update README with KoalaFilter

This seems necessary because of how pytest works. If a task is cancelled, pytest
will know the task has been cancelled even if # `asyncio.CancelledError` is
handled internally in the task.

.github/workflows/android.yaml

@@ -1,48 +0,0 @@
-name: android
-
-on:
-  push:
-    branches:
-      - main
-    paths:
-      - "examples/simple-chatbot/client/android/**"
-  pull_request:
-    branches:
-      - "**"
-    paths:
-      - "examples/simple-chatbot/client/android/**"
-  workflow_dispatch:
-    inputs:
-      sdk_git_ref:
-        type: string
-        description: "Which git ref of the app to build"
-
-concurrency:
-  group: build-android-${{ github.event.pull_request.number || github.ref }}
-  cancel-in-progress: true
-
-jobs:
-  sdk:
-    name: "Simple chatbot demo"
-    runs-on: ubuntu-latest
-    steps:
-      - name: Checkout repo
-        uses: actions/checkout@v4
-        with:
-          ref: ${{ github.event.inputs.sdk_git_ref || github.ref }}
-
-      - name: "Install Java"
-        uses: actions/setup-java@v4
-        with:
-          distribution: 'temurin'
-          java-version: '17'
-
-      - name: Build demo app
-        working-directory: examples/simple-chatbot/client/android
-        run: ./gradlew :simple-chatbot-client:assembleDebug
-
-      - name: Upload demo APK
-        uses: actions/upload-artifact@v4
-        with:
-          name: Simple Chatbot Android Client
-          path: examples/simple-chatbot/client/android/simple-chatbot-client/build/outputs/apk/debug/simple-chatbot-client-debug.apk

.github/workflows/tests.yaml

@@ -1,4 +1,4 @@
-name: tests
+name: test
 
 on:
   workflow_dispatch:

.pre-commit-config.yaml

@@ -1,7 +0,0 @@
-repos:
-  - repo: local
-    hooks:
-      - id: ruff-format-hook
-        name: Check ruff formatting
-        entry: sh scripts/pre-commit.sh
-        language: system

CHANGELOG.md

@@ -9,165 +9,6 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 
 ### Added
 
-- It is now possible to specify the period of the `PipelineTask` heartbeat
-  frames with `heartbeats_period_secs`.
-
-### Changed
-
-- Modified `TranscriptProcessor` to use TTS text frames for more accurate assistant
-  transcripts. Assistant messages are now aggregated based on bot speaking boundaries
-  rather than LLM context, providing better handling of interruptions and partial
-  utterances.
-
-- Updated foundational examples `28a-transcription-processor-openai.py`,
-  `28b-transcript-processor-anthropic.py`, and
-  `28c-transcription-processor-gemini.py` to use the updated
-  `TranscriptProcessor`.
-
-### Fixed
-
-- Fixed a type error when using `voice_settings` in `ElevenLabsHttpTTSService`.
-
-- Fixed an issue where `OpenAIRealtimeBetaLLMService` function calling resulted
-  in an error.
-
-### Performance
-
-- Replaced audio resampling library `resampy` with `soxr`. Resampling a 2:21s
-  audio file from 24KHz to 16KHz took 1.41s with `resampy` and 0.031s with
-  `soxr` with similar audio quality.
-
-### Other
-
-- Added initial unit test infrastructure.
-
-## [0.0.53] - 2025-01-18
-
-### Added
-
-- Added `ElevenLabsHttpTTSService` which uses EleveLabs' HTTP API instead of the
-  websocket one.
-
-- Introduced pipeline frame observers. Observers can view all the frames that go
-  through the pipeline without the need to inject processors in the
-  pipeline. This can be useful, for example, to implement frame loggers or
-  debuggers among other things. The example
-  `examples/foundational/30-observer.py` shows how to add an observer to a
-  pipeline for debugging.
-
-- Introduced heartbeat frames. The pipeline task can now push periodic
-  heartbeats down the pipeline when `enable_heartbeats=True`. Heartbeats are
-  system frames that are supposed to make it all the way to the end of the
-  pipeline. When a heartbeat frame is received the traversing time (i.e. the
-  time it took to go through the whole pipeline) will be displayed (with TRACE
-  logging) otherwise a warning will be shown. The example
-  `examples/foundational/31-heartbeats.py` shows how to enable heartbeats and
-  forces warnings to be displayed.
-
-- Added `LLMTextFrame` and `TTSTextFrame` which should be pushed by LLM and TTS
-  services respectively instead of `TextFrame`s.
-
-- Added `OpenRouter` for OpenRouter integration with an OpenAI-compatible
-  interface. Added foundational example `14m-function-calling-openrouter.py`.
-
-- Added a new `WebsocketService` based class for TTS services, containing
-  base functions and retry logic.
-
-- Added `DeepSeekLLMService` for DeepSeek integration with an OpenAI-compatible
-  interface. Added foundational example `14l-function-calling-deepseek.py`.
-
-- Added `FunctionCallResultProperties` dataclass to provide a structured way to
-  control function call behavior, including:
-
-  - `run_llm`: Controls whether to trigger LLM completion
-  - `on_context_updated`: Optional callback triggered after context update
-
-- Added a new foundational example `07e-interruptible-playht-http.py` for easy
-  testing of `PlayHTHttpTTSService`.
-
-- Added support for Google TTS Journey voices in `GoogleTTSService`.
-
-- Added `29-livekit-audio-chat.py`, as a new foundational examples for
-  `LiveKitTransportLayer`.
-
-- Added `enable_prejoin_ui`, `max_participants` and `start_video_off` params
-  to `DailyRoomProperties`.
-
-- Added `session_timeout` to `FastAPIWebsocketTransport` and
-  `WebsocketServerTransport` for configuring session timeouts (in
-  seconds). Triggers `on_session_timeout` for custom timeout handling.
-  See [examples/websocket-server/bot.py](https://github.com/pipecat-ai/pipecat/blob/main/examples/websocket-server/bot.py).
-
-- Added the new modalities option and helper function to set Gemini output
-  modalities.
-
-- 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
-
-- Modified `UserIdleProcessor` to start monitoring only after first
-  conversation activity (`UserStartedSpeakingFrame` or
-  `BotStartedSpeakingFrame`) instead of immediately.
-
-- Modified `OpenAIAssistantContextAggregator` to support controlled completions
-  and to emit context update callbacks via `FunctionCallResultProperties`.
-
-- Added `aws_session_token` to the `PollyTTSService`.
-
-- Changed the default model for `PlayHTHttpTTSService` to `Play3.0-mini-http`.
-
-- `api_key`, `aws_access_key_id` and `region` are no longer required parameters
-  for the PollyTTSService (AWSTTSService)
-
-- Added `session_timeout` example in `examples/websocket-server/bot.py` to
-  handle session timeout event.
-
-- Changed `InputParams` in
-  `src/pipecat/services/gemini_multimodal_live/gemini.py` to support different
-  modalities.
-
-- Changed `DeepgramSTTService` to send `finalize` event whenever VAD detects
-  `UserStoppedSpeakingFrame`. This helps in faster transcriptions and clearing
-  the `Deepgram` audio buffer.
-
-### Fixed
-
-- Fixed an issue where `DeepgramSTTService` was not generating metrics using
-  pipeline's VAD.
-
-- Fixed `UserIdleProcessor` not properly propagating `EndFrame`s through the
-  pipeline.
-
-- Fixed an issue where websocket based TTS services could incorrectly terminate
-  their connection due to a retry counter not resetting.
-
-- Fixed a `PipelineTask` issue that would cause a dangling task after stopping
-  the pipeline with an `EndFrame`.
-
-- Fixed an import issue for `PlayHTHttpTTSService`.
-
-- Fixed an issue where languages couldn't be used with the `PlayHTHttpTTSService`.
-
-- Fixed an issue where `OpenAIRealtimeBetaLLMService` audio chunks were hitting
-  an error when truncating audio content.
-
-- Fixed an issue where setting the voice and model for `RimeHttpTTSService`
-  wasn't working.
-
-- Fixed an issue where `IdleFrameProcessor` and `UserIdleProcessor` were getting
-  initialized before the start of the pipeline.
-
-## [0.0.52] - 2024-12-24
-
-### Added
-
-- Constructor arguments for GoogleLLMService to directly set tools and tool_config.
-
-- Smart turn detection example (`22d-natural-conversation-gemini-audio.py`) that
-  leverages Gemini 2.0 capabilities ().
-  (see https://x.com/kwindla/status/1870974144831275410)
-
 - Added `DailyTransport.send_dtmf()` to send dial-out DTMF tones.
 
 - Added `DailyTransport.sip_call_transfer()` to forward SIP and PSTN calls to
@@ -234,8 +75,8 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 
 ### Fixed
 
-- Fixed token counting in `GoogleLLMService`. Tokens were summed incorrectly
-  (double-counted in many cases).
+- Fixed LLM response aggregators to support more uses cases such as delayed
+  transcriptions.
 
 - Fixed an issue that could cause the bot to stop talking if there was a user
   interruption before getting any audio from the TTS service.
@@ -251,8 +92,8 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
   only be pushed downstream after the audio generated from `TTSSpeakFrame` has
   been spoken.
 
-- Fixed a `DeepgramSTTService` issue that was causing language to be passed as
-  an object instead of a string resulting in the connection to fail.
+- Fixed a [bug](https://github.com/pipecat-ai/pipecat/issues/868) in `DeepgramSTTService` that was causing Language to be passed
+  as python object instead of a string, causing connection to fail.
 
 ## [0.0.51] - 2024-12-16
 

LICENSE

@@ -1,6 +1,6 @@
 BSD 2-Clause License
 
-Copyright (c) 2024–2025, Daily
+Copyright (c) 2024, Daily
 
 Redistribution and use in source and binary forms, with or without
 modification, are permitted provided that the following conditions are met:

README.md

@@ -2,7 +2,7 @@
  <img alt="pipecat" width="300px" height="auto" src="https://raw.githubusercontent.com/pipecat-ai/pipecat/main/pipecat.png">
 </div></h1>
 
-[![PyPI](https://img.shields.io/pypi/v/pipecat-ai)](https://pypi.org/project/pipecat-ai) ![Tests](https://github.com/pipecat-ai/pipecat/actions/workflows/tests.yaml/badge.svg) [![Docs](https://img.shields.io/badge/Documentation-blue)](https://docs.pipecat.ai) [![Discord](https://img.shields.io/discord/1239284677165056021)](https://discord.gg/pipecat) <a href="https://app.commanddash.io/agent/github_pipecat-ai_pipecat"><img src="https://img.shields.io/badge/AI-Code%20Agent-EB9FDA"></a>
+[![PyPI](https://img.shields.io/pypi/v/pipecat-ai)](https://pypi.org/project/pipecat-ai) [![Docs](https://img.shields.io/badge/Documentation-blue)](https://docs.pipecat.ai) [![Discord](https://img.shields.io/discord/1239284677165056021)](https://discord.gg/pipecat) <a href="https://app.commanddash.io/agent/github_pipecat-ai_pipecat"><img src="https://img.shields.io/badge/AI-Code%20Agent-EB9FDA"></a>
 
 Pipecat is an open source Python framework for building voice and multimodal conversational agents. It handles the complex orchestration of AI services, network transport, audio processing, and multimodal interactions, letting you focus on creating engaging experiences.
 
@@ -55,17 +55,17 @@ pip install "pipecat-ai[option,...]"
 
 Available options include:
 
