changed data type of callback signature

Pipecat Cloud: Companion server to handle webhooks for pinless dial-in

.pre-commit-config.yaml

@@ -1,7 +1,8 @@
 repos:
-  - repo: local
+  - repo: https://github.com/astral-sh/ruff-pre-commit
+    rev: v0.9.7
     hooks:
-      - id: ruff-format-hook
+      - id: ruff
-        name: Check ruff formatting
+        language_version: python3
-        entry: sh scripts/pre-commit.sh
+        args: [ --select,  I, ]
-        language: system
+      - id: ruff-format

CHANGELOG.md

@@ -9,6 +9,521 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 
 ### Added
 
+- Added new processors `ProducerProcessor` and `ConsumerProcessor`. The
+  producer processor processes frames from the pipeline and decides whether the
+  consumers should consume it or not. If so, the same frame that is received by
+  the producer is sent to the consumer. There can be multiple consumers per
+  producer. These processors can be useful to push frames from one part of a
+  pipeline to a different one (e.g. when using `ParallelPipeline`).
+
+- Improvements for the `SmallWebRTCTransport`:
+  - Wait until the pipeline is ready before triggering the `connected` event.
+  - Queue messages if the data channel is not ready.
+  - Update the aiortc dependency to fix an issue where the 'video/rtx' MIME
+    type was incorrectly handled as a codec retransmission.
+  - Avoid initial video delays.
+
+### Fixed
+
+- Fixed an issue in the Azure TTS services where the language was being set
+  incorrectly.
+
+- Fixed `SmallWebRTCTransport` to support dynamic values for
+  `TransportParams.audio_out_10ms_chunks`. Previously, it only worked with 20ms
+  chunks.
+
+- Fixed an issue where `LLMAssistantContextAggregator` would prevent a
+  `BotStoppedSpeakingFrame` from moving through the pipeline.
+
+## [0.0.62] - 2025-04-01 "An April Fools' release"
+
+### Added
+
+- Added `TransportParams.audio_out_10ms_chunks` parameter to allow controlling
+  the amount of audio being sent by the output transport. It defaults to 4, so
+  40ms audio chunks are sent.
+
+- Added `QwenLLMService` for Qwen integration with an OpenAI-compatible
+  interface. Added foundational example `14q-function-calling-qwen.py`.
+
+- Added `Mem0MemoryService`. Mem0 is a self-improving memory layer for LLM
+  applications. Learn more at: https://mem0.ai/.
+
+- Added `WhisperSTTServiceMLX` for Whisper transcription on Apple Silicon.
+  See example in `examples/foundational/13e-whisper-mlx.py`. Latency of
+  completed transcription using Whisper large-v3-turbo on an M4 macbook is
+  ~500ms.
+
+- Added `SmallWebRTCTransport`, a new P2P WebRTC transport.
+
+  - Created two examples in `p2p-webrtc`:
+    - **video-transform**: Demonstrates sending and receiving audio/video with
+      `SmallWebRTCTransport` using `TypeScript`. Includes video frame
+      processing with OpenCV.
+    - **voice-agent**: A minimal example of creating a voice agent with
+      `SmallWebRTCTransport`.
+
+- `GladiaSTTService` now have comprehensive support for the latest API config
+  options, including model, language detection, preprocessing, custom
+  vocabulary, custom spelling, translation, and message filtering options.
+
+- Added `SmallWebRTCTransport`, a new P2P WebRTC transport.
+
+  - Created two examples in `p2p-webrtc`:
+    - **video-transform**: Demonstrates sending and receiving audio/video with
+      `SmallWebRTCTransport` using `TypeScript`. Includes video frame
+      processing with OpenCV.
+    - **voice-agent**: A minimal example of creating a voice agent with
+      `SmallWebRTCTransport`.
+
+- Added support to `ProtobufFrameSerializer` to send the messages from
+  `TransportMessageFrame` and `TransportMessageUrgentFrame`.
+
+- Added support for a new TTS service, `PiperTTSService`.
+  (see https://github.com/rhasspy/piper/)
+
+- It is now possible to tell whether `UserStartedSpeakingFrame` or
+  `UserStoppedSpeakingFrame` have been generated because of emulation frames.
+
+### Changed
+
+- `FunctionCallResultFrame`a are now system frames. This is to prevent function
+  call results to be discarded during interruptions.
+
+- Pipecat services have been reorganized into packages. Each package can have
+  one or more of the following modules (in the future new module names might be
+  needed) depending on the services implemented:
+
+  - image: for image generation services
+  - llm: for LLM services
+  - memory: for memory services
+  - stt: for Speech-To-Text services
+  - tts: for Text-To-Speech services
+  - video: for video generation services
+  - vision: for video recognition services
+
+- Base classes for AI services have been reorganized into modules. They can now
+  be found in
+  `pipecat.services.[ai_service,image_service,llm_service,stt_service,vision_service]`.
+
+- `GladiaSTTService` now uses the `solaria-1` model by default. Other params
+  use Gladia's default values. Added support for more language codes.
+
+### Deprecated
+
+- All Pipecat services imports have been deprecated and a warning will be shown
+  when using the old import. The new import should be
+  `pipecat.services.[service].[image,llm,memory,stt,tts,video,vision]`. For
+  example, `from pipecat.services.openai.llm import OpenAILLMService`.
+
+- Import for AI services base classes from `pipecat.services.ai_services` is now
+  deprecated, use one of
+  `pipecat.services.[ai_service,image_service,llm_service,stt_service,vision_service]`.
+
+- Deprecated the `language` parameter in `GladiaSTTService.InputParams` in
+  favor of `language_config`, which better aligns with Gladia's API.
+
+- Deprecated using `GladiaSTTService.InputParams` directly. Use the new
+  `GladiaInputParams` class instead.
+
+### Fixed
+
+- Fixed a `FastAPIWebsocketTransport` and `WebsocketClientTransport` issue that
+  would cause the transport to be closed prematurely, preventing the internally
+  queued audio to be sent. The same issue could also cause an infinite loop
+  while using an output mixer and when sending an `EndFrame`, preventing the bot
+  to finish.
+
+- Fixed an issue that could cause the `TranscriptionUpdateFrame` being pushed
+  because of an interruption to be discarded.
+
+- Fixed an issue that would cause `SegmentedSTTService` based services
+  (e.g. `OpenAISTTService`) to try to transcribe non-spoken audio, causing
+  invalid transcriptions.
+
+- Fixed an issue where `GoogleTTSService` was emitting two `TTSStoppedFrames`.
+
+### Performance
+
+- Output transports now send 40ms audio chunks instead of 20ms. This should
+  improve performance.
+
+- `BotSpeakingFrame`s are now sent every 200ms. If the output transport audio chunks
+  are higher than 200ms then they will be sent at every audio chunk.
+
+### Other
+
+- Added foundational example `37-mem0.py` demonstrating how to use the
+  `Mem0MemoryService`.
+
+- Added foundational example `13e-whisper-mlx.py` demonstrating how to use the
+  `WhisperSTTServiceMLX`.
+
+## [0.0.61] - 2025-03-26
+
+### Added
+
+- Added a new frame, `LLMSetToolChoiceFrame`, which provides a mechanism
+  for modifying the `tool_choice` in the context.
+
+- Added `GroqTTSService` which provides text-to-speech functionality using
+  Groq's API.
+
+- Added support in `DailyTransport` for updating remote participants'
+  `canReceive` permission via the `update_remote_participants()` method, by
+  bumping the daily-python dependency to >= 0.16.0.
+
+- ElevenLabs TTS services now support a sample rate of 8000.
+
+- Added support for `instructions` in `OpenAITTSService`.
+
+- Added support for `base_url` in `OpenAIImageGenService` and
+  `OpenAITTSService`.
+
+### Fixed
+
+- Fixed an issue in `RTVIObserver` that prevented handling of Google LLM
+  context messages. The observer now processes both OpenAI-style and
+  Google-style contexts.
+
+- Fixed an issue in Daily involving switching virtual devices, by bumping the
+  daily-python dependency to >= 0.16.1.
+
+- Fixed a `GoogleAssistantContextAggregator` issue where function calls
+  placeholders where not being updated when then function call result was
+  different from a string.
+
+- Fixed an issue that would cause `LLMAssistantContextAggregator` to block
+  processing more frames while processing a function call result.
+
+- 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.
 
@@ -45,6 +560,10 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 - ⚠️ `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.
@@ -78,6 +597,12 @@ stt = DeepgramSTTService(..., live_options=LiveOptions(model="nova-2-general"))
 
 ### 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.
@@ -91,10 +616,6 @@ stt = DeepgramSTTService(..., live_options=LiveOptions(model="nova-2-general"))
 
 - Fixed `match_endofsentence` support for ellipses.
 
-- Fixed an issue that would cause undesired interruptions via
-  `EmulateUserStartedSpeakingFrame` when only interim transcriptions (i.e. no
-  final transcriptions) where received.
-
 - Fixed an issue where `EndTaskFrame` was not triggering
   `on_client_disconnected` or closing the WebSocket in FastAPI.
 
@@ -124,6 +645,9 @@ 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
@@ -1756,7 +2280,7 @@ 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
-  canceled.
+  cancelled.
 
 ### Fixed
 

CONTRIBUTING.md

@@ -26,11 +26,52 @@ git commit -m "Description of your changes"
 git push origin your-branch-name
 ```
 
-9. **Submit a Pull Request (PR)**: Open a PR from your forked repository to the main branch of this repo. 
+8. **Submit a Pull Request (PR)**: Open a PR from your forked repository to the main branch of this repo.
-> Important: Describe the changes you've made clearly!
+   > Important: Describe the changes you've made clearly!
 
 Our maintainers will review your PR, and once everything is good, your contributions will be merged!
 
+## Code Style and Documentation
+
+### Python Code Style
+
+We use Ruff for code linting and formatting. Please ensure your code passes all linting checks before submitting a PR.
+
+### Docstring Conventions
+
+We follow Google-style docstrings with these specific conventions:
+
+- Class docstrings should fully document all parameters used in `__init__`
+- We don't require separate docstrings for `__init__` methods when parameters are documented in the class docstring
+- Property methods should have docstrings explaining their purpose and return value
+
+Example of correctly documented class:
+
+```python
+class MyClass:
+    """Class description.
+
+    Additional details about the class.
+
+    Args:
+        param1: Description of first parameter.
+        param2: Description of second parameter.
+    """
+
+    def __init__(self, param1, param2):
+        # No docstring required here as parameters are documented above
+        self.param1 = param1
+        self.param2 = param2
+
+    @property
+    def some_property(self) -> str:
+        """Get the formatted property value.
+
+        Returns:
+            A string representation of the property.
+        """
+        return f"Property: {self.param1}"
+```
 
 # Contributor Covenant Code of Conduct
 
@@ -51,23 +92,23 @@ diverse, inclusive, and healthy community.
 Examples of behavior that contributes to a positive environment for our
 community include:
 
-* Demonstrating empathy and kindness toward other people
+- Demonstrating empathy and kindness toward other people
-* Being respectful of differing opinions, viewpoints, and experiences
+- Being respectful of differing opinions, viewpoints, and experiences
-* Giving and gracefully accepting constructive feedback
+- Giving and gracefully accepting constructive feedback
-* Accepting responsibility and apologizing to those affected by our mistakes,
+- Accepting responsibility and apologizing to those affected by our mistakes,
   and learning from the experience
-* Focusing on what is best not just for us as individuals, but for the overall
+- Focusing on what is best not just for us as individuals, but for the overall
   community
 
 Examples of unacceptable behavior include:
 
-* The use of sexualized language or imagery, and sexual attention or advances of
+- The use of sexualized language or imagery, and sexual attention or advances of
   any kind
-* Trolling, insulting or derogatory comments, and personal or political attacks
+- Trolling, insulting or derogatory comments, and personal or political attacks
-* Public or private harassment
+- Public or private harassment
-* Publishing others' private information, such as a physical or email address,
+- Publishing others' private information, such as a physical or email address,
   without their explicit permission
-* Other conduct which could reasonably be considered inappropriate in a
+- Other conduct which could reasonably be considered inappropriate in a
   professional setting
 
 ## Enforcement Responsibilities
@@ -162,4 +203,4 @@ For answers to common questions about this code of conduct, see the FAQ at
 [v2.1]: https://www.contributor-covenant.org/version/2/1/code_of_conduct.html
 [Mozilla CoC]: https://github.com/mozilla/diversity
 [FAQ]: https://www.contributor-covenant.org/faq
-[translations]: https://www.contributor-covenant.org/translations
+[translations]: https://www.contributor-covenant.org/translations

README.md

@@ -55,17 +55,18 @@ pip install "pipecat-ai[option,...]"
 
 ### Available services
 
-| Category            | Services                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                | Install Command Example                 |
+| 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), [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]"`    |
+| 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]"`    |
-| 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]"`      |
+| 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), [Qwen](https://docs.pipecat.ai/server/services/llm/qwen), [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), [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]"`    |
+| 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), [Piper](https://docs.pipecat.ai/server/services/tts/piper), [PlayHT](https://docs.pipecat.ai/server/services/tts/playht), [Rime](https://docs.pipecat.ai/server/services/tts/rime), [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]"`      |
+| 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]"`       |
+| Transport           | [Daily (WebRTC)](https://docs.pipecat.ai/server/services/transport/daily), [FastAPI Websocket](https://docs.pipecat.ai/server/services/transport/fastapi-websocket), [SmallWebRTCTransport](https://docs.pipecat.ai/server/services/transport/small-webrtc), [WebSocket Server](https://docs.pipecat.ai/server/services/transport/websocket-server), Local                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        | `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]"` |
+| 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      | [Moondream](https://docs.pipecat.ai/server/services/vision/moondream), [fal](https://docs.pipecat.ai/server/services/image-generation/fal)                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              | `pip install "pipecat-ai[moondream]"`   |
+| Memory              | [mem0](https://docs.pipecat.ai/server/services/memory/mem0)                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                       | `pip install "pipecat-ai[mem0]"`        |
-| 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]"`      |
+| 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]"`   |
-| 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]"`   |
+| 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]"`   |
 
 📚 [View full services documentation →](https://docs.pipecat.ai/server/services/supported-services)
 

dev-requirements.txt

@@ -3,10 +3,11 @@ coverage~=7.6.12
 grpcio-tools~=1.67.1
 pip-tools~=7.4.1
 pre-commit~=4.0.1
-pyright~=1.1.394
+pyright~=1.1.397
 pytest~=8.3.4
 pytest-asyncio~=0.25.3
-ruff~=0.9.7
+pytest-aiohttp==1.1.0
+ruff~=0.11.1
 setuptools~=70.0.0
 setuptools_scm~=8.1.0
 python-dotenv~=1.0.1

docs/api/conf.py

@@ -50,6 +50,14 @@ autodoc_mock_imports = [
     "pyht.protos",
     "pyht.protos.api_pb2",
     "pipecat_ai_playht",  # PlayHT wrapper
+    "vllm",
+    "aiortc",
+    "aiortc.mediastreams",
+    "cv2",
+    "av",
+    "pyneuphonic",
+    "mem0",
+    "mlx_whisper",
     "anthropic",
     "assemblyai",
     "boto3",

docs/api/index.rst

@@ -45,8 +45,10 @@ Transport & Serialization
 Utilities
 ~~~~~~~~~
 
