CartesiaTTSService: reset context id when flushing audio

github: add coverage support

.github/workflows/coverage.yaml

@@ -0,0 +1,54 @@
+name: coverage
+
+on:
+  workflow_dispatch:
+  push:
+    branches:
+      - main
+  pull_request:
+    branches:
+      - "**"
+    paths-ignore:
+      - "docs/**"
+
+jobs:
+  coverage:
+    name: "Coverage"
+    runs-on: ubuntu-latest
+    steps:
+      - name: Checkout repo
+        uses: actions/checkout@v4
+      - name: Set up Python
+        id: setup_python
+        uses: actions/setup-python@v4
+        with:
+          python-version: "3.10"
+      - name: Cache virtual environment
+        uses: actions/cache@v3
+        with:
+          # We are hashing dev-requirements.txt and test-requirements.txt which
+          # contain all dependencies needed to run the tests.
+          key: venv-${{ runner.os }}-${{ steps.setup_python.outputs.python-version}}-${{ hashFiles('dev-requirements.txt') }}-${{ hashFiles('test-requirements.txt') }}
+          path: .venv
+      - name: Install system packages
+        id: install_system_packages
+        run: |
+          sudo apt-get install -y portaudio19-dev
+      - name: Setup virtual environment
+        run: |
+          python -m venv .venv
+      - name: Install basic Python dependencies
+        run: |
+          source .venv/bin/activate
+          python -m pip install --upgrade pip
+          pip install -r dev-requirements.txt -r test-requirements.txt
+      - name: Run tests with coverage
+        run: |
+          source .venv/bin/activate
+          coverage run
+          coverage xml
+      - name: Upload coverage to Codecov
+        uses: codecov/codecov-action@v5
+        with:
+          token: ${{ secrets.CODECOV_TOKEN }}
+          slug: pipecat-ai/pipecat

CHANGELOG.md

@@ -9,6 +9,114 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 
 ### Added
 
+- Added support for Groq's Whisper API through the new `GroqSTTService` and
+  OpenAI's Whisper API through the new `OpenAISTTService`. Introduced a new
+  base class `BaseWhisperSTTService` to handle common Whisper API
+  functionality.
+
+- Added `PerplexityLLMService` for Perplexity NIM API integration, with an
+  OpenAI-compatible interface. Also, added foundational example
+  `14n-function-calling-perplexity.py`.
+
+- Added `DailyTransport.update_remote_participants()`. This allows you to update remote participant's settings, like their permissions or which of their devices are enabled. Requires that the local participant have participant admin permission.
+
+### Changed
+
+- Improved error handling in `AzureTTSService` to properly detect and log
+  synthesis cancellation errors.
+
+- Enhanced `WhisperSTTService` with full language support and improved model
+  documentation.
+
+- Updated foundation example `14f-function-calling-groq.py` to use
+  `GroqSTTService` for transcription.
+
+- Updated `GroqLLMService` to use `llama-3.3-70b-versatile` as the default
+  model.
+
+- `RTVIObserver` doesn't handle `LLMSearchResponseFrame` frames anymore. For
+  now, to handle those frames you need to create a `GoogleRTVIObserver` instead.
+
+### Deprecated
+
+- `STTMuteFilter` constructor's `stt_service` parameter is now deprecated and
+  will be removed in a future version. The filter now manages mute state
+  internally instead of querying the STT service.
+
+- `RTVI.observer()` is now deprecated, instantiate an `RTVIObserver` directly
+  instead.
+
+- All RTVI frame processors (e.g. `RTVISpeakingProcessor`,
+  `RTVIBotLLMProcessor`) are now deprecated, instantiate an `RTVIObserver`
+  instead.
+
+### Fixed
+
+- Fixed a `CartesiaTTSService` issue that could cause audio overlapping in some
+  cases.
+
+- Fixed an issue that was causing `AudioBufferProcessor` to not record
+  synchronized audio.
+
+- Fixed an `RTVI` issue that was causing `bot-tts-text` messages to be sent
+  before being processed by the output transport.
+
+- Fixed an issue[#1192] in 11labs where we are trying to reconnect/disconnect the
+  websocket connection even when the connection is already closed.
+
+## [0.0.56] - 2025-02-06
+
+### Changed
+
+- Use `gemini-2.0-flash-001` as the default model for `GoogleLLMSerivce`.
+
+- Improved foundational examples 22b, 22c, and 22d to support function calling.
+  With these base examples, `FunctionCallInProgressFrame` and
+  `FunctionCallResultFrame` will no longer be blocked by the gates.
+
+### Fixed
+
+- Fixed a `TkLocalTransport` and `LocalAudioTransport` issues that was causing
+  errors on cleanup.
+
+- Fixed an issue that was causing `tests.utils` import to fail because of
+  logging setup.
+
+- Fixed a `SentryMetrics` issue that was preventing any metrics to be sent to
+  Sentry and also was preventing from metrics frames to be pushed to the pipeline.
+
+- Fixed an issue in `BaseOutputTransport` where incoming audio would not be
+  resampled to the desired output sample rate.
+
+- Fixed an issue with the `TwilioFrameSerializer` and `TelnyxFrameSerializer`
+  where `twilio_sample_rate` and `telnyx_sample_rate` were incorrectly
+  initialized to `audio_in_sample_rate`. Those values currently default to 8000
+  and should be set manually from the serializer constructor if a different
+  value is needed.
+
+### Other
+
+- Added a new `sentry-metrics` example.
+
+## [0.0.55] - 2025-02-05
+
+### Added
+
+- Added a new `start_metadata` field to `PipelineParams`. The provided metadata
+  will be set to the initial `StartFrame` being pushed from the `PipelineTask`.
+
+- Added new fields to `PipelineParams` to control audio input and output sample
+  rates for the whole pipeline. This allows controlling sample rates from a
+  single place instead of having to specify sample rates in each
+  service. Setting a sample rate to a service is still possible and will
+  override the value from `PipelineParams`.
+
+- Introduce audio resamplers (`BaseAudioResampler`). This is just a base class
+  to implement audio resamplers. Currently, two implementations are provided
+  `SOXRAudioResampler` and `ResampyResampler`. A new
+  `create_default_resampler()` has been added (replacing the now deprecated
+  `resample_audio()`).
+
 - It is now possible to specify the asyncio event loop that a `PipelineTask` and
   all the processors should run on by passing it as a new argument to the
   `PipelineRunner`. This could allow running pipelines in multiple threads each
@@ -41,6 +149,12 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 
 ### Changed
 
+- `GatedOpenAILLMContextAggregator` now require keyword arguments. Also, a new
+  `start_open` argument has been added to set the initial state of the gate.
+
+- Added `organization` and `project` level authentication to
+  `OpenAILLMService`.
+
 - Improved the language checking logic in `ElevenLabsTTSService` and
   `ElevenLabsHttpTTSService` to properly handle language codes based on model
   compatibility, with appropriate warnings when language codes cannot be
@@ -50,8 +164,30 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
   contain a combination of function calls, function call responses, system
   messages, or just messages.
 
+- `InputDTMFFrame` is now based on `DTMFFrame`. There's also a new
+  `OutputDTMFFrame` frame.
+
+### Deprecated
+
+- `resample_audio()` is now deprecated, use `create_default_resampler()`
+  instead.
+
+### Removed
+
+- `AudioBufferProcessor.reset_audio_buffers()` has been removed, use
+  `AudioBufferProcessor.start_recording()` and
+  `AudioBufferProcessor.stop_recording()` instead.
+
 ### Fixed
 
+- Fixed a `AudioBufferProcessor` that would cause crackling in some recordings.
+
+- Fixed an issue in `AudioBufferProcessor` where user callback would not be
+  called on task cancellation.
+
+- Fixed an issue in `AudioBufferProcessor` that would cause wrong silence
+  padding in some cases.
+
 - Fixed an issue where `ElevenLabsTTSService` messages would return a 1009
   websocket error by increasing the max message size limit to 16MB.
 
@@ -67,11 +203,22 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 
 ### Other
 
-- Updated all examples to use `task.cancel()` instead of pushing an `EndFrame`
-  when a participant leaves/disconnects. If you push an `EndFrame` this will
-  cause the bot to run through everything that is internally queued (which could
-  take seconds). Instead, if a participant disconnects there is nothing else to
-  be sent and therefore we should stop immediately.
+- Improved Unit Test `run_test()` to use `PipelineTask` and
+  `PipelineRunner`. There's now also some control around `StartFrame` and
+  `EndFrame`. The `EndTaskFrame` has been removed since it doesn't seem
+  necessary with this new approach.
+
+- Updated `twilio-chatbot` with a few new features: use 8000 sample rate and
+  avoid resampling, a new client useful for stress testing and testing locally
+  without the need to make phone calls. Also, added audio recording on both the
+  client and the server to make sure the audio sounds good.
+
+- Updated examples to use `task.cancel()` to immediately exit the example when a
+  participant leaves or disconnects, instead of pushing an `EndFrame`. Pushing
+  an `EndFrame` causes the bot to run through everything that is internally
+  queued (which could take some seconds). Note that using `task.cancel()` might
+  not always be the best option and pushing an `EndFrame` could still be
+  desirable to make sure all the pipeline is flushed.
 
 ## [0.0.54] - 2025-01-27
 
@@ -1520,6 +1667,9 @@ async def on_connected(processor):
 
 ### Changed
 
+- `FrameSerializer.serialize()` and `FrameSerializer.deserialize()` are now
+  `async`.
+
 - `Filter` has been renamed to `FrameFilter` and it's now under
   `processors/filters`.
 

README.md

@@ -2,7 +2,7 @@
  <img alt="pipecat" width="300px" height="auto" src="https://raw.githubusercontent.com/pipecat-ai/pipecat/main/pipecat.png">
 </div></h1>
 
-[![PyPI](https://img.shields.io/pypi/v/pipecat-ai)](https://pypi.org/project/pipecat-ai) ![Tests](https://github.com/pipecat-ai/pipecat/actions/workflows/tests.yaml/badge.svg) [![Docs](https://img.shields.io/badge/Documentation-blue)](https://docs.pipecat.ai) [![Discord](https://img.shields.io/discord/1239284677165056021)](https://discord.gg/pipecat) <a href="https://app.commanddash.io/agent/github_pipecat-ai_pipecat"><img src="https://img.shields.io/badge/AI-Code%20Agent-EB9FDA"></a>
+[![PyPI](https://img.shields.io/pypi/v/pipecat-ai)](https://pypi.org/project/pipecat-ai) ![Tests](https://github.com/pipecat-ai/pipecat/actions/workflows/tests.yaml/badge.svg) [![codecov](https://codecov.io/gh/pipecat-ai/pipecat/graph/badge.svg?token=LNVUIVO4Y9)](https://codecov.io/gh/pipecat-ai/pipecat) [![Docs](https://img.shields.io/badge/Documentation-blue)](https://docs.pipecat.ai) [![Discord](https://img.shields.io/discord/1239284677165056021)](https://discord.gg/pipecat)
 
 Pipecat is an open source Python framework for building voice and multimodal conversational agents. It handles the complex orchestration of AI services, network transport, audio processing, and multimodal interactions, letting you focus on creating engaging experiences.
 
@@ -55,17 +55,17 @@ pip install "pipecat-ai[option,...]"
 
 ### Available services
 
-| Category            | Services                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    | Install Command Example                 |
-| ------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | --------------------------------------- |
-| Speech-to-Text      | [AssemblyAI](https://docs.pipecat.ai/server/services/stt/assemblyai), [Azure](https://docs.pipecat.ai/server/services/stt/azure), [Deepgram](https://docs.pipecat.ai/server/services/stt/deepgram), [Gladia](https://docs.pipecat.ai/server/services/stt/gladia), [Whisper](https://docs.pipecat.ai/server/services/stt/whisper)                                                                                                                                                                                                                                                                                                                                                                                                                                                            | `pip install "pipecat-ai[deepgram]"`    |
-| LLMs                | [Anthropic](https://docs.pipecat.ai/server/services/llm/anthropic), [Azure](https://docs.pipecat.ai/server/services/llm/azure), [Cerebras](https://docs.pipecat.ai/server/services/llm/cerebras), [DeepSeek](https://docs.pipecat.ai/server/services/llm/deepseek), [Fireworks AI](https://docs.pipecat.ai/server/services/llm/fireworks), [Gemini](https://docs.pipecat.ai/server/services/llm/gemini), [Grok](https://docs.pipecat.ai/server/services/llm/grok), [Groq](https://docs.pipecat.ai/server/services/llm/groq), [NVIDIA NIM](https://docs.pipecat.ai/server/services/llm/nim), [Ollama](https://docs.pipecat.ai/server/services/llm/ollama), [OpenAI](https://docs.pipecat.ai/server/services/llm/openai), [OpenRouter](https://docs.pipecat.ai/server/services/llm/openrouter), [Together AI](https://docs.pipecat.ai/server/services/llm/together) | `pip install "pipecat-ai[openai]"`      |
-| Text-to-Speech      | [AWS](https://docs.pipecat.ai/server/services/tts/aws), [Azure](https://docs.pipecat.ai/server/services/tts/azure), [Cartesia](https://docs.pipecat.ai/server/services/tts/cartesia), [Deepgram](https://docs.pipecat.ai/server/services/tts/deepgram), [ElevenLabs](https://docs.pipecat.ai/server/services/tts/elevenlabs), [Fish](https://docs.pipecat.ai/server/services/tts/fish), [Google](https://docs.pipecat.ai/server/services/tts/google), [LMNT](https://docs.pipecat.ai/server/services/tts/lmnt), [OpenAI](https://docs.pipecat.ai/server/services/tts/openai), [PlayHT](https://docs.pipecat.ai/server/services/tts/playht), [Rime](https://docs.pipecat.ai/server/services/tts/rime), [XTTS](https://docs.pipecat.ai/server/services/tts/xtts)                              | `pip install "pipecat-ai[cartesia]"`    |
-| Speech-to-Speech    | [Gemini Multimodal Live](https://docs.pipecat.ai/server/services/s2s/gemini), [OpenAI Realtime](https://docs.pipecat.ai/server/services/s2s/openai)                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                         | `pip install "pipecat-ai[openai]"`      |
-| Transport           | [Daily (WebRTC)](https://docs.pipecat.ai/server/services/transport/daily), [FastAPI Websocket](https://docs.pipecat.ai/server/services/transport/fastapi-websocket), [WebSocket Server](https://docs.pipecat.ai/server/services/transport/websocket-server), Local                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          | `pip install "pipecat-ai[daily]"`       |
-| Video               | [Tavus](https://docs.pipecat.ai/server/services/video/tavus), [Simli](https://docs.pipecat.ai/server/services/video/simli)                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                  | `pip install "pipecat-ai[tavus,simli]"` |
-| Vision & Image      | [Moondream](https://docs.pipecat.ai/server/services/vision/moondream), [fal](https://docs.pipecat.ai/server/services/image-generation/fal)                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                  | `pip install "pipecat-ai[moondream]"`   |
-| Audio Processing    | [Silero VAD](https://docs.pipecat.ai/server/utilities/audio/silero-vad-analyzer), [Krisp](https://docs.pipecat.ai/server/utilities/audio/krisp-filter), [Koala](https://docs.pipecat.ai/server/utilities/audio/koala-filter), [Noisereduce](https://docs.pipecat.ai/server/utilities/audio/noisereduce-filter)                                                                                                                                                                                                                                                                                                                                                                                                                                                                              | `pip install "pipecat-ai[silero]"`      |
-| Analytics & Metrics | [Canonical AI](https://docs.pipecat.ai/server/services/analytics/canonical), [Sentry](https://docs.pipecat.ai/server/services/analytics/sentry)                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                             | `pip install "pipecat-ai[canonical]"`   |
+| Category            | Services                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                | Install Command Example                 |
+| ------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | --------------------------------------- |
+| Speech-to-Text      | [AssemblyAI](https://docs.pipecat.ai/server/services/stt/assemblyai), [Azure](https://docs.pipecat.ai/server/services/stt/azure), [Deepgram](https://docs.pipecat.ai/server/services/stt/deepgram), [Gladia](https://docs.pipecat.ai/server/services/stt/gladia), [Groq (Whisper)](https://docs.pipecat.ai/server/services/stt/groq), [OpenAI (Whisper)](https://docs.pipecat.ai/server/services/stt/openai), [Whisper](https://docs.pipecat.ai/server/services/stt/whisper)                                                                                                                                                                                                                                                                                                                                                                                                                                                            | `pip install "pipecat-ai[deepgram]"`    |
+| LLMs                | [Anthropic](https://docs.pipecat.ai/server/services/llm/anthropic), [Azure](https://docs.pipecat.ai/server/services/llm/azure), [Cerebras](https://docs.pipecat.ai/server/services/llm/cerebras), [DeepSeek](https://docs.pipecat.ai/server/services/llm/deepseek), [Fireworks AI](https://docs.pipecat.ai/server/services/llm/fireworks), [Gemini](https://docs.pipecat.ai/server/services/llm/gemini), [Grok](https://docs.pipecat.ai/server/services/llm/grok), [Groq](https://docs.pipecat.ai/server/services/llm/groq), [NVIDIA NIM](https://docs.pipecat.ai/server/services/llm/nim), [Ollama](https://docs.pipecat.ai/server/services/llm/ollama), [OpenAI](https://docs.pipecat.ai/server/services/llm/openai), [OpenRouter](https://docs.pipecat.ai/server/services/llm/openrouter), [Perplexity](https://docs.pipecat.ai/server/services/llm/perplexity), [Together AI](https://docs.pipecat.ai/server/services/llm/together) | `pip install "pipecat-ai[openai]"`      |
+| Text-to-Speech      | [AWS](https://docs.pipecat.ai/server/services/tts/aws), [Azure](https://docs.pipecat.ai/server/services/tts/azure), [Cartesia](https://docs.pipecat.ai/server/services/tts/cartesia), [Deepgram](https://docs.pipecat.ai/server/services/tts/deepgram), [ElevenLabs](https://docs.pipecat.ai/server/services/tts/elevenlabs), [Fish](https://docs.pipecat.ai/server/services/tts/fish), [Google](https://docs.pipecat.ai/server/services/tts/google), [LMNT](https://docs.pipecat.ai/server/services/tts/lmnt), [OpenAI](https://docs.pipecat.ai/server/services/tts/openai), [PlayHT](https://docs.pipecat.ai/server/services/tts/playht), [Rime](https://docs.pipecat.ai/server/services/tts/rime), [XTTS](https://docs.pipecat.ai/server/services/tts/xtts)                                                                                                                                                                          | `pip install "pipecat-ai[cartesia]"`    |
+| Speech-to-Speech    | [Gemini Multimodal Live](https://docs.pipecat.ai/server/services/s2s/gemini), [OpenAI Realtime](https://docs.pipecat.ai/server/services/s2s/openai)                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                     | `pip install "pipecat-ai[google]"`      |
+| Transport           | [Daily (WebRTC)](https://docs.pipecat.ai/server/services/transport/daily), [FastAPI Websocket](https://docs.pipecat.ai/server/services/transport/fastapi-websocket), [WebSocket Server](https://docs.pipecat.ai/server/services/transport/websocket-server), Local                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      | `pip install "pipecat-ai[daily]"`       |
+| Video               | [Tavus](https://docs.pipecat.ai/server/services/video/tavus), [Simli](https://docs.pipecat.ai/server/services/video/simli)                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              | `pip install "pipecat-ai[tavus,simli]"` |
+| Vision & Image      | [Moondream](https://docs.pipecat.ai/server/services/vision/moondream), [fal](https://docs.pipecat.ai/server/services/image-generation/fal)                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              | `pip install "pipecat-ai[moondream]"`   |
+| Audio Processing    | [Silero VAD](https://docs.pipecat.ai/server/utilities/audio/silero-vad-analyzer), [Krisp](https://docs.pipecat.ai/server/utilities/audio/krisp-filter), [Koala](https://docs.pipecat.ai/server/utilities/audio/koala-filter), [Noisereduce](https://docs.pipecat.ai/server/utilities/audio/noisereduce-filter)                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          | `pip install "pipecat-ai[silero]"`      |
+| Analytics & Metrics | [Canonical AI](https://docs.pipecat.ai/server/services/analytics/canonical), [Sentry](https://docs.pipecat.ai/server/services/analytics/sentry)                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                         | `pip install "pipecat-ai[canonical]"`   |
 
 📚 [View full services documentation →](https://docs.pipecat.ai/server/services/supported-services)
 
@@ -149,36 +149,40 @@ Sign up [here](https://dashboard.daily.co/u/signup) and [create a room](https://
 
 ## Hacking on the framework itself
 
-_Note that you may need to set up a virtual environment before following the instructions below. For instance, you might need to run the following from the root of the repo:_
+_Note: You may need to set up a virtual environment before following these instructions. From the root of the repo:_
 
 ```shell
 python3 -m venv venv
 source venv/bin/activate
 ```
 
-From the root of this repo, run the following:
+Install the development dependencies:
 
 ```shell
 pip install -r dev-requirements.txt
 ```
 
-This will install the necessary development dependencies. Also, make sure you install the git pre-commit hooks:
+Install the git pre-commit hooks (these help ensure your code follows project rules):
 
 ```shell
 pre-commit install
 ```
 
-The hooks will just save you time when you submit a PR by making sure your code follows the project rules.
-
-To use the package locally (e.g. to run sample files), run:
+Install the `pipecat-ai` package locally in editable mode:
 
 ```shell
-pip install --editable ".[option,...]"
+pip install -e .
 ```
 
-The `--editable` option makes sure you don't have to run `pip install` again and you can just edit the project files locally.
+The `-e` or `--editable` option allows you to modify the code without reinstalling.
 
