            # Set openai TurnDetection parameters. Not setting this at all will turn it
            # on by default
-            turn_detection=SemanticTurnDetection(),
+            turn_detection=TurnDetection(silence_duration_ms=1000),
            # Or set to False to disable openai turn detection and use transport VAD
            # turn_detection=False,
-            input_audio_noise_reduction=InputAudioNoiseReduction(type="near_field"),
            # tools=tools,
            instructions="""Your knowledge cutoff is 2023-10. You are a helpful and friendly AI.

@@ -148,14 +147,14 @@ Remember, your responses should be short. Just one or two sentences, usually."""
                transport.input(),  # Transport user input
                context_aggregator.user(),
                llm,  # LLM
-                transport.output(),  # Transport bot output
                context_aggregator.assistant(),
+                transport.output(),  # Transport bot output
            ]
        )

        task = PipelineTask(
            pipeline,
-            params=PipelineParams(
+            PipelineParams(
                allow_interruptions=True,
                enable_metrics=True,
                enable_usage_metrics=True,
--- a/examples/foundational/19a-azure-realtime-beta.py
+++ b/examples/foundational/19a-azure-realtime-beta.py
@@ -1,177 +0,0 @@
-#
-# Copyright (c) 2024–2025, Daily
-#
-# SPDX-License-Identifier: BSD 2-Clause License
-#
-
-import asyncio
-import os
-import sys
-from datetime import datetime
-
-import aiohttp
-from dotenv import load_dotenv
-from loguru import logger
-from runner import configure
-
-from pipecat.adapters.schemas.function_schema import FunctionSchema
-from pipecat.adapters.schemas.tools_schema import ToolsSchema
-from pipecat.audio.vad.silero import SileroVADAnalyzer
-from pipecat.audio.vad.vad_analyzer import VADParams
-from pipecat.pipeline.pipeline import Pipeline
-from pipecat.pipeline.runner import PipelineRunner
-from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
-from pipecat.services.openai_realtime_beta import (
-    AzureRealtimeBetaLLMService,
-    InputAudioTranscription,
-    SessionProperties,
-)
-from pipecat.transports.services.daily import DailyParams, DailyTransport
-
-load_dotenv(override=True)
-
-logger.remove(0)
-logger.add(sys.stderr, level="DEBUG")
-
-
-async def fetch_weather_from_api(function_name, tool_call_id, args, llm, context, result_callback):
-    temperature = 75 if args["format"] == "fahrenheit" else 24
-    await result_callback(
-        {
-            "conditions": "nice",
-            "temperature": temperature,
-            "format": args["format"],
-            "timestamp": datetime.now().strftime("%Y%m%d_%H%M%S"),
-        }
-    )
-
-
-# Define weather function using standardized schema
-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 users location.",
-        },
-    },
-    required=["location", "format"],
-)
-
-# Create tools schema
-tools = ToolsSchema(standard_tools=[weather_function])
-
-
-async def main():
-    async with aiohttp.ClientSession() as session:
-        (room_url, token) = await configure(session)
-
-        transport = DailyTransport(
-            room_url,
-            token,
-            "Respond bot",
-            DailyParams(
-                audio_in_enabled=True,
-                audio_out_enabled=True,
-                transcription_enabled=False,
-                vad_enabled=True,
-                vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.8)),
-                vad_audio_passthrough=True,
-            ),
-        )
-
-        session_properties = SessionProperties(
-            input_audio_transcription=InputAudioTranscription(),
