Remove extra code

.github/workflows/publish.yaml

@@ -5,25 +5,25 @@ on:
     inputs:
       gitref:
         type: string
-        description: 'what git tag to build (e.g. v0.0.74)'
+        description: "what git tag to build (e.g. v0.0.74)"
         required: true
 
 jobs:
   build:
-    name: 'Build and upload wheels'
+    name: "Build and upload wheels"
     runs-on: ubuntu-latest
     steps:
       - name: Checkout repo
         uses: actions/checkout@v4
         with:
           ref: ${{ github.event.inputs.gitref }}
-
+      
       - name: Install uv
         uses: astral-sh/setup-uv@v3
         with:
-          version: 'latest'
+          version: "latest"
       - name: Set up Python
-        run: uv python install 3.12
+        run: uv python install 3.10
       - name: Install development dependencies
         run: uv sync --group dev
       - name: Build project
@@ -35,9 +35,9 @@ jobs:
           path: ./dist
 
   publish-to-pypi:
-    name: 'Publish to PyPI'
+    name: "Publish to PyPI"
     runs-on: ubuntu-latest
-    needs: [build]
+    needs: [ build ]
     environment:
       name: pypi
       url: https://pypi.org/p/pipecat-ai
@@ -56,12 +56,12 @@ jobs:
           print-hash: true
 
   publish-to-test-pypi:
-    name: 'Publish to Test PyPI'
+    name: "Publish to Test PyPI"
     runs-on: ubuntu-latest
-    needs: [build]
+    needs: [ build ]
     environment:
       name: testpypi
-      url: https://test.pypi.org/p/pipecat-ai
+      url: https://pypi.org/p/pipecat-ai
     permissions:
       id-token: write
     steps:
@@ -70,7 +70,7 @@ jobs:
         with:
           name: wheels
           path: ./dist
-      - name: Publish to Test PyPI
+      - name: Publish to PyPI
         uses: pypa/gh-action-pypi-publish@release/v1
         with:
           verbose: true

.github/workflows/publish_test.yaml

@@ -4,7 +4,7 @@ on: workflow_dispatch
 
 jobs:
   build:
-    name: 'Build and upload wheels'
+    name: "Build and upload wheels"
     runs-on: ubuntu-latest
     steps:
       - name: Checkout repo
@@ -15,9 +15,9 @@ jobs:
       - name: Install uv
         uses: astral-sh/setup-uv@v3
         with:
-          version: 'latest'
+          version: "latest"
       - name: Set up Python
-        run: uv python install 3.12
+        run: uv python install 3.10
       - name: Install development dependencies
         run: uv sync --group dev
       - name: Build project
@@ -29,12 +29,12 @@ jobs:
           path: ./dist
 
   publish-to-test-pypi:
-    name: 'Publish to Test PyPI'
+    name: "Publish to Test PyPI"
     runs-on: ubuntu-latest
     needs: [build]
     environment:
       name: testpypi
-      url: https://test.pypi.org/p/pipecat-ai
+      url: https://pypi.org/p/pipecat-ai
     permissions:
       id-token: write
     steps:
@@ -43,7 +43,7 @@ jobs:
         with:
           name: wheels
           path: ./dist
-      - name: Publish to Test PyPI
+      - name: Publish to PyPI
         uses: pypa/gh-action-pypi-publish@release/v1
         with:
           verbose: true

CHANGELOG.md

@@ -9,744 +9,6 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 
 ### Added
 
-- Added `ElevenLabsRealtimeSTTService` which implements the Realtime STT
-  service from ElevenLabs.
-
-- Added a `TTSService.includes_inter_frame_spaces` property getter, so that TTS
-  services that subclass `TTSService` can indicate whether the text in the
-  `TTSTextFrame`s they push already contain any necessary inter-frame spaces.
-
-### Changed
-
-- Updated all STT and TTS services to use consistent error handling pattern with
-  `push_error()` method for better pipeline error event integration.
-
-- Added Hindi support for Rime TTS services.
-
-- Updated `GeminiTTSService` to use Google Cloud Text-to-Speech streaming API
-  instead of the deprecated Gemini API. Now uses `credentials` /
-  `credentials_path` for authentication. The `api_key` parameter is deprecated.
-  Also, added support for `prompt` parameter for style instructions and
-  expressive markup tags. Significantly improved latency with streaming
-  synthesis.
-
-- Updated language mappings for the Google and Gemini TTS services to match
-  official documentation.
-
-### Deprecated
-
-- The `api_key` parameter in `GeminiTTSService` is deprecated. Use
-  `credentials` or `credentials_path` instead for Google Cloud authentication.
-
-### Fixed
-
-- Fixed subtle issue of assistant context messages ending up with double spaces
-  between words or sentences.
-
-- Fixed an issue where `NeuphonicTTSService` wasn't pushing `TTSTextFrame`s,
-  meaning assistant messages weren't being written to context.
-
-- Fixed an issue with OpenTelemetry where tracing wasn't correctly displaying
-  LLM completions and tools when using the universal `LLMContext`.
-
-- Fixed issue where `DeepgramFluxSTTService` failed to connect if passing a
-  `keyterm` or `tag` containing a space.
-
-- Prevented `HeyGenVideoService` from automatically disconnecting after 5 minutes.
-
-### Added
-
-- Added ai-coustics integrated VAD (`AICVADAnalyzer`) with `AICFilter` factory and 
-  example wiring; leverages the enhancement model for robust detection with no 
-  ONNX dependency or added processing complexity.
-
-## [0.0.94] - 2025-11-10
-
-### Changed
-
-- Added support for retrying `SpeechmaticsTTSService` when it returns a 503
-  error. Default values in `InputParams`.
-
-### Deprecated
-
-- The `KrispFilter` is deprecated and will be removed in a future version. Use
-  the `KrispVivaFilter` instead.
-
-### Removed
-
-- `LivekitFrameSerializer` has been removed. Use `LiveKitTransport` instead.
-
-### Fixed
-
-- Fixed a bug related to `LLMAssistantAggregator` where spaces were sometimes
-  missing from assistant messages in context.
-
-## [0.0.93] - 2025-11-07
-
-### Added
-
-- Added support for Sarvam Speech-to-Text service (`SarvamSTTService`) with
-  streaming WebSocket support for `saarika` (STT) and `saaras` (STT-translate)
-  models.
-
-- Added support for passing in a `ToolsSchema` in lieu of a list of provider-
-  specific dicts when initializing `OpenAIRealtimeLLMService` or when updating
-  it using `LLMUpdateSettingsFrame`.
-
-- Added `TransportParams.audio_out_silence_secs`, which specifies how many
-  seconds of silence to output when an `EndFrame` reaches the output
-  transport. This can help ensure that all audio data is fully delivered to
-  clients.
-
-- Added new `FrameProcessor.broadcast_frame()` method. This will push two
-  instances of a given frame class, one upstream and the other downstream.
-
-  ```python
-  await self.broadcast_frame(UserSpeakingFrame)
-  ```
-
-- Added `MetricsLogObserver` for logging performance metrics from `MetricsFrame`
-  instances. Supports filtering via `include_metrics` parameter to control which
-  metrics types are logged (TTFB, processing time, LLM token usage, TTS usage,
-  smart turn metrics).
-
-- Added `pronunciation_dictionary_locators` to `ElevenLabsTTSService` and
-  `ElevenLabsHttpTTSService`.
-
-- Added support for loading external observers. You can now register custom
-  pipeline observers by setting the `PIPECAT_OBSERVER_FILES` environment
-  variable. This variable should contain a colon-separated list of Python files
-  (e.g. `export PIPECAT_OBSERVER_FILES="observer1.py:observer2.py:..."`). Each
-  file must define a function with the following signature:
-
-  ```python
-  async def create_observers(task: PipelineTask) -> Iterable[BaseObserver]:
-      ...
-  ```
-
-- Added support for new sonic-3 languages in `CartesiaTTSService` and
-  `CartesiaHttpTTSService`.
-
-- `EndFrame` and `EndTaskFrame` have an optional `reason` field to indicate why
-  the pipeline is being ended.
-
-- `CancelFrame` and `CancelTaskFrame` have an optional `reason` field to
-  indicate why the pipeline is being canceled. This can be also specified when
-  you cancel a task with `PipelineTask.cancel(reason="cancellation reason")`.
-
-- Added `include_prob_metrics` parameter to Whisper STT services to enable access
-  to probability metrics from transcription results.
-
-- Added utility functions `extract_whisper_probability()`,
-  `extract_openai_gpt4o_probability()`, and `extract_deepgram_probability()` to
-  extract probability metrics from `TranscriptionFrame` objects for Whisper-based,
-  OpenAI GPT-4o-transcribe, and Deepgram STT services respectively.
-
-- Added `LLMSwitcher.register_direct_function()`. It works much like
-  `LLMSwitcher.register_function()` in that it's a shorthand for registering
-  functions on all LLMs in the switcher, but for direct functions.
-
-- Added `LLMSwitcher.register_direct_function()`. It works much like
-  `LLMSwitcher.register_function()` in that it's a shorthand for registering
-  a function on all LLMs in the switcher, except this new method takes a direct
-  function (a `FunctionSchema`-less function).
-
-- Added `MCPClient.get_tools_schema()` and `MCPClient.register_tools_schema()`
-  as a two-step alternative to `MCPClient.register_tools()`, to allow users to
-  pass MCP tools to, say, `GeminiLiveLLMService` (as well as other
-  speech-to-speech services) in the constructor.
-
-- Added support for passing in an `LLMSwicher` to `MCPClient.register_tools()`
-  (as well as the new `MCPClient.register_tools_schema()`).
-
-- Added `cpu_count` parameter to `LocalSmartTurnAnalyzerV3`. This is set to `1`
-  by default for more predictable performance on low-CPU systems.
-
-### Changed
-
-- Updated `simli-ai` to 0.1.25.
-
-- `STTMuteFilter` no longer sends `STTMuteFrame` to the STT service. The filter
-  now blocks frames locally without instructing the STT service to stop
-  processing audio. This prevents inactivity-related errors (such as 409 errors
-  from Google STT) while maintaining the same muting behavior at the application
-  level. Important: The STTMuteFilter should be placed _after_ the STT service
-  itself.
-
-- Improved `GoogleSTTService` error handling to properly catch gRPC `Aborted`
-  exceptions (corresponding to 409 errors) caused by stream inactivity. These
-  exceptions are now logged at DEBUG level instead of ERROR level, since they
-  indicate expected behavior when no audio is sent for 10+ seconds (e.g., during
-  long silences or when audio input is blocked). The service automatically
-  reconnects when this occurs.
-
-- Bumped the `fastapi` dependency's upperbound to `<0.122.0`.
-
-- Updated the default model for `GoogleVertexLLMService` to `gemini-2.5-flash`.
-
-- Updated the `GoogleVertexLLMService` to use the `GoogleLLMService` as a base
-  class instead of the `OpenAILLMService`.
-
-- Updated STT and TTS services to pass through unverified language codes with a
-  warning instead of returning None. This allows developers to use newly
-  supported languages before Pipecat's service classes are updated, while still
-  providing guidance on verified languages.
-
-### Removed
-
-- Removed `needs_mcp_alternate_schema()` from `LLMService`. The mechanism that
-  relied on it went away.
-
-### Fixed
-
-- Restore backwards compatibility for vision/image features (broken in 0.0.92)
-  when using non-universal context and assistant aggregators.
-
-- Fixed `DeepgramSTTService._disconnect()` to properly await `is_connected()`
-  method call, which is an async coroutine in the Deepgram SDK.
-
-- Fixed an issue where the `SmallWebRTCRequest` dataclass in runner would scrub
-  arbitrary request data from client due to camelCase typing. This fixes data
-  passthrough for JS clients where `APIRequest` is used.
-
-- Fixed a bug in `GeminiLiveLLMService` where in some circumstances it wouldn't
-  respond after a tool call.
-
-- Fixed `GeminiLiveLLMService` session resumption after a connection timeout.
-
-- `GeminiLiveLLMService` now properly supports context-provided system
-  instruction and tools.
-
-- Fixed `GoogleLLMService` token counting to avoid double-counting tokens when
-  Gemini sends usage metadata across multiple streaming chunks.
-
-## [0.0.92] - 2025-10-31 🎃 "The Haunted Edition" 👻
-
-### Added
-
-- Added a new `DeepgramHttpTTSService`, which delivers a meaningful reduction
-  in latency when compared to the `DeepgramTTSService`.
-
-- Add support for `speaking_rate` input parameter in `GoogleHttpTTSService`.
-
-- Added `enable_speaker_diarization` and `enable_language_identification` to
-  `SonioxSTTService`.
-
-- Added `SpeechmaticsTTSService`, which uses Speechmatic's TTS API. Updated
-  examples 07a\* to use the new TTS service.
-
-- Added support for including images or audio to LLM context messages using
-  `LLMContext.create_image_message()` or `LLMContext.create_image_url_message()`
-  (not all LLMs support URLs) and `LLMContext.create_audio_message()`. For
-  example, when creating `LLMMessagesAppendFrame`:
-
-  ```python
-  message = LLMContext.create_image_message(image=..., size= ...)
-  await self.push_frame(LLMMessagesAppendFrame(messages=[message], run_llm=True))
-  ```
-
-- New event handlers for the `DeepgramFluxSTTService`: `on_start_of_turn`,
-  `on_turn_resumed`, `on_end_of_turn`, `on_eager_end_of_turn`, `on_update`.
-
-- Added `generation_config` parameter support to `CartesiaTTSService` and
-  `CartesiaHttpTTSService` for Cartesia Sonic-3 models. Includes a new
-  `GenerationConfig` class with `volume` (0.5-2.0), `speed` (0.6-1.5),
-  and `emotion` (60+ options) parameters for fine-grained speech generation
-  control.
-
-- Expanded support for univeral `LLMContext` to `OpenAIRealtimeLLMService`.
-  As a reminder, the context-setup pattern when using `LLMContext` is:
-
-  ```python
-  context = LLMContext(messages, tools)
-  context_aggregator = LLMContextAggregatorPair(context)
-  ```
-
-  (Note that even though `OpenAIRealtimeLLMService` now supports the universal
-  `LLMContext`, it is not meant to be swapped out for another LLM service at
-  runtime with `LLMSwitcher`.)
-
-  Note: `TranscriptionFrame`s and `InterimTranscriptionFrame`s now go upstream
-  from `OpenAIRealtimeLLMService`, so if you're using `TranscriptProcessor`,
-  say, you'll want to adjust accordingly:
-
-  ```python
-  pipeline = Pipeline(
-    [
-      transport.input(),
-      context_aggregator.user(),
-
-      # BEFORE
-      llm,
-      transcript.user(),
-
-      # AFTER
-      transcript.user(),
-      llm,
-
-      transport.output(),
-      transcript.assistant(),
-      context_aggregator.assistant(),
-    ]
-  )
-  ```
-
-  Also worth noting: whether or not you use the new context-setup pattern with
-  `OpenAIRealtimeLLMService`, some types have changed under the hood:
-
-  ```python
-  ## BEFORE:
-
-  # Context aggregator type
-  context_aggregator: OpenAIContextAggregatorPair
-
-  # Context frame type
-  frame: OpenAILLMContextFrame
-
-  # Context type
-  context: OpenAIRealtimeLLMContext
-  # or
-  context: OpenAILLMContext
-
-  ## AFTER:
-
-  # Context aggregator type
-  context_aggregator: LLMContextAggregatorPair
-
-  # Context frame type
-  frame: LLMContextFrame
-
-  # Context type
-  context: LLMContext
-  ```
-
-  Also note that `RealtimeMessagesUpdateFrame` and
-  `RealtimeFunctionCallResultFrame` have been deprecated, since they're no
-  longer used by `OpenAIRealtimeLLMService`. OpenAI Realtime now works more
-  like other LLM services in Pipecat, relying on updates to its context, pushed
-  by context aggregators, to update its internal state. Listen for
-  `LLMContextFrame`s for context updates.
-
-  Finally, `LLMTextFrame`s are no longer pushed from `OpenAIRealtimeLLMService`
-  when it's configured with `output_modalities=['audio']`. If you need
-  to process its output, listen for `TTSTextFrame`s instead.
-
-- Expanded support for universal `LLMContext` to `GeminiLiveLLMService`.
-  As a reminder, the context-setup pattern when using `LLMContext` is:
-
-  ```python
-  context = LLMContext(messages, tools)
-  context_aggregator = LLMContextAggregatorPair(context)
-  ```
-
-  (Note that even though `GeminiLiveLLMService` now supports the universal
-  `LLMContext`, it is not meant to be swapped out for another LLM service at
-  runtime with `LLMSwitcher`.)
-
-  Worth noting: whether or not you use the new context-setup pattern with
-  `GeminiLiveLLMService`, some types have changed under the hood:
-
-  ```python
-  ## BEFORE:
-
-  # Context aggregator type
-  context_aggregator: GeminiLiveContextAggregatorPair
-
-  # Context frame type
-  frame: OpenAILLMContextFrame
-
-  # Context type
-  context: GeminiLiveLLMContext
-  # or
-  context: OpenAILLMContext
-
-  ## AFTER:
-
-  # Context aggregator type
-  context_aggregator: LLMContextAggregatorPair
-
-  # Context frame type
-  frame: LLMContextFrame
-
-  # Context type
-  context: LLMContext
-  ```
-
-  Also note that `LLMTextFrame`s are no longer pushed from `GeminiLiveLLMService`
-  when it's configured with `modalities=GeminiModalities.AUDIO`. If you need
-  to process its output, listen for `TTSTextFrame`s instead.
-
-### Changed
-
-- The development runner's `/start` endpoint now supports passing
-  `dailyRoomProperties` and `dailyMeetingTokenProperties` in the request body
-  when `createDailyRoom` is true. Properties are validated against the
-  `DailyRoomProperties` and `DailyMeetingTokenProperties` types respectively
-  and passed to Daily's room and token creation APIs.
-
-- `UserImageRawFrame` new fields `append_to_context` and `text`. The
-  `append_to_context` field indicates if this image and text should be added to
-  the LLM context (by the LLM assistant aggregator). The `text` field, if set,
-  might also guide the LLM or the vision service on how to analyze the image.
-
-- `UserImageRequestFrame` new fiels `append_to_context` and `text`. Both fields
-  will be used to set the same fields on the captured `UserImageRawFrame`.
-
-- `UserImageRequestFrame` don't require function call name and ID anymore.
-
-- Updated `MoondreamService` to process `UserImageRawFrame`.
-
-- `VisionService` expects `UserImageRawFrame` in order to analyze images.
-
-- `DailyTransport` triggers `on_error` event if transcription can't be started
-  or stopped.
-
-- `DailyTransport` updates: `start_dialout()` now returns two values:
-  `session_id` and `error`. `start_recording()` now returns two values:
-  `stream_id` and `error`.
-
-- Updated `daily-python` to 0.21.0.
-
-- `SimliVideoService` now accepts `api_key` and `face_id` parameters directly,
-  with optional `params` for `max_session_length` and `max_idle_time`
-  configuration, aligning with other Pipecat service patterns.
-
-- Updated the default model to `sonic-3` for `CartesiaTTSService` and
-  `CartesiaHttpTTSService`.
-
-- `FunctionFilter` now has a `filter_system_frames` arg, which controls whether
-  or not SystemFrames are filtered.
-
-- Upgraded `aws_sdk_bedrock_runtime` to v0.1.1 to resolve potential CPU issues
-  when running `AWSNovaSonicLLMService`.
-
-### Deprecated
-
-- The `expect_stripped_words` parameter of `LLMAssistantAggregatorParams` is
-  ignored when used with the newer `LLMAssistantAggregator`, which now handles
-  word spacing automatically.
-
-- `LLMService.request_image_frame()` is deprecated, push a
-  `UserImageRequestFrame` instead.
-
-- `UserResponseAggregator` is deprecated and will be removed in a future version.
-
-- The `send_transcription_frames` argument to `OpenAIRealtimeLLMService` is
-  deprecated. Transcription frames are now always sent. They go upstream, to be
-  handled by the user context aggregator. See "Added" section for details.
-
-- Types in `pipecat.services.openai.realtime.context` and
-  `pipecat.services.openai.realtime.frames` are deprecated, as they're no
-  longer used by `OpenAIRealtimeLLMService`. See "Added" section for details.
-
-- `SimliVideoService` `simli_config` parameter is deprecated. Use `api_key` and
-  `face_id` parameters instead.
-
-### Removed
-
-- Removed `enable_non_final_tokens` and `max_non_final_tokens_duration_ms` from
-  `SonioxSTTService`.
-
-- Removed the `aiohttp_session` arg from `SarvamTTSService` as it's no longer
-  used.
-
-### Fixed
-
-- Fixed a `PipelineTask` issue that was causing an idle timeout for frames that
-  were being generated but not reaching the end of the pipeline. Since the exact
-  point when frames are discarded is unknown, we now monitor pipeline frames
-  using an observer. If the observer detects frames are being generated, it will
-  prevent the pipeline from being considered idle.
-
-- Fixed an issue in `HumeTTSService` that was only using Octave 2, which does
-  not support the `description` field. Now, if a description is provided, it
-  switches to Octave 1.
-
-- Fixed an issue where `DailyTransport` would timeout prematurely on join and on
-  leave.
-
-- Fixed an issue in the runner where starting a DailyTransport room via
-  `/start` didn't support using the `DAILY_SAMPLE_ROOM_URL` env var.
-
-- Fixed an issue in `ServiceSwitcher` where the `STTService`s would result in
-  all STT services producing `TranscriptionFrame`s.
-
-### Other
-
-- Updated all vision 12-series foundational examples to load images from a file.
-
-- Added 14-series video examples for different services. These new examples
-  request an image from the user camera through a function call.
-
-## [0.0.91] - 2025-10-21
-
-### Added
-
-- It is now possible to start a bot from the `/start` endpoint when using the
-  runner Daily's transport. This follows the Pipecat Cloud format with
-  `createDailyRoom` and `body` fields in the POST request body.
-
-- Added an ellipsis character (`…`) to the end of sentence detection in the
-  string utils.
-
-- Expanded support for universal `LLMContext` to `AWSNovaSonicLLMService`.
-  As a reminder, the context-setup pattern when using `LLMContext` is:
-
-  ```python
-  context = LLMContext(messages, tools)
-  context_aggregator = LLMContextAggregatorPair(context)
-  ```
-
-  (Note that even though `AWSNovaSonicLLMService` now supports the universal
-  `LLMContext`, it is not meant to be swapped out for another LLM service at
-  runtime with `LLMSwitcher`.)
-
-  Worth noting: whether or not you use the new context-setup pattern with
-  `AWSNovaSonicLLMService`, some types have changed under the hood:
-
-  ```python
-  ## BEFORE:
-
-  # Context aggregator type
-  context_aggregator: AWSNovaSonicContextAggregatorPair
-
-  # Context frame type
-  frame: OpenAILLMContextFrame
-
-  # Context type
-  context: AWSNovaSonicLLMContext
-  # or
-  context: OpenAILLMContext
-
-  ## AFTER:
-
-  # Context aggregator type
-  context_aggregator: LLMContextAggregatorPair
-
-  # Context frame type
-  frame: LLMContextFrame
-
-  # Context type
-  context: LLMContext
-  ```
-
-- Added support for `bulbul:v3` model in `SarvamTTSService` and
-  `SarvamHttpTTSService`.
-
-- Added `keyterms_prompt` parameter to `AssemblyAIConnectionParams`.
-
-- Added `speech_model` parameter to `AssemblyAIConnectionParams` to access the
-  multilingual model.
-
-- Added support for trickle ICE to the `SmallWebRTCTransport`.
-
-- Added support for updating `OpenAITTSService` settings (`instructions` and
-  `speed`) at runtime via `TTSUpdateSettingsFrame`.
-
-- Added `--whatsapp` flag to runner to better surface WhatsApp transport logs.
-
-- Added `on_connected` and `on_disconnected` events to TTS and STT
-  websocket-based services.
-
-- Added an `aggregate_sentences` arg in `ElevenLabsHttpTTSService`, where the
-  default value is True.
-
-- Added a `room_properties` arg to the Daily runner's `configure()` method,
-  allowing `DailyRoomProperties` to be provided.
-
-- The runner `--folder` argument now supports downloading files from
-  subdirectories.
-
-### Changed
-
-- `RunnerArguments` now include the `body` field, so there's no need to add it
-  to subclasses. Also, all `RunnerArguments` fields are now keyword-only.
-
-- `CartesiaSTTService` now inherits from `WebsocketSTTService`.
-
-- Package upgrades:
-
-  - `daily-python` upgraded to 0.20.0.
-  - `openai` upgraded to support up to 2.x.x.
-  - `openpipe` upgraded to support up to 5.x.x.
-
-- `SpeechmaticsSTTService` updated dependencies for `speechmatics-rt>=0.5.0`.
-
-### Deprecated
-
-- The `send_transcription_frames` argument to `AWSNovaSonicLLMService` is
-  deprecated. Transcription frames are now always sent. They go upstream, to be
-  handled by the user context aggregator. See "Added" section for details.
-
-- Types in `pipecat.services.aws.nova_sonic.context` are deprecated, as they're
-  no longer used by `AWSNovaSonicLLMService`. See "Added" section for
-  details.
-
-### Fixed
-
-- Fixed an issue where the `RTVIProcessor` was sending duplicate
-  `UserStartedSpeakingFrame` and `UserStoppedSpeakingFrame` messages.
-
-- Fixed an issue in `AWSBedrockLLMService` where both `temperature` and `top_p`
-  were always sent together, causing conflicts with models like Claude Sonnet 4.5
-  that don't allow both parameters simultaneously. The service now only includes
-  inference parameters that are explicitly set, and `InputParams` defaults have
-  been changed to `None` to rely on AWS Bedrock's built-in model defaults.
-
-- Fixed an issue in `RivaSegmentedSTTService` where a runtime error occurred due
-  to a mismatch in the `_handle_transcription` method's signature.
-
-- Fixed multiple pipeline task cancellation issues. `asyncio.CancelledError` is
-  now handled properly in `PipelineTask` making it possible to cancel an asyncio
-  task that it's executing a `PipelineRunner` cleanly. Also,
-  `PipelineTask.cancel()` does not block anymore waiting for the `CancelFrame`
-  to reach the end of the pipeline (going back to the behavior in < 0.0.83).
-
-- Fixed an issue in `ElevenLabsTTSService` and `ElevenLabsHttpTTSService` where
-  the Flash models would split words, resulting in a space being inserted
-  between words.
-
-- Fixed an issue where audio filters' `stop()` would not be called when using
-  `CancelFrame`.
-
-- Fixed an issue in `ElevenLabsHttpTTSService`, where
-  `apply_text_normalization` was incorrectly set as a query parameter. It's now
-  being added as a request parameter.
-
-- Fixed an issue where `RimeHttpTTSService` and `PiperTTSService` could generate
-  incorrectly 16-bit aligned audio frames, potentially leading to internal
-  errors or static audio.
-
-- Fixed an issue in `SpeechmaticsSTTService` where `AdditionalVocabEntry` items
-  needed to have `sounds_like` for the session to start.
-
-### Other
-
-- Added foundational example `47-sentry-metrics.py`, demonstrating how to use the
-  `SentryMetrics` processor.
-
-- Added foundational example `14x-function-calling-openpipe.py`.
-
-## [0.0.90] - 2025-10-10
-
-### Added
-
-- Added audio filter `KrispVivaFilter` using the Krisp VIVA SDK.
-
-- Added `--folder` argument to the runner, allowing files saved in that folder
-  to be downloaded from `http://HOST:PORT/file/FILE`.
-
-- Added `GeminiLiveVertexLLMService`, for accessing Gemini Live via Google
-  Vertex AI.
-
-- Added some new configuration options to `GeminiLiveLLMService`:
-
-  - `thinking`
-  - `enable_affective_dialog`
-  - `proactivity`
-
-  Note that these new configuration options require using a newer model than
-  the default, like "gemini-2.5-flash-native-audio-preview-09-2025". The last
-  two require specifying `http_options=HttpOptions(api_version="v1alpha")`.
-
-- Added `on_pipeline_error` event to `PipelineTask`. This event will get fired
-  when an `ErrorFrame` is pushed (use `FrameProcessor.push_error()`).
-
-  ```python
-  @task.event_handler("on_pipeline_error")
-  async def on_pipeline_error(task: PipelineTask, frame: ErrorFrame):
-      ...
-  ```
-
-- Added a `service_tier` `InputParam` to the `BaseOpenAILLMService`. This
-  parameter can influence the latency of the response. For example `"priority"`
-  will result in faster completions, but in exchange for a higher price.
-
-### Changed
-
-- Updated `GeminiLiveLLMService` to use the `google-genai` library rather than
-  use WebSockets directly.
-
-### Deprecated
-
-- `LivekitFrameSerializer` is now deprecated. Use `LiveKitTransport` instead.
-
-- `pipecat.service.openai_realtime` is now deprecated, use
-  `pipecat.services.openai.realtime` instead or
-  `pipecat.services.azure.realtime` for Azure Realtime.
-
-- `pipecat.service.aws_nova_sonic` is now deprecated, use
-  `pipecat.services.aws.nova_sonic` instead.
-
-- `GeminiMultimodalLiveLLMService` is now deprecated, use
-  `GeminiLiveLLMService`.
-
-### Fixed
-
-- Fixed a `GoogleVertexLLMService` issue that would generate an error if no
-  token information was returned.
-
-- `GeminiLiveLLMService` will now end gracefully (i.e. after the bot has
-  finished) upon receiving an `EndFrame`.
-
-- `GeminiLiveLLMService` will try to seamlessly reconnect when it loses its
-  connection.
-
-## [0.0.89] - 2025-10-07
-
-### Fixed
-
-- Reverted a change introduced in 0.0.88 that was causing pipelines to be frozen
-  when using interruption strategies and processors that block interruption
-  frames (e.g. `STTMuteFilter`).
-
-## [0.0.88] - 2025-10-07
-
-### Added
-
-- Added support for Nano Banana models to `GoogleLLMService`. For example, you
-  can now use the `gemini-2.5-flash-image` model to generate images.
-
-- Added `HumeTTSService` for text-to-speech synthesis using Hume AI's expressive
-  voice models. Provides high-quality, emotionally expressive speech synthesis
-  with support for various voice models. Includes example in
-  `examples/foundational/07ad-interruptible-hume.py`. Use with:
-  `uv pip install pipecat-ai[hume]`.
-
-### Changed
-
-- Updated default `GoogleLLMService` model to `gemini-2.5-flash`.
-
-### Deprecated
-
-- PlayHT is shutting down their API on December 31st, 2025. As a result,
-  `PlayHTTTSService` and `PlayHTHttpTTSService` are deprecated and will be
-  removed in a future version.
-
-### Fixed
-
-- Fixed an issue with `AWSNovaSonicLLMService` where the client wouldn't
-  connect due to a breaking change in the AWS dependency chain.
-
-- `PermissionError` is now caught if NLTK's `punkt_tab` can't be downloaded.
-
-- Fixed an issue that would cause wrong user/assistant context ordering when
-  using interruption strategies.
-
-- Fixed RTVI incoming message handling, broken in 0.0.87.
-
-## [0.0.87] - 2025-10-02
-
-### Added
-
-- Added `WebsocketSTTService` base class for websocket-based STT services.
-  Combines STT functionality with websocket connectivity, providing automatic
-  error handling and reconnection capabilities with exponential backoff.
-
-- Added `DeepgramFluxSTTService` for real-time speech recognition using
-  Deepgram's Flux WebSocket API. Flux understands conversational flow and
-  automatically handles turn-taking.
-
 - Added RTVI messages for user/bot audio levels and system logs.
 