-If you want to use this package from another directory, you can run:
+To include optional dependencies, add them to the install command. For example:
+
+```shell
+pip install -e ".[daily,deepgram,cartesia,openai,silero]"     # Updated for the services you're using
+```
+
+If you want to use this package from another directory:
 
 ```shell
 pip install "path_to_this_repo[option,...]"

dev-requirements.txt

@@ -1,11 +1,12 @@
 build~=1.2.2
-grpcio-tools~=1.69.0
+coverage~=7.6.12
+grpcio-tools~=1.67.1
 pip-tools~=7.4.1
 pre-commit~=4.0.1
-pyright~=1.1.392
+pyright~=1.1.393
 pytest~=8.3.4
 pytest-asyncio~=0.25.2
-ruff~=0.9.1
-setuptools~=75.8.0
+ruff~=0.9.5
+setuptools~=70.0.0
 setuptools_scm~=8.1.0
 python-dotenv~=1.0.1

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

@@ -12,7 +12,7 @@
         "@daily-co/daily-js": "0.74.0"
       },
       "devDependencies": {
-        "vite": "^6.0.2"
+        "vite": "^6.0.9"
       }
     },
     "node_modules/@babel/runtime": {
@@ -1007,15 +1007,14 @@
       }
     },
     "node_modules/vite": {
-      "version": "6.0.7",
-      "resolved": "https://registry.npmjs.org/vite/-/vite-6.0.7.tgz",
-      "integrity": "sha512-RDt8r/7qx9940f8FcOIAH9PTViRrghKaK2K1jY3RaAURrEUbm9Du1mJ72G+jlhtG3WwodnfzY8ORQZbBavZEAQ==",
+      "version": "6.1.0",
+      "resolved": "https://registry.npmjs.org/vite/-/vite-6.1.0.tgz",
+      "integrity": "sha512-RjjMipCKVoR4hVfPY6GQTgveinjNuyLw+qruksLDvA5ktI1150VmcMBKmQaEWJhg/j6Uaf6dNCNA0AfdzUb/hQ==",
       "dev": true,
-      "license": "MIT",
       "dependencies": {
         "esbuild": "^0.24.2",
-        "postcss": "^8.4.49",
-        "rollup": "^4.23.0"
+        "postcss": "^8.5.1",
+        "rollup": "^4.30.1"
       },
       "bin": {
         "vite": "bin/vite.js"

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

@@ -12,7 +12,7 @@
   "license": "ISC",
   "description": "",
   "devDependencies": {
-    "vite": "^6.0.2"
+    "vite": "^6.0.9"
   },
   "dependencies": {
     "@daily-co/daily-js": "0.74.0"

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

@@ -6,6 +6,7 @@
 
 import argparse
 import os
+from typing import Optional
 
 import aiohttp
 
@@ -18,7 +19,7 @@ async def configure(aiohttp_session: aiohttp.ClientSession):
 
 
 async def configure_with_args(
-    aiohttp_session: aiohttp.ClientSession, parser: argparse.ArgumentParser | None = None
+    aiohttp_session: aiohttp.ClientSession, parser: Optional[argparse.ArgumentParser] = None
 ):
     if not parser:
         parser = argparse.ArgumentParser(description="Daily AI SDK Bot Sample")

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

@@ -17,7 +17,7 @@ from runner import configure
 from pipecat.frames.frames import AudioRawFrame, EndFrame, OutputAudioRawFrame, TTSSpeakFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
-from pipecat.pipeline.task import PipelineTask
+from pipecat.pipeline.task import PipelineParams, PipelineTask
 from pipecat.services.cartesia import CartesiaTTSService
 from pipecat.transports.services.daily import DailyParams, DailyTransport
 
@@ -31,16 +31,15 @@ logger.add(sys.stderr, level="DEBUG")
 class SilenceFrame(OutputAudioRawFrame):
     def __init__(
         self,
-        audio: bytes = None,
-        sample_rate: int = 16000,
-        num_channels: int = 1,
-        duration: float = 0.1,
+        *,
+        sample_rate: int,
+        duration: float,
     ):
         # Initialize the parent class with the silent frame's data
         super().__init__(
-            audio=self.create_silent_audio_frame(sample_rate, num_channels, duration).audio,
+            audio=self.create_silent_audio_frame(sample_rate, 1, duration).audio,
             sample_rate=sample_rate,
-            num_channels=num_channels,
+            num_channels=1,
         )
 
     @staticmethod
@@ -80,7 +79,10 @@ async def main():
                 return
             await task.queue_frames(
                 [
-                    SilenceFrame(duration=0.5),
+                    SilenceFrame(
+                        sample_rate=task.params.audio_out_sample_rate,
+                        duration=0.5,
+                    ),
                     TTSSpeakFrame(f"Hello there, how are you doing today ?"),
                     EndFrame(),
                 ]

examples/canonical-metrics/bot.py

@@ -65,7 +65,6 @@ async def main():
             # English
             #
             voice_id="cgSgspJ2msm6clMCkdW9",
-            aiohttp_session=session,
             #
             # Spanish
             #
@@ -124,6 +123,7 @@ async def main():
 
         @transport.event_handler("on_first_participant_joined")
         async def on_first_participant_joined(transport, participant):
+            await audio_buffer_processor.start_recording()
             await transport.capture_participant_transcription(participant["id"])
             await task.queue_frames([context_aggregator.user().get_context_frame()])
 

examples/chatbot-audio-recording/bot.py

@@ -82,7 +82,6 @@ async def main():
             # English
             #
             voice_id="cgSgspJ2msm6clMCkdW9",
-            aiohttp_session=session,
             #
             # Spanish
             #
@@ -109,8 +108,9 @@ async def main():
         context = OpenAILLMContext(messages)
         context_aggregator = llm.create_context_aggregator(context)
 
-        # Save audio every 10 seconds.
-        audiobuffer = AudioBufferProcessor(buffer_size=480000)
+        # NOTE: Watch out! This will save all the conversation in memory. You
+        # can pass `buffer_size` to get periodic callbacks.
+        audiobuffer = AudioBufferProcessor()
 
         pipeline = Pipeline(
             [
@@ -132,6 +132,7 @@ async def main():
 
         @transport.event_handler("on_first_participant_joined")
         async def on_first_participant_joined(transport, participant):
+            await audiobuffer.start_recording()
             await transport.capture_participant_transcription(participant["id"])
             await task.queue_frames([context_aggregator.user().get_context_frame()])
 

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

@@ -16,8 +16,7 @@ from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineTask
 from pipecat.services.cartesia import CartesiaTTSService
-from pipecat.transports.base_transport import TransportParams
-from pipecat.transports.local.audio import LocalAudioTransport
+from pipecat.transports.local.audio import LocalAudioTransport, LocalTransportParams
 
 load_dotenv(override=True)
 
@@ -26,7 +25,7 @@ logger.add(sys.stderr, level="DEBUG")
 
 
 async def main():
-    transport = LocalAudioTransport(TransportParams(audio_out_enabled=True))
+    transport = LocalAudioTransport(LocalTransportParams(audio_out_enabled=True))
 
     tts = CartesiaTTSService(
         api_key=os.getenv("CARTESIA_API_KEY"),
@@ -41,7 +40,7 @@ async def main():
         await asyncio.sleep(1)
         await task.queue_frames([TTSSpeakFrame("Hello there, how is it going!"), EndFrame()])
 
-    runner = PipelineRunner()
+    runner = PipelineRunner(handle_sigint=False if sys.platform == "win32" else True)
 
     await asyncio.gather(runner.run(task), say_something())
 

examples/foundational/04-utterance-and-speech.py

@@ -51,7 +51,6 @@ async def main():
         )
 
         elevenlabs_tts = ElevenLabsTTSService(
-            aiohttp_session=session,
             api_key=os.getenv("ELEVENLABS_API_KEY"),
             voice_id=os.getenv("ELEVENLABS_VOICE_ID"),
         )

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

@@ -18,7 +18,7 @@ from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
 from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
-from pipecat.services.openai import OpenAILLMService, OpenAITTSService
+from pipecat.services.openai import OpenAILLMService, OpenAISTTService, OpenAITTSService
 from pipecat.transports.services.daily import DailyParams, DailyTransport
 
 load_dotenv(override=True)
@@ -38,12 +38,21 @@ async def main():
             DailyParams(
                 audio_out_enabled=True,
                 audio_out_sample_rate=24000,
-                transcription_enabled=True,
+                transcription_enabled=False,
                 vad_enabled=True,
                 vad_analyzer=SileroVADAnalyzer(),
+                vad_audio_passthrough=True,
             ),
         )
 
+        # You can use the OpenAI compatible API like Groq.
+        # stt = OpenAISTTService(
+        #     base_url="https://api.groq.com/openai/v1",
+        #     api_key="gsk_***",
+        #     model="whisper-large-v3",
+        # )
+        stt = OpenAISTTService(api_key=os.getenv("OPENAI_API_KEY"), model="whisper-1")
+
         tts = OpenAITTSService(api_key=os.getenv("OPENAI_API_KEY"), voice="alloy")
 
         llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
@@ -61,6 +70,7 @@ async def main():
         pipeline = Pipeline(
             [
                 transport.input(),  # Transport user input
+                stt,  # STT
                 context_aggregator.user(),  # User responses
                 llm,  # LLM
                 tts,  # TTS

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

@@ -38,7 +38,6 @@ async def main():
             "Respond bot",
             DailyParams(
                 audio_out_enabled=True,
-                audio_out_sample_rate=24000,
                 transcription_enabled=True,
                 vad_enabled=True,
                 vad_analyzer=SileroVADAnalyzer(),

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

@@ -40,7 +40,6 @@ async def main():
             "Respond bot",
             DailyParams(
                 audio_out_enabled=True,
-                audio_out_sample_rate=24000,
                 vad_enabled=True,
                 vad_analyzer=SileroVADAnalyzer(),
                 vad_audio_passthrough=True,

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

@@ -216,11 +216,7 @@ async def main():
             voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22",  # British Lady
         )
 
-        llm = GoogleLLMService(
-            model="gemini-1.5-flash-latest",
-            # model="gemini-exp-1114",
-            api_key=os.getenv("GOOGLE_API_KEY"),
-        )
+        llm = GoogleLLMService(api_key=os.getenv("GOOGLE_API_KEY"), model="gemini-2.0-flash-001")
 
         messages = [
             {

examples/foundational/08-bots-arguing.py

@@ -48,7 +48,6 @@ async def main():
             region=os.getenv("AZURE_SPEECH_REGION"),
         )
         tts2 = ElevenLabsTTSService(
-            aiohttp_session=session,
             api_key=os.getenv("ELEVENLABS_API_KEY"),
             voice_id="jBpfuIE2acCO8z3wKNLl",
         )

examples/foundational/09-mirror.py

@@ -21,7 +21,7 @@ from pipecat.frames.frames import (
 )
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
-from pipecat.pipeline.task import PipelineTask
+from pipecat.pipeline.task import PipelineParams, PipelineTask
 from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
 from pipecat.transports.services.daily import DailyParams, DailyTransport
 
@@ -61,7 +61,6 @@ async def main():
             "Test",
             DailyParams(
                 audio_in_enabled=True,
-                audio_in_sample_rate=24000,
                 audio_out_enabled=True,
                 camera_out_enabled=True,
                 camera_out_is_live=True,
@@ -78,7 +77,9 @@ async def main():
 
         runner = PipelineRunner()
 
-        task = PipelineTask(pipeline)
+        task = PipelineTask(
+            pipeline, PipelineParams(audio_in_sample_rate=24000, audio_out_sample_rate=24000)
+        )
 
         await runner.run(task)
 

examples/foundational/09a-local-mirror.py

@@ -22,7 +22,7 @@ from pipecat.frames.frames import (
 )
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
-from pipecat.pipeline.task import PipelineTask
+from pipecat.pipeline.task import PipelineParams, PipelineTask
 from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
 from pipecat.transports.base_transport import TransportParams
 from pipecat.transports.local.tk import TkLocalTransport
@@ -62,7 +62,7 @@ async def main():
         tk_root.title("Local Mirror")
 
         daily_transport = DailyTransport(
-            room_url, token, "Test", DailyParams(audio_in_enabled=True, audio_in_sample_rate=24000)
+            room_url, token, "Test", DailyParams(audio_in_enabled=True)
         )
 
         tk_transport = TkLocalTransport(
@@ -82,7 +82,9 @@ async def main():
 
         pipeline = Pipeline([daily_transport.input(), MirrorProcessor(), tk_transport.output()])
 
-        task = PipelineTask(pipeline)
+        task = PipelineTask(
+            pipeline, PipelineParams(audio_in_sample_rate=24000, audio_out_sample_rate=24000)
+        )
 
         async def run_tk():
             while not task.has_finished():

examples/foundational/12-describe-video.py

@@ -7,6 +7,7 @@
 import asyncio
 import os
 import sys
+from typing import Optional
 
 import aiohttp
 from dotenv import load_dotenv
@@ -32,7 +33,7 @@ logger.add(sys.stderr, level="DEBUG")
 
 
 class UserImageRequester(FrameProcessor):
-    def __init__(self, participant_id: str | None = None):
+    def __init__(self, participant_id: Optional[str] = None):
         super().__init__()
         self._participant_id = participant_id
 

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

@@ -7,6 +7,7 @@
 import asyncio
 import os
 import sys
+from typing import Optional
 
 import aiohttp
 from dotenv import load_dotenv
@@ -32,7 +33,7 @@ logger.add(sys.stderr, level="DEBUG")
 
 
 class UserImageRequester(FrameProcessor):
-    def __init__(self, participant_id: str | None = None):
+    def __init__(self, participant_id: Optional[str] = None):
         super().__init__()
         self._participant_id = participant_id
 
@@ -72,9 +73,7 @@ async def main():
 
         vision_aggregator = VisionImageFrameAggregator()
 
-        google = GoogleLLMService(
-            model="gemini-1.5-flash-latest", api_key=os.getenv("GOOGLE_API_KEY")
-        )
+        google = GoogleLLMService(model="gemini-2.0-flash-001", api_key=os.getenv("GOOGLE_API_KEY"))
 
         tts = CartesiaTTSService(
             api_key=os.getenv("CARTESIA_API_KEY"),

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

@@ -7,6 +7,7 @@
 import asyncio
 import os
 import sys
+from typing import Optional
 
 import aiohttp
 from dotenv import load_dotenv
@@ -32,7 +33,7 @@ logger.add(sys.stderr, level="DEBUG")
 
 
 class UserImageRequester(FrameProcessor):
-    def __init__(self, participant_id: str | None = None):
+    def __init__(self, participant_id: Optional[str] = None):
         super().__init__()
         self._participant_id = participant_id
 

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

@@ -7,6 +7,7 @@
 import asyncio
 import os
 import sys
+from typing import Optional
 
 import aiohttp
 from dotenv import load_dotenv
@@ -32,7 +33,7 @@ logger.add(sys.stderr, level="DEBUG")
 
 
 class UserImageRequester(FrameProcessor):
-    def __init__(self, participant_id: str | None = None):
+    def __init__(self, participant_id: Optional[str] = None):
         super().__init__()
         self._participant_id = participant_id
 

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

@@ -16,8 +16,7 @@ from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineTask
 from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
 from pipecat.services.whisper import WhisperSTTService
-from pipecat.transports.base_transport import TransportParams
-from pipecat.transports.local.audio import LocalAudioTransport
+from pipecat.transports.local.audio import LocalAudioTransport, LocalTransportParams
 
 load_dotenv(override=True)
 
@@ -34,7 +33,7 @@ class TranscriptionLogger(FrameProcessor):
 
 
 async def main():
-    transport = LocalAudioTransport(TransportParams(audio_in_enabled=True))
+    transport = LocalAudioTransport(LocalTransportParams(audio_in_enabled=True))
 
     stt = WhisperSTTService()
 
@@ -44,7 +43,7 @@ async def main():
 
     task = PipelineTask(pipeline)
 
-    runner = PipelineRunner()
+    runner = PipelineRunner(handle_sigint=False if sys.platform == "win32" else True)
 
     await runner.run(task)
 

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

@@ -62,11 +62,7 @@ async def main():
             voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22",  # British Lady
         )
 
-        llm = GoogleLLMService(
-            model="gemini-1.5-flash-latest",
-            # model="gemini-exp-1114",
-            api_key=os.getenv("GOOGLE_API_KEY"),
-        )
+        llm = GoogleLLMService(api_key=os.getenv("GOOGLE_API_KEY"), model="gemini-2.0-flash-001")
         llm.register_function("get_weather", get_weather)
         llm.register_function("get_image", get_image)
 

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

@@ -20,7 +20,7 @@ from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
 from pipecat.services.cartesia import CartesiaTTSService
-from pipecat.services.groq import GroqLLMService
+from pipecat.services.groq import GroqLLMService, GroqSTTService
 from pipecat.services.openai import OpenAILLMContext
 from pipecat.transports.services.daily import DailyParams, DailyTransport
 
@@ -50,20 +50,20 @@ async def main():
             "Respond bot",
             DailyParams(
                 audio_out_enabled=True,
-                transcription_enabled=True,
                 vad_enabled=True,
                 vad_analyzer=SileroVADAnalyzer(),
+                vad_audio_passthrough=True,
             ),
         )
 
+        stt = GroqSTTService(api_key=os.getenv("GROQ_API_KEY"), model="distil-whisper-large-v3-en")
+
         tts = CartesiaTTSService(
             api_key=os.getenv("CARTESIA_API_KEY"),
             voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22",  # British Lady
         )
 
-        llm = GroqLLMService(
-            api_key=os.getenv("GROQ_API_KEY"), model="llama3-groq-70b-8192-tool-use-preview"
-        )
+        llm = GroqLLMService(api_key=os.getenv("GROQ_API_KEY"), model="llama-3.3-70b-versatile")
         # Register a function_name of None to get all functions
         # sent to the same callback with an additional function_name parameter.
         llm.register_function(None, fetch_weather_from_api, start_callback=start_fetch_weather)
@@ -105,6 +105,7 @@ async def main():
         pipeline = Pipeline(
             [
                 transport.input(),
+                stt,
                 context_aggregator.user(),
                 llm,
                 tts,

examples/foundational/14n-function-calling-perplexity.py

@@ -0,0 +1,106 @@
+#
+# Copyright (c) 2024–2025, Daily
+#
+# SPDX-License-Identifier: BSD 2-Clause License
+#
+
+"""This example demonstrates using the Perplexity API as a drop-in replacement for OpenAI.
+
+Note that while this file is in the function-calling examples, Perplexity's API does not
+currently support function calling. The example shows basic chat completion functionality
+using Perplexity's API while maintaining compatibility with the OpenAI interface.
+"""
+
+import asyncio
+import os
+import sys
+
+import aiohttp
+from dotenv import load_dotenv
+from loguru import logger
+from openai.types.chat import ChatCompletionToolParam
+from runner import configure
+
+from pipecat.audio.vad.silero import SileroVADAnalyzer
+from pipecat.frames.frames import TTSSpeakFrame
+from pipecat.pipeline.pipeline import Pipeline
+from pipecat.pipeline.runner import PipelineRunner
+from pipecat.pipeline.task import PipelineParams, PipelineTask
+from pipecat.services.cartesia import CartesiaTTSService
+from pipecat.services.openai import OpenAILLMContext, OpenAILLMService
+from pipecat.services.perplexity import PerplexityLLMService
+from pipecat.transports.services.daily import DailyParams, DailyTransport
+
+load_dotenv(override=True)
+
+logger.remove(0)
+logger.add(sys.stderr, level="DEBUG")
+
+
+async def main():
+    async with aiohttp.ClientSession() as session:
+        (room_url, token) = await configure(session)
+
+        transport = DailyTransport(
+            room_url,
+            token,
+            "Respond bot",
+            DailyParams(
+                audio_out_enabled=True,
+                transcription_enabled=True,
+                vad_enabled=True,
+                vad_analyzer=SileroVADAnalyzer(),
+            ),
+        )
+
+        tts = CartesiaTTSService(
+            api_key=os.getenv("CARTESIA_API_KEY"),
+            voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22",  # British Lady
+        )
+
+        llm = PerplexityLLMService(api_key=os.getenv("PERPLEXITY_API_KEY"), model="sonar")
+
+        messages = [
+            {
+                "role": "user",
+                "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,
+            PipelineParams(
+                allow_interruptions=True,
+                enable_metrics=True,
+                enable_usage_metrics=True,
+                report_only_initial_ttfb=True,
+            ),
+        )
+
+        @transport.event_handler("on_first_participant_joined")
+        async def on_first_participant_joined(transport, participant):
+            await transport.capture_participant_transcription(participant["id"])
+            # Kick off the conversation.
+            await task.queue_frames([context_aggregator.user().get_context_frame()])
+
+        runner = PipelineRunner()
+
+        await runner.run(task)
+
+
+if __name__ == "__main__":
+    asyncio.run(main())

examples/foundational/18-gstreamer-filesrc.py

@@ -51,8 +51,6 @@ async def main():
             out_params=GStreamerPipelineSource.OutputParams(
                 video_width=1280,
                 video_height=720,
-                audio_sample_rate=24000,
-                audio_channels=1,
             ),
         )
 

examples/foundational/19-openai-realtime-beta.py

@@ -80,9 +80,7 @@ async def main():
             "Respond bot",
             DailyParams(
                 audio_in_enabled=True,
-                audio_in_sample_rate=24000,
                 audio_out_enabled=True,
-                audio_out_sample_rate=24000,
                 transcription_enabled=False,
                 vad_enabled=True,
                 vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.8)),

examples/foundational/20b-persistent-context-openai-realtime.py

@@ -177,9 +177,7 @@ async def main():
             "Respond bot",
             DailyParams(
                 audio_in_enabled=True,
-                audio_in_sample_rate=24000,
                 audio_out_enabled=True,
-                audio_out_sample_rate=24000,
                 transcription_enabled=False,
                 vad_enabled=True,
                 vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.8)),

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

@@ -237,7 +237,7 @@ async def main():
             voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22",  # British Lady
