Update CHANGELOG

Add Whisper STT service using OpenAI API

.github/workflows/android.yaml

@@ -0,0 +1,48 @@
+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/format.yaml

@@ -1,4 +1,4 @@
-name: lint
+name: format
 
 on:
   workflow_dispatch:
@@ -12,12 +12,12 @@ on:
       - "docs/**"
 
 concurrency:
-  group: build-lint-${{ github.event.pull_request.number || github.ref }}
+  group: build-format-${{ github.event.pull_request.number || github.ref }}
   cancel-in-progress: true
 
 jobs:
-  autopep8:
-    name: "Formatting lints"
+  ruff-format:
+    name: "Formatting checker"
     runs-on: ubuntu-latest
     steps:
       - name: Checkout repo
@@ -25,7 +25,7 @@ jobs:
       - name: Set up Python
         uses: actions/setup-python@v4
         with:
-          python-version: '3.10'
+          python-version: "3.10"
       - name: Setup virtual environment
         run: |
           python -m venv .venv
@@ -34,11 +34,13 @@ jobs:
           source .venv/bin/activate
           python -m pip install --upgrade pip
           pip install -r dev-requirements.txt
-      - name: autopep8
-        id: autopep8
+      - name: Ruff formatter
+        id: ruff-format
         run: |
           source .venv/bin/activate
-          autopep8 --max-line-length 100 --exit-code -r -d --exclude "*_pb2.py" -a -a src/
-      - name: Fail if autopep8 requires changes
-        if: steps.autopep8.outputs.exit-code == 2
-        run: exit 1
+          ruff format --diff
+      - name: Ruff import linter
+        id: ruff-check
+        run: |
+          source .venv/bin/activate
+          ruff check --select I

.github/workflows/publish_test.yaml

@@ -1,10 +1,6 @@
 name: publish-test
 
-on:
-  workflow_dispatch:
-  push:
-    branches:
-      - main
+on: workflow_dispatch
 
 jobs:
   build:
@@ -14,7 +10,6 @@ jobs:
       - name: Checkout repo
         uses: actions/checkout@v4
         with:
-          ref: ${{ github.event.inputs.gitref }}
           fetch-tags: true
           fetch-depth: 100
       - name: Set up Python

.github/workflows/tests.yaml

@@ -1,4 +1,4 @@
-name: test
+name: tests
 
 on:
   workflow_dispatch:
@@ -20,21 +20,24 @@ jobs:
     name: "Unit and Integration Tests"
     runs-on: ubuntu-latest
     steps:
-      - uses: actions/checkout@v4
+      - name: Checkout repo
+        uses: actions/checkout@v4
       - name: Set up Python
         id: setup_python
         uses: actions/setup-python@v4
         with:
-          python-version: '3.10'
+          python-version: "3.10"
       - name: Cache virtual environment
         uses: actions/cache@v3
         with:
-          # We are hashing requirements-dev.txt and requirements-extra.txt which
-          # contain all dependencies needed to run the tests and examples.
-          key: venv-${{ runner.os }}-${{ steps.setup_python.outputs.python-version}}-${{ hashFiles('linux-py3.10-requirements.txt') }}-${{ hashFiles('dev-requirements.txt') }}
+          # We are hashing dev-requirements.txt and test-requirements.txt which
+          # contain all dependencies needed to run the tests.
+          key: venv-${{ runner.os }}-${{ steps.setup_python.outputs.python-version}}-${{ hashFiles('dev-requirements.txt') }}-${{ hashFiles('test-requirements.txt') }}
           path: .venv
       - name: Install system packages
-        run: sudo apt-get install -y portaudio19-dev
+        id: install_system_packages
+        run: |
+          sudo apt-get install -y portaudio19-dev
       - name: Setup virtual environment
         run: |
           python -m venv .venv
@@ -42,8 +45,8 @@ jobs:
         run: |
           source .venv/bin/activate
           python -m pip install --upgrade pip
-          pip install -r linux-py3.10-requirements.txt -r dev-requirements.txt
+          pip install -r dev-requirements.txt -r test-requirements.txt
       - name: Test with pytest
         run: |
           source .venv/bin/activate
-          pytest --doctest-modules --ignore-glob="*to_be_updated*" src tests
+          pytest

.gitignore

@@ -4,6 +4,7 @@ __pycache__/
 *~
 venv
 .venv
+/.idea
 #*#
 
 # Distribution / packaging
@@ -27,4 +28,11 @@ share/python-wheels/
 MANIFEST
 .DS_Store
 .env
-fly.toml
+fly.toml
+
+# Example files
+pipecat/examples/twilio-chatbot/templates/streams.xml
+
+# Documentation
+docs/api/_build/
+docs/api/api

.pre-commit-config.yaml

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

.readthedocs.yaml

@@ -0,0 +1,36 @@
+version: 2
+
+build:
+  os: ubuntu-22.04
+  tools:
+    python: '3.12'
+  apt_packages:
+    - portaudio19-dev
+    - python3-dev
+    - libasound2-dev
+  jobs:
+    pre_build:
+      - python -m pip install --upgrade pip
+      - pip install wheel setuptools
+    post_build:
+      - echo "Build completed"
+
+sphinx:
+  configuration: docs/api/conf.py
+  fail_on_warning: false
+
+python:
+  install:
+    - requirements: docs/api/requirements.txt
+    - method: pip
+      path: .
+
+search:
+  ranking:
+    api/*: 5
+    getting-started/*: 4
+    guides/*: 3
+
+submodules:
+  include: all
+  recursive: true

CONTRIBUTING.md

@@ -0,0 +1,165 @@
+## Contributing to Pipecat
+
+We welcome contributions of all kinds! Your help is appreciated. Follow these steps to get involved:
+
+1. **Fork this repository**: Start by forking the Pipecat Documentation repository to your GitHub account.
+
+2. **Clone the repository**: Clone your forked repository to your local machine.
+   ```bash
+   git clone https://github.com/your-username/pipecat
+   ```
+3. **Create a branch**: For your contribution, create a new branch.
+   ```bash
+   git checkout -b your-branch-name
+   ```
+4. **Make your changes**: Edit or add files as necessary.
+5. **Test your changes**: Ensure that your changes look correct and follow the style set in the codebase.
+6. **Commit your changes**: Once you're satisfied with your changes, commit them with a meaningful message.
+
+```bash
+git commit -m "Description of your changes"
+```
+
+7. **Push your changes**: Push your branch to your forked repository.
+
+```bash
+git push origin your-branch-name
+```
+
+9. **Submit a Pull Request (PR)**: Open a PR from your forked repository to the main branch of this repo. 
+> Important: Describe the changes you've made clearly!
+
+Our maintainers will review your PR, and once everything is good, your contributions will be merged!
+
+
+# Contributor Covenant Code of Conduct
+
+## Our Pledge
+
+We as members, contributors, and leaders pledge to make participation in our
+community a harassment-free experience for everyone, regardless of age, body
+size, visible or invisible disability, ethnicity, sex characteristics, gender
+identity and expression, level of experience, education, socio-economic status,
+nationality, personal appearance, race, caste, color, religion, or sexual
+identity and orientation.
+
+We pledge to act and interact in ways that contribute to an open, welcoming,
+diverse, inclusive, and healthy community.
+
+## Our Standards
+
+Examples of behavior that contributes to a positive environment for our
+community include:
+
+* Demonstrating empathy and kindness toward other people
+* Being respectful of differing opinions, viewpoints, and experiences
+* Giving and gracefully accepting constructive feedback
+* Accepting responsibility and apologizing to those affected by our mistakes,
+  and learning from the experience
+* Focusing on what is best not just for us as individuals, but for the overall
+  community
+
+Examples of unacceptable behavior include:
+
+* The use of sexualized language or imagery, and sexual attention or advances of
+  any kind
+* Trolling, insulting or derogatory comments, and personal or political attacks
+* Public or private harassment
+* Publishing others' private information, such as a physical or email address,
+  without their explicit permission
+* Other conduct which could reasonably be considered inappropriate in a
+  professional setting
+
+## Enforcement Responsibilities
+
+Community leaders are responsible for clarifying and enforcing our standards of
+acceptable behavior and will take appropriate and fair corrective action in
+response to any behavior that they deem inappropriate, threatening, offensive,
+or harmful.
+
+Community leaders have the right and responsibility to remove, edit, or reject
+comments, commits, code, wiki edits, issues, and other contributions that are
+not aligned to this Code of Conduct, and will communicate reasons for moderation
+decisions when appropriate.
+
+## Scope
+
+This Code of Conduct applies within all community spaces, and also applies when
+an individual is officially representing the community in public spaces.
+Examples of representing our community include using an official email address,
+posting via an official social media account, or acting as an appointed
+representative at an online or offline event.
+
+## Enforcement
+
+Instances of abusive, harassing, or otherwise unacceptable behavior may be
+reported to the community leaders responsible for enforcement at pipecat-ai@daily.co.
+All complaints will be reviewed and investigated promptly and fairly.
+
+All community leaders are obligated to respect the privacy and security of the
+reporter of any incident.
+
+## Enforcement Guidelines
+
+Community leaders will follow these Community Impact Guidelines in determining
+the consequences for any action they deem in violation of this Code of Conduct:
+
+### 1. Correction
+
+**Community Impact**: Use of inappropriate language or other behavior deemed
+unprofessional or unwelcome in the community.
+
+**Consequence**: A private, written warning from community leaders, providing
+clarity around the nature of the violation and an explanation of why the
+behavior was inappropriate. A public apology may be requested.
+
+### 2. Warning
+
+**Community Impact**: A violation through a single incident or series of
+actions.
+
+**Consequence**: A warning with consequences for continued behavior. No
+interaction with the people involved, including unsolicited interaction with
+those enforcing the Code of Conduct, for a specified period of time. This
+includes avoiding interactions in community spaces as well as external channels
+like social media. Violating these terms may lead to a temporary or permanent
+ban.
+
+### 3. Temporary Ban
+
+**Community Impact**: A serious violation of community standards, including
+sustained inappropriate behavior.
+
+**Consequence**: A temporary ban from any sort of interaction or public
+communication with the community for a specified period of time. No public or
+private interaction with the people involved, including unsolicited interaction
+with those enforcing the Code of Conduct, is allowed during this period.
+Violating these terms may lead to a permanent ban.
+
+### 4. Permanent Ban
+
+**Community Impact**: Demonstrating a pattern of violation of community
+standards, including sustained inappropriate behavior, harassment of an
+individual, or aggression toward or disparagement of classes of individuals.
+
+**Consequence**: A permanent ban from any sort of public interaction within the
+community.
+
+## Attribution
+
+This Code of Conduct is adapted from the [Contributor Covenant][homepage],
+version 2.1, available at
+[https://www.contributor-covenant.org/version/2/1/code_of_conduct.html][v2.1].
+
+Community Impact Guidelines were inspired by
+[Mozilla's code of conduct enforcement ladder][Mozilla CoC].
+
+For answers to common questions about this code of conduct, see the FAQ at
+[https://www.contributor-covenant.org/faq][FAQ]. Translations are available at
+[https://www.contributor-covenant.org/translations][translations].
+
+[homepage]: https://www.contributor-covenant.org
+[v2.1]: https://www.contributor-covenant.org/version/2/1/code_of_conduct.html
+[Mozilla CoC]: https://github.com/mozilla/diversity
+[FAQ]: https://www.contributor-covenant.org/faq
+[translations]: https://www.contributor-covenant.org/translations

LICENSE

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

README.md

@@ -1,15 +1,21 @@
-<div align="center">
+<h1><div align="center">
  <img alt="pipecat" width="300px" height="auto" src="https://raw.githubusercontent.com/pipecat-ai/pipecat/main/pipecat.png">
-</div>
+</div></h1>
 
-# Pipecat
+[![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) [![Discord](https://img.shields.io/discord/1239284677165056021
-)](https://discord.gg/pipecat)
+Pipecat is an open source Python framework for building voice and multimodal conversational agents. It handles the complex orchestration of AI services, network transport, audio processing, and multimodal interactions, letting you focus on creating engaging experiences.
 
-`pipecat` is a framework for building voice (and multimodal) conversational agents. Things like personal coaches, meeting assistants, [story-telling toys for kids](https://storytelling-chatbot.fly.dev/), customer support bots, [intake flows](https://www.youtube.com/watch?v=lDevgsp9vn0), and snarky social companions.
+## What you can build
 
-Take a look at some example apps:
+- **Voice Assistants**: [Natural, real-time conversations with AI](https://demo.dailybots.ai/)
+- **Interactive Agents**: Personal coaches and meeting assistants
+- **Multimodal Apps**: Combine voice, video, images, and text
+- **Creative Tools**: [Story-telling experiences](https://storytelling-chatbot.fly.dev/) and social companions
+- **Business Solutions**: [Customer intake flows](https://www.youtube.com/watch?v=lDevgsp9vn0) and support bots
+- **Complex conversational flows**: [Refer to Pipecat Flows](https://github.com/pipecat-ai/pipecat-flows) to learn more
+
+## See it in action
 
 <p float="left">
     <a href="https://github.com/pipecat-ai/pipecat/tree/main/examples/simple-chatbot"><img src="https://raw.githubusercontent.com/pipecat-ai/pipecat/main/examples/simple-chatbot/image.png" width="280" /></a>&nbsp;
@@ -19,86 +25,107 @@ Take a look at some example apps:
     <a href="https://github.com/pipecat-ai/pipecat/tree/main/examples/moondream-chatbot"><img src="https://raw.githubusercontent.com/pipecat-ai/pipecat/main/examples/moondream-chatbot/image.png" width="280" /></a>
 </p>
 
-## Getting started with voice agents
+## Key features
+
+- **Voice-first Design**: Built-in speech recognition, TTS, and conversation handling
+- **Flexible Integration**: Works with popular AI services (OpenAI, ElevenLabs, etc.)
+- **Pipeline Architecture**: Build complex apps from simple, reusable components
+- **Real-time Processing**: Frame-based pipeline architecture for fluid interactions
+- **Production Ready**: Enterprise-grade WebRTC and Websocket support
+
+💡 Looking to build structured conversations? Check out [Pipecat Flows](https://github.com/pipecat-ai/pipecat-flows) for managing complex conversational states and transitions.
+
+## Getting started
 
 You can get started with Pipecat running on your local machine, then move your agent processes to the cloud when you’re ready. You can also add a 📞 telephone number, 🖼️ image output, 📺 video input, use different LLMs, and more.
 
 ```shell
-# install the module
+# Install the module
 pip install pipecat-ai
 
-# set up an .env file with API keys
+# Set up your environment
 cp dot-env.template .env
 ```
 
-By default, in order to minimize dependencies, only the basic framework functionality is available. Some third-party AI services require additional dependencies that you can install with:
+To keep things lightweight, only the core framework is included by default. If you need support for third-party AI services, you can add the necessary dependencies with:
 
 ```shell
 pip install "pipecat-ai[option,...]"
 ```
 
-Your project may or may not need these, so they're made available as optional requirements. Here is a list:
+### Available services
 
-- **AI services**: `anthropic`, `azure`, `deepgram`, `google`, `fal`, `moondream`, `openai`, `playht`, `silero`, `whisper`
-- **Transports**: `local`, `websocket`, `daily`
+| 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), [OpenAI (Whisper)](https://docs.pipecat.ai/server/services/stt/openai), [Whisper](https://docs.pipecat.ai/server/services/stt/whisper)                                                                                                                                                                                                                                                                                                                                                                                                                                                          | `pip install "pipecat-ai[deepgram]"`    |
+| LLMs                | [Anthropic](https://docs.pipecat.ai/server/services/llm/anthropic), [Azure](https://docs.pipecat.ai/server/services/llm/azure), [Cerebras](https://docs.pipecat.ai/server/services/llm/cerebras), [DeepSeek](https://docs.pipecat.ai/server/services/llm/deepseek), [Fireworks AI](https://docs.pipecat.ai/server/services/llm/fireworks), [Gemini](https://docs.pipecat.ai/server/services/llm/gemini), [Grok](https://docs.pipecat.ai/server/services/llm/grok), [Groq](https://docs.pipecat.ai/server/services/llm/groq), [NVIDIA NIM](https://docs.pipecat.ai/server/services/llm/nim), [Ollama](https://docs.pipecat.ai/server/services/llm/ollama), [OpenAI](https://docs.pipecat.ai/server/services/llm/openai), [OpenRouter](https://docs.pipecat.ai/server/services/llm/openrouter), [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]"`   |
+
+📚 [View full services documentation →](https://docs.pipecat.ai/server/services/supported-services)
 
 ## Code examples
 
-- [foundational](https://github.com/pipecat-ai/pipecat/tree/main/examples/foundational) — small snippets that build on each other, introducing one or two concepts at a time
-- [example apps](https://github.com/pipecat-ai/pipecat/tree/main/examples/) — complete applications that you can use as starting points for development
+- [Foundational](https://github.com/pipecat-ai/pipecat/tree/main/examples/foundational) — small snippets that build on each other, introducing one or two concepts at a time
+- [Example apps](https://github.com/pipecat-ai/pipecat/tree/main/examples/) — complete applications that you can use as starting points for development
 
 ## A simple voice agent running locally
 
-Here is a very basic Pipecat bot that greets a user when they join a real-time session. We'll use [Daily](https://daily.co) for real-time media transport, and [ElevenLabs](https://elevenlabs.io/) for text-to-speech.
+Here is a very basic Pipecat bot that greets a user when they join a real-time session. We'll use [Daily](https://daily.co) for real-time media transport, and [Cartesia](https://cartesia.ai/) for text-to-speech.
 
 ```python
-#app.py
-
 import asyncio
-import aiohttp
 
-from pipecat.frames.frames import EndFrame, TextFrame
+from pipecat.frames.frames import TextFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.task import PipelineTask
 from pipecat.pipeline.runner import PipelineRunner
-from pipecat.services.elevenlabs import ElevenLabsTTSService
+from pipecat.services.cartesia import CartesiaTTSService
 from pipecat.transports.services.daily import DailyParams, DailyTransport
 
 async def main():
-  async with aiohttp.ClientSession() as session:
-    # Use Daily as a real-time media transport (WebRTC)
-    transport = DailyTransport(
-      room_url=...,
-      token=...,
-      "Bot Name",
-      DailyParams(audio_out_enabled=True))
+  # Use Daily as a real-time media transport (WebRTC)
+  transport = DailyTransport(
+    room_url=...,
+    token="", # leave empty. Note: token is _not_ your api key
+    bot_name="Bot Name",
+    params=DailyParams(audio_out_enabled=True))
 
-    # Use Eleven Labs for Text-to-Speech
-    tts = ElevenLabsTTSService(
-      aiohttp_session=session,
-      api_key=...,
-      voice_id=...,
-      )
+  # Use Cartesia for Text-to-Speech
+  tts = CartesiaTTSService(
+    api_key=...,
+    voice_id=...
+  )
 
-    # Simple pipeline that will process text to speech and output the result
-    pipeline = Pipeline([tts, transport.output()])
+  # Simple pipeline that will process text to speech and output the result
+  pipeline = Pipeline([tts, transport.output()])
 
-    # Create Pipecat processor that can run one or more pipelines tasks
-    runner = PipelineRunner()
+  # Create Pipecat processor that can run one or more pipelines tasks
+  runner = PipelineRunner()
 
-    # Assign the task callable to run the pipeline
-    task = PipelineTask(pipeline)
+  # Assign the task callable to run the pipeline
+  task = PipelineTask(pipeline)
 
-    # Register an event handler to play audio when a
-    # participant joins the transport WebRTC session
-    @transport.event_handler("on_participant_joined")
-    async def on_new_participant_joined(transport, participant):
-      participant_name = participant["info"]["userName"] or ''
-      # Queue a TextFrame that will get spoken by the TTS service (Eleven Labs)
-      await task.queue_frames([TextFrame(f"Hello there, {participant_name}!"), EndFrame()])
+  # Register an event handler to play audio when a
+  # participant joins the transport WebRTC session
+  @transport.event_handler("on_first_participant_joined")
+  async def on_first_participant_joined(transport, participant):
+    participant_name = participant.get("info", {}).get("userName", "")
+    # Queue a TextFrame that will get spoken by the TTS service (Cartesia)
+    await task.queue_frame(TextFrame(f"Hello there, {participant_name}!"))
 
-    # Run the pipeline task
-    await runner.run(task)
+  # Register an event handler to exit the application when the user leaves.
+  @transport.event_handler("on_participant_left")
+  async def on_participant_left(transport, participant, reason):
+    await task.cancel()
+
+  # Run the pipeline task
+  await runner.run(task)
 
 if __name__ == "__main__":
   asyncio.run(main())
@@ -110,8 +137,7 @@ Run it with:
 python app.py
 ```
 
-Daily provides a prebuilt WebRTC user interface. Whilst the app is running, you can visit at `https://<yourdomain>.daily.co/<room_url>` and listen to the bot say hello!
-
+Daily provides a prebuilt WebRTC user interface. While the app is running, you can visit at `https://<yourdomain>.daily.co/<room_url>` and listen to the bot say hello!
 
 ## WebRTC for production use
 
@@ -121,19 +147,6 @@ One way to get up and running quickly with WebRTC is to sign up for a Daily deve
 
 Sign up [here](https://dashboard.daily.co/u/signup) and [create a room](https://docs.daily.co/reference/rest-api/rooms) in the developer Dashboard.
 
-## What is VAD?
-
-Voice Activity Detection &mdash; very important for knowing when a user has finished speaking to your bot. If you are not using press-to-talk, and want Pipecat to detect when the user has finished talking, VAD is an essential component for a natural feeling conversation.
-
-Pipecast makes use of WebRTC VAD by default when using a WebRTC transport layer. Optionally, you can use Silero VAD for improved accuracy at the cost of higher CPU usage.
-
-```shell
-pip install pipecat-ai[silero]
-```
-
-The first time your run your bot with Silero, startup may take a while whilst it downloads and caches the model in the background. You can check the progress of this in the console.
-
-
 ## Hacking on the framework itself
 
 _Note that you may need to set up a virtual environment before following the instructions below. For instance, you might need to run the following from the root of the repo:_
@@ -146,20 +159,29 @@ source venv/bin/activate
 From the root of this repo, run the following:
 
 ```shell
-pip install -r dev-requirements.txt -r {env}-requirements.txt
-python -m build
+pip install -r dev-requirements.txt
 ```
 
-This builds the package. To use the package locally (eg to run sample files), run
+This will install the necessary development dependencies. Also, make sure you install the git pre-commit hooks:
 
 ```shell
-pip install --editable .
+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:
+
+```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
-pip install path_to_this_repo
+pip install "path_to_this_repo[option,...]"
 ```
 
 ### Running tests
@@ -167,55 +189,74 @@ pip install path_to_this_repo
 From the root directory, run:
 
 ```shell
-pytest --doctest-modules --ignore-glob="*to_be_updated*" src tests
+pytest
 ```
 
 ## Setting up your editor
 
-This project uses strict [PEP 8](https://peps.python.org/pep-0008/) formatting.
+This project uses strict [PEP 8](https://peps.python.org/pep-0008/) formatting via [Ruff](https://github.com/astral-sh/ruff).
 
 ### Emacs
 
-You can use [use-package](https://github.com/jwiegley/use-package) to install [py-autopep8](https://codeberg.org/ideasman42/emacs-py-autopep8) package and configure `autopep8` arguments:
+You can use [use-package](https://github.com/jwiegley/use-package) to install [emacs-lazy-ruff](https://github.com/christophermadsen/emacs-lazy-ruff) package and configure `ruff` arguments:
 
 ```elisp
-(use-package py-autopep8
+(use-package lazy-ruff
   :ensure t
-  :defer t
-  :hook ((python-mode . py-autopep8-mode))
+  :hook ((python-mode . lazy-ruff-mode))
   :config
-  (setq py-autopep8-options '("-a" "-a", "--max-line-length=100")))
+  (setq lazy-ruff-format-command "ruff format")
+  (setq lazy-ruff-check-command "ruff check --select I"))
 ```
 
-`autopep8` was installed in the `venv` environment described before, so you should be able to use [pyvenv-auto](https://github.com/ryotaro612/pyvenv-auto) to automatically load that environment inside Emacs.
+`ruff` was installed in the `venv` environment described before, so you should be able to use [pyvenv-auto](https://github.com/ryotaro612/pyvenv-auto) to automatically load that environment inside Emacs.
 
 ```elisp
 (use-package pyvenv-auto
   :ensure t
   :defer t
   :hook ((python-mode . pyvenv-auto-run)))
-
 ```
 
 ### Visual Studio Code
 
 Install the
-[autopep8](https://marketplace.visualstudio.com/items?itemName=ms-python.autopep8) extension. Then edit the user settings (_Ctrl-Shift-P_ `Open User Settings (JSON)`) and set it as the default Python formatter, enable formatting on save and configure `autopep8` arguments:
+[Ruff](https://marketplace.visualstudio.com/items?itemName=charliermarsh.ruff) extension. Then edit the user settings (_Ctrl-Shift-P_ `Open User Settings (JSON)`) and set it as the default Python formatter, and enable formatting on save:
 
 ```json
 "[python]": {
-    "editor.defaultFormatter": "ms-python.autopep8",
+    "editor.defaultFormatter": "charliermarsh.ruff",
     "editor.formatOnSave": true
-},
-"autopep8.args": [
-    "-a",
-    "-a",
-    "--max-line-length=100"
-],
+}
 ```
 
+### PyCharm
+
+`ruff` was installed in the `venv` environment described before, now to enable autoformatting on save, go to `File` -> `Settings` -> `Tools` -> `File Watchers` and add a new watcher with the following settings:
+
+1. **Name**: `Ruff formatter`
+2. **File type**: `Python`
+3. **Working directory**: `$ContentRoot$`
+4. **Arguments**: `format $FilePath$`
+5. **Program**: `$PyInterpreterDirectory$/ruff`
+
+## Contributing
+
+We welcome contributions from the community! Whether you're fixing bugs, improving documentation, or adding new features, here's how you can help:
+
+- **Found a bug?** Open an [issue](https://github.com/pipecat-ai/pipecat/issues)
+- **Have a feature idea?** Start a [discussion](https://discord.gg/pipecat)
+- **Want to contribute code?** Check our [CONTRIBUTING.md](CONTRIBUTING.md) guide
+- **Documentation improvements?** [Docs](https://github.com/pipecat-ai/docs) PRs are always welcome
+
+Before submitting a pull request, please check existing issues and PRs to avoid duplicates.
+
+We aim to review all contributions promptly and provide constructive feedback to help get your changes merged.
+
 ## Getting help
 
 ➡️ [Join our Discord](https://discord.gg/pipecat)
 
+➡️ [Read the docs](https://docs.pipecat.ai)
+
 ➡️ [Reach us on X](https://x.com/pipecat_ai)

dev-requirements.txt

@@ -1,7 +1,11 @@
-autopep8~=2.1.0
-build~=1.2.1
-grpcio-tools~=1.62.2
+build~=1.2.2
+grpcio-tools~=1.67.1
 pip-tools~=7.4.1
-pytest~=8.2.0
-setuptools~=69.5.1
+pre-commit~=4.0.1
+pyright~=1.1.393
+pytest~=8.3.4
+pytest-asyncio~=0.25.2
+ruff~=0.9.5
+setuptools~=70.0.0
 setuptools_scm~=8.1.0
+python-dotenv~=1.0.1

docs/ISSUE_TEMPLATE.md

@@ -0,0 +1,22 @@
+# Description
+Is this reporting a bug or feature request?
+
+
+If reporting a bug, please fill out the following:
+
+### Environment
+- pipecat-ai version:
+- python version:
+- OS:
+
+### Issue description
+Provide a clear description of the issue.
+
+### Repro steps
+List the steps to reproduce the issue.
+
+### Expected behavior
+
+### Actual behavior
+
+### Logs

docs/PULL_REQUEST_TEMPLATE.md

@@ -0,0 +1 @@
+#### Please describe the changes in your PR. If it is addressing an issue, please reference that as well.

docs/api/Makefile

@@ -0,0 +1,20 @@
+# Minimal makefile for Sphinx documentation
+#
+
+# You can set these variables from the command line, and also
+# from the environment for the first two.
+SPHINXOPTS    ?=
+SPHINXBUILD   ?= sphinx-build
+SOURCEDIR     = .
+BUILDDIR      = _build
+
+# Put it first so that "make" without argument is like "make help".
+help:
+	@$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)
+
+.PHONY: help Makefile
+
+# Catch-all target: route all unknown targets to Sphinx using the new
+# "make mode" option.  $(O) is meant as a shortcut for $(SPHINXOPTS).
+%: Makefile
+	@$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)

docs/api/README.md

@@ -0,0 +1,109 @@
+# Pipecat Documentation
+
+This directory contains the source files for auto-generating Pipecat's server API reference documentation.
+
+## Setup
+
+1. Install documentation dependencies:
+
+```bash
+pip install -r requirements.txt
+```
+
+2. Make the build scripts executable:
+
+```bash
+chmod +x build-docs.sh rtd-test.py
+```
+
+## Building Documentation
+
+From this directory, you can build the documentation in several ways:
+
+### Local Build
+
+```bash
+# Using the build script (automatically opens docs when done)
+./build-docs.sh
+
+# Or directly with sphinx-build
+sphinx-build -b html . _build/html -W --keep-going
+```
+
+### ReadTheDocs Test Build
+
+To test the documentation build process exactly as it would run on ReadTheDocs:
+
+```bash
+./rtd-test.py
+```
+
+This script:
+
+- Creates a fresh virtual environment
+- Installs all dependencies as specified in requirements files
+- Handles conflicting dependencies (like grpcio versions for Riva and PlayHT)
+- Builds the documentation in an isolated environment
+- Provides detailed logging of the build process
+
+Use this script to verify your documentation will build correctly on ReadTheDocs before pushing changes.
+
+## Viewing Documentation
+
+The built documentation will be available at `_build/html/index.html`. To open:
+
+```bash
+# On MacOS
+open _build/html/index.html
+
+# On Linux
+xdg-open _build/html/index.html
+
+# On Windows
+start _build/html/index.html
+```
+
+## Directory Structure
+
+```
+.
+├── api/            # Auto-generated API documentation
+├── _build/         # Built documentation
+├── _static/        # Static files (images, css, etc.)
+├── conf.py         # Sphinx configuration
+├── index.rst       # Main documentation entry point
+├── requirements-base.txt    # Base documentation dependencies
+├── requirements-riva.txt    # Riva-specific dependencies
+├── requirements-playht.txt  # PlayHT-specific dependencies
+├── build-docs.sh   # Local build script
+└── rtd-test.py     # ReadTheDocs test build script
+```
+
+## Notes
+
+- Documentation is auto-generated from Python docstrings
+- Service modules are automatically detected and included
+- The build process matches our ReadTheDocs configuration
+- Warnings are treated as errors (-W flag) to maintain consistency
+- The --keep-going flag ensures all errors are reported
+- Dependencies are split into multiple requirements files to handle version conflicts
+
+## Troubleshooting
+
+If you encounter missing service modules:
+
+1. Verify the service is installed with its extras: `pip install pipecat-ai[service-name]`
+2. Check the build logs for import errors
+3. Ensure the service module is properly initialized in the package
+4. Run `./rtd-test.py` to test in an isolated environment matching ReadTheDocs
+
+For dependency conflicts:
+
+1. Check the requirements files for version specifications
+2. Use `rtd-test.py` to verify dependency resolution
+3. Consider adding service-specific requirements files if needed
+
+For more information:
+
+- [ReadTheDocs Configuration](.readthedocs.yaml)
+- [Sphinx Documentation](https://www.sphinx-doc.org/)

docs/api/build-docs.sh

@@ -0,0 +1,10 @@
+#!/bin/bash
+
+# Clean previous build
+rm -rf _build
+
+# Build docs matching ReadTheDocs configuration
+sphinx-build -b html -d _build/doctrees . _build/html -W --keep-going
+
+# Open docs (MacOS)
+open _build/html/index.html

docs/api/conf.py

@@ -0,0 +1,252 @@
+import logging
+import sys
+from pathlib import Path
+
+# Configure logging
+logging.basicConfig(level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s")
+logger = logging.getLogger("sphinx-build")
+
+# Add source directory to path
+docs_dir = Path(__file__).parent
+project_root = docs_dir.parent.parent
+sys.path.insert(0, str(project_root / "src"))
+
+# Project information
+project = "pipecat-ai"
+copyright = "2024, Daily"
+author = "Daily"
+
+# General configuration
+extensions = [
+    "sphinx.ext.autodoc",
+    "sphinx.ext.napoleon",
+    "sphinx.ext.viewcode",
+    "sphinx.ext.intersphinx",
+]
+
+# Napoleon settings
+napoleon_google_docstring = True
+napoleon_numpy_docstring = False
+napoleon_include_init_with_doc = True
+
+# AutoDoc settings
+autodoc_default_options = {
+    "members": True,
+    "member-order": "bysource",
+    "special-members": "__init__",
+    "undoc-members": True,
+    "exclude-members": "__weakref__",
+    "no-index": True,
+    "show-inheritance": True,
+}
+
+# Mock imports for optional dependencies
+autodoc_mock_imports = [
+    "riva",
+    "livekit",
+    "pyht",  # Base PlayHT package
+    "pyht.async_client",  # PlayHT specific imports
+    "pyht.client",
+    "pyht.protos",
+    "pyht.protos.api_pb2",
+    "pipecat_ai_playht",  # PlayHT wrapper
+    "anthropic",
+    "assemblyai",
+    "boto3",
+    "azure",
+    "cartesia",
+    "deepgram",
+    "elevenlabs",
+    "fal",
+    "gladia",
+    "google",
+    "krisp",
+    "langchain",
+    "lmnt",
+    "noisereduce",
+    "openai",
+    "openpipe",
+    "simli",
+    "soundfile",
+    # Existing mocks
+    "pipecat_ai_krisp",
+    "pyaudio",
+    "_tkinter",
+    "tkinter",
+    "daily",
+    "daily_python",
+    "pydantic.BaseModel",
+    "pydantic.Field",
+    "pydantic._internal._model_construction",
+    "pydantic._internal._fields",
+]
+
+# HTML output settings
+html_theme = "sphinx_rtd_theme"
+html_static_path = ["_static"]
+autodoc_typehints = "description"
+html_show_sphinx = False
+
+
+def verify_modules():
+    """Verify that required modules are available."""
+    required_modules = {
+        "services": [
+            "assemblyai",
+            "aws",
+            "cartesia",
+            "deepgram",
+            "google",
+            "lmnt",
+            "riva",
+            "simli",
+        ],
+        "serializers": ["livekit"],
+        "vad": ["silero", "vad_analyzer"],
+        "transports": {
+            "services": ["daily", "livekit"],
+            "local": ["audio", "tk"],
+            "network": ["fastapi_websocket", "websocket_server"],
+        },
+    }
+
+    missing = []
+    for category, modules in required_modules.items():
+        if isinstance(modules, dict):
+            # Handle nested structure
+            for subcategory, submodules in modules.items():
+                for module in submodules:
+                    try:
+                        __import__(f"pipecat.{category}.{subcategory}.{module}")
+                        logger.info(
+                            f"Successfully imported pipecat.{category}.{subcategory}.{module}"
+                        )
+                    except (ImportError, TypeError, NameError) as e:
+                        missing.append(f"pipecat.{category}.{subcategory}.{module}")
+                        logger.warning(
+                            f"Optional module not available: pipecat.{category}.{subcategory}.{module} - {str(e)}"
+                        )
+        else:
+            # Handle flat structure
+            for module in modules:
+                try:
+                    __import__(f"pipecat.{category}.{module}")
+                    logger.info(f"Successfully imported pipecat.{category}.{module}")
+                except (ImportError, TypeError, NameError) as e:
+                    missing.append(f"pipecat.{category}.{module}")
+                    logger.warning(
+                        f"Optional module not available: pipecat.{category}.{module} - {str(e)}"
+                    )
+
+    if missing:
+        logger.warning(f"Some optional modules are not available: {missing}")
+
+
+def clean_title(title: str) -> str:
+    """Automatically clean module titles."""
+    # Remove everything after space (like 'module', 'processor', etc.)
+    title = title.split(" ")[0]
+
+    # Get the last part of the dot-separated path
+    parts = title.split(".")
+    title = parts[-1]
+
+    # Special cases for service names and common acronyms
+    special_cases = {
+        "ai": "AI",
+        "aws": "AWS",
+        "api": "API",
+        "vad": "VAD",
+        "assemblyai": "AssemblyAI",
+        "deepgram": "Deepgram",
+        "elevenlabs": "ElevenLabs",
+        "openai": "OpenAI",
+        "openpipe": "OpenPipe",
+        "playht": "PlayHT",
+        "xtts": "XTTS",
+        "lmnt": "LMNT",
+    }
+
+    # Check if the entire title is a special case
+    if title.lower() in special_cases:
+        return special_cases[title.lower()]
+
+    # Otherwise, capitalize each word
+    words = title.split("_")
+    cleaned_words = []
+    for word in words:
+        if word.lower() in special_cases:
+            cleaned_words.append(special_cases[word.lower()])
+        else:
+            cleaned_words.append(word.capitalize())
+
+    return " ".join(cleaned_words)
+
+
+def setup(app):
+    """Generate API documentation during Sphinx build."""
+    from sphinx.ext.apidoc import main
+
+    docs_dir = Path(__file__).parent
+    project_root = docs_dir.parent.parent
+    output_dir = str(docs_dir / "api")
+    source_dir = str(project_root / "src" / "pipecat")
+
+    # Clean existing files
+    if Path(output_dir).exists():
+        import shutil
+
+        shutil.rmtree(output_dir)
+        logger.info(f"Cleaned existing documentation in {output_dir}")
+
+    logger.info(f"Generating API documentation...")
+    logger.info(f"Output directory: {output_dir}")
+    logger.info(f"Source directory: {source_dir}")
+
+    excludes = [
+        str(project_root / "src/pipecat/pipeline/to_be_updated"),
+        str(project_root / "src/pipecat/processors/gstreamer"),
+        str(project_root / "src/pipecat/services/to_be_updated"),
+        str(project_root / "src/pipecat/vad"),  # deprecated
+        "**/test_*.py",
+        "**/tests/*.py",
+    ]
+
+    try:
+        main(
+            [
+                "-f",  # Force overwriting
+                "-e",  # Don't generate empty files
+                "-M",  # Put module documentation before submodule documentation
+                "--no-toc",  # Don't create a table of contents file
+                "--separate",  # Put documentation for each module in its own page
+                "--module-first",  # Module documentation before submodule documentation
+                "--implicit-namespaces",  # Added: Handle implicit namespace packages
+                "-o",
+                output_dir,
+                source_dir,
+            ]
+            + excludes
+        )
+
+        logger.info("API documentation generated successfully!")
+
+        # Process generated RST files to update titles
+        for rst_file in Path(output_dir).glob("**/*.rst"):  # Changed to recursive glob
+            content = rst_file.read_text()
+            lines = content.split("\n")
+
+            # Find and clean up the title
+            if lines and "=" in lines[1]:  # Title is typically the first line
+                old_title = lines[0]
+                new_title = clean_title(old_title)
+                content = content.replace(old_title, new_title)
+                rst_file.write_text(content)
+                logger.info(f"Updated title: {old_title} -> {new_title}")
+
+    except Exception as e:
+        logger.error(f"Error generating API documentation: {e}", exc_info=True)
+
+
+# Run module verification
+verify_modules()

docs/api/index.rst

@@ -0,0 +1,77 @@
+Pipecat API Reference Docs
+==========================
+
+Welcome to Pipecat's API reference documentation!
+
+Pipecat is an open source framework for building voice and multimodal assistants.
+It provides a flexible pipeline architecture for connecting various AI services,
+audio processing, and transport layers.
+
+Quick Links
+-----------
+
+* `GitHub Repository <https://github.com/pipecat-ai/pipecat>`_
+* `Website <https://pipecat.ai>`_
+
+API Reference
+-------------
+
+Core Components
+~~~~~~~~~~~~~~~
+
+* :mod:`Frames <pipecat.frames>`
+* :mod:`Processors <pipecat.processors>`
+* :mod:`Pipeline <pipecat.pipeline>`
+
+Audio Processing
+~~~~~~~~~~~~~~~~
+
+* :mod:`Audio <pipecat.audio>`
+
+Services
+~~~~~~~~
+
+* :mod:`Services <pipecat.services>`
+
+Transport & Serialization
+~~~~~~~~~~~~~~~~~~~~~~~~~
+
+* :mod:`Transports <pipecat.transports>`
+   * :mod:`Local <pipecat.transports.local>`
+   * :mod:`Network <pipecat.transports.network>`
+   * :mod:`Services <pipecat.transports.services>`
+* :mod:`Serializers <pipecat.serializers>`
+
+Utilities
+~~~~~~~~~
+
+* :mod:`Clocks <pipecat.clocks>`
+* :mod:`Metrics <pipecat.metrics>`
+* :mod:`Sync <pipecat.sync>`
+* :mod:`Transcriptions <pipecat.transcriptions>`
+* :mod:`Utils <pipecat.utils>`
+
+.. toctree::
+   :maxdepth: 3
+   :caption: API Reference
+   :hidden:
+
+   Audio <api/pipecat.audio>
+   Clocks <api/pipecat.clocks>
+   Frames <api/pipecat.frames>
+   Metrics <api/pipecat.metrics>
+   Pipeline <api/pipecat.pipeline>
+   Processors <api/pipecat.processors>
+   Serializers <api/pipecat.serializers>
+   Services <api/pipecat.services>
+   Sync <api/pipecat.sync>
+   Transcriptions <api/pipecat.transcriptions>
+   Transports <api/pipecat.transports>
+   Utils <api/pipecat.utils>
+
+Indices and tables
+==================
+
+* :ref:`genindex`
+* :ref:`modindex`
+* :ref:`search`

docs/api/make.bat

@@ -0,0 +1,35 @@
+@ECHO OFF
+
+pushd %~dp0
+
+REM Command file for Sphinx documentation
+
+if "%SPHINXBUILD%" == "" (
+	set SPHINXBUILD=sphinx-build
+)
+set SOURCEDIR=.
+set BUILDDIR=_build
+
+%SPHINXBUILD% >NUL 2>NUL
+if errorlevel 9009 (
+	echo.
+	echo.The 'sphinx-build' command was not found. Make sure you have Sphinx
+	echo.installed, then set the SPHINXBUILD environment variable to point
+	echo.to the full path of the 'sphinx-build' executable. Alternatively you
+	echo.may add the Sphinx directory to PATH.
+	echo.
+	echo.If you don't have Sphinx installed, grab it from
+	echo.https://www.sphinx-doc.org/
+	exit /b 1
+)
+
+if "%1" == "" goto help
+
+%SPHINXBUILD% -M %1 %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O%
+goto end
+
+:help
+%SPHINXBUILD% -M help %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O%
+
+:end
+popd

docs/api/requirements.txt

@@ -0,0 +1,40 @@
+# Sphinx dependencies
+sphinx>=8.1.3
+sphinx-rtd-theme
+sphinx-markdown-builder
+sphinx-autodoc-typehints
+toml
+
+# Install all extras individually to ensure they're properly resolved
+pipecat-ai[anthropic]
+pipecat-ai[assemblyai]
+pipecat-ai[aws]
+pipecat-ai[azure]
+pipecat-ai[canonical]
+pipecat-ai[cartesia]
+pipecat-ai[daily]
+pipecat-ai[deepgram]
+pipecat-ai[elevenlabs]
+pipecat-ai[fal]
+pipecat-ai[fireworks]
+pipecat-ai[gladia]
+pipecat-ai[google]
+pipecat-ai[grok]
+pipecat-ai[groq]
+# pipecat-ai[krisp] # Mocked instead
+pipecat-ai[langchain]
+pipecat-ai[livekit]
+pipecat-ai[lmnt]
+pipecat-ai[local]
+pipecat-ai[moondream]
+pipecat-ai[nim]
+pipecat-ai[noisereduce]
+pipecat-ai[openai]
+# pipecat-ai[openpipe]
+# pipecat-ai[playht] # Mocked due to grpcio conflict with riva
+pipecat-ai[riva]
+pipecat-ai[silero]
+pipecat-ai[simli]
+pipecat-ai[soundfile]
+pipecat-ai[websocket]
+pipecat-ai[whisper]

docs/api/rtd-test.sh

@@ -0,0 +1,38 @@
+#!/bin/bash
+set -e
+
+# Configuration
+DOCS_DIR=$(pwd)
+PROJECT_ROOT=$(cd ../../ && pwd)
+TEST_DIR="/tmp/rtd-test-$(date +%Y%m%d_%H%M%S)"
+
+echo "Creating test directory: $TEST_DIR"
+mkdir -p "$TEST_DIR"
+cd "$TEST_DIR"
+
+# Create virtual environment
+python -m venv venv
+source venv/bin/activate
+
+echo "Installing build dependencies..."
+pip install --upgrade pip wheel setuptools
+
+echo "Installing documentation dependencies..."
+pip install -r "$DOCS_DIR/requirements.txt"
+
+echo "Building documentation..."
+cd "$DOCS_DIR"
+sphinx-build -b html . "_build/html"
+
+echo "Build complete. Check _build/html directory for output."
+
+# Print summary
+echo -e "\n=== Build Summary ==="
+echo "Documentation: $DOCS_DIR/_build/html"
+echo "Test environment: $TEST_DIR"
+echo -e "\nTo view the documentation:"
+echo "open $DOCS_DIR/_build/html/index.html"
+
+# Print installed packages for verification
+echo -e "\n=== Installed Packages ==="
+pip freeze | grep -E "sphinx|pipecat"

docs/frame.md

@@ -0,0 +1,110 @@
+# Understanding Different Frame Types in the Pipecat System
+
+In the Pipecat system, frames are used to represent different types of data and control signals that flow through the pipeline. Understanding these frame types is crucial for working with the system effectively. This tutorial will cover the main categories of frames and their specific uses.
+
+## 1. Base Frame Classes
+
+### Frame
+The `Frame` class is the base class for all frames. It includes:
+- `id`: A unique identifier
+- `name`: A descriptive name
+- `pts`: Presentation timestamp (optional)
+
+### DataFrame
+`DataFrame` is a subclass of `Frame` and serves as a base for most data-carrying frames.
+
+## 2. Audio Frames
+
+### AudioRawFrame
+Represents a chunk of audio with properties:
+- `audio`: Raw audio data
+- `sample_rate`: Audio sample rate
+- `num_channels`: Number of audio channels
+
+Subclasses include:
+- `InputAudioRawFrame`: For audio from input sources
+- `OutputAudioRawFrame`: For audio to be played by output devices
+- `TTSAudioRawFrame`: For audio generated by Text-to-Speech services
+
+## 3. Image Frames
+
+### ImageRawFrame
+Represents an image with properties:
+- `image`: Raw image data
+- `size`: Image dimensions
+- `format`: Image format (e.g., JPEG, PNG)
+
+Subclasses include:
+- `InputImageRawFrame`: For images from input sources
+- `OutputImageRawFrame`: For images to be displayed
+- `UserImageRawFrame`: For images associated with a specific user
+- `VisionImageRawFrame`: For images with associated text for description
+- `URLImageRawFrame`: For images with an associated URL
+
+### SpriteFrame
+Represents an animated sprite, containing a list of `ImageRawFrame` objects.
+
+## 4. Text and Transcription Frames
+
+### TextFrame
+Represents a chunk of text, used for various purposes in the pipeline.
+
+### TranscriptionFrame
+A specialized `TextFrame` for speech transcriptions, including:
+- `user_id`: ID of the speaking user
+- `timestamp`: When the transcription was generated
+- `language`: Detected language of the speech
+
+### InterimTranscriptionFrame
+Similar to `TranscriptionFrame`, but for interim (not final) transcriptions.
+
+## 5. LLM (Language Model) Frames
+
+### LLMMessagesFrame
+Contains a list of messages for an LLM service to process.
+
+### LLMMessagesAppendFrame and LLMMessagesUpdateFrame
+Used to modify the current context of LLM messages.
+
+### LLMSetToolsFrame
+Specifies tools (functions) available for the LLM to use.
+
+### LLMEnablePromptCachingFrame
+Controls prompt caching in certain LLMs.
+
+## 6. System and Control Frames
+
+### SystemFrame
+Base class for system-level frames.
+
+Important system frames include:
+- `StartFrame`: Initiates a pipeline
+- `CancelFrame`: Stops a pipeline immediately
+- `ErrorFrame`: Notifies of errors (with `FatalErrorFrame` for unrecoverable errors)
+- `EndTaskFrame` and `CancelTaskFrame`: Control pipeline tasks
+- `StartInterruptionFrame` and `StopInterruptionFrame`: Indicate user speech for interruptions
+
+### ControlFrame
+Base class for control-flow frames.
+
+Notable control frames:
+- `EndFrame`: Signals the end of a pipeline
+- `LLMFullResponseStartFrame` and `LLMFullResponseEndFrame`: Bracket LLM responses
+- `UserStartedSpeakingFrame` and `UserStoppedSpeakingFrame`: Indicate user speech activity
+- `BotStartedSpeakingFrame` and `BotStoppedSpeakingFrame`: Indicate bot speech activity
+- `TTSStartedFrame` and `TTSStoppedFrame`: Bracket Text-to-Speech responses
+
+## 7. Special Purpose Frames
+
+### MetricsFrame
+Contains performance metrics data.
+
+### FunctionCallInProgressFrame and FunctionCallResultFrame
+Used for handling LLM function (tool) calls.
+
+### ServiceUpdateSettingsFrame
+Base class for updating service settings, with specific subclasses for LLM, TTS, and STT services.
+
+## Conclusion
+
+Understanding these frame types is essential for working with the Pipecat system. Each frame type serves a specific purpose in the pipeline, whether it's carrying data (like audio or images), controlling the flow of the pipeline, or managing system-level operations. By using the appropriate frame types, you can effectively process and transmit various kinds of information through your pipeline.

dot-env.template

@@ -1,6 +1,11 @@
 # Anthropic
 ANTHROPIC_API_KEY=...
 
+# AWS
+AWS_SECRET_ACCESS_KEY=...
+AWS_ACCESS_KEY_ID=...
+AWS_REGION=...
+
 # Azure
 AZURE_SPEECH_REGION=...
 AZURE_SPEECH_API_KEY=...
@@ -27,6 +32,13 @@ FAL_KEY=...
 # Fireworks
 FIREWORKS_API_KEY=...
 
+# Gladia
+GLADIA_API_KEY=...
+
+# LMNT
+LMNT_API_KEY=...
+LMNT_VOICE_ID=...
+
 # PlayHT
 PLAY_HT_USER_ID=...
 PLAY_HT_API_KEY=...
@@ -34,5 +46,41 @@ PLAY_HT_API_KEY=...
 # OpenAI
 OPENAI_API_KEY=...
 
-#OpenPipe
+# OpenPipe
 OPENPIPE_API_KEY=...
+
+# Tavus
+TAVUS_API_KEY=...
+TAVUS_REPLICA_ID=...
+TAVUS_PERSONA_ID=...
+
+# Simli
+SIMLI_API_KEY=...
+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

@@ -32,14 +32,17 @@ Next, follow the steps in the README for each demo.
 
 ## Projects:
 
-| Project                                      | Description                                                                                                                                | Services                                       |
-| -------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------ | ---------------------------------------------- |
-| [Simple Chatbot](simple-chatbot)             | Basic voice-driven conversational bot. A good starting point for learning the flow of the framework.                                       | Deepgram, OpenAI, Daily, Daily Prebuilt UI            |
-| [Storytelling Chatbot](storytelling-chatbot) | Stitches together multiple third-party services to create a collaborative storytime experience.                                            | Deepgram, ElevenLabs, Open AI, Fal, Daily, Custom UI  |
-| [Translation Chatbot](translation-chatbot)   | Listens for user speech, then translates that speech to Spanish and speaks the translation back. Demonstrates multi-participant use-cases. | Deepgram, Azure, OpenAI, Daily, Daily Prebuilt UI     |
-| [Moondream Chatbot](moondream-chatbot)       | Demonstrates how to add vision capabilities to GPT4. **Note: works best with a GPU**                                                       | Deepgram, OpenAI, Moondream, Daily, Daily Prebuilt UI |
-| Function-calling Chatbot (TBC)               | A chatbot that can call functions in response to user input.                                                                                | Deepgram, OpenAI, Fireworks, Daily, Daily Prebuilt UI |
-| [Dialin Chatbot](dialin-chatbot)             | A chatbot that connects to an incoming phone call from Daily or Twilio.                                                                                | Deepgram, OpenAI, ElevenLabs, Daily, Twilio |
+| Project                                      | Description                                                                                                                                | Services                                                          |
+|----------------------------------------------|--------------------------------------------------------------------------------------------------------------------------------------------|-------------------------------------------------------------------|
+| [Simple Chatbot](simple-chatbot)             | Basic voice-driven conversational bot. A good starting point for learning the flow of the framework.                                       | Deepgram, ElevenLabs, OpenAI, Daily, Daily Prebuilt UI            |
+| [Storytelling Chatbot](storytelling-chatbot) | Stitches together multiple third-party services to create a collaborative storytime experience.                                            | Deepgram, ElevenLabs, OpenAI, Fal, Daily, Custom UI               |
+| [Translation Chatbot](translation-chatbot)   | Listens for user speech, then translates that speech to Spanish and speaks the translation back. Demonstrates multi-participant use-cases. | Deepgram, Azure, OpenAI, Daily, Daily Prebuilt UI                 |
+| [Moondream Chatbot](moondream-chatbot)       | Demonstrates how to add vision capabilities to GPT4. **Note: works best with a GPU**                                                       | Deepgram, ElevenLabs, OpenAI, Moondream, Daily, Daily Prebuilt UI |
+| [Patient intake](patient-intake)             | A chatbot that can call functions in response to user input.                                                                               | Deepgram, ElevenLabs, OpenAI, Daily, Daily Prebuilt UI            |
+| [Phone Chatbot](phone-chatbot)             | A chatbot that connects to PSTN/SIP phone calls, powered by 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 |
 
 > [!IMPORTANT]
 > These example projects use Daily as a WebRTC transport and can be joined using their hosted Prebuilt UI.

examples/bot-ready-signalling/README.md

@@ -0,0 +1,45 @@
+# Bot ready signaling
+
+A simple Pipecat example demonstrating how to handle signaling between the client and the bot, 
+ensuring that the bot starts sending audio only when the client is available, 
+thereby avoiding the risk of cutting off the beginning of the audio.
+
+## Quick Start
+
+### First, start the bot server:
+
+1. Navigate to the server directory:
+   ```bash
+   cd server
+   ```
+2. Create and activate a virtual environment:
+   ```bash
+   python3 -m venv venv
+   source venv/bin/activate  # On Windows: venv\Scripts\activate
+   ```
+3. Install requirements:
+   ```bash
+   pip install -r requirements.txt
+   ```
+4. Copy env.example to .env and configure:
+   - Add your API keys
+5. Start the server:
+   ```bash
+   python server.py
+   ```
+
+### Next, connect using the client app:
+
+For client-side setup, refer to the [JavaScript Guide](client/javascript/README.md).
+
+## Important Note
+
+Ensure the bot server is running before using any client implementations.
+
+## Requirements
+
+- Python 3.10+
+- Node.js 16+ (for JavaScript)
+- Daily API key
+- Cartesia API key
+- Modern web browser with WebRTC support

examples/bot-ready-signalling/client/javascript/README.md

@@ -0,0 +1,27 @@
+# JavaScript Implementation
+
+Basic implementation using the [Pipecat JavaScript SDK](https://docs.pipecat.ai/client/js/introduction).
+
+## Setup
+
+1. Run the bot server. See the [server README](../../README).
+
+2. Navigate to the `client/javascript` directory:
+
+```bash
+cd client/javascript
+```
+
+3. Install dependencies:
+
+```bash
+npm install
+```
+
+4. Run the client app:
+
+```
+npm run dev
+```
+
+5. Visit http://localhost:5173 in your browser.

examples/bot-ready-signalling/client/javascript/index.html

@@ -0,0 +1,34 @@
+<!DOCTYPE html>
+<html lang="en">
+
+<head>
+  <meta charset="UTF-8">
+  <meta name="viewport" content="width=device-width, initial-scale=1.0">
+  <title>AI Chatbot</title>
+</head>
+
+<body>
+  <div class="container">
+    <div class="status-bar">
+      <div class="status">
+        Status: <span id="connection-status">Disconnected</span>
+      </div>
+      <div class="controls">
+        <button id="connect-btn">Connect</button>
+        <button id="disconnect-btn" disabled>Disconnect</button>
+      </div>
+    </div>
+
+    <audio id="bot-audio" autoplay></audio>
+
+    <div class="debug-panel">
+      <h3>Debug Info</h3>
+      <div id="debug-log"></div>
+    </div>
+  </div>
+
+  <script type="module" src="/src/app.js"></script>
+  <link rel="stylesheet" href="/src/style.css">
+</body>
+
+</html>

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

@@ -0,0 +1,20 @@
+{
+  "name": "client",
+  "version": "1.0.0",
+  "main": "index.js",
+  "scripts": {
+    "dev": "vite",
+    "build": "vite build",
+    "preview": "vite preview"
+  },
+  "keywords": [],
+  "author": "",
+  "license": "ISC",
+  "description": "",
+  "devDependencies": {
+    "vite": "^6.0.2"
+  },
+  "dependencies": {
+    "@daily-co/daily-js": "0.74.0"
+  }
+}

examples/bot-ready-signalling/client/javascript/src/app.js

@@ -0,0 +1,216 @@
+/**
+ * Copyright (c) 2024–2025, Daily
+ *
+ * SPDX-License-Identifier: BSD 2-Clause License
+ */
+
+import Daily from "@daily-co/daily-js";
+
+/**
+ * ChatbotClient handles the connection and media management for a real-time
+ * voice interaction with an AI bot.
+ */
+class ChatbotClient {
+  constructor() {
+    // Initialize client state
+    this.dailyCallObject = null;
+    this.setupDOMElements();
+    this.setupEventListeners();
+  }
+
+  /**
+   * Set up references to DOM elements and create necessary media elements
+   */
+  setupDOMElements() {
+    // Get references to UI control elements
+    this.connectBtn = document.getElementById('connect-btn');
+    this.disconnectBtn = document.getElementById('disconnect-btn');
+    this.statusSpan = document.getElementById('connection-status');
+    this.debugLog = document.getElementById('debug-log');
+
+    // Create an audio element for bot's voice output
+    this.botAudio = document.createElement('audio');
+    this.botAudio.autoplay = true;
+    this.botAudio.playsInline = true;
+    document.body.appendChild(this.botAudio);
+  }
+
+  /**
+   * Set up event listeners for connect/disconnect buttons
+   */
+  setupEventListeners() {
+    this.connectBtn.addEventListener('click', () => this.connect());
+    this.disconnectBtn.addEventListener('click', () => this.disconnect());
+  }
+
+  /**
+   * Add a timestamped message to the debug log
+   */
+  log(message) {
+    const entry = document.createElement('div');
+    entry.textContent = `${new Date().toISOString()} - ${message}`;
+
+    // Add styling based on message type
+    if (message.startsWith('User: ')) {
+      entry.style.color = '#2196F3'; // blue for user
+    } else if (message.startsWith('Bot: ')) {
+      entry.style.color = '#4CAF50'; // green for bot
+    }
+
+    this.debugLog.appendChild(entry);
+    this.debugLog.scrollTop = this.debugLog.scrollHeight;
+    console.log(message);
+  }
+
+  /**
+   * Update the connection status display
+   */
+  updateStatus(status) {
+    this.statusSpan.textContent = status;
+    this.log(`Status: ${status}`);
+  }
+
+  handleEventToConsole (evt) {
+    this.log(`Received event: ${evt.action}`);
+  };
+
+  /**
+   * Set up listeners for track events (start/stop)
+   * This handles new tracks being added during the session
+   */
+  setupTrackListeners() {
+    if (!this.dailyCallObject) return;
+
+    this.dailyCallObject.on("joined-meeting", () => {
+      this.updateStatus('Connected');
+      this.connectBtn.disabled = true;
+      this.disconnectBtn.disabled = false;
+      this.log('Client connected');
+    });
+    this.dailyCallObject.on("track-started", (evt) => {
+      if (evt.track.kind === "audio" && evt.participant.local === false) {
+        this.log("Audio track started.")
+        this.setupAudioTrack(evt.track);
+      }
+    });
+    this.dailyCallObject.on("track-stopped", this.handleEventToConsole.bind(this));
+    this.dailyCallObject.on("participant-joined", this.handleEventToConsole.bind(this));
+    this.dailyCallObject.on("participant-updated", this.handleEventToConsole.bind(this));
+    this.dailyCallObject.on("participant-left", () => {
+      // When the bot leaves, we are also disconnecting from the call
+      this.disconnect()
+    });
+    this.dailyCallObject.on("left-meeting", () => {
+      this.updateStatus('Disconnected');
+      this.connectBtn.disabled = false;
+      this.disconnectBtn.disabled = true;
+      this.log('Client disconnected');
+    });
+    this.dailyCallObject.on("error", this.handleEventToConsole.bind(this));
+  }
+
+  /**
+   * Set up an audio track for playback
+   * Handles both initial setup and track updates
+   */
+  setupAudioTrack(track) {
+    this.log(`Setting up audio track, track state: ${track.readyState}, muted: ${track.muted}`);
+
+    // Check if we're already playing this track
+    if (this.botAudio.srcObject) {
+      const oldTrack = this.botAudio.srcObject.getAudioTracks()[0];
+      if (oldTrack?.id === track.id) return;
+    }
+    // Create a new MediaStream with the track and set it as the audio source
+    this.botAudio.srcObject = new MediaStream([track]);
+    this.botAudio.onplaying = async (event) => {
+      this.log("onplaying")
+      this.log("Will send the audio message to play the audio at the next tick")
+      this.dailyCallObject.sendAppMessage("playable")
+    }
+  }
+
+  async fetchRoomInfo() {
+    let connectUrl = '/connect'
+    let res = await fetch(connectUrl, {
+      method: "POST",
+      mode: "cors",
+      headers: new Headers({
+        "Content-Type": "application/json"
+      }),
+    })
+    if (res.ok) {
+      return res.json();
+    }
+  }
+
+  /**
+   * Initialize and connect to the bot
+   * This sets up the RTVI client, initializes devices, and establishes the connection
+   */
+  async connect() {
+    try {
+      // Initialize the client
+      this.dailyCallObject = Daily.createCallObject({
+        subscribeToTracksAutomatically: true,
+      });
+
+      // Set up listeners for media track events
+      this.setupTrackListeners();
+
+      this.log('Creating the bot...');
+      let roomInfo = await this.fetchRoomInfo()
+
+      // Connect to the bot
+      this.log('Connecting to bot...');
+      // Only for making debugger easier
+      window.callObject = this.dailyCallObject;
+      await this.dailyCallObject.join({
+        url: roomInfo.room_url,
+      });
+
+      this.log('Connection complete');
+    } catch (error) {
+      // Handle any errors during connection
+      this.log(`Error connecting: ${error.message}`);
+      this.log(`Error stack: ${error.stack}`);
+      this.updateStatus('Error');
+
+      // Clean up if there's an error
+      if (this.dailyCallObject) {
+        try {
+          await this.dailyCallObject.leave();
+        } catch (disconnectError) {
+          this.log(`Error during disconnect: ${disconnectError.message}`);
+        }
+      }
+    }
+  }
+
+  /**
+   * Disconnect from the bot and clean up media resources
+   */
+  async disconnect() {
+    if (this.dailyCallObject) {
+      try {
+        // Disconnect the RTVI client
+        await this.dailyCallObject.leave();
+        await this.dailyCallObject.destroy();
+        this.dailyCallObject = null;
+
+        // Clean up audio
+        if (this.botAudio.srcObject) {
+          this.botAudio.srcObject.getTracks().forEach((track) => track.stop());
+          this.botAudio.srcObject = null;
+        }
+      } catch (error) {
+        this.log(`Error disconnecting: ${error.message}`);
+      }
+    }
+  }
+}
+
+// Initialize the client when the page loads
+window.addEventListener('DOMContentLoaded', () => {
+  new ChatbotClient();
+});

examples/bot-ready-signalling/client/javascript/src/style.css

@@ -0,0 +1,98 @@
+body {
+  margin: 0;
+  padding: 20px;
+  font-family: Arial, sans-serif;
+  background-color: #f0f0f0;
+}
+
+.container {
+  max-width: 1200px;
+  margin: 0 auto;
+}
+
+.status-bar {
+  display: flex;
+  justify-content: space-between;
+  align-items: center;
+  padding: 10px;
+  background-color: #fff;
+  border-radius: 8px;
+  margin-bottom: 20px;
+}
+
+.controls button {
+  padding: 8px 16px;
+  margin-left: 10px;
+  border: none;
+  border-radius: 4px;
+  cursor: pointer;
+}
+
+#connect-btn {
+  background-color: #4caf50;
+  color: white;
+}
+
+#disconnect-btn {
+  background-color: #f44336;
+  color: white;
+}
+
+button:disabled {
+  opacity: 0.5;
+  cursor: not-allowed;
+}
+
+.main-content {
+  background-color: #fff;
+  border-radius: 8px;
+  padding: 20px;
+  margin-bottom: 20px;
+}
+
+.bot-container {
+  display: flex;
+  flex-direction: column;
+  align-items: center;
+}
+
+#bot-video-container {
+  width: 640px;
+  height: 360px;
+  background-color: #e0e0e0;
+  border-radius: 8px;
+  margin: 20px auto;
+  overflow: hidden;
+  display: flex;
+  align-items: center;
+  justify-content: center;
+}
+
+#bot-video-container video {
+  width: 100%;
+  height: 100%;
+  object-fit: cover;
+}
+
+.debug-panel {
+  background-color: #fff;
+  border-radius: 8px;
+  padding: 20px;
+}
+
+.debug-panel h3 {
+  margin: 0 0 10px 0;
+  font-size: 16px;
+  font-weight: bold;
+}
+
+#debug-log {
+  height: 200px;
+  overflow-y: auto;
+  background-color: #f8f8f8;
+  padding: 10px;
+  border-radius: 4px;
+  font-family: monospace;
+  font-size: 12px;
+  line-height: 1.4;
+}

examples/bot-ready-signalling/client/javascript/vite.config.js

@@ -0,0 +1,13 @@
+import { defineConfig } from 'vite';
+
+export default defineConfig({
+    server: {
+        proxy: {
+            // Proxy /api requests to the backend server
+            '/connect': {
+                target: 'http://0.0.0.0:7860', // Replace with your backend URL
+                changeOrigin: true,
+            },
+        },
+    },
+});

examples/bot-ready-signalling/server/README.md

@@ -0,0 +1,50 @@
+# Bot ready signaling Server
+
+A FastAPI server that manages bot instances and provide endpoint for Pipecat client connections.
+
+## Endpoints
+
+- `POST /connect` - Pipecat client connection endpoint
+
+## Environment Variables
+
+Copy `env.example` to `.env` and configure:
+
+```ini
+# Required API Keys
+DAILY_API_KEY=           # Your Daily API key
+CARTESIA_API_KEY=        # Your Cartesia API key
+
+# Optional Configuration
+DAILY_API_URL=           # Optional: Daily API URL (defaults to https://api.daily.co/v1)
+DAILY_SAMPLE_ROOM_URL=   # Optional: Fixed room URL for development
+HOST=                    # Optional: Host address (defaults to 0.0.0.0)
+FAST_API_PORT=           # Optional: Port number (defaults to 7860)
+```
+
+## Running the Server
+
+Set up and activate your virtual environment:
+
+```bash
+python3 -m venv venv
+source venv/bin/activate  # On Windows: venv\Scripts\activate
+```
+
+Install dependencies:
+
+```bash
+pip install -r requirements.txt
+```
+
+If you want to use the local version of `pipecat` in this repo rather than the last published version, also run:
+
+```bash
+pip install --editable "../../../[daily,cartesia,openai]"
+```
+
+Run the server:
+
+```bash
+python server.py
+```

examples/bot-ready-signalling/server/env.example

@@ -0,0 +1,3 @@
+DAILY_SAMPLE_ROOM_URL=https://yourdomain.daily.co/yourroom # (for joining the bot to the same room repeatedly for local dev)
+DAILY_API_KEY=
+CARTESIA_API_KEY=

examples/bot-ready-signalling/server/requirements.txt

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

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

@@ -0,0 +1,64 @@
+#
+# Copyright (c) 2024–2025, Daily
+#
+# SPDX-License-Identifier: BSD 2-Clause License
+#
+
+import argparse
+import os
+from typing import Optional
+
+import aiohttp
+
+from pipecat.transports.services.helpers.daily_rest import DailyRESTHelper
+
+
+async def configure(aiohttp_session: aiohttp.ClientSession):
+    (url, token, _) = await configure_with_args(aiohttp_session)
+    return (url, token)
+
+
+async def configure_with_args(
+    aiohttp_session: aiohttp.ClientSession, parser: Optional[argparse.ArgumentParser] = None
+):
+    if not parser:
+        parser = argparse.ArgumentParser(description="Daily AI SDK Bot Sample")
+    parser.add_argument(
+        "-u", "--url", type=str, required=False, help="URL of the Daily room to join"
+    )
+    parser.add_argument(
+        "-k",
+        "--apikey",
+        type=str,
+        required=False,
+        help="Daily API Key (needed to create an owner token for the room)",
+    )
+
+    args, unknown = parser.parse_known_args()
+
+    url = args.url or os.getenv("DAILY_SAMPLE_ROOM_URL")
+    key = args.apikey or os.getenv("DAILY_API_KEY")
+
+    if not url:
+        raise Exception(
+            "No Daily room specified. use the -u/--url option from the command line, or set DAILY_SAMPLE_ROOM_URL in your environment to specify a Daily room URL."
+        )
+
+    if not key:
+        raise Exception(
+            "No Daily API key specified. use the -k/--apikey option from the command line, or set DAILY_API_KEY in your environment to specify a Daily API key, available from https://dashboard.daily.co/developers."
+        )
+
+    daily_rest_helper = DailyRESTHelper(
+        daily_api_key=key,
+        daily_api_url=os.getenv("DAILY_API_URL", "https://api.daily.co/v1"),
+        aiohttp_session=aiohttp_session,
+    )
+
+    # Create a meeting token for the given room with an expiration 1 hour in
+    # the future.
+    expiry_time: float = 60 * 60
+
+    token = await daily_rest_helper.get_token(url, expiry_time)
+
+    return (url, token, args)

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

@@ -0,0 +1,147 @@
+#
+# Copyright (c) 2025, Daily
+#
+# SPDX-License-Identifier: BSD 2-Clause License
+#
+
+import argparse
+import os
+import subprocess
+from contextlib import asynccontextmanager
+from typing import Any, Dict
+
+import aiohttp
+from dotenv import load_dotenv
+from fastapi import FastAPI, HTTPException, Request
+from fastapi.middleware.cors import CORSMiddleware
+from fastapi.responses import JSONResponse
+
+from pipecat.transports.services.helpers.daily_rest import DailyRESTHelper, DailyRoomParams
+
+# Load environment variables from .env file
+load_dotenv(override=True)
+
+# Dictionary to track bot processes: {pid: (process, room_url)}
+bot_procs = {}
+
+# Store Daily API helpers
+daily_helpers = {}
+
+
+def cleanup():
+    """Cleanup function to terminate all bot processes.
+
+    Called during server shutdown.
+    """
+    for entry in bot_procs.values():
+        proc = entry[0]
+        proc.terminate()
+        proc.wait()
+
+
+@asynccontextmanager
+async def lifespan(app: FastAPI):
+    """FastAPI lifespan manager that handles startup and shutdown tasks.
+
+    - Creates aiohttp session
+    - Initializes Daily API helper
+    - Cleans up resources on shutdown
+    """
+    aiohttp_session = aiohttp.ClientSession()
+    daily_helpers["rest"] = DailyRESTHelper(
+        daily_api_key=os.getenv("DAILY_API_KEY", ""),
+        daily_api_url=os.getenv("DAILY_API_URL", "https://api.daily.co/v1"),
+        aiohttp_session=aiohttp_session,
+    )
+    yield
+    await aiohttp_session.close()
+    cleanup()
+
+
+# Initialize FastAPI app with lifespan manager
+app = FastAPI(lifespan=lifespan)
+
+# Configure CORS to allow requests from any origin
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+
+async def create_room_and_token() -> tuple[str, str]:
+    """Helper function to create a Daily room and generate an access token.
+
+    Returns:
+        tuple[str, str]: A tuple containing (room_url, token)
+
+    Raises:
+        HTTPException: If room creation or token generation fails
+    """
+    room = await daily_helpers["rest"].create_room(DailyRoomParams())
+    if not room.url:
+        raise HTTPException(status_code=500, detail="Failed to create room")
+
+    token = await daily_helpers["rest"].get_token(room.url)
+    if not token:
+        raise HTTPException(status_code=500, detail=f"Failed to get token for room: {room.url}")
+
+    return room.url, token
+
+
+@app.post("/connect")
+async def bot_connect(request: Request) -> Dict[Any, Any]:
+    """Connect endpoint that creates a room and returns connection credentials.
+
+    This endpoint is called by client to establish a connection.
+
+    Returns:
+        Dict[Any, Any]: Authentication bundle containing room_url and token
+
+    Raises:
+        HTTPException: If room creation, token generation, or bot startup fails
+    """
+    print("Creating room for RTVI connection")
+    room_url, token = await create_room_and_token()
+    print(f"Room URL: {room_url}")
+
+    # Start the bot process
+    try:
+        bot_file = "signalling_bot"
+        proc = subprocess.Popen(
+            [f"python3 -m {bot_file} -u {room_url} -t {token}"],
+            shell=True,
+            bufsize=1,
+            cwd=os.path.dirname(os.path.abspath(__file__)),
+        )
+        bot_procs[proc.pid] = (proc, room_url)
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=f"Failed to start subprocess: {e}")
+
+    # Return the authentication bundle in format expected by DailyTransport
+    return {"room_url": room_url, "token": token}
+
+
+if __name__ == "__main__":
+    import uvicorn
+
+    # Parse command line arguments for server configuration
+    default_host = os.getenv("HOST", "0.0.0.0")
+    default_port = int(os.getenv("FAST_API_PORT", "7860"))
+
+    parser = argparse.ArgumentParser(description="Daily Travel Companion FastAPI server")
+    parser.add_argument("--host", type=str, default=default_host, help="Host address")
+    parser.add_argument("--port", type=int, default=default_port, help="Port number")
+    parser.add_argument("--reload", action="store_true", help="Reload code on change")
+
+    config = parser.parse_args()
+
+    # Start the FastAPI server
+    uvicorn.run(
+        "server:app",
+        host=config.host,
+        port=config.port,
+        reload=config.reload,
+    )

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

@@ -0,0 +1,95 @@
+#
+# Copyright (c) 2024–2025, Daily
+#
+# SPDX-License-Identifier: BSD 2-Clause License
+#
+
+import asyncio
+import os
+import sys
+from dataclasses import dataclass
+
+import aiohttp
+from dotenv import load_dotenv
+from loguru import logger
+from runner import configure
+
+from pipecat.frames.frames import AudioRawFrame, EndFrame, OutputAudioRawFrame, TTSSpeakFrame
+from pipecat.pipeline.pipeline import Pipeline
+from pipecat.pipeline.runner import PipelineRunner
+from pipecat.pipeline.task import PipelineParams, PipelineTask
+from pipecat.services.cartesia import CartesiaTTSService
+from pipecat.transports.services.daily import DailyParams, DailyTransport
+
+load_dotenv(override=True)
+
+logger.remove(0)
+logger.add(sys.stderr, level="DEBUG")
+
+
+@dataclass
+class SilenceFrame(OutputAudioRawFrame):
+    def __init__(
+        self,
+        *,
+        sample_rate: int,
+        duration: float,
+    ):
+        # Initialize the parent class with the silent frame's data
+        super().__init__(
+            audio=self.create_silent_audio_frame(sample_rate, 1, duration).audio,
+            sample_rate=sample_rate,
+            num_channels=1,
+        )
+
+    @staticmethod
+    def create_silent_audio_frame(
+        sample_rate: int, num_channels: int, duration: float
+    ) -> AudioRawFrame:
+        """Create an AudioRawFrame containing silence."""
+        frame_size = num_channels * 2  # 2 bytes per sample for 16-bit audio
+        total_frames = int(sample_rate * duration)
+        total_bytes = total_frames * frame_size
+        silent_audio = bytes(total_bytes)  # Create a byte array filled with zeros
+        return AudioRawFrame(audio=silent_audio, sample_rate=sample_rate, num_channels=num_channels)
+
+
+async def main():
+    async with aiohttp.ClientSession() as session:
+        (room_url, _) = await configure(session)
+
+        transport = DailyTransport(
+            room_url, None, "Say One Thing", DailyParams(audio_out_enabled=True)
+        )
+
+        tts = CartesiaTTSService(
+            api_key=os.getenv("CARTESIA_API_KEY"),
+            voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22",  # British Lady
+        )
+
+        runner = PipelineRunner()
+
+        task = PipelineTask(Pipeline([tts, transport.output()]))
+
+        # Register an event handler so we can play the audio when we receive a specific message
+        @transport.event_handler("on_app_message")
+        async def on_app_message(transport, message, sender):
+            logger.debug(f"Received app message: {message} - {sender}")
+            if "playable" not in message:
+                return
+            await task.queue_frames(
+                [
+                    SilenceFrame(
+                        sample_rate=task.params.audio_out_sample_rate,
+                        duration=0.5,
+                    ),
+                    TTSSpeakFrame(f"Hello there, how are you doing today ?"),
+                    EndFrame(),
+                ]
+            )
+
+        await runner.run(task)
+
+
+if __name__ == "__main__":
+    asyncio.run(main())

examples/canonical-metrics/.gitignore

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

examples/canonical-metrics/Dockerfile

@@ -0,0 +1,10 @@
+FROM python:3.10-bullseye
+RUN mkdir /app
+COPY *.py /app/
+COPY requirements.txt /app/
+WORKDIR /app
+RUN pip3 install -r requirements.txt
+
+EXPOSE 7860
+
+CMD ["python3", "server.py"]

examples/canonical-metrics/README.md

@@ -0,0 +1,66 @@
+# Chatbot with canonical-metrics
+
+This project implements a chatbot using a pipeline architecture that integrates audio processing, transcription, and a language model for conversational interactions. The chatbot operates within a daily communication environment, utilizing various services for text-to-speech and language model responses.
+
+## Features
+
+- **Audio Input and Output**: Captures microphone input and plays back audio responses.
+- **Voice Activity Detection**: Utilizes Silero VAD to manage audio input intelligently.
+- **Text-to-Speech**: Integrates ElevenLabs TTS service to convert text responses into audio.
+- **Language Model Interaction**: Uses OpenAI's GPT-4 model to generate responses based on user input.
+- **Transcription Services**: Captures and transcribes participant speech for analytics.
+- **Metrics Collection**: Sends audio data for analysis via Canonical Metrics Service.
+
+## Requirements
+
+- Python 3.10+
+- `python-dotenv`
+- Additional libraries from the `pipecat` package.
+
+## Setup
+
+1. Clone the repository.
+2. Install the required packages.
+3. Set up environment variables for API keys:
+   - `OPENAI_API_KEY`
+   - `ELEVENLABS_API_KEY`
+   - `CANONICAL_API_KEY`
+   - `CANONICAL_API_URL`
+4. Run the script.
+
+## Usage
+
+The chatbot introduces itself and engages in conversations, providing brief and creative responses. Designed for flexibility, it can support multiple languages with appropriate configuration.
+
+## Events
+
+- Participants joining or leaving the call are handled dynamically, adjusting the chatbot's behavior accordingly.
+
+
+ℹ️ The first time, things might take extra time to get started since VAD (Voice Activity Detection) model needs to be downloaded.
+
+## Get started
+
+```python
+python3 -m venv venv
+source venv/bin/activate
+pip install -r requirements.txt
+
+cp env.example .env # and add your credentials
+
+```
+
+## Run the server
+
+```bash
+python server.py
+```
+
+Then, visit `http://localhost:7860/` in your browser to start a chatbot session.
+
+## Build and test the Docker image
+
+```
+docker build -t chatbot .
+docker run --env-file .env -p 7860:7860 chatbot
+```

examples/canonical-metrics/bot.py

@@ -0,0 +1,148 @@
+#
+# Copyright (c) 2024–2025, Daily
+#
+# SPDX-License-Identifier: BSD 2-Clause License
+#
+
+import asyncio
+import os
+import sys
+import uuid
+
+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.processors.audio.audio_buffer_processor import AudioBufferProcessor
+from pipecat.services.canonical import CanonicalMetricsService
+from pipecat.services.elevenlabs import ElevenLabsTTSService
+from pipecat.services.openai import OpenAILLMService
+from pipecat.transports.services.daily import DailyParams, DailyTransport
+
+load_dotenv(override=True)
+
+logger.remove(0)
+logger.add(sys.stderr, level="DEBUG")
+
+
+async def main():
+    async with aiohttp.ClientSession() as session:
+        (room_url, token) = await configure(session)
+
+        transport = DailyTransport(
+            room_url,
+            token,
+            "Chatbot",
+            DailyParams(
+                audio_out_enabled=True,
+                audio_in_enabled=True,
+                camera_out_enabled=False,
+                vad_enabled=True,
+                vad_audio_passthrough=True,
+                vad_analyzer=SileroVADAnalyzer(),
+                transcription_enabled=True,
+                #
+                # Spanish
+                #
+                # transcription_settings=DailyTranscriptionSettings(
+                #     language="es",
+                #     tier="nova",
+                #     model="2-general"
+                # )
+            ),
+        )
+
+        tts = ElevenLabsTTSService(
+            api_key=os.getenv("ELEVENLABS_API_KEY"),
+            #
+            # English
+            #
+            voice_id="cgSgspJ2msm6clMCkdW9",
+            #
+            # Spanish
+            #
+            # model="eleven_multilingual_v2",
+            # voice_id="gD1IexrzCvsXPHUuT0s3",
+        )
+
+        llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
+
+        messages = [
+            {
+                "role": "system",
+                #
+                # English
+                #
+                "content": "You are Chatbot, a friendly, helpful robot. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way, but keep your responses brief. Start by introducing yourself. Keep all your responses to 12 words or fewer.",
+                #
+                # Spanish
+                #
+                # "content": "Eres Chatbot, un amigable y útil robot. Tu objetivo es demostrar tus capacidades de una manera breve. Tus respuestas se convertiran a audio así que nunca no debes incluir caracteres especiales. Contesta a lo que el usuario pregunte de una manera creativa, útil y breve. Empieza por presentarte a ti mismo.",
+            },
+        ]
+
+        context = OpenAILLMContext(messages)
+        context_aggregator = llm.create_context_aggregator(context)
+
+        """
+        CanonicalMetrics uses AudioBufferProcessor under the hood to buffer the audio. On
+        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)
+        canonical = CanonicalMetricsService(
+            audio_buffer_processor=audio_buffer_processor,
+            aiohttp_session=session,
+            api_key=os.getenv("CANONICAL_API_KEY"),
+            call_id=str(uuid.uuid4()),
+            assistant="pipecat-chatbot",
+            assistant_speaks_first=True,
+            context=context,
+        )
+        pipeline = Pipeline(
+            [
+                transport.input(),  # microphone
+                context_aggregator.user(),
+                llm,
+                tts,
+                transport.output(),
+                audio_buffer_processor,  # captures audio into a buffer
+                canonical,  # uploads audio buffer to Canonical AI for metrics
+                context_aggregator.assistant(),
+            ]
+        )
+
+        task = PipelineTask(pipeline, PipelineParams(allow_interruptions=True))
+
+        @transport.event_handler("on_first_participant_joined")
+        async def on_first_participant_joined(transport, participant):
+            await audio_buffer_processor.start_recording()
+            await transport.capture_participant_transcription(participant["id"])
+            await task.queue_frames([context_aggregator.user().get_context_frame()])
+
+        @transport.event_handler("on_participant_left")
+        async def on_participant_left(transport, participant, reason):
+            print(f"Participant left: {participant}")
+            await task.cancel()
+
+        @transport.event_handler("on_call_state_updated")
+        async def on_call_state_updated(transport, state):
+            if state == "left":
+                # Here we don't want to cancel, we just want to finish sending
+                # whatever is queued, so we use an EndFrame().
+                await task.queue_frame(EndFrame())
+
+        runner = PipelineRunner()
+
+        await runner.run(task)
+
+
+if __name__ == "__main__":
+    asyncio.run(main())

examples/canonical-metrics/env.example

@@ -0,0 +1,6 @@
+DAILY_SAMPLE_ROOM_URL=https://yourdomain.daily.co/yourroom # (for joining the bot to the same room repeatedly for local dev)
+DAILY_API_KEY=7df...
+OPENAI_API_KEY=sk-PL...
+ELEVENLABS_API_KEY=aeb...
+CANONICAL_API_KEY=can...
+CANONICAL_API_URL=

examples/canonical-metrics/requirements.txt

@@ -0,0 +1,5 @@
+python-dotenv
+fastapi[all]
+uvicorn
+pipecat-ai[daily,openai,silero,elevenlabs,canonical]
+

examples/canonical-metrics/runner.py

@@ -0,0 +1,55 @@
+#
+# Copyright (c) 2024–2025, Daily
+#
+# SPDX-License-Identifier: BSD 2-Clause License
+#
+
+import argparse
+import os
+
+import aiohttp
+
+from pipecat.transports.services.helpers.daily_rest import DailyRESTHelper
+
+
+async def configure(aiohttp_session: aiohttp.ClientSession):
+    parser = argparse.ArgumentParser(description="Daily AI SDK Bot Sample")
+    parser.add_argument(
+        "-u", "--url", type=str, required=False, help="URL of the Daily room to join"
+    )
+    parser.add_argument(
+        "-k",
+        "--apikey",
+        type=str,
+        required=False,
+        help="Daily API Key (needed to create an owner token for the room)",
+    )
+
+    args, unknown = parser.parse_known_args()
+
+    url = args.url or os.getenv("DAILY_SAMPLE_ROOM_URL")
+    key = args.apikey or os.getenv("DAILY_API_KEY")
+
+    if not url:
+        raise Exception(
+            "No Daily room specified. use the -u/--url option from the command line, or set DAILY_SAMPLE_ROOM_URL in your environment to specify a Daily room URL."
+        )
+
+    if not key:
+        raise Exception(
+            "No Daily API key specified. use the -k/--apikey option from the command line, or set DAILY_API_KEY in your environment to specify a Daily API key, available from https://dashboard.daily.co/developers."
+        )
+
+    daily_rest_helper = DailyRESTHelper(
+        daily_api_key=key,
+        daily_api_url=os.getenv("DAILY_API_URL", "https://api.daily.co/v1"),
+        aiohttp_session=aiohttp_session,
+    )
+
+    # Create a meeting token for the given room with an expiration 1 hour in
+    # the future.
+    expiry_time: float = 60 * 60
+
+    token = await daily_rest_helper.get_token(url, expiry_time)
+
+    return (url, token)

examples/canonical-metrics/server.py

@@ -0,0 +1,139 @@
+#
+# Copyright (c) 2024–2025, Daily
+#
+# SPDX-License-Identifier: BSD 2-Clause License
+#
+
+import argparse
+import os
+import subprocess
+from contextlib import asynccontextmanager
+
+import aiohttp
+from dotenv import load_dotenv
+from fastapi import FastAPI, HTTPException, Request
+from fastapi.middleware.cors import CORSMiddleware
+from fastapi.responses import JSONResponse, RedirectResponse
+
+from pipecat.transports.services.helpers.daily_rest import DailyRESTHelper, DailyRoomParams
+
+MAX_BOTS_PER_ROOM = 1
+
+# Bot sub-process dict for status reporting and concurrency control
+bot_procs = {}
+
+daily_helpers = {}
+
+load_dotenv(override=True)
+
+
+def cleanup():
+    # Clean up function, just to be extra safe
+    for entry in bot_procs.values():
+        proc = entry[0]
+        proc.terminate()
+        proc.wait()
+
+
+@asynccontextmanager
+async def lifespan(app: FastAPI):
+    aiohttp_session = aiohttp.ClientSession()
+    daily_helpers["rest"] = DailyRESTHelper(
+        daily_api_key=os.getenv("DAILY_API_KEY", ""),
+        daily_api_url=os.getenv("DAILY_API_URL", "https://api.daily.co/v1"),
+        aiohttp_session=aiohttp_session,
+    )
+    yield
+    await aiohttp_session.close()
+    cleanup()
+
+
+app = FastAPI(lifespan=lifespan)
+
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+
+@app.get("/")
+async def start_agent(request: Request):
+    print(f"!!! Creating room")
+    room = await daily_helpers["rest"].create_room(DailyRoomParams())
+    print(f"!!! Room URL: {room.url}")
+    # Ensure the room property is present
+    if not room.url:
+        raise HTTPException(
+            status_code=500,
+            detail="Missing 'room' property in request data. Cannot start agent without a target room!",
+        )
+
+    # Check if there is already an existing process running in this room
+    num_bots_in_room = sum(
+        1 for proc in bot_procs.values() if proc[1] == room.url and proc[0].poll() is None
+    )
+    if num_bots_in_room >= MAX_BOTS_PER_ROOM:
+        raise HTTPException(status_code=500, detail=f"Max bot limited reach for room: {room.url}")
+
+    # Get the token for the room
+    token = await daily_helpers["rest"].get_token(room.url)
+
+    if not token:
+        raise HTTPException(status_code=500, detail=f"Failed to get token for room: {room.url}")
+
+    # Spawn a new agent, and join the user session
+    # Note: this is mostly for demonstration purposes (refer to 'deployment' in README)
+    try:
+        proc = subprocess.Popen(
+            [f"python3 -m bot -u {room.url} -t {token}"],
+            shell=True,
+            bufsize=1,
+            cwd=os.path.dirname(os.path.abspath(__file__)),
+        )
+        bot_procs[proc.pid] = (proc, room.url)
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=f"Failed to start subprocess: {e}")
+
+    return RedirectResponse(room.url)
+
+
+@app.get("/status/{pid}")
+def get_status(pid: int):
+    # Look up the subprocess
+    proc = bot_procs.get(pid)
+
+    # If the subprocess doesn't exist, return an error
+    if not proc:
+        raise HTTPException(status_code=404, detail=f"Bot with process id: {pid} not found")
+
+    # Check the status of the subprocess
+    if proc[0].poll() is None:
+        status = "running"
+    else:
+        status = "finished"
+
+    return JSONResponse({"bot_id": pid, "status": status})
+
+
+if __name__ == "__main__":
+    import uvicorn
+
+    default_host = os.getenv("HOST", "0.0.0.0")
+    default_port = int(os.getenv("FAST_API_PORT", "7860"))
+
+    parser = argparse.ArgumentParser(description="Daily Storyteller FastAPI server")
+    parser.add_argument("--host", type=str, default=default_host, help="Host address")
+    parser.add_argument("--port", type=int, default=default_port, help="Port number")
+    parser.add_argument("--reload", action="store_true", help="Reload code on change")
+
+    config = parser.parse_args()
+
+    uvicorn.run(
+        "server:app",
+        host=config.host,
+        port=config.port,
+        reload=config.reload,
+    )

examples/chatbot-audio-recording/.gitignore

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

examples/chatbot-audio-recording/Dockerfile

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

examples/chatbot-audio-recording/README.md

@@ -0,0 +1,37 @@
+# Simple Chatbot
+
+<img src="image.png" width="420px">
+
+This app connects you to a chatbot powered by GPT-4, complete with animations generated by Stable Video Diffusion.
+
+See a video of it in action: https://x.com/kwindla/status/1778628911817183509
+
+And a quick video walkthrough of the code: https://www.loom.com/share/13df1967161f4d24ade054e7f8753416
+
+ℹ️ The first time, things might take extra time to get started since VAD (Voice Activity Detection) model needs to be downloaded.
+
+## Get started
+
+```python
+python3 -m venv venv
+source venv/bin/activate
+pip install -r requirements.txt
+
+cp env.example .env # and add your credentials
+
+```
+
+## Run the server
+
+```bash
+python server.py
+```
+
+Then, visit `http://localhost:7860/` in your browser to start a chatbot session.
+
+## Build and test the Docker image
+
+```
+docker build -t chatbot .
+docker run --env-file .env -p 7860:7860 chatbot
+```

examples/chatbot-audio-recording/bot.py

@@ -0,0 +1,150 @@
+#
+# Copyright (c) 2024–2025, Daily
+#
+# SPDX-License-Identifier: BSD 2-Clause License
+#
+
+import asyncio
+import datetime
+import io
+import os
+import sys
+import wave
+
+import aiofiles
+import aiohttp
+from dotenv import load_dotenv
+from loguru import logger
+from runner import configure
+
+from pipecat.audio.vad.silero import SileroVADAnalyzer
+from pipecat.pipeline.pipeline import Pipeline
+from pipecat.pipeline.runner import PipelineRunner
+from pipecat.pipeline.task import PipelineParams, PipelineTask
+from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
+from pipecat.processors.audio.audio_buffer_processor import AudioBufferProcessor
+from pipecat.services.elevenlabs import ElevenLabsTTSService
+from pipecat.services.openai import OpenAILLMService
+from pipecat.transports.services.daily import DailyParams, DailyTransport
+
+load_dotenv(override=True)
+
+logger.remove(0)
+logger.add(sys.stderr, level="DEBUG")
+
+
+async def save_audio(audio: bytes, sample_rate: int, num_channels: int):
+    if len(audio) > 0:
+        filename = f"conversation_recording{datetime.datetime.now().strftime('%Y%m%d_%H%M%S')}.wav"
+        with io.BytesIO() as buffer:
+            with wave.open(buffer, "wb") as wf:
+                wf.setsampwidth(2)
+                wf.setnchannels(num_channels)
+                wf.setframerate(sample_rate)
+                wf.writeframes(audio)
+            async with aiofiles.open(filename, "wb") as file:
+                await file.write(buffer.getvalue())
+        print(f"Merged audio saved to {filename}")
+    else:
+        print("No audio data to save")
+
+
+async def main():
+    async with aiohttp.ClientSession() as session:
+        (room_url, token) = await configure(session)
+
+        transport = DailyTransport(
+            room_url,
+            token,
+            "Chatbot",
+            DailyParams(
+                audio_out_enabled=True,
+                audio_in_enabled=True,
+                camera_out_enabled=False,
+                vad_enabled=True,
+                vad_audio_passthrough=True,
+                vad_analyzer=SileroVADAnalyzer(),
+                transcription_enabled=True,
+                #
+                # Spanish
+                #
+                # transcription_settings=DailyTranscriptionSettings(
+                #     language="es",
+                #     tier="nova",
+                #     model="2-general"
+                # )
+            ),
+        )
+
+        tts = ElevenLabsTTSService(
+            api_key=os.getenv("ELEVENLABS_API_KEY"),
+            #
+            # English
+            #
+            voice_id="cgSgspJ2msm6clMCkdW9",
+            #
+            # Spanish
+            #
+            # model="eleven_multilingual_v2",
+            # voice_id="gD1IexrzCvsXPHUuT0s3",
+        )
+
+        llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
+
+        messages = [
+            {
+                "role": "system",
+                #
+                # English
+                #
+                "content": "You are Chatbot, a friendly, helpful robot. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way, but keep your responses brief. Start by introducing yourself. Keep all your response to 12 words or fewer.",
+                #
+                # Spanish
+                #
+                # "content": "Eres Chatbot, un amigable y útil robot. Tu objetivo es demostrar tus capacidades de una manera breve. Tus respuestas se convertiran a audio así que nunca no debes incluir caracteres especiales. Contesta a lo que el usuario pregunte de una manera creativa, útil y breve. Empieza por presentarte a ti mismo.",
+            },
+        ]
+
+        context = OpenAILLMContext(messages)
+        context_aggregator = llm.create_context_aggregator(context)
+
+        # NOTE: Watch out! This will save all the conversation in memory. You
+        # can pass `buffer_size` to get periodic callbacks.
+        audiobuffer = AudioBufferProcessor()
+
+        pipeline = Pipeline(
+            [
+                transport.input(),  # microphone
+                context_aggregator.user(),
+                llm,
+                tts,
+                transport.output(),
+                audiobuffer,  # used to buffer the audio in the pipeline
+                context_aggregator.assistant(),
+            ]
+        )
+
+        task = PipelineTask(pipeline, PipelineParams(allow_interruptions=True))
+
+        @audiobuffer.event_handler("on_audio_data")
+        async def on_audio_data(buffer, audio, sample_rate, num_channels):
+            await save_audio(audio, sample_rate, num_channels)
+
+        @transport.event_handler("on_first_participant_joined")
+        async def on_first_participant_joined(transport, participant):
+            await audiobuffer.start_recording()
+            await transport.capture_participant_transcription(participant["id"])
+            await task.queue_frames([context_aggregator.user().get_context_frame()])
+
+        @transport.event_handler("on_participant_left")
+        async def on_participant_left(transport, participant, reason):
+            print(f"Participant left: {participant}")
+            await task.cancel()
+
+        runner = PipelineRunner()
+
+        await runner.run(task)
+
+
+if __name__ == "__main__":
+    asyncio.run(main())

examples/chatbot-audio-recording/requirements.txt

@@ -0,0 +1,5 @@
+aiofiles
+python-dotenv
+fastapi[all]
+uvicorn
+pipecat-ai[daily,openai,silero,elevenlabs]

examples/chatbot-audio-recording/runner.py

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

examples/chatbot-audio-recording/server.py

@@ -0,0 +1,139 @@
+#
+# Copyright (c) 2024–2025, Daily
+#
+# SPDX-License-Identifier: BSD 2-Clause License
+#
+
+import argparse
+import os
+import subprocess
+from contextlib import asynccontextmanager
+
+import aiohttp
+from dotenv import load_dotenv
+from fastapi import FastAPI, HTTPException, Request
+from fastapi.middleware.cors import CORSMiddleware
+from fastapi.responses import JSONResponse, RedirectResponse
+
+from pipecat.transports.services.helpers.daily_rest import DailyRESTHelper, DailyRoomParams
+
+MAX_BOTS_PER_ROOM = 1
+
+# Bot sub-process dict for status reporting and concurrency control
+bot_procs = {}
+
+daily_helpers = {}
+
+load_dotenv(override=True)
+
+
+def cleanup():
+    # Clean up function, just to be extra safe
+    for entry in bot_procs.values():
+        proc = entry[0]
+        proc.terminate()
+        proc.wait()
+
+
+@asynccontextmanager
+async def lifespan(app: FastAPI):
+    aiohttp_session = aiohttp.ClientSession()
+    daily_helpers["rest"] = DailyRESTHelper(
+        daily_api_key=os.getenv("DAILY_API_KEY", ""),
+        daily_api_url=os.getenv("DAILY_API_URL", "https://api.daily.co/v1"),
+        aiohttp_session=aiohttp_session,
+    )
+    yield
+    await aiohttp_session.close()
+    cleanup()
+
+
+app = FastAPI(lifespan=lifespan)
+
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+
+@app.get("/")
+async def start_agent(request: Request):
+    print(f"!!! Creating room")
+    room = await daily_helpers["rest"].create_room(DailyRoomParams())
+    print(f"!!! Room URL: {room.url}")
+    # Ensure the room property is present
+    if not room.url:
+        raise HTTPException(
+            status_code=500,
+            detail="Missing 'room' property in request data. Cannot start agent without a target room!",
+        )
+
+    # Check if there is already an existing process running in this room
+    num_bots_in_room = sum(
+        1 for proc in bot_procs.values() if proc[1] == room.url and proc[0].poll() is None
+    )
+    if num_bots_in_room >= MAX_BOTS_PER_ROOM:
+        raise HTTPException(status_code=500, detail=f"Max bot limited reach for room: {room.url}")
+
+    # Get the token for the room
+    token = await daily_helpers["rest"].get_token(room.url)
+
+    if not token:
+        raise HTTPException(status_code=500, detail=f"Failed to get token for room: {room.url}")
+
+    # Spawn a new agent, and join the user session
+    # Note: this is mostly for demonstration purposes (refer to 'deployment' in README)
+    try:
+        proc = subprocess.Popen(
+            [f"python3 -m bot -u {room.url} -t {token}"],
+            shell=True,
+            bufsize=1,
+            cwd=os.path.dirname(os.path.abspath(__file__)),
+        )
+        bot_procs[proc.pid] = (proc, room.url)
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=f"Failed to start subprocess: {e}")
+
+    return RedirectResponse(room.url)
+
+
+@app.get("/status/{pid}")
+def get_status(pid: int):
+    # Look up the subprocess
+    proc = bot_procs.get(pid)
+
+    # If the subprocess doesn't exist, return an error
+    if not proc:
+        raise HTTPException(status_code=404, detail=f"Bot with process id: {pid} not found")
+
+    # Check the status of the subprocess
+    if proc[0].poll() is None:
+        status = "running"
+    else:
+        status = "finished"
+
+    return JSONResponse({"bot_id": pid, "status": status})
+
+
+if __name__ == "__main__":
+    import uvicorn
+
+    default_host = os.getenv("HOST", "0.0.0.0")
+    default_port = int(os.getenv("FAST_API_PORT", "7860"))
+
+    parser = argparse.ArgumentParser(description="Daily Storyteller FastAPI server")
+    parser.add_argument("--host", type=str, default=default_host, help="Host address")
+    parser.add_argument("--port", type=int, default=default_port, help="Port number")
+    parser.add_argument("--reload", action="store_true", help="Reload code on change")
+
+    config = parser.parse_args()
+
+    uvicorn.run(
+        "server:app",
+        host=config.host,
+        port=config.port,
+        reload=config.reload,
+    )

examples/deployment/flyio-example/Dockerfile

@@ -0,0 +1,13 @@
+FROM python:3.11-bullseye
+
+# Open port 7860 for http service
+ENV FAST_API_PORT=7860
+EXPOSE 7860
+
+# Install Python dependencies
+COPY *.py .
+COPY ./requirements.txt requirements.txt
+RUN pip3 install --no-cache-dir --upgrade -r requirements.txt
+
+# Start the FastAPI server
+CMD python3 bot_runner.py --port ${FAST_API_PORT}

examples/deployment/flyio-example/README.md

@@ -0,0 +1,39 @@
+# Fly.io deployment example
+
+This project modifies the `bot_runner.py` server to launch a new machine for each user session. This is a recommended approach for production vs. running shell processess as your deployment will quickly run out of system resources under load.
+
+For this example, we are using Daily as a WebRTC transport and provisioning a new room and token for each session. You can use another transport, such as WebSockets, by modifying the `bot.py` and `bot_runner.py` files accordingly.
+
+## Setting up your fly.io deployment
+
+### Create your fly.toml file
+
+You can copy the `example-fly.toml` as a reference. Be sure to change the app name to something unique.
+
+### Create your .env file
+
+Copy the base `env.example` to `.env` and enter the necessary API keys.
+
+`FLY_APP_NAME` should match that in the `fly.toml` file.
+
+### Launch a new fly.io project
+
+`fly launch` or `fly launch --org your-org-name`
+
+### Set the necessary app secrets from your .env
+
+Note: you can do this manually via the fly.io dashboard under the "secrets" sub-section of your deployment (e.g. "https://fly.io/apps/fly-app-name/secrets") or run the following terminal command:
+
+`cat .env | tr '\n' ' ' | xargs flyctl secrets set`
+
+### Deploy your machine
+
+`fly deploy`
+
+## Connecting to your bot
+
+Send a post request to your running fly.io instance:
+
+`curl --location --request POST 'https://YOUR_FLY_APP_NAME/'`
+
+This request will wait until the machine enters into a `starting` state, before returning the a room URL and token to join.

examples/deployment/flyio-example/bot.py

@@ -0,0 +1,102 @@
+import argparse
+import asyncio
+import os
+import sys
+
+from dotenv import load_dotenv
+from loguru import logger
+
+from pipecat.audio.vad.silero import SileroVADAnalyzer
+from pipecat.frames.frames import EndFrame
+from pipecat.pipeline.pipeline import Pipeline
+from pipecat.pipeline.runner import PipelineRunner
+from pipecat.pipeline.task import PipelineParams, PipelineTask
+from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
+from pipecat.services.elevenlabs import ElevenLabsTTSService
+from pipecat.services.openai import OpenAILLMService
+from pipecat.transports.services.daily import DailyParams, DailyTransport
+
+load_dotenv(override=True)
+
+logger.remove(0)
+logger.add(sys.stderr, level="DEBUG")
+
+daily_api_key = os.getenv("DAILY_API_KEY", "")
+daily_api_url = os.getenv("DAILY_API_URL", "https://api.daily.co/v1")
+
+
+async def main(room_url: str, token: str):
+    transport = DailyTransport(
+        room_url,
+        token,
+        "Chatbot",
+        DailyParams(
+            api_url=daily_api_url,
+            api_key=daily_api_key,
+            audio_in_enabled=True,
+            audio_out_enabled=True,
+            camera_out_enabled=False,
+            vad_enabled=True,
+            vad_analyzer=SileroVADAnalyzer(),
+            transcription_enabled=True,
+        ),
+    )
+
+    tts = ElevenLabsTTSService(
+        api_key=os.getenv("ELEVENLABS_API_KEY", ""),
+        voice_id=os.getenv("ELEVENLABS_VOICE_ID", ""),
+    )
+
+    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
+
+    messages = [
+        {
+            "role": "system",
+            "content": "You are Chatbot, a friendly, helpful robot. Your output will be converted to audio so don't include special characters other than '!' or '?' in your answers. Respond to what the user said in a creative and helpful way, but keep your responses brief. Start by saying hello.",
+        },
+    ]
+
+    context = OpenAILLMContext(messages)
+    context_aggregator = llm.create_context_aggregator(context)
+
+    pipeline = Pipeline(
+        [
+            transport.input(),
+            context_aggregator.user(),
+            llm,
+            tts,
+            transport.output(),
+            context_aggregator.assistant(),
+        ]
+    )
+
+    task = PipelineTask(pipeline, PipelineParams(allow_interruptions=True))
+
+    @transport.event_handler("on_first_participant_joined")
+    async def on_first_participant_joined(transport, participant):
+        await transport.capture_participant_transcription(participant["id"])
+        await task.queue_frames([context_aggregator.user().get_context_frame()])
+
+    @transport.event_handler("on_participant_left")
+    async def on_participant_left(transport, participant, reason):
+        await task.cancel()
+
+    @transport.event_handler("on_call_state_updated")
+    async def on_call_state_updated(transport, state):
+        if state == "left":
+            # Here we don't want to cancel, we just want to finish sending
+            # whatever is queued, so we use an EndFrame().
+            await task.queue_frame(EndFrame())
+
+    runner = PipelineRunner()
+
+    await runner.run(task)
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(description="Pipecat Bot")
+    parser.add_argument("-u", type=str, help="Room URL")
+    parser.add_argument("-t", type=str, help="Token")
+    config = parser.parse_args()
+
+    asyncio.run(main(config.u, config.t))

examples/deployment/flyio-example/bot_runner.py

@@ -0,0 +1,209 @@
+#
+# Copyright (c) 2024–2025, Daily
+#
+# SPDX-License-Identifier: BSD 2-Clause License
+#
+
+import argparse
+import os
+import subprocess
+from contextlib import asynccontextmanager
+
+import aiohttp
+from dotenv import load_dotenv
+from fastapi import FastAPI, HTTPException, Request
+from fastapi.middleware.cors import CORSMiddleware
+from fastapi.responses import JSONResponse
+
+from pipecat.transports.services.helpers.daily_rest import (
+    DailyRESTHelper,
+    DailyRoomObject,
+    DailyRoomParams,
+    DailyRoomProperties,
+)
+
+load_dotenv(override=True)
+
+
+# ------------ Configuration ------------ #
+
+MAX_SESSION_TIME = 5 * 60  # 5 minutes
+REQUIRED_ENV_VARS = [
+    "DAILY_API_KEY",
+    "OPENAI_API_KEY",
+    "ELEVENLABS_API_KEY",
+    "ELEVENLABS_VOICE_ID",
+    "FLY_API_KEY",
+    "FLY_APP_NAME",
+]
+
+FLY_API_HOST = os.getenv("FLY_API_HOST", "https://api.machines.dev/v1")
+FLY_APP_NAME = os.getenv("FLY_APP_NAME", "pipecat-fly-example")
+FLY_API_KEY = os.getenv("FLY_API_KEY", "")
+FLY_HEADERS = {"Authorization": f"Bearer {FLY_API_KEY}", "Content-Type": "application/json"}
+
+daily_helpers = {}
+
+
+# ----------------- API ----------------- #
+
+
+@asynccontextmanager
+async def lifespan(app: FastAPI):
+    aiohttp_session = aiohttp.ClientSession()
+    daily_helpers["rest"] = DailyRESTHelper(
+        daily_api_key=os.getenv("DAILY_API_KEY", ""),
+        daily_api_url=os.getenv("DAILY_API_URL", "https://api.daily.co/v1"),
+        aiohttp_session=aiohttp_session,
+    )
+    yield
+    await aiohttp_session.close()
+
+
+app = FastAPI(lifespan=lifespan)
+
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+# ----------------- Main ----------------- #
+
+
+async def spawn_fly_machine(room_url: str, token: str):
+    async with aiohttp.ClientSession() as session:
+        # Use the same image as the bot runner
+        async with session.get(
+            f"{FLY_API_HOST}/apps/{FLY_APP_NAME}/machines", headers=FLY_HEADERS
+        ) as r:
+            if r.status != 200:
+                text = await r.text()
+                raise Exception(f"Unable to get machine info from Fly: {text}")
+
+            data = await r.json()
+            image = data[0]["config"]["image"]
+
+        # Machine configuration
+        cmd = f"python3 bot.py -u {room_url} -t {token}"
+        cmd = cmd.split()
+        worker_props = {
+            "config": {
+                "image": image,
+                "auto_destroy": True,
+                "init": {"cmd": cmd},
+                "restart": {"policy": "no"},
+                "guest": {"cpu_kind": "shared", "cpus": 1, "memory_mb": 1024},
+            },
+        }
+
+        # Spawn a new machine instance
+        async with session.post(
+            f"{FLY_API_HOST}/apps/{FLY_APP_NAME}/machines", headers=FLY_HEADERS, json=worker_props
+        ) as r:
+            if r.status != 200:
+                text = await r.text()
+                raise Exception(f"Problem starting a bot worker: {text}")
+
+            data = await r.json()
+            # Wait for the machine to enter the started state
+            vm_id = data["id"]
+
+        async with session.get(
+            f"{FLY_API_HOST}/apps/{FLY_APP_NAME}/machines/{vm_id}/wait?state=started",
+            headers=FLY_HEADERS,
+        ) as r:
+            if r.status != 200:
+                text = await r.text()
+                raise Exception(f"Bot was unable to enter started state: {text}")
+
+    print(f"Machine joined room: {room_url}")
+
+
+@app.post("/")
+async def start_bot(request: Request) -> JSONResponse:
+    try:
+        data = await request.json()
+        # Is this a webhook creation request?
+        if "test" in data:
+            return JSONResponse({"test": True})
+    except Exception as e:
+        pass
+
+    # Use specified room URL, or create a new one if not specified
+    room_url = os.getenv("DAILY_SAMPLE_ROOM_URL", "")
+
+    if not room_url:
+        params = DailyRoomParams(properties=DailyRoomProperties())
+        try:
+            room: DailyRoomObject = await daily_helpers["rest"].create_room(params=params)
+        except Exception as e:
+            raise HTTPException(status_code=500, detail=f"Unable to provision room {e}")
+    else:
+        # Check passed room URL exists, we should assume that it already has a sip set up
+        try:
+            room: DailyRoomObject = await daily_helpers["rest"].get_room_from_url(room_url)
+        except Exception:
+            raise HTTPException(status_code=500, detail=f"Room not found: {room_url}")
+
+    # Give the agent a token to join the session
+    token = await daily_helpers["rest"].get_token(room.url, MAX_SESSION_TIME)
+
+    if not room or not token:
+        raise HTTPException(status_code=500, detail=f"Failed to get token for room: {room_url}")
+
+    # Launch a new fly.io machine, or run as a shell process (not recommended)
+    run_as_process = os.getenv("RUN_AS_PROCESS", False)
+
+    if run_as_process:
+        try:
+            subprocess.Popen(
+                [f"python3 -m bot -u {room.url} -t {token}"],
+                shell=True,
+                bufsize=1,
+                cwd=os.path.dirname(os.path.abspath(__file__)),
+            )
+        except Exception as e:
+            raise HTTPException(status_code=500, detail=f"Failed to start subprocess: {e}")
+    else:
+        try:
+            await spawn_fly_machine(room.url, token)
+        except Exception as e:
+            raise HTTPException(status_code=500, detail=f"Failed to spawn VM: {e}")
+
+    # Grab a token for the user to join with
+    user_token = await daily_helpers["rest"].get_token(room.url, MAX_SESSION_TIME)
+
+    return JSONResponse(
+        {
+            "room_url": room.url,
+            "token": user_token,
+        }
+    )
+
+
+if __name__ == "__main__":
+    # Check environment variables
+    for env_var in REQUIRED_ENV_VARS:
+        if env_var not in os.environ:
+            raise Exception(f"Missing environment variable: {env_var}.")
+
+    parser = argparse.ArgumentParser(description="Pipecat Bot Runner")
+    parser.add_argument(
+        "--host", type=str, default=os.getenv("HOST", "0.0.0.0"), help="Host address"
+    )
+    parser.add_argument("--port", type=int, default=os.getenv("PORT", 7860), help="Port number")
+    parser.add_argument(
+        "--reload", action="store_true", default=False, help="Reload code on change"
+    )
+
+    config = parser.parse_args()
+
+    try:
+        import uvicorn
+
+        uvicorn.run("bot_runner:app", host=config.host, port=config.port, reload=config.reload)
+    except KeyboardInterrupt:
+        print("Pipecat runner shutting down...")

examples/deployment/flyio-example/env.example

@@ -0,0 +1,8 @@
+DAILY_API_KEY=
+DAILY_SAMPLE_ROOM_URL= # Enter a Daily room URL to use a set room URL each time (useful for local testing)
+OPENAI_API_KEY=
+ELEVENLABS_API_KEY=
+ELEVENLABS_VOICE_ID=
+FLY_API_KEY=
+FLY_APP_NAME=
+RUN_AS_PROCESS= # Spawn fly.io machine for each session or run as local process

examples/deployment/flyio-example/example-fly.toml

@@ -0,0 +1,25 @@
+# fly.toml app configuration file generated for pipecat-fly-example on 2024-07-01T15:04:53+01:00
+#
+# See https://fly.io/docs/reference/configuration/ for information about how to use this file.
+#
+
+app = 'pipecat-fly-example'
+primary_region = 'sjc'
+
+[build]
+
+[env]
+  FLY_APP_NAME = 'pipecat-fly-example'
+
+[http_service]
+  internal_port = 7860
+  force_https = true
+  auto_stop_machines = true
+  auto_start_machines = true
+  min_machines_running = 0
+  processes = ['app']
+
+[[vm]]
+  memory = 512
+  cpu_kind = 'shared'
+  cpus = 1

examples/deployment/flyio-example/requirements.txt

@@ -1,7 +1,5 @@
 pipecat-ai[daily,openai,silero]
 fastapi
 uvicorn
-requests
 python-dotenv
 loguru
-twilio

examples/deployment/modal-example/.gitignore

@@ -0,0 +1,91 @@
+# Python
+__pycache__/
+*.py[cod]
+*$py.class
+*.so
+.Python
+build/
+dist/
+*.egg-info/
+*.egg
+.installed.cfg
+.eggs/
+downloads/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+share/python-wheels/
+MANIFEST
+
+# Virtual Environments
+venv/
+env/
+.env
+.venv/
+ENV/
+env.bak/
+venv.bak/
+
+# IDE
+.idea/
+.vscode/
+.spyderproject
+.spyproject
+.ropeproject
+
+# Testing and Coverage
+.coverage
+.coverage.*
+htmlcov/
+.pytest_cache/
+.tox/
+.nox/
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+.hypothesis/
+cover/
+
+# Logs and Databases
+*.log
+*.db
+db.sqlite3
+db.sqlite3-journal
+pip-log.txt
+
+# System Files
+.DS_Store
+Thumbs.db
+desktop.ini
+*.swp
+*.swo
+*.bak
+*.tmp
+*~
+
+# Build and Documentation
+docs/_build/
+.pybuilder/
+target/
+instance/
+.webassets-cache
+.pdm.toml
+.pdm-python
+.pdm-build/
+__pypackages__/
+
+# Other
+*.mo
+*.pot
+*.sage.py
+.mypy_cache/
+.dmypy.json
+dmypy.json
+.pyre/
+.pytype/
+cython_debug/
+.ipynb_checkpoints

examples/deployment/modal-example/README.md

@@ -0,0 +1,37 @@
+# Deploying Pipecat to Modal.com
+
+Barebones deployment example for [modal.com](https://www.modal.com)
+
+1. Install dependencies
+
+```bash
+python -m venv venv
+source venv/bin/active # or OS equivalent
+pip install -r requirements.txt
+```
+
+2. Setup .env
+
+```bash
+cp env.example .env
+```
+
+Alternatively, you can configure your Modal app to use [secrets](https://modal.com/docs/guide/secrets)
+
+3. Test the app locally
+
+```bash
+modal serve app.py
+```
+
+4. Deploy to production
+
+```bash
+modal deploy app.py
+```
+
+## Configuration options
+
+This app sets some sensible defaults for reducing cold starts, such as `minkeep_warm=1`, which will keep at least 1 warm instance ready for your bot function.
+
+It has been configured to only allow a concurrency of 1 (`max_inputs=1`) as each user will require their own running function.

examples/deployment/modal-example/app.py

@@ -0,0 +1,74 @@
+import os
+
+import aiohttp
+import modal
+from bot import _voice_bot_process
+from fastapi import HTTPException
+from fastapi.responses import JSONResponse
+from loguru import logger
+
+MAX_SESSION_TIME = 15 * 60  # 15 minutes
+
+app = modal.App("pipecat-modal")
+
+
+image = modal.Image.debian_slim(python_version="3.12").pip_install_from_requirements(
+    "requirements.txt"
+)
+
+
+@app.function(
+    image=image,
+    cpu=1.0,
+    secrets=[modal.Secret.from_dotenv()],
+    keep_warm=1,
+    enable_memory_snapshot=True,
+    max_inputs=1,  # Do not reuse instances across requests
+    retries=0,
+)
+def launch_bot_process(room_url: str, token: str):
+    _voice_bot_process(room_url, token)
+
+
+@app.function(
+    image=image,
+    secrets=[modal.Secret.from_dotenv()],
+)
+@modal.web_endpoint(method="POST")
+async def start():
+    from pipecat.transports.services.helpers.daily_rest import (
+        DailyRESTHelper,
+        DailyRoomParams,
+    )
+
+    logger.info("Request received")
+
+    async with aiohttp.ClientSession() as session:
+        daily_rest_helper = DailyRESTHelper(
+            daily_api_key=os.getenv("DAILY_API_KEY", ""),
+            daily_api_url=os.getenv("DAILY_API_URL", "https://api.daily.co/v1"),
+            aiohttp_session=session,
+        )
+
+        # Create new Daily room
+        room = await daily_rest_helper.create_room(DailyRoomParams())
+        if not room.url:
+            raise HTTPException(
+                status_code=500,
+                detail="Unable to create room",
+            )
+        logger.info(f"Created room: {room.url}")
+
+        # Create bot token for room
+        token = await daily_rest_helper.get_token(room.url, MAX_SESSION_TIME)
+        if not token:
+            raise HTTPException(status_code=500, detail=f"Failed to get token for room: {room.url}")
+
+        logger.info(f"Bot token created: {token}")
+
+        # Spawn a new bot process
+        launch_bot_process.spawn(room_url=room.url, token=token)
+
+        # Return room URL to the user to join
+        # Note: in production, you would want to return a token to the user
+        return JSONResponse(content={"room_url": room.url, token: token})

examples/deployment/modal-example/bot.py

@@ -0,0 +1,89 @@
+import asyncio
+import os
+import sys
+
+from dotenv import load_dotenv
+from loguru import logger
+
+from pipecat.audio.vad.silero import SileroVADAnalyzer
+from pipecat.pipeline.pipeline import Pipeline
+from pipecat.pipeline.runner import PipelineRunner
+from pipecat.pipeline.task import PipelineParams, PipelineTask
+from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
+from pipecat.services.cartesia import CartesiaTTSService
+from pipecat.services.openai import OpenAILLMService
+from pipecat.transports.services.daily import DailyParams, DailyTransport
+
+load_dotenv(override=True)
+
+logger.remove(0)
+logger.add(sys.stderr, level="DEBUG")
+
+
+async def main(room_url: str, token: str):
+    transport = DailyTransport(
+        room_url,
+        token,
+        "bot",
+        DailyParams(
+            audio_out_enabled=True,
+            transcription_enabled=True,
+            vad_enabled=True,
+            vad_analyzer=SileroVADAnalyzer(),
+        ),
+    )
+
+    tts = CartesiaTTSService(
+        api_key=os.getenv("CARTESIA_API_KEY", ""), voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22"
+    )
+
+    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
+
+    messages = [
+        {
+            "role": "system",
+            "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
+        },
+    ]
+
+    context = OpenAILLMContext(messages)
+    context_aggregator = llm.create_context_aggregator(context)
+
+    pipeline = Pipeline(
+        [
+            transport.input(),
+            context_aggregator.user(),
+            llm,
+            tts,
+            transport.output(),
+            context_aggregator.assistant(),
+        ]
+    )
+
+    task = PipelineTask(
+        pipeline,
+        PipelineParams(
+            allow_interruptions=True,
+            enable_metrics=True,
+            enable_usage_metrics=True,
+            report_only_initial_ttfb=True,
+        ),
+    )
+
+    @transport.event_handler("on_first_participant_joined")
+    async def on_first_participant_joined(transport, participant):
+        await transport.capture_participant_transcription(participant["id"])
+        messages.append({"role": "system", "content": "Please introduce yourself to the user."})
+        await task.queue_frames([context_aggregator.user().get_context_frame()])
+
+    @transport.event_handler("on_participant_left")
+    async def on_participant_left(transport, participant, reason):
+        await task.cancel()
+
+    runner = PipelineRunner()
+
+    await runner.run(task)
+
+
+def _voice_bot_process(room_url: str, token: str):
+    asyncio.run(main(room_url, token))

examples/deployment/modal-example/env.example

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

examples/deployment/modal-example/requirements.txt

@@ -0,0 +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

examples/dialin-chatbot/README.md

@@ -1,85 +0,0 @@
-<div align="center">
- <img alt="pipecat" width="300px" height="auto" src="image.png">
-</div>
-
-# Dialin example
-
-Example project that demonstrates how to add phone number dialin to your Pipecat bots. We include examples for both Daily (`bot_daily.py`) and Twilio (`bot_twilio.py`), depending on who you want to use as a phone vendor.
-
-- 🔁 Transport: Daily WebRTC
-- 💬 Speech-to-Text: Deepgram via Daily transport
-- 🤖 LLM: GPT4-o / OpenAI
-- 🔉 Text-to-Speech: ElevenLabs
-
-#### Should I use Daily or Twilio as a vendor?
-
-If you're starting from scratch, using Daily to provision phone numbers alongside Daily as a transport offers some convenience (such as automatic call forwarding.)
-
-If you already have Twilio numbers and workflows that you want to connect to your Pipecat bots, there is some additional configuration required (you'll need to create a `on_dialin_ready` and use the Twilio client to trigger the forward.)
-
-You can read more about this, as well as see respective walkthroughs in our docs.
-
-## Setup
-
-```shell
-# Install the requirements
-pip install -r requirements.txt
-
-# Setup your env
-mv env.example .env
-```
-
-## Using Daily numbers
-
-Run `bot_runner.py` to handle incoming HTTP requests:
-
-`python bot_runner.py --host localhost`
-
-Then target the following URL:
-
-`POST /daily_start_bot`
-
-For more configuration options, please consult Daily's API documentation.
-
-
-## Using Twilio numbers
-
-As above, but target the following URL:
-
-`POST /twilio_start_bot`
-
-For more configuration options, please consult Twilio's API documentation.
-
-## Deployment example
-
-A Dockerfile is included in this demo for convenience. Here is an example of how to build and deploy your bot to [fly.io](https://fly.io).
-
-*Please note: This demo spawns agents as subprocesses for convenience / demonstration purposes. You would likely not want to do this in production as it would limit concurrency to available system resources. For more information on how to deploy your bots using VMs, refer to the Pipecat documentation.*
-
-### Build the docker image
-
-`docker build -t tag:project .`
-
-### Launch the fly project
-
-`mv fly.example.toml fly.toml`
-
-`fly launch` (using the included fly.toml)
-
-### Setup your secrets on Fly
-
-Set the necessary secrets (found in `env.example`)
-
-`fly secrets set DAILY_API_KEY=... OPENAI_API_KEY=... ELEVENLABS_API_KEY=... ELEVENLABS_VOICE_ID=...`
-
-If you're using Twilio as a number vendor:
-
-`fly secrets set TWILIO_ACCOUNT_SID=... TWILIO_AUTH_TOKEN=...`
-
-### Deploy!
-
-`fly deploy`
-
-## 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).

examples/dialin-chatbot/bot_daily.py

@@ -1,111 +0,0 @@
-import asyncio
-import aiohttp
-import os
-import sys
-import argparse
-
-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 LLMAssistantResponseAggregator, LLMUserResponseAggregator
-from pipecat.frames.frames import (
-    LLMMessagesFrame,
-    EndFrame
-)
-from pipecat.services.elevenlabs import ElevenLabsTTSService
-from pipecat.services.openai import OpenAILLMService
-from pipecat.transports.services.daily import DailyParams, DailyTransport, DailyDialinSettings
-from pipecat.vad.silero import SileroVADAnalyzer
-from loguru import logger
-
-from dotenv import load_dotenv
-load_dotenv(override=True)
-
-logger.remove(0)
-logger.add(sys.stderr, level="DEBUG")
-
-daily_api_key = os.getenv("DAILY_API_KEY", "")
-daily_api_url = os.getenv("DAILY_API_URL", "https://api.daily.co/v1")
-
-
-async def main(room_url: str, token: str, callId: str, callDomain: str):
-    async with aiohttp.ClientSession() as session:
-        # diallin_settings are only needed if Daily's SIP URI is used
-        # If you are handling this via Twilio, Telnyx, set this to None
-        # and handle call-forwarding when on_dialin_ready fires.
-        diallin_settings = DailyDialinSettings(
-            call_id=callId,
-            call_domain=callDomain
-        )
-
-        transport = DailyTransport(
-            room_url,
-            token,
-            "Chatbot",
-            DailyParams(
-                api_url=daily_api_url,
-                api_key=daily_api_key,
-                dialin_settings=diallin_settings,
-                audio_in_enabled=True,
-                audio_out_enabled=True,
-                camera_out_enabled=False,
-                vad_enabled=True,
-                vad_analyzer=SileroVADAnalyzer(),
-                transcription_enabled=True,
-            )
-        )
-
-        tts = ElevenLabsTTSService(
-            aiohttp_session=session,
-            api_key=os.getenv("ELEVENLABS_API_KEY", ""),
-            voice_id=os.getenv("ELEVENLABS_VOICE_ID", ""),
-        )
-
-        llm = OpenAILLMService(
-            api_key=os.getenv("OPENAI_API_KEY"),
-            model="gpt-4o")
-
-        messages = [
-            {
-                "role": "system",
-                "content": "You are Chatbot, a friendly, helpful robot. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way, but keep your responses brief. Start by saying 'Oh, hello! Who dares dial me at this hour?!'.",
-            },
-        ]
-
-        tma_in = LLMUserResponseAggregator(messages)
-        tma_out = LLMAssistantResponseAggregator(messages)
-
-        pipeline = Pipeline([
-            transport.input(),
-            tma_in,
-            llm,
-            tts,
-            transport.output(),
-            tma_out,
-        ])
-
-        task = PipelineTask(pipeline, PipelineParams(allow_interruptions=True))
-
-        @transport.event_handler("on_first_participant_joined")
-        async def on_first_participant_joined(transport, participant):
-            transport.capture_participant_transcription(participant["id"])
-            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)
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser(description="Pipecat Simple ChatBot")
-    parser.add_argument("-u", type=str, help="Room URL")
-    parser.add_argument("-t", type=str, help="Token")
-    parser.add_argument("-i", type=str, help="Call ID")
-    parser.add_argument("-d", type=str, help="Call Domain")
-    config = parser.parse_args()
-
-    asyncio.run(main(config.u, config.t, config.i, config.d))

examples/dialin-chatbot/bot_twilio.py

@@ -1,125 +0,0 @@
-import asyncio
-import aiohttp
-import os
-import sys
-import argparse
-
-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 LLMAssistantResponseAggregator, LLMUserResponseAggregator
-from pipecat.frames.frames import (
-    LLMMessagesFrame,
-    EndFrame
-)
-from pipecat.services.elevenlabs import ElevenLabsTTSService
-from pipecat.services.openai import OpenAILLMService
-from pipecat.transports.services.daily import DailyParams, DailyTransport
-from pipecat.vad.silero import SileroVADAnalyzer
-
-from twilio.rest import Client
-
-from loguru import logger
-
-from dotenv import load_dotenv
-load_dotenv(override=True)
-
-logger.remove(0)
-logger.add(sys.stderr, level="DEBUG")
-
-
-twilio_account_sid = os.getenv('TWILIO_ACCOUNT_SID')
-twilio_auth_token = os.getenv('TWILIO_AUTH_TOKEN')
-twilioclient = Client(twilio_account_sid, twilio_auth_token)
-
-daily_api_key = os.getenv("DAILY_API_KEY", "")
-
-
-async def main(room_url: str, token: str, callId: str, sipUri: str):
-    async with aiohttp.ClientSession() as session:
-        # diallin_settings are only needed if Daily's SIP URI is used
-        # If you are handling this via Twilio, Telnyx, set this to None
-        # and handle call-forwarding when on_dialin_ready fires.
-        transport = DailyTransport(
-            room_url,
-            token,
-            "Chatbot",
-            DailyParams(
-                api_key=daily_api_key,
-                dialin_settings=None,  # Not required for Twilio
-                audio_in_enabled=True,
-                audio_out_enabled=True,
-                camera_out_enabled=False,
-                vad_enabled=True,
-                vad_analyzer=SileroVADAnalyzer(),
-                transcription_enabled=True,
-            )
-        )
-
-        tts = ElevenLabsTTSService(
-            aiohttp_session=session,
-            api_key=os.getenv("ELEVENLABS_API_KEY", ""),
-            voice_id=os.getenv("ELEVENLABS_VOICE_ID", ""),
-        )
-
-        llm = OpenAILLMService(
-            api_key=os.getenv("OPENAI_API_KEY"),
-            model="gpt-4o")
-
-        messages = [
-            {
-                "role": "system",
-                "content": "You are Chatbot, a friendly, helpful robot. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way, but keep your responses brief. Start by saying 'Hello! Who dares dial me at this hour?!'.",
-            },
-        ]
-
-        tma_in = LLMUserResponseAggregator(messages)
-        tma_out = LLMAssistantResponseAggregator(messages)
-
-        pipeline = Pipeline([
-            transport.input(),
-            tma_in,
-            llm,
-            tts,
-            transport.output(),
-            tma_out,
-        ])
-
-        task = PipelineTask(pipeline, PipelineParams(allow_interruptions=True))
-
-        @transport.event_handler("on_first_participant_joined")
-        async def on_first_participant_joined(transport, participant):
-            transport.capture_participant_transcription(participant["id"])
-            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(transport, cdata):
-            # For Twilio, Telnyx, etc. You need to update the state of the call
-            # and forward it to the sip_uri..
-            print(f"Forwarding call: {callId} {sipUri}")
-
-            try:
-                # The TwiML is updated using Twilio's client library
-                call = twilioclient.calls(callId).update(
-                    twiml=f'<Response><Dial><Sip>{sipUri}</Sip></Dial></Response>'
-                )
-            except Exception as e:
-                raise Exception(f"Failed to forward call: {str(e)}")
-
-        runner = PipelineRunner()
-        await runner.run(task)
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser(description="Pipecat Simple ChatBot")
-    parser.add_argument("-u", type=str, help="Room URL")
-    parser.add_argument("-t", type=str, help="Token")
-    parser.add_argument("-i", type=str, help="Call ID")
-    parser.add_argument("-s", type=str, help="SIP URI")
-    config = parser.parse_args()
-
-    asyncio.run(main(config.u, config.t, config.i, config.s))

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

@@ -1,41 +1,42 @@
 #
-# Copyright (c) 2024, Daily
+# Copyright (c) 2024–2025, Daily
 #
 # SPDX-License-Identifier: BSD 2-Clause License
 #
 
 import asyncio
-import aiohttp
 import os
 import sys
 
-from pipecat.frames.frames import EndFrame, TextFrame
-from pipecat.pipeline.pipeline import Pipeline
-from pipecat.pipeline.task import PipelineTask
-from pipecat.pipeline.runner import PipelineRunner
-from pipecat.services.elevenlabs import ElevenLabsTTSService
-from pipecat.transports.services.daily import DailyParams, DailyTransport
-
+import aiohttp
+from dotenv import load_dotenv
+from loguru import logger
 from runner import configure
 
-from loguru import logger
+from pipecat.frames.frames import EndFrame, TTSSpeakFrame
+from pipecat.pipeline.pipeline import Pipeline
+from pipecat.pipeline.runner import PipelineRunner
+from pipecat.pipeline.task import PipelineTask
+from pipecat.services.cartesia import CartesiaTTSService
+from pipecat.transports.services.daily import DailyParams, DailyTransport
 
-from dotenv import load_dotenv
 load_dotenv(override=True)
 
 logger.remove(0)
 logger.add(sys.stderr, level="DEBUG")
 
 
-async def main(room_url):
+async def main():
     async with aiohttp.ClientSession() as session:
-        transport = DailyTransport(
-            room_url, None, "Say One Thing", DailyParams(audio_out_enabled=True))
+        (room_url, _) = await configure(session)
 
-        tts = ElevenLabsTTSService(
-            aiohttp_session=session,
-            api_key=os.getenv("ELEVENLABS_API_KEY"),
-            voice_id=os.getenv("ELEVENLABS_VOICE_ID"),
+        transport = DailyTransport(
+            room_url, None, "Say One Thing", DailyParams(audio_out_enabled=True)
+        )
+
+        tts = CartesiaTTSService(
+            api_key=os.getenv("CARTESIA_API_KEY"),
+            voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22",  # British Lady
         )
 
         runner = PipelineRunner()
@@ -44,13 +45,15 @@ async def main(room_url):
 
         # Register an event handler so we can play the audio when the
         # participant joins.
-        @transport.event_handler("on_participant_joined")
-        async def on_new_participant_joined(transport, participant):
-            participant_name = participant["info"]["userName"] or ''
-            await task.queue_frames([TextFrame(f"Hello there, {participant_name}!"), EndFrame()])
+        @transport.event_handler("on_first_participant_joined")
+        async def on_first_participant_joined(transport, participant):
+            participant_name = participant.get("info", {}).get("userName", "")
+            await task.queue_frames(
+                [TTSSpeakFrame(f"Hello there, {participant_name}!"), EndFrame()]
+            )
 
         await runner.run(task)
 
+
 if __name__ == "__main__":
-    (url, token) = configure()
-    asyncio.run(main(url))
+    asyncio.run(main())

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

@@ -1,25 +1,24 @@
 #
-# Copyright (c) 2024, Daily
+# Copyright (c) 2024–2025, Daily
 #
 # SPDX-License-Identifier: BSD 2-Clause License
 #
 
 import asyncio
-import aiohttp
 import os
 import sys
 
-from pipecat.frames.frames import EndFrame, TextFrame
+from dotenv import load_dotenv
+from loguru import logger
+
+from pipecat.frames.frames import EndFrame, TTSSpeakFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineTask
-from pipecat.services.elevenlabs import ElevenLabsTTSService
+from pipecat.services.cartesia import CartesiaTTSService
 from pipecat.transports.base_transport import TransportParams
 from pipecat.transports.local.audio import LocalAudioTransport
 
-from loguru import logger
-
-from dotenv import load_dotenv
 load_dotenv(override=True)
 
 logger.remove(0)
@@ -27,26 +26,24 @@ logger.add(sys.stderr, level="DEBUG")
 
 
 async def main():
-    async with aiohttp.ClientSession() as session:
-        transport = LocalAudioTransport(TransportParams(audio_out_enabled=True))
+    transport = LocalAudioTransport(TransportParams(audio_out_enabled=True))
 
-        tts = ElevenLabsTTSService(
-            aiohttp_session=session,
-            api_key=os.getenv("ELEVENLABS_API_KEY"),
-            voice_id=os.getenv("ELEVENLABS_VOICE_ID"),
-        )
+    tts = CartesiaTTSService(
+        api_key=os.getenv("CARTESIA_API_KEY"),
+        voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22",  # British Lady
+    )
 
-        pipeline = Pipeline([tts, transport.output()])
+    pipeline = Pipeline([tts, transport.output()])
 
-        task = PipelineTask(pipeline)
+    task = PipelineTask(pipeline)
 
-        async def say_something():
-            await asyncio.sleep(1)
-            await task.queue_frames([TextFrame("Hello there!"), EndFrame()])
+    async def say_something():
+        await asyncio.sleep(1)
+        await task.queue_frames([TTSSpeakFrame("Hello there, how is it going!"), EndFrame()])
 
-        runner = PipelineRunner()
+    runner = PipelineRunner()
 
-        await asyncio.gather(runner.run(task), say_something())
+    await asyncio.gather(runner.run(task), say_something())
 
 
 if __name__ == "__main__":

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

@@ -0,0 +1,108 @@
+import argparse
+import asyncio
+import os
+import sys
+
+import aiohttp
+from dotenv import load_dotenv
+from livekit import api
+from loguru import logger
+
+from pipecat.frames.frames import TextFrame
+from pipecat.pipeline.pipeline import Pipeline
+from pipecat.pipeline.runner import PipelineRunner
+from pipecat.pipeline.task import PipelineTask
+from pipecat.services.cartesia import CartesiaTTSService
+from pipecat.transports.services.livekit import LiveKitParams, LiveKitTransport
+
+load_dotenv(override=True)
+
+logger.remove(0)
+logger.add(sys.stderr, level="DEBUG")
+
+
+def generate_token(room_name: str, participant_name: str, api_key: str, api_secret: str) -> str:
+    token = api.AccessToken(api_key, api_secret)
+    token.with_identity(participant_name).with_name(participant_name).with_grants(
+        api.VideoGrants(
+            room_join=True,
+            room=room_name,
+        )
+    )
+
+    return token.to_jwt()
+
+
+async def configure_livekit():
+    parser = argparse.ArgumentParser(description="LiveKit AI SDK Bot Sample")
+    parser.add_argument(
+        "-r", "--room", type=str, required=False, help="Name of the LiveKit room to join"
+    )
+    parser.add_argument("-u", "--url", type=str, required=False, help="URL of the LiveKit server")
+
+    args, unknown = parser.parse_known_args()
+
+    room_name = args.room or os.getenv("LIVEKIT_ROOM_NAME")
+    url = args.url or os.getenv("LIVEKIT_URL")
+    api_key = os.getenv("LIVEKIT_API_KEY")
+    api_secret = os.getenv("LIVEKIT_API_SECRET")
+
+    if not room_name:
+        raise Exception(
+            "No LiveKit room specified. Use the -r/--room option from the command line, or set LIVEKIT_ROOM_NAME in your environment."
+        )
+
+    if not url:
+        raise Exception(
+            "No LiveKit server URL specified. Use the -u/--url option from the command line, or set LIVEKIT_URL in your environment."
+        )
+
+    if not api_key or not api_secret:
+        raise Exception(
+            "LIVEKIT_API_KEY and LIVEKIT_API_SECRET must be set in environment variables."
+        )
+
+    token = generate_token(room_name, "Say One Thing", api_key, api_secret)
+
+    user_token = generate_token(room_name, "User", api_key, api_secret)
+    logger.info(f"User token: {user_token}")
+
+    return (url, token, room_name)
+
+
+async def main():
+    async with aiohttp.ClientSession() as session:
+        (url, token, room_name) = await configure_livekit()
+
+        transport = LiveKitTransport(
+            url=url,
+            token=token,
+            room_name=room_name,
+            params=LiveKitParams(audio_out_enabled=True),
+        )
+
+        tts = CartesiaTTSService(
+            api_key=os.getenv("CARTESIA_API_KEY"),
+            voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22",  # British Lady
+        )
+
+        runner = PipelineRunner()
+
+        task = PipelineTask(Pipeline([tts, transport.output()]))
+
+        # Register an event handler so we can play the audio when the
+        # participant joins.
+        @transport.event_handler("on_first_participant_joined")
+        async def on_first_participant_joined(transport, participant_id):
+            await asyncio.sleep(1)
+            await task.queue_frame(
+                TextFrame(
+                    "Hello there! How are you doing today? Would you like to talk about the weather?"
+                )
+            )
+
+        await runner.run(task)
+
+
+if __name__ == "__main__":
+    asyncio.run(main())

examples/foundational/01c-fastpitch.py

@@ -0,0 +1,54 @@
+#
+# Copyright (c) 2024–2025, Daily
+#
+# SPDX-License-Identifier: BSD 2-Clause License
+#
+
+import asyncio
+import os
+import sys
+
+import aiohttp
+from dotenv import load_dotenv
+from loguru import logger
+from runner import configure
+
+from pipecat.frames.frames import EndFrame, TTSSpeakFrame
+from pipecat.pipeline.pipeline import Pipeline
+from pipecat.pipeline.runner import PipelineRunner
+from pipecat.pipeline.task import PipelineTask
+from pipecat.services.riva import FastPitchTTSService
+from pipecat.transports.services.daily import DailyParams, DailyTransport
+
+load_dotenv(override=True)
+
+logger.remove(0)
+logger.add(sys.stderr, level="DEBUG")
+
+
+async def main():
+    async with aiohttp.ClientSession() as session:
+        (room_url, _) = await configure(session)
+
+        transport = DailyTransport(
+            room_url, None, "Say One Thing", DailyParams(audio_out_enabled=True)
+        )
+
+        tts = FastPitchTTSService(api_key=os.getenv("NVIDIA_API_KEY"))
+
+        runner = PipelineRunner()
+
+        task = PipelineTask(Pipeline([tts, transport.output()]))
+
+        # Register an event handler so we can play the audio when the
+        # participant joins.
+        @transport.event_handler("on_first_participant_joined")
+        async def on_first_participant_joined(transport, participant):
+            participant_name = participant.get("info", {}).get("userName", "")
+            await task.queue_frames([TTSSpeakFrame(f"Aloha, {participant_name}!"), EndFrame()])
+
+        await runner.run(task)
+
+
+if __name__ == "__main__":
+    asyncio.run(main())

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

@@ -1,56 +1,53 @@
 #
-# Copyright (c) 2024, Daily
+# Copyright (c) 2024–2025, Daily
 #
 # SPDX-License-Identifier: BSD 2-Clause License
 #
 
 import asyncio
-import aiohttp
 import os
 import sys
 
+import aiohttp
+from dotenv import load_dotenv
+from loguru import logger
+from runner import configure
+
 from pipecat.frames.frames import EndFrame, LLMMessagesFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineTask
-from pipecat.services.elevenlabs import ElevenLabsTTSService
+from pipecat.services.cartesia import CartesiaTTSService
 from pipecat.services.openai import OpenAILLMService
 from pipecat.transports.services.daily import DailyParams, DailyTransport
 
-from runner import configure
-
-from loguru import logger
-
-from dotenv import load_dotenv
 load_dotenv(override=True)
 
 logger.remove(0)
 logger.add(sys.stderr, level="DEBUG")
 
 
-async def main(room_url):
+async def main():
     async with aiohttp.ClientSession() as session:
-        transport = DailyTransport(
-            room_url,
-            None,
-            "Say One Thing From an LLM",
-            DailyParams(audio_out_enabled=True))
+        (room_url, _) = await configure(session)
 
-        tts = ElevenLabsTTSService(
-            aiohttp_session=session,
-            api_key=os.getenv("ELEVENLABS_API_KEY"),
-            voice_id=os.getenv("ELEVENLABS_VOICE_ID"),
+        transport = DailyTransport(
+            room_url, None, "Say One Thing From an LLM", DailyParams(audio_out_enabled=True)
         )
 
-        llm = OpenAILLMService(
-            api_key=os.getenv("OPENAI_API_KEY"),
-            model="gpt-4o")
+        tts = CartesiaTTSService(
+            api_key=os.getenv("CARTESIA_API_KEY"),
+            voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22",  # British Lady
+        )
+
+        llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
 
         messages = [
             {
                 "role": "system",
                 "content": "You are an LLM in a WebRTC session, and this is a 'hello world' demo. Say hello to the world.",
-            }]
+            }
+        ]
 
         runner = PipelineRunner()
 
@@ -64,5 +61,4 @@ async def main(room_url):
 
 
 if __name__ == "__main__":
-    (url, token) = configure()
-    asyncio.run(main(url))
+    asyncio.run(main())

examples/foundational/03-still-frame.py

@@ -1,14 +1,18 @@
 #
-# Copyright (c) 2024, Daily
+# Copyright (c) 2024–2025, Daily
 #
 # SPDX-License-Identifier: BSD 2-Clause License
 #
 
 import asyncio
-import aiohttp
 import os
 import sys
 
+import aiohttp
+from dotenv import load_dotenv
+from loguru import logger
+from runner import configure
+
 from pipecat.frames.frames import TextFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
@@ -16,34 +20,25 @@ from pipecat.pipeline.task import PipelineTask
 from pipecat.services.fal import FalImageGenService
 from pipecat.transports.services.daily import DailyParams, DailyTransport
 
-from runner import configure
-
-from loguru import logger
-
-from dotenv import load_dotenv
 load_dotenv(override=True)
 
 logger.remove(0)
 logger.add(sys.stderr, level="DEBUG")
 
 
-async def main(room_url):
+async def main():
     async with aiohttp.ClientSession() as session:
+        (room_url, _) = await configure(session)
+
         transport = DailyTransport(
             room_url,
             None,
             "Show a still frame image",
-            DailyParams(
-                camera_out_enabled=True,
-                camera_out_width=1024,
-                camera_out_height=1024
-            )
+            DailyParams(camera_out_enabled=True, camera_out_width=1024, camera_out_height=1024),
         )
 
         imagegen = FalImageGenService(
-            params=FalImageGenService.InputParams(
-                image_size="square_hd"
-            ),
+            params=FalImageGenService.InputParams(image_size="square_hd"),
             aiohttp_session=session,
             key=os.getenv("FAL_KEY"),
         )
@@ -54,15 +49,14 @@ async def main(room_url):
 
         @transport.event_handler("on_first_participant_joined")
         async def on_first_participant_joined(transport, participant):
-            # Note that we do not put an EndFrame() item in the pipeline for this demo.
-            # This means that the bot will stay in the channel until it times out.
-            # An EndFrame() in the pipeline would cause the transport to shut
-            # down.
-            await task.queue_frames([TextFrame("a cat in the style of picasso")])
+            await task.queue_frame(TextFrame("a cat in the style of picasso"))
+
+        @transport.event_handler("on_participant_left")
+        async def on_participant_left(transport, participant, reason):
+            await task.cancel()
 
         await runner.run(task)
 
 
 if __name__ == "__main__":
-    (url, token) = configure()
-    asyncio.run(main(url))
+    asyncio.run(main())

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

@@ -1,16 +1,18 @@
 #
-# Copyright (c) 2024, Daily
+# Copyright (c) 2024–2025, Daily
 #
 # SPDX-License-Identifier: BSD 2-Clause License
 #
 
 import asyncio
-import aiohttp
 import os
 import sys
-
 import tkinter as tk
 
+import aiohttp
+from dotenv import load_dotenv
+from loguru import logger
+
 from pipecat.frames.frames import TextFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
@@ -19,9 +21,6 @@ from pipecat.services.fal import FalImageGenService
 from pipecat.transports.base_transport import TransportParams
 from pipecat.transports.local.tk import TkLocalTransport
 
-from loguru import logger
-
-from dotenv import load_dotenv
 load_dotenv(override=True)
 
 logger.remove(0)
@@ -35,15 +34,11 @@ async def main():
 
         transport = TkLocalTransport(
             tk_root,
-            TransportParams(
-                camera_out_enabled=True,
-                camera_out_width=1024,
-                camera_out_height=1024))
+            TransportParams(camera_out_enabled=True, camera_out_width=1024, camera_out_height=1024),
+        )
 
         imagegen = FalImageGenService(
-            params=FalImageGenService.InputParams(
-                image_size="square_hd"
-            ),
+            params=FalImageGenService.InputParams(image_size="square_hd"),
             aiohttp_session=session,
             key=os.getenv("FAL_KEY"),
         )
@@ -56,7 +51,7 @@ async def main():
         runner = PipelineRunner()
 
         async def run_tk():
-            while runner.is_active():
+            while not task.has_finished():
                 tk_root.update()
                 tk_root.update_idletasks()
                 await asyncio.sleep(0.1)

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

@@ -0,0 +1,64 @@
+#
+# Copyright (c) 2024–2025, Daily
+#
+# SPDX-License-Identifier: BSD 2-Clause License
+#
+
+import asyncio
+import os
+import sys
+
+import aiohttp
+from dotenv import load_dotenv
+from loguru import logger
+from runner import configure
+
+from pipecat.frames.frames import EndFrame, TextFrame
+from pipecat.pipeline.pipeline import Pipeline
+from pipecat.pipeline.runner import PipelineRunner
+from pipecat.pipeline.task import PipelineParams, PipelineTask
+from pipecat.services.google import GoogleImageGenService
+from pipecat.transports.services.daily import DailyParams, DailyTransport
+
+load_dotenv(override=True)
+
+logger.remove(0)
+logger.add(sys.stderr, level="DEBUG")
+
+
+async def main():
+    async with aiohttp.ClientSession() as session:
+        (room_url, _) = await configure(session)
+
+        transport = DailyTransport(
+            room_url,
+            None,
+            "Show a still frame image",
+            DailyParams(camera_out_enabled=True, camera_out_width=1024, camera_out_height=1024),
+        )
+
+        imagegen = GoogleImageGenService(
+            api_key=os.getenv("GOOGLE_API_KEY"),
+        )
+
+        runner = PipelineRunner()
+
+        task = PipelineTask(
+            Pipeline([imagegen, transport.output()]), PipelineParams(enable_metrics=True)
+        )
+
+        @transport.event_handler("on_first_participant_joined")
+        async def on_first_participant_joined(transport, participant):
+            await task.queue_frame(TextFrame("a cat in the style of picasso"))
+            await task.queue_frame(TextFrame("a dog in the style of picasso"))
+            await task.queue_frame(TextFrame("a fish in the style of picasso"))
+
+        @transport.event_handler("on_participant_left")
+        async def on_participant_left(transport, participant, reason):
+            await task.queue_frame(EndFrame())
+
+        await runner.run(task)
+
+
+if __name__ == "__main__":
+    asyncio.run(main())

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

@@ -1,37 +1,41 @@
 #
-# Copyright (c) 2024, Daily
+# Copyright (c) 2024–2025, Daily
 #
 # SPDX-License-Identifier: BSD 2-Clause License
 #
 
-import aiohttp
+#
+# This example broken on latest pipecat and needs updating.
+#
+
 import asyncio
 import os
 import sys
 
-from pipecat.pipeline.merge_pipeline import SequentialMergePipeline
-from pipecat.pipeline.pipeline import Pipeline
+import aiohttp
+from dotenv import load_dotenv
+from loguru import logger
+from runner import configure
 
 from pipecat.frames.frames import EndPipeFrame, LLMMessagesFrame, TextFrame
+from pipecat.pipeline.merge_pipeline import SequentialMergePipeline
+from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.task import PipelineTask
 from pipecat.services.azure import AzureLLMService, AzureTTSService
 from pipecat.services.elevenlabs import ElevenLabsTTSService
 from pipecat.services.transport_services import TransportServiceOutput
 from pipecat.services.transports.daily_transport import DailyTransport
 
-from runner import configure
-
-from loguru import logger
-
-from dotenv import load_dotenv
 load_dotenv(override=True)
 
 logger.remove(0)
 logger.add(sys.stderr, level="DEBUG")
 
 
-async def main(room_url: str):
+async def main():
     async with aiohttp.ClientSession() as session:
+        (room_url, _) = await configure(session)
+
         transport = DailyTransport(room_url, None, "Static And Dynamic Speech")
 
         meeting = TransportServiceOutput(transport, mic_enabled=True)
@@ -47,13 +51,11 @@ async def main(room_url: str):
         )
 
         elevenlabs_tts = ElevenLabsTTSService(
-            aiohttp_session=session,
             api_key=os.getenv("ELEVENLABS_API_KEY"),
             voice_id=os.getenv("ELEVENLABS_VOICE_ID"),
         )
 
-        messages = [{"role": "system",
-                     "content": "tell the user a joke about llamas"}]
+        messages = [{"role": "system", "content": "tell the user a joke about llamas"}]
 
         # Start a task to run the LLM to create a joke, and convert the LLM
         # output to audio frames. This task will run in parallel with generating
@@ -71,8 +73,7 @@ async def main(room_url: str):
             ]
         )
 
-        merge_pipeline = SequentialMergePipeline(
-            [simple_tts_pipeline, llm_pipeline])
+        merge_pipeline = SequentialMergePipeline([simple_tts_pipeline, llm_pipeline])
 
         await asyncio.gather(
             transport.run(merge_pipeline),
@@ -82,5 +83,4 @@ async def main(room_url: str):
 
 
 if __name__ == "__main__":
-    (url, token) = configure()
-    asyncio.run(main(url))
+    asyncio.run(main())

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

@@ -1,43 +1,37 @@
 #
-# Copyright (c) 2024, Daily
+# Copyright (c) 2024–2025, Daily
 #
 # SPDX-License-Identifier: BSD 2-Clause License
 #
 
 import asyncio
-import aiohttp
 import os
 import sys
-
 from dataclasses import dataclass
 
+import aiohttp
+from dotenv import load_dotenv
+from loguru import logger
+from runner import configure
+
 from pipecat.frames.frames import (
-    AppFrame,
-    EndFrame,
+    DataFrame,
     Frame,
-    ImageRawFrame,
     LLMFullResponseStartFrame,
     LLMMessagesFrame,
-    TextFrame
+    TextFrame,
 )
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
+from pipecat.pipeline.sync_parallel_pipeline import SyncParallelPipeline
 from pipecat.pipeline.task import PipelineTask
-from pipecat.pipeline.parallel_task import ParallelTask
-from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
-from pipecat.processors.aggregators.gated import GatedAggregator
-from pipecat.processors.aggregators.llm_response import LLMFullResponseAggregator
 from pipecat.processors.aggregators.sentence import SentenceAggregator
-from pipecat.services.openai import OpenAILLMService
-from pipecat.services.elevenlabs import ElevenLabsTTSService
+from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
+from pipecat.services.cartesia import CartesiaHttpTTSService
 from pipecat.services.fal import FalImageGenService
+from pipecat.services.openai import OpenAILLMService
 from pipecat.transports.services.daily import DailyParams, DailyTransport
 
-from runner import configure
-
-from loguru import logger
-
-from dotenv import load_dotenv
 load_dotenv(override=True)
 
 logger.remove(0)
@@ -45,7 +39,7 @@ logger.add(sys.stderr, level="DEBUG")
 
 
 @dataclass
-class MonthFrame(AppFrame):
+class MonthFrame(DataFrame):
     month: str
 
     def __str__(self):
@@ -73,8 +67,10 @@ class MonthPrepender(FrameProcessor):
             await self.push_frame(frame, direction)
 
 
-async def main(room_url):
+async def main():
     async with aiohttp.ClientSession() as session:
+        (room_url, _) = await configure(session)
+
         transport = DailyTransport(
             room_url,
             None,
@@ -83,48 +79,46 @@ async def main(room_url):
                 audio_out_enabled=True,
                 camera_out_enabled=True,
                 camera_out_width=1024,
-                camera_out_height=1024
-            )
+                camera_out_height=1024,
+            ),
         )
 
-        tts = ElevenLabsTTSService(
-            aiohttp_session=session,
-            api_key=os.getenv("ELEVENLABS_API_KEY"),
-            voice_id=os.getenv("ELEVENLABS_VOICE_ID"),
-        )
+        llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
 
-        llm = OpenAILLMService(
-            api_key=os.getenv("OPENAI_API_KEY"),
-            model="gpt-4o")
+        tts = CartesiaHttpTTSService(
+            api_key=os.getenv("CARTESIA_API_KEY"),
+            voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22",  # British Lady
+        )
 
         imagegen = FalImageGenService(
-            params=FalImageGenService.InputParams(
-                image_size="square_hd"
-            ),
+            params=FalImageGenService.InputParams(image_size="square_hd"),
             aiohttp_session=session,
             key=os.getenv("FAL_KEY"),
         )
 
-        gated_aggregator = GatedAggregator(
-            gate_open_fn=lambda frame: isinstance(frame, ImageRawFrame),
-            gate_close_fn=lambda frame: isinstance(frame, LLMFullResponseStartFrame),
-            start_open=False
-        )
-
         sentence_aggregator = SentenceAggregator()
         month_prepender = MonthPrepender()
-        llm_full_response_aggregator = LLMFullResponseAggregator()
 
-        pipeline = Pipeline([
-            llm,                     # LLM
-            sentence_aggregator,     # Aggregates LLM output into full sentences
-            ParallelTask(            # Run pipelines in parallel aggregating the result
-                [month_prepender, tts],                   # Create "Month: sentence" and output audio
-                [llm_full_response_aggregator, imagegen]  # Aggregate full LLM response
-            ),
-            gated_aggregator,        # Queues everything until an image is available
-            transport.output()       # Transport output
-        ])
+        # With `SyncParallelPipeline` we synchronize audio and images by pushing
+        # them basically in order (e.g. I1 A1 A1 A1 I2 A2 A2 A2 A2 I3 A3). To do
+        # that, each pipeline runs concurrently and `SyncParallelPipeline` will
+        # wait for the input frame to be processed.
+        #
+        # Note that `SyncParallelPipeline` requires the last processor in each
+        # of the pipelines to be synchronous. In this case, we use
+        # `CartesiaHttpTTSService` and `FalImageGenService` which make HTTP
+        # requests and wait for the response.
+        pipeline = Pipeline(
+            [
+                llm,  # LLM
+                sentence_aggregator,  # Aggregates LLM output into full sentences
+                SyncParallelPipeline(  # Run pipelines in parallel aggregating the result
+                    [month_prepender, tts],  # Create "Month: sentence" and output audio
+                    [imagegen],  # Generate image
+                ),
+                transport.output(),  # Transport output
+            ]
+        )
 
         frames = []
         for month in [
@@ -150,8 +144,6 @@ async def main(room_url):
             frames.append(MonthFrame(month=month))
             frames.append(LLMMessagesFrame(messages))
 
-        frames.append(EndFrame())
-
         runner = PipelineRunner()
 
         task = PipelineTask(pipeline)
@@ -162,5 +154,4 @@ async def main(room_url):
 
 
 if __name__ == "__main__":
-    (url, token) = configure()
-    asyncio.run(main(url))
+    asyncio.run(main())

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

@@ -1,32 +1,37 @@
 #
-# Copyright (c) 2024, Daily
+# Copyright (c) 2024–2025, Daily
 #
 # SPDX-License-Identifier: BSD 2-Clause License
 #
 
-import aiohttp
 import asyncio
 import os
 import sys
-
 import tkinter as tk
 
-from pipecat.frames.frames import AudioRawFrame, Frame, URLImageRawFrame, LLMMessagesFrame, TextFrame
-from pipecat.pipeline.parallel_pipeline import ParallelPipeline
-from pipecat.pipeline.pipeline import Pipeline
-from pipecat.pipeline.runner import PipelineRunner
-from pipecat.pipeline.task import PipelineTask
-from pipecat.processors.aggregators.llm_response import LLMFullResponseAggregator
-from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
-from pipecat.services.openai import OpenAILLMService
-from pipecat.services.elevenlabs import ElevenLabsTTSService
-from pipecat.services.fal import FalImageGenService
-from pipecat.transports.base_transport import TransportParams
-from pipecat.transports.local.tk import TkLocalTransport
-
+import aiohttp
+from dotenv import load_dotenv
 from loguru import logger
 
-from dotenv import load_dotenv
+from pipecat.frames.frames import (
+    Frame,
+    LLMMessagesFrame,
+    OutputAudioRawFrame,
+    TextFrame,
+    TTSAudioRawFrame,
+    URLImageRawFrame,
+)
+from pipecat.pipeline.pipeline import Pipeline
+from pipecat.pipeline.runner import PipelineRunner
+from pipecat.pipeline.sync_parallel_pipeline import SyncParallelPipeline
+from pipecat.pipeline.task import PipelineTask
+from pipecat.processors.aggregators.sentence import SentenceAggregator
+from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
+from pipecat.services.cartesia import CartesiaHttpTTSService
+from pipecat.services.fal import FalImageGenService
+from pipecat.services.openai import OpenAILLMService
+from pipecat.transports.base_transport import TransportParams
+from pipecat.transports.local.tk import TkLocalTransport, TkOutputTransport
 
 load_dotenv(override=True)
 
@@ -42,7 +47,12 @@ async def main():
         runner = PipelineRunner()
 
         async def get_month_data(month):
-            messages = [{"role": "system", "content": f"Describe a nature photograph suitable for use in a calendar, for the month of {month}. Include only the image description with no preamble. Limit the description to one sentence, please.", }]
+            messages = [
+                {
+                    "role": "system",
+                    "content": f"Describe a nature photograph suitable for use in a calendar, for the month of {month}. Include only the image description with no preamble. Limit the description to one sentence, please.",
+                }
+            ]
 
             class ImageDescription(FrameProcessor):
                 def __init__(self):
@@ -60,14 +70,17 @@ async def main():
                 def __init__(self):
                     super().__init__()
                     self.audio = bytearray()
+                    self.frame = None
 
                 async def process_frame(self, frame: Frame, direction: FrameDirection):
                     await super().process_frame(frame, direction)
 
-                    if isinstance(frame, AudioRawFrame):
+                    if isinstance(frame, TTSAudioRawFrame):
                         self.audio.extend(frame.audio)
-                        self.frame = AudioRawFrame(
-                            bytes(self.audio), frame.sample_rate, frame.num_channels)
+                        self.frame = OutputAudioRawFrame(
+                            bytes(self.audio), frame.sample_rate, frame.num_channels
+                        )
+                    await self.push_frame(frame, direction)
 
             class ImageGrabber(FrameProcessor):
                 def __init__(self):
@@ -79,24 +92,22 @@ async def main():
 
                     if isinstance(frame, URLImageRawFrame):
                         self.frame = frame
+                    await self.push_frame(frame, direction)
 
-            llm = OpenAILLMService(
-                api_key=os.getenv("OPENAI_API_KEY"),
-                model="gpt-4o")
+            llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
 
-            tts = ElevenLabsTTSService(
-                aiohttp_session=session,
-                api_key=os.getenv("ELEVENLABS_API_KEY"),
-                voice_id=os.getenv("ELEVENLABS_VOICE_ID"))
+            tts = CartesiaHttpTTSService(
+                api_key=os.getenv("CARTESIA_API_KEY"),
+                voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22",  # British Lady
+            )
 
             imagegen = FalImageGenService(
-                params=FalImageGenService.InputParams(
-                    image_size="square_hd"
-                ),
+                params=FalImageGenService.InputParams(image_size="square_hd"),
                 aiohttp_session=session,
-                key=os.getenv("FAL_KEY"))
+                key=os.getenv("FAL_KEY"),
+            )
 
-            aggregator = LLMFullResponseAggregator()
+            sentence_aggregator = SentenceAggregator()
 
             description = ImageDescription()
 
@@ -104,13 +115,27 @@ async def main():
 
             image_grabber = ImageGrabber()
 
-            pipeline = Pipeline([
-                llm,
-                aggregator,
-                description,
-                ParallelPipeline([tts, audio_grabber],
-                                 [imagegen, image_grabber])
-            ])
+            # With `SyncParallelPipeline` we synchronize audio and images by
+            # pushing them basically in order (e.g. I1 A1 A1 A1 I2 A2 A2 A2 A2
+            # I3 A3). To do that, each pipeline runs concurrently and
+            # `SyncParallelPipeline` will wait for the input frame to be
+            # processed.
+            #
+            # Note that `SyncParallelPipeline` requires the last processor in
+            # each of the pipelines to be synchronous. In this case, we use
+            # `CartesiaHttpTTSService` and `FalImageGenService` which make HTTP
+            # requests and wait for the response.
+            pipeline = Pipeline(
+                [
+                    llm,  # LLM
+                    sentence_aggregator,  # Aggregates LLM output into full sentences
+                    description,  # Store sentence
+                    SyncParallelPipeline(
+                        [tts, audio_grabber],  # Generate and store audio for the given sentence
+                        [imagegen, image_grabber],  # Generate and storeimage for the given sentence
+                    ),
+                ]
+            )
 
             task = PipelineTask(pipeline)
             await task.queue_frame(LLMMessagesFrame(messages))
@@ -131,20 +156,19 @@ async def main():
                 audio_out_enabled=True,
                 camera_out_enabled=True,
                 camera_out_width=1024,
-                camera_out_height=1024))
+                camera_out_height=1024,
+            ),
+        )
 
         pipeline = Pipeline([transport.output()])
 
         task = PipelineTask(pipeline)
 
-        # We only specify 5 months as we create tasks all at once and we might
-        # get rate limited otherwise.
+        # We only specify a few months as we create tasks all at once and we
+        # might get rate limited otherwise.
         months: list[str] = [
             "January",
             "February",
-            # "March",
-            # "April",
-            # "May",
         ]
 
         # We create one task per month. This will be executed concurrently.

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

@@ -1,41 +1,65 @@
 #
-# Copyright (c) 2024, Daily
+# Copyright (c) 2024–2025, Daily
 #
 # SPDX-License-Identifier: BSD 2-Clause License
 #
 
 import asyncio
-import aiohttp
 import os
 import sys
 
-from pipecat.frames.frames import LLMMessagesFrame
-from pipecat.pipeline.pipeline import Pipeline
-from pipecat.pipeline.runner import PipelineRunner
-from pipecat.pipeline.task import PipelineTask
-from pipecat.processors.aggregators.llm_response import (
-    LLMAssistantResponseAggregator,
-    LLMUserResponseAggregator,
-)
-from pipecat.processors.logger import FrameLogger
-from pipecat.services.elevenlabs import ElevenLabsTTSService
-from pipecat.services.openai import OpenAILLMService
-from pipecat.transports.services.daily import DailyParams, DailyTransport
-from pipecat.vad.silero import SileroVADAnalyzer
-
+import aiohttp
+from dotenv import load_dotenv
+from loguru import logger
 from runner import configure
 
-from loguru import logger
+from pipecat.audio.vad.silero import SileroVADAnalyzer
+from pipecat.frames.frames import Frame, MetricsFrame
+from pipecat.metrics.metrics import (
+    LLMUsageMetricsData,
+    ProcessingMetricsData,
+    TTFBMetricsData,
+    TTSUsageMetricsData,
+)
+from pipecat.pipeline.pipeline import Pipeline
+from pipecat.pipeline.runner import PipelineRunner
+from pipecat.pipeline.task import PipelineParams, PipelineTask
+from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
+from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
+from pipecat.services.cartesia import CartesiaTTSService
+from pipecat.services.openai import OpenAILLMService
+from pipecat.transports.services.daily import DailyParams, DailyTransport
 
-from dotenv import load_dotenv
 load_dotenv(override=True)
 
 logger.remove(0)
 logger.add(sys.stderr, level="DEBUG")
 
 
-async def main(room_url: str, token):
+class MetricsLogger(FrameProcessor):
+    async def process_frame(self, frame: Frame, direction: FrameDirection):
+        await super().process_frame(frame, direction)
+
+        if isinstance(frame, MetricsFrame):
+            for d in frame.data:
+                if isinstance(d, TTFBMetricsData):
+                    print(f"!!! MetricsFrame: {frame}, ttfb: {d.value}")
+                elif isinstance(d, ProcessingMetricsData):
+                    print(f"!!! MetricsFrame: {frame}, processing: {d.value}")
+                elif isinstance(d, LLMUsageMetricsData):
+                    tokens = d.value
+                    print(
+                        f"!!! MetricsFrame: {frame}, tokens: {tokens.prompt_tokens}, characters: {tokens.completion_tokens}"
+                    )
+                elif isinstance(d, TTSUsageMetricsData):
+                    print(f"!!! MetricsFrame: {frame}, characters: {d.value}")
+        await self.push_frame(frame, direction)
+
+
+async def main():
     async with aiohttp.ClientSession() as session:
+        (room_url, token) = await configure(session)
+
         transport = DailyTransport(
             room_url,
             token,
@@ -44,23 +68,18 @@ async def main(room_url: str, token):
                 audio_out_enabled=True,
                 transcription_enabled=True,
                 vad_enabled=True,
-                vad_analyzer=SileroVADAnalyzer()
-            )
+                vad_analyzer=SileroVADAnalyzer(),
+            ),
         )
 
-        tts = ElevenLabsTTSService(
-            aiohttp_session=session,
-            api_key=os.getenv("ELEVENLABS_API_KEY"),
-            voice_id=os.getenv("ELEVENLABS_VOICE_ID"),
+        tts = CartesiaTTSService(
+            api_key=os.getenv("CARTESIA_API_KEY"),
+            voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22",  # British Lady
         )
 
-        llm = OpenAILLMService(
-            api_key=os.getenv("OPENAI_API_KEY"),
-            model="gpt-4o")
+        llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
 
-        fl = FrameLogger("!!! after LLM", "red")
-        fltts = FrameLogger("@@@ out of tts", "green")
-        flend = FrameLogger("### out of the end", "magenta")
+        ml = MetricsLogger()
 
         messages = [
             {
@@ -68,30 +87,37 @@ async def main(room_url: str, token):
                 "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.",
             },
         ]
-        tma_in = LLMUserResponseAggregator(messages)
-        tma_out = LLMAssistantResponseAggregator(messages)
 
-        pipeline = Pipeline([
-            transport.input(),
-            tma_in,
-            llm,
-            fl,
-            tts,
-            fltts,
-            transport.output(),
-            tma_out,
-            flend
-        ])
+        context = OpenAILLMContext(messages)
+        context_aggregator = llm.create_context_aggregator(context)
 
-        task = PipelineTask(pipeline)
+        pipeline = Pipeline(
+            [
+                transport.input(),
+                context_aggregator.user(),
+                llm,
+                tts,
+                ml,
+                transport.output(),
+                context_aggregator.assistant(),
+            ]
+        )
+
+        task = PipelineTask(
+            pipeline,
+            PipelineParams(enable_metrics=True, enable_usage_metrics=True),
+        )
 
         @transport.event_handler("on_first_participant_joined")
         async def on_first_participant_joined(transport, participant):
-            transport.capture_participant_transcription(participant["id"])
+            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([LLMMessagesFrame(messages)])
+            messages.append({"role": "system", "content": "Please introduce yourself to the user."})
+            await task.queue_frames([context_aggregator.user().get_context_frame()])
+
+        @transport.event_handler("on_participant_left")
+        async def on_participant_left(transport, participant, reason):
+            await task.cancel()
 
         runner = PipelineRunner()
 
@@ -99,5 +125,4 @@ async def main(room_url: str, token):
 
 
 if __name__ == "__main__":
-    (url, token) = configure()
-    asyncio.run(main(url, token))
+    asyncio.run(main())

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

@@ -1,36 +1,36 @@
 #
-# Copyright (c) 2024, Daily
+# Copyright (c) 2024–2025, Daily
 #
 # SPDX-License-Identifier: BSD 2-Clause License
 #
 
 import asyncio
-import aiohttp
 import os
 import sys
 
+import aiohttp
+from dotenv import load_dotenv
+from loguru import logger
 from PIL import Image
-
-from pipecat.frames.frames import ImageRawFrame, Frame, SystemFrame, TextFrame
-from pipecat.pipeline.pipeline import Pipeline
-from pipecat.pipeline.runner import PipelineRunner
-from pipecat.pipeline.task import PipelineTask
-from pipecat.processors.aggregators.llm_response import (
-    LLMAssistantResponseAggregator,
-    LLMUserResponseAggregator,
-)
-from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
-from pipecat.services.openai import OpenAILLMService
-from pipecat.services.elevenlabs import ElevenLabsTTSService
-from pipecat.transports.services.daily import DailyTransport
-from pipecat.vad.silero import SileroVADAnalyzer
-
-from pipecat.transports.services.daily import DailyParams
 from runner import configure
 
-from loguru import logger
+from pipecat.audio.vad.silero import SileroVADAnalyzer
+from pipecat.frames.frames import (
+    BotStartedSpeakingFrame,
+    BotStoppedSpeakingFrame,
+    Frame,
+    OutputImageRawFrame,
+    TextFrame,
+)
+from pipecat.pipeline.pipeline import Pipeline
+from pipecat.pipeline.runner import PipelineRunner
+from pipecat.pipeline.task import PipelineParams, PipelineTask
+from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
+from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
+from pipecat.services.cartesia import CartesiaHttpTTSService
+from pipecat.services.openai import OpenAILLMService
+from pipecat.transports.services.daily import DailyParams, DailyTransport
 
-from dotenv import load_dotenv
 load_dotenv(override=True)
 
 logger.remove(0)
@@ -51,39 +51,52 @@ class ImageSyncAggregator(FrameProcessor):
     async def process_frame(self, frame: Frame, direction: FrameDirection):
         await super().process_frame(frame, direction)
 
-        if not isinstance(frame, SystemFrame):
-            await self.push_frame(ImageRawFrame(image=self._speaking_image_bytes, size=(1024, 1024), format=self._speaking_image_format))
-            await self.push_frame(frame)
-            await self.push_frame(ImageRawFrame(image=self._waiting_image_bytes, size=(1024, 1024), format=self._waiting_image_format))
-        else:
-            await self.push_frame(frame)
+        if isinstance(frame, BotStartedSpeakingFrame):
+            await self.push_frame(
+                OutputImageRawFrame(
+                    image=self._speaking_image_bytes,
+                    size=(1024, 1024),
+                    format=self._speaking_image_format,
+                )
+            )
+
+        elif isinstance(frame, BotStoppedSpeakingFrame):
+            await self.push_frame(
+                OutputImageRawFrame(
+                    image=self._waiting_image_bytes,
+                    size=(1024, 1024),
+                    format=self._waiting_image_format,
+                )
+            )
+
+        await self.push_frame(frame)
 
 
-async def main(room_url: str, token):
+async def main():
     async with aiohttp.ClientSession() as session:
+        (room_url, token) = await configure(session)
+
         transport = DailyTransport(
             room_url,
             token,
             "Respond bot",
             DailyParams(
                 audio_out_enabled=True,
+                camera_out_enabled=True,
                 camera_out_width=1024,
                 camera_out_height=1024,
                 transcription_enabled=True,
                 vad_enabled=True,
-                vad_analyzer=SileroVADAnalyzer()
-            )
+                vad_analyzer=SileroVADAnalyzer(),
+            ),
         )
 
-        tts = ElevenLabsTTSService(
-            aiohttp_session=session,
-            api_key=os.getenv("ELEVENLABS_API_KEY"),
-            voice_id=os.getenv("ELEVENLABS_VOICE_ID"),
+        tts = CartesiaHttpTTSService(
+            api_key=os.getenv("CARTESIA_API_KEY"),
+            voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22",  # British Lady
         )
 
-        llm = OpenAILLMService(
-            api_key=os.getenv("OPENAI_API_KEY"),
-            model="gpt-4o")
+        llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
 
         messages = [
             {
@@ -92,31 +105,45 @@ async def main(room_url: str, token):
             },
         ]
 
-        tma_in = LLMUserResponseAggregator(messages)
-        tma_out = LLMAssistantResponseAggregator(messages)
+        context = OpenAILLMContext(messages)
+        context_aggregator = llm.create_context_aggregator(context)
 
         image_sync_aggregator = ImageSyncAggregator(
             os.path.join(os.path.dirname(__file__), "assets", "speaking.png"),
             os.path.join(os.path.dirname(__file__), "assets", "waiting.png"),
         )
 
-        pipeline = Pipeline([
-            transport.input(),
-            image_sync_aggregator,
-            tma_in,
-            llm,
-            tts,
-            transport.output(),
-            tma_out
-        ])
+        pipeline = Pipeline(
+            [
+                transport.input(),
+                context_aggregator.user(),
+                llm,
+                tts,
+                image_sync_aggregator,
+                transport.output(),
+                context_aggregator.assistant(),
+            ]
+        )
 
-        task = PipelineTask(pipeline)
+        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):
-            participant_name = participant["info"]["userName"] or ''
-            transport.capture_participant_transcription(participant["id"])
-            await task.queue_frames([TextFrame(f"Hi, this is {participant_name}.")])
+            participant_name = participant.get("info", {}).get("userName", "")
+            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.cancel()
 
         runner = PipelineRunner()
 
@@ -124,5 +151,4 @@ async def main(room_url: str, token):
 
 
 if __name__ == "__main__":
-    (url, token) = configure()
-    asyncio.run(main(url, token))
+    asyncio.run(main())

examples/foundational/07-interruptible-vad.py

@@ -1,61 +1,56 @@
 #
-# Copyright (c) 2024, Daily
+# Copyright (c) 2024–2025, Daily
 #
 # SPDX-License-Identifier: BSD 2-Clause License
 #
 
 import asyncio
-import aiohttp
 import os
 import sys
 
-from pipecat.frames.frames import LLMMessagesFrame
+import aiohttp
+from dotenv import load_dotenv
+from loguru import logger
+from runner import configure
+
 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 (
-    LLMAssistantResponseAggregator, LLMUserResponseAggregator)
+from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
+from pipecat.processors.audio.vad.silero import SileroVAD
 from pipecat.services.cartesia import CartesiaTTSService
 from pipecat.services.openai import OpenAILLMService
 from pipecat.transports.services.daily import DailyParams, DailyTransport
-from pipecat.vad.silero import SileroVADAnalyzer
 
-
-from runner import configure
-
-from loguru import logger
-
-from dotenv import load_dotenv
 load_dotenv(override=True)
 
 logger.remove(0)
 logger.add(sys.stderr, level="DEBUG")
 
 
-async def main(room_url: str, token):
+async def main():
     async with aiohttp.ClientSession() as session:
+        (room_url, token) = await configure(session)
+
         transport = DailyTransport(
             room_url,
             token,
             "Respond bot",
             DailyParams(
+                audio_in_enabled=True,
                 audio_out_enabled=True,
-                audio_out_sample_rate=44100,
                 transcription_enabled=True,
-                vad_enabled=True,
-                vad_analyzer=SileroVADAnalyzer()
-            )
+            ),
         )
 
+        vad = SileroVAD()
+
         tts = CartesiaTTSService(
             api_key=os.getenv("CARTESIA_API_KEY"),
-            voice_name="British Lady",
-            output_format="pcm_44100"
+            voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22",  # British Lady
         )
 
-        llm = OpenAILLMService(
-            api_key=os.getenv("OPENAI_API_KEY"),
-            model="gpt-4o")
+        llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
 
         messages = [
             {
@@ -64,27 +59,41 @@ async def main(room_url: str, token):
             },
         ]
 
-        tma_in = LLMUserResponseAggregator(messages)
-        tma_out = LLMAssistantResponseAggregator(messages)
+        context = OpenAILLMContext(messages)
+        context_aggregator = llm.create_context_aggregator(context)
 
-        pipeline = Pipeline([
-            transport.input(),   # Transport user input
-            tma_in,              # User responses
-            llm,                 # LLM
-            tts,                 # TTS
-            transport.output(),  # Transport bot output
-            tma_out              # Assistant spoken responses
-        ])
+        pipeline = Pipeline(
+            [
+                transport.input(),
+                vad,
+                context_aggregator.user(),
+                llm,
+                tts,
+                transport.output(),
+                context_aggregator.assistant(),
+            ]
+        )
 
-        task = PipelineTask(pipeline, PipelineParams(allow_interruptions=True))
+        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):
-            transport.capture_participant_transcription(participant["id"])
+            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([LLMMessagesFrame(messages)])
+            messages.append({"role": "system", "content": "Please introduce yourself to the user."})
+            await task.queue_frames([context_aggregator.user().get_context_frame()])
+
+        @transport.event_handler("on_participant_left")
+        async def on_participant_left(transport, participant, reason):
+            await task.cancel()
 
         runner = PipelineRunner()
 
@@ -92,5 +101,4 @@ async def main(room_url: str, token):
 
 
 if __name__ == "__main__":
-    (url, token) = configure()
-    asyncio.run(main(url, token))
+    asyncio.run(main())

examples/foundational/07-interruptible.py

@@ -1,38 +1,37 @@
 #
-# Copyright (c) 2024, Daily
+# Copyright (c) 2024–2025, Daily
 #
 # SPDX-License-Identifier: BSD 2-Clause License
 #
 
 import asyncio
-import aiohttp
 import os
 import sys
 
-from pipecat.frames.frames import LLMMessagesFrame
+import aiohttp
+from dotenv import load_dotenv
+from loguru import logger
+from runner import configure
+
+from pipecat.audio.vad.silero import SileroVADAnalyzer
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.aggregators.llm_response import (
-    LLMAssistantResponseAggregator, LLMUserResponseAggregator)
-from pipecat.services.elevenlabs import ElevenLabsTTSService
+from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
+from pipecat.services.cartesia import CartesiaTTSService
 from pipecat.services.openai import OpenAILLMService
 from pipecat.transports.services.daily import DailyParams, DailyTransport
-from pipecat.vad.silero import SileroVADAnalyzer
 
-from runner import configure
-
-from loguru import logger
-
-from dotenv import load_dotenv
 load_dotenv(override=True)
 
 logger.remove(0)
 logger.add(sys.stderr, level="DEBUG")
 
 
-async def main(room_url: str, token):
+async def main():
     async with aiohttp.ClientSession() as session:
+        (room_url, token) = await configure(session)
+
         transport = DailyTransport(
             room_url,
             token,
@@ -41,19 +40,16 @@ async def main(room_url: str, token):
                 audio_out_enabled=True,
                 transcription_enabled=True,
                 vad_enabled=True,
-                vad_analyzer=SileroVADAnalyzer()
-            )
+                vad_analyzer=SileroVADAnalyzer(),
+            ),
         )
 
-        tts = ElevenLabsTTSService(
-            aiohttp_session=session,
-            api_key=os.getenv("ELEVENLABS_API_KEY"),
-            voice_id=os.getenv("ELEVENLABS_VOICE_ID"),
+        tts = CartesiaTTSService(
+            api_key=os.getenv("CARTESIA_API_KEY"),
+            voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22",  # British Lady
         )
 
-        llm = OpenAILLMService(
-            api_key=os.getenv("OPENAI_API_KEY"),
-            model="gpt-4o")
+        llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
 
         messages = [
             {
@@ -62,31 +58,40 @@ async def main(room_url: str, token):
             },
         ]
 
-        tma_in = LLMUserResponseAggregator(messages)
-        tma_out = LLMAssistantResponseAggregator(messages)
+        context = OpenAILLMContext(messages)
+        context_aggregator = llm.create_context_aggregator(context)
 
-        pipeline = Pipeline([
-            transport.input(),   # Transport user input
-            tma_in,              # User responses
-            llm,                 # LLM
-            tts,                 # TTS
-            transport.output(),  # Transport bot output
-            tma_out              # Assistant spoken responses
-        ])
+        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,
-            report_only_initial_ttfb=True,
-        ))
+        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):
-            transport.capture_participant_transcription(participant["id"])
+            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([LLMMessagesFrame(messages)])
+            messages.append({"role": "system", "content": "Please introduce yourself to the user."})
+            await task.queue_frames([context_aggregator.user().get_context_frame()])
+
+        @transport.event_handler("on_participant_left")
+        async def on_participant_left(transport, participant, reason):
+            await task.cancel()
 
         runner = PipelineRunner()
 
@@ -94,5 +99,4 @@ async def main(room_url: str, token):
 
 
 if __name__ == "__main__":
-    (url, token) = configure()
-    asyncio.run(main(url, token))
+    asyncio.run(main())

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

@@ -1,38 +1,37 @@
 #
-# Copyright (c) 2024, Daily
+# Copyright (c) 2024–2025, Daily
 #
 # SPDX-License-Identifier: BSD 2-Clause License
 #
 
 import asyncio
-import aiohttp
 import os
 import sys
 
-from pipecat.frames.frames import LLMMessagesFrame
+import aiohttp
+from dotenv import load_dotenv
+from loguru import logger
+from runner import configure
+
+from pipecat.audio.vad.silero import SileroVADAnalyzer
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.aggregators.llm_response import (
-    LLMAssistantResponseAggregator, LLMUserResponseAggregator)
-from pipecat.services.elevenlabs import ElevenLabsTTSService
+from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
 from pipecat.services.anthropic import AnthropicLLMService
+from pipecat.services.cartesia import CartesiaTTSService
 from pipecat.transports.services.daily import DailyParams, DailyTransport
-from pipecat.vad.silero import SileroVADAnalyzer
 
-from runner import configure
-
-from loguru import logger
-
-from dotenv import load_dotenv
 load_dotenv(override=True)
 
 logger.remove(0)
 logger.add(sys.stderr, level="DEBUG")
 
 
-async def main(room_url: str, token):
+async def main():
     async with aiohttp.ClientSession() as session:
+        (room_url, token) = await configure(session)
+
         transport = DailyTransport(
             room_url,
             token,
@@ -41,19 +40,18 @@ async def main(room_url: str, token):
                 audio_out_enabled=True,
                 transcription_enabled=True,
                 vad_enabled=True,
-                vad_analyzer=SileroVADAnalyzer()
-            )
+                vad_analyzer=SileroVADAnalyzer(),
+            ),
         )
 
-        tts = ElevenLabsTTSService(
-            aiohttp_session=session,
-            api_key=os.getenv("ELEVENLABS_API_KEY"),
-            voice_id=os.getenv("ELEVENLABS_VOICE_ID"),
+        tts = CartesiaTTSService(
+            api_key=os.getenv("CARTESIA_API_KEY"),
+            voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22",  # British Lady
         )
 
         llm = AnthropicLLMService(
-            api_key=os.getenv("ANTHROPIC_API_KEY"),
-            model="claude-3-opus-20240229")
+            api_key=os.getenv("ANTHROPIC_API_KEY"), model="claude-3-opus-20240229"
+        )
 
         # todo: think more about how to handle system prompts in a more general way. OpenAI,
         # Google, and Anthropic all have slightly different approaches to providing a system
@@ -65,25 +63,39 @@ async def main(room_url: str, token):
             },
         ]
 
-        tma_in = LLMUserResponseAggregator(messages)
-        tma_out = LLMAssistantResponseAggregator(messages)
+        context = OpenAILLMContext(messages)
+        context_aggregator = llm.create_context_aggregator(context)
 
-        pipeline = Pipeline([
-            transport.input(),   # Transport user input
-            tma_in,              # User responses
-            llm,                 # LLM
-            tts,                 # TTS
-            transport.output(),  # Transport bot output
-            tma_out              # Assistant spoken responses
-        ])
+        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))
+        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):
-            transport.capture_participant_transcription(participant["id"])
+            await transport.capture_participant_transcription(participant["id"])
             # Kick off the conversation.
-            await task.queue_frames([LLMMessagesFrame(messages)])
+            await task.queue_frames([context_aggregator.user().get_context_frame()])
+
+        @transport.event_handler("on_participant_left")
+        async def on_participant_left(transport, participant, reason):
+            await task.cancel()
 
         runner = PipelineRunner()
 
@@ -91,5 +103,4 @@ async def main(room_url: str, token):
 
 
 if __name__ == "__main__":
-    (url, token) = configure()
-    asyncio.run(main(url, token))
+    asyncio.run(main())

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

@@ -1,5 +1,5 @@
 #
-# Copyright (c) 2024, Daily
+# Copyright (c) 2024–2025, Daily
 #
 # SPDX-License-Identifier: BSD 2-Clause License
 #
@@ -9,29 +9,28 @@ import os
 import sys
 
 import aiohttp
-
-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.llm_response import (
-    LLMAssistantResponseAggregator, LLMUserResponseAggregator)
-from pipecat.processors.frameworks.langchain import LangchainProcessor
-from pipecat.services.elevenlabs import ElevenLabsTTSService
-from pipecat.transports.services.daily import DailyParams, DailyTransport
-from pipecat.vad.silero import SileroVADAnalyzer
-
+from dotenv import load_dotenv
 from langchain.prompts import ChatPromptTemplate, MessagesPlaceholder
 from langchain_community.chat_message_histories import ChatMessageHistory
 from langchain_core.chat_history import BaseChatMessageHistory
 from langchain_core.runnables.history import RunnableWithMessageHistory
 from langchain_openai import ChatOpenAI
-
 from loguru import logger
-
 from runner import configure
 
-from dotenv import load_dotenv
+from pipecat.audio.vad.silero import SileroVADAnalyzer
+from pipecat.frames.frames import LLMMessagesFrame
+from pipecat.pipeline.pipeline import Pipeline
+from pipecat.pipeline.runner import PipelineRunner
+from pipecat.pipeline.task import PipelineParams, PipelineTask
+from pipecat.processors.aggregators.llm_response import (
+    LLMAssistantResponseAggregator,
+    LLMUserResponseAggregator,
+)
+from pipecat.processors.frameworks.langchain import LangchainProcessor
+from pipecat.services.cartesia import CartesiaTTSService
+from pipecat.transports.services.daily import DailyParams, DailyTransport
+
 load_dotenv(override=True)
 
 
@@ -47,8 +46,10 @@ def get_session_history(session_id: str) -> BaseChatMessageHistory:
     return message_store[session_id]
 
 
-async def main(room_url: str, token):
+async def main():
     async with aiohttp.ClientSession() as session:
+        (room_url, token) = await configure(session)
+
         transport = DailyTransport(
             room_url,
             token,
@@ -61,27 +62,29 @@ async def main(room_url: str, token):
             ),
         )
 
-        tts = ElevenLabsTTSService(
-            aiohttp_session=session,
-            api_key=os.getenv("ELEVENLABS_API_KEY"),
-            voice_id=os.getenv("ELEVENLABS_VOICE_ID"),
+        tts = CartesiaTTSService(
+            api_key=os.getenv("CARTESIA_API_KEY"),
+            voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22",  # British Lady
         )
 
         prompt = ChatPromptTemplate.from_messages(
             [
-                ("system",
-                 "Be nice and helpful. Answer very briefly and without special characters like `#` or `*`. "
-                 "Your response will be synthesized to voice and those characters will create unnatural sounds.",
-                 ),
+                (
+                    "system",
+                    "Be nice and helpful. Answer very briefly and without special characters like `#` or `*`. "
+                    "Your response will be synthesized to voice and those characters will create unnatural sounds.",
+                ),
                 MessagesPlaceholder("chat_history"),
                 ("human", "{input}"),
-            ])
+            ]
+        )
         chain = prompt | ChatOpenAI(model="gpt-4o", temperature=0.7)
         history_chain = RunnableWithMessageHistory(
             chain,
             get_session_history,
             history_messages_key="chat_history",
-            input_messages_key="input")
+            input_messages_key="input",
+        )
         lc = LangchainProcessor(history_chain)
 
         tma_in = LLMUserResponseAggregator()
@@ -89,37 +92,44 @@ async def main(room_url: str, token):
 
         pipeline = Pipeline(
             [
-                transport.input(),      # Transport user input
-                tma_in,                 # User responses
-                lc,                     # Langchain
-                tts,                    # TTS
-                transport.output(),     # Transport bot output
-                tma_out,                # Assistant spoken responses
+                transport.input(),  # Transport user input
+                tma_in,  # User responses
+                lc,  # Langchain
+                tts,  # TTS
+                transport.output(),  # Transport bot output
+                tma_out,  # Assistant spoken responses
             ]
         )
 
-        task = PipelineTask(pipeline, PipelineParams(allow_interruptions=True))
+        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):
-            transport.capture_participant_transcription(participant["id"])
+            await transport.capture_participant_transcription(participant["id"])
             lc.set_participant_id(participant["id"])
             # Kick off the conversation.
             # the `LLMMessagesFrame` will be picked up by the LangchainProcessor using
             # only the content of the last message to inject it in the prompt defined
             # above. So no role is required here.
-            messages = [(
-                {
-                    "content": "Please briefly introduce yourself to the user."
-                }
-            )]
+            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.cancel()
+
         runner = PipelineRunner()
 
         await runner.run(task)
 
 
 if __name__ == "__main__":
-    (url, token) = configure()
-    asyncio.run(main(url, token))
+    asyncio.run(main())

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

@@ -0,0 +1,116 @@
+#
+# Copyright (c) 2024–2025, Daily
+#
+# SPDX-License-Identifier: BSD 2-Clause License
+#
+
+import asyncio
+import os
+import sys
+
+import aiohttp
+from deepgram import LiveOptions
+from dotenv import load_dotenv
+from loguru import logger
+from runner import configure
+
+from pipecat.frames.frames import (
+    BotInterruptionFrame,
+    StopInterruptionFrame,
+    UserStartedSpeakingFrame,
+    UserStoppedSpeakingFrame,
+)
+from pipecat.pipeline.pipeline import Pipeline
+from pipecat.pipeline.runner import PipelineRunner
+from pipecat.pipeline.task import PipelineParams, PipelineTask
+from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
+from pipecat.services.deepgram import DeepgramSTTService, DeepgramTTSService
+from pipecat.services.openai import OpenAILLMService
+from pipecat.transports.services.daily import DailyParams, DailyTransport
+
+load_dotenv(override=True)
+
+logger.remove(0)
+logger.add(sys.stderr, level="DEBUG")
+
+
+async def main():
+    async with aiohttp.ClientSession() as session:
+        (room_url, _) = await configure(session)
+
+        transport = DailyTransport(
+            room_url,
+            None,
+            "Respond bot",
+            DailyParams(
+                audio_in_enabled=True,
+                audio_out_enabled=True,
+            ),
+        )
+
+        stt = DeepgramSTTService(
+            api_key=os.getenv("DEEPGRAM_API_KEY"),
+            live_options=LiveOptions(vad_events=True, utterance_end_ms="1000"),
+        )
+
+        tts = DeepgramTTSService(api_key=os.getenv("DEEPGRAM_API_KEY"), voice="aura-helios-en")
+
+        llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
+
+        messages = [
+            {
+                "role": "system",
+                "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
+            },
+        ]
+
+        context = OpenAILLMContext(messages)
+        context_aggregator = llm.create_context_aggregator(context)
+
+        pipeline = Pipeline(
+            [
+                transport.input(),  # Transport user input
+                stt,  # STT
+                context_aggregator.user(),  # User responses
+                llm,  # LLM
+                tts,  # TTS
+                transport.output(),  # Transport bot output
+                context_aggregator.assistant(),  # Assistant spoken responses
+            ]
+        )
+
+        task = PipelineTask(
+            pipeline,
+            PipelineParams(
+                allow_interruptions=True,
+                enable_metrics=True,
+                enable_usage_metrics=True,
+                report_only_initial_ttfb=True,
+            ),
+        )
+
+        @stt.event_handler("on_speech_started")
+        async def on_speech_started(stt, *args, **kwargs):
+            await task.queue_frames([BotInterruptionFrame(), UserStartedSpeakingFrame()])
+
+        @stt.event_handler("on_utterance_end")
+        async def on_utterance_end(stt, *args, **kwargs):
+            await task.queue_frames([StopInterruptionFrame(), UserStoppedSpeakingFrame()])
+
+        @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.cancel()
+
+        runner = PipelineRunner()
+
+        await runner.run(task)
+
+
+if __name__ == "__main__":
+    asyncio.run(main())

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

@@ -1,61 +1,54 @@
 #
-# Copyright (c) 2024, Daily
+# Copyright (c) 2024–2025, Daily
 #
 # SPDX-License-Identifier: BSD 2-Clause License
 #
 
 import asyncio
-import aiohttp
 import os
 import sys
 
-from pipecat.frames.frames import LLMMessagesFrame
+import aiohttp
+from dotenv import load_dotenv
+from loguru import logger
+from runner import configure
+
+from pipecat.audio.vad.silero import SileroVADAnalyzer
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.aggregators.llm_response import (
-    LLMAssistantResponseAggregator, LLMUserResponseAggregator)
+from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
 from pipecat.services.deepgram import DeepgramSTTService, DeepgramTTSService
 from pipecat.services.openai import OpenAILLMService
 from pipecat.transports.services.daily import DailyParams, DailyTransport
-from pipecat.vad.silero import SileroVADAnalyzer
 
-from runner import configure
-
-from loguru import logger
-
-from dotenv import load_dotenv
 load_dotenv(override=True)
 
 logger.remove(0)
 logger.add(sys.stderr, level="DEBUG")
 
 
-async def main(room_url: str, token):
+async def main():
     async with aiohttp.ClientSession() as session:
+        (room_url, _) = await configure(session)
+
         transport = DailyTransport(
             room_url,
-            token,
+            None,
             "Respond bot",
             DailyParams(
                 audio_out_enabled=True,
                 vad_enabled=True,
                 vad_analyzer=SileroVADAnalyzer(),
-                vad_audio_passthrough=True
-            )
+                vad_audio_passthrough=True,
+            ),
         )
 
         stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
 
-        tts = DeepgramTTSService(
-            aiohttp_session=session,
-            api_key=os.getenv("DEEPGRAM_API_KEY"),
-            voice="aura-helios-en"
-        )
+        tts = DeepgramTTSService(api_key=os.getenv("DEEPGRAM_API_KEY"), voice="aura-helios-en")
 
-        llm = OpenAILLMService(
-            api_key=os.getenv("OPENAI_API_KEY"),
-            model="gpt-4o")
+        llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
 
         messages = [
             {
@@ -64,28 +57,40 @@ async def main(room_url: str, token):
             },
         ]
 
-        tma_in = LLMUserResponseAggregator(messages)
-        tma_out = LLMAssistantResponseAggregator(messages)
+        context = OpenAILLMContext(messages)
+        context_aggregator = llm.create_context_aggregator(context)
 
-        pipeline = Pipeline([
-            transport.input(),   # Transport user input
-            stt,                 # STT
-            tma_in,              # User responses
-            llm,                 # LLM
-            tts,                 # TTS
-            transport.output(),  # Transport bot output
-            tma_out              # Assistant spoken responses
-        ])
+        pipeline = Pipeline(
+            [
+                transport.input(),  # Transport user input
+                stt,  # STT
+                context_aggregator.user(),  # User responses
+                llm,  # LLM
+                tts,  # TTS
+                transport.output(),  # Transport bot output
+                context_aggregator.assistant(),  # Assistant spoken responses
+            ]
+        )
 
-        task = PipelineTask(pipeline, PipelineParams(allow_interruptions=True))
+        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):
-            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([LLMMessagesFrame(messages)])
+            messages.append({"role": "system", "content": "Please introduce yourself to the user."})
+            await task.queue_frames([context_aggregator.user().get_context_frame()])
+
+        @transport.event_handler("on_participant_left")
+        async def on_participant_left(transport, participant, reason):
+            await task.cancel()
 
         runner = PipelineRunner()
 
@@ -93,5 +98,4 @@ async def main(room_url: str, token):
 
 
 if __name__ == "__main__":
-    (url, token) = configure()
-    asyncio.run(main(url, token))
+    asyncio.run(main())

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

@@ -0,0 +1,102 @@
+#
+# Copyright (c) 2024–2025, Daily
+#
+# SPDX-License-Identifier: BSD 2-Clause License
+#
+
+import asyncio
+import os
+import sys
+
+import aiohttp
+from dotenv import load_dotenv
+from loguru import logger
+from runner import configure
+
+from pipecat.audio.vad.silero import SileroVADAnalyzer
+from pipecat.pipeline.pipeline import Pipeline
+from pipecat.pipeline.runner import PipelineRunner
+from pipecat.pipeline.task import PipelineParams, PipelineTask
+from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
+from pipecat.services.elevenlabs import ElevenLabsTTSService
+from pipecat.services.openai import OpenAILLMService
+from pipecat.transports.services.daily import DailyParams, DailyTransport
+
+load_dotenv(override=True)
+
+logger.remove(0)
+logger.add(sys.stderr, level="DEBUG")
+
+
+async def main():
+    async with aiohttp.ClientSession() as session:
+        (room_url, token) = await configure(session)
+
+        transport = DailyTransport(
+            room_url,
+            token,
+            "Respond bot",
+            DailyParams(
+                audio_out_enabled=True,
+                transcription_enabled=True,
+                vad_enabled=True,
+                vad_analyzer=SileroVADAnalyzer(),
+            ),
+        )
+
+        tts = ElevenLabsTTSService(
+            api_key=os.getenv("ELEVENLABS_API_KEY", ""),
+            voice_id=os.getenv("ELEVENLABS_VOICE_ID", ""),
+        )
+
+        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.cancel()
+
+        runner = PipelineRunner()
+
+        await runner.run(task)
+
+
+if __name__ == "__main__":
+    asyncio.run(main())

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

@@ -0,0 +1,103 @@
+#
+# Copyright (c) 2024–2025, Daily
+#
+# SPDX-License-Identifier: BSD 2-Clause License
+#
+
+import asyncio
+import os
+import sys
+
+import aiohttp
+from dotenv import load_dotenv
+from loguru import logger
+from runner import configure
+
+from pipecat.audio.vad.silero import SileroVADAnalyzer
+from pipecat.pipeline.pipeline import Pipeline
+from pipecat.pipeline.runner import PipelineRunner
+from pipecat.pipeline.task import PipelineParams, PipelineTask
+from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
+from pipecat.services.openai import OpenAILLMService
+from pipecat.services.playht import PlayHTHttpTTSService
+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.cancel()
+
+        runner = PipelineRunner()
+
+        await runner.run(task)
+
+
+if __name__ == "__main__":
+    asyncio.run(main())

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

@@ -1,61 +1,58 @@
 #
-# Copyright (c) 2024, Daily
+# Copyright (c) 2024–2025, Daily
 #
 # SPDX-License-Identifier: BSD 2-Clause License
 #
 
 import asyncio
-import aiohttp
 import os
 import sys
 
-from pipecat.frames.frames import LLMMessagesFrame
+import aiohttp
+from dotenv import load_dotenv
+from loguru import logger
+from runner import configure
+
+from pipecat.audio.vad.silero import SileroVADAnalyzer
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.aggregators.llm_response import (
-    LLMAssistantResponseAggregator, LLMUserResponseAggregator)
-from pipecat.services.playht import PlayHTTTSService
+from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
 from pipecat.services.openai import OpenAILLMService
+from pipecat.services.playht import PlayHTTTSService
+from pipecat.transcriptions.language import Language
 from pipecat.transports.services.daily import DailyParams, DailyTransport
-from pipecat.vad.silero import SileroVADAnalyzer
-from pipecat.processors.logger import FrameLogger
 
-from runner import configure
-
-from loguru import logger
-
-from dotenv import load_dotenv
 load_dotenv(override=True)
 
 logger.remove(0)
 logger.add(sys.stderr, level="DEBUG")
 
 
-async def main(room_url: str, token):
+async def main():
     async with aiohttp.ClientSession() as session:
+        (room_url, token) = await configure(session)
+
         transport = DailyTransport(
             room_url,
             token,
             "Respond bot",
             DailyParams(
                 audio_out_enabled=True,
-                audio_out_sample_rate=16000,
                 transcription_enabled=True,
                 vad_enabled=True,
-                vad_analyzer=SileroVADAnalyzer()
-            )
+                vad_analyzer=SileroVADAnalyzer(),
+            ),
         )
 
         tts = PlayHTTTSService(
             user_id=os.getenv("PLAYHT_USER_ID"),
             api_key=os.getenv("PLAYHT_API_KEY"),
-            voice_url="s3://voice-cloning-zero-shot/801a663f-efd0-4254-98d0-5c175514c3e8/jennifer/manifest.json",
+            voice_url="s3://voice-cloning-zero-shot/d9ff78ba-d016-47f6-b0ef-dd630f59414e/female-cs/manifest.json",
+            params=PlayHTTTSService.InputParams(language=Language.EN),
         )
 
-        llm = OpenAILLMService(
-            api_key=os.getenv("OPENAI_API_KEY"),
-            model="gpt-4o")
+        llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
 
         messages = [
             {
@@ -64,27 +61,40 @@ async def main(room_url: str, token):
             },
         ]
 
-        tma_in = LLMUserResponseAggregator(messages)
-        tma_out = LLMAssistantResponseAggregator(messages)
+        context = OpenAILLMContext(messages)
+        context_aggregator = llm.create_context_aggregator(context)
 
-        pipeline = Pipeline([
-            transport.input(),   # Transport user input
-            tma_in,              # User responses
-            llm,                 # LLM
-            tts,                 # TTS
-            transport.output(),  # Transport bot output
-            tma_out              # Assistant spoken responses
-        ])
+        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))
+        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):
-            transport.capture_participant_transcription(participant["id"])
+            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([LLMMessagesFrame(messages)])
+            messages.append({"role": "system", "content": "Please introduce yourself to the user."})
+            await task.queue_frames([context_aggregator.user().get_context_frame()])
+
+        @transport.event_handler("on_participant_left")
+        async def on_participant_left(transport, participant, reason):
+            await task.cancel()
 
         runner = PipelineRunner()
 
@@ -92,5 +102,4 @@ async def main(room_url: str, token):
 
 
 if __name__ == "__main__":
-    (url, token) = configure()
-    asyncio.run(main(url, token))
+    asyncio.run(main())

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

@@ -1,95 +0,0 @@
-#
-# Copyright (c) 2024, Daily
-#
-# SPDX-License-Identifier: BSD 2-Clause License
-#
-
-import asyncio
-import aiohttp
-import os
-import sys
-
-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.llm_response import (
-    LLMAssistantResponseAggregator, LLMUserResponseAggregator)
-from pipecat.services.azure import AzureTTSService
-from pipecat.services.openai import OpenAILLMService
-from pipecat.transports.services.daily import DailyParams, DailyTransport
-from pipecat.vad.silero import SileroVADAnalyzer
-
-
-from runner import configure
-
-from loguru import logger
-
-from dotenv import load_dotenv
-load_dotenv(override=True)
-
-logger.remove(0)
-logger.add(sys.stderr, level="DEBUG")
-
-
-async def main(room_url: str, token):
-    async with aiohttp.ClientSession() as session:
-        transport = DailyTransport(
-            room_url,
-            token,
-            "Respond bot",
-            DailyParams(
-                audio_out_enabled=True,
-                audio_out_sample_rate=16000,
-                transcription_enabled=True,
-                vad_enabled=True,
-                vad_analyzer=SileroVADAnalyzer()
-            )
-        )
-
-        tts = AzureTTSService(
-            api_key=os.getenv("AZURE_SPEECH_API_KEY"),
-            region=os.getenv("AZURE_SPEECH_REGION"),
-        )
-
-        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.",
-            },
-        ]
-
-        tma_in = LLMUserResponseAggregator(messages)
-        tma_out = LLMAssistantResponseAggregator(messages)
-
-        pipeline = Pipeline([
-            transport.input(),   # Transport user input
-            tma_in,              # User responses
-            llm,                 # LLM
-            tts,                 # TTS
-            transport.output(),  # Transport bot output
-            tma_out              # Assistant spoken responses
-        ])
-
-        task = PipelineTask(pipeline, PipelineParams(allow_interruptions=True))
-
-        @transport.event_handler("on_first_participant_joined")
-        async def on_first_participant_joined(transport, participant):
-            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([LLMMessagesFrame(messages)])
-
-        runner = PipelineRunner()
-
-        await runner.run(task)
-
-
-if __name__ == "__main__":
-    (url, token) = configure()
-    asyncio.run(main(url, token))

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

@@ -0,0 +1,111 @@
+#
+# Copyright (c) 2024–2025, Daily
+#
+# SPDX-License-Identifier: BSD 2-Clause License
+#
+
+import asyncio
+import os
+import sys
+
+import aiohttp
+from dotenv import load_dotenv
+from loguru import logger
+from runner import configure
+
+from pipecat.audio.vad.silero import SileroVADAnalyzer
+from pipecat.pipeline.pipeline import Pipeline
+from pipecat.pipeline.runner import PipelineRunner
+from pipecat.pipeline.task import PipelineParams, PipelineTask
+from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
+from pipecat.services.azure import AzureLLMService, AzureSTTService, AzureTTSService
+from pipecat.transports.services.daily import DailyParams, DailyTransport
+
+load_dotenv(override=True)
+
+logger.remove(0)
+logger.add(sys.stderr, level="DEBUG")
+
+
+async def main():
+    async with aiohttp.ClientSession() as session:
+        (room_url, token) = await configure(session)
+
+        transport = DailyTransport(
+            room_url,
+            token,
+            "Respond bot",
+            DailyParams(
+                audio_out_enabled=True,
+                vad_enabled=True,
+                vad_analyzer=SileroVADAnalyzer(),
+                vad_audio_passthrough=True,
+            ),
+        )
+
+        stt = AzureSTTService(
+            api_key=os.getenv("AZURE_SPEECH_API_KEY"),
+            region=os.getenv("AZURE_SPEECH_REGION"),
+        )
+
+        tts = AzureTTSService(
+            api_key=os.getenv("AZURE_SPEECH_API_KEY"),
+            region=os.getenv("AZURE_SPEECH_REGION"),
+        )
+
+        llm = AzureLLMService(
+            api_key=os.getenv("AZURE_CHATGPT_API_KEY"),
+            endpoint=os.getenv("AZURE_CHATGPT_ENDPOINT"),
+            model=os.getenv("AZURE_CHATGPT_MODEL"),
+        )
+
+        messages = [
+            {
+                "role": "system",
+                "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
+            },
+        ]
+
+        context = OpenAILLMContext(messages)
+        context_aggregator = llm.create_context_aggregator(context)
+
+        pipeline = Pipeline(
+            [
+                transport.input(),  # Transport user input
+                stt,  # STT
+                context_aggregator.user(),  # User responses
+                llm,  # LLM
+                tts,  # TTS
+                transport.output(),  # Transport bot output
+                context_aggregator.assistant(),  # Assistant spoken responses
+            ]
+        )
+
+        task = PipelineTask(
+            pipeline,
+            PipelineParams(
+                allow_interruptions=True,
+                enable_metrics=True,
+                enable_usage_metrics=True,
+                report_only_initial_ttfb=True,
+            ),
+        )
+
+        @transport.event_handler("on_first_participant_joined")
+        async def on_first_participant_joined(transport, participant):
+            await transport.capture_participant_transcription(participant["id"])
+            # Kick off the conversation.
+            messages.append({"role": "system", "content": "Please introduce yourself to the user."})
+            await task.queue_frames([context_aggregator.user().get_context_frame()])
+
+        @transport.event_handler("on_participant_left")
+        async def on_participant_left(transport, participant, reason):
+            await task.cancel()
+
+        runner = PipelineRunner()
+
+        await runner.run(task)
+
+
+if __name__ == "__main__":
+    asyncio.run(main())

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

@@ -1,94 +0,0 @@
-#
-# Copyright (c) 2024, Daily
-#
-# SPDX-License-Identifier: BSD 2-Clause License
-#
-
-import asyncio
-import aiohttp
-import os
-import sys
-
-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.llm_response import (
-    LLMAssistantResponseAggregator, LLMUserResponseAggregator)
-from pipecat.services.openai import OpenAITTSService
-from pipecat.services.openai import OpenAILLMService
-from pipecat.transports.services.daily import DailyParams, DailyTransport
-from pipecat.vad.silero import SileroVADAnalyzer
-
-from runner import configure
-
-from loguru import logger
-
-from dotenv import load_dotenv
-load_dotenv(override=True)
-
-logger.remove(0)
-logger.add(sys.stderr, level="DEBUG")
-
-
-async def main(room_url: str, token):
-    async with aiohttp.ClientSession() as session:
-        transport = DailyTransport(
-            room_url,
-            token,
-            "Respond bot",
-            DailyParams(
-                audio_out_enabled=True,
-                audio_out_sample_rate=24000,
-                transcription_enabled=True,
-                vad_enabled=True,
-                vad_analyzer=SileroVADAnalyzer()
-            )
-        )
-
-        tts = OpenAITTSService(
-            api_key=os.getenv("OPENAI_API_KEY"),
-            voice="alloy"
-        )
-
-        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.",
-            },
-        ]
-
-        tma_in = LLMUserResponseAggregator(messages)
-        tma_out = LLMAssistantResponseAggregator(messages)
-
-        pipeline = Pipeline([
-            transport.input(),   # Transport user input
-            tma_in,              # User responses
-            llm,                 # LLM
-            tts,                 # TTS
-            transport.output(),  # Transport bot output
-            tma_out              # Assistant spoken responses
-        ])
-
-        task = PipelineTask(pipeline, PipelineParams(allow_interruptions=True))
-
-        @transport.event_handler("on_first_participant_joined")
-        async def on_first_participant_joined(transport, participant):
-            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([LLMMessagesFrame(messages)])
-
-        runner = PipelineRunner()
-
-        await runner.run(task)
-
-
-if __name__ == "__main__":
-    (url, token) = configure()
-    asyncio.run(main(url, token))

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

@@ -0,0 +1,109 @@
+#
+# Copyright (c) 2024–2025, Daily
+#
+# SPDX-License-Identifier: BSD 2-Clause License
+#
+
+import asyncio
+import os
+import sys
+
+import aiohttp
+from dotenv import load_dotenv
+from loguru import logger
+from runner import configure
+
+from pipecat.audio.vad.silero import SileroVADAnalyzer
+from pipecat.pipeline.pipeline import Pipeline
+from pipecat.pipeline.runner import PipelineRunner
+from pipecat.pipeline.task import PipelineParams, PipelineTask
+from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
+from pipecat.services.openai import OpenAILLMService, OpenAISTTService, OpenAITTSService
+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,
+                audio_out_sample_rate=24000,
+                transcription_enabled=False,
+                vad_enabled=True,
+                vad_analyzer=SileroVADAnalyzer(),
+                vad_audio_passthrough=True,
+            ),
+        )
+
+        # You can use the OpenAI compatible API like Groq.
+        # stt = OpenAISTTService(
+        #     base_url="https://api.groq.com/openai/v1",
+        #     api_key="gsk_***",
+        #     model="whisper-large-v3",
+        # )
+        stt = OpenAISTTService(api_key=os.getenv("OPENAI_API_KEY"), model="whisper-1")
+
+        tts = OpenAITTSService(api_key=os.getenv("OPENAI_API_KEY"), voice="alloy")
+
+        llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
+
+        messages = [
+            {
+                "role": "system",
+                "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
+            },
+        ]
+
+        context = OpenAILLMContext(messages)
+        context_aggregator = llm.create_context_aggregator(context)
+
+        pipeline = Pipeline(
+            [
+                transport.input(),  # Transport user input
+                stt,  # STT
+                context_aggregator.user(),  # User responses
+                llm,  # LLM
+                tts,  # TTS
+                transport.output(),  # Transport bot output
+                context_aggregator.assistant(),  # Assistant spoken responses
+            ]
+        )
+
+        task = PipelineTask(
+            pipeline,
+            PipelineParams(
+                allow_interruptions=True,
+                enable_metrics=True,
+                enable_usage_metrics=True,
+                report_only_initial_ttfb=True,
+            ),
+        )
+
+        @transport.event_handler("on_first_participant_joined")
+        async def on_first_participant_joined(transport, participant):
+            await transport.capture_participant_transcription(participant["id"])
+            # Kick off the conversation.
+            messages.append({"role": "system", "content": "Please introduce yourself to the user."})
+            await task.queue_frames([context_aggregator.user().get_context_frame()])
+
+        @transport.event_handler("on_participant_left")
+        async def on_participant_left(transport, participant, reason):
+            await task.cancel()
+
+        runner = PipelineRunner()
+
+        await runner.run(task)
+
+
+if __name__ == "__main__":
+    asyncio.run(main())

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

@@ -1,41 +1,38 @@
 #
-# Copyright (c) 2024, Daily
+# Copyright (c) 2024–2025, Daily
 #
 # SPDX-License-Identifier: BSD 2-Clause License
 #
 
 import asyncio
-import aiohttp
 import os
 import sys
+import time
 
-from pipecat.frames.frames import LLMMessagesFrame
+import aiohttp
+from dotenv import load_dotenv
+from loguru import logger
+from runner import configure
+
+from pipecat.audio.vad.silero import SileroVADAnalyzer
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.aggregators.llm_response import (
-    LLMAssistantResponseAggregator,
-    LLMUserResponseAggregator,
-)
-from pipecat.services.elevenlabs import ElevenLabsTTSService
+from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
+from pipecat.services.cartesia import CartesiaTTSService
 from pipecat.services.openpipe import OpenPipeLLMService
 from pipecat.transports.services.daily import DailyParams, DailyTransport
-from pipecat.vad.silero import SileroVADAnalyzer
 
-from runner import configure
-
-from loguru import logger
-import time
-
-from dotenv import load_dotenv
 load_dotenv(override=True)
 
 logger.remove(0)
 logger.add(sys.stderr, level="DEBUG")
 
 
-async def main(room_url: str, token):
+async def main():
     async with aiohttp.ClientSession() as session:
+        (room_url, token) = await configure(session)
+
         transport = DailyTransport(
             room_url,
             token,
@@ -44,14 +41,13 @@ async def main(room_url: str, token):
                 audio_out_enabled=True,
                 transcription_enabled=True,
                 vad_enabled=True,
-                vad_analyzer=SileroVADAnalyzer()
-            )
+                vad_analyzer=SileroVADAnalyzer(),
+            ),
         )
 
-        tts = ElevenLabsTTSService(
-            aiohttp_session=session,
-            api_key=os.getenv("ELEVENLABS_API_KEY"),
-            voice_id=os.getenv("ELEVENLABS_VOICE_ID"),
+        tts = CartesiaTTSService(
+            api_key=os.getenv("CARTESIA_API_KEY"),
+            voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22",  # British Lady
         )
 
         timestamp = int(time.time())
@@ -59,9 +55,7 @@ async def main(room_url: str, token):
             api_key=os.getenv("OPENAI_API_KEY"),
             openpipe_api_key=os.getenv("OPENPIPE_API_KEY"),
             model="gpt-4o",
-            tags={
-                "conversation_id": f"pipecat-{timestamp}"
-            }
+            tags={"conversation_id": f"pipecat-{timestamp}"},
         )
 
         messages = [
@@ -70,27 +64,41 @@ async def main(room_url: str, token):
                 "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.",
             },
         ]
-        tma_in = LLMUserResponseAggregator(messages)
-        tma_out = LLMAssistantResponseAggregator(messages)
 
-        pipeline = Pipeline([
-            transport.input(),   # Transport user input
-            tma_in,              # User responses
-            llm,                 # LLM
-            tts,                 # TTS
-            transport.output(),  # Transport bot output
-            tma_out              # Assistant spoken responses
-        ])
+        context = OpenAILLMContext(messages)
+        context_aggregator = llm.create_context_aggregator(context)
 
-        task = PipelineTask(pipeline, params=PipelineParams(allow_interruptions=True))
+        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):
-            transport.capture_participant_transcription(participant["id"])
+            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([LLMMessagesFrame(messages)])
+            messages.append({"role": "system", "content": "Please introduce yourself to the user."})
+            await task.queue_frames([context_aggregator.user().get_context_frame()])
+
+        @transport.event_handler("on_participant_left")
+        async def on_participant_left(transport, participant, reason):
+            await task.cancel()
 
         runner = PipelineRunner()
 
@@ -98,5 +106,4 @@ async def main(room_url: str, token):
 
 
 if __name__ == "__main__":
-    (url, token) = configure()
-    asyncio.run(main(url, token))
+    asyncio.run(main())