+* :mod:`Adapters <pipecat.adapters>`
 * :mod:`Clocks <pipecat.clocks>`
 * :mod:`Metrics <pipecat.metrics>`
+* :mod:`Observers <pipecat.observers>`
 * :mod:`Sync <pipecat.sync>`
 * :mod:`Transcriptions <pipecat.transcriptions>`
 * :mod:`Utils <pipecat.utils>`
@@ -56,10 +58,12 @@ Utilities
    :caption: API Reference
    :hidden:
 
+   Adapters <api/pipecat.adapters>
    Audio <api/pipecat.audio>
    Clocks <api/pipecat.clocks>
    Frames <api/pipecat.frames>
    Metrics <api/pipecat.metrics>
+   Observers <api/pipecat.observers>
    Pipeline <api/pipecat.pipeline>
    Processors <api/pipecat.processors>
    Serializers <api/pipecat.serializers>

docs/api/requirements.txt

@@ -12,22 +12,29 @@ pipecat-ai[aws]
 pipecat-ai[azure]
 pipecat-ai[canonical]
 pipecat-ai[cartesia]
+pipecat-ai[cerebras]
+pipecat-ai[deepseek]
 pipecat-ai[daily]
 pipecat-ai[deepgram]
 pipecat-ai[elevenlabs]
 pipecat-ai[fal]
 pipecat-ai[fireworks]
+pipecat-ai[fish]
 pipecat-ai[gladia]
 pipecat-ai[google]
 pipecat-ai[grok]
 pipecat-ai[groq]
-# pipecat-ai[krisp] # Mocked instead
+# pipecat-ai[krisp] # Mocked
+pipecat-ai[koala]
 pipecat-ai[langchain]
 pipecat-ai[livekit]
 pipecat-ai[lmnt]
 pipecat-ai[local]
+# pipecat-ai[mem0] # Mocked
+# pipecat-ai[mlx-whisper] # Mocked
 pipecat-ai[moondream]
 pipecat-ai[nim]
+# pipecat-ai[neuphonic] # Mocked
 pipecat-ai[noisereduce]
 pipecat-ai[openai]
 # pipecat-ai[openpipe]
@@ -36,5 +43,9 @@ pipecat-ai[riva]
 pipecat-ai[silero]
 pipecat-ai[simli]
 pipecat-ai[soundfile]
+pipecat-ai[tavus]
+pipecat-ai[together]
+# pipecat-ai[ultravox] # Mocked
+# pipecat-ai[webrtc] # Mocked
 pipecat-ai[websocket]
 pipecat-ai[whisper]

dot-env.template

@@ -29,6 +29,9 @@ DAILY_SAMPLE_ROOM_URL=https://...
 ELEVENLABS_API_KEY=...
 ELEVENLABS_VOICE_ID=...
 
+# Neuphonic
+NEUPHONIC_API_KEY=...
+
 # Fal
 FAL_KEY=...
 
@@ -87,3 +90,6 @@ ASSEMBLYAI_API_KEY=...
 
 # OpenRouter
 OPENROUTER_API_KEY=...
+
+# Piper
+PIPER_BASE_URL=...

examples/bot-ready-signalling/server/signalling_bot.py

@@ -18,7 +18,7 @@ from pipecat.frames.frames import AudioRawFrame, EndFrame, OutputAudioRawFrame,
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineTask
-from pipecat.services.cartesia import CartesiaTTSService
+from pipecat.services.cartesia.tts import CartesiaTTSService
 from pipecat.transports.services.daily import DailyParams, DailyTransport
 
 load_dotenv(override=True)
@@ -64,7 +64,7 @@ async def main():
 
         tts = CartesiaTTSService(
             api_key=os.getenv("CARTESIA_API_KEY"),
-            voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22",  # British Lady
+            voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
         )
 
         runner = PipelineRunner()

examples/canonical-metrics/bot.py

@@ -21,9 +21,9 @@ from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
 from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
 from pipecat.processors.audio.audio_buffer_processor import AudioBufferProcessor
-from pipecat.services.canonical import CanonicalMetricsService
+from pipecat.services.canonical.metrics import CanonicalMetricsService
-from pipecat.services.elevenlabs import ElevenLabsTTSService
+from pipecat.services.elevenlabs.tts import ElevenLabsTTSService
-from pipecat.services.openai import OpenAILLMService
+from pipecat.services.openai.llm import OpenAILLMService
 from pipecat.transports.services.daily import DailyParams, DailyTransport
 
 load_dotenv(override=True)
@@ -113,8 +113,8 @@ async def main():
                 llm,
                 tts,
                 transport.output(),
-                audio_buffer_processor,  # captures audio into a buffer
                 canonical,  # uploads audio buffer to Canonical AI for metrics
+                audio_buffer_processor,  # captures audio into a buffer
                 context_aggregator.assistant(),
             ]
         )

examples/chatbot-audio-recording/bot.py

@@ -23,8 +23,8 @@ from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
 from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
 from pipecat.processors.audio.audio_buffer_processor import AudioBufferProcessor
-from pipecat.services.elevenlabs import ElevenLabsTTSService
+from pipecat.services.elevenlabs.tts import ElevenLabsTTSService
-from pipecat.services.openai import OpenAILLMService
+from pipecat.services.openai.llm import OpenAILLMService
 from pipecat.transports.services.daily import DailyParams, DailyTransport
 
 load_dotenv(override=True)
@@ -32,10 +32,16 @@ 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):
+
+async def save_audio(audio: bytes, sample_rate: int, num_channels: int, name: str):
     if len(audio) > 0:
-        filename = f"conversation_recording{datetime.datetime.now().strftime('%Y%m%d_%H%M%S')}.wav"
+        filename = os.path.join(
+            "recordings",
+            f"{name}_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)
@@ -110,7 +116,7 @@ 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()
+        audiobuffer = AudioBufferProcessor(enable_turn_audio=True)
 
         pipeline = Pipeline(
             [
@@ -128,7 +134,15 @@ async def main():
 
         @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)
+            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")
 
         @transport.event_handler("on_first_participant_joined")
         async def on_first_participant_joined(transport, participant):

examples/deployment/flyio-example/bot.py

@@ -1,3 +1,9 @@
+#
+# Copyright (c) 2024–2025, Daily
+#
+# SPDX-License-Identifier: BSD 2-Clause License
+#
+
 import argparse
 import asyncio
 import os
@@ -12,8 +18,8 @@ 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 ElevenLabsTTSService
+from pipecat.services.elevenlabs.tts import ElevenLabsTTSService
-from pipecat.services.openai import OpenAILLMService
+from pipecat.services.openai.llm import OpenAILLMService
 from pipecat.transports.services.daily import DailyParams, DailyTransport
 
 load_dotenv(override=True)

examples/deployment/modal-example/app.py

@@ -1,3 +1,9 @@
+#
+# Copyright (c) 2024–2025, Daily
+#
+# SPDX-License-Identifier: BSD 2-Clause License
+#
+
 import os
 
 import aiohttp

examples/deployment/modal-example/bot.py

@@ -1,3 +1,9 @@
+#
+# Copyright (c) 2024–2025, Daily
+#
+# SPDX-License-Identifier: BSD 2-Clause License
+#
+
 import asyncio
 import os
 import sys
@@ -10,8 +16,8 @@ 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.cartesia.tts import CartesiaTTSService
-from pipecat.services.openai import OpenAILLMService
+from pipecat.services.openai.llm import OpenAILLMService
 from pipecat.transports.services.daily import DailyParams, DailyTransport
 
 load_dotenv(override=True)
@@ -34,7 +40,7 @@ async def main(room_url: str, token: str):
     )
 
     tts = CartesiaTTSService(
-        api_key=os.getenv("CARTESIA_API_KEY", ""), voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22"
+        api_key=os.getenv("CARTESIA_API_KEY", ""), voice_id="71a7ad14-091c-4e8e-a314-022ece01c121"
     )
 
     llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")