-| Category            | Services                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    | Install Command Example                 |
-| ------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | --------------------------------------- |
-| Speech-to-Text      | [AssemblyAI](https://docs.pipecat.ai/server/services/stt/assemblyai), [Azure](https://docs.pipecat.ai/server/services/stt/azure), [Deepgram](https://docs.pipecat.ai/server/services/stt/deepgram), [Gladia](https://docs.pipecat.ai/server/services/stt/gladia), [Whisper](https://docs.pipecat.ai/server/services/stt/whisper)                                                                                                                                                                                                                                                                                                                                                                                                                                                            | `pip install "pipecat-ai[deepgram]"`    |
-| LLMs                | [Anthropic](https://docs.pipecat.ai/server/services/llm/anthropic), [Azure](https://docs.pipecat.ai/server/services/llm/azure), [Cerebras](https://docs.pipecat.ai/server/services/llm/cerebras), [DeepSeek](https://docs.pipecat.ai/server/services/llm/deepseek), [Fireworks AI](https://docs.pipecat.ai/server/services/llm/fireworks), [Gemini](https://docs.pipecat.ai/server/services/llm/gemini), [Grok](https://docs.pipecat.ai/server/services/llm/grok), [Groq](https://docs.pipecat.ai/server/services/llm/groq), [NVIDIA NIM](https://docs.pipecat.ai/server/services/llm/nim), [Ollama](https://docs.pipecat.ai/server/services/llm/ollama), [OpenAI](https://docs.pipecat.ai/server/services/llm/openai), [OpenRouter](https://docs.pipecat.ai/server/services/llm/openrouter), [Together AI](https://docs.pipecat.ai/server/services/llm/together) | `pip install "pipecat-ai[openai]"`      |
-| Text-to-Speech      | [AWS](https://docs.pipecat.ai/server/services/tts/aws), [Azure](https://docs.pipecat.ai/server/services/tts/azure), [Cartesia](https://docs.pipecat.ai/server/services/tts/cartesia), [Deepgram](https://docs.pipecat.ai/server/services/tts/deepgram), [ElevenLabs](https://docs.pipecat.ai/server/services/tts/elevenlabs), [Fish](https://docs.pipecat.ai/server/services/tts/fish), [Google](https://docs.pipecat.ai/server/services/tts/google), [LMNT](https://docs.pipecat.ai/server/services/tts/lmnt), [OpenAI](https://docs.pipecat.ai/server/services/tts/openai), [PlayHT](https://docs.pipecat.ai/server/services/tts/playht), [Rime](https://docs.pipecat.ai/server/services/tts/rime), [XTTS](https://docs.pipecat.ai/server/services/tts/xtts)                              | `pip install "pipecat-ai[cartesia]"`    |
-| Speech-to-Speech    | [Gemini Multimodal Live](https://docs.pipecat.ai/server/services/s2s/gemini), [OpenAI Realtime](https://docs.pipecat.ai/server/services/s2s/openai)                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                         | `pip install "pipecat-ai[openai]"`      |
-| Transport           | [Daily (WebRTC)](https://docs.pipecat.ai/server/services/transport/daily), [FastAPI Websocket](https://docs.pipecat.ai/server/services/transport/fastapi-websocket), [WebSocket Server](https://docs.pipecat.ai/server/services/transport/websocket-server), Local                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          | `pip install "pipecat-ai[daily]"`       |
-| Video               | [Tavus](https://docs.pipecat.ai/server/services/video/tavus), [Simli](https://docs.pipecat.ai/server/services/video/simli)                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                  | `pip install "pipecat-ai[tavus,simli]"` |
-| Vision & Image      | [Moondream](https://docs.pipecat.ai/server/services/vision/moondream), [fal](https://docs.pipecat.ai/server/services/image-generation/fal)                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                  | `pip install "pipecat-ai[moondream]"`   |
-| Audio Processing    | [Silero VAD](https://docs.pipecat.ai/server/utilities/audio/silero-vad-analyzer), [Krisp](https://docs.pipecat.ai/server/utilities/audio/krisp-filter), [Koala](https://docs.pipecat.ai/server/utilities/audio/koala-filter), [Noisereduce](https://docs.pipecat.ai/server/utilities/audio/noisereduce-filter)                                                                                                                                                                                                                                                                                                                                                                                                                                                                              | `pip install "pipecat-ai[silero]"`      |
-| Analytics & Metrics | [Canonical AI](https://docs.pipecat.ai/server/services/analytics/canonical), [Sentry](https://docs.pipecat.ai/server/services/analytics/sentry)                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                             | `pip install "pipecat-ai[canonical]"`   |
+| Category            | Services                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                       | Install Command Example                 |
+| ------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | --------------------------------------- |
+| Speech-to-Text      | [AssemblyAI](https://docs.pipecat.ai/server/services/stt/assemblyai), [Azure](https://docs.pipecat.ai/server/services/stt/azure), [Deepgram](https://docs.pipecat.ai/server/services/stt/deepgram), [Gladia](https://docs.pipecat.ai/server/services/stt/gladia), [Whisper](https://docs.pipecat.ai/server/services/stt/whisper)                                                                                                                                                                                                                                                                                                                                                                                                                               | `pip install "pipecat-ai[deepgram]"`    |
+| LLMs                | [Anthropic](https://docs.pipecat.ai/server/services/llm/anthropic), [Azure](https://docs.pipecat.ai/server/services/llm/azure), [Cerebras](https://docs.pipecat.ai/server/services/llm/cerebras), [Fireworks AI](https://docs.pipecat.ai/server/services/llm/fireworks), [Gemini](https://docs.pipecat.ai/server/services/llm/gemini), [Grok](https://docs.pipecat.ai/server/services/llm/grok), [Groq](https://docs.pipecat.ai/server/services/llm/groq), [NVIDIA NIM](https://docs.pipecat.ai/server/services/llm/nim), [Ollama](https://docs.pipecat.ai/server/services/llm/ollama), [OpenAI](https://docs.pipecat.ai/server/services/llm/openai), [Together AI](https://docs.pipecat.ai/server/services/llm/together)                                      | `pip install "pipecat-ai[openai]"`      |
+| Text-to-Speech      | [AWS](https://docs.pipecat.ai/server/services/tts/aws), [Azure](https://docs.pipecat.ai/server/services/tts/azure), [Cartesia](https://docs.pipecat.ai/server/services/tts/cartesia), [Deepgram](https://docs.pipecat.ai/server/services/tts/deepgram), [ElevenLabs](https://docs.pipecat.ai/server/services/tts/elevenlabs), [Fish](https://docs.pipecat.ai/server/services/tts/fish), [Google](https://docs.pipecat.ai/server/services/tts/google), [LMNT](https://docs.pipecat.ai/server/services/tts/lmnt), [OpenAI](https://docs.pipecat.ai/server/services/tts/openai), [PlayHT](https://docs.pipecat.ai/server/services/tts/playht), [Rime](https://docs.pipecat.ai/server/services/tts/rime), [XTTS](https://docs.pipecat.ai/server/services/tts/xtts) | `pip install "pipecat-ai[cartesia]"`    |
+| Speech-to-Speech    | [Gemini Multimodal Live](https://docs.pipecat.ai/server/services/s2s/gemini), [OpenAI Realtime](https://docs.pipecat.ai/server/services/s2s/openai)                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            | `pip install "pipecat-ai[openai]"`      |
+| Transport           | [Daily (WebRTC)](https://docs.pipecat.ai/server/services/transport/daily), WebSocket, Local                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    | `pip install "pipecat-ai[daily]"`       |
+| Video               | [Tavus](https://docs.pipecat.ai/server/services/video/tavus), [Simli](https://docs.pipecat.ai/server/services/video/simli)                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                     | `pip install "pipecat-ai[tavus,simli]"` |
+| Vision & Image      | [Moondream](https://docs.pipecat.ai/server/services/vision/moondream), [fal](https://docs.pipecat.ai/server/services/image-generation/fal)                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                     | `pip install "pipecat-ai[moondream]"`   |
+| Audio Processing    | [Silero VAD](https://docs.pipecat.ai/server/utilities/audio/silero-vad-analyzer), [Krisp](https://docs.pipecat.ai/server/utilities/audio/krisp-filter), [Koala](https://docs.pipecat.ai/server/utilities/audio/koala-filter)                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                   | `pip install "pipecat-ai[silero]"`      |
+| Analytics & Metrics | [Canonical AI](https://docs.pipecat.ai/server/services/analytics/canonical), [Sentry](https://docs.pipecat.ai/server/services/analytics/sentry)                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                | `pip install "pipecat-ai[canonical]"`   |
 
 📚 [View full services documentation →](https://docs.pipecat.ai/server/services/supported-services)
 
@@ -160,24 +160,15 @@ From the root of this repo, run the following:
 
 ```shell
 pip install -r dev-requirements.txt
+python -m build
 ```
 
-This will install the necessary development dependencies. Also, make sure you install the git pre-commit hooks:
-
-```shell
-pre-commit install
-```
-
-The hooks will just save you time when you submit a PR by making sure your code follows the project rules.
-
-To use the package locally (e.g. to run sample files), run:
+This builds the package. To use the package locally (e.g. to run sample files), run
 
 ```shell
 pip install --editable ".[option,...]"
 ```
 
-The `--editable` option makes sure you don't have to run `pip install` again and you can just edit the project files locally.
-
 If you want to use this package from another directory, you can run:
 
 ```shell
@@ -189,7 +180,7 @@ pip install "path_to_this_repo[option,...]"
 From the root directory, run:
 
