Progress on LLM failover support

Rename new `LLMUser/AssistantContextAggregator`s, adding a `_Universal` suffix, allowing old ones to be used while we migrate services gradually to use new universal `LLMContext` and associated patterns.

.github/workflows/android.yaml

@@ -6,11 +6,13 @@ on:
       - main
     paths:
       - "examples/simple-chatbot/client/android/**"
+      - "examples/p2p-webrtc/video-transform/client/android/**"
   pull_request:
     branches:
       - "**"
     paths:
       - "examples/simple-chatbot/client/android/**"
+      - "examples/p2p-webrtc/video-transform/client/android/**"
   workflow_dispatch:
     inputs:
       sdk_git_ref:
@@ -23,7 +25,7 @@ concurrency:
 
 jobs:
   sdk:
-    name: "Simple chatbot demo"
+    name: "Demo apps"
     runs-on: ubuntu-latest
     steps:
       - name: Checkout repo
@@ -37,12 +39,22 @@ jobs:
           distribution: 'temurin'
           java-version: '17'
 
-      - name: Build demo app
+      - name: "Example app: Simple Chatbot"
         working-directory: examples/simple-chatbot/client/android
         run: ./gradlew :simple-chatbot-client:assembleDebug
 
-      - name: Upload demo APK
+      - name: Upload Simple Chatbot APK
         uses: actions/upload-artifact@v4
         with:
           name: Simple Chatbot Android Client
           path: examples/simple-chatbot/client/android/simple-chatbot-client/build/outputs/apk/debug/simple-chatbot-client-debug.apk
+
+      - name: "Example app: Small WebRTC Client"
+        working-directory: examples/p2p-webrtc/video-transform/client/android
+        run: ./gradlew :small-webrtc-client:assembleDebug
+
+      - name: Upload Small WebRTC APK
+        uses: actions/upload-artifact@v4
+        with:
+          name: Small WebRTC Android Client
+          path: examples/p2p-webrtc/video-transform/client/android/small-webrtc-client/build/outputs/apk/debug/small-webrtc-client-debug.apk

.github/workflows/format.yaml

@@ -17,7 +17,7 @@ concurrency:
 
 jobs:
   ruff-format:
-    name: "Formatting checker"
+    name: "Code quality checks"
     runs-on: ubuntu-latest
     steps:
       - name: Checkout repo
@@ -39,8 +39,8 @@ jobs:
         run: |
           source .venv/bin/activate
           ruff format --diff
-      - name: Ruff import linter
+      - name: Ruff linter (all rules)
         id: ruff-check
         run: |
           source .venv/bin/activate
-          ruff check --select I
+          ruff check

.github/workflows/publish.yaml

@@ -5,7 +5,7 @@ on:
     inputs:
       gitref:
         type: string
-        description: "what git ref to build"
+        description: "what git tag to build (e.g. v0.0.74)"
         required: true
 
 jobs:

.pre-commit-config.yaml

@@ -4,5 +4,5 @@ repos:
     hooks:
       - id: ruff
         language_version: python3
-        args: [ --select,  I, ]
+        args: [--fix]
       - id: ruff-format

CHANGELOG.md

@@ -5,6 +5,757 @@ All notable changes to **Pipecat** will be documented in this file.
 The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/),
 and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html).
 
+## [Unreleased]
+
+### Added
+
+- Added a new field `handle_sigterm` to `PipelineRunner`. It defaults to `False`.
+  This field handles SIGTERM signals. The `handle_sigint` field still defaults
+  to `True`, but now it handles only SIGINT signals.
+
+- Added foundational example `14u-function-calling-ollama.py` for Ollama
+  function calling.
+
+- Added `LocalSmartTurnAnalyzerV2`, which supports local on-device inference
+  with the new `smart-turn-v2` turn detection model.
+
+### Changed
+
+- Updated `MiniMaxHttpTTSService` with a `base_url` arg where you can specify
+  the Global endpoint (default) or Mainland China.
+
+- Replaced regex-based sentence detection in `match_endofsentence` with NLTK's
+  punkt_tab tokenizer for more reliable sentence boundary detection.
+
+- Changed the `livekit` optional dependency for `tenacity` to
+  `tenacity>=8.2.3,<10.0.0` in order to support the `google-genai` package.
+
+- For `LmntTTSService`, changed the default `model` to `blizzard`, LMNT's
+  recommended model.
+
+### Fixed
+
+- Fixed a dependency issue for uv users where an `llvmlite` version required python 3.9.
+
+- Fixed an issue in `MiniMaxHttpTTSService` where the `pitch` param was the
+  incorrect type.
+
+- Fixed an issue with OpenTelemetry tracing where the `enable_tracing` flag did
+  not disable the internal tracing decorator functions.
+
+- Fixed an issue in `OLLamaLLMService` where kwargs were not passed correctly
+  to the parent class.
+
+- Fixed an issue in `ElevenLabsTTSService` where the word/timestamp pairs were
+  calculating word boundaries incorrectly.
+
+- Fixed an issue where, in some edge cases, the `EmulateUserStartedSpeakingFrame`
+  could be created even if we didn't have a transcription.
+
+- Fixed an issue in `GoogleLLMContext` where it would inject the
+  `system_message` as a "user" message into cases where it was not meant to;
+  it was only meant to do that when there were no "regular" (non-function-call)
+  messages in the context, to ensure that inference would run properly.
+
+## [0.0.76] - 2025-07-11
+
+### Added
+
+- Added `SpeechControlParamsFrame`, a new `SystemFrame` that notifies
+  downstream processors of the VAD and Turn analyzer params. This frame is
+  pushed by the `BaseInputTransport` at Start and any time a
+  `VADParamsUpdateFrame` is received.
+
+### Changed
+
+- Two package dependencies have been updated:
+  - `numpy` now supports 1.26.0 and newer
+  - `transformers` now supports 4.48.0 and newer
+
+### Fixed
+
+- Fixed an issue with RTVI's handling of `append-to-context`.
+
+- Fixed an issue where using audio input with a sample rate requiring resampling
+  could result in empty audio being passed to STT services, causing errors.
+
+- Fixed the VAD analyzer to process the full audio buffer as long as it contains
+  more than the minimum required bytes per iteration, instead of only analyzing
+  the first chunk.
+
+- Fixed an issue in ParallelPipeline that caused errors when attempting to drain
+  the queues.
+
+- Fixed an issue with emulated VAD timeout inconsistency in
+  `LLMUserContextAggregator`. Previously, emulated VAD scenarios (where
+  transcription is received without VAD detection) used a hardcoded
+  `aggregation_timeout` (default 0.5s) instead of matching the VAD's
+  `stop_secs` parameter (default 0.8s). This created different user experiences
+  between real VAD and emulated VAD scenarios. Now, emulated VAD timeouts
+  automatically synchronize with the VAD's `stop_secs` parameter.
+
+- Fix a pipeline freeze when using AWS Nova Sonic, which would occur if the
+  user started early, while the bot was still working through
+  `trigger_assistant_response()`.
+
+## [0.0.75] - 2025-07-08
+
+### Added
+
+- Added an `aggregate_sentences` arg in `CartesiaTTSService`,
+  `ElevenLabsTTSService`, `NeuphonicTTSService` and `RimeTTSService`, where the
+  default value is True. When `aggregate_sentences` is True, the `TTSService`
+  aggregates the LLM streamed tokens into sentences by default. Note: setting
+  the value to False requires a custom processor before the `TTSService` to
+  aggregate LLM tokens.
+
+- Added `kwargs` to the `OLLamaLLMService` to allow for configuration args to
+  be passed to Ollama.
+
+- Added call hang-up error handling in `TwilioFrameSerializer`, which handles
+  the case where the user has hung up before the `TwilioFrameSerializer` hangs
+  up the call.
+
+### Changed
+
+- Updated `RTVIObserver` and `RTVIProcessor` to match the new RTVI 1.0.0 protocol.
+  This includes:
+
+  - Deprecating support for all messages related to service configuaration and
+    actions.
+  - Adding support for obtaining and logging data about client, including its
+    RTVI version and optionally included system information (OS/browser/etc.)
+  - Adding support for handling the new `client-message` RTVI message through
+    either a `on_client_message` event handler or listening for a new
+    `RTVIClientMessageFrame`
+  - Adding support for responding to a `client-message` with a `server-response`
+    via either a direct call on the `RTVIProcessor` or via pushing a new
+    `RTVIServerResponseFrame`
+  - Adding built-in support for handling the new `append-to-context` RTVI message
+    which allows a client to add to the user or assistant llm context. No extra
+    code is required for supporting this behavior.
+  - Updating all JavaScript and React client RTVI examples to use versions 1.0.0
+    of the clients.
+
+  Get started migrating to RTVI protocol 1.0.0 by following the migration guide:
+  https://docs.pipecat.ai/client/migration-guide
+
+- Refactored `AWSBedrockLLMService` and `AWSPollyTTSService` to work
+  asynchronously using `aioboto3` instead of the `boto3` library.
+
+- The `UserIdleProcessor` now handles the scenario where function calls take
+  longer than the idle timeout duration. This allows you to use the
+  `UserIdleProcessor` in conjunction with function calls that take a while to
+  return a result.
+
+### Fixed
+
+- Updated the `NeuphonicTTSService` to work with the updated websocket API.
+
+- Fixed an issue with `RivaSTTService` where the watchdog feature was causing
+  an error on initialization.
+
+### Performance
+
+- Remove unncessary push task in each `FrameProcessor`.
+
+## [0.0.74] - 2025-07-03
+
+### Added
+
+- Added a new STT service, `SpeechmaticsSTTService`. This service provides
+  real-time speech-to-text transcription using the Speechmatics API. It supports
+  partial and final transcriptions, multiple languages, various audio formats,
+  and speaker diarization.
+
+- Added `normalize` and `model_id` to `FishAudioTTSService`.
+
+- Added `http_options` argument to `GoogleLLMService`.
+
+- Added `run_llm` field to `LLMMessagesAppendFrame` and `LLMMessagesUpdateFrame`
+  frames. If true, a context frame will be pushed triggering the LLM to respond.
+
+- Added a new `SOXRStreamAudioResampler` for processing audio in chunks or
+  streams. If you write your own processor and need to use an audio resampler,
+  use the new `create_stream_resampler()`.
+
+- Added new `DailyParams.audio_in_user_tracks` to allow receiving one track per
+  user (default) or a single track from the room (all participants mixed).
+
+- Added support for providing "direct" functions, which don't need an
+  accompanying `FunctionSchema` or function definition dict. Instead, metadata
+  (i.e. `name`, `description`, `properties`, and `required`) are automatically
+  extracted from a combination of the function signature and docstring.
+
+  Usage:
+
+  ```python
+  # "Direct" function
+  # `params` must be the first parameter
+  async def do_something(params: FunctionCallParams, foo: int, bar: str = ""):
+    """
+    Do something interesting.
+
+    Args:
+      foo (int): The foo to do something interesting with.
+      bar (string): The bar to do something interesting with.
+    """
+
+    result = await process(foo, bar)
+    await params.result_callback({"result": result})
+
+  # ...
+
+  llm.register_direct_function(do_something)
+
+  # ...
+
+  tools = ToolsSchema(standard_tools=[do_something])
+  ```
+
+- `user_id` is now populated in the `TranscriptionFrame` and
+  `InterimTranscriptionFrame` when using a transport that provides a `user_id`,
+  like `DailyTransport` or `LiveKitTransport`.
+
+- Added `watchdog_coroutine()`. This is a watchdog helper for couroutines. So,
+  if you have a coroutine that is waiting for a result and that takes a long
+  time, you will need to wrap it with `watchdog_coroutine()` so the watchdog
+  timers are reset regularly.
+
+- Added `session_token` parameter to `AWSNovaSonicLLMService`.
+
+- Added Gemini Multimodal Live File API for uploading, fetching, listing, and
+  deleting files. See `26f-gemini-multimodal-live-files-api.py` for example usage.
+
+### Changed
+
+- Updated all the services to use the new `SOXRStreamAudioResampler`, ensuring smooth
+  transitions and eliminating clicks.
+
+- Upgraded `daily-python` to 0.19.4.
+
+- Updated `google` optional dependency to use `google-genai` version `1.24.0`.
+
+### Fixed
+
+- Fixed an issue where audio would get stuck in the queue when an interrupt occurs
+  during Azure TTS synthesis.
+
+- Fixed a race condition that occurs in Python 3.10+ where the task could miss
+  the `CancelledError` and continue running indefinitely, freezing the pipeline.
+
+- Fixed a `AWSNovaSonicLLMService` issue introduced in 0.0.72.
+
+### Deprecated
+
+- In `FishAudioTTSService`, deprecated `model` and replaced with
+  `reference_id`. This change is to better align with Fish Audio's variable
+  naming and to reduce confusion about what functionality the variable
+  controls.
+
+## [0.0.73] - 2025-06-26
+
+### Fixed
+
+- Fixed an issue introduced in 0.0.72 that would cause `ElevenLabsTTSService`,
+  `GladiaSTTService`, `NeuphonicTTSService` and `OpenAIRealtimeBetaLLMService`
+  to throw an error.
+
+## [0.0.72] - 2025-06-26
+
+### Added
+
+- Added logging and improved error handling to help diagnose and prevent potential
+  Pipeline freezes.
+
+- Added `WatchdogQueue`, `WatchdogPriorityQueue`, `WatchdogEvent` and
+  `WatchdogAsyncIterator`. These helper utilities reset watchdog timers
+  appropriately before they expire. When watchdog timers are disabled, the
+  utilities behave as standard counterparts without side effects.
+
+- Introduce task watchdog timers. Watchdog timers are used to detect if a
+  Pipecat task is taking longer than expected (by default 5 seconds). Watchdog
+  timers are disabled by default and can be enabled globally by passing
+  `enable_watchdog_timers` argument to `PipelineTask` constructor. It is
+  possible to change the default watchdog timer timeout by using the
+  `watchdog_timeout` argument. You can also log how long it takes to reset the
+  watchdog timers which is done with the `enable_watchdog_logging`. You can
+  control all these settings per each frame processor or even per task. That is,
+  you can set `enable_watchdog_timers`, `enable_watchdog_logging` and
+  `watchdog_timeout` when creating any frame processor through their constructor
+  arguments or when you create a task with `FrameProcessor.create_task()`. Note
+  that watchdog timers only work with Pipecat tasks and will not work if you use
+  `asycio.create_task()` or similar.
+
+- Added `lexicon_names` parameter to `AWSPollyTTSService.InputParams`.
+
+- Added reconnection logic and audio buffer management to `GladiaSTTService`.
+
+- The `TurnTrackingObserver` now ends a turn upon observing an `EndFrame` or
+  `CancelFrame`.
+
+- Added Polish support to `AWSTranscribeSTTService`.
+
+- Added new frames `FrameProcessorPauseFrame` and `FrameProcessorResumeFrame`
+  which allow pausing and resuming frame processing for a given frame
+  processor. These are control frames, so they are ordered. Pausing frame
+  processor will keep old frames in the internal queues until resume takes
+  place. Frames being pushed while a frame processor is paused will be pushed to
+  the queues. When frame processing is resumed all queued frames will be
+  processed in order. Also added `FrameProcessorPauseUrgentFrame` and
+  `FrameProcessorResumeUrgentFrame` which are system frames and therefore they
+  have high priority.
+
+- Added a property called `has_function_calls_in_progress` in
+  `LLMAssistantContextAggregator` that exposes whether a function call is in
+  progress.
+
+- Added `SambaNovaLLMService` which provides llm api integration with an
+  OpenAI-compatible interface.
+
+- Added `SambaNovaTTSService` which provides speech-to-text functionality using
+  SambaNovas's (whisper) API.
+
+- Add fundational examples for function calling and transcription
+  `14s-function-calling-sambanova.py`, `13g-sambanova-transcription.py`
+
+### Changed
+
+- `HeartbeatFrame`s are now control frames. This will make it easier to detect
+  pipeline freezes. Previously, heartbeat frames were system frames which meant
+  they were not get queued with other frames, making it difficult to detect
+  pipeline stalls.
+
+- Updated `OpenAIRealtimeBetaLLMService` to accept `language` in the
+  `InputAudioTranscription` class for all models.
+
+- Updated the default model for `OpenAIRealtimeBetaLLMService` to
+  `gpt-4o-realtime-preview-2025-06-03`.
+
+- The `PipelineParams` arg `allow_interruptions` now defaults to `True`.
+
+- `TavusTransport` and `TavusVideoService` now send audio to Tavus using WebRTC
+  audio tracks instead of `app-messages` over WebSocket. This should improve the
+  overall audio quality.
+
+- Upgraded `daily-python` to 0.19.3.
+
+### Fixed
+
+- Fixed an issue that would cause heartbeat frames to be sent before processors
+  were started.
+
+- Fixed an event loop blocking issue when using `SentryMetrics`.
+
+- Fixed an issue in `FastAPIWebsocketClient` to ensure proper disconnection
+  when the websocket is already closed.
+
+- Fixed an issue where the `UserStoppedSpeakingFrame` was not received if the
+  transport was not receiving new audio frames.
+
+- Fixed an edge case where if the user interrupted the bot but no new aggregation
+  was received, the bot would not resume speaking.
+
+- Fixed an issue with `TelnyxFrameSerializer` where it would throw an exception
+  when the user hung up the call.
+
+- Fixed an issue with `ElevenLabsTTSService` where the context was not being
+  closed.
+
+- Fixed function calling in `AWSNovaSonicLLMService`.
+
+- Fixed an issue that would cause multiple `PipelineTask.on_idle_timeout`
+  events to be triggered repeatedly.
+
+- Fixed an issue that was causing user and bot speech to not be synchronized
+  during recordings.
+
+- Fixed an issue where voice settings weren't applied to ElevenLabsTTSService.
+
+- Fixed an issue with `GroqTTSService` where it was not properly parsing the
+  WAV file header.
+
+- Fixed an issue with `GoogleSTTService` where it was constantly reconnecting
+  before starting to receive audio from the user.
+
+- Fixed an issue where `GoogleLLMService`'s TTFB value was incorrect.
+
+### Deprecated
+
+- `AudioBufferProcessor` parameter `user_continuos_stream` is deprecated.
+
+### Other
+
+- Rename `14e-function-calling-gemini.py` to `14e-function-calling-google.py`.
+
+## [0.0.71] - 2025-06-10
+
+### Added
+
+- Adds a parameter called `additional_span_attributes` to PipelineTask that
+  lets you add any additional attributes you'd like to the conversation span.
+
+### Fixed
+
+- Fixed an issue with `CartesiaSTTService` initialization.
+
+## [0.0.70] - 2025-06-10
+
+### Added
+
+- Added `ExotelFrameSerializer` to handle telephony calls via Exotel.
+
+- Added the option `informal` to `TranslationConfig` on Gladia config.
+  Allowing to force informal language forms when available.
+
+- Added `CartesiaSTTService` which is a websocket based implementation to
+  transcribe audio. Added a foundational example in
+  `13f-cartesia-transcription.py`
+
+- Added an `websocket` example, showing how to use the new Pipecat client
+  `WebsocketTransport` to connect with Pipecat `FastAPIWebsocketTransport` or
+  `WebsocketServerTransport`.
+
+- Added language support to `RimeHttpTTSService`. Extended languages to include
+  German and French for both `RimeTTSService` and `RimeHttpTTSService`.
+
+### Changed
+
+- Upgraded `daily-python` to 0.19.2.
+
+- Make `PipelineTask.add_observer()` synchronous. This allows callers to call it
+  before doing the work of running the `PipelineTask` (i.e. without invoking
+  `PipelineTask.set_event_loop()` first).
+
+- Pipecat 0.0.69 forced `uvloop` event loop on Linux on macOS. Unfortunately,
+  this is causing issue in some systems. So, `uvloop` is not enabled by default
+  anymore. If you want to use `uvloop` you can just set the `asyncio` event
+  policy before starting your agent with:
+
+```python
+asyncio.set_event_loop_policy(uvloop.EventLoopPolicy())
+```
+
+### Fixed
+
+- Fixed an issue with various TTS services that would cause audio glitches at
+  the start of every bot turn.
+
+- Fixed an `ElevenLabsTTSService` issue where a context warning was printed
+  when pushing a `TTSSpeakFrame`.
+
+- Fixed an `AssemblyAISTTService` issue that could cause unexpected behavior
+  when yielding empty `Frame()`s.
+
+- Fixed an issue where `OutputAudioRawFrame.transport_destination` was being
+  reset to `None` instead of retaining its intended value before sending the
+  audio frame to `write_audio_frame`.
+
+- Fixed a typo in Livekit transport that prevented initialization.
+
+## [0.0.69] - 2025-06-02 "AI Engineer World's Fair release" ✨
+
+### Added
+
+- Added a new frame `FunctionCallsStartedFrame`. This frame is pushed both
+  upstream and downstream from the LLM service to indicate that one or more
+  function calls are going to be executed.
+
+- Added LLM services `on_function_calls_started` event. This event will be
+  triggered when the LLM service receives function calls from the model and is
+  going to start executing them.
+
+- Function calls can now be executed sequentially (in the order received in the
+  completion) by passing `run_in_parallel=False` when creating your LLM
+  service. By default, if the LLM completion returns 2 or more function calls
+  they run concurrently. In both cases, concurrently and sequentially, a new LLM
+  completion will run when the last function call finishes.
+
+- Added OpenTelemetry tracing for `GeminiMultimodalLiveLLMService` and
+  `OpenAIRealtimeBetaLLMService`.
+
+- Added initial support for interruption strategies, which determine if the user
+  should interrupt the bot while the bot is speaking. Interruption strategies
+  can be based on factors such as audio volume or the number of words spoken by
+  the user. These can be specified via the new `interruption_strategies` field
+  in `PipelineParams`. A new `MinWordsInterruptionStrategy` strategy has been
+  introduced which triggers an interruption if the user has spoken a minimum
+  number of words. If no interruption strategies are specified, the normal
+  interruption behavior applies. If multiple strategies are provided, the first
+  one that evaluates to true will trigger the interruption.
+
+- `BaseInputTransport` now handles `StopFrame`. When a `StopFrame` is received
+  the transport will pause sending frames downstream until a new `StartFrame` is
+  received. This allows the transport to be reused (keeping the same connection)
+  in a different pipeline.
+
+- Updated AssemblyAI STT service to support their latest streaming
+  speech-to-text model with improved transcription latency and endpointing.
+
+- You can now access STT service results through the new
+  `TranscriptionFrame.result` and `InterimTranscriptionFrame.result` field. This
+  is useful in case you use some specific settings for the STT and you want to
+  access the STT results.
+
+- The examples runner is now public from the `pipecat.examples` package. This
+  allows everyone to build their own examples and run them easily.
+
+- It is now possible to push `OutputDTMFFrame` or `OutputDTMFUrgentFrame` with
+  `DailyTransport`. This will be sent properly if a Daily dial-out connection
+  has been established.
+
+- Added `OutputDTMFUrgentFrame` to send a DTMF keypress quickly. The previous
+  `OutputDTMFFrame` queues the keypress with the rest of data frames.
+
+- Added `DTMFAggregator`, which aggregates keypad presses into
+  `TranscriptionFrame`s. Aggregation occurs after a timeout, termination key
+  press, or user interruption. You can specify the prefix of the
+  `TranscriptionFrame`.
+
+- Added new functions `DailyTransport.start_transcription()` and
+  `DailyTransport.stop_transcription()` to be able to start and stop Daily
+  transcription dynamically (maybe with different settings).
+
+### Changed
+
+- Reverted the default model for `GeminiMultimodalLiveLLMService` back to
+  `models/gemini-2.0-flash-live-001`.
+  `gemini-2.5-flash-preview-native-audio-dialog` has inconsistent performance.
+  You can opt in to using this model by setting the `model` arg.
+
+- Function calls are now cancelled by default if there's an interruption. To
+  disable this behavior you can set `cancel_on_interruption=False` when
+  registering the function call. Since function calls are executed as tasks you
+  can tell if a function call has been cancelled by catching the
+  `asyncio.CancelledError` exception (and don't forget to raise it again!).
+
+- Updated OpenTelemetry tracing attribute `metrics.ttfb_ms` to `metrics.ttfb`.
+  The attribute reports TTFB in seconds.
+
+### Deprecated
+
+- `DailyTransport.send_dtmf()` is deprecated, push an `OutputDTMFFrame` or an
+  `OutputDTMFUrgentFrame` instead.
+
+### Fixed
+
+- Fixed an issue with `ElevenLabsTTSService` where long responses would
+  continue generating output even after an interruption.
+
+- Fixed an issue with the `OpenAILLMContext` where non-Roman characters were
+  being incorrectly encoded as Unicode escape sequences. This was a logging
+  issue and did not impact the actual conversation.
+
+- In `AWSBedrockLLMService`, worked around a possible bug in AWS Bedrock where
+  a `toolConfig` is required if there has been previous tool use in the
+  messages array. This workaround includes a no_op factory function call is
+  used to satisfy the requirement.
+
+- Fixed `WebsocketClientTransport` to use `FrameProcessorSetup.task_manager`
+  instead of `StartFrame.task_manager`.
+
+### Performance
+
+- Use `uvloop` as the new event loop on Linux and macOS systems.
+
+## [0.0.68] - 2025-05-28
+
+### Added
+
+- Added `GoogleHttpTTSService` which uses Google's HTTP TTS API.
+
+- Added `TavusTransport`, a new transport implementation compatible with any
+  Pipecat pipeline. When using the `TavusTransport`the Pipecat bot will
+  connect in the same room as the Tavus Avatar and the user.
+
+- Added `PlivoFrameSerializer` to support Plivo calls. A full running example
+  has also been added to `examples/plivo-chatbot`.
+
+- Added `UserBotLatencyLogObserver`. This is an observer that logs the latency
+  between when the user stops speaking and when the bot starts speaking. This
+  gives you an initial idea on how quickly the AI services respond.
+
+- Added `SarvamTTSService`, which implements Sarvam AI's TTS API:
+  https://docs.sarvam.ai/api-reference-docs/text-to-speech/convert.
+
+- Added `PipelineTask.add_observer()` and `PipelineTask.remove_observer()` to
+  allow mangaging observers at runtime. This is useful for cases where the task
+  is passed around to other code components that might want to observe the
+  pipeline dynamically.
+
+- Added `user_id` field to `TranscriptionMessage`. This allows identifying the
+  user in a multi-user scenario. Note that this requires that
+  `TranscriptionFrame` has the `user_id` properly set.
+
+- Added new `PipelineTask` event handlers `on_pipeline_started`,
+  `on_pipeline_stopped`, `on_pipeline_ended` and `on_pipeline_cancelled`, which
+  correspond to the `StartFrame`, `StopFrame`, `EndFrame` and `CancelFrame`
+  respectively.
+
+- Added additional languages to `LmntTTSService`. Languages include: `hi`,
+  `id`, `it`, `ja`, `nl`, `pl`, `ru`, `sv`, `th`, `tr`, `uk`, `vi`.
+
+- Added a `model` parameter to the `LmntTTSService` constructor, allowing
+  switching between LMNT models.
+
+- Added `MiniMaxHttpTTSService`, which implements MiniMax's T2A API for TTS.
+  Learn more: https://www.minimax.io/platform_overview
+
+- A new function `FrameProcessor.setup()` has been added to allow setting up
+  frame processors before receiving a `StartFrame`. This is what's happening
+  internally: `FrameProcessor.setup()` is called, `StartFrame` is pushed from
+  the beginning of the pipeline, your regular pipeline operations, `EndFrame`
+  or `CancelFrame` are pushed from the beginning of the pipeline and finally
+  `FrameProcessor.cleanup()` is called.
+
+- Added support for OpenTelemetry tracing in Pipecat. This initial
+  implementation includes:
+
+  - A `setup_tracing` method where you can specify your OpenTelemetry exporter
+  - Service decorators for STT (`@traced_stt`), LLM (`@traced_llm`), and TTS
+    (`@traced_tts`) which trace the execution and collect properties and
+    metrics (TTFB, token usage, character counts, etc.)
+  - Class decorators that provide execution tracking; these are generic and can
+    be used for service tracking as needed
+  - Spans that help track traces on a per conversations and turn basis:
+
+  ```
+  conversation-uuid
+  ├── turn-1
+  │   ├── stt_deepgramsttservice
+  │   ├── llm_openaillmservice
+  │   └── tts_cartesiattsservice
+  ...
+  └── turn-n
+      └── ...
+  ```
+
+  By default, Pipecat has implemented service decorators to trace execution of
+  STT, LLM, and TTS services. You can enable tracing by setting
+  `enable_tracing` to `True` in the PipelineTask.
+
+- Added `TurnTrackingObserver`, which tracks the start and end of a user/bot
+  turn pair and emits events `on_turn_started` and `on_turn_stopped`
+  corresponding to the start and end of a turn, respectively.
+
+- Allow passing observers to `run_test()` while running unit tests.
+
+### Changed
+
+- Upgraded `daily-python` to 0.19.1.
+
+- ⚠️ Updated `SmallWebRTCTransport` to align with how other transports handle
+  `on_client_disconnected`. Now, when the connection is closed and no reconnection
+  is attempted, `on_client_disconnected` is called instead of `on_client_close`. The
+  `on_client_close` callback is no longer used, use `on_client_disconnected` instead.
+
+- Check if `PipelineTask` has already been cancelled.
+
+- Don't raise an exception if event handler is not registered.
+
+- Upgraded `deepgram-sdk` to 4.1.0.
+
+- Updated `GoogleTTSService` to use Google's streaming TTS API. The default
+  voice also updated to `en-US-Chirp3-HD-Charon`.
+
+- ⚠️ Refactored the `TavusVideoService`, so it acts like a proxy, sending audio
+  to Tavus and receiving both audio and video. This will make
+  `TavusVideoService` usable with any Pipecat pipeline and with any transport.
+  This is a **breaking change**, check the
+  `examples/foundational/21a-tavus-layer-small-webrtc.py` to see how to use it.
+
+- `DailyTransport` now uses custom microphone audio tracks instead of virtual
+  microphones. Now, multiple Daily transports can be used in the same process.
+
+- `DailyTransport` now captures audio from individual participants instead of
+  the whole room. This allows identifying audio frames per participant.
+
+- Updated the default model for `AnthropicLLMService` to
+  `claude-sonnet-4-20250514`.
+
+- Updated the default model for `GeminiMultimodalLiveLLMService` to
+  `models/gemini-2.5-flash-preview-native-audio-dialog`.
+
+- `BaseTextFilter` methods `filter()`, `update_settings()`,
+  `handle_interruption()` and `reset_interruption()` are now async.
+
+- `BaseTextAggregator` methods `aggregate()`, `handle_interruption()` and
+  `reset()` are now async.
+
+- The API version for `CartesiaTTSService` and `CartesiaHttpTTSService` has
+  been updated. Also, the `cartesia` dependency has been updated to 2.x.
+
+- `CartesiaTTSService` and `CartesiaHttpTTSService` now support Cartesia's new
+  `speed` parameter which accepts values of `slow`, `normal`, and `fast`.
+
+- `GeminiMultimodalLiveLLMService` now uses the user transcription and usage
+  metrics provided by Gemini Live.
+
+- `GoogleLLMService` has been updated to use `google-genai` instead of the
+  deprecated `google-generativeai`.
+
+### Deprecated
+
+- In `CartesiaTTSService` and `CartesiaHttpTTSService`, `emotion` has been
+  deprecated by Cartesia. Pipecat is following suit and deprecating `emotion`
+  as well.
+
+### Removed
+
+- Since `GeminiMultimodalLiveLLMService` now transcribes it's own audio, the
+  `transcribe_user_audio` arg has been removed. Audio is now transcribed
+  automatically.
+
+- Removed `SileroVAD` frame processor, just use `SileroVADAnalyzer`
+  instead. Also removed, `07a-interruptible-vad.py` example.
+
+### Fixed
+
+- Fixed a `DailyTransport` issue that was not allow capturing video frames if
+  framerate was greater than zero.
+
+- Fixed a `DeegramSTTService` connection issue when the user provided their own
+  `LiveOptions`.
+
+- Fixed a `DailyTransport` issue that would cause images needing resize to block
+  the event loop.
+
+- Fixed an issue with `ElevenLabsTTSService` where changing the model or voice
+  while the service is running wasn't working.
+
+- Fixed an issue that would cause multiple instances of the same class to behave
+  incorrectly if any of the given constructor arguments defaulted to a mutable
+  value (e.g. lists, dictionaries, objects).
+
+- Fixed an issue with `CartesiaTTSService` where `TTSTextFrame` messages weren't
+  being emitted when the model was set to `sonic`. This resulted in the
+  assistant context not being updated with assistant messages.
+
+### Performance
+
+- `DailyTransport`: process audio, video and events in separate tasks.
+
+- Don't create event handler tasks if no user event handlers have been
+  registered.
+
+### Other
+
+- It is now possible to run all (or most) foundational example with multiple
+  transports. By default, they run with P2P (Peer-To-Peer) WebRTC so you can try
+  everything locally. You can also run them with Daily or even with a Twilio
+  phone number.
+
+- Added foundation examples `07y-interruptible-minimax.py` and
+  `07z-interruptible-sarvam.py`to show how to use the `MiniMaxHttpTTSService`
+  and `SarvamTTSService`, respectively.
+
+- Added an `open-telemetry-tracing` example, showing how to setup tracing. The
+  example also includes Jaeger as an open source OpenTelemetry client to review
+  traces from the example runs.
+
+- Added foundational example `29-turn-tracking-observer.py` to show how to use
+  the `TurnTrackingObserver`.
+
 ## [0.0.67] - 2025-05-07
 