examples/deployment/pipecat-cloud-daily-pstn-server/README.md

@@ -0,0 +1,178 @@
+# Handling PSTN/SIP Dial-in on Pipecat Cloud
+
+This repository contains two server implementations for handling
+the pinless dial-in workflow in Pipecat Cloud. This is the companion to the
+Pipecat Cloud [pstn_sip starter image](https://github.com/daily-co/pipecat-cloud-images/tree/main/pipecat-starters/pstn_sip).
+In addition you can use `/api/dial` to trigger dial-out, and
+eventually, call-transfers.
+
+1. [FastAPI Server](fastapi-webhook-server/README.md) -
+   A FastAPI implementation that handles PSTN (Public Switched Telephone
+   Network) and SIP (Session Initiation Protocol) calls using the Daily API.
+
+2. [Next.js Serverless](nextjs-webhook-server/README.md) -
+   A Next.js API implementation designed for deployment on Vercel's
+   serverless platform.
+
+Both implementations provide:
+
+- HMAC signature validation for pinless webhook
+- Structured logging
+- Support for dial-in and dial-out settings
+- Voicemail detection and call transfer functionality (coming soon)
+- Test request handling
+
+## Choosing an Implementation
+
+- Use the **FastAPI Server** if you:
+
+  - Need a standalone server
+  - Prefer Python and FastAPI
+  - Want to deploy to traditional hosting platforms
+
+- Use the **Next.js Serverless** implementation if you:
+  - Want serverless deployment
+  - Prefer JavaScript/TypeScript
+  - Already use Next.js and Vercel for other projects
+  - Need quick scaling and zero maintenance
+
+## Prerequisites
+
+### Environment Variables
+
+Both implementations require similar environment variables:
+
+- `PIPECAT_CLOUD_API_KEY`: Pipecat Cloud API Key, begins with pk\_\*
+- `AGENT_NAME`: Your Daily agent name
+- `PINLESS_HMAC_SECRET`: Your HMAC secret for request verification
+- `LOG_LEVEL`: (Optional) Logging level (defaults to 'info')
+
+See the individual README files in each implementation directory for
+specific setup instructions.
+
+### Phone number setup
+
+You can buy a phone number through the Pipecat Cloud Dashboard:
+
+1. Go to `Settings` > `Telephony`
+2. Follow the UI to purchase a phone number
+3. Configure the webhook URL to receive incoming calls (e.g. `https://my-webhook-url.com/api/dial`)
+
+Or purchase the number using Daily's
+[PhoneNumbers API](https://docs.daily.co/reference/rest-api/phone-numbers).
+
+```bash
+curl --request POST \
+--url https://api.daily.co/v1/domain-dialin-config \
+--header 'Authorization: Bearer $TOKEN' \
+--header 'Content-Type: application/json' \
+--data-raw '{
+	"type": "pinless_dialin",
+	"name_prefix": "Customer1",
+    "phone_number": "+1PURCHASED_NUM",
+	"room_creation_api": "https://example.com/api/dial",
+    "hold_music_url": "https://example.com/static/ringtone.mp3",
+	"timeout_config": {
+		"message": "No agent is available right now"
+	}
+}'
+```
+
+The API will return a static SIP URI (`sip_uri`) that can be called
+from other SIP services.
+
+### `room_creation_api`
+
+To make and receive calls currently you have to host a server that
+handles incoming calls. In the coming weeks, incoming calls will be
+directly handled within Daily and we will expose an endpoint similar
+to `{service}/start` that will manage this for you.
+
+In the meantime, the server described below serves as the webhook
+handler for the `room_creation_api`. Configure your pinless phone
+number or SIP interconnect to the `ngrok` tunnel or
+the actual server URL, append `/api/dial` to the webhook URL.
+
+## Example curl commands
+
+Note: Replace `http://localhost:3000` with your actual server URL and
+phone numbers with valid values for your use case.
+
+### Dialin Request
+
+The server will receive a request when a call is received from Daily.
+
+### Dialout Request
+
+Dial a number, will use any purchased number
+
+```bash
+curl -X POST http://localhost:3000/api/dial \
+  -H "Content-Type: application/json" \
+  -d '{
+    "dialout_settings": [
+      {
+        "phoneNumber": "+1234567890",
+      }
+    ]
+  }'
+```
+
+Dial a number with callerId, which is the UUID of a purchased number.
+
+```bash
+curl -X POST http://localhost:3000/api/dial \
+  -H "Content-Type: application/json" \
+  -d '{
+    "dialout_settings": [
+      {
+        "phoneNumber": "+1234567890",
+        "callerId": "purchased_phone_uuid"
+      }
+    ]
+  }'
+```
+
+Dial a number
+
+```bash
+curl -X POST http://localhost:3000/api/dial \
+  -H "Content-Type: application/json" \
+  -d '{
+    "dialout_settings": [
+      {
+        "phoneNumber": "+1234567890",
+        "callerId": "purchased_phone_uuid"
+      }
+    ]
+  }'
+```
+
+### Advanced Request with Voicemail Detection
+
+```bash
+curl -X POST http://localhost:3000/api/dial \
+  -H "Content-Type: application/json" \
+  -d '{
+    "To": "+1234567890",
+    "From": "+1987654321",
+    "callId": "call-uuid-123",
+    "callDomain": "domain-uuid-456",
+    "dialout_settings": [
+      {
+        "phoneNumber": "+1234567890",
+        "callerId": "purchased_phone_uuid"
+      }
+    ],
+    "voicemail_detection": {
+      "testInPrebuilt": true
+    },
+    "call_transfer": {
+      "mode": "dialout",
+      "speakSummary": true,
+      "storeSummary": true,
+      "operatorNumber": "+1234567890",
+      "testInPrebuilt": true
+    }
+  }'
+```

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

@@ -0,0 +1,98 @@
+# FastAPI server for handling Daily PSTN/SIP Webhook
+
+A FastAPI server that handles PSTN (Public Switched Telephone Network) and SIP (Session Initiation Protocol) calls using the Daily API.
+
+## Setup
+
+1. Clone the repository
+
+2. Navigate to the `fastapi-webhook-server` directory:
+
+   ```bash
+   cd fastapi-webhook-server
+   ```
+
+3. Install dependencies:
+
+   ```bash
+   pip install -r requirements.txt
+   ```
+
+4. Copy `env.example` to `.env`:
+
+   ```bash
+   cp env.example .env
+   ```
+
+5. Update `.env` with your credentials:
+
+   - `AGENT_NAME`: Your Daily agent name
+   - `PIPECAT_CLOUD_API_KEY`: Your Daily API key
+   - `PINLESS_HMAC_SECRET`: Your HMAC secret for request verification
+
+## Running the Server
+
+Start the server:
+
+```bash
+python server.py
+```
+
+The server will run on `http://localhost:7860` and you can expose it via ngrok for testing:
+
+```bash
+`ngrok http 7860`
+```
+
+> Tip: Use a subdomain for a consistent URL (e.g. `ngrok http -subdomain=mydomain http://localhost:7860`)
+
+## API Endpoints
+
+### GET /
+
+Health check endpoint that returns a "Hello, World!" message.
+
+### POST /api/dial
+
+Initiates a PSTN/SIP call with the following request body format:
+
+```json
+{
+  "To": "+14152251493",
+  "From": "+14158483432",
+  "callId": "string-contains-uuid",
+  "callDomain": "string-contains-uuid",
+  "dialout_settings": [
+    {
+      "phoneNumber": "+14158483432",
+      "callerId": "+14152251493"
+    }
+  ],
+  "voicemail_detection": {
+    "testInPrebuilt": true
+  },
+  "call_transfer": {
+    "mode": "dialout",
+    "speakSummary": true,
+    "storeSummary": true,
+    "operatorNumber": "+14152250006",
+    "testInPrebuilt": true
+  }
+}
+```
+
+#### Response
+
+Returns a JSON object containing:
+
+- `status`: Success/failure status
+- `data`: Response from Daily API
+- `room_properties`: Properties of the created Daily room
+
+## Error Handling
+
+- 401: Invalid signature
+- 400: Invalid authorization header (e.g. missing Daily API key in bot.py)
+- 405: Method not allowed (e.g. incorrect route on the webhook URL)
+- 500: Server errors (missing API key, network issues)
+- Other status codes are passed through from the Daily API

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

@@ -0,0 +1,3 @@
+AGENT_NAME="your-agent-name"
+PIPECAT_CLOUD_API_KEY="your-daily-api-key"
+PINLESS_HMAC_SECRET="hmac-secret-pinless-dialin"

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

@@ -0,0 +1,6 @@
+fastapi
+uvicorn
+python-dotenv
+requests
+pydantic
+loguru

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

@@ -0,0 +1,201 @@
+#
+# Copyright (c) 2025, Daily
+#
+# SPDX-License-Identifier: BSD 2-Clause License
+#
+
+# server.py
+
+
+import base64  # for calculating hmac signature
+import hmac
+import os  # for accessing environment variables
+import time  # for setting expiration time
+from typing import Any, Dict, List, Optional
+
+import requests
+from dotenv import load_dotenv
+from fastapi import FastAPI, HTTPException, Request
+from loguru import logger
+from pydantic import BaseModel, Field
+
+load_dotenv(override=True)
+
+app = FastAPI()
+
+
+class RoomRequest(BaseModel):
+    test: Optional[str] = Field(None, alias="Test", description="Test field")
+    To: Optional[str] = Field(None, alias="to", description="Destination phone number")
+    From: Optional[str] = Field(None, alias="from", description="Source phone number")
+    callId: Optional[str] = Field(None, alias="call_id", description="Unique call identifier")
+    callDomain: Optional[str] = Field(
+        None, alias="call_domain", description="Call domain identifier"
+    )
+    dialout_settings: Optional[List[Dict[str, Any]]] = Field(
+        None, description="An array of phone numbers or SIP URIs to dialout to"
+    )
+    voicemail_detection: Optional[Dict[str, Any]] = Field(
+        None, description="A flag to perform voicemail or answeing-machine detection"
+    )
+    call_transfer: Optional[Dict[str, Any]] = Field(None, description="to initiate a call transfer")
+
+    class Config:
+        populate_by_name = True
+        alias_generator = None
+
+
+"""
+    body can contain any fields, but for handling PSTN/SIP, 
+    we recommend sending the following custom values:
+    dialin, dialout, voicemail detection, and call transfer
+    
+    
+    "To": "+14152251493",
+    "From": "+14158483432",
+    "callId": "string-contains-uuid",
+    "callDomain": "string-contains-uuid"
+    These need to be remapped to dialin_settings
+
+    "dialout_settings": [
+        {"phoneNumber": "+14158483432", "callerId": "+14152251493"}, 
+        {"sipUri": "sip:username@sip.hostname"}
+        ],
+    },
+
+    voicemail_detection:{
+        testInPrebuilt: true
+    },
+
+    "call_transfer": {
+        "mode": "dialout",
+        "speakSummary": true,
+        "storeSummary": true,
+        "operatorNumber": "+14152250006",
+        "testInPrebuilt": true
+    }
+"""
+
+
+@app.get("/")
+async def read_root():
+    return {"message": "Hello, World!"}
+
+
+@app.post("/api/dial")
+async def dial(request: RoomRequest, raw_request: Request):
+    logger.info("Incoming request to /dial:")
+    logger.info(f"Headers: {dict(raw_request.headers)}")
+    raw_body = await raw_request.body()
+    raw_body_str = raw_body.decode()
+    logger.info(f"Raw body: {raw_body_str}")
+    logger.info(f"Parsed body: {request.dict()}")
+
+    # calculate signature and compare/verify
+    hmac_secret = os.getenv("PINLESS_HMAC_SECRET")
+    timestamp = raw_request.headers.get("x-pinless-timestamp")
+    signature = raw_request.headers.get("x-pinless-signature")
+
+    if not hmac_secret:
+        logger.debug("Skipping HMAC validation - PINLESS_HMAC_SECRET not set")
+    elif timestamp and signature:
+        message = timestamp + "." + raw_body_str
+
+        base64_decoded_secret = base64.b64decode(hmac_secret)
+        computed_signature = base64.b64encode(
+            hmac.new(base64_decoded_secret, message.encode(), "sha256").digest()
+        ).decode()
+
+        if computed_signature != signature:
+            logger.error(f"Invalid signature. Expected {signature}, got {computed_signature}")
+            raise HTTPException(status_code=401, detail="Invalid signature")
+    else:
+        logger.debug("Skipping HMAC validation - no signature headers present")
+
+    if request.test == "test":
+        logger.debug("Test request received")
+        return {"status": "success", "message": "Test request received"}
+
+    dialin_settings = None
+    # these fields are camelCase in the request
+    required_fields = ["To", "From", "callId", "callDomain"]
+    if all(
+        field in request.dict() and request.dict()[field] is not None for field in required_fields
+    ):
+        # transform from camelCase to snake_case because daily-python expects snake_case
+        dialin_settings = {
+            "From": request.From,
+            "To": request.To,
+            "call_id": request.callId,
+            "call_domain": request.callDomain,
+            # transform from camelCase to snake_case
+        }
+        logger.debug(f"Populated dialin_settings from request: {dialin_settings}")
+
+    daily_room_properties = {
+        "enable_dialout": request.dialout_settings is not None,
+    }
+
+    if dialin_settings is not None:
+        sip_config = {
+            "display_name": request.From,
+            "sip_mode": "dial-in",
+            "num_endpoints": 2 if request.call_transfer is not None else 1,
+        }
+        daily_room_properties["sip"] = sip_config
+
+    # Setting default expiry to 5 minutes from now
+    daily_room_properties["exp"] = int(time.time()) + (5 * 60)
+
+    logger.debug(f"Daily room properties: {daily_room_properties}")
+    payload = {
+        "createDailyRoom": True,
+        "dailyRoomProperties": daily_room_properties,
+        "body": {
+            "dialin_settings": dialin_settings,
+            "dialout_settings": request.dialout_settings,
+            "voicemail_detection": request.voicemail_detection,
+            "call_transfer": request.call_transfer,
+        },
+    }
+
+    pcc_api_key = os.getenv("PIPECAT_CLOUD_API_KEY")
+    agent_name = os.getenv("AGENT_NAME", "my-first-agent")
+
+    if not pcc_api_key:
+        raise HTTPException(status_code=500, detail="DAILY_API_KEY environment variable is not set")
+
+    headers = {"Authorization": f"Bearer {pcc_api_key}", "Content-Type": "application/json"}
+
+    url = f"https://api.pipecat.daily.co/v1/public/{agent_name}/start"
+
+    logger.debug(f"Making API call to Daily: {url} {headers} {payload}")
+
+    try:
+        response = requests.post(url, json=payload, headers=headers)
+        response.raise_for_status()
+        response_data = response.json()
+        logger.debug(f"Response: {response_data}")
+        return {
+            "status": "success",
+            "data": response_data,
+            "room_properties": daily_room_properties,
+        }
+    except requests.exceptions.HTTPError as e:
+        # Pass through the status code and error details from the Daily API
+        status_code = e.response.status_code
+        error_detail = e.response.json() if e.response.content else str(e)
+        logger.error(f"HTTP error: {error_detail}")
+        raise HTTPException(status_code=status_code, detail=error_detail)
+    except requests.exceptions.RequestException as e:
+        logger.error(f"Request error: {str(e)}")
+        raise HTTPException(status_code=500, detail=str(e))
+
+
+if __name__ == "__main__":
+    try:
+        import uvicorn
+
+        uvicorn.run(app, host="0.0.0.0", port=7860)
+    except KeyboardInterrupt:
+        logger.info("Server stopped manually")

examples/deployment/pipecat-cloud-daily-pstn-server/nextjs-webhook-server/.gitignore