 - Include OpenAI-based LLM services cached tokens to `MetricsFrame`.
@@ -758,21 +20,6 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 
 ### Deprecated
 
-- `DailyTransportMessageFrame` and `DailyTransportMessageUrgentFrame` are
-  deprecated, use `DailyOutputTransportMessageFrame` and
-  `DailyOutputTransportMessageUrgentFrame` respectively instead.
-
-- `LiveKitTransportMessageFrame` and `LiveKitTransportMessageUrgentFrame` are
-  deprecated, use `LiveKitOutputTransportMessageFrame` and
-  `LiveKitOutputTransportMessageUrgentFrame` respectively instead.
-
-- `TransportMessageFrame` and `TransportMessageUrgentFrame` are deprecated, use
-  `OutputTransportMessageFrame` and `OutputTransportMessageUrgentFrame`
-  respectively instead.
-
-- `InputTransportMessageUrgentFrame` is deprecated, use
-  `InputTransportMessageFrame` instead.
-
 - `DailyUpdateRemoteParticipantsFrame` is deprecated and will be removed in a
   future version. Instead, create your own custom frame and handle it in the
   `@transport.output().event_handler("on_after_push_frame")` event handler or a
@@ -780,9 +27,6 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 
 ## Fixed
 
-- Fixed an issue in `AWSBedrockLLMService` where timeout exceptions weren't
-  being detected.
-
 - Fixed a `PipelineTask` issue that could prevent the application to exit if
   `task.cancel()` was called when the task was already finished.
 
@@ -1618,8 +862,6 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 
 ### Added
 
-- Added `SonioxSTTService` using Soniox's STT websocket API.
-
 - Added `enable_emulated_vad_interruptions` to `LLMUserAggregatorParams`.
   When user speech is emulated (e.g. when a transcription is received but
   VAD doesn't detect speech), this parameter controls whether the emulated
@@ -2111,7 +1353,7 @@ quality and critical bugs impacting `ParallelPipelines` functionality.**
 - Added `session_token` parameter to `AWSNovaSonicLLMService`.
 
 - Added Gemini Multimodal Live File API for uploading, fetching, listing, and
-  deleting files. See `26f-gemini-live-files-api.py` for example usage.
+  deleting files. See `26f-gemini-multimodal-live-files-api.py` for example usage.
 
 ### Changed
 
@@ -4117,7 +3359,7 @@ stt = DeepgramSTTService(..., live_options=LiveOptions(model="nova-2-general"))
 - Added the new modalities option and helper function to set Gemini output
   modalities.
 
-- Added `examples/foundational/26d-gemini-live-text.py` which is
+- Added `examples/foundational/26d-gemini-multimodal-live-text.py` which is
   using Gemini as TEXT modality and using another TTS provider for TTS process.
 
 ### Changed
@@ -4304,9 +3546,9 @@ stt = DeepgramSTTService(..., live_options=LiveOptions(model="nova-2-general"))
 - Added new foundational examples for `GeminiMultimodalLiveLLMService`:
 
   - `26-gemini-multimodal-live.py`
-  - `26a-gemini-live-transcription.py`
-  - `26b-gemini-live-video.py`
-  - `26c-gemini-live-video.py`
+  - `26a-gemini-multimodal-live-transcription.py`
+  - `26b-gemini-multimodal-live-video.py`
+  - `26c-gemini-multimodal-live-video.py`
 
 - Added `SimliVideoService`. This is an integration for Simli AI avatars.
   (see https://www.simli.com)

COMMUNITY_INTEGRATIONS.md

@@ -1,336 +0,0 @@
-# Community Integrations Guide
-
-Pipecat welcomes community-maintained integrations! As our ecosystem grows, we've established a process for any developer to create and maintain their own service integrations while ensuring discoverability for the Pipecat community.
-
-## Overview
-
-**What we support:** Community-maintained integrations that live in separate repositories and are maintained by their authors.
-
-**What we don't do:** The Pipecat team does not code review, test, or maintain community integrations. We provide guidance and list approved integrations for discoverability.
-
-**Why this approach:** This allows the community to move quickly while keeping the Pipecat core team focused on maintaining the framework itself.
-
-## Submitting your Integration
-
-To be listed as an official community integration, follow these steps:
-
-### Step 1: Build Your Integration
-
-Create your integration following the patterns and examples shown in the "Integration Patterns and Examples" section below.
-
-### Step 2: Set Up Your Repository
-
-Your repository must contain these components:
-
-- **Source code** - Complete implementation following Pipecat patterns
-- **Foundational example** - Single file example showing basic usage (see [Pipecat examples](https://github.com/pipecat-ai/pipecat/tree/main/examples/foundational))
-- **README.md** - Must include:
-
-  - Introduction and explanation of your integration
-  - Installation instructions
-  - Usage instructions with Pipecat Pipeline
-  - How to run your example
-  - Pipecat version compatibility (e.g., "Tested with Pipecat v0.0.86")
-  - Company attribution: If you work for the company providing the service, please mention this in your README. This helps build confidence that the integration will be actively maintained.
-
-- **LICENSE** - Permissive license (BSD-2 like Pipecat, or equivalent open source terms)
-- **Code documentation** - Source code with docstrings (we recommend following [Pipecat's docstring conventions](https://github.com/pipecat-ai/pipecat/blob/main/CONTRIBUTING.md#docstring-conventions))
-- **Changelog** - Maintain a changelog for version updates
-
-### Step 3: Join Discord
-
-Join our Discord: https://discord.gg/pipecat
-
-### Step 4: Submit for Listing
-
-Submit a pull request to add your integration to our [Community Integrations documentation page](https://docs.pipecat.ai/server/services/community-integrations).
-
-**To submit:**
-
-1. Fork the [Pipecat docs repository](https://github.com/pipecat-ai/docs)
-2. Edit the file `server/services/community-integrations.mdx`
-3. Add your integration to the appropriate service category table with:
-   - Service name
-   - Link to your repository
-   - Maintainer GitHub username(s)
-4. Include a link to your demo video (approx 30-60 seconds) in your PR description showing:
-   - Core functionality of your integration
-   - Handling of an interruption (if applicable to service type)
-5. Submit your pull request
-
-Once your PR is submitted, post in the `#community-integrations` Discord channel to let us know.
-
-## Integration Patterns and Examples
-
-### STT (Speech-to-Text) Services
-
-#### Websocket-based Services
-
-**Base class:** `STTService`
-
-**Examples:**
-
-- [DeepgramSTTService](https://github.com/pipecat-ai/pipecat/blob/main/src/pipecat/services/deepgram/stt.py)
-- [SpeechmaticsSTTService](https://github.com/pipecat-ai/pipecat/blob/main/src/pipecat/services/speechmatics/stt.py)
-
-#### File-based Services
-
-**Base class:** `SegmentedSTTService`
-
-**Examples:**
-
-- [RivaSTTService](https://github.com/pipecat-ai/pipecat/blob/main/src/pipecat/services/riva/stt.py)
-- [FalSTTService](https://github.com/pipecat-ai/pipecat/blob/main/src/pipecat/services/fal/stt.py)
-
-#### Key requirements:
-
-- STT services should push `InterimTranscriptionFrames` and `TranscriptionFrames`
-- If confidence values are available, filter for values >50% confidence
-
-### LLM (Large Language Model) Services
-
-#### OpenAI-Compatible Services
-
-**Base class:** `OpenAILLMService`
-
-**Examples:**
-
-- [AzureLLMService](https://github.com/pipecat-ai/pipecat/blob/main/src/pipecat/services/azure/llm.py)
-- [GrokLLMService](https://github.com/pipecat-ai/pipecat/blob/main/src/pipecat/services/grok/llm.py) - Shows overriding the base class where needed
-
-#### Non-OpenAI Compatible Services
-
-**Requires:** Full implementation
-
-**Examples:**
-
-- [AnthropicLLMService](https://github.com/pipecat-ai/pipecat/blob/main/src/pipecat/services/anthropic/llm.py)
-- [GoogleLLMService](https://github.com/pipecat-ai/pipecat/blob/main/src/pipecat/services/google/llm.py)
-
-#### Key requirements:
-
-- **Frame sequence:** Output must follow this frame sequence pattern:
-
-  - `LLMFullResponseStartFrame` - Signals the start of an LLM response
-  - `LLMTextFrame` - Contains LLM content, typically streamed as tokens
-  - `LLMFullResponseEndFrame` - Signals the end of an LLM response
-
-- **Context aggregation:** Implement context aggregation to collect user and assistant content:
-  - Aggregators come in pairs with a `user()` instance and `assistant()` instance
-  - Context must adhere to the `LLMContext` universal format
-  - Aggregators should handle adding messages, function calls, and images to the context
-
-### TTS (Text-to-Speech) Services
-
-#### AudioContextWordTTSService
-
-**Use for:** Websocket-based services supporting word/timestamp alignment
-
-**Example:**
-
-- [CartesiaTTSService](https://github.com/pipecat-ai/pipecat/blob/main/src/pipecat/services/cartesia/tts.py)
-
-#### InterruptibleTTSService
-
-**Use for:** Websocket-based services without word/timestamp alignment, requiring disconnection on interruption
-
-**Example:**
-
-- [SarvamTTSService](https://github.com/pipecat-ai/pipecat/blob/main/src/pipecat/services/sarvam/tts.py)
-
-#### WordTTSService
-
-**Use for:** HTTP-based services supporting word/timestamp alignment
-
-**Example:**
-
-- [ElevenLabsHttpTTSService](https://github.com/pipecat-ai/pipecat/blob/main/src/pipecat/services/elevenlabs/tts.py)
-
-#### TTSService
-
-**Use for:** HTTP-based services without word/timestamp alignment
-
-**Example:**
-
-- [GoogleHttpTTSService](https://github.com/pipecat-ai/pipecat/blob/main/src/pipecat/services/google/tts.py)
-
-#### Key requirements:
-
-- For websocket services, use asyncio WebSocket implementation (required for v13+ support)
-- Handle idle service timeouts with keepalives
-- TTSServices push both audio (`TTSRawAudioFrame`) and text (`TTSTextFrame`) frames
-
-### Telephony Serializers
-
-Pipecat supports telephony provider integration using websocket connections to exchange MediaStreams. These services use a FrameSerializer to serialize and deserialize inputs from the FastAPIWebsocketTransport.
-
-**Examples:**
-
-- [Twilio](https://github.com/pipecat-ai/pipecat/blob/main/src/pipecat/serializers/twilio.py)
-- [Telnyx](https://github.com/pipecat-ai/pipecat/blob/main/src/pipecat/serializers/telnyx.py)
-
-#### Key requirements:
-
-- Include hang-up functionality using the provider's native API, ideally using `aiohttp`
-- Support DTMF (dual-tone multi-frequency) events if the provider supports them:
-  - Deserialize DTMF events from the provider's protocol to `InputDTMFFrame`
-  - Use `KeypadEntry` enum for valid keypad entries (0-9, \*, #, A-D)
-  - Handle invalid DTMF digits gracefully by returning `None`
-
-### Image Generation Services
-
-**Base class:** `ImageGenService`
-
-**Examples:**
-
-- [FalImageGenService](https://github.com/pipecat-ai/pipecat/blob/main/src/pipecat/services/fal/image.py)
-- [GoogleImageGenService](https://github.com/pipecat-ai/pipecat/blob/main/src/pipecat/services/google/image.py)
-
-#### Key requirements:
-
-- Must implement `run_image_gen` method returning an `AsyncGenerator`
-
-### Vision Services
-
-Vision services process images and provide analysis such as descriptions, object detection, or visual question answering.
-
-**Base class:** `VisionService`
-
-**Example:**
-
-- [MoondreamVisionService](https://github.com/pipecat-ai/pipecat/blob/main/src/pipecat/services/moondream/vision.py)
-
-#### Key requirements:
-
-- Must implement `run_vision` method that takes an `LLMContext` and returns an `AsyncGenerator[Frame, None]`
-- The method processes the latest image in the context and yields frames with analysis results
-- Typically yields `TextFrame` objects containing descriptions or answers
-
-## Implementation Guidelines
-
-### Naming Conventions
-
-- **STT:** `VendorSTTService`
-- **LLM:** `VendorLLMService`
-- **TTS:**
-  - Websocket: `VendorTTSService`
-  - HTTP: `VendorHttpTTSService`
-- **Image:** `VendorImageGenService`
-- **Vision:** `VendorVisionService`
-- **Telephony:** `VendorFrameSerializer`
-
-### Metrics Support
-
-Enable metrics in your service:
-
-```python
-def can_generate_metrics(self) -> bool:
-    """Check if this service can generate processing metrics.
-
-    Returns:
-        True, as this service supports metrics.
-    """
-    return True
-```
-
-### Dynamic Settings Updates
-
-STT, LLM, and TTS services support `ServiceUpdateSettingsFrame` for dynamic configuration changes. The base STTService has an `_update_settings()` method that handles settings, and the private `_settings` `Dict` is used to store settings and provide access to the subclass.
-
-```python
-async def set_language(self, language: Language):
-    """Set the recognition language and reconnect.
-
-    Args:
-        language: The language to use for speech recognition.
-    """
-    logger.info(f"Switching STT language to: [{language}]")
-    self._settings["language"] = language
-    await self._disconnect()
-    await self._connect()
-```
-
-Note that, in this example, Deepgram requires the websocket connection be disconnected and reconnected to reinitialize the service with the new value. Consider if your service requires reconnection.
-
-### Sample Rate Handling
-
-Sample rates are set via PipelineParams and passed to each frame processor at initialization. The pattern is to _not_ set the sample rate value in the constructor of a given service. Instead, use the `start()` method to initialize sample rates from the frame:
-
-```python
-async def start(self, frame: StartFrame):
-    """Start the service."""
-    await super().start(frame)
-    self._settings["output_format"]["sample_rate"] = self.sample_rate
-    await self._connect()
-```
-
-Note that `self.sample_rate` is a `@property` set in the TTSService base class, which provides access to the private sample rate value obtained from the StartFrame.
-
-### Tracing Decorators
-
-Use Pipecat's tracing decorators:
-
-- **STT:** `@traced_stt` - decorate a function that handles `transcript`, `is_final`, `language` as args
-- **LLM:** `@traced_llm` - decorate the `_process_context()` method
-- **TTS:** `@traced_tts` - decorate the `run_tts()` method
-
-## Best Practices
-
-### Packaging and Distribution
-
-- Use [uv](https://docs.astral.sh/uv/) for packaging (encouraged)
-- Consider releasing to PyPI for easier installation
-- Follow semantic versioning principles
-- Maintain a changelog
-
-### HTTP Communication
-
-For REST-based communication, use aiohttp. Pipecat includes this as a required dependency, so using it prevents adding an additional dependency to your integration.
-
-### Error Handling
-
-- Wrap API calls in appropriate try/catch blocks
-- Handle rate limits and network failures gracefully
-- Provide meaningful error messages
-- When errors occur, raise exceptions AND push `ErrorFrame`s to notify the pipeline:
-
-```python
-from pipecat.frames.frames import ErrorFrame
-
-try:
-    # Your API call
-    result = await self._make_api_call()
-except Exception as e:
-    # Push error frame to pipeline
-    await self.push_error(ErrorFrame(error=f"{self} error: {e}"))
-    # Raise or handle as appropriate
-    raise
-```
-
-### Testing
-
-- Your foundational example serves as a valuable integration-level test
-- Unit tests are nice to have. As the Pipecat teams provides better guidance, we will encourage unit testing more
-
-## Disclaimer
-
-Community integrations are community-maintained and not officially supported by the Pipecat team. Users should evaluate these integrations independently. The Pipecat team reserves the right to remove listings that become unmaintained or problematic.
-
-## Staying Up to Date
-
-Pipecat evolves rapidly to support the latest AI technologies and patterns. While we strive to minimize breaking changes, they do occur as the framework matures.
-
-**We strongly recommend:**
-
-- Join our Discord at https://discord.gg/pipecat and monitor the `#announcements` channel for release notifications
-- Follow our changelog: https://github.com/pipecat-ai/pipecat/blob/main/CHANGELOG.md
-- Test your integration against new Pipecat releases promptly
-- Update your README with the last tested Pipecat version
-
-This helps ensure your integration remains compatible and your users have clear expectations about version support.
-
-## Questions?
-
-Join our Discord community at https://discord.gg/pipecat and post in the `#community-integrations` channel for guidance and support.
-
-For additional questions, you can also reach out to us at pipecat-ai@daily.co.

CONTRIBUTING.md

@@ -1,9 +1,5 @@
 ## Contributing to Pipecat
 
-**Want to add a new service integration?**
-We encourage community-maintained integrations! Please see our [Community Integration Guide](COMMUNITY_INTEGRATIONS.md) for the process and requirements.
-
-**Want to contribute to Pipecat core?**
 We welcome contributions of all kinds! Your help is appreciated. Follow these steps to get involved:
 
 1. **Fork this repository**: Start by forking the Pipecat Documentation repository to your GitHub account.

README.md

@@ -3,7 +3,6 @@
 </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) [![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) [![Ask DeepWiki](https://deepwiki.com/badge.svg)](https://deepwiki.com/pipecat-ai/pipecat)
-[![](https://getmanta.ai/api/badges?text=Manta%20Graph&link=manta)](https://getmanta.ai/pipecat)
 
 # 🎙️ Pipecat: Real-Time Voice & Multimodal AI Agents
 
@@ -20,6 +19,10 @@
 - **Business Agents** – customer intake, support bots, guided flows
 - **Complex Dialog Systems** – design logic with structured conversations
 
+🧭 Looking to build structured conversations? Check out [Pipecat Flows](https://github.com/pipecat-ai/pipecat-flows) for managing complex conversational states and transitions.
+
+🔍 Looking for help debugging your pipeline and processors? Check out [Whisker](https://github.com/pipecat-ai/whisker), a real-time Pipecat debugger.
+
 ## 🧠 Why Pipecat?
 
 - **Voice-first**: Integrates speech recognition, text-to-speech, and conversation handling
@@ -27,38 +30,40 @@
 - **Composable Pipelines**: Build complex behavior from modular components
 - **Real-Time**: Ultra-low latency interaction with different transports (e.g. WebSockets or WebRTC)
 
-## 🌐 Pipecat Ecosystem
+## 📱 Client SDKs
 
-### 📱 Client SDKs
+You can connect to Pipecat from any platform using our official SDKs:
 
-Building client applications? You can connect to Pipecat from any platform using our official SDKs:
-
-<a href="https://docs.pipecat.ai/client/js/introduction">JavaScript</a> | <a href="https://docs.pipecat.ai/client/react/introduction">React</a> | <a href="https://docs.pipecat.ai/client/react-native/introduction">React Native</a> |
-<a href="https://docs.pipecat.ai/client/ios/introduction">Swift</a> | <a href="https://docs.pipecat.ai/client/android/introduction">Kotlin</a> | <a href="https://docs.pipecat.ai/client/c++/introduction">C++</a> | <a href="https://github.com/pipecat-ai/pipecat-esp32">ESP32</a>
-
-### 🧭 Structured conversations
-
-Looking to build structured conversations? Check out [Pipecat Flows](https://github.com/pipecat-ai/pipecat-flows) for managing complex conversational states and transitions.
-
-### 🪄 Beautiful UIs
-
-Want to build beautiful and engaging experiences? Checkout the [Voice UI Kit](https://github.com/pipecat-ai/voice-ui-kit), a collection of components, hooks and templates for building voice AI applications quickly.
-
-### 🛠️ Create and deploy projects
-
-Create a new project in under a minute with the [Pipecat CLI](https://github.com/pipecat-ai/pipecat-cli). Then use the CLI to monitor and deploy your agent to production.
-
-### 🔍 Debugging
-
-Looking for help debugging your pipeline and processors? Check out [Whisker](https://github.com/pipecat-ai/whisker), a real-time Pipecat debugger.
-
-### 🖥️ Terminal
-
-Love terminal applications? Check out [Tail](https://github.com/pipecat-ai/tail), a terminal dashboard for Pipecat.
-
-### 📺️ Pipecat TV Channel
-
-Catch new features, interviews, and how-tos on our [Pipecat TV](https://www.youtube.com/playlist?list=PLzU2zoMTQIHjqC3v4q2XVSR3hGSzwKFwH) channel.
+<table>
+  <tr>
+    <td>
+      <img src="https://cdn.jsdelivr.net/gh/devicons/devicon/icons/javascript/javascript-original.svg" width="40" height="40" alt="JavaScript"/>
+      <a href="https://docs.pipecat.ai/client/js/introduction">JavaScript</a>
+    </td>
+    <td>
+      <img src="https://cdn.jsdelivr.net/gh/devicons/devicon/icons/react/react-original.svg" width="40" height="40" alt="React"/>
+      <a href="https://docs.pipecat.ai/client/react/introduction">React</a>
+    </td>
+    <td>
+      <img src="https://cdn.jsdelivr.net/gh/devicons/devicon/icons/react/react-original.svg" width="40" height="40" alt="React Native"/>
+      <a href="https://docs.pipecat.ai/client/react-native/introduction">React Native</a>
+    </td>
+  </tr>
+  <tr>
+    <td>
+      <img src="https://cdn.jsdelivr.net/gh/devicons/devicon/icons/swift/swift-original.svg" width="40" height="40" alt="Swift"/>
+      <a href="https://docs.pipecat.ai/client/ios/introduction">Swift</a>
+    </td>
+    <td>
+      <img src="https://cdn.jsdelivr.net/gh/devicons/devicon/icons/kotlin/kotlin-original.svg" width="40" height="40" alt="Kotlin"/>
+      <a href="https://docs.pipecat.ai/client/android/introduction">Kotlin</a>
+    </td>
+    <td>
+      <img src="https://cdn.jsdelivr.net/gh/devicons/devicon/icons/cplusplus/cplusplus-original.svg" width="40" height="40" alt="JavaScript"/>
+      <a href="https://docs.pipecat.ai/client/c++/introduction">C++</a>
+    </td>
+  </tr>
+</table>
 
 ## 🎬 See it in action
 
@@ -67,24 +72,24 @@ Catch new features, interviews, and how-tos on our [Pipecat TV](https://www.yout
     <a href="https://github.com/pipecat-ai/pipecat-examples/tree/main/storytelling-chatbot"><img src="https://raw.githubusercontent.com/pipecat-ai/pipecat-examples/main/storytelling-chatbot/image.png" width="400" /></a>
     <br/>
     <a href="https://github.com/pipecat-ai/pipecat-examples/tree/main/translation-chatbot"><img src="https://raw.githubusercontent.com/pipecat-ai/pipecat-examples/main/translation-chatbot/image.png" width="400" /></a>&nbsp;
-    <a href="https://github.com/pipecat-ai/pipecat/blob/main/examples/foundational/12-describe-video.py"><img src="https://github.com/pipecat-ai/pipecat/blob/main/examples/foundational/assets/moondream.png" width="400" /></a>
+    <a href="https://github.com/pipecat-ai/pipecat-examples/tree/main/moondream-chatbot"><img src="https://raw.githubusercontent.com/pipecat-ai/pipecat-examples/main/moondream-chatbot/image.png" width="400" /></a>
 </p>
 