 ### Added

CONTRIBUTING.md

@@ -41,36 +41,150 @@ We use Ruff for code linting and formatting. Please ensure your code passes all
 
 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
+**Regular Classes:**
 
-Example of correctly documented class:
+- Class docstring describes the class purpose and key functionality
+- `__init__` method has its own docstring with complete `Args:` section documenting all parameters
+- All public methods must have docstrings with `Args:` and `Returns:` sections as appropriate
+
+**Dataclasses:**
+
+- Class docstring describes the purpose and documents all fields in a `Parameters:` section
+- No `__init__` docstring (auto-generated)
+
+**Properties:**
+
+- Must have docstrings with `Returns:` section
+
+**Abstract Methods:**
+
+- Must have docstrings explaining what subclasses should implement
+
+**`__init__.py` Files:**
+
+- **Skip docstrings** for pure import/re-export modules
+- **Add brief docstrings** for top-level packages or those with initialization logic
+
+**Enums:**
+
+- Class docstring describes the enumeration purpose
+- Use `Parameters:` section to document each enum value and its meaning
+- No `__init__` docstring (Enums don't have custom constructors)
+
+**Code Examples in Docstrings:**
+
+- Use `Examples:` as a section header for multiple examples
+- Use descriptive text followed by double colons (`::`) for each example
+- **Always include a blank line after the `::"`**
+- Indent all code consistently within each block
+- Separate multiple examples with blank lines for readability
+
+**Lists and Bullets in Docstrings:**
+
+- Use dashes (`-`) for bullet points, not asterisks (`*`)
+- **Add a blank line before bullet lists** when they follow a colon
+- Use section headers like "Supported features:" or "Behavior:" before lists
+- For complex nested information, consider using paragraph format instead
+
+**Deprecations:**
+
+- Use `warnings.warn()` in code for runtime deprecation warnings
+- Add `.. deprecated::` directive in docstrings for documentation visibility
+- Include version information and describe current status
+- Describe parameters in present tense, use directive to indicate deprecation status
+
+#### Examples:
 
 ```python
-class MyClass:
-    """Class description.
+# Regular class
+class MyService(BaseService):
+    """Description of what the service does.
 
-    Additional details about the class.
+    Provides detailed explanation of the service's functionality,
+    key features, and usage patterns.
 
-    Args:
-        param1: Description of first parameter.
-        param2: Description of second parameter.
+    Supported features:
+
+    - Feature one with detailed explanation
+    - Feature two with additional context
+    - Feature three for advanced use cases
     """
 
-    def __init__(self, param1, param2):
-        # No docstring required here as parameters are documented above
-        self.param1 = param1
-        self.param2 = param2
+    def __init__(self, param1: str, old_param: str = None, **kwargs):
+        """Initialize the service.
+
+        Args:
+            param1: Description of param1.
+            old_param: Controls legacy behavior.
+
+                .. deprecated:: 1.2.0
+                    This parameter no longer has any effect and will be removed in version 2.0.
+
+            **kwargs: Additional arguments passed to parent.
+        """
+        if old_param is not None:
+            import warnings
+            warnings.warn(
+                "Parameter 'old_param' is deprecated and will be removed in version 2.0.",
+                DeprecationWarning,
+            )
+        super().__init__(**kwargs)
 
     @property
-    def some_property(self) -> str:
-        """Get the formatted property value.
+    def sample_rate(self) -> int:
+        """Get the current sample rate.
 
         Returns:
-            A string representation of the property.
+            The sample rate in Hz.
         """
-        return f"Property: {self.param1}"
+        return self._sample_rate
+
+    async def process_data(self, data: str) -> bool:
+        """Process the provided data.
+
+        Args:
+            data: The data to process.
+
+        Returns:
+            True if processing succeeded.
+        """
+        pass
+
+# Dataclass with code examples
+@dataclass
+class MessageFrame:
+    """Frame containing messages in OpenAI format.
+
+    Supports both simple and content list message formats.
+
+    Example::
+
+        [
+            {"role": "user", "content": "Hello"},
+            {"role": "assistant", "content": "Hi there!"}
+        ]
+
+    Parameters:
+        messages: List of messages in OpenAI format.
+    """
+
+    messages: List[dict]
+
+# Enum class
+class Status(Enum):
+    """Status codes for processing operations.
+
+    Parameters:
+        PENDING: Operation is queued but not started.
+        RUNNING: Operation is currently in progress.
+        COMPLETED: Operation finished successfully.
+        FAILED: Operation encountered an error.
+    """
+
+    PENDING = "pending"
+    RUNNING = "running"
+    COMPLETED = "completed"
+    FAILED = "failed"
 ```
 
 # Contributor Covenant Code of Conduct

MANIFEST.in

@@ -0,0 +1,4 @@
+prune docs
+prune examples
+prune scripts
+prune tests

README.md

@@ -8,6 +8,8 @@
 
 **Pipecat** is an open-source Python framework for building real-time voice and multimodal conversational agents. Orchestrate audio and video, AI services, different transports, and conversation pipelines effortlessly—so you can focus on what makes your agent unique.
 
+> Want to dive right in? [Install Pipecat](https://docs.pipecat.ai/getting-started/installation) then try the [quickstart](https://docs.pipecat.ai/getting-started/quickstart).
+
 ## 🚀 What You Can Build
 
 - **Voice Assistants** – natural, streaming conversations with AI
@@ -49,18 +51,19 @@ You can connect to Pipecat from any platform using our official SDKs:
 
 ## 🧩 Available services
 
-| Category            | Services                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                  |
-|---------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
-| Speech-to-Text      | [AssemblyAI](https://docs.pipecat.ai/server/services/stt/assemblyai), [AWS](https://docs.pipecat.ai/server/services/stt/aws), [Azure](https://docs.pipecat.ai/server/services/stt/azure), [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)                                                                                                                                                                                                                                            |
-| LLMs                | [Anthropic](https://docs.pipecat.ai/server/services/llm/anthropic), [AWS](https://docs.pipecat.ai/server/services/llm/aws), [Azure](https://docs.pipecat.ai/server/services/llm/azure), [Cerebras](https://docs.pipecat.ai/server/services/llm/cerebras), [DeepSeek](https://docs.pipecat.ai/server/services/llm/deepseek), [Fireworks AI](https://docs.pipecat.ai/server/services/llm/fireworks), [Gemini](https://docs.pipecat.ai/server/services/llm/gemini), [Grok](https://docs.pipecat.ai/server/services/llm/grok), [Groq](https://docs.pipecat.ai/server/services/llm/groq), [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) |
-| 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)                                                                               |
-| Speech-to-Speech    | [Gemini Multimodal Live](https://docs.pipecat.ai/server/services/s2s/gemini), [OpenAI Realtime](https://docs.pipecat.ai/server/services/s2s/openai)                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                       |
-| Transport           | [Daily (WebRTC)](https://docs.pipecat.ai/server/services/transport/daily), [FastAPI Websocket](https://docs.pipecat.ai/server/services/transport/fastapi-websocket), [SmallWebRTCTransport](https://docs.pipecat.ai/server/services/transport/small-webrtc), [WebSocket Server](https://docs.pipecat.ai/server/services/transport/websocket-server), Local                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                |
-| Video               | [Tavus](https://docs.pipecat.ai/server/services/video/tavus), [Simli](https://docs.pipecat.ai/server/services/video/simli)                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                |
-| Memory              | [mem0](https://docs.pipecat.ai/server/services/memory/mem0)                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                               |
-| Vision & Image      | [fal](https://docs.pipecat.ai/server/services/image-generation/fal), [Google Imagen](https://docs.pipecat.ai/server/services/image-generation/fal), [Moondream](https://docs.pipecat.ai/server/services/vision/moondream)                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                 |
-| Audio Processing    | [Silero VAD](https://docs.pipecat.ai/server/utilities/audio/silero-vad-analyzer), [Krisp](https://docs.pipecat.ai/server/utilities/audio/krisp-filter), [Koala](https://docs.pipecat.ai/server/utilities/audio/koala-filter), [Noisereduce](https://docs.pipecat.ai/server/utilities/audio/noisereduce-filter)                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            |
-| Analytics & Metrics | [Sentry](https://docs.pipecat.ai/server/services/analytics/sentry)                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        |
+| Category            | Services                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                     |
+| ------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| Speech-to-Text      | [AssemblyAI](https://docs.pipecat.ai/server/services/stt/assemblyai), [AWS](https://docs.pipecat.ai/server/services/stt/aws), [Azure](https://docs.pipecat.ai/server/services/stt/azure), [Cartesia](https://docs.pipecat.ai/server/services/stt/cartesia), [Deepgram](https://docs.pipecat.ai/server/services/stt/deepgram), [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), [SambaNova (Whisper)](https://docs.pipecat.ai/server/services/stt/sambanova), [Soniox](https://docs.pipecat.ai/server/services/stt/soniox), [Speechmatics](https://docs.pipecat.ai/server/services/stt/speechmatics), [Ultravox](https://docs.pipecat.ai/server/services/stt/ultravox), [Whisper](https://docs.pipecat.ai/server/services/stt/whisper)                       |
+| LLMs                | [Anthropic](https://docs.pipecat.ai/server/services/llm/anthropic), [AWS](https://docs.pipecat.ai/server/services/llm/aws), [Azure](https://docs.pipecat.ai/server/services/llm/azure), [Cerebras](https://docs.pipecat.ai/server/services/llm/cerebras), [DeepSeek](https://docs.pipecat.ai/server/services/llm/deepseek), [Fireworks AI](https://docs.pipecat.ai/server/services/llm/fireworks), [Gemini](https://docs.pipecat.ai/server/services/llm/gemini), [Grok](https://docs.pipecat.ai/server/services/llm/grok), [Groq](https://docs.pipecat.ai/server/services/llm/groq), [NVIDIA NIM](https://docs.pipecat.ai/server/services/llm/nim), [Ollama](https://docs.pipecat.ai/server/services/llm/ollama), [OpenAI](https://docs.pipecat.ai/server/services/llm/openai), [OpenRouter](https://docs.pipecat.ai/server/services/llm/openrouter), [Perplexity](https://docs.pipecat.ai/server/services/llm/perplexity), [Qwen](https://docs.pipecat.ai/server/services/llm/qwen), [SambaNova](https://docs.pipecat.ai/server/services/llm/sambanova) [Together AI](https://docs.pipecat.ai/server/services/llm/together) |
+| Text-to-Speech      | [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), [MiniMax](https://docs.pipecat.ai/server/services/tts/minimax), [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), [Sarvam](https://docs.pipecat.ai/server/services/tts/sarvam), [XTTS](https://docs.pipecat.ai/server/services/tts/xtts)                    |
+| Speech-to-Speech    | [AWS Nova Sonic](https://docs.pipecat.ai/server/services/s2s/aws), [Gemini Multimodal Live](https://docs.pipecat.ai/server/services/s2s/gemini), [OpenAI Realtime](https://docs.pipecat.ai/server/services/s2s/openai)                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                       |
+| Transport           | [Daily (WebRTC)](https://docs.pipecat.ai/server/services/transport/daily), [FastAPI Websocket](https://docs.pipecat.ai/server/services/transport/fastapi-websocket), [SmallWebRTCTransport](https://docs.pipecat.ai/server/services/transport/small-webrtc), [WebSocket Server](https://docs.pipecat.ai/server/services/transport/websocket-server), Local                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                   |
+| Serializers         | [Plivo](https://docs.pipecat.ai/server/utilities/serializers/plivo), [Twilio](https://docs.pipecat.ai/server/utilities/serializers/twilio), [Telnyx](https://docs.pipecat.ai/server/utilities/serializers/telnyx)                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            |
+| Video               | [Tavus](https://docs.pipecat.ai/server/services/video/tavus), [Simli](https://docs.pipecat.ai/server/services/video/simli)                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                   |
+| Memory              | [mem0](https://docs.pipecat.ai/server/services/memory/mem0)                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                  |
+| Vision & Image      | [fal](https://docs.pipecat.ai/server/services/image-generation/fal), [Google Imagen](https://docs.pipecat.ai/server/services/image-generation/fal), [Moondream](https://docs.pipecat.ai/server/services/vision/moondream)                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    |
+| Audio Processing    | [Silero VAD](https://docs.pipecat.ai/server/utilities/audio/silero-vad-analyzer), [Krisp](https://docs.pipecat.ai/server/utilities/audio/krisp-filter), [Koala](https://docs.pipecat.ai/server/utilities/audio/koala-filter), [Noisereduce](https://docs.pipecat.ai/server/utilities/audio/noisereduce-filter)                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                               |
+| Analytics & Metrics | [OpenTelemetry](https://docs.pipecat.ai/server/utilities/opentelemetry), [Sentry](https://docs.pipecat.ai/server/services/analytics/sentry)                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                  |
 
 📚 [View full services documentation →](https://docs.pipecat.ai/server/services/supported-services)
 

dev-requirements.txt

@@ -1,13 +1,13 @@
 build~=1.2.2
-coverage~=7.6.12
+coverage~=7.9.1
 grpcio-tools~=1.67.1
 pip-tools~=7.4.1
-pre-commit~=4.0.1
-pyright~=1.1.397
-pytest~=8.3.4
-pytest-asyncio~=0.25.3
+pre-commit~=4.2.0
+pyright~=1.1.402
+pytest~=8.4.1
+pytest-asyncio~=1.0.0
 pytest-aiohttp==1.1.0
-ruff~=0.11.1
-setuptools~=70.0.0
-setuptools_scm~=8.1.0
-python-dotenv~=1.0.1
+ruff~=0.12.1
+setuptools~=78.1.1
+setuptools_scm~=8.3.1
+python-dotenv~=1.1.1

docs/api/conf.py

@@ -1,5 +1,6 @@
 import logging
 import sys
+from datetime import datetime
 from pathlib import Path
 
 # Configure logging
@@ -13,7 +14,8 @@ sys.path.insert(0, str(project_root / "src"))
 
 # Project information
 project = "pipecat-ai"
-copyright = "2024, Daily"
+current_year = datetime.now().year
+copyright = f"2024-{current_year}, Daily" if current_year > 2024 else "2024, Daily"
 author = "Daily"
 
 # General configuration
@@ -24,19 +26,20 @@ extensions = [
     "sphinx.ext.intersphinx",
 ]
 
+suppress_warnings = [
+    "autodoc.mocked_object",
+]
+
 # Napoleon settings
 napoleon_google_docstring = True
-napoleon_numpy_docstring = False
 napoleon_include_init_with_doc = True
 
 # AutoDoc settings
 autodoc_default_options = {
     "members": True,
     "member-order": "bysource",
-    "special-members": "__init__",
-    "undoc-members": True,
-    "exclude-members": "__weakref__",
-    "no-index": True,
+    "undoc-members": False,
+    "exclude-members": "__weakref__,model_config",
     "show-inheritance": True,
 }
 