@@ -0,0 +1,53 @@
+# dependencies
+/node_modules
+/.pnp
+.pnp.js
+
+# testing
+/coverage
+
+# next.js
+/.next/
+/out/
+
+# production
+/build
+
+# misc
+.DS_Store
+*.pem
+
+# debug
+npm-debug.log*
+yarn-debug.log*
+yarn-error.log*
+.pnpm-debug.log*
+
+# local env files
+.env*.local
+
+# vercel
+.vercel
+
+# typescript
+*.tsbuildinfo
+next-env.d.ts
+
+# IDE specific files
+.idea/
+.vscode/
+*.swp
+*.swo
+
+# Logs
+logs
+*.log
+
+# OS generated files
+.DS_Store
+.DS_Store?
+._*
+.Spotlight-V100
+.Trashes
+ehthumbs.db
+Thumbs.db

examples/deployment/pipecat-cloud-daily-pstn-server/nextjs-webhook-server/README.md

@@ -0,0 +1,115 @@
+# Next.js server for handling Daily PSTN/SIP Webhook
+
+Next.js API routes for handling Daily PSTN/SIP Pipecat requests.
+
+## Features
+
+- API endpoint for handling Daily PSTN/SIP Pipecat requests
+- HMAC signature validation
+- Structured logging with Pino
+- Support for dial-in and dial-out settings
+- Voicemail detection and call transfer functionality
+- Test request handling
+
+## Setup
+
+1. Clone the repository
+
+2. Navigate to the `nextjs-webhook-server` directory:
+
+   ```bash
+   cd nextjs-webhook-server
+   ```
+
+3. Install dependencies:
+
+   ```bash
+   npm install
+   ```
+
+4. Create `.env.local` file with your credentials:
+
+   ```bash
+   cp env.local.example .env.local
+   ```
+
+5. Update your `.env` with your secrets:
+
+   ```bash
+   PIPECAT_CLOUD_API_KEY=pk_*
+   AGENT_NAME=my-first-agent
+   PINLESS_HMAC_SECRET=your_hmac_secret
+   LOG_LEVEL=info
+   ```
+
+### Running the server
+
+Run the development server:
+
+```bash
+npm run dev
+```
+
+The server will run on `http://localhost:7860` and you can expose it via ngrok for testing:
+
+```bash
+`ngrok http 7860`
+```
+
+> Tip: Use a subdomain for a consistent URL (e.g. `ngrok http -subdomain=mydomain http://localhost:7860`)
+
+## API Endpoints
+
+### GET /api
+
+Returns a simple "Hello, World!" message with a cute cat emoji to verify the server is running.
+
+### POST /api/dial
+
+Handles dial-in and dial-out requests for Pipecat Cloud.
+
+#### Test Requests
+
+The endpoint handles test requests when a webhook is configured. Send a request with `"Test": "test"` to verify your setup:
+
+```json
+{
+  "Test": "test"
+}
+```
+
+#### Production Request Format
+
+```json
+{
+  // for dial-in from webhook
+  "To": "+14152251493",
+  "From": "+14158483432",
+  "callId": "string-contains-uuid",
+  "callDomain": "string-contains-uuid",
+  // for making a dial out to a phone or SIP
+  "dialout_settings": [
+    { "phoneNumber": "+14158483432", "callerId": "purchased_phone_uuid" },
+    { "sipUri": "sip:username@sip.hostname.com" }
+  ]
+}
+```
+
+## Deployment
+
+The application is configured for Vercel deployment:
+
+1. Push your code to a Git repository
+2. Import your project in Vercel dashboard
+3. Configure environment variables:
+   - `PIPECAT_CLOUD_API_KEY`
+   - `AGENT_NAME`
+   - `PINLESS_HMAC_SECRET`
+   - `LOG_LEVEL` (optional, defaults to 'info')
+4. Deploy!
+
+## Security
+
+- HMAC signature validation for request authentication
+- Environment variables for sensitive credentials
+- Method validation (POST only for /dial)

examples/deployment/pipecat-cloud-daily-pstn-server/nextjs-webhook-server/env.local.example

@@ -0,0 +1,4 @@
+AGENT_NAME=my-first-agent
+PIPECAT_CLOUD_API_KEY=your_daily_api_key
+PINLESS_HMAC_SECRET=your_hmac_secret
+LOG_LEVEL="info"

examples/deployment/pipecat-cloud-daily-pstn-server/nextjs-webhook-server/package.json

@@ -0,0 +1,22 @@
+{
+  "name": "my-daily-app",
+  "version": "0.1.0",
+  "private": true,
+  "scripts": {
+    "dev": "next dev -p 7860",
+    "build": "next build",
+    "start": "next start -p 7860",
+    "lint": "next lint"
+  },
+  "dependencies": {
+    "axios": "^1.6.0",
+    "next": "^14.0.0",
+    "pino": "^8.15.0",
+    "react": "^18.2.0",
+    "react-dom": "^18.2.0"
+  },
+  "devDependencies": {
+    "eslint": "^8.46.0",
+    "eslint-config-next": "^14.0.0"
+  }
+}

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

@@ -0,0 +1,175 @@
+import { logger } from '../../lib/utils'; 
+import axios from 'axios';
+import crypto from 'crypto';
+
+const validateSignature = (body, signature, timestamp, secret) => {
+  // Skip if any required fields are missing
+  if (!signature || !timestamp || !secret) {
+    logger.warn('Missing required fields for HMAC validation');
+    return true;
+  }
+
+  try {
+    const decodedSecret = Buffer.from(secret, 'base64');
+    const hmac = crypto.createHmac('sha256', decodedSecret);
+    const signatureData = `${timestamp}.${body}`;
+    const computedSignature = hmac.update(signatureData).digest('base64');
+    
+    logger.debug('Signature validation:', {
+      timestamp,
+      signatureData: signatureData.substring(0, 50) + '...',
+      computedSignature,
+      receivedSignature: signature
+    });
+    
+    return computedSignature === signature;
+  } catch (error) {
+    logger.error('Error validating signature:', error);
+    return true; // Allow request to proceed on error
+  }
+};
+
+export default async function handler(req, res) {
+  // Only allow POST requests
+  if (req.method !== 'POST') {
+    return res.status(405).json({ error: 'Method not allowed' });
+  }
+
+  try {
+    logger.info('Incoming request to /api/dial:');
+    logger.info(`Headers: ${JSON.stringify(req.headers)}`);
+    
+    const rawBody = JSON.stringify(req.body);
+    logger.info(`Raw body: ${rawBody}`);
+
+    const signature = req.headers['x-pinless-signature'];
+    const timestamp = req.headers['x-pinless-timestamp'];
+    
+    if (signature && timestamp) {
+      logger.info('Validating HMAC signature');
+      if (!validateSignature(rawBody, signature, timestamp, process.env.PINLESS_HMAC_SECRET)) {
+        logger.error('Invalid HMAC signature', { signature, timestamp });
+        return res.status(401).json({ 
+          error: 'Invalid signature',
+          message: 'Invalid HMAC signature'
+        });
+      }
+    } else {
+      logger.info('Skipping HMAC validation - no signature headers present');
+    }
+
+    // Extract request data
+    const {
+      Test: test,
+      To,
+      From,
+      callId,
+      callDomain,
+      dialout_settings,
+      voicemail_detection,
+      call_transfer
+    } = req.body;
+
+    // Handle test requests when a webhook is configured
+    if (test === 'test') {
+      logger.debug('Test request received');
+      return res.status(200).json({ status: 'success', message: 'Test request received' });
+    }
+
+    // Process dialin settings
+    let dialin_settings = null;
+    const requiredFields = ['To', 'From', 'callId', 'callDomain'];
+    
+    if (requiredFields.every(field => req.body[field] !== undefined && req.body[field] !== null)) {
+      dialin_settings = {
+        // snake_case because pipecat expects this format
+        From,
+        To,
+        call_id: callId, 
+        call_domain: callDomain,
+      };
+      logger.debug(`Populated dialin_settings from request: ${JSON.stringify(dialin_settings)}`);
+    }
+
+    // Set up Daily room properties
+    const daily_room_properties = {
+      enable_dialout: dialout_settings !== undefined && dialout_settings !== null,
+      exp: Math.floor(Date.now() / 1000) + (5 * 60), // 5 minutes from now
+    };
+
+    // Configure SIP if dialin settings are provided
+    if (dialin_settings !== null) {
+      const sip_config = {
+        display_name: From,
+        sip_mode: 'dial-in',
+        num_endpoints: call_transfer !== null ? 2 : 1,
+      };
+      daily_room_properties.sip = sip_config;
+    }
+
+    // Prepare payload for {service}/start API call
+    const payload = {
+      createDailyRoom: true,
+      dailyRoomProperties: daily_room_properties,
+      body: {
+        dialin_settings,
+        dialout_settings,
+        voicemail_detection,
+        call_transfer,
+      },
+    };
+
+    logger.debug(`Daily room properties: ${JSON.stringify(daily_room_properties)}`);
+
+    // Get Daily API key and agent name from environment variables
+    const pccApiKey = process.env.PIPECAT_CLOUD_API_KEY;
+    const agentName = process.env.AGENT_NAME || 'my-first-agent';
+
+    if (!pccApiKey) {
+      throw new Error('PIPECAT_CLOUD_API_KEY environment variable is not set');
+    }
+
+    // Set up headers for Daily API call
+    const headers = {
+      'Authorization': `Bearer ${pccApiKey}`,
+      'Content-Type': 'application/json',
+    };
+
+    const url = `https://api.pipecat.daily.co/v1/public/${agentName}/start`;
+    logger.debug(`Making API call to Daily: ${url} ${JSON.stringify(headers)} ${JSON.stringify(payload)}`);
+    
+    try {
+      const response = await axios.post(url, payload, { headers });
+      logger.debug(`Response: ${JSON.stringify(response.data)}`);
+      
+      return res.status(200).json({
+        status: 'success',
+        data: response.data,
+        room_properties: daily_room_properties,
+      });
+    } catch (error) {
+      if (error.response) {
+        // Pass through status code and error details from the Daily API
+        const statusCode = error.response.status;
+        const errorDetail = error.response.data || error.message;
+        logger.error(`HTTP error: ${JSON.stringify(errorDetail)}`);
+        return res.status(statusCode).json(errorDetail);
+      } else {
+        logger.error(`Request error: ${error.message}`);
+        return res.status(500).json({ error: error.message });
+      }
+    }    
+  } catch (error) {
+    logger.error(`Unexpected error: ${error.message}`);
+    return res.status(500).json({ error: 'Internal server error', message: error.message });
+  }
+}
+
+// Configure body parser to preserve raw body text
+export const config = {
+  api: {
+    bodyParser: {
+      sizeLimit: '1mb',
+    },
+  },
+};

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

@@ -0,0 +1,6 @@
+import { logger } from '../../lib/utils';
+
+export default function handler(req, res) {
+  logger.info('Received request to /api');
+  res.status(200).json({ message: 'Hello, World! from ᓚᘏᗢ' });
+}

examples/deployment/pipecat-cloud-daily-pstn-server/nextjs-webhook-server/vercel.js

@@ -0,0 +1,6 @@
+module.exports = {
+  version: 2,
+  buildCommand: "next build",
+  outputDirectory: ".next",
+  cleanUrls: true
+};

examples/deployment/pipecat-cloud-example/.gitignore

@@ -0,0 +1,94 @@
+# 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

examples/deployment/pipecat-cloud-example/Dockerfile

@@ -0,0 +1,7 @@
+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

examples/deployment/pipecat-cloud-example/README.md

@@ -0,0 +1,196 @@
+# 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.