 ```shell
-pytest
+pytest --doctest-modules --ignore-glob="*to_be_updated*" --ignore-glob=*pipeline_source* src tests
 ```
 
 ## Setting up your editor

dev-requirements.txt

@@ -1,11 +1,9 @@
 build~=1.2.2
-grpcio-tools~=1.69.0
+grpcio-tools~=1.68.1
 pip-tools~=7.4.1
-pre-commit~=4.0.1
-pyright~=1.1.392
+pyright~=1.1.390
 pytest~=8.3.4
-pytest-asyncio~=0.25.2
-ruff~=0.9.1
-setuptools~=75.8.0
+ruff~=0.8.3
+setuptools~=75.6.0
 setuptools_scm~=8.1.0
 python-dotenv~=1.0.1

dot-env.template

@@ -60,27 +60,3 @@ SIMLI_FACE_ID=...
 
 # Krisp
 KRISP_MODEL_PATH=...
-
-# DeepSeek
-DEEPSEEK_API_KEY=...
-
-# Groq
-GROQ_API_KEY=...
-
-# Grok
-GROK_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=...

examples/README.md

@@ -42,7 +42,7 @@ Next, follow the steps in the README for each demo.
 | [Dialin Chatbot](dialin-chatbot)             | A chatbot that connects to an incoming phone call from Daily or Twilio.                                                                    | Deepgram, ElevenLabs, OpenAI, Daily, Twilio                       |
 | [Twilio Chatbot](twilio-chatbot)             | A chatbot that connects to an incoming phone call from Twilio.                                                                             | Deepgram, ElevenLabs, OpenAI, Daily, Twilio                       |
 | [studypal](studypal)                         | A chatbot to have a conversation about any article on the web                                                                              |                                                                   |
-| [WebSocket Chatbot Server](websocket-server) | A real-time websocket server that handles audio streaming and bot interactions with speech-to-text and text-to-speech capabilities. | Cartesia, Deepgram, OpenAI, Websockets |
+| [WebSocket Chatbot Server](websocket-server) | A real-time websocket server that handles audio streaming and bot interactions with speech-to-text and text-to-speech capabilities | `python-websockets`, `openai`, `deepgram`, `silero-tts`, `numpy` |
 
 > [!IMPORTANT]
 > These example projects use Daily as a WebRTC transport and can be joined using their hosted Prebuilt UI.

examples/canonical-metrics/bot.py

@@ -1,5 +1,5 @@
 #
-# Copyright (c) 2024–2025, Daily
+# Copyright (c) 2024, Daily
 #
 # SPDX-License-Identifier: BSD 2-Clause License
 #
@@ -15,7 +15,7 @@ from loguru import logger
 from runner import configure
 
 from pipecat.audio.vad.silero import SileroVADAnalyzer
-from pipecat.frames.frames import EndFrame
+from pipecat.frames.frames import EndFrame, LLMMessagesFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
@@ -97,7 +97,7 @@ async def main():
         call completion, CanonicalMetrics will send the audio buffer to Canonical for
         analysis. Visit https://voice.canonical.chat to learn more.
         """
-        audio_buffer_processor = AudioBufferProcessor(num_channels=2)
+        audio_buffer_processor = AudioBufferProcessor()
         canonical = CanonicalMetricsService(
             audio_buffer_processor=audio_buffer_processor,
             aiohttp_session=session,
@@ -105,7 +105,6 @@ async def main():
             call_id=str(uuid.uuid4()),
             assistant="pipecat-chatbot",
             assistant_speaks_first=True,
-            context=context,
         )
         pipeline = Pipeline(
             [
@@ -125,7 +124,7 @@ async def main():
         @transport.event_handler("on_first_participant_joined")
         async def on_first_participant_joined(transport, participant):
             await transport.capture_participant_transcription(participant["id"])
-            await task.queue_frames([context_aggregator.user().get_context_frame()])
+            await task.queue_frames([LLMMessagesFrame(messages)])
 
         @transport.event_handler("on_participant_left")
         async def on_participant_left(transport, participant, reason):

examples/canonical-metrics/runner.py

@@ -1,5 +1,5 @@
 #
-# Copyright (c) 2024–2025, Daily
+# Copyright (c) 2024, Daily
 #
 # SPDX-License-Identifier: BSD 2-Clause License
 #

examples/canonical-metrics/server.py

@@ -1,5 +1,5 @@
 #
-# Copyright (c) 2024–2025, Daily
+# Copyright (c) 2024, Daily
 #
 # SPDX-License-Identifier: BSD 2-Clause License
 #

examples/chatbot-audio-recording/bot.py

@@ -1,5 +1,5 @@
 #
-# Copyright (c) 2024–2025, Daily
+# Copyright (c) 2024, Daily
 #
 # SPDX-License-Identifier: BSD 2-Clause License
 #
@@ -18,7 +18,7 @@ from loguru import logger
 from runner import configure
 
 from pipecat.audio.vad.silero import SileroVADAnalyzer
-from pipecat.frames.frames import EndFrame
+from pipecat.frames.frames import EndFrame, LLMMessagesFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
@@ -134,7 +134,7 @@ async def main():
         @transport.event_handler("on_first_participant_joined")
         async def on_first_participant_joined(transport, participant):
             await transport.capture_participant_transcription(participant["id"])
-            await task.queue_frames([context_aggregator.user().get_context_frame()])
+            await task.queue_frames([LLMMessagesFrame(messages)])
 
         @transport.event_handler("on_participant_left")
         async def on_participant_left(transport, participant, reason):

examples/chatbot-audio-recording/runner.py

@@ -1,5 +1,5 @@
 #
-# Copyright (c) 2024–2025, Daily
+# Copyright (c) 2024, Daily
 #
 # SPDX-License-Identifier: BSD 2-Clause License
 #

examples/chatbot-audio-recording/server.py

@@ -1,5 +1,5 @@
 #
-# Copyright (c) 2024–2025, Daily
+# Copyright (c) 2024, Daily
 #
 # SPDX-License-Identifier: BSD 2-Clause License
 #

examples/deployment/flyio-example/bot.py

@@ -7,7 +7,7 @@ from dotenv import load_dotenv
 from loguru import logger
 
 from pipecat.audio.vad.silero import SileroVADAnalyzer
-from pipecat.frames.frames import EndFrame
+from pipecat.frames.frames import EndFrame, LLMMessagesFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
@@ -75,7 +75,7 @@ async def main(room_url: str, token: str):
     @transport.event_handler("on_first_participant_joined")
     async def on_first_participant_joined(transport, participant):
         await transport.capture_participant_transcription(participant["id"])
-        await task.queue_frames([context_aggregator.user().get_context_frame()])
+        await task.queue_frames([LLMMessagesFrame(messages)])
 
     @transport.event_handler("on_participant_left")
     async def on_participant_left(transport, participant, reason):

examples/deployment/flyio-example/bot_runner.py

@@ -1,5 +1,5 @@
 #
-# Copyright (c) 2024–2025, Daily
+# Copyright (c) 2024, Daily
 #
 # SPDX-License-Identifier: BSD 2-Clause License
 #

examples/deployment/modal-example/bot.py

@@ -13,7 +13,7 @@ logger.add(sys.stderr, level="DEBUG")
 
 async def main(room_url: str, token: str):
     from pipecat.audio.vad.silero import SileroVADAnalyzer
-    from pipecat.frames.frames import EndFrame
+    from pipecat.frames.frames import EndFrame, LLMMessagesFrame
     from pipecat.pipeline.pipeline import Pipeline
     from pipecat.pipeline.runner import PipelineRunner
     from pipecat.pipeline.task import PipelineParams, PipelineTask
@@ -75,7 +75,7 @@ async def main(room_url: str, token: str):
     async def on_first_participant_joined(transport, participant):
         await transport.capture_participant_transcription(participant["id"])
         messages.append({"role": "system", "content": "Please introduce yourself to the user."})
-        await task.queue_frames([context_aggregator.user().get_context_frame()])
+        await task.queue_frames([LLMMessagesFrame(messages)])
 
     @transport.event_handler("on_participant_left")
     async def on_participant_left(transport, participant, reason):

examples/deployment/modal-example/requirements.txt

@@ -1,5 +1,5 @@
 python-dotenv==1.0.1
-modal==0.71.3
-pipecat-ai[daily,silero,cartesia,openai]==0.0.52
-fastapi==0.115.6
-aiohttp==3.11.11
+modal==0.65.48
+pipecat-ai[daily,silero,cartesia,openai]==0.0.48
+fastapi==0.115.4
+aiohttp==3.11.9

examples/dialin-chatbot/README.md

@@ -37,16 +37,7 @@ Run `bot_runner.py` to handle incoming HTTP requests:
 
 Then target the following URL:
 
-```bash
-curl -X POST 'http://localhost:7860/daily_start_bot' \
-  -H 'Content-Type: application/json' \
-  -d '{
-      "callId": "callId-from-call",
-      "callDomain": "callDomain-from-call"
-  }'
-```
-
-Use [this guide](https://docs.pipecat.ai/guides/telephony/daily-webrtc) to connect a phone number purchased from Daily to the bot.
+`POST /daily_start_bot`
 
 For more configuration options, please consult Daily's API documentation.
 
@@ -91,4 +82,4 @@ If you're using Twilio as a number vendor:
 
 ## Need to do something more advanced?
 
-This demo covers the basics of bot telephony. If you want to know more about working with PSTN / SIP, please ping us on [Discord](https://discord.gg/pipecat).
+This demo covers the basics of bot telephony. If you want to know more about working with PSTN / SIP, please ping us on [Discord](https://discord.gg/pipecat).

examples/dialin-chatbot/bot_daily.py

@@ -1,8 +1,3 @@
-# Copyright (c) 2024–2025, Daily
-#
-# SPDX-License-Identifier: BSD 2-Clause License
-#
-
 import argparse
 import asyncio
 import os
@@ -10,16 +5,13 @@ import sys
 
 from dotenv import load_dotenv
 from loguru import logger
-from openai.types.chat import ChatCompletionToolParam
 
 from pipecat.audio.vad.silero import SileroVADAnalyzer
-from pipecat.frames.frames import EndFrame, TextFrame
+from pipecat.frames.frames import EndFrame, LLMMessagesFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
 from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
-from pipecat.services.ai_services import LLMService
-from pipecat.services.deepgram import DeepgramSTTService
 from pipecat.services.elevenlabs import ElevenLabsTTSService
 from pipecat.services.openai import OpenAILLMService
 from pipecat.transports.services.daily import DailyDialinSettings, DailyParams, DailyTransport
@@ -63,62 +55,16 @@ async def main(room_url: str, token: str, callId: str, callDomain: str):
 
     llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
 
-    content = f"""
-You are a delivery service customer support specialist supporting customers with their orders. 
-Begin with: "Hello, this is Hailey from customer support. What can I help you with today?"
-    """
-
     messages = [
         {
             "role": "system",
-            "content": content,
+            "content": "You are Chatbot, a friendly, helpful robot. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way, but keep your responses brief. Start by saying 'Oh, hello! Who dares dial me at this hour?!'.",
         },
     ]
 
-    tools = [
-        ChatCompletionToolParam(
-            type="function",
-            function={
-                "name": "transfer_call",
-                "description": "Transfer the call to a person. This function is used to connect the call to a real person. Examples of real people are: managers, supervisors, or other customer support specialists. Any person is okay as long as they are not a bot.",
-                "parameters": {
-                    "type": "object",
-                    "properties": {
-                        "call_id": {
-                            "type": "string",
-                            "description": "This is always {callId}.",
-                        },
-                        "summary": {
-                            "type": "string",
-                            "description": """
-Provide a concise summary in 3-5 sentences. Highlight any  important details or unusual aspects of the conversation.
-                            """,
-                        },
-                    },
-                },
-            },
-        )
-    ]
-
-    context = OpenAILLMContext(messages, tools)
+    context = OpenAILLMContext(messages)
     context_aggregator = llm.create_context_aggregator(context)
 
-    async def default_transfer_call(
-        function_name, tool_call_id, args, llm: LLMService, context, result_callback
-    ):
-        logger.debug(f"default_transfer_call: {function_name} {tool_call_id} {args}")
-        await result_callback(
-            {
-                "transfer_call": False,
-                "reason": "To transfer call calls, please dial in to the room using a phone or a SIP client.",
-            }
-        )
-
-    llm.register_function(
-        function_name="transfer_call",
-        callback=default_transfer_call,
-    )
-
     pipeline = Pipeline(
         [
             transport.input(),
@@ -135,50 +81,12 @@ Provide a concise summary in 3-5 sentences. Highlight any  important details or
     @transport.event_handler("on_first_participant_joined")
     async def on_first_participant_joined(transport, participant):
         await transport.capture_participant_transcription(participant["id"])
-        await task.queue_frames([context_aggregator.user().get_context_frame()])
+        await task.queue_frames([LLMMessagesFrame(messages)])
 
     @transport.event_handler("on_participant_left")
     async def on_participant_left(transport, participant, reason):
         await task.queue_frame(EndFrame())
 
-    @transport.event_handler("on_dialin_ready")
-    async def on_dialin_ready(_, sip_endpoint):
-        logger.info(f"on_dialin_ready: {sip_endpoint}")
-
-    @transport.event_handler("on_dialin_connected")
-    async def on_dialin_connected(transport, event):
-        logger.info(f"on_dialin_connected: {event}")
-        sip_session_id = event["sessionId"]
-
-        async def transfer_call(
-            function_name, tool_call_id, args, llm: LLMService, context, result_callback
-        ):
-            logger.debug(f"transfer_call: {function_name} {tool_call_id} {args}")
-
-            # sip_url = "sip:your_user_name@sip.linphone.org"
-
-            sip_url = (
-                f"sip:your_username@dailyco.sip.twilio.com?x-daily_id={room_url.split('/')[-1]}"
-            )
-
-            try:
-                await transport.sip_refer(
-                    settings={
-                        "sessionId": sip_session_id,
-                        "toEndPoint": sip_url,
-                    }
-                )
-            except Exception as e:
-                logger.error(f"An error occurred during SIP refer: {e}")
-                await result_callback({"transfer_call": False})
-
-            await result_callback({"transfer_call": True})
-
-        llm.register_function(
-            function_name="transfer_call",
-            callback=transfer_call,
-        )
-
     runner = PipelineRunner()
 
     await runner.run(task)

examples/dialin-chatbot/bot_twilio.py

@@ -8,7 +8,7 @@ from loguru import logger
 from twilio.rest import Client
 
 from pipecat.audio.vad.silero import SileroVADAnalyzer
-from pipecat.frames.frames import EndFrame
+from pipecat.frames.frames import EndFrame, LLMMessagesFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
@@ -83,7 +83,7 @@ async def main(room_url: str, token: str, callId: str, sipUri: str):
     @transport.event_handler("on_first_participant_joined")
     async def on_first_participant_joined(transport, participant):
         await transport.capture_participant_transcription(participant["id"])
-        await task.queue_frames([context_aggregator.user().get_context_frame()])
+        await task.queue_frames([LLMMessagesFrame(messages)])
 
     @transport.event_handler("on_participant_left")
     async def on_participant_left(transport, participant, reason):

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

@@ -1,5 +1,5 @@
 #
-# Copyright (c) 2024–2025, Daily
+# Copyright (c) 2024, Daily
 #
 # SPDX-License-Identifier: BSD 2-Clause License
 #

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

@@ -1,5 +1,5 @@
 #
-# Copyright (c) 2024–2025, Daily
+# Copyright (c) 2024, Daily
 #
 # SPDX-License-Identifier: BSD 2-Clause License
 #

examples/foundational/01c-fastpitch.py

@@ -1,5 +1,5 @@
 #
-# Copyright (c) 2024–2025, Daily
+# Copyright (c) 2024, Daily
 #
 # SPDX-License-Identifier: BSD 2-Clause License
 #

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

@@ -1,5 +1,5 @@
 #
-# Copyright (c) 2024–2025, Daily
+# Copyright (c) 2024, Daily
 #
 # SPDX-License-Identifier: BSD 2-Clause License
 #

examples/foundational/03-still-frame.py

@@ -1,5 +1,5 @@
 #
-# Copyright (c) 2024–2025, Daily
+# Copyright (c) 2024, Daily
 #
 # SPDX-License-Identifier: BSD 2-Clause License
 #

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

@@ -1,5 +1,5 @@
 #
-# Copyright (c) 2024–2025, Daily
+# Copyright (c) 2024, Daily
 #
 # SPDX-License-Identifier: BSD 2-Clause License
 #

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

@@ -1,5 +1,5 @@
 #
-# Copyright (c) 2024–2025, Daily
+# Copyright (c) 2024, Daily
 #
 # SPDX-License-Identifier: BSD 2-Clause License
 #

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

@@ -1,5 +1,5 @@
 #
-# Copyright (c) 2024–2025, Daily
+# Copyright (c) 2024, Daily
 #
 # SPDX-License-Identifier: BSD 2-Clause License
 #

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

@@ -1,5 +1,5 @@
 #
-# Copyright (c) 2024–2025, Daily
+# Copyright (c) 2024, Daily
 #
 # SPDX-License-Identifier: BSD 2-Clause License
 #

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

@@ -1,5 +1,5 @@
 #
-# Copyright (c) 2024–2025, Daily
+# Copyright (c) 2024, Daily
 #
 # SPDX-License-Identifier: BSD 2-Clause License
 #
@@ -14,7 +14,7 @@ from loguru import logger
 from runner import configure
 
 from pipecat.audio.vad.silero import SileroVADAnalyzer
-from pipecat.frames.frames import EndFrame, Frame, MetricsFrame
+from pipecat.frames.frames import Frame, LLMMessagesFrame, MetricsFrame
 from pipecat.metrics.metrics import (
     LLMUsageMetricsData,
     ProcessingMetricsData,
@@ -47,7 +47,9 @@ class MetricsLogger(FrameProcessor):
                 elif isinstance(d, LLMUsageMetricsData):
                     tokens = d.value
                     print(
-                        f"!!! MetricsFrame: {frame}, tokens: {tokens.prompt_tokens}, characters: {tokens.completion_tokens}"
+                        f"!!! MetricsFrame: {frame}, tokens: {
+                            tokens.prompt_tokens}, characters: {
+                            tokens.completion_tokens}"
                     )
                 elif isinstance(d, TTSUsageMetricsData):