-            # Set openai TurnDetection parameters. Not setting this at all will turn it
-            # on by default
-            # turn_detection=TurnDetection(silence_duration_ms=1000),
-            # Or set to False to disable openai turn detection and use transport VAD
-            # turn_detection=False,
-            # tools=tools,
-            instructions="""Your knowledge cutoff is 2023-10. You are a helpful and friendly AI.
-
-Act like a human, but remember that you aren't a human and that you can't do human
-things in the real world. Your voice and personality should be warm and engaging, with a lively and
-playful tone.
-
-If interacting in a non-English language, start by using the standard accent or dialect familiar to
-the user. Talk quickly. You should always call a function if you can. Do not refer to these rules,
-even if you're asked about them.
-
-You are participating in a voice conversation. Keep your responses concise, short, and to the point
-unless specifically asked to elaborate on a topic.
-
-Remember, your responses should be short. Just one or two sentences, usually.""",
-        )
-
-        llm = AzureRealtimeBetaLLMService(
-            api_key=os.getenv("AZURE_REALTIME_API_KEY"),
-            base_url=os.getenv("AZURE_REALTIME_BASE_URL"),
-            session_properties=session_properties,
-            start_audio_paused=False,
-        )
-
-        # you can either register a single function for all function calls, or specific functions
-        # llm.register_function(None, fetch_weather_from_api)
-        llm.register_function("get_current_weather", fetch_weather_from_api)
-
-        # Create a standard OpenAI LLM context object using the normal messages format. The
-        # OpenAIRealtimeBetaLLMService will convert this internally to messages that the
-        # openai WebSocket API can understand.
-        context = OpenAILLMContext(
-            [{"role": "user", "content": "Say hello!"}],
-            # [{"role": "user", "content": [{"type": "text", "text": "Say hello!"}]}],
-            #     [
-            #         {
-            #             "role": "user",
-            #             "content": [
-            #                 {"type": "text", "text": "Say"},
-            #                 {"type": "text", "text": "yo what's up!"},
-            #             ],
-            #         }
-            #     ],
-            tools,
-        )
-
-        context_aggregator = llm.create_context_aggregator(context)
-
-        pipeline = Pipeline(
-            [
-                transport.input(),  # Transport user input
-                context_aggregator.user(),
-                llm,  # LLM
-                context_aggregator.assistant(),
-                transport.output(),  # Transport bot output
-            ]
-        )
-
-        task = PipelineTask(
-            pipeline,
-            params=PipelineParams(
-                allow_interruptions=True,
-                enable_metrics=True,
-                enable_usage_metrics=True,
-                # report_only_initial_ttfb=True,
-            ),
-        )
-
-        @transport.event_handler("on_first_participant_joined")
-        async def on_first_participant_joined(transport, participant):
-            await transport.capture_participant_transcription(participant["id"])
-            # Kick off the conversation.
-            await task.queue_frames([context_aggregator.user().get_context_frame()])
-
-        runner = PipelineRunner()
-
-        await runner.run(task)
-
-
-if __name__ == "__main__":
-    asyncio.run(main())
--- a/examples/foundational/20a-persistent-context-openai.py
+++ b/examples/foundational/20a-persistent-context-openai.py
@@ -184,7 +184,7 @@ async def main():