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

@@ -0,0 +1,161 @@
+#
+# 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.tts import CartesiaTTSService
+from pipecat.services.openai.llm 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}")

examples/deployment/pipecat-cloud-example/env.example

@@ -0,0 +1,2 @@
+CARTESIA_API_KEY=
+OPENAI_API_KEY=

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

@@ -0,0 +1,46 @@
+#
+# 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)

examples/deployment/pipecat-cloud-example/pcc-deploy.toml

@@ -0,0 +1,6 @@
+agent_name = "my-first-agent"
+image = "your-username/my-first-agent:0.1"
+secret_set = "my-first-agent-secrets"
+
+[scaling]
+	min_instances = 0

examples/deployment/pipecat-cloud-example/requirements.txt

@@ -0,0 +1,3 @@
+pipecatcloud
+pipecat-ai[cartesia,daily,openai,silero]>=0.0.58
+python-dotenv~=1.0.1

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

@@ -0,0 +1,57 @@
+#
+# 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.frames.frames import EndFrame, TTSSpeakFrame
+from pipecat.pipeline.pipeline import Pipeline
+from pipecat.pipeline.runner import PipelineRunner
+from pipecat.pipeline.task import PipelineTask
+from pipecat.services.piper.tts import PiperTTSService
+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, _) = await configure(session)
+
+        transport = DailyTransport(
+            room_url, None, "Say One Thing", DailyParams(audio_out_enabled=True)
+        )
+
+        tts = PiperTTSService(
+            base_url=os.getenv("PIPER_BASE_URL"), aiohttp_session=session, sample_rate=24000
+        )
+
+        runner = PipelineRunner()
+
+        task = PipelineTask(Pipeline([tts, transport.output()]))
+
+        # Register an event handler so we can play the audio when the
+        # participant joins.
+        @transport.event_handler("on_first_participant_joined")
+        async def on_first_participant_joined(transport, participant):
+            await task.queue_frames(
+                [TTSSpeakFrame(f"Hello there, how are you today ?"), EndFrame()]
+            )
+
+        await runner.run(task)
+
+
+if __name__ == "__main__":
+    asyncio.run(main())

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

@@ -17,7 +17,7 @@ from pipecat.frames.frames import EndFrame, TTSSpeakFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineTask
-from pipecat.services.cartesia import CartesiaTTSService
+from pipecat.services.cartesia.tts import CartesiaTTSService
 from pipecat.transports.services.daily import DailyParams, DailyTransport
 
 load_dotenv(override=True)
@@ -36,7 +36,7 @@ async def main():
 
         tts = CartesiaTTSService(
             api_key=os.getenv("CARTESIA_API_KEY"),
-            voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22",  # British Lady
+            voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
         )
 
         runner = PipelineRunner()

examples/foundational/01a-local-audio.py

@@ -15,7 +15,7 @@ from pipecat.frames.frames import EndFrame, TTSSpeakFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineTask
-from pipecat.services.cartesia import CartesiaTTSService
+from pipecat.services.cartesia.tts import CartesiaTTSService
 from pipecat.transports.local.audio import LocalAudioTransport, LocalAudioTransportParams
 
 load_dotenv(override=True)
@@ -29,7 +29,7 @@ async def main():
 
     tts = CartesiaTTSService(
         api_key=os.getenv("CARTESIA_API_KEY"),
-        voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22",  # British Lady
+        voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
     )
 
     pipeline = Pipeline([tts, transport.output()])

examples/foundational/01b-livekit-audio.py

@@ -1,3 +1,9 @@
+#
+# Copyright (c) 2024–2025, Daily
+#
+# SPDX-License-Identifier: BSD 2-Clause License
+#
+
 import argparse
 import asyncio
 import os
@@ -12,7 +18,7 @@ from pipecat.frames.frames import TextFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineTask
-from pipecat.services.cartesia import CartesiaTTSService
+from pipecat.services.cartesia.tts import CartesiaTTSService
 from pipecat.transports.services.livekit import LiveKitParams, LiveKitTransport
 
 load_dotenv(override=True)
@@ -83,7 +89,7 @@ async def main():
 
         tts = CartesiaTTSService(
             api_key=os.getenv("CARTESIA_API_KEY"),
-            voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22",  # British Lady
+            voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
         )
 
         runner = PipelineRunner()

examples/foundational/01c-fastpitch.py

@@ -17,7 +17,7 @@ from pipecat.frames.frames import EndFrame, TTSSpeakFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineTask
-from pipecat.services.riva import FastPitchTTSService
+from pipecat.services.riva.tts import FastPitchTTSService
 from pipecat.transports.services.daily import DailyParams, DailyTransport
 
 load_dotenv(override=True)

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

@@ -17,8 +17,8 @@ from pipecat.frames.frames import EndFrame, LLMMessagesFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineTask
-from pipecat.services.cartesia import CartesiaTTSService
+from pipecat.services.cartesia.tts import CartesiaTTSService
-from pipecat.services.openai import OpenAILLMService
+from pipecat.services.openai.llm import OpenAILLMService
 from pipecat.transports.services.daily import DailyParams, DailyTransport
 
 load_dotenv(override=True)
@@ -37,7 +37,7 @@ async def main():
 
         tts = CartesiaTTSService(
             api_key=os.getenv("CARTESIA_API_KEY"),
-            voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22",  # British Lady
+            voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
         )
 
         llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")

examples/foundational/03-still-frame.py

@@ -17,7 +17,7 @@ from pipecat.frames.frames import TextFrame
 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.services.fal.image import FalImageGenService
 from pipecat.transports.services.daily import DailyParams, DailyTransport
 
 load_dotenv(override=True)

examples/foundational/03a-local-still-frame.py

@@ -17,7 +17,7 @@ from pipecat.frames.frames import TextFrame
 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.services.fal.image import FalImageGenService
 from pipecat.transports.local.tk import TkLocalTransport, TkTransportParams
 
 load_dotenv(override=True)

examples/foundational/03b-still-frame-imagen.py

@@ -17,7 +17,7 @@ from pipecat.frames.frames import EndFrame, TextFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.services.google import GoogleImageGenService
+from pipecat.services.google.image import GoogleImageGenService
 from pipecat.transports.services.daily import DailyParams, DailyTransport
 
 load_dotenv(override=True)

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

@@ -27,9 +27,9 @@ from pipecat.pipeline.sync_parallel_pipeline import SyncParallelPipeline
 from pipecat.pipeline.task import PipelineTask
 from pipecat.processors.aggregators.sentence import SentenceAggregator
 from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
-from pipecat.services.cartesia import CartesiaHttpTTSService
+from pipecat.services.cartesia.tts import CartesiaHttpTTSService
-from pipecat.services.fal import FalImageGenService
+from pipecat.services.fal.image import FalImageGenService
-from pipecat.services.openai import OpenAILLMService
+from pipecat.services.openai.llm import OpenAILLMService
 from pipecat.transports.services.daily import DailyParams, DailyTransport
 
 load_dotenv(override=True)
@@ -87,7 +87,7 @@ async def main():
 
         tts = CartesiaHttpTTSService(
             api_key=os.getenv("CARTESIA_API_KEY"),
-            voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22",  # British Lady
+            voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
         )
 
         imagegen = FalImageGenService(

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

@@ -27,9 +27,9 @@ from pipecat.pipeline.sync_parallel_pipeline import SyncParallelPipeline
 from pipecat.pipeline.task import PipelineTask
 from pipecat.processors.aggregators.sentence import SentenceAggregator
 from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
-from pipecat.services.cartesia import CartesiaHttpTTSService
+from pipecat.services.cartesia.tts import CartesiaHttpTTSService
-from pipecat.services.fal import FalImageGenService
+from pipecat.services.fal.image import FalImageGenService
-from pipecat.services.openai import OpenAILLMService
+from pipecat.services.openai.llm import OpenAILLMService
 from pipecat.transports.local.tk import TkLocalTransport, TkTransportParams
 
 load_dotenv(override=True)
@@ -97,7 +97,7 @@ async def main():
 
             tts = CartesiaHttpTTSService(
                 api_key=os.getenv("CARTESIA_API_KEY"),
-                voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22",  # British Lady
+                voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
             )
 
             imagegen = FalImageGenService(

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

@@ -26,8 +26,8 @@ from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
 from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
 from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
-from pipecat.services.cartesia import CartesiaTTSService
+from pipecat.services.cartesia.tts import CartesiaTTSService
-from pipecat.services.openai import OpenAILLMService
+from pipecat.services.openai.llm import OpenAILLMService
 from pipecat.transports.services.daily import DailyParams, DailyTransport
 
 load_dotenv(override=True)
@@ -74,7 +74,7 @@ async def main():
 
         tts = CartesiaTTSService(
             api_key=os.getenv("CARTESIA_API_KEY"),
-            voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22",  # British Lady
+            voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
         )
 
         llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")

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

@@ -27,8 +27,8 @@ from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
 from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
 from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
-from pipecat.services.cartesia import CartesiaTTSService
+from pipecat.services.cartesia.tts import CartesiaTTSService
-from pipecat.services.openai import OpenAILLMService
+from pipecat.services.openai.llm import OpenAILLMService
 from pipecat.transports.services.daily import DailyParams, DailyTransport
 
 load_dotenv(override=True)
@@ -93,7 +93,7 @@ async def main():
 
         tts = CartesiaTTSService(
             api_key=os.getenv("CARTESIA_API_KEY"),
-            voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22",  # British Lady
+            voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
         )
 
         llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")

examples/foundational/07-interruptible-vad.py

@@ -18,8 +18,8 @@ from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
 from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
 from pipecat.processors.audio.vad.silero import SileroVAD
-from pipecat.services.cartesia import CartesiaTTSService
+from pipecat.services.cartesia.tts import CartesiaTTSService
-from pipecat.services.openai import OpenAILLMService
+from pipecat.services.openai.llm import OpenAILLMService
 from pipecat.transports.services.daily import DailyParams, DailyTransport
 
 load_dotenv(override=True)
@@ -47,7 +47,7 @@ async def main():
 
         tts = CartesiaTTSService(
             api_key=os.getenv("CARTESIA_API_KEY"),
-            voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22",  # British Lady
+            voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
         )
 
         llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")

examples/foundational/07-interruptible.py

@@ -18,8 +18,8 @@ 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.cartesia.tts import CartesiaTTSService
-from pipecat.services.openai import OpenAILLMService
+from pipecat.services.openai.llm import OpenAILLMService
 from pipecat.transports.services.daily import DailyParams, DailyTransport
 
 load_dotenv(override=True)
@@ -46,7 +46,7 @@ async def main():
 
         tts = CartesiaTTSService(
             api_key=os.getenv("CARTESIA_API_KEY"),
-            voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22",  # British Lady
+            voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
         )
 
         llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")

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

@@ -18,8 +18,8 @@ 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.anthropic import AnthropicLLMService
+from pipecat.services.anthropic.llm import AnthropicLLMService
-from pipecat.services.cartesia import CartesiaTTSService
+from pipecat.services.cartesia.tts import CartesiaTTSService
 from pipecat.transports.services.daily import DailyParams, DailyTransport
 
 load_dotenv(override=True)
@@ -46,7 +46,7 @@ async def main():
 
         tts = CartesiaTTSService(
             api_key=os.getenv("CARTESIA_API_KEY"),
-            voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22",  # British Lady
+            voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
         )
 
         llm = AnthropicLLMService(

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

@@ -28,7 +28,7 @@ from pipecat.processors.aggregators.llm_response import (
     LLMUserResponseAggregator,
 )
 from pipecat.processors.frameworks.langchain import LangchainProcessor
-from pipecat.services.cartesia import CartesiaTTSService
+from pipecat.services.cartesia.tts import CartesiaTTSService
 from pipecat.transports.services.daily import DailyParams, DailyTransport
 
 load_dotenv(override=True)
@@ -64,7 +64,7 @@ async def main():
 
         tts = CartesiaTTSService(
             api_key=os.getenv("CARTESIA_API_KEY"),
-            voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22",  # British Lady
+            voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
         )
 
         prompt = ChatPromptTemplate.from_messages(

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

@@ -24,8 +24,9 @@ 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.deepgram import DeepgramSTTService, DeepgramTTSService
+from pipecat.services.deepgram.stt import DeepgramSTTService
-from pipecat.services.openai import OpenAILLMService
+from pipecat.services.deepgram.tts import DeepgramTTSService
+from pipecat.services.openai.llm import OpenAILLMService
 from pipecat.transports.services.daily import DailyParams, DailyTransport
 
 load_dotenv(override=True)

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

@@ -18,8 +18,9 @@ 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.deepgram import DeepgramSTTService, DeepgramTTSService
+from pipecat.services.deepgram.stt import DeepgramSTTService
-from pipecat.services.openai import OpenAILLMService
+from pipecat.services.deepgram.tts import DeepgramTTSService
+from pipecat.services.openai.llm import OpenAILLMService
 from pipecat.transports.services.daily import DailyParams, DailyTransport
 
 load_dotenv(override=True)

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

@@ -0,0 +1,103 @@
+#
+# 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.tts import ElevenLabsHttpTTSService
+from pipecat.services.openai.llm 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())

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

@@ -18,8 +18,8 @@ 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 ElevenLabsTTSService
+from pipecat.services.elevenlabs.tts import ElevenLabsTTSService
-from pipecat.services.openai import OpenAILLMService
+from pipecat.services.openai.llm import OpenAILLMService
 from pipecat.transports.services.daily import DailyParams, DailyTransport
 
 load_dotenv(override=True)

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

@@ -18,8 +18,8 @@ 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.openai.llm import OpenAILLMService
-from pipecat.services.playht import PlayHTHttpTTSService
+from pipecat.services.playht.tts import PlayHTHttpTTSService
 from pipecat.transports.services.daily import DailyParams, DailyTransport
 
 load_dotenv(override=True)