 ## 🧩 Available services
 
-| Category            | Services                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                  |
-| ------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| Speech-to-Text      | [AssemblyAI](https://docs.pipecat.ai/server/services/stt/assemblyai), [AWS](https://docs.pipecat.ai/server/services/stt/aws), [Azure](https://docs.pipecat.ai/server/services/stt/azure), [Cartesia](https://docs.pipecat.ai/server/services/stt/cartesia), [Deepgram](https://docs.pipecat.ai/server/services/stt/deepgram), [ElevenLabs](https://docs.pipecat.ai/server/services/stt/elevenlabs), [Fal Wizper](https://docs.pipecat.ai/server/services/stt/fal), [Gladia](https://docs.pipecat.ai/server/services/stt/gladia), [Google](https://docs.pipecat.ai/server/services/stt/google), [Groq (Whisper)](https://docs.pipecat.ai/server/services/stt/groq), [NVIDIA Riva](https://docs.pipecat.ai/server/services/stt/riva), [OpenAI (Whisper)](https://docs.pipecat.ai/server/services/stt/openai), [SambaNova (Whisper)](https://docs.pipecat.ai/server/services/stt/sambanova), [Sarvam](https://docs.pipecat.ai/server/services/stt/sarvam), [Soniox](https://docs.pipecat.ai/server/services/stt/soniox), [Speechmatics](https://docs.pipecat.ai/server/services/stt/speechmatics), [Ultravox](https://docs.pipecat.ai/server/services/stt/ultravox), [Whisper](https://docs.pipecat.ai/server/services/stt/whisper)                                                                                                                                                                                          |
-| LLMs                | [Anthropic](https://docs.pipecat.ai/server/services/llm/anthropic), [AWS](https://docs.pipecat.ai/server/services/llm/aws), [Azure](https://docs.pipecat.ai/server/services/llm/azure), [Cerebras](https://docs.pipecat.ai/server/services/llm/cerebras), [DeepSeek](https://docs.pipecat.ai/server/services/llm/deepseek), [Fireworks AI](https://docs.pipecat.ai/server/services/llm/fireworks), [Gemini](https://docs.pipecat.ai/server/services/llm/gemini), [Grok](https://docs.pipecat.ai/server/services/llm/grok), [Groq](https://docs.pipecat.ai/server/services/llm/groq), [Mistral](https://docs.pipecat.ai/server/services/llm/mistral), [NVIDIA NIM](https://docs.pipecat.ai/server/services/llm/nim), [Ollama](https://docs.pipecat.ai/server/services/llm/ollama), [OpenAI](https://docs.pipecat.ai/server/services/llm/openai), [OpenRouter](https://docs.pipecat.ai/server/services/llm/openrouter), [Perplexity](https://docs.pipecat.ai/server/services/llm/perplexity), [Qwen](https://docs.pipecat.ai/server/services/llm/qwen), [SambaNova](https://docs.pipecat.ai/server/services/llm/sambanova) [Together AI](https://docs.pipecat.ai/server/services/llm/together)                                                                                                                                                                                                                              |
-| Text-to-Speech      | [Async](https://docs.pipecat.ai/server/services/tts/asyncai), [AWS](https://docs.pipecat.ai/server/services/tts/aws), [Azure](https://docs.pipecat.ai/server/services/tts/azure), [Cartesia](https://docs.pipecat.ai/server/services/tts/cartesia), [Deepgram](https://docs.pipecat.ai/server/services/tts/deepgram), [ElevenLabs](https://docs.pipecat.ai/server/services/tts/elevenlabs), [Fish](https://docs.pipecat.ai/server/services/tts/fish), [Google](https://docs.pipecat.ai/server/services/tts/google), [Groq](https://docs.pipecat.ai/server/services/tts/groq), [Hume](https://docs.pipecat.ai/server/services/tts/hume), [Inworld](https://docs.pipecat.ai/server/services/tts/inworld), [LMNT](https://docs.pipecat.ai/server/services/tts/lmnt), [MiniMax](https://docs.pipecat.ai/server/services/tts/minimax), [Neuphonic](https://docs.pipecat.ai/server/services/tts/neuphonic), [NVIDIA Riva](https://docs.pipecat.ai/server/services/tts/riva), [OpenAI](https://docs.pipecat.ai/server/services/tts/openai), [Piper](https://docs.pipecat.ai/server/services/tts/piper), [PlayHT](https://docs.pipecat.ai/server/services/tts/playht), [Rime](https://docs.pipecat.ai/server/services/tts/rime), [Sarvam](https://docs.pipecat.ai/server/services/tts/sarvam), [Speechmatics](https://docs.pipecat.ai/server/services/tts/speechmatics), [XTTS](https://docs.pipecat.ai/server/services/tts/xtts) |
-| Speech-to-Speech    | [AWS Nova Sonic](https://docs.pipecat.ai/server/services/s2s/aws), [Gemini Multimodal Live](https://docs.pipecat.ai/server/services/s2s/gemini), [OpenAI Realtime](https://docs.pipecat.ai/server/services/s2s/openai)                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    |
-| Transport           | [Daily (WebRTC)](https://docs.pipecat.ai/server/services/transport/daily), [FastAPI Websocket](https://docs.pipecat.ai/server/services/transport/fastapi-websocket), [SmallWebRTCTransport](https://docs.pipecat.ai/server/services/transport/small-webrtc), [WebSocket Server](https://docs.pipecat.ai/server/services/transport/websocket-server), Local                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                |
-| Serializers         | [Plivo](https://docs.pipecat.ai/server/utilities/serializers/plivo), [Twilio](https://docs.pipecat.ai/server/utilities/serializers/twilio), [Telnyx](https://docs.pipecat.ai/server/utilities/serializers/telnyx)                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                         |
-| Video               | [HeyGen](https://docs.pipecat.ai/server/services/video/heygen), [Tavus](https://docs.pipecat.ai/server/services/video/tavus), [Simli](https://docs.pipecat.ai/server/services/video/simli)                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                |
-| Memory              | [mem0](https://docs.pipecat.ai/server/services/memory/mem0)                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                               |
-| Vision & Image      | [fal](https://docs.pipecat.ai/server/services/image-generation/fal), [Google Imagen](https://docs.pipecat.ai/server/services/image-generation/fal), [Moondream](https://docs.pipecat.ai/server/services/vision/moondream)                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                 |
-| Audio Processing    | [Silero VAD](https://docs.pipecat.ai/server/utilities/audio/silero-vad-analyzer), [Krisp](https://docs.pipecat.ai/server/utilities/audio/krisp-filter), [Koala](https://docs.pipecat.ai/server/utilities/audio/koala-filter), [ai-coustics](https://docs.pipecat.ai/server/utilities/audio/aic-filter)                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    |
-| Analytics & Metrics | [OpenTelemetry](https://docs.pipecat.ai/server/utilities/opentelemetry), [Sentry](https://docs.pipecat.ai/server/services/analytics/sentry)                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                               |
+| Category            | Services                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              |
+| ------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| Speech-to-Text      | [AssemblyAI](https://docs.pipecat.ai/server/services/stt/assemblyai), [AWS](https://docs.pipecat.ai/server/services/stt/aws), [Azure](https://docs.pipecat.ai/server/services/stt/azure), [Cartesia](https://docs.pipecat.ai/server/services/stt/cartesia), [Deepgram](https://docs.pipecat.ai/server/services/stt/deepgram), [ElevenLabs](https://docs.pipecat.ai/server/services/stt/elevenlabs), [Fal Wizper](https://docs.pipecat.ai/server/services/stt/fal), [Gladia](https://docs.pipecat.ai/server/services/stt/gladia), [Google](https://docs.pipecat.ai/server/services/stt/google), [Groq (Whisper)](https://docs.pipecat.ai/server/services/stt/groq), [NVIDIA Riva](https://docs.pipecat.ai/server/services/stt/riva), [OpenAI (Whisper)](https://docs.pipecat.ai/server/services/stt/openai), [SambaNova (Whisper)](https://docs.pipecat.ai/server/services/stt/sambanova), [Soniox](https://docs.pipecat.ai/server/services/stt/soniox), [Speechmatics](https://docs.pipecat.ai/server/services/stt/speechmatics), [Ultravox](https://docs.pipecat.ai/server/services/stt/ultravox), [Whisper](https://docs.pipecat.ai/server/services/stt/whisper)                                                                                                                    |
+| LLMs                | [Anthropic](https://docs.pipecat.ai/server/services/llm/anthropic), [AWS](https://docs.pipecat.ai/server/services/llm/aws), [Azure](https://docs.pipecat.ai/server/services/llm/azure), [Cerebras](https://docs.pipecat.ai/server/services/llm/cerebras), [DeepSeek](https://docs.pipecat.ai/server/services/llm/deepseek), [Fireworks AI](https://docs.pipecat.ai/server/services/llm/fireworks), [Gemini](https://docs.pipecat.ai/server/services/llm/gemini), [Grok](https://docs.pipecat.ai/server/services/llm/grok), [Groq](https://docs.pipecat.ai/server/services/llm/groq), [Mistral](https://docs.pipecat.ai/server/services/llm/mistral), [NVIDIA NIM](https://docs.pipecat.ai/server/services/llm/nim), [Ollama](https://docs.pipecat.ai/server/services/llm/ollama), [OpenAI](https://docs.pipecat.ai/server/services/llm/openai), [OpenRouter](https://docs.pipecat.ai/server/services/llm/openrouter), [Perplexity](https://docs.pipecat.ai/server/services/llm/perplexity), [Qwen](https://docs.pipecat.ai/server/services/llm/qwen), [SambaNova](https://docs.pipecat.ai/server/services/llm/sambanova) [Together AI](https://docs.pipecat.ai/server/services/llm/together)                                                                                          |
+| Text-to-Speech      | [Async](https://docs.pipecat.ai/server/services/tts/asyncai), [AWS](https://docs.pipecat.ai/server/services/tts/aws), [Azure](https://docs.pipecat.ai/server/services/tts/azure), [Cartesia](https://docs.pipecat.ai/server/services/tts/cartesia), [Deepgram](https://docs.pipecat.ai/server/services/tts/deepgram), [ElevenLabs](https://docs.pipecat.ai/server/services/tts/elevenlabs), [Fish](https://docs.pipecat.ai/server/services/tts/fish), [Google](https://docs.pipecat.ai/server/services/tts/google), [Groq](https://docs.pipecat.ai/server/services/tts/groq), [Inworld](https://docs.pipecat.ai/server/services/tts/inworld), [LMNT](https://docs.pipecat.ai/server/services/tts/lmnt), [MiniMax](https://docs.pipecat.ai/server/services/tts/minimax), [Neuphonic](https://docs.pipecat.ai/server/services/tts/neuphonic), [NVIDIA Riva](https://docs.pipecat.ai/server/services/tts/riva), [OpenAI](https://docs.pipecat.ai/server/services/tts/openai), [Piper](https://docs.pipecat.ai/server/services/tts/piper), [PlayHT](https://docs.pipecat.ai/server/services/tts/playht), [Rime](https://docs.pipecat.ai/server/services/tts/rime), [Sarvam](https://docs.pipecat.ai/server/services/tts/sarvam), [XTTS](https://docs.pipecat.ai/server/services/tts/xtts) |
+| Speech-to-Speech    | [AWS Nova Sonic](https://docs.pipecat.ai/server/services/s2s/aws), [Gemini Multimodal Live](https://docs.pipecat.ai/server/services/s2s/gemini), [OpenAI Realtime](https://docs.pipecat.ai/server/services/s2s/openai)                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                |
+| Transport           | [Daily (WebRTC)](https://docs.pipecat.ai/server/services/transport/daily), [FastAPI Websocket](https://docs.pipecat.ai/server/services/transport/fastapi-websocket), [SmallWebRTCTransport](https://docs.pipecat.ai/server/services/transport/small-webrtc), [WebSocket Server](https://docs.pipecat.ai/server/services/transport/websocket-server), Local                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            |
+| Serializers         | [Plivo](https://docs.pipecat.ai/server/utilities/serializers/plivo), [Twilio](https://docs.pipecat.ai/server/utilities/serializers/twilio), [Telnyx](https://docs.pipecat.ai/server/utilities/serializers/telnyx)                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                     |
+| Video               | [HeyGen](https://docs.pipecat.ai/server/services/video/heygen), [Tavus](https://docs.pipecat.ai/server/services/video/tavus), [Simli](https://docs.pipecat.ai/server/services/video/simli)                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            |
+| Memory              | [mem0](https://docs.pipecat.ai/server/services/memory/mem0)                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                           |
+| Vision & Image      | [fal](https://docs.pipecat.ai/server/services/image-generation/fal), [Google Imagen](https://docs.pipecat.ai/server/services/image-generation/fal), [Moondream](https://docs.pipecat.ai/server/services/vision/moondream)                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                             |
+| Audio Processing    | [Silero VAD](https://docs.pipecat.ai/server/utilities/audio/silero-vad-analyzer), [Krisp](https://docs.pipecat.ai/server/utilities/audio/krisp-filter), [Koala](https://docs.pipecat.ai/server/utilities/audio/koala-filter), [ai-coustics](https://docs.pipecat.ai/server/utilities/audio/aic-filter)                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                |
+| Analytics & Metrics | [OpenTelemetry](https://docs.pipecat.ai/server/utilities/opentelemetry), [Sentry](https://docs.pipecat.ai/server/services/analytics/sentry)                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                           |
 
 📚 [View full services documentation →](https://docs.pipecat.ai/server/services/supported-services)
 