        tts = CartesiaTTSService(
            api_key=os.getenv("CARTESIA_API_KEY"),
-            voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
+            voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22",  # British Lady
        )

        llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
@@ -212,7 +212,7 @@ async def main():

        task = PipelineTask(
            pipeline,
-            params=PipelineParams(
+            PipelineParams(
                allow_interruptions=True,
                enable_metrics=True,
                enable_usage_metrics=True,
--- a/examples/foundational/20b-persistent-context-openai-realtime.py
+++ b/examples/foundational/20b-persistent-context-openai-realtime.py
@@ -237,7 +237,7 @@ Remember, your responses should be short. Just one or two sentences, usually."""

        task = PipelineTask(
            pipeline,
-            params=PipelineParams(
+            PipelineParams(
                allow_interruptions=True,
                enable_metrics=True,
                enable_usage_metrics=True,
--- a/examples/foundational/20c-persistent-context-anthropic.py
+++ b/examples/foundational/20c-persistent-context-anthropic.py
@@ -179,7 +179,7 @@ async def main():

        tts = CartesiaTTSService(
            api_key=os.getenv("CARTESIA_API_KEY"),
-            voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
+            voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22",  # British Lady
        )

        llm = AnthropicLLMService(
@@ -209,7 +209,7 @@ async def main():

        task = PipelineTask(
            pipeline,
-            params=PipelineParams(
+            PipelineParams(
                allow_interruptions=True,
                enable_metrics=True,
                enable_usage_metrics=True,
--- a/examples/foundational/20d-persistent-context-gemini.py
+++ b/examples/foundational/20d-persistent-context-gemini.py
@@ -54,12 +54,7 @@ async def fetch_weather_from_api(function_name, tool_call_id, args, llm, context

 async def get_image(function_name, tool_call_id, arguments, llm, context, result_callback):
    question = arguments["question"]
-    await llm.request_image_frame(
-        user_id=video_participant_id,
-        function_name=function_name,
-        tool_call_id=tool_call_id,
-        text_content=question,
-    )
+    await llm.request_image_frame(user_id=video_participant_id, text_content=question)


 async def get_saved_conversation_filenames(
@@ -239,7 +234,7 @@ async def main():

        tts = CartesiaTTSService(
            api_key=os.getenv("CARTESIA_API_KEY"),
-            voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
+            voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22",  # British Lady
        )

        llm = GoogleLLMService(model="gemini-2.0-flash-001", api_key=os.getenv("GOOGLE_API_KEY"))
@@ -268,7 +263,7 @@ async def main():

        task = PipelineTask(
            pipeline,
-            params=PipelineParams(
+            PipelineParams(
                allow_interruptions=True,
                enable_metrics=True,
                enable_usage_metrics=True,
--- a/examples/foundational/21-tavus-layer.py
+++ b/examples/foundational/21-tavus-layer.py
@@ -87,7 +87,7 @@ async def main():

        task = PipelineTask(
            pipeline,
-            params=PipelineParams(
+            PipelineParams(
                # We just use 16000 because that's what Tavus is expecting and
                # we avoid resampling.
                audio_in_sample_rate=16000,
--- a/examples/foundational/22-natural-conversation.py
+++ b/examples/foundational/22-natural-conversation.py
@@ -56,7 +56,7 @@ async def main():

        tts = CartesiaTTSService(
            api_key=os.getenv("CARTESIA_API_KEY"),
-            voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
+            voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22",  # British Lady
        )

        # This is the LLM that will be used to detect if the user has finished a
@@ -145,7 +145,7 @@ async def main():

        task = PipelineTask(
            pipeline,
-            params=PipelineParams(
+            PipelineParams(
                allow_interruptions=True,
                enable_metrics=True,
                enable_usage_metrics=True,
--- a/examples/foundational/22b-natural-conversation-proposal.py
+++ b/examples/foundational/22b-natural-conversation-proposal.py
@@ -138,7 +138,6 @@ class OutputGate(FrameProcessor):
        self._gate_open = start_open
        self._frames_buffer = []
        self._notifier = notifier
-        self._gate_task = None

    def close_gate(self):
        self._gate_open = False
@@ -179,13 +178,10 @@ class OutputGate(FrameProcessor):

    async def _start(self):
        self._frames_buffer = []
-        if not self._gate_task:
-            self._gate_task = self.create_task(self._gate_task_handler())
+        self._gate_task = self.create_task(self._gate_task_handler())

    async def _stop(self):
-        if self._gate_task:
-            await self.cancel_task(self._gate_task)
-            self._gate_task = None
+        await self.cancel_task(self._gate_task)

    async def _gate_task_handler(self):
        while True:
@@ -199,8 +195,13 @@ class OutputGate(FrameProcessor):
                break


-async def fetch_weather_from_api(function_name, tool_call_id, args, llm, context, result_callback):
+async def start_fetch_weather(function_name, llm, context):
+    """Push a frame to the LLM; this is handy when the LLM response might take a while."""
    await llm.push_frame(TTSSpeakFrame("Let me check on that."))
+    logger.debug(f"Starting fetch_weather_from_api with function_name: {function_name}")
+
+
+async def fetch_weather_from_api(function_name, tool_call_id, args, llm, context, result_callback):
    await result_callback({"conditions": "nice", "temperature": "75"})