                     print(f"!!! MetricsFrame: {frame}, characters: {d.value}")
@@ -111,11 +113,7 @@ async def main():
             await transport.capture_participant_transcription(participant["id"])
             # Kick off the conversation.
             messages.append({"role": "system", "content": "Please introduce yourself to the user."})
-            await task.queue_frames([context_aggregator.user().get_context_frame()])
-
-        @transport.event_handler("on_participant_left")
-        async def on_participant_left(transport, participant, reason):
-            await task.queue_frame(EndFrame())
+            await task.queue_frames([LLMMessagesFrame(messages)])
 
         runner = PipelineRunner()
 

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

@@ -1,5 +1,5 @@
 #
-# Copyright (c) 2024–2025, Daily
+# Copyright (c) 2024, Daily
 #
 # SPDX-License-Identifier: BSD 2-Clause License
 #
@@ -15,7 +15,7 @@ from PIL import Image
 from runner import configure
 
 from pipecat.audio.vad.silero import SileroVADAnalyzer
-from pipecat.frames.frames import EndFrame, Frame, OutputImageRawFrame, SystemFrame, TextFrame
+from pipecat.frames.frames import Frame, OutputImageRawFrame, SystemFrame, TextFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineTask
@@ -126,10 +126,6 @@ async def main():
             await transport.capture_participant_transcription(participant["id"])
             await task.queue_frames([TextFrame(f"Hi there {participant_name}!")])
 
-        @transport.event_handler("on_participant_left")
-        async def on_participant_left(transport, participant, reason):
-            await task.queue_frame(EndFrame())
-
         runner = PipelineRunner()
 
         await runner.run(task)

examples/foundational/07-interruptible-vad.py

@@ -1,5 +1,5 @@
 #
-# Copyright (c) 2024–2025, Daily
+# Copyright (c) 2024, Daily
 #
 # SPDX-License-Identifier: BSD 2-Clause License
 #
@@ -13,7 +13,7 @@ from dotenv import load_dotenv
 from loguru import logger
 from runner import configure
 
-from pipecat.frames.frames import EndFrame
+from pipecat.frames.frames import LLMMessagesFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
@@ -90,11 +90,7 @@ async def main():
             await transport.capture_participant_transcription(participant["id"])
             # Kick off the conversation.
             messages.append({"role": "system", "content": "Please introduce yourself to the user."})
-            await task.queue_frames([context_aggregator.user().get_context_frame()])
-
-        @transport.event_handler("on_participant_left")
-        async def on_participant_left(transport, participant, reason):
-            await task.queue_frame(EndFrame())
+            await task.queue_frames([LLMMessagesFrame(messages)])
 
         runner = PipelineRunner()
 

examples/foundational/07-interruptible.py

@@ -1,5 +1,5 @@
 #
-# Copyright (c) 2024–2025, Daily
+# Copyright (c) 2024, Daily
 #
 # SPDX-License-Identifier: BSD 2-Clause License
 #
@@ -14,7 +14,7 @@ from loguru import logger
 from runner import configure
 
 from pipecat.audio.vad.silero import SileroVADAnalyzer
-from pipecat.frames.frames import EndFrame
+from pipecat.frames.frames import LLMMessagesFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
@@ -88,11 +88,7 @@ async def main():
             await transport.capture_participant_transcription(participant["id"])
             # Kick off the conversation.
             messages.append({"role": "system", "content": "Please introduce yourself to the user."})
-            await task.queue_frames([context_aggregator.user().get_context_frame()])
-
-        @transport.event_handler("on_participant_left")
-        async def on_participant_left(transport, participant, reason):
-            await task.queue_frame(EndFrame())
+            await task.queue_frames([LLMMessagesFrame(messages)])
 
         runner = PipelineRunner()
 

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

@@ -1,5 +1,5 @@
 #
-# Copyright (c) 2024–2025, Daily
+# Copyright (c) 2024, Daily
 #
 # SPDX-License-Identifier: BSD 2-Clause License
 #
@@ -14,7 +14,7 @@ from loguru import logger
 from runner import configure
 
 from pipecat.audio.vad.silero import SileroVADAnalyzer
-from pipecat.frames.frames import EndFrame
+from pipecat.frames.frames import LLMMessagesFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
@@ -78,25 +78,13 @@ async def main():
             ]
         )
 
-        task = PipelineTask(
-            pipeline,
-            PipelineParams(
-                allow_interruptions=True,
-                enable_metrics=True,
-                enable_usage_metrics=True,
-                report_only_initial_ttfb=True,
-            ),
-        )
+        task = PipelineTask(pipeline, PipelineParams(allow_interruptions=True))
 
         @transport.event_handler("on_first_participant_joined")
         async def on_first_participant_joined(transport, participant):
             await transport.capture_participant_transcription(participant["id"])
             # Kick off the conversation.
-            await task.queue_frames([context_aggregator.user().get_context_frame()])
-
-        @transport.event_handler("on_participant_left")
-        async def on_participant_left(transport, participant, reason):
-            await task.queue_frame(EndFrame())
+            await task.queue_frames([LLMMessagesFrame(messages)])
 
         runner = PipelineRunner()
 

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

@@ -1,5 +1,5 @@
 #
-# Copyright (c) 2024–2025, Daily
+# Copyright (c) 2024, Daily
 #
 # SPDX-License-Identifier: BSD 2-Clause License
 #
@@ -19,7 +19,7 @@ from loguru import logger
 from runner import configure
 
 from pipecat.audio.vad.silero import SileroVADAnalyzer
-from pipecat.frames.frames import EndFrame, LLMMessagesFrame
+from pipecat.frames.frames import LLMMessagesFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
@@ -101,15 +101,7 @@ async def main():
             ]
         )
 
-        task = PipelineTask(
-            pipeline,
-            PipelineParams(
-                allow_interruptions=True,
-                enable_metrics=True,
-                enable_usage_metrics=True,
-                report_only_initial_ttfb=True,
-            ),
-        )
+        task = PipelineTask(pipeline, PipelineParams(allow_interruptions=True))
 
         @transport.event_handler("on_first_participant_joined")
         async def on_first_participant_joined(transport, participant):
@@ -122,10 +114,6 @@ async def main():
             messages = [({"content": "Please briefly introduce yourself to the user."})]
             await task.queue_frames([LLMMessagesFrame(messages)])
 
-        @transport.event_handler("on_participant_left")
-        async def on_participant_left(transport, participant, reason):
-            await task.queue_frame(EndFrame())
-
         runner = PipelineRunner()
 
         await runner.run(task)

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

@@ -1,5 +1,5 @@
 #
-# Copyright (c) 2024–2025, Daily
+# Copyright (c) 2024, Daily
 #
 # SPDX-License-Identifier: BSD 2-Clause License
 #
@@ -16,7 +16,7 @@ from runner import configure
 
 from pipecat.frames.frames import (
     BotInterruptionFrame,
-    EndFrame,
+    LLMMessagesFrame,
     StopInterruptionFrame,
     UserStartedSpeakingFrame,
     UserStoppedSpeakingFrame,
@@ -80,15 +80,7 @@ async def main():
             ]
         )
 
-        task = PipelineTask(
-            pipeline,
-            PipelineParams(
-                allow_interruptions=True,
-                enable_metrics=True,
-                enable_usage_metrics=True,
-                report_only_initial_ttfb=True,
-            ),
-        )
+        task = PipelineTask(pipeline, PipelineParams(allow_interruptions=True))
 
         @stt.event_handler("on_speech_started")
         async def on_speech_started(stt, *args, **kwargs):
@@ -102,11 +94,7 @@ async def main():
         async def on_first_participant_joined(transport, participant):
             # Kick off the conversation.
             messages.append({"role": "system", "content": "Please introduce yourself to the user."})
-            await task.queue_frames([context_aggregator.user().get_context_frame()])
-
-        @transport.event_handler("on_participant_left")
-        async def on_participant_left(transport, participant, reason):
-            await task.queue_frame(EndFrame())
+            await task.queue_frames([LLMMessagesFrame(messages)])
 
         runner = PipelineRunner()
 

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

@@ -1,5 +1,5 @@
 #
-# Copyright (c) 2024–2025, Daily
+# Copyright (c) 2024, Daily
 #
 # SPDX-License-Identifier: BSD 2-Clause License
 #
@@ -14,7 +14,7 @@ from loguru import logger
 from runner import configure
 
 from pipecat.audio.vad.silero import SileroVADAnalyzer
-from pipecat.frames.frames import EndFrame
+from pipecat.frames.frames import LLMMessagesFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
@@ -73,25 +73,13 @@ async def main():
             ]
         )
 
-        task = PipelineTask(
-            pipeline,
-            PipelineParams(
-                allow_interruptions=True,
-                enable_metrics=True,
-                enable_usage_metrics=True,
-                report_only_initial_ttfb=True,
-            ),
-        )
+        task = PipelineTask(pipeline, PipelineParams(allow_interruptions=True))
 
         @transport.event_handler("on_first_participant_joined")
         async def on_first_participant_joined(transport, participant):
             # Kick off the conversation.
             messages.append({"role": "system", "content": "Please introduce yourself to the user."})
-            await task.queue_frames([context_aggregator.user().get_context_frame()])
-
-        @transport.event_handler("on_participant_left")
-        async def on_participant_left(transport, participant, reason):
-            await task.queue_frame(EndFrame())
+            await task.queue_frames([LLMMessagesFrame(messages)])
 
         runner = PipelineRunner()
 

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

@@ -1,5 +1,5 @@
 #
-# Copyright (c) 2024–2025, Daily
+# Copyright (c) 2024, Daily
 #
 # SPDX-License-Identifier: BSD 2-Clause License
 #
@@ -14,7 +14,7 @@ from loguru import logger
 from runner import configure
 
 from pipecat.audio.vad.silero import SileroVADAnalyzer
-from pipecat.frames.frames import EndFrame
+from pipecat.frames.frames import LLMMessagesFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
@@ -88,11 +88,7 @@ async def main():
             await transport.capture_participant_transcription(participant["id"])
             # Kick off the conversation.
             messages.append({"role": "system", "content": "Please introduce yourself to the user."})
-            await task.queue_frames([context_aggregator.user().get_context_frame()])
-
-        @transport.event_handler("on_participant_left")
-        async def on_participant_left(transport, participant, reason):
-            await task.queue_frame(EndFrame())
+            await task.queue_frames([LLMMessagesFrame(messages)])
 
         runner = PipelineRunner()
 

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

@@ -1,105 +0,0 @@
-#
-# Copyright (c) 2024–2025, Daily
-#
-# SPDX-License-Identifier: BSD 2-Clause License
-#
-
-import asyncio
-import os
-import sys
-
-import aiohttp
-from dotenv import load_dotenv
-from loguru import logger
-from runner import configure
-
-from pipecat.audio.vad.silero import SileroVADAnalyzer
-from pipecat.frames.frames import EndFrame
-from pipecat.pipeline.pipeline import Pipeline
-from pipecat.pipeline.runner import PipelineRunner
-from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
-from pipecat.services.openai import OpenAILLMService
-from pipecat.services.playht import PlayHTHttpTTSService
-from pipecat.transcriptions.language import Language
-from pipecat.transports.services.daily import DailyParams, DailyTransport
-
-load_dotenv(override=True)
-
-logger.remove(0)
-logger.add(sys.stderr, level="DEBUG")
-
-
-async def main():
-    async with aiohttp.ClientSession() as session:
-        (room_url, token) = await configure(session)
-
-        transport = DailyTransport(
-            room_url,
-            token,
-            "Respond bot",
-            DailyParams(
-                audio_out_enabled=True,
-                transcription_enabled=True,
-                vad_enabled=True,
-                vad_analyzer=SileroVADAnalyzer(),
-            ),
-        )
-
-        tts = PlayHTHttpTTSService(
-            user_id=os.getenv("PLAYHT_USER_ID"),
-            api_key=os.getenv("PLAYHT_API_KEY"),
-            voice_url="s3://voice-cloning-zero-shot/d9ff78ba-d016-47f6-b0ef-dd630f59414e/female-cs/manifest.json",
-        )
-
-        llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
-
-        messages = [
-            {
-                "role": "system",
-                "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
-            },
-        ]
-
-        context = OpenAILLMContext(messages)
-        context_aggregator = llm.create_context_aggregator(context)
-
-        pipeline = Pipeline(
-            [
-                transport.input(),  # Transport user input
-                context_aggregator.user(),  # User responses
-                llm,  # LLM
-                tts,  # TTS
-                transport.output(),  # Transport bot output
-                context_aggregator.assistant(),  # Assistant spoken responses
-            ]
-        )
-
-        task = PipelineTask(
-            pipeline,
-            PipelineParams(
-                allow_interruptions=True,
-                enable_metrics=True,
-                enable_usage_metrics=True,
-                report_only_initial_ttfb=True,
-            ),
-        )
-
-        @transport.event_handler("on_first_participant_joined")
-        async def on_first_participant_joined(transport, participant):
-            await transport.capture_participant_transcription(participant["id"])
-            # Kick off the conversation.
-            messages.append({"role": "system", "content": "Please introduce yourself to the user."})
-            await task.queue_frames([context_aggregator.user().get_context_frame()])
-
-        @transport.event_handler("on_participant_left")
-        async def on_participant_left(transport, participant, reason):
-            await task.queue_frame(EndFrame())
-
-        runner = PipelineRunner()
-
-        await runner.run(task)
-
-
-if __name__ == "__main__":
-    asyncio.run(main())

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

@@ -1,5 +1,5 @@
 #
-# Copyright (c) 2024–2025, Daily
+# Copyright (c) 2024, Daily
 #
 # SPDX-License-Identifier: BSD 2-Clause License
 #
@@ -14,7 +14,7 @@ from loguru import logger
 from runner import configure
 
 from pipecat.audio.vad.silero import SileroVADAnalyzer
-from pipecat.frames.frames import EndFrame
+from pipecat.frames.frames import LLMMessagesFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
@@ -91,11 +91,7 @@ async def main():
             await transport.capture_participant_transcription(participant["id"])
             # Kick off the conversation.
             messages.append({"role": "system", "content": "Please introduce yourself to the user."})
-            await task.queue_frames([context_aggregator.user().get_context_frame()])
-
-        @transport.event_handler("on_participant_left")
-        async def on_participant_left(transport, participant, reason):
-            await task.queue_frame(EndFrame())
+            await task.queue_frames([LLMMessagesFrame(messages)])
 
         runner = PipelineRunner()
 

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

@@ -1,5 +1,5 @@
 #
-# Copyright (c) 2024–2025, Daily
+# Copyright (c) 2024, Daily
 #
 # SPDX-License-Identifier: BSD 2-Clause License
 #
@@ -14,7 +14,7 @@ from loguru import logger
 from runner import configure
 
 from pipecat.audio.vad.silero import SileroVADAnalyzer
-from pipecat.frames.frames import EndFrame
+from pipecat.frames.frames import LLMMessagesFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
@@ -82,26 +82,14 @@ async def main():
             ]
         )
 
-        task = PipelineTask(
-            pipeline,
-            PipelineParams(
-                allow_interruptions=True,
-                enable_metrics=True,
-                enable_usage_metrics=True,
-                report_only_initial_ttfb=True,
-            ),
-        )
+        task = PipelineTask(pipeline, PipelineParams(allow_interruptions=True))
 
         @transport.event_handler("on_first_participant_joined")
         async def on_first_participant_joined(transport, participant):
             await transport.capture_participant_transcription(participant["id"])
             # Kick off the conversation.
             messages.append({"role": "system", "content": "Please introduce yourself to the user."})
-            await task.queue_frames([context_aggregator.user().get_context_frame()])
-
-        @transport.event_handler("on_participant_left")
-        async def on_participant_left(transport, participant, reason):
-            await task.queue_frame(EndFrame())
+            await task.queue_frames([LLMMessagesFrame(messages)])
 
         runner = PipelineRunner()
 

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

@@ -1,5 +1,5 @@
 #
-# Copyright (c) 2024–2025, Daily
+# Copyright (c) 2024, Daily
 #
 # SPDX-License-Identifier: BSD 2-Clause License
 #
@@ -14,7 +14,7 @@ from loguru import logger
 from runner import configure
 
 from pipecat.audio.vad.silero import SileroVADAnalyzer
-from pipecat.frames.frames import EndFrame
+from pipecat.frames.frames import LLMMessagesFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
@@ -70,26 +70,14 @@ async def main():
             ]
         )
 
-        task = PipelineTask(
-            pipeline,
-            PipelineParams(
-                allow_interruptions=True,
-                enable_metrics=True,
-                enable_usage_metrics=True,
-                report_only_initial_ttfb=True,
-            ),
-        )
+        task = PipelineTask(pipeline, PipelineParams(allow_interruptions=True))
 
         @transport.event_handler("on_first_participant_joined")
         async def on_first_participant_joined(transport, participant):