         )
 
-        llm = GoogleLLMService(model="gemini-1.5-flash-latest", api_key=os.getenv("GOOGLE_API_KEY"))
+        llm = GoogleLLMService(model="gemini-2.0-flash-001", api_key=os.getenv("GOOGLE_API_KEY"))
 
         # you can either register a single function for all function calls, or specific functions
         # llm.register_function(None, fetch_weather_from_api)

examples/foundational/21-tavus-layer.py

@@ -88,6 +88,10 @@ async def main():
         task = PipelineTask(
             pipeline,
             PipelineParams(
+                # We just use 16000 because that's what Tavus is expecting and
+                # we avoid resampling.
+                audio_in_sample_rate=16000,
+                audio_out_sample_rate=16000,
                 allow_interruptions=True,
                 enable_metrics=True,
                 enable_usage_metrics=True,

examples/foundational/22-natural-conversation.py

@@ -104,8 +104,11 @@ async def main():
         )
 
         # This processor keeps the last context and will let it through once the
-        # notifier is woken up.
-        gated_context_aggregator = GatedOpenAILLMContextAggregator(notifier)
+        # notifier is woken up. We start with the gate open because we send an
+        # initial context frame to start the conversation.
+        gated_context_aggregator = GatedOpenAILLMContextAggregator(
+            notifier=notifier, start_open=True
+        )
 
         # Notify if the user hasn't said anything.
         async def user_idle_notifier(frame):

examples/foundational/22b-natural-conversation-proposal.py

@@ -12,6 +12,7 @@ import time
 import aiohttp
 from dotenv import load_dotenv
 from loguru import logger
+from openai.types.chat import ChatCompletionToolParam
 from runner import configure
 
 from pipecat.audio.vad.silero import SileroVADAnalyzer
@@ -19,6 +20,8 @@ from pipecat.frames.frames import (
     CancelFrame,
     EndFrame,
     Frame,
+    FunctionCallInProgressFrame,
+    FunctionCallResultFrame,
     LLMMessagesFrame,
     StartFrame,
     StartInterruptionFrame,
@@ -26,6 +29,7 @@ from pipecat.frames.frames import (
     SystemFrame,
     TextFrame,
     TranscriptionFrame,
+    TTSSpeakFrame,
     UserStartedSpeakingFrame,
     UserStoppedSpeakingFrame,
 )
@@ -129,9 +133,9 @@ class CompletenessCheck(FrameProcessor):
 
 
 class OutputGate(FrameProcessor):
-    def __init__(self, notifier: BaseNotifier, **kwargs):
+    def __init__(self, *, notifier: BaseNotifier, start_open: bool = False, **kwargs):
         super().__init__(**kwargs)
-        self._gate_open = False
+        self._gate_open = start_open
         self._frames_buffer = []
         self._notifier = notifier
 
@@ -156,6 +160,11 @@ class OutputGate(FrameProcessor):
             await self.push_frame(frame, direction)
             return
 
+        # Don't block function call frames
+        if isinstance(frame, (FunctionCallInProgressFrame, FunctionCallResultFrame)):
+            await self.push_frame(frame, direction)
+            return
+
         # Ignore frames that are not following the direction of this gate.
         if direction != FrameDirection.DOWNSTREAM:
             await self.push_frame(frame, direction)
@@ -186,6 +195,16 @@ class OutputGate(FrameProcessor):
                 break
 
 
+async def start_fetch_weather(function_name, llm, context):
+    """Push a frame to the LLM; this is handy when the LLM response might take a while."""
+    await llm.push_frame(TTSSpeakFrame("Let me check on that."))
+    logger.debug(f"Starting fetch_weather_from_api with function_name: {function_name}")
+
+
+async def fetch_weather_from_api(function_name, tool_call_id, args, llm, context, result_callback):
+    await result_callback({"conditions": "nice", "temperature": "75"})
+
+
 async def main():
     async with aiohttp.ClientSession() as session:
         (room_url, _) = await configure(session)
@@ -216,6 +235,34 @@ async def main():
 
         # This is the regular LLM.
         llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
+        # Register a function_name of None to get all functions
+        # sent to the same callback with an additional function_name parameter.
+        llm.register_function(None, fetch_weather_from_api, start_callback=start_fetch_weather)
+
+        tools = [
+            ChatCompletionToolParam(
+                type="function",
+                function={
+                    "name": "get_current_weather",
+                    "description": "Get the current weather",
+                    "parameters": {
+                        "type": "object",
+                        "properties": {
+                            "location": {
+                                "type": "string",
+                                "description": "The city and state, e.g. San Francisco, CA",
+                            },
+                            "format": {
+                                "type": "string",
+                                "enum": ["celsius", "fahrenheit"],
+                                "description": "The temperature unit to use. Infer this from the users location.",
+                            },
+                        },
+                        "required": ["location", "format"],
+                    },
+                },
+            )
+        ]
 
         messages = [
             {
@@ -224,7 +271,7 @@ async def main():
             },
         ]
 
-        context = OpenAILLMContext(messages)
+        context = OpenAILLMContext(messages, tools)
         context_aggregator = llm.create_context_aggregator(context)
 
         # We have instructed the LLM to return 'YES' if it thinks the user
@@ -252,7 +299,9 @@ async def main():
         # sentence, this will wake up the notifier if that happens.
         user_idle = UserIdleProcessor(callback=user_idle_notifier, timeout=5.0)
 
-        bot_output_gate = OutputGate(notifier=notifier)
+        # We start with the gate open because we send an initial context frame
+        # to start the conversation.
+        bot_output_gate = OutputGate(notifier=notifier, start_open=True)
 
         async def block_user_stopped_speaking(frame):
             return not isinstance(frame, UserStoppedSpeakingFrame)
@@ -263,6 +312,8 @@ async def main():
                 or isinstance(frame, LLMMessagesFrame)
                 or isinstance(frame, StartInterruptionFrame)
                 or isinstance(frame, StopInterruptionFrame)
+                or isinstance(frame, FunctionCallInProgressFrame)
+                or isinstance(frame, FunctionCallResultFrame)
             )
 
         pipeline = Pipeline(

examples/foundational/22c-natural-conversation-mixed-llms.py

@@ -12,6 +12,7 @@ import time
 import aiohttp
 from dotenv import load_dotenv
 from loguru import logger
+from openai.types.chat import ChatCompletionToolParam
 from runner import configure
 
 from pipecat.audio.vad.silero import SileroVADAnalyzer
@@ -19,6 +20,8 @@ from pipecat.frames.frames import (
     CancelFrame,
     EndFrame,
     Frame,
+    FunctionCallInProgressFrame,
+    FunctionCallResultFrame,
     LLMMessagesFrame,
     StartFrame,
     StartInterruptionFrame,
@@ -26,6 +29,7 @@ from pipecat.frames.frames import (
     SystemFrame,
     TextFrame,
     TranscriptionFrame,
+    TTSSpeakFrame,
     UserStartedSpeakingFrame,
     UserStoppedSpeakingFrame,
 )
@@ -333,9 +337,9 @@ class CompletenessCheck(FrameProcessor):
 
 
 class OutputGate(FrameProcessor):
-    def __init__(self, notifier: BaseNotifier, **kwargs):
+    def __init__(self, *, notifier: BaseNotifier, start_open: bool = False, **kwargs):
         super().__init__(**kwargs)
-        self._gate_open = False
+        self._gate_open = start_open
         self._frames_buffer = []
         self._notifier = notifier
 
@@ -360,6 +364,11 @@ class OutputGate(FrameProcessor):
             await self.push_frame(frame, direction)
             return
 
+        # Don't block function call frames
+        if isinstance(frame, (FunctionCallInProgressFrame, FunctionCallResultFrame)):
+            await self.push_frame(frame, direction)
+            return
+
         # Ignore frames that are not following the direction of this gate.
         if direction != FrameDirection.DOWNSTREAM:
             await self.push_frame(frame, direction)
@@ -390,6 +399,16 @@ class OutputGate(FrameProcessor):
                 break
 
 
+async def start_fetch_weather(function_name, llm, context):
+    """Push a frame to the LLM; this is handy when the LLM response might take a while."""
+    await llm.push_frame(TTSSpeakFrame("Let me check on that."))
+    logger.debug(f"Starting fetch_weather_from_api with function_name: {function_name}")
+
+
+async def fetch_weather_from_api(function_name, tool_call_id, args, llm, context, result_callback):
+    await result_callback({"conditions": "nice", "temperature": "75"})
+
+
 async def main():
     async with aiohttp.ClientSession() as session:
         (room_url, _) = await configure(session)
@@ -426,6 +445,34 @@ async def main():
             api_key=os.getenv("OPENAI_API_KEY"),
             model="gpt-4o",
         )
+        # Register a function_name of None to get all functions
+        # sent to the same callback with an additional function_name parameter.
+        llm.register_function(None, fetch_weather_from_api, start_callback=start_fetch_weather)
+
+        tools = [
+            ChatCompletionToolParam(
+                type="function",
+                function={
+                    "name": "get_current_weather",
+                    "description": "Get the current weather",
+                    "parameters": {
+                        "type": "object",
+                        "properties": {
+                            "location": {
+                                "type": "string",
+                                "description": "The city and state, e.g. San Francisco, CA",
+                            },
+                            "format": {
+                                "type": "string",
+                                "enum": ["celsius", "fahrenheit"],
+                                "description": "The temperature unit to use. Infer this from the users location.",
+                            },
+                        },
+                        "required": ["location", "format"],
+                    },
+                },
+            )
+        ]
 
         messages = [
             {
@@ -434,7 +481,7 @@ async def main():
             },
         ]
 
-        context = OpenAILLMContext(messages)
+        context = OpenAILLMContext(messages, tools)
         context_aggregator = llm.create_context_aggregator(context)
 
         # We have instructed the LLM to return 'YES' if it thinks the user
@@ -461,7 +508,9 @@ async def main():
         # sentence, this will wake up the notifier if that happens.
         user_idle = UserIdleProcessor(callback=user_idle_notifier, timeout=5.0)
 
-        bot_output_gate = OutputGate(notifier=notifier)
+        # We start with the gate open because we send an initial context frame
+        # to start the conversation.
+        bot_output_gate = OutputGate(notifier=notifier, start_open=True)
 
         async def block_user_stopped_speaking(frame):
             return not isinstance(frame, UserStoppedSpeakingFrame)
@@ -472,6 +521,8 @@ async def main():
                 or isinstance(frame, LLMMessagesFrame)
                 or isinstance(frame, StartInterruptionFrame)
                 or isinstance(frame, StopInterruptionFrame)
+                or isinstance(frame, FunctionCallInProgressFrame)
+                or isinstance(frame, FunctionCallResultFrame)
             )
 
         pipeline = Pipeline(

examples/foundational/22d-natural-conversation-gemini-audio.py

@@ -20,6 +20,8 @@ from pipecat.frames.frames import (
     CancelFrame,
     EndFrame,
     Frame,
+    FunctionCallInProgressFrame,
+    FunctionCallResultFrame,
     InputAudioRawFrame,
     LLMFullResponseEndFrame,
     LLMFullResponseStartFrame,
@@ -55,13 +57,9 @@ load_dotenv(override=True)
 logger.remove(0)
 logger.add(sys.stderr, level="DEBUG")
 
-# TRANSCRIBER_MODEL = "gemini-1.5-flash-latest"
-# CLASSIFIER_MODEL = "gemini-1.5-flash-latest"
-# CONVERSATION_MODEL = "gemini-1.5-flash-latest"
-
-TRANSCRIBER_MODEL = "gemini-2.0-flash-exp"
-CLASSIFIER_MODEL = "gemini-2.0-flash-exp"
-CONVERSATION_MODEL = "gemini-2.0-flash-exp"
+TRANSCRIBER_MODEL = "gemini-2.0-flash-001"
+CLASSIFIER_MODEL = "gemini-2.0-flash-001"
+CONVERSATION_MODEL = "gemini-2.0-flash-001"
 
 transcriber_system_instruction = """You are an audio transcriber. You are receiving audio from a user. Your job is to
 transcribe the input audio to text exactly as it was said by the user.
@@ -579,6 +577,11 @@ class OutputGate(FrameProcessor):
             await self.push_frame(frame, direction)
             return
 
+        # Don't block function call frames
+        if isinstance(frame, (FunctionCallInProgressFrame, FunctionCallResultFrame)):
+            await self.push_frame(frame, direction)
+            return
+
         # Ignore frames that are not following the direction of this gate.
         if direction != FrameDirection.DOWNSTREAM:
             await self.push_frame(frame, direction)
@@ -639,7 +642,6 @@ async def main():
                 vad_enabled=True,
                 vad_analyzer=SileroVADAnalyzer(),
                 vad_audio_passthrough=True,
-                audio_in_sample_rate=16000,
             ),
         )
 
@@ -677,12 +679,6 @@ async def main():
         context = OpenAILLMContext()
         context_aggregator = conversation_llm.create_context_aggregator(context)
 
-        # We have instructed the LLM to return 'True' if it thinks the user
-        # completed a sentence. So, if it's 'True' we will return true in this
-        # predicate which will wake up the notifier.
-        async def wake_check_filter(frame):
-            return frame.text == "True"
-
         # This is a notifier that we use to synchronize the two LLMs.
         notifier = EventNotifier()
 
@@ -699,14 +695,6 @@ async def main():
         async def block_user_stopped_speaking(frame):
             return not isinstance(frame, UserStoppedSpeakingFrame)
 
-        async def pass_only_llm_trigger_frames(frame):
-            return (
-                isinstance(frame, OpenAILLMContextFrame)
-                or isinstance(frame, LLMMessagesFrame)
-                or isinstance(frame, StartInterruptionFrame)
-                or isinstance(frame, StopInterruptionFrame)
-            )
-
         conversation_audio_context_assembler = ConversationAudioContextAssembler(context=context)
 
         user_aggregator_buffer = UserAggregatorBuffer()

examples/foundational/24-stt-mute-filter.py

@@ -61,7 +61,6 @@ async def main():
         stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
         # Configure the mute processor with both strategies
         stt_mute_processor = STTMuteFilter(
-            stt_service=stt,
             config=STTMuteConfig(
                 strategies={STTMuteStrategy.FIRST_SPEECH, STTMuteStrategy.FUNCTION_CALL}
             ),

examples/foundational/25-google-audio-in.py

@@ -292,7 +292,7 @@ async def main():
 
         conversation_llm = GoogleLLMService(
             name="Conversation",
-            model="gemini-1.5-flash-latest",
+            model="gemini-2.0-flash-001",
             # model="gemini-exp-1121",
             api_key=os.getenv("GOOGLE_API_KEY"),
             # we can give the GoogleLLMService a system instruction to use directly
@@ -303,7 +303,7 @@ async def main():
 
         input_transcription_llm = GoogleLLMService(
             name="Transcription",
-            model="gemini-1.5-flash-latest",
+            model="gemini-2.0-flash-001",
             # model="gemini-exp-1121",
             api_key=os.getenv("GOOGLE_API_KEY"),
             system_instruction=transcriber_system_message,

examples/foundational/26-gemini-multimodal-live.py

@@ -37,8 +37,6 @@ async def main():
             token,
             "Respond bot",
             DailyParams(
-                audio_in_sample_rate=16000,
-                audio_out_sample_rate=24000,
                 audio_out_enabled=True,
                 vad_enabled=True,
                 vad_audio_passthrough=True,

examples/foundational/26a-gemini-multimodal-live-transcription.py

@@ -37,8 +37,6 @@ async def main():
             token,
             "Respond bot",
             DailyParams(
-                audio_in_sample_rate=16000,
-                audio_out_sample_rate=24000,
                 audio_out_enabled=True,
                 vad_enabled=True,
                 vad_audio_passthrough=True,

examples/foundational/26b-gemini-multimodal-live-function-calling.py

@@ -84,8 +84,6 @@ async def main():
             token,
             "Respond bot",
             DailyParams(
-                audio_in_sample_rate=16000,
-                audio_out_sample_rate=24000,
                 audio_out_enabled=True,
                 vad_enabled=True,
                 vad_audio_passthrough=True,

examples/foundational/26c-gemini-multimodal-live-video.py

@@ -37,8 +37,6 @@ async def main():
             token,
             "Respond bot",
             DailyParams(
-                audio_in_sample_rate=16000,
-                audio_out_sample_rate=24000,
                 audio_out_enabled=True,
                 vad_enabled=True,
                 vad_audio_passthrough=True,
@@ -47,8 +45,6 @@ async def main():
                 # matter because we can only use the Multimodal Live API's phrase
                 # endpointing, for now.
                 vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.5)),
-                start_audio_paused=True,
-                start_video_paused=True,
             ),
         )
 

examples/foundational/26d-gemini-multimodal-live-text.py

@@ -52,8 +52,6 @@ async def main():
             token,
             "Respond bot",
             DailyParams(
-                audio_in_sample_rate=16000,
-                audio_out_sample_rate=24000,
                 audio_out_enabled=True,
                 vad_enabled=True,
                 vad_audio_passthrough=True,

examples/foundational/28c-transcription-processor-gemini.py

@@ -6,6 +6,7 @@
 
 import asyncio
 import os
+import sqlite3
 import sys
 from typing import List, Optional
 
@@ -44,22 +45,33 @@ class TranscriptHandler:
         output_file: Optional path to file where transcript is saved. If None, outputs to log only.
     """
 
-    def __init__(self, output_file: Optional[str] = None):
-        """Initialize handler with optional file output.
+    def __init__(self, output_file: Optional[str] = None, output_db: Optional[str] = None):
+        """Initialize handler with optional file or database output.
 
         Args:
             output_file: Path to output file. If None, outputs to log only.
         """
         self.messages: List[TranscriptionMessage] = []
         self.output_file: Optional[str] = output_file
+        self.output_db: Optional[str] = output_db
+
+        if self.output_db:
+            self.con = sqlite3.connect("example.db")
+            self.db = self.con.cursor()
+
+            table = self.db.execute("SELECT name FROM sqlite_master WHERE name='messages'")
+            if not (table.fetchone()):
+                self.db.execute(
+                    "CREATE TABLE messages(role TEXT, content TEXT, timestamp DATETIME DEFAULT CURRENT_TIMESTAMP )"
+                )
         logger.debug(
-            f"TranscriptHandler initialized {'with output_file=' + output_file if output_file else 'with log output only'}"
+            f"TranscriptHandler initialized; output file: {output_file}, output DB: {output_db}"
         )
 
     async def save_message(self, message: TranscriptionMessage):
         """Save a single transcript message.
 
-        Outputs the message to the log and optionally to a file.
+        Outputs the message to the log and optionally to a SQLite database or file.
 
         Args:
             message: The message to save
@@ -78,6 +90,14 @@ class TranscriptHandler:
             except Exception as e:
                 logger.error(f"Error saving transcript message to file: {e}")
 
+        # and/or to a SQLite database
+        if self.output_db:
+            self.db.execute(
+                "INSERT INTO messages VALUES (?, ?, ?)",
+                (message.role, message.content, message.timestamp),
+            )
+            self.con.commit()
+
     async def on_transcript_update(
         self, processor: TranscriptProcessor, frame: TranscriptionUpdateFrame
     ):
@@ -136,8 +156,11 @@ async def main():
 
         # Create transcript processor and handler
         transcript = TranscriptProcessor()
+        # Select a TranscriptHandler output method
+        # Uncomment out only one of the following lines:
         transcript_handler = TranscriptHandler()  # Output to log only
         # transcript_handler = TranscriptHandler(output_file="transcript.txt") # Output to file and log
+        # transcript_handler = TranscriptHandler(output_db="example.db")  # Output to SQLite DB and log
 
         pipeline = Pipeline(
             [

examples/foundational/29-livekit-audio-chat.py

@@ -38,8 +38,6 @@ load_dotenv(override=True)
 logger.remove(0)
 logger.add(sys.stderr, level="DEBUG")
 
-DESIRED_SAMPLE_RATE = 16000
-
 
 def generate_token(room_name: str, participant_name: str, api_key: str, api_secret: str) -> str:
     token = api.AccessToken(api_key, api_secret)
@@ -114,11 +112,8 @@ async def main():
             token=token,
             room_name=room_name,
             params=LiveKitParams(
-                audio_in_channels=1,
                 audio_in_enabled=True,
                 audio_out_enabled=True,
-                audio_in_sample_rate=DESIRED_SAMPLE_RATE,
-                audio_out_sample_rate=DESIRED_SAMPLE_RATE,
                 vad_analyzer=SileroVADAnalyzer(),
                 vad_enabled=True,
                 vad_audio_passthrough=True,
@@ -128,7 +123,6 @@ async def main():
         stt = DeepgramSTTService(
             api_key=os.getenv("DEEPGRAM_API_KEY"),
             live_options=LiveOptions(
-                sample_rate=DESIRED_SAMPLE_RATE,
                 vad_events=True,
             ),
         )
@@ -138,7 +132,6 @@ async def main():
         tts = CartesiaTTSService(
             api_key=os.getenv("CARTESIA_API_KEY"),
             voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22",  # British Lady
-            sample_rate=DESIRED_SAMPLE_RATE,
         )
 
         messages = [

examples/foundational/32-gemini-grounding-metadata.py

@@ -89,6 +89,7 @@ async def main():
             api_key=os.getenv("GOOGLE_API_KEY"),
             system_instruction=system_instruction,
             tools=tools,
+            model="gemini-1.5-flash-002",
         )
 
         context = OpenAILLMContext(