@@ -224,7 +225,7 @@ async def main():

        tts = CartesiaTTSService(
            api_key=os.getenv("CARTESIA_API_KEY"),
-            voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
+            voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22",  # British Lady
        )

        # This is the LLM that will be used to detect if the user has finished a
@@ -234,9 +235,9 @@ async def main():

        # This is the regular LLM.
        llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
-        # You can also register a function_name of None to get all functions
+        # 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(None, fetch_weather_from_api, start_callback=start_fetch_weather)

        tools = [
            ChatCompletionToolParam(
@@ -350,7 +351,7 @@ async def main():

        task = PipelineTask(
            pipeline,
-            params=PipelineParams(
+            PipelineParams(
                allow_interruptions=True,
                enable_metrics=True,
                enable_usage_metrics=True,
--- a/examples/foundational/22c-natural-conversation-mixed-llms.py
+++ b/examples/foundational/22c-natural-conversation-mixed-llms.py
@@ -342,7 +342,6 @@ class OutputGate(FrameProcessor):
        self._gate_open = start_open
        self._frames_buffer = []
        self._notifier = notifier
-        self._gate_task = None

    def close_gate(self):
        self._gate_open = False
@@ -383,13 +382,10 @@ class OutputGate(FrameProcessor):

    async def _start(self):
        self._frames_buffer = []
-        if not self._gate_task:
-            self._gate_task = self.create_task(self._gate_task_handler())
+        self._gate_task = self.create_task(self._gate_task_handler())

    async def _stop(self):
-        if self._gate_task:
-            await self.cancel_task(self._gate_task)
-            self._gate_task = None
+        await self.cancel_task(self._gate_task)

    async def _gate_task_handler(self):
        while True:
@@ -403,8 +399,13 @@ class OutputGate(FrameProcessor):
                break


-async def fetch_weather_from_api(function_name, tool_call_id, args, llm, context, result_callback):
+async def start_fetch_weather(function_name, llm, context):
+    """Push a frame to the LLM; this is handy when the LLM response might take a while."""
    await llm.push_frame(TTSSpeakFrame("Let me check on that."))
+    logger.debug(f"Starting fetch_weather_from_api with function_name: {function_name}")
+
+
+async def fetch_weather_from_api(function_name, tool_call_id, args, llm, context, result_callback):
    await result_callback({"conditions": "nice", "temperature": "75"})


@@ -428,7 +429,7 @@ async def main():

        tts = CartesiaTTSService(
            api_key=os.getenv("CARTESIA_API_KEY"),
-            voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
+            voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22",  # British Lady
        )

        # This is the LLM that will be used to detect if the user has finished a
@@ -446,7 +447,7 @@ async def main():
        )
        # 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(None, fetch_weather_from_api, start_callback=start_fetch_weather)

        tools = [
            ChatCompletionToolParam(
@@ -559,7 +560,7 @@ async def main():

        task = PipelineTask(
            pipeline,
-            params=PipelineParams(
+            PipelineParams(
                allow_interruptions=True,
                enable_metrics=True,
                enable_usage_metrics=True,
--- a/examples/foundational/22d-natural-conversation-gemini-audio.py
+++ b/examples/foundational/22d-natural-conversation-gemini-audio.py
@@ -23,9 +23,12 @@ from pipecat.frames.frames import (
    FunctionCallInProgressFrame,
    FunctionCallResultFrame,
    InputAudioRawFrame,
+    LLMFullResponseEndFrame,
    LLMFullResponseStartFrame,
+    LLMMessagesFrame,
    StartFrame,
    StartInterruptionFrame,
+    StopInterruptionFrame,
    SystemFrame,
    TextFrame,
    TranscriptionFrame,
@@ -36,7 +39,7 @@ from pipecat.pipeline.parallel_pipeline import ParallelPipeline
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.aggregators.llm_response import LLMAssistantResponseAggregator
+from pipecat.processors.aggregators.llm_response import LLMResponseAggregator
 from pipecat.processors.aggregators.openai_llm_context import (
    OpenAILLMContext,
    OpenAILLMContextFrame,
@@ -388,7 +391,7 @@ class AudioAccumulator(FrameProcessor):
            )
            self._user_speaking = False
            context = GoogleLLMContext()
-            context.add_audio_frames_message(audio_frames=self._audio_frames)
+            context.add_audio_frames_message(text="Audio follows", audio_frames=self._audio_frames)
            await self.push_frame(OpenAILLMContextFrame(context=context))
        elif isinstance(frame, InputAudioRawFrame):
            # Append the audio frame to our buffer. Treat the buffer as a ring buffer, dropping the oldest
@@ -431,11 +434,7 @@ class CompletenessCheck(FrameProcessor):
    async def process_frame(self, frame: Frame, direction: FrameDirection):
        await super().process_frame(frame, direction)