             await transport.capture_participant_transcription(participant["id"])
             # Kick off the conversation.
             messages.append({"role": "system", "content": "Please introduce yourself to the user."})
-            await task.queue_frames([context_aggregator.user().get_context_frame()])
-
-        @transport.event_handler("on_participant_left")
-        async def on_participant_left(transport, participant, reason):
-            await task.queue_frame(EndFrame())
+            await task.queue_frames([LLMMessagesFrame(messages)])
 
         runner = PipelineRunner()
 

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

@@ -1,5 +1,5 @@
 #
-# Copyright (c) 2024–2025, Daily
+# Copyright (c) 2024, Daily
 #
 # SPDX-License-Identifier: BSD 2-Clause License
 #
@@ -15,7 +15,7 @@ from loguru import logger
 from runner import configure
 
 from pipecat.audio.vad.silero import SileroVADAnalyzer
-from pipecat.frames.frames import EndFrame
+from pipecat.frames.frames import LLMMessagesFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
@@ -80,26 +80,14 @@ async def main():
             ]
         )
 
-        task = PipelineTask(
-            pipeline,
-            PipelineParams(
-                allow_interruptions=True,
-                enable_metrics=True,
-                enable_usage_metrics=True,
-                report_only_initial_ttfb=True,
-            ),
-        )
+        task = PipelineTask(pipeline, params=PipelineParams(allow_interruptions=True))
 
         @transport.event_handler("on_first_participant_joined")
         async def on_first_participant_joined(transport, participant):
             await transport.capture_participant_transcription(participant["id"])
             # Kick off the conversation.
             messages.append({"role": "system", "content": "Please introduce yourself to the user."})
-            await task.queue_frames([context_aggregator.user().get_context_frame()])
-
-        @transport.event_handler("on_participant_left")
-        async def on_participant_left(transport, participant, reason):
-            await task.queue_frame(EndFrame())
+            await task.queue_frames([LLMMessagesFrame(messages)])
 
         runner = PipelineRunner()
 

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

@@ -1,5 +1,5 @@
 #
-# Copyright (c) 2024–2025, Daily
+# Copyright (c) 2024, Daily
 #
 # SPDX-License-Identifier: BSD 2-Clause License
 #
@@ -14,7 +14,7 @@ from loguru import logger
 from runner import configure
 
 from pipecat.audio.vad.silero import SileroVADAnalyzer
-from pipecat.frames.frames import EndFrame
+from pipecat.frames.frames import LLMMessagesFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
@@ -74,26 +74,14 @@ async def main():
             ]
         )
 
-        task = PipelineTask(
-            pipeline,
-            PipelineParams(
-                allow_interruptions=True,
-                enable_metrics=True,
-                enable_usage_metrics=True,
-                report_only_initial_ttfb=True,
-            ),
-        )
+        task = PipelineTask(pipeline, PipelineParams(allow_interruptions=True))
 
         @transport.event_handler("on_first_participant_joined")
         async def on_first_participant_joined(transport, participant):
             await transport.capture_participant_transcription(participant["id"])
             # Kick off the conversation.
             messages.append({"role": "system", "content": "Please introduce yourself to the user."})
-            await task.queue_frames([context_aggregator.user().get_context_frame()])
-
-        @transport.event_handler("on_participant_left")
-        async def on_participant_left(transport, participant, reason):
-            await task.queue_frame(EndFrame())
+            await task.queue_frames([LLMMessagesFrame(messages)])
 
         runner = PipelineRunner()
 

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

@@ -1,5 +1,5 @@
 #
-# Copyright (c) 2024–2025, Daily
+# Copyright (c) 2024, Daily
 #
 # SPDX-License-Identifier: BSD 2-Clause License
 #
@@ -14,7 +14,7 @@ from loguru import logger
 from runner import configure
 
 from pipecat.audio.vad.silero import SileroVADAnalyzer
-from pipecat.frames.frames import EndFrame
+from pipecat.frames.frames import EndFrame, LLMMessagesFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
@@ -79,22 +79,14 @@ async def main():
             ]
         )
 
-        task = PipelineTask(
-            pipeline,
-            PipelineParams(
-                allow_interruptions=True,
-                enable_metrics=True,
-                enable_usage_metrics=True,
-                report_only_initial_ttfb=True,
-            ),
-        )
+        task = PipelineTask(pipeline, PipelineParams(allow_interruptions=True))
 
         @transport.event_handler("on_first_participant_joined")
         async def on_first_participant_joined(transport, participant):
             await transport.capture_participant_transcription(participant["id"])
             # Kick off the conversation.
             messages.append({"role": "system", "content": "Please introduce yourself to the user."})
-            await task.queue_frames([context_aggregator.user().get_context_frame()])
+            await task.queue_frames([LLMMessagesFrame(messages)])
 
         # Register an event handler to exit the application when the user leaves.
         @transport.event_handler("on_participant_left")

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

@@ -1,5 +1,5 @@
 #
-# Copyright (c) 2024–2025, Daily
+# Copyright (c) 2024, Daily
 #
 # SPDX-License-Identifier: BSD 2-Clause License
 #
@@ -14,7 +14,7 @@ from loguru import logger
 from runner import configure
 
 from pipecat.audio.vad.silero import SileroVADAnalyzer
-from pipecat.frames.frames import EndFrame
+from pipecat.frames.frames import LLMMessagesFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
@@ -71,26 +71,14 @@ async def main():
             ]
         )
 
-        task = PipelineTask(
-            pipeline,
-            PipelineParams(
-                allow_interruptions=True,
-                enable_metrics=True,
-                enable_usage_metrics=True,
-                report_only_initial_ttfb=True,
-            ),
-        )
+        task = PipelineTask(pipeline, PipelineParams(allow_interruptions=True))
 
         @transport.event_handler("on_first_participant_joined")
         async def on_first_participant_joined(transport, participant):
             await transport.capture_participant_transcription(participant["id"])
             # Kick off the conversation.
             messages.append({"role": "system", "content": "Please introduce yourself to the user."})
-            await task.queue_frames([context_aggregator.user().get_context_frame()])
-
-        @transport.event_handler("on_participant_left")
-        async def on_participant_left(transport, participant, reason):
-            await task.queue_frame(EndFrame())
+            await task.queue_frames([LLMMessagesFrame(messages)])
 
         runner = PipelineRunner()
 

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

@@ -1,5 +1,5 @@
 #
-# Copyright (c) 2024–2025, Daily
+# Copyright (c) 2024, Daily
 #
 # SPDX-License-Identifier: BSD 2-Clause License
 #
@@ -14,7 +14,7 @@ from loguru import logger
 from runner import configure
 
 from pipecat.audio.vad.silero import SileroVADAnalyzer
-from pipecat.frames.frames import EndFrame
+from pipecat.frames.frames import LLMMessagesFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
@@ -90,10 +90,7 @@ async def main():
         task = PipelineTask(
             pipeline,
             PipelineParams(
-                allow_interruptions=True,
-                enable_metrics=True,
-                enable_usage_metrics=True,
-                report_only_initial_ttfb=True,
+                allow_interruptions=True, enable_metrics=True, enable_usage_metrics=True
             ),
         )
 
@@ -101,11 +98,7 @@ async def main():
         async def on_first_participant_joined(transport, participant):
             await transport.capture_participant_transcription(participant["id"])
             # Kick off the conversation.
-            await task.queue_frames([context_aggregator.user().get_context_frame()])
-
-        @transport.event_handler("on_participant_left")
-        async def on_participant_left(transport, participant, reason):
-            await task.queue_frame(EndFrame())
+            await task.queue_frames([LLMMessagesFrame(messages)])
 
         runner = PipelineRunner()
 

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

@@ -1,5 +1,5 @@
 #
-# Copyright (c) 2024–2025, Daily
+# Copyright (c) 2024, Daily
 #
 # SPDX-License-Identifier: BSD 2-Clause License
 #
@@ -14,7 +14,7 @@ from loguru import logger
 from runner import configure
 
 from pipecat.audio.vad.silero import SileroVADAnalyzer
-from pipecat.frames.frames import EndFrame
+from pipecat.frames.frames import LLMMessagesFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
@@ -80,26 +80,14 @@ async def main():
             ]
         )
 
-        task = PipelineTask(
-            pipeline,
-            PipelineParams(
-                allow_interruptions=True,
-                enable_metrics=True,
-                enable_usage_metrics=True,
-                report_only_initial_ttfb=True,
-            ),
-        )
+        task = PipelineTask(pipeline, PipelineParams(allow_interruptions=True))
 
         @transport.event_handler("on_first_participant_joined")
         async def on_first_participant_joined(transport, participant):
             await transport.capture_participant_transcription(participant["id"])
             # Kick off the conversation.
             messages.append({"role": "system", "content": "Please introduce yourself to the user."})
-            await task.queue_frames([context_aggregator.user().get_context_frame()])
-
-        @transport.event_handler("on_participant_left")
-        async def on_participant_left(transport, participant, reason):
-            await task.queue_frame(EndFrame())
+            await task.queue_frames([LLMMessagesFrame(messages)])
 
         runner = PipelineRunner()
 

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

@@ -1,5 +1,5 @@
 #
-# Copyright (c) 2024–2025, Daily
+# Copyright (c) 2024, Daily
 #
 # SPDX-License-Identifier: BSD 2-Clause License
 #
@@ -14,7 +14,7 @@ from loguru import logger
 from runner import configure
 
 from pipecat.audio.vad.silero import SileroVADAnalyzer
-from pipecat.frames.frames import EndFrame
+from pipecat.frames.frames import LLMMessagesFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
@@ -22,7 +22,6 @@ from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
 from pipecat.services.deepgram import DeepgramSTTService
 from pipecat.services.google import GoogleTTSService
 from pipecat.services.openai import OpenAILLMService
-from pipecat.transcriptions.language import Language
 from pipecat.transports.services.daily import DailyParams, DailyTransport
 
 load_dotenv(override=True)
@@ -51,8 +50,8 @@ async def main():
         stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
 
         tts = GoogleTTSService(
-            voice_id="en-US-Journey-F",
-            params=GoogleTTSService.InputParams(language=Language.EN_US),
+            voice_id="en-US-Neural2-J",
+            params=GoogleTTSService.InputParams(language="en-US", rate="1.05"),
         )
 
         llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
@@ -79,26 +78,14 @@ async def main():
             ]
         )
 
-        task = PipelineTask(
-            pipeline,
-            PipelineParams(
-                allow_interruptions=True,
-                enable_metrics=True,
-                enable_usage_metrics=True,
-                report_only_initial_ttfb=True,
-            ),
-        )
+        task = PipelineTask(pipeline, PipelineParams(allow_interruptions=True))
 
         @transport.event_handler("on_first_participant_joined")
         async def on_first_participant_joined(transport, participant):
             await transport.capture_participant_transcription(participant["id"])
             # Kick off the conversation.
             messages.append({"role": "system", "content": "Please introduce yourself to the user."})
-            await task.queue_frames([context_aggregator.user().get_context_frame()])
-
-        @transport.event_handler("on_participant_left")
-        async def on_participant_left(transport, participant, reason):
-            await task.queue_frame(EndFrame())
+            await task.queue_frames([LLMMessagesFrame(messages)])
 
         runner = PipelineRunner()
 

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

@@ -1,5 +1,5 @@
 #
-# Copyright (c) 2024–2025, Daily
+# Copyright (c) 2024, Daily
 #
 # SPDX-License-Identifier: BSD 2-Clause License
 #
@@ -14,7 +14,7 @@ from loguru import logger
 from runner import configure
 
 from pipecat.audio.vad.silero import SileroVADAnalyzer
-from pipecat.frames.frames import EndFrame
+from pipecat.frames.frames import LLMMessagesFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
@@ -79,26 +79,14 @@ async def main():
             ]
         )
 
-        task = PipelineTask(
-            pipeline,
-            PipelineParams(
-                allow_interruptions=True,
-                enable_metrics=True,
-                enable_usage_metrics=True,
-                report_only_initial_ttfb=True,
-            ),
-        )
+        task = PipelineTask(pipeline, PipelineParams(allow_interruptions=True))
 
         @transport.event_handler("on_first_participant_joined")
         async def on_first_participant_joined(transport, participant):
             await transport.capture_participant_transcription(participant["id"])
             # Kick off the conversation.
             messages.append({"role": "system", "content": "Please introduce yourself to the user."})
-            await task.queue_frames([context_aggregator.user().get_context_frame()])
-
-        @transport.event_handler("on_participant_left")
-        async def on_participant_left(transport, participant, reason):
-            await task.queue_frame(EndFrame())
+            await task.queue_frames([LLMMessagesFrame(messages)])
 
         runner = PipelineRunner()
 

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

@@ -1,5 +1,5 @@
 #
-# Copyright (c) 2024–2025, Daily
+# Copyright (c) 2024, Daily
 #
 # SPDX-License-Identifier: BSD 2-Clause License
 #
@@ -14,11 +14,14 @@ from loguru import logger
 from runner import configure
 
 from pipecat.audio.filters.krisp_filter import KrispFilter
-from pipecat.frames.frames import EndFrame
+from pipecat.frames.frames import LLMMessagesFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
+from pipecat.processors.aggregators.llm_response import (
+    LLMAssistantResponseAggregator,
+    LLMUserResponseAggregator,
+)
 from pipecat.services.deepgram import DeepgramSTTService, DeepgramTTSService
 from pipecat.services.openai import OpenAILLMService
 from pipecat.transports.services.daily import DailyParams, DailyTransport
@@ -60,40 +63,28 @@ async def main():
             },
         ]
 
-        context = OpenAILLMContext(messages)
-        context_aggregator = llm.create_context_aggregator(context)
+        tma_in = LLMUserResponseAggregator(messages)
+        tma_out = LLMAssistantResponseAggregator(messages)
 
         pipeline = Pipeline(
             [
                 transport.input(),  # Transport user input
                 stt,  # STT
-                context_aggregator.user(),  # User responses
+                tma_in,  # User responses
                 llm,  # LLM
                 tts,  # TTS
                 transport.output(),  # Transport bot output
-                context_aggregator.assistant(),  # Assistant spoken responses
+                tma_out,  # Assistant spoken responses
             ]
         )
 
-        task = PipelineTask(
-            pipeline,
-            PipelineParams(
-                allow_interruptions=True,
-                enable_metrics=True,
-                enable_usage_metrics=True,
-                report_only_initial_ttfb=True,
-            ),
-        )
+        task = PipelineTask(pipeline, PipelineParams(allow_interruptions=True))
 
         @transport.event_handler("on_first_participant_joined")
         async def on_first_participant_joined(transport, participant):
             # Kick off the conversation.
             messages.append({"role": "system", "content": "Please introduce yourself to the user."})
-            await task.queue_frames([context_aggregator.user().get_context_frame()])
-
-        @transport.event_handler("on_participant_left")
-        async def on_participant_left(transport, participant, reason):
-            await task.queue_frame(EndFrame())
+            await task.queue_frames([LLMMessagesFrame(messages)])
 
         runner = PipelineRunner()
 

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

@@ -1,5 +1,5 @@
 #
-# Copyright (c) 2024–2025, Daily
+# Copyright (c) 2024, Daily
 #
 # SPDX-License-Identifier: BSD 2-Clause License
 #
@@ -14,7 +14,7 @@ from loguru import logger
 from runner import configure
 
 from pipecat.audio.vad.silero import SileroVADAnalyzer
-from pipecat.frames.frames import EndFrame
+from pipecat.frames.frames import LLMMessagesFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
@@ -89,11 +89,7 @@ async def main():
             await transport.capture_participant_transcription(participant["id"])
             # Kick off the conversation.
             messages.append({"role": "system", "content": "Please introduce yourself to the user."})
-            await task.queue_frames([context_aggregator.user().get_context_frame()])
-
-        @transport.event_handler("on_participant_left")
-        async def on_participant_left(transport, participant, reason):
-            await task.queue_frame(EndFrame())
+            await task.queue_frames([LLMMessagesFrame(messages)])
 
         runner = PipelineRunner()
 

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

@@ -1,5 +1,5 @@
 #
-# Copyright (c) 2024–2025, Daily
+# Copyright (c) 2024, Daily
 #
 # SPDX-License-Identifier: BSD 2-Clause License
 #
@@ -14,7 +14,7 @@ from loguru import logger
 from runner import configure
 
 from pipecat.audio.vad.silero import SileroVADAnalyzer
-from pipecat.frames.frames import EndFrame
+from pipecat.frames.frames import LLMMessagesFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
@@ -81,11 +81,7 @@ async def main():
         async def on_first_participant_joined(transport, participant):
             # Kick off the conversation.
             messages.append({"role": "system", "content": "Please introduce yourself to the user."})
-            await task.queue_frames([context_aggregator.user().get_context_frame()])
-
-        @transport.event_handler("on_participant_left")
-        async def on_participant_left(transport, participant, reason):
-            await task.queue_frame(EndFrame())
+            await task.queue_frames([LLMMessagesFrame(messages)])
 
         runner = PipelineRunner()
 

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

@@ -1,5 +1,5 @@
 #
-# Copyright (c) 2024–2025, Daily
+# Copyright (c) 2024, Daily
 #
 # SPDX-License-Identifier: BSD 2-Clause License
 #
@@ -17,7 +17,6 @@ from runner import configure
 
 from pipecat.audio.vad.silero import SileroVADAnalyzer
 from pipecat.frames.frames import (
-    EndFrame,
     Frame,
     InputAudioRawFrame,
     LLMFullResponseEndFrame,
@@ -269,10 +268,6 @@ async def main():
             # Kick off the conversation.
             await task.queue_frames([context_aggregator.user().get_context_frame()])
 
-        @transport.event_handler("on_participant_left")
-        async def on_participant_left(transport, participant, reason):
-            await task.queue_frame(EndFrame())
-
         runner = PipelineRunner()
 
         await runner.run(task)

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

@@ -1,5 +1,5 @@
 #
-# Copyright (c) 2024–2025, Daily
+# Copyright (c) 2024, Daily
 #
 # SPDX-License-Identifier: BSD 2-Clause License
 #
@@ -14,7 +14,7 @@ from loguru import logger
 from runner import configure
 
 from pipecat.audio.vad.silero import SileroVADAnalyzer
-from pipecat.frames.frames import EndFrame
+from pipecat.frames.frames import LLMMessagesFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
@@ -88,11 +88,7 @@ async def main():
             await transport.capture_participant_transcription(participant["id"])
             # Kick off the conversation.
             messages.append({"role": "system", "content": "Please introduce yourself to the user."})
-            await task.queue_frames([context_aggregator.user().get_context_frame()])
-
-        @transport.event_handler("on_participant_left")
-        async def on_participant_left(transport, participant, reason):
-            await task.queue_frame(EndFrame())
+            await task.queue_frames([LLMMessagesFrame(messages)])
 
         runner = PipelineRunner()
 

examples/foundational/09-mirror.py

@@ -1,5 +1,5 @@
 #
-# Copyright (c) 2024–2025, Daily
+# Copyright (c) 2024, Daily
 #
 # SPDX-License-Identifier: BSD 2-Clause License
 #

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

@@ -1,5 +1,5 @@
 #
-# Copyright (c) 2024–2025, Daily
+# Copyright (c) 2024, Daily
 #
 # SPDX-License-Identifier: BSD 2-Clause License
 #

examples/foundational/10-wake-phrase.py

@@ -1,5 +1,5 @@
 #
-# Copyright (c) 2024–2025, Daily
+# Copyright (c) 2024, Daily
 #
 # SPDX-License-Identifier: BSD 2-Clause License
 #

examples/foundational/11-sound-effects.py

@@ -1,5 +1,5 @@
 #
-# Copyright (c) 2024–2025, Daily
+# Copyright (c) 2024, Daily
 #
 # SPDX-License-Identifier: BSD 2-Clause License
 #

examples/foundational/12-describe-video.py

@@ -1,5 +1,5 @@
 #
-# Copyright (c) 2024–2025, Daily
+# Copyright (c) 2024, Daily
 #
 # SPDX-License-Identifier: BSD 2-Clause License
 #

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

@@ -1,5 +1,5 @@
 #
-# Copyright (c) 2024–2025, Daily
+# Copyright (c) 2024, Daily
 #
 # SPDX-License-Identifier: BSD 2-Clause License
 #

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

@@ -1,5 +1,5 @@
 #
-# Copyright (c) 2024–2025, Daily
+# Copyright (c) 2024, Daily
 #
 # SPDX-License-Identifier: BSD 2-Clause License
 #

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

@@ -1,5 +1,5 @@
 #
-# Copyright (c) 2024–2025, Daily
+# Copyright (c) 2024, Daily
 #
 # SPDX-License-Identifier: BSD 2-Clause License
 #

examples/foundational/13-whisper-transcription.py

@@ -1,5 +1,5 @@
 #
-# Copyright (c) 2024–2025, Daily
+# Copyright (c) 2024, Daily
 #
 # SPDX-License-Identifier: BSD 2-Clause License
 #

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

@@ -1,5 +1,5 @@
 #
-# Copyright (c) 2024–2025, Daily
+# Copyright (c) 2024, Daily
 #
 # SPDX-License-Identifier: BSD 2-Clause License
 #

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

@@ -1,5 +1,5 @@
 #
-# Copyright (c) 2024–2025, Daily
+# Copyright (c) 2024, Daily
 #
 # SPDX-License-Identifier: BSD 2-Clause License
 #

examples/foundational/13c-gladia-transcription.py

@@ -1,5 +1,5 @@
 #
-# Copyright (c) 2024–2025, Daily
+# Copyright (c) 2024, Daily
 #
 # SPDX-License-Identifier: BSD 2-Clause License
 #

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

@@ -1,5 +1,5 @@
 #
-# Copyright (c) 2024–2025, Daily
+# Copyright (c) 2024, Daily
 #
 # SPDX-License-Identifier: BSD 2-Clause License
 #

examples/foundational/14-function-calling.py

@@ -1,5 +1,5 @@
 #
-# Copyright (c) 2024–2025, Daily
+# Copyright (c) 2024, Daily
 #
 # SPDX-License-Identifier: BSD 2-Clause License
 #

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

@@ -1,5 +1,5 @@
 #
-# Copyright (c) 2024–2025, Daily
+# Copyright (c) 2024, Daily
 #
 # SPDX-License-Identifier: BSD 2-Clause License
 #

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

@@ -1,5 +1,5 @@
 #
-# Copyright (c) 2024–2025, Daily
+# Copyright (c) 2024, Daily
 #
 # SPDX-License-Identifier: BSD 2-Clause License
 #

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

@@ -1,5 +1,5 @@
 #
-# Copyright (c) 2024–2025, Daily
+# Copyright (c) 2024, Daily
 #
 # SPDX-License-Identifier: BSD 2-Clause License
 #

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

@@ -1,5 +1,5 @@
 #
-# Copyright (c) 2024–2025, Daily
+# Copyright (c) 2024, Daily
 #
 # SPDX-License-Identifier: BSD 2-Clause License
 #

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

@@ -1,5 +1,5 @@
 #
-# Copyright (c) 2024–2025, Daily
+# Copyright (c) 2024, Daily
 #
 # SPDX-License-Identifier: BSD 2-Clause License
 #

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

@@ -1,5 +1,5 @@
 #
-# Copyright (c) 2024–2025, Daily
+# Copyright (c) 2024, Daily
 #
 # SPDX-License-Identifier: BSD 2-Clause License
 #

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

@@ -1,5 +1,5 @@
 #
-# Copyright (c) 2024–2025, Daily
+# Copyright (c) 2024, Daily
 #
 # SPDX-License-Identifier: BSD 2-Clause License
 #

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

@@ -1,5 +1,5 @@
 #
-# Copyright (c) 2024–2025, Daily
+# Copyright (c) 2024, Daily
 #
 # SPDX-License-Identifier: BSD 2-Clause License
 #

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

@@ -1,5 +1,5 @@
 #
-# Copyright (c) 2024–2025, Daily
+# Copyright (c) 2024, Daily
 #
 # SPDX-License-Identifier: BSD 2-Clause License
 #

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

@@ -1,5 +1,5 @@
 #
-# Copyright (c) 2024–2025, Daily
+# Copyright (c) 2024, Daily
 #
 # SPDX-License-Identifier: BSD 2-Clause License
 #

examples/foundational/14k-function-calling-cerebras.py

@@ -1,5 +1,5 @@
 #
-# Copyright (c) 2024–2025, Daily
+# Copyright (c) 2024, Daily
 #
 # SPDX-License-Identifier: BSD 2-Clause License
 #

examples/foundational/14l-function-calling-deepseek.py

@@ -1,148 +0,0 @@
-#
-# Copyright (c) 2025, Daily
-#
-# SPDX-License-Identifier: BSD 2-Clause License
-#
-
-import asyncio
-import os
-import sys
-
-import aiohttp
-from dotenv import load_dotenv
-from loguru import logger
-from openai.types.chat import ChatCompletionToolParam
-from runner import configure
-
-from pipecat.audio.vad.silero import SileroVADAnalyzer
-from pipecat.pipeline.pipeline import Pipeline
-from pipecat.pipeline.runner import PipelineRunner
-from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.services.cartesia import CartesiaTTSService
-from pipecat.services.deepseek import DeepSeekLLMService
-from pipecat.services.openai import OpenAILLMContext
-from pipecat.transports.services.daily import DailyParams, DailyTransport
-
-load_dotenv(override=True)
-
-logger.remove(0)
-logger.add(sys.stderr, level="DEBUG")
-
-
-async def start_fetch_weather(function_name, llm, context):
-    # note: we can't push a frame to the LLM here. the bot
-    # can interrupt itself and/or cause audio overlapping glitches.
-    # possible question for Aleix and Chad about what the right way
-    # to trigger speech is, now, with the new queues/async/sync refactors.
-    # await llm.push_frame(TextFrame("Let me check on that."))
-    logger.debug(f"Starting fetch_weather_from_api with function_name: {function_name}")
-
-
-async def fetch_weather_from_api(function_name, tool_call_id, args, llm, context, result_callback):
-    await result_callback({"conditions": "nice", "temperature": "75"})
-
-
-async def main():
-    async with aiohttp.ClientSession() as session:
-        (room_url, token) = await configure(session)
-
-        transport = DailyTransport(
-            room_url,
-            token,
-            "Respond bot",
-            DailyParams(
-                audio_out_enabled=True,
-                transcription_enabled=True,
-                vad_enabled=True,
-                vad_analyzer=SileroVADAnalyzer(),
-            ),
-        )
-
-        tts = CartesiaTTSService(
-            api_key=os.getenv("CARTESIA_API_KEY"),
-            voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22",  # British Lady
-        )
-
-        llm = DeepSeekLLMService(api_key=os.getenv("DEEPSEEK_API_KEY"), model="deepseek-chat")
-        # Register a function_name of None to get all functions
-        # sent to the same callback with an additional function_name parameter.
-        llm.register_function(None, fetch_weather_from_api, start_callback=start_fetch_weather)
-
-        tools = [
-            ChatCompletionToolParam(
-                type="function",
-                function={
-                    "name": "get_current_weather",
-                    "description": "Get the current weather for a specific location. You MUST use this function whenever asked about weather.",
-                    "parameters": {
-                        "type": "object",
-                        "properties": {
-                            "location": {
-                                "type": "string",
-                                "description": "The city and state, e.g. San Francisco, CA",
-                            },
-                            "format": {
-                                "type": "string",
-                                "enum": ["celsius", "fahrenheit"],
-                                "description": "The temperature unit to use. Use fahrenheit for US locations, celsius for others.",
-                            },
-                        },
-                        "required": ["location", "format"],
-                    },
-                },
-            )
-        ]
-        messages = [
-            {
-                "role": "system",
-                "content": """You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. 
-
-You have one functions available:
-
-1. get_current_weather is used to get current weather information.
-
-Infer whether to use Fahrenheit or Celsius automatically based on the location, unless the user specifies a preference.
-
-Start by asking me for my location. Then, use 'get_weather_current' to give me a forecast.
-
-        Respond to what the user said in a creative and helpful way.""",
-            },
-        ]
-
-        context = OpenAILLMContext(messages, tools)
-        context_aggregator = llm.create_context_aggregator(context)
-
-        pipeline = Pipeline(
-            [
-                transport.input(),
-                context_aggregator.user(),
-                llm,
-                tts,
-                transport.output(),
-                context_aggregator.assistant(),
-            ]
-        )
-
-        task = PipelineTask(
-            pipeline,
-            PipelineParams(
-                allow_interruptions=True,
-                enable_metrics=True,
-                enable_usage_metrics=True,
-                report_only_initial_ttfb=True,
-            ),
-        )
-
-        @transport.event_handler("on_first_participant_joined")
-        async def on_first_participant_joined(transport, participant):
-            await transport.capture_participant_transcription(participant["id"])
-            # Kick off the conversation.
-            await task.queue_frames([context_aggregator.user().get_context_frame()])
-
-        runner = PipelineRunner()
-
-        await runner.run(task)
-
-
-if __name__ == "__main__":
-    asyncio.run(main())

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

@@ -1,142 +0,0 @@
-#
-# Copyright (c) 2024–2025, Daily
-#
-# SPDX-License-Identifier: BSD 2-Clause License
-#
-
-import asyncio
-import os
-import sys
-
-import aiohttp
-from dotenv import load_dotenv
-from loguru import logger
-from openai.types.chat import ChatCompletionToolParam
-from runner import configure
-
-from pipecat.audio.vad.silero import SileroVADAnalyzer
-from pipecat.pipeline.pipeline import Pipeline
-from pipecat.pipeline.runner import PipelineRunner
-from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
-from pipecat.services.azure import AzureTTSService
-from pipecat.services.openrouter import OpenRouterLLMService
-from pipecat.transports.services.daily import DailyParams, DailyTransport
-
-load_dotenv(override=True)
-
-logger.remove(0)
-logger.add(sys.stderr, level="DEBUG")
-
-
-async def start_fetch_weather(function_name, llm, context):
-    # note: we can't push a frame to the LLM here. the bot
-    # can interrupt itself and/or cause audio overlapping glitches.
-    # possible question for Aleix and Chad about what the right way
-    # to trigger speech is, now, with the new queues/async/sync refactors.
-    # await llm.push_frame(TextFrame("Let me check on that."))
-    logger.debug(f"Starting fetch_weather_from_api with function_name: {function_name}")
-
-
-async def fetch_weather_from_api(function_name, tool_call_id, args, llm, context, result_callback):
-    await result_callback({"conditions": "nice", "temperature": "75"})
-
-
-async def main():
-    async with aiohttp.ClientSession() as session:
-        (room_url, token) = await configure(session)
-
-        transport = DailyTransport(
-            room_url,
-            token,
-            "Respond bot",
-            DailyParams(
-                audio_out_enabled=True,
-                transcription_enabled=True,
-                vad_enabled=True,
-                vad_analyzer=SileroVADAnalyzer(),
-            ),
-        )
-
-        tts = AzureTTSService(
-            api_key=os.getenv("AZURE_API_KEY"),
-            region="eastus",
-            voice="en-US-JennyNeural",
-            params=AzureTTSService.InputParams(language="en-US", rate="1.1", style="cheerful"),
-        )
-
-        llm = OpenRouterLLMService(
-            api_key=os.getenv("OPENROUTER_API_KEY"), model="openai/gpt-4o-2024-11-20"
-        )
-        # Register a function_name of None to get all functions
-        # sent to the same callback with an additional function_name parameter.
-        llm.register_function(None, fetch_weather_from_api, start_callback=start_fetch_weather)
-
-        tools = [
-            ChatCompletionToolParam(
-                type="function",
-                function={
-                    "name": "get_current_weather",
-                    "description": "Get the current weather",
-                    "parameters": {
-                        "type": "object",
-                        "properties": {
-                            "location": {
-                                "type": "string",
-                                "description": "The city and state, e.g. San Francisco, CA",
-                            },
-                            "format": {
-                                "type": "string",
-                                "enum": ["celsius", "fahrenheit"],
-                                "description": "The temperature unit to use. Infer this from the users location.",
-                            },
-                        },
-                        "required": ["location", "format"],
-                    },
-                },
-            )
-        ]
-        messages = [
-            {
-                "role": "system",
-                "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
-            },
-        ]
-
-        context = OpenAILLMContext(messages, tools)
-        context_aggregator = llm.create_context_aggregator(context)
-
-        pipeline = Pipeline(
-            [
-                transport.input(),
-                context_aggregator.user(),
-                llm,
-                tts,
-                transport.output(),
-                context_aggregator.assistant(),
-            ]
-        )
-
-        task = PipelineTask(
-            pipeline,
-            PipelineParams(
-                allow_interruptions=True,
-                enable_metrics=True,
-                enable_usage_metrics=True,
-                report_only_initial_ttfb=True,
-            ),
-        )
-
-        @transport.event_handler("on_first_participant_joined")
-        async def on_first_participant_joined(transport, participant):
-            await transport.capture_participant_transcription(participant["id"])
-            # Kick off the conversation.
-            await task.queue_frames([context_aggregator.user().get_context_frame()])
-
-        runner = PipelineRunner()
-
-        await runner.run(task)
-
-
-if __name__ == "__main__":
-    asyncio.run(main())

examples/foundational/15-switch-voices.py

@@ -1,5 +1,5 @@
 #
-# Copyright (c) 2024–2025, Daily
+# Copyright (c) 2024, Daily
 #
 # SPDX-License-Identifier: BSD 2-Clause License
 #
@@ -15,6 +15,7 @@ from openai.types.chat import ChatCompletionToolParam
 from runner import configure
 
 from pipecat.audio.vad.silero import SileroVADAnalyzer
+from pipecat.frames.frames import LLMMessagesFrame
 from pipecat.pipeline.parallel_pipeline import ParallelPipeline
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
@@ -145,7 +146,7 @@ async def main():
                     "content": f"Please introduce yourself to the user and let them know the voices you can do. Your initial responses should be as if you were a {current_voice}.",
                 }
             )
-            await task.queue_frames([context_aggregator.user().get_context_frame()])
+            await task.queue_frames([LLMMessagesFrame(messages)])
 
         runner = PipelineRunner()
 

examples/foundational/15a-switch-languages.py

@@ -1,5 +1,5 @@
 #
-# Copyright (c) 2024–2025, Daily
+# Copyright (c) 2024, Daily
 #
 # SPDX-License-Identifier: BSD 2-Clause License
 #
@@ -16,6 +16,7 @@ from openai.types.chat import ChatCompletionToolParam
 from runner import configure
 
 from pipecat.audio.vad.silero import SileroVADAnalyzer
+from pipecat.frames.frames import LLMMessagesFrame
 from pipecat.pipeline.parallel_pipeline import ParallelPipeline
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
@@ -138,7 +139,7 @@ async def main():
                     "content": f"Please introduce yourself to the user and let them know the languages you speak. Your initial responses should be in {current_language}.",
                 }
             )
-            await task.queue_frames([context_aggregator.user().get_context_frame()])
+            await task.queue_frames([LLMMessagesFrame(messages)])
 
         runner = PipelineRunner()
 

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

@@ -1,5 +1,5 @@
 #
-# Copyright (c) 2024–2025, Daily
+# Copyright (c) 2024, Daily
 #
 # SPDX-License-Identifier: BSD 2-Clause License
 #
@@ -14,6 +14,7 @@ from loguru import logger
 from runner import configure
 
 from pipecat.audio.vad.silero import SileroVADAnalyzer
+from pipecat.frames.frames import LLMMessagesFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
@@ -97,7 +98,7 @@ async def main():
         @transport.event_handler("on_first_participant_joined")
         async def on_first_participant_joined(transport, participant):
             messages.append({"role": "system", "content": "Please introduce yourself to the user."})
-            await task.queue_frames([context_aggregator.user().get_context_frame()])
+            await task.queue_frames([LLMMessagesFrame(messages)])
 
         # Handle "latency-ping" messages. The client will send app messages that look like
         # this:

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

@@ -1,5 +1,5 @@
 #
-# Copyright (c) 2024–2025, Daily
+# Copyright (c) 2024, Daily
 #
 # SPDX-License-Identifier: BSD 2-Clause License
 #
@@ -100,7 +100,7 @@ async def main():
             await transport.capture_participant_transcription(participant["id"])
             # Kick off the conversation.
             messages.append({"role": "system", "content": "Please introduce yourself to the user."})
-            await task.queue_frames([context_aggregator.user().get_context_frame()])
+            await task.queue_frames([LLMMessagesFrame(messages)])
 
         runner = PipelineRunner()
 

examples/foundational/18-gstreamer-filesrc.py

@@ -1,5 +1,5 @@
 #
-# Copyright (c) 2024–2025, Daily
+# Copyright (c) 2024, Daily
 #
 # SPDX-License-Identifier: BSD 2-Clause License
 #

examples/foundational/18a-gstreamer-videotestsrc.py

@@ -1,5 +1,5 @@
 #
-# Copyright (c) 2024–2025, Daily
+# Copyright (c) 2024, Daily
 #
 # SPDX-License-Identifier: BSD 2-Clause License
 #

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

@@ -1,5 +1,5 @@
 #
-# Copyright (c) 2024–2025, Daily
+# Copyright (c) 2024, Daily
 #
 # SPDX-License-Identifier: BSD 2-Clause License
 #

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

@@ -1,5 +1,5 @@
 #
-# Copyright (c) 2024–2025, Daily
+# Copyright (c) 2024, Daily
 #
 # SPDX-License-Identifier: BSD 2-Clause License
 #

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

@@ -1,5 +1,5 @@
 #
-# Copyright (c) 2024–2025, Daily
+# Copyright (c) 2024, Daily
 #
 # SPDX-License-Identifier: BSD 2-Clause License
 #

examples/foundational/20c-persistent-context-anthropic.py

@@ -1,5 +1,5 @@
 #
-# Copyright (c) 2024–2025, Daily
+# Copyright (c) 2024, Daily
 #
 # SPDX-License-Identifier: BSD 2-Clause License
 #

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

@@ -1,5 +1,5 @@
 #
-# Copyright (c) 2024–2025, Daily
+# Copyright (c) 2024, Daily
 #
 # SPDX-License-Identifier: BSD 2-Clause License
 #

examples/foundational/21-tavus-layer.py

@@ -1,5 +1,5 @@
 #
-# Copyright (c) 2024–2025, Daily
+# Copyright (c) 2024, Daily
 #
 # SPDX-License-Identifier: BSD 2-Clause License
 #
@@ -14,11 +14,14 @@ from dotenv import load_dotenv
 from loguru import logger
 
 from pipecat.audio.vad.silero import SileroVADAnalyzer
-from pipecat.frames.frames import EndFrame
+from pipecat.frames.frames import LLMMessagesFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
+from pipecat.processors.aggregators.llm_response import (
+    LLMAssistantResponseAggregator,
+    LLMUserResponseAggregator,
+)
 from pipecat.services.cartesia import CartesiaTTSService
 from pipecat.services.deepgram import DeepgramSTTService
 from pipecat.services.openai import OpenAILLMService
@@ -36,6 +39,7 @@ async def main():
         tavus = TavusVideoService(
             api_key=os.getenv("TAVUS_API_KEY"),
             replica_id=os.getenv("TAVUS_REPLICA_ID"),
+            persona_id=os.getenv("TAVUS_PERSONA_ID", "pipecat0"),
             session=session,
         )
 
@@ -70,19 +74,19 @@ async def main():
             },
         ]
 
-        context = OpenAILLMContext(messages)
-        context_aggregator = llm.create_context_aggregator(context)
+        tma_in = LLMUserResponseAggregator(messages)
+        tma_out = LLMAssistantResponseAggregator(messages)
 
         pipeline = Pipeline(
             [
                 transport.input(),  # Transport user input
                 stt,  # STT
-                context_aggregator.user(),  # User responses
+                tma_in,  # User responses
                 llm,  # LLM
                 tts,  # TTS
                 tavus,  # Tavus output layer
                 transport.output(),  # Transport bot output
-                context_aggregator.assistant(),  # Assistant spoken responses
+                tma_out,  # Assistant spoken responses
             ]
         )
 
@@ -116,11 +120,7 @@ async def main():
                 messages.append(
                     {"role": "system", "content": "Please introduce yourself to the user."}
                 )
-                await task.queue_frames([context_aggregator.user().get_context_frame()])
-
-        @transport.event_handler("on_participant_left")
-        async def on_participant_left(transport, participant, reason):
-            await task.queue_frame(EndFrame())
+                await task.queue_frames([LLMMessagesFrame(messages)])
 
         runner = PipelineRunner()
 

examples/foundational/22-natural-conversation.py

@@ -1,5 +1,5 @@
 #
-# Copyright (c) 2024–2025, Daily
+# Copyright (c) 2024, Daily
 #
 # SPDX-License-Identifier: BSD 2-Clause License
 #
@@ -14,7 +14,7 @@ from loguru import logger
 from runner import configure
 
 from pipecat.audio.vad.silero import SileroVADAnalyzer
-from pipecat.frames.frames import TextFrame
+from pipecat.frames.frames import LLMMessagesFrame, TextFrame
 from pipecat.pipeline.parallel_pipeline import ParallelPipeline
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
@@ -155,7 +155,7 @@ async def main():
             await transport.capture_participant_transcription(participant["id"])
             # Kick off the conversation.
             messages.append({"role": "system", "content": "Please introduce yourself to the user."})
-            await task.queue_frames([context_aggregator.user().get_context_frame()])
+            await task.queue_frames([LLMMessagesFrame(messages)])
 
         runner = PipelineRunner()
 

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

@@ -1,5 +1,5 @@
 #
-# Copyright (c) 2024–2025, Daily
+# Copyright (c) 2024, Daily
 #
 # SPDX-License-Identifier: BSD 2-Clause License
 #
@@ -311,7 +311,7 @@ async def main():
             await transport.capture_participant_transcription(participant["id"])
             # Kick off the conversation.
             messages.append({"role": "system", "content": "Please introduce yourself to the user."})
-            await task.queue_frames([context_aggregator.user().get_context_frame()])
+            await task.queue_frames([LLMMessagesFrame(messages)])
 
         @transport.event_handler("on_app_message")
         async def on_app_message(transport, message, sender):

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

@@ -1,5 +1,5 @@
 #
-# Copyright (c) 2024–2025, Daily
+# Copyright (c) 2024, Daily
 #
 # SPDX-License-Identifier: BSD 2-Clause License
 #
@@ -525,7 +525,7 @@ async def main():
                     "content": "Start by just saying \"Hello I'm ready.\" Don't say anything else.",
                 }
             )
-            await task.queue_frames([context_aggregator.user().get_context_frame()])
+            await task.queue_frames([LLMMessagesFrame(messages)])
 
         @transport.event_handler("on_app_message")
         async def on_app_message(transport, message, sender):

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

@@ -1,5 +1,5 @@
 #
-# Copyright (c) 2024–2025, Daily
+# Copyright (c) 2024, Daily
 #
 # SPDX-License-Identifier: BSD 2-Clause License
 #
@@ -10,7 +10,6 @@ import sys
 import time
 
 import aiohttp
-import google.ai.generativelanguage as glm
 from dotenv import load_dotenv
 from loguru import logger
 from runner import configure
@@ -21,8 +20,6 @@ from pipecat.frames.frames import (
     EndFrame,
     Frame,
     InputAudioRawFrame,
-    LLMFullResponseEndFrame,
-    LLMFullResponseStartFrame,
     LLMMessagesFrame,
     StartFrame,
     StartInterruptionFrame,
@@ -37,7 +34,6 @@ from pipecat.pipeline.parallel_pipeline import ParallelPipeline
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.aggregators.llm_response import LLMResponseAggregator
 from pipecat.processors.aggregators.openai_llm_context import (
     OpenAILLMContext,
     OpenAILLMContextFrame,
@@ -57,321 +53,39 @@ load_dotenv(override=True)
 logger.remove(0)
 logger.add(sys.stderr, level="DEBUG")
 
-# TRANSCRIBER_MODEL = "gemini-1.5-flash-latest"
-# CLASSIFIER_MODEL = "gemini-1.5-flash-latest"
-# CONVERSATION_MODEL = "gemini-1.5-flash-latest"
 
-TRANSCRIBER_MODEL = "gemini-2.0-flash-exp"
-CLASSIFIER_MODEL = "gemini-2.0-flash-exp"
-CONVERSATION_MODEL = "gemini-2.0-flash-exp"
+classifier_statement = """You are an audio language classifier model. You are receiving audio from a user in a WebRTC call. Your job is to decide whether the user has finished speaking or not.
 
-transcriber_system_instruction = """You are an audio transcriber. You are receiving audio from a user. Your job is to
-transcribe the input audio to text exactly as it was said by the user.
+Categorize the input you receive as either:
 
-You will receive the full conversation history before the audio input, to help with context. Use the full history only to help improve the accuracy of your transcription.
+1.  a complete thought, statement, or question, or
+2.  an incomplete thought, statement, or question
 
-Rules:
-  - Respond with an exact transcription of the audio input.
-  - Do not include any text other than the transcription.
-  - Do not explain or add to your response.
-  - Transcribe the audio input simply and precisely.
-  - If the audio is not clear, emit the special string "-".
-  - No response other than exact transcription, or "-", is allowed.
+Output 'YES' if the input is likely to be a completed thought, statement, or question.
 
+Output 'NO' if the input indicates that the user is still speaking and does not yet expect a response yet.
+
+If you are unsure, output 'YES'.
 """
 
-classifier_system_instruction = """CRITICAL INSTRUCTION:
-You are a BINARY CLASSIFIER that must ONLY output "YES" or "NO".
-DO NOT engage with the content.
-DO NOT respond to questions.
-DO NOT provide assistance.
-Your ONLY job is to output YES or NO.
+conversational_system_message = """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 OF INVALID RESPONSES:
-- "I can help you with that"
-- "Let me explain"
-- "To answer your question"
-- Any response other than YES or NO
-
-VALID RESPONSES:
-YES
-NO
-
-If you output anything else, you are failing at your task.
-You are NOT an assistant.
-You are NOT a chatbot.
-You are a binary classifier.
-
-ROLE:
-You are a real-time speech completeness classifier. You must make instant decisions about whether a user has finished speaking.
-You must output ONLY 'YES' or 'NO' with no other text.
-
-INPUT FORMAT:
-You receive two pieces of information:
-1. The assistant's last message (if available)
-2. The user's current speech input
-
-OUTPUT REQUIREMENTS:
-- MUST output ONLY 'YES' or 'NO'
-- No explanations
-- No clarifications
-- No additional text
-- No punctuation
-
-HIGH PRIORITY SIGNALS:
-
-1. Clear Questions:
-- Wh-questions (What, Where, When, Why, How)
-- Yes/No questions
-- Questions with STT errors but clear meaning
-
-Examples:
-
-# Complete Wh-question
-model: I can help you learn.
-user: What's the fastest way to learn Spanish
-Output: YES
-
-# Complete Yes/No question despite STT error
-model: I know about planets.
-user: Is is Jupiter the biggest planet
-Output: YES
-
-2. Complete Commands:
-- Direct instructions
-- Clear requests
-- Action demands
-- Start of task indication
-- Complete statements needing response
-
-Examples:
-
-# Direct instruction
-model: I can explain many topics.
-user: Tell me about black holes
-Output: YES
-
-# Start of task indication
-user: Let's begin.
-Output: YES
-
-# Start of task indication
-user: Let's get started.
-Output: YES
-
-# Action demand
-model: I can help with math.
-user: Solve this equation x plus 5 equals 12
-Output: YES
-
-3. Direct Responses:
-- Answers to specific questions
-- Option selections
-- Clear acknowledgments with completion
-- Providing information with a known format - mailing address
-- Providing information with a known format - phone number
-- Providing information with a known format - credit card number
-
-Examples:
-
-# Specific answer
-model: What's your favorite color?
-user: I really like blue
-Output: YES
-
-# Option selection
-model: Would you prefer morning or evening?
-user: Morning
-Output: YES
-
-# Providing information with a known format - mailing address
-model: What's your address?
-user: 1234 Main Street
-Output: NO
-
-# Providing information with a known format - mailing address
-model: What's your address?
-user: 1234 Main Street Irving Texas 75063
-Output: Yes
-
-# Providing information with a known format - phone number
-model: What's your phone number?
-user: 41086753
-Output: NO
-
-# Providing information with a known format - phone number
-model: What's your phone number?
-user: 4108675309
-Output: Yes
-
-# Providing information with a known format - phone number
-model: What's your phone number?
-user: 220
-Output: No
-
-# Providing information with a known format - credit card number
-model: What's your credit card number?
-user: 5556
-Output: NO
-
-# Providing information with a known format - phone number
-model: What's your credit card number?
-user: 5556710454680800
-Output: Yes
-
-model: What's your credit card number?
-user: 414067
-Output: NO
-
-
-MEDIUM PRIORITY SIGNALS:
-
-1. Speech Pattern Completions:
-- Self-corrections reaching completion
-- False starts with clear ending
-- Topic changes with complete thought
-- Mid-sentence completions
-
-Examples:
-
-# Self-correction reaching completion
-model: What would you like to know?
-user: Tell me about... no wait, explain how rainbows form
-Output: YES
-
-# Topic change with complete thought
-model: The weather is nice today.
-user: Actually can you tell me who invented the telephone
-Output: YES
-
-# Mid-sentence completion
-model: Hello I'm ready.
-user: What's the capital of? France
-Output: YES
-
-2. Context-Dependent Brief Responses:
-- Acknowledgments (okay, sure, alright)
-- Agreements (yes, yeah)
-- Disagreements (no, nah)
-- Confirmations (correct, exactly)
-
-Examples:
-
-# Acknowledgment
-model: Should we talk about history?
-user: Sure
-Output: YES
-
-# Disagreement with completion
-model: Is that what you meant?
-user: No not really
-Output: YES
-
-LOW PRIORITY SIGNALS:
-
-1. STT Artifacts (Consider but don't over-weight):
-- Repeated words
-- Unusual punctuation
-- Capitalization errors
-- Word insertions/deletions
-
-Examples:
-
-# Word repetition but complete
-model: I can help with that.
-user: What what is the time right now
-Output: YES
-
-# Missing punctuation but complete
-model: I can explain that.
-user: Please tell me how computers work
-Output: YES
-
-2. Speech Features:
-- Filler words (um, uh, like)
-- Thinking pauses
-- Word repetitions
-- Brief hesitations
-
-Examples:
-
-# Filler words but complete
-model: What would you like to know?
-user: Um uh how do airplanes fly
-Output: YES
-
-# Thinking pause but incomplete
-model: I can explain anything.
-user: Well um I want to know about the
-Output: NO
-
-DECISION RULES:
-
-1. Return YES if:
-- ANY high priority signal shows clear completion
-- Medium priority signals combine to show completion
-- Meaning is clear despite low priority artifacts
-
-2. Return NO if:
-- No high priority signals present
-- Thought clearly trails off
-- Multiple incomplete indicators
-- User appears mid-formulation
-
-3. When uncertain:
-- If you can understand the intent → YES
-- If meaning is unclear → NO
-- Always make a binary decision
-- Never request clarification
-
-Examples:
-
-# Incomplete despite corrections
-model: What would you like to know about?
-user: Can you tell me about
-Output: NO
-
-# Complete despite multiple artifacts
-model: I can help you learn.
-user: How do you I mean what's the best way to learn programming
-Output: YES
-
-# Trailing off incomplete
-model: I can explain anything.
-user: I was wondering if you could tell me why
-Output: NO
-"""
-
-conversation_system_instruction = """You are a helpful assistant participating in a voice converation.
-
-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.
-
-If you know that a number string is a phone number from the context of the conversation, write it as a phone number. For example 210-333-4567.
-
-If you know that a number string is a credit card number, write it as a credit card number. For example 4111-1111-1111-1111.
-
-Please be very concise in your responses. Unless you are explicitly asked to do otherwise, give me shortest complete answer possible without unnecessary elaboration. Generally you should answer with a single sentence.
+Please be very concise in your responses. Unless you are explicitly asked to do otherwise, give me the shortest complete answer possible without unnecessary elaboration. Generally you should answer with a single sentence.
 """
 
 
-class AudioAccumulator(FrameProcessor):
-    """Buffers user audio until the user stops speaking.
-
-    Always pushes a fresh context with a single audio message.
-    """
-
-    def __init__(self, **kwargs):
+class StatementJudgeAudioContextAccumulator(FrameProcessor):
+    def __init__(self, *, notifier: BaseNotifier, **kwargs):
         super().__init__(**kwargs)
+        self._notifier = notifier
+        self._audio_frames = []
         self._audio_frames = []
         self._start_secs = 0.2  # this should match VAD start_secs (hardcoding for now)
-        self._max_buffer_size_secs = 30
-        self._user_speaking_vad_state = False
-        self._user_speaking_utterance_state = False
+        self._user_speaking = False
 
     async def reset(self):
         self._audio_frames = []
-        self._user_speaking_vad_state = False
-        self._user_speaking_utterance_state = False
+        self._user_speaking = False
 
     async def process_frame(self, frame: Frame, direction: FrameDirection):
         await super().process_frame(frame, direction)
@@ -385,33 +99,22 @@ class AudioAccumulator(FrameProcessor):
             # but let's leave that as an exercise to the reader. :-)
             return
         if isinstance(frame, UserStartedSpeakingFrame):
-            self._user_speaking_vad_state = True
-            self._user_speaking_utterance_state = True
-
+            self._user_speaking = True
         elif isinstance(frame, UserStoppedSpeakingFrame):
-            data = b"".join(frame.audio for frame in self._audio_frames)
-            logger.debug(
-                f"Processing audio buffer seconds: ({len(self._audio_frames)}) ({len(data)}) {len(data) / 2 / 16000}"
-            )
             self._user_speaking = False
             context = GoogleLLMContext()
-            context.add_audio_frames_message(text="Audio follows", audio_frames=self._audio_frames)
+            context.set_messages([{"role": "system", "content": classifier_statement}])
+            context.add_audio_frames_message(audio_frames=self._audio_frames)
             await self.push_frame(OpenAILLMContextFrame(context=context))
         elif isinstance(frame, InputAudioRawFrame):
-            # Append the audio frame to our buffer. Treat the buffer as a ring buffer, dropping the oldest
-            # frames as necessary.
-            # Use a small buffer size when an utterance is not in progress. Just big enough to backfill the start_secs.
-            # Use a larger buffer size when an utterance is in progress.
-            # Assume all audio frames have the same duration.
-            self._audio_frames.append(frame)
-            frame_duration = len(frame.audio) / 2 * frame.num_channels / frame.sample_rate
-            buffer_duration = frame_duration * len(self._audio_frames)
-            #  logger.debug(f"!!! Frame duration: {frame_duration}")
-            if self._user_speaking_utterance_state:
-                while buffer_duration > self._max_buffer_size_secs:
-                    self._audio_frames.pop(0)
-                    buffer_duration -= frame_duration
+            if self._user_speaking:
+                self._audio_frames.append(frame)
             else:
+                # Append the audio frame to our buffer. Treat the buffer as a ring buffer, dropping the oldest
+                # frames as necessary. Assume all audio frames have the same duration.
+                self._audio_frames.append(frame)
+                frame_duration = len(frame.audio) / 16 * frame.num_channels / frame.sample_rate
+                buffer_duration = frame_duration * len(self._audio_frames)
                 while buffer_duration > self._start_secs:
                     self._audio_frames.pop(0)
                     buffer_duration -= frame_duration
@@ -420,143 +123,32 @@ class AudioAccumulator(FrameProcessor):
 
 
 class CompletenessCheck(FrameProcessor):
-    """Checks the result of the classifier LLM to determine if the user has finished speaking.
-
-    Triggers the notifier if the user has finished speaking. Also triggers the notifier if an
-    idle timeout is reached.
-    """
-
-    wait_time = 5.0
-
-    def __init__(self, notifier: BaseNotifier, audio_accumulator: AudioAccumulator, **kwargs):
+    def __init__(
+        self, notifier: BaseNotifier, audio_accumulator: StatementJudgeAudioContextAccumulator
+    ):
         super().__init__()
         self._notifier = notifier
         self._audio_accumulator = audio_accumulator
-        self._idle_task = None
-        self._wakeup_time = 0
 
     async def process_frame(self, frame: Frame, direction: FrameDirection):
         await super().process_frame(frame, direction)
 
-        if isinstance(frame, UserStartedSpeakingFrame):
-            if self._idle_task:
-                self._idle_task.cancel()
-        elif isinstance(frame, TextFrame) and frame.text.startswith("YES"):
+        if isinstance(frame, TextFrame) and frame.text.startswith("YES"):
             logger.debug("Completeness check YES")
-            if self._idle_task:
-                self._idle_task.cancel()
             await self.push_frame(UserStoppedSpeakingFrame())
             await self._audio_accumulator.reset()
             await self._notifier.notify()
         elif isinstance(frame, TextFrame):
             if frame.text.strip():
                 logger.debug(f"Completeness check NO - '{frame.text}'")
-                # start timer to wake up if necessary
-                if self._wakeup_time:
-                    self._wakeup_time = time.time() + self.wait_time
-                else:
-                    # logger.debug("!!! CompletenessCheck idle wait START")
-                    self._wakeup_time = time.time() + self.wait_time
-                    self._idle_task = self.get_event_loop().create_task(self._idle_task_handler())
-
-    async def _idle_task_handler(self):
-        try:
-            while time.time() < self._wakeup_time:
-                await asyncio.sleep(0.01)
-            # logger.debug(f"!!! CompletenessCheck idle wait OVER")
-            await self._audio_accumulator.reset()
-            await self._notifier.notify()
-        except asyncio.CancelledError:
-            # logger.debug(f"!!! CompletenessCheck idle wait CANCEL")
-            pass
-        except Exception as e:
-            logger.error(f"CompletenessCheck idle wait error: {e}")
-            raise e
-        finally:
-            # logger.debug(f"!!! CompletenessCheck idle wait FINALLY")
-            self._wakeup_time = 0
-            self._idle_task = None
-
-
-class UserAggregatorBuffer(LLMResponseAggregator):
-    """Buffers the output of the transcription LLM. Used by the bot output gate."""
-
-    def __init__(self, **kwargs):
-        super().__init__(
-            messages=None,
-            role=None,
-            start_frame=LLMFullResponseStartFrame,
-            end_frame=LLMFullResponseEndFrame,
-            accumulator_frame=TextFrame,
-            handle_interruptions=True,
-            expect_stripped_words=False,
-        )
-        self._transcription = ""
-
-    async def process_frame(self, frame: Frame, direction: FrameDirection):
-        await super().process_frame(frame, direction)
-        # parent method pushes frames
-        if isinstance(frame, UserStartedSpeakingFrame):
-            self._transcription = ""
-
-    async def _push_aggregation(self):
-        if self._aggregation:
-            self._transcription = self._aggregation
-            self._aggregation = ""
-
-            logger.debug(f"[Transcription] {self._transcription}")
-
-    async def wait_for_transcription(self):
-        while not self._transcription:
-            await asyncio.sleep(0.01)
-        tx = self._transcription
-        self._transcription = ""
-        return tx
-
-
-class ConversationAudioContextAssembler(FrameProcessor):
-    """Takes the single-message context generated by the AudioAccumulator and adds it to the conversation LLM's context."""
-
-    def __init__(self, context: OpenAILLMContext, **kwargs):
-        super().__init__(**kwargs)
-        self._context = context
-
-    async def process_frame(self, frame: Frame, direction: FrameDirection):
-        await super().process_frame(frame, direction)
-
-        # We must not block system frames.
-        if isinstance(frame, SystemFrame):
-            await self.push_frame(frame, direction)
-            return
-
-        if isinstance(frame, OpenAILLMContextFrame):
-            GoogleLLMContext.upgrade_to_google(self._context)
-            last_message = frame.context.messages[-1]
-            self._context._messages.append(last_message)
-            await self.push_frame(OpenAILLMContextFrame(context=self._context))
 
 
 class OutputGate(FrameProcessor):
-    """Buffers output frames until the notifier is triggered.
-
-    When the notifier fires, waits until a transcription is ready, then:
-      1. Replaces the last user audio message with the transcription.
-      2. Flushes the frames buffer.
-    """
-
-    def __init__(
-        self,
-        notifier: BaseNotifier,
-        context: OpenAILLMContext,
-        user_transcription_buffer: "UserAggregatorBuffer",
-        **kwargs,
-    ):
+    def __init__(self, notifier: BaseNotifier, **kwargs):
         super().__init__(**kwargs)
         self._gate_open = False
         self._frames_buffer = []
         self._notifier = notifier
-        self._context = context
-        self._transcription_buffer = user_transcription_buffer
 
     def close_gate(self):
         self._gate_open = False
@@ -584,13 +176,6 @@ class OutputGate(FrameProcessor):
             await self.push_frame(frame, direction)
             return
 
-        if isinstance(frame, LLMFullResponseStartFrame):
-            # Remove the audio message from the context. We will never need it again.
-            # If the completeness check fails, a new audio message will be appended to the context.
-            # If the completeness check succeeds, our notifier will fire and we will append the
-            # transcription to the context.
-            self._context._messages.pop()
-
         if self._gate_open:
             await self.push_frame(frame, direction)
             return
@@ -609,22 +194,12 @@ class OutputGate(FrameProcessor):
         while True:
             try:
                 await self._notifier.wait()
-
-                transcription = await self._transcription_buffer.wait_for_transcription() or "-"
-                self._context._messages.append(
-                    glm.Content(role="user", parts=[glm.Part(text=transcription)])
-                )
-
                 self.open_gate()
                 for frame, direction in self._frames_buffer:
                     await self.push_frame(frame, direction)
                 self._frames_buffer = []
             except asyncio.CancelledError:
                 break
-            except Exception as e:
-                logger.error(f"OutputGate error: {e}")
-                raise e
-                break
 
 
 async def main():
@@ -640,63 +215,64 @@ async def main():
                 vad_enabled=True,
                 vad_analyzer=SileroVADAnalyzer(),
                 vad_audio_passthrough=True,
-                audio_in_sample_rate=16000,
             ),
         )
 
+        stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
+
         tts = CartesiaTTSService(
             api_key=os.getenv("CARTESIA_API_KEY"),
             voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22",  # British Lady
         )
 
-        # This is the LLM that will transcribe user speech.
-        tx_llm = GoogleLLMService(
-            name="Transcriber",
-            model=TRANSCRIBER_MODEL,
-            api_key=os.getenv("GOOGLE_API_KEY"),
-            temperature=0.0,
-            system_instruction=transcriber_system_instruction,
+        # This is the LLM that will be used to detect if the user has finished a
+        # statement. This doesn't really need to be an LLM, we could use NLP
+        # libraries for that, but we have the machinery to use an LLM, so we might as well!
+        statement_llm = GoogleLLMService(
+            model="gemini-1.5-flash-latest", api_key=os.getenv("GOOGLE_API_KEY")
         )
 
-        # This is the LLM that will classify user speech as complete or incomplete.
-        classifier_llm = GoogleLLMService(
-            name="Classifier",
-            model=CLASSIFIER_MODEL,
-            api_key=os.getenv("GOOGLE_API_KEY"),
-            temperature=0.0,
-            system_instruction=classifier_system_instruction,
-        )
+        # This is the regular LLM.
+        llm = GoogleLLMService(model="gemini-1.5-flash-latest", api_key=os.getenv("GOOGLE_API_KEY"))
 
-        # This is the regular LLM that responds conversationally.
-        conversation_llm = GoogleLLMService(
-            name="Conversation",
-            model=CONVERSATION_MODEL,
-            api_key=os.getenv("GOOGLE_API_KEY"),
-            system_instruction=conversation_system_instruction,
-        )
+        messages = [
+            {
+                "role": "system",
+                "content": conversational_system_message,
+            },
+        ]
 
-        context = OpenAILLMContext()
-        context_aggregator = conversation_llm.create_context_aggregator(context)
+        context = OpenAILLMContext(messages)
+        context_aggregator = llm.create_context_aggregator(context)
 
-        # We have instructed the LLM to return 'True' if it thinks the user
-        # completed a sentence. So, if it's 'True' we will return true in this
+        # We have instructed the LLM to return 'YES' if it thinks the user
+        # completed a sentence. So, if it's 'YES' we will return true in this
         # predicate which will wake up the notifier.
         async def wake_check_filter(frame):
-            return frame.text == "True"
+            return frame.text == "YES"
 
         # This is a notifier that we use to synchronize the two LLMs.
         notifier = EventNotifier()
 
         # This turns the LLM context into an inference request to classify the user's speech
         # as complete or incomplete.
-        # statement_judge_context_filter = StatementJudgeAudioContextAccumulator(notifier=notifier)
+        statement_judge_context_filter = StatementJudgeAudioContextAccumulator(notifier=notifier)
 
-        audio_accumulater = AudioAccumulator()
         # This sends a UserStoppedSpeakingFrame and triggers the notifier event
         completeness_check = CompletenessCheck(
-            notifier=notifier, audio_accumulator=audio_accumulater
+            notifier=notifier, audio_accumulator=statement_judge_context_filter
         )
 
+        # # Notify if the user hasn't said anything.
+        async def user_idle_notifier(frame):
+            await notifier.notify()
+
+        # Sometimes the LLM will fail detecting if a user has completed a
+        # sentence, this will wake up the notifier if that happens.
+        user_idle = UserIdleProcessor(callback=user_idle_notifier, timeout=5.0)
+
+        bot_output_gate = OutputGate(notifier=notifier)
+
         async def block_user_stopped_speaking(frame):
             return not isinstance(frame, UserStoppedSpeakingFrame)
 
@@ -708,18 +284,9 @@ async def main():
                 or isinstance(frame, StopInterruptionFrame)
             )
 
-        conversation_audio_context_assembler = ConversationAudioContextAssembler(context=context)
-
-        user_aggregator_buffer = UserAggregatorBuffer()
-
-        bot_output_gate = OutputGate(
-            notifier=notifier, context=context, user_transcription_buffer=user_aggregator_buffer
-        )
-
         pipeline = Pipeline(
             [
                 transport.input(),
-                audio_accumulater,
                 ParallelPipeline(
                     [
                         # Pass everything except UserStoppedSpeaking to the elements after
@@ -727,28 +294,24 @@ async def main():
                         FunctionFilter(filter=block_user_stopped_speaking),
                     ],
                     [
-                        ParallelPipeline(
-                            [
-                                classifier_llm,
-                                completeness_check,
-                            ],
-                            [
-                                tx_llm,
-                                user_aggregator_buffer,
-                            ],
-                        )
+                        statement_judge_context_filter,
+                        statement_llm,
+                        completeness_check,
                     ],
                     [
-                        conversation_audio_context_assembler,
-                        conversation_llm,
-                        bot_output_gate,  # buffer output until notified, then flush frames and update context
-                        # TempPrinter(),
+                        stt,
+                        context_aggregator.user(),
+                        # Block everything except OpenAILLMContextFrame and LLMMessagesFrame
+                        FunctionFilter(filter=pass_only_llm_trigger_frames),
+                        llm,
+                        bot_output_gate,  # Buffer all llm/tts output until notified.
                     ],
                 ),
                 tts,
+                user_idle,
                 transport.output(),
                 context_aggregator.assistant(),
-            ],
+            ]
         )
 
         task = PipelineTask(