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

@@ -18,8 +18,8 @@ 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.openai.llm import OpenAILLMService
-from pipecat.services.playht import PlayHTTTSService
+from pipecat.services.playht.tts import PlayHTTTSService
 from pipecat.transcriptions.language import Language
 from pipecat.transports.services.daily import DailyParams, DailyTransport
 
@@ -48,7 +48,7 @@ async def main():
         tts = PlayHTTTSService(
             user_id=os.getenv("PLAYHT_USER_ID"),
             api_key=os.getenv("PLAYHT_API_KEY"),
-            voice_url="s3://voice-cloning-zero-shot/d9ff78ba-d016-47f6-b0ef-dd630f59414e/female-cs/manifest.json",
+            voice_url="s3://voice-cloning-zero-shot/e46b4027-b38d-4d24-b292-38fbca2be0ef/original/manifest.json",
             params=PlayHTTTSService.InputParams(language=Language.EN),
         )
 

examples/foundational/07f-interruptible-azure.py

@@ -18,7 +18,9 @@ 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.azure import AzureLLMService, AzureSTTService, AzureTTSService
+from pipecat.services.azure.llm import AzureLLMService
+from pipecat.services.azure.stt import AzureSTTService
+from pipecat.services.azure.tts import AzureTTSService
 from pipecat.transports.services.daily import DailyParams, DailyTransport
 
 load_dotenv(override=True)

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

@@ -18,7 +18,9 @@ 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, OpenAISTTService, OpenAITTSService
+from pipecat.services.openai.llm import OpenAILLMService
+from pipecat.services.openai.stt import OpenAISTTService
+from pipecat.services.openai.tts import OpenAITTSService
 from pipecat.transports.services.daily import DailyParams, DailyTransport
 
 load_dotenv(override=True)
@@ -51,16 +53,20 @@ async def main():
         #     api_key="gsk_***",
         #     model="whisper-large-v3",
         # )
-        stt = OpenAISTTService(api_key=os.getenv("OPENAI_API_KEY"), model="whisper-1")
+        stt = OpenAISTTService(
+            api_key=os.getenv("OPENAI_API_KEY"),
+            model="gpt-4o-transcribe-latest",
+            prompt="Expect words related to dogs, such as breed names.",
+        )
 
-        tts = OpenAITTSService(api_key=os.getenv("OPENAI_API_KEY"), voice="alloy")
+        tts = OpenAITTSService(api_key=os.getenv("OPENAI_API_KEY"), voice="ballad")
 
         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.",
+                "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.",
             },
         ]
 

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

@@ -19,8 +19,8 @@ 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.cartesia.tts import CartesiaTTSService
-from pipecat.services.openpipe import OpenPipeLLMService
+from pipecat.services.openpipe.llm import OpenPipeLLMService
 from pipecat.transports.services.daily import DailyParams, DailyTransport
 
 load_dotenv(override=True)
@@ -47,7 +47,7 @@ async def main():
 
         tts = CartesiaTTSService(
             api_key=os.getenv("CARTESIA_API_KEY"),
-            voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22",  # British Lady
+            voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
         )
 
         timestamp = int(time.time())

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

@@ -18,8 +18,8 @@ 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.openai.llm import OpenAILLMService
-from pipecat.services.xtts import XTTSService
+from pipecat.services.xtts.tts import XTTSService
 from pipecat.transports.services.daily import DailyParams, DailyTransport
 
 load_dotenv(override=True)

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

@@ -18,9 +18,11 @@ 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.cartesia.tts import CartesiaTTSService
-from pipecat.services.gladia import GladiaSTTService
+from pipecat.services.gladia.config import GladiaInputParams, LanguageConfig
-from pipecat.services.openai import OpenAILLMService
+from pipecat.services.gladia.stt import GladiaSTTService
+from pipecat.services.openai.llm import OpenAILLMService
+from pipecat.transcriptions.language import Language
 from pipecat.transports.services.daily import DailyParams, DailyTransport
 
 load_dotenv(override=True)
@@ -47,11 +49,16 @@ async def main():
 
         stt = GladiaSTTService(
             api_key=os.getenv("GLADIA_API_KEY"),
+            params=GladiaInputParams(
+                language_config=LanguageConfig(
+                    languages=[Language.EN],
+                )
+            ),
         )
 
         tts = CartesiaTTSService(
             api_key=os.getenv("CARTESIA_API_KEY"),
-            voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22",  # British Lady
+            voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
         )
 
         llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")

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

@@ -18,8 +18,8 @@ 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.lmnt import LmntTTSService
+from pipecat.services.lmnt.tts import LmntTTSService
-from pipecat.services.openai import OpenAILLMService
+from pipecat.services.openai.llm import OpenAILLMService
 from pipecat.transports.services.daily import DailyParams, DailyTransport
 
 load_dotenv(override=True)

examples/foundational/07l-interruptible-together.py

@@ -17,9 +17,9 @@ 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.services.ai_services import OpenAILLMContext
+from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
-from pipecat.services.cartesia import CartesiaTTSService
+from pipecat.services.cartesia.tts import CartesiaTTSService
-from pipecat.services.together import TogetherLLMService
+from pipecat.services.together.llm import TogetherLLMService
 from pipecat.transports.services.daily import DailyParams, DailyTransport
 
 load_dotenv(override=True)
@@ -46,7 +46,7 @@ async def main():
 
         tts = CartesiaTTSService(
             api_key=os.getenv("CARTESIA_API_KEY"),
-            voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22",  # British Lady
+            voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
         )
 
         llm = TogetherLLMService(

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

@@ -18,9 +18,9 @@ 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.aws import PollyTTSService
+from pipecat.services.aws.tts import PollyTTSService
-from pipecat.services.deepgram import DeepgramSTTService
+from pipecat.services.deepgram.stt import DeepgramSTTService
-from pipecat.services.openai import OpenAILLMService
+from pipecat.services.openai.llm import OpenAILLMService
 from pipecat.transports.services.daily import DailyParams, DailyTransport
 
 load_dotenv(override=True)

examples/foundational/07n-interruptible-google.py

@@ -18,7 +18,9 @@ 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.google import GoogleLLMService, GoogleSTTService, GoogleTTSService
+from pipecat.services.google.llm import GoogleLLMService
+from pipecat.services.google.stt import GoogleSTTService
+from pipecat.services.google.tts import GoogleTTSService
 from pipecat.transcriptions.language import Language
 from pipecat.transports.services.daily import DailyParams, DailyTransport
 

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

@@ -18,9 +18,9 @@ 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.assemblyai import AssemblyAISTTService
+from pipecat.services.assemblyai.stt import AssemblyAISTTService
-from pipecat.services.cartesia import CartesiaTTSService
+from pipecat.services.cartesia.tts import CartesiaTTSService
-from pipecat.services.openai import OpenAILLMService
+from pipecat.services.openai.llm import OpenAILLMService
 from pipecat.transports.services.daily import DailyParams, DailyTransport
 
 load_dotenv(override=True)
@@ -51,7 +51,7 @@ async def main():
 
         tts = CartesiaTTSService(
             api_key=os.getenv("CARTESIA_API_KEY"),
-            voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22",  # British Lady
+            voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
         )
 
         llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")

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

@@ -14,14 +14,15 @@ from loguru import logger
 from runner import configure
 
 from pipecat.audio.filters.krisp_filter import KrispFilter
+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.deepgram import DeepgramSTTService, DeepgramTTSService
+from pipecat.services.deepgram.stt import DeepgramSTTService
-from pipecat.services.openai import OpenAILLMService
+from pipecat.services.deepgram.tts import DeepgramTTSService
+from pipecat.services.openai.llm import OpenAILLMService
 from pipecat.transports.services.daily import DailyParams, DailyTransport
-from pipecat.vad.silero import SileroVADAnalyzer
 
 load_dotenv(override=True)
 

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

@@ -0,0 +1,103 @@
+#
+# 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.llm import OpenAILLMService
+from pipecat.services.rime.tts 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())

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

@@ -18,8 +18,8 @@ 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.openai.llm import OpenAILLMService
-from pipecat.services.rime import RimeTTSService
+from pipecat.services.rime.tts import RimeTTSService
 from pipecat.transports.services.daily import DailyParams, DailyTransport
 
 load_dotenv(override=True)

examples/foundational/07r-interruptible-riva-nim.py

@@ -18,8 +18,9 @@ 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.nim import NimLLMService
+from pipecat.services.nim.llm import NimLLMService
-from pipecat.services.riva import FastPitchTTSService, ParakeetSTTService
+from pipecat.services.riva.stt import ParakeetSTTService
+from pipecat.services.riva.tts import FastPitchTTSService
 from pipecat.transports.services.daily import DailyParams, DailyTransport
 
 load_dotenv(override=True)

examples/foundational/07s-interruptible-google-audio-in.py

@@ -32,8 +32,8 @@ from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
 from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
 from pipecat.processors.frame_processor import FrameProcessor
-from pipecat.services.cartesia import CartesiaTTSService
+from pipecat.services.cartesia.tts import CartesiaTTSService
-from pipecat.services.google import GoogleLLMService
+from pipecat.services.google.llm import GoogleLLMService
 from pipecat.transports.services.daily import DailyParams, DailyTransport
 
 load_dotenv(override=True)
@@ -213,7 +213,7 @@ async def main():
 
         tts = CartesiaTTSService(
             api_key=os.getenv("CARTESIA_API_KEY"),
-            voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22",  # British Lady
+            voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
         )
 
         llm = GoogleLLMService(api_key=os.getenv("GOOGLE_API_KEY"), model="gemini-2.0-flash-001")

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

@@ -18,8 +18,8 @@ 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.fish import FishAudioTTSService
+from pipecat.services.fish.tts import FishAudioTTSService
-from pipecat.services.openai import OpenAILLMService
+from pipecat.services.openai.llm import OpenAILLMService
 from pipecat.transports.services.daily import DailyParams, DailyTransport
 
 load_dotenv(override=True)

examples/foundational/07u-interruptible-ultravox.py

@@ -0,0 +1,91 @@
+#
+# 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.tts import CartesiaTTSService
+from pipecat.services.ultravox.stt 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_name="fixie-ai/ultravox-v0_5-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())

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

@@ -0,0 +1,102 @@
+#
+# 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.tts import NeuphonicHttpTTSService
+from pipecat.services.openai.llm 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())

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

@@ -0,0 +1,102 @@
+#
+# 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.tts import NeuphonicTTSService
+from pipecat.services.openai.llm 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())

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

@@ -0,0 +1,109 @@
+#
+# 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.tts import CartesiaTTSService
+from pipecat.services.fal.stt import FalSTTService
+from pipecat.services.openai.llm 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())

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

@@ -0,0 +1,91 @@
+#
+# 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.tts import CartesiaTTSService
+from pipecat.services.deepgram.stt import DeepgramSTTService
+from pipecat.services.openai.llm 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())

examples/foundational/07y-interruptible-groq.py

@@ -0,0 +1,103 @@
+#
+# 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.groq.llm import GroqLLMService
+from pipecat.services.groq.stt import GroqSTTService
+from pipecat.services.groq.tts import GroqTTSService
+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(),
+                vad_audio_passthrough=True,
+            ),
+        )
+
+        stt = GroqSTTService(api_key=os.getenv("GROQ_API_KEY"))
+
+        llm = GroqLLMService(api_key=os.getenv("GROQ_API_KEY"), model="llama-3.3-70b-versatile")
+
+        tts = GroqTTSService(api_key=os.getenv("GROQ_API_KEY"))
+
+        messages = [
+            {
+                "role": "system",
+                "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
+            },
+        ]
+
+        context = OpenAILLMContext(messages)
+        context_aggregator = llm.create_context_aggregator(context)
+
+        pipeline = Pipeline(
+            [
+                transport.input(),  # Transport user input
+                stt,
+                context_aggregator.user(),  # User responses
+                llm,  # LLM
+                tts,  # TTS
+                transport.output(),  # Transport bot output
+                context_aggregator.assistant(),  # Assistant spoken responses
+            ]
+        )
+
+        task = PipelineTask(
+            pipeline,
+            params=PipelineParams(
+                allow_interruptions=True,
+                enable_metrics=True,
+                enable_usage_metrics=True,
+            ),
+        )
+
+        @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())

examples/foundational/10-wake-phrase.py

@@ -19,8 +19,8 @@ from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
 from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
 from pipecat.processors.filters.wake_check_filter import WakeCheckFilter
-from pipecat.services.cartesia import CartesiaTTSService
+from pipecat.services.cartesia.tts import CartesiaTTSService
-from pipecat.services.openai import OpenAILLMService
+from pipecat.services.openai.llm import OpenAILLMService
 from pipecat.transports.services.daily import DailyParams, DailyTransport
 
 load_dotenv(override=True)
@@ -47,7 +47,7 @@ async def main():
 
         tts = CartesiaTTSService(
             api_key=os.getenv("CARTESIA_API_KEY"),
-            voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22",  # British Lady
+            voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
         )
 
         llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")

examples/foundational/11-sound-effects.py

@@ -29,8 +29,8 @@ from pipecat.processors.aggregators.openai_llm_context import (
 )
 from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
 from pipecat.processors.logger import FrameLogger
-from pipecat.services.cartesia import CartesiaTTSService
+from pipecat.services.cartesia.tts import CartesiaTTSService
-from pipecat.services.openai import OpenAILLMService
+from pipecat.services.openai.llm import OpenAILLMService
 from pipecat.transports.services.daily import DailyParams, DailyTransport
 
 load_dotenv(override=True)
@@ -100,7 +100,7 @@ async def main():
 
         tts = CartesiaTTSService(
             api_key=os.getenv("CARTESIA_API_KEY"),
-            voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22",  # British Lady
+            voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
         )
 
         messages = [

examples/foundational/12-describe-video.py

@@ -22,8 +22,8 @@ from pipecat.pipeline.task import PipelineTask
 from pipecat.processors.aggregators.user_response import UserResponseAggregator
 from pipecat.processors.aggregators.vision_image_frame import VisionImageFrameAggregator
 from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
-from pipecat.services.cartesia import CartesiaTTSService
+from pipecat.services.cartesia.tts import CartesiaTTSService
-from pipecat.services.moondream import MoondreamService
+from pipecat.services.moondream.vision import MoondreamService
 from pipecat.transports.services.daily import DailyParams, DailyTransport
 
 load_dotenv(override=True)
@@ -77,7 +77,7 @@ async def main():
 
         tts = CartesiaTTSService(
             api_key=os.getenv("CARTESIA_API_KEY"),
-            voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22",  # British Lady
+            voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
         )
 