@@ -179,6 +184,54 @@ Run a specific test suite:
 uv run pytest tests/test_name.py
 ```
 
+### Setting up your editor
+
+This project uses strict [PEP 8](https://peps.python.org/pep-0008/) formatting via [Ruff](https://github.com/astral-sh/ruff).
+
+#### Emacs
+
+You can use [use-package](https://github.com/jwiegley/use-package) to install [emacs-lazy-ruff](https://github.com/christophermadsen/emacs-lazy-ruff) package and configure `ruff` arguments:
+
+```elisp
+(use-package lazy-ruff
+  :ensure t
+  :hook ((python-mode . lazy-ruff-mode))
+  :config
+  (setq lazy-ruff-format-command "ruff format")
+  (setq lazy-ruff-check-command "ruff check --select I"))
+```
+
+`ruff` was installed in the `venv` environment described before, so you should be able to use [pyvenv-auto](https://github.com/ryotaro612/pyvenv-auto) to automatically load that environment inside Emacs.
+
+```elisp
+(use-package pyvenv-auto
+  :ensure t
+  :defer t
+  :hook ((python-mode . pyvenv-auto-run)))
+```
+
+#### Visual Studio Code
+
+Install the
+[Ruff](https://marketplace.visualstudio.com/items?itemName=charliermarsh.ruff) extension. Then edit the user settings (_Ctrl-Shift-P_ `Open User Settings (JSON)`) and set it as the default Python formatter, and enable formatting on save:
+
+```json
+"[python]": {
+    "editor.defaultFormatter": "charliermarsh.ruff",
+    "editor.formatOnSave": true
+}
+```
+
+#### PyCharm
+
+`ruff` was installed in the `venv` environment described before, now to enable autoformatting on save, go to `File` -> `Settings` -> `Tools` -> `File Watchers` and add a new watcher with the following settings:
+
+1. **Name**: `Ruff formatter`
+2. **File type**: `Python`
+3. **Working directory**: `$ContentRoot$`
+4. **Arguments**: `format $FilePath$`
+5. **Program**: `$PyInterpreterDirectory$/ruff`
+
 ## 🤝 Contributing
 
 We welcome contributions from the community! Whether you're fixing bugs, improving documentation, or adding new features, here's how you can help:

SECURITY.md

@@ -1,5 +0,0 @@
-# Security Policy
-
-## Reporting a Vulnerability
-
-Please email `disclosures@daily.co`.

docs/api/conf.py

@@ -50,7 +50,6 @@ autodoc_mock_imports = [
     # Krisp - has build issues on some platforms
     "pipecat_ai_krisp",
     "krisp",
-    "krisp_audio",
     # System-specific GUI libraries
     "_tkinter",
     "tkinter",

env.example

@@ -4,9 +4,6 @@ AICOUSTICS_LICENSE_KEY=...
 # Anthropic
 ANTHROPIC_API_KEY=...
 
-# Assembly AI
-ASSEMBLYAI_API_KEY=...
-
 # Async
 ASYNCAI_API_KEY=...
 ASYNCAI_VOICE_ID=...
@@ -24,19 +21,12 @@ AZURE_CHATGPT_API_KEY=...
 AZURE_CHATGPT_ENDPOINT=https://...
 AZURE_CHATGPT_MODEL=...
 
-AZURE_REALTIME_API_KEY=...
-AZURE_REALTIME_BASE_URL=...
-
 AZURE_DALLE_API_KEY=...
 AZURE_DALLE_ENDPOINT=https://...
 AZURE_DALLE_MODEL=...
 
 # Cartesia
 CARTESIA_API_KEY=...
-CARTESIA_VOICE_ID=...
-
-# Cerebras
-CEREBRAS_API_KEY=...
 
 # Daily
 DAILY_API_KEY=...
@@ -45,75 +35,39 @@ DAILY_SAMPLE_ROOM_URL=https://...
 # Deepgram
 DEEPGRAM_API_KEY=...
 
-# DeepSeek
-DEEPSEEK_API_KEY=...
-
 # ElevenLabs
 ELEVENLABS_API_KEY=...
 ELEVENLABS_VOICE_ID=...
 
+# Neuphonic
+NEUPHONIC_API_KEY=...
+
 # Fal
 FAL_KEY=...
 
 # Fireworks
 FIREWORKS_API_KEY=...
 
-# Fish Audio
-FISH_API_KEY=...
-
 # Gladia
 GLADIA_API_KEY=...
 GLADIA_REGION=...
 
 # Google
 GOOGLE_API_KEY=...
-GOOGLE_VERTEX_TEST_CREDENTIALS=...
 GOOGLE_CLOUD_PROJECT_ID=...
-GOOGLE_CLOUD_LOCATION=...
 GOOGLE_TEST_CREDENTIALS=...
-
-# Grok
-GROK_API_KEY=...
-
-# Groq
-GROQ_API_KEY=...
-
-# Heygen
-HEYGEN_API_KEY=...
-
-# Hume
-HUME_API_KEY=...
-HUME_VOICE_ID=...
-
-# Inworld
-INWORLD_API_KEY=...
-
-# Krisp
-KRISP_MODEL_PATH=...
-
-# Krisp Viva
-KRISP_VIVA_MODEL_PATH=...
-
-# LiveKit
-LIVEKIT_API_KEY=...
-LIVEKIT_API_SECRET=...
+GOOGLE_VERTEX_TEST_CREDENTIALS=...
 
 # LMNT
 LMNT_API_KEY=...
 LMNT_VOICE_ID=...
 
-# MiniMax
-MINIMAX_API_KEY=...
-MINIMAX_GROUP_ID=...
+# Perplexity
+PERPLEXITY_API_KEY=...
 
-# Mistral
-MISTRAL_API_KEY=...
-
-# Neuphonic
-NEUPHONIC_API_KEY=...
-
-# NVIDIA
-NVIDIA_API_KEY=...
+# PlayHT
+PLAYHT_USER_ID=...
+PLAYHT_API_KEY=...
 
 # OpenAI
 OPENAI_API_KEY=...
@@ -121,73 +75,83 @@ OPENAI_API_KEY=...
 # OpenPipe
 OPENPIPE_API_KEY=...
 
-# OpenRouter
-OPENROUTER_API_KEY=...
-
-# Perplexity
-PERPLEXITY_API_KEY=...
-
-# Picovoice Koala
-KOALA_ACCESS_KEY=...
-
-# Piper
-PIPER_BASE_URL=...
-
-# PlayHT
-PLAYHT_USER_ID=...
-PLAYHT_API_KEY=...
-
-# Plivo
-PLIVO_AUTH_ID=...
-PLIVO_AUTH_TOKEN=...
-
-# Qwen
-QWEN_API_KEY=...
-
-# Rime
-RIME_API_KEY=...
-RIME_VOICE_ID=...
-
-# SambaNova
-SAMBANOVA_API_KEY=...
-
-# Sarvam AI
-SARVAM_API_KEY=...
-
-# Sentry
-SENTRY_DSN=...
+# Tavus
+TAVUS_API_KEY=...
+TAVUS_REPLICA_ID=...
+TAVUS_PERSONA_ID=...
 
 # Simli
 SIMLI_API_KEY=...
 SIMLI_FACE_ID=...
 
-# Smart turn
-LOCAL_SMART_TURN_MODEL_PATH=...
-FAL_SMART_TURN_API_KEY=...
+# Krisp
+KRISP_MODEL_PATH=...
 
-# Soniox
-SONIOX_API_KEY=...
+# DeepSeek
+DEEPSEEK_API_KEY=...
 
-# Speechmatics
-SPEECHMATICS_API_KEY=...
+# Groq
+GROQ_API_KEY=...
 
-# Tavus
-TAVUS_API_KEY=...
-TAVUS_REPLICA_ID=...
+# Grok
+GROK_API_KEY=...
 
-# Telnyx
-TELNYX_API_KEY=...
-TELNYX_ACCOUNT_SID=...
+# Inworld
+INWORLD_API_KEY=...
 
 # Together.ai
 TOGETHER_API_KEY=...
 
+# Cerebras
+CEREBRAS_API_KEY=...
+
+# Fish Audio
+FISH_API_KEY=...
+
+# Assembly AI
+ASSEMBLYAI_API_KEY=...
+
+# OpenRouter
+OPENROUTER_API_KEY=...
+
+# Piper
+PIPER_BASE_URL=...
+
+# Smart turn
+LOCAL_SMART_TURN_MODEL_PATH=...
+FAL_SMART_TURN_API_KEY=...
+
 # Twilio
 TWILIO_ACCOUNT_SID=...
 TWILIO_AUTH_TOKEN=...
 
-# WhatsApp
-WHATSAPP_TOKEN=...
-WHATSAPP_WEBHOOK_VERIFICATION_TOKEN=...
-WHATSAPP_PHONE_NUMBER_ID=...
-WHATSAPP_APP_SECRET=...
+# MiniMax
+MINIMAX_API_KEY=...
+MINIMAX_GROUP_ID=...
+
+# Sarvam AI
+SARVAM_API_KEY=...
+
+# Soniox
+SONIOX_API_KEY=
+
+# Speechmatics
+SPEECHMATICS_API_KEY=...
+
+# SambaNova
+SAMBANOVA_API_KEY=...
+
+# Sentry
+SENTRY_DSN=...
+
+# Heygen
+HEYGEN_API_KEY=...
+
+# Mistral
+MISTRAL_API_KEY=...
+
+# NVIDIA
+NVIDIA_API_KEY=...
+
+# Qwen
+QWEN_API_KEY=...

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

@@ -77,7 +77,7 @@ async def run_example(webrtc_connection: SmallWebRTCConnection):
     messages = [
         {
             "role": "system",
-            "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
+            "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
         },
     ]
 

examples/foundational/04a-transports-daily.py

@@ -25,7 +25,7 @@ from pipecat.processors.aggregators.llm_response_universal import LLMContextAggr
 from pipecat.runner.daily import configure
 from pipecat.services.cartesia.tts import CartesiaTTSService
 from pipecat.services.openai.llm import OpenAILLMService
-from pipecat.transports.daily.transport import DailyParams, DailyTransport
+from pipecat.transports.daily.transport import DailyLogLevel, DailyParams, DailyTransport
 
 load_dotenv(override=True)
 
@@ -49,6 +49,7 @@ async def main():
                 turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
             ),
         )
+        transport.set_log_level(DailyLogLevel.Info)
 
         tts = CartesiaTTSService(
             api_key=os.getenv("CARTESIA_API_KEY"),
@@ -60,7 +61,7 @@ async def main():
         messages = [
             {
                 "role": "system",
-                "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
+                "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
             },
         ]
 

examples/foundational/04b-transports-livekit.py

@@ -69,7 +69,7 @@ async def main():
             "role": "system",
             "content": "You are a helpful LLM in a WebRTC call. "
             "Your goal is to demonstrate your capabilities in a succinct way. "
-            "Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. "
+            "Your output will be converted to audio so don't include special characters in your answers. "
             "Respond to what the user said in a creative and helpful way.",
         },
     ]

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

@@ -100,7 +100,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
     messages = [
         {
             "role": "system",
-            "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
+            "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
         },
     ]
 

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

@@ -113,7 +113,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
     messages = [
         {
             "role": "system",
-            "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
+            "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
         },
     ]
 

examples/foundational/07-interruptible-cartesia-http.py

@@ -21,8 +21,8 @@ from pipecat.processors.aggregators.llm_context import LLMContext
 from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
 from pipecat.runner.types import RunnerArguments
 from pipecat.runner.utils import create_transport
-from pipecat.services.cartesia.stt import CartesiaSTTService
 from pipecat.services.cartesia.tts import CartesiaHttpTTSService
+from pipecat.services.deepgram.stt import DeepgramSTTService
 from pipecat.services.openai.llm import OpenAILLMService
 from pipecat.transports.base_transport import BaseTransport, TransportParams
 from pipecat.transports.daily.transport import DailyParams
@@ -59,7 +59,7 @@ transport_params = {
 async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
     logger.info(f"Starting bot")
 
-    stt = CartesiaSTTService(api_key=os.getenv("CARTESIA_API_KEY"))
+    stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
 
     tts = CartesiaHttpTTSService(
         api_key=os.getenv("CARTESIA_API_KEY"),
@@ -71,7 +71,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
     messages = [
         {
             "role": "system",
-            "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
+            "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
         },
     ]
 

examples/foundational/07-interruptible.py

@@ -5,6 +5,7 @@
 #
 
 import os
+import wave
 
 from dotenv import load_dotenv
 from loguru import logger
@@ -13,7 +14,14 @@ from pipecat.audio.turn.smart_turn.base_smart_turn import SmartTurnParams
 from pipecat.audio.turn.smart_turn.local_smart_turn_v3 import LocalSmartTurnAnalyzerV3
 from pipecat.audio.vad.silero import SileroVADAnalyzer
 from pipecat.audio.vad.vad_analyzer import VADParams
-from pipecat.frames.frames import LLMRunFrame
+from pipecat.frames.frames import (
+    LLMFullResponseEndFrame,
+    LLMFullResponseStartFrame,
+    LLMRunFrame,
+    LLMTextFrame,
+    OutputAudioRawFrame,
+    TextFrame,
+)
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
@@ -70,7 +78,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
     messages = [
         {
             "role": "system",
-            "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
+            "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
         },
     ]
 
@@ -103,7 +111,27 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         logger.info(f"Client connected")
         # Kick off the conversation.
         messages.append({"role": "system", "content": "Please introduce yourself to the user."})
-        await task.queue_frames([LLMRunFrame()])
+
+        audio_file_path = os.path.join(os.path.dirname(__file__), "assets", "pre-recorded.wav")
+
+        with wave.open(audio_file_path, "rb") as wav_file:
+            llm_text_frame = TextFrame(text="This is a pre-recorded message.")
+            llm_text_frame.skip_tts = True
+
+            audio_data = wav_file.readframes(wav_file.getnframes())
+            output_audio_raw_frame = OutputAudioRawFrame(
+                audio=audio_data, sample_rate=44100, num_channels=1
+            )
+
+            await task.queue_frames(
+                [
+                    LLMRunFrame(),
+                    LLMFullResponseStartFrame(),
+                    llm_text_frame,
+                    output_audio_raw_frame,
+                    LLMFullResponseEndFrame(),
+                ]
+            )
 
     @transport.event_handler("on_client_disconnected")
     async def on_client_disconnected(transport, client):

examples/foundational/07a-interruptible-speechmatics-vad.py

@@ -6,7 +6,6 @@
 
 import os
 
-import aiohttp
 from dotenv import load_dotenv
 from loguru import logger
 
@@ -21,10 +20,10 @@ from pipecat.processors.aggregators.llm_response import (
 from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
 from pipecat.runner.types import RunnerArguments
 from pipecat.runner.utils import create_transport
+from pipecat.services.elevenlabs.tts import ElevenLabsTTSService
 from pipecat.services.openai.base_llm import BaseOpenAILLMService
 from pipecat.services.openai.llm import OpenAILLMService
 from pipecat.services.speechmatics.stt import SpeechmaticsSTTService
-from pipecat.services.speechmatics.tts import SpeechmaticsTTSService
 from pipecat.transcriptions.language import Language
 from pipecat.transports.base_transport import BaseTransport, TransportParams
 from pipecat.transports.daily.transport import DailyParams
@@ -52,127 +51,121 @@ transport_params = {
 
 
 async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
-    """Speechmatics STT and TTS Service Example
+    """Speechmatics STT Service Example
 
-    This example demonstrates using Speechmatics Speech-to-Text and Text-to-Speech services
-    with speaker diarization and intelligent speaker management. Key features:
+    This example demonstrates using Speechmatics Speech-to-Text service with speaker diarization and intelligent speaker management. Key features:
 
-    1. Speaker Diarization (STT)
+    1. Speaker Diarization
        - Automatically identifies and distinguishes between different speakers
        - First speaker is identified as 'S1', others get subsequent IDs
        - Uses `enable_diarization` parameter to manage speaker detection
 
-    2. Smart Speaker Control (STT)
+    2. Smart Speaker Control
        - `focus_speakers` parameter lets you target specific speakers (e.g. ["S1"])
        - Other speakers will be wrapped in PASSIVE tags
        - Only processes speech from focused speakers
        - Words from all speakers are wrapped with XML tags for clear speaker identification
        - Other speakers' speech only sent when focused speaker is active
 
-    3. Voice Activity Detection (STT)
+    3. Voice Activity Detection
        - Built-in VAD using `enable_vad` parameter
        - Remove `vad_analyzer` from `transport` config to use module's VAD
        - Emits speaker started/stopped events
 
-    4. Text-to-Speech (TTS)
-       - Low latency streaming audio synthesis
-       - Multiple voice options available including `sarah`, `theo`, and `megan`
-
-    5. Configuration Options
+    4. Configuration Options
        - `operating_point` parameter defaults to `ENHANCED` for optimal accuracy
        - Configurable `end_of_utterance_silence_trigger` (default 0.5s)
        - Customizable speaker formatting
        - Additional diarization settings available
 
-    For detailed information:
-    - STT: https://docs.speechmatics.com/rt-api-ref
-    - TTS: https://docs.speechmatics.com/text-to-speech/quickstart
+    For detailed information about operating points and configuration:
+    https://docs.speechmatics.com/rt-api-ref
     """
 
     logger.info(f"Starting bot")
-    async with aiohttp.ClientSession() as session:
-        stt = SpeechmaticsSTTService(
-            api_key=os.getenv("SPEECHMATICS_API_KEY"),
-            params=SpeechmaticsSTTService.InputParams(
-                language=Language.EN,
-                enable_vad=True,
-                enable_diarization=True,
-                focus_speakers=["S1"],
-                end_of_utterance_silence_trigger=0.5,
-                speaker_active_format="<{speaker_id}>{text}</{speaker_id}>",
-                speaker_passive_format="<PASSIVE><{speaker_id}>{text}</{speaker_id}></PASSIVE>",
+
+    stt = SpeechmaticsSTTService(
+        api_key=os.getenv("SPEECHMATICS_API_KEY"),
+        params=SpeechmaticsSTTService.InputParams(
+            language=Language.EN,
+            enable_vad=True,
+            enable_diarization=True,
+            focus_speakers=["S1"],
+            end_of_utterance_silence_trigger=0.5,
+            speaker_active_format="<{speaker_id}>{text}</{speaker_id}>",
+            speaker_passive_format="<PASSIVE><{speaker_id}>{text}</{speaker_id}></PASSIVE>",
+        ),
+    )
+
+    tts = ElevenLabsTTSService(
+        api_key=os.getenv("ELEVENLABS_API_KEY"),
+        voice_id=os.getenv("ELEVENLABS_VOICE_ID"),
+        model="eleven_turbo_v2_5",
+    )
+
+    llm = OpenAILLMService(
+        api_key=os.getenv("OPENAI_API_KEY"),
+        params=BaseOpenAILLMService.InputParams(temperature=0.75),
+    )
+
+    messages = [
+        {
+            "role": "system",
+            "content": (
+                "You are a helpful British assistant called Alfred. "
+                "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. "
+                "Always include punctuation in your responses. "
+                "Give very short replies - do not give longer replies unless strictly necessary. "
+                "Respond to what the user said in a concise, funny, creative and helpful way. "
+                "Use `<Sn/>` tags to identify different speakers - do not use tags in your replies. "
+                "Do not respond to speakers within `<PASSIVE/>` tags unless explicitly asked to. "
             ),
-        )
+        },
+    ]
 
-        tts = SpeechmaticsTTSService(
-            api_key=os.getenv("SPEECHMATICS_API_KEY"),
-            voice_id="sarah",
-            aiohttp_session=session,
-        )
+    context = LLMContext(messages)
+    context_aggregator = LLMContextAggregatorPair(
+        context,
+        user_params=LLMUserAggregatorParams(aggregation_timeout=0.005),
+    )
 
-        llm = OpenAILLMService(
-            api_key=os.getenv("OPENAI_API_KEY"),
-            params=BaseOpenAILLMService.InputParams(temperature=0.75),
-        )
-
-        messages = [
-            {
-                "role": "system",
-                "content": (
-                    "You are a helpful British assistant called Sarah. "
-                    "Your goal is to demonstrate your capabilities in a succinct way. "
-                    "Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. "
-                    "Always include punctuation in your responses. "
-                    "Give very short replies - do not give longer replies unless strictly necessary. "
-                    "Respond to what the user said in a concise, funny, creative and helpful way. "
-                    "Use `<Sn/>` tags to identify different speakers - do not use tags in your replies. "
-                    "Do not respond to speakers within `<PASSIVE/>` tags unless explicitly asked to. "
-                ),
-            },
+    pipeline = Pipeline(
+        [
+            transport.input(),  # Transport user input
+            stt,
+            context_aggregator.user(),  # User responses
+            llm,  # LLM
+            tts,  # TTS
+            transport.output(),  # Transport bot output
+            context_aggregator.assistant(),  # Assistant spoken responses
         ]
+    )
 
-        context = LLMContext(messages)
-        context_aggregator = LLMContextAggregatorPair(
-            context,
-            user_params=LLMUserAggregatorParams(aggregation_timeout=0.005),
-        )
+    task = PipelineTask(
+        pipeline,
+        params=PipelineParams(
+            enable_metrics=True,
+            enable_usage_metrics=True,
+        ),
+        idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
+    )
 
-        pipeline = Pipeline(
-            [
-                transport.input(),  # Transport user input
-                stt,
-                context_aggregator.user(),  # User responses
-                llm,  # LLM
-                tts,  # TTS
-                transport.output(),  # Transport bot output
-                context_aggregator.assistant(),  # Assistant spoken responses
-            ]
-        )
+    @transport.event_handler("on_client_connected")
+    async def on_client_connected(transport, client):
+        logger.info(f"Client connected")
+        # Kick off the conversation.
+        messages.append({"role": "system", "content": "Say a short hello to the user."})
+        await task.queue_frames([LLMRunFrame()])
 
-        task = PipelineTask(
-            pipeline,
-            params=PipelineParams(
-                enable_metrics=True,
-                enable_usage_metrics=True,
-            ),
-            idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
-        )
+    @transport.event_handler("on_client_disconnected")
+    async def on_client_disconnected(transport, client):
+        logger.info(f"Client disconnected")
+        await task.cancel()
 
-        @transport.event_handler("on_client_connected")
-        async def on_client_connected(transport, client):
-            logger.info(f"Client connected")
-            # Kick off the conversation.
-            messages.append({"role": "system", "content": "Say a short hello to the user."})
-            await task.queue_frames([LLMRunFrame()])
+    runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
 
-        @transport.event_handler("on_client_disconnected")
-        async def on_client_disconnected(transport, client):
-            logger.info(f"Client disconnected")
-            await task.cancel()
-
-        runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
-
-        await runner.run(task)
+    await runner.run(task)
 
 
 async def bot(runner_args: RunnerArguments):

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

@@ -6,7 +6,6 @@
 
 import os
 
-import aiohttp
 from dotenv import load_dotenv
 from loguru import logger
 
@@ -25,10 +24,10 @@ from pipecat.processors.aggregators.llm_response import (
 from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
 from pipecat.runner.types import RunnerArguments
 from pipecat.runner.utils import create_transport
+from pipecat.services.elevenlabs.tts import ElevenLabsTTSService
 from pipecat.services.openai.base_llm import BaseOpenAILLMService
 from pipecat.services.openai.llm import OpenAILLMService
 from pipecat.services.speechmatics.stt import SpeechmaticsSTTService
-from pipecat.services.speechmatics.tts import SpeechmaticsTTSService
 from pipecat.transcriptions.language import Language
 from pipecat.transports.base_transport import BaseTransport, TransportParams
 from pipecat.transports.daily.transport import DailyParams
@@ -62,106 +61,100 @@ transport_params = {
 
 
 async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
-    """Run example using Speechmatics STT and TTS.
+    """Run example using Speechmatics STT.
 
-    This example demonstrates a complete Speechmatics integration with both Speech-to-Text
-    and Text-to-Speech services:
+    This example will use diarization within our STT service and output the words spoken by
+    each individual speaker and wrap them with XML tags for the LLM to process. Note the
+    instructions in the system context for the LLM. This greatly improves the conversation
+    experience by allowing the LLM to understand who is speaking in a multi-party call.
 
-    STT Features:
-    - Diarization to identify and distinguish between different speakers
-    - Words spoken by each speaker are wrapped with XML tags for LLM processing
-    - System context instructions help the LLM understand multi-party conversations
-    - ENHANCED operating point by default for optimal accuracy
+    By default, this example will use our ENHANCED operating point, which is optimized for
+    high accuracy. You can change this by setting the `operating_point` parameter to a different
+    value.
 
-    TTS Features:
-    - Low latency streaming audio synthesis
-    - Multiple voice options available including `sarah`, `theo`, and `megan`
-
-    For more information:
-    - STT: https://docs.speechmatics.com/rt-api-ref
-    - TTS: https://docs.speechmatics.com/text-to-speech/quickstart
+    For more information on operating points, see the Speechmatics documentation:
+    https://docs.speechmatics.com/rt-api-ref
     """
     logger.info(f"Starting bot")
 
-    async with aiohttp.ClientSession() as session:
-        stt = SpeechmaticsSTTService(
-            api_key=os.getenv("SPEECHMATICS_API_KEY"),
-            params=SpeechmaticsSTTService.InputParams(
-                language=Language.EN,
-                enable_diarization=True,
-                end_of_utterance_silence_trigger=0.5,
-                speaker_active_format="<{speaker_id}>{text}</{speaker_id}>",
+    stt = SpeechmaticsSTTService(
+        api_key=os.getenv("SPEECHMATICS_API_KEY"),
+        params=SpeechmaticsSTTService.InputParams(
+            language=Language.EN,
+            enable_diarization=True,
+            end_of_utterance_silence_trigger=0.5,
+            speaker_active_format="<{speaker_id}>{text}</{speaker_id}>",
+        ),
+    )
+
+    tts = ElevenLabsTTSService(
+        api_key=os.getenv("ELEVENLABS_API_KEY"),
+        voice_id=os.getenv("ELEVENLABS_VOICE_ID"),
+        model="eleven_turbo_v2_5",
+    )
+
+    llm = OpenAILLMService(
+        api_key=os.getenv("OPENAI_API_KEY"),
+        params=BaseOpenAILLMService.InputParams(temperature=0.75),
+    )
+
+    messages = [
+        {
+            "role": "system",
+            "content": (
+                "You are a helpful British assistant called Alfred. "
+                "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. "
+                "Always include punctuation in your responses. "
+                "Give very short replies - do not give longer replies unless strictly necessary. "
+                "Respond to what the user said in a concise, funny, creative and helpful way. "
+                "Use `<Sn/>` tags to identify different speakers - do not use tags in your replies."
             ),
-        )
+        },
+    ]
 
-        tts = SpeechmaticsTTSService(
-            api_key=os.getenv("SPEECHMATICS_API_KEY"),
-            voice_id="sarah",
-            aiohttp_session=session,
-        )
+    context = LLMContext(messages)
+    context_aggregator = LLMContextAggregatorPair(
+        context,
+        user_params=LLMUserAggregatorParams(aggregation_timeout=0.005),
+    )
 
-        llm = OpenAILLMService(
-            api_key=os.getenv("OPENAI_API_KEY"),
-            params=BaseOpenAILLMService.InputParams(temperature=0.75),
-        )
-
-        messages = [
-            {
-                "role": "system",
-                "content": (
-                    "You are a helpful British assistant called Sarah. "
-                    "Your goal is to demonstrate your capabilities in a succinct way. "
-                    "Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. "
-                    "Always include punctuation in your responses. "
-                    "Give very short replies - do not give longer replies unless strictly necessary. "
-                    "Respond to what the user said in a concise, funny, creative and helpful way. "
-                    "Use `<Sn/>` tags to identify different speakers - do not use tags in your replies."
-                ),
-            },
+    pipeline = Pipeline(
+        [
+            transport.input(),  # Transport user input
+            stt,  # STT
+            context_aggregator.user(),  # User responses
+            llm,  # LLM
+            tts,  # TTS
+            transport.output(),  # Transport bot output
+            context_aggregator.assistant(),  # Assistant spoken responses
         ]
+    )
 
-        context = LLMContext(messages)
-        context_aggregator = LLMContextAggregatorPair(
-            context,
-            user_params=LLMUserAggregatorParams(aggregation_timeout=0.005),
-        )
+    task = PipelineTask(
+        pipeline,
+        params=PipelineParams(
+            enable_metrics=True,
+            enable_usage_metrics=True,
+        ),
+        idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
+    )
 
-        pipeline = Pipeline(
-            [
-                transport.input(),  # Transport user input
-                stt,  # STT
-                context_aggregator.user(),  # User responses
-                llm,  # LLM
-                tts,  # TTS
-                transport.output(),  # Transport bot output
-                context_aggregator.assistant(),  # Assistant spoken responses
-            ]
-        )
+    @transport.event_handler("on_client_connected")
+    async def on_client_connected(transport, client):
+        logger.info(f"Client connected")
+        # Kick off the conversation.
+        messages.append({"role": "system", "content": "Say a short hello to the user."})
+        await task.queue_frames([LLMRunFrame()])
 
-        task = PipelineTask(
-            pipeline,
-            params=PipelineParams(
-                enable_metrics=True,
-                enable_usage_metrics=True,
-            ),
-            idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
-        )
+    @transport.event_handler("on_client_disconnected")
+    async def on_client_disconnected(transport, client):
+        logger.info(f"Client disconnected")
+        await task.cancel()
 
-        @transport.event_handler("on_client_connected")
-        async def on_client_connected(transport, client):
-            logger.info(f"Client connected")
-            # Kick off the conversation.
-            messages.append({"role": "system", "content": "Say a short hello to the user."})
-            await task.queue_frames([LLMRunFrame()])
+    runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
 
-        @transport.event_handler("on_client_disconnected")
-        async def on_client_disconnected(transport, client):
-            logger.info(f"Client disconnected")
-            await task.cancel()
-
-        runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
-
-        await runner.run(task)
+    await runner.run(task)
 
 
 async def bot(runner_args: RunnerArguments):

examples/foundational/07aa-interruptible-soniox.py

@@ -70,7 +70,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
     messages = [
         {
             "role": "system",
-            "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
+            "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
         },
     ]
 

examples/foundational/07ab-interruptible-inworld-http.py

@@ -81,7 +81,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         messages = [
             {
                 "role": "system",
-                "content": "You are very knowledgable about dogs. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
+                "content": "You are very knowledgable about dogs. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
             },
         ]
 

examples/foundational/07ac-interruptible-asyncai-http.py

@@ -76,7 +76,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         messages = [
             {
                 "role": "system",
-                "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
+                "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
             },
         ]
 

examples/foundational/07ac-interruptible-asyncai.py

@@ -72,7 +72,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
     messages = [
         {
             "role": "system",
-            "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
+            "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
         },
     ]
 

examples/foundational/07ad-interruptible-aicoustics.py

@@ -15,6 +15,7 @@ from loguru import logger
 from pipecat.audio.filters.aic_filter import AICFilter
 from pipecat.audio.turn.smart_turn.base_smart_turn import SmartTurnParams
 from pipecat.audio.turn.smart_turn.local_smart_turn_v3 import LocalSmartTurnAnalyzerV3
+from pipecat.audio.vad.silero import SileroVADAnalyzer
 from pipecat.audio.vad.vad_analyzer import VADParams
 from pipecat.frames.frames import LLMRunFrame
 from pipecat.pipeline.pipeline import Pipeline
@@ -47,7 +48,7 @@ def _create_aic_filter() -> AICFilter:
 
     return AICFilter(
         license_key=license_key,
-        enhancement_level=0.5,
+        enhancement_level=1.0,
     )
 
 
@@ -55,33 +56,27 @@ def _create_aic_filter() -> AICFilter:
 # instantiated. The function will be called when the desired transport gets
 # selected.
 transport_params = {
-    "daily": lambda: (
-        lambda aic: DailyParams(
-            audio_in_enabled=True,
-            audio_out_enabled=True,
-            vad_analyzer=aic.create_vad_analyzer(lookback_buffer_size=6.0, sensitivity=6.0),
-            turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
-            audio_in_filter=aic,
-        )
-    )(_create_aic_filter()),
-    "twilio": lambda: (
-        lambda aic: FastAPIWebsocketParams(
-            audio_in_enabled=True,
-            audio_out_enabled=True,
-            vad_analyzer=aic.create_vad_analyzer(lookback_buffer_size=6.0, sensitivity=6.0),
-            turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
-            audio_in_filter=aic,
-        )
-    )(_create_aic_filter()),
-    "webrtc": lambda: (
-        lambda aic: TransportParams(
-            audio_in_enabled=True,
-            audio_out_enabled=True,
-            vad_analyzer=aic.create_vad_analyzer(lookback_buffer_size=6.0, sensitivity=6.0),
-            turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
-            audio_in_filter=aic,
-        )
-    )(_create_aic_filter()),
+    "daily": lambda: DailyParams(
+        audio_in_enabled=True,
+        audio_out_enabled=True,
+        vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
+        turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
+        audio_in_filter=_create_aic_filter(),
+    ),
+    "twilio": lambda: FastAPIWebsocketParams(
+        audio_in_enabled=True,
+        audio_out_enabled=True,
+        vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
+        turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
+        audio_in_filter=_create_aic_filter(),
+    ),
+    "webrtc": lambda: TransportParams(
+        audio_in_enabled=True,
+        audio_out_enabled=True,
+        vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
+        turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
+        audio_in_filter=_create_aic_filter(),
+    ),
 }
 
 
@@ -100,7 +95,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
     messages = [
         {
             "role": "system",
-            "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
+            "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
         },
     ]
 

examples/foundational/07ae-interruptible-hume.py

@@ -1,138 +0,0 @@
-#
-# Copyright (c) 2024–2025, Daily
-#
-# SPDX-License-Identifier: BSD 2-Clause License
-#
-
-import os
-
-from dotenv import load_dotenv
-from loguru import logger
-
-from pipecat.audio.turn.smart_turn.base_smart_turn import SmartTurnParams
-from pipecat.audio.turn.smart_turn.local_smart_turn_v3 import LocalSmartTurnAnalyzerV3
-from pipecat.audio.vad.silero import SileroVADAnalyzer
-from pipecat.audio.vad.vad_analyzer import VADParams
-from pipecat.frames.frames import LLMRunFrame
-from pipecat.pipeline.pipeline import Pipeline
-from pipecat.pipeline.runner import PipelineRunner
-from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.aggregators.llm_context import LLMContext
-from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
-from pipecat.processors.frameworks.rtvi import RTVIConfig, RTVIObserver, RTVIProcessor
-from pipecat.runner.types import RunnerArguments
-from pipecat.runner.utils import create_transport
-from pipecat.services.deepgram.stt import DeepgramSTTService
-from pipecat.services.hume.tts import HUME_SAMPLE_RATE, HumeTTSService
-from pipecat.services.openai.llm import OpenAILLMService
-from pipecat.transports.base_transport import BaseTransport, TransportParams
-from pipecat.transports.daily.transport import DailyParams
-from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
-
-load_dotenv(override=True)
-
-# We store functions so objects (e.g. SileroVADAnalyzer) don't get
-# instantiated. The function will be called when the desired transport gets
-# selected.
-transport_params = {
-    "daily": lambda: DailyParams(
-        audio_in_enabled=True,
-        audio_out_enabled=True,
-        vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
-        turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
-    ),
-    "twilio": lambda: FastAPIWebsocketParams(
-        audio_in_enabled=True,
-        audio_out_enabled=True,
-        vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
-        turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
-    ),
-    "webrtc": lambda: TransportParams(
-        audio_in_enabled=True,
-        audio_out_enabled=True,
-        vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
-        turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
-    ),
-}
-
-
-async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
-    logger.info(f"Starting bot")
-
-    stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
-
-    tts = HumeTTSService(
-        api_key=os.getenv("HUME_API_KEY"),
-        # Replace with your Hume voice ID
-        voice_id="f898a92e-685f-43fa-985b-a46920f0650b",
-    )
-
-    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
-
-    messages = [
-        {
-            "role": "system",
-            "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
-        },
-    ]
-
-    context = LLMContext(messages)
-    context_aggregator = LLMContextAggregatorPair(context)
-
-    rtvi = RTVIProcessor(config=RTVIConfig(config=[]))
-
-    pipeline = Pipeline(
-        [
-            transport.input(),  # Transport user input
-            rtvi,
-            stt,
-            context_aggregator.user(),  # User responses
-            llm,  # LLM
-            tts,  # TTS
-            transport.output(),  # Transport bot output
-            context_aggregator.assistant(),  # Assistant spoken responses
-        ]
-    )
-
-    task = PipelineTask(
-        pipeline,
-        params=PipelineParams(
-            enable_metrics=True,
-            enable_usage_metrics=True,
-            audio_out_sample_rate=HUME_SAMPLE_RATE,
-        ),
-        idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
-        observers=[RTVIObserver(rtvi)],
-    )
-
-    @rtvi.event_handler("on_client_ready")
-    async def on_client_ready(rtvi):
-        await rtvi.set_bot_ready()
-
-    @transport.event_handler("on_client_connected")
-    async def on_client_connected(transport, client):
-        logger.info(f"Client connected")
-        # Kick off the conversation.
-        messages.append({"role": "system", "content": "Please introduce yourself to the user."})
-        await task.queue_frames([LLMRunFrame()])
-
-    @transport.event_handler("on_client_disconnected")
-    async def on_client_disconnected(transport, client):
-        logger.info(f"Client disconnected")
-        await task.cancel()
-
-    runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
-
-    await runner.run(task)
-
-
-async def bot(runner_args: RunnerArguments):
-    """Main bot entry point compatible with Pipecat Cloud."""
-    transport = await create_transport(runner_args, transport_params)
-    await run_bot(transport, runner_args)
-
-
-if __name__ == "__main__":
-    from pipecat.runner.run import main
-
-    main()

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

@@ -1,122 +0,0 @@
-#
-# Copyright (c) 2024–2025, Daily
-#
-# SPDX-License-Identifier: BSD 2-Clause License
-#
-
-
-import os
-
-from dotenv import load_dotenv
-from loguru import logger
-
-from pipecat.frames.frames import LLMRunFrame
-from pipecat.pipeline.pipeline import Pipeline
-from pipecat.pipeline.runner import PipelineRunner
-from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.aggregators.llm_response_universal import (
-    LLMContext,
-    LLMContextAggregatorPair,
-)
-from pipecat.runner.types import RunnerArguments
-from pipecat.runner.utils import create_transport
-from pipecat.services.deepgram.flux.stt import DeepgramFluxSTTService
-from pipecat.services.deepgram.tts import DeepgramTTSService
-from pipecat.services.openai.llm import OpenAILLMService
-from pipecat.transports.base_transport import BaseTransport, TransportParams
-from pipecat.transports.daily.transport import DailyParams
-from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
-
-load_dotenv(override=True)
-
-
-# We store functions so objects (e.g. SileroVADAnalyzer) don't get
-# instantiated. The function will be called when the desired transport gets
-# selected.