@@ -71,20 +74,16 @@ autodoc_mock_imports = [
     "langchain",
     "lmnt",
     "noisereduce",
-    "openai",
     "openpipe",
     "simli",
     "soundfile",
+    "soniox",
     "pipecat_ai_krisp",
     "pyaudio",
     "_tkinter",
     "tkinter",
     "daily",
     "daily_python",
-    "pydantic.BaseModel",
-    "pydantic.Field",
-    "pydantic._internal._model_construction",
-    "pydantic._internal._fields",
     # Moondream dependencies
     "torch",
     "transformers",
@@ -145,85 +144,76 @@ autodoc_mock_imports = [
     "transformers.AutoFeatureExtractor",
     # Also add specific classes that are imported
     "AutoFeatureExtractor",
+    # Sentry dependencies
+    "sentry_sdk",
+    # AWS Nova Sonic dependencies
+    "aws_sdk_bedrock_runtime",
+    "aws_sdk_bedrock_runtime.client",
+    "aws_sdk_bedrock_runtime.config",
+    "aws_sdk_bedrock_runtime.models",
+    "smithy_aws_core",
+    "smithy_aws_core.credentials_resolvers",
+    "smithy_aws_core.credentials_resolvers.static",
+    "smithy_aws_core.identity",
+    "smithy_core",
+    "smithy_core.aio",
+    "smithy_core.aio.eventstream",
+    # MCP dependencies (you may already have these)
+    "mcp",
+    "mcp.client",
+    "mcp.client.session_group",
+    "mcp.client.sse",
+    "mcp.client.stdio",
+    "mcp.ClientSession",
+    "mcp.StdioServerParameters",
+    # gstreamer
+    "gi",
+    "gi.require_version",
+    "gi.repository",
+    # Protobuf mocks
+    "pipecat.frames.protobufs.frames_pb2",
+    "pipecat.serializers.protobuf",
+    "google.protobuf",
+    "google.protobuf.descriptor",
+    "google.protobuf.descriptor_pool",
+    "google.protobuf.runtime_version",
+    "google.protobuf.symbol_database",
+    "google.protobuf.internal.builder",
 ]
 
 # HTML output settings
 html_theme = "sphinx_rtd_theme"
 html_static_path = ["_static"]
-autodoc_typehints = "description"
+autodoc_typehints = "signature"  # Show type hints in the signature only, not in the docstring
 html_show_sphinx = False
 
 
-def verify_modules():
-    """Verify that required modules are available."""
-    required_modules = {
-        "services": [
-            "assemblyai",
-            "aws",
-            "cartesia",
-            "deepgram",
-            "google",
-            "lmnt",
-            "riva",
-            "simli",
-        ],
-        "serializers": ["livekit"],
-        "vad": ["silero", "vad_analyzer"],
-        "transports": {
-            "services": ["daily", "livekit"],
-            "local": ["audio", "tk"],
-            "network": ["fastapi_websocket", "websocket_server"],
-        },
-    }
+def import_core_modules():
+    """Import core pipecat modules for autodoc to discover."""
+    core_modules = [
+        "pipecat",
+        "pipecat.frames",
+        "pipecat.pipeline",
+        "pipecat.processors",
+        "pipecat.services",
+        "pipecat.transports",
+        "pipecat.audio",
+        "pipecat.adapters",
+        "pipecat.clocks",
+        "pipecat.metrics",
+        "pipecat.observers",
+        "pipecat.serializers",
+        "pipecat.sync",
+        "pipecat.transcriptions",
+        "pipecat.utils",
+    ]
 
-    # Skip importing modules that are in autodoc_mock_imports
-    skipped_modules = set(autodoc_mock_imports)
-
-    missing = []
-    for category, modules in required_modules.items():
-        if isinstance(modules, dict):
-            # Handle nested structure
-            for subcategory, submodules in modules.items():
-                for module in submodules:
-                    # Check if module is in autodoc_mock_imports
-                    if (
-                        f"pipecat.{category}.{subcategory}.{module}" in skipped_modules
-                        or module in skipped_modules
-                    ):
-                        logger.info(
-                            f"Skipping import of mocked module: pipecat.{category}.{subcategory}.{module}"
-                        )
-                        continue
-
-                    try:
-                        __import__(f"pipecat.{category}.{subcategory}.{module}")
-                        logger.info(
-                            f"Successfully imported pipecat.{category}.{subcategory}.{module}"
-                        )
-                    except (ImportError, TypeError, NameError) as e:
-                        missing.append(f"pipecat.{category}.{subcategory}.{module}")
-                        logger.warning(
-                            f"Optional module not available: pipecat.{category}.{subcategory}.{module} - {str(e)}"
-                        )
-        else:
-            # Handle flat structure
-            for module in modules:
-                # Check if module is in autodoc_mock_imports
-                if f"pipecat.{category}.{module}" in skipped_modules or module in skipped_modules:
-                    logger.info(f"Skipping import of mocked module: pipecat.{category}.{module}")
-                    continue
-
-                try:
-                    __import__(f"pipecat.{category}.{module}")
-                    logger.info(f"Successfully imported pipecat.{category}.{module}")
-                except (ImportError, TypeError, NameError) as e:
-                    missing.append(f"pipecat.{category}.{module}")
-                    logger.warning(
-                        f"Optional module not available: pipecat.{category}.{module} - {str(e)}"
-                    )
-
-    if missing:
-        logger.warning(f"Some optional modules are not available: {missing}")
+    for module_name in core_modules:
+        try:
+            __import__(module_name)
+            logger.info(f"Successfully imported {module_name}")
+        except ImportError as e:
+            logger.warning(f"Failed to import {module_name}: {e}")
 
 
 def clean_title(title: str) -> str:
@@ -235,36 +225,7 @@ def clean_title(title: str) -> str:
     parts = title.split(".")
     title = parts[-1]
 
-    # Special cases for service names and common acronyms
-    special_cases = {
-        "ai": "AI",
-        "aws": "AWS",
-        "api": "API",
-        "vad": "VAD",
-        "assemblyai": "AssemblyAI",
-        "deepgram": "Deepgram",
-        "elevenlabs": "ElevenLabs",
-        "openai": "OpenAI",
-        "openpipe": "OpenPipe",
-        "playht": "PlayHT",
-        "xtts": "XTTS",
-        "lmnt": "LMNT",
-    }
-
-    # Check if the entire title is a special case
-    if title.lower() in special_cases:
-        return special_cases[title.lower()]
-
-    # Otherwise, capitalize each word
-    words = title.split("_")
-    cleaned_words = []
-    for word in words:
-        if word.lower() in special_cases:
-            cleaned_words.append(special_cases[word.lower()])
-        else:
-            cleaned_words.append(word.capitalize())
-
-    return " ".join(cleaned_words)
+    return title
 
 
 def setup(app):
@@ -289,9 +250,8 @@ def setup(app):
 
     excludes = [
         str(project_root / "src/pipecat/pipeline/to_be_updated"),
-        str(project_root / "src/pipecat/processors/gstreamer"),
-        str(project_root / "src/pipecat/services/to_be_updated"),
-        str(project_root / "src/pipecat/vad"),  # deprecated
+        str(project_root / "src/pipecat/examples"),
+        str(project_root / "src/pipecat/tests"),
         "**/test_*.py",
         "**/tests/*.py",
     ]
@@ -332,5 +292,4 @@ def setup(app):
         logger.error(f"Error generating API documentation: {e}", exc_info=True)
 
 
-# Run module verification
-verify_modules()
+import_core_modules()

docs/api/index.rst

@@ -1,57 +1,17 @@
-Pipecat API Reference Docs
-==========================
+Pipecat API Reference
+=====================
 
-Welcome to Pipecat's API reference documentation!
+Welcome to the Pipecat API reference.
 
-Pipecat is an open source framework for building voice and multimodal assistants.
-It provides a flexible pipeline architecture for connecting various AI services,
-audio processing, and transport layers.
+Use the navigation on the left to browse modules, or search using the search box.
+
+**New to Pipecat?** Check out the `main documentation <https://docs.pipecat.ai>`_ for tutorials, guides, and client SDK information.
 
 Quick Links
 -----------
 
 * `GitHub Repository <https://github.com/pipecat-ai/pipecat>`_
-* `Website <https://pipecat.ai>`_
-
-API Reference
--------------
-
-Core Components
-~~~~~~~~~~~~~~~
-
-* :mod:`Frames <pipecat.frames>`
-* :mod:`Processors <pipecat.processors>`
-* :mod:`Pipeline <pipecat.pipeline>`
-
-Audio Processing
-~~~~~~~~~~~~~~~~
-
-* :mod:`Audio <pipecat.audio>`
-
-Services
-~~~~~~~~
-
-* :mod:`Services <pipecat.services>`
-
-Transport & Serialization
-~~~~~~~~~~~~~~~~~~~~~~~~~
-
-* :mod:`Transports <pipecat.transports>`
-   * :mod:`Local <pipecat.transports.local>`
-   * :mod:`Network <pipecat.transports.network>`
-   * :mod:`Services <pipecat.transports.services>`
-* :mod:`Serializers <pipecat.serializers>`
-
-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>`
+* `Join our Community <https://discord.gg/pipecat>`_
 
 .. toctree::
    :maxdepth: 3
@@ -71,11 +31,4 @@ Utilities
    Sync <api/pipecat.sync>
    Transcriptions <api/pipecat.transcriptions>
    Transports <api/pipecat.transports>
-   Utils <api/pipecat.utils>
-
-Indices and tables
-==================
-
-* :ref:`genindex`
-* :ref:`modindex`
-* :ref:`search`
+   Utils <api/pipecat.utils>

docs/api/requirements.txt

@@ -42,9 +42,12 @@ pipecat-ai[openai]
 pipecat-ai[qwen]
 pipecat-ai[remote-smart-turn]
 # pipecat-ai[riva] # Mocked
+pipecat-ai[sambanova]
 pipecat-ai[silero]
 pipecat-ai[simli]
 pipecat-ai[soundfile]
+pipecat-ai[soniox]
+pipecat-ai[speechmatics]
 pipecat-ai[tavus]
 pipecat-ai[together]
 # pipecat-ai[ultravox] # Mocked

dot-env.template

@@ -95,9 +95,29 @@ OPENROUTER_API_KEY=...
 PIPER_BASE_URL=...
 
 # Smart turn
-LOCAL_SMART_TURN_MODEL_PATH=
+LOCAL_SMART_TURN_MODEL_PATH=...
 FAL_SMART_TURN_API_KEY=...
 
 # Twilio
-TWILIO_ACCOUNT_SID=
-TWILIO_AUTH_TOKEN=
+TWILIO_ACCOUNT_SID=...
+TWILIO_AUTH_TOKEN=...
+
+# MiniMax
+MINIMAX_API_KEY=...
+MINIMAX_GROUP_ID=...
+
+# Sarvam AI
+SARVAM_API_KEY=...
+
+# Soniox
+SONIOX_API_KEY=
+
+# Speechmatics
+SPEECHMATICS_API_KEY=...
+
+
+# SambaNova
+SAMBANOVA_API_KEY=...
+
+# Sentry
+SENTRY_DSN=...

examples/aws-strands/README.md

@@ -0,0 +1,60 @@
+# AWS Strands Examples
+
+This folder contains two Python examples demonstrating how to use Pipecat with the AWS Strands agent.
+
+## Overview
+
+These examples show how to delegate complex, multi-step tasks to a Strands agent, which can reason step-by-step and call tools to accomplish user requests.
+
+These examples are intentionally simplified for demonstration, using mock API calls. They work best if you ask it:
+
+> What's the weather where the Golden Gate Bridge is?
+
+## Example Scripts
+
+### `black-box.py`
+
+A minimal example that demonstrates how to use the Strands agent with Pipecat. The agent can handle multi-step queries by calling tools, but does not explain its reasoning out loud.
+
+### `explain-thinking.py`
+
+An enhanced example where the Strands agent explains each step of its reasoning in clear, simple language as it works through a multi-step task.
+
+## Quick Start
+
+1. **Clone the repository and navigate to this example:**
+
+   ```bash
+   git clone https://github.com/pipecat-ai/pipecat.git
+   cd pipecat/examples/aws-strands
+   ```
+
+2. **Set up a virtual environment:**
+
+   ```bash
+   python -m venv venv
+   source venv/bin/activate  # On Windows: venv\Scripts\activate
+   ```
+
+3. **Install dependencies:**
+
+   ```bash
+   pip install -r requirements.txt
+   ```
+
+4. **Configure environment variables:**
+
+   Copy the provided `env.example` file to `.env` and fill in the necessary credentials:
+
+   ```bash
+   cp env.example .env
+   # Then edit .env with your preferred editor
+   ```
+
+5. **Run an example:**
+
+   ```bash
+   python black-box.py
+   # or
+   python explain-thinking.py
+   ```

examples/aws-strands/black-box.py

@@ -0,0 +1,206 @@
+#
+# Copyright (c) 2025, Daily
+#
+# SPDX-License-Identifier: BSD 2-Clause License
+#
+
+import argparse
+import asyncio
+import os
+
+from dotenv import load_dotenv
+from loguru import logger
+from strands import Agent, tool
+from strands.models import BedrockModel
+
+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.processors.aggregators.openai_llm_context import OpenAILLMContext
+from pipecat.services.cartesia.tts import CartesiaTTSService
+from pipecat.services.deepgram.stt import DeepgramSTTService
+from pipecat.services.llm_service import FunctionCallParams
+from pipecat.services.openai.llm import OpenAILLMService
+from pipecat.transports.base_transport import BaseTransport, TransportParams
+from pipecat.transports.network.fastapi_websocket import FastAPIWebsocketParams
+from pipecat.transports.services.daily import DailyParams
+
+load_dotenv(override=True)
+
+"""This example demonstrates how to use the Strands agent with Pipecat.
+
+You can delegate complex, multi-step tasks to the Strands agent, which can cycle through LLM-based reasoning and tool calls to accomplish the task.
+
+Try asking: "What's the weather where the Golden Gate Bridge is?"
+"""
+
+# Strands agent tools
+
+
+@tool
+def get_location_name_from_landmark(landmark: str) -> str:
+    """
+    Get the location name from a landmark.
+
+    Args:
+        landmark (str): The name of the landmark, e.g. "Golden Gate Bridge".
+    """
+    # Simulate fetching location
+    return "San Francisco, CA"
+
+
+@tool
+def get_lat_long_from_location_name(location: str) -> dict:
+    """
+    Get the latitude and longitude for a location name.
+
+    Args:
+        location (str): The city and state, e.g. "San Francisco, CA".
+    """
+    # Simulate fetching lat/long from a geocoding service
+    return {"lat": 37.7749, "long": -122.4194}
+
+
+@tool
+def get_current_weather_from_lat_long(lat: float, long: float) -> dict:
+    """
+    Get the current weather for a specific latitude and longitude.
+
+    Args:
+        lat (float): The latitude of the location.
+        long (float): The longitude of the location.
+    """
+    # Simulate fetching weather data from a weather service
+    return {"conditions": "nice", "temperature": "75"}
+
+
+# We store functions so objects (e.g. SileroVADAnalyzer) don't get
+# instantiated. The function will be called when the desired transport gets
+# selected.
+transport_params = {
+    "daily": lambda: DailyParams(
+        audio_in_enabled=True,
+        audio_out_enabled=True,
+        vad_analyzer=SileroVADAnalyzer(),
+    ),
+    "twilio": lambda: FastAPIWebsocketParams(
+        audio_in_enabled=True,
+        audio_out_enabled=True,
+        vad_analyzer=SileroVADAnalyzer(),
+    ),
+    "webrtc": lambda: TransportParams(
+        audio_in_enabled=True,
+        audio_out_enabled=True,
+        vad_analyzer=SileroVADAnalyzer(),
+    ),
+}
+
+
+async def run_example(transport: BaseTransport, _: argparse.Namespace, handle_sigint: bool):
+    logger.info(f"Starting bot")
+
+    strands_agent = Agent(
+        model=BedrockModel(
+            model_id="us.anthropic.claude-3-7-sonnet-20250219-v1:0", max_tokens=64000
+        ),
+        tools=[
+            get_location_name_from_landmark,
+            get_lat_long_from_location_name,
+            get_current_weather_from_lat_long,
+        ],
+        system_prompt="""
+        You are a helpful personal assistant who can look up information about places and weather.
+
+        Your key capabilities:
+        1. Look up where landmarks are located.
+        2. Find latitude and longitude for a location.
+        3. Look up the current weather for a specific latitude and longitude.
+
+        Explain each step of your reasoning in clear, simple, and concise language. Your responses will be converted to audio, so avoid special characters and numbered lists.
+        """,
+    )
+
+    async def handle_location_or_weather_related_queries(params: FunctionCallParams, query: str):
+        """
+        Handle location or weather related queries.
+
+        Args:
+            query (str): The user's query, e.g. "What's the weather where the Golden Gate Bridge is?".
+        """
+        # Run in a background thread
+        # (Otherwise the agent blocks the event loop; one effect of that is that we don't hear
+        # "let me check on that" until the agent finishes)
+        loop = asyncio.get_running_loop()
+        result = await loop.run_in_executor(None, strands_agent, query)
+        await params.result_callback(result.message)
+
+    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"))
+
+    llm.register_direct_function(handle_location_or_weather_related_queries)
+
+    @llm.event_handler("on_function_calls_started")
+    async def on_function_calls_started(service, function_calls):
+        await tts.queue_frame(TTSSpeakFrame("Let me check on that."))
+
+    tools = ToolsSchema(standard_tools=[handle_location_or_weather_related_queries])
+
+    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. Start by suggesting that the user ask about the weather where the Golden Gate Bridge is.",
+        },
+    ]
+
+    context = OpenAILLMContext(messages, tools)
+    context_aggregator = llm.create_context_aggregator(context)
+
+    pipeline = Pipeline(
+        [
+            transport.input(),
+            stt,
+            context_aggregator.user(),
+            llm,
+            tts,
+            transport.output(),
+            context_aggregator.assistant(),
+        ]
+    )
+
+    task = PipelineTask(
+        pipeline,
+        params=PipelineParams(
+            enable_metrics=True,
+            enable_usage_metrics=True,
+        ),
+    )
+
+    @transport.event_handler("on_client_connected")
+    async def on_client_connected(transport, client):
+        logger.info(f"Client connected")
+        # Kick off the conversation.
+        await task.queue_frames([context_aggregator.user().get_context_frame()])
+
+    @transport.event_handler("on_client_disconnected")
+    async def on_client_disconnected(transport, client):
+        logger.info(f"Client disconnected")
+        await task.cancel()
+
+    runner = PipelineRunner(handle_sigint=handle_sigint)
+
+    await runner.run(task)
+
+
+if __name__ == "__main__":
+    from pipecat.examples.run import main
+
+    main(run_example, transport_params=transport_params)

examples/aws-strands/env.example

@@ -0,0 +1,8 @@
+OPENAI_API_KEY=
+CARTESIA_API_KEY=
+DEEPGRAM_API_KEY=
+DAILY_API_KEY=
+DAILY_SAMPLE_ROOM_URL=
+AWS_SECRET_ACCESS_KEY=
+AWS_ACCESS_KEY_ID=
+AWS_REGION=

examples/aws-strands/explain-thinking.py

@@ -0,0 +1,249 @@
+#
+# Copyright (c) 2025, Daily
+#
+# SPDX-License-Identifier: BSD 2-Clause License
+#
+
+import argparse
+import asyncio
+import os
+import threading
+import time
+
+from dotenv import load_dotenv
+from loguru import logger
+from strands import Agent, tool
+from strands.models import BedrockModel
+
+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.processors.aggregators.openai_llm_context import OpenAILLMContext
+from pipecat.services.cartesia.tts import CartesiaTTSService
+from pipecat.services.deepgram.stt import DeepgramSTTService
+from pipecat.services.llm_service import FunctionCallParams
+from pipecat.services.openai.llm import OpenAILLMService
+from pipecat.transports.base_transport import BaseTransport, TransportParams
+from pipecat.transports.network.fastapi_websocket import FastAPIWebsocketParams
+from pipecat.transports.services.daily import DailyParams
+
+load_dotenv(override=True)
+
+"""This example demonstrates how to use the Strands agent with Pipecat in a way where the agent explains its reasoning step-by-step.
+
+You can delegate complex, multi-step tasks to the Strands agent, which can cycle through LLM-based reasoning and tool calls to accomplish the task.
+
+Try asking: "What's the weather where the Golden Gate Bridge is?"
+"""
+
+
+# Strands agent tools
+
+
+@tool
+def get_location_name_from_landmark(landmark: str) -> str:
+    """
+    Get the location name from a landmark.
+
+    Args:
+        landmark (str): The name of the landmark, e.g. "Golden Gate Bridge".
+    """
+    # Simulate fetching location (slowly)
+    time.sleep(3)
+    return "San Francisco, CA"
+
+
+@tool
+def get_lat_long_from_location_name(location: str) -> dict:
+    """
+    Get the latitude and longitude for a location name.
+
+    Args:
+        location (str): The city and state, e.g. "San Francisco, CA".
+    """
+    # Simulate fetching lat/long from a geocoding service (slowly)
+    time.sleep(3)
+    return {"lat": 37.7749, "long": -122.4194}
+
+
+@tool
+def get_current_weather_from_lat_long(lat: float, long: float) -> dict:
+    """
+    Get the current weather for a specific latitude and longitude.
+
+    Args:
+        lat (float): The latitude of the location.
+        long (float): The longitude of the location.
+    """
+    # Simulate fetching weather data from a weather service (slowly)
+    time.sleep(3)
+    return {"conditions": "nice", "temperature": "75"}
+
+
+# We store functions so objects (e.g. SileroVADAnalyzer) don't get
+# instantiated. The function will be called when the desired transport gets
+# selected.
+transport_params = {
+    "daily": lambda: DailyParams(
+        audio_in_enabled=True,
+        audio_out_enabled=True,
+        vad_analyzer=SileroVADAnalyzer(),
+    ),
+    "twilio": lambda: FastAPIWebsocketParams(
+        audio_in_enabled=True,
+        audio_out_enabled=True,
+        vad_analyzer=SileroVADAnalyzer(),
+    ),
+    "webrtc": lambda: TransportParams(
+        audio_in_enabled=True,
+        audio_out_enabled=True,
+        vad_analyzer=SileroVADAnalyzer(),
+    ),
+}
+
+
+async def run_example(transport: BaseTransport, _: argparse.Namespace, handle_sigint: bool):
+    logger.info(f"Starting bot")
+
+    stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
+
+    tts = CartesiaTTSService(
+        api_key=os.getenv("CARTESIA_API_KEY"),
+        voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
+    )
+
+    next_strands_message_is_last = False
+    strands_messages_queue = asyncio.Queue()
+
+    def strands_callback_handler(**kwargs):
+        """
+        Handle events from the Strands agent.
+        """
+        nonlocal next_strands_message_is_last
+        if "event" in kwargs:
+            event_obj = kwargs["event"]
+            if event_obj and "messageStop" in event_obj:
+                message_stop = event_obj["messageStop"]
+                if message_stop and "stopReason" in message_stop:
+                    stop_reason = message_stop["stopReason"]
+                    if stop_reason == "end_turn":
+                        next_strands_message_is_last = True
+        elif "message" in kwargs:
+            message_obj = kwargs["message"]
+            if message_obj and "content" in message_obj and "role" in message_obj:
+                role = message_obj["role"]
+                content = message_obj["content"]
+                if role == "assistant" and isinstance(content, list):
+                    for content_obj in content:
+                        if isinstance(content_obj, dict) and "text" in content_obj:
+                            message = content_obj["text"]
+                            if not next_strands_message_is_last:
+                                strands_messages_queue.put_nowait(message)
+
+    async def process_strands_messages():
+        while True:
+            message = await strands_messages_queue.get()
+            await tts.queue_frame(TTSSpeakFrame(message))
+            strands_messages_queue.task_done()
+
+    asyncio.create_task(process_strands_messages())
+
+    strands_agent = Agent(
+        model=BedrockModel(
+            model_id="us.anthropic.claude-3-7-sonnet-20250219-v1:0", max_tokens=64000
+        ),
+        tools=[
+            get_location_name_from_landmark,
+            get_lat_long_from_location_name,
+            get_current_weather_from_lat_long,
+        ],
+        system_prompt="""
+        You are a helpful personal assistant who can look up information about places and weather.
+
+        Your key capabilities:
+        1. Look up where landmarks are located.
+        2. Find latitude and longitude for a location.
+        3. Look up the current weather for a specific latitude and longitude.
+
+        Explain each step of your reasoning in clear, simple, and concise language. Your responses will be converted to audio, so avoid special characters and numbered lists.
+        """,
+        callback_handler=strands_callback_handler,
+    )
+
+    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
+
+    async def handle_location_or_weather_related_queries(params: FunctionCallParams, query: str):
+        """
+        Handle location or weather related queries.
+
+        Args:
+            query (str): The user's query, e.g. "What's the weather where the Golden Gate Bridge is?".
+        """
+        # Run in a background thread
+        # (Otherwise the agent blocks the event loop; one effect of that is that we don't hear
+        # the agent's "thinking" messages until the agent finishes)
+        loop = asyncio.get_running_loop()
+        result = await loop.run_in_executor(None, strands_agent, query)
+        await params.result_callback(result.message)
+
+    llm.register_direct_function(handle_location_or_weather_related_queries)
+
+    @llm.event_handler("on_function_calls_started")
+    async def on_function_calls_started(service, function_calls):
+        await tts.queue_frame(TTSSpeakFrame("Let me check on that."))
+
+    tools = ToolsSchema(standard_tools=[handle_location_or_weather_related_queries])
+
+    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. Start by suggesting that the user ask about the weather where the Golden Gate Bridge is.",
+        },
+    ]
+
+    context = OpenAILLMContext(messages, tools)
+    context_aggregator = llm.create_context_aggregator(context)
+
+    pipeline = Pipeline(
+        [
+            transport.input(),
+            stt,
+            context_aggregator.user(),
+            llm,
+            tts,
+            transport.output(),
+            context_aggregator.assistant(),
+        ]
+    )
+
+    task = PipelineTask(
+        pipeline,
+        params=PipelineParams(
+            enable_metrics=True,
+            enable_usage_metrics=True,
+        ),
+    )
+
+    @transport.event_handler("on_client_connected")
+    async def on_client_connected(transport, client):
+        logger.info(f"Client connected")
+        # Kick off the conversation.
+        await task.queue_frames([context_aggregator.user().get_context_frame()])
+
+    @transport.event_handler("on_client_disconnected")
+    async def on_client_disconnected(transport, client):
+        logger.info(f"Client disconnected")
+        await task.cancel()
+
+    runner = PipelineRunner(handle_sigint=handle_sigint)
+
+    await runner.run(task)
+
+
+if __name__ == "__main__":
+    from pipecat.examples.run import main
+
+    main(run_example, transport_params=transport_params)