examples/foundational/33-gemini-rag.py

@@ -0,0 +1,254 @@
+#
+# Copyright (c) 2024–2025, Daily
+#
+# SPDX-License-Identifier: BSD 2-Clause License
+#
+"""CrossFit Games 2025 Rulebook RAG Demo.
+
+This example demonstrates a Model-Assisted Generation (MAG) chatbot using Google's Gemini model.
+This example uses 2 Gemini models:
+- Gemini 2.0 Flash: This is the voice model that is used to generate the response.
+- Gemini 2.0 Flash Lite: This is the model that is used to answer questions about the CrossFit Games 2025 rulebook - information that isn't yet publicly
+indexed by Gemini (or any other LLM).
+
+How it works:
+- The voice model (Gemini 2.0 Flash) is configured to call a function whenever the user asks a question.
+- The function call is a tool call to the MAG model (Gemini 2.0 Flash Lite).
+- The MAG model generates a response based on the question. The MAG model has the entire contents of the CrossFit Games 2025 rulebook in it's context window.
+- The response is returned to the voice model (Gemini 2.0 Flash), which then generates the response to the user.
+
+Why this works:
+- Gemini 2.0 Flash is fast
+- Gemini 2.0 Flash Lite is faster
+- Gemini 2.0 Flash Lite has a large (1 million tokens) context window
+- IMPORTANT: The generated response from Gemini 2.0 Flash Lite is limited to 50 words or less and 64 tokens.
+You can see this in the RAG_PROMPT variable and the generation_config in the query_knowledge_base function.
+Long generations are slower and more expensive, in the world of Voice AI, we don't need long generations.
+
+Example questions to ask and compare to other RAG solutions:
+- What lenses are not allowed?
+- How many people can be on a team?
+- What do winning gyms get?
+- What happens if I skip a workout?
+- Can I switch my team members for the Games?
+- What happens if I start too early?
+
+Notes:
+- The RAG model is Gemini 2.0 Flash Lite.
+- The voice model is Gemini 2.0 Flash.
+- The RAG content is stored in the assets/rag-content.txt file.
+- The model for voice is Gemini 2.0 Flash, but can be easily switched to any other model.
+
+Customization options:
+- update assets/rag-content.txt with your own knowledge base
+- increase/decrease the RAG_MODEL's generation length
+- use a different voice model
+- play with the RAG_PROMPT
+- change the function calling logic
+"""
+
+import asyncio
+import json
+import os
+import sys
+import time
+
+import aiohttp
+import google.generativeai as genai
+from dotenv import load_dotenv
+from loguru import logger
+from runner import configure
+
+from pipecat.audio.vad.silero import SileroVADAnalyzer
+from pipecat.pipeline.pipeline import Pipeline
+from pipecat.pipeline.runner import PipelineRunner
+from pipecat.pipeline.task import PipelineParams, PipelineTask
+from pipecat.services.cartesia import CartesiaTTSService
+from pipecat.services.google import GoogleLLMService
+from pipecat.services.openai import OpenAILLMContext
+from pipecat.transports.services.daily import DailyParams, DailyTransport
+
+load_dotenv(override=True)
+
+logger.remove(0)
+logger.add(sys.stderr, level="INFO")
+
+video_participant_id = None
+
+
+def get_rag_content():
+    """Get the RAG content from the file."""
+    script_dir = os.path.dirname(os.path.abspath(__file__))
+    rag_content_path = os.path.join(script_dir, "assets", "rag-content.txt")
+    with open(rag_content_path, "r") as f:
+        return f.read()
+
+
+RAG_MODEL = "gemini-2.0-flash-lite-preview-02-05"
+VOICE_MODEL = "gemini-2.0-flash"
+RAG_CONTENT = get_rag_content()
+RAG_PROMPT = f"""
+You are a helpful assistant designed to answer user questions based solely on the provided knowledge base.
+
+**Instructions:**
+
+1.  **Knowledge Base Only:** Answer questions *exclusively* using the information in the "Knowledge Base" section below. Do not use any outside information.
+2.  **Conversation History:** Use the "Conversation History" (ordered oldest to newest) to understand the context of the current question.
+3.  **Concise Response:**  Respond in 50 words or fewer.  The response will be spoken, so avoid symbols, abbreviations, or complex formatting. Use plain, natural language.
+4.  **Unknown Answer:** If the answer is not found within the "Knowledge Base," respond with "I don't know." Do not guess or make up an answer.
+5. Do not introduce your response. Just provide the answer.
+6. You must follow all instructions.
+
+**Input Format:**
+
+Each request will include:
+
+*   **Conversation History:**  (A list of previous user and assistant messages, if any)
+
+**Knowledge Base:**
+Here is the knowledge base you have access to:
+{RAG_CONTENT}
+"""
+genai.configure(api_key=os.environ["GOOGLE_API_KEY"])
+
+
+async def query_knowledge_base(
+    function_name, tool_call_id, arguments, llm, context, result_callback
+):
+    """Query the knowledge base for the answer to the question."""
+    logger.info(f"Querying knowledge base for question: {arguments['question']}")
+    client = genai.GenerativeModel(
+        model_name=RAG_MODEL,
+        system_instruction=RAG_PROMPT,
+        generation_config=genai.types.GenerationConfig(
+            temperature=0.1,
+            max_output_tokens=64,
+        ),
+    )
+    # for our case, the first two messages are the instructions and the user message
+    # so we remove them.
+    conversation_turns = context.messages[2:]
+    # convert to standard messages
+    messages = []
+    for turn in conversation_turns:
+        messages.extend(context.to_standard_messages(turn))
+
+    def _is_tool_call(turn):
+        if turn.get("role", None) == "tool":
+            return True
+        if turn.get("tool_calls", None):
+            return True
+        return False
+
+    # filter out tool calls
+    messages = [turn for turn in messages if not _is_tool_call(turn)]
+    # use the last 3 turns as the conversation history/context
+    messages = messages[-3:]
+    messages_json = json.dumps(messages, ensure_ascii=False, indent=2)
+
+    logger.info(f"Conversation turns: {messages_json}")
+
+    start = time.perf_counter()
+    response = client.generate_content(
+        contents=[messages_json],
+    )
+    end = time.perf_counter()
+    logger.info(f"Time taken: {end - start:.2f} seconds")
+    logger.info(response.text)
+    await result_callback(response.text)
+
+
+async def main():
+    async with aiohttp.ClientSession() as session:
+        (room_url, token) = await configure(session)
+
+        transport = DailyTransport(
+            room_url,
+            token,
+            "Gemini RAG Bot",
+            DailyParams(
+                audio_out_enabled=True,
+                transcription_enabled=True,
+                vad_enabled=True,
+                vad_analyzer=SileroVADAnalyzer(),
+            ),
+        )
+
+        tts = CartesiaTTSService(
+            api_key=os.getenv("CARTESIA_API_KEY"),
+            voice_id="f9836c6e-a0bd-460e-9d3c-f7299fa60f94",  # Southern Lady
+        )
+
+        llm = GoogleLLMService(
+            model=VOICE_MODEL,
+            api_key=os.getenv("GOOGLE_API_KEY"),
+        )
+        llm.register_function("query_knowledge_base", query_knowledge_base)
+        tools = [
+            {
+                "function_declarations": [
+                    {
+                        "name": "query_knowledge_base",
+                        "description": "Query the knowledge base for the answer to the question.",
+                        "parameters": {
+                            "type": "object",
+                            "properties": {
+                                "question": {
+                                    "type": "string",
+                                    "description": "The question to query the knowledge base with.",
+                                },
+                            },
+                        },
+                    },
+                ],
+            },
+        ]
+        system_prompt = """\
+You are a helpful assistant who converses with a user and answers questions.
+
+You have access to the tool, query_knowledge_base, that allows you to query the knowledge base for the answer to the user's question.
+
+Your response will be turned into speech so use only simple words and punctuation.
+"""
+        messages = [
+            {"role": "system", "content": system_prompt},
+            {"role": "user", "content": "Greet the user."},
+        ]
+
+        context = OpenAILLMContext(messages, tools)
+        context_aggregator = llm.create_context_aggregator(context)
+
+        pipeline = Pipeline(
+            [
+                transport.input(),
+                context_aggregator.user(),
+                llm,
+                tts,
+                transport.output(),
+                context_aggregator.assistant(),
+            ]
+        )
+        task = PipelineTask(
+            pipeline,
+            PipelineParams(
+                allow_interruptions=True,
+                enable_metrics=True,
+                enable_usage_metrics=True,
+            ),
+        )
+
+        @transport.event_handler("on_first_participant_joined")
+        async def on_first_participant_joined(transport, participant):
+            global video_participant_id
+            video_participant_id = participant["id"]
+            await transport.capture_participant_transcription(participant["id"])
+            await transport.capture_participant_video(video_participant_id, framerate=0)
+            # Kick off the conversation.
+            await task.queue_frames([context_aggregator.user().get_context_frame()])
+
+        runner = PipelineRunner()
+        await runner.run(task)
+
+
+if __name__ == "__main__":
+    asyncio.run(main())

examples/foundational/runner.py

@@ -6,6 +6,7 @@
 
 import argparse
 import os
+from typing import Optional
 
 import aiohttp
 
@@ -18,7 +19,7 @@ async def configure(aiohttp_session: aiohttp.ClientSession):
 
 
 async def configure_with_args(
-    aiohttp_session: aiohttp.ClientSession, parser: argparse.ArgumentParser | None = None
+    aiohttp_session: aiohttp.ClientSession, parser: Optional[argparse.ArgumentParser] = None
 ):
     if not parser:
         parser = argparse.ArgumentParser(description="Daily AI SDK Bot Sample")

examples/local-input-select-stt/README.md

@@ -0,0 +1,88 @@
+
+# Pipecat Audio Transcription Example 🚀🎙️
+
+Welcome to the **Pipecat Audio Transcription Example**! 
+
+This project showcases how to integrate the awesome [pipecat](https://github.com/pipecat-ai/pipecat) library with a neat textual interface (powered by [Textual](https://github.com/Textualize/textual)) to select audio devices, perform real-time speech-to-text (STT) transcription using [Whisper](https://github.com/openai/whisper).
+
+> **Note:** Although the script allows you to select both input and output audio devices, this example only utilizes the audio **input** for transcription.
+
+---
+
+## 🎉 Features
+
+- **Interactive Audio Device Selection:**  
+  Choose your preferred audio input device using a cool, textual UI.
+  
+- **State-of-the-Art Transcription:**  
+  Leverage Whisper's large model (running on CUDA) for high-quality, real-time STT.
+  
+- **Live Transcription Logging:**  
+  Watch your spoken words transform into text on your console instantly.
+  
+- **Easy Setup:**  
+  Everything you need is in the [`requirements.txt`](./requirements.txt).
+
+---
+
+## 🎥 Demo
+
+Get a quick glimpse of the app in action!  
+*(Don't worry – I'll be adding a GIF demo here soon!)*
+
+![Demo GIF](demo.gif)
+
+---
+
+## 🔧 Installation
+
+
+Install Dependencies:
+
+```bash
+pip install -r requirements.txt
+```
+
+
+---
+
+## 🚀 Usage
+
+Run the main script:
+
+```bash
+python bot.py
+```
+
+When the app launches, you'll see a textual interface that lets you select your audio input device. Once selected, the app will begin capturing audio, transcribing it using Whisper.
+
+---
+
+## ⚙️ How It Works
+
+
+1. **LocalAudioTransport:**  
+   Captures audio from your chosen input device.
+   
+2. **WhisperSTTService:**  
+   Processes the audio stream using Whisper's large model for speech-to-text conversion.
+   
+3. **TranscriptionLogger:**  
+   Logs the transcribed text to the console as soon as it's processed.
+
+
+---
+
+## 📦 Dependencies
+
+The project relies on:
+
+- [pipecat](https://github.com/yourusername/pipecat) – For building the audio processing pipeline.
+- [Textual](https://github.com/Textualize/textual) – For the interactive terminal UI.
+- [Whisper](https://github.com/openai/whisper) – For state-of-the-art STT transcription.
+
+---
+
+## Example improvements:
+
+I plan to improve this example with local LLM calls and audio output.

examples/local-input-select-stt/bot.py

@@ -0,0 +1,65 @@
+#
+# Copyright (c) 2024–2025, Daily
+#
+# SPDX-License-Identifier: BSD 2-Clause License
+#
+
+import asyncio
+import sys
+from typing import Tuple
+
+from dotenv import load_dotenv
+from loguru import logger
+from select_audio_device import AudioDevice, run_device_selector
+
+from pipecat.frames.frames import Frame, TranscriptionFrame
+from pipecat.pipeline.pipeline import Pipeline
+from pipecat.pipeline.runner import PipelineRunner
+from pipecat.pipeline.task import PipelineTask
+from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
+from pipecat.services.whisper import Model, WhisperSTTService
+from pipecat.transports.local.audio import LocalAudioTransport, LocalTransportParams
+
+load_dotenv(override=True)
+
+logger.remove(0)
+logger.add(sys.stderr, level="DEBUG")
+
+
+class TranscriptionLogger(FrameProcessor):
+    async def process_frame(self, frame: Frame, direction: FrameDirection):
+        await super().process_frame(frame, direction)
+
+        if isinstance(frame, TranscriptionFrame):
+            print(f"Transcription: {frame.text}")
+
+
+async def main(input_device: int, output_device: int):
+    transport = LocalAudioTransport(
+        LocalTransportParams(
+            audio_in_enabled=True,
+            audio_out_enabled=False,
+            input_device_index=input_device,
+            output_device_index=output_device,
+        )
+    )
+
+    stt = WhisperSTTService(device="cuda", model=Model.LARGE, no_speech_prob=0.3)
+
+    tl = TranscriptionLogger()
+
+    pipeline = Pipeline([transport.input(), stt, tl])
+
+    task = PipelineTask(pipeline)
+
+    runner = PipelineRunner(handle_sigint=False if sys.platform == "win32" else True)
+
+    await asyncio.gather(runner.run(task))
+
+
+if __name__ == "__main__":
+    res: Tuple[AudioDevice, AudioDevice, int] = asyncio.run(
+        run_device_selector()  # runs the textual app that allows to select input device
+    )
+
+    asyncio.run(main(res[0].index, res[1].index))

examples/local-input-select-stt/requirements.txt

@@ -0,0 +1,8 @@
+--extra-index-url https://download.pytorch.org/whl/cu124
+torch==2.5.0+cu124
+torchvision 
+torchaudio
+pipecat[whisper, openai]
+textual==1.0.0
+pydantic-settings==2.7.1
+pyaudio==0.2.14

examples/local-input-select-stt/select_audio_device.py

@@ -0,0 +1,247 @@
+from typing import List, Optional, Tuple
+
+import pyaudio
+from pydantic import BaseModel, ConfigDict, Field
+from pydantic_settings import BaseSettings
+from textual.app import App, ComposeResult
+from textual.containers import Container
+from textual.widgets import Footer, Header, Label, ListItem, ListView, Select
+from textual.widgets.option_list import Option
+
+# ─── DATA MODELS ───────────────────────────────────────────────────────────────
+
+
+class HostApi(BaseModel):
+    index: int
+    struct_version: int = Field(..., alias="structVersion")
+    type: int
+    name: str
+    device_count: int = Field(..., alias="deviceCount")
+    default_input_device: int = Field(..., alias="defaultInputDevice")
+    default_output_device: int = Field(..., alias="defaultOutputDevice")
+
+
+class AudioDevice(BaseModel):
+    model_config = ConfigDict(populate_by_name=True)
+    index: int
+    struct_version: int = Field(..., alias="structVersion")
+    name: str
+    host_api: int = Field(..., alias="hostApi")
+    max_input_channels: int = Field(..., alias="maxInputChannels")
+    max_output_channels: int = Field(..., alias="maxOutputChannels")
+    default_low_input_latency: float = Field(..., alias="defaultLowInputLatency")
+    default_low_output_latency: float = Field(..., alias="defaultLowOutputLatency")
+    default_high_input_latency: float = Field(..., alias="defaultHighInputLatency")
+    default_high_output_latency: float = Field(..., alias="defaultHighOutputLatency")
+    default_sample_rate: float = Field(..., alias="defaultSampleRate")
+
+
+# ─── SETTINGS MODEL ───────────────────────────────────────────────────────────
+
+
+class AudioSettings(BaseSettings):  # to save settings to a file
+    host_api: Optional[int] = None
+    input_device: Optional[AudioDevice] = None
+    output_device: Optional[AudioDevice] = None
+
+    class Config:
+        env_file = "settings.env"  # or adjust as needed
+
+    def save_to_json(self, filepath: str) -> None:
+        with open(filepath, "w") as f:
+            f.write(self.model_dump_json(indent=2))
+
+
+# ─── TEXTUAL APP ──────────────────────────────────────────────────────────────
+
+
+class AudioDeviceSelectorApp(App):
+    CSS = """
+    Screen {
+        align: center middle;
+    }
+    #container {
+        width: 80%;
+        border: round green;
+        padding: 1 2;
+    }
+    """
+
+    def __init__(
+        self,
+        default_host_api: Optional[int] = None,
+        default_input_device: Optional[AudioDevice] = None,
+        default_output_device: Optional[AudioDevice] = None,
+        **kwargs,
+    ) -> None:
+        super().__init__(**kwargs)
+        # Save defaults passed from settings.
+        self.default_host_api: Optional[int] = default_host_api
+        self.default_input_device: Optional[AudioDevice] = default_input_device
+        self.default_output_device: Optional[AudioDevice] = default_output_device
+
+        self.pyaudio_instance = pyaudio.PyAudio()
+
+        # Static datastructures: host APIs and devices as well‐typed models.
+        self.host_apis: List[HostApi] = []
+        self.current_host_api: Optional[int] = None
+
+        self.all_input_devices: List[AudioDevice] = []
+        self.all_output_devices: List[AudioDevice] = []
+        self.input_devices: List[AudioDevice] = []
+        self.output_devices: List[AudioDevice] = []
+
+        # Stage management: first select input, then output.
+        self.stage: str = "input"
+        self.selected_input_device: Optional[AudioDevice] = None
+        self.selected_output_device: Optional[AudioDevice] = None
+        host_api_count: int = self.pyaudio_instance.get_host_api_count()
+        for i in range(host_api_count):
+            raw_api = self.pyaudio_instance.get_host_api_info_by_index(i)
+            # Inject the index (if not already present)
+            raw_api["index"] = i
+            try:
+                api = HostApi.parse_obj(raw_api)
+                self.host_apis.append(api)
+            except Exception as e:
+                # Skip APIs that don't conform.
+                continue
+
+    def compose(self) -> ComposeResult:
+        options: List[Tuple[str, Option]] = [
+            (
+                api.name,
+                Option(
+                    prompt=str(api.name) if api.name else f"Host API {api.index}",
+                    id=str(api.index),
+                ),
+            )
+            for api in self.host_apis
+        ]
+
+        yield Header()
+
+        yield Footer()
+        with Container(id="container"):
+            yield Label("Select Host API:", id="host-api-label")
+            # Create the Select widget with no options initially.
+            self.host_api_select: Select[HostApi] = Select(options=options, id="host-api-select")
+            yield self.host_api_select
+            self.prompt = Label("Select Input Audio Device:", id="prompt")
+            yield self.prompt
+            self.list_view = ListView(id="device-list")
+            yield self.list_view
+
+    def on_mount(self) -> None:
+        # Populate host APIs from PyAudio.
+
+        # Build the dropdown options.
+
+        self.host_api_select.refresh()  # Force a redraw
+
+        # Determine the default host API.
+        if self.default_host_api is not None:
+            self.current_host_api = self.default_host_api
+        else:
+            default_api_info = self.pyaudio_instance.get_default_host_api_info()
+            self.current_host_api = default_api_info["index"]
+
+        # Delay setting the dropdown's value until the widget is fully initialized.
+        self.set_timer(
+            0,
+            lambda: setattr(self.host_api_select, "value", str(self.current_host_api)),
+        )
+
+        # Load all devices and parse them into AudioDevice objects.
+        device_count: int = self.pyaudio_instance.get_device_count()
+        for i in range(device_count):
+            raw_device = self.pyaudio_instance.get_device_info_by_index(i)
+            raw_device["index"] = i
+            try:
+                device = AudioDevice.parse_obj(raw_device)
+            except Exception as e:
+                # Skip devices missing required fields.
+                continue
+            if device.max_input_channels > 0:
+                self.all_input_devices.append(device)
+            if device.max_output_channels > 0:
+                self.all_output_devices.append(device)
+
+        self.filter_devices()
+        self.populate_list(self.input_devices)
+        if self.default_input_device:
+            self._select_default_in_list(self.default_input_device)
+
+    def filter_devices(self) -> None:
+        """Filter devices based on the selected host API."""
+        self.input_devices = [
+            d for d in self.all_input_devices if d.host_api == self.current_host_api
+        ]
+        self.output_devices = [
+            d for d in self.all_output_devices if d.host_api == self.current_host_api
+        ]
+
+    def populate_list(self, devices: List[AudioDevice]) -> None:
+        """Populate the ListView with a list of AudioDevice objects."""
+        self.list_view.clear()
+        for dev in devices:
+            item_text: str = f"{dev.name} (Index: {dev.index})"
+            item = ListItem(Label(item_text))
+            # Attach the AudioDevice instance to the widget.
+            item.device_info = dev  # type: ignore
+            self.list_view.append(item)
+
+    def _select_default_in_list(self, default_device: AudioDevice) -> None:
+        """Pre-select the default device if present in the current list."""
+        for idx, item in enumerate(self.list_view.children):
+            if hasattr(item, "device_info") and item.device_info.index == default_device.index:
+                self.list_view.index = idx
+                break
+
+    async def on_select_changed(self, event: Select.Changed) -> None:
+        """Handle changes in the host API dropdown."""
+        if event.select.id == "host-api-select":
+            self.current_host_api = int(event.value.id)
+            self.filter_devices()
+            if self.stage == "input":
+                self.populate_list(self.input_devices)
+                if self.default_input_device:
+                    self._select_default_in_list(self.default_input_device)
+            elif self.stage == "output":
+                self.populate_list(self.output_devices)
+                if self.default_output_device:
+                    self._select_default_in_list(self.default_output_device)
+
+    async def on_list_view_selected(self, message: ListView.Selected) -> None:
+        """Record device selection and switch stages."""
+        selected_item = message.item
+        device_info: AudioDevice = selected_item.device_info  # type: ignore
+        if self.stage == "input":
+            self.selected_input_device = device_info
+            self.stage = "output"
+            self.prompt.update("Select Output Audio Device:")
+            self.populate_list(self.output_devices)
+            if self.default_output_device:
+                self._select_default_in_list(self.default_output_device)
+        elif self.stage == "output":
+            self.selected_output_device = device_info
+            await self.action_quit()
+
+
+# ─── HELPER FUNCTIONS ─────────────────────────────────────────────────────────
+
+
+async def run_device_selector(
+    default_host_api: Optional[int] = None,
+    default_input_device: Optional[AudioDevice] = None,
+    default_output_device: Optional[AudioDevice] = None,
+) -> Tuple[AudioDevice, AudioDevice, int]:
+    app = AudioDeviceSelectorApp(
+        default_host_api=default_host_api,
+        default_input_device=default_input_device,
+        default_output_device=default_output_device,
+    )
+    await app.run_async()
+
+    # The current_host_api is guaranteed to be set.
+    return app.selected_input_device, app.selected_output_device, app.current_host_api  # type: ignore

examples/moondream-chatbot/bot.py

@@ -106,8 +106,8 @@ class UserImageRequester(FrameProcessor):
                     UserImageRequestFrame(self.participant_id), FrameDirection.UPSTREAM
                 )
                 await self.push_frame(TextFrame("Describe the image in a short sentence."))
-        elif isinstance(frame, UserImageRawFrame):
-            await self.push_frame(frame)
+        else:
+            await self.push_frame(frame, direction)
 
 
 class TextFilterProcessor(FrameProcessor):

examples/news-chatbot/client/javascript/package-lock.json

@@ -13,7 +13,7 @@
         "@pipecat-ai/daily-transport": "^0.3.4"
       },
       "devDependencies": {
-        "vite": "^6.0.2"
+        "vite": "^6.0.9"
       }
     },
     "node_modules/@babel/runtime": {
@@ -45,14 +45,13 @@
       }
     },
     "node_modules/@esbuild/aix-ppc64": {
-      "version": "0.24.0",
-      "resolved": "https://registry.npmjs.org/@esbuild/aix-ppc64/-/aix-ppc64-0.24.0.tgz",
-      "integrity": "sha512-WtKdFM7ls47zkKHFVzMz8opM7LkcsIp9amDUBIAWirg70RM71WRSjdILPsY5Uv1D42ZpUfaPILDlfactHgsRkw==",
+      "version": "0.24.2",
+      "resolved": "https://registry.npmjs.org/@esbuild/aix-ppc64/-/aix-ppc64-0.24.2.tgz",
+      "integrity": "sha512-thpVCb/rhxE/BnMLQ7GReQLLN8q9qbHmI55F4489/ByVg2aQaQ6kbcLb6FHkocZzQhxc4gx0sCk0tJkKBFzDhA==",
       "cpu": [
         "ppc64"
       ],
       "dev": true,
-      "license": "MIT",
       "optional": true,
       "os": [
         "aix"
@@ -62,14 +61,13 @@
       }
     },
     "node_modules/@esbuild/android-arm": {
-      "version": "0.24.0",
-      "resolved": "https://registry.npmjs.org/@esbuild/android-arm/-/android-arm-0.24.0.tgz",
-      "integrity": "sha512-arAtTPo76fJ/ICkXWetLCc9EwEHKaeya4vMrReVlEIUCAUncH7M4bhMQ+M9Vf+FFOZJdTNMXNBrWwW+OXWpSew==",
+      "version": "0.24.2",
+      "resolved": "https://registry.npmjs.org/@esbuild/android-arm/-/android-arm-0.24.2.tgz",
+      "integrity": "sha512-tmwl4hJkCfNHwFB3nBa8z1Uy3ypZpxqxfTQOcHX+xRByyYgunVbZ9MzUUfb0RxaHIMnbHagwAxuTL+tnNM+1/Q==",
       "cpu": [
         "arm"
       ],
       "dev": true,
-      "license": "MIT",
       "optional": true,
       "os": [
         "android"
@@ -79,14 +77,13 @@
       }
     },
     "node_modules/@esbuild/android-arm64": {
-      "version": "0.24.0",
-      "resolved": "https://registry.npmjs.org/@esbuild/android-arm64/-/android-arm64-0.24.0.tgz",
-      "integrity": "sha512-Vsm497xFM7tTIPYK9bNTYJyF/lsP590Qc1WxJdlB6ljCbdZKU9SY8i7+Iin4kyhV/KV5J2rOKsBQbB77Ab7L/w==",
+      "version": "0.24.2",
+      "resolved": "https://registry.npmjs.org/@esbuild/android-arm64/-/android-arm64-0.24.2.tgz",
+      "integrity": "sha512-cNLgeqCqV8WxfcTIOeL4OAtSmL8JjcN6m09XIgro1Wi7cF4t/THaWEa7eL5CMoMBdjoHOTh/vwTO/o2TRXIyzg==",
       "cpu": [
         "arm64"
       ],
       "dev": true,
-      "license": "MIT",
       "optional": true,
       "os": [
         "android"
@@ -96,14 +93,13 @@
       }
     },
     "node_modules/@esbuild/android-x64": {
-      "version": "0.24.0",
-      "resolved": "https://registry.npmjs.org/@esbuild/android-x64/-/android-x64-0.24.0.tgz",
-      "integrity": "sha512-t8GrvnFkiIY7pa7mMgJd7p8p8qqYIz1NYiAoKc75Zyv73L3DZW++oYMSHPRarcotTKuSs6m3hTOa5CKHaS02TQ==",
+      "version": "0.24.2",
+      "resolved": "https://registry.npmjs.org/@esbuild/android-x64/-/android-x64-0.24.2.tgz",
+      "integrity": "sha512-B6Q0YQDqMx9D7rvIcsXfmJfvUYLoP722bgfBlO5cGvNVb5V/+Y7nhBE3mHV9OpxBf4eAS2S68KZztiPaWq4XYw==",
       "cpu": [
         "x64"
       ],
       "dev": true,
-      "license": "MIT",
       "optional": true,
       "os": [
         "android"
@@ -113,14 +109,13 @@
       }
     },
     "node_modules/@esbuild/darwin-arm64": {
-      "version": "0.24.0",
-      "resolved": "https://registry.npmjs.org/@esbuild/darwin-arm64/-/darwin-arm64-0.24.0.tgz",
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     "node_modules/postcss": {
-      "version": "8.4.49",
-      "resolved": "https://registry.npmjs.org/postcss/-/postcss-8.4.49.tgz",
-      "integrity": "sha512-OCVPnIObs4N29kxTjzLfUryOkvZEq+pf8jTF0lg8E7uETuWHA+v7j3c/xJmiqpX450191LlmZfUKkXxkTry7nA==",
+      "version": "8.5.2",
+      "resolved": "https://registry.npmjs.org/postcss/-/postcss-8.5.2.tgz",
+      "integrity": "sha512-MjOadfU3Ys9KYoX0AdkBlFEF1Vx37uCCeN4ZHnmwm9FfpbsGWMZeBLMmmpY+6Ocqod7mkdZ0DT31OlbsFrLlkA==",
       "dev": true,
       "funding": [
         {
@@ -985,9 +968,8 @@
           "url": "https://github.com/sponsors/ai"
         }
       ],
-      "license": "MIT",
       "dependencies": {
-        "nanoid": "^3.3.7",
+        "nanoid": "^3.3.8",
         "picocolors": "^1.1.1",
         "source-map-js": "^1.2.1"
       },
@@ -1002,11 +984,10 @@
       "license": "MIT"
     },
     "node_modules/rollup": {
-      "version": "4.28.0",
-      "resolved": "https://registry.npmjs.org/rollup/-/rollup-4.28.0.tgz",
-      "integrity": "sha512-G9GOrmgWHBma4YfCcX8PjH0qhXSdH8B4HDE2o4/jaxj93S4DPCIDoLcXz99eWMji4hB29UFCEd7B2gwGJDR9cQ==",
+      "version": "4.34.6",
+      "resolved": "https://registry.npmjs.org/rollup/-/rollup-4.34.6.tgz",
+      "integrity": "sha512-wc2cBWqJgkU3Iz5oztRkQbfVkbxoz5EhnCGOrnJvnLnQ7O0WhQUYyv18qQI79O8L7DdHrrlJNeCHd4VGpnaXKQ==",
       "dev": true,
-      "license": "MIT",
       "dependencies": {
         "@types/estree": "1.0.6"
       },
@@ -1018,24 +999,25 @@
         "npm": ">=8.0.0"
       },
       "optionalDependencies": {
-        "@rollup/rollup-android-arm-eabi": "4.28.0",
-        "@rollup/rollup-android-arm64": "4.28.0",
-        "@rollup/rollup-darwin-arm64": "4.28.0",
-        "@rollup/rollup-darwin-x64": "4.28.0",
-        "@rollup/rollup-freebsd-arm64": "4.28.0",
-        "@rollup/rollup-freebsd-x64": "4.28.0",
-        "@rollup/rollup-linux-arm-gnueabihf": "4.28.0",
-        "@rollup/rollup-linux-arm-musleabihf": "4.28.0",
-        "@rollup/rollup-linux-arm64-gnu": "4.28.0",
-        "@rollup/rollup-linux-arm64-musl": "4.28.0",
-        "@rollup/rollup-linux-powerpc64le-gnu": "4.28.0",
-        "@rollup/rollup-linux-riscv64-gnu": "4.28.0",
-        "@rollup/rollup-linux-s390x-gnu": "4.28.0",
-        "@rollup/rollup-linux-x64-gnu": "4.28.0",
-        "@rollup/rollup-linux-x64-musl": "4.28.0",
-        "@rollup/rollup-win32-arm64-msvc": "4.28.0",
-        "@rollup/rollup-win32-ia32-msvc": "4.28.0",
-        "@rollup/rollup-win32-x64-msvc": "4.28.0",
+        "@rollup/rollup-android-arm-eabi": "4.34.6",
+        "@rollup/rollup-android-arm64": "4.34.6",
+        "@rollup/rollup-darwin-arm64": "4.34.6",
+        "@rollup/rollup-darwin-x64": "4.34.6",
+        "@rollup/rollup-freebsd-arm64": "4.34.6",
+        "@rollup/rollup-freebsd-x64": "4.34.6",
+        "@rollup/rollup-linux-arm-gnueabihf": "4.34.6",
+        "@rollup/rollup-linux-arm-musleabihf": "4.34.6",
+        "@rollup/rollup-linux-arm64-gnu": "4.34.6",
+        "@rollup/rollup-linux-arm64-musl": "4.34.6",
+        "@rollup/rollup-linux-loongarch64-gnu": "4.34.6",
+        "@rollup/rollup-linux-powerpc64le-gnu": "4.34.6",
+        "@rollup/rollup-linux-riscv64-gnu": "4.34.6",
+        "@rollup/rollup-linux-s390x-gnu": "4.34.6",
+        "@rollup/rollup-linux-x64-gnu": "4.34.6",
+        "@rollup/rollup-linux-x64-musl": "4.34.6",
+        "@rollup/rollup-win32-arm64-msvc": "4.34.6",
+        "@rollup/rollup-win32-ia32-msvc": "4.34.6",
+        "@rollup/rollup-win32-x64-msvc": "4.34.6",
         "fsevents": "~2.3.2"
       }
     },
@@ -1066,7 +1048,6 @@
       "resolved": "https://registry.npmjs.org/source-map-js/-/source-map-js-1.2.1.tgz",
       "integrity": "sha512-UXWMKhLOwVKb728IUtQPXxfYU+usdybtUrK/8uGE8CQMvrhOpwvzDBwj0QhSL7MQc7vIsISBG8VQ8+IDQxpfQA==",
       "dev": true,
-      "license": "BSD-3-Clause",
       "engines": {
         "node": ">=0.10.0"
       }
@@ -1101,15 +1082,14 @@
       }
     },
     "node_modules/vite": {
-      "version": "6.0.2",
-      "resolved": "https://registry.npmjs.org/vite/-/vite-6.0.2.tgz",
-      "integrity": "sha512-XdQ+VsY2tJpBsKGs0wf3U/+azx8BBpYRHFAyKm5VeEZNOJZRB63q7Sc8Iup3k0TrN3KO6QgyzFf+opSbfY1y0g==",
+      "version": "6.1.0",
+      "resolved": "https://registry.npmjs.org/vite/-/vite-6.1.0.tgz",
+      "integrity": "sha512-RjjMipCKVoR4hVfPY6GQTgveinjNuyLw+qruksLDvA5ktI1150VmcMBKmQaEWJhg/j6Uaf6dNCNA0AfdzUb/hQ==",
       "dev": true,
-      "license": "MIT",
       "dependencies": {
-        "esbuild": "^0.24.0",
-        "postcss": "^8.4.49",
-        "rollup": "^4.23.0"
+        "esbuild": "^0.24.2",
+        "postcss": "^8.5.1",
+        "rollup": "^4.30.1"
       },
       "bin": {
         "vite": "bin/vite.js"

examples/news-chatbot/client/javascript/package.json

@@ -12,7 +12,7 @@
   "license": "ISC",
   "description": "",
   "devDependencies": {
-    "vite": "^6.0.2"
+    "vite": "^6.0.9"
   },
   "dependencies": {
     "@pipecat-ai/client-js": "^0.3.2",

examples/news-chatbot/server/news_bot.py

@@ -23,7 +23,7 @@ from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
 from pipecat.processors.frameworks.rtvi import RTVIConfig, RTVIProcessor
 from pipecat.services.cartesia import CartesiaTTSService
 from pipecat.services.deepgram import DeepgramSTTService
-from pipecat.services.google import GoogleLLMService, LLMSearchResponseFrame
+from pipecat.services.google import GoogleLLMService, GoogleRTVIObserver, LLMSearchResponseFrame
 from pipecat.transports.services.daily import DailyParams, DailyTransport
 from pipecat.utils.text.markdown_text_filter import MarkdownTextFilter
 
@@ -102,6 +102,7 @@ async def main():
 
         llm = GoogleLLMService(
             api_key=os.getenv("GOOGLE_API_KEY"),
+            model="gemini-1.5-flash-002",
             system_instruction=system_instruction,
             tools=tools,
         )
@@ -141,7 +142,7 @@ async def main():
             pipeline,
             PipelineParams(
                 allow_interruptions=True,
-                observers=[rtvi.observer()],
+                observers=[GoogleRTVIObserver(rtvi)],
             ),
         )
 

examples/news-chatbot/server/runner.py

@@ -6,6 +6,7 @@
 
 import argparse
 import os
+from typing import Optional
 
 import aiohttp
 
@@ -18,7 +19,7 @@ async def configure(aiohttp_session: aiohttp.ClientSession):
 
 
 async def configure_with_args(
-    aiohttp_session: aiohttp.ClientSession, parser: argparse.ArgumentParser | None = None
+    aiohttp_session: aiohttp.ClientSession, parser: Optional[argparse.ArgumentParser] = None
 ):
     if not parser:
         parser = argparse.ArgumentParser(description="Daily AI SDK Bot Sample")

examples/phone-chatbot/bot_daily.py

@@ -2,13 +2,14 @@ import argparse
 import asyncio
 import os
 import sys
+from typing import Optional
 
 from dotenv import load_dotenv
 from loguru import logger
 from openai.types.chat import ChatCompletionToolParam
 
 from pipecat.audio.vad.silero import SileroVADAnalyzer
-from pipecat.frames.frames import EndFrame, EndTaskFrame
+from pipecat.frames.frames import EndTaskFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
@@ -42,7 +43,7 @@ async def main(
     callId: str,
     callDomain: str,
     detect_voicemail: bool,
-    dialout_number: str | None,
+    dialout_number: Optional[str],
 ):
     # dialin_settings are only needed if Daily's SIP URI is used
     # If you are handling this via Twilio, Telnyx, set this to None
@@ -99,14 +100,14 @@ async def main(
             - **ASSUME IT IS A VOICEMAIL. DO NOT WAIT FOR MORE CONFIRMATION.**
 
             #### **Step 2: Leave a Voicemail Message**
-            - Immediately say:  
+            - Immediately say:
             *"Hello, this is a message for Pipecat example user. This is Chatbot. Please call back on 123-456-7891. Thank you."*
             - **IMMEDIATELY AFTER LEAVING THE MESSAGE, CALL `terminate_call`.**
             - **DO NOT SPEAK AFTER CALLING `terminate_call`.**
             - **FAILURE TO CALL `terminate_call` IMMEDIATELY IS A MISTAKE.**
 
             #### **Step 3: If Speaking to a Human**
-            - If the call is answered by a human, say:  
+            - If the call is answered by a human, say:
             *"Oh, hello! I'm a friendly chatbot. Is there anything I can help you with?"*
             - Keep responses **brief and helpful**.
             - If the user no longer needs assistance, **call `terminate_call` immediately.**

examples/sentry-metrics/.gitignore

@@ -0,0 +1,161 @@
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+cover/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+.pybuilder/
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+#   For a library or package, you might want to ignore these files since the code is
+#   intended to run in multiple environments; otherwise, check them in:
+# .python-version
+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+#Pipfile.lock
+
+# poetry
+#   Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
+#   This is especially recommended for binary packages to ensure reproducibility, and is more
+#   commonly ignored for libraries.
+#   https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
+#poetry.lock
+
+# pdm
+#   Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
+#pdm.lock
+#   pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it
+#   in version control.
+#   https://pdm.fming.dev/#use-with-ide
+.pdm.toml
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+
+# pytype static type analyzer
+.pytype/
+
+# Cython debug symbols
+cython_debug/
+
+# PyCharm
+#  JetBrains specific template is maintained in a separate JetBrains.gitignore that can
+#  be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
+#  and can be added to the global gitignore or merged into this file.  For a more nuclear
+#  option (not recommended) you can uncomment the following to ignore the entire idea folder.
+#.idea/
+runpod.toml

examples/sentry-metrics/Dockerfile

@@ -0,0 +1,15 @@
+FROM python:3.10-bullseye
+
+RUN mkdir /app
+RUN mkdir /app/assets
+RUN mkdir /app/utils
+COPY *.py /app/
+COPY requirements.txt /app/
+
+
+WORKDIR /app
+RUN pip3 install -r requirements.txt
+
+EXPOSE 7860
+
+CMD ["python3", "server.py"]

examples/sentry-metrics/README.md

@@ -0,0 +1,29 @@
+# Sentry Metrics
+
+This app connects you to a chatbot powered by GPT-4. It provides TTFB (Time-To-First-Byte) and processing metrics to Sentry.
+
+## Get started
+
+```python
+python3 -m venv venv
+source venv/bin/activate
+pip install -r requirements.txt
+
+cp env.example .env # and add your credentials
+
+```
+
+## Run the server
+
+```bash
+python server.py
+```
+
+Then, visit `http://localhost:7860/` in your browser to start a chatbot session.
+
+## Build and test the Docker image
+
+```
+docker build -t chatbot .
+docker run --env-file .env -p 7860:7860 chatbot
+```

examples/sentry-metrics/bot.py

@@ -0,0 +1,112 @@
+#
+# Copyright (c) 2024–2025, Daily
+#
+# SPDX-License-Identifier: BSD 2-Clause License
+#
+
+import asyncio
+import os
+import sys
+
+import aiohttp
+import sentry_sdk
+from dotenv import load_dotenv
+from loguru import logger
+from runner import configure
+
+from pipecat.audio.vad.silero import SileroVADAnalyzer
+from pipecat.pipeline.pipeline import Pipeline
+from pipecat.pipeline.runner import PipelineRunner
+from pipecat.pipeline.task import PipelineParams, PipelineTask
+from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
+from pipecat.processors.metrics.sentry import SentryMetrics
+from pipecat.services.elevenlabs import ElevenLabsTTSService
+from pipecat.services.openai import OpenAILLMService
+from pipecat.transports.services.daily import DailyParams, DailyTransport
+
+load_dotenv(override=True)
+
+logger.remove(0)
+logger.add(sys.stderr, level="DEBUG")
+
+
+async def main():
+    async with aiohttp.ClientSession() as session:
+        (room_url, token) = await configure(session)
+
+        transport = DailyTransport(
+            room_url,
+            token,
+            "Chatbot",
+            DailyParams(
+                audio_out_enabled=True,
+                audio_in_enabled=True,
+                camera_out_enabled=False,
+                vad_enabled=True,
+                vad_audio_passthrough=True,
+                vad_analyzer=SileroVADAnalyzer(),
+                transcription_enabled=True,
+            ),
+        )
+
+        # Initialize Sentry
+        sentry_sdk.init(
+            dsn="your-project-dsn",
+            traces_sample_rate=1.0,
+        )
+
+        tts = ElevenLabsTTSService(
+            api_key=os.getenv("ELEVENLABS_API_KEY"),
+            voice_id="cgSgspJ2msm6clMCkdW9",
+            metrics=SentryMetrics(),
+        )
+
+        llm = OpenAILLMService(
+            api_key=os.getenv("OPENAI_API_KEY"),
+            model="gpt-4o",
+            metrics=SentryMetrics(),
+        )
+
+        messages = [
+            {
+                "role": "system",
+                "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.",
+            },
+        ]
+
+        context = OpenAILLMContext(messages)
+        context_aggregator = llm.create_context_aggregator(context)
+
+        pipeline = Pipeline(
+            [
+                transport.input(),  # microphone
+                context_aggregator.user(),
+                llm,
+                tts,
+                transport.output(),
+                context_aggregator.assistant(),
+            ]
+        )
+
+        task = PipelineTask(
+            pipeline,
+            PipelineParams(allow_interruptions=True, enable_metrics=True),
+        )
+
+        @transport.event_handler("on_first_participant_joined")
+        async def on_first_participant_joined(transport, participant):
+            await transport.capture_participant_transcription(participant["id"])
+            await task.queue_frames([context_aggregator.user().get_context_frame()])
+
+        @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)
+
+
+if __name__ == "__main__":
+    asyncio.run(main())

examples/sentry-metrics/env.example

@@ -0,0 +1,4 @@
+DAILY_SAMPLE_ROOM_URL=https://yourdomain.daily.co/yourroom # (for joining the bot to the same room repeatedly for local dev)
+DAILY_API_KEY=7df...
+OPENAI_API_KEY=sk-PL...
+ELEVENLABS_API_KEY=aeb...

examples/sentry-metrics/requirements.txt

@@ -0,0 +1,4 @@
+python-dotenv
+fastapi[all]
+uvicorn
+pipecat-ai[daily,openai,sentry,silero,elevenlabs]

examples/sentry-metrics/runner.py

@@ -0,0 +1,56 @@
+#
+# Copyright (c) 2024–2025, Daily
+#
+# SPDX-License-Identifier: BSD 2-Clause License
+#
+
+import argparse
+import os
+
+import aiohttp
+
+from pipecat.transports.services.helpers.daily_rest import DailyRESTHelper
+
+
+async def configure(aiohttp_session: aiohttp.ClientSession):
+    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(
+        "-k",
+        "--apikey",
+        type=str,
+        required=False,
+        help="Daily API Key (needed to create an owner token for the room)",
+    )
+
+    args, unknown = parser.parse_known_args()
+
+    url = args.url or os.getenv("DAILY_SAMPLE_ROOM_URL")
+    key = args.apikey or os.getenv("DAILY_API_KEY")
+
+    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."
+        )
+
+    if not key:
+        raise Exception(
+            "No Daily API key specified. use the -k/--apikey option from the command line, or set DAILY_API_KEY in your environment to specify a Daily API key, available from https://dashboard.daily.co/developers."
+        )
+
+    daily_rest_helper = DailyRESTHelper(
+        daily_api_key=key,
+        daily_api_url=os.getenv("DAILY_API_URL", "https://api.daily.co/v1"),
+        aiohttp_session=aiohttp_session,
+    )
+
+    # Create a meeting token for the given room with an expiration 1 hour in
+    # the future.
+    expiry_time: float = 60 * 60
+
+    token = await daily_rest_helper.get_token(url, expiry_time)
+
+    return (url, token)
+    return (url, token)

examples/sentry-metrics/server.py

@@ -0,0 +1,139 @@
+#
+# Copyright (c) 2024–2025, Daily
+#
+# SPDX-License-Identifier: BSD 2-Clause License
+#
+
+import argparse
+import os
+import subprocess
+from contextlib import asynccontextmanager
+
+import aiohttp
+from dotenv import load_dotenv
+from fastapi import FastAPI, HTTPException, Request
+from fastapi.middleware.cors import CORSMiddleware
+from fastapi.responses import JSONResponse, RedirectResponse
+
+from pipecat.transports.services.helpers.daily_rest import DailyRESTHelper, DailyRoomParams
+
+MAX_BOTS_PER_ROOM = 1
+
+# Bot sub-process dict for status reporting and concurrency control
+bot_procs = {}
+
+daily_helpers = {}
+
+load_dotenv(override=True)
+
+
+def cleanup():
+    # Clean up function, just to be extra safe
+    for entry in bot_procs.values():
+        proc = entry[0]
+        proc.terminate()
+        proc.wait()
+
+
+@asynccontextmanager
+async def lifespan(app: FastAPI):
+    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()
+
+
+app = FastAPI(lifespan=lifespan)
+