-transport_params = {
-    "daily": lambda: DailyParams(
-        audio_in_enabled=True,
-        audio_out_enabled=True,
-    ),
-    "twilio": lambda: FastAPIWebsocketParams(
-        audio_in_enabled=True,
-        audio_out_enabled=True,
-    ),
-    "webrtc": lambda: TransportParams(
-        audio_in_enabled=True,
-        audio_out_enabled=True,
-    ),
-}
-
-
-async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
-    logger.info(f"Starting bot")
-
-    stt = DeepgramFluxSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
-
-    tts = DeepgramTTSService(api_key=os.getenv("DEEPGRAM_API_KEY"), voice="aura-2-andromeda-en")
-
-    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
-
-    messages = [
-        {
-            "role": "system",
-            "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
-        },
-    ]
-
-    context = LLMContext(messages)
-    context_aggregator = LLMContextAggregatorPair(context)
-
-    pipeline = Pipeline(
-        [
-            transport.input(),  # Transport user input
-            stt,  # STT
-            context_aggregator.user(),  # User responses
-            llm,  # LLM
-            tts,  # TTS
-            transport.output(),  # Transport bot output
-            context_aggregator.assistant(),  # Assistant spoken responses
-        ]
-    )
-
-    task = PipelineTask(
-        pipeline,
-        params=PipelineParams(
-            enable_metrics=True,
-            enable_usage_metrics=True,
-        ),
-        idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
-    )
-
-    @transport.event_handler("on_client_connected")
-    async def on_client_connected(transport, client):
-        logger.info(f"Client connected")
-        # Kick off the conversation.
-        messages.append({"role": "system", "content": "Please introduce yourself to the user."})
-        await task.queue_frames([LLMRunFrame()])
-
-    @transport.event_handler("on_client_disconnected")
-    async def on_client_disconnected(transport, client):
-        logger.info(f"Client disconnected")
-        await task.cancel()
-
-    @stt.event_handler("on_update")
-    async def on_deepgram_flux_update(stt, transcript):
-        logger.debug(f"On deeggram flux update: {transcript}")
-
-    runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
-
-    await runner.run(task)
-
-
-async def bot(runner_args: RunnerArguments):
-    """Main bot entry point compatible with Pipecat Cloud."""
-    transport = await create_transport(runner_args, transport_params)
-    await run_bot(transport, runner_args)
-
-
-if __name__ == "__main__":
-    from pipecat.runner.run import main
-
-    main()

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

@@ -1,132 +0,0 @@
-#
-# Copyright (c) 2024–2025, Daily
-#
-# SPDX-License-Identifier: BSD 2-Clause License
-#
-
-
-import os
-
-import aiohttp
-from dotenv import load_dotenv
-from loguru import logger
-
-from pipecat.audio.turn.smart_turn.base_smart_turn import SmartTurnParams
-from pipecat.audio.turn.smart_turn.local_smart_turn_v3 import LocalSmartTurnAnalyzerV3
-from pipecat.audio.vad.silero import SileroVADAnalyzer
-from pipecat.audio.vad.vad_analyzer import VADParams
-from pipecat.frames.frames import LLMRunFrame
-from pipecat.pipeline.pipeline import Pipeline
-from pipecat.pipeline.runner import PipelineRunner
-from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.aggregators.llm_context import LLMContext
-from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
-from pipecat.runner.types import RunnerArguments
-from pipecat.runner.utils import create_transport
-from pipecat.services.deepgram.stt import DeepgramSTTService
-from pipecat.services.deepgram.tts import DeepgramHttpTTSService
-from pipecat.services.openai.llm import OpenAILLMService
-from pipecat.transports.base_transport import BaseTransport, TransportParams
-from pipecat.transports.daily.transport import DailyParams
-from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
-
-load_dotenv(override=True)
-
-
-# We store functions so objects (e.g. SileroVADAnalyzer) don't get
-# instantiated. The function will be called when the desired transport gets
-# selected.
-transport_params = {
-    "daily": lambda: DailyParams(
-        audio_in_enabled=True,
-        audio_out_enabled=True,
-        vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
-        turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
-    ),
-    "twilio": lambda: FastAPIWebsocketParams(
-        audio_in_enabled=True,
-        audio_out_enabled=True,
-        vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
-        turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
-    ),
-    "webrtc": lambda: TransportParams(
-        audio_in_enabled=True,
-        audio_out_enabled=True,
-        vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
-        turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
-    ),
-}
-
-
-async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
-    logger.info(f"Starting bot")
-
-    async with aiohttp.ClientSession() as session:
-        stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
-
-        tts = DeepgramHttpTTSService(
-            api_key=os.getenv("DEEPGRAM_API_KEY"),
-            voice="aura-2-andromeda-en",
-            aiohttp_session=session,
-        )
-
-        llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
-
-        messages = [
-            {
-                "role": "system",
-                "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
-            },
-        ]
-
-        context = LLMContext(messages)
-        context_aggregator = LLMContextAggregatorPair(context)
-
-        pipeline = Pipeline(
-            [
-                transport.input(),  # Transport user input
-                stt,  # STT
-                context_aggregator.user(),  # User responses
-                llm,  # LLM
-                tts,  # TTS
-                transport.output(),  # Transport bot output
-                context_aggregator.assistant(),  # Assistant spoken responses
-            ]
-        )
-
-        task = PipelineTask(
-            pipeline,
-            params=PipelineParams(
-                enable_metrics=True,
-                enable_usage_metrics=True,
-            ),
-            idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
-        )
-
-        @transport.event_handler("on_client_connected")
-        async def on_client_connected(transport, client):
-            logger.info(f"Client connected")
-            # Kick off the conversation.
-            messages.append({"role": "system", "content": "Please introduce yourself to the user."})
-            await task.queue_frames([LLMRunFrame()])
-
-        @transport.event_handler("on_client_disconnected")
-        async def on_client_disconnected(transport, client):
-            logger.info(f"Client disconnected")
-            await task.cancel()
-
-        runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
-
-        await runner.run(task)
-
-
-async def bot(runner_args: RunnerArguments):
-    """Main bot entry point compatible with Pipecat Cloud."""
-    transport = await create_transport(runner_args, transport_params)
-    await run_bot(transport, runner_args)
-
-
-if __name__ == "__main__":
-    from pipecat.runner.run import main
-
-    main()

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

@@ -68,7 +68,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
     messages = [
         {
             "role": "system",
-            "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
+            "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
         },
     ]
 

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

@@ -69,7 +69,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
     messages = [
         {
             "role": "system",
-            "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
+            "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
         },
     ]
 

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

@@ -23,6 +23,7 @@ from pipecat.processors.aggregators.llm_context import LLMContext
 from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
 from pipecat.runner.types import RunnerArguments
 from pipecat.runner.utils import create_transport
+from pipecat.services.deepgram.stt import DeepgramSTTService
 from pipecat.services.elevenlabs.stt import ElevenLabsSTTService
 from pipecat.services.elevenlabs.tts import ElevenLabsHttpTTSService
 from pipecat.services.openai.llm import OpenAILLMService
@@ -79,7 +80,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         messages = [
             {
                 "role": "system",
-                "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
+                "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
             },
         ]
 

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

@@ -22,7 +22,7 @@ from pipecat.processors.aggregators.llm_context import LLMContext
 from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
 from pipecat.runner.types import RunnerArguments
 from pipecat.runner.utils import create_transport
-from pipecat.services.elevenlabs.stt import ElevenLabsRealtimeSTTService
+from pipecat.services.deepgram.stt import DeepgramSTTService
 from pipecat.services.elevenlabs.tts import ElevenLabsTTSService
 from pipecat.services.openai.llm import OpenAILLMService
 from pipecat.transports.base_transport import BaseTransport, TransportParams
@@ -60,7 +60,7 @@ transport_params = {
 async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
     logger.info(f"Starting bot")
 
-    stt = ElevenLabsRealtimeSTTService(api_key=os.getenv("ELEVENLABS_API_KEY"))
+    stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
 
     tts = ElevenLabsTTSService(
         api_key=os.getenv("ELEVENLABS_API_KEY", ""),
@@ -72,7 +72,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
     messages = [
         {
             "role": "system",
-            "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
+            "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
         },
     ]
 

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

@@ -72,7 +72,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
     messages = [
         {
             "role": "system",
-            "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
+            "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
         },
     ]
 

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

@@ -74,7 +74,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
     messages = [
         {
             "role": "system",
-            "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
+            "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
         },
     ]
 

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

@@ -1,135 +0,0 @@
-#
-# Copyright (c) 2024–2025, Daily
-#
-# SPDX-License-Identifier: BSD 2-Clause License
-#
-
-
-import os
-
-from dotenv import load_dotenv
-from loguru import logger
-
-from pipecat.audio.turn.smart_turn.base_smart_turn import SmartTurnParams
-from pipecat.audio.turn.smart_turn.local_smart_turn_v3 import LocalSmartTurnAnalyzerV3
-from pipecat.audio.vad.silero import SileroVADAnalyzer
-from pipecat.audio.vad.vad_analyzer import VADParams
-from pipecat.frames.frames import LLMRunFrame
-from pipecat.pipeline.pipeline import Pipeline
-from pipecat.pipeline.runner import PipelineRunner
-from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.aggregators.llm_context import LLMContext
-from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
-from pipecat.runner.types import RunnerArguments
-from pipecat.runner.utils import create_transport
-from pipecat.services.azure.llm import AzureLLMService
-from pipecat.services.azure.stt import AzureSTTService
-from pipecat.services.azure.tts import AzureHttpTTSService
-from pipecat.transports.base_transport import BaseTransport, TransportParams
-from pipecat.transports.daily.transport import DailyParams
-from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
-
-load_dotenv(override=True)
-
-# We store functions so objects (e.g. SileroVADAnalyzer) don't get
-# instantiated. The function will be called when the desired transport gets
-# selected.
-transport_params = {
-    "daily": lambda: DailyParams(
-        audio_in_enabled=True,
-        audio_out_enabled=True,
-        vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
-        turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
-    ),
-    "twilio": lambda: FastAPIWebsocketParams(
-        audio_in_enabled=True,
-        audio_out_enabled=True,
-        vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
-        turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
-    ),
-    "webrtc": lambda: TransportParams(
-        audio_in_enabled=True,
-        audio_out_enabled=True,
-        vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
-        turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
-    ),
-}
-
-
-async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
-    logger.info(f"Starting bot")
-
-    stt = AzureSTTService(
-        api_key=os.getenv("AZURE_SPEECH_API_KEY"),
-        region=os.getenv("AZURE_SPEECH_REGION"),
-    )
-
-    tts = AzureHttpTTSService(
-        api_key=os.getenv("AZURE_SPEECH_API_KEY"),
-        region=os.getenv("AZURE_SPEECH_REGION"),
-    )
-
-    llm = AzureLLMService(
-        api_key=os.getenv("AZURE_CHATGPT_API_KEY"),
-        endpoint=os.getenv("AZURE_CHATGPT_ENDPOINT"),
-        model=os.getenv("AZURE_CHATGPT_MODEL"),
-    )
-
-    messages = [
-        {
-            "role": "system",
-            "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
-        },
-    ]
-
-    context = LLMContext(messages)
-    context_aggregator = LLMContextAggregatorPair(context)
-
-    pipeline = Pipeline(
-        [
-            transport.input(),  # Transport user input
-            stt,  # STT
-            context_aggregator.user(),  # User responses
-            llm,  # LLM
-            tts,  # TTS
-            transport.output(),  # Transport bot output
-            context_aggregator.assistant(),  # Assistant spoken responses
-        ]
-    )
-
-    task = PipelineTask(
-        pipeline,
-        params=PipelineParams(
-            enable_metrics=True,
-            enable_usage_metrics=True,
-        ),
-        idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
-    )
-
-    @transport.event_handler("on_client_connected")
-    async def on_client_connected(transport, client):
-        logger.info(f"Client connected")
-        # Kick off the conversation.
-        messages.append({"role": "system", "content": "Please introduce yourself to the user."})
-        await task.queue_frames([LLMRunFrame()])
-
-    @transport.event_handler("on_client_disconnected")
-    async def on_client_disconnected(transport, client):
-        logger.info(f"Client disconnected")
-        await task.cancel()
-
-    runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
-
-    await runner.run(task)
-
-
-async def bot(runner_args: RunnerArguments):
-    """Main bot entry point compatible with Pipecat Cloud."""
-    transport = await create_transport(runner_args, transport_params)
-    await run_bot(transport, runner_args)
-
-
-if __name__ == "__main__":
-    from pipecat.runner.run import main
-
-    main()

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

@@ -78,7 +78,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
     messages = [
         {
             "role": "system",
-            "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
+            "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
         },
     ]
 

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

@@ -72,7 +72,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
     messages = [
         {
             "role": "system",
-            "content": "You are very knowledgable about dogs. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
+            "content": "You are very knowledgable about dogs. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
         },
     ]
 

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

@@ -77,7 +77,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
     messages = [
         {
             "role": "system",
-            "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
+            "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
         },
     ]
 

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

@@ -75,7 +75,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         messages = [
             {
                 "role": "system",
-                "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
+                "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
             },
         ]
 

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

@@ -81,7 +81,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
     messages = [
         {
             "role": "system",
-            "content": f"You are a helpful LLM. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
+            "content": f"You are a helpful LLM. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
         },
     ]
 

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

@@ -68,7 +68,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
     messages = [
         {
             "role": "system",
-            "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
+            "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
         },
     ]
 

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

@@ -71,7 +71,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
     messages = [
         {
             "role": "system",
-            "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
+            "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
         },
     ]
 

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

@@ -67,14 +67,14 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
 
     llm = AWSBedrockLLMService(
         aws_region="us-west-2",
-        model="us.anthropic.claude-haiku-4-5-20251001-v1:0",
-        params=AWSBedrockLLMService.InputParams(temperature=0.8),
+        model="us.anthropic.claude-3-5-haiku-20241022-v1:0",
+        params=AWSBedrockLLMService.InputParams(temperature=0.8, latency="optimized"),
     )
 
     messages = [
         {
             "role": "system",
-            "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
+            "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
         },
     ]
 

examples/foundational/07n-interruptible-gemini-image.py

@@ -1,151 +0,0 @@
-#
-# Copyright (c) 2024–2025, Daily
-#
-# SPDX-License-Identifier: BSD 2-Clause License
-#
-
-"""
-A conversational AI bot using Gemini for both LLM, STT and TTS.
-
-This example demonstrates how to use Gemini's image generation capabilities.
-
-Features showcased:
-- Gemini LLM for conversation and image generation
-- Google TTS and STT
-
-Run with:
-    python examples/foundational/07n-interruptible-gemini-image.py
-
-Make sure to set your environment variables:
-    export GOOGLE_API_KEY=your_api_key_here
-"""
-
-import os
-
-from dotenv import load_dotenv
-from loguru import logger
-
-from pipecat.audio.turn.smart_turn.base_smart_turn import SmartTurnParams
-from pipecat.audio.turn.smart_turn.local_smart_turn_v3 import LocalSmartTurnAnalyzerV3
-from pipecat.audio.vad.silero import SileroVADAnalyzer
-from pipecat.audio.vad.vad_analyzer import VADParams
-from pipecat.frames.frames import LLMRunFrame
-from pipecat.pipeline.pipeline import Pipeline
-from pipecat.pipeline.runner import PipelineRunner
-from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.aggregators.llm_context import LLMContext
-from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
-from pipecat.runner.types import RunnerArguments
-from pipecat.runner.utils import create_transport
-from pipecat.services.google.llm import GoogleLLMService
-from pipecat.services.google.stt import GoogleSTTService
-from pipecat.services.google.tts import GoogleTTSService
-from pipecat.transcriptions.language import Language
-from pipecat.transports.base_transport import BaseTransport, TransportParams
-from pipecat.transports.daily.transport import DailyParams
-from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
-
-load_dotenv(override=True)
-
-# We store functions so objects (e.g. SileroVADAnalyzer) don't get
-# instantiated. The function will be called when the desired transport gets
-# selected.
-transport_params = {
-    "daily": lambda: DailyParams(
-        audio_in_enabled=True,
-        audio_out_enabled=True,
-        video_out_enabled=True,
-        video_out_width=1024,
-        video_out_height=1024,
-        vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
-        turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
-    ),
-    "webrtc": lambda: TransportParams(
-        audio_in_enabled=True,
-        audio_out_enabled=True,
-        video_out_enabled=True,
-        video_out_width=1024,
-        video_out_height=1024,
-        vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
-        turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
-    ),
-}
-
-
-async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
-    logger.info(f"Starting bot")
-
-    stt = GoogleSTTService(
-        params=GoogleSTTService.InputParams(languages=Language.EN_US),
-        credentials=os.getenv("GOOGLE_TEST_CREDENTIALS"),
-    )
-
-    tts = GoogleTTSService(
-        voice_id="en-US-Chirp3-HD-Charon",
-        params=GoogleTTSService.InputParams(language=Language.EN_US),
-        credentials=os.getenv("GOOGLE_TEST_CREDENTIALS"),
-    )
-
-    llm = GoogleLLMService(
-        api_key=os.getenv("GOOGLE_API_KEY"),
-        model="gemini-2.5-flash-image",
-    )
-
-    messages = [
-        {
-            "role": "system",
-            "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
-        },
-    ]
-
-    context = LLMContext(messages)
-    context_aggregator = LLMContextAggregatorPair(context)
-
-    pipeline = Pipeline(
-        [
-            transport.input(),  # Transport user input
-            stt,  # STT
-            context_aggregator.user(),  # User responses
-            llm,  # LLM
-            tts,  # Gemini TTS
-            transport.output(),  # Transport bot output
-            context_aggregator.assistant(),  # Assistant spoken responses
-        ]
-    )
-
-    task = PipelineTask(
-        pipeline,
-        params=PipelineParams(
-            enable_metrics=True,
-            enable_usage_metrics=True,
-        ),
-        idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
-    )
-
-    @transport.event_handler("on_client_connected")
-    async def on_client_connected(transport, client):
-        logger.info(f"Client connected")
-        # Kick off the conversation with a styled introduction
-        messages.append({"role": "system", "content": "Please introduce yourself to the user."})
-        await task.queue_frames([LLMRunFrame()])
-
-    @transport.event_handler("on_client_disconnected")
-    async def on_client_disconnected(transport, client):
-        logger.info(f"Client disconnected")
-        await task.cancel()
-
-    runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
-
-    await runner.run(task)
-
-
-async def bot(runner_args: RunnerArguments):
-    """Main bot entry point compatible with Pipecat Cloud."""
-    transport = await create_transport(runner_args, transport_params)
-    await run_bot(transport, runner_args)
-
-
-if __name__ == "__main__":
-    from pipecat.runner.run import main
-
-    main()

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

@@ -4,6 +4,24 @@
 # SPDX-License-Identifier: BSD 2-Clause License
 #
 
+"""
+A conversational AI bot using Gemini for both LLM and TTS.
+
+This example demonstrates how to use Gemini's TTS capabilities with the new
+GeminiTTSService, which uses Gemini's TTS-specific models instead of Google Cloud TTS.
+
+Features showcased:
+- Gemini LLM for conversation
+- Gemini TTS with natural voice control
+- Support for different voice personalities
+- Style and tone control through natural language prompts
+
+Run with:
+    python examples/foundational/gemini-tts.py
+
+Make sure to set your environment variables:
+    export GOOGLE_API_KEY=your_api_key_here
+"""
 
 import os
 
@@ -66,13 +84,10 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
     )
 
     tts = GeminiTTSService(
-        credentials=os.getenv("GOOGLE_TEST_CREDENTIALS"),
-        model="gemini-2.5-flash-tts",
+        api_key=os.getenv("GOOGLE_API_KEY"),
+        model="gemini-2.5-flash-preview-tts",  # TTS-specific model
         voice_id="Charon",
-        params=GeminiTTSService.InputParams(
-            language=Language.EN_US,
-            prompt="You are a helpful AI assistant. Speak in a natural, conversational tone.",
-        ),
+        params=GeminiTTSService.InputParams(language=Language.EN_US),
     )
 
     llm = GoogleLLMService(
@@ -86,22 +101,15 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
             "role": "system",
             "content": """You are a helpful AI assistant in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way.
 
-            IMPORTANT: You're using Gemini TTS which supports expressive markup tags. You can use these tags in your responses:
-            - [sigh] - Insert a sigh sound
-            - [laughing] - Insert a laugh
-            - [uhm] - Insert a hesitation sound
-            - [whispering] - Speak the next part in a whisper
-            - [shouting] - Speak the next part louder
-            - [extremely fast] - Speak the next part very quickly
-            - [short pause], [medium pause], [long pause] - Add pauses for dramatic effect
+            IMPORTANT: Since you're using Gemini TTS which supports natural voice control, you can include speaking instructions in your responses. For example:
+            - "Say cheerfully: Welcome to our conversation!"
+            - "Read this in a calm, professional tone: Here are the details you requested."
+            - "Speak in an excited whisper: I have some great news to share!"
+            - "Say slowly and clearly: Let me explain this step by step."
 
-            Examples:
-            - "Well [sigh] that's a tricky question."
-            - "[laughing] That's a great joke!"
-            - "[whispering] Let me tell you a secret."
-            - "The answer is... [long pause] ...42!"
+            Feel free to use natural language instructions to control your voice style, tone, pace, and emotion. The TTS system will interpret these instructions and adjust the speech accordingly.
 
-            Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.""",
+            Your output will be converted to audio, so avoid special characters in your answers. Respond to what the user said in a creative and helpful way.""",
         },
     ]
 
@@ -132,11 +140,11 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
     @transport.event_handler("on_client_connected")
     async def on_client_connected(transport, client):
         logger.info(f"Client connected")
-        # Kick off the conversation
+        # Kick off the conversation with a styled introduction
         messages.append(
             {
                 "role": "system",
-                "content": "Hello! I'm your AI assistant. I can help you with a variety of tasks. What would you like to know?",
+                "content": "Say cheerfully and warmly: Hello! I'm your AI assistant powered by Gemini's new TTS technology. I can speak with different voices, tones, and styles. How can I help you today?",
             }
         )
         await task.queue_frames([LLMRunFrame()])

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

@@ -1,139 +0,0 @@
-#
-# Copyright (c) 2024–2025, Daily
-#
-# SPDX-License-Identifier: BSD 2-Clause License
-#
-
-
-import os
-
-from dotenv import load_dotenv
-from loguru import logger
-
-from pipecat.audio.turn.smart_turn.base_smart_turn import SmartTurnParams
-from pipecat.audio.turn.smart_turn.local_smart_turn_v3 import LocalSmartTurnAnalyzerV3
-from pipecat.audio.vad.silero import SileroVADAnalyzer
-from pipecat.audio.vad.vad_analyzer import VADParams
-from pipecat.frames.frames import LLMRunFrame
-from pipecat.pipeline.pipeline import Pipeline
-from pipecat.pipeline.runner import PipelineRunner
-from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.aggregators.llm_context import LLMContext
-from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
-from pipecat.runner.types import RunnerArguments
-from pipecat.runner.utils import create_transport
-from pipecat.services.google.llm import GoogleLLMService
-from pipecat.services.google.stt import GoogleSTTService
-from pipecat.services.google.tts import GoogleHttpTTSService, GoogleTTSService
-from pipecat.transcriptions.language import Language
-from pipecat.transports.base_transport import BaseTransport, TransportParams
-from pipecat.transports.daily.transport import DailyParams
-from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
-
-load_dotenv(override=True)
-
-# We store functions so objects (e.g. SileroVADAnalyzer) don't get
-# instantiated. The function will be called when the desired transport gets
-# selected.
-transport_params = {
-    "daily": lambda: DailyParams(
-        audio_in_enabled=True,
-        audio_out_enabled=True,
-        vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
-        turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
-    ),
-    "twilio": lambda: FastAPIWebsocketParams(
-        audio_in_enabled=True,
-        audio_out_enabled=True,
-        vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
-        turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
-    ),
-    "webrtc": lambda: TransportParams(
-        audio_in_enabled=True,
-        audio_out_enabled=True,
-        vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
-        turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
-    ),
-}
-
-
-async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
-    logger.info(f"Starting bot")
-
-    stt = GoogleSTTService(
-        params=GoogleSTTService.InputParams(languages=Language.EN_US, model="chirp_3"),
-        credentials=os.getenv("GOOGLE_TEST_CREDENTIALS"),
-        location="us",
-    )
-
-    tts = GoogleHttpTTSService(
-        voice_id="en-US-Chirp3-HD-Charon",
-        params=GoogleHttpTTSService.InputParams(language=Language.EN_US),
-        credentials=os.getenv("GOOGLE_TEST_CREDENTIALS"),
-    )
-
-    llm = GoogleLLMService(
-        api_key=os.getenv("GOOGLE_API_KEY"),
-        model="gemini-2.5-flash",
-        # turn on thinking if you want it
-        # params=GoogleLLMService.InputParams(extra={"thinking_config": {"thinking_budget": 4096}}),)
-    )
-
-    messages = [
-        {
-            "role": "system",
-            "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
-        },
-    ]
-
-    context = LLMContext(messages)
-    context_aggregator = LLMContextAggregatorPair(context)
-
-    pipeline = Pipeline(
-        [
-            transport.input(),  # Transport user input
-            stt,  # STT
-            context_aggregator.user(),  # User respones
-            llm,  # LLM
-            tts,  # TTS
-            transport.output(),  # Transport bot output
-            context_aggregator.assistant(),  # Assistant spoken responses
-        ]
-    )
-
-    task = PipelineTask(
-        pipeline,
-        params=PipelineParams(
-            enable_metrics=True,
-            enable_usage_metrics=True,
-        ),
-        idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
-    )
-
-    @transport.event_handler("on_client_connected")
-    async def on_client_connected(transport, client):
-        logger.info(f"Client connected")
-        # Kick off the conversation.
-        messages.append({"role": "system", "content": "Please introduce yourself to the user."})
-        await task.queue_frames([LLMRunFrame()])
-
-    @transport.event_handler("on_client_disconnected")
-    async def on_client_disconnected(transport, client):
-        logger.info(f"Client disconnected")
-        await task.cancel()
-
-    runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
-
-    await runner.run(task)
-
-
-async def bot(runner_args: RunnerArguments):
-    """Main bot entry point compatible with Pipecat Cloud."""
-    transport = await create_transport(runner_args, transport_params)
-    await run_bot(transport, runner_args)
-
-
-if __name__ == "__main__":
-    from pipecat.runner.run import main
-
-    main()

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

@@ -61,9 +61,8 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
     logger.info(f"Starting bot")
 
     stt = GoogleSTTService(
-        params=GoogleSTTService.InputParams(languages=Language.EN_US, model="chirp_3"),
+        params=GoogleSTTService.InputParams(languages=Language.EN_US),
         credentials=os.getenv("GOOGLE_TEST_CREDENTIALS"),
-        location="us",
     )
 
     tts = GoogleTTSService(
@@ -82,7 +81,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
     messages = [
         {
             "role": "system",
-            "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
+            "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
         },
     ]
 

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

@@ -74,7 +74,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
     messages = [
         {
             "role": "system",
-            "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
+            "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
         },
     ]
 

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

@@ -1,129 +0,0 @@
-#
-# Copyright (c) 2024–2025, Daily
-#
-# SPDX-License-Identifier: BSD 2-Clause License
-#
-
-
-import os
-
-from dotenv import load_dotenv
-from loguru import logger
-
-from pipecat.audio.filters.krisp_viva_filter import KrispVivaFilter
-from pipecat.audio.turn.smart_turn.base_smart_turn import SmartTurnParams
-from pipecat.audio.turn.smart_turn.local_smart_turn_v3 import LocalSmartTurnAnalyzerV3
-from pipecat.audio.vad.silero import SileroVADAnalyzer
-from pipecat.audio.vad.vad_analyzer import VADParams
-from pipecat.frames.frames import LLMRunFrame
-from pipecat.pipeline.pipeline import Pipeline
-from pipecat.pipeline.runner import PipelineRunner
-from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.aggregators.llm_context import LLMContext
-from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
-from pipecat.runner.types import RunnerArguments
-from pipecat.runner.utils import create_transport
-from pipecat.services.deepgram.stt import DeepgramSTTService
-from pipecat.services.deepgram.tts import DeepgramTTSService
-from pipecat.services.openai.llm import OpenAILLMService
-from pipecat.transports.base_transport import BaseTransport, TransportParams
-from pipecat.transports.daily.transport import DailyParams
-from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
-
-load_dotenv(override=True)
-
-# We store functions so objects (e.g. SileroVADAnalyzer) don't get
-# instantiated. The function will be called when the desired transport gets
-# selected.
-transport_params = {
-    "daily": lambda: DailyParams(
-        audio_in_enabled=True,
-        audio_out_enabled=True,
-        vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
-        turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
-        audio_in_filter=KrispVivaFilter(),
-    ),
-    "twilio": lambda: FastAPIWebsocketParams(
-        audio_in_enabled=True,
-        audio_out_enabled=True,
-        vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
-        turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
-        audio_in_filter=KrispVivaFilter(),
-    ),
-    "webrtc": lambda: TransportParams(
-        audio_in_enabled=True,
-        audio_out_enabled=True,
-        vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
-        turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
-        audio_in_filter=KrispVivaFilter(),
-    ),
-}
-
-
-async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
-    logger.info(f"Starting bot")
-
-    stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
-
-    tts = DeepgramTTSService(api_key=os.getenv("DEEPGRAM_API_KEY"), voice="aura-helios-en")
-
-    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
-
-    messages = [
-        {
-            "role": "system",
-            "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
-        },
-    ]
-
-    context = LLMContext(messages)
-    context_aggregator = LLMContextAggregatorPair(context)
-
-    pipeline = Pipeline(
-        [
-            transport.input(),  # Transport user input
-            stt,  # STT
-            context_aggregator.user(),  # User responses
-            llm,  # LLM
-            tts,  # TTS
-            transport.output(),  # Transport bot output
-            context_aggregator.assistant(),  # Assistant spoken responses
-        ]
-    )
-
-    task = PipelineTask(
-        pipeline,
-        params=PipelineParams(
-            enable_metrics=True,
-            enable_usage_metrics=True,
-        ),
-        idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
-    )
-
-    @transport.event_handler("on_client_connected")
-    async def on_client_connected(transport, client):
-        logger.info(f"Client connected")
-        # Kick off the conversation.
-        messages.append({"role": "system", "content": "Please introduce yourself to the user."})
-        await task.queue_frames([LLMRunFrame()])
-
-    @transport.event_handler("on_client_disconnected")
-    async def on_client_disconnected(transport, client):
-        logger.info(f"Client disconnected")
-        await task.cancel()
-
-    runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
-
-    await runner.run(task)
-
-
-async def bot(runner_args: RunnerArguments):
-    """Main bot entry point compatible with Pipecat Cloud."""
-    transport = await create_transport(runner_args, transport_params)
-    await run_bot(transport, runner_args)
-
-
-if __name__ == "__main__":
-    from pipecat.runner.run import main
-
-    main()

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

@@ -72,7 +72,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
     messages = [
         {
             "role": "system",
-            "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
+            "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
         },
     ]
 

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

@@ -77,7 +77,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         messages = [
             {
                 "role": "system",
-                "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
+                "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
             },
         ]
 

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

@@ -71,7 +71,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
     messages = [
         {
             "role": "system",
-            "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
+            "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
         },
     ]
 

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

@@ -68,7 +68,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
     messages = [
         {
             "role": "system",
-            "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
+            "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
         },
     ]
 

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

@@ -53,7 +53,7 @@ You are a helpful LLM in a WebRTC call. Your goals are to be helpful and brief i
 You are expert at transcribing audio to text. You will receive a mixture of audio and text input. When
 asked to transcribe what the user said, output an exact, word-for-word transcription.
 
-Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points.
+Your output will be converted to audio so don't include special characters in your answers.
 
 Each time you answer, you should respond in three parts.
 

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

@@ -72,7 +72,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
     messages = [
         {
             "role": "system",
-            "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
+            "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
         },
     ]
 

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

@@ -76,7 +76,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         messages = [
             {