examples/aws-strands/requirements.txt

@@ -0,0 +1,6 @@
+fastapi
+uvicorn
+python-dotenv
+pipecat-ai[webrtc,daily,deepgram,cartesia]
+pipecat-ai-small-webrtc-prebuilt
+strands-agents

examples/bot-ready-signalling/client/javascript/package-lock.json

@@ -12,7 +12,7 @@
         "@daily-co/daily-js": "0.74.0"
       },
       "devDependencies": {
-        "vite": "^6.0.9"
+        "vite": "^6.3.5"
       }
     },
     "node_modules/@babel/runtime": {
@@ -999,9 +999,9 @@
       }
     },
     "node_modules/vite": {
-      "version": "6.3.3",
-      "resolved": "https://registry.npmjs.org/vite/-/vite-6.3.3.tgz",
-      "integrity": "sha512-5nXH+QsELbFKhsEfWLkHrvgRpTdGJzqOZ+utSdmPTvwHmvU6ITTm3xx+mRusihkcI8GeC7lCDyn3kDtiki9scw==",
+      "version": "6.3.5",
+      "resolved": "https://registry.npmjs.org/vite/-/vite-6.3.5.tgz",
+      "integrity": "sha512-cZn6NDFE7wdTpINgs++ZJ4N49W2vRp8LCKrn3Ob1kYNtOo21vfDoaV5GzBfLU4MovSAB8uNRm4jgzVQZ+mBzPQ==",
       "dev": true,
       "dependencies": {
         "esbuild": "^0.25.0",

examples/bot-ready-signalling/client/javascript/package.json

@@ -12,7 +12,7 @@
   "license": "ISC",
   "description": "",
   "devDependencies": {
-    "vite": "^6.0.9"
+    "vite": "^6.3.5"
   },
   "dependencies": {
     "@daily-co/daily-js": "0.74.0"

examples/bot-ready-signalling/client/react-native/package-lock.json

@@ -4364,9 +4364,9 @@
       }
     },
     "node_modules/brace-expansion": {
-      "version": "1.1.11",
-      "resolved": "https://registry.npmjs.org/brace-expansion/-/brace-expansion-1.1.11.tgz",
-      "integrity": "sha512-iCuPHDFgrHX7H2vEI/5xpz07zSHB00TpugqhmYtVmMO6518mCuRMoOYFldEBl0g187ufozdaHgWKcYFb61qGiA==",
+      "version": "1.1.12",
+      "resolved": "https://registry.npmjs.org/brace-expansion/-/brace-expansion-1.1.12.tgz",
+      "integrity": "sha512-9T9UjW3r0UW5c1Q7GTwllptXwhvYmEzFhzMfZ9H7FQWt+uZePjZPjBP/W1ZEyZ1twGWom5/56TF4lPcqjnDHcg==",
       "dependencies": {
         "balanced-match": "^1.0.0",
         "concat-map": "0.0.1"
@@ -6081,9 +6081,9 @@
       }
     },
     "node_modules/glob/node_modules/brace-expansion": {
-      "version": "2.0.1",
-      "resolved": "https://registry.npmjs.org/brace-expansion/-/brace-expansion-2.0.1.tgz",
-      "integrity": "sha512-XnAIvQ8eM+kC6aULx6wuQiwVsnzsi9d3WxzV3FpWTGA19F621kwdbsAcFKXgKUHZWsy+mY6iL1sHTxWEFCytDA==",
+      "version": "2.0.2",
+      "resolved": "https://registry.npmjs.org/brace-expansion/-/brace-expansion-2.0.2.tgz",
+      "integrity": "sha512-Jt0vHyM+jmUBqojB7E1NIYadt0vI0Qxjxd2TErW94wDz+E2LAm5vKMXXwg6ZZBTHPuUlDgQHKXvjGBdfcF1ZDQ==",
       "dependencies": {
         "balanced-match": "^1.0.0"
       }

examples/chatbot-audio-recording/bot.py

@@ -128,7 +128,15 @@ async def main():
             ]
         )
 
-        task = PipelineTask(pipeline, params=PipelineParams(allow_interruptions=True))
+        task = PipelineTask(
+            pipeline,
+            params=PipelineParams(
+                audio_in_sample_rate=16000,
+                audio_out_sample_rate=16000,
+                enable_metrics=True,
+                enable_usage_metrics=True,
+            ),
+        )
 
         @audiobuffer.event_handler("on_audio_data")
         async def on_audio_data(buffer, audio, sample_rate, num_channels):

examples/daily-custom-tracks/bot.py

@@ -71,6 +71,8 @@ async def main():
             params=PipelineParams(
                 audio_in_sample_rate=16000,
                 audio_out_sample_rate=16000,
+                enable_metrics=True,
+                enable_usage_metrics=True,
             ),
         )
 

examples/daily-multi-translation/bot.py

@@ -148,10 +148,8 @@ async def main():
             params=PipelineParams(
                 audio_in_sample_rate=16000,
                 audio_out_sample_rate=16000,
-                allow_interruptions=True,
                 enable_metrics=True,
                 enable_usage_metrics=True,
-                report_only_initial_ttfb=True,
             ),
             observers=[TranscriptionLogObserver()],
         )

examples/daily-multi-translation/requirements.txt

@@ -2,4 +2,4 @@ aiofiles
 python-dotenv
 fastapi[all]
 uvicorn
-pipecat-ai[daily,deepgram,openai,silero,cartesia]
+pipecat-ai[daily,deepgram,openai,silero,cartesia,soundfile]

examples/deployment/flyio-example/bot.py

@@ -75,7 +75,13 @@ async def main(room_url: str, token: str):
         ]
     )
 
-    task = PipelineTask(pipeline, params=PipelineParams(allow_interruptions=True))
+    task = PipelineTask(
+        pipeline,
+        params=PipelineParams(
+            enable_metrics=True,
+            enable_usage_metrics=True,
+        ),
+    )
 
     @transport.event_handler("on_first_participant_joined")
     async def on_first_participant_joined(transport, participant):

examples/deployment/modal-example/.gitignore

@@ -1,3 +1,6 @@
+# Modal clone
+modal-examples
+
 # Python
 __pycache__/
 *.py[cod]

examples/deployment/modal-example/README.md

@@ -1,37 +1,91 @@
 # Deploying Pipecat to Modal.com
 
-Barebones deployment example for [modal.com](https://www.modal.com)
+Deployment example for [modal.com](https://www.modal.com). This example demonstrates how to deploy a FastAPI webapp to Modal with an RTVI compatible `/connect` endpoint that launches a Pipecat pipeline in a separate Modal container and returns a room/token for the client to join. This example also supports providing a parameter to the `/connect` endpoint for specifying which Pipecat pipeline to launch; openai, gemini, or vllm. The vllm pipeline points to a self-hosted OpenAI compatible LLM, using a llama model (neuralmagic/Meta-Llama-3.1-8B-Instruct-quantized.w4a16), deployed to Modal.
 
-1. Install dependencies
+![](diagram.jpg)
 
-```bash
-python -m venv venv
-source venv/bin/active # or OS equivalent
-pip install -r requirements.txt
-```
+# Running this Example
 
-2. Setup .env
+## Install the Modal CLI
 
-```bash
-cp env.example .env
-```
+Setup a Modal account and install it on your machine if you have not already, following their easy 3 steps in their [Getting Started Guide](https://modal.com/docs/guide#getting-started)
 
-Alternatively, you can configure your Modal app to use [secrets](https://modal.com/docs/guide/secrets)
+## Deploy a self-serve LLM
 
-3. Test the app locally
+1. Deploy Modal's OpenAI-compatible LLM service:
 
-```bash
-modal serve app.py
-```
+   ```bash
+   git clone https://github.com/modal-labs/modal-examples
+   cd modal-examples
+   modal deploy 06_gpu_and_ml/llm-serving/vllm_inference.py
+   ```
+
+   Refer to Modal's guide and example for [Deploying an OpenAI-compatible LLM service with vLLM](https://modal.com/docs/examples/vllm_inference) for more details.
+
+2. Take note of the endpoint URL from the previous step, which will look like:
+   ```
+   https://{your-workspace}--example-vllm-openai-compatible-serve.modal.run
+   ```
+   You'll need this for the `bot_vllm.py` file in the next section.
+
+    **Note:**  The default Modal LLM example uses Llama-3.1 and will shut down after 15 minutes of inactivity. Cold starts take 5-10 minutes. To prepare the service, we recommend visiting the `/docs` endpoint (`https://<Modal workspace>--example-vllm-openai-compatible-serve.modal.run/docs`) for your deployed LLM and wait for it to fully load before connecting your client.
+
+## Deploy FastAPI App and Pipecat pipeline to Modal 
+
+1. Setup environment variables
+
+   ```bash
+   cd server
+   cp env.example .env
+   # Modify .env to provide your service API Keys
+   ```
+
+   Alternatively, you can configure your Modal app to use [secrets](https://modal.com/docs/guide/secrets)
+
+2. Update the `modal_url` in `server/src/bot_vllm.py` to point to the url produced from the self-serve llm deploy, mentioned above.
+
+3. From within the `server` directory, test the app locally:
+
+   ```bash
+   modal serve app.py
+   ```
 
 4. Deploy to production
 
-```bash
-modal deploy app.py
-```
+   ```bash
+   modal deploy app.py
+   ```
 
-## Configuration options
+5. Note the endpoint URL produced from this deployment. It will look like:
 
-This app sets some sensible defaults for reducing cold starts, such as `minkeep_warm=1`, which will keep at least 1 warm instance ready for your bot function.
+   ```bash
+   https://{your-workspace}--pipecat-modal-fastapi-app.modal.run
+   ```
 
-It has been configured to only allow a concurrency of 1 (`max_inputs=1`) as each user will require their own running function.
+   You'll need this URL for the client's `app.js` configuration mentioned in its README.
+
+## Launch your bots on Modal
+
+### Option 1: Direct Link
+
+Simply click on the url displayed after running the server or deploy step to launch an agent and be redirected to a Daily room to talk with the launched bot. This will use the OpenAI pipeline.
+
+### Option 2: Connect via an RTVI Client
+
+Follow the instructions provided in the [client folder's README](client/javascript/README.md) for building and running a custom client that connects to your Modal endpoint. The provided client provides a dropdown for choosing which bot pipeline to run.
+
+# Navigating your llm, server, and Pipecat logs
+
+In your [Modal dashboard](https://modal.com/apps), you should have two Apps listed under Live Apps:
+
+1. `example-vllm-openai-compatible`: This App contains the containers and logs used to run your self-hosted LLM. There will be just one App Function listed: `serve`. Click on this function to view logs for your LLM.
+2. `pipecat-modal`: This App contains the containers and logs used to run your `connect` endpoints and Pipecat pipelines. It will list two App Functions:
+    1. `fastapi_app`: This function is running the endpoints that your client will interact with and initiate starting a new pipeline (`/`, `/connect`, `/status`). Click on this function to see logs for each endpoint hit.
+    2. `bot_runner`: This function handles launching and running a bot pipeline. Click on this function to get a list of all pipeline runs and access each run's logs.
+
+# Modal + Pipecat Tips
+
+- In most other Pipecat examples, we use `Popen` to launch the pipeline process from the `/connect` endpoint. In this example, we use a Modal function instead. This allows us to run the pipelines using a separately defined Modal image as well as run each pipeline in an isolated container.
+- For the FastAPI and most common Pipecat Pipeline containers, a default `debian_slim` CPU-only should be all that's required to run. GPU containers are needed for self-hosted services.
+- To minimize cold starts of the pipeline and reduce latency for users, set `min_containers=1` on the Modal Function that launches the pipeline to ensure at least one warm instance of your function is always available.
+- For next steps on running a self-hosted llm and reducing latency, check out all of [Modal's LLM examples](https://modal.com/docs/examples/vllm_inference).

examples/deployment/modal-example/app.py

@@ -1,80 +0,0 @@
-#
-# Copyright (c) 2024–2025, Daily
-#
-# SPDX-License-Identifier: BSD 2-Clause License
-#
-
-import os
-
-import aiohttp
-import modal
-from bot import _voice_bot_process
-from fastapi import HTTPException
-from fastapi.responses import JSONResponse
-from loguru import logger
-
-MAX_SESSION_TIME = 15 * 60  # 15 minutes
-
-app = modal.App("pipecat-modal")
-
-
-image = modal.Image.debian_slim(python_version="3.12").pip_install_from_requirements(
-    "requirements.txt"
-)
-
-
-@app.function(
-    image=image,
-    cpu=1.0,
-    secrets=[modal.Secret.from_dotenv()],
-    keep_warm=1,
-    enable_memory_snapshot=True,
-    max_inputs=1,  # Do not reuse instances across requests
-    retries=0,
-)
-def launch_bot_process(room_url: str, token: str):
-    _voice_bot_process(room_url, token)
-
-
-@app.function(
-    image=image,
-    secrets=[modal.Secret.from_dotenv()],
-)
-@modal.web_endpoint(method="POST")
-async def start():
-    from pipecat.transports.services.helpers.daily_rest import (
-        DailyRESTHelper,
-        DailyRoomParams,
-    )
-
-    logger.info("Request received")
-
-    async with aiohttp.ClientSession() as session:
-        daily_rest_helper = DailyRESTHelper(
-            daily_api_key=os.getenv("DAILY_API_KEY", ""),
-            daily_api_url=os.getenv("DAILY_API_URL", "https://api.daily.co/v1"),
-            aiohttp_session=session,
-        )
-
-        # Create new Daily room
-        room = await daily_rest_helper.create_room(DailyRoomParams())
-        if not room.url:
-            raise HTTPException(
-                status_code=500,
-                detail="Unable to create room",
-            )
-        logger.info(f"Created room: {room.url}")
-
-        # Create bot token for room
-        token = await daily_rest_helper.get_token(room.url, MAX_SESSION_TIME)
-        if not token:
-            raise HTTPException(status_code=500, detail=f"Failed to get token for room: {room.url}")
-
-        logger.info(f"Bot token created: {token}")
-
-        # Spawn a new bot process
-        launch_bot_process.spawn(room_url=room.url, token=token)
-
-        # Return room URL to the user to join
-        # Note: in production, you would want to return a token to the user
-        return JSONResponse(content={"room_url": room.url, token: token})

examples/deployment/modal-example/bot.py

@@ -1,95 +0,0 @@
-#
-# Copyright (c) 2024–2025, Daily
-#
-# SPDX-License-Identifier: BSD 2-Clause License
-#
-
-import asyncio
-import os
-import sys
-
-from dotenv import load_dotenv
-from loguru import logger
-
-from pipecat.audio.vad.silero import SileroVADAnalyzer
-from pipecat.pipeline.pipeline import Pipeline
-from pipecat.pipeline.runner import PipelineRunner
-from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
-from pipecat.services.cartesia.tts import CartesiaTTSService
-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(room_url: str, token: str):
-    transport = DailyTransport(
-        room_url,
-        token,
-        "bot",
-        DailyParams(
-            audio_in_enabled=True,
-            audio_out_enabled=True,
-            transcription_enabled=True,
-            vad_analyzer=SileroVADAnalyzer(),
-        ),
-    )
-
-    tts = CartesiaTTSService(
-        api_key=os.getenv("CARTESIA_API_KEY", ""), voice_id="71a7ad14-091c-4e8e-a314-022ece01c121"
-    )
-
-    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
-
-    messages = [
-        {
-            "role": "system",
-            "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
-        },
-    ]
-
-    context = 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):
-        await transport.capture_participant_transcription(participant["id"])
-        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)
-
-
-def _voice_bot_process(room_url: str, token: str):
-    asyncio.run(main(room_url, token))

examples/deployment/modal-example/client/javascript/.gitignore

@@ -0,0 +1 @@
+node_modules

examples/deployment/modal-example/client/javascript/README.md

@@ -0,0 +1,29 @@
+# JavaScript Implementation
+
+Basic implementation using the [Pipecat JavaScript SDK](https://docs.pipecat.ai/client/js/introduction).
+
+## Setup
+
+1. Deploy the Modal server. See the main [README](../../README).
+
+2. Navigate to the `client/javascript` directory:
+
+```bash
+cd client/javascript
+```
+
+3. Modify the baseUrl in src/app.js to point to your deployed Modal endpoint
+
+4. Install dependencies:
+
+```bash
+npm install
+```
+
+5. Run the client app:
+
+```
+npm run dev
+```
+
+6. Visit http://localhost:5173 in your browser.

examples/deployment/modal-example/client/javascript/index.html

@@ -0,0 +1,49 @@
+<!DOCTYPE html>
+<html lang="en">
+  <head>
+    <meta charset="UTF-8" />
+    <meta name="viewport" content="width=device-width, initial-scale=1.0" />
+    <title>AI Chatbot</title>
+  </head>
+
+  <body>
+    <div class="container">
+      <div class="status-bar">
+        <div class="status">
+          Status: <span id="connection-status">Disconnected</span>
+        </div>
+        <div class="controls">
+          <select id="bot-selector">
+            <option value="openai">OpenAI</option>
+            <option value="gemini">Gemini</option>
+            <option value="vllm">Llama</option>
+          </select>
+          <button id="connect-btn">Connect</button>
+          <button id="disconnect-btn" disabled>Disconnect</button>
+        </div>
+      </div>
+
+      <div class="main-content">
+        <div class="bot-container">
+          <div id="bot-video-container"></div>
+          <audio id="bot-audio" autoplay></audio>
+        </div>
+      </div>
+
+      <div class="device-bar">
+        <div class="device-controls">
+          <select id="device-selector"></select>
+          <button id="mic-toggle-btn">Mute Mic</button>
+        </div>
+      </div>
+
+      <div class="debug-panel">
+        <h3>Debug Info</h3>
+        <div id="debug-log"></div>
+      </div>
+    </div>
+
+    <script type="module" src="/src/app.js"></script>
+    <link rel="stylesheet" href="/src/style.css" />
+  </body>
+</html>

examples/deployment/modal-example/client/javascript/package.json

@@ -0,0 +1,21 @@
+{
+  "name": "client",
+  "version": "1.0.0",
+  "main": "index.js",
+  "scripts": {
+    "dev": "vite",
+    "build": "vite build",
+    "preview": "vite preview"
+  },
+  "keywords": [],
+  "author": "",
+  "license": "ISC",
+  "description": "",
+  "devDependencies": {
+    "vite": "^6.3.5"
+  },
+  "dependencies": {
+    "@pipecat-ai/client-js": "^1.0.0",
+    "@pipecat-ai/daily-transport": "^1.0.0"
+  }
+}