         @transport.event_handler("on_first_participant_joined")

examples/foundational/12a-describe-video-gemini-flash.py

@@ -22,8 +22,8 @@ from pipecat.pipeline.task import PipelineTask
 from pipecat.processors.aggregators.user_response import UserResponseAggregator
 from pipecat.processors.aggregators.vision_image_frame import VisionImageFrameAggregator
 from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
-from pipecat.services.cartesia import CartesiaTTSService
+from pipecat.services.cartesia.tts import CartesiaTTSService
-from pipecat.services.google import GoogleLLMService
+from pipecat.services.google.llm import GoogleLLMService
 from pipecat.transports.services.daily import DailyParams, DailyTransport
 
 load_dotenv(override=True)
@@ -77,7 +77,7 @@ async def main():
 
         tts = CartesiaTTSService(
             api_key=os.getenv("CARTESIA_API_KEY"),
-            voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22",  # British Lady
+            voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
         )
 
         @transport.event_handler("on_first_participant_joined")

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

@@ -22,8 +22,8 @@ from pipecat.pipeline.task import PipelineTask
 from pipecat.processors.aggregators.user_response import UserResponseAggregator
 from pipecat.processors.aggregators.vision_image_frame import VisionImageFrameAggregator
 from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
-from pipecat.services.cartesia import CartesiaTTSService
+from pipecat.services.cartesia.tts import CartesiaTTSService
-from pipecat.services.openai import OpenAILLMService
+from pipecat.services.openai.llm import OpenAILLMService
 from pipecat.transports.services.daily import DailyParams, DailyTransport
 
 load_dotenv(override=True)
@@ -76,7 +76,7 @@ async def main():
 
         tts = CartesiaTTSService(
             api_key=os.getenv("CARTESIA_API_KEY"),
-            voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22",  # British Lady
+            voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
         )
 
         @transport.event_handler("on_first_participant_joined")

examples/foundational/12c-describe-video-anthropic.py

@@ -22,8 +22,8 @@ from pipecat.pipeline.task import PipelineTask
 from pipecat.processors.aggregators.user_response import UserResponseAggregator
 from pipecat.processors.aggregators.vision_image_frame import VisionImageFrameAggregator
 from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
-from pipecat.services.anthropic import AnthropicLLMService
+from pipecat.services.anthropic.llm import AnthropicLLMService
-from pipecat.services.cartesia import CartesiaTTSService
+from pipecat.services.cartesia.tts import CartesiaTTSService
 from pipecat.transports.services.daily import DailyParams, DailyTransport
 
 load_dotenv(override=True)
@@ -76,7 +76,7 @@ async def main():
 
         tts = CartesiaTTSService(
             api_key=os.getenv("CARTESIA_API_KEY"),
-            voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22",  # British Lady
+            voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
         )
 
         @transport.event_handler("on_first_participant_joined")

examples/foundational/13-whisper-transcription.py

@@ -12,12 +12,13 @@ from dotenv import load_dotenv
 from loguru import logger
 from runner import configure
 
+from pipecat.audio.vad.silero import SileroVADAnalyzer
 from pipecat.frames.frames import Frame, TranscriptionFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineTask
 from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
-from pipecat.services.whisper import WhisperSTTService
+from pipecat.services.whisper.stt import WhisperSTTService
 from pipecat.transports.services.daily import DailyParams, DailyTransport
 
 load_dotenv(override=True)
@@ -39,7 +40,15 @@ async def main():
         (room_url, _) = await configure(session)
 
         transport = DailyTransport(
-            room_url, None, "Transcription bot", DailyParams(audio_in_enabled=True)
+            room_url,
+            None,
+            "Transcription bot",
+            DailyParams(
+                audio_in_enabled=True,
+                vad_enabled=True,
+                vad_analyzer=SileroVADAnalyzer(),
+                vad_audio_passthrough=True,
+            ),
         )
 
         stt = WhisperSTTService()

examples/foundational/13a-whisper-local.py

@@ -10,12 +10,13 @@ import sys
 from dotenv import load_dotenv
 from loguru import logger
 
+from pipecat.audio.vad.silero import SileroVADAnalyzer
 from pipecat.frames.frames import Frame, TranscriptionFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineTask
 from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
-from pipecat.services.whisper import WhisperSTTService
+from pipecat.services.whisper.stt import WhisperSTTService
 from pipecat.transports.local.audio import LocalAudioTransport, LocalAudioTransportParams
 
 load_dotenv(override=True)
@@ -33,7 +34,14 @@ class TranscriptionLogger(FrameProcessor):
 
 
 async def main():
-    transport = LocalAudioTransport(LocalAudioTransportParams(audio_in_enabled=True))
+    transport = LocalAudioTransport(
+        LocalAudioTransportParams(
+            audio_in_enabled=True,
+            vad_enabled=True,
+            vad_analyzer=SileroVADAnalyzer(),
+            vad_audio_passthrough=True,
+        )
+    )
 
     stt = WhisperSTTService()
 

examples/foundational/13b-deepgram-transcription.py

@@ -18,7 +18,7 @@ from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineTask
 from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
-from pipecat.services.deepgram import DeepgramSTTService, Language, LiveOptions
+from pipecat.services.deepgram.stt import DeepgramSTTService, Language, LiveOptions
 from pipecat.transports.services.daily import DailyParams, DailyTransport
 
 load_dotenv(override=True)
@@ -45,7 +45,7 @@ async def main():
 
         stt = DeepgramSTTService(
             api_key=os.getenv("DEEPGRAM_API_KEY"),
-            # live_options=LiveOptions(language=Language.FR),
+            #            live_options=LiveOptions(language=Language.FR),
         )
 
         tl = TranscriptionLogger()

examples/foundational/13d-assemblyai-transcription.py

@@ -18,7 +18,7 @@ from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineTask
 from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
-from pipecat.services.assemblyai import AssemblyAISTTService
+from pipecat.services.assemblyai.stt import AssemblyAISTTService
 from pipecat.transports.services.daily import DailyParams, DailyTransport
 
 load_dotenv(override=True)

examples/foundational/13e-whisper-mlx.py

@@ -0,0 +1,95 @@
+#
+# Copyright (c) 2024–2025, Daily
+#
+# SPDX-License-Identifier: BSD 2-Clause License
+#
+
+import asyncio
+import sys
+import time
+
+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.frames.frames import Frame, TranscriptionFrame, UserStoppedSpeakingFrame
+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.services.whisper.stt import MLXModel, WhisperSTTServiceMLX
+from pipecat.transports.services.daily import DailyParams, DailyTransport
+
+load_dotenv(override=True)
+
+logger.remove(0)
+logger.add(sys.stderr, level="DEBUG")
+
+
+STOP_SECS = 2.0
+
+
+class TranscriptionLogger(FrameProcessor):
+    """Measures transcription latency.
+
+    Uses the (intentionally) long STOP_SECS parameter to give the transcription time to finish,
+    then outputs the timing between when the VAD first classified audio input as not-speech and
+    the delivery of the last transcription frame.
+    """
+
+    def __init__(self):
+        super().__init__()
+        self._last_transcription_time = time.time()
+
+    async def process_frame(self, frame: Frame, direction: FrameDirection):
+        await super().process_frame(frame, direction)
+
+        if isinstance(frame, UserStoppedSpeakingFrame):
+            logger.debug(
+                f"Transcription latency: {(STOP_SECS - (time.time() - self._last_transcription_time)):.2f}"
+            )
+
+        if isinstance(frame, TranscriptionFrame):
+            self._last_transcription_time = time.time()
+
+
+async def main():
+    async with aiohttp.ClientSession() as session:
+        (room_url, _) = await configure(session)
+
+        transport = DailyTransport(
+            room_url,
+            None,
+            "Transcription bot",
+            DailyParams(
+                audio_in_enabled=True,
+                vad_enabled=True,
+                vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=STOP_SECS)),
+                vad_audio_passthrough=True,
+            ),
+        )
+
+        stt = WhisperSTTServiceMLX(model=MLXModel.LARGE_V3_TURBO)
+
+        tl = TranscriptionLogger()
+
+        pipeline = Pipeline([transport.input(), stt, tl])
+
+        task = PipelineTask(
+            pipeline,
+            params=PipelineParams(
+                enable_metrics=True,
+                report_only_initial_ttfb=False,
+            ),
+        )
+
+        runner = PipelineRunner()
+
+        await runner.run(task)
+
+
+if __name__ == "__main__":
+    asyncio.run(main())

examples/foundational/14-function-calling.py

@@ -11,16 +11,18 @@ 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
-from pipecat.services.cartesia import CartesiaTTSService
+from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
-from pipecat.services.openai import OpenAILLMContext, OpenAILLMService
+from pipecat.services.cartesia.tts import CartesiaTTSService
+from pipecat.services.openai.llm import OpenAILLMService
 from pipecat.transports.services.daily import DailyParams, DailyTransport
 
 load_dotenv(override=True)
@@ -29,13 +31,8 @@ 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 llm.push_frame(TTSSpeakFrame("Let me check on that."))
     await result_callback({"conditions": "nice", "temperature": "75"})
 
 
@@ -57,38 +54,33 @@ async def main():
 
         tts = CartesiaTTSService(
             api_key=os.getenv("CARTESIA_API_KEY"),
-            voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22",  # British Lady
+            voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
         )
 
         llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
-        # Register a function_name of None to get all functions
-        # sent to the same callback with an additional function_name parameter.
-        llm.register_function(None, fetch_weather_from_api, start_callback=start_fetch_weather)
 
-        tools = [
+        # You can also register a function_name of None to get all functions
-            ChatCompletionToolParam(
+        # sent to the same callback with an additional function_name parameter.
-                type="function",
+        llm.register_function("get_current_weather", fetch_weather_from_api)
-                function={
+
-                    "name": "get_current_weather",
+        weather_function = FunctionSchema(
-                    "description": "Get the current weather",
+            name="get_current_weather",
-                    "parameters": {
+            description="Get the current weather",
-                        "type": "object",
+            properties={
-                        "properties": {
+                "location": {
-                            "location": {
+                    "type": "string",
-                                "type": "string",
+                    "description": "The city and state, e.g. San Francisco, CA",
-                                "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",

examples/foundational/14a-function-calling-anthropic.py

@@ -13,13 +13,15 @@ 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
 from pipecat.pipeline.task import PipelineParams, PipelineTask
 from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
-from pipecat.services.anthropic import AnthropicLLMService
+from pipecat.services.anthropic.llm import AnthropicLLMService
-from pipecat.services.cartesia import CartesiaTTSService
+from pipecat.services.cartesia.tts import CartesiaTTSService
 from pipecat.transports.services.daily import DailyParams, DailyTransport
 
 load_dotenv(override=True)
@@ -51,30 +53,26 @@ async def main():
 
         tts = CartesiaTTSService(
             api_key=os.getenv("CARTESIA_API_KEY"),
-            voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22",  # British Lady
+            voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
         )
 
         llm = AnthropicLLMService(
-            api_key=os.getenv("ANTHROPIC_API_KEY"), model="claude-3-5-sonnet-20240620"
+            api_key=os.getenv("ANTHROPIC_API_KEY"), model="claude-3-7-sonnet-latest"
         )
         llm.register_function("get_weather", get_weather)
 