                 "role": "system",
-                "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
+                "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
             },
         ]
 

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

@@ -71,7 +71,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
     messages = [
         {
             "role": "system",
-            "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
+            "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
         },
     ]
 

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

@@ -74,7 +74,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
     messages = [
         {
             "role": "system",
-            "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
+            "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
         },
     ]
 

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

@@ -56,7 +56,7 @@ async def main():
     messages = [
         {
             "role": "system",
-            "content": "You are a helpful LLM. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
+            "content": "You are a helpful LLM. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
         },
     ]
 

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

@@ -78,7 +78,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         messages = [
             {
                 "role": "system",
-                "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
+                "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
             },
         ]
 

examples/foundational/07z-interruptible-sarvam-http.py

@@ -15,7 +15,6 @@ from pipecat.audio.turn.smart_turn.base_smart_turn import SmartTurnParams
 from pipecat.audio.turn.smart_turn.local_smart_turn_v3 import LocalSmartTurnAnalyzerV3
 from pipecat.audio.vad.silero import SileroVADAnalyzer
 from pipecat.audio.vad.vad_analyzer import VADParams
-from pipecat.frames.frames import LLMRunFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
@@ -23,8 +22,8 @@ from pipecat.processors.aggregators.llm_context import LLMContext
 from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
 from pipecat.runner.types import RunnerArguments
 from pipecat.runner.utils import create_transport
+from pipecat.services.deepgram.stt import DeepgramSTTService
 from pipecat.services.openai.llm import OpenAILLMService
-from pipecat.services.sarvam.stt import SarvamSTTService
 from pipecat.services.sarvam.tts import SarvamHttpTTSService
 from pipecat.transcriptions.language import Language
 from pipecat.transports.base_transport import BaseTransport, TransportParams
@@ -64,10 +63,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
 
     # Create an HTTP session
     async with aiohttp.ClientSession() as session:
-        stt = SarvamSTTService(
-            api_key=os.getenv("SARVAM_API_KEY"),
-            model="saarika:v2.5",
-        )
+        stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
 
         tts = SarvamHttpTTSService(
             api_key=os.getenv("SARVAM_API_KEY"),
@@ -80,7 +76,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         messages = [
             {
                 "role": "system",
-                "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
+                "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
             },
         ]
 
@@ -113,7 +109,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
             logger.info(f"Client connected")
             # Kick off the conversation.
             messages.append({"role": "system", "content": "Please introduce yourself to the user."})
-            await task.queue_frames([LLMRunFrame()])
+            await task.queue_frames([context_aggregator.user().get_context_frame()])
 
         @transport.event_handler("on_client_disconnected")
         async def on_client_disconnected(transport, client):

examples/foundational/07z-interruptible-sarvam.py

@@ -24,8 +24,8 @@ from pipecat.processors.aggregators.llm_context import LLMContext
 from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
 from pipecat.runner.types import RunnerArguments
 from pipecat.runner.utils import create_transport
+from pipecat.services.deepgram.stt import DeepgramSTTService
 from pipecat.services.openai.llm import OpenAILLMService
-from pipecat.services.sarvam.stt import SarvamSTTService
 from pipecat.services.sarvam.tts import SarvamTTSService
 from pipecat.transports.base_transport import BaseTransport, TransportParams
 from pipecat.transports.daily.transport import DailyParams
@@ -62,10 +62,7 @@ transport_params = {
 async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
     logger.info(f"Starting bot")
 
-    stt = SarvamSTTService(
-        api_key=os.getenv("SARVAM_API_KEY"),
-        model="saarika:v2.5",
-    )
+    stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
 
     tts = SarvamTTSService(
         api_key=os.getenv("SARVAM_API_KEY"),
@@ -77,7 +74,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
     messages = [
         {
             "role": "system",
-            "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
+            "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
         },
     ]
 

examples/foundational/08-bots-arguing.py

@@ -0,0 +1,147 @@
+import asyncio
+import logging
+import os
+from typing import Tuple
+
+import aiohttp
+from dotenv import load_dotenv
+
+from pipecat.frames.frames import AudioFrame, EndFrame, ImageFrame, LLMContextFrame, TextFrame
+from pipecat.pipeline.pipeline import Pipeline
+from pipecat.processors.aggregators import SentenceAggregator
+from pipecat.processors.aggregators.llm_context import LLMContext
+from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
+from pipecat.runner.daily import configure
+from pipecat.services.azure import AzureLLMService, AzureTTSService
+from pipecat.services.elevenlabs import ElevenLabsTTSService
+from pipecat.services.fal import FalImageGenService
+from pipecat.transports.daily.transport import DailyTransport
+
+load_dotenv(override=True)
+
+logging.basicConfig(format=f"%(levelno)s %(asctime)s %(message)s")
+logger = logging.getLogger("pipecat")
+logger.setLevel(logging.DEBUG)
+
+
+async def main():
+    async with aiohttp.ClientSession() as session:
+        (room_url, _) = await configure(session)
+
+        transport = DailyTransport(
+            room_url,
+            None,
+            "Respond bot",
+            duration_minutes=10,
+            mic_enabled=True,
+            mic_sample_rate=16000,
+            camera_enabled=True,
+            camera_width=1024,
+            camera_height=1024,
+        )
+
+        llm = AzureLLMService(
+            api_key=os.getenv("AZURE_CHATGPT_API_KEY"),
+            endpoint=os.getenv("AZURE_CHATGPT_ENDPOINT"),
+            model=os.getenv("AZURE_CHATGPT_MODEL"),
+        )
+        tts1 = AzureTTSService(
+            api_key=os.getenv("AZURE_SPEECH_API_KEY"),
+            region=os.getenv("AZURE_SPEECH_REGION"),
+        )
+        tts2 = ElevenLabsTTSService(
+            api_key=os.getenv("ELEVENLABS_API_KEY"),
+            voice_id="jBpfuIE2acCO8z3wKNLl",
+        )
+        dalle = FalImageGenService(
+            params=FalImageGenService.InputParams(image_size="1024x1024"),
+            aiohttp_session=session,
+            key=os.getenv("FAL_KEY"),
+        )
+
+        bot1_messages = [
+            {
+                "role": "system",
+                "content": "You are a stern librarian. You strongly believe that a hot dog is a sandwich. Start by stating this fact in a few sentences, then be prepared to debate this with the user. You shouldn't ever compromise on the fundamental truth that a hot dog is a sandwich. Your responses should only be a few sentences long.",
+            },
+        ]
+        bot2_messages = [
+            {
+                "role": "system",
+                "content": "You are a silly cat, and you strongly believe that a hot dog is not a sandwich. Debate this with the user, only responding with a few sentences. Don't ever accept that a hot dog is a sandwich.",
+            },
+        ]
+
+        async def get_text_and_audio(messages) -> Tuple[str, bytearray]:
+            """This function streams text from the LLM and uses the TTS service to convert
+            that text to speech as it's received.
+            """
+            source_queue = asyncio.Queue()
+            sink_queue = asyncio.Queue()
+            sentence_aggregator = SentenceAggregator()
+            pipeline = Pipeline([llm, sentence_aggregator, tts1], source_queue, sink_queue)
+
+            await source_queue.put(LLMContextFrame(LLMContext(messages)))
+            await source_queue.put(EndFrame())
+            await pipeline.run_pipeline()
+
+            message = ""
+            all_audio = bytearray()
+            while sink_queue.qsize():
+                frame = sink_queue.get_nowait()
+                if isinstance(frame, TextFrame):
+                    message += frame.text
+                elif isinstance(frame, AudioFrame):
+                    all_audio.extend(frame.audio)
+
+            return (message, all_audio)
+
+        async def get_bot1_statement():
+            message, audio = await get_text_and_audio(bot1_messages)
+
+            bot1_messages.append({"role": "assistant", "content": message})
+            bot2_messages.append({"role": "user", "content": message})
+
+            return audio
+
+        async def get_bot2_statement():
+            message, audio = await get_text_and_audio(bot2_messages)
+
+            bot2_messages.append({"role": "assistant", "content": message})
+            bot1_messages.append({"role": "user", "content": message})
+
+            return audio
+
+        async def argue():
+            for i in range(100):
+                print(f"In iteration {i}")
+
+                bot1_description = "A woman conservatively dressed as a librarian in a library surrounded by books, cartoon, serious, highly detailed"
+
+                (audio1, image_data1) = await asyncio.gather(
+                    get_bot1_statement(), dalle.run_image_gen(bot1_description)
+                )
+                await transport.send_queue.put(
+                    [
+                        ImageFrame(image_data1[1], image_data1[2]),
+                        AudioFrame(audio1),
+                    ]
+                )
+
+                bot2_description = "A cat dressed in a hot dog costume, cartoon, bright colors, funny, highly detailed"
+
+                (audio2, image_data2) = await asyncio.gather(
+                    get_bot2_statement(), dalle.run_image_gen(bot2_description)
+                )
+                await transport.send_queue.put(
+                    [
+                        ImageFrame(image_data2[1], image_data2[2]),
+                        AudioFrame(audio2),
+                    ]
+                )
+
+        await asyncio.gather(transport.run(), argue())
+
+
+if __name__ == "__main__":
+    asyncio.run(main())

examples/foundational/11-sound-effects.py

@@ -121,7 +121,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
     messages = [
         {
             "role": "system",
-            "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
+            "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio. Respond to what the user said in a creative and helpful way.",
         },
     ]
 

examples/foundational/12-describe-image-openai.py

@@ -1,141 +0,0 @@
-#
-# Copyright (c) 2024–2025, Daily
-#
-# SPDX-License-Identifier: BSD 2-Clause License
-#
-
-
-import os
-
-from dotenv import load_dotenv
-from loguru import logger
-from PIL import Image
-
-from pipecat.audio.turn.smart_turn.base_smart_turn import SmartTurnParams
-from pipecat.audio.turn.smart_turn.local_smart_turn_v3 import LocalSmartTurnAnalyzerV3
-from pipecat.audio.vad.silero import SileroVADAnalyzer
-from pipecat.audio.vad.vad_analyzer import VADParams
-from pipecat.frames.frames import LLMRunFrame
-from pipecat.pipeline.pipeline import Pipeline
-from pipecat.pipeline.runner import PipelineRunner
-from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.aggregators.llm_context import LLMContext
-from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
-from pipecat.runner.types import RunnerArguments
-from pipecat.runner.utils import create_transport
-from pipecat.services.cartesia.tts import CartesiaTTSService
-from pipecat.services.deepgram.stt import DeepgramSTTService
-from pipecat.services.openai.llm import OpenAILLMService
-from pipecat.transports.base_transport import BaseTransport, TransportParams
-from pipecat.transports.daily.transport import DailyParams
-
-load_dotenv(override=True)
-
-
-# We store functions so objects (e.g. SileroVADAnalyzer) don't get
-# instantiated. The function will be called when the desired transport gets
-# selected.
-transport_params = {
-    "daily": lambda: DailyParams(
-        audio_in_enabled=True,
-        audio_out_enabled=True,
-        vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
-        turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
-    ),
-    "webrtc": lambda: TransportParams(
-        audio_in_enabled=True,
-        audio_out_enabled=True,
-        vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
-        turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
-    ),
-}
-
-
-async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
-    logger.info(f"Starting bot")
-
-    stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
-
-    tts = CartesiaTTSService(
-        api_key=os.getenv("CARTESIA_API_KEY"),
-        voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
-    )
-
-    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
-
-    messages = [
-        {
-            "role": "system",
-            "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way. You are also able to describe images.",
-        },
-    ]
-
-    context = LLMContext(messages)
-    context_aggregator = LLMContextAggregatorPair(context)
-
-    pipeline = Pipeline(
-        [
-            transport.input(),  # Transport user input
-            stt,  # STT
-            context_aggregator.user(),  # User responses
-            llm,  # LLM
-            tts,  # TTS
-            transport.output(),  # Transport bot output
-            context_aggregator.assistant(),  # Assistant spoken responses
-        ]
-    )
-
-    task = PipelineTask(
-        pipeline,
-        params=PipelineParams(
-            enable_metrics=True,
-            enable_usage_metrics=True,
-        ),
-        idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
-    )
-
-    @transport.event_handler("on_client_connected")
-    async def on_client_connected(transport, client):
-        logger.info(f"Client connected")
-
-        if not runner_args.body:
-            script_dir = os.path.dirname(__file__)
-            runner_args.body = {
-                "image_path": os.path.join(script_dir, "assets", "cat.jpg"),
-                "question": "Describe this image",
-            }
-
-        image_path = runner_args.body["image_path"]
-        question = runner_args.body["question"]
-
-        # Kick off the conversation.
-        image = Image.open(image_path)
-        message = LLMContext.create_image_message(
-            image=image.tobytes(),
-            format="RGB",
-            size=image.size,
-            text=question,
-        )
-        messages.append(message)
-        await task.queue_frames([LLMRunFrame()])
-
-    @transport.event_handler("on_client_disconnected")
-    async def on_client_disconnected(transport, client):
-        logger.info(f"Client disconnected")
-        await task.cancel()
-
-    runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
-
-    await runner.run(task)
-
-
-async def bot(runner_args: RunnerArguments):
-    """Main bot entry point compatible with Pipecat Cloud."""
-    transport = await create_transport(runner_args, transport_params)
-    await run_bot(transport, runner_args)
-
-
-if __name__ == "__main__":
-    from pipecat.runner.run import main
-
-    main()

examples/foundational/12-describe-video.py

@@ -0,0 +1,180 @@
+#
+# Copyright (c) 2024–2025, Daily
+#
+# SPDX-License-Identifier: BSD 2-Clause License
+#
+
+import os
+from typing import Optional
+
+from dotenv import load_dotenv
+from loguru import logger
+
+from pipecat.audio.turn.smart_turn.base_smart_turn import SmartTurnParams
+from pipecat.audio.turn.smart_turn.local_smart_turn_v3 import LocalSmartTurnAnalyzerV3
+from pipecat.audio.vad.silero import SileroVADAnalyzer
+from pipecat.audio.vad.vad_analyzer import VADParams
+from pipecat.frames.frames import (
+    Frame,
+    LLMContextFrame,
+    TextFrame,
+    TTSSpeakFrame,
+    UserImageRawFrame,
+    UserImageRequestFrame,
+)
+from pipecat.pipeline.pipeline import Pipeline
+from pipecat.pipeline.runner import PipelineRunner
+from pipecat.pipeline.task import PipelineTask
+from pipecat.processors.aggregators.llm_context import LLMContext
+from pipecat.processors.aggregators.user_response import UserResponseAggregator
+from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
+from pipecat.runner.types import RunnerArguments
+from pipecat.runner.utils import (
+    create_transport,
+    get_transport_client_id,
+    maybe_capture_participant_camera,
+)
+from pipecat.services.cartesia.tts import CartesiaTTSService
+from pipecat.services.deepgram.stt import DeepgramSTTService
+from pipecat.services.moondream.vision import MoondreamService
+from pipecat.transports.base_transport import BaseTransport, TransportParams
+from pipecat.transports.daily.transport import DailyParams
+
+load_dotenv(override=True)
+
+
+class UserImageRequester(FrameProcessor):
+    """Converts incoming text into requests for user images."""
+
+    def __init__(self, participant_id: Optional[str] = None):
+        super().__init__()
+        self._participant_id = participant_id
+
+    def set_participant_id(self, participant_id: str):
+        self._participant_id = participant_id
+
+    async def process_frame(self, frame: Frame, direction: FrameDirection):
+        await super().process_frame(frame, direction)
+
+        if self._participant_id and isinstance(frame, TextFrame):
+            await self.push_frame(
+                UserImageRequestFrame(self._participant_id, context=frame.text),
+                FrameDirection.UPSTREAM,
+            )
+        else:
+            await self.push_frame(frame, direction)
+
+
+class UserImageProcessor(FrameProcessor):
+    """Converts incoming user images into context frames."""
+
+    async def process_frame(self, frame: Frame, direction: FrameDirection):
+        await super().process_frame(frame, direction)
+
+        if isinstance(frame, UserImageRawFrame):
+            if frame.request and frame.request.context:
+                context = LLMContext()
+                context.add_image_frame_message(
+                    image=frame.image,
+                    text=frame.request.context,
+                    size=frame.size,
+                    format=frame.format,
+                )
+                frame = LLMContextFrame(context)
+                await self.push_frame(frame)
+        else:
+            await self.push_frame(frame, direction)
+
+
+# We store functions so objects (e.g. SileroVADAnalyzer) don't get
+# instantiated. The function will be called when the desired transport gets
+# selected.
+transport_params = {
+    "daily": lambda: DailyParams(
+        audio_in_enabled=True,
+        audio_out_enabled=True,
+        video_in_enabled=True,
+        vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
+        turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
+    ),
+    "webrtc": lambda: TransportParams(
+        audio_in_enabled=True,
+        audio_out_enabled=True,
+        video_in_enabled=True,
+        vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
+        turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
+    ),
+}
+
+
+async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
+    logger.info(f"Starting bot")
+
+    user_response = UserResponseAggregator()
+
+    # Initialize the image requester without setting the participant ID yet
+    image_requester = UserImageRequester()
+
+    image_processor = UserImageProcessor()
+
+    # If you run into weird description, try with use_cpu=True
+    moondream = MoondreamService()
+
+    stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
+
+    tts = CartesiaTTSService(
+        api_key=os.getenv("CARTESIA_API_KEY"),
+        voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
+    )
+
+    pipeline = Pipeline(
+        [
+            transport.input(),
+            stt,
+            user_response,
+            image_requester,
+            image_processor,
+            moondream,
+            tts,
+            transport.output(),
+        ]
+    )
+
+    task = PipelineTask(
+        pipeline,
+        idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
+    )
+
+    @transport.event_handler("on_client_connected")
+    async def on_client_connected(transport, client):
+        logger.info(f"Client connected: {client}")
+
+        await maybe_capture_participant_camera(transport, client)
+
+        # Set the participant ID in the image requester
+        client_id = get_transport_client_id(transport, client)
+        image_requester.set_participant_id(client_id)
+
+        # Welcome message
+        await task.queue_frame(TTSSpeakFrame("Hi there! Feel free to ask me about what I see."))
+
+    @transport.event_handler("on_client_disconnected")
+    async def on_client_disconnected(transport, client):
+        logger.info(f"Client disconnected")
+        await task.cancel()
+
+    runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
+
+    await runner.run(task)
+
+
+async def bot(runner_args: RunnerArguments):
+    """Main bot entry point compatible with Pipecat Cloud."""
+    transport = await create_transport(runner_args, transport_params)
+    await run_bot(transport, runner_args)
+
+
+if __name__ == "__main__":
+    from pipecat.runner.run import main
+
+    main()

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

@@ -5,23 +5,29 @@
 #
 
 import os
+from typing import Optional
 
 from dotenv import load_dotenv
 from loguru import logger
 
-from pipecat.adapters.schemas.function_schema import FunctionSchema
-from pipecat.adapters.schemas.tools_schema import ToolsSchema
 from pipecat.audio.turn.smart_turn.base_smart_turn import SmartTurnParams
 from pipecat.audio.turn.smart_turn.local_smart_turn_v3 import LocalSmartTurnAnalyzerV3
 from pipecat.audio.vad.silero import SileroVADAnalyzer
 from pipecat.audio.vad.vad_analyzer import VADParams
-from pipecat.frames.frames import LLMRunFrame, UserImageRequestFrame
+from pipecat.frames.frames import (
+    Frame,
+    LLMContextFrame,
+    TextFrame,
+    TTSSpeakFrame,
+    UserImageRawFrame,
+    UserImageRequestFrame,
+)
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
 from pipecat.processors.aggregators.llm_context import LLMContext
-from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
-from pipecat.processors.frame_processor import FrameDirection
+from pipecat.processors.aggregators.user_response import UserResponseAggregator
+from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
 from pipecat.runner.types import RunnerArguments
 from pipecat.runner.utils import (
     create_transport,
@@ -31,37 +37,53 @@ from pipecat.runner.utils import (
 from pipecat.services.cartesia.tts import CartesiaTTSService
 from pipecat.services.deepgram.stt import DeepgramSTTService
 from pipecat.services.google.llm import GoogleLLMService
-from pipecat.services.llm_service import FunctionCallParams
 from pipecat.transports.base_transport import BaseTransport, TransportParams
 from pipecat.transports.daily.transport import DailyParams
 
 load_dotenv(override=True)
 
 
-async def fetch_user_image(params: FunctionCallParams):
-    """Fetch the user image and push it to the LLM.
+class UserImageRequester(FrameProcessor):
+    """Converts incoming text into requests for user images."""
 
-    When called, this function pushes a UserImageRequestFrame upstream to the
-    transport. As a result, the transport will request the user image and push a
-    UserImageRawFrame downstream which will be added to the context by the LLM
-    assistant aggregator.
-    """
-    user_id = params.arguments["user_id"]
-    question = params.arguments["question"]
-    logger.debug(f"Requesting image with user_id={user_id}, question={question}")
+    def __init__(self, participant_id: Optional[str] = None):
+        super().__init__()
+        self._participant_id = participant_id
 
-    # Request a user image frame and indicate that it should be added to the
-    # context.
-    await params.llm.push_frame(
-        UserImageRequestFrame(user_id=user_id, text=question, append_to_context=True),
-        FrameDirection.UPSTREAM,
-    )
+    def set_participant_id(self, participant_id: str):
+        self._participant_id = participant_id
 
-    await params.result_callback(None)
+    async def process_frame(self, frame: Frame, direction: FrameDirection):
+        await super().process_frame(frame, direction)
 
-    # Instead of None, it's possible to also provide a tool call answer to
-    # tell the LLM that we are grabbing the image to analyze.
-    # await params.result_callback({"result": "Image is being captured."})
+        if self._participant_id and isinstance(frame, TextFrame):
+            await self.push_frame(
+                UserImageRequestFrame(self._participant_id, context=frame.text),
+                FrameDirection.UPSTREAM,
+            )
+        else:
+            await self.push_frame(frame, direction)
+
+
+class UserImageProcessor(FrameProcessor):
+    """Converts incoming user images into context frames."""
+
+    async def process_frame(self, frame: Frame, direction: FrameDirection):
+        await super().process_frame(frame, direction)
+
+        if isinstance(frame, UserImageRawFrame):
+            if frame.request and frame.request.context:
+                context = LLMContext()
+                context.add_image_frame_message(
+                    image=frame.image,
+                    text=frame.request.context,
+                    size=frame.size,
+                    format=frame.format,
+                )
+                frame = LLMContextFrame(context)
+                await self.push_frame(frame)
+        else:
+            await self.push_frame(frame, direction)
 
 
 # We store functions so objects (e.g. SileroVADAnalyzer) don't get
@@ -88,53 +110,33 @@ transport_params = {
 async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
     logger.info(f"Starting bot")
 
+    user_response = UserResponseAggregator()
+
+    # Initialize the image requester without setting the participant ID yet
+    image_requester = UserImageRequester()
+
+    image_processor = UserImageProcessor()
+
     stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
 
+    # Google Gemini model for vision analysis
+    google = GoogleLLMService(model="gemini-2.0-flash-001", api_key=os.getenv("GOOGLE_API_KEY"))
+
     tts = CartesiaTTSService(
         api_key=os.getenv("CARTESIA_API_KEY"),
         voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
     )
 
-    # Google Gemini model for vision analysis
-    llm = GoogleLLMService(api_key=os.getenv("GOOGLE_API_KEY"))
-    llm.register_function("fetch_user_image", fetch_user_image)
-
-    fetch_image_function = FunctionSchema(
-        name="fetch_user_image",
-        description="Called when the user requests a description of their camera feed",
-        properties={
-            "user_id": {
-                "type": "string",
-                "description": "The ID of the user to grab the image from",
-            },
-            "question": {
-                "type": "string",
-                "description": "The question that the user is asking about the image",
-            },
-        },
-        required=["user_id", "question"],
-    )
-    tools = ToolsSchema(standard_tools=[fetch_image_function])
-
-    messages = [
-        {
-            "role": "system",
-            "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way. You are able to describe images from the user camera.",
-        },
-    ]
-
-    context = LLMContext(messages, tools)
-    context_aggregator = LLMContextAggregatorPair(context)
-
     pipeline = Pipeline(
         [
-            transport.input(),  # Transport user input
-            stt,  # STT
-            context_aggregator.user(),  # User responses
-            llm,  # LLM
-            tts,  # TTS
-            transport.output(),  # Transport bot output
-            context_aggregator.assistant(),  # Assistant spoken responses
+            transport.input(),
+            stt,
+            user_response,
+            image_requester,
+            image_processor,
+            google,
+            tts,
+            transport.output(),
         ]
     )
 
@@ -155,15 +157,10 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
 
         # Set the participant ID in the image requester
         client_id = get_transport_client_id(transport, client)
+        image_requester.set_participant_id(client_id)
 
-        # Kick off the conversation.
-        messages.append(
-            {
-                "role": "system",
-                "content": f"Please introduce yourself to the user. Use '{client_id}' as the user ID during function calls.",
-            }
-        )
-        await task.queue_frames([LLMRunFrame()])
+        # Welcome message
+        await task.queue_frame(TTSSpeakFrame("Hi there! Feel free to ask me about what I see."))
 
     @transport.event_handler("on_client_disconnected")
     async def on_client_disconnected(transport, client):

examples/foundational/12b-describe-image-aws.py

@@ -1,148 +0,0 @@
-#
-# Copyright (c) 2024–2025, Daily
-#
-# SPDX-License-Identifier: BSD 2-Clause License
-#
-
-
-import os
-
-from dotenv import load_dotenv
-from loguru import logger
-from PIL import Image
-
-from pipecat.audio.turn.smart_turn.base_smart_turn import SmartTurnParams
-from pipecat.audio.turn.smart_turn.local_smart_turn_v3 import LocalSmartTurnAnalyzerV3
-from pipecat.audio.vad.silero import SileroVADAnalyzer
-from pipecat.audio.vad.vad_analyzer import VADParams
-from pipecat.frames.frames import LLMRunFrame
-from pipecat.pipeline.pipeline import Pipeline
-from pipecat.pipeline.runner import PipelineRunner
-from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.aggregators.llm_context import LLMContext
-from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
-from pipecat.runner.types import RunnerArguments
-from pipecat.runner.utils import create_transport
-from pipecat.services.aws.llm import AWSBedrockLLMService
-from pipecat.services.cartesia.tts import CartesiaTTSService
-from pipecat.services.deepgram.stt import DeepgramSTTService
-from pipecat.transports.base_transport import BaseTransport, TransportParams
-from pipecat.transports.daily.transport import DailyParams
-
-load_dotenv(override=True)
-
-
-# We store functions so objects (e.g. SileroVADAnalyzer) don't get
-# instantiated. The function will be called when the desired transport gets
-# selected.
-transport_params = {
-    "daily": lambda: DailyParams(
-        audio_in_enabled=True,
-        audio_out_enabled=True,
-        vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
-        turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
-    ),
-    "webrtc": lambda: TransportParams(
-        audio_in_enabled=True,
-        audio_out_enabled=True,
-        vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
-        turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
-    ),
-}
-
-
-async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
-    logger.info(f"Starting bot")
-
-    stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
-
-    tts = CartesiaTTSService(
-        api_key=os.getenv("CARTESIA_API_KEY"),
-        voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
-    )
-
-    llm = AWSBedrockLLMService(
-        aws_region="us-west-2",
-        model="us.anthropic.claude-3-7-sonnet-20250219-v1:0",
-        # Note: usually, prefer providing latency="optimized" param.
-        # Here we can't because AWS Bedrock doesn't support it for Claude 3.7,
-        # which we need for image input.
-        params=AWSBedrockLLMService.InputParams(temperature=0.8),
-    )
-
-    messages = [
-        {
-            "role": "system",
-            "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way. You are also able to describe images.",
-        },
-    ]
-
-    context = LLMContext(messages)
-    context_aggregator = LLMContextAggregatorPair(context)
-
-    pipeline = Pipeline(
-        [
-            transport.input(),  # Transport user input
-            stt,  # STT
-            context_aggregator.user(),  # User responses
-            llm,  # LLM
-            tts,  # TTS
-            transport.output(),  # Transport bot output
-            context_aggregator.assistant(),  # Assistant spoken responses
-        ]
-    )
-
-    task = PipelineTask(
-        pipeline,
-        params=PipelineParams(
-            enable_metrics=True,
-            enable_usage_metrics=True,
-        ),
-        idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
-    )
-
-    @transport.event_handler("on_client_connected")
-    async def on_client_connected(transport, client):
-        logger.info(f"Client connected")
-
-        if not runner_args.body:
-            script_dir = os.path.dirname(__file__)
-            runner_args.body = {
-                "image_path": os.path.join(script_dir, "assets", "cat.jpg"),
-                "question": "Describe this image",
-            }
-
-        image_path = runner_args.body["image_path"]
-        question = runner_args.body["question"]
-
-        # Kick off the conversation.
-        image = Image.open(image_path)
-        message = LLMContext.create_image_message(
-            image=image.tobytes(),
-            format="RGB",
-            size=image.size,
-            text=question,
-        )
-        messages.append(message)
-        await task.queue_frames([LLMRunFrame()])
-
-    @transport.event_handler("on_client_disconnected")
-    async def on_client_disconnected(transport, client):
-        logger.info(f"Client disconnected")
-        await task.cancel()
-
-    runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
-
-    await runner.run(task)
-
-
-async def bot(runner_args: RunnerArguments):
-    """Main bot entry point compatible with Pipecat Cloud."""
-    transport = await create_transport(runner_args, transport_params)
-    await run_bot(transport, runner_args)
-
-
-if __name__ == "__main__":
-    from pipecat.runner.run import main
-
-    main()

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

@@ -4,9 +4,8 @@
 # SPDX-License-Identifier: BSD 2-Clause License
 #
 
-import io
 import os
-import re
+from typing import Optional
 
 from dotenv import load_dotenv
 from loguru import logger
@@ -17,17 +16,24 @@ from pipecat.audio.vad.silero import SileroVADAnalyzer
 from pipecat.audio.vad.vad_analyzer import VADParams
 from pipecat.frames.frames import (
     Frame,
-    LLMRunFrame,
-    MetricsFrame,
+    LLMContextFrame,
+    TextFrame,
+    TTSSpeakFrame,
+    UserImageRawFrame,
+    UserImageRequestFrame,
 )
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
 from pipecat.processors.aggregators.llm_context import LLMContext
-from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
+from pipecat.processors.aggregators.user_response import UserResponseAggregator
 from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
 from pipecat.runner.types import RunnerArguments
-from pipecat.runner.utils import create_transport
+from pipecat.runner.utils import (
+    create_transport,
+    get_transport_client_id,
+    maybe_capture_participant_camera,
+)
 from pipecat.services.cartesia.tts import CartesiaTTSService
 from pipecat.services.deepgram.stt import DeepgramSTTService
 from pipecat.services.openai.llm import OpenAILLMService
@@ -37,41 +43,46 @@ from pipecat.transports.daily.transport import DailyParams
 load_dotenv(override=True)
 
 
-def format_metrics(metrics, indent=0):
-    lines = []
-    tab = "\t" * indent
+class UserImageRequester(FrameProcessor):
+    """Converts incoming text into requests for user images."""
 
-    for metric in metrics:
-        lines.append(tab + type(metric).__name__)
-        for field, value in vars(metric).items():
-            if hasattr(value, "__dict__") and not isinstance(
-                value, (str, int, float, bool, type(None))
-            ):
-                lines.append(f"{tab}\t{field}={type(value).__name__}")
-                for k, v in vars(value).items():
-                    lines.append(f"{tab}\t\t{k}={repr(v)}")
-            else:
-                lines.append(f"{tab}\t{field}={repr(value)}")
+    def __init__(self, participant_id: Optional[str] = None):
+        super().__init__()
+        self._participant_id = participant_id
 
-    return "\n".join(lines)
-
-
-class MetricsFrameLogger(FrameProcessor):
-    """MetricsFrameLogger formats and logs all MetericsFrames"""
-
-    def __init__(self, **kwargs):
-        super().__init__(**kwargs)
+    def set_participant_id(self, participant_id: str):
+        self._participant_id = participant_id
 
     async def process_frame(self, frame: Frame, direction: FrameDirection):
         await super().process_frame(frame, direction)
 
-        if isinstance(frame, MetricsFrame):
-            logger.info(f"{frame.name}\n    {format_metrics(frame.data)}")
+        if self._participant_id and isinstance(frame, TextFrame):
+            await self.push_frame(
+                UserImageRequestFrame(self._participant_id, context=frame.text),
+                FrameDirection.UPSTREAM,
+            )
+        else:
             await self.push_frame(frame, direction)
 
-        # ALWAYS push all frames
+
+class UserImageProcessor(FrameProcessor):
+    """Converts incoming user images into context frames."""
+
+    async def process_frame(self, frame: Frame, direction: FrameDirection):
+        await super().process_frame(frame, direction)
+
+        if isinstance(frame, UserImageRawFrame):
+            if frame.request and frame.request.context:
+                context = LLMContext()
+                context.add_image_frame_message(
+                    image=frame.image,
+                    text=frame.request.context,
+                    size=frame.size,
+                    format=frame.format,
+                )
+                frame = LLMContextFrame(context)
+                await self.push_frame(frame)
         else:
-            # SUPER IMPORTANT: always push every frame!
             await self.push_frame(frame, direction)
 
 
@@ -82,13 +93,14 @@ transport_params = {
     "daily": lambda: DailyParams(
         audio_in_enabled=True,
         audio_out_enabled=True,
+        video_in_enabled=True,
         vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
         turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
     ),
     "webrtc": lambda: TransportParams(
         audio_in_enabled=True,
         audio_out_enabled=True,
-        video_out_enabled=True,
+        video_in_enabled=True,
         vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
         turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
     ),
@@ -98,37 +110,33 @@ transport_params = {
 async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
     logger.info(f"Starting bot")
 
+    user_response = UserResponseAggregator()
+
+    # Initialize the image requester without setting the participant ID yet
+    image_requester = UserImageRequester()
+
+    image_processor = UserImageProcessor()
+
     stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
 
+    # OpenAI GPT-4o for vision analysis
+    openai = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
+
     tts = CartesiaTTSService(
         api_key=os.getenv("CARTESIA_API_KEY"),
         voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
     )
 
-    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
-
-    messages = [
-        {
-            "role": "system",
-            "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
-        },
-    ]
-
-    context = LLMContext(messages)
-    context_aggregator = LLMContextAggregatorPair(context)
-
-    metrics_frame_processor = MetricsFrameLogger()
-
     pipeline = Pipeline(
         [
             transport.input(),
             stt,
-            context_aggregator.user(),
-            llm,
+            user_response,
+            image_requester,