examples/deployment/modal-example/client/javascript/src/app.js

@@ -0,0 +1,376 @@
+/**
+ * Copyright (c) 2024–2025, Daily
+ *
+ * SPDX-License-Identifier: BSD 2-Clause License
+ */
+
+/**
+ * Pipecat Client Implementation
+ *
+ * This client connects to an RTVI-compatible bot server using WebRTC (via Daily).
+ * It handles audio/video streaming and manages the connection lifecycle.
+ *
+ * Requirements:
+ * - A running RTVI bot server (defaults to http://localhost:7860)
+ * - The server must implement the /connect endpoint that returns Daily.co room credentials
+ * - Browser with WebRTC support
+ */
+
+import { PipecatClient, RTVIEvent } from '@pipecat-ai/client-js';
+import { DailyTransport } from '@pipecat-ai/daily-transport';
+
+/**
+ * ChatbotClient handles the connection and media management for a real-time
+ * voice and video interaction with an AI bot.
+ */
+class ChatbotClient {
+  constructor() {
+    // Initialize client state
+    this.pcClient = null;
+    this.setupDOMElements();
+    this.initializeClientAndTransport();
+    this.setupEventListeners();
+  }
+
+  /**
+   * Set up references to DOM elements and create necessary media elements
+   */
+  setupDOMElements() {
+    // Get references to UI control elements
+    this.connectBtn = document.getElementById('connect-btn');
+    this.disconnectBtn = document.getElementById('disconnect-btn');
+    this.statusSpan = document.getElementById('connection-status');
+    this.debugLog = document.getElementById('debug-log');
+    this.botVideoContainer = document.getElementById('bot-video-container');
+    this.deviceSelector = document.getElementById('device-selector');
+
+    // Create an audio element for bot's voice output
+    this.botAudio = document.createElement('audio');
+    this.botAudio.autoplay = true;
+    this.botAudio.playsInline = true;
+    document.body.appendChild(this.botAudio);
+  }
+
+  /**
+   * Set up event listeners for connect/disconnect buttons
+   */
+  setupEventListeners() {
+    this.connectBtn.addEventListener('click', () => this.connect());
+    this.disconnectBtn.addEventListener('click', () => this.disconnect());
+
+    // Populate device selector
+    this.pcClient.getAllMics().then((mics) => {
+      console.log('Available mics:', mics);
+      mics.forEach((device) => {
+        const option = document.createElement('option');
+        option.value = device.deviceId;
+        option.textContent = device.label || `Microphone ${device.deviceId}`;
+        this.deviceSelector.appendChild(option);
+      });
+    });
+    this.deviceSelector.addEventListener('change', (event) => {
+      const selectedDeviceId = event.target.value;
+      console.log('Selected device ID:', selectedDeviceId);
+      this.pcClient.updateMic(selectedDeviceId);
+    });
+
+    // Handle mic mute/unmute toggle
+    const micToggleBtn = document.getElementById('mic-toggle-btn');
+
+    micToggleBtn.addEventListener('click', () => {
+      let micEnabled = this.pcClient.isMicEnabled;
+      micToggleBtn.textContent = micEnabled ? 'Unmute Mic' : 'Mute Mic';
+      this.pcClient.enableMic(!micEnabled);
+      // Add logic to mute/unmute the mic
+      if (micEnabled) {
+        console.log('Mic muted');
+        // Add code to mute the mic
+      } else {
+        console.log('Mic unmuted');
+        // Add code to unmute the mic
+      }
+    });
+  }
+
+  /**
+   * Set up the Pipecat client and Daily transport
+   */
+  async initializeClientAndTransport() {
+    // Initialize the Pipecat client with a DailyTransport and our configuration
+    this.pcClient = new PipecatClient({
+      transport: new DailyTransport(),
+      enableMic: true, // Enable microphone for user input
+      enableCam: false,
+      callbacks: {
+        // Handle connection state changes
+        onConnected: () => {
+          this.updateStatus('Connected');
+          this.connectBtn.disabled = true;
+          this.disconnectBtn.disabled = false;
+          this.log('Client connected');
+        },
+        onDisconnected: () => {
+          this.updateStatus('Disconnected');
+          this.connectBtn.disabled = false;
+          this.disconnectBtn.disabled = true;
+          this.log('Client disconnected');
+        },
+        // Handle transport state changes
+        onTransportStateChanged: (state) => {
+          this.updateStatus(`Transport: ${state}`);
+          this.log(`Transport state changed: ${state}`);
+          if (state === 'connecting') {
+            window.startTime = Date.now();
+          }
+          if (state === 'ready') {
+            this.setupMediaTracks();
+            console.warn('TIME TO BOT READY:', Date.now() - window.startTime);
+          }
+        },
+        // Handle bot connection events
+        onBotConnected: (participant) => {
+          this.log(`Bot connected: ${JSON.stringify(participant)}`);
+        },
+        onBotDisconnected: (participant) => {
+          this.log(`Bot disconnected: ${JSON.stringify(participant)}`);
+        },
+        onBotReady: (data) => {
+          this.log(`Bot ready: ${JSON.stringify(data)}`);
+          this.setupMediaTracks();
+        },
+        // Transcript events
+        onUserTranscript: (data) => {
+          // Only log final transcripts
+          if (data.final) {
+            this.log(`User: ${data.text}`);
+          }
+        },
+        onBotTranscript: (data) => {
+          this.log(`Bot: ${data.text}`);
+        },
+        // Error handling
+        onMessageError: (error) => {
+          console.log('Message error:', error);
+        },
+        onMicUpdated: (data) => {
+          console.log('Mic updated:', data);
+          this.deviceSelector.value = data.deviceId;
+        },
+        onError: (error) => {
+          console.log('Error:', JSON.stringify(error));
+        },
+      },
+    });
+
+    // Set up listeners for media track events
+    this.setupTrackListeners();
+
+    await this.pcClient.initDevices();
+    window.client = this.pcClient;
+  }
+
+  /**
+   * Add a timestamped message to the debug log
+   */
+  log(message) {
+    const entry = document.createElement('div');
+    entry.textContent = `${new Date().toISOString()} - ${message}`;
+
+    // Add styling based on message type
+    if (message.startsWith('User: ')) {
+      entry.style.color = '#2196F3'; // blue for user
+    } else if (message.startsWith('Bot: ')) {
+      entry.style.color = '#4CAF50'; // green for bot
+    }
+
+    this.debugLog.appendChild(entry);
+    this.debugLog.scrollTop = this.debugLog.scrollHeight;
+    console.log(message);
+  }
+
+  /**
+   * Update the connection status display
+   */
+  updateStatus(status) {
+    this.statusSpan.textContent = status;
+    this.log(`Status: ${status}`);
+  }
+
+  /**
+   * Check for available media tracks and set them up if present
+   * This is called when the bot is ready or when the transport state changes to ready
+   */
+  setupMediaTracks() {
+    if (!this.pcClient) return;
+
+    // Get current tracks from the client
+    const tracks = this.pcClient.tracks();
+
+    // Set up any available bot tracks
+    if (tracks.bot?.audio) {
+      this.setupAudioTrack(tracks.bot.audio);
+    }
+    if (tracks.bot?.video) {
+      this.setupVideoTrack(tracks.bot.video);
+    }
+  }
+
+  /**
+   * Set up listeners for track events (start/stop)
+   * This handles new tracks being added during the session
+   */
+  setupTrackListeners() {
+    if (!this.pcClient) return;
+
+    // Listen for new tracks starting
+    this.pcClient.on(RTVIEvent.TrackStarted, (track, participant) => {
+      // Only handle non-local (bot) tracks
+      if (!participant?.local) {
+        if (track.kind === 'audio') {
+          this.setupAudioTrack(track);
+        } else if (track.kind === 'video') {
+          this.setupVideoTrack(track);
+        }
+        this.log(
+          `Track started event: ${track.kind} from ${
+            participant?.name || 'unknown'
+          }`
+        );
+      } else {
+        this.log('Local mic unmuted');
+      }
+    });
+
+    // Listen for tracks stopping
+    this.pcClient.on(RTVIEvent.TrackStopped, (track, participant) => {
+      if (participant.local) {
+        this.log('Local mic muted');
+        return;
+      }
+      this.log(
+        `Track stopped event: ${track.kind} from ${
+          participant?.name || 'unknown'
+        }`
+      );
+    });
+  }
+
+  /**
+   * Set up an audio track for playback
+   * Handles both initial setup and track updates
+   */
+  setupAudioTrack(track) {
+    this.log('Setting up audio track');
+    // Check if we're already playing this track
+    if (this.botAudio.srcObject) {
+      const oldTrack = this.botAudio.srcObject.getAudioTracks()[0];
+      if (oldTrack?.id === track.id) return;
+    }
+    // Create a new MediaStream with the track and set it as the audio source
+    this.botAudio.srcObject = new MediaStream([track]);
+  }
+
+  /**
+   * Set up a video track for display
+   * Handles both initial setup and track updates
+   */
+  setupVideoTrack(track) {
+    this.log('Setting up video track');
+    const videoEl = document.createElement('video');
+    videoEl.autoplay = true;
+    videoEl.playsInline = true;
+    videoEl.muted = true;
+    videoEl.style.width = '100%';
+    videoEl.style.height = '100%';
+    videoEl.style.objectFit = 'cover';
+
+    // Check if we're already displaying this track
+    if (this.botVideoContainer.querySelector('video')?.srcObject) {
+      const oldTrack = this.botVideoContainer
+        .querySelector('video')
+        .srcObject.getVideoTracks()[0];
+      if (oldTrack?.id === track.id) return;
+    }
+
+    // Create a new MediaStream with the track and set it as the video source
+    videoEl.srcObject = new MediaStream([track]);
+    this.botVideoContainer.innerHTML = '';
+    this.botVideoContainer.appendChild(videoEl);
+  }
+
+  /**
+   * Initialize and connect to the bot
+   * This sets up the Pipecat client, initializes devices, and establishes the connection
+   */
+  async connect() {
+    try {
+      const botSelector = document.getElementById('bot-selector');
+      const selectedBot = botSelector.value;
+
+      // Initialize audio/video devices
+      this.log('Initializing devices...');
+      await this.pcClient.initDevices();
+
+      // Connect to the bot
+      this.log(`Connecting to bot: ${selectedBot}`);
+      await this.pcClient.connect({
+        // REPLACE WITH YOUR MODAL URL ENDPOINT
+        endpoint:
+          'https://<your-workspace>--pipecat-modal-fastapi-app.modal.run/connect',
+        requestData: {
+          bot_name: selectedBot,
+        },
+      });
+
+      this.log('Connection complete');
+    } catch (error) {
+      // Handle any errors during connection
+      console.error('Connection error:', error);
+      this.log(`Error connecting: ${JSON.stringify(error.message)}`);
+      this.log(`Error stack: ${error.stack}`);
+      this.updateStatus('Error');
+
+      // Clean up if there's an error
+      if (this.pcClient) {
+        try {
+          await this.pcClient.disconnect();
+        } catch (disconnectError) {
+          this.log(`Error during disconnect: ${disconnectError.message}`);
+        }
+      }
+    }
+  }
+
+  /**
+   * Disconnect from the bot and clean up media resources
+   */
+  async disconnect() {
+    if (this.pcClient) {
+      try {
+        // Disconnect the Pipecat client
+        await this.pcClient.disconnect();
+
+        // Clean up audio
+        if (this.botAudio.srcObject) {
+          this.botAudio.srcObject.getTracks().forEach((track) => track.stop());
+          this.botAudio.srcObject = null;
+        }
+
+        // Clean up video
+        if (this.botVideoContainer.querySelector('video')?.srcObject) {
+          const video = this.botVideoContainer.querySelector('video');
+          video.srcObject.getTracks().forEach((track) => track.stop());
+          video.srcObject = null;
+        }
+        this.botVideoContainer.innerHTML = '';
+      } catch (error) {
+        this.log(`Error disconnecting: ${error.message}`);
+      }
+    }
+  }
+}
+
+// Initialize the client when the page loads
+window.addEventListener('DOMContentLoaded', () => {
+  new ChatbotClient();
+});

examples/deployment/modal-example/client/javascript/src/style.css

@@ -0,0 +1,135 @@
+body {
+  margin: 0;
+  padding: 20px;
+  font-family: Arial, sans-serif;
+  background-color: #f0f0f0;
+}
+
+.container {
+  max-width: 1200px;
+  margin: 0 auto;
+}
+
+.status-bar,
+.device-bar {
+  display: flex;
+  justify-content: space-between;
+  align-items: center;
+  padding: 10px;
+  background-color: #fff;
+  border-radius: 8px;
+  margin-bottom: 20px;
+}
+
+.controls,
+.device-controls {
+  display: flex;
+  align-items: center;
+  gap: 10px; /* Adds spacing between elements */
+}
+
+.device-controls {
+  margin-left: auto;
+}
+
+.controls button,
+.device-controls button {
+  padding: 8px 16px;
+  margin-left: 10px;
+  border: none;
+  border-radius: 4px;
+  cursor: pointer;
+}
+
+#bot-selector,
+#device-selector {
+  padding: 8px 16px;
+  padding-right: 40px;
+  border: none;
+  border-radius: 4px;
+  background-color: #6c757d; /* Gray background */
+  color: white; /* White text */
+  cursor: pointer;
+  appearance: none; /* Removes default browser styling for dropdowns */
+  background-image: url("data:image/svg+xml,%3Csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 24 24' fill='white'%3E%3Cpath d='M7 10l5 5 5-5z'/%3E%3C/svg%3E"); /* Custom arrow */
+  background-repeat: no-repeat;
+  background-position: right 8px center; /* Position the arrow */
+}
+
+#bot-selector:focus,
+#device-selector:focus {
+  outline: none;
+  box-shadow: 0 0 4px rgba(0, 0, 0, 0.3); /* Add a subtle focus effect */
+}
+
+#connect-btn {
+  background-color: #4caf50;
+  color: white;
+}
+
+#disconnect-btn {
+  background-color: #f44336;
+  color: white;
+}
+
+#mic-toggle-btn {
+}
+
+button:disabled {
+  opacity: 0.5;
+  cursor: not-allowed;
+}
+
+.main-content {
+  background-color: #fff;
+  border-radius: 8px;
+  padding: 20px;
+  margin-bottom: 20px;
+}
+
+.bot-container {
+  display: flex;
+  flex-direction: column;
+  align-items: center;
+}
+
+#bot-video-container {
+  width: 640px;
+  height: 360px;
+  background-color: #e0e0e0;
+  border-radius: 8px;
+  margin: 20px auto;
+  overflow: hidden;
+  display: flex;
+  align-items: center;
+  justify-content: center;
+}
+
+#bot-video-container video {
+  width: 100%;
+  height: 100%;
+  object-fit: cover;
+}
+
+.debug-panel {
+  background-color: #fff;
+  border-radius: 8px;
+  padding: 20px;
+}
+
+.debug-panel h3 {
+  margin: 0 0 10px 0;
+  font-size: 16px;
+  font-weight: bold;
+}
+
+#debug-log {
+  height: 200px;
+  overflow-y: auto;
+  background-color: #f8f8f8;
+  padding: 10px;
+  border-radius: 4px;
+  font-family: monospace;
+  font-size: 12px;
+  line-height: 1.4;
+}

examples/deployment/modal-example/env.example

@@ -1,3 +0,0 @@
-DAILY_API_KEY=
-OPENAI_API_KEY=
-CARTESIA_API_KEY=

examples/deployment/modal-example/requirements.txt

@@ -1,4 +0,0 @@
-python-dotenv==1.0.1
-modal==0.71.3
-pipecat-ai[daily,silero,cartesia,openai]
-fastapi==0.115.6

examples/deployment/modal-example/server/app.py

@@ -0,0 +1,307 @@
+"""modal_example.
+
+This module shows a simple example of how to deploy a bot using Modal and FastAPI.
+
+It includes:
+- FastAPI endpoints for starting agents and checking bot statuses.
+- Dynamic loading of bot implementations.
+- Use of a Daily transport for bot communication.
+"""
+
+#
+# Copyright (c) 2024–2025, Daily
+#
+# SPDX-License-Identifier: BSD 2-Clause License
+#
+
+import importlib
+import os
+from contextlib import asynccontextmanager
+from typing import Any, Dict, Literal
+
+import aiohttp
+import modal
+from fastapi import APIRouter, FastAPI, HTTPException
+from fastapi.responses import JSONResponse, RedirectResponse
+from pydantic import BaseModel
+
+# container specifications for the FastAPI web server
+web_image = (
+    modal.Image.debian_slim(python_version="3.13")
+    .pip_install_from_requirements("requirements.txt")
+    .pip_install("pipecat-ai[daily]")
+    .add_local_dir("src", remote_path="/root/src")
+)
+
+# container specifications for the Pipecat pipeline
+bot_image = (
+    modal.Image.debian_slim(python_version="3.13")
+    .apt_install("ffmpeg")
+    .pip_install_from_requirements("requirements.txt")
+    .pip_install("pipecat-ai[daily,elevenlabs,openai,silero,google]")
+    .add_local_dir("src", remote_path="/root/src")
+)
+
+app = modal.App("pipecat-modal", secrets=[modal.Secret.from_dotenv()])
+
+router = APIRouter()
+
+bot_jobs = {}
+daily_helpers = {}
+
+# Names of all supported bot implementations
+# These correspond to the bot files in the src directory
+BotName = Literal["openai", "gemini", "vllm"]
+
+
+def cleanup():
+    """Cleanup function to terminate all bot processes.
+
+    Called during server shutdown.
+    """
+    for entry in bot_jobs.values():
+        func = modal.FunctionCall.from_id(entry[0])
+        if func:
+            func.cancel()
+
+
+def get_bot_file(bot_name: BotName) -> str:
+    """Retrieve the bot file name corresponding to the provided bot_name.
+
+    Args:
+        bot_name (BotName): The name of the bot (e.g., 'openai', 'gemini', 'vllm').
+
+    Returns:
+        str: The file name corresponding to the bot implementation.
+
+    Raises:
+        ValueError: If the bot name is invalid or not supported.
+    """
+    # bot_implementation = os.getenv("BOT_IMPLEMENTATION", "openai").lower().strip()
+    bot_implementation = bot_name.lower().strip()
+    if not bot_implementation:
+        bot_implementation = "openai"
+    if bot_implementation not in ["openai", "gemini", "vllm"]:
+        raise ValueError(
+            f"Invalid BOT_IMPLEMENTATION: {bot_implementation}. Must be 'openai' or 'gemini' or 'vllm'"
+        )
+
+    return f"bot_{bot_implementation}"
+
+
+def get_runner(path: str, bot_file: str) -> callable:
+    """Dynamically import the run_bot function based on the bot name.
+
+    Args:
+        path (str): The path to the bot files (e.g., 'src').
+        bot_file (str): The file name of the bot implementation (e.g., 'openai', 'gemini', 'vllm').
+
+    Returns:
+        function: The run_bot function from the specified bot module.
+
+    Raises:
+        ImportError: If the specified bot module or run_bot function is not found.
+    """
+    try:
+        # Dynamically construct the module name
+        module_name = f"{path}.{bot_file}"
+        # Import the module
+        module = importlib.import_module(module_name)
+        # Get the run_bot function from the module
+        return getattr(module, "run_bot")
+    except (ImportError, AttributeError) as e:
+        raise ImportError(f"Failed to import run_bot from {module_name}: {e}")
+
+
+async def create_room_and_token() -> tuple[str, str]:
+    """Create a Daily room and generate an authentication token.
+
+    This function checks for existing room URL and token in the environment variables.
+    If not found, it creates a new room using the Daily API and generates a token for it.
+
+    Returns:
+        tuple[str, str]: A tuple containing the room URL and the authentication token.
+
+    Raises:
+        HTTPException: If room creation or token generation fails.
+    """
+    from pipecat.transports.services.helpers.daily_rest import DailyRoomParams
+
+    room_url = os.getenv("DAILY_SAMPLE_ROOM_URL", None)
+    token = os.getenv("DAILY_SAMPLE_ROOM_TOKEN", None)
+    if not room_url:
+        room = await daily_helpers["rest"].create_room(DailyRoomParams())
+        if not room.url:
+            raise HTTPException(status_code=500, detail="Failed to create room")
+        room_url = room.url
+
+        token = await daily_helpers["rest"].get_token(room_url)
+        if not token:
+            raise HTTPException(status_code=500, detail=f"Failed to get token for room: {room_url}")
+
+    return room_url, token
+
+
+@app.function(image=bot_image, min_containers=1)
+async def bot_runner(room_url, token, bot_name: BotName = "openai"):
+    """Launch the provided bot process, providing the given room URL and token for the bot to join.
+
+    Args:
+        room_url (str): The URL of the Daily room where the bot and client will communicate.
+        token (str): The authentication token for the room.
+        bot_name (BotName): The name of the bot implementation to use. Defaults to "openai".
+
+    Raises:
+        HTTPException: If the bot pipeline fails to start.
+    """
+    try:
+        path = "src"
+        bot_file = get_bot_file(bot_name)
+        run_bot = get_runner(path, bot_file)
+
+        print(f"Starting bot process: {bot_file} -u {room_url} -t {token}")
+        await run_bot(room_url, token)
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=f"Failed to start bot pipeline: {e}")
+
+
+@asynccontextmanager
+async def lifespan(app: FastAPI):
+    """FastAPI lifespan manager that handles startup and shutdown tasks.
+
+    - Creates aiohttp session
+    - Initializes Daily API helper
+    - Cleans up resources on shutdown
+    """
+    from pipecat.transports.services.helpers.daily_rest import DailyRESTHelper
+
+    aiohttp_session = aiohttp.ClientSession()
+    daily_helpers["rest"] = DailyRESTHelper(
+        daily_api_key=os.getenv("DAILY_API_KEY", ""),
+        daily_api_url=os.getenv("DAILY_API_URL", "https://api.daily.co/v1"),
+        aiohttp_session=aiohttp_session,
+    )
+    yield
+    await aiohttp_session.close()
+    cleanup()
+
+
+class ConnectData(BaseModel):
+    """Data provided by client to specify the bot pipeline.
+
+    Attributes:
+        bot_name (BotName): The name of the bot to connect to. Defaults to "openai".
+    """
+
+    bot_name: BotName = "openai"
+
+
+async def start(data: ConnectData):
+    """Internal method to start a bot agent and return the room URL and token.
+
+    Args:
+        data (ConnectData): The data containing the bot name to use.
+
+    Returns:
+        tuple[str, str]: A tuple containing the room URL and token.
+    """
+    room_url, token = await create_room_and_token()
+    launch_bot_func = modal.Function.from_name("pipecat-modal", "bot_runner")
+    function_id = launch_bot_func.spawn(room_url, token, data.bot_name)
+    bot_jobs[function_id] = (function_id, room_url)
+
+    return room_url, token
+
+
+@router.get("/")
+async def start_agent():
+    """A user endpoint for launching a bot agent and redirecting to the created room URL.
+
+    This function retrieves the bot implementation from the environment,
+    starts the bot agent, and redirects the user to the room URL to
+    interact with the bot through a Daily Prebuilt Interface.
+
+    Returns:
+        RedirectResponse: A response that redirects to the room URL.
+    """
+    bot_name = os.getenv("BOT_IMPLEMENTATION", "openai").lower().strip()
+    print(f"Starting bot: {bot_name}")
+    room_url, token = await start(ConnectData(bot_name=bot_name))
+
+    return RedirectResponse(room_url)
+
+
+@router.post("/connect")
+async def rtvi_connect(data: ConnectData) -> Dict[Any, Any]:
+    """A user endpoint for launching a bot agent and retrieving the room/token credentials.
+
+    This function retrieves the bot implementation from the request, if provided,
+    starts the bot agent, and returns the room URL and token for the bot. This allows the
+    client to then connect to the bot using their own RTVI interface.
+
+    Args:
+        data (ConnectData): Optional. The data containing the bot name to use.
+
+    Returns:
+        Dict[Any, Any]: A dictionary containing the room URL and token.
+    """
+    print(f"Starting bot: {data.bot_name}")
+    if data is None or not data.bot_name:
+        data.bot_name = os.getenv("BOT_IMPLEMENTATION", "openai").lower().strip()
+    room_url, token = await start(data)
+
+    return {"room_url": room_url, "token": token}
+
+
+@router.get("/status/{fid}")
+def get_status(fid: str):
+    """Retrieve the status of a bot process by its function ID.
+
+    Args:
+        fid (str): The function ID of the bot process.
+
+    Returns:
+        JSONResponse: A JSON response containing the bot's status and result code.
+
+    Raises:
+        HTTPException: If the bot process with the given ID is not found.
+    """
+    func = modal.FunctionCall.from_id(fid)
+    if not func:
+        raise HTTPException(status_code=404, detail=f"Bot with process id: {fid} not found")
+
+    try:
+        result = func.get(timeout=0)
+        return JSONResponse({"bot_id": fid, "status": "finished", "code": result})
+    except modal.exception.OutputExpiredError:
+        return JSONResponse({"bot_id": fid, "status": "finished", "code": 404})
+    except TimeoutError:
+        return JSONResponse({"bot_id": fid, "status": "running", "code": 202})
+
+
+@app.function(image=web_image, min_containers=1)
+@modal.concurrent(max_inputs=1)
+@modal.asgi_app()
+def fastapi_app():
+    """Create and configure the FastAPI application.
+
+    This function initializes the FastAPI app with middleware, routes, and lifespan management.
+    It is decorated to be used as a Modal ASGI app.
+    """
+    from fastapi.middleware.cors import CORSMiddleware
+
+    # Initialize FastAPI app
+    web_app = FastAPI(lifespan=lifespan)
+
+    web_app.add_middleware(
+        CORSMiddleware,
+        allow_origins=["*"],
+        allow_credentials=True,
+        allow_methods=["*"],
+        allow_headers=["*"],
+    )
+
+    # Include the endpoints from this file
+    web_app.include_router(router)
+
+    return web_app

examples/deployment/modal-example/server/env.example

@@ -0,0 +1,14 @@
+DAILY_API_KEY=
+
+# determines which bot file to default to: 'openai', 'gemini', or 'vllm'
+BOT_IMPLEMENTATION=openai
+
+# needed for the openai bot pipeline
+OPENAI_API_KEY=
+ELEVENLABS_API_KEY=
+
+# needed for the gemini live bot pipeline
+GOOGLE_API_KEY=
+
+# needed if you modified the API Key for your self-hosted LLM
+VLLM_API_KEY=

examples/deployment/modal-example/server/requirements.txt

@@ -0,0 +1,3 @@
+python-dotenv==1.0.1
+modal==1.0.5
+fastapi[all]