-        tools = [
+        weather_function = FunctionSchema(
-            {
+            name="get_weather",
-                "name": "get_weather",
+            description="Get the current weather",
-                "description": "Get the current weather in a given location",
+            properties={
-                "input_schema": {
+                "location": {
-                    "type": "object",
+                    "type": "string",
-                    "properties": {
+                    "description": "The city and state, e.g. San Francisco, CA",
-                        "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
 

examples/foundational/14b-function-calling-anthropic-video.py

@@ -13,13 +13,15 @@ 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
 from pipecat.pipeline.task import PipelineParams, PipelineTask
 from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
-from pipecat.services.anthropic import AnthropicLLMService
+from pipecat.services.anthropic.llm import AnthropicLLMService
-from pipecat.services.cartesia import CartesiaTTSService
+from pipecat.services.cartesia.tts import CartesiaTTSService
 from pipecat.transports.services.daily import DailyParams, DailyTransport
 
 load_dotenv(override=True)
@@ -37,7 +39,12 @@ 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, text_content=question)
+    await llm.request_image_frame(
+        user_id=video_participant_id,
+        function_name=function_name,
+        tool_call_id=tool_call_id,
+        text_content=question,
+    )
 
 
 async def main():
@@ -60,48 +67,40 @@ async def main():
 
         tts = CartesiaTTSService(
             api_key=os.getenv("CARTESIA_API_KEY"),
-            voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22",  # British Lady
+            voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
         )
 
         llm = AnthropicLLMService(
             api_key=os.getenv("ANTHROPIC_API_KEY"),
-            # model="claude-3-5-sonnet-20240620",
+            model="claude-3-7-sonnet-latest",
-            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)
 
-        tools = [
+        weather_function = FunctionSchema(
-            {
+            name="get_weather",
-                "name": "get_weather",
+            description="Get the current weather",
-                "description": "Get the current weather in a given location",
+            properties={
-                "input_schema": {
+                "location": {
-                    "type": "object",
+                    "type": "string",
-                    "properties": {
+                    "description": "The city and state, e.g. San Francisco, CA",
-                        "location": {
-                            "type": "string",
-                            "description": "The city and state, e.g. San Francisco, CA",
-                        }
-                    },
-                    "required": ["location"],
                 },
             },
-            {
+            required=["location"],
-                "name": "get_image",
+        )
-                "description": "Get an image from the video stream.",
+        get_image_function = FunctionSchema(
-                "input_schema": {
+            name="get_image",
-                    "type": "object",
+            description="Get an image from the video stream.",
-                    "properties": {
+            properties={
-                        "question": {
+                "question": {
-                            "type": "string",
+                    "type": "string",
-                            "description": "The question that the user is asking about the image.",
+                    "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
 

examples/foundational/14c-function-calling-together.py

@@ -11,17 +11,18 @@ 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 PipelineTask
-from pipecat.services.cartesia import CartesiaTTSService
+from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
-from pipecat.services.openai import OpenAILLMContext
+from pipecat.services.cartesia.tts import CartesiaTTSService
-from pipecat.services.together import TogetherLLMService
+from pipecat.services.together.llm import TogetherLLMService
 from pipecat.transports.services.daily import DailyParams, DailyTransport
 
 load_dotenv(override=True)
@@ -30,13 +31,8 @@ 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 llm.push_frame(TTSSpeakFrame("Let me check on that."))
     await result_callback({"conditions": "nice", "temperature": "75"})
 
 
@@ -58,41 +54,34 @@ async def main():
 
         tts = CartesiaTTSService(
             api_key=os.getenv("CARTESIA_API_KEY"),
-            voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22",  # British Lady
+            voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
         )
 
         llm = TogetherLLMService(
             api_key=os.getenv("TOGETHER_API_KEY"),
             model="meta-llama/Meta-Llama-3.1-70B-Instruct-Turbo",
         )
-        # Register a function_name of None to get all functions
+        # You can also register a function_name of None to get all functions
         # sent to the same callback with an additional function_name parameter.
-        llm.register_function(None, fetch_weather_from_api, start_callback=start_fetch_weather)
+        llm.register_function("get_current_weather", fetch_weather_from_api)
 
-        tools = [
+        weather_function = FunctionSchema(
-            ChatCompletionToolParam(
+            name="get_current_weather",
-                type="function",
+            description="Get the current weather",
-                function={
+            properties={
-                    "name": "get_current_weather",
+                "location": {
-                    "description": "Get the current weather",
+                    "type": "string",
-                    "parameters": {
+                    "description": "The city and state, e.g. San Francisco, CA",
-                        "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",

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

@@ -11,15 +11,17 @@ 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
 from pipecat.pipeline.task import PipelineTask
-from pipecat.services.cartesia import CartesiaTTSService
+from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
-from pipecat.services.openai import OpenAILLMContext, OpenAILLMService
+from pipecat.services.cartesia.tts import CartesiaTTSService
+from pipecat.services.openai.llm import OpenAILLMService
 from pipecat.transports.services.daily import DailyParams, DailyTransport
 
 load_dotenv(override=True)
@@ -38,7 +40,12 @@ 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, text_content=question)
+    await llm.request_image_frame(
+        user_id=video_participant_id,
+        function_name=function_name,
+        tool_call_id=tool_call_id,
+        text_content=question,
+    )
 
 
 async def main():
@@ -59,54 +66,41 @@ async def main():
 
         tts = CartesiaTTSService(
             api_key=os.getenv("CARTESIA_API_KEY"),
-            voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22",  # British Lady
+            voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading 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)
 
-        tools = [
+        weather_function = FunctionSchema(
-            ChatCompletionToolParam(
+            name="get_weather",
-                type="function",
+            description="Get the current weather",
-                function={
+            properties={
-                    "name": "get_weather",
+                "location": {
-                    "description": "Get the current weather",
+                    "type": "string",
-                    "parameters": {
+                    "description": "The city and state, e.g. San Francisco, CA",
-                        "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": {
-            ChatCompletionToolParam(
+                    "type": "string",
-                type="function",
+                    "enum": ["celsius", "fahrenheit"],
-                function={
+                    "description": "The temperature unit to use. Infer this from the user's location.",
-                    "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.
@@ -153,7 +147,7 @@ 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 tts.say("Hi! Ask me about the weather in San Francisco.")
+            await task.queue_frames([context_aggregator.user().get_context_frame()])
 
         runner = PipelineRunner()
 

examples/foundational/14e-function-calling-gemini.py

@@ -13,14 +13,16 @@ 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.cartesia import CartesiaTTSService
+from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
-from pipecat.services.google import GoogleLLMService
+from pipecat.services.cartesia.tts import CartesiaTTSService
-from pipecat.services.openai import OpenAILLMContext
+from pipecat.services.google.llm import GoogleLLMService
 from pipecat.transports.services.daily import DailyParams, DailyTransport
 
 load_dotenv(override=True)
@@ -31,13 +33,8 @@ logger.add(sys.stderr, level="DEBUG")
 video_participant_id = None
 
 
-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):
+    await llm.push_frame(TTSSpeakFrame("Let me check on that."))
     location = arguments["location"]
     await result_callback(f"The weather in {location} is currently 72 degrees and sunny.")
 
@@ -45,7 +42,12 @@ 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, text_content=question)
+    await llm.request_image_frame(
+        user_id=video_participant_id,
+        function_name=function_name,
+        tool_call_id=tool_call_id,
+        text_content=question,
+    )
 
 
 async def main():
@@ -66,52 +68,41 @@ async def main():
 
         tts = CartesiaTTSService(
             api_key=os.getenv("CARTESIA_API_KEY"),
-            voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22",  # British Lady
+            voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
         )
 
         llm = GoogleLLMService(api_key=os.getenv("GOOGLE_API_KEY"), model="gemini-2.0-flash-001")
-        llm.register_function("get_weather", get_weather, start_fetch_weather)
+        llm.register_function("get_weather", get_weather)
         llm.register_function("get_image", get_image)
 
-        tools = [
+        weather_function = FunctionSchema(
-            {
+            name="get_weather",
-                "function_declarations": [
+            description="Get the current weather",
-                    {
+            properties={
-                        "name": "get_weather",
+                "location": {
-                        "description": "Get the current weather",
+                    "type": "string",
-                        "parameters": {
+                    "description": "The city and state, e.g. San Francisco, CA",
-                            "type": "object",
+                },
-                            "properties": {
+                "format": {
-                                "location": {
+                    "type": "string",
-                                    "type": "string",
+                    "enum": ["celsius", "fahrenheit"],
-                                    "description": "The city and state, e.g. San Francisco, CA",
+                    "description": "The temperature unit to use. Infer this from the user's location.",
-                                },
+                },
-                                "format": {
+            },
-                                    "type": "string",
+            required=["location", "format"],
-                                    "enum": ["celsius", "fahrenheit"],
+        )
-                                    "description": "The temperature unit to use. Infer this from the users location.",
+        get_image_function = FunctionSchema(
-                                },
+            name="get_image",
-                            },
+            description="Get an image from the video stream.",
-                            "required": ["location", "format"],
+            properties={
-                        },
+                "question": {
-                    },
+                    "type": "string",
-                    {
+                    "description": "The question that the user is asking about the image.",
-                        "name": "get_image",
+                }
-                        "description": "Get and image from the camera or video stream.",
+            },
-                        "parameters": {
+            required=["question"],
-                            "type": "object",
+        )
-                            "properties": {
+        tools = ToolsSchema(standard_tools=[weather_function, get_image_function])
-                                "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.

examples/foundational/14f-function-calling-groq.py

@@ -11,17 +11,19 @@ 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
-from pipecat.services.cartesia import CartesiaTTSService
+from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
-from pipecat.services.groq import GroqLLMService, GroqSTTService
+from pipecat.services.cartesia.tts import CartesiaTTSService
-from pipecat.services.openai import OpenAILLMContext
+from pipecat.services.groq.llm import GroqLLMService
+from pipecat.services.groq.stt import GroqSTTService
 from pipecat.transports.services.daily import DailyParams, DailyTransport
 
 load_dotenv(override=True)
@@ -30,13 +32,8 @@ 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 llm.push_frame(TTSSpeakFrame("Let me check on that."))
     await result_callback({"conditions": "nice", "temperature": "75"})
 
 
@@ -60,38 +57,31 @@ async def main():
 
         tts = CartesiaTTSService(
             api_key=os.getenv("CARTESIA_API_KEY"),
-            voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22",  # British Lady
+            voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
         )
 
         llm = GroqLLMService(api_key=os.getenv("GROQ_API_KEY"), model="llama-3.3-70b-versatile")
-        # Register a function_name of None to get all functions
+        # You can also register a function_name of None to get all functions
         # sent to the same callback with an additional function_name parameter.
-        llm.register_function(None, fetch_weather_from_api, start_callback=start_fetch_weather)
+        llm.register_function("get_current_weather", fetch_weather_from_api)
 
-        tools = [
+        weather_function = FunctionSchema(
-            ChatCompletionToolParam(
+            name="get_current_weather",
-                type="function",
+            description="Get the current weather",
-                function={
+            properties={
-                    "name": "get_current_weather",
+                "location": {
-                    "description": "Get the current weather",
+                    "type": "string",
-                    "parameters": {
+                    "description": "The city and state, e.g. San Francisco, CA",
-                        "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",

examples/foundational/14g-function-calling-grok.py

@@ -11,17 +11,17 @@ 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
-from pipecat.services.cartesia import CartesiaTTSService
+from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
-from pipecat.services.grok import GrokLLMService
+from pipecat.services.cartesia.tts import CartesiaTTSService
-from pipecat.services.openai import OpenAILLMContext
+from pipecat.services.grok.llm import GrokLLMService
 from pipecat.transports.services.daily import DailyParams, DailyTransport
 
 load_dotenv(override=True)
@@ -30,12 +30,6 @@ 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"})
 
@@ -58,38 +52,31 @@ async def main():
 
         tts = CartesiaTTSService(
             api_key=os.getenv("CARTESIA_API_KEY"),
-            voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22",  # British Lady
+            voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
         )
 
         llm = GrokLLMService(api_key=os.getenv("GROK_API_KEY"))
-        # Register a function_name of None to get all functions
+        # You can also register a function_name of None to get all functions
         # sent to the same callback with an additional function_name parameter.
-        llm.register_function(None, fetch_weather_from_api, start_callback=start_fetch_weather)
+        llm.register_function("get_current_weather", fetch_weather_from_api)
 
-        tools = [
+        weather_function = FunctionSchema(
-            ChatCompletionToolParam(
+            name="get_current_weather",
-                type="function",
+            description="Get the current weather",
-                function={
+            properties={
-                    "name": "get_current_weather",
+                "location": {
-                    "description": "Get the current weather",
+                    "type": "string",
-                    "parameters": {
+                    "description": "The city and state, e.g. San Francisco, CA",
-                        "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",