+            image_processor,
+            openai,
             tts,
             transport.output(),
-            context_aggregator.assistant(),
-            metrics_frame_processor,  # pretty print metrics frames
         ]
     )
 
@@ -144,9 +152,15 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
     @transport.event_handler("on_client_connected")
     async def on_client_connected(transport, client):
         logger.info(f"Client connected: {client}")
-        # Kick off the conversation.
-        messages.append({"role": "system", "content": "Please introduce yourself to the user."})
-        await task.queue_frames([LLMRunFrame()])
+
+        await maybe_capture_participant_camera(transport, client)
+
+        # Set the participant ID in the image requester
+        client_id = get_transport_client_id(transport, client)
+        image_requester.set_participant_id(client_id)
+
+        # Welcome message
+        await task.queue_frame(TTSSpeakFrame("Hi there! Feel free to ask me about what I see."))
 
     @transport.event_handler("on_client_disconnected")
     async def on_client_disconnected(transport, client):

examples/foundational/12c-describe-image-gemini-flash.py

@@ -1,141 +0,0 @@
-#
-# Copyright (c) 2024–2025, Daily
-#
-# SPDX-License-Identifier: BSD 2-Clause License
-#
-
-
-import os
-
-from dotenv import load_dotenv
-from loguru import logger
-from PIL import Image
-
-from pipecat.audio.turn.smart_turn.base_smart_turn import SmartTurnParams
-from pipecat.audio.turn.smart_turn.local_smart_turn_v3 import LocalSmartTurnAnalyzerV3
-from pipecat.audio.vad.silero import SileroVADAnalyzer
-from pipecat.audio.vad.vad_analyzer import VADParams
-from pipecat.frames.frames import LLMRunFrame
-from pipecat.pipeline.pipeline import Pipeline
-from pipecat.pipeline.runner import PipelineRunner
-from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.aggregators.llm_context import LLMContext
-from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
-from pipecat.runner.types import RunnerArguments
-from pipecat.runner.utils import create_transport
-from pipecat.services.cartesia.tts import CartesiaTTSService
-from pipecat.services.deepgram.stt import DeepgramSTTService
-from pipecat.services.google.llm import GoogleLLMService
-from pipecat.transports.base_transport import BaseTransport, TransportParams
-from pipecat.transports.daily.transport import DailyParams
-
-load_dotenv(override=True)
-
-
-# We store functions so objects (e.g. SileroVADAnalyzer) don't get
-# instantiated. The function will be called when the desired transport gets
-# selected.
-transport_params = {
-    "daily": lambda: DailyParams(
-        audio_in_enabled=True,
-        audio_out_enabled=True,
-        vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
-        turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
-    ),
-    "webrtc": lambda: TransportParams(
-        audio_in_enabled=True,
-        audio_out_enabled=True,
-        vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
-        turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
-    ),
-}
-
-
-async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
-    logger.info(f"Starting bot")
-
-    stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
-
-    tts = CartesiaTTSService(
-        api_key=os.getenv("CARTESIA_API_KEY"),
-        voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
-    )
-
-    llm = GoogleLLMService(api_key=os.getenv("GOOGLE_API_KEY"))
-
-    messages = [
-        {
-            "role": "system",
-            "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way. You are also able to describe images.",
-        },
-    ]
-
-    context = LLMContext(messages)
-    context_aggregator = LLMContextAggregatorPair(context)
-
-    pipeline = Pipeline(
-        [
-            transport.input(),  # Transport user input
-            stt,  # STT
-            context_aggregator.user(),  # User responses
-            llm,  # LLM
-            tts,  # TTS
-            transport.output(),  # Transport bot output
-            context_aggregator.assistant(),  # Assistant spoken responses
-        ]
-    )
-
-    task = PipelineTask(
-        pipeline,
-        params=PipelineParams(
-            enable_metrics=True,
-            enable_usage_metrics=True,
-        ),
-        idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
-    )
-
-    @transport.event_handler("on_client_connected")
-    async def on_client_connected(transport, client):
-        logger.info(f"Client connected")
-
-        if not runner_args.body:
-            script_dir = os.path.dirname(__file__)
-            runner_args.body = {
-                "image_path": os.path.join(script_dir, "assets", "cat.jpg"),
-                "question": "Describe this image",
-            }
-
-        image_path = runner_args.body["image_path"]
-        question = runner_args.body["question"]
-
-        # Kick off the conversation.
-        image = Image.open(image_path)
-        message = LLMContext.create_image_message(
-            image=image.tobytes(),
-            format="RGB",
-            size=image.size,
-            text=question,
-        )
-        messages.append(message)
-        await task.queue_frames([LLMRunFrame()])
-
-    @transport.event_handler("on_client_disconnected")
-    async def on_client_disconnected(transport, client):
-        logger.info(f"Client disconnected")
-        await task.cancel()
-
-    runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
-
-    await runner.run(task)
-
-
-async def bot(runner_args: RunnerArguments):
-    """Main bot entry point compatible with Pipecat Cloud."""
-    transport = await create_transport(runner_args, transport_params)
-    await run_bot(transport, runner_args)
-
-
-if __name__ == "__main__":
-    from pipecat.runner.run import main
-
-    main()

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

@@ -4,25 +4,36 @@
 # SPDX-License-Identifier: BSD 2-Clause License
 #
 
-
 import os
+from typing import Optional
 
 from dotenv import load_dotenv
 from loguru import logger
-from PIL import Image
 
 from pipecat.audio.turn.smart_turn.base_smart_turn import SmartTurnParams
 from pipecat.audio.turn.smart_turn.local_smart_turn_v3 import LocalSmartTurnAnalyzerV3
 from pipecat.audio.vad.silero import SileroVADAnalyzer
 from pipecat.audio.vad.vad_analyzer import VADParams
-from pipecat.frames.frames import LLMRunFrame
+from pipecat.frames.frames import (
+    Frame,
+    LLMContextFrame,
+    TextFrame,
+    TTSSpeakFrame,
+    UserImageRawFrame,
+    UserImageRequestFrame,
+)
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
 from pipecat.processors.aggregators.llm_context import LLMContext
-from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
+from pipecat.processors.aggregators.user_response import UserResponseAggregator
+from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
 from pipecat.runner.types import RunnerArguments
-from pipecat.runner.utils import create_transport
+from pipecat.runner.utils import (
+    create_transport,
+    get_transport_client_id,
+    maybe_capture_participant_camera,
+)
 from pipecat.services.anthropic.llm import AnthropicLLMService
 from pipecat.services.cartesia.tts import CartesiaTTSService
 from pipecat.services.deepgram.stt import DeepgramSTTService
@@ -32,6 +43,49 @@ from pipecat.transports.daily.transport import DailyParams
 load_dotenv(override=True)
 
 
+class UserImageRequester(FrameProcessor):
+    """Converts incoming text into requests for user images."""
+
+    def __init__(self, participant_id: Optional[str] = None):
+        super().__init__()
+        self._participant_id = participant_id
+
+    def set_participant_id(self, participant_id: str):
+        self._participant_id = participant_id
+
+    async def process_frame(self, frame: Frame, direction: FrameDirection):
+        await super().process_frame(frame, direction)
+
+        if self._participant_id and isinstance(frame, TextFrame):
+            await self.push_frame(
+                UserImageRequestFrame(self._participant_id, context=frame.text),
+                FrameDirection.UPSTREAM,
+            )
+        else:
+            await self.push_frame(frame, direction)
+
+
+class UserImageProcessor(FrameProcessor):
+    """Converts incoming user images into context frames."""
+
+    async def process_frame(self, frame: Frame, direction: FrameDirection):
+        await super().process_frame(frame, direction)
+
+        if isinstance(frame, UserImageRawFrame):
+            if frame.request and frame.request.context:
+                context = LLMContext()
+                context.add_image_frame_message(
+                    image=frame.image,
+                    text=frame.request.context,
+                    size=frame.size,
+                    format=frame.format,
+                )
+                frame = LLMContextFrame(context)
+                await self.push_frame(frame)
+        else:
+            await self.push_frame(frame, direction)
+
+
 # We store functions so objects (e.g. SileroVADAnalyzer) don't get
 # instantiated. The function will be called when the desired transport gets
 # selected.
@@ -39,12 +93,14 @@ transport_params = {
     "daily": lambda: DailyParams(
         audio_in_enabled=True,
         audio_out_enabled=True,
+        video_in_enabled=True,
         vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
         turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
     ),
     "webrtc": lambda: TransportParams(
         audio_in_enabled=True,
         audio_out_enabled=True,
+        video_in_enabled=True,
         vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
         turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
     ),
@@ -54,34 +110,33 @@ transport_params = {
 async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
     logger.info(f"Starting bot")
 
+    user_response = UserResponseAggregator()
+
+    # Initialize the image requester without setting the participant ID yet
+    image_requester = UserImageRequester()
+
+    image_processor = UserImageProcessor()
+
     stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
 
+    # Anthropic for vision analysis
+    anthropic = AnthropicLLMService(api_key=os.getenv("ANTHROPIC_API_KEY"))
+
     tts = CartesiaTTSService(
         api_key=os.getenv("CARTESIA_API_KEY"),
         voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
     )
 
-    llm = AnthropicLLMService(api_key=os.getenv("ANTHROPIC_API_KEY"))
-
-    messages = [
-        {
-            "role": "system",
-            "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way. You are also able to describe images.",
-        },
-    ]
-
-    context = LLMContext(messages)
-    context_aggregator = LLMContextAggregatorPair(context)
-
     pipeline = Pipeline(
         [
-            transport.input(),  # Transport user input
-            stt,  # STT
-            context_aggregator.user(),  # User responses
-            llm,  # LLM
-            tts,  # TTS
-            transport.output(),  # Transport bot output
-            context_aggregator.assistant(),  # Assistant spoken responses
+            transport.input(),
+            stt,
+            user_response,
+            image_requester,
+            image_processor,
+            anthropic,
+            tts,
+            transport.output(),
         ]
     )
 
@@ -96,28 +151,16 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
 
     @transport.event_handler("on_client_connected")
     async def on_client_connected(transport, client):
-        logger.info(f"Client connected")
+        logger.info(f"Client connected: {client}")
 
-        if not runner_args.body:
-            script_dir = os.path.dirname(__file__)
-            runner_args.body = {
-                "image_path": os.path.join(script_dir, "assets", "cat.jpg"),
-                "question": "Describe this image",
-            }
+        await maybe_capture_participant_camera(transport, client)
 
-        image_path = runner_args.body["image_path"]
-        question = runner_args.body["question"]
+        # Set the participant ID in the image requester
+        client_id = get_transport_client_id(transport, client)
+        image_requester.set_participant_id(client_id)
 
-        # Kick off the conversation.
-        image = Image.open(image_path)
-        message = LLMContext.create_image_message(
-            image=image.tobytes(),
-            format="RGB",
-            size=image.size,
-            text=question,
-        )
-        messages.append(message)
-        await task.queue_frames([LLMRunFrame()])
+        # Welcome message
+        await task.queue_frame(TTSSpeakFrame("Hi there! Feel free to ask me about what I see."))
 
     @transport.event_handler("on_client_disconnected")
     async def on_client_disconnected(transport, client):

examples/foundational/12d-describe-image-moondream.py

@@ -1,122 +0,0 @@
-#
-# Copyright (c) 2024–2025, Daily
-#
-# SPDX-License-Identifier: BSD 2-Clause License
-#
-
-
-import os
-
-from dotenv import load_dotenv
-from loguru import logger
-from PIL import Image
-
-from pipecat.audio.turn.smart_turn.base_smart_turn import SmartTurnParams
-from pipecat.audio.turn.smart_turn.local_smart_turn_v3 import LocalSmartTurnAnalyzerV3
-from pipecat.audio.vad.silero import SileroVADAnalyzer
-from pipecat.audio.vad.vad_analyzer import VADParams
-from pipecat.frames.frames import UserImageRawFrame
-from pipecat.pipeline.pipeline import Pipeline
-from pipecat.pipeline.runner import PipelineRunner
-from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.runner.types import RunnerArguments
-from pipecat.runner.utils import create_transport
-from pipecat.services.cartesia.tts import CartesiaTTSService
-from pipecat.services.moondream.vision import MoondreamService
-from pipecat.transports.base_transport import BaseTransport, TransportParams
-from pipecat.transports.daily.transport import DailyParams
-
-load_dotenv(override=True)
-
-
-# We store functions so objects (e.g. SileroVADAnalyzer) don't get
-# instantiated. The function will be called when the desired transport gets
-# selected.
-transport_params = {
-    "daily": lambda: DailyParams(
-        audio_out_enabled=True,
-        vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
-        turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
-    ),
-    "webrtc": lambda: TransportParams(
-        audio_out_enabled=True,
-        vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
-        turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
-    ),
-}
-
-
-async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
-    logger.info(f"Starting bot")
-
-    tts = CartesiaTTSService(
-        api_key=os.getenv("CARTESIA_API_KEY"),
-        voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
-    )
-
-    vision = MoondreamService()
-
-    pipeline = Pipeline(
-        [
-            vision,  # Vision
-            tts,  # TTS
-            transport.output(),  # Transport bot output
-        ]
-    )
-
-    task = PipelineTask(
-        pipeline,
-        params=PipelineParams(
-            enable_metrics=True,
-            enable_usage_metrics=True,
-        ),
-        idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
-    )
-
-    @transport.event_handler("on_client_connected")
-    async def on_client_connected(transport, client):
-        logger.info(f"Client connected")
-
-        if not runner_args.body:
-            script_dir = os.path.dirname(__file__)
-            runner_args.body = {
-                "image_path": os.path.join(script_dir, "assets", "cat.jpg"),
-                "question": "Describe this image",
-            }
-
-        image_path = runner_args.body["image_path"]
-        question = runner_args.body["question"]
-
-        # Describe the image.
-        image = Image.open(image_path)
-        await task.queue_frames(
-            [
-                UserImageRawFrame(
-                    image=image.tobytes(),
-                    format="RGB",
-                    size=image.size,
-                    text=question,
-                )
-            ]
-        )
-
-    @transport.event_handler("on_client_disconnected")
-    async def on_client_disconnected(transport, client):
-        logger.info(f"Client disconnected")
-        await task.cancel()
-
-    runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
-
-    await runner.run(task)
-
-
-async def bot(runner_args: RunnerArguments):
-    """Main bot entry point compatible with Pipecat Cloud."""
-    transport = await create_transport(runner_args, transport_params)
-    await run_bot(transport, runner_args)
-
-
-if __name__ == "__main__":
-    from pipecat.runner.run import main
-
-    main()

examples/foundational/12d-describe-video-aws.py

@@ -5,23 +5,29 @@
 #
 
 import os
+from typing import Optional
 
 from dotenv import load_dotenv
 from loguru import logger
 
-from pipecat.adapters.schemas.function_schema import FunctionSchema
-from pipecat.adapters.schemas.tools_schema import ToolsSchema
 from pipecat.audio.turn.smart_turn.base_smart_turn import SmartTurnParams
 from pipecat.audio.turn.smart_turn.local_smart_turn_v3 import LocalSmartTurnAnalyzerV3
 from pipecat.audio.vad.silero import SileroVADAnalyzer
 from pipecat.audio.vad.vad_analyzer import VADParams
-from pipecat.frames.frames import LLMRunFrame, UserImageRequestFrame
+from pipecat.frames.frames import (
+    Frame,
+    LLMContextFrame,
+    TextFrame,
+    TTSSpeakFrame,
+    UserImageRawFrame,
+    UserImageRequestFrame,
+)
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
 from pipecat.processors.aggregators.llm_context import LLMContext
-from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
-from pipecat.processors.frame_processor import FrameDirection
+from pipecat.processors.aggregators.user_response import UserResponseAggregator
+from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
 from pipecat.runner.types import RunnerArguments
 from pipecat.runner.utils import (
     create_transport,
@@ -31,37 +37,54 @@ from pipecat.runner.utils import (
 from pipecat.services.aws.llm import AWSBedrockLLMService
 from pipecat.services.cartesia.tts import CartesiaTTSService
 from pipecat.services.deepgram.stt import DeepgramSTTService
-from pipecat.services.llm_service import FunctionCallParams
 from pipecat.transports.base_transport import BaseTransport, TransportParams
 from pipecat.transports.daily.transport import DailyParams
 
 load_dotenv(override=True)
 
 
-async def fetch_user_image(params: FunctionCallParams):
-    """Fetch the user image and push it to the LLM.
+class UserImageRequester(FrameProcessor):
+    """Converts incoming text into requests for user images."""
 
-    When called, this function pushes a UserImageRequestFrame upstream to the
-    transport. As a result, the transport will request the user image and push a
-    UserImageRawFrame downstream which will be added to the context by the LLM
-    assistant aggregator.
-    """
-    user_id = params.arguments["user_id"]
-    question = params.arguments["question"]
-    logger.debug(f"Requesting image with user_id={user_id}, question={question}")
+    def __init__(self, participant_id: Optional[str] = None):
+        super().__init__()
+        self._participant_id = participant_id
 
-    # Request a user image frame and indicate that it should be added to the
-    # context.
-    await params.llm.push_frame(
-        UserImageRequestFrame(user_id=user_id, text=question, append_to_context=True),
-        FrameDirection.UPSTREAM,
-    )
+    def set_participant_id(self, participant_id: str):
+        self._participant_id = participant_id
 
-    await params.result_callback(None)
+    async def process_frame(self, frame: Frame, direction: FrameDirection):
+        await super().process_frame(frame, direction)
 
-    # Instead of None, it's possible to also provide a tool call answer to
-    # tell the LLM that we are grabbing the image to analyze.
-    # await params.result_callback({"result": "Image is being captured."})
+        if self._participant_id and isinstance(frame, TextFrame):
+            await self.push_frame(
+                UserImageRequestFrame(self._participant_id, context=frame.text),
+                FrameDirection.UPSTREAM,
+            )
+        else:
+            await self.push_frame(frame, direction)
+
+
+class UserImageProcessor(FrameProcessor):
+    """Converts incoming user images into context frames."""
+
+    async def process_frame(self, frame: Frame, direction: FrameDirection):
+        await super().process_frame(frame, direction)
+
+        if isinstance(frame, UserImageRawFrame):
+            if frame.request and frame.request.context:
+                # Note: AWS Bedrock does not yet support the universal LLMContext
+                context = LLMContext()
+                context.add_image_frame_message(
+                    image=frame.image,
+                    text=frame.request.context,
+                    size=frame.size,
+                    format=frame.format,
+                )
+                frame = LLMContextFrame(context)
+                await self.push_frame(frame)
+        else:
+            await self.push_frame(frame, direction)
 
 
 # We store functions so objects (e.g. SileroVADAnalyzer) don't get
@@ -88,15 +111,17 @@ transport_params = {
 async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
     logger.info(f"Starting bot")
 
+    user_response = UserResponseAggregator()
+
+    # Initialize the image requester without setting the participant ID yet
+    image_requester = UserImageRequester()
+
+    image_processor = UserImageProcessor()
+
     stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
 
-    tts = CartesiaTTSService(
-        api_key=os.getenv("CARTESIA_API_KEY"),
-        voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
-    )
-
     # AWS for vision analysis
-    llm = AWSBedrockLLMService(
+    aws = AWSBedrockLLMService(
         aws_region="us-west-2",
         model="us.anthropic.claude-3-7-sonnet-20250219-v1:0",
         # Note: usually, prefer providing latency="optimized" param.
@@ -104,44 +129,22 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         # which we need for image input.
         params=AWSBedrockLLMService.InputParams(temperature=0.8),
     )
-    llm.register_function("fetch_user_image", fetch_user_image)
 
-    fetch_image_function = FunctionSchema(
-        name="fetch_user_image",
-        description="Called when the user requests a description of their camera feed",
-        properties={
-            "user_id": {
-                "type": "string",
-                "description": "The ID of the user to grab the image from",
-            },
-            "question": {
-                "type": "string",
-                "description": "The question that the user is asking about the image",
-            },
-        },
-        required=["user_id", "question"],
+    tts = CartesiaTTSService(
+        api_key=os.getenv("CARTESIA_API_KEY"),
+        voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
     )
-    tools = ToolsSchema(standard_tools=[fetch_image_function])
-
-    messages = [
-        {
-            "role": "system",
-            "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way. You are able to describe images from the user camera.",
-        },
-    ]
-
-    context = LLMContext(messages, tools)
-    context_aggregator = LLMContextAggregatorPair(context)
 
     pipeline = Pipeline(
         [
-            transport.input(),  # Transport user input
-            stt,  # STT
-            context_aggregator.user(),  # User responses
-            llm,  # LLM
-            tts,  # TTS
-            transport.output(),  # Transport bot output
-            context_aggregator.assistant(),  # Assistant spoken responses
+            transport.input(),
+            stt,
+            user_response,
+            image_requester,
+            image_processor,
+            aws,
+            tts,
+            transport.output(),
         ]
     )
 
@@ -162,15 +165,10 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
 
         # Set the participant ID in the image requester
         client_id = get_transport_client_id(transport, client)
+        image_requester.set_participant_id(client_id)
 
-        # Kick off the conversation.
-        messages.append(
-            {
-                "role": "system",
-                "content": f"Please introduce yourself to the user. Use '{client_id}' as the user ID during function calls.",
-            }
-        )
-        await task.queue_frames([LLMRunFrame()])
+        # Welcome message
+        await task.queue_frame(TTSSpeakFrame("Hi there! Feel free to ask me about what I see."))
 
     @transport.event_handler("on_client_disconnected")
     async def on_client_disconnected(transport, client):

examples/foundational/13f-cartesia-transcription.py

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

examples/foundational/14-function-calling.py

@@ -120,7 +120,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
     messages = [
         {
             "role": "system",
-            "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
+            "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
         },
     ]
 

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

@@ -4,6 +4,8 @@
 # SPDX-License-Identifier: BSD 2-Clause License
 #
 
+
+import asyncio
 import os
 
 from dotenv import load_dotenv
@@ -15,13 +17,12 @@ from pipecat.audio.turn.smart_turn.base_smart_turn import SmartTurnParams
 from pipecat.audio.turn.smart_turn.local_smart_turn_v3 import LocalSmartTurnAnalyzerV3
 from pipecat.audio.vad.silero import SileroVADAnalyzer
 from pipecat.audio.vad.vad_analyzer import VADParams
-from pipecat.frames.frames import LLMRunFrame, UserImageRequestFrame
+from pipecat.frames.frames import LLMRunFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
 from pipecat.processors.aggregators.llm_context import LLMContext
 from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
-from pipecat.processors.frame_processor import FrameDirection
 from pipecat.runner.types import RunnerArguments
 from pipecat.runner.utils import (
     create_transport,
@@ -38,30 +39,34 @@ from pipecat.transports.daily.transport import DailyParams
 load_dotenv(override=True)
 
 
-async def fetch_user_image(params: FunctionCallParams):
-    """Fetch the user image and push it to the LLM.
+# Global variable to store the client ID
+client_id = ""
 
-    When called, this function pushes a UserImageRequestFrame upstream to the
-    transport. As a result, the transport will request the user image and push a
-    UserImageRawFrame downstream which will be added to the context by the LLM
-    assistant aggregator.
-    """
-    user_id = params.arguments["user_id"]
+
+async def get_weather(params: FunctionCallParams):
+    location = params.arguments["location"]
+    await params.result_callback(f"The weather in {location} is currently 72 degrees and sunny.")
+
+
+async def get_image(params: FunctionCallParams):
     question = params.arguments["question"]
-    logger.debug(f"Requesting image with user_id={user_id}, question={question}")
+    logger.debug(f"Requesting image with user_id={client_id}, question={question}")
 
-    # Request a user image frame and indicate that it should be added to the
-    # context.
-    await params.llm.push_frame(
-        UserImageRequestFrame(user_id=user_id, text=question, append_to_context=True),
-        FrameDirection.UPSTREAM,
+    # Request the image frame
+    await params.llm.request_image_frame(
+        user_id=client_id,
+        function_name=params.function_name,
+        tool_call_id=params.tool_call_id,
+        text_content=question,
     )
 
-    await params.result_callback(None)
+    # Wait a short time for the frame to be processed
+    await asyncio.sleep(0.5)
 
-    # Instead of None, it's possible to also provide a tool call answer to
-    # tell the LLM that we are grabbing the image to analyze.
-    # await params.result_callback({"result": "Image is being captured."})
+    # Return a result to complete the function call
+    await params.result_callback(
+        f"I've captured an image from your camera and I'm analyzing what you asked about: {question}"
+    )
 
 
 # We store functions so objects (e.g. SileroVADAnalyzer) don't get
@@ -95,32 +100,70 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
     )
 
-    # Anthropic for vision analysis
-    llm = AnthropicLLMService(api_key=os.getenv("ANTHROPIC_API_KEY"))
-    llm.register_function("fetch_user_image", fetch_user_image)
+    llm = AnthropicLLMService(
+        api_key=os.getenv("ANTHROPIC_API_KEY"),
+        model="claude-3-7-sonnet-latest",
+        params=AnthropicLLMService.InputParams(enable_prompt_caching=True),
+    )
+    llm.register_function("get_weather", get_weather)
+    llm.register_function("get_image", get_image)
 
-    fetch_image_function = FunctionSchema(
-        name="fetch_user_image",
-        description="Called when the user requests a description of their camera feed",
+    weather_function = FunctionSchema(
+        name="get_weather",
+        description="Get the current weather",
         properties={
-            "user_id": {
+            "location": {
                 "type": "string",
-                "description": "The ID of the user to grab the image from",
-            },
-            "question": {
-                "type": "string",
-                "description": "The question that the user is asking about the image",
+                "description": "The city and state, e.g. San Francisco, CA",
             },
         },
-        required=["user_id", "question"],
+        required=["location"],
     )
-    tools = ToolsSchema(standard_tools=[fetch_image_function])
+    get_image_function = FunctionSchema(
+        name="get_image",
+        description="Get an image from the video stream.",
+        properties={
+            "question": {
+                "type": "string",
+                "description": "The question that the user is asking about the image.",
+            }
+        },
+        required=["question"],
+    )
+    tools = ToolsSchema(standard_tools=[weather_function, get_image_function])
+
+    system_prompt = """\
+You are a helpful assistant who converses with a user and answers questions. Respond concisely to general questions.
+
+Your response will be turned into speech so use only simple words and punctuation.
+
+You have access to two tools: get_weather and get_image.
+
+You can respond to questions about the weather using the get_weather tool.
+
+You can answer questions about the user's video stream using the get_image tool. Some examples of phrases that \
+indicate you should use the get_image tool are:
+- What do you see?
+- What's in the video?
+- Can you describe the video?
+- Tell me about what you see.
+- Tell me something interesting about what you see.
+- What's happening in the video?
+
+If you need to use a tool, simply use the tool. Do not tell the user the tool you are using. Be brief and concise.
+    """
 
     messages = [
         {
             "role": "system",
-            "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way. You are able to describe images from the user camera.",
+            "content": [
+                {
+                    "type": "text",
+                    "text": system_prompt,
+                }
+            ],
         },
+        {"role": "user", "content": "Start the conversation by introducing yourself."},
     ]
 
     context = LLMContext(messages, tools)
@@ -130,11 +173,11 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         [
             transport.input(),  # Transport user input
             stt,  # STT
-            context_aggregator.user(),  # User responses
+            context_aggregator.user(),  # User speech to text
             llm,  # LLM
             tts,  # TTS
             transport.output(),  # Transport bot output
-            context_aggregator.assistant(),  # Assistant spoken responses
+            context_aggregator.assistant(),  # Assistant spoken responses and tool context
         ]
     )
 
@@ -153,16 +196,10 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
 
         await maybe_capture_participant_camera(transport, client)
 
-        # Set the participant ID in the image requester
+        global client_id
         client_id = get_transport_client_id(transport, client)
 
         # Kick off the conversation.
-        messages.append(
-            {
-                "role": "system",
-                "content": f"Please introduce yourself to the user. Use '{client_id}' as the user ID during function calls.",
-            }
-        )
         await task.queue_frames([LLMRunFrame()])
 
     @transport.event_handler("on_client_disconnected")

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

@@ -106,7 +106,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
     messages = [
         {
             "role": "system",
-            "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
+            "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
         },
     ]
 

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

@@ -1,190 +0,0 @@
-#
-# Copyright (c) 2024–2025, Daily
-#
-# SPDX-License-Identifier: BSD 2-Clause License
-#
-
-import os
-
-from dotenv import load_dotenv
-from loguru import logger
-
-from pipecat.adapters.schemas.function_schema import FunctionSchema
-from pipecat.adapters.schemas.tools_schema import ToolsSchema
-from pipecat.audio.turn.smart_turn.base_smart_turn import SmartTurnParams
-from pipecat.audio.turn.smart_turn.local_smart_turn_v3 import LocalSmartTurnAnalyzerV3
-from pipecat.audio.vad.silero import SileroVADAnalyzer
-from pipecat.audio.vad.vad_analyzer import VADParams
-from pipecat.frames.frames import LLMRunFrame, UserImageRequestFrame
-from pipecat.pipeline.parallel_pipeline import ParallelPipeline
-from pipecat.pipeline.pipeline import Pipeline
-from pipecat.pipeline.runner import PipelineRunner
-from pipecat.pipeline.task import PipelineTask
-from pipecat.processors.aggregators.llm_context import LLMContext
-from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
-from pipecat.processors.frame_processor import FrameDirection
-from pipecat.runner.types import RunnerArguments
-from pipecat.runner.utils import (
-    create_transport,
-    get_transport_client_id,
-    maybe_capture_participant_camera,
-)
-from pipecat.services.cartesia.tts import CartesiaTTSService
-from pipecat.services.deepgram.stt import DeepgramSTTService
-from pipecat.services.llm_service import FunctionCallParams
-from pipecat.services.moondream.vision import MoondreamService
-from pipecat.services.openai.llm import OpenAILLMService
-from pipecat.transports.base_transport import BaseTransport, TransportParams
-from pipecat.transports.daily.transport import DailyParams
-
-load_dotenv(override=True)
-
-
-async def fetch_user_image(params: FunctionCallParams):
-    """Fetch the user image.
-
-    When called, this function pushes a UserImageRequestFrame upstream to the
-    transport. As a result, the transport will request the user image and push a
-    UserImageRawFrame downstream.
-    """
-    user_id = params.arguments["user_id"]
-    question = params.arguments["question"]
-    logger.debug(f"Requesting image with user_id={user_id}, question={question}")
-
-    # Request a user image frame. In this case, we don't want the requested
-    # image to be added to the context because we will process it with
-    # Moondream.
-    await params.llm.push_frame(
-        UserImageRequestFrame(user_id=user_id, text=question, append_to_context=False),
-        FrameDirection.UPSTREAM,
-    )
-
-    await params.result_callback(None)
-
-    # Instead of None, it's possible to also provide a tool call answer to
-    # tell the LLM that we are grabbing the image to analyze.
-    # await params.result_callback({"result": "Image is being captured."})
-
-
-# We store functions so objects (e.g. SileroVADAnalyzer) don't get
-# instantiated. The function will be called when the desired transport gets
-# selected.
-transport_params = {
-    "daily": lambda: DailyParams(
-        audio_in_enabled=True,
-        audio_out_enabled=True,
-        video_in_enabled=True,
-        vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
-        turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
-    ),
-    "webrtc": lambda: TransportParams(
-        audio_in_enabled=True,
-        audio_out_enabled=True,
-        video_in_enabled=True,
-        vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
-        turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
-    ),
-}
-
-
-async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
-    logger.info(f"Starting bot")
-
-    stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
-
-    tts = CartesiaTTSService(
-        api_key=os.getenv("CARTESIA_API_KEY"),
-        voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
-    )
-
-    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
-    llm.register_function("fetch_user_image", fetch_user_image)
-
-    fetch_image_function = FunctionSchema(
-        name="fetch_user_image",
-        description="Called when the user requests a description of their camera feed",
-        properties={
-            "user_id": {
-                "type": "string",
-                "description": "The ID of the user to grab the image from",
-            },
-            "question": {
-                "type": "string",
-                "description": "The question that the user is asking about the image",
-            },
-        },
-        required=["user_id", "question"],
-    )
-    tools = ToolsSchema(standard_tools=[fetch_image_function])
-
-    messages = [
-        {
-            "role": "system",
-            "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way. You are able to describe images from the user camera.",
-        },
-    ]
-
-    context = LLMContext(messages, tools)
-    context_aggregator = LLMContextAggregatorPair(context)
-
-    # If you run into weird description, try with use_cpu=True
-    moondream = MoondreamService()
-
-    pipeline = Pipeline(
-        [
-            transport.input(),  # Transport user input
-            stt,  # STT
-            context_aggregator.user(),  # User responses
-            ParallelPipeline(
-                [llm],  # LLM
-                [moondream],
-            ),
-            tts,  # TTS
-            transport.output(),  # Transport bot output
-            context_aggregator.assistant(),  # Assistant spoken responses
-        ]
-    )
-
-    task = PipelineTask(
-        pipeline,
-        idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
-    )
-
-    @transport.event_handler("on_client_connected")
-    async def on_client_connected(transport, client):
-        logger.info(f"Client connected: {client}")
-
-        await maybe_capture_participant_camera(transport, client)
-
-        # Set the participant ID in the image requester
-        client_id = get_transport_client_id(transport, client)
-
-        # Kick off the conversation.
-        messages.append(
-            {
-                "role": "system",
-                "content": f"Please introduce yourself to the user. Use '{client_id}' as the user ID during function calls.",
-            }
-        )
-        await task.queue_frames([LLMRunFrame()])
-
-    @transport.event_handler("on_client_disconnected")
-    async def on_client_disconnected(transport, client):
-        logger.info(f"Client disconnected")
-        await task.cancel()
-
-    runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
-
-    await runner.run(task)
-
-
-async def bot(runner_args: RunnerArguments):
-    """Main bot entry point compatible with Pipecat Cloud."""
-    transport = await create_transport(runner_args, transport_params)
-    await run_bot(transport, runner_args)
-
-
-if __name__ == "__main__":
-    from pipecat.runner.run import main
-
-    main()

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

@@ -1,186 +0,0 @@
-#
-# Copyright (c) 2024–2025, Daily
-#
-# SPDX-License-Identifier: BSD 2-Clause License
-#
-
-
-import os
-
-from dotenv import load_dotenv
-from loguru import logger
-
-from pipecat.adapters.schemas.function_schema import FunctionSchema
-from pipecat.adapters.schemas.tools_schema import ToolsSchema
-from pipecat.audio.turn.smart_turn.base_smart_turn import SmartTurnParams
-from pipecat.audio.turn.smart_turn.local_smart_turn_v3 import LocalSmartTurnAnalyzerV3
-from pipecat.audio.vad.silero import SileroVADAnalyzer
-from pipecat.audio.vad.vad_analyzer import VADParams
-from pipecat.frames.frames import LLMRunFrame, UserImageRequestFrame