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+
+@app.get("/")
+async def start_agent(request: Request):
+    print(f"!!! Creating room")
+    room = await daily_helpers["rest"].create_room(DailyRoomParams())
+    print(f"!!! Room URL: {room.url}")
+    # Ensure the room property is present
+    if not room.url:
+        raise HTTPException(
+            status_code=500,
+            detail="Missing 'room' property in request data. Cannot start agent without a target room!",
+        )
+
+    # Check if there is already an existing process running in this room
+    num_bots_in_room = sum(
+        1 for proc in bot_procs.values() if proc[1] == room.url and proc[0].poll() is None
+    )
+    if num_bots_in_room >= MAX_BOTS_PER_ROOM:
+        raise HTTPException(status_code=500, detail=f"Max bot limited reach for room: {room.url}")
+
+    # Get the token for the room
+    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}")
+
+    # Spawn a new agent, and join the user session
+    # Note: this is mostly for demonstration purposes (refer to 'deployment' in README)
+    try:
+        proc = subprocess.Popen(
+            [f"python3 -m bot -u {room.url} -t {token}"],
+            shell=True,
+            bufsize=1,
+            cwd=os.path.dirname(os.path.abspath(__file__)),
+        )
+        bot_procs[proc.pid] = (proc, room.url)
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=f"Failed to start subprocess: {e}")
+
+    return RedirectResponse(room.url)
+
+
+@app.get("/status/{pid}")
+def get_status(pid: int):
+    # Look up the subprocess
+    proc = bot_procs.get(pid)
+
+    # If the subprocess doesn't exist, return an error
+    if not proc:
+        raise HTTPException(status_code=404, detail=f"Bot with process id: {pid} not found")
+
+    # Check the status of the subprocess
+    if proc[0].poll() is None:
+        status = "running"
+    else:
+        status = "finished"
+
+    return JSONResponse({"bot_id": pid, "status": status})
+
+
+if __name__ == "__main__":
+    import uvicorn
+
+    default_host = os.getenv("HOST", "0.0.0.0")
+    default_port = int(os.getenv("FAST_API_PORT", "7860"))
+
+    parser = argparse.ArgumentParser(description="Daily Storyteller FastAPI server")
+    parser.add_argument("--host", type=str, default=default_host, help="Host address")
+    parser.add_argument("--port", type=int, default=default_port, help="Port number")
+    parser.add_argument("--reload", action="store_true", help="Reload code on change")
+
+    config = parser.parse_args()
+
+    uvicorn.run(
+        "server:app",
+        host=config.host,
+        port=config.port,
+        reload=config.reload,
+    )

examples/simple-chatbot/client/javascript/package-lock.json

@@ -13,7 +13,7 @@
         "@pipecat-ai/daily-transport": "^0.3.4"
       },
       "devDependencies": {
-        "vite": "^6.0.2"
+        "vite": "^6.0.9"
       }
     },
     "node_modules/@babel/runtime": {
@@ -45,14 +45,13 @@
       }
     },
     "node_modules/@esbuild/aix-ppc64": {
-      "version": "0.24.0",
-      "resolved": "https://registry.npmjs.org/@esbuild/aix-ppc64/-/aix-ppc64-0.24.0.tgz",
-      "integrity": "sha512-WtKdFM7ls47zkKHFVzMz8opM7LkcsIp9amDUBIAWirg70RM71WRSjdILPsY5Uv1D42ZpUfaPILDlfactHgsRkw==",
+      "version": "0.24.2",
+      "resolved": "https://registry.npmjs.org/@esbuild/aix-ppc64/-/aix-ppc64-0.24.2.tgz",
+      "integrity": "sha512-thpVCb/rhxE/BnMLQ7GReQLLN8q9qbHmI55F4489/ByVg2aQaQ6kbcLb6FHkocZzQhxc4gx0sCk0tJkKBFzDhA==",
       "cpu": [
         "ppc64"
       ],
       "dev": true,
-      "license": "MIT",
       "optional": true,
       "os": [
         "aix"
@@ -62,14 +61,13 @@
       }
     },
     "node_modules/@esbuild/android-arm": {
-      "version": "0.24.0",
-      "resolved": "https://registry.npmjs.org/@esbuild/android-arm/-/android-arm-0.24.0.tgz",
-      "integrity": "sha512-arAtTPo76fJ/ICkXWetLCc9EwEHKaeya4vMrReVlEIUCAUncH7M4bhMQ+M9Vf+FFOZJdTNMXNBrWwW+OXWpSew==",
+      "version": "0.24.2",
+      "resolved": "https://registry.npmjs.org/@esbuild/android-arm/-/android-arm-0.24.2.tgz",
+      "integrity": "sha512-tmwl4hJkCfNHwFB3nBa8z1Uy3ypZpxqxfTQOcHX+xRByyYgunVbZ9MzUUfb0RxaHIMnbHagwAxuTL+tnNM+1/Q==",
       "cpu": [
         "arm"
       ],
       "dev": true,
-      "license": "MIT",
       "optional": true,
       "os": [
         "android"
@@ -79,14 +77,13 @@
       }
     },
     "node_modules/@esbuild/android-arm64": {
-      "version": "0.24.0",
-      "resolved": "https://registry.npmjs.org/@esbuild/android-arm64/-/android-arm64-0.24.0.tgz",
-      "integrity": "sha512-Vsm497xFM7tTIPYK9bNTYJyF/lsP590Qc1WxJdlB6ljCbdZKU9SY8i7+Iin4kyhV/KV5J2rOKsBQbB77Ab7L/w==",
+      "version": "0.24.2",
+      "resolved": "https://registry.npmjs.org/@esbuild/android-arm64/-/android-arm64-0.24.2.tgz",
+      "integrity": "sha512-cNLgeqCqV8WxfcTIOeL4OAtSmL8JjcN6m09XIgro1Wi7cF4t/THaWEa7eL5CMoMBdjoHOTh/vwTO/o2TRXIyzg==",
       "cpu": [
         "arm64"
       ],
       "dev": true,
-      "license": "MIT",
       "optional": true,
       "os": [
         "android"
@@ -96,14 +93,13 @@
       }
     },
     "node_modules/@esbuild/android-x64": {
-      "version": "0.24.0",
-      "resolved": "https://registry.npmjs.org/@esbuild/android-x64/-/android-x64-0.24.0.tgz",
-      "integrity": "sha512-t8GrvnFkiIY7pa7mMgJd7p8p8qqYIz1NYiAoKc75Zyv73L3DZW++oYMSHPRarcotTKuSs6m3hTOa5CKHaS02TQ==",
+      "version": "0.24.2",
+      "resolved": "https://registry.npmjs.org/@esbuild/android-x64/-/android-x64-0.24.2.tgz",
+      "integrity": "sha512-B6Q0YQDqMx9D7rvIcsXfmJfvUYLoP722bgfBlO5cGvNVb5V/+Y7nhBE3mHV9OpxBf4eAS2S68KZztiPaWq4XYw==",
       "cpu": [
         "x64"
       ],
       "dev": true,
-      "license": "MIT",
       "optional": true,
       "os": [
         "android"
@@ -113,14 +109,13 @@
       }
     },
     "node_modules/@esbuild/darwin-arm64": {
-      "version": "0.24.0",
-      "resolved": "https://registry.npmjs.org/@esbuild/darwin-arm64/-/darwin-arm64-0.24.0.tgz",
-      "integrity": "sha512-CKyDpRbK1hXwv79soeTJNHb5EiG6ct3efd/FTPdzOWdbZZfGhpbcqIpiD0+vwmpu0wTIL97ZRPZu8vUt46nBSw==",
+      "version": "0.24.2",
+      "resolved": "https://registry.npmjs.org/@esbuild/darwin-arm64/-/darwin-arm64-0.24.2.tgz",
+      "integrity": "sha512-kj3AnYWc+CekmZnS5IPu9D+HWtUI49hbnyqk0FLEJDbzCIQt7hg7ucF1SQAilhtYpIujfaHr6O0UHlzzSPdOeA==",
       "cpu": [
         "arm64"
       ],
       "dev": true,
-      "license": "MIT",
       "optional": true,
       "os": [
         "darwin"
@@ -130,14 +125,13 @@
       }
     },
     "node_modules/@esbuild/darwin-x64": {
-      "version": "0.24.0",
-      "resolved": "https://registry.npmjs.org/@esbuild/darwin-x64/-/darwin-x64-0.24.0.tgz",
-      "integrity": "sha512-rgtz6flkVkh58od4PwTRqxbKH9cOjaXCMZgWD905JOzjFKW+7EiUObfd/Kav+A6Gyud6WZk9w+xu6QLytdi2OA==",
+      "version": "0.24.2",
+      "resolved": "https://registry.npmjs.org/@esbuild/darwin-x64/-/darwin-x64-0.24.2.tgz",
+      "integrity": "sha512-WeSrmwwHaPkNR5H3yYfowhZcbriGqooyu3zI/3GGpF8AyUdsrrP0X6KumITGA9WOyiJavnGZUwPGvxvwfWPHIA==",
       "cpu": [
         "x64"
       ],
       "dev": true,
-      "license": "MIT",
       "optional": true,
       "os": [
         "darwin"
@@ -147,14 +141,13 @@
       }
     },
     "node_modules/@esbuild/freebsd-arm64": {
-      "version": "0.24.0",
-      "resolved": "https://registry.npmjs.org/@esbuild/freebsd-arm64/-/freebsd-arm64-0.24.0.tgz",
-      "integrity": "sha512-6Mtdq5nHggwfDNLAHkPlyLBpE5L6hwsuXZX8XNmHno9JuL2+bg2BX5tRkwjyfn6sKbxZTq68suOjgWqCicvPXA==",
+      "version": "0.24.2",
+      "resolved": "https://registry.npmjs.org/@esbuild/freebsd-arm64/-/freebsd-arm64-0.24.2.tgz",
+      "integrity": "sha512-UN8HXjtJ0k/Mj6a9+5u6+2eZ2ERD7Edt1Q9IZiB5UZAIdPnVKDoG7mdTVGhHJIeEml60JteamR3qhsr1r8gXvg==",
       "cpu": [
         "arm64"
       ],
       "dev": true,
-      "license": "MIT",
       "optional": true,
       "os": [
         "freebsd"
@@ -164,14 +157,13 @@
       }
     },
     "node_modules/@esbuild/freebsd-x64": {
-      "version": "0.24.0",
-      "resolved": "https://registry.npmjs.org/@esbuild/freebsd-x64/-/freebsd-x64-0.24.0.tgz",
-      "integrity": "sha512-D3H+xh3/zphoX8ck4S2RxKR6gHlHDXXzOf6f/9dbFt/NRBDIE33+cVa49Kil4WUjxMGW0ZIYBYtaGCa2+OsQwQ==",
+      "version": "0.24.2",
+      "resolved": "https://registry.npmjs.org/@esbuild/freebsd-x64/-/freebsd-x64-0.24.2.tgz",
+      "integrity": "sha512-TvW7wE/89PYW+IevEJXZ5sF6gJRDY/14hyIGFXdIucxCsbRmLUcjseQu1SyTko+2idmCw94TgyaEZi9HUSOe3Q==",
       "cpu": [
         "x64"
       ],
       "dev": true,
-      "license": "MIT",
       "optional": true,
       "os": [
         "freebsd"
@@ -181,14 +173,13 @@
       }
     },
     "node_modules/@esbuild/linux-arm": {
-      "version": "0.24.0",
-      "resolved": "https://registry.npmjs.org/@esbuild/linux-arm/-/linux-arm-0.24.0.tgz",
-      "integrity": "sha512-gJKIi2IjRo5G6Glxb8d3DzYXlxdEj2NlkixPsqePSZMhLudqPhtZ4BUrpIuTjJYXxvF9njql+vRjB2oaC9XpBw==",
+      "version": "0.24.2",
+      "resolved": "https://registry.npmjs.org/@esbuild/linux-arm/-/linux-arm-0.24.2.tgz",
+      "integrity": "sha512-n0WRM/gWIdU29J57hJyUdIsk0WarGd6To0s+Y+LwvlC55wt+GT/OgkwoXCXvIue1i1sSNWblHEig00GBWiJgfA==",
       "cpu": [
         "arm"
       ],
       "dev": true,
-      "license": "MIT",
       "optional": true,
       "os": [
         "linux"
@@ -198,14 +189,13 @@
       }
     },
     "node_modules/@esbuild/linux-arm64": {
-      "version": "0.24.0",
-      "resolved": "https://registry.npmjs.org/@esbuild/linux-arm64/-/linux-arm64-0.24.0.tgz",
-      "integrity": "sha512-TDijPXTOeE3eaMkRYpcy3LarIg13dS9wWHRdwYRnzlwlA370rNdZqbcp0WTyyV/k2zSxfko52+C7jU5F9Tfj1g==",
+      "version": "0.24.2",
+      "resolved": "https://registry.npmjs.org/@esbuild/linux-arm64/-/linux-arm64-0.24.2.tgz",
+      "integrity": "sha512-7HnAD6074BW43YvvUmE/35Id9/NB7BeX5EoNkK9obndmZBUk8xmJJeU7DwmUeN7tkysslb2eSl6CTrYz6oEMQg==",
       "cpu": [
         "arm64"
       ],
       "dev": true,
-      "license": "MIT",
       "optional": true,
       "os": [
         "linux"
@@ -215,14 +205,13 @@
       }
     },
     "node_modules/@esbuild/linux-ia32": {
-      "version": "0.24.0",
-      "resolved": "https://registry.npmjs.org/@esbuild/linux-ia32/-/linux-ia32-0.24.0.tgz",
-      "integrity": "sha512-K40ip1LAcA0byL05TbCQ4yJ4swvnbzHscRmUilrmP9Am7//0UjPreh4lpYzvThT2Quw66MhjG//20mrufm40mA==",
+      "version": "0.24.2",
+      "resolved": "https://registry.npmjs.org/@esbuild/linux-ia32/-/linux-ia32-0.24.2.tgz",
+      "integrity": "sha512-sfv0tGPQhcZOgTKO3oBE9xpHuUqguHvSo4jl+wjnKwFpapx+vUDcawbwPNuBIAYdRAvIDBfZVvXprIj3HA+Ugw==",
       "cpu": [
         "ia32"
       ],
       "dev": true,
-      "license": "MIT",
       "optional": true,
       "os": [
         "linux"
@@ -232,14 +221,13 @@
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     },
     "node_modules/@esbuild/linux-loong64": {
-      "version": "0.24.0",
-      "resolved": "https://registry.npmjs.org/@esbuild/linux-loong64/-/linux-loong64-0.24.0.tgz",
-      "integrity": "sha512-0mswrYP/9ai+CU0BzBfPMZ8RVm3RGAN/lmOMgW4aFUSOQBjA31UP8Mr6DDhWSuMwj7jaWOT0p0WoZ6jeHhrD7g==",
+      "version": "0.24.2",
+      "resolved": "https://registry.npmjs.org/@esbuild/linux-loong64/-/linux-loong64-0.24.2.tgz",
+      "integrity": "sha512-CN9AZr8kEndGooS35ntToZLTQLHEjtVB5n7dl8ZcTZMonJ7CCfStrYhrzF97eAecqVbVJ7APOEe18RPI4KLhwQ==",
       "cpu": [
         "loong64"
       ],
       "dev": true,
-      "license": "MIT",
       "optional": true,
       "os": [
         "linux"
@@ -249,14 +237,13 @@
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     },
     "node_modules/@esbuild/linux-mips64el": {
-      "version": "0.24.0",
-      "resolved": "https://registry.npmjs.org/@esbuild/linux-mips64el/-/linux-mips64el-0.24.0.tgz",
-      "integrity": "sha512-hIKvXm0/3w/5+RDtCJeXqMZGkI2s4oMUGj3/jM0QzhgIASWrGO5/RlzAzm5nNh/awHE0A19h/CvHQe6FaBNrRA==",
+      "version": "0.24.2",
+      "resolved": "https://registry.npmjs.org/@esbuild/linux-mips64el/-/linux-mips64el-0.24.2.tgz",
+      "integrity": "sha512-iMkk7qr/wl3exJATwkISxI7kTcmHKE+BlymIAbHO8xanq/TjHaaVThFF6ipWzPHryoFsesNQJPE/3wFJw4+huw==",
       "cpu": [
         "mips64el"
       ],
       "dev": true,
-      "license": "MIT",
       "optional": true,
       "os": [
         "linux"
@@ -266,14 +253,13 @@
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     },
     "node_modules/@esbuild/linux-ppc64": {
-      "version": "0.24.0",
-      "resolved": "https://registry.npmjs.org/@esbuild/linux-ppc64/-/linux-ppc64-0.24.0.tgz",
-      "integrity": "sha512-HcZh5BNq0aC52UoocJxaKORfFODWXZxtBaaZNuN3PUX3MoDsChsZqopzi5UupRhPHSEHotoiptqikjN/B77mYQ==",
+      "version": "0.24.2",
+      "resolved": "https://registry.npmjs.org/@esbuild/linux-ppc64/-/linux-ppc64-0.24.2.tgz",
+      "integrity": "sha512-shsVrgCZ57Vr2L8mm39kO5PPIb+843FStGt7sGGoqiiWYconSxwTiuswC1VJZLCjNiMLAMh34jg4VSEQb+iEbw==",
       "cpu": [
         "ppc64"
       ],
       "dev": true,
-      "license": "MIT",
       "optional": true,
       "os": [
         "linux"
@@ -283,14 +269,13 @@
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     "node_modules/@esbuild/linux-riscv64": {
-      "version": "0.24.0",
-      "resolved": "https://registry.npmjs.org/@esbuild/linux-riscv64/-/linux-riscv64-0.24.0.tgz",
-      "integrity": "sha512-bEh7dMn/h3QxeR2KTy1DUszQjUrIHPZKyO6aN1X4BCnhfYhuQqedHaa5MxSQA/06j3GpiIlFGSsy1c7Gf9padw==",
+      "version": "0.24.2",
+      "resolved": "https://registry.npmjs.org/@esbuild/linux-riscv64/-/linux-riscv64-0.24.2.tgz",
+      "integrity": "sha512-4eSFWnU9Hhd68fW16GD0TINewo1L6dRrB+oLNNbYyMUAeOD2yCK5KXGK1GH4qD/kT+bTEXjsyTCiJGHPZ3eM9Q==",
       "cpu": [
         "riscv64"
       ],
       "dev": true,
-      "license": "MIT",
       "optional": true,
       "os": [
         "linux"
@@ -300,14 +285,13 @@
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     "node_modules/@esbuild/linux-s390x": {
-      "version": "0.24.0",
-      "resolved": "https://registry.npmjs.org/@esbuild/linux-s390x/-/linux-s390x-0.24.0.tgz",
-      "integrity": "sha512-ZcQ6+qRkw1UcZGPyrCiHHkmBaj9SiCD8Oqd556HldP+QlpUIe2Wgn3ehQGVoPOvZvtHm8HPx+bH20c9pvbkX3g==",
+      "version": "0.24.2",
+      "resolved": "https://registry.npmjs.org/@esbuild/linux-s390x/-/linux-s390x-0.24.2.tgz",
+      "integrity": "sha512-S0Bh0A53b0YHL2XEXC20bHLuGMOhFDO6GN4b3YjRLK//Ep3ql3erpNcPlEFed93hsQAjAQDNsvcK+hV90FubSw==",
       "cpu": [
         "s390x"
       ],
       "dev": true,
-      "license": "MIT",
       "optional": true,
       "os": [
         "linux"
@@ -317,14 +301,13 @@
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     "node_modules/@esbuild/linux-x64": {
-      "version": "0.24.0",
-      "resolved": "https://registry.npmjs.org/@esbuild/linux-x64/-/linux-x64-0.24.0.tgz",
-      "integrity": "sha512-vbutsFqQ+foy3wSSbmjBXXIJ6PL3scghJoM8zCL142cGaZKAdCZHyf+Bpu/MmX9zT9Q0zFBVKb36Ma5Fzfa8xA==",
+      "version": "0.24.2",
+      "resolved": "https://registry.npmjs.org/@esbuild/linux-x64/-/linux-x64-0.24.2.tgz",
+      "integrity": "sha512-8Qi4nQcCTbLnK9WoMjdC9NiTG6/E38RNICU6sUNqK0QFxCYgoARqVqxdFmWkdonVsvGqWhmm7MO0jyTqLqwj0Q==",
       "cpu": [
         "x64"
       ],
       "dev": true,
-      "license": "MIT",
       "optional": true,
       "os": [
         "linux"
@@ -333,15 +316,30 @@
         "node": ">=18"
       }
     },
+    "node_modules/@esbuild/netbsd-arm64": {
+      "version": "0.24.2",
+      "resolved": "https://registry.npmjs.org/@esbuild/netbsd-arm64/-/netbsd-arm64-0.24.2.tgz",
+      "integrity": "sha512-wuLK/VztRRpMt9zyHSazyCVdCXlpHkKm34WUyinD2lzK07FAHTq0KQvZZlXikNWkDGoT6x3TD51jKQ7gMVpopw==",
+      "cpu": [
+        "arm64"
+      ],
+      "dev": true,
+      "optional": true,
+      "os": [
+        "netbsd"
+      ],
+      "engines": {
+        "node": ">=18"
+      }
+    },
     "node_modules/@esbuild/netbsd-x64": {
-      "version": "0.24.0",
-      "resolved": "https://registry.npmjs.org/@esbuild/netbsd-x64/-/netbsd-x64-0.24.0.tgz",
-      "integrity": "sha512-hjQ0R/ulkO8fCYFsG0FZoH+pWgTTDreqpqY7UnQntnaKv95uP5iW3+dChxnx7C3trQQU40S+OgWhUVwCjVFLvg==",
+      "version": "0.24.2",
+      "resolved": "https://registry.npmjs.org/@esbuild/netbsd-x64/-/netbsd-x64-0.24.2.tgz",
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       "cpu": [
         "x64"
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       "dev": true,
-      "license": "MIT",
       "optional": true,
       "os": [
         "netbsd"
@@ -351,14 +349,13 @@
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     "node_modules/@esbuild/openbsd-arm64": {
-      "version": "0.24.0",
-      "resolved": "https://registry.npmjs.org/@esbuild/openbsd-arm64/-/openbsd-arm64-0.24.0.tgz",
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+      "resolved": "https://registry.npmjs.org/@esbuild/openbsd-arm64/-/openbsd-arm64-0.24.2.tgz",
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       "cpu": [
         "arm64"
       ],
       "dev": true,
-      "license": "MIT",
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@@ -368,14 +365,13 @@
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-      "resolved": "https://registry.npmjs.org/@esbuild/openbsd-x64/-/openbsd-x64-0.24.0.tgz",
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@@ -951,7 +936,6 @@
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@@ -963,13 +947,12 @@
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-      "license": "ISC"
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@@ -985,9 +968,8 @@
           "url": "https://github.com/sponsors/ai"
         }
       ],
-      "license": "MIT",
       "dependencies": {
-        "nanoid": "^3.3.7",
+        "nanoid": "^3.3.8",
         "picocolors": "^1.1.1",
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@@ -1002,11 +984,10 @@
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@@ -1018,24 +999,25 @@
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-        "@rollup/rollup-win32-ia32-msvc": "4.28.0",
-        "@rollup/rollup-win32-x64-msvc": "4.28.0",
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+        "@rollup/rollup-android-arm64": "4.34.6",
+        "@rollup/rollup-darwin-arm64": "4.34.6",
+        "@rollup/rollup-darwin-x64": "4.34.6",
+        "@rollup/rollup-freebsd-arm64": "4.34.6",
+        "@rollup/rollup-freebsd-x64": "4.34.6",
+        "@rollup/rollup-linux-arm-gnueabihf": "4.34.6",
+        "@rollup/rollup-linux-arm-musleabihf": "4.34.6",
+        "@rollup/rollup-linux-arm64-gnu": "4.34.6",
+        "@rollup/rollup-linux-arm64-musl": "4.34.6",
+        "@rollup/rollup-linux-loongarch64-gnu": "4.34.6",
+        "@rollup/rollup-linux-powerpc64le-gnu": "4.34.6",
+        "@rollup/rollup-linux-riscv64-gnu": "4.34.6",
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@@ -1101,15 +1082,14 @@
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-        "postcss": "^8.4.49",
-        "rollup": "^4.23.0"
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examples/simple-chatbot/client/javascript/package.json

@@ -12,7 +12,7 @@
   "license": "ISC",
   "description": "",
   "devDependencies": {
-    "vite": "^6.0.2"
+    "vite": "^6.0.9"
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   "dependencies": {
     "@pipecat-ai/client-js": "^0.3.2",

examples/simple-chatbot/client/react/package-lock.json

@@ -25,7 +25,7 @@
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@@ -370,14 +369,13 @@
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-      "license": "MIT",
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@@ -387,14 +385,13 @@
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@@ -404,14 +401,13 @@
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+      "version": "0.24.2",
+      "resolved": "https://registry.npmjs.org/@esbuild/android-x64/-/android-x64-0.24.2.tgz",
+      "integrity": "sha512-B6Q0YQDqMx9D7rvIcsXfmJfvUYLoP722bgfBlO5cGvNVb5V/+Y7nhBE3mHV9OpxBf4eAS2S68KZztiPaWq4XYw==",
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@@ -421,14 +417,13 @@
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+      "resolved": "https://registry.npmjs.org/@esbuild/darwin-arm64/-/darwin-arm64-0.24.2.tgz",
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@@ -438,14 +433,13 @@
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+      "resolved": "https://registry.npmjs.org/@esbuild/darwin-x64/-/darwin-x64-0.24.2.tgz",
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@@ -455,14 +449,13 @@
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+      "resolved": "https://registry.npmjs.org/@esbuild/freebsd-arm64/-/freebsd-arm64-0.24.2.tgz",
+      "integrity": "sha512-UN8HXjtJ0k/Mj6a9+5u6+2eZ2ERD7Edt1Q9IZiB5UZAIdPnVKDoG7mdTVGhHJIeEml60JteamR3qhsr1r8gXvg==",
       "cpu": [
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       "dev": true,
-      "license": "MIT",
       "optional": true,
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@@ -472,14 +465,13 @@
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+      "version": "0.24.2",
+      "resolved": "https://registry.npmjs.org/@esbuild/freebsd-x64/-/freebsd-x64-0.24.2.tgz",
+      "integrity": "sha512-TvW7wE/89PYW+IevEJXZ5sF6gJRDY/14hyIGFXdIucxCsbRmLUcjseQu1SyTko+2idmCw94TgyaEZi9HUSOe3Q==",
       "cpu": [
         "x64"
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       "dev": true,
-      "license": "MIT",
       "optional": true,
       "os": [
         "freebsd"
@@ -489,14 +481,13 @@
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@@ -3213,7 +3197,6 @@
       "resolved": "https://registry.npmjs.org/source-map-js/-/source-map-js-1.2.1.tgz",
       "integrity": "sha512-UXWMKhLOwVKb728IUtQPXxfYU+usdybtUrK/8uGE8CQMvrhOpwvzDBwj0QhSL7MQc7vIsISBG8VQ8+IDQxpfQA==",
       "dev": true,
-      "license": "BSD-3-Clause",
       "engines": {
         "node": ">=0.10.0"
       }
@@ -3395,15 +3378,14 @@
       }
     },
     "node_modules/vite": {
-      "version": "6.0.2",
-      "resolved": "https://registry.npmjs.org/vite/-/vite-6.0.2.tgz",
-      "integrity": "sha512-XdQ+VsY2tJpBsKGs0wf3U/+azx8BBpYRHFAyKm5VeEZNOJZRB63q7Sc8Iup3k0TrN3KO6QgyzFf+opSbfY1y0g==",
+      "version": "6.1.0",
+      "resolved": "https://registry.npmjs.org/vite/-/vite-6.1.0.tgz",
+      "integrity": "sha512-RjjMipCKVoR4hVfPY6GQTgveinjNuyLw+qruksLDvA5ktI1150VmcMBKmQaEWJhg/j6Uaf6dNCNA0AfdzUb/hQ==",
       "dev": true,
-      "license": "MIT",
       "dependencies": {
-        "esbuild": "^0.24.0",
-        "postcss": "^8.4.49",
-        "rollup": "^4.23.0"
+        "esbuild": "^0.24.2",
+        "postcss": "^8.5.1",
+        "rollup": "^4.30.1"
       },
       "bin": {
         "vite": "bin/vite.js"

examples/simple-chatbot/client/react/package.json

@@ -27,6 +27,6 @@
     "globals": "^15.12.0",
     "typescript": "~5.6.2",
     "typescript-eslint": "^8.15.0",
-    "vite": "^6.0.1"
+    "vite": "^6.0.9"
   }
 }

examples/simple-chatbot/server/bot-gemini.py

@@ -40,7 +40,7 @@ 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, RTVIProcessor