examples/deployment/modal-example/server/src/bot_gemini.py

@@ -0,0 +1,197 @@
+#
+# Copyright (c) 2024–2025, Daily
+#
+# SPDX-License-Identifier: BSD 2-Clause License
+#
+
+"""Gemini Bot Implementation.
+
+This module implements a chatbot using Google's Gemini Multimodal Live model.
+It includes:
+- Real-time audio/video interaction through Daily
+- Animated robot avatar
+- Speech-to-speech model
+
+The bot runs as part of a pipeline that processes audio/video frames and manages
+the conversation flow using Gemini's streaming capabilities.
+"""
+
+import os
+import sys
+
+from dotenv import load_dotenv
+from loguru import logger
+from PIL import Image
+
+from pipecat.audio.vad.silero import SileroVADAnalyzer
+from pipecat.audio.vad.vad_analyzer import VADParams
+from pipecat.frames.frames import (
+    BotStartedSpeakingFrame,
+    BotStoppedSpeakingFrame,
+    Frame,
+    OutputImageRawFrame,
+    SpriteFrame,
+)
+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.processors.frame_processor import FrameDirection, FrameProcessor
+from pipecat.processors.frameworks.rtvi import RTVIConfig, RTVIObserver, RTVIProcessor
+from pipecat.services.gemini_multimodal_live.gemini import GeminiMultimodalLiveLLMService
+from pipecat.transports.services.daily import DailyParams, DailyTransport
+
+load_dotenv(override=True)
+
+try:
+    logger.remove(0)
+    logger.add(sys.stderr, level="DEBUG")
+except ValueError:
+    # Handle the case where logger is already initialized
+    pass
+
+sprites = []
+script_dir = os.path.dirname(__file__)
+
+for i in range(1, 26):
+    # Build the full path to the image file
+    full_path = os.path.join(script_dir, f"assets/robot0{i}.png")
+    # Get the filename without the extension to use as the dictionary key
+    # Open the image and convert it to bytes
+    with Image.open(full_path) as img:
+        sprites.append(OutputImageRawFrame(image=img.tobytes(), size=img.size, format=img.format))
+
+# Create a smooth animation by adding reversed frames
+flipped = sprites[::-1]
+sprites.extend(flipped)
+
+# Define static and animated states
+quiet_frame = sprites[0]  # Static frame for when bot is listening
+talking_frame = SpriteFrame(images=sprites)  # Animation sequence for when bot is talking
+
+
+class TalkingAnimation(FrameProcessor):
+    """Manages the bot's visual animation states.
+
+    Switches between static (listening) and animated (talking) states based on
+    the bot's current speaking status.
+    """
+
+    def __init__(self):
+        super().__init__()
+        self._is_talking = False
+
+    async def process_frame(self, frame: Frame, direction: FrameDirection):
+        """Process incoming frames and update animation state.
+
+        Args:
+            frame: The incoming frame to process
+            direction: The direction of frame flow in the pipeline
+        """
+        await super().process_frame(frame, direction)
+
+        # Switch to talking animation when bot starts speaking
+        if isinstance(frame, BotStartedSpeakingFrame):
+            if not self._is_talking:
+                await self.push_frame(talking_frame)
+                self._is_talking = True
+        # Return to static frame when bot stops speaking
+        elif isinstance(frame, BotStoppedSpeakingFrame):
+            await self.push_frame(quiet_frame)
+            self._is_talking = False
+
+        await self.push_frame(frame, direction)
+
+
+async def run_bot(room_url: str, token: str):
+    """Main bot execution function.
+
+    Sets up and runs the bot pipeline including:
+    - Daily video transport with specific audio parameters
+    - Gemini Live multimodal model integration
+    - Voice activity detection
+    - Animation processing
+    - RTVI event handling
+    """
+    # Set up Daily transport with specific audio/video parameters for Gemini
+    transport = DailyTransport(
+        room_url,
+        token,
+        "Chatbot",
+        DailyParams(
+            audio_out_enabled=True,
+            camera_out_enabled=True,
+            camera_out_width=1024,
+            camera_out_height=576,
+            vad_enabled=True,
+            vad_audio_passthrough=True,
+            vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.5)),
+        ),
+    )
+
+    # Initialize the Gemini Multimodal Live model
+    llm = GeminiMultimodalLiveLLMService(
+        api_key=os.getenv("GOOGLE_API_KEY"),
+        voice_id="Puck",  # Aoede, Charon, Fenrir, Kore, Puck
+        transcribe_user_audio=True,
+    )
+
+    messages = [
+        {
+            "role": "user",
+            "content": "You are Chatbot, a friendly, helpful robot. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way, but keep your responses brief. Start by introducing yourself.",
+        },
+    ]
+
+    # Set up conversation context and management
+    # The context_aggregator will automatically collect conversation context
+    context = OpenAILLMContext(messages)
+    context_aggregator = llm.create_context_aggregator(context)
+
+    ta = TalkingAnimation()
+
+    #
+    # RTVI events for Pipecat client UI
+    #
+    rtvi = RTVIProcessor(config=RTVIConfig(config=[]))
+
+    pipeline = Pipeline(
+        [
+            transport.input(),
+            rtvi,
+            context_aggregator.user(),
+            llm,
+            ta,
+            transport.output(),
+            context_aggregator.assistant(),
+        ]
+    )
+
+    task = PipelineTask(
+        pipeline,
+        params=PipelineParams(
+            enable_metrics=True,
+            enable_usage_metrics=True,
+        ),
+        observers=[RTVIObserver(rtvi)],
+    )
+    await task.queue_frame(quiet_frame)
+
+    @rtvi.event_handler("on_client_ready")
+    async def on_client_ready(rtvi):
+        await rtvi.set_bot_ready()
+        # Kick off the conversation
+        await task.queue_frames([context_aggregator.user().get_context_frame()])
+
+    @transport.event_handler("on_first_participant_joined")
+    async def on_first_participant_joined(transport, participant):
+        await transport.capture_participant_transcription(participant["id"])
+
+    @transport.event_handler("on_participant_left")
+    async def on_participant_left(transport, participant, reason):
+        print(f"Participant left: {participant}")
+        await task.cancel()
+
+    runner = PipelineRunner()
+
+    await runner.run(task)

examples/deployment/modal-example/server/src/bot_openai.py

@@ -0,0 +1,225 @@
+#
+# Copyright (c) 2024–2025, Daily
+#
+# SPDX-License-Identifier: BSD 2-Clause License
+#
+
+"""OpenAI Bot Implementation.
+
+This module implements a chatbot using OpenAI's GPT-4 model for natural language
+processing. It includes:
+- Real-time audio/video interaction through Daily
+- Animated robot avatar
+- Text-to-speech using ElevenLabs
+- Support for both English and Spanish
+
+The bot runs as part of a pipeline that processes audio/video frames and manages
+the conversation flow.
+"""
+
+import os
+import sys
+
+from dotenv import load_dotenv
+from loguru import logger
+from PIL import Image
+
+from pipecat.audio.vad.silero import SileroVADAnalyzer
+from pipecat.frames.frames import (
+    BotStartedSpeakingFrame,
+    BotStoppedSpeakingFrame,
+    Frame,
+    OutputImageRawFrame,
+    SpriteFrame,
+)
+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.processors.frame_processor import FrameDirection, FrameProcessor
+from pipecat.processors.frameworks.rtvi import RTVIConfig, RTVIObserver, RTVIProcessor
+from pipecat.services.elevenlabs.tts import ElevenLabsTTSService
+from pipecat.services.openai.llm import OpenAILLMService
+from pipecat.transports.services.daily import DailyParams, DailyTransport
+
+load_dotenv(override=True)
+
+try:
+    logger.remove(0)
+    logger.add(sys.stderr, level="DEBUG")
+except ValueError:
+    # Handle the case where logger is already initialized
+    pass
+
+sprites = []
+script_dir = os.path.dirname(__file__)
+
+# Load sequential animation frames
+for i in range(1, 26):
+    # Build the full path to the image file
+    full_path = os.path.join(script_dir, f"assets/robot0{i}.png")
+    # Get the filename without the extension to use as the dictionary key
+    # Open the image and convert it to bytes
+    with Image.open(full_path) as img:
+        sprites.append(OutputImageRawFrame(image=img.tobytes(), size=img.size, format=img.format))
+
+# Create a smooth animation by adding reversed frames
+flipped = sprites[::-1]
+sprites.extend(flipped)
+
+# Define static and animated states
+quiet_frame = sprites[0]  # Static frame for when bot is listening
+talking_frame = SpriteFrame(images=sprites)  # Animation sequence for when bot is talking
+
+
+class TalkingAnimation(FrameProcessor):
+    """Manages the bot's visual animation states.
+
+    Switches between static (listening) and animated (talking) states based on
+    the bot's current speaking status.
+    """
+
+    def __init__(self):
+        super().__init__()
+        self._is_talking = False
+
+    async def process_frame(self, frame: Frame, direction: FrameDirection):
+        """Process incoming frames and update animation state.
+
+        Args:
+            frame: The incoming frame to process
+            direction: The direction of frame flow in the pipeline
+        """
+        await super().process_frame(frame, direction)
+
+        # Switch to talking animation when bot starts speaking
+        if isinstance(frame, BotStartedSpeakingFrame):
+            if not self._is_talking:
+                await self.push_frame(talking_frame)
+                self._is_talking = True
+        # Return to static frame when bot stops speaking
+        elif isinstance(frame, BotStoppedSpeakingFrame):
+            await self.push_frame(quiet_frame)
+            self._is_talking = False
+
+        await self.push_frame(frame, direction)
+
+
+async def run_bot(room_url: str, token: str):
+    """Main bot execution function.
+
+    Sets up and runs the bot pipeline including:
+    - Daily video transport
+    - Speech-to-text and text-to-speech services
+    - Language model integration
+    - Animation processing
+    - RTVI event handling
+    """
+    # Set up Daily transport with video/audio parameters
+    transport = DailyTransport(
+        room_url,
+        token,
+        "Chatbot",
+        DailyParams(
+            audio_out_enabled=True,
+            camera_out_enabled=True,
+            camera_out_width=1024,
+            camera_out_height=576,
+            vad_enabled=True,
+            vad_analyzer=SileroVADAnalyzer(),
+            transcription_enabled=True,
+            #
+            # Spanish
+            #
+            # transcription_settings=DailyTranscriptionSettings(
+            #     language="es",
+            #     tier="nova",
+            #     model="2-general"
+            # )
+        ),
+    )
+
+    # Initialize text-to-speech service
+    tts = ElevenLabsTTSService(
+        api_key=os.getenv("ELEVENLABS_API_KEY"),
+        #
+        # English
+        #
+        voice_id="SAz9YHcvj6GT2YYXdXww",
+        #
+        # Spanish
+        #
+        # model="eleven_multilingual_v2",
+        # voice_id="gD1IexrzCvsXPHUuT0s3",
+    )
+
+    # Initialize LLM service
+    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
+
+    messages = [
+        {
+            "role": "system",
+            #
+            # English
+            #
+            "content": "You are an incessant one-upper. Start by asking the user how their day is going.",
+            #
+            # Spanish
+            #
+            # "content": "Eres Chatbot, un amigable y útil robot. Tu objetivo es demostrar tus capacidades de una manera breve. Tus respuestas se convertiran a audio así que nunca no debes incluir caracteres especiales. Contesta a lo que el usuario pregunte de una manera creativa, útil y breve. Empieza por presentarte a ti mismo.",
+        },
+    ]
+
+    # Set up conversation context and management
+    # The context_aggregator will automatically collect conversation context
+    context = OpenAILLMContext(messages)
+    context_aggregator = llm.create_context_aggregator(context)
+
+    ta = TalkingAnimation()
+
+    #
+    # RTVI events for Pipecat client UI
+    #
+    rtvi = RTVIProcessor(config=RTVIConfig(config=[]))
+
+    pipeline = Pipeline(
+        [
+            transport.input(),
+            rtvi,
+            context_aggregator.user(),
+            llm,
+            tts,
+            ta,
+            transport.output(),
+            context_aggregator.assistant(),
+        ]
+    )
+
+    task = PipelineTask(
+        pipeline,
+        params=PipelineParams(
+            enable_metrics=True,
+            enable_usage_metrics=True,
+        ),
+        observers=[RTVIObserver(rtvi)],
+    )
+    await task.queue_frame(quiet_frame)
+
+    @rtvi.event_handler("on_client_ready")
+    async def on_client_ready(rtvi):
+        await rtvi.set_bot_ready()
+        # Kick off the conversation
+        await task.queue_frames([context_aggregator.user().get_context_frame()])
+
+    @transport.event_handler("on_first_participant_joined")
+    async def on_first_participant_joined(transport, participant):
+        await transport.capture_participant_transcription(participant["id"])
+
+    @transport.event_handler("on_participant_left")
+    async def on_participant_left(transport, participant, reason):
+        print(f"Participant left: {participant}")
+        await task.cancel()
+
+    runner = PipelineRunner()
+
+    await runner.run(task)

examples/deployment/modal-example/server/src/bot_vllm.py

@@ -0,0 +1,238 @@
+#
+# Copyright (c) 2024–2025, Daily
+#
+# SPDX-License-Identifier: BSD 2-Clause License
+#
+
+"""OpenAI Bot Implementation.
+
+This module implements a chatbot using OpenAI's GPT-4 model for natural language
+processing. It includes:
+- Real-time audio/video interaction through Daily
+- Animated robot avatar
+- Text-to-speech using ElevenLabs
+- Support for both English and Spanish
+
+The bot runs as part of a pipeline that processes audio/video frames and manages
+the conversation flow.
+"""
+
+import os
+import sys
+from typing import List
+
+from dotenv import load_dotenv
+from loguru import logger
+from openai.types.chat import ChatCompletionMessageParam
+from PIL import Image
+
+from pipecat.audio.vad.silero import SileroVADAnalyzer
+from pipecat.frames.frames import (
+    BotStartedSpeakingFrame,
+    BotStoppedSpeakingFrame,
+    Frame,
+    OutputImageRawFrame,
+    SpriteFrame,
+)
+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.processors.frame_processor import FrameDirection, FrameProcessor
+from pipecat.processors.frameworks.rtvi import RTVIConfig, RTVIObserver, RTVIProcessor
+from pipecat.services.elevenlabs.tts import ElevenLabsTTSService
+from pipecat.services.openai.llm import OpenAILLMService
+from pipecat.transports.services.daily import DailyParams, DailyTransport
+
+load_dotenv(override=True)
+
+try:
+    logger.remove(0)
+    logger.add(sys.stderr, level="DEBUG")
+except ValueError:
+    # Handle the case where logger is already initialized
+    pass
+
+# REPLACE WITH YOUR MODAL URL ENDPOINT
+modal_url = "https://<Modal workspace>--example-vllm-openai-compatible-serve.modal.run"
+api_key = os.getenv("VLLM_API_KEY", "super-secret-key")
+
+
+sprites = []
+script_dir = os.path.dirname(__file__)
+
+# Load sequential animation frames
+for i in range(1, 26):
+    # Build the full path to the image file
+    full_path = os.path.join(script_dir, f"assets/robot0{i}.png")
+    # Get the filename without the extension to use as the dictionary key
+    # Open the image and convert it to bytes
+    with Image.open(full_path) as img:
+        sprites.append(OutputImageRawFrame(image=img.tobytes(), size=img.size, format=img.format))
+
+# Create a smooth animation by adding reversed frames
+flipped = sprites[::-1]
+sprites.extend(flipped)
+
+# Define static and animated states
+quiet_frame = sprites[0]  # Static frame for when bot is listening
+talking_frame = SpriteFrame(images=sprites)  # Animation sequence for when bot is talking
+
+
+class TalkingAnimation(FrameProcessor):
+    """Manages the bot's visual animation states.
+
+    Switches between static (listening) and animated (talking) states based on
+    the bot's current speaking status.
+    """
+
+    def __init__(self):
+        super().__init__()
+        self._is_talking = False
+
+    async def process_frame(self, frame: Frame, direction: FrameDirection):
+        """Process incoming frames and update animation state.
+
+        Args:
+            frame: The incoming frame to process
+            direction: The direction of frame flow in the pipeline
+        """
+        await super().process_frame(frame, direction)
+
+        # Switch to talking animation when bot starts speaking
+        if isinstance(frame, BotStartedSpeakingFrame):
+            if not self._is_talking:
+                await self.push_frame(talking_frame)
+                self._is_talking = True
+        # Return to static frame when bot stops speaking
+        elif isinstance(frame, BotStoppedSpeakingFrame):
+            await self.push_frame(quiet_frame)
+            self._is_talking = False
+
+        await self.push_frame(frame, direction)
+
+
+async def run_bot(room_url: str, token: str):
+    """Main bot execution function.
+
+    Sets up and runs the bot pipeline including:
+    - Daily video transport
+    - Speech-to-text and text-to-speech services
+    - Language model integration
+    - Animation processing
+    - RTVI event handling
+    """
+    # Set up Daily transport with video/audio parameters
+    transport = DailyTransport(
+        room_url,
+        token,
+        "Chatbot",
+        DailyParams(
+            audio_out_enabled=True,
+            camera_out_enabled=True,
+            camera_out_width=1024,
+            camera_out_height=576,
+            vad_enabled=True,
+            vad_analyzer=SileroVADAnalyzer(),
+            transcription_enabled=True,
+            #
+            # Spanish
+            #
+            # transcription_settings=DailyTranscriptionSettings(
+            #     language="es",
+            #     tier="nova",
+            #     model="2-general"
+            # )
+        ),
+    )
+
+    # Initialize text-to-speech service
+    tts = ElevenLabsTTSService(
+        api_key=os.getenv("ELEVENLABS_API_KEY"),
+        #
+        # English
+        #
+        voice_id="D38z5RcWu1voky8WS1ja",
+        #
+        # Spanish
+        #
+        # model="eleven_multilingual_v2",
+        # voice_id="gD1IexrzCvsXPHUuT0s3",
+    )
+
+    # Initialize LLM service
+    llm = OpenAILLMService(
+        # To use OpenAI
+        api_key=api_key,
+        # Or, to use a local vLLM (or similar) api server
+        model="neuralmagic/Meta-Llama-3.1-8B-Instruct-quantized.w4a16",
+        base_url=f"{modal_url}/v1",
+    )
+
+    messages = [
+        {
+            "role": "system",
+            #
+            # English
+            #
+            "content": "You are a salesman for Modal, the cloud-native serverless Python computing platform.",
+            #
+            # Spanish
+            #
+            # "content": "Eres Chatbot, un amigable y útil robot. Tu objetivo es demostrar tus capacidades de una manera breve. Tus respuestas se convertiran a audio así que nunca no debes incluir caracteres especiales. Contesta a lo que el usuario pregunte de una manera creativa, útil y breve. Empieza por presentarte a ti mismo.",
+        },
+    ]
+
+    # Set up conversation context and management
+    # The context_aggregator will automatically collect conversation context
+    context = OpenAILLMContext(messages)
+    context_aggregator = llm.create_context_aggregator(context)
+
+    ta = TalkingAnimation()
+
+    #
+    # RTVI events for Pipecat client UI
+    #
+    rtvi = RTVIProcessor(config=RTVIConfig(config=[]))
+
+    pipeline = Pipeline(
+        [
+            transport.input(),
+            rtvi,
+            context_aggregator.user(),
+            llm,
+            tts,
+            ta,
+            transport.output(),
+            context_aggregator.assistant(),
+        ]
+    )
+
+    task = PipelineTask(
+        pipeline,
+        params=PipelineParams(
+            enable_metrics=True,
+            enable_usage_metrics=True,
+        ),
+        observers=[RTVIObserver(rtvi)],
+    )
+    await task.queue_frame(quiet_frame)
+
+    @rtvi.event_handler("on_client_ready")
+    async def on_client_ready(rtvi):
+        await rtvi.set_bot_ready()
+        # Kick off the conversation
+        await task.queue_frames([context_aggregator.user().get_context_frame()])
+
+    @transport.event_handler("on_first_participant_joined")
+    async def on_first_participant_joined(transport, participant):
+        await transport.capture_participant_transcription(participant["id"])
+
+    @transport.event_handler("on_participant_left")
+    async def on_participant_left(transport, participant, reason):
+        print(f"Participant left: {participant}")
+        await task.cancel()
+
+    runner = PipelineRunner()
+
+    await runner.run(task)

examples/deployment/modal-example/server/src/runner.py

@@ -0,0 +1,84 @@
+#
+# Copyright (c) 2024–2025, Daily
+#
+# SPDX-License-Identifier: BSD 2-Clause License
+#
+
+import argparse
+import asyncio
+import importlib
+import os
+
+
+def get_bot_file(arg_bot: str | None) -> str:
+    bot_implementation = arg_bot or os.getenv("BOT_IMPLEMENTATION", "openai").lower().strip()
+    if not bot_implementation:
+        bot_implementation = "openai"
+    if bot_implementation not in ["openai", "gemini", "vllm"]:
+        raise ValueError(
+            f"Invalid BOT_IMPLEMENTATION: {bot_implementation}. Must be 'openai' or 'gemini'"
+        )
+    return f"bot_{bot_implementation}"
+
+
+def get_runner(bot_file: str):
+    """Dynamically import the run_bot function based on the bot name.
+
+    Args:
+        bot_name (str): The name of the bot implementation (e.g., 'openai', 'gemini').
+
+    Returns:
+        function: The run_bot function from the specified bot module.
+
+    Raises:
+        ImportError: If the specified bot module or run_bot function is not found.
+    """
+    try:
+        # Dynamically construct the module name
+        module_name = f"{bot_file}"
+        # Import the module
+        module = importlib.import_module(module_name)
+        # Get the run_bot function from the module
+        return getattr(module, "run_bot")
+    except (ImportError, AttributeError) as e:
+        raise ImportError(f"Failed to import run_bot from {module_name}: {e}")
+
+
+def main():
+    """Parse the args to launch the appropriate bot using the given room/token."""
+    parser = argparse.ArgumentParser(description="Daily AI SDK Bot Sample")
+    parser.add_argument(
+        "-u", "--url", type=str, required=False, help="URL of the Daily room to join"
+    )
+    parser.add_argument(
+        "-t",
+        "--token",
+        type=str,
+        required=False,
+        help="Daily room token",
+    )
+    parser.add_argument(
+        "-b",
+        "--bot",
+        type=str,
+        required=False,
+        help="Bot runner to use (e.g., openai, gemini)",
+    )
+
+    args, unknown = parser.parse_known_args()
+
+    url = args.url or os.getenv("DAILY_SAMPLE_ROOM_URL")
+    token = args.token or os.getenv("DAILY_SAMPLE_ROOM_TOKEN")
+    bot_file = get_bot_file(args.bot)
+
+    if not url:
+        raise Exception(
+            "No Daily room specified. use the -u/--url option from the command line, or set DAILY_SAMPLE_ROOM_URL in your environment to specify a Daily room URL."
+        )
+
+    run_bot = get_runner(bot_file)
+    asyncio.run(run_bot(url, token))
+
+
+if __name__ == "__main__":
+    main()

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

@@ -100,7 +100,28 @@ phone numbers with valid values for your use case.
 
 ### Dialin Request
 
-The server will receive a request when a call is received from Daily.
+The server will receive a request when a call is received from Daily. 
+The payload that the webhook received is as follows:
+```json
+{
+  // for dial-in from webhook
+  "To": "+14152251493",
+  "From": "+14158483432",
+  "callId": "string-contains-uuid",
+  "callDomain": "string-contains-uuid",
+  "sipHeaders": {
+    "X-My-Custom-Header": "value",
+    "x-caller": "+1234567890",
+    "x-called": "+1987654321", 
+   },
+}
+```
+The `To`, `From`, `callId`, `callDomain` fields are converted to 
+`snake_case` and mapped to `dialin_settings`. In addition, `sipHeader` 
+contains any custom SIP headers received by Daily on the SIP 
+interconnect address (`sip_uri`). These are headers sent from 
+Twilio or other external SIP platforms, for example, to send the 
+caller's phone number.
 
