Update demos with RunnerArguments, runner_args

This reverts commit 471311b18f.

.github/workflows/build.yaml

@@ -21,20 +21,24 @@ jobs:
     runs-on: ubuntu-latest
     steps:
       - uses: actions/checkout@v4
-      
-      - name: Install uv
-        uses: astral-sh/setup-uv@v3
-        with:
-          version: "latest"
-      
       - name: Set up Python
-        run: uv python install 3.10
-      
-      - name: Install development dependencies
-        run: uv sync --group dev
-      
+        id: setup_python
+        uses: actions/setup-python@v4
+        with:
+          python-version: '3.10'
+      - name: Setup virtual environment
+        run: |
+          python -m venv .venv
+      - name: Install basic Python dependencies
+        run: |
+          source .venv/bin/activate
+          python -m pip install --upgrade pip
+          pip install -r dev-requirements.txt
       - name: Build project
-        run: uv build
-      
-      - name: Install project in editable mode
-        run: uv pip install --editable .
+        run: |
+          source .venv/bin/activate
+          python -m build
+      - name: Install project and other Python dependencies
+        run: |
+          source .venv/bin/activate
+          pip install --editable .

.github/workflows/coverage.yaml

@@ -18,28 +18,35 @@ jobs:
     steps:
       - name: Checkout repo
         uses: actions/checkout@v4
-
-      - name: Install uv
-        uses: astral-sh/setup-uv@v3
-        with:
-          version: "latest"
-
       - name: Set up Python
-        run: uv python install 3.12
-
+        id: setup_python
+        uses: actions/setup-python@v4
+        with:
+          python-version: "3.10"
+      - name: Cache virtual environment
+        uses: actions/cache@v3
+        with:
+          # We are hashing dev-requirements.txt and test-requirements.txt which
+          # contain all dependencies needed to run the tests.
+          key: venv-${{ runner.os }}-${{ steps.setup_python.outputs.python-version}}-${{ hashFiles('dev-requirements.txt') }}-${{ hashFiles('test-requirements.txt') }}
+          path: .venv
       - name: Install system packages
+        id: install_system_packages
         run: |
           sudo apt-get install -y portaudio19-dev
-
-      - name: Install dependencies
+      - name: Setup virtual environment
         run: |
-          uv sync --group dev --extra anthropic --extra aws --extra google --extra langchain
-
+          python -m venv .venv
+      - name: Install basic Python dependencies
+        run: |
+          source .venv/bin/activate
+          python -m pip install --upgrade pip
+          pip install -r dev-requirements.txt -r test-requirements.txt
       - name: Run tests with coverage
         run: |
-          uv run coverage run
-          uv run coverage xml
-
+          source .venv/bin/activate
+          coverage run
+          coverage xml
       - name: Upload coverage to Codecov
         uses: codecov/codecov-action@v5
         with:

.github/workflows/format.yaml

@@ -22,22 +22,25 @@ jobs:
     steps:
       - name: Checkout repo
         uses: actions/checkout@v4
-      
-      - name: Install uv
-        uses: astral-sh/setup-uv@v3
-        with:
-          version: "latest"
-      
       - name: Set up Python
-        run: uv python install 3.10
-      
-      - name: Install development dependencies
-        run: uv sync --group dev
-      
+        uses: actions/setup-python@v4
+        with:
+          python-version: "3.10"
+      - name: Setup virtual environment
+        run: |
+          python -m venv .venv
+      - name: Install development Python dependencies
+        run: |
+          source .venv/bin/activate
+          python -m pip install --upgrade pip
+          pip install -r dev-requirements.txt
       - name: Ruff formatter
         id: ruff-format
-        run: uv run ruff format --diff
-      
+        run: |
+          source .venv/bin/activate
+          ruff format --diff
       - name: Ruff linter (all rules)
         id: ruff-check
-        run: uv run ruff check
+        run: |
+          source .venv/bin/activate
+          ruff check

.github/workflows/publish.yaml

@@ -17,17 +17,23 @@ jobs:
         uses: actions/checkout@v4
         with:
           ref: ${{ github.event.inputs.gitref }}
-      
-      - name: Install uv
-        uses: astral-sh/setup-uv@v3
-        with:
-          version: "latest"
       - name: Set up Python
-        run: uv python install 3.10
-      - name: Install development dependencies
-        run: uv sync --group dev
+        id: setup_python
+        uses: actions/setup-python@v4
+        with:
+          python-version: '3.10'
+      - name: Setup virtual environment
+        run: |
+          python -m venv .venv
+      - name: Install basic Python dependencies
+        run: |
+          source .venv/bin/activate
+          python -m pip install --upgrade pip
+          pip install -r dev-requirements.txt
       - name: Build project
-        run: uv build
+        run: |
+          source .venv/bin/activate
+          python -m build
       - name: Upload wheels
         uses: actions/upload-artifact@v4
         with:

.github/workflows/publish_test.yaml

@@ -12,16 +12,23 @@ jobs:
         with:
           fetch-tags: true
           fetch-depth: 100
-      - name: Install uv
-        uses: astral-sh/setup-uv@v3
-        with:
-          version: "latest"
       - name: Set up Python
-        run: uv python install 3.10
-      - name: Install development dependencies
-        run: uv sync --group dev
+        id: setup_python
+        uses: actions/setup-python@v4
+        with:
+          python-version: '3.10'
+      - name: Setup virtual environment
+        run: |
+          python -m venv .venv
+      - name: Install basic Python dependencies
+        run: |
+          source .venv/bin/activate
+          python -m pip install --upgrade pip
+          pip install -r dev-requirements.txt
       - name: Build project
-        run: uv build
+        run: |
+          source .venv/bin/activate
+          python -m build
       - name: Upload wheels
         uses: actions/upload-artifact@v4
         with:
@@ -31,7 +38,7 @@ jobs:
   publish-to-test-pypi:
     name: "Publish to Test PyPI"
     runs-on: ubuntu-latest
-    needs: [build]
+    needs: [ build ]
     environment:
       name: testpypi
       url: https://pypi.org/p/pipecat-ai

.github/workflows/python-compatibility.yaml

@@ -1,61 +0,0 @@
-name: Python Compatibility Test
-
-on:
-  push:
-    branches: [main, develop]
-    paths: ['pyproject.toml']
-  pull_request:
-    branches: [main, develop]
-    paths: ['pyproject.toml']
-
-jobs:
-  test-compatibility:
-    runs-on: ubuntu-latest
-    strategy:
-      fail-fast: false
-      matrix:
-        python-version: ['3.10.18', '3.11.13', '3.12.11', '3.13.5']
-
-    name: Python ${{ matrix.python-version }}
-    steps:
-      - name: Checkout code
-        uses: actions/checkout@v4
-
-      - name: Install system dependencies
-        run: |
-          sudo apt-get update
-          sudo apt-get install -y \
-            portaudio19-dev \
-            libcairo2-dev \
-            libgirepository1.0-dev \
-            pkg-config
-
-      - name: Install uv
-        uses: astral-sh/setup-uv@v4
-        with:
-          version: 'latest'
-
-      - name: Set up Python ${{ matrix.python-version }}
-        run: |
-          uv python install ${{ matrix.python-version }}
-          uv python pin ${{ matrix.python-version }}
-
-      - name: Test uv sync with all extras (Python < 3.13)
-        if: "!startsWith(matrix.python-version, '3.13.')"
-        run: |
-          uv sync --group dev --all-extras --no-extra krisp
-
-      - name: Test uv sync without PyTorch extras (Python 3.13+)
-        if: startsWith(matrix.python-version, '3.13.')
-        run: |
-          uv sync --group dev --all-extras \
-            --no-extra krisp \
-            --no-extra ultravox \
-            --no-extra local-smart-turn \
-            --no-extra moondream \
-            --no-extra mlx-whisper
-
-      - name: Verify installation
-        run: |
-          uv run python --version
-          uv run python -c "import pipecat; print('✅ Pipecat imports successfully')"

.github/workflows/sync-quickstart.yaml

@@ -1,51 +0,0 @@
-name: Sync Quickstart to pipecat-quickstart repo
-
-on:
-  push:
-    branches: [main]
-    paths:
-      - 'examples/quickstart/**'
-  workflow_dispatch: # Manual trigger
-
-jobs:
-  sync-quickstart:
-    runs-on: ubuntu-latest
-    steps:
-      - name: Checkout main repo
-        uses: actions/checkout@v4
-        with:
-          fetch-depth: 0
-
-      - name: Checkout quickstart repo
-        uses: actions/checkout@v4
-        with:
-          repository: pipecat-ai/pipecat-quickstart
-          token: ${{ secrets.QUICKSTART_SYNC_TOKEN }}
-          path: quickstart-repo
-
-      - name: Sync files (excluding uv.lock and README.md)
-        run: |
-          # Copy all files except uv.lock and README.md
-          find examples/quickstart -type f \
-            -not -name "README.md" \
-            -not -name "uv.lock" \
-            -exec cp {} quickstart-repo/ \;
-
-      - name: Commit and push changes
-        run: |
-          cd quickstart-repo
-          git config user.name "GitHub Action"
-          git config user.email "action@github.com"
-          git add .
-
-          # Only commit if there are changes
-          if ! git diff --staged --quiet; then
-            git commit -m "Sync from pipecat main repo
-            
-            Updated files from examples/quickstart/
-            Commit: ${{ github.sha }}
-            "
-            git push
-          else
-            echo "No changes to sync"
-          fi

.github/workflows/tests.yaml

@@ -22,23 +22,31 @@ jobs:
     steps:
       - name: Checkout repo
         uses: actions/checkout@v4
-
-      - name: Install uv
-        uses: astral-sh/setup-uv@v3
-        with:
-          version: "latest"
-
       - name: Set up Python
-        run: uv python install 3.12
-
+        id: setup_python
+        uses: actions/setup-python@v4
+        with:
+          python-version: "3.10"
+      - name: Cache virtual environment
+        uses: actions/cache@v3
+        with:
+          # We are hashing dev-requirements.txt and test-requirements.txt which
+          # contain all dependencies needed to run the tests.
+          key: venv-${{ runner.os }}-${{ steps.setup_python.outputs.python-version}}-${{ hashFiles('dev-requirements.txt') }}-${{ hashFiles('test-requirements.txt') }}
+          path: .venv
       - name: Install system packages
+        id: install_system_packages
         run: |
           sudo apt-get install -y portaudio19-dev
-
-      - name: Install dependencies
+      - name: Setup virtual environment
         run: |
-          uv sync --group dev --extra anthropic --extra aws --extra google --extra langchain
-
+          python -m venv .venv
+      - name: Install basic Python dependencies
+        run: |
+          source .venv/bin/activate
+          python -m pip install --upgrade pip
+          pip install -r dev-requirements.txt -r test-requirements.txt
       - name: Test with pytest
         run: |
-          uv run pytest
+          source .venv/bin/activate
+          pytest

.pre-commit-config.yaml

@@ -1,6 +1,6 @@
 repos:
   - repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: v0.12.1
+    rev: v0.9.7
     hooks:
       - id: ruff
         language_version: python3

.readthedocs.yaml

@@ -9,14 +9,22 @@ build:
     - python3-dev
     - libasound2-dev
   jobs:
-    post_install:
-      - pip install uv
-      - UV_PROJECT_ENVIRONMENT=$READTHEDOCS_VIRTUALENV_PATH uv sync --group docs --all-extras --no-extra krisp --no-extra gstreamer --no-extra ultravox --no-extra local_smart_turn --no-extra moondream --no-extra riva --no-extra mlx-whisper
+    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

CONTRIBUTING.md

@@ -31,23 +31,6 @@ git push origin your-branch-name
 
 Our maintainers will review your PR, and once everything is good, your contributions will be merged!
 
-## Dependency Management
-
-This project uses [uv](https://docs.astral.sh/uv/) for dependency management. The `uv.lock` file is committed to ensure reproducible builds.
-
-### Adding or Updating Dependencies
-
-1. Edit `pyproject.toml` to add/update dependencies
-2. Run `uv lock` to update the lockfile with new dependency resolution
-3. Run `uv sync` to install the updated dependencies locally
-4. Always commit both files together:
-   ```bash
-   git add pyproject.toml uv.lock
-   git commit -m "feat: add new dependency for feature X"
-   ```
-
-**Important:** Never manually edit `uv.lock`. It's auto-generated by `uv lock`.
-
 ## Code Style and Documentation
 
 ### Python Code Style

README.md

@@ -2,13 +2,13 @@
  <img alt="pipecat" width="300px" height="auto" src="https://raw.githubusercontent.com/pipecat-ai/pipecat/main/pipecat.png">
 </div></h1>
 
-[![PyPI](https://img.shields.io/pypi/v/pipecat-ai)](https://pypi.org/project/pipecat-ai) ![Tests](https://github.com/pipecat-ai/pipecat/actions/workflows/tests.yaml/badge.svg) [![codecov](https://codecov.io/gh/pipecat-ai/pipecat/graph/badge.svg?token=LNVUIVO4Y9)](https://codecov.io/gh/pipecat-ai/pipecat) [![Docs](https://img.shields.io/badge/Documentation-blue)](https://docs.pipecat.ai) [![Discord](https://img.shields.io/discord/1239284677165056021)](https://discord.gg/pipecat) [![Ask DeepWiki](https://deepwiki.com/badge.svg)](https://deepwiki.com/pipecat-ai/pipecat)
+[![PyPI](https://img.shields.io/pypi/v/pipecat-ai)](https://pypi.org/project/pipecat-ai) ![Tests](https://github.com/pipecat-ai/pipecat/actions/workflows/tests.yaml/badge.svg) [![codecov](https://codecov.io/gh/pipecat-ai/pipecat/graph/badge.svg?token=LNVUIVO4Y9)](https://codecov.io/gh/pipecat-ai/pipecat) [![Docs](https://img.shields.io/badge/Documentation-blue)](https://docs.pipecat.ai) [![Discord](https://img.shields.io/discord/1239284677165056021)](https://discord.gg/pipecat)
 
 # 🎙️ Pipecat: Real-Time Voice & Multimodal AI Agents
 
 **Pipecat** is an open-source Python framework for building real-time voice and multimodal conversational agents. Orchestrate audio and video, AI services, different transports, and conversation pipelines effortlessly—so you can focus on what makes your agent unique.
 
-> Want to dive right in? Try the [quickstart](https://docs.pipecat.ai/getting-started/quickstart).
+> Want to dive right in? [Install Pipecat](https://docs.pipecat.ai/getting-started/installation) then try the [quickstart](https://docs.pipecat.ai/getting-started/quickstart).
 
 ## 🚀 What You Can Build
 
@@ -21,8 +21,6 @@
 
 🧭 Looking to build structured conversations? Check out [Pipecat Flows](https://github.com/pipecat-ai/pipecat-flows) for managing complex conversational states and transitions.
 
-🔍 Looking for help debugging your pipeline and processors? Check out [Whisker](https://github.com/pipecat-ai/whisker), a real-time Pipecat debugger.
-
 ## 🧠 Why Pipecat?
 
 - **Voice-first**: Integrates speech recognition, text-to-speech, and conversation handling
@@ -30,41 +28,6 @@
 - **Composable Pipelines**: Build complex behavior from modular components
 - **Real-Time**: Ultra-low latency interaction with different transports (e.g. WebSockets or WebRTC)
 
-## 📱 Client SDKs
-
-You can connect to Pipecat from any platform using our official SDKs:
-
-<table>
-  <tr>
-    <td>
-      <img src="https://cdn.jsdelivr.net/gh/devicons/devicon/icons/javascript/javascript-original.svg" width="40" height="40" alt="JavaScript"/>
-      <a href="https://docs.pipecat.ai/client/js/introduction">JavaScript</a>
-    </td>
-    <td>
-      <img src="https://cdn.jsdelivr.net/gh/devicons/devicon/icons/react/react-original.svg" width="40" height="40" alt="React"/>
-      <a href="https://docs.pipecat.ai/client/react/introduction">React</a>
-    </td>
-    <td>
-      <img src="https://cdn.jsdelivr.net/gh/devicons/devicon/icons/react/react-original.svg" width="40" height="40" alt="React Native"/>
-      <a href="https://docs.pipecat.ai/client/react-native/introduction">React Native</a>
-    </td>
-  </tr>
-  <tr>
-    <td>
-      <img src="https://cdn.jsdelivr.net/gh/devicons/devicon/icons/swift/swift-original.svg" width="40" height="40" alt="Swift"/>
-      <a href="https://docs.pipecat.ai/client/ios/introduction">Swift</a>
-    </td>
-    <td>
-      <img src="https://cdn.jsdelivr.net/gh/devicons/devicon/icons/kotlin/kotlin-original.svg" width="40" height="40" alt="Kotlin"/>
-      <a href="https://docs.pipecat.ai/client/android/introduction">Kotlin</a>
-    </td>
-    <td>
-      <img src="https://cdn.jsdelivr.net/gh/devicons/devicon/icons/cplusplus/cplusplus-original.svg" width="40" height="40" alt="JavaScript"/>
-      <a href="https://docs.pipecat.ai/client/c++/introduction">C++</a>
-    </td>
-  </tr>
-</table>
-
 ## 🎬 See it in action
 
 <p float="left">
@@ -75,113 +38,111 @@ You can connect to Pipecat from any platform using our official SDKs:
     <a href="https://github.com/pipecat-ai/pipecat-examples/tree/main/moondream-chatbot"><img src="https://raw.githubusercontent.com/pipecat-ai/pipecat-examples/main/moondream-chatbot/image.png" width="400" /></a>
 </p>
 
+## 📱 Client SDKs
+
+You can connect to Pipecat from any platform using our official SDKs:
+
+| Platform | SDK Repo                                                                       | Description                      |
+| -------- | ------------------------------------------------------------------------------ | -------------------------------- |
+| Web      | [pipecat-client-web](https://github.com/pipecat-ai/pipecat-client-web)         | JavaScript and React client SDKs |
+| iOS      | [pipecat-client-ios](https://github.com/pipecat-ai/pipecat-client-ios)         | Swift SDK for iOS                |
+| Android  | [pipecat-client-android](https://github.com/pipecat-ai/pipecat-client-android) | Kotlin SDK for Android           |
+| C++      | [pipecat-client-cxx](https://github.com/pipecat-ai/pipecat-client-cxx)         | C++ client SDK                   |
+
 ## 🧩 Available services
 
-| Category            | Services                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              |
-| ------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| Speech-to-Text      | [AssemblyAI](https://docs.pipecat.ai/server/services/stt/assemblyai), [AWS](https://docs.pipecat.ai/server/services/stt/aws), [Azure](https://docs.pipecat.ai/server/services/stt/azure), [Cartesia](https://docs.pipecat.ai/server/services/stt/cartesia), [Deepgram](https://docs.pipecat.ai/server/services/stt/deepgram), [ElevenLabs](https://docs.pipecat.ai/server/services/stt/elevenlabs), [Fal Wizper](https://docs.pipecat.ai/server/services/stt/fal), [Gladia](https://docs.pipecat.ai/server/services/stt/gladia), [Google](https://docs.pipecat.ai/server/services/stt/google), [Groq (Whisper)](https://docs.pipecat.ai/server/services/stt/groq), [NVIDIA Riva](https://docs.pipecat.ai/server/services/stt/riva), [OpenAI (Whisper)](https://docs.pipecat.ai/server/services/stt/openai), [SambaNova (Whisper)](https://docs.pipecat.ai/server/services/stt/sambanova), [Soniox](https://docs.pipecat.ai/server/services/stt/soniox), [Speechmatics](https://docs.pipecat.ai/server/services/stt/speechmatics), [Ultravox](https://docs.pipecat.ai/server/services/stt/ultravox), [Whisper](https://docs.pipecat.ai/server/services/stt/whisper)                                                                                                                    |
-| LLMs                | [Anthropic](https://docs.pipecat.ai/server/services/llm/anthropic), [AWS](https://docs.pipecat.ai/server/services/llm/aws), [Azure](https://docs.pipecat.ai/server/services/llm/azure), [Cerebras](https://docs.pipecat.ai/server/services/llm/cerebras), [DeepSeek](https://docs.pipecat.ai/server/services/llm/deepseek), [Fireworks AI](https://docs.pipecat.ai/server/services/llm/fireworks), [Gemini](https://docs.pipecat.ai/server/services/llm/gemini), [Grok](https://docs.pipecat.ai/server/services/llm/grok), [Groq](https://docs.pipecat.ai/server/services/llm/groq), [Mistral](https://docs.pipecat.ai/server/services/llm/mistral), [NVIDIA NIM](https://docs.pipecat.ai/server/services/llm/nim), [Ollama](https://docs.pipecat.ai/server/services/llm/ollama), [OpenAI](https://docs.pipecat.ai/server/services/llm/openai), [OpenRouter](https://docs.pipecat.ai/server/services/llm/openrouter), [Perplexity](https://docs.pipecat.ai/server/services/llm/perplexity), [Qwen](https://docs.pipecat.ai/server/services/llm/qwen), [SambaNova](https://docs.pipecat.ai/server/services/llm/sambanova) [Together AI](https://docs.pipecat.ai/server/services/llm/together)                                                                                          |
-| Text-to-Speech      | [Async](https://docs.pipecat.ai/server/services/tts/asyncai), [AWS](https://docs.pipecat.ai/server/services/tts/aws), [Azure](https://docs.pipecat.ai/server/services/tts/azure), [Cartesia](https://docs.pipecat.ai/server/services/tts/cartesia), [Deepgram](https://docs.pipecat.ai/server/services/tts/deepgram), [ElevenLabs](https://docs.pipecat.ai/server/services/tts/elevenlabs), [Fish](https://docs.pipecat.ai/server/services/tts/fish), [Google](https://docs.pipecat.ai/server/services/tts/google), [Groq](https://docs.pipecat.ai/server/services/tts/groq), [Inworld](https://docs.pipecat.ai/server/services/tts/inworld), [LMNT](https://docs.pipecat.ai/server/services/tts/lmnt), [MiniMax](https://docs.pipecat.ai/server/services/tts/minimax), [Neuphonic](https://docs.pipecat.ai/server/services/tts/neuphonic), [NVIDIA Riva](https://docs.pipecat.ai/server/services/tts/riva), [OpenAI](https://docs.pipecat.ai/server/services/tts/openai), [Piper](https://docs.pipecat.ai/server/services/tts/piper), [PlayHT](https://docs.pipecat.ai/server/services/tts/playht), [Rime](https://docs.pipecat.ai/server/services/tts/rime), [Sarvam](https://docs.pipecat.ai/server/services/tts/sarvam), [XTTS](https://docs.pipecat.ai/server/services/tts/xtts) |
-| Speech-to-Speech    | [AWS Nova Sonic](https://docs.pipecat.ai/server/services/s2s/aws), [Gemini Multimodal Live](https://docs.pipecat.ai/server/services/s2s/gemini), [OpenAI Realtime](https://docs.pipecat.ai/server/services/s2s/openai)                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                |
-| Transport           | [Daily (WebRTC)](https://docs.pipecat.ai/server/services/transport/daily), [FastAPI Websocket](https://docs.pipecat.ai/server/services/transport/fastapi-websocket), [SmallWebRTCTransport](https://docs.pipecat.ai/server/services/transport/small-webrtc), [WebSocket Server](https://docs.pipecat.ai/server/services/transport/websocket-server), Local                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            |
-| Serializers         | [Plivo](https://docs.pipecat.ai/server/utilities/serializers/plivo), [Twilio](https://docs.pipecat.ai/server/utilities/serializers/twilio), [Telnyx](https://docs.pipecat.ai/server/utilities/serializers/telnyx)                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                     |
-| Video               | [HeyGen](https://docs.pipecat.ai/server/services/video/heygen), [Tavus](https://docs.pipecat.ai/server/services/video/tavus), [Simli](https://docs.pipecat.ai/server/services/video/simli)                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            |
-| Memory              | [mem0](https://docs.pipecat.ai/server/services/memory/mem0)                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                           |
-| Vision & Image      | [fal](https://docs.pipecat.ai/server/services/image-generation/fal), [Google Imagen](https://docs.pipecat.ai/server/services/image-generation/fal), [Moondream](https://docs.pipecat.ai/server/services/vision/moondream)                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                             |
-| Audio Processing    | [Silero VAD](https://docs.pipecat.ai/server/utilities/audio/silero-vad-analyzer), [Krisp](https://docs.pipecat.ai/server/utilities/audio/krisp-filter), [Koala](https://docs.pipecat.ai/server/utilities/audio/koala-filter), [ai-coustics](https://docs.pipecat.ai/server/utilities/audio/aic-filter)                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                |
-| Analytics & Metrics | [OpenTelemetry](https://docs.pipecat.ai/server/utilities/opentelemetry), [Sentry](https://docs.pipecat.ai/server/services/analytics/sentry)                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                           |
+| Category            | Services                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                |
+| ------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| Speech-to-Text      | [AssemblyAI](https://docs.pipecat.ai/server/services/stt/assemblyai), [AWS](https://docs.pipecat.ai/server/services/stt/aws), [Azure](https://docs.pipecat.ai/server/services/stt/azure), [Cartesia](https://docs.pipecat.ai/server/services/stt/cartesia), [Deepgram](https://docs.pipecat.ai/server/services/stt/deepgram), [Fal Wizper](https://docs.pipecat.ai/server/services/stt/fal), [Gladia](https://docs.pipecat.ai/server/services/stt/gladia), [Google](https://docs.pipecat.ai/server/services/stt/google), [Groq (Whisper)](https://docs.pipecat.ai/server/services/stt/groq), [NVIDIA Riva](https://docs.pipecat.ai/server/services/stt/riva), [OpenAI (Whisper)](https://docs.pipecat.ai/server/services/stt/openai), [SambaNova (Whisper)](https://docs.pipecat.ai/server/services/stt/sambanova), [Soniox](https://docs.pipecat.ai/server/services/stt/soniox), [Speechmatics](https://docs.pipecat.ai/server/services/stt/speechmatics), [Ultravox](https://docs.pipecat.ai/server/services/stt/ultravox), [Whisper](https://docs.pipecat.ai/server/services/stt/whisper)                                                                                                                            |
+| LLMs                | [Anthropic](https://docs.pipecat.ai/server/services/llm/anthropic), [AWS](https://docs.pipecat.ai/server/services/llm/aws), [Azure](https://docs.pipecat.ai/server/services/llm/azure), [Cerebras](https://docs.pipecat.ai/server/services/llm/cerebras), [DeepSeek](https://docs.pipecat.ai/server/services/llm/deepseek), [Fireworks AI](https://docs.pipecat.ai/server/services/llm/fireworks), [Gemini](https://docs.pipecat.ai/server/services/llm/gemini), [Grok](https://docs.pipecat.ai/server/services/llm/grok), [Groq](https://docs.pipecat.ai/server/services/llm/groq), [NVIDIA NIM](https://docs.pipecat.ai/server/services/llm/nim), [Ollama](https://docs.pipecat.ai/server/services/llm/ollama), [OpenAI](https://docs.pipecat.ai/server/services/llm/openai), [OpenRouter](https://docs.pipecat.ai/server/services/llm/openrouter), [Perplexity](https://docs.pipecat.ai/server/services/llm/perplexity), [Qwen](https://docs.pipecat.ai/server/services/llm/qwen), [SambaNova](https://docs.pipecat.ai/server/services/llm/sambanova) [Together AI](https://docs.pipecat.ai/server/services/llm/together)                                                                                            |
+| Text-to-Speech      | [AWS](https://docs.pipecat.ai/server/services/tts/aws), [Azure](https://docs.pipecat.ai/server/services/tts/azure), [Cartesia](https://docs.pipecat.ai/server/services/tts/cartesia), [Deepgram](https://docs.pipecat.ai/server/services/tts/deepgram), [ElevenLabs](https://docs.pipecat.ai/server/services/tts/elevenlabs), [Fish](https://docs.pipecat.ai/server/services/tts/fish), [Google](https://docs.pipecat.ai/server/services/tts/google), [Groq](https://docs.pipecat.ai/server/services/tts/groq), [Inworld](https://docs.pipecat.ai/server/services/tts/inworld), [LMNT](https://docs.pipecat.ai/server/services/tts/lmnt), [MiniMax](https://docs.pipecat.ai/server/services/tts/minimax), [Neuphonic](https://docs.pipecat.ai/server/services/tts/neuphonic), [NVIDIA Riva](https://docs.pipecat.ai/server/services/tts/riva), [OpenAI](https://docs.pipecat.ai/server/services/tts/openai), [Piper](https://docs.pipecat.ai/server/services/tts/piper), [PlayHT](https://docs.pipecat.ai/server/services/tts/playht), [Rime](https://docs.pipecat.ai/server/services/tts/rime), [Sarvam](https://docs.pipecat.ai/server/services/tts/sarvam), [XTTS](https://docs.pipecat.ai/server/services/tts/xtts) |
+| Speech-to-Speech    | [AWS Nova Sonic](https://docs.pipecat.ai/server/services/s2s/aws), [Gemini Multimodal Live](https://docs.pipecat.ai/server/services/s2s/gemini), [OpenAI Realtime](https://docs.pipecat.ai/server/services/s2s/openai)                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                  |
+| Transport           | [Daily (WebRTC)](https://docs.pipecat.ai/server/services/transport/daily), [FastAPI Websocket](https://docs.pipecat.ai/server/services/transport/fastapi-websocket), [SmallWebRTCTransport](https://docs.pipecat.ai/server/services/transport/small-webrtc), [WebSocket Server](https://docs.pipecat.ai/server/services/transport/websocket-server), Local                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              |
+| Serializers         | [Plivo](https://docs.pipecat.ai/server/utilities/serializers/plivo), [Twilio](https://docs.pipecat.ai/server/utilities/serializers/twilio), [Telnyx](https://docs.pipecat.ai/server/utilities/serializers/telnyx)                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                       |
+| Video               | [Tavus](https://docs.pipecat.ai/server/services/video/tavus), [Simli](https://docs.pipecat.ai/server/services/video/simli)                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              |
+| Memory              | [mem0](https://docs.pipecat.ai/server/services/memory/mem0)                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                             |
+| Vision & Image      | [fal](https://docs.pipecat.ai/server/services/image-generation/fal), [Google Imagen](https://docs.pipecat.ai/server/services/image-generation/fal), [Moondream](https://docs.pipecat.ai/server/services/vision/moondream)                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                               |
+| Audio Processing    | [Silero VAD](https://docs.pipecat.ai/server/utilities/audio/silero-vad-analyzer), [Krisp](https://docs.pipecat.ai/server/utilities/audio/krisp-filter), [Koala](https://docs.pipecat.ai/server/utilities/audio/koala-filter), [Noisereduce](https://docs.pipecat.ai/server/utilities/audio/noisereduce-filter)                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          |
+| Analytics & Metrics | [OpenTelemetry](https://docs.pipecat.ai/server/utilities/opentelemetry), [Sentry](https://docs.pipecat.ai/server/services/analytics/sentry)                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                             |
 
 📚 [View full services documentation →](https://docs.pipecat.ai/server/services/supported-services)
 
 ## ⚡ 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 get started with Pipecat running on your local machine, then move your agent processes to the cloud when you’re ready.
 
-1. Install uv
+```shell
+# Install the module
+pip install pipecat-ai
 
-   ```bash
-   curl -LsSf https://astral.sh/uv/install.sh | sh
-   ```
+# Set up your environment
+cp dot-env.template .env
+```
 
-   > **Need help?** Refer to the [uv install documentation](https://docs.astral.sh/uv/getting-started/installation/).
+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:
 
-2. Install the module
-
-   ```bash
-   # For new projects
-   uv init my-pipecat-app
-   cd my-pipecat-app
-   uv add pipecat-ai
-
-   # Or for existing projects
-   uv add pipecat-ai
-   ```
-
-3. Set up your environment
-
-   ```bash
-   cp env.example .env
-   ```
-
-4. 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:
-
-   ```bash
-   uv add "pipecat-ai[option,...]"
-   ```
-
-> **Using pip?** You can still use `pip install pipecat-ai` and `pip install "pipecat-ai[option,...]"` to get set up.
+```shell
+pip install "pipecat-ai[option,...]"
+```
 
 ## 🧪 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-examples) — complete applications that you can use as starting points for development
 
-## 🛠️ Contributing to the framework
+## 🛠️ Hacking on the framework itself
 
-### Prerequisites
+1. Set up a virtual environment before following these instructions. From the root of the repo:
 
-**Minimum Python Version:** 3.10
-**Recommended Python Version:** 3.12
-
-### Setup Steps
-
-1. Clone the repository and navigate to it:
-
-   ```bash
-   git clone https://github.com/pipecat-ai/pipecat.git
-   cd pipecat
+   ```shell
+   python3 -m venv venv
+   source venv/bin/activate
    ```
 
-2. Install development and testing dependencies:
+2. Install the development dependencies:
 
-   ```bash
-   uv sync --group dev --all-extras \
-     --no-extra gstreamer \
-     --no-extra krisp \
-     --no-extra local \
-     --no-extra ultravox # (ultravox not fully supported on macOS)
+   ```shell
+   pip install -r dev-requirements.txt
    ```
 
-3. Install the git pre-commit hooks:
+3. Install the git pre-commit hooks (these help ensure your code follows project rules):
 
-   ```bash
-   uv run pre-commit install
+   ```shell
+   pre-commit install
    ```
 
-> **Note**: Some extras (local, gstreamer) require system dependencies. See documentation if you encounter build errors.
+4. Install the `pipecat-ai` package locally in editable mode:
+
+   ```shell
+   pip install -e .
+   ```
+
+   > The `-e` or `--editable` option allows you to modify the code without reinstalling.
+
+5. Include optional dependencies as needed. For example:
+
+   ```shell
+   pip install -e ".[daily,deepgram,cartesia,openai,silero]"
+   ```
+
+6. (Optional) If you want to use this package from another directory:
+
+   ```shell
+   pip install "path_to_this_repo[option,...]"
+   ```
 
 ### Running tests
 
-To run all tests, from the root directory:
+Install the test dependencies:
 
-```bash
-uv run pytest
+```shell
+pip install -r test-requirements.txt
 ```
 
-Run a specific test suite:
+From the root directory, run:
 
-```bash
-uv run pytest tests/test_name.py
+```shell
+pytest
 ```
 
 ### Setting up your editor

dev-requirements.txt

@@ -0,0 +1,13 @@
+build~=1.2.2
+coverage~=7.9.1
+grpcio-tools~=1.67.1
+pip-tools~=7.4.1
+pre-commit~=4.2.0
+pyright~=1.1.402
+pytest~=8.4.1
+pytest-asyncio~=1.0.0
+pytest-aiohttp==1.1.0
+ruff~=0.12.1
+setuptools~=78.1.1
+setuptools_scm~=8.3.1
+python-dotenv~=1.1.1

docs/README.md

@@ -0,0 +1,10 @@
+# Pipecat Docs
+
+## [Architecture Overview](architecture.md)
+
+Learn about the thinking behind the framework's design.
+
+## [A Frame's Progress](frame-progress.md)
+
+See how a Frame is processed through a Transport, a Pipeline, and a series of Frame Processors.
+

docs/api/build-docs.sh

@@ -1,27 +1,17 @@
 #!/bin/bash
 
-# Build docs using uv
-echo "Installing dependencies with uv..."
-uv sync --group docs --all-extras --no-extra krisp --no-extra gstreamer --no-extra ultravox --no-extra local_smart_turn --no-extra moondream --no-extra riva --no-extra mlx-whisper
-
-# Check if sphinx-build is available
-if ! uv run sphinx-build --version &> /dev/null; then
-    echo "Error: sphinx-build is not available" >&2
+# Check if sphinx-build is installed
+if ! command -v sphinx-build &> /dev/null; then
+    echo "Error: sphinx-build is not installed or not in PATH" >&2
+    echo "Please install Sphinx using: pip install -r requirements.txt" >&2
     exit 1
 fi
 
 # Clean previous build
 rm -rf _build
 
-echo "Building documentation..."
 # Build docs matching ReadTheDocs configuration
-uv run sphinx-build -b html -d _build/doctrees . _build/html -W --keep-going
+sphinx-build -b html -d _build/doctrees . _build/html -W --keep-going
 
-if [ $? -eq 0 ]; then
-    echo "Documentation built successfully!"
-    # Open docs (MacOS)
-    open _build/html/index.html
-else
-    echo "Documentation build failed!" >&2
-    exit 1
-fi
+# Open docs (MacOS)
+open _build/html/index.html

docs/api/conf.py

@@ -1,5 +1,4 @@
 import logging
-import os
 import sys
 from datetime import datetime
 from pathlib import Path
@@ -29,7 +28,6 @@ extensions = [
 
 suppress_warnings = [
     "autodoc.mocked_object",
-    "toc.not_included",
 ]
 
 # Napoleon settings
@@ -47,40 +45,85 @@ autodoc_default_options = {
 
 # Mock imports for optional dependencies
 autodoc_mock_imports = [
-    # Krisp - has build issues on some platforms
-    "pipecat_ai_krisp",
+    "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
+    "aiortc",
+    "aiortc.mediastreams",
+    "cv2",
+    "av",
+    "pyneuphonic",
+    "mem0",
+    "mlx_whisper",
+    "anthropic",
+    "assemblyai",
+    "boto3",
+    "azure",
+    "cartesia",
+    "deepgram",
+    "elevenlabs",
+    "fal",
+    "gladia",
+    "google",
     "krisp",
-    # System-specific GUI libraries
+    "langchain",
+    "lmnt",
+    "noisereduce",
+    "openpipe",
+    "simli",
+    "soundfile",
+    "soniox",
+    "pipecat_ai_krisp",
+    "pyaudio",
     "_tkinter",
     "tkinter",
-    # Platform-specific audio libraries (if needed)
-    "gi",
-    "gi.require_version",
-    "gi.repository",
-    # OpenCV - sometimes has import issues during docs build
-    "cv2",
-    # Heavy ML packages excluded from ReadTheDocs
-    # ultravox dependencies
+    "daily",
+    "daily_python",
+    # Moondream dependencies
+    "torch",
+    "transformers",
+    "intel_extension_for_pytorch",
+    # Ultravox dependencies
+    "huggingface_hub",
     "vllm",
     "vllm.engine.arg_utils",
-    # local-smart-turn dependencies
-    "coremltools",
-    "coremltools.models",
-    "coremltools.models.MLModel",
-    "torch",
-    "torch.nn",
-    "torch.nn.functional",
-    "torchaudio",
-    # moondream dependencies
-    "transformers",
     "transformers.AutoTokenizer",
-    "transformers.AutoFeatureExtractor",
-    "AutoFeatureExtractor",
-    "timm",
-    "einops",
-    "intel_extension_for_pytorch",
-    "huggingface_hub",
-    # riva dependencies
+    # Langchain dependencies
+    "langchain_core",
+    "langchain_core.messages",
+    "langchain_core.runnables",
+    "langchain_core.messages.AIMessageChunk",
+    "langchain_core.runnables.Runnable",
+    # LiveKit dependencies
+    "livekit",
+    "livekit.rtc",
+    "livekit_api",
+    "livekit_protocol",
+    "tenacity",
+    "tenacity.retry",
+    "tenacity.stop_after_attempt",
+    "tenacity.wait_exponential",
+    "rtc",
+    "rtc.Room",
+    "rtc.RoomOptions",
+    "rtc.AudioSource",
+    "rtc.LocalAudioTrack",
+    "rtc.TrackPublishOptions",
+    "rtc.TrackSource",
+    "rtc.AudioStream",
+    "rtc.AudioFrameEvent",
+    "rtc.AudioFrame",
+    "rtc.Track",
+    "rtc.TrackKind",
+    "rtc.RemoteParticipant",
+    "rtc.RemoteTrackPublication",
+    "rtc.DataPacket",
+    # Riva dependencies
     "riva",
     "riva.client",
     "riva.client.Auth",
@@ -90,14 +133,57 @@ autodoc_mock_imports = [
     "riva.client.AudioEncoding",
     "riva.client.proto.riva_tts_pb2",
     "riva.client.SpeechSynthesisService",
-    # MLX dependencies (Apple Silicon specific)
-    "mlx",
-    "mlx_whisper",  # Note: might need underscore format too
+    # Local CoreML Smart Turn dependencies
+    "coremltools",
+    "coremltools.models",
+    "coremltools.models.MLModel",
+    "torch",
+    "torch.nn",
+    "torch.nn.functional",
+    "transformers",
+    "transformers.AutoFeatureExtractor",
+    # Also add specific classes that are imported
+    "AutoFeatureExtractor",
+    # Sentry dependencies
+    "sentry_sdk",
+    # AWS Nova Sonic dependencies
+    "aws_sdk_bedrock_runtime",
+    "aws_sdk_bedrock_runtime.client",
+    "aws_sdk_bedrock_runtime.config",
+    "aws_sdk_bedrock_runtime.models",
+    "smithy_aws_core",
+    "smithy_aws_core.credentials_resolvers",
+    "smithy_aws_core.credentials_resolvers.static",
+    "smithy_aws_core.identity",
+    "smithy_core",
+    "smithy_core.aio",
+    "smithy_core.aio.eventstream",
+    # MCP dependencies (you may already have these)
+    "mcp",
+    "mcp.client",
+    "mcp.client.session_group",
+    "mcp.client.sse",
+    "mcp.client.stdio",
+    "mcp.ClientSession",
+    "mcp.StdioServerParameters",
+    # gstreamer
+    "gi",
+    "gi.require_version",
+    "gi.repository",
+    # Protobuf mocks
+    "pipecat.frames.protobufs.frames_pb2",
+    "pipecat.serializers.protobuf",
+    "google.protobuf",
+    "google.protobuf.descriptor",
+    "google.protobuf.descriptor_pool",
+    "google.protobuf.runtime_version",
+    "google.protobuf.symbol_database",
+    "google.protobuf.internal.builder",
 ]
 
 # HTML output settings
 html_theme = "sphinx_rtd_theme"
-html_static_path = ["_static"] if os.path.exists("_static") else []
+html_static_path = ["_static"]
 autodoc_typehints = "signature"  # Show type hints in the signature only, not in the docstring
 html_show_sphinx = False
 

docs/api/index.rst

@@ -14,14 +14,13 @@ Quick Links
 * `Join our Community <https://discord.gg/pipecat>`_
 
 .. toctree::
-   :maxdepth: 2
+   :maxdepth: 3
    :caption: API Reference
    :hidden:
 
    Adapters <api/pipecat.adapters>
    Audio <api/pipecat.audio>
    Clocks <api/pipecat.clocks>
-   Extensions <api/pipecat.extensions>
    Frames <api/pipecat.frames>
    Metrics <api/pipecat.metrics>
    Observers <api/pipecat.observers>

docs/api/requirements.txt

@@ -0,0 +1,59 @@
+# 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[asyncai]
+pipecat-ai[aws]
+pipecat-ai[azure]
+pipecat-ai[cartesia]
+pipecat-ai[cerebras]
+pipecat-ai[deepseek]
+pipecat-ai[daily]
+pipecat-ai[deepgram]
+pipecat-ai[elevenlabs]
+pipecat-ai[fal]
+pipecat-ai[fireworks]
+pipecat-ai[fish]
+pipecat-ai[gladia]
+pipecat-ai[google]
+pipecat-ai[grok]
+pipecat-ai[groq]
+pipecat-ai[inworld]
+# pipecat-ai[krisp] # Mocked
+pipecat-ai[koala]
+# pipecat-ai[langchain] # Mocked
+# pipecat-ai[livekit] # Mocked
+pipecat-ai[lmnt]
+pipecat-ai[local]
+# pipecat-ai[local-smart-turn] # Mocked
+# pipecat-ai[mem0] # Mocked
+# pipecat-ai[mlx-whisper] # Mocked
+# pipecat-ai[moondream] # Mocked
+pipecat-ai[nim]
+# pipecat-ai[neuphonic] # Mocked
+pipecat-ai[noisereduce]
+pipecat-ai[openai]
+# pipecat-ai[openpipe]
+# pipecat-ai[playht] # Mocked due to grpcio conflict with riva
+pipecat-ai[qwen]
+pipecat-ai[remote-smart-turn]
+# pipecat-ai[riva] # Mocked
+pipecat-ai[runner]
+pipecat-ai[sambanova]
+pipecat-ai[silero]
+pipecat-ai[simli]
+pipecat-ai[soundfile]
+pipecat-ai[soniox]
+pipecat-ai[speechmatics]
+pipecat-ai[tavus]
+pipecat-ai[together]
+# pipecat-ai[ultravox] # Mocked
+# pipecat-ai[webrtc] # Mocked
+pipecat-ai[websocket]
+pipecat-ai[whisper]

docs/architecture.md

@@ -0,0 +1,17 @@
+# Pipecat architecture guide
+
+## Frames
+
+Frames can represent discrete chunks of data, for instance a chunk of text, a chunk of audio, or an image. They can also be used to as control flow, for instance a frame that indicates that there is no more data available, or that a user started or stopped talking. They can also represent more complex data structures, such as a message array used for an LLM completion.
+
+## FrameProcessors
+
+Frame processors operate on frames. Every frame processor implements a `process_frame` method that consumes one frame and produces zero or more frames. Frame processors can do simple transforms, such as concatenating text fragments into sentences, or they can treat frames as input for an AI Service, and emit chat completions based on message arrays or transform text into audio or images.
+
+## Pipelines
+
+Pipelines are lists of frame processors linked together. Frame processors can push frames upstream or downstream to their peers. A very simple pipeline might chain an LLM frame processor to a text-to-speech frame processor, with a transport as an output.
+
+## Transports
+
+Transports provide input and output frame processors to receive or send frames respectively. For example, the `DailyTransport` does this with a WebRTC session joined to a Daily.co room.

docs/frame-progress.md

@@ -0,0 +1,46 @@
+# A Frame's Progress
+
+1. A user says “Hello, LLM” and the cloud transcription service delivers a transcription to the Transport.
+![A transcript frame arrives](images/frame-progress-01.png)
+
+2. The Transport places a Transcription frame in the Pipeline’s source queue.
+![Frame in source queue](images/frame-progress-02.png)
+
+3. The Pipeline passes the Transcription frame to the first Frame Processor in its list, the LLM User Message Aggregator.
+![To UMA](images/frame-progress-03.png)
+
+4. The LLM User Message Aggregator updates the LLM Context with a `{“user”: “Hello LLM”}` message.
+![Update context](images/frame-progress-04.png)
+
+5. The LLM User Message Aggregator yields an LLM Message Frame, containing the updated LLM Context. The Pipeline passes this frame to the LLM Frame Processor.
+![Update context](images/frame-progress-05.png)
+
+6. The LLM Frame Processor creates a streaming chat completion based on the LLM context and yields the first chunk of a response, Text Frame with the value “Hi, “. The Pipeline passes this frame to the TTS Frame Processor. The TTS Frame Processor aggregates this response but doesn’t yield anything, yet, because it’s waiting for a full sentence.
+![LLM yields Text](images/frame-progress-06.png)
+
+7. The LLM Frame Processor yields another Text Frame with the value “there.”. The Pipeline passes this frame to the TTS Frame Processor.
+![LLM yields more Text](images/frame-progress-07.png)
+
+8. The TTS Frame Processor now has a full sentence, so it starts streaming audio based on “Hi, there.” It yields the first chunk of streaming audio as an Audio frame, which the Pipeline passes to the LLM Assistant Message Aggregator.
+![TTS yields Audio](images/frame-progress-08.png)
+
+9. The LLM Assistant Message Aggregator doesn’t do anything with Audio frames, so it immediately yields the frame, unchanged. This is the convention for all Frame Processors: frames that the processor doesn’t process should be immediately yielded.
+![pass-through](images/frame-progress-09.png)
+
+10. The Pipeline places the first Audio frame in its sink queue, which is being watched by the Transport. Since the frame is now in a queue, the Pipeline can continue processing other frames. Note that the source and sink queues form a sort of “boundary of concurrent processing” between a Pipeline and the outside world. In a Pipeline, Frames are processed sequentially; once a Frame is on a queue it can be processed in parallel with the frames being processed by the Pipeline. TODO: link to a more in-depth section about this.
+![sink queue](images/frame-progress-10.png)
+
+11. The TTS Frame Processor yields another Audio frame as the Transport transmits the first Audio frame.
+![parallel audio](images/frame-progress-11.png)
+
+12. As before, the LLM Assistant Message Aggregator immediately yields the Audio frame and the Pipeline places the Audio frame in the sink queue.
+![sink queue 2](images/frame-progress-12.png)
+
+13. The TTS Frame Processor has no more frames to yield. The LLM Frame Processor emits an LLM Response End Frame, which the Pipeline passes to the TTS Frame Processor.
+![response end](images/frame-progress-13.png)
+
+14. The TTS Frame Processor immediately yields the LLM Response End Frame, so the Pipeline passes it along to the LLM Assistant Message Aggregator. The LLM Assistant Message Aggregator updates the LLM Context with the full response from the LLM. TODO TODO: I realized I forgot that the TSS Frame Processor also yields the Text frames that the LLM emitted so that the LLM Assistant Message Aggregator could accumulate them, arrggh.
+![response end](images/frame-progress-14.png)
+
+15. The system is quiet, and waiting for the next message from the Transport.
+![response end](images/frame-progress-15.png)

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 +1,3 @@
-# AI-COUSTICS
-AICOUSTICS_LICENSE_KEY=...
-
 # Anthropic
 ANTHROPIC_API_KEY=...
 
@@ -32,9 +29,6 @@ CARTESIA_API_KEY=...
 DAILY_API_KEY=...
 DAILY_SAMPLE_ROOM_URL=https://...
 
-# Deepgram
-DEEPGRAM_API_KEY=...
-
 # ElevenLabs
 ELEVENLABS_API_KEY=...
 ELEVENLABS_VOICE_ID=...
@@ -50,24 +44,14 @@ FIREWORKS_API_KEY=...
 
 # Gladia
 GLADIA_API_KEY=...
-GLADIA_REGION=...
-
-# Google
-GOOGLE_API_KEY=...
-GOOGLE_CLOUD_PROJECT_ID=...
-GOOGLE_TEST_CREDENTIALS=...
-GOOGLE_VERTEX_TEST_CREDENTIALS=...
 
 # LMNT
 LMNT_API_KEY=...
 LMNT_VOICE_ID=...
 
-# Perplexity
-PERPLEXITY_API_KEY=...
-
 # PlayHT
-PLAYHT_USER_ID=...
-PLAYHT_API_KEY=...
+PLAY_HT_USER_ID=...
+PLAY_HT_API_KEY=...
 
 # OpenAI
 OPENAI_API_KEY=...
@@ -138,20 +122,9 @@ SONIOX_API_KEY=
 # Speechmatics
 SPEECHMATICS_API_KEY=...
 
+
 # SambaNova
 SAMBANOVA_API_KEY=...
 
 # Sentry
 SENTRY_DSN=...
-
-# Heygen
-HEYGEN_API_KEY=...
-
-# Mistral
-MISTRAL_API_KEY=...
-
-# NVIDIA
-NVIDIA_API_KEY=...
-
-# Qwen
-QWEN_API_KEY=...

examples/client-server-web/README.md

@@ -0,0 +1,111 @@
+# Client Server Web Example
+
+Learn how to build web applications using Pipecat's client/server architecture. This approach separates your bot logic from your user interface, giving you full control over the client experience while maintaining real-time voice communication.
+
+This example demonstrates:
+
+- Server-side bot running with Pipecat
+- React client using [Pipecat's client SDK](https://docs.pipecat.ai/client/introduction)
+- Real-time voice communication between client and server
+- UI components from [voice-ui-kit](https://github.com/pipecat-ai/voice-ui-kit) for common voice interface patterns
+
+This is the recommended architecture for web applications that need custom interfaces or client-side functionality.
+
+## Prerequisites
+
+- Python 3.10+
+- `npm` installed
+- AI Service API keys for: [Deepgram](https://console.deepgram.com/signup), [OpenAI](https://auth.openai.com/create-account), and [Cartesia](https://play.cartesia.ai/sign-up)
+
+## Setup
+
+This example requires running both a server and client in **two separate terminal windows**.
+
+### Terminal 1: Server Setup
+
+1. Set up a virtual environment
+
+From the `examples/client-server-web` directory, run:
+
+```bash
+cd server
+python -m venv .venv
+source .venv/bin/activate  # On Windows: .venv\Scripts\activate
+```
+
+> Using `uv`? Create your venv using: `uv venv && source .venv/bin/activate`.
+
+2. Install dependencies
+
+```bash
+pip install -r requirements.txt
+```
+
+> Using `uv`? Install requirements using: `uv pip install -r requirements.txt`.
+
+3. Configure environment variables
+
+Create a `.env` file:
+
+```bash
+cp env.example .env
+```
+
+Then, add your API keys:
+
+```
+DEEPGRAM_API_KEY=your_deepgram_api_key
+OPENAI_API_KEY=your_openai_api_key
+CARTESIA_API_KEY=your_cartesia_api_key
+```
+
+4. Run the example
+
+Run your bot using:
+
+```bash
+python bot.py
+```
+
+> Using `uv`? Run your bot using: `uv run bot.py`.
+
+> 💡 First run note: The initial startup may take ~10 seconds as Pipecat downloads required models, like the Silero VAD model.
+
+### Terminal 2: Client Setup
+
+1. Open a new terminal window and navigate to the `client` folder:
+
+From the `examples/client-server-web` directory, run:
+
+```bash
+cd client
+```
+
+2. Install dependencies:
+
+```bash
+npm i
+```
+
+3. Run the client:
+
+```bash
+npm run dev
+```
+
+4. **Open http://localhost:5173 in your browser** and click `Connect` to start talking to your bot.
+
+> 💡 **Tip**: Check your server terminal for debug logs showing Pipecat's internal workings.
+
+## Troubleshooting
+
+- **Browser permissions**: Make sure to allow microphone access when prompted by your browser.
+- **Connection issues**: If the WebRTC connection fails, first try a different browser. If that fails, make sure you don't have a VPN or firewall rules blocking traffic. WebRTC uses UDP to communicate.
+- **Audio issues**: Check that your microphone and speakers are working and not muted.
+
+## Next Steps
+
+- **Explore the client SDK**: Learn more about [Pipecat's client SDKs](https://docs.pipecat.ai/client/introduction) for web, mobile, and other platforms
+- **Learn about the voice-ui-kit**: Explore [voice-ui-kit](https://github.com/pipecat-ai/voice-ui-kit) to simplify your front end development
+- **Advanced examples**: Check out [pipecat-examples](https://github.com/pipecat-ai/pipecat-examples) for more complex client/server applications
+- **Join Discord**: Connect with other developers on [Discord](https://discord.gg/pipecat)

examples/client-server-web/client/.gitignore

@@ -0,0 +1,24 @@
+# Logs
+logs
+*.log
+npm-debug.log*
+yarn-debug.log*
+yarn-error.log*
+pnpm-debug.log*
+lerna-debug.log*
+
+node_modules
+dist
+dist-ssr
+*.local
+
+# Editor directories and files
+.vscode/*
+!.vscode/extensions.json
+.idea
+.DS_Store
+*.suo
+*.ntvs*
+*.njsproj
+*.sln
+*.sw?

examples/client-server-web/client/index.html

@@ -0,0 +1,17 @@
+<!DOCTYPE html>
+<html lang="en">
+
+<head>
+  <meta charset="UTF-8" />
+  <meta name="viewport" content="width=device-width, initial-scale=1.0" />
+  <title>Pipecat UI</title>
+  <link rel="icon" href="/favicon.svg" type="image/svg+xml">
+  <link rel="stylesheet" href="/src/style.css" />
+</head>
+
+<body>
+  <div id="root"></div>
+  <script type="module" src="/src/index.tsx"></script>
+</body>
+
+</html>

examples/client-server-web/client/package.json

@@ -0,0 +1,33 @@
+{
+  "name": "client",
+  "version": "1.0.0",
+  "main": "index.js",
+  "scripts": {
+    "dev": "node_modules/.bin/vite",
+    "build": "node_modules/.bin/tsc && vite build",
+    "preview": "node_modules/.bin/vite preview"
+  },
+  "keywords": [],
+  "author": "",
+  "license": "ISC",
+  "description": "",
+  "devDependencies": {
+    "@types/node": "^22.14.0",
+    "@types/react": "^19.1.8",
+    "@types/react-dom": "^19.1.6",
+    "@vitejs/plugin-react-swc": "^3.8.1",
+    "typescript": "^5.8.3",
+    "vite": "^6.2.5"
+  },
+  "dependencies": {
+    "@fontsource-variable/geist": "^5.2.6",
+    "@fontsource-variable/geist-mono": "^5.2.6",
+    "@pipecat-ai/client-js": "^1.0.1",
+    "@pipecat-ai/client-react": "^1.0.1",
+    "@pipecat-ai/small-webrtc-transport": "^1.0.0",
+    "@pipecat-ai/daily-transport": "^1.0.0",
+    "@pipecat-ai/voice-ui-kit": "^0.1.0",
+    "react": "^19.1.0",
+    "react-dom": "^19.1.0"
+  }
+}

examples/client-server-web/client/public/favicon.svg

@@ -0,0 +1,7 @@
+<svg width="24" height="24" viewBox="0 0 24 24" fill="none" xmlns="http://www.w3.org/2000/svg">
+<path d="M3.3088 5.05615C3.64682 4.92779 4.02833 5.02411 4.26653 5.29797L7.36884 8.86461H16.6312L19.7335 5.29797C19.9717 5.02411 20.3532 4.92779 20.6912 5.05615C21.0292 5.18452 21.253 5.51072 21.253 5.87504V13.75H24V15.5H19.5181V8.19909L17.6762 10.3167C17.5115 10.506 17.2738 10.6146 17.0241 10.6146H6.9759C6.72616 10.6146 6.48854 10.506 6.32383 10.3167L4.48193 8.19909V15.5H0V13.75H2.74699V5.87504C2.74699 5.51072 2.97078 5.18452 3.3088 5.05615Z" fill="black"/>
+<path d="M19.5181 17.25H24V19H19.5181V17.25Z" fill="black"/>
+<path d="M0 17.25H4.48193V19H0V17.25Z" fill="black"/>
+<path d="M9.25301 14.3333C9.25301 14.9777 8.73517 15.5 8.09639 15.5C7.4576 15.5 6.93976 14.9777 6.93976 14.3333C6.93976 13.689 7.4576 13.1667 8.09639 13.1667C8.73517 13.1667 9.25301 13.689 9.25301 14.3333Z" fill="black"/>
+<path d="M17.0602 14.3333C17.0602 14.9777 16.5424 15.5 15.9036 15.5C15.2648 15.5 14.747 14.9777 14.747 14.3333C14.747 13.689 15.2648 13.1667 15.9036 13.1667C16.5424 13.1667 17.0602 13.689 17.0602 14.3333Z" fill="black"/>
+</svg>

examples/client-server-web/client/public/pipecat-logo.svg

@@ -0,0 +1,7 @@
+<svg width="332" height="192" viewBox="0 0 332 192" fill="none" xmlns="http://www.w3.org/2000/svg">
+<path d="M45.7718 0.769635C50.4477 -0.990844 55.7252 0.330188 59.0204 4.08595L101.936 53H230.064L272.98 4.08595C276.275 0.330188 281.552 -0.990844 286.228 0.769635C290.904 2.53011 294 7.00367 294 12V120H332V144H270V43.8728L244.52 72.9141C242.242 75.5111 238.955 77 235.5 77H96.5C93.0452 77 89.7581 75.5111 87.4796 72.9141L62 43.8728V144H0V120H38V12C38 7.00367 41.0958 2.53011 45.7718 0.769635Z" fill="black"/>
+<path d="M270 168H332V192H270V168Z" fill="black"/>
+<path d="M0 168H62V192H0V168Z" fill="black"/>
+<path d="M128 128C128 136.837 120.837 144 112 144C103.163 144 96 136.837 96 128C96 119.164 103.163 112 112 112C120.837 112 128 119.164 128 128Z" fill="black"/>
+<path d="M236 128C236 136.837 228.837 144 220 144C211.163 144 204 136.837 204 128C204 119.164 211.163 112 220 112C228.837 112 236 119.164 236 128Z" fill="black"/>
+</svg>

examples/client-server-web/client/src/index.tsx

@@ -0,0 +1,28 @@
+import {
+  ConsoleTemplate,
+  FullScreenContainer,
+  ThemeProvider,
+} from '@pipecat-ai/voice-ui-kit';
+import { StrictMode } from 'react';
+import { createRoot } from 'react-dom/client';
+
+//@ts-ignore - fontsource-variable/geist is not typed
+import '@fontsource-variable/geist';
+//@ts-ignore - fontsource-variable/geist is not typed
+import '@fontsource-variable/geist-mono';
+
+createRoot(document.getElementById('root')!).render(
+  // @ts-ignore
+  <StrictMode>
+    <ThemeProvider>
+      <FullScreenContainer>
+        <ConsoleTemplate
+          transportType="smallwebrtc"
+          connectParams={{
+            connectionUrl: '/api/offer',
+          }}
+        />
+      </FullScreenContainer>
+    </ThemeProvider>
+  </StrictMode>
+);

examples/client-server-web/client/src/style.css

@@ -0,0 +1,7 @@
+@import "@pipecat-ai/voice-ui-kit/styles.css";
+
+html,
+body {
+  height: 100%;
+  margin: 0;
+}

examples/client-server-web/client/tsconfig.json

@@ -0,0 +1,27 @@
+{
+  "compilerOptions": {
+    "target": "ES2016",
+    "module": "ESNext",
+    "lib": ["ES2016", "DOM", "DOM.Iterable"],
+    "types": ["node"],
+    "skipLibCheck": true,
+    "jsx": "preserve",
+
+    /* Bundler mode */
+    "moduleResolution": "bundler",
+    "allowImportingTsExtensions": true,
+    "allowJs": true,
+    "noEmit": true,
+    "resolveJsonModule": true,
+    "isolatedModules": true,
+    "moduleDetection": "force",
+    "esModuleInterop": true,
+
+    /* Linting */
+    "strict": true,
+    "noUnusedLocals": true,
+    "noUnusedParameters": false,
+    "noFallthroughCasesInSwitch": true
+  },
+  "include": ["src"]
+}

examples/client-server-web/client/vite.config.js

@@ -0,0 +1,18 @@
+import { defineConfig } from "vite";
+import react from "@vitejs/plugin-react-swc";
+
+export default defineConfig({
+  base: "./", //Use relative paths so it works at any mount path
+  plugins: [react()],
+  publicDir: "public",
+  server: {
+    allowedHosts: true, // Allows external connections like ngrok
+    proxy: {
+      // Proxy /api requests to the backend server
+      "/api": {
+        target: "http://0.0.0.0:7860", // Replace with your backend URL
+        changeOrigin: true,
+      },
+    },
+  },
+});

examples/client-server-web/server/bot.py

@@ -0,0 +1,127 @@
+#
+# Copyright (c) 2024–2025, Daily
+#
+# SPDX-License-Identifier: BSD 2-Clause License
+#
+
+"""Client-Server Web Example.
+
+This is the server-side bot implementation for the Pipecat client-server
+web example. It runs a simple voice AI bot that you can connect to using a
+web browser and speak with it.
+
+Required AI services:
+- Deepgram (Speech-to-Text)
+- OpenAI (LLM)
+- Cartesia (Text-to-Speech)
+
+The example connects between client and server using a P2P WebRTC connection.
+
+Run the bot using::
+
+    python bot.py
+"""
+
+import os
+
+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.processors.frameworks.rtvi import RTVIConfig, RTVIObserver, RTVIProcessor
+from pipecat.runner.types import RunnerArguments
+from pipecat.services.cartesia.tts import CartesiaTTSService
+from pipecat.services.deepgram.stt import DeepgramSTTService
+from pipecat.services.openai.llm import OpenAILLMService
+from pipecat.transports.base_transport import BaseTransport, TransportParams
+from pipecat.transports.network.small_webrtc import SmallWebRTCTransport
+
+load_dotenv(override=True)
+
+
+async def run_bot(transport: BaseTransport):
+    logger.info(f"Starting bot")
+
+    stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
+
+    tts = CartesiaTTSService(
+        api_key=os.getenv("CARTESIA_API_KEY"),
+        voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
+    )
+
+    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
+
+    messages = [
+        {
+            "role": "system",
+            "content": "You are a friendly AI assistant. Respond naturally and keep your answers conversational.",
+        },
+    ]
+
+    context = OpenAILLMContext(messages)
+    context_aggregator = llm.create_context_aggregator(context)
+
+    rtvi = RTVIProcessor(config=RTVIConfig(config=[]))
+
+    pipeline = Pipeline(
+        [
+            transport.input(),  # Transport user input
+            rtvi,  # RTVI processor
+            stt,
+            context_aggregator.user(),  # User responses
+            llm,  # LLM
+            tts,  # TTS
+            transport.output(),  # Transport bot output
+            context_aggregator.assistant(),  # Assistant spoken responses
+        ]
+    )
+
+    task = PipelineTask(
+        pipeline,
+        params=PipelineParams(
+            enable_metrics=True,
+            enable_usage_metrics=True,
+        ),
+        observers=[RTVIObserver(rtvi)],
+    )
+
+    @transport.event_handler("on_client_connected")
+    async def on_client_connected(transport, client):
+        logger.info(f"Client connected")
+        # Kick off the conversation.
+        messages.append({"role": "system", "content": "Say hello and briefly introduce yourself."})
+        await task.queue_frames([context_aggregator.user().get_context_frame()])
+
+    @transport.event_handler("on_client_disconnected")
+    async def on_client_disconnected(transport, client):
+        logger.info(f"Client disconnected")
+        await task.cancel()
+
+    runner = PipelineRunner(handle_sigint=False)
+
+    await runner.run(task)
+
+
+async def bot(runner_args: RunnerArguments):
+    """Main bot entry point for the bot starter."""
+
+    transport = SmallWebRTCTransport(
+        params=TransportParams(
+            audio_in_enabled=True,
+            audio_out_enabled=True,
+            vad_analyzer=SileroVADAnalyzer(),
+        ),
+        webrtc_connection=runner_args.webrtc_connection,
+    )
+
+    await run_bot(transport)
+
+
+if __name__ == "__main__":
+    from pipecat.runner.run import main
+
+    main()

examples/client-server-web/server/env.example

@@ -0,0 +1,3 @@
+DEEPGRAM_API_KEY=your_deepgram_api_key
+OPENAI_API_KEY=your_openai_api_key
+CARTESIA_API_KEY=your_cartesia_api_key

examples/client-server-web/server/requirements.txt

@@ -0,0 +1 @@
+pipecat-ai[webrtc,silero,deepgram,openai,cartesia,runner]

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

@@ -4,6 +4,7 @@
 # SPDX-License-Identifier: BSD 2-Clause License
 #
 
+import argparse
 import os
 
 import aiohttp
@@ -14,12 +15,10 @@ 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.runner.types import RunnerArguments
-from pipecat.runner.utils import create_transport
 from pipecat.services.piper.tts import PiperTTSService
 from pipecat.transports.base_transport import BaseTransport, TransportParams
-from pipecat.transports.daily.transport import DailyParams
-from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
+from pipecat.transports.network.fastapi_websocket import FastAPIWebsocketParams
+from pipecat.transports.services.daily import DailyParams
 
 load_dotenv(override=True)
 
@@ -34,7 +33,7 @@ transport_params = {
 }
 
 
-async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
+async def run_example(transport: BaseTransport, _: argparse.Namespace, handle_sigint: bool):
     logger.info(f"Starting bot")
 
     # Create an HTTP session
@@ -43,28 +42,19 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
             base_url=os.getenv("PIPER_BASE_URL"), aiohttp_session=session, sample_rate=24000
         )
 
-        task = PipelineTask(
-            Pipeline([tts, transport.output()]),
-            idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
-        )
+        task = PipelineTask(Pipeline([tts, transport.output()]))
 
         # Register an event handler so we can play the audio when the client joins
         @transport.event_handler("on_client_connected")
         async def on_client_connected(transport, client):
             await task.queue_frames([TTSSpeakFrame(f"Hello there!"), EndFrame()])
 
-        runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
+        runner = PipelineRunner(handle_sigint=handle_sigint)
 
         await runner.run(task)
 
 
-async def bot(runner_args: RunnerArguments):
-    """Main bot entry point compatible with Pipecat Cloud."""
-    transport = await create_transport(runner_args, transport_params)
-    await run_bot(transport, runner_args)
-
-
 if __name__ == "__main__":
-    from pipecat.runner.run import main
+    from pipecat.examples.run import main
 
-    main()
+    main(run_example, transport_params=transport_params)

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

@@ -4,6 +4,7 @@
 # SPDX-License-Identifier: BSD 2-Clause License
 #
 
+import argparse
 import os
 
 import aiohttp
@@ -14,12 +15,10 @@ 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.runner.types import RunnerArguments
-from pipecat.runner.utils import create_transport
 from pipecat.services.rime.tts import RimeHttpTTSService
 from pipecat.transports.base_transport import BaseTransport, TransportParams
-from pipecat.transports.daily.transport import DailyParams
-from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
+from pipecat.transports.network.fastapi_websocket import FastAPIWebsocketParams
+from pipecat.transports.services.daily import DailyParams
 
 load_dotenv(override=True)
 
@@ -33,7 +32,7 @@ transport_params = {
 }
 
 
-async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
+async def run_example(transport: BaseTransport, _: argparse.Namespace, handle_sigint: bool):
     logger.info(f"Starting bot")
 
     # Create an HTTP session
@@ -44,28 +43,19 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
             aiohttp_session=session,
         )
 
-        task = PipelineTask(
-            Pipeline([tts, transport.output()]),
-            idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
-        )
+        task = PipelineTask(Pipeline([tts, transport.output()]))
 
         # Register an event handler so we can play the audio when the client joins
         @transport.event_handler("on_client_connected")
         async def on_client_connected(transport, client):
             await task.queue_frames([TTSSpeakFrame(f"Hello there!"), EndFrame()])
 
-        runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
+        runner = PipelineRunner(handle_sigint=handle_sigint)
 
         await runner.run(task)
 
 
-async def bot(runner_args: RunnerArguments):
-    """Main bot entry point compatible with Pipecat Cloud."""
-    transport = await create_transport(runner_args, transport_params)
-    await run_bot(transport, runner_args)
-
-
 if __name__ == "__main__":
-    from pipecat.runner.run import main
+    from pipecat.examples.run import main
 
-    main()
+    main(run_example, transport_params=transport_params)

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

@@ -4,6 +4,7 @@
 # SPDX-License-Identifier: BSD 2-Clause License
 #
 
+import argparse
 import os
 
 from dotenv import load_dotenv
@@ -13,12 +14,10 @@ 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.runner.types import RunnerArguments
-from pipecat.runner.utils import create_transport
 from pipecat.services.cartesia.tts import CartesiaTTSService
 from pipecat.transports.base_transport import BaseTransport, TransportParams
-from pipecat.transports.daily.transport import DailyParams
-from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
+from pipecat.transports.network.fastapi_websocket import FastAPIWebsocketParams
+from pipecat.transports.services.daily import DailyParams
 
 load_dotenv(override=True)
 
@@ -33,7 +32,7 @@ transport_params = {
 }
 
 
-async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
+async def run_example(transport: BaseTransport, _: argparse.Namespace, handle_sigint: bool):
     logger.info(f"Starting bot")
 
     tts = CartesiaTTSService(
@@ -41,28 +40,19 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
     )
 
-    task = PipelineTask(
-        Pipeline([tts, transport.output()]),
-        idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
-    )
+    task = PipelineTask(Pipeline([tts, transport.output()]))
 
     # Register an event handler so we can play the audio when the client joins
     @transport.event_handler("on_client_connected")
     async def on_client_connected(transport, client):
         await task.queue_frames([TTSSpeakFrame(f"Hello there!"), EndFrame()])
 
-    runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
+    runner = PipelineRunner(handle_sigint=handle_sigint)
 
     await runner.run(task)
 
 
-async def bot(runner_args: RunnerArguments):
-    """Main bot entry point compatible with Pipecat Cloud."""
-    transport = await create_transport(runner_args, transport_params)
-    await run_bot(transport, runner_args)
-
-
 if __name__ == "__main__":
-    from pipecat.runner.run import main
+    from pipecat.examples.run import main
 
-    main()
+    main(run_example, transport_params=transport_params)

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

@@ -4,20 +4,22 @@
 # SPDX-License-Identifier: BSD 2-Clause License
 #
 
+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 TTSSpeakFrame
+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.runner.livekit import configure
 from pipecat.services.cartesia.tts import CartesiaTTSService
-from pipecat.transports.livekit.transport import LiveKitParams, LiveKitTransport
+from pipecat.transports.services.livekit import LiveKitParams, LiveKitTransport
 
 load_dotenv(override=True)
 
@@ -25,8 +27,57 @@ 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():
-    (url, token, room_name) = await configure()
+    (url, token, room_name) = await configure_livekit()
 
     transport = LiveKitTransport(
         url=url,
@@ -50,7 +101,7 @@ async def main():
     async def on_first_participant_joined(transport, participant_id):
         await asyncio.sleep(1)
         await task.queue_frame(
-            TTSSpeakFrame(
+            TextFrame(
                 "Hello there! How are you doing today? Would you like to talk about the weather?"
             )
         )

examples/foundational/01c-fastpitch.py

@@ -4,6 +4,7 @@
 # SPDX-License-Identifier: BSD 2-Clause License
 #
 
+import argparse
 import os
 
 from dotenv import load_dotenv
@@ -13,12 +14,10 @@ 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.runner.types import RunnerArguments
-from pipecat.runner.utils import create_transport
 from pipecat.services.riva.tts import FastPitchTTSService
 from pipecat.transports.base_transport import BaseTransport, TransportParams
-from pipecat.transports.daily.transport import DailyParams
-from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
+from pipecat.transports.network.fastapi_websocket import FastAPIWebsocketParams
+from pipecat.transports.services.daily import DailyParams
 
 load_dotenv(override=True)
 
@@ -33,33 +32,24 @@ transport_params = {
 }
 
 
-async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
+async def run_example(transport: BaseTransport, _: argparse.Namespace, handle_sigint: bool):
     logger.info(f"Starting bot")
 
     tts = FastPitchTTSService(api_key=os.getenv("NVIDIA_API_KEY"))
 
-    task = PipelineTask(
-        Pipeline([tts, transport.output()]),
-        idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
-    )
+    task = PipelineTask(Pipeline([tts, transport.output()]))
 
     # Register an event handler so we can play the audio when the client joins
     @transport.event_handler("on_client_connected")
     async def on_client_connected(transport, client):
         await task.queue_frames([TTSSpeakFrame(f"Hello there!"), EndFrame()])
 
-    runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
+    runner = PipelineRunner(handle_sigint=handle_sigint)
 
     await runner.run(task)
 
 
-async def bot(runner_args: RunnerArguments):
-    """Main bot entry point compatible with Pipecat Cloud."""
-    transport = await create_transport(runner_args, transport_params)
-    await run_bot(transport, runner_args)
-
-
 if __name__ == "__main__":
-    from pipecat.runner.run import main
+    from pipecat.examples.run import main
 
-    main()
+    main(run_example, transport_params=transport_params)

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

@@ -4,23 +4,21 @@
 # SPDX-License-Identifier: BSD 2-Clause License
 #
 
+import argparse
 import os
 
 from dotenv import load_dotenv
 from loguru import logger
 
-from pipecat.frames.frames import EndFrame, LLMContextFrame
+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.processors.aggregators.llm_context import LLMContext
-from pipecat.runner.types import RunnerArguments
-from pipecat.runner.utils import create_transport
 from pipecat.services.cartesia.tts import CartesiaTTSService
 from pipecat.services.openai.llm import OpenAILLMService
 from pipecat.transports.base_transport import BaseTransport, TransportParams
-from pipecat.transports.daily.transport import DailyParams
-from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
+from pipecat.transports.network.fastapi_websocket import FastAPIWebsocketParams
+from pipecat.transports.services.daily import DailyParams
 
 load_dotenv(override=True)
 
@@ -35,7 +33,7 @@ transport_params = {
 }
 
 
-async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
+async def run_example(transport: BaseTransport, _: argparse.Namespace, handle_sigint: bool):
     logger.info(f"Starting bot")
 
     tts = CartesiaTTSService(
@@ -52,28 +50,19 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         }
     ]
 
-    task = PipelineTask(
-        Pipeline([llm, tts, transport.output()]),
-        idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
-    )
+    task = PipelineTask(Pipeline([llm, tts, transport.output()]))
 
     # Register an event handler so we can play the audio when the client joins
     @transport.event_handler("on_client_connected")
     async def on_client_connected(transport, client):
-        await task.queue_frames([LLMContextFrame(LLMContext(messages)), EndFrame()])
+        await task.queue_frames([LLMMessagesFrame(messages), EndFrame()])
 
-    runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
+    runner = PipelineRunner(handle_sigint=handle_sigint)
 
     await runner.run(task)
 
 
-async def bot(runner_args: RunnerArguments):
-    """Main bot entry point compatible with Pipecat Cloud."""
-    transport = await create_transport(runner_args, transport_params)
-    await run_bot(transport, runner_args)
-
-
 if __name__ == "__main__":
-    from pipecat.runner.run import main
+    from pipecat.examples.run import main
 
-    main()
+    main(run_example, transport_params=transport_params)

examples/foundational/03-still-frame.py

@@ -4,6 +4,7 @@
 # SPDX-License-Identifier: BSD 2-Clause License
 #
 
+import argparse
 import os
 
 import aiohttp
@@ -14,11 +15,9 @@ 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.runner.types import RunnerArguments
-from pipecat.runner.utils import create_transport
 from pipecat.services.fal.image import FalImageGenService
 from pipecat.transports.base_transport import BaseTransport, TransportParams
-from pipecat.transports.daily.transport import DailyParams
+from pipecat.transports.services.daily import DailyParams
 
 load_dotenv(override=True)
 
@@ -40,7 +39,7 @@ transport_params = {
 }
 
 
-async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
+async def run_example(transport: BaseTransport, _: argparse.Namespace, handle_sigint: bool):
     logger.info(f"Starting bot")
 
     # Create an HTTP session
@@ -51,10 +50,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
             key=os.getenv("FAL_KEY"),
         )
 
-        task = PipelineTask(
-            Pipeline([imagegen, transport.output()]),
-            idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
-        )
+        task = PipelineTask(Pipeline([imagegen, transport.output()]))
 
         # Register an event handler so we can play the audio when the client joins
         @transport.event_handler("on_client_connected")
@@ -66,18 +62,12 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
             logger.info(f"Client disconnected")
             await task.cancel()
 
-        runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
+        runner = PipelineRunner(handle_sigint=handle_sigint)
 
         await runner.run(task)
 
 
-async def bot(runner_args: RunnerArguments):
-    """Main bot entry point compatible with Pipecat Cloud."""
-    transport = await create_transport(runner_args, transport_params)
-    await run_bot(transport, runner_args)
-
-
 if __name__ == "__main__":
-    from pipecat.runner.run import main
+    from pipecat.examples.run import main
 
-    main()
+    main(run_example, transport_params=transport_params)

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

@@ -4,6 +4,7 @@
 # SPDX-License-Identifier: BSD 2-Clause License
 #
 
+import argparse
 import os
 
 from dotenv import load_dotenv
@@ -13,11 +14,9 @@ from pipecat.frames.frames import TextFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.runner.types import RunnerArguments
-from pipecat.runner.utils import create_transport
 from pipecat.services.google.image import GoogleImageGenService
 from pipecat.transports.base_transport import BaseTransport, TransportParams
-from pipecat.transports.daily.transport import DailyParams
+from pipecat.transports.services.daily import DailyParams
 
 load_dotenv(override=True)
 
@@ -39,7 +38,7 @@ transport_params = {
 }
 
 
-async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
+async def run_example(transport: BaseTransport, _: argparse.Namespace, handle_sigint: bool):
     logger.info(f"Starting bot")
 
     imagegen = GoogleImageGenService(
@@ -52,7 +51,6 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
             enable_metrics=True,
             enable_usage_metrics=True,
         ),
-        idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
     )
 
     # Register an event handler so we can play the audio when the client joins
@@ -67,18 +65,12 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         logger.info(f"Client disconnected")
         await task.cancel()
 
-    runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
+    runner = PipelineRunner(handle_sigint=handle_sigint)
 
     await runner.run(task)
 
 
-async def bot(runner_args: RunnerArguments):
-    """Main bot entry point compatible with Pipecat Cloud."""
-    transport = await create_transport(runner_args, transport_params)
-    await run_bot(transport, runner_args)
-
-
 if __name__ == "__main__":
-    from pipecat.runner.run import main
+    from pipecat.examples.run import main
 
-    main()
+    main(run_example, transport_params=transport_params)

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

@@ -17,22 +17,17 @@ from fastapi.responses import RedirectResponse
 from loguru import logger
 from pipecat_ai_small_webrtc_prebuilt.frontend import SmallWebRTCPrebuiltUI
 
-from pipecat.audio.turn.smart_turn.base_smart_turn import SmartTurnParams
-from pipecat.audio.turn.smart_turn.local_smart_turn_v3 import LocalSmartTurnAnalyzerV3
 from pipecat.audio.vad.silero import SileroVADAnalyzer
-from pipecat.audio.vad.vad_analyzer import VADParams
-from pipecat.frames.frames import LLMRunFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.aggregators.llm_context import LLMContext
-from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
+from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
 from pipecat.services.cartesia.tts import CartesiaTTSService
 from pipecat.services.deepgram.stt import DeepgramSTTService
 from pipecat.services.openai.llm import OpenAILLMService
 from pipecat.transports.base_transport import TransportParams
-from pipecat.transports.smallwebrtc.connection import IceServer, SmallWebRTCConnection
-from pipecat.transports.smallwebrtc.transport import SmallWebRTCTransport
+from pipecat.transports.network.small_webrtc import SmallWebRTCTransport
+from pipecat.transports.network.webrtc_connection import IceServer, SmallWebRTCConnection
 
 load_dotenv(override=True)
 
@@ -60,8 +55,7 @@ async def run_example(webrtc_connection: SmallWebRTCConnection):
         params=TransportParams(
             audio_in_enabled=True,
             audio_out_enabled=True,
-            vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
-            turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
+            vad_analyzer=SileroVADAnalyzer(),
         ),
     )
 
@@ -81,8 +75,8 @@ async def run_example(webrtc_connection: SmallWebRTCConnection):
         },
     ]
 
-    context = LLMContext(messages)
-    context_aggregator = LLMContextAggregatorPair(context)
+    context = OpenAILLMContext(messages)
+    context_aggregator = llm.create_context_aggregator(context)
 
     pipeline = Pipeline(
         [
@@ -109,7 +103,7 @@ async def run_example(webrtc_connection: SmallWebRTCConnection):
         logger.info(f"Client connected")
         # Kick off the conversation.
         messages.append({"role": "system", "content": "Please introduce yourself to the user."})
-        await task.queue_frames([LLMRunFrame()])
+        await task.queue_frames([context_aggregator.user().get_context_frame()])
 
     @transport.event_handler("on_client_disconnected")
     async def on_client_disconnected(transport, client):

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

@@ -12,20 +12,15 @@ import aiohttp
 from dotenv import load_dotenv
 from loguru import logger
 
-from pipecat.audio.turn.smart_turn.base_smart_turn import SmartTurnParams
-from pipecat.audio.turn.smart_turn.local_smart_turn_v3 import LocalSmartTurnAnalyzerV3
 from pipecat.audio.vad.silero import SileroVADAnalyzer
-from pipecat.audio.vad.vad_analyzer import VADParams
-from pipecat.frames.frames import LLMRunFrame
+from pipecat.examples.daily_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_context import LLMContext
-from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
-from pipecat.runner.daily import configure
+from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
 from pipecat.services.cartesia.tts import CartesiaTTSService
 from pipecat.services.openai.llm import OpenAILLMService
-from pipecat.transports.daily.transport import DailyLogLevel, DailyParams, DailyTransport
+from pipecat.transports.services.daily import DailyLogLevel, DailyParams, DailyTransport
 
 load_dotenv(override=True)
 
@@ -45,8 +40,7 @@ async def main():
                 audio_in_enabled=True,
                 audio_out_enabled=True,
                 transcription_enabled=True,
-                vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
-                turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
+                vad_analyzer=SileroVADAnalyzer(),
             ),
         )
         transport.set_log_level(DailyLogLevel.Info)
@@ -65,8 +59,8 @@ async def main():
             },
         ]
 
-        context = LLMContext(messages)
-        context_aggregator = LLMContextAggregatorPair(context)
+        context = OpenAILLMContext(messages)
+        context_aggregator = llm.create_context_aggregator(context)
 
         pipeline = Pipeline(
             [
@@ -92,7 +86,7 @@ async def main():
             await transport.capture_participant_transcription(participant["id"])
             # Kick off the conversation.
             messages.append({"role": "system", "content": "Please introduce yourself to the user."})
-            await task.queue_frames([LLMRunFrame()])
+            await task.queue_frames([context_aggregator.user().get_context_frame()])
 
         @transport.event_handler("on_participant_left")
         async def on_participant_left(transport, participant, reason):

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

@@ -4,35 +4,33 @@
 # SPDX-License-Identifier: BSD 2-Clause License
 #
 
+import argparse
 import asyncio
 import json
 import os
 import sys
 
+from deepgram import LiveOptions
 from dotenv import load_dotenv
+from livekit import api
 from loguru import logger
 
-from pipecat.audio.turn.smart_turn.base_smart_turn import SmartTurnParams
-from pipecat.audio.turn.smart_turn.local_smart_turn_v3 import LocalSmartTurnAnalyzerV3
 from pipecat.audio.vad.silero import SileroVADAnalyzer
-from pipecat.audio.vad.vad_analyzer import VADParams
 from pipecat.frames.frames import (
-    InterruptionFrame,
+    BotInterruptionFrame,
+    TextFrame,
     TranscriptionFrame,
-    TTSSpeakFrame,
     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.llm_context import LLMContext
-from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
-from pipecat.runner.livekit import configure
+from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
 from pipecat.services.cartesia.tts import CartesiaTTSService
 from pipecat.services.deepgram.stt import DeepgramSTTService
 from pipecat.services.openai.llm import OpenAILLMService
-from pipecat.transports.livekit.transport import LiveKitParams, LiveKitTransport
+from pipecat.transports.services.livekit import LiveKitParams, LiveKitTransport
 
 load_dotenv(override=True)
 
@@ -40,8 +38,72 @@ 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()
+
+
+def generate_token_with_agent(
+    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,
+            agent=True,  # This is the only difference, this makes livekit client know agent has joined
+        )
+    )
+
+    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_with_agent(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():
-    (url, token, room_name) = await configure()
+    (url, token, room_name) = await configure_livekit()
 
     transport = LiveKitTransport(
         url=url,
@@ -50,12 +112,16 @@ async def main():
         params=LiveKitParams(
             audio_in_enabled=True,
             audio_out_enabled=True,
-            vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
-            turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
+            vad_analyzer=SileroVADAnalyzer(),
         ),
     )
 
-    stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
+    stt = DeepgramSTTService(
+        api_key=os.getenv("DEEPGRAM_API_KEY"),
+        live_options=LiveOptions(
+            vad_events=True,
+        ),
+    )
 
     llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
 
@@ -74,23 +140,23 @@ async def main():
         },
     ]
 
-    context = LLMContext(messages)
-    context_aggregator = LLMContextAggregatorPair(context)
+    context = OpenAILLMContext(messages)
+    context_aggregator = llm.create_context_aggregator(context)
 
-    pipeline = Pipeline(
-        [
-            transport.input(),  # Transport user input
-            stt,
-            context_aggregator.user(),  # User responses
-            llm,  # LLM
-            tts,  # TTS
-            transport.output(),  # Transport bot output
-            context_aggregator.assistant(),  # Assistant spoken responses
-        ]
-    )
+    runner = PipelineRunner()
 
     task = PipelineTask(
-        pipeline,
+        Pipeline(
+            [
+                transport.input(),
+                stt,
+                context_aggregator.user(),
+                llm,
+                tts,
+                transport.output(),
+                context_aggregator.assistant(),
+            ],
+        ),
         params=PipelineParams(
             enable_metrics=True,
             enable_usage_metrics=True,
@@ -103,7 +169,7 @@ async def main():
     async def on_first_participant_joined(transport, participant_id):
         await asyncio.sleep(1)
         await task.queue_frame(
-            TTSSpeakFrame(
+            TextFrame(
                 "Hello there! How are you doing today? Would you like to talk about the weather?"
             )
         )
@@ -120,7 +186,7 @@ async def main():
 
         await task.queue_frames(
             [
-                InterruptionFrame(),
+                BotInterruptionFrame(),
                 UserStartedSpeakingFrame(),
                 TranscriptionFrame(
                     user_id=participant_id,
@@ -131,8 +197,6 @@ async def main():
             ],
         )
 
-    runner = PipelineRunner()
-
     await runner.run(task)
 
 

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

@@ -4,6 +4,7 @@
 # SPDX-License-Identifier: BSD 2-Clause License
 #
 
+import argparse
 import os
 from dataclasses import dataclass
 
@@ -14,25 +15,21 @@ from loguru import logger
 from pipecat.frames.frames import (
     DataFrame,
     Frame,
-    LLMContextFrame,
     LLMFullResponseStartFrame,
+    LLMMessagesFrame,
     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.processors.aggregators.llm_context import LLMContext
-from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
 from pipecat.processors.aggregators.sentence import SentenceAggregator
 from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
-from pipecat.runner.types import RunnerArguments
-from pipecat.runner.utils import create_transport
 from pipecat.services.cartesia.tts import CartesiaHttpTTSService
 from pipecat.services.fal.image import FalImageGenService
 from pipecat.services.openai.llm import OpenAILLMService
 from pipecat.transports.base_transport import BaseTransport, TransportParams
-from pipecat.transports.daily.transport import DailyParams
+from pipecat.transports.services.daily import DailyParams
 
 load_dotenv(override=True)
 
@@ -85,7 +82,7 @@ transport_params = {
 }
 
 
-async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
+async def run_example(transport: BaseTransport, _: argparse.Namespace, handle_sigint: bool):
     """Run the Calendar Month Narration bot using WebRTC transport.
 
     Args:
@@ -155,12 +152,9 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
                 }
             ]
             frames.append(MonthFrame(month=month))
-            frames.append(LLMContextFrame(LLMContext(messages)))
+            frames.append(LLMMessagesFrame(messages))
 
-        task = PipelineTask(
-            pipeline,
-            idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
-        )
+        task = PipelineTask(pipeline)
 
         # Set up transport event handlers
         @transport.event_handler("on_client_connected")
@@ -175,17 +169,11 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
             await task.cancel()
 
         # Run the pipeline
-        runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
+        runner = PipelineRunner(handle_sigint=handle_sigint)
         await runner.run(task)
 
 
-async def bot(runner_args: RunnerArguments):
-    """Main bot entry point compatible with Pipecat Cloud."""
-    transport = await create_transport(runner_args, transport_params)
-    await run_bot(transport, runner_args)
-
-
 if __name__ == "__main__":
-    from pipecat.runner.run import main
+    from pipecat.examples.run import main
 
-    main()
+    main(run_example, transport_params=transport_params)

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

@@ -15,7 +15,7 @@ from loguru import logger
 
 from pipecat.frames.frames import (
     Frame,
-    LLMContextFrame,
+    LLMMessagesFrame,
     OutputAudioRawFrame,
     TextFrame,
     TTSAudioRawFrame,
@@ -25,8 +25,6 @@ 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.llm_context import LLMContext
-from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
 from pipecat.processors.aggregators.sentence import SentenceAggregator
 from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
 from pipecat.services.cartesia.tts import CartesiaHttpTTSService
@@ -139,7 +137,7 @@ async def main():
             )
 
             task = PipelineTask(pipeline)
-            await task.queue_frame(LLMContextFrame(LLMContext(messages)))
+            await task.queue_frame(LLMMessagesFrame(messages))
             await task.stop_when_done()
 
             await runner.run(task)

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

@@ -4,16 +4,14 @@
 # SPDX-License-Identifier: BSD 2-Clause License
 #
 
+import argparse
 import os
 
 from dotenv import load_dotenv
 from loguru import logger
 
-from pipecat.audio.turn.smart_turn.base_smart_turn import SmartTurnParams
-from pipecat.audio.turn.smart_turn.local_smart_turn_v3 import LocalSmartTurnAnalyzerV3
 from pipecat.audio.vad.silero import SileroVADAnalyzer
-from pipecat.audio.vad.vad_analyzer import VADParams
-from pipecat.frames.frames import Frame, LLMRunFrame, MetricsFrame
+from pipecat.frames.frames import Frame, MetricsFrame
 from pipecat.metrics.metrics import (
     LLMUsageMetricsData,
     ProcessingMetricsData,
@@ -23,17 +21,14 @@ from pipecat.metrics.metrics import (
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.aggregators.llm_context import LLMContext
-from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
+from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
 from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
-from pipecat.runner.types import RunnerArguments
-from pipecat.runner.utils import create_transport
 from pipecat.services.cartesia.tts import CartesiaTTSService
 from pipecat.services.deepgram.stt import DeepgramSTTService
 from pipecat.services.openai.llm import OpenAILLMService
 from pipecat.transports.base_transport import BaseTransport, TransportParams
-from pipecat.transports.daily.transport import DailyParams
-from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
+from pipecat.transports.network.fastapi_websocket import FastAPIWebsocketParams
+from pipecat.transports.services.daily import DailyParams
 
 load_dotenv(override=True)
 
@@ -65,25 +60,22 @@ transport_params = {
     "daily": lambda: DailyParams(
         audio_in_enabled=True,
         audio_out_enabled=True,
-        vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
-        turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
+        vad_analyzer=SileroVADAnalyzer(),
     ),
     "twilio": lambda: FastAPIWebsocketParams(
         audio_in_enabled=True,
         audio_out_enabled=True,
-        vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
-        turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
+        vad_analyzer=SileroVADAnalyzer(),
     ),
     "webrtc": lambda: TransportParams(
         audio_in_enabled=True,
         audio_out_enabled=True,
-        vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
-        turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
+        vad_analyzer=SileroVADAnalyzer(),
     ),
 }
 
 
-async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
+async def run_example(transport: BaseTransport, _: argparse.Namespace, handle_sigint: bool):
     logger.info(f"Starting bot")
 
     stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
@@ -104,8 +96,8 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         },
     ]
 
-    context = LLMContext(messages)
-    context_aggregator = LLMContextAggregatorPair(context)
+    context = OpenAILLMContext(messages)
+    context_aggregator = llm.create_context_aggregator(context)
 
     pipeline = Pipeline(
         [
@@ -126,7 +118,6 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
             enable_metrics=True,
             enable_usage_metrics=True,
         ),
-        idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
     )
 
     @transport.event_handler("on_client_connected")
@@ -134,24 +125,18 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         logger.info(f"Client connected")
         # Kick off the conversation.
         messages.append({"role": "system", "content": "Please introduce yourself to the user."})
-        await task.queue_frames([LLMRunFrame()])
+        await task.queue_frames([context_aggregator.user().get_context_frame()])
 
     @transport.event_handler("on_client_disconnected")
     async def on_client_disconnected(transport, client):
         logger.info(f"Client disconnected")
         await task.cancel()
 
-    runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
+    runner = PipelineRunner(handle_sigint=handle_sigint)
     await runner.run(task)
 
 
-async def bot(runner_args: RunnerArguments):
-    """Main bot entry point compatible with Pipecat Cloud."""
-    transport = await create_transport(runner_args, transport_params)
-    await run_bot(transport, runner_args)
-
-
 if __name__ == "__main__":
-    from pipecat.runner.run import main
+    from pipecat.examples.run import main
 
-    main()
+    main(run_example, transport_params=transport_params)

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

@@ -4,36 +4,30 @@
 # SPDX-License-Identifier: BSD 2-Clause License
 #
 
+import argparse
 import os
 
 from dotenv import load_dotenv
 from loguru import logger
 from PIL import Image
 
-from pipecat.audio.turn.smart_turn.base_smart_turn import SmartTurnParams
-from pipecat.audio.turn.smart_turn.local_smart_turn_v3 import LocalSmartTurnAnalyzerV3
 from pipecat.audio.vad.silero import SileroVADAnalyzer
-from pipecat.audio.vad.vad_analyzer import VADParams
 from pipecat.frames.frames import (
     BotStartedSpeakingFrame,
     BotStoppedSpeakingFrame,
     Frame,
-    LLMRunFrame,
     OutputImageRawFrame,
 )
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.aggregators.llm_context import LLMContext
-from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
+from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
 from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
-from pipecat.runner.types import RunnerArguments
-from pipecat.runner.utils import create_transport
 from pipecat.services.cartesia.tts import CartesiaTTSService
 from pipecat.services.deepgram.stt import DeepgramSTTService
 from pipecat.services.openai.llm import OpenAILLMService
 from pipecat.transports.base_transport import BaseTransport, TransportParams
-from pipecat.transports.daily.transport import DailyParams
+from pipecat.transports.services.daily import DailyParams
 
 load_dotenv(override=True)
 
@@ -70,7 +64,7 @@ class ImageSyncAggregator(FrameProcessor):
                 )
             )
 
-        await self.push_frame(frame, direction)
+        await self.push_frame(frame)
 
 
 # We store functions so objects (e.g. SileroVADAnalyzer) don't get
@@ -83,8 +77,7 @@ transport_params = {
         video_out_enabled=True,
         video_out_width=1024,
         video_out_height=1024,
-        vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
-        turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
+        vad_analyzer=SileroVADAnalyzer(),
     ),
     "webrtc": lambda: TransportParams(
         audio_in_enabled=True,
@@ -92,13 +85,12 @@ transport_params = {
         video_out_enabled=True,
         video_out_width=1024,
         video_out_height=1024,
-        vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
-        turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
+        vad_analyzer=SileroVADAnalyzer(),
     ),
 }
 
 
-async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
+async def run_example(transport: BaseTransport, _: argparse.Namespace, handle_sigint: bool):
     logger.info(f"Starting bot")
 
     stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
@@ -117,8 +109,8 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         },
     ]
 
-    context = LLMContext(messages)
-    context_aggregator = LLMContextAggregatorPair(context)
+    context = OpenAILLMContext(messages)
+    context_aggregator = llm.create_context_aggregator(context)
 
     image_sync_aggregator = ImageSyncAggregator(
         os.path.join(os.path.dirname(__file__), "assets", "speaking.png"),
@@ -144,31 +136,24 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
             enable_metrics=True,
             enable_usage_metrics=True,
         ),
-        idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
     )
 
     @transport.event_handler("on_client_connected")
     async def on_client_connected(transport, client):
         logger.info(f"Client connected")
         # Kick off the conversation.
-        await task.queue_frames([LLMRunFrame()])
+        await task.queue_frames([context_aggregator.user().get_context_frame()])
 
     @transport.event_handler("on_client_disconnected")
     async def on_client_disconnected(transport, client):
         logger.info(f"Client disconnected")
         await task.cancel()
 
-    runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
+    runner = PipelineRunner(handle_sigint=handle_sigint)
     await runner.run(task)
 
 
-async def bot(runner_args: RunnerArguments):
-    """Main bot entry point compatible with Pipecat Cloud."""
-    transport = await create_transport(runner_args, transport_params)
-    await run_bot(transport, runner_args)
-
-
 if __name__ == "__main__":
-    from pipecat.runner.run import main
+    from pipecat.examples.run import main
 
-    main()
+    main(run_example, transport_params=transport_params)

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

@@ -4,29 +4,23 @@
 # SPDX-License-Identifier: BSD 2-Clause License
 #
 
+import argparse
 import os
 
 from dotenv import load_dotenv
 from loguru import logger
 
-from pipecat.audio.turn.smart_turn.base_smart_turn import SmartTurnParams
-from pipecat.audio.turn.smart_turn.local_smart_turn_v3 import LocalSmartTurnAnalyzerV3
 from pipecat.audio.vad.silero import SileroVADAnalyzer
-from pipecat.audio.vad.vad_analyzer import VADParams
-from pipecat.frames.frames import LLMRunFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.aggregators.llm_context import LLMContext
-from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
-from pipecat.runner.types import RunnerArguments
-from pipecat.runner.utils import create_transport
+from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
 from pipecat.services.cartesia.tts import CartesiaHttpTTSService
 from pipecat.services.deepgram.stt import DeepgramSTTService
 from pipecat.services.openai.llm import OpenAILLMService
 from pipecat.transports.base_transport import BaseTransport, TransportParams
-from pipecat.transports.daily.transport import DailyParams
-from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
+from pipecat.transports.network.fastapi_websocket import FastAPIWebsocketParams
+from pipecat.transports.services.daily import DailyParams
 
 load_dotenv(override=True)
 
@@ -38,25 +32,22 @@ transport_params = {
     "daily": lambda: DailyParams(
         audio_in_enabled=True,
         audio_out_enabled=True,
-        vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
-        turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
+        vad_analyzer=SileroVADAnalyzer(),
     ),
     "twilio": lambda: FastAPIWebsocketParams(
         audio_in_enabled=True,
         audio_out_enabled=True,
-        vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
-        turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
+        vad_analyzer=SileroVADAnalyzer(),
     ),
     "webrtc": lambda: TransportParams(
         audio_in_enabled=True,
         audio_out_enabled=True,
-        vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
-        turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
+        vad_analyzer=SileroVADAnalyzer(),
     ),
 }
 
 
-async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
+async def run_example(transport: BaseTransport, _: argparse.Namespace, handle_sigint: bool):
     logger.info(f"Starting bot")
 
     stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
@@ -75,8 +66,8 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         },
     ]
 
-    context = LLMContext(messages)
-    context_aggregator = LLMContextAggregatorPair(context)
+    context = OpenAILLMContext(messages)
+    context_aggregator = llm.create_context_aggregator(context)
 
     pipeline = Pipeline(
         [
@@ -96,7 +87,6 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
             enable_metrics=True,
             enable_usage_metrics=True,
         ),
-        idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
     )
 
     @transport.event_handler("on_client_connected")
@@ -104,25 +94,19 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         logger.info(f"Client connected")
         # Kick off the conversation.
         messages.append({"role": "system", "content": "Please introduce yourself to the user."})
-        await task.queue_frames([LLMRunFrame()])
+        await task.queue_frames([context_aggregator.user().get_context_frame()])
 
     @transport.event_handler("on_client_disconnected")
     async def on_client_disconnected(transport, client):
         logger.info(f"Client disconnected")
         await task.cancel()
 
-    runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
+    runner = PipelineRunner(handle_sigint=handle_sigint)
 
     await runner.run(task)
 
 
-async def bot(runner_args: RunnerArguments):
-    """Main bot entry point compatible with Pipecat Cloud."""
-    transport = await create_transport(runner_args, transport_params)
-    await run_bot(transport, runner_args)
-
-
 if __name__ == "__main__":
-    from pipecat.runner.run import main
+    from pipecat.examples.run import main
 
-    main()
+    main(run_example, transport_params=transport_params)

examples/foundational/07-interruptible.py

@@ -4,37 +4,23 @@
 # SPDX-License-Identifier: BSD 2-Clause License
 #
 
+import argparse
 import os
-import wave
 
 from dotenv import load_dotenv
 from loguru import logger
 
-from pipecat.audio.turn.smart_turn.base_smart_turn import SmartTurnParams
-from pipecat.audio.turn.smart_turn.local_smart_turn_v3 import LocalSmartTurnAnalyzerV3
 from pipecat.audio.vad.silero import SileroVADAnalyzer
-from pipecat.audio.vad.vad_analyzer import VADParams
-from pipecat.frames.frames import (
-    LLMFullResponseEndFrame,
-    LLMFullResponseStartFrame,
-    LLMRunFrame,
-    LLMTextFrame,
-    OutputAudioRawFrame,
-    TextFrame,
-)
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.aggregators.llm_context import LLMContext
-from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
-from pipecat.runner.types import RunnerArguments
-from pipecat.runner.utils import create_transport
+from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
 from pipecat.services.cartesia.tts import CartesiaTTSService
 from pipecat.services.deepgram.stt import DeepgramSTTService
 from pipecat.services.openai.llm import OpenAILLMService
 from pipecat.transports.base_transport import BaseTransport, TransportParams
-from pipecat.transports.daily.transport import DailyParams
-from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
+from pipecat.transports.network.fastapi_websocket import FastAPIWebsocketParams
+from pipecat.transports.services.daily import DailyParams
 
 load_dotenv(override=True)
 
@@ -45,25 +31,22 @@ transport_params = {
     "daily": lambda: DailyParams(
         audio_in_enabled=True,
         audio_out_enabled=True,
-        vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
-        turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
+        vad_analyzer=SileroVADAnalyzer(),
     ),
     "twilio": lambda: FastAPIWebsocketParams(
         audio_in_enabled=True,
         audio_out_enabled=True,
-        vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
-        turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
+        vad_analyzer=SileroVADAnalyzer(),
     ),
     "webrtc": lambda: TransportParams(
         audio_in_enabled=True,
         audio_out_enabled=True,
-        vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
-        turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
+        vad_analyzer=SileroVADAnalyzer(),
     ),
 }
 
 
-async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
+async def run_example(transport: BaseTransport, _: argparse.Namespace, handle_sigint: bool):
     logger.info(f"Starting bot")
 
     stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
@@ -82,8 +65,8 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         },
     ]
 
-    context = LLMContext(messages)
-    context_aggregator = LLMContextAggregatorPair(context)
+    context = OpenAILLMContext(messages)
+    context_aggregator = llm.create_context_aggregator(context)
 
     pipeline = Pipeline(
         [
@@ -103,7 +86,6 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
             enable_metrics=True,
             enable_usage_metrics=True,
         ),
-        idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
     )
 
     @transport.event_handler("on_client_connected")
@@ -111,45 +93,19 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         logger.info(f"Client connected")
         # Kick off the conversation.
         messages.append({"role": "system", "content": "Please introduce yourself to the user."})
-
-        audio_file_path = os.path.join(os.path.dirname(__file__), "assets", "pre-recorded.wav")
-
-        with wave.open(audio_file_path, "rb") as wav_file:
-            llm_text_frame = TextFrame(text="This is a pre-recorded message.")
-            llm_text_frame.skip_tts = True
-
-            audio_data = wav_file.readframes(wav_file.getnframes())
-            output_audio_raw_frame = OutputAudioRawFrame(
-                audio=audio_data, sample_rate=44100, num_channels=1
-            )
-
-            await task.queue_frames(
-                [
-                    LLMRunFrame(),
-                    LLMFullResponseStartFrame(),
-                    llm_text_frame,
-                    output_audio_raw_frame,
-                    LLMFullResponseEndFrame(),
-                ]
-            )
+        await task.queue_frames([context_aggregator.user().get_context_frame()])
 
     @transport.event_handler("on_client_disconnected")
     async def on_client_disconnected(transport, client):
         logger.info(f"Client disconnected")
         await task.cancel()
 
-    runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
+    runner = PipelineRunner(handle_sigint=handle_sigint)
 
     await runner.run(task)
 
 
-async def bot(runner_args: RunnerArguments):
-    """Main bot entry point compatible with Pipecat Cloud."""
-    transport = await create_transport(runner_args, transport_params)
-    await run_bot(transport, runner_args)
-
-
 if __name__ == "__main__":
-    from pipecat.runner.run import main
+    from pipecat.examples.run import main
 
-    main()
+    main(run_example, transport_params=transport_params)

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

@@ -1,180 +0,0 @@
-#
-# Copyright (c) 2024–2025, Daily
-#
-# SPDX-License-Identifier: BSD 2-Clause License
-#
-
-import os
-
-from dotenv import load_dotenv
-from loguru import logger
-
-from pipecat.frames.frames import LLMRunFrame
-from pipecat.pipeline.pipeline import Pipeline
-from pipecat.pipeline.runner import PipelineRunner
-from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.aggregators.llm_context import LLMContext
-from pipecat.processors.aggregators.llm_response import (
-    LLMUserAggregatorParams,
-)
-from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
-from pipecat.runner.types import RunnerArguments
-from pipecat.runner.utils import create_transport
-from pipecat.services.elevenlabs.tts import ElevenLabsTTSService
-from pipecat.services.openai.base_llm import BaseOpenAILLMService
-from pipecat.services.openai.llm import OpenAILLMService
-from pipecat.services.speechmatics.stt import SpeechmaticsSTTService
-from pipecat.transcriptions.language import Language
-from pipecat.transports.base_transport import BaseTransport, TransportParams
-from pipecat.transports.daily.transport import DailyParams
-from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
-
-load_dotenv(override=True)
-
-# We store functions so objects (e.g. SileroVADAnalyzer) don't get
-# instantiated. The function will be called when the desired transport gets
-# selected.
-transport_params = {
-    "daily": lambda: DailyParams(
-        audio_in_enabled=True,
-        audio_out_enabled=True,
-    ),
-    "twilio": lambda: FastAPIWebsocketParams(
-        audio_in_enabled=True,
-        audio_out_enabled=True,
-    ),
-    "webrtc": lambda: TransportParams(
-        audio_in_enabled=True,
-        audio_out_enabled=True,
-    ),
-}
-
-
-async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
-    """Speechmatics STT Service Example
-
-    This example demonstrates using Speechmatics Speech-to-Text service with speaker diarization and intelligent speaker management. Key features:
-
-    1. Speaker Diarization
-       - Automatically identifies and distinguishes between different speakers
-       - First speaker is identified as 'S1', others get subsequent IDs
-       - Uses `enable_diarization` parameter to manage speaker detection
-
-    2. Smart Speaker Control
-       - `focus_speakers` parameter lets you target specific speakers (e.g. ["S1"])
-       - Other speakers will be wrapped in PASSIVE tags
-       - Only processes speech from focused speakers
-       - Words from all speakers are wrapped with XML tags for clear speaker identification
-       - Other speakers' speech only sent when focused speaker is active
-
-    3. Voice Activity Detection
-       - Built-in VAD using `enable_vad` parameter
-       - Remove `vad_analyzer` from `transport` config to use module's VAD
-       - Emits speaker started/stopped events
-
-    4. Configuration Options
-       - `operating_point` parameter defaults to `ENHANCED` for optimal accuracy
-       - Configurable `end_of_utterance_silence_trigger` (default 0.5s)
-       - Customizable speaker formatting
-       - Additional diarization settings available
-
-    For detailed information about operating points and configuration:
-    https://docs.speechmatics.com/rt-api-ref
-    """
-
-    logger.info(f"Starting bot")
-
-    stt = SpeechmaticsSTTService(
-        api_key=os.getenv("SPEECHMATICS_API_KEY"),
-        params=SpeechmaticsSTTService.InputParams(
-            language=Language.EN,
-            enable_vad=True,
-            enable_diarization=True,
-            focus_speakers=["S1"],
-            end_of_utterance_silence_trigger=0.5,
-            speaker_active_format="<{speaker_id}>{text}</{speaker_id}>",
-            speaker_passive_format="<PASSIVE><{speaker_id}>{text}</{speaker_id}></PASSIVE>",
-        ),
-    )
-
-    tts = ElevenLabsTTSService(
-        api_key=os.getenv("ELEVENLABS_API_KEY"),
-        voice_id=os.getenv("ELEVENLABS_VOICE_ID"),
-        model="eleven_turbo_v2_5",
-    )
-
-    llm = OpenAILLMService(
-        api_key=os.getenv("OPENAI_API_KEY"),
-        params=BaseOpenAILLMService.InputParams(temperature=0.75),
-    )
-
-    messages = [
-        {
-            "role": "system",
-            "content": (
-                "You are a helpful British assistant called Alfred. "
-                "Your goal is to demonstrate your capabilities in a succinct way. "
-                "Your output will be converted to audio so don't include special characters in your answers. "
-                "Always include punctuation in your responses. "
-                "Give very short replies - do not give longer replies unless strictly necessary. "
-                "Respond to what the user said in a concise, funny, creative and helpful way. "
-                "Use `<Sn/>` tags to identify different speakers - do not use tags in your replies. "
-                "Do not respond to speakers within `<PASSIVE/>` tags unless explicitly asked to. "
-            ),
-        },
-    ]
-
-    context = LLMContext(messages)
-    context_aggregator = LLMContextAggregatorPair(
-        context,
-        user_params=LLMUserAggregatorParams(aggregation_timeout=0.005),
-    )
-
-    pipeline = Pipeline(
-        [
-            transport.input(),  # Transport user input
-            stt,
-            context_aggregator.user(),  # User responses
-            llm,  # LLM
-            tts,  # TTS
-            transport.output(),  # Transport bot output
-            context_aggregator.assistant(),  # Assistant spoken responses
-        ]
-    )
-
-    task = PipelineTask(
-        pipeline,
-        params=PipelineParams(
-            enable_metrics=True,
-            enable_usage_metrics=True,
-        ),
-        idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
-    )
-
-    @transport.event_handler("on_client_connected")
-    async def on_client_connected(transport, client):
-        logger.info(f"Client connected")
-        # Kick off the conversation.
-        messages.append({"role": "system", "content": "Say a short hello to the user."})
-        await task.queue_frames([LLMRunFrame()])
-
-    @transport.event_handler("on_client_disconnected")
-    async def on_client_disconnected(transport, client):
-        logger.info(f"Client disconnected")
-        await task.cancel()
-
-    runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
-
-    await runner.run(task)
-
-
-async def bot(runner_args: RunnerArguments):
-    """Main bot entry point compatible with Pipecat Cloud."""
-    transport = await create_transport(runner_args, transport_params)
-    await run_bot(transport, runner_args)
-
-
-if __name__ == "__main__":
-    from pipecat.runner.run import main
-
-    main()

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

@@ -4,34 +4,28 @@
 # SPDX-License-Identifier: BSD 2-Clause License
 #
 
+import argparse
 import os
 
 from dotenv import load_dotenv
 from loguru import logger
 
-from pipecat.audio.turn.smart_turn.base_smart_turn import SmartTurnParams
-from pipecat.audio.turn.smart_turn.local_smart_turn_v3 import LocalSmartTurnAnalyzerV3
 from pipecat.audio.vad.silero import SileroVADAnalyzer
-from pipecat.audio.vad.vad_analyzer import VADParams
-from pipecat.frames.frames import LLMRunFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.aggregators.llm_context import LLMContext
 from pipecat.processors.aggregators.llm_response import (
     LLMUserAggregatorParams,
 )
-from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
-from pipecat.runner.types import RunnerArguments
-from pipecat.runner.utils import create_transport
+from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
 from pipecat.services.elevenlabs.tts import ElevenLabsTTSService
 from pipecat.services.openai.base_llm import BaseOpenAILLMService
 from pipecat.services.openai.llm import OpenAILLMService
 from pipecat.services.speechmatics.stt import SpeechmaticsSTTService
 from pipecat.transcriptions.language import Language
 from pipecat.transports.base_transport import BaseTransport, TransportParams
-from pipecat.transports.daily.transport import DailyParams
-from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
+from pipecat.transports.network.fastapi_websocket import FastAPIWebsocketParams
+from pipecat.transports.services.daily import DailyParams
 
 load_dotenv(override=True)
 
@@ -42,25 +36,22 @@ transport_params = {
     "daily": lambda: DailyParams(
         audio_in_enabled=True,
         audio_out_enabled=True,
-        vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
-        turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
+        vad_analyzer=SileroVADAnalyzer(),
     ),
     "twilio": lambda: FastAPIWebsocketParams(
         audio_in_enabled=True,
         audio_out_enabled=True,
-        vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
-        turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
+        vad_analyzer=SileroVADAnalyzer(),
     ),
     "webrtc": lambda: TransportParams(
         audio_in_enabled=True,
         audio_out_enabled=True,
-        vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
-        turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
+        vad_analyzer=SileroVADAnalyzer(),
     ),
 }
 
 
-async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
+async def run_example(transport: BaseTransport, _: argparse.Namespace, handle_sigint: bool):
     """Run example using Speechmatics STT.
 
     This example will use diarization within our STT service and output the words spoken by
@@ -68,6 +59,9 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
     instructions in the system context for the LLM. This greatly improves the conversation
     experience by allowing the LLM to understand who is speaking in a multi-party call.
 
+    If you do not wish to use diarization, then set the `enable_speaker_diarization` parameter
+    to `False` or omit it altogether. The `text_format` will only be used if diarization is enabled.
+
     By default, this example will use our ENHANCED operating point, which is optimized for
     high accuracy. You can change this by setting the `operating_point` parameter to a different
     value.
@@ -79,17 +73,14 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
 
     stt = SpeechmaticsSTTService(
         api_key=os.getenv("SPEECHMATICS_API_KEY"),
-        params=SpeechmaticsSTTService.InputParams(
-            language=Language.EN,
-            enable_diarization=True,
-            end_of_utterance_silence_trigger=0.5,
-            speaker_active_format="<{speaker_id}>{text}</{speaker_id}>",
-        ),
+        language=Language.EN,
+        enable_speaker_diarization=True,
+        text_format="<{speaker_id}>{text}</{speaker_id}>",
     )
 
     tts = ElevenLabsTTSService(
-        api_key=os.getenv("ELEVENLABS_API_KEY"),
-        voice_id=os.getenv("ELEVENLABS_VOICE_ID"),
+        api_key=os.getenv("ELEVENLABS_API_KEY", ""),
+        voice_id=os.getenv("ELEVENLABS_VOICE_ID", ""),
         model="eleven_turbo_v2_5",
     )
 
@@ -113,8 +104,8 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         },
     ]
 
-    context = LLMContext(messages)
-    context_aggregator = LLMContextAggregatorPair(
+    context = OpenAILLMContext(messages)
+    context_aggregator = llm.create_context_aggregator(
         context,
         user_params=LLMUserAggregatorParams(aggregation_timeout=0.005),
     )
@@ -137,7 +128,6 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
             enable_metrics=True,
             enable_usage_metrics=True,
         ),
-        idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
     )
 
     @transport.event_handler("on_client_connected")
@@ -145,25 +135,19 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         logger.info(f"Client connected")
         # Kick off the conversation.
         messages.append({"role": "system", "content": "Say a short hello to the user."})
-        await task.queue_frames([LLMRunFrame()])
+        await task.queue_frames([context_aggregator.user().get_context_frame()])
 
     @transport.event_handler("on_client_disconnected")
     async def on_client_disconnected(transport, client):
         logger.info(f"Client disconnected")
         await task.cancel()
 
-    runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
+    runner = PipelineRunner(handle_sigint=handle_sigint)
 
     await runner.run(task)
 
 
-async def bot(runner_args: RunnerArguments):
-    """Main bot entry point compatible with Pipecat Cloud."""
-    transport = await create_transport(runner_args, transport_params)
-    await run_bot(transport, runner_args)
-
-
 if __name__ == "__main__":
-    from pipecat.runner.run import main
+    from pipecat.examples.run import main
 
-    main()
+    main(run_example, transport_params=transport_params)

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

@@ -4,30 +4,23 @@
 # SPDX-License-Identifier: BSD 2-Clause License
 #
 
-
+import argparse
 import os
 
 from dotenv import load_dotenv
 from loguru import logger
 
-from pipecat.audio.turn.smart_turn.base_smart_turn import SmartTurnParams
-from pipecat.audio.turn.smart_turn.local_smart_turn_v3 import LocalSmartTurnAnalyzerV3
 from pipecat.audio.vad.silero import SileroVADAnalyzer
-from pipecat.audio.vad.vad_analyzer import VADParams
-from pipecat.frames.frames import LLMRunFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.aggregators.llm_context import LLMContext
-from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
-from pipecat.runner.types import RunnerArguments
-from pipecat.runner.utils import create_transport
+from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
 from pipecat.services.cartesia.tts import CartesiaTTSService
 from pipecat.services.openai.llm import OpenAILLMService
 from pipecat.services.soniox.stt import SonioxSTTService
 from pipecat.transports.base_transport import BaseTransport, TransportParams
-from pipecat.transports.daily.transport import DailyParams
-from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
+from pipecat.transports.network.fastapi_websocket import FastAPIWebsocketParams
+from pipecat.transports.services.daily import DailyParams
 
 load_dotenv(override=True)
 
@@ -35,25 +28,22 @@ transport_params = {
     "daily": lambda: DailyParams(
         audio_in_enabled=True,
         audio_out_enabled=True,
-        vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
-        turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
+        vad_analyzer=SileroVADAnalyzer(),
     ),
     "twilio": lambda: FastAPIWebsocketParams(
         audio_in_enabled=True,
         audio_out_enabled=True,
-        vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
-        turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
+        vad_analyzer=SileroVADAnalyzer(),
     ),
     "webrtc": lambda: TransportParams(
         audio_in_enabled=True,
         audio_out_enabled=True,
-        vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
-        turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
+        vad_analyzer=SileroVADAnalyzer(),
     ),
 }
 
 
-async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
+async def run_example(transport: BaseTransport, _: argparse.Namespace, handle_sigint: bool):
     logger.info(f"Starting bot")
 
     stt = SonioxSTTService(
@@ -74,8 +64,8 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         },
     ]
 
-    context = LLMContext(messages)
-    context_aggregator = LLMContextAggregatorPair(context)
+    context = OpenAILLMContext(messages)
+    context_aggregator = llm.create_context_aggregator(context)
 
     pipeline = Pipeline(
         [
@@ -94,7 +84,6 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
             enable_metrics=True,
             enable_usage_metrics=True,
         ),
-        idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
     )
 
     @transport.event_handler("on_client_connected")
@@ -102,25 +91,19 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         logger.info(f"Client connected")
         # Kick off the conversation.
         messages.append({"role": "system", "content": "Please introduce yourself to the user."})
-        await task.queue_frames([LLMRunFrame()])
+        await task.queue_frames([context_aggregator.user().get_context_frame()])
 
     @transport.event_handler("on_client_disconnected")
     async def on_client_disconnected(transport, client):
         logger.info(f"Client disconnected")
         await task.cancel()
 
-    runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
+    runner = PipelineRunner(handle_sigint=handle_sigint)
 
     await runner.run(task)
 
 
-async def bot(runner_args: RunnerArguments):
-    """Main bot entry point compatible with Pipecat Cloud."""
-    transport = await create_transport(runner_args, transport_params)
-    await run_bot(transport, runner_args)
-
-
 if __name__ == "__main__":
-    from pipecat.runner.run import main
+    from pipecat.examples.run import main
 
-    main()
+    main(run_example, transport_params=transport_params)

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

@@ -4,31 +4,24 @@
 # SPDX-License-Identifier: BSD 2-Clause License
 #
 
-
+import argparse
 import os
 
 import aiohttp
 from dotenv import load_dotenv
 from loguru import logger
 
-from pipecat.audio.turn.smart_turn.base_smart_turn import SmartTurnParams
-from pipecat.audio.turn.smart_turn.local_smart_turn_v3 import LocalSmartTurnAnalyzerV3
 from pipecat.audio.vad.silero import SileroVADAnalyzer
-from pipecat.audio.vad.vad_analyzer import VADParams
-from pipecat.frames.frames import LLMRunFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.aggregators.llm_context import LLMContext
-from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
-from pipecat.runner.types import RunnerArguments
-from pipecat.runner.utils import create_transport
+from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
 from pipecat.services.deepgram.stt import DeepgramSTTService
 from pipecat.services.inworld.tts import InworldTTSService
 from pipecat.services.openai.llm import OpenAILLMService
 from pipecat.transports.base_transport import BaseTransport, TransportParams
-from pipecat.transports.daily.transport import DailyParams
-from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
+from pipecat.transports.network.fastapi_websocket import FastAPIWebsocketParams
+from pipecat.transports.services.daily import DailyParams
 
 load_dotenv(override=True)
 
@@ -39,25 +32,22 @@ transport_params = {
     "daily": lambda: DailyParams(
         audio_in_enabled=True,
         audio_out_enabled=True,
-        vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
-        turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
+        vad_analyzer=SileroVADAnalyzer(),
     ),
     "twilio": lambda: FastAPIWebsocketParams(
         audio_in_enabled=True,
         audio_out_enabled=True,
-        vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
-        turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
+        vad_analyzer=SileroVADAnalyzer(),
     ),
     "webrtc": lambda: TransportParams(
         audio_in_enabled=True,
         audio_out_enabled=True,
-        vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
-        turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
+        vad_analyzer=SileroVADAnalyzer(),
     ),
 }
 
 
-async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
+async def run_example(transport: BaseTransport, _: argparse.Namespace, handle_sigint: bool):
     logger.info(f"Starting bot")
 
     # Create an HTTP session
@@ -74,6 +64,9 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
             voice_id="Ashley",
             model="inworld-tts-1",
             streaming=streaming,  # True: real-time chunks, False: complete audio then playback
+            params=InworldTTSService.InputParams(
+                temperature=0.8,
+            ),
         )
 
         llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
@@ -85,8 +78,8 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
             },
         ]
 
-        context = LLMContext(messages)
-        context_aggregator = LLMContextAggregatorPair(context)
+        context = OpenAILLMContext(messages)
+        context_aggregator = llm.create_context_aggregator(context)
 
         pipeline = Pipeline(
             [
@@ -106,7 +99,6 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
                 enable_metrics=True,
                 enable_usage_metrics=True,
             ),
-            idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
         )
 
         @transport.event_handler("on_client_connected")
@@ -114,25 +106,19 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
             logger.info(f"Client connected")
             # Kick off the conversation.
             messages.append({"role": "system", "content": "Please introduce yourself to the user."})
-            await task.queue_frames([LLMRunFrame()])
+            await task.queue_frames([context_aggregator.user().get_context_frame()])
 
         @transport.event_handler("on_client_disconnected")
         async def on_client_disconnected(transport, client):
             logger.info(f"Client disconnected")
             await task.cancel()
 
-        runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
+        runner = PipelineRunner(handle_sigint=handle_sigint)
 
         await runner.run(task)
 
 
-async def bot(runner_args: RunnerArguments):
-    """Main bot entry point compatible with Pipecat Cloud."""
-    transport = await create_transport(runner_args, transport_params)
-    await run_bot(transport, runner_args)
-
-
 if __name__ == "__main__":
-    from pipecat.runner.run import main
+    from pipecat.examples.run import main
 
-    main()
+    main(run_example, transport_params=transport_params)

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

@@ -4,31 +4,24 @@
 # SPDX-License-Identifier: BSD 2-Clause License
 #
 
-
+import argparse
 import os
 
 import aiohttp
 from dotenv import load_dotenv
 from loguru import logger
 
-from pipecat.audio.turn.smart_turn.base_smart_turn import SmartTurnParams
-from pipecat.audio.turn.smart_turn.local_smart_turn_v3 import LocalSmartTurnAnalyzerV3
 from pipecat.audio.vad.silero import SileroVADAnalyzer
-from pipecat.audio.vad.vad_analyzer import VADParams
-from pipecat.frames.frames import LLMRunFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.aggregators.llm_context import LLMContext
-from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
-from pipecat.runner.types import RunnerArguments
-from pipecat.runner.utils import create_transport
+from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
 from pipecat.services.asyncai.tts import AsyncAIHttpTTSService
 from pipecat.services.deepgram.stt import DeepgramSTTService
 from pipecat.services.openai.llm import OpenAILLMService
 from pipecat.transports.base_transport import BaseTransport, TransportParams
-from pipecat.transports.daily.transport import DailyParams
-from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
+from pipecat.transports.network.fastapi_websocket import FastAPIWebsocketParams
+from pipecat.transports.services.daily import DailyParams
 
 load_dotenv(override=True)
 
@@ -40,25 +33,22 @@ transport_params = {
     "daily": lambda: DailyParams(
         audio_in_enabled=True,
         audio_out_enabled=True,
-        vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
-        turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
+        vad_analyzer=SileroVADAnalyzer(),
     ),
     "twilio": lambda: FastAPIWebsocketParams(
         audio_in_enabled=True,
         audio_out_enabled=True,
-        vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
-        turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
+        vad_analyzer=SileroVADAnalyzer(),
     ),
     "webrtc": lambda: TransportParams(
         audio_in_enabled=True,
         audio_out_enabled=True,
-        vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
-        turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
+        vad_analyzer=SileroVADAnalyzer(),
     ),
 }
 
 
-async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
+async def run_example(transport: BaseTransport, _: argparse.Namespace, handle_sigint: bool):
     logger.info(f"Starting bot")
 
     # Create an HTTP session
@@ -80,8 +70,8 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
             },
         ]
 
-        context = LLMContext(messages)
-        context_aggregator = LLMContextAggregatorPair(context)
+        context = OpenAILLMContext(messages)
+        context_aggregator = llm.create_context_aggregator(context)
 
         pipeline = Pipeline(
             [
@@ -101,7 +91,6 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
                 enable_metrics=True,
                 enable_usage_metrics=True,
             ),
-            idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
         )
 
         @transport.event_handler("on_client_connected")
@@ -109,25 +98,19 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
             logger.info(f"Client connected")
             # Kick off the conversation.
             messages.append({"role": "system", "content": "Please introduce yourself to the user."})
-            await task.queue_frames([LLMRunFrame()])
+            await task.queue_frames([context_aggregator.user().get_context_frame()])
 
         @transport.event_handler("on_client_disconnected")
         async def on_client_disconnected(transport, client):
             logger.info(f"Client disconnected")
             await task.cancel()
 
-        runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
+        runner = PipelineRunner(handle_sigint=handle_sigint)
 
         await runner.run(task)
 
 
-async def bot(runner_args: RunnerArguments):
-    """Main bot entry point compatible with Pipecat Cloud."""
-    transport = await create_transport(runner_args, transport_params)
-    await run_bot(transport, runner_args)
-
-
 if __name__ == "__main__":
-    from pipecat.runner.run import main
+    from pipecat.examples.run import main
 
-    main()
+    main(run_example, transport_params=transport_params)

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

@@ -4,30 +4,23 @@
 # SPDX-License-Identifier: BSD 2-Clause License
 #
 
-
+import argparse
 import os
 
 from dotenv import load_dotenv
 from loguru import logger
 
-from pipecat.audio.turn.smart_turn.base_smart_turn import SmartTurnParams
-from pipecat.audio.turn.smart_turn.local_smart_turn_v3 import LocalSmartTurnAnalyzerV3
 from pipecat.audio.vad.silero import SileroVADAnalyzer
-from pipecat.audio.vad.vad_analyzer import VADParams
-from pipecat.frames.frames import LLMRunFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.aggregators.llm_context import LLMContext
-from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
-from pipecat.runner.types import RunnerArguments
-from pipecat.runner.utils import create_transport
+from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
 from pipecat.services.asyncai.tts import AsyncAITTSService
 from pipecat.services.deepgram.stt import DeepgramSTTService
 from pipecat.services.openai.llm import OpenAILLMService
 from pipecat.transports.base_transport import BaseTransport, TransportParams
-from pipecat.transports.daily.transport import DailyParams
-from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
+from pipecat.transports.network.fastapi_websocket import FastAPIWebsocketParams
+from pipecat.transports.services.daily import DailyParams
 
 load_dotenv(override=True)
 
@@ -39,25 +32,22 @@ transport_params = {
     "daily": lambda: DailyParams(
         audio_in_enabled=True,
         audio_out_enabled=True,
-        vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
-        turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
+        vad_analyzer=SileroVADAnalyzer(),
     ),
     "twilio": lambda: FastAPIWebsocketParams(
         audio_in_enabled=True,
         audio_out_enabled=True,
-        vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
-        turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
+        vad_analyzer=SileroVADAnalyzer(),
     ),
     "webrtc": lambda: TransportParams(
         audio_in_enabled=True,
         audio_out_enabled=True,
-        vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
-        turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
+        vad_analyzer=SileroVADAnalyzer(),
     ),
 }
 
 
-async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
+async def run_example(transport: BaseTransport, _: argparse.Namespace, handle_sigint: bool):
     logger.info(f"Starting bot")
 
     stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
@@ -76,8 +66,8 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         },
     ]
 
-    context = LLMContext(messages)
-    context_aggregator = LLMContextAggregatorPair(context)
+    context = OpenAILLMContext(messages)
+    context_aggregator = llm.create_context_aggregator(context)
 
     pipeline = Pipeline(
         [
@@ -97,7 +87,6 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
             enable_metrics=True,
             enable_usage_metrics=True,
         ),
-        idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
     )
 
     @transport.event_handler("on_client_connected")
@@ -105,25 +94,19 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         logger.info(f"Client connected")
         # Kick off the conversation.
         messages.append({"role": "system", "content": "Please introduce yourself to the user."})
-        await task.queue_frames([LLMRunFrame()])
+        await task.queue_frames([context_aggregator.user().get_context_frame()])
 
     @transport.event_handler("on_client_disconnected")
     async def on_client_disconnected(transport, client):
         logger.info(f"Client disconnected")
         await task.cancel()
 
-    runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
+    runner = PipelineRunner(handle_sigint=handle_sigint)
 
     await runner.run(task)
 
 
-async def bot(runner_args: RunnerArguments):
-    """Main bot entry point compatible with Pipecat Cloud."""
-    transport = await create_transport(runner_args, transport_params)
-    await run_bot(transport, runner_args)
-
-
 if __name__ == "__main__":
-    from pipecat.runner.run import main
+    from pipecat.examples.run import main
 
-    main()
+    main(run_example, transport_params=transport_params)

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

@@ -1,169 +0,0 @@
-#
-# Copyright (c) 2024–2025, Daily
-#
-# SPDX-License-Identifier: BSD 2-Clause License
-#
-
-
-import datetime
-import os
-import wave
-
-from dotenv import load_dotenv
-from loguru import logger
-
-from pipecat.audio.filters.aic_filter import AICFilter
-from pipecat.audio.turn.smart_turn.base_smart_turn import SmartTurnParams
-from pipecat.audio.turn.smart_turn.local_smart_turn_v3 import LocalSmartTurnAnalyzerV3
-from pipecat.audio.vad.silero import SileroVADAnalyzer
-from pipecat.audio.vad.vad_analyzer import VADParams
-from pipecat.frames.frames import LLMRunFrame
-from pipecat.pipeline.pipeline import Pipeline
-from pipecat.pipeline.runner import PipelineRunner
-from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.aggregators.llm_context import LLMContext
-from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
-from pipecat.processors.audio.audio_buffer_processor import AudioBufferProcessor
-from pipecat.runner.types import RunnerArguments
-from pipecat.runner.utils import create_transport
-from pipecat.services.cartesia.tts import CartesiaTTSService
-from pipecat.services.deepgram.stt import DeepgramSTTService
-from pipecat.services.openai.llm import OpenAILLMService
-from pipecat.transports.base_transport import BaseTransport, TransportParams
-from pipecat.transports.daily.transport import DailyParams
-from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
-
-load_dotenv(override=True)
-
-
-# Create audio buffer processor so we can hear the audio fitler results.
-audiobuffer = AudioBufferProcessor(
-    num_channels=2,  # 1 for mono, 2 for stereo (user left, bot right)
-    enable_turn_audio=False,  # Enable per-turn audio recording
-)
-
-
-def _create_aic_filter() -> AICFilter:
-    license_key = os.getenv("AICOUSTICS_LICENSE_KEY", "")
-
-    return AICFilter(
-        license_key=license_key,
-        enhancement_level=1.0,
-    )
-
-
-# We store functions so objects (e.g. SileroVADAnalyzer) don't get
-# instantiated. The function will be called when the desired transport gets
-# selected.
-transport_params = {
-    "daily": lambda: DailyParams(
-        audio_in_enabled=True,
-        audio_out_enabled=True,
-        vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
-        turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
-        audio_in_filter=_create_aic_filter(),
-    ),
-    "twilio": lambda: FastAPIWebsocketParams(
-        audio_in_enabled=True,
-        audio_out_enabled=True,
-        vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
-        turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
-        audio_in_filter=_create_aic_filter(),
-    ),
-    "webrtc": lambda: TransportParams(
-        audio_in_enabled=True,
-        audio_out_enabled=True,
-        vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
-        turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
-        audio_in_filter=_create_aic_filter(),
-    ),
-}
-
-
-async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
-    logger.info(f"Starting bot")
-
-    stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
-
-    tts = CartesiaTTSService(
-        api_key=os.getenv("CARTESIA_API_KEY"),
-        voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
-    )
-
-    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
-
-    messages = [
-        {
-            "role": "system",
-            "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be 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 = LLMContext(messages)
-    context_aggregator = LLMContextAggregatorPair(context)
-
-    pipeline = Pipeline(
-        [
-            transport.input(),  # Transport user input
-            stt,  # STT
-            context_aggregator.user(),  # User responses
-            llm,  # LLM
-            tts,  # TTS
-            transport.output(),  # Transport bot output
-            audiobuffer,  # write audio data to a file
-            context_aggregator.assistant(),  # Assistant spoken responses
-        ]
-    )
-
-    task = PipelineTask(
-        pipeline,
-        params=PipelineParams(
-            enable_metrics=True,
-            enable_usage_metrics=True,
-        ),
-        idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
-    )
-
-    @transport.event_handler("on_client_connected")
-    async def on_client_connected(transport, client):
-        logger.info(f"Client connected")
-        await audiobuffer.start_recording()
-        # Kick off the conversation.
-        messages.append({"role": "system", "content": "Please introduce yourself to the user."})
-        await task.queue_frames([LLMRunFrame()])
-
-    @audiobuffer.event_handler("on_audio_data")
-    async def on_audio_data(buffer, audio, sample_rate, num_channels):
-        # Save or process the composite audio
-        timestamp = datetime.datetime.now().strftime("%Y%m%d_%H%M%S")
-        filename = f"./conversation_{timestamp}.wav"
-
-        # Create the WAV file
-        with wave.open(filename, "wb") as wf:
-            wf.setnchannels(num_channels)
-            wf.setsampwidth(2)  # 16-bit audio
-            wf.setframerate(sample_rate)
-            wf.writeframes(audio)
-
-        logger.info(f"Saved recording to {filename}")
-
-    @transport.event_handler("on_client_disconnected")
-    async def on_client_disconnected(transport, client):
-        logger.info(f"Client disconnected")
-        await task.cancel()
-
-    runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
-
-    await runner.run(task)
-
-
-async def bot(runner_args: RunnerArguments):
-    """Main bot entry point compatible with Pipecat Cloud."""
-    transport = await create_transport(runner_args, transport_params)
-    await run_bot(transport, runner_args)
-
-
-if __name__ == "__main__":
-    from pipecat.runner.run import main
-
-    main()

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

@@ -4,7 +4,7 @@
 # SPDX-License-Identifier: BSD 2-Clause License
 #
 
-
+import argparse
 import os
 
 from dotenv import load_dotenv
@@ -15,24 +15,21 @@ from langchain_core.runnables.history import RunnableWithMessageHistory
 from langchain_openai import ChatOpenAI
 from loguru import logger
 
-from pipecat.audio.turn.smart_turn.base_smart_turn import SmartTurnParams
-from pipecat.audio.turn.smart_turn.local_smart_turn_v3 import LocalSmartTurnAnalyzerV3
 from pipecat.audio.vad.silero import SileroVADAnalyzer
-from pipecat.audio.vad.vad_analyzer import VADParams
-from pipecat.frames.frames import LLMMessagesUpdateFrame
+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_context import LLMContext
-from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
+from pipecat.processors.aggregators.llm_response import (
+    LLMAssistantResponseAggregator,
+    LLMUserResponseAggregator,
+)
 from pipecat.processors.frameworks.langchain import LangchainProcessor
-from pipecat.runner.types import RunnerArguments
-from pipecat.runner.utils import create_transport
 from pipecat.services.cartesia.tts import CartesiaTTSService
 from pipecat.services.deepgram.stt import DeepgramSTTService
 from pipecat.transports.base_transport import BaseTransport, TransportParams
-from pipecat.transports.daily.transport import DailyParams
-from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
+from pipecat.transports.network.fastapi_websocket import FastAPIWebsocketParams
+from pipecat.transports.services.daily import DailyParams
 
 load_dotenv(override=True)
 
@@ -53,25 +50,22 @@ transport_params = {
     "daily": lambda: DailyParams(
         audio_in_enabled=True,
         audio_out_enabled=True,
-        vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
-        turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
+        vad_analyzer=SileroVADAnalyzer(),
     ),
     "twilio": lambda: FastAPIWebsocketParams(
         audio_in_enabled=True,
         audio_out_enabled=True,
-        vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
-        turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
+        vad_analyzer=SileroVADAnalyzer(),
     ),
     "webrtc": lambda: TransportParams(
         audio_in_enabled=True,
         audio_out_enabled=True,
-        vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
-        turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
+        vad_analyzer=SileroVADAnalyzer(),
     ),
 }
 
 
-async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
+async def run_example(transport: BaseTransport, _: argparse.Namespace, handle_sigint: bool):
     logger.info(f"Starting bot")
 
     stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
@@ -101,18 +95,18 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
     )
     lc = LangchainProcessor(history_chain)
 
-    context = LLMContext()
-    context_aggregator = LLMContextAggregatorPair(context)
+    tma_in = LLMUserResponseAggregator()
+    tma_out = LLMAssistantResponseAggregator()
 
     pipeline = Pipeline(
         [
             transport.input(),  # Transport user input
             stt,
-            context_aggregator.user(),  # User responses
+            tma_in,  # User responses
             lc,  # Langchain
             tts,  # TTS
             transport.output(),  # Transport bot output
-            context_aggregator.assistant(),  # Assistant spoken responses
+            tma_out,  # Assistant spoken responses
         ]
     )
 
@@ -122,36 +116,29 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
             enable_metrics=True,
             enable_usage_metrics=True,
         ),
-        idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
     )
 
     @transport.event_handler("on_client_connected")
     async def on_client_connected(transport, client):
         logger.info(f"Client connected")
         # Kick off the conversation.
-        # An `LLMContextFrame` will be picked up by the LangchainProcessor using
+        # 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."})]
-        await task.queue_frames([LLMMessagesUpdateFrame(messages, run_llm=True)])
+        await task.queue_frames([LLMMessagesFrame(messages)])
 
     @transport.event_handler("on_client_disconnected")
     async def on_client_disconnected(transport, client):
         logger.info(f"Client disconnected")
         await task.cancel()
 
-    runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
+    runner = PipelineRunner(handle_sigint=handle_sigint)
 
     await runner.run(task)
 
 
-async def bot(runner_args: RunnerArguments):
-    """Main bot entry point compatible with Pipecat Cloud."""
-    transport = await create_transport(runner_args, transport_params)
-    await run_bot(transport, runner_args)
-
-
 if __name__ == "__main__":
-    from pipecat.runner.run import main
+    from pipecat.examples.run import main
 
-    main()
+    main(run_example, transport_params=transport_params)

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

@@ -4,7 +4,7 @@
 # SPDX-License-Identifier: BSD 2-Clause License
 #
 
-
+import argparse
 import os
 
 from deepgram import LiveOptions
@@ -12,24 +12,21 @@ from dotenv import load_dotenv
 from loguru import logger
 
 from pipecat.frames.frames import (
-    InterruptionFrame,
-    LLMRunFrame,
+    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.llm_context import LLMContext
-from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
-from pipecat.runner.types import RunnerArguments
-from pipecat.runner.utils import create_transport
+from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
 from pipecat.services.deepgram.stt import DeepgramSTTService
 from pipecat.services.deepgram.tts import DeepgramTTSService
 from pipecat.services.openai.llm import OpenAILLMService
 from pipecat.transports.base_transport import BaseTransport, TransportParams
-from pipecat.transports.daily.transport import DailyParams
-from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
+from pipecat.transports.network.fastapi_websocket import FastAPIWebsocketParams
+from pipecat.transports.services.daily import DailyParams
 
 load_dotenv(override=True)
 
@@ -53,7 +50,7 @@ transport_params = {
 }
 
 
-async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
+async def run_example(transport: BaseTransport, _: argparse.Namespace, handle_sigint: bool):
     logger.info(f"Starting bot")
 
     stt = DeepgramSTTService(
@@ -72,8 +69,8 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         },
     ]
 
-    context = LLMContext(messages)
-    context_aggregator = LLMContextAggregatorPair(context)
+    context = OpenAILLMContext(messages)
+    context_aggregator = llm.create_context_aggregator(context)
 
     pipeline = Pipeline(
         [
@@ -93,41 +90,34 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
             enable_metrics=True,
             enable_usage_metrics=True,
         ),
-        idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
     )
 
     @stt.event_handler("on_speech_started")
     async def on_speech_started(stt, *args, **kwargs):
-        await task.queue_frames([InterruptionFrame(), UserStartedSpeakingFrame()])
+        await task.queue_frames([BotInterruptionFrame(), UserStartedSpeakingFrame()])
 
     @stt.event_handler("on_utterance_end")
     async def on_utterance_end(stt, *args, **kwargs):
-        await task.queue_frames([UserStoppedSpeakingFrame()])
+        await task.queue_frames([StopInterruptionFrame(), UserStoppedSpeakingFrame()])
 
     @transport.event_handler("on_client_connected")
     async def on_client_connected(transport, client):
         logger.info(f"Client connected")
         # Kick off the conversation.
         messages.append({"role": "system", "content": "Please introduce yourself to the user."})
-        await task.queue_frames([LLMRunFrame()])
+        await task.queue_frames([context_aggregator.user().get_context_frame()])
 
     @transport.event_handler("on_client_disconnected")
     async def on_client_disconnected(transport, client):
         logger.info(f"Client disconnected")
         await task.cancel()
 
-    runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
+    runner = PipelineRunner(handle_sigint=handle_sigint)
 
     await runner.run(task)
 
 
-async def bot(runner_args: RunnerArguments):
-    """Main bot entry point compatible with Pipecat Cloud."""
-    transport = await create_transport(runner_args, transport_params)
-    await run_bot(transport, runner_args)
-
-
 if __name__ == "__main__":
-    from pipecat.runner.run import main
+    from pipecat.examples.run import main
 
-    main()
+    main(run_example, transport_params=transport_params)

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

@@ -4,30 +4,23 @@
 # SPDX-License-Identifier: BSD 2-Clause License
 #
 
-
+import argparse
 import os
 
 from dotenv import load_dotenv
 from loguru import logger
 
-from pipecat.audio.turn.smart_turn.base_smart_turn import SmartTurnParams
-from pipecat.audio.turn.smart_turn.local_smart_turn_v3 import LocalSmartTurnAnalyzerV3
 from pipecat.audio.vad.silero import SileroVADAnalyzer
-from pipecat.audio.vad.vad_analyzer import VADParams
-from pipecat.frames.frames import LLMRunFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.aggregators.llm_context import LLMContext
-from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
-from pipecat.runner.types import RunnerArguments
-from pipecat.runner.utils import create_transport
+from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
 from pipecat.services.deepgram.stt import DeepgramSTTService
 from pipecat.services.deepgram.tts import DeepgramTTSService
 from pipecat.services.openai.llm import OpenAILLMService
 from pipecat.transports.base_transport import BaseTransport, TransportParams
-from pipecat.transports.daily.transport import DailyParams
-from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
+from pipecat.transports.network.fastapi_websocket import FastAPIWebsocketParams
+from pipecat.transports.services.daily import DailyParams
 
 load_dotenv(override=True)
 
@@ -39,25 +32,22 @@ transport_params = {
     "daily": lambda: DailyParams(
         audio_in_enabled=True,
         audio_out_enabled=True,
-        vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
-        turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
+        vad_analyzer=SileroVADAnalyzer(),
     ),
     "twilio": lambda: FastAPIWebsocketParams(
         audio_in_enabled=True,
         audio_out_enabled=True,
-        vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
-        turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
+        vad_analyzer=SileroVADAnalyzer(),
     ),
     "webrtc": lambda: TransportParams(
         audio_in_enabled=True,
         audio_out_enabled=True,
-        vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
-        turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
+        vad_analyzer=SileroVADAnalyzer(),
     ),
 }
 
 
-async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
+async def run_example(transport: BaseTransport, _: argparse.Namespace, handle_sigint: bool):
     logger.info(f"Starting bot")
 
     stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
@@ -73,8 +63,8 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         },
     ]
 
-    context = LLMContext(messages)
-    context_aggregator = LLMContextAggregatorPair(context)
+    context = OpenAILLMContext(messages)
+    context_aggregator = llm.create_context_aggregator(context)
 
     pipeline = Pipeline(
         [
@@ -94,7 +84,6 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
             enable_metrics=True,
             enable_usage_metrics=True,
         ),
-        idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
     )
 
     @transport.event_handler("on_client_connected")
@@ -102,25 +91,19 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         logger.info(f"Client connected")
         # Kick off the conversation.
         messages.append({"role": "system", "content": "Please introduce yourself to the user."})
-        await task.queue_frames([LLMRunFrame()])
+        await task.queue_frames([context_aggregator.user().get_context_frame()])
 
     @transport.event_handler("on_client_disconnected")
     async def on_client_disconnected(transport, client):
         logger.info(f"Client disconnected")
         await task.cancel()
 
-    runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
+    runner = PipelineRunner(handle_sigint=handle_sigint)
 
     await runner.run(task)
 
 
-async def bot(runner_args: RunnerArguments):
-    """Main bot entry point compatible with Pipecat Cloud."""
-    transport = await create_transport(runner_args, transport_params)
-    await run_bot(transport, runner_args)
-
-
 if __name__ == "__main__":
-    from pipecat.runner.run import main
+    from pipecat.examples.run import main
 
-    main()
+    main(run_example, transport_params=transport_params)

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

@@ -4,32 +4,24 @@
 # SPDX-License-Identifier: BSD 2-Clause License
 #
 
-
+import argparse
 import os
 
 import aiohttp
 from dotenv import load_dotenv
 from loguru import logger
 
-from pipecat.audio.turn.smart_turn.base_smart_turn import SmartTurnParams
-from pipecat.audio.turn.smart_turn.local_smart_turn_v3 import LocalSmartTurnAnalyzerV3
 from pipecat.audio.vad.silero import SileroVADAnalyzer
-from pipecat.audio.vad.vad_analyzer import VADParams
-from pipecat.frames.frames import LLMRunFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.aggregators.llm_context import LLMContext
-from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
-from pipecat.runner.types import RunnerArguments
-from pipecat.runner.utils import create_transport
+from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
 from pipecat.services.deepgram.stt import DeepgramSTTService
-from pipecat.services.elevenlabs.stt import ElevenLabsSTTService
 from pipecat.services.elevenlabs.tts import ElevenLabsHttpTTSService
 from pipecat.services.openai.llm import OpenAILLMService
 from pipecat.transports.base_transport import BaseTransport, TransportParams
-from pipecat.transports.daily.transport import DailyParams
-from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
+from pipecat.transports.network.fastapi_websocket import FastAPIWebsocketParams
+from pipecat.transports.services.daily import DailyParams
 
 load_dotenv(override=True)
 
@@ -41,33 +33,27 @@ transport_params = {
     "daily": lambda: DailyParams(
         audio_in_enabled=True,
         audio_out_enabled=True,
-        vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
-        turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
+        vad_analyzer=SileroVADAnalyzer(),
     ),
     "twilio": lambda: FastAPIWebsocketParams(
         audio_in_enabled=True,
         audio_out_enabled=True,
-        vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
-        turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
+        vad_analyzer=SileroVADAnalyzer(),
     ),
     "webrtc": lambda: TransportParams(
         audio_in_enabled=True,
         audio_out_enabled=True,
-        vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
-        turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
+        vad_analyzer=SileroVADAnalyzer(),
     ),
 }
 
 
-async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
+async def run_example(transport: BaseTransport, _: argparse.Namespace, handle_sigint: bool):
     logger.info(f"Starting bot")
 
     # Create an HTTP session
     async with aiohttp.ClientSession() as session:
-        stt = ElevenLabsSTTService(
-            api_key=os.getenv("ELEVENLABS_API_KEY"),
-            aiohttp_session=session,
-        )
+        stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
 
         tts = ElevenLabsHttpTTSService(
             api_key=os.getenv("ELEVENLABS_API_KEY", ""),
@@ -84,8 +70,8 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
             },
         ]
 
-        context = LLMContext(messages)
-        context_aggregator = LLMContextAggregatorPair(context)
+        context = OpenAILLMContext(messages)
+        context_aggregator = llm.create_context_aggregator(context)
 
         pipeline = Pipeline(
             [
@@ -105,7 +91,6 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
                 enable_metrics=True,
                 enable_usage_metrics=True,
             ),
-            idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
         )
 
         @transport.event_handler("on_client_connected")
@@ -113,25 +98,19 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
             logger.info(f"Client connected")
             # Kick off the conversation.
             messages.append({"role": "system", "content": "Please introduce yourself to the user."})
-            await task.queue_frames([LLMRunFrame()])
+            await task.queue_frames([context_aggregator.user().get_context_frame()])
 
         @transport.event_handler("on_client_disconnected")
         async def on_client_disconnected(transport, client):
             logger.info(f"Client disconnected")
             await task.cancel()
 
-        runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
+        runner = PipelineRunner(handle_sigint=handle_sigint)
 
         await runner.run(task)
 
 
-async def bot(runner_args: RunnerArguments):
-    """Main bot entry point compatible with Pipecat Cloud."""
-    transport = await create_transport(runner_args, transport_params)
-    await run_bot(transport, runner_args)
-
-
 if __name__ == "__main__":
-    from pipecat.runner.run import main
+    from pipecat.examples.run import main
 
-    main()
+    main(run_example, transport_params=transport_params)

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

@@ -4,30 +4,23 @@
 # SPDX-License-Identifier: BSD 2-Clause License
 #
 
-
+import argparse
 import os
 
 from dotenv import load_dotenv
 from loguru import logger
 
-from pipecat.audio.turn.smart_turn.base_smart_turn import SmartTurnParams
-from pipecat.audio.turn.smart_turn.local_smart_turn_v3 import LocalSmartTurnAnalyzerV3
 from pipecat.audio.vad.silero import SileroVADAnalyzer
-from pipecat.audio.vad.vad_analyzer import VADParams
-from pipecat.frames.frames import LLMRunFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.aggregators.llm_context import LLMContext
-from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
-from pipecat.runner.types import RunnerArguments
-from pipecat.runner.utils import create_transport
+from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
 from pipecat.services.deepgram.stt import DeepgramSTTService
 from pipecat.services.elevenlabs.tts import ElevenLabsTTSService
 from pipecat.services.openai.llm import OpenAILLMService
 from pipecat.transports.base_transport import BaseTransport, TransportParams
-from pipecat.transports.daily.transport import DailyParams
-from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
+from pipecat.transports.network.fastapi_websocket import FastAPIWebsocketParams
+from pipecat.transports.services.daily import DailyParams
 
 load_dotenv(override=True)
 
@@ -39,25 +32,22 @@ transport_params = {
     "daily": lambda: DailyParams(
         audio_in_enabled=True,
         audio_out_enabled=True,
-        vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
-        turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
+        vad_analyzer=SileroVADAnalyzer(),
     ),
     "twilio": lambda: FastAPIWebsocketParams(
         audio_in_enabled=True,
         audio_out_enabled=True,
-        vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
-        turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
+        vad_analyzer=SileroVADAnalyzer(),
     ),
     "webrtc": lambda: TransportParams(
         audio_in_enabled=True,
         audio_out_enabled=True,
-        vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
-        turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
+        vad_analyzer=SileroVADAnalyzer(),
     ),
 }
 
 
-async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
+async def run_example(transport: BaseTransport, _: argparse.Namespace, handle_sigint: bool):
     logger.info(f"Starting bot")
 
     stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
@@ -76,8 +66,8 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         },
     ]
 
-    context = LLMContext(messages)
-    context_aggregator = LLMContextAggregatorPair(context)
+    context = OpenAILLMContext(messages)
+    context_aggregator = llm.create_context_aggregator(context)
 
     pipeline = Pipeline(
         [
@@ -97,7 +87,6 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
             enable_metrics=True,
             enable_usage_metrics=True,
         ),
-        idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
     )
 
     @transport.event_handler("on_client_connected")
@@ -105,25 +94,19 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         logger.info(f"Client connected")
         # Kick off the conversation.
         messages.append({"role": "system", "content": "Please introduce yourself to the user."})
-        await task.queue_frames([LLMRunFrame()])
+        await task.queue_frames([context_aggregator.user().get_context_frame()])
 
     @transport.event_handler("on_client_disconnected")
     async def on_client_disconnected(transport, client):
         logger.info(f"Client disconnected")
         await task.cancel()
 
-    runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
+    runner = PipelineRunner(handle_sigint=handle_sigint)
 
     await runner.run(task)
 
 
-async def bot(runner_args: RunnerArguments):
-    """Main bot entry point compatible with Pipecat Cloud."""
-    transport = await create_transport(runner_args, transport_params)
-    await run_bot(transport, runner_args)
-
-
 if __name__ == "__main__":
-    from pipecat.runner.run import main
+    from pipecat.examples.run import main
 
-    main()
+    main(run_example, transport_params=transport_params)

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

@@ -4,30 +4,23 @@
 # SPDX-License-Identifier: BSD 2-Clause License
 #
 
-
+import argparse
 import os
 
 from dotenv import load_dotenv
 from loguru import logger
 
-from pipecat.audio.turn.smart_turn.base_smart_turn import SmartTurnParams
-from pipecat.audio.turn.smart_turn.local_smart_turn_v3 import LocalSmartTurnAnalyzerV3
 from pipecat.audio.vad.silero import SileroVADAnalyzer
-from pipecat.audio.vad.vad_analyzer import VADParams
-from pipecat.frames.frames import LLMRunFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.aggregators.llm_context import LLMContext
-from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
-from pipecat.runner.types import RunnerArguments
-from pipecat.runner.utils import create_transport
+from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
 from pipecat.services.deepgram.stt import DeepgramSTTService
 from pipecat.services.openai.llm import OpenAILLMService
 from pipecat.services.playht.tts import PlayHTHttpTTSService
 from pipecat.transports.base_transport import BaseTransport, TransportParams
-from pipecat.transports.daily.transport import DailyParams
-from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
+from pipecat.transports.network.fastapi_websocket import FastAPIWebsocketParams
+from pipecat.transports.services.daily import DailyParams
 
 load_dotenv(override=True)
 
@@ -38,25 +31,22 @@ transport_params = {
     "daily": lambda: DailyParams(
         audio_in_enabled=True,
         audio_out_enabled=True,
-        vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
-        turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
+        vad_analyzer=SileroVADAnalyzer(),
     ),
     "twilio": lambda: FastAPIWebsocketParams(
         audio_in_enabled=True,
         audio_out_enabled=True,
-        vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
-        turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
+        vad_analyzer=SileroVADAnalyzer(),
     ),
     "webrtc": lambda: TransportParams(
         audio_in_enabled=True,
         audio_out_enabled=True,
-        vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
-        turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
+        vad_analyzer=SileroVADAnalyzer(),
     ),
 }
 
 
-async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
+async def run_example(transport: BaseTransport, _: argparse.Namespace, handle_sigint: bool):
     logger.info(f"Starting bot")
 
     stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
@@ -76,8 +66,8 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         },
     ]
 
-    context = LLMContext(messages)
-    context_aggregator = LLMContextAggregatorPair(context)
+    context = OpenAILLMContext(messages)
+    context_aggregator = llm.create_context_aggregator(context)
 
     pipeline = Pipeline(
         [
@@ -97,7 +87,6 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
             enable_metrics=True,
             enable_usage_metrics=True,
         ),
-        idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
     )
 
     @transport.event_handler("on_client_connected")
@@ -105,25 +94,19 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         logger.info(f"Client connected")
         # Kick off the conversation.
         messages.append({"role": "system", "content": "Please introduce yourself to the user."})
-        await task.queue_frames([LLMRunFrame()])
+        await task.queue_frames([context_aggregator.user().get_context_frame()])
 
     @transport.event_handler("on_client_disconnected")
     async def on_client_disconnected(transport, client):
         logger.info(f"Client disconnected")
         await task.cancel()
 
-    runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
+    runner = PipelineRunner(handle_sigint=handle_sigint)
 
     await runner.run(task)
 
 
-async def bot(runner_args: RunnerArguments):
-    """Main bot entry point compatible with Pipecat Cloud."""
-    transport = await create_transport(runner_args, transport_params)
-    await run_bot(transport, runner_args)
-
-
 if __name__ == "__main__":
-    from pipecat.runner.run import main
+    from pipecat.examples.run import main
 
-    main()
+    main(run_example, transport_params=transport_params)

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

@@ -4,31 +4,24 @@
 # SPDX-License-Identifier: BSD 2-Clause License
 #
 
-
+import argparse
 import os
 
 from dotenv import load_dotenv
 from loguru import logger
 
-from pipecat.audio.turn.smart_turn.base_smart_turn import SmartTurnParams
-from pipecat.audio.turn.smart_turn.local_smart_turn_v3 import LocalSmartTurnAnalyzerV3
 from pipecat.audio.vad.silero import SileroVADAnalyzer
-from pipecat.audio.vad.vad_analyzer import VADParams
-from pipecat.frames.frames import LLMRunFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.aggregators.llm_context import LLMContext
-from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
-from pipecat.runner.types import RunnerArguments
-from pipecat.runner.utils import create_transport
+from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
 from pipecat.services.deepgram.stt import DeepgramSTTService
 from pipecat.services.openai.llm import OpenAILLMService
 from pipecat.services.playht.tts import PlayHTTTSService
 from pipecat.transcriptions.language import Language
 from pipecat.transports.base_transport import BaseTransport, TransportParams
-from pipecat.transports.daily.transport import DailyParams
-from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
+from pipecat.transports.network.fastapi_websocket import FastAPIWebsocketParams
+from pipecat.transports.services.daily import DailyParams
 
 load_dotenv(override=True)
 
@@ -39,25 +32,22 @@ transport_params = {
     "daily": lambda: DailyParams(
         audio_in_enabled=True,
         audio_out_enabled=True,
-        vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
-        turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
+        vad_analyzer=SileroVADAnalyzer(),
     ),
     "twilio": lambda: FastAPIWebsocketParams(
         audio_in_enabled=True,
         audio_out_enabled=True,
-        vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
-        turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
+        vad_analyzer=SileroVADAnalyzer(),
     ),
     "webrtc": lambda: TransportParams(
         audio_in_enabled=True,
         audio_out_enabled=True,
-        vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
-        turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
+        vad_analyzer=SileroVADAnalyzer(),
     ),
 }
 
 
-async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
+async def run_example(transport: BaseTransport, _: argparse.Namespace, handle_sigint: bool):
     logger.info(f"Starting bot")
 
     stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
@@ -78,8 +68,8 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         },
     ]
 
-    context = LLMContext(messages)
-    context_aggregator = LLMContextAggregatorPair(context)
+    context = OpenAILLMContext(messages)
+    context_aggregator = llm.create_context_aggregator(context)
 
     pipeline = Pipeline(
         [
@@ -99,7 +89,6 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
             enable_metrics=True,
             enable_usage_metrics=True,
         ),
-        idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
     )
 
     @transport.event_handler("on_client_connected")
@@ -107,25 +96,19 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         logger.info(f"Client connected")
         # Kick off the conversation.
         messages.append({"role": "system", "content": "Please introduce yourself to the user."})
-        await task.queue_frames([LLMRunFrame()])
+        await task.queue_frames([context_aggregator.user().get_context_frame()])
 
     @transport.event_handler("on_client_disconnected")
     async def on_client_disconnected(transport, client):
         logger.info(f"Client disconnected")
         await task.cancel()
 
-    runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
+    runner = PipelineRunner(handle_sigint=handle_sigint)
 
     await runner.run(task)
 
 
-async def bot(runner_args: RunnerArguments):
-    """Main bot entry point compatible with Pipecat Cloud."""
-    transport = await create_transport(runner_args, transport_params)
-    await run_bot(transport, runner_args)
-
-
 if __name__ == "__main__":
-    from pipecat.runner.run import main
+    from pipecat.examples.run import main
 
-    main()
+    main(run_example, transport_params=transport_params)

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

@@ -4,30 +4,23 @@
 # SPDX-License-Identifier: BSD 2-Clause License
 #
 
-
+import argparse
 import os
 
 from dotenv import load_dotenv
 from loguru import logger
 
-from pipecat.audio.turn.smart_turn.base_smart_turn import SmartTurnParams
-from pipecat.audio.turn.smart_turn.local_smart_turn_v3 import LocalSmartTurnAnalyzerV3
 from pipecat.audio.vad.silero import SileroVADAnalyzer
-from pipecat.audio.vad.vad_analyzer import VADParams
-from pipecat.frames.frames import LLMRunFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.aggregators.llm_context import LLMContext
-from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
-from pipecat.runner.types import RunnerArguments
-from pipecat.runner.utils import create_transport
+from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
 from pipecat.services.azure.llm import AzureLLMService
 from pipecat.services.azure.stt import AzureSTTService
 from pipecat.services.azure.tts import AzureTTSService
 from pipecat.transports.base_transport import BaseTransport, TransportParams
-from pipecat.transports.daily.transport import DailyParams
-from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
+from pipecat.transports.network.fastapi_websocket import FastAPIWebsocketParams
+from pipecat.transports.services.daily import DailyParams
 
 load_dotenv(override=True)
 
@@ -38,25 +31,22 @@ transport_params = {
     "daily": lambda: DailyParams(
         audio_in_enabled=True,
         audio_out_enabled=True,
-        vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
-        turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
+        vad_analyzer=SileroVADAnalyzer(),
     ),
     "twilio": lambda: FastAPIWebsocketParams(
         audio_in_enabled=True,
         audio_out_enabled=True,
-        vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
-        turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
+        vad_analyzer=SileroVADAnalyzer(),
     ),
     "webrtc": lambda: TransportParams(
         audio_in_enabled=True,
         audio_out_enabled=True,
-        vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
-        turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
+        vad_analyzer=SileroVADAnalyzer(),
     ),
 }
 
 
-async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
+async def run_example(transport: BaseTransport, _: argparse.Namespace, handle_sigint: bool):
     logger.info(f"Starting bot")
 
     stt = AzureSTTService(
@@ -82,8 +72,8 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         },
     ]
 
-    context = LLMContext(messages)
-    context_aggregator = LLMContextAggregatorPair(context)
+    context = OpenAILLMContext(messages)
+    context_aggregator = llm.create_context_aggregator(context)
 
     pipeline = Pipeline(
         [
@@ -103,7 +93,6 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
             enable_metrics=True,
             enable_usage_metrics=True,
         ),
-        idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
     )
 
     @transport.event_handler("on_client_connected")
@@ -111,25 +100,19 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         logger.info(f"Client connected")
         # Kick off the conversation.
         messages.append({"role": "system", "content": "Please introduce yourself to the user."})
-        await task.queue_frames([LLMRunFrame()])
+        await task.queue_frames([context_aggregator.user().get_context_frame()])
 
     @transport.event_handler("on_client_disconnected")
     async def on_client_disconnected(transport, client):
         logger.info(f"Client disconnected")
         await task.cancel()
 
-    runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
+    runner = PipelineRunner(handle_sigint=handle_sigint)
 
     await runner.run(task)
 
 
-async def bot(runner_args: RunnerArguments):
-    """Main bot entry point compatible with Pipecat Cloud."""
-    transport = await create_transport(runner_args, transport_params)
-    await run_bot(transport, runner_args)
-
-
 if __name__ == "__main__":
-    from pipecat.runner.run import main
+    from pipecat.examples.run import main
 
-    main()
+    main(run_example, transport_params=transport_params)

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

@@ -4,30 +4,23 @@
 # SPDX-License-Identifier: BSD 2-Clause License
 #
 
-
+import argparse
 import os
 
 from dotenv import load_dotenv
 from loguru import logger
 
-from pipecat.audio.turn.smart_turn.base_smart_turn import SmartTurnParams
-from pipecat.audio.turn.smart_turn.local_smart_turn_v3 import LocalSmartTurnAnalyzerV3
 from pipecat.audio.vad.silero import SileroVADAnalyzer
-from pipecat.audio.vad.vad_analyzer import VADParams
-from pipecat.frames.frames import LLMRunFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.aggregators.llm_context import LLMContext
-from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
-from pipecat.runner.types import RunnerArguments
-from pipecat.runner.utils import create_transport
+from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
 from pipecat.services.openai.llm import OpenAILLMService
 from pipecat.services.openai.stt import OpenAISTTService
 from pipecat.services.openai.tts import OpenAITTSService
 from pipecat.transports.base_transport import BaseTransport, TransportParams
-from pipecat.transports.daily.transport import DailyParams
-from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
+from pipecat.transports.network.fastapi_websocket import FastAPIWebsocketParams
+from pipecat.transports.services.daily import DailyParams
 
 load_dotenv(override=True)
 
@@ -38,25 +31,22 @@ transport_params = {
     "daily": lambda: DailyParams(
         audio_in_enabled=True,
         audio_out_enabled=True,
-        vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
-        turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
+        vad_analyzer=SileroVADAnalyzer(),
     ),
     "twilio": lambda: FastAPIWebsocketParams(
         audio_in_enabled=True,
         audio_out_enabled=True,
-        vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
-        turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
+        vad_analyzer=SileroVADAnalyzer(),
     ),
     "webrtc": lambda: TransportParams(
         audio_in_enabled=True,
         audio_out_enabled=True,
-        vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
-        turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
+        vad_analyzer=SileroVADAnalyzer(),
     ),
 }
 
 
-async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
+async def run_example(transport: BaseTransport, _: argparse.Namespace, handle_sigint: bool):
     logger.info(f"Starting bot")
 
     stt = OpenAISTTService(
@@ -76,8 +66,8 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         },
     ]
 
-    context = LLMContext(messages)
-    context_aggregator = LLMContextAggregatorPair(context)
+    context = OpenAILLMContext(messages)
+    context_aggregator = llm.create_context_aggregator(context)
 
     pipeline = Pipeline(
         [
@@ -98,7 +88,6 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
             enable_metrics=True,
             enable_usage_metrics=True,
         ),
-        idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
     )
 
     @transport.event_handler("on_client_connected")
@@ -106,25 +95,19 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         logger.info(f"Client connected")
         # Kick off the conversation.
         messages.append({"role": "system", "content": "Please introduce yourself to the user."})
-        await task.queue_frames([LLMRunFrame()])
+        await task.queue_frames([context_aggregator.user().get_context_frame()])
 
     @transport.event_handler("on_client_disconnected")
     async def on_client_disconnected(transport, client):
         logger.info(f"Client disconnected")
         await task.cancel()
 
-    runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
+    runner = PipelineRunner(handle_sigint=handle_sigint)
 
     await runner.run(task)
 
 
-async def bot(runner_args: RunnerArguments):
-    """Main bot entry point compatible with Pipecat Cloud."""
-    transport = await create_transport(runner_args, transport_params)
-    await run_bot(transport, runner_args)
-
-
 if __name__ == "__main__":
-    from pipecat.runner.run import main
+    from pipecat.examples.run import main
 
-    main()
+    main(run_example, transport_params=transport_params)

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

@@ -4,31 +4,24 @@
 # SPDX-License-Identifier: BSD 2-Clause License
 #
 
-
+import argparse
 import os
 import time
 
 from dotenv import load_dotenv
 from loguru import logger
 
-from pipecat.audio.turn.smart_turn.base_smart_turn import SmartTurnParams
-from pipecat.audio.turn.smart_turn.local_smart_turn_v3 import LocalSmartTurnAnalyzerV3
 from pipecat.audio.vad.silero import SileroVADAnalyzer
-from pipecat.audio.vad.vad_analyzer import VADParams
-from pipecat.frames.frames import LLMRunFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.aggregators.llm_context import LLMContext
-from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
-from pipecat.runner.types import RunnerArguments
-from pipecat.runner.utils import create_transport
+from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
 from pipecat.services.cartesia.tts import CartesiaTTSService
 from pipecat.services.deepgram.stt import DeepgramSTTService
 from pipecat.services.openpipe.llm import OpenPipeLLMService
 from pipecat.transports.base_transport import BaseTransport, TransportParams
-from pipecat.transports.daily.transport import DailyParams
-from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
+from pipecat.transports.network.fastapi_websocket import FastAPIWebsocketParams
+from pipecat.transports.services.daily import DailyParams
 
 load_dotenv(override=True)
 
@@ -39,25 +32,22 @@ transport_params = {
     "daily": lambda: DailyParams(
         audio_in_enabled=True,
         audio_out_enabled=True,
-        vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
-        turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
+        vad_analyzer=SileroVADAnalyzer(),
     ),
     "twilio": lambda: FastAPIWebsocketParams(
         audio_in_enabled=True,
         audio_out_enabled=True,
-        vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
-        turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
+        vad_analyzer=SileroVADAnalyzer(),
     ),
     "webrtc": lambda: TransportParams(
         audio_in_enabled=True,
         audio_out_enabled=True,
-        vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
-        turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
+        vad_analyzer=SileroVADAnalyzer(),
     ),
 }
 
 
-async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
+async def run_example(transport: BaseTransport, _: argparse.Namespace, handle_sigint: bool):
     logger.info(f"Starting bot")
 
     stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
@@ -81,8 +71,8 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         },
     ]
 
-    context = LLMContext(messages)
-    context_aggregator = LLMContextAggregatorPair(context)
+    context = OpenAILLMContext(messages)
+    context_aggregator = llm.create_context_aggregator(context)
 
     pipeline = Pipeline(
         [
@@ -102,7 +92,6 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
             enable_metrics=True,
             enable_usage_metrics=True,
         ),
-        idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
     )
 
     @transport.event_handler("on_client_connected")
@@ -110,25 +99,19 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         logger.info(f"Client connected")
         # Kick off the conversation.
         messages.append({"role": "system", "content": "Please introduce yourself to the user."})
-        await task.queue_frames([LLMRunFrame()])
+        await task.queue_frames([context_aggregator.user().get_context_frame()])
 
     @transport.event_handler("on_client_disconnected")
     async def on_client_disconnected(transport, client):
         logger.info(f"Client disconnected")
         await task.cancel()
 
-    runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
+    runner = PipelineRunner(handle_sigint=handle_sigint)
 
     await runner.run(task)
 
 
-async def bot(runner_args: RunnerArguments):
-    """Main bot entry point compatible with Pipecat Cloud."""
-    transport = await create_transport(runner_args, transport_params)
-    await run_bot(transport, runner_args)
-
-
 if __name__ == "__main__":
-    from pipecat.runner.run import main
+    from pipecat.examples.run import main
 
-    main()
+    main(run_example, transport_params=transport_params)

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

@@ -4,31 +4,24 @@
 # SPDX-License-Identifier: BSD 2-Clause License
 #
 
-
+import argparse
 import os
 
 import aiohttp
 from dotenv import load_dotenv
 from loguru import logger
 
-from pipecat.audio.turn.smart_turn.base_smart_turn import SmartTurnParams
-from pipecat.audio.turn.smart_turn.local_smart_turn_v3 import LocalSmartTurnAnalyzerV3
 from pipecat.audio.vad.silero import SileroVADAnalyzer
-from pipecat.audio.vad.vad_analyzer import VADParams
-from pipecat.frames.frames import LLMRunFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.aggregators.llm_context import LLMContext
-from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
-from pipecat.runner.types import RunnerArguments
-from pipecat.runner.utils import create_transport
+from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
 from pipecat.services.deepgram.stt import DeepgramSTTService
 from pipecat.services.openai.llm import OpenAILLMService
 from pipecat.services.xtts.tts import XTTSService
 from pipecat.transports.base_transport import BaseTransport, TransportParams
-from pipecat.transports.daily.transport import DailyParams
-from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
+from pipecat.transports.network.fastapi_websocket import FastAPIWebsocketParams
+from pipecat.transports.services.daily import DailyParams
 
 load_dotenv(override=True)
 
@@ -39,25 +32,22 @@ transport_params = {
     "daily": lambda: DailyParams(
         audio_in_enabled=True,
         audio_out_enabled=True,
-        vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
-        turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
+        vad_analyzer=SileroVADAnalyzer(),
     ),
     "twilio": lambda: FastAPIWebsocketParams(
         audio_in_enabled=True,
         audio_out_enabled=True,
-        vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
-        turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
+        vad_analyzer=SileroVADAnalyzer(),
     ),
     "webrtc": lambda: TransportParams(
         audio_in_enabled=True,
         audio_out_enabled=True,
-        vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
-        turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
+        vad_analyzer=SileroVADAnalyzer(),
     ),
 }
 
 
-async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
+async def run_example(transport: BaseTransport, _: argparse.Namespace, handle_sigint: bool):
     logger.info(f"Starting bot")
 
     # Create an HTTP session
@@ -79,8 +69,8 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
             },
         ]
 
-        context = LLMContext(messages)
-        context_aggregator = LLMContextAggregatorPair(context)
+        context = OpenAILLMContext(messages)
+        context_aggregator = llm.create_context_aggregator(context)
 
         pipeline = Pipeline(
             [
@@ -100,7 +90,6 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
                 enable_metrics=True,
                 enable_usage_metrics=True,
             ),
-            idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
         )
 
         @transport.event_handler("on_client_connected")
@@ -108,25 +97,19 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
             logger.info(f"Client connected")
             # Kick off the conversation.
             messages.append({"role": "system", "content": "Please introduce yourself to the user."})
-            await task.queue_frames([LLMRunFrame()])
+            await task.queue_frames([context_aggregator.user().get_context_frame()])
 
         @transport.event_handler("on_client_disconnected")
         async def on_client_disconnected(transport, client):
             logger.info(f"Client disconnected")
             await task.cancel()
 
-        runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
+        runner = PipelineRunner(handle_sigint=handle_sigint)
 
         await runner.run(task)
 
 
-async def bot(runner_args: RunnerArguments):
-    """Main bot entry point compatible with Pipecat Cloud."""
-    transport = await create_transport(runner_args, transport_params)
-    await run_bot(transport, runner_args)
-
-
 if __name__ == "__main__":
-    from pipecat.runner.run import main
+    from pipecat.examples.run import main
 
-    main()
+    main(run_example, transport_params=transport_params)

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

@@ -4,32 +4,25 @@
 # SPDX-License-Identifier: BSD 2-Clause License
 #
 
-
+import argparse
 import os
 
 from dotenv import load_dotenv
 from loguru import logger
 
-from pipecat.audio.turn.smart_turn.base_smart_turn import SmartTurnParams
-from pipecat.audio.turn.smart_turn.local_smart_turn_v3 import LocalSmartTurnAnalyzerV3
 from pipecat.audio.vad.silero import SileroVADAnalyzer
-from pipecat.audio.vad.vad_analyzer import VADParams
-from pipecat.frames.frames import LLMRunFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.aggregators.llm_context import LLMContext
-from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
-from pipecat.runner.types import RunnerArguments
-from pipecat.runner.utils import create_transport
+from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
 from pipecat.services.cartesia.tts import CartesiaTTSService
 from pipecat.services.gladia.config import GladiaInputParams, LanguageConfig
 from pipecat.services.gladia.stt import GladiaSTTService
 from pipecat.services.openai.llm import OpenAILLMService
 from pipecat.transcriptions.language import Language
 from pipecat.transports.base_transport import BaseTransport, TransportParams
-from pipecat.transports.daily.transport import DailyParams
-from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
+from pipecat.transports.network.fastapi_websocket import FastAPIWebsocketParams
+from pipecat.transports.services.daily import DailyParams
 
 load_dotenv(override=True)
 
@@ -40,30 +33,26 @@ transport_params = {
     "daily": lambda: DailyParams(
         audio_in_enabled=True,
         audio_out_enabled=True,
-        vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
-        turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
+        vad_analyzer=SileroVADAnalyzer(),
     ),
     "twilio": lambda: FastAPIWebsocketParams(
         audio_in_enabled=True,
         audio_out_enabled=True,
-        vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
-        turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
+        vad_analyzer=SileroVADAnalyzer(),
     ),
     "webrtc": lambda: TransportParams(
         audio_in_enabled=True,
         audio_out_enabled=True,
-        vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
-        turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
+        vad_analyzer=SileroVADAnalyzer(),
     ),
 }
 
 
-async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
+async def run_example(transport: BaseTransport, _: argparse.Namespace, handle_sigint: bool):
     logger.info(f"Starting bot")
 