-        if isinstance(frame, (EndFrame, CancelFrame)):
-            if self._idle_task:
-                await self.cancel_task(self._idle_task)
-                self._idle_task = None
-        elif isinstance(frame, UserStartedSpeakingFrame):
+        if isinstance(frame, UserStartedSpeakingFrame):
            if self._idle_task:
                await self.cancel_task(self._idle_task)
        elif isinstance(frame, TextFrame) and frame.text.startswith("YES"):
@@ -477,11 +476,19 @@ class CompletenessCheck(FrameProcessor):
            self._idle_task = None


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

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

    async def process_frame(self, frame: Frame, direction: FrameDirection):
@@ -539,7 +546,7 @@ class OutputGate(FrameProcessor):
        self,
        notifier: BaseNotifier,
        context: OpenAILLMContext,
-        llm_transcription_buffer: LLMAggregatorBuffer,
+        user_transcription_buffer: "UserAggregatorBuffer",
        **kwargs,
    ):
        super().__init__(**kwargs)
@@ -547,8 +554,7 @@ class OutputGate(FrameProcessor):
        self._frames_buffer = []
        self._notifier = notifier
        self._context = context
-        self._transcription_buffer = llm_transcription_buffer
-        self._gate_task = None
+        self._transcription_buffer = user_transcription_buffer

    def close_gate(self):
        self._gate_open = False
@@ -596,13 +602,10 @@ class OutputGate(FrameProcessor):

    async def _start(self):
        self._frames_buffer = []
-        if not self._gate_task:
-            self._gate_task = self.create_task(self._gate_task_handler())
+        self._gate_task = self.create_task(self._gate_task_handler())

    async def _stop(self):
-        if self._gate_task:
-            await self.cancel_task(self._gate_task)
-            self._gate_task = None
+        await self.cancel_task(self._gate_task)

    async def _gate_task_handler(self):
        while True:
@@ -644,7 +647,7 @@ async def main():

        tts = CartesiaTTSService(
            api_key=os.getenv("CARTESIA_API_KEY"),
-            voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
+            voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22",  # British Lady
        )

        # This is the LLM that will transcribe user speech.
@@ -694,10 +697,10 @@ async def main():

        conversation_audio_context_assembler = ConversationAudioContextAssembler(context=context)

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

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

        pipeline = Pipeline(
@@ -718,7 +721,7 @@ async def main():
                            ],
                            [
                                tx_llm,
-                                llm_aggregator_buffer,
+                                user_aggregator_buffer,
                            ],
                        )
                    ],
@@ -737,7 +740,7 @@ async def main():

        task = PipelineTask(
            pipeline,
-            params=PipelineParams(
+            PipelineParams(
                allow_interruptions=True,
                enable_metrics=True,
                enable_usage_metrics=True,
--- a/Show More
+++ b/Show More
Author	SHA1	Message	Date
James Hush	b5934783a7	Update comment	2025-02-20 15:12:12 +08:00
James Hush	95b28f635a	Change prompt to make it about vacuums and tvs	2025-02-20 15:07:09 +08:00