 ### Dialout Request
 
@@ -158,6 +179,7 @@ curl -X POST http://localhost:3000/api/dial \
     "From": "+1987654321",
     "callId": "call-uuid-123",
     "callDomain": "domain-uuid-456",
+    "sipHeader": {},
     "dialout_settings": [
       {
         "phoneNumber": "+1234567890",

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

@@ -39,6 +39,11 @@ class RoomRequest(BaseModel):
         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")
+    sipHeaders: Optional[Dict[str, Any]] = Field(
+        None,
+        alias="sip_headers",
+        description="Custom SIP headers received from the external SIP provider",
+    )
 
     class Config:
         populate_by_name = True
@@ -57,6 +62,14 @@ class RoomRequest(BaseModel):
     "callDomain": "string-contains-uuid"
     These need to be remapped to dialin_settings
 
+    In addition, we may receive in the body that can be 
+    sent to the bot as a custom field, sip_headers
+    "sipHeaders": {
+        "X-My-Custom-Header": "value",
+        "x-caller": "+14158483432",
+        "x-called": "+14152251493",
+    },
+
     "dialout_settings": [
         {"phoneNumber": "+14158483432", "callerId": "+14152251493"}, 
         {"sipUri": "sip:username@sip.hostname"}
@@ -157,6 +170,7 @@ async def dial(request: RoomRequest, raw_request: Request):
             "dialout_settings": request.dialout_settings,
             "voicemail_detection": request.voicemail_detection,
             "call_transfer": request.call_transfer,
+            "sip_headers": request.sipHeaders,  # passing the SIP headers to the bot
         },
     }
 

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

@@ -8,7 +8,7 @@
       "name": "my-daily-app",
       "version": "0.1.0",
       "dependencies": {
-        "axios": "^1.6.0",
+        "axios": "^1.10.0",
         "next": "^14.0.0",
         "pino": "^8.15.0",
         "react": "^18.2.0",
@@ -215,10 +215,9 @@
       }
     },
     "node_modules/@next/env": {
-      "version": "14.2.26",
-      "resolved": "https://registry.npmjs.org/@next/env/-/env-14.2.26.tgz",
-      "integrity": "sha512-vO//GJ/YBco+H7xdQhzJxF7ub3SUwft76jwaeOyVVQFHCi5DCnkP16WHB+JBylo4vOKPoZBlR94Z8xBxNBdNJA==",
-      "license": "MIT"
+      "version": "14.2.30",
+      "resolved": "https://registry.npmjs.org/@next/env/-/env-14.2.30.tgz",
+      "integrity": "sha512-KBiBKrDY6kxTQWGzKjQB7QirL3PiiOkV7KW98leHFjtVRKtft76Ra5qSA/SL75xT44dp6hOcqiiJ6iievLOYug=="
     },
     "node_modules/@next/eslint-plugin-next": {
       "version": "14.2.25",
@@ -231,13 +230,12 @@
       }
     },
     "node_modules/@next/swc-darwin-arm64": {
-      "version": "14.2.26",
-      "resolved": "https://registry.npmjs.org/@next/swc-darwin-arm64/-/swc-darwin-arm64-14.2.26.tgz",
-      "integrity": "sha512-zDJY8gsKEseGAxG+C2hTMT0w9Nk9N1Sk1qV7vXYz9MEiyRoF5ogQX2+vplyUMIfygnjn9/A04I6yrUTRTuRiyQ==",
+      "version": "14.2.30",
+      "resolved": "https://registry.npmjs.org/@next/swc-darwin-arm64/-/swc-darwin-arm64-14.2.30.tgz",
+      "integrity": "sha512-EAqfOTb3bTGh9+ewpO/jC59uACadRHM6TSA9DdxJB/6gxOpyV+zrbqeXiFTDy9uV6bmipFDkfpAskeaDcO+7/g==",
       "cpu": [
         "arm64"
       ],
-      "license": "MIT",
       "optional": true,
       "os": [
         "darwin"
@@ -247,13 +245,12 @@
       }
     },
     "node_modules/@next/swc-darwin-x64": {
-      "version": "14.2.26",
-      "resolved": "https://registry.npmjs.org/@next/swc-darwin-x64/-/swc-darwin-x64-14.2.26.tgz",
-      "integrity": "sha512-U0adH5ryLfmTDkahLwG9sUQG2L0a9rYux8crQeC92rPhi3jGQEY47nByQHrVrt3prZigadwj/2HZ1LUUimuSbg==",
+      "version": "14.2.30",
+      "resolved": "https://registry.npmjs.org/@next/swc-darwin-x64/-/swc-darwin-x64-14.2.30.tgz",
+      "integrity": "sha512-TyO7Wz1IKE2kGv8dwQ0bmPL3s44EKVencOqwIY69myoS3rdpO1NPg5xPM5ymKu7nfX4oYJrpMxv8G9iqLsnL4A==",
       "cpu": [
         "x64"
       ],
-      "license": "MIT",
       "optional": true,
       "os": [
         "darwin"
@@ -263,13 +260,12 @@
       }
     },
     "node_modules/@next/swc-linux-arm64-gnu": {
-      "version": "14.2.26",
-      "resolved": "https://registry.npmjs.org/@next/swc-linux-arm64-gnu/-/swc-linux-arm64-gnu-14.2.26.tgz",
-      "integrity": "sha512-SINMl1I7UhfHGM7SoRiw0AbwnLEMUnJ/3XXVmhyptzriHbWvPPbbm0OEVG24uUKhuS1t0nvN/DBvm5kz6ZIqpg==",
+      "version": "14.2.30",
+      "resolved": "https://registry.npmjs.org/@next/swc-linux-arm64-gnu/-/swc-linux-arm64-gnu-14.2.30.tgz",
+      "integrity": "sha512-I5lg1fgPJ7I5dk6mr3qCH1hJYKJu1FsfKSiTKoYwcuUf53HWTrEkwmMI0t5ojFKeA6Vu+SfT2zVy5NS0QLXV4Q==",
       "cpu": [
         "arm64"
       ],
-      "license": "MIT",
       "optional": true,
       "os": [
         "linux"
@@ -279,13 +275,12 @@
       }
     },
     "node_modules/@next/swc-linux-arm64-musl": {
-      "version": "14.2.26",
-      "resolved": "https://registry.npmjs.org/@next/swc-linux-arm64-musl/-/swc-linux-arm64-musl-14.2.26.tgz",
-      "integrity": "sha512-s6JaezoyJK2DxrwHWxLWtJKlqKqTdi/zaYigDXUJ/gmx/72CrzdVZfMvUc6VqnZ7YEvRijvYo+0o4Z9DencduA==",
+      "version": "14.2.30",
+      "resolved": "https://registry.npmjs.org/@next/swc-linux-arm64-musl/-/swc-linux-arm64-musl-14.2.30.tgz",
+      "integrity": "sha512-8GkNA+sLclQyxgzCDs2/2GSwBc92QLMrmYAmoP2xehe5MUKBLB2cgo34Yu242L1siSkwQkiV4YLdCnjwc/Micw==",
       "cpu": [
         "arm64"
       ],
-      "license": "MIT",
       "optional": true,
       "os": [
         "linux"
@@ -295,13 +290,12 @@
       }
     },
     "node_modules/@next/swc-linux-x64-gnu": {
-      "version": "14.2.26",
-      "resolved": "https://registry.npmjs.org/@next/swc-linux-x64-gnu/-/swc-linux-x64-gnu-14.2.26.tgz",
-      "integrity": "sha512-FEXeUQi8/pLr/XI0hKbe0tgbLmHFRhgXOUiPScz2hk0hSmbGiU8aUqVslj/6C6KA38RzXnWoJXo4FMo6aBxjzg==",
+      "version": "14.2.30",
+      "resolved": "https://registry.npmjs.org/@next/swc-linux-x64-gnu/-/swc-linux-x64-gnu-14.2.30.tgz",
+      "integrity": "sha512-8Ly7okjssLuBoe8qaRCcjGtcMsv79hwzn/63wNeIkzJVFVX06h5S737XNr7DZwlsbTBDOyI6qbL2BJB5n6TV/w==",
       "cpu": [
         "x64"
       ],
-      "license": "MIT",
       "optional": true,
       "os": [
         "linux"
@@ -311,13 +305,12 @@
       }
     },
     "node_modules/@next/swc-linux-x64-musl": {
-      "version": "14.2.26",
-      "resolved": "https://registry.npmjs.org/@next/swc-linux-x64-musl/-/swc-linux-x64-musl-14.2.26.tgz",
-      "integrity": "sha512-BUsomaO4d2DuXhXhgQCVt2jjX4B4/Thts8nDoIruEJkhE5ifeQFtvW5c9JkdOtYvE5p2G0hcwQ0UbRaQmQwaVg==",
+      "version": "14.2.30",
+      "resolved": "https://registry.npmjs.org/@next/swc-linux-x64-musl/-/swc-linux-x64-musl-14.2.30.tgz",
+      "integrity": "sha512-dBmV1lLNeX4mR7uI7KNVHsGQU+OgTG5RGFPi3tBJpsKPvOPtg9poyav/BYWrB3GPQL4dW5YGGgalwZ79WukbKQ==",
       "cpu": [
         "x64"
       ],
-      "license": "MIT",
       "optional": true,
       "os": [
         "linux"
@@ -327,13 +320,12 @@
       }
     },
     "node_modules/@next/swc-win32-arm64-msvc": {
-      "version": "14.2.26",
-      "resolved": "https://registry.npmjs.org/@next/swc-win32-arm64-msvc/-/swc-win32-arm64-msvc-14.2.26.tgz",
-      "integrity": "sha512-5auwsMVzT7wbB2CZXQxDctpWbdEnEW/e66DyXO1DcgHxIyhP06awu+rHKshZE+lPLIGiwtjo7bsyeuubewwxMw==",
+      "version": "14.2.30",
+      "resolved": "https://registry.npmjs.org/@next/swc-win32-arm64-msvc/-/swc-win32-arm64-msvc-14.2.30.tgz",
+      "integrity": "sha512-6MMHi2Qc1Gkq+4YLXAgbYslE1f9zMGBikKMdmQRHXjkGPot1JY3n5/Qrbg40Uvbi8//wYnydPnyvNhI1DMUW1g==",
       "cpu": [
         "arm64"
       ],
-      "license": "MIT",
       "optional": true,
       "os": [
         "win32"
@@ -343,13 +335,12 @@
       }
     },
     "node_modules/@next/swc-win32-ia32-msvc": {
-      "version": "14.2.26",
-      "resolved": "https://registry.npmjs.org/@next/swc-win32-ia32-msvc/-/swc-win32-ia32-msvc-14.2.26.tgz",
-      "integrity": "sha512-GQWg/Vbz9zUGi9X80lOeGsz1rMH/MtFO/XqigDznhhhTfDlDoynCM6982mPCbSlxJ/aveZcKtTlwfAjwhyxDpg==",
+      "version": "14.2.30",
+      "resolved": "https://registry.npmjs.org/@next/swc-win32-ia32-msvc/-/swc-win32-ia32-msvc-14.2.30.tgz",
+      "integrity": "sha512-pVZMnFok5qEX4RT59mK2hEVtJX+XFfak+/rjHpyFh7juiT52r177bfFKhnlafm0UOSldhXjj32b+LZIOdswGTg==",
       "cpu": [
         "ia32"
       ],
-      "license": "MIT",
       "optional": true,
       "os": [
         "win32"
@@ -359,13 +350,12 @@
       }
     },
     "node_modules/@next/swc-win32-x64-msvc": {
-      "version": "14.2.26",
-      "resolved": "https://registry.npmjs.org/@next/swc-win32-x64-msvc/-/swc-win32-x64-msvc-14.2.26.tgz",
-      "integrity": "sha512-2rdB3T1/Gp7bv1eQTTm9d1Y1sv9UuJ2LAwOE0Pe2prHKe32UNscj7YS13fRB37d0GAiGNR+Y7ZcW8YjDI8Ns0w==",
+      "version": "14.2.30",
+      "resolved": "https://registry.npmjs.org/@next/swc-win32-x64-msvc/-/swc-win32-x64-msvc-14.2.30.tgz",
+      "integrity": "sha512-4KCo8hMZXMjpTzs3HOqOGYYwAXymXIy7PEPAXNEcEOyKqkjiDlECumrWziy+JEF0Oi4ILHGxzgQ3YiMGG2t/Lg==",
       "cpu": [
         "x64"
       ],
-      "license": "MIT",
       "optional": true,
       "os": [
         "win32"
@@ -620,11 +610,10 @@
       }
     },
     "node_modules/@typescript-eslint/typescript-estree/node_modules/brace-expansion": {
-      "version": "2.0.1",
-      "resolved": "https://registry.npmjs.org/brace-expansion/-/brace-expansion-2.0.1.tgz",
-      "integrity": "sha512-XnAIvQ8eM+kC6aULx6wuQiwVsnzsi9d3WxzV3FpWTGA19F621kwdbsAcFKXgKUHZWsy+mY6iL1sHTxWEFCytDA==",
+      "version": "2.0.2",
+      "resolved": "https://registry.npmjs.org/brace-expansion/-/brace-expansion-2.0.2.tgz",
+      "integrity": "sha512-Jt0vHyM+jmUBqojB7E1NIYadt0vI0Qxjxd2TErW94wDz+E2LAm5vKMXXwg6ZZBTHPuUlDgQHKXvjGBdfcF1ZDQ==",
       "dev": true,
-      "license": "MIT",
       "dependencies": {
         "balanced-match": "^1.0.0"
       }
@@ -1176,9 +1165,9 @@
       }
     },
     "node_modules/axios": {
-      "version": "1.8.4",
-      "resolved": "https://registry.npmjs.org/axios/-/axios-1.8.4.tgz",
-      "integrity": "sha512-eBSYY4Y68NNlHbHBMdeDmKNtDgXWhQsJcGqzO3iLUM0GraQFSS9cVgPX5I9b3lbdFKyYoAEGAZF1DwhTaljNAw==",
+      "version": "1.10.0",
+      "resolved": "https://registry.npmjs.org/axios/-/axios-1.10.0.tgz",
+      "integrity": "sha512-/1xYAC4MP/HEG+3duIhFr4ZQXR4sQXOIe+o6sdqzeykGLx6Upp/1p8MHqhINOvGeP7xyNHe7tsiJByc4SSVUxw==",
       "license": "MIT",
       "dependencies": {
         "follow-redirects": "^1.15.6",
@@ -1224,11 +1213,10 @@
       "license": "MIT"
     },
     "node_modules/brace-expansion": {
-      "version": "1.1.11",
-      "resolved": "https://registry.npmjs.org/brace-expansion/-/brace-expansion-1.1.11.tgz",
-      "integrity": "sha512-iCuPHDFgrHX7H2vEI/5xpz07zSHB00TpugqhmYtVmMO6518mCuRMoOYFldEBl0g187ufozdaHgWKcYFb61qGiA==",
+      "version": "1.1.12",
+      "resolved": "https://registry.npmjs.org/brace-expansion/-/brace-expansion-1.1.12.tgz",
+      "integrity": "sha512-9T9UjW3r0UW5c1Q7GTwllptXwhvYmEzFhzMfZ9H7FQWt+uZePjZPjBP/W1ZEyZ1twGWom5/56TF4lPcqjnDHcg==",
       "dev": true,
-      "license": "MIT",
       "dependencies": {
         "balanced-match": "^1.0.0",
         "concat-map": "0.0.1"
@@ -2448,14 +2436,15 @@
       }
     },
     "node_modules/form-data": {
-      "version": "4.0.2",
-      "resolved": "https://registry.npmjs.org/form-data/-/form-data-4.0.2.tgz",
-      "integrity": "sha512-hGfm/slu0ZabnNt4oaRZ6uREyfCj6P4fT/n6A1rGV+Z0VdGXjfOhVUpkn6qVQONHGIFwmveGXyDs75+nr6FM8w==",
+      "version": "4.0.4",
+      "resolved": "https://registry.npmjs.org/form-data/-/form-data-4.0.4.tgz",
+      "integrity": "sha512-KrGhL9Q4zjj0kiUt5OO4Mr/A/jlI2jDYs5eHBpYHPcBEVSiipAvn2Ko2HnPe20rmcuuvMHNdZFp+4IlGTMF0Ow==",
       "license": "MIT",
       "dependencies": {
         "asynckit": "^0.4.0",
         "combined-stream": "^1.0.8",
         "es-set-tostringtag": "^2.1.0",
+        "hasown": "^2.0.2",
         "mime-types": "^2.1.12"
       },
       "engines": {
@@ -2614,11 +2603,10 @@
       }
     },
     "node_modules/glob/node_modules/brace-expansion": {
-      "version": "2.0.1",
-      "resolved": "https://registry.npmjs.org/brace-expansion/-/brace-expansion-2.0.1.tgz",
-      "integrity": "sha512-XnAIvQ8eM+kC6aULx6wuQiwVsnzsi9d3WxzV3FpWTGA19F621kwdbsAcFKXgKUHZWsy+mY6iL1sHTxWEFCytDA==",
+      "version": "2.0.2",
+      "resolved": "https://registry.npmjs.org/brace-expansion/-/brace-expansion-2.0.2.tgz",
+      "integrity": "sha512-Jt0vHyM+jmUBqojB7E1NIYadt0vI0Qxjxd2TErW94wDz+E2LAm5vKMXXwg6ZZBTHPuUlDgQHKXvjGBdfcF1ZDQ==",
       "dev": true,
-      "license": "MIT",
       "dependencies": {
         "balanced-match": "^1.0.0"
       }
@@ -3613,12 +3601,11 @@
       "license": "MIT"
     },
     "node_modules/next": {
-      "version": "14.2.26",
-      "resolved": "https://registry.npmjs.org/next/-/next-14.2.26.tgz",
-      "integrity": "sha512-b81XSLihMwCfwiUVRRja3LphLo4uBBMZEzBBWMaISbKTwOmq3wPknIETy/8000tr7Gq4WmbuFYPS7jOYIf+ZJw==",
-      "license": "MIT",
+      "version": "14.2.30",
+      "resolved": "https://registry.npmjs.org/next/-/next-14.2.30.tgz",
+      "integrity": "sha512-+COdu6HQrHHFQ1S/8BBsCag61jZacmvbuL2avHvQFbWa2Ox7bE+d8FyNgxRLjXQ5wtPyQwEmk85js/AuaG2Sbg==",
       "dependencies": {
-        "@next/env": "14.2.26",
+        "@next/env": "14.2.30",
         "@swc/helpers": "0.5.5",
         "busboy": "1.6.0",
         "caniuse-lite": "^1.0.30001579",
@@ -3633,15 +3620,15 @@
         "node": ">=18.17.0"
       },
       "optionalDependencies": {
-        "@next/swc-darwin-arm64": "14.2.26",
-        "@next/swc-darwin-x64": "14.2.26",
-        "@next/swc-linux-arm64-gnu": "14.2.26",
-        "@next/swc-linux-arm64-musl": "14.2.26",
-        "@next/swc-linux-x64-gnu": "14.2.26",
-        "@next/swc-linux-x64-musl": "14.2.26",
-        "@next/swc-win32-arm64-msvc": "14.2.26",
-        "@next/swc-win32-ia32-msvc": "14.2.26",
-        "@next/swc-win32-x64-msvc": "14.2.26"
+        "@next/swc-darwin-arm64": "14.2.30",
+        "@next/swc-darwin-x64": "14.2.30",
+        "@next/swc-linux-arm64-gnu": "14.2.30",
+        "@next/swc-linux-arm64-musl": "14.2.30",
+        "@next/swc-linux-x64-gnu": "14.2.30",
+        "@next/swc-linux-x64-musl": "14.2.30",
+        "@next/swc-win32-arm64-msvc": "14.2.30",
+        "@next/swc-win32-ia32-msvc": "14.2.30",
+        "@next/swc-win32-x64-msvc": "14.2.30"
       },
       "peerDependencies": {
         "@opentelemetry/api": "^1.1.0",

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

@@ -9,7 +9,7 @@
     "lint": "next lint"
   },
   "dependencies": {
-    "axios": "^1.6.0",
+    "axios": "^1.10.0",
     "next": "^14.0.0",
     "pino": "^8.15.0",
     "react": "^18.2.0",

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

@@ -65,6 +65,7 @@ export default async function handler(req, res) {
       From,
       callId,
       callDomain,
+      sipHeaders,
       dialout_settings,
       voicemail_detection,
       call_transfer
@@ -102,7 +103,7 @@ export default async function handler(req, res) {
       const sip_config = {
         display_name: From,
         sip_mode: 'dial-in',
-        num_endpoints: call_transfer !== null ? 2 : 1,
+        num_endpoints: (call_transfer !== undefined && call_transfer !== null) ? 2 : 1,
         codecs: {"audio": ["OPUS"]},
       };
       daily_room_properties.sip = sip_config;
@@ -117,6 +118,7 @@ export default async function handler(req, res) {
         dialout_settings,
         voicemail_detection,
         call_transfer,
+        sip_headers: sipHeaders,
       },
     };
 

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

@@ -67,10 +67,8 @@ async def main(transport: DailyTransport):
     task = PipelineTask(
         pipeline,
         params=PipelineParams(
-            allow_interruptions=True,
             enable_metrics=True,
             enable_usage_metrics=True,
-            report_only_initial_ttfb=True,
         ),
     )
 
@@ -92,7 +90,7 @@ async def main(transport: DailyTransport):
         logger.info("Participant left: {}", participant)
         await task.cancel()
 
-    runner = PipelineRunner()
+    runner = PipelineRunner(handle_sigint=False, force_gc=True)
 
     await runner.run(task)
 

examples/fal-smart-turn/README.md

@@ -44,7 +44,7 @@ Try the hosted version of the demo here: https://pcc-smart-turn.vercel.app/.
 4. Run the server:
 
    ```bash
-   LOCAL=1 python server.py
+   LOCAL_RUN=1 python server.py
    ```
 
 ### Run the client

examples/fal-smart-turn/client/package.json

@@ -9,9 +9,9 @@
     "lint": "next lint"
   },
   "dependencies": {
-    "@pipecat-ai/client-js": "^0.3.5",
-    "@pipecat-ai/client-react": "^0.3.5",
-    "@pipecat-ai/daily-transport": "^0.3.10",
+    "@pipecat-ai/client-js": "^1.0.0",
+    "@pipecat-ai/client-react": "^1.0.0",
+    "@pipecat-ai/daily-transport": "^1.0.0",
     "next": "15.3.1",
     "react": "^19.0.0",
     "react-dom": "^19.0.0"

examples/fal-smart-turn/client/src/app/layout.tsx

@@ -1,5 +1,5 @@
 import './globals.css';
-import { RTVIProvider } from '@/providers/RTVIProvider';
+import { PipecatProvider } from '@/providers/PipecatProvider';
 
 export const metadata = {
   title: 'Pipecat React Client',
@@ -20,7 +20,7 @@ export default function RootLayout({
         <link rel="icon" href="/favicon.svg" type="image/svg+xml" />
       </head>
       <body>
-        <RTVIProvider>{children}</RTVIProvider>
+        <PipecatProvider>{children}</PipecatProvider>
       </body>
     </html>
   );

examples/fal-smart-turn/client/src/app/page.tsx

@@ -1,22 +1,22 @@
 'use client';
 
 import {
-  RTVIClientAudio,
-  RTVIClientVideo,
-  useRTVIClientTransportState,
+  PipecatClientAudio,
+  PipecatClientVideo,
+  usePipecatClientTransportState,
 } from '@pipecat-ai/client-react';
 import { ConnectButton } from '../components/ConnectButton';
 import { StatusDisplay } from '../components/StatusDisplay';
 import { DebugDisplay } from '../components/DebugDisplay';
 
 function BotVideo() {
-  const transportState = useRTVIClientTransportState();
+  const transportState = usePipecatClientTransportState();
   const isConnected = transportState !== 'disconnected';
 
   return (
     <div className="bot-container">
       <div className="video-container">
-        {isConnected && <RTVIClientVideo participant="bot" fit="cover" />}
+        {isConnected && <PipecatClientVideo participant="bot" fit="cover" />}
       </div>
     </div>
   );
@@ -35,7 +35,7 @@ export default function Home() {
       </div>
 
       <DebugDisplay />
-      <RTVIClientAudio />
+      <PipecatClientAudio />
     </div>
   );
 }

examples/fal-smart-turn/client/src/components/ConnectButton.tsx

@@ -1,11 +1,17 @@
 import {
-  useRTVIClient,
-  useRTVIClientTransportState,
+  usePipecatClient,
+  usePipecatClientTransportState,
 } from '@pipecat-ai/client-react';
 
+// Get the API base URL from environment variables
+// Default to "/api" if not specified
+// "/api" is the default for Next.js API routes and used
+// for the Pipecat Cloud deployed agent
+const API_BASE_URL = process.env.NEXT_PUBLIC_API_BASE_URL || '/api';
+
 export function ConnectButton() {
-  const client = useRTVIClient();
-  const transportState = useRTVIClientTransportState();
+  const client = usePipecatClient();
+  const transportState = usePipecatClientTransportState();
   const isConnected = ['connected', 'ready'].includes(transportState);
 
   const handleClick = async () => {
@@ -18,7 +24,10 @@ export function ConnectButton() {
       if (isConnected) {
         await client.disconnect();
       } else {
-        await client.connect();
+        await client.connect({
+          endpoint: `${API_BASE_URL}/connect`,
+          requestData: { foo: 'bar' },
+        });
       }
     } catch (error) {
       console.error('Connection error:', error);

examples/fal-smart-turn/client/src/components/DebugDisplay.tsx

@@ -6,7 +6,7 @@ import {
   TranscriptData,
   BotLLMTextData,
 } from '@pipecat-ai/client-js';
-import { useRTVIClient, useRTVIClientEvent } from '@pipecat-ai/client-react';
+import { usePipecatClient, useRTVIClientEvent } from '@pipecat-ai/client-react';
 import './DebugDisplay.css';
 
 interface SmartTurnResultData {
@@ -20,7 +20,7 @@ interface SmartTurnResultData {
 
 export function DebugDisplay() {
   const debugLogRef = useRef<HTMLDivElement>(null);
-  const client = useRTVIClient();
+  const client = usePipecatClient();
 
   const log = useCallback((message: string) => {
     if (!debugLogRef.current) return;

examples/fal-smart-turn/client/src/components/StatusDisplay.tsx

@@ -1,7 +1,7 @@
-import { useRTVIClientTransportState } from '@pipecat-ai/client-react';
+import { usePipecatClientTransportState } from '@pipecat-ai/client-react';
 
 export function StatusDisplay() {
-  const transportState = useRTVIClientTransportState();
+  const transportState = usePipecatClientTransportState();
 
   return (
     <div className="status">

examples/fal-smart-turn/client/src/providers/PipecatProvider.tsx

@@ -0,0 +1,28 @@
+'use client';
+
+import { PipecatClient } from '@pipecat-ai/client-js';
+import { DailyTransport } from '@pipecat-ai/daily-transport';
+import { PipecatClientProvider } from '@pipecat-ai/client-react';
+import { PropsWithChildren, useEffect, useState } from 'react';
+
+export function PipecatProvider({ children }: PropsWithChildren) {
+  const [client, setClient] = useState<PipecatClient | null>(null);
+
+  useEffect(() => {
+    const pcClient = new PipecatClient({
+      transport: new DailyTransport(),
+      enableMic: true,
+      enableCam: false,
+    });
+
+    setClient(pcClient);
+  }, []);
+
+  if (!client) {
+    return null;
+  }
+
+  return (
+    <PipecatClientProvider client={client}>{children}</PipecatClientProvider>
+  );
+}

examples/fal-smart-turn/client/src/providers/RTVIProvider.tsx

@@ -1,43 +0,0 @@
-'use client';
-
-import { RTVIClient } from '@pipecat-ai/client-js';
-import { DailyTransport } from '@pipecat-ai/daily-transport';
-import { RTVIClientProvider } from '@pipecat-ai/client-react';
-import { PropsWithChildren, useEffect, useState } from 'react';
-
-// Get the API base URL from environment variables
-// Default to "/api" if not specified
-// "/api" is the default for Next.js API routes and used
-// for the Pipecat Cloud deployed agent
-const API_BASE_URL = process.env.NEXT_PUBLIC_API_BASE_URL || '/api';
-
-console.log('Using API base URL:', API_BASE_URL);
-
-export function RTVIProvider({ children }: PropsWithChildren) {
-  const [client, setClient] = useState<RTVIClient | null>(null);
-
-  useEffect(() => {
-    const transport = new DailyTransport();
-
-    const rtviClient = new RTVIClient({
-      transport,
-      params: {
-        baseUrl: API_BASE_URL,
-        endpoints: {
-          connect: '/connect',
-        },
-        requestData: { foo: 'bar' },
-      },
-      enableMic: true,
-      enableCam: false,
-    });
-
-    setClient(rtviClient);
-  }, []);
-
-  if (!client) {
-    return null;
-  }
-
-  return <RTVIClientProvider client={client}>{children}</RTVIClientProvider>;
-}

examples/fal-smart-turn/server/bot.py

@@ -45,7 +45,7 @@ from pipecat.transports.services.daily import DailyParams, DailyTransport
 load_dotenv(override=True)
 
 # Check if we're in local development mode
-LOCAL = os.getenv("LOCAL")
+LOCAL = os.getenv("LOCAL_RUN")
 
 logger.remove()
 logger.add(sys.stderr, level="DEBUG")
@@ -192,7 +192,6 @@ async def main(transport: DailyTransport):
     task = PipelineTask(
         pipeline,
         params=PipelineParams(
-            allow_interruptions=True,
             enable_metrics=True,
             enable_usage_metrics=True,
         ),

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

@@ -16,23 +16,25 @@ 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.base_transport import TransportParams
-from pipecat.transports.network.small_webrtc import SmallWebRTCTransport
-from pipecat.transports.network.webrtc_connection import SmallWebRTCConnection
+from pipecat.transports.base_transport import BaseTransport, TransportParams
+from pipecat.transports.network.fastapi_websocket import FastAPIWebsocketParams
+from pipecat.transports.services.daily import DailyParams
 
 load_dotenv(override=True)
 
 
-async def run_bot(webrtc_connection: SmallWebRTCConnection, _: argparse.Namespace):
-    logger.info(f"Starting bot")
+# We store functions so objects (e.g. SileroVADAnalyzer) don't get
+# instantiated. The function will be called when the desired transport gets
+# selected.
+transport_params = {
+    "daily": lambda: DailyParams(audio_out_enabled=True),
+    "twilio": lambda: FastAPIWebsocketParams(audio_out_enabled=True),
+    "webrtc": lambda: TransportParams(audio_out_enabled=True),
+}
 
-    # Create a transport using the WebRTC connection
-    transport = SmallWebRTCTransport(
-        webrtc_connection=webrtc_connection,
-        params=TransportParams(
-            audio_out_enabled=True,
-        ),
-    )
+
+async def run_example(transport: BaseTransport, _: argparse.Namespace, handle_sigint: bool):
+    logger.info(f"Starting bot")
 
     # Create an HTTP session
     async with aiohttp.ClientSession() as session:
@@ -47,12 +49,12 @@ async def run_bot(webrtc_connection: SmallWebRTCConnection, _: argparse.Namespac
         async def on_client_connected(transport, client):
             await task.queue_frames([TTSSpeakFrame(f"Hello there!"), EndFrame()])
 
-        runner = PipelineRunner(handle_sigint=False)
+        runner = PipelineRunner(handle_sigint=handle_sigint)
 
         await runner.run(task)
 
 
 if __name__ == "__main__":
-    from run import main
+    from pipecat.examples.run import main
 
-    main()
+    main(run_example, transport_params=transport_params)

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

@@ -16,24 +16,25 @@ from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineTask
 from pipecat.services.rime.tts import RimeHttpTTSService
-from pipecat.transports.base_transport import TransportParams
-from pipecat.transports.network.small_webrtc import SmallWebRTCTransport
-from pipecat.transports.network.webrtc_connection import SmallWebRTCConnection
+from pipecat.transports.base_transport import BaseTransport, TransportParams
+from pipecat.transports.network.fastapi_websocket import FastAPIWebsocketParams
+from pipecat.transports.services.daily import DailyParams
 
 load_dotenv(override=True)
 
+# We store functions so objects (e.g. SileroVADAnalyzer) don't get
+# instantiated. The function will be called when the desired transport gets
+# selected.
+transport_params = {
+    "daily": lambda: DailyParams(audio_out_enabled=True),
+    "twilio": lambda: FastAPIWebsocketParams(audio_out_enabled=True),
+    "webrtc": lambda: TransportParams(audio_out_enabled=True),
+}
 
-async def run_bot(webrtc_connection: SmallWebRTCConnection, _: argparse.Namespace):
+
+async def run_example(transport: BaseTransport, _: argparse.Namespace, handle_sigint: bool):
     logger.info(f"Starting bot")
 
-    # Create a transport using the WebRTC connection
-    transport = SmallWebRTCTransport(
-        webrtc_connection=webrtc_connection,
-        params=TransportParams(
-            audio_out_enabled=True,
-        ),
-    )
-
     # Create an HTTP session
     async with aiohttp.ClientSession() as session:
         tts = RimeHttpTTSService(
@@ -49,12 +50,12 @@ async def run_bot(webrtc_connection: SmallWebRTCConnection, _: argparse.Namespac
         async def on_client_connected(transport, client):
             await task.queue_frames([TTSSpeakFrame(f"Hello there!"), EndFrame()])
 
-        runner = PipelineRunner(handle_sigint=False)
+        runner = PipelineRunner(handle_sigint=handle_sigint)
 
         await runner.run(task)
 
 
 if __name__ == "__main__":
-    from run import main
+    from pipecat.examples.run import main
 
-    main()
+    main(run_example, transport_params=transport_params)

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

@@ -15,23 +15,25 @@ from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineTask
 from pipecat.services.cartesia.tts import CartesiaTTSService
-from pipecat.transports.base_transport import TransportParams
-from pipecat.transports.network.small_webrtc import SmallWebRTCTransport
-from pipecat.transports.network.webrtc_connection import SmallWebRTCConnection
+from pipecat.transports.base_transport import BaseTransport, TransportParams
+from pipecat.transports.network.fastapi_websocket import FastAPIWebsocketParams
+from pipecat.transports.services.daily import DailyParams
 
 load_dotenv(override=True)
 
 
-async def run_bot(webrtc_connection: SmallWebRTCConnection, _: argparse.Namespace):
-    logger.info(f"Starting bot")
+# We store functions so objects (e.g. SileroVADAnalyzer) don't get
+# instantiated. The function will be called when the desired transport gets
+# selected.
+transport_params = {
+    "daily": lambda: DailyParams(audio_out_enabled=True),
+    "twilio": lambda: FastAPIWebsocketParams(audio_out_enabled=True),
+    "webrtc": lambda: TransportParams(audio_out_enabled=True),
+}
 
-    # Create a transport using the WebRTC connection
-    transport = SmallWebRTCTransport(
-        webrtc_connection=webrtc_connection,
-        params=TransportParams(
-            audio_out_enabled=True,
-        ),
-    )
+
+async def run_example(transport: BaseTransport, _: argparse.Namespace, handle_sigint: bool):
+    logger.info(f"Starting bot")
 
     tts = CartesiaTTSService(
         api_key=os.getenv("CARTESIA_API_KEY"),
@@ -45,12 +47,12 @@ async def run_bot(webrtc_connection: SmallWebRTCConnection, _: argparse.Namespac
     async def on_client_connected(transport, client):
         await task.queue_frames([TTSSpeakFrame(f"Hello there!"), EndFrame()])
 
-    runner = PipelineRunner(handle_sigint=False)
+    runner = PipelineRunner(handle_sigint=handle_sigint)
 
     await runner.run(task)
 
 
 if __name__ == "__main__":
-    from run import main
+    from pipecat.examples.run import main
 
-    main()
+    main(run_example, transport_params=transport_params)

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

@@ -77,37 +77,36 @@ async def configure_livekit():
 
 
 async def main():
-    async with aiohttp.ClientSession() as session:
-        (url, token, room_name) = await configure_livekit()
+    (url, token, room_name) = await configure_livekit()
 
-        transport = LiveKitTransport(
-            url=url,
-            token=token,
-            room_name=room_name,
-            params=LiveKitParams(audio_out_enabled=True),
-        )
+    transport = LiveKitTransport(
+        url=url,
+        token=token,
+        room_name=room_name,
+        params=LiveKitParams(audio_out_enabled=True),
+    )
 
-        tts = CartesiaTTSService(
-            api_key=os.getenv("CARTESIA_API_KEY"),
-            voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
-        )
+    tts = CartesiaTTSService(
+        api_key=os.getenv("CARTESIA_API_KEY"),
+        voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
+    )
 
-        runner = PipelineRunner()
+    runner = PipelineRunner()
 
-        task = PipelineTask(Pipeline([tts, transport.output()]))
+    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_id):
-            await asyncio.sleep(1)
-            await task.queue_frame(
-                TextFrame(
-                    "Hello there! How are you doing today? Would you like to talk about the weather?"
-                )
+    # 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_id):
+        await asyncio.sleep(1)
+        await task.queue_frame(
+            TextFrame(
+                "Hello there! How are you doing today? Would you like to talk about the weather?"
             )
+        )
 
-        await runner.run(task)
+    await runner.run(task)
 
 
 if __name__ == "__main__":

examples/foundational/01c-fastpitch.py

@@ -15,23 +15,25 @@ from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineTask
 from pipecat.services.riva.tts import FastPitchTTSService
-from pipecat.transports.base_transport import TransportParams
-from pipecat.transports.network.small_webrtc import SmallWebRTCTransport
-from pipecat.transports.network.webrtc_connection import SmallWebRTCConnection
+from pipecat.transports.base_transport import BaseTransport, TransportParams
+from pipecat.transports.network.fastapi_websocket import FastAPIWebsocketParams
+from pipecat.transports.services.daily import DailyParams
 
 load_dotenv(override=True)
 
 
-async def run_bot(webrtc_connection: SmallWebRTCConnection, _: argparse.Namespace):
-    logger.info(f"Starting bot")
+# We store functions so objects (e.g. SileroVADAnalyzer) don't get
+# instantiated. The function will be called when the desired transport gets
+# selected.
+transport_params = {
+    "daily": lambda: DailyParams(audio_out_enabled=True),
+    "twilio": lambda: FastAPIWebsocketParams(audio_out_enabled=True),
+    "webrtc": lambda: TransportParams(audio_out_enabled=True),
+}
 
-    # Create a transport using the WebRTC connection
-    transport = SmallWebRTCTransport(
-        webrtc_connection=webrtc_connection,
-        params=TransportParams(
-            audio_out_enabled=True,
-        ),
-    )
+
+async def run_example(transport: BaseTransport, _: argparse.Namespace, handle_sigint: bool):
+    logger.info(f"Starting bot")
 
     tts = FastPitchTTSService(api_key=os.getenv("NVIDIA_API_KEY"))
 
@@ -42,12 +44,12 @@ async def run_bot(webrtc_connection: SmallWebRTCConnection, _: argparse.Namespac
     async def on_client_connected(transport, client):
         await task.queue_frames([TTSSpeakFrame(f"Hello there!"), EndFrame()])
 
-    runner = PipelineRunner(handle_sigint=False)
+    runner = PipelineRunner(handle_sigint=handle_sigint)
 
     await runner.run(task)
 
 
 if __name__ == "__main__":
-    from run import main
+    from pipecat.examples.run import main
 
-    main()
+    main(run_example, transport_params=transport_params)

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

@@ -16,23 +16,25 @@ from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineTask
 from pipecat.services.cartesia.tts import CartesiaTTSService
 from pipecat.services.openai.llm import OpenAILLMService
-from pipecat.transports.base_transport import TransportParams
-from pipecat.transports.network.small_webrtc import SmallWebRTCTransport
-from pipecat.transports.network.webrtc_connection import SmallWebRTCConnection
+from pipecat.transports.base_transport import BaseTransport, TransportParams
+from pipecat.transports.network.fastapi_websocket import FastAPIWebsocketParams
+from pipecat.transports.services.daily import DailyParams
 
 load_dotenv(override=True)
 
 
-async def run_bot(webrtc_connection: SmallWebRTCConnection, _: argparse.Namespace):
-    logger.info(f"Starting bot")
+# We store functions so objects (e.g. SileroVADAnalyzer) don't get
+# instantiated. The function will be called when the desired transport gets
+# selected.
+transport_params = {
+    "daily": lambda: DailyParams(audio_out_enabled=True),
+    "twilio": lambda: FastAPIWebsocketParams(audio_out_enabled=True),
+    "webrtc": lambda: TransportParams(audio_out_enabled=True),
+}
 
-    # Create a transport using the WebRTC connection
-    transport = SmallWebRTCTransport(
-        webrtc_connection=webrtc_connection,
-        params=TransportParams(
-            audio_out_enabled=True,
-        ),
-    )
+
+async def run_example(transport: BaseTransport, _: argparse.Namespace, handle_sigint: bool):
+    logger.info(f"Starting bot")
 
     tts = CartesiaTTSService(
         api_key=os.getenv("CARTESIA_API_KEY"),
@@ -55,12 +57,12 @@ async def run_bot(webrtc_connection: SmallWebRTCConnection, _: argparse.Namespac
     async def on_client_connected(transport, client):
         await task.queue_frames([LLMMessagesFrame(messages), EndFrame()])
 
-    runner = PipelineRunner(handle_sigint=False)
+    runner = PipelineRunner(handle_sigint=handle_sigint)
 
     await runner.run(task)
 
 
 if __name__ == "__main__":
-    from run import main
+    from pipecat.examples.run import main
 
-    main()
+    main(run_example, transport_params=transport_params)

examples/foundational/03-still-frame.py

@@ -16,25 +16,31 @@ from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineTask
 from pipecat.services.fal.image import FalImageGenService
-from pipecat.transports.base_transport import TransportParams
-from pipecat.transports.network.small_webrtc import SmallWebRTCTransport
-from pipecat.transports.network.webrtc_connection import SmallWebRTCConnection
+from pipecat.transports.base_transport import BaseTransport, TransportParams
+from pipecat.transports.services.daily import DailyParams
 
 load_dotenv(override=True)
 
 
-async def run_bot(webrtc_connection: SmallWebRTCConnection, _: argparse.Namespace):
-    logger.info(f"Starting bot")
+# We store functions so objects (e.g. SileroVADAnalyzer) don't get
+# instantiated. The function will be called when the desired transport gets
+# selected.
+transport_params = {
+    "daily": lambda: DailyParams(
+        video_out_enabled=True,
+        video_out_width=1024,
+        video_out_height=1024,
+    ),
+    "webrtc": lambda: TransportParams(
+        video_out_enabled=True,
+        video_out_width=1024,
+        video_out_height=1024,
+    ),
+}
 
-    # Create a transport using the WebRTC connection
-    transport = SmallWebRTCTransport(
-        webrtc_connection=webrtc_connection,
-        params=TransportParams(
-            video_out_enabled=True,
-            video_out_width=1024,
-            video_out_height=1024,
-        ),
-    )
+
+async def run_example(transport: BaseTransport, _: argparse.Namespace, handle_sigint: bool):
+    logger.info(f"Starting bot")
 
     # Create an HTTP session
     async with aiohttp.ClientSession() as session:
@@ -54,18 +60,14 @@ async def run_bot(webrtc_connection: SmallWebRTCConnection, _: argparse.Namespac
         @transport.event_handler("on_client_disconnected")
         async def on_client_disconnected(transport, client):
             logger.info(f"Client disconnected")
-
-        @transport.event_handler("on_client_closed")
-        async def on_client_closed(transport, client):
-            logger.info(f"Client closed connection")
             await task.cancel()
 
-        runner = PipelineRunner(handle_sigint=False)
+        runner = PipelineRunner(handle_sigint=handle_sigint)
 
         await runner.run(task)
 
 
 if __name__ == "__main__":
-    from run import main
+    from pipecat.examples.run import main
 
-    main()
+    main(run_example, transport_params=transport_params)

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

@@ -15,25 +15,31 @@ from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
 from pipecat.services.google.image import GoogleImageGenService
-from pipecat.transports.base_transport import TransportParams
-from pipecat.transports.network.small_webrtc import SmallWebRTCTransport
-from pipecat.transports.network.webrtc_connection import SmallWebRTCConnection
+from pipecat.transports.base_transport import BaseTransport, TransportParams
+from pipecat.transports.services.daily import DailyParams
 
 load_dotenv(override=True)
 
 
-async def run_bot(webrtc_connection: SmallWebRTCConnection, _: argparse.Namespace):
-    logger.info(f"Starting bot")
+# We store functions so objects (e.g. SileroVADAnalyzer) don't get
+# instantiated. The function will be called when the desired transport gets
+# selected.
+transport_params = {
+    "daily": lambda: DailyParams(
+        video_out_enabled=True,
+        video_out_width=1024,
+        video_out_height=1024,
+    ),
+    "webrtc": lambda: TransportParams(
+        video_out_enabled=True,
+        video_out_width=1024,
+        video_out_height=1024,
+    ),
+}
 
-    # Create a transport using the WebRTC connection
-    transport = SmallWebRTCTransport(
-        webrtc_connection=webrtc_connection,
-        params=TransportParams(
-            video_out_enabled=True,
-            video_out_width=1024,
-            video_out_height=1024,
-        ),
-    )
+
+async def run_example(transport: BaseTransport, _: argparse.Namespace, handle_sigint: bool):
+    logger.info(f"Starting bot")
 
     imagegen = GoogleImageGenService(
         api_key=os.getenv("GOOGLE_API_KEY"),
@@ -41,7 +47,10 @@ async def run_bot(webrtc_connection: SmallWebRTCConnection, _: argparse.Namespac
 
     task = PipelineTask(
         Pipeline([imagegen, transport.output()]),
-        params=PipelineParams(enable_metrics=True),
+        params=PipelineParams(
+            enable_metrics=True,
+            enable_usage_metrics=True,
+        ),
     )
 
     # Register an event handler so we can play the audio when the client joins
@@ -54,18 +63,14 @@ async def run_bot(webrtc_connection: SmallWebRTCConnection, _: argparse.Namespac
     @transport.event_handler("on_client_disconnected")
     async def on_client_disconnected(transport, client):
         logger.info(f"Client disconnected")
-
-    @transport.event_handler("on_client_closed")
-    async def on_client_closed(transport, client):
-        logger.info(f"Client closed connection")
         await task.cancel()
 
-    runner = PipelineRunner(handle_sigint=False)
+    runner = PipelineRunner(handle_sigint=handle_sigint)
 
     await runner.run(task)
 
 
 if __name__ == "__main__":
-    from run import main
+    from pipecat.examples.run import main
 
-    main()
+    main(run_example, transport_params=transport_params)

examples/foundational/04-transports-small-webrtc.py

@@ -5,10 +5,17 @@
 #
 
 import argparse
+import asyncio
 import os
+from contextlib import asynccontextmanager
+from typing import Dict
 
+import uvicorn
 from dotenv import load_dotenv
+from fastapi import BackgroundTasks, FastAPI
+from fastapi.responses import RedirectResponse
 from loguru import logger
+from pipecat_ai_small_webrtc_prebuilt.frontend import SmallWebRTCPrebuiltUI
 
 from pipecat.audio.vad.silero import SileroVADAnalyzer
 from pipecat.pipeline.pipeline import Pipeline
@@ -20,14 +27,29 @@ from pipecat.services.deepgram.stt import DeepgramSTTService
 from pipecat.services.openai.llm import OpenAILLMService
 from pipecat.transports.base_transport import TransportParams
 from pipecat.transports.network.small_webrtc import SmallWebRTCTransport
-from pipecat.transports.network.webrtc_connection import SmallWebRTCConnection
+from pipecat.transports.network.webrtc_connection import IceServer, SmallWebRTCConnection
 
 load_dotenv(override=True)
 
+app = FastAPI()
 
-async def run_bot(webrtc_connection: SmallWebRTCConnection, _: argparse.Namespace):
+# Store connections by pc_id
+pcs_map: Dict[str, SmallWebRTCConnection] = {}
+
+ice_servers = [
+    IceServer(
+        urls="stun:stun.l.google.com:19302",
+    )
+]
+
+# Mount the frontend at /
+app.mount("/client", SmallWebRTCPrebuiltUI)
+
+
+async def run_example(webrtc_connection: SmallWebRTCConnection):
     logger.info(f"Starting bot")
 
+    # Create a transport using the WebRTC connection
     transport = SmallWebRTCTransport(
         webrtc_connection=webrtc_connection,
         params=TransportParams(
@@ -71,10 +93,8 @@ async def run_bot(webrtc_connection: SmallWebRTCConnection, _: argparse.Namespac
     task = PipelineTask(
         pipeline,
         params=PipelineParams(
-            allow_interruptions=True,
             enable_metrics=True,
             enable_usage_metrics=True,
-            report_only_initial_ttfb=True,
         ),
     )
 
@@ -88,10 +108,6 @@ async def run_bot(webrtc_connection: SmallWebRTCConnection, _: argparse.Namespac
     @transport.event_handler("on_client_disconnected")
     async def on_client_disconnected(transport, client):
         logger.info(f"Client disconnected")
-
-    @transport.event_handler("on_client_closed")
-    async def on_client_closed(transport, client):
-        logger.info(f"Client closed connection")
         await task.cancel()
 
     runner = PipelineRunner(handle_sigint=False)
@@ -99,7 +115,58 @@ async def run_bot(webrtc_connection: SmallWebRTCConnection, _: argparse.Namespac
     await runner.run(task)
 
 
-if __name__ == "__main__":
-    from run import main
+@app.get("/", include_in_schema=False)
+async def root_redirect():
+    return RedirectResponse(url="/client/")
 
-    main()
+
+@app.post("/api/offer")
+async def offer(request: dict, background_tasks: BackgroundTasks):
+    pc_id = request.get("pc_id")
+
+    if pc_id and pc_id in pcs_map:
+        pipecat_connection = pcs_map[pc_id]
+        logger.info(f"Reusing existing connection for pc_id: {pc_id}")
+        await pipecat_connection.renegotiate(
+            sdp=request["sdp"],
+            type=request["type"],
+            restart_pc=request.get("restart_pc", False),
+        )
+    else:
+        pipecat_connection = SmallWebRTCConnection(ice_servers)
+        await pipecat_connection.initialize(sdp=request["sdp"], type=request["type"])
+
+        @pipecat_connection.event_handler("closed")
+        async def handle_disconnected(webrtc_connection: SmallWebRTCConnection):
+            logger.info(f"Discarding peer connection for pc_id: {webrtc_connection.pc_id}")
+            pcs_map.pop(webrtc_connection.pc_id, None)
+
+        # Run example function with SmallWebRTC transport arguments.
+        background_tasks.add_task(run_example, pipecat_connection)
+
+    answer = pipecat_connection.get_answer()
+    # Updating the peer connection inside the map
+    pcs_map[answer["pc_id"]] = pipecat_connection
+
+    return answer
+
+
+@asynccontextmanager
+async def lifespan(app: FastAPI):
+    yield  # Run app
+    coros = [pc.disconnect() for pc in pcs_map.values()]
+    await asyncio.gather(*coros)
+    pcs_map.clear()
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(description="Pipecat Bot Runner")
+    parser.add_argument(
+        "--host", default="localhost", help="Host for HTTP server (default: localhost)"
+    )
+    parser.add_argument(
+        "--port", type=int, default=7860, help="Port for HTTP server (default: 7860)"
+    )
+    args = parser.parse_args()
+
+    uvicorn.run(app, host=args.host, port=args.port)