-from pipecat.pipeline.pipeline import Pipeline
-from pipecat.pipeline.runner import PipelineRunner
-from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.aggregators.llm_context import LLMContext
-from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
-from pipecat.processors.frame_processor import FrameDirection
-from pipecat.runner.types import RunnerArguments
-from pipecat.runner.utils import (
-    create_transport,
-    get_transport_client_id,
-    maybe_capture_participant_camera,
-)
-from pipecat.services.cartesia.tts import CartesiaTTSService
-from pipecat.services.deepgram.stt import DeepgramSTTService
-from pipecat.services.llm_service import FunctionCallParams
-from pipecat.services.openai.llm import OpenAILLMService
-from pipecat.transports.base_transport import BaseTransport, TransportParams
-from pipecat.transports.daily.transport import DailyParams
-
-load_dotenv(override=True)
-
-
-async def fetch_user_image(params: FunctionCallParams):
-    """Fetch the user image and push it to the LLM.
-
-    When called, this function pushes a UserImageRequestFrame upstream to the
-    transport. As a result, the transport will request the user image and push a
-    UserImageRawFrame downstream which will be added to the context by the LLM
-    assistant aggregator.
-    """
-    user_id = params.arguments["user_id"]
-    question = params.arguments["question"]
-    logger.debug(f"Requesting image with user_id={user_id}, question={question}")
-
-    # Request a user image frame and indicate that it should be added to the
-    # context.
-    await params.llm.push_frame(
-        UserImageRequestFrame(user_id=user_id, text=question, append_to_context=True),
-        FrameDirection.UPSTREAM,
-    )
-
-    await params.result_callback(None)
-
-    # Instead of None, it's possible to also provide a tool call answer to
-    # tell the LLM that we are grabbing the image to analyze.
-    # await params.result_callback({"result": "Image is being captured."})
-
-
-# We store functions so objects (e.g. SileroVADAnalyzer) don't get
-# instantiated. The function will be called when the desired transport gets
-# selected.
-transport_params = {
-    "daily": lambda: DailyParams(
-        audio_in_enabled=True,
-        audio_out_enabled=True,
-        video_in_enabled=True,
-        vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
-        turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
-    ),
-    "webrtc": lambda: TransportParams(
-        audio_in_enabled=True,
-        audio_out_enabled=True,
-        video_in_enabled=True,
-        vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
-        turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
-    ),
-}
-
-
-async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
-    logger.info(f"Starting bot")
-
-    stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
-
-    tts = CartesiaTTSService(
-        api_key=os.getenv("CARTESIA_API_KEY"),
-        voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
-    )
-
-    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
-    llm.register_function("fetch_user_image", fetch_user_image)
-
-    fetch_image_function = FunctionSchema(
-        name="fetch_user_image",
-        description="Called when the user requests a description of their camera feed",
-        properties={
-            "user_id": {
-                "type": "string",
-                "description": "The ID of the user to grab the image from",
-            },
-            "question": {
-                "type": "string",
-                "description": "The question that the user is asking about the image",
-            },
-        },
-        required=["user_id", "question"],
-    )
-    tools = ToolsSchema(standard_tools=[fetch_image_function])
-
-    messages = [
-        {
-            "role": "system",
-            "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way. You are able to describe images from the user camera.",
-        },
-    ]
-
-    context = LLMContext(messages, tools)
-    context_aggregator = LLMContextAggregatorPair(context)
-
-    pipeline = Pipeline(
-        [
-            transport.input(),  # Transport user input
-            stt,  # STT
-            context_aggregator.user(),  # User responses
-            llm,  # LLM
-            tts,  # TTS
-            transport.output(),  # Transport bot output
-            context_aggregator.assistant(),  # Assistant spoken responses
-        ]
-    )
-
-    task = PipelineTask(
-        pipeline,
-        params=PipelineParams(
-            enable_metrics=True,
-            enable_usage_metrics=True,
-        ),
-        idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
-    )
-
-    @transport.event_handler("on_client_connected")
-    async def on_client_connected(transport, client):
-        logger.info(f"Client connected")
-
-        await maybe_capture_participant_camera(transport, client)
-
-        client_id = get_transport_client_id(transport, client)
-
-        # Kick off the conversation.
-        messages.append(
-            {
-                "role": "system",
-                "content": f"Please introduce yourself to the user. Use '{client_id}' as the user ID during function calls.",
-            }
-        )
-        await task.queue_frames([LLMRunFrame()])
-
-    @transport.event_handler("on_client_disconnected")
-    async def on_client_disconnected(transport, client):
-        logger.info(f"Client disconnected")
-        await task.cancel()
-
-    runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
-
-    await runner.run(task)
-
-
-async def bot(runner_args: RunnerArguments):
-    """Main bot entry point compatible with Pipecat Cloud."""
-    transport = await create_transport(runner_args, transport_params)
-    await run_bot(transport, runner_args)
-
-
-if __name__ == "__main__":
-    from pipecat.runner.run import main
-
-    main()

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

@@ -4,8 +4,9 @@
 # SPDX-License-Identifier: BSD 2-Clause License
 #
 
+
+import asyncio
 import os
-import time
 
 from dotenv import load_dotenv
 from loguru import logger
@@ -16,31 +17,56 @@ from pipecat.audio.turn.smart_turn.base_smart_turn import SmartTurnParams
 from pipecat.audio.turn.smart_turn.local_smart_turn_v3 import LocalSmartTurnAnalyzerV3
 from pipecat.audio.vad.silero import SileroVADAnalyzer
 from pipecat.audio.vad.vad_analyzer import VADParams
-from pipecat.frames.frames import LLMRunFrame, TTSSpeakFrame
+from pipecat.frames.frames import LLMRunFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
 from pipecat.processors.aggregators.llm_context import LLMContext
 from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
 from pipecat.runner.types import RunnerArguments
-from pipecat.runner.utils import create_transport
+from pipecat.runner.utils import (
+    create_transport,
+    get_transport_client_id,
+    maybe_capture_participant_camera,
+)
 from pipecat.services.cartesia.tts import CartesiaTTSService
 from pipecat.services.deepgram.stt import DeepgramSTTService
 from pipecat.services.llm_service import FunctionCallParams
-from pipecat.services.openpipe.llm import OpenPipeLLMService
+from pipecat.services.openai.llm import OpenAILLMService
 from pipecat.transports.base_transport import BaseTransport, TransportParams
 from pipecat.transports.daily.transport import DailyParams
-from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
 
 load_dotenv(override=True)
 
 
-async def fetch_weather_from_api(params: FunctionCallParams):
-    await params.result_callback({"conditions": "nice", "temperature": "75"})
+# Global variable to store the client ID
+client_id = ""
 
 
-async def fetch_restaurant_recommendation(params: FunctionCallParams):
-    await params.result_callback({"name": "The Golden Dragon"})
+async def get_weather(params: FunctionCallParams):
+    location = params.arguments["location"]
+    await params.result_callback(f"The weather in {location} is currently 72 degrees and sunny.")
+
+
+async def get_image(params: FunctionCallParams):
+    question = params.arguments["question"]
+    logger.debug(f"Requesting image with user_id={client_id}, question={question}")
+
+    # Request the image frame
+    await params.llm.request_image_frame(
+        user_id=client_id,
+        function_name=params.function_name,
+        tool_call_id=params.tool_call_id,
+        text_content=question,
+    )
+
+    # Wait a short time for the frame to be processed
+    await asyncio.sleep(0.5)
+
+    # Return a result to complete the function call
+    await params.result_callback(
+        f"I've captured an image from your camera and I'm analyzing what you asked about: {question}"
+    )
 
 
 # We store functions so objects (e.g. SileroVADAnalyzer) don't get
@@ -50,18 +76,14 @@ transport_params = {
     "daily": lambda: DailyParams(
         audio_in_enabled=True,
         audio_out_enabled=True,
-        vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
-        turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
-    ),
-    "twilio": lambda: FastAPIWebsocketParams(
-        audio_in_enabled=True,
-        audio_out_enabled=True,
+        video_in_enabled=True,
         vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
         turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
     ),
     "webrtc": lambda: TransportParams(
         audio_in_enabled=True,
         audio_out_enabled=True,
+        video_in_enabled=True,
         vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
         turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
     ),
@@ -78,24 +100,12 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
     )
 
-    timestamp = int(time.time())
-    llm = OpenPipeLLMService(
-        api_key=os.getenv("OPENAI_API_KEY"),
-        openpipe_api_key=os.getenv("OPENPIPE_API_KEY"),
-        tags={"conversation_id": f"pipecat-{timestamp}"},
-    )
-
-    # You can also register a function_name of None to get all functions
-    # sent to the same callback with an additional function_name parameter.
-    llm.register_function("get_current_weather", fetch_weather_from_api)
-    llm.register_function("get_restaurant_recommendation", fetch_restaurant_recommendation)
-
-    @llm.event_handler("on_function_calls_started")
-    async def on_function_calls_started(service, function_calls):
-        await tts.queue_frame(TTSSpeakFrame("Let me check on that."))
+    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
+    llm.register_function("get_weather", get_weather)
+    llm.register_function("get_image", get_image)
 
     weather_function = FunctionSchema(
-        name="get_current_weather",
+        name="get_weather",
         description="Get the current weather",
         properties={
             "location": {
@@ -108,26 +118,41 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
                 "description": "The temperature unit to use. Infer this from the user's location.",
             },
         },
-        required=["location", "format"],
-    )
-    restaurant_function = FunctionSchema(
-        name="get_restaurant_recommendation",
-        description="Get a restaurant recommendation",
-        properties={
-            "location": {
-                "type": "string",
-                "description": "The city and state, e.g. San Francisco, CA",
-            },
-        },
         required=["location"],
     )
-    tools = ToolsSchema(standard_tools=[weather_function, restaurant_function])
-
-    messages = [
-        {
-            "role": "system",
-            "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
+    get_image_function = FunctionSchema(
+        name="get_image",
+        description="Get an image from the video stream.",
+        properties={
+            "question": {
+                "type": "string",
+                "description": "The question that the user is asking about the image.",
+            }
         },
+        required=["question"],
+    )
+    tools = ToolsSchema(standard_tools=[weather_function, get_image_function])
+
+    system_prompt = """\
+You are a helpful assistant who converses with a user and answers questions. Respond concisely to general questions.
+
+Your response will be turned into speech so use only simple words and punctuation.
+
+You have access to two tools: get_weather and get_image.
+
+You can respond to questions about the weather using the get_weather tool.
+
+You can answer questions about the user's video stream using the get_image tool. Some examples of phrases that \
+indicate you should use the get_image tool are:
+- What do you see?
+- What's in the video?
+- Can you describe the video?
+- Tell me about what you see.
+- Tell me something interesting about what you see.
+- What's happening in the video?
+"""
+    messages = [
+        {"role": "system", "content": system_prompt},
     ]
 
     context = LLMContext(messages, tools)
@@ -157,6 +182,12 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
     @transport.event_handler("on_client_connected")
     async def on_client_connected(transport, client):
         logger.info(f"Client connected")
+
+        await maybe_capture_participant_camera(transport, client)
+
+        global client_id
+        client_id = get_transport_client_id(transport, client)
+
         # Kick off the conversation.
         await task.queue_frames([LLMRunFrame()])
 

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

@@ -104,7 +104,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
     messages = [
         {
             "role": "system",
-            "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
+            "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
         },
     ]
 

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

@@ -99,7 +99,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
     messages = [
         {
             "role": "system",
-            "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
+            "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
         },
     ]
 

examples/foundational/14h-function-calling-azure.py

@@ -107,7 +107,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
     messages = [
         {
             "role": "system",
-            "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
+            "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
         },
     ]
 

examples/foundational/14i-function-calling-fireworks.py

@@ -110,7 +110,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
     messages = [
         {
             "role": "system",
-            "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way. Start by saying hello.",
+            "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way. Start by saying hello.",
         },
     ]
 

examples/foundational/14j-function-calling-nim.py

@@ -75,12 +75,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         # text_filters=[MarkdownTextFilter()],
     )
 
-    llm = NimLLMService(
-        api_key=os.getenv("NVIDIA_API_KEY"),
-        model="nvidia/llama-3.3-nemotron-super-49b-v1.5",
-        # Recommended when turning thinking off
-        params=NimLLMService.InputParams(temperature=0.0),
-    )
+    llm = NimLLMService(api_key=os.getenv("NVIDIA_API_KEY"), model="meta/llama-3.3-70b-instruct")
     # You can also register a function_name of None to get all functions
     # sent to the same callback with an additional function_name parameter.
     llm.register_function("get_current_weather", fetch_weather_from_api)
@@ -107,12 +102,9 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
     )
     tools = ToolsSchema(standard_tools=[weather_function])
     messages = [
-        # Disable thinking by sending this message first
-        # Check the model for the corresponding "no thinking" message
-        {"role": "system", "content": "/no_think"},
         {
             "role": "system",
-            "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
+            "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
         },
     ]
 

examples/foundational/14m-function-calling-openrouter.py

@@ -107,7 +107,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
     messages = [
         {
             "role": "system",
-            "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
+            "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
         },
     ]
 

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

@@ -77,7 +77,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
     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 spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way, but try to be brief.",
+            "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.",
         },
     ]
 

examples/foundational/14p-function-calling-gemini-vertex-ai.py

@@ -76,8 +76,9 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
 
     llm = GoogleVertexLLMService(
         credentials=os.getenv("GOOGLE_VERTEX_TEST_CREDENTIALS"),
-        project_id=os.getenv("GOOGLE_CLOUD_PROJECT_ID"),
-        location=os.getenv("GOOGLE_CLOUD_LOCATION"),
+        params=GoogleVertexLLMService.InputParams(
+            project_id=os.getenv("GOOGLE_CLOUD_PROJECT_ID"),
+        ),
     )
     # You can aslo register a function_name of None to get all functions
     # sent to the same callback with an additional function_name parameter.

examples/foundational/14q-function-calling-qwen.py

@@ -105,7 +105,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
     messages = [
         {
             "role": "system",
-            "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
+            "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
         },
     ]
 

examples/foundational/14r-function-calling-aws.py

@@ -79,8 +79,8 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
 
     llm = AWSBedrockLLMService(
         aws_region="us-west-2",
-        model="us.anthropic.claude-haiku-4-5-20251001-v1:0",
-        params=AWSBedrockLLMService.InputParams(temperature=0.8),
+        model="us.anthropic.claude-3-5-haiku-20241022-v1:0",
+        params=AWSBedrockLLMService.InputParams(temperature=0.8, latency="optimized"),
     )
 
     # You can also register a function_name of None to get all functions
@@ -120,7 +120,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
     messages = [
         {
             "role": "system",
-            "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
+            "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
         },
     ]
 

examples/foundational/14s-function-calling-sambanova.py

@@ -110,7 +110,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
     messages = [
         {
             "role": "system",
-            "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
+            "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
         },
     ]
 

examples/foundational/14t-function-calling-direct.py

@@ -106,7 +106,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
     messages = [
         {
             "role": "system",
-            "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
+            "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
         },
     ]
 

examples/foundational/14u-function-calling-ollama.py

@@ -122,7 +122,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
     messages = [
         {
             "role": "system",
-            "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
+            "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
         },
     ]
 

examples/foundational/14v-function-calling-openai.py

@@ -128,7 +128,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
     messages = [
         {
             "role": "system",
-            "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
+            "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
         },
     ]
 

examples/foundational/14w-function-calling-mistral.py

@@ -116,7 +116,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
     messages = [
         {
             "role": "system",
-            "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
+            "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
         },
     ]
 

examples/foundational/16-gpu-container-local-bot.py

@@ -26,11 +26,7 @@ from pipecat.services.deepgram.stt import DeepgramSTTService
 from pipecat.services.deepgram.tts import DeepgramTTSService
 from pipecat.services.openai.llm import OpenAILLMService
 from pipecat.transports.base_transport import BaseTransport, TransportParams
-from pipecat.transports.daily.transport import (
-    DailyOutputTransportMessageFrame,
-    DailyOutputTransportMessageUrgentFrame,
-    DailyParams,
-)
+from pipecat.transports.daily.transport import DailyParams, DailyTransportMessageFrame
 from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
 
 load_dotenv(override=True)
@@ -82,7 +78,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
     messages = [
         {
             "role": "system",
-            "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
+            "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
         },
     ]
 
@@ -132,14 +128,14 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
                 logger.debug(f"Received latency ping app message: {message}")
                 ts = message["latency-ping"]["ts"]
                 # Send immediately
-                await task.queue_frame(
-                    DailyOutputTransportMessageUrgentFrame(
+                transport.output().send_message(
+                    DailyTransportMessageFrame(
                         message={"latency-pong-msg-handler": {"ts": ts}}, participant_id=sender
                     )
                 )
                 # And push to the pipeline for the Daily transport.output to send
                 await task.queue_frame(
-                    DailyOutputTransportMessageFrame(
+                    DailyTransportMessageFrame(
                         message={"latency-pong-pipeline-delivery": {"ts": ts}},
                         participant_id=sender,
                     )

examples/foundational/17-detect-user-idle.py

@@ -72,7 +72,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
     messages = [
         {
             "role": "system",
-            "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
+            "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
         },
     ]
 

examples/foundational/19-openai-realtime.py

@@ -5,7 +5,6 @@
 #
 
 
-import asyncio
 import os
 from datetime import datetime
 
@@ -14,34 +13,25 @@ from loguru import logger
 
 from pipecat.adapters.schemas.function_schema import FunctionSchema
 from pipecat.adapters.schemas.tools_schema import ToolsSchema
-from pipecat.adapters.services.open_ai_realtime_adapter import OpenAIRealtimeLLMAdapter
 from pipecat.audio.vad.silero import SileroVADAnalyzer
-from pipecat.frames.frames import (
-    LLMRunFrame,
-    LLMSetToolsFrame,
-    LLMUpdateSettingsFrame,
-    TranscriptionMessage,
-)
+from pipecat.frames.frames import LLMRunFrame, TranscriptionMessage
 from pipecat.observers.loggers.transcription_log_observer import TranscriptionLogObserver
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.aggregators.llm_context import LLMContext
-from pipecat.processors.aggregators.llm_response import LLMAssistantAggregatorParams
-from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
+from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
 from pipecat.processors.transcript_processor import TranscriptProcessor
 from pipecat.runner.types import RunnerArguments
 from pipecat.runner.utils import create_transport
 from pipecat.services.llm_service import FunctionCallParams
-from pipecat.services.openai.realtime.events import (
-    AudioConfiguration,
-    AudioInput,
+from pipecat.services.openai_realtime import (
     InputAudioNoiseReduction,
     InputAudioTranscription,
+    OpenAIRealtimeLLMService,
     SemanticTurnDetection,
     SessionProperties,
 )
-from pipecat.services.openai.realtime.llm import OpenAIRealtimeLLMService
+from pipecat.services.openai_realtime.events import AudioConfiguration, AudioInput
 from pipecat.transports.base_transport import BaseTransport, TransportParams
 from pipecat.transports.daily.transport import DailyParams
 from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
@@ -61,18 +51,6 @@ async def fetch_weather_from_api(params: FunctionCallParams):
     )
 
 
-async def get_news(params: FunctionCallParams):
-    await params.result_callback(
-        {
-            "news": [
-                "Massive UFO currently hovering above New York City",
-                "Stock markets reach all-time highs",
-                "Living dinosaur species discovered in the Amazon rainforest",
-            ],
-        }
-    )
-
-
 async def fetch_restaurant_recommendation(params: FunctionCallParams):
     await params.result_callback({"name": "The Golden Dragon"})
 
@@ -94,13 +72,6 @@ weather_function = FunctionSchema(
     required=["location", "format"],
 )
 
-get_news_function = FunctionSchema(
-    name="get_news",
-    description="Get the current news.",
-    properties={},
-    required=[],
-)
-
 restaurant_function = FunctionSchema(
     name="get_restaurant_recommendation",
     description="Get a restaurant recommendation",
@@ -154,8 +125,6 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
                 noise_reduction=InputAudioNoiseReduction(type="near_field"),
             )
         ),
-        # In this example we provide tools through the context, but you could
-        # alternatively provide them here.
         # tools=tools,
         instructions="""You are a helpful and friendly AI.
 
@@ -170,6 +139,10 @@ even if you're asked about them.
 You are participating in a voice conversation. Keep your responses concise, short, and to the point
 unless specifically asked to elaborate on a topic.
 
+You have access to the following tools:
+- get_current_weather: Get the current weather for a given location.
+- get_restaurant_recommendation: Get a restaurant recommendation for a given location.
+
 Remember, your responses should be short. Just one or two sentences, usually. Respond in English.""",
     )
 
@@ -183,26 +156,25 @@ Remember, your responses should be short. Just one or two sentences, usually. Re
     # llm.register_function(None, fetch_weather_from_api)
     llm.register_function("get_current_weather", fetch_weather_from_api)
     llm.register_function("get_restaurant_recommendation", fetch_restaurant_recommendation)
-    llm.register_function("get_news", get_news)
 
     transcript = TranscriptProcessor()
 
     # Create a standard OpenAI LLM context object using the normal messages format. The
     # OpenAIRealtimeLLMService will convert this internally to messages that the
     # openai WebSocket API can understand.
-    context = LLMContext(
+    context = OpenAILLMContext(
         [{"role": "user", "content": "Say hello!"}],
         tools,
     )
 
-    context_aggregator = LLMContextAggregatorPair(context)
+    context_aggregator = llm.create_context_aggregator(context)
 
     pipeline = Pipeline(
         [
             transport.input(),  # Transport user input
             context_aggregator.user(),
-            transcript.user(),  # LLM pushes TranscriptionFrames upstream
             llm,  # LLM
+            transcript.user(),  # Placed after the LLM, as LLM pushes TranscriptionFrames downstream
             transport.output(),  # Transport bot output
             transcript.assistant(),  # After the transcript output, to time with the audio output
             context_aggregator.assistant(),
@@ -225,22 +197,6 @@ Remember, your responses should be short. Just one or two sentences, usually. Re
         # Kick off the conversation.
         await task.queue_frames([LLMRunFrame()])
 
-        # Add a new tool at runtime after a delay.
-        await asyncio.sleep(15)
-        new_tools = ToolsSchema(
-            standard_tools=[weather_function, restaurant_function, get_news_function]
-        )
-        await task.queue_frames([LLMSetToolsFrame(tools=new_tools)])
-        # Alternative pattern, useful if you're changing other session properties, too.
-        # (Though note that tools in your LLMContext take precedence over those
-        # in session properties, so if you have context-provided tools, prefer
-        # LLMSetToolsFrame instead, as it updates your context. Ditto for
-        # updating system instructions: send an LLMMessagesUpdateFrame with
-        # context messages updated with your new desired system message.)
-        # await task.queue_frames(
-        #     [LLMUpdateSettingsFrame(settings=SessionProperties(tools=new_tools).model_dump())]
-        # )
-
     @transport.event_handler("on_client_disconnected")
     async def on_client_disconnected(transport, client):
         logger.info(f"Client disconnected")

examples/foundational/19a-azure-realtime.py

@@ -18,19 +18,16 @@ from pipecat.frames.frames import LLMRunFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.aggregators.llm_context import LLMContext
-from pipecat.processors.aggregators.llm_response import LLMAssistantAggregatorParams
-from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
+from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
 from pipecat.runner.types import RunnerArguments
 from pipecat.runner.utils import create_transport
-from pipecat.services.azure.realtime.llm import AzureRealtimeLLMService
 from pipecat.services.llm_service import FunctionCallParams
-from pipecat.services.openai.realtime.events import (
-    AudioConfiguration,
-    AudioInput,
+from pipecat.services.openai_realtime import (
+    AzureRealtimeLLMService,
     InputAudioTranscription,
     SessionProperties,
 )
+from pipecat.services.openai_realtime.events import AudioConfiguration, AudioInput
 from pipecat.transports.base_transport import BaseTransport, TransportParams
 from pipecat.transports.daily.transport import DailyParams
 from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
@@ -157,10 +154,10 @@ Remember, your responses should be short. Just one or two sentences, usually. Re
     llm.register_function("get_current_weather", fetch_weather_from_api)
     llm.register_function("get_restaurant_recommendation", fetch_restaurant_recommendation)
 
-    # Create a standard LLM context object using the normal messages format. The
+    # Create a standard OpenAI LLM context object using the normal messages format. The
     # OpenAIRealtimeBetaLLMService will convert this internally to messages that the
     # openai WebSocket API can understand.
-    context = LLMContext(
+    context = OpenAILLMContext(
         [{"role": "user", "content": "Say hello!"}],
         # [{"role": "user", "content": [{"type": "text", "text": "Say hello!"}]}],
         #     [
@@ -175,7 +172,7 @@ Remember, your responses should be short. Just one or two sentences, usually. Re
         tools,
     )
 
-    context_aggregator = LLMContextAggregatorPair(context)
+    context_aggregator = llm.create_context_aggregator(context)
 
     pipeline = Pipeline(
         [

examples/foundational/19b-openai-realtime-text.py

@@ -18,22 +18,20 @@ from pipecat.frames.frames import LLMRunFrame, TranscriptionMessage
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.aggregators.llm_context import LLMContext
-from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
+from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
 from pipecat.processors.transcript_processor import TranscriptProcessor
 from pipecat.runner.types import RunnerArguments
 from pipecat.runner.utils import create_transport
-from pipecat.services.cartesia.tts import CartesiaTTSService
+from pipecat.services.cartesia import CartesiaTTSService
 from pipecat.services.llm_service import FunctionCallParams
-from pipecat.services.openai.realtime.events import (
-    AudioConfiguration,
-    AudioInput,
+from pipecat.services.openai_realtime import (
     InputAudioNoiseReduction,
     InputAudioTranscription,
+    OpenAIRealtimeLLMService,
     SemanticTurnDetection,
     SessionProperties,
 )
-from pipecat.services.openai.realtime.llm import OpenAIRealtimeLLMService
+from pipecat.services.openai_realtime.events import AudioConfiguration, AudioInput
 from pipecat.transports.base_transport import BaseTransport, TransportParams
 from pipecat.transports.daily.transport import DailyParams
 from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
@@ -170,20 +168,20 @@ Remember, your responses should be short. Just one or two sentences, usually. Re
     # Create a standard OpenAI LLM context object using the normal messages format. The
     # OpenAIRealtimeLLMService will convert this internally to messages that the
     # openai WebSocket API can understand.
-    context = LLMContext(
+    context = OpenAILLMContext(
         [{"role": "user", "content": "Say hello!"}],
         tools,
     )
 
-    context_aggregator = LLMContextAggregatorPair(context)
+    context_aggregator = llm.create_context_aggregator(context)
 
     pipeline = Pipeline(
         [
             transport.input(),  # Transport user input
             context_aggregator.user(),
-            transcript.user(),  # LLM pushes TranscriptionFrames upstream
             llm,  # LLM
             tts,  # TTS
+            transcript.user(),  # Placed after the LLM, as LLM pushes TranscriptionFrames downstream
             transport.output(),  # Transport bot output
             transcript.assistant(),  # After the transcript output, to time with the audio output
             context_aggregator.assistant(),

examples/foundational/20a-persistent-context-openai.py

@@ -98,7 +98,7 @@ async def load_conversation(params: FunctionCallParams):
 messages = [
     {
         "role": "system",
-        "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
+        "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
     },
 ]
 

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

@@ -13,27 +13,25 @@ from datetime import datetime
 from dotenv import load_dotenv
 from loguru import logger
 
-from pipecat.adapters.schemas.function_schema import FunctionSchema
-from pipecat.adapters.schemas.tools_schema import ToolsSchema
 from pipecat.audio.vad.silero import SileroVADAnalyzer
 from pipecat.frames.frames import LLMRunFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.aggregators.llm_context import LLMContext
-from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
+from pipecat.processors.aggregators.openai_llm_context import (
+    OpenAILLMContext,
+)
 from pipecat.runner.types import RunnerArguments
 from pipecat.runner.utils import create_transport
 from pipecat.services.deepgram.stt import DeepgramSTTService
 from pipecat.services.llm_service import FunctionCallParams
-from pipecat.services.openai.realtime.events import (
-    AudioConfiguration,
-    AudioInput,
+from pipecat.services.openai_realtime import (
     InputAudioTranscription,
+    OpenAIRealtimeLLMService,
     SessionProperties,
     TurnDetection,
 )
-from pipecat.services.openai.realtime.llm import OpenAIRealtimeLLMService
+from pipecat.services.openai_realtime.events import AudioConfiguration, AudioInput
 from pipecat.transports.base_transport import BaseTransport, TransportParams
 from pipecat.transports.daily.transport import DailyParams
 from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
@@ -70,11 +68,11 @@ async def save_conversation(params: FunctionCallParams):
     timestamp = datetime.now().strftime("%Y-%m-%d_%H:%M:%S")
     filename = f"{BASE_FILENAME}{timestamp}.json"
     logger.debug(
-        f"writing conversation to {filename}\n{json.dumps(params.context.get_messages(), indent=4)}"
+        f"writing conversation to {filename}\n{json.dumps(params.context.messages, indent=4)}"
     )
     try:
         with open(filename, "w") as file:
-            messages = params.context.get_messages()
+            messages = params.context.get_messages_for_persistent_storage()
             # remove the last message, which is the instruction we just gave to save the conversation
             messages.pop()
             json.dump(messages, file, indent=2)
@@ -91,10 +89,6 @@ async def load_conversation(params: FunctionCallParams):
             with open(filename, "r") as file:
                 params.context.set_messages(json.load(file))
                 await params.llm.reset_conversation()
-                # NOTE: we manually create a response here rather than relying
-                # on the function callback to trigger one since we've reset the
-                # conversation so the remote service doesn't know about the
-                # in-progress tool call.
                 await params.llm._create_response()
         except Exception as e:
             await params.result_callback({"success": False, "error": str(e)})
@@ -102,12 +96,14 @@ async def load_conversation(params: FunctionCallParams):
     asyncio.create_task(_reset())
 
 
-tools = ToolsSchema(
-    standard_tools=[
-        FunctionSchema(
-            name="get_current_weather",
-            description="Get the current weather",
-            properties={
+tools = [
+    {
+        "type": "function",
+        "name": "get_current_weather",
+        "description": "Get the current weather",
+        "parameters": {
+            "type": "object",
+            "properties": {
                 "location": {
                     "type": "string",
                     "description": "The city and state, e.g. San Francisco, CA",
@@ -118,33 +114,45 @@ tools = ToolsSchema(
                     "description": "The temperature unit to use. Infer this from the users location.",
                 },
             },
-            required=["location", "format"],
-        ),
-        FunctionSchema(
-            name="save_conversation",
-            description="Save the current conversatione. Use this function to persist the current conversation to external storage.",
-            properties={},
-            required=[],
-        ),
-        FunctionSchema(
-            name="get_saved_conversation_filenames",
-            description="Get a list of saved conversation histories. Returns a list of filenames. Each filename includes a date and timestamp. Each file is conversation history that can be loaded into this session.",
-            properties={},
-            required=[],
-        ),
-        FunctionSchema(
-            name="load_conversation",
-            description="Load a conversation history. Use this function to load a conversation history into the current session.",
-            properties={
+            "required": ["location", "format"],
+        },
+    },
+    {
+        "type": "function",
+        "name": "save_conversation",
+        "description": "Save the current conversatione. Use this function to persist the current conversation to external storage.",
+        "parameters": {
+            "type": "object",
+            "properties": {},
+            "required": [],
+        },
+    },
+    {
+        "type": "function",
+        "name": "get_saved_conversation_filenames",
+        "description": "Get a list of saved conversation histories. Returns a list of filenames. Each filename includes a date and timestamp. Each file is conversation history that can be loaded into this session.",
+        "parameters": {
+            "type": "object",
+            "properties": {},
+            "required": [],
+        },
+    },
+    {
+        "type": "function",
+        "name": "load_conversation",
+        "description": "Load a conversation history. Use this function to load a conversation history into the current session.",
+        "parameters": {
+            "type": "object",
+            "properties": {
                 "filename": {
                     "type": "string",
                     "description": "The filename of the conversation history to load.",
                 }
             },
-            required=["filename"],
-        ),
-    ]
-)
+            "required": ["filename"],
+        },
+    },
+]
 
 
 # We store functions so objects (e.g. SileroVADAnalyzer) don't get
@@ -215,8 +223,8 @@ Remember, your responses should be short. Just one or two sentences, usually."""
     llm.register_function("get_saved_conversation_filenames", get_saved_conversation_filenames)
     llm.register_function("load_conversation", load_conversation)
 
-    context = LLMContext([{"role": "user", "content": "Say hello!"}], tools)
-    context_aggregator = LLMContextAggregatorPair(context)
+    context = OpenAILLMContext([], tools)
+    context_aggregator = llm.create_context_aggregator(context)
 
     pipeline = Pipeline(
         [