+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
 
@@ -121,8 +121,6 @@ async def main():
             token,
             "Chatbot",
             DailyParams(
-                audio_in_sample_rate=16000,
-                audio_out_sample_rate=24000,
                 audio_out_enabled=True,
                 camera_out_enabled=True,
                 camera_out_width=1024,
@@ -178,7 +176,7 @@ async def main():
                 allow_interruptions=True,
                 enable_metrics=True,
                 enable_usage_metrics=True,
-                observers=[rtvi.observer()],
+                observers=[RTVIObserver(rtvi)],
             ),
         )
         await task.queue_frame(quiet_frame)

examples/simple-chatbot/server/bot-openai.py

@@ -40,7 +40,7 @@ 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, RTVIProcessor
+from pipecat.processors.frameworks.rtvi import RTVIConfig, RTVIObserver, RTVIProcessor
 from pipecat.services.elevenlabs import ElevenLabsTTSService
 from pipecat.services.openai import OpenAILLMService
 from pipecat.transports.services.daily import DailyParams, DailyTransport
@@ -202,7 +202,7 @@ async def main():
                 allow_interruptions=True,
                 enable_metrics=True,
                 enable_usage_metrics=True,
-                observers=[rtvi.observer()],
+                observers=[RTVIObserver(rtvi)],
             ),
         )
         await task.queue_frame(quiet_frame)

examples/storytelling-chatbot/README.md

@@ -74,6 +74,8 @@ If you'd like to run a custom domain or port:
 
 ➡️ Open the host URL in your browser `http://localhost:7860`
 
+If you've run previous versions of the demo, make sure to set `ENV=dev`, and remove the `RUN_AS_VM` line from the .env file.
+
 ---
 
 ## Improvements to make

examples/storytelling-chatbot/env.example

@@ -3,6 +3,4 @@ DAILY_SAMPLE_ROOM_URL=
 ELEVENLABS_API_KEY=
 ELEVENLABS_VOICE_ID=
 GOOGLE_API_KEY=
-
-ENV= # dev | production
-RUN_AS_VM= # Set this if you want to run bots on process (not launch a new VM)
+ENV=dev

examples/storytelling-chatbot/requirements.txt

@@ -2,5 +2,4 @@ async_timeout
 fastapi
 uvicorn
 python-dotenv
--e "../..[daily,silero,openai,fal,cartesia,google]"
--e "../../../python-genai"
+pipecat-ai[daily,silero,openai,cartesia,google]

examples/storytelling-chatbot/src/bot.py

@@ -23,8 +23,7 @@ from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
 from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
 from pipecat.services.elevenlabs import ElevenLabsTTSService
-from pipecat.services.fal import FalImageGenService
-from pipecat.services.google import GoogleLLMService
+from pipecat.services.google import GoogleImageGenService, GoogleLLMService
 from pipecat.transports.services.daily import (
     DailyParams,
     DailyTransport,

examples/studypal/requirements.txt

@@ -1,5 +1,5 @@
 beautifulsoup4==4.12.3
 pypdf==4.3.1
 tiktoken==0.7.0
-pipecat-ai[daily,cartesia,openai,silero]==0.0.40
+pipecat-ai[daily,cartesia,openai,silero]
 python-dotenv==1.0.1

examples/studypal/runner.py

@@ -6,6 +6,7 @@
 
 import argparse
 import os
+from typing import Optional
 
 import aiohttp
 
@@ -18,7 +19,7 @@ async def configure(aiohttp_session: aiohttp.ClientSession):
 
 
 async def configure_with_args(
-    aiohttp_session: aiohttp.ClientSession, parser: argparse.ArgumentParser | None = None
+    aiohttp_session: aiohttp.ClientSession, parser: Optional[argparse.ArgumentParser] = None
 ):
     if not parser:
         parser = argparse.ArgumentParser(description="Daily AI SDK Bot Sample")

examples/studypal/studypal.py

@@ -112,7 +112,6 @@ async def main():
             token,
             "studypal",
             DailyParams(
-                audio_out_sample_rate=44100,
                 audio_out_enabled=True,
                 transcription_enabled=True,
                 vad_enabled=True,
@@ -124,7 +123,6 @@ async def main():
             api_key=os.getenv("CARTESIA_API_KEY"),
             voice_id=os.getenv("CARTESIA_VOICE_ID", "4d2fd738-3b3d-4368-957a-bb4805275bd9"),
             # British Narration Lady: 4d2fd738-3b3d-4368-957a-bb4805275bd9
-            sample_rate=44100,
         )
 
         llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o-mini")
@@ -155,7 +153,12 @@ Your task is to help the user understand and learn from this article in 2 senten
             ]
         )
 
-        task = PipelineTask(pipeline, PipelineParams(allow_interruptions=True, enable_metrics=True))
+        task = PipelineTask(
+            pipeline,
+            PipelineParams(
+                audio_out_sample_rate=44100, allow_interruptions=True, enable_metrics=True
+            ),
+        )
 
         @transport.event_handler("on_first_participant_joined")
         async def on_first_participant_joined(transport, participant):

examples/telnyx-chatbot/bot.py

@@ -11,14 +11,13 @@ from dotenv import load_dotenv
 from loguru import logger
 
 from pipecat.audio.vad.silero import SileroVADAnalyzer
-from pipecat.frames.frames import EndFrame
 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.serializers.telnyx import TelnyxFrameSerializer
+from pipecat.services.cartesia import CartesiaTTSService
 from pipecat.services.deepgram import DeepgramSTTService
-from pipecat.services.elevenlabs import ElevenLabsTTSService, Language
 from pipecat.services.openai import OpenAILLMService
 from pipecat.transports.network.fastapi_websocket import (
     FastAPIWebsocketParams,
@@ -31,7 +30,12 @@ logger.remove(0)
 logger.add(sys.stderr, level="DEBUG")
 
 
-async def run_bot(websocket_client, stream_id, outbound_encoding, inbound_encoding):
+async def run_bot(
+    websocket_client,
+    stream_id: str,
+    outbound_encoding: str,
+    inbound_encoding: str,
+):
     transport = FastAPIWebsocketTransport(
         websocket=websocket_client,
         params=FastAPIWebsocketParams(
@@ -48,11 +52,9 @@ async def run_bot(websocket_client, stream_id, outbound_encoding, inbound_encodi
 
     stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
 
-    tts = ElevenLabsTTSService(
-        api_key=os.getenv("ELEVENLABS_API_KEY"),
-        voice_id="CwhRBWXzGAHq8TQ4Fs17",
-        output_format="pcm_24000",
-        params=ElevenLabsTTSService.InputParams(language=Language.EN),
+    tts = CartesiaTTSService(
+        api_key=os.getenv("CARTESIA_API_KEY"),
+        voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22",  # British Lady
     )
 
     messages = [
@@ -77,7 +79,14 @@ async def run_bot(websocket_client, stream_id, outbound_encoding, inbound_encodi
         ]
     )
 
-    task = PipelineTask(pipeline, params=PipelineParams(allow_interruptions=True))
+    task = PipelineTask(
+        pipeline,
+        params=PipelineParams(
+            audio_in_sample_rate=8000,
+            audio_out_sample_rate=8000,
+            allow_interruptions=True,
+        ),
+    )
 
     @transport.event_handler("on_client_connected")
     async def on_client_connected(transport, client):
@@ -87,7 +96,7 @@ async def run_bot(websocket_client, stream_id, outbound_encoding, inbound_encodi
 
     @transport.event_handler("on_client_disconnected")
     async def on_client_disconnected(transport, client):
-        await task.queue_frames([EndFrame()])
+        await task.cancel()
 
     runner = PipelineRunner(handle_sigint=False)
 

examples/twilio-chatbot/README.md

@@ -107,3 +107,34 @@ The server will start on port 8765. Keep this running while you test with Twilio
 ## Usage
 
 To start a call, simply make a call to your configured Twilio phone number. The webhook URL will direct the call to your FastAPI application, which will handle it accordingly.
+
+## Testing
+
+It is also possible to automatically test the server without making phone calls by using a software client.
+
+First, update `templates/streams.xml` to point to your server's websocket endpoint. For example:
+
+```
+<?xml version="1.0" encoding="UTF-8"?>
+<Response>
+  <Connect>
+    <Stream url="ws://localhost:8765/ws"></Stream>
+  </Connect>
+  <Pause length="40"/>
+</Response>
+```
+
+Then, start the server with `-t` to indicate we are testing:
+
+```sh
+# Make sure you’re in the project directory and your virtual environment is activated
+python server.py -t
+```
+
+Finally, just point the client to the server's URL:
+
+```sh
+python client.py -u http://localhost:8765 -c 2
+```
+
+where `-c` allows you to create multiple concurrent clients.

examples/twilio-chatbot/bot.py

@@ -4,10 +4,15 @@
 # SPDX-License-Identifier: BSD 2-Clause License
 #
 
+import datetime
+import io
 import os
 import sys
+import wave
 
+import aiofiles
 from dotenv import load_dotenv
+from fastapi import WebSocket
 from loguru import logger
 
 from pipecat.audio.vad.silero import SileroVADAnalyzer
@@ -15,6 +20,7 @@ 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.audio.audio_buffer_processor import AudioBufferProcessor
 from pipecat.serializers.twilio import TwilioFrameSerializer
 from pipecat.services.cartesia import CartesiaTTSService
 from pipecat.services.deepgram import DeepgramSTTService
@@ -30,10 +36,29 @@ logger.remove(0)
 logger.add(sys.stderr, level="DEBUG")
 
 
-async def run_bot(websocket_client, stream_sid):
+async def save_audio(server_name: str, audio: bytes, sample_rate: int, num_channels: int):
+    if len(audio) > 0:
+        filename = (
+            f"{server_name}_recording_{datetime.datetime.now().strftime('%Y%m%d_%H%M%S')}.wav"
+        )
+        with io.BytesIO() as buffer:
+            with wave.open(buffer, "wb") as wf:
+                wf.setsampwidth(2)
+                wf.setnchannels(num_channels)
+                wf.setframerate(sample_rate)
+                wf.writeframes(audio)
+            async with aiofiles.open(filename, "wb") as file:
+                await file.write(buffer.getvalue())
+        logger.info(f"Merged audio saved to {filename}")
+    else:
+        logger.info("No audio data to save")
+
+
+async def run_bot(websocket_client: WebSocket, stream_sid: str, testing: bool):
     transport = FastAPIWebsocketTransport(
         websocket=websocket_client,
         params=FastAPIWebsocketParams(
+            audio_in_enabled=True,
             audio_out_enabled=True,
             add_wav_header=False,
             vad_enabled=True,
@@ -45,23 +70,28 @@ async def run_bot(websocket_client, stream_sid):
 
     llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
 
-    stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
+    stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"), audio_passthrough=True)
 
     tts = CartesiaTTSService(
         api_key=os.getenv("CARTESIA_API_KEY"),
         voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22",  # British Lady
+        push_silence_after_stop=testing,
     )
 
     messages = [
         {
             "role": "system",
-            "content": "You are a helpful LLM in an audio call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
+            "content": "You are an elementary teacher in an audio call. Your output will be converted to audio so don't include special characters in your answers. Respond to what the student said in a short short sentence.",
         },
     ]
 
     context = OpenAILLMContext(messages)
     context_aggregator = llm.create_context_aggregator(context)
 
+    # NOTE: Watch out! This will save all the conversation in memory. You can
+    # pass `buffer_size` to get periodic callbacks.
+    audiobuffer = AudioBufferProcessor(user_continuous_stream=not testing)
+
     pipeline = Pipeline(
         [
             transport.input(),  # Websocket input from client
@@ -70,14 +100,22 @@ async def run_bot(websocket_client, stream_sid):
             llm,  # LLM
             tts,  # Text-To-Speech
             transport.output(),  # Websocket output to client
+            audiobuffer,  # Used to buffer the audio in the pipeline
             context_aggregator.assistant(),
         ]
     )
 
-    task = PipelineTask(pipeline, params=PipelineParams(allow_interruptions=True))
+    task = PipelineTask(
+        pipeline,
+        params=PipelineParams(
+            audio_in_sample_rate=8000, audio_out_sample_rate=8000, allow_interruptions=True
+        ),
+    )
 
     @transport.event_handler("on_client_connected")
     async def on_client_connected(transport, client):
+        # Start recording.
+        await audiobuffer.start_recording()
         # Kick off the conversation.
         messages.append({"role": "system", "content": "Please introduce yourself to the user."})
         await task.queue_frames([context_aggregator.user().get_context_frame()])
@@ -86,6 +124,15 @@ async def run_bot(websocket_client, stream_sid):
     async def on_client_disconnected(transport, client):
         await task.cancel()
 
-    runner = PipelineRunner(handle_sigint=False)
+    @audiobuffer.event_handler("on_audio_data")
+    async def on_audio_data(buffer, audio, sample_rate, num_channels):
+        server_name = f"server_{websocket_client.client.port}"
+        await save_audio(server_name, audio, sample_rate, num_channels)
+
+    # We use `handle_sigint=False` because `uvicorn` is controlling keyboard
+    # interruptions. We use `force_gc=True` to force garbage collection after
+    # the runner finishes running a task which could be useful for long running
+    # applications with multiple clients connecting.
+    runner = PipelineRunner(handle_sigint=False, force_gc=True)
 
     await runner.run(task)

examples/twilio-chatbot/client.py

@@ -0,0 +1,199 @@
+#
+# Copyright (c) 2025, Daily
+#
+# SPDX-License-Identifier: BSD 2-Clause License
+#
+
+import argparse
+import asyncio
+import datetime
+import io
+import os
+import sys
+import wave
+import xml.etree.ElementTree as ET
+from uuid import uuid4
+
+import aiofiles
+import aiohttp
+from dotenv import load_dotenv
+from loguru import logger
+
+from pipecat.audio.vad.silero import SileroVADAnalyzer
+from pipecat.audio.vad.vad_analyzer import VADParams
+from pipecat.frames.frames import EndFrame, TransportMessageUrgentFrame
+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.audio.audio_buffer_processor import AudioBufferProcessor
+from pipecat.serializers.twilio import TwilioFrameSerializer
+from pipecat.services.cartesia import CartesiaTTSService
+from pipecat.services.deepgram import DeepgramSTTService
+from pipecat.services.openai import OpenAILLMService
+from pipecat.transports.network.websocket_client import (
+    WebsocketClientParams,
+    WebsocketClientTransport,
+)
+
+load_dotenv(override=True)
+
+logger.remove(0)
+logger.add(sys.stderr, level="DEBUG")
+
+
+DEFAULT_CLIENT_DURATION = 30
+
+
+async def download_twiml(server_url: str) -> str:
+    # TODO(aleix): add error checking.
+    async with aiohttp.ClientSession() as session:
+        async with session.post(server_url) as response:
+            return await response.text()
+
+
+def get_stream_url_from_twiml(twiml: str) -> str:
+    root = ET.fromstring(twiml)
+    # TODO(aleix): add error checking.
+    stream_element = root.find(".//Stream")  # Finds the first <Stream> element
+    url = stream_element.get("url")
+    return url
+
+
+async def save_audio(client_name: str, audio: bytes, sample_rate: int, num_channels: int):
+    if len(audio) > 0:
+        filename = (
+            f"{client_name}_recording_{datetime.datetime.now().strftime('%Y%m%d_%H%M%S')}.wav"
+        )
+        with io.BytesIO() as buffer:
+            with wave.open(buffer, "wb") as wf:
+                wf.setsampwidth(2)
+                wf.setnchannels(num_channels)
+                wf.setframerate(sample_rate)
+                wf.writeframes(audio)
+            async with aiofiles.open(filename, "wb") as file:
+                await file.write(buffer.getvalue())
+        logger.info(f"Merged audio saved to {filename}")
+    else:
+        logger.info("No audio data to save")
+
+
+async def run_client(client_name: str, server_url: str, duration_secs: int):
+    twiml = await download_twiml(server_url)
+
+    stream_url = get_stream_url_from_twiml(twiml)
+
+    stream_sid = str(uuid4())
+
+    transport = WebsocketClientTransport(
+        uri=stream_url,
+        params=WebsocketClientParams(
+            audio_in_enabled=True,
+            audio_out_enabled=True,
+            add_wav_header=False,
+            serializer=TwilioFrameSerializer(stream_sid),
+            vad_enabled=True,
+            vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=1.5)),
+            vad_audio_passthrough=True,
+        ),
+    )
+
+    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
+
+    # We let the audio passthrough so we can record the conversation.
+    stt = DeepgramSTTService(
+        api_key=os.getenv("DEEPGRAM_API_KEY"),
+        audio_passthrough=True,
+    )
+
+    tts = CartesiaTTSService(
+        api_key=os.getenv("CARTESIA_API_KEY"),
+        voice_id="e13cae5c-ec59-4f71-b0a6-266df3c9bb8e",  # Madame Mischief
+        push_silence_after_stop=True,
+    )
+
+    messages = [
+        {
+            "role": "system",
+            "content": "You are an 8 year old child. A teacher will explain you new concepts you want to know about. Feel free to change topics whnever you want. Once you are taught something you need to keep asking for clarifications if you think someone your age would not understand what you are being taught.",
+        },
+    ]
+
+    context = OpenAILLMContext(messages)
+    context_aggregator = llm.create_context_aggregator(context)
+
+    # NOTE: Watch out! This will save all the conversation in memory. You can
+    # pass `buffer_size` to get periodic callbacks.
+    audiobuffer = AudioBufferProcessor(user_continuous_stream=False)
+
+    pipeline = Pipeline(
+        [
+            transport.input(),  # Websocket input from server
+            stt,  # Speech-To-Text
+            context_aggregator.user(),
+            llm,  # LLM
+            tts,  # Text-To-Speech
+            transport.output(),  # Websocket output to server
+            audiobuffer,  # Used to buffer the audio in the pipeline
+            context_aggregator.assistant(),
+        ]
+    )
+
+    task = PipelineTask(
+        pipeline,
+        params=PipelineParams(
+            audio_in_sample_rate=8000, audio_out_sample_rate=8000, allow_interruptions=True
+        ),
+    )
+
+    @transport.event_handler("on_connected")
+    async def on_connected(transport: WebsocketClientTransport, client):
+        # Start recording.
+        await audiobuffer.start_recording()
+
+        message = TransportMessageUrgentFrame(
+            message={"event": "connected", "protocol": "Call", "version": "1.0.0"}
+        )
+        await transport.output().send_message(message)
+
+        message = TransportMessageUrgentFrame(
+            message={"event": "start", "streamSid": stream_sid, "start": {"streamSid": stream_sid}}
+        )
+        await transport.output().send_message(message)
+
+    @audiobuffer.event_handler("on_audio_data")
+    async def on_audio_data(buffer, audio, sample_rate, num_channels):
+        await save_audio(client_name, audio, sample_rate, num_channels)
+
+    async def end_call():
+        await asyncio.sleep(duration_secs)
+        await task.queue_frame(EndFrame())
+
+    runner = PipelineRunner()
+
+    await asyncio.gather(runner.run(task), end_call())
+
+
+async def main():
+    parser = argparse.ArgumentParser(description="Pipecat Twilio Chatbot Client")
+    parser.add_argument("-u", "--url", type=str, required=True, help="specify the server URL")
+    parser.add_argument(
+        "-c", "--clients", type=int, required=True, help="number of concurrent clients"
+    )
+    parser.add_argument(
+        "-d",
+        "--duration",
+        type=int,
+        default=DEFAULT_CLIENT_DURATION,
+        help=f"duration of each client in seconds (default: {DEFAULT_CLIENT_DURATION})",
+    )
+    args, _ = parser.parse_known_args()
+
+    clients = []
+    for i in range(args.clients):
+        clients.append(asyncio.create_task(run_client(f"client_{i}", args.url, args.duration)))
+    await asyncio.gather(*clients)
+
+
+if __name__ == "__main__":
+    asyncio.run(main())

examples/twilio-chatbot/server.py

@@ -4,6 +4,7 @@
 # SPDX-License-Identifier: BSD 2-Clause License
 #
 
+import argparse
 import json
 
 import uvicorn
@@ -38,8 +39,16 @@ async def websocket_endpoint(websocket: WebSocket):
     print(call_data, flush=True)
     stream_sid = call_data["start"]["streamSid"]
     print("WebSocket connection accepted")
-    await run_bot(websocket, stream_sid)
+    await run_bot(websocket, stream_sid, app.state.testing)
 
 
 if __name__ == "__main__":
+    parser = argparse.ArgumentParser(description="Pipecat Twilio Chatbot Server")
+    parser.add_argument(
+        "-t", "--test", action="store_true", default=False, help="set the server in testing mode"
+    )
+    args, _ = parser.parse_known_args()
+
+    app.state.testing = args.test
+
     uvicorn.run(app, host="0.0.0.0", port=8765)

examples/websocket-server/bot.py

@@ -17,6 +17,7 @@ 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.serializers.protobuf import ProtobufFrameSerializer
 from pipecat.services.cartesia import CartesiaTTSService
 from pipecat.services.deepgram import DeepgramSTTService
 from pipecat.services.openai import OpenAILLMService
@@ -80,7 +81,7 @@ class SessionTimeoutHandler:
 async def main():
     transport = WebsocketServerTransport(
         params=WebsocketServerParams(
-            audio_out_sample_rate=16000,
+            serializer=ProtobufFrameSerializer(),
             audio_out_enabled=True,
             add_wav_header=True,
             vad_enabled=True,
@@ -97,7 +98,6 @@ async def main():
     tts = CartesiaTTSService(
         api_key=os.getenv("CARTESIA_API_KEY"),
         voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22",  # British Lady
-        sample_rate=16000,
     )
 
     messages = [
@@ -122,7 +122,12 @@ 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, allow_interruptions=True
+        ),
+    )
 
     @transport.event_handler("on_client_connected")
     async def on_client_connected(transport, client):

pyproject.toml

@@ -32,6 +32,7 @@ dependencies = [
     "protobuf~=5.29.3",
     "pydantic~=2.10.5",
     "pyloudnorm~=0.1.1",
+    "resampy~=0.4.3",
     "soxr~=0.5.0"
 ]
 
@@ -40,7 +41,7 @@ Source = "https://github.com/pipecat-ai/pipecat"
 Website = "https://pipecat.ai"
 
 [project.optional-dependencies]
-anthropic = [ "anthropic~=0.39.0" ]
+anthropic = [ "anthropic~=0.45.2" ]
 assemblyai = [ "assemblyai~=0.36.0" ]
 aws = [ "boto3~=1.35.99" ]
 azure = [ "azure-cognitiveservices-speech~=1.42.0", "openai~=1.59.6" ]
@@ -54,7 +55,7 @@ elevenlabs = [ "websockets~=13.1" ]
 fal = [ "fal-client~=0.5.6" ]
 fish = [ "ormsgpack~=1.7.0", "websockets~=13.1" ]
 gladia = [ "websockets~=13.1" ]
-google = [ "google-generativeai~=0.8.3", "google-cloud-texttospeech~=2.24.0", "google-genai~=0.7.0" ]
+google = [ "google-generativeai~=0.8.3", "google-cloud-texttospeech~=2.24.0", "google-genai~=1.0.0" ]
 grok = [ "openai~=1.59.6" ]
 groq = [ "openai~=1.59.6" ]
 gstreamer = [ "pygobject~=3.50.0" ]
@@ -69,9 +70,11 @@ moondream = [ "einops~=0.8.0", "timm~=1.0.13", "transformers~=4.48.0" ]
 nim = [ "openai~=1.59.6" ]
 noisereduce = [ "noisereduce~=3.0.3" ]
 openai = [ "openai~=1.59.6", "websockets~=13.1", "python-deepcompare~=2.1.0" ]
-openpipe = [ "openpipe~=4.43.0" ]
+openpipe = [ "openpipe~=4.45.0" ]
+perplexity = [ "openai~=1.59.6" ]
 playht = [ "pyht~=0.1.6", "websockets~=13.1" ]
 riva = [ "nvidia-riva-client~=2.18.0" ]
+sentry = [ "sentry-sdk~=2.20.0" ]
 silero = [ "onnxruntime~=1.20.1" ]
 simli = [ "simli-ai~=0.1.10"]
 soundfile = [ "soundfile~=0.13.0" ]
@@ -109,3 +112,8 @@ select = [
 
 [tool.ruff.lint.pydocstyle]
 convention = "google"
+
+[tool.coverage.run]
+command_line = "--module pytest"
+source = ["src"]
+omit = ["*/tests/*"]

src/pipecat/audio/filters/krisp_filter.py

@@ -20,6 +20,22 @@ except ModuleNotFoundError as e:
     raise Exception(f"Missing module: {e}")
 
 
+class KrispProcessorManager:
+    """
+    Ensures that only one KrispAudioProcessor instance exists for the entire program.
+    """
+
+    _krisp_instance = None
+
+    @classmethod
+    def get_processor(cls, sample_rate: int, sample_type: str, channels: int, model_path: str):
+        if cls._krisp_instance is None:
+            cls._krisp_instance = KrispAudioProcessor(
+                sample_rate, sample_type, channels, model_path
+            )
+        return cls._krisp_instance
+
+
 class KrispFilter(BaseAudioFilter):
     def __init__(
         self, sample_type: str = "PCM_16", channels: int = 1, model_path: str = None
@@ -48,7 +64,7 @@ class KrispFilter(BaseAudioFilter):
 
     async def start(self, sample_rate: int):
         self._sample_rate = sample_rate
-        self._krisp_processor = KrispAudioProcessor(
+        self._krisp_processor = KrispProcessorManager.get_processor(
             self._sample_rate, self._sample_type, self._channels, self._model_path
         )
 

src/pipecat/audio/resamplers/__init__.py

@@ -0,0 +1 @@
+

src/pipecat/audio/resamplers/base_audio_resampler.py

@@ -0,0 +1,30 @@
+#
+# Copyright (c) 2024–2025, Daily
+#
+# SPDX-License-Identifier: BSD 2-Clause License
+#
+
+from abc import ABC, abstractmethod
+
+
+class BaseAudioResampler(ABC):
+    """Abstract base class for audio resampling. This class defines an
+    interface for audio resampling implementations.
+    """
+
+    @abstractmethod
+    async def resample(self, audio: bytes, in_rate: int, out_rate: int) -> bytes:
+        """
+        Resamples the given audio data to a different sample rate.
+
+        This is an abstract method that must be implemented in subclasses.
+
+        Parameters:
+            audio (bytes): The audio data to be resampled, represented as a byte string.
+            in_rate (int): The original sample rate of the audio data (in Hz).
+            out_rate (int): The desired sample rate for the resampled audio data (in Hz).
+
+        Returns:
+            bytes: The resampled audio data as a byte string.
+        """
+        pass

src/pipecat/audio/resamplers/resampy_resampler.py

@@ -0,0 +1,25 @@
+#
+# Copyright (c) 2024–2025, Daily
+#
+# SPDX-License-Identifier: BSD 2-Clause License
+#
+
+import numpy as np
+import resampy
+
+from pipecat.audio.resamplers.base_audio_resampler import BaseAudioResampler
+
+
+class ResampyResampler(BaseAudioResampler):
+    """Audio resampler implementation using the resampy library."""
+
+    def __init__(self, **kwargs):
+        pass
+
+    async def resample(self, audio: bytes, in_rate: int, out_rate: int) -> bytes:
+        if in_rate == out_rate:
+            return audio
+        audio_data = np.frombuffer(audio, dtype=np.int16)
+        resampled_audio = resampy.resample(audio_data, in_rate, out_rate, filter="kaiser_fast")
+        result = resampled_audio.astype(np.int16).tobytes()
+        return result

src/pipecat/audio/resamplers/soxr_resampler.py

@@ -0,0 +1,25 @@
+#
+# Copyright (c) 2024–2025, Daily
+#
+# SPDX-License-Identifier: BSD 2-Clause License
+#
+
+import numpy as np
+import soxr
+
+from pipecat.audio.resamplers.base_audio_resampler import BaseAudioResampler
+
+
+class SOXRAudioResampler(BaseAudioResampler):
+    """Audio resampler implementation using the SoX resampler library."""
+
+    def __init__(self, **kwargs):
+        pass
+
+    async def resample(self, audio: bytes, in_rate: int, out_rate: int) -> bytes:
+        if in_rate == out_rate:
+            return audio
+        audio_data = np.frombuffer(audio, dtype=np.int16)
+        resampled_audio = soxr.resample(audio_data, in_rate, out_rate, quality="VHQ")
+        result = resampled_audio.astype(np.int16).tobytes()
+        return result

src/pipecat/audio/utils.py

@@ -10,8 +10,24 @@ import numpy as np
 import pyloudnorm as pyln
 import soxr
 
+from pipecat.audio.resamplers.base_audio_resampler import BaseAudioResampler
+from pipecat.audio.resamplers.soxr_resampler import SOXRAudioResampler
+
+
+def create_default_resampler(**kwargs) -> BaseAudioResampler:
+    return SOXRAudioResampler(**kwargs)
+
 
 def resample_audio(audio: bytes, original_rate: int, target_rate: int) -> bytes:
+    import warnings
+
+    with warnings.catch_warnings():
+        warnings.simplefilter("always")
+        warnings.warn(
+            "'resample_audio()' is deprecated, use 'create_default_resampler()' instead.",
+            DeprecationWarning,
+        )
+
     if original_rate == target_rate:
         return audio
     audio_data = np.frombuffer(audio, dtype=np.int16)
@@ -75,41 +91,45 @@ def exp_smoothing(value: float, prev_value: float, factor: float) -> float:
     return prev_value + factor * (value - prev_value)
 
 
-def ulaw_to_pcm(ulaw_bytes: bytes, in_sample_rate: int, out_sample_rate: int):
+async def ulaw_to_pcm(
+    ulaw_bytes: bytes, in_rate: int, out_rate: int, resampler: BaseAudioResampler
+):
     # Convert μ-law to PCM
     in_pcm_bytes = audioop.ulaw2lin(ulaw_bytes, 2)
 
     # Resample
-    out_pcm_bytes = resample_audio(in_pcm_bytes, in_sample_rate, out_sample_rate)
+    out_pcm_bytes = await resampler.resample(in_pcm_bytes, in_rate, out_rate)
 
     return out_pcm_bytes
 
 
-def pcm_to_ulaw(pcm_bytes: bytes, in_sample_rate: int, out_sample_rate: int):
+async def pcm_to_ulaw(pcm_bytes: bytes, in_rate: int, out_rate: int, resampler: BaseAudioResampler):
     # Resample
-    in_pcm_bytes = resample_audio(pcm_bytes, in_sample_rate, out_sample_rate)
+    in_pcm_bytes = await resampler.resample(pcm_bytes, in_rate, out_rate)
 
     # Convert PCM to μ-law
-    ulaw_bytes = audioop.lin2ulaw(in_pcm_bytes, 2)
+    out_ulaw_bytes = audioop.lin2ulaw(in_pcm_bytes, 2)
 
-    return ulaw_bytes
+    return out_ulaw_bytes
 
 
-def alaw_to_pcm(alaw_bytes: bytes, in_sample_rate: int, out_sample_rate: int) -> bytes:
+async def alaw_to_pcm(
+    alaw_bytes: bytes, in_rate: int, out_rate: int, resampler: BaseAudioResampler
+) -> bytes:
     # Convert a-law to PCM
     in_pcm_bytes = audioop.alaw2lin(alaw_bytes, 2)
 
     # Resample
-    out_pcm_bytes = resample_audio(in_pcm_bytes, in_sample_rate, out_sample_rate)
+    out_pcm_bytes = await resampler.resample(in_pcm_bytes, in_rate, out_rate)
 
     return out_pcm_bytes
 
 
-def pcm_to_alaw(pcm_bytes: bytes, in_sample_rate: int, out_sample_rate: int):
+async def pcm_to_alaw(pcm_bytes: bytes, in_rate: int, out_rate: int, resampler: BaseAudioResampler):
     # Resample
-    in_pcm_bytes = resample_audio(pcm_bytes, in_sample_rate, out_sample_rate)
+    in_pcm_bytes = await resampler.resample(pcm_bytes, in_rate, out_rate)
 
     # Convert PCM to μ-law
-    alaw_bytes = audioop.lin2alaw(in_pcm_bytes, 2)
+    out_alaw_bytes = audioop.lin2alaw(in_pcm_bytes, 2)
 
-    return alaw_bytes
+    return out_alaw_bytes

src/pipecat/audio/vad/silero.py

@@ -5,6 +5,7 @@
 #
 
 import time
+from typing import Optional
 
 import numpy as np
 from loguru import logger
@@ -104,11 +105,8 @@ class SileroOnnxModel:
 
 
 class SileroVADAnalyzer(VADAnalyzer):
-    def __init__(self, *, sample_rate: int = 16000, params: VADParams = VADParams()):
-        super().__init__(sample_rate=sample_rate, num_channels=1, params=params)
-
-        if sample_rate != 16000 and sample_rate != 8000:
-            raise ValueError("Silero VAD sample rate needs to be 16000 or 8000")
+    def __init__(self, *, sample_rate: Optional[int] = None, params: VADParams = VADParams()):
+        super().__init__(sample_rate=sample_rate, params=params)
 
         logger.debug("Loading Silero VAD model...")
 
@@ -138,6 +136,12 @@ class SileroVADAnalyzer(VADAnalyzer):
     # VADAnalyzer
     #
 
+    def set_sample_rate(self, sample_rate: int):
+        if sample_rate != 16000 and sample_rate != 8000:
+            raise ValueError("Silero VAD sample rate needs to be 16000 or 8000")
+
+        super().set_sample_rate(sample_rate)
+
     def num_frames_required(self) -> int:
         return 512 if self.sample_rate == 16000 else 256
 

src/pipecat/audio/vad/vad_analyzer.py

@@ -6,6 +6,7 @@
 
 from abc import abstractmethod
 from enum import Enum
+from typing import Optional
 
 from loguru import logger
 from pydantic import BaseModel
@@ -33,11 +34,11 @@ class VADParams(BaseModel):
 
 
 class VADAnalyzer:
-    def __init__(self, *, sample_rate: int, num_channels: int, params: VADParams):
-        self._sample_rate = sample_rate
-        self._num_channels = num_channels
-
-        self.set_params(params)
+    def __init__(self, *, sample_rate: Optional[int] = None, params: VADParams):
+        self._init_sample_rate = sample_rate
+        self._sample_rate = 0
+        self._params = params
+        self._num_channels = 1
 
         self._vad_buffer = b""
 
@@ -65,13 +66,17 @@ class VADAnalyzer:
     def voice_confidence(self, buffer) -> float:
         pass
 
+    def set_sample_rate(self, sample_rate: int):
+        self._sample_rate = self._init_sample_rate or sample_rate
+        self.set_params(self._params)
+
     def set_params(self, params: VADParams):
         logger.info(f"Setting VAD params to: {params}")
         self._params = params
         self._vad_frames = self.num_frames_required()
         self._vad_frames_num_bytes = self._vad_frames * self._num_channels * 2
 
-        vad_frames_per_sec = self._vad_frames / self._sample_rate
+        vad_frames_per_sec = self._vad_frames / self.sample_rate
 
         self._vad_start_frames = round(self._params.start_secs / vad_frames_per_sec)
         self._vad_stop_frames = round(self._params.stop_secs / vad_frames_per_sec)
@@ -80,7 +85,7 @@ class VADAnalyzer:
         self._vad_state: VADState = VADState.QUIET
 
     def _get_smoothed_volume(self, audio: bytes) -> float:
-        volume = calculate_audio_volume(audio, self._sample_rate)
+        volume = calculate_audio_volume(audio, self.sample_rate)
         return exp_smoothing(volume, self._prev_volume, self._smoothing_factor)
 
     def analyze_audio(self, buffer) -> VADState:

src/pipecat/frames/frames.py

@@ -6,7 +6,18 @@
 
 from dataclasses import dataclass, field
 from enum import Enum
-from typing import TYPE_CHECKING, Any, Awaitable, Callable, List, Literal, Mapping, Optional, Tuple
+from typing import (
+    TYPE_CHECKING,
+    Any,
+    Awaitable,
+    Callable,
+    Dict,
+    List,
+    Literal,
+    Mapping,
+    Optional,
+    Tuple,
+)
 
 from pipecat.audio.vad.vad_analyzer import VADParams
 from pipecat.clocks.base_clock import BaseClock
@@ -37,7 +48,7 @@ class KeypadEntry(str, Enum):
     STAR = "*"
 
 
-def format_pts(pts: int | None):
+def format_pts(pts: Optional[int]):
     return nanoseconds_to_str(pts) if pts else None
 
 
@@ -48,13 +59,13 @@ class Frame:
     id: int = field(init=False)
     name: str = field(init=False)
     pts: Optional[int] = field(init=False)
-    metadata: dict = field(init=False)
+    metadata: Dict[str, Any] = field(init=False)
 
     def __post_init__(self):
         self.id: int = obj_id()
         self.name: str = f"{self.__class__.__name__}#{obj_count(self)}"
         self.pts: Optional[int] = None
-        self.metadata: dict = {}
+        self.metadata: Dict[str, Any] = {}
 
     def __str__(self):
         return self.name
@@ -115,7 +126,7 @@ class ImageRawFrame:
 
     image: bytes
     size: Tuple[int, int]
-    format: str | None
+    format: Optional[str]
 
 
 #
@@ -165,7 +176,7 @@ class URLImageRawFrame(OutputImageRawFrame):
 
     """
 
-    url: str | None
+    url: Optional[str]
 
     def __str__(self):
         pts = format_pts(self.pts)
@@ -224,7 +235,7 @@ class TranscriptionFrame(TextFrame):
 
     user_id: str
     timestamp: str
-    language: Language | None = None
+    language: Optional[Language] = None
 
     def __str__(self):
         return f"{self.name}(user: {self.user_id}, text: [{self.text}], language: {self.language}, timestamp: {self.timestamp})"
@@ -239,7 +250,7 @@ class InterimTranscriptionFrame(TextFrame):
     text: str
     user_id: str
     timestamp: str
-    language: Language | None = None
+    language: Optional[Language] = None
 
     def __str__(self):
         return f"{self.name}(user: {self.user_id}, text: [{self.text}], language: {self.language}, timestamp: {self.timestamp})"
@@ -261,7 +272,7 @@ class TranscriptionMessage:
 
     role: Literal["user", "assistant"]
     content: str
-    timestamp: str | None = None
+    timestamp: Optional[str] = None
 
 
 @dataclass
@@ -397,12 +408,26 @@ class TransportMessageFrame(DataFrame):
 
 
 @dataclass
-class InputDTMFFrame(DataFrame):
-    """A DTMF button input"""
+class DTMFFrame(DataFrame):
+    """A DTMF button frame"""
 
     button: KeypadEntry
 
 
+@dataclass
+class InputDTMFFrame(DTMFFrame):
+    """A DTMF button input"""
+
+    pass
+
+
+@dataclass
+class OutputDTMFFrame(DTMFFrame):
+    """A DTMF button output"""
+
+    pass
+
+
 #
 # System frames
 #
@@ -414,11 +439,13 @@ class StartFrame(SystemFrame):
 
     clock: BaseClock
     task_manager: TaskManager
+    audio_in_sample_rate: int = 16000
+    audio_out_sample_rate: int = 24000
     allow_interruptions: bool = False
     enable_metrics: bool = False
     enable_usage_metrics: bool = False
-    report_only_initial_ttfb: bool = False
     observer: Optional["BaseObserver"] = None
+    report_only_initial_ttfb: bool = False
 
 
 @dataclass
@@ -647,7 +674,7 @@ class UserImageRawFrame(InputImageRawFrame):
 class VisionImageRawFrame(InputImageRawFrame):
     """An image with an associated text to ask for a description of it."""
 
-    text: str | None
+    text: Optional[str]
 
     def __str__(self):
         pts = format_pts(self.pts)

src/pipecat/pipeline/runner.py

@@ -19,7 +19,7 @@ class PipelineRunner:
     def __init__(
         self,
         *,
-        name: str | None = None,
+        name: Optional[str] = None,
         handle_sigint: bool = True,
         force_gc: bool = False,
         loop: Optional[asyncio.AbstractEventLoop] = None,

src/pipecat/pipeline/task.py

@@ -5,7 +5,7 @@
 #
 
 import asyncio
-from typing import AsyncIterable, Iterable, List
+from typing import Any, AsyncIterable, Dict, Iterable, List
 
 from loguru import logger
 from pydantic import BaseModel, ConfigDict
@@ -38,24 +38,48 @@ HEARTBEAT_MONITOR_SECONDS = HEARTBEAT_SECONDS * 5
 
 
 class PipelineParams(BaseModel):
+    """Configuration parameters for pipeline execution.
+
+    Attributes:
+        allow_interruptions: Whether to allow pipeline interruptions.
+        audio_in_sample_rate: Input audio sample rate in Hz.
+        audio_out_sample_rate: Output audio sample rate in Hz.
+        enable_heartbeats: Whether to enable heartbeat monitoring.
+        enable_metrics: Whether to enable metrics collection.
+        enable_usage_metrics: Whether to enable usage metrics.
+        heartbeats_period_secs: Period between heartbeats in seconds.
+        observers: List of observers for monitoring pipeline execution.
+        report_only_initial_ttfb: Whether to report only initial time to first byte.
+        send_initial_empty_metrics: Whether to send initial empty metrics.
+        start_metadata: Additional metadata for pipeline start.
+    """
+
     model_config = ConfigDict(arbitrary_types_allowed=True)
 
     allow_interruptions: bool = False
+    audio_in_sample_rate: int = 16000
+    audio_out_sample_rate: int = 24000
     enable_heartbeats: bool = False
     enable_metrics: bool = False
     enable_usage_metrics: bool = False
-    send_initial_empty_metrics: bool = True
-    report_only_initial_ttfb: bool = False
-    observers: List[BaseObserver] = []
     heartbeats_period_secs: float = HEARTBEAT_SECONDS
+    observers: List[BaseObserver] = []
+    report_only_initial_ttfb: bool = False
+    send_initial_empty_metrics: bool = True
+    start_metadata: Dict[str, Any] = {}
 
 
 class PipelineTaskSource(FrameProcessor):
-    """This is the source processor that is linked at the beginning of the
+    """Source processor for pipeline tasks that handles frame routing.
+
+    This is the source processor that is linked at the beginning of the
     pipeline given to the pipeline task. It allows us to easily push frames
     downstream to the pipeline and also receive upstream frames coming from the
     pipeline.
 
+    Args:
+        up_queue: Queue for upstream frame processing.
+
     """
 
     def __init__(self, up_queue: asyncio.Queue, **kwargs):
@@ -73,10 +97,14 @@ class PipelineTaskSource(FrameProcessor):
 
 
 class PipelineTaskSink(FrameProcessor):
-    """This is the sink processor that is linked at the end of the pipeline
+    """Sink processor for pipeline tasks that handles final frame processing.
+
+    This is the sink processor that is linked at the end of the pipeline
     given to the pipeline task. It allows us to receive downstream frames and
     act on them, for example, waiting to receive an EndFrame.
 
+    Args:
+        down_queue: Queue for downstream frame processing.
     """
 
     def __init__(self, down_queue: asyncio.Queue, **kwargs):
@@ -89,6 +117,14 @@ class PipelineTaskSink(FrameProcessor):
 
 
 class PipelineTask(BaseTask):
+    """Manages the execution of a pipeline, handling frame processing and task lifecycle.
+
+    Args:
+        pipeline: The pipeline to execute.
+        params: Configuration parameters for the pipeline.
+        clock: Clock implementation for timing operations.
+    """
+
     def __init__(
         self,
         pipeline: BasePipeline,
@@ -136,6 +172,11 @@ class PipelineTask(BaseTask):
         """Returns the name of this task."""
         return self._name
 
+    @property
+    def params(self) -> PipelineParams:
+        """Returns the pipeline parameters of this task."""
+        return self._params
+
     def set_event_loop(self, loop: asyncio.AbstractEventLoop):
         self._task_manager.set_event_loop(loop)
 
@@ -155,9 +196,7 @@ class PipelineTask(BaseTask):
         await self.queue_frame(EndFrame())
 
     async def cancel(self):
-        """
-        Stops the running pipeline immediately.
-        """
+        """Stops the running pipeline immediately."""
         logger.debug(f"Canceling pipeline task {self}")
         # Make sure everything is cleaned up downstream. This is sent
         # out-of-band from the main streaming task which is what we want since
@@ -167,9 +206,7 @@ class PipelineTask(BaseTask):
         await self._task_manager.cancel_task(self._process_push_task)
 
     async def run(self):
-        """
-        Starts running the given pipeline.
-        """
+        """Starts and manages the pipeline execution until completion or cancellation."""
         if self.has_finished():
             return
         try:
@@ -187,14 +224,18 @@ class PipelineTask(BaseTask):
         self._finished = True
 
     async def queue_frame(self, frame: Frame):
-        """
-        Queue a frame to be pushed down the pipeline.
+        """Queue a single frame to be pushed down the pipeline.
+
+        Args:
+            frame: The frame to be processed.
         """
         await self._push_queue.put(frame)
 
     async def queue_frames(self, frames: Iterable[Frame] | AsyncIterable[Frame]):
-        """
-        Queues multiple frames to be pushed down the pipeline.
+        """Queues multiple frames to be pushed down the pipeline.
+
+        Args:
+            frames: An iterable or async iterable of frames to be processed.
         """
         if isinstance(frames, AsyncIterable):
             async for frame in frames:
@@ -271,11 +312,14 @@ class PipelineTask(BaseTask):
             clock=self._clock,
             task_manager=self._task_manager,
             allow_interruptions=self._params.allow_interruptions,
+            audio_in_sample_rate=self._params.audio_in_sample_rate,
+            audio_out_sample_rate=self._params.audio_out_sample_rate,
             enable_metrics=self._params.enable_metrics,
             enable_usage_metrics=self._params.enable_usage_metrics,
-            report_only_initial_ttfb=self._params.report_only_initial_ttfb,
             observer=self._observer,
+            report_only_initial_ttfb=self._params.report_only_initial_ttfb,
         )
+        start_frame.metadata = self._params.start_metadata
         await self._source.queue_frame(start_frame, FrameDirection.DOWNSTREAM)
 
         if self._params.enable_metrics and self._params.send_initial_empty_metrics:
@@ -337,9 +381,7 @@ class PipelineTask(BaseTask):
             self._down_queue.task_done()
 
     async def _heartbeat_push_handler(self):
-        """
-        This tasks pushes a heartbeat frame every heartbeat period.
-        """
+        """This tasks pushes a heartbeat frame every heartbeat period."""
         while True:
             # Don't use `queue_frame()` because if an EndFrame is queued the
             # task will just stop waiting for the pipeline to finish not

src/pipecat/processors/aggregators/gated_openai_llm_context.py

@@ -16,9 +16,10 @@ class GatedOpenAILLMContextAggregator(FrameProcessor):
 
     """
 
-    def __init__(self, notifier: BaseNotifier, **kwargs):
+    def __init__(self, *, notifier: BaseNotifier, start_open: bool = False, **kwargs):
         super().__init__(**kwargs)
         self._notifier = notifier
+        self._start_open = start_open
         self._last_context_frame = None
 
     async def process_frame(self, frame: Frame, direction: FrameDirection):
@@ -31,7 +32,11 @@ class GatedOpenAILLMContextAggregator(FrameProcessor):
             await self._stop()
             await self.push_frame(frame)
         elif isinstance(frame, OpenAILLMContextFrame):
-            self._last_context_frame = frame
+            if self._start_open:
+                self._start_open = False
+                await self.push_frame(frame, direction)
+            else:
+                self._last_context_frame = frame
         else:
             await self.push_frame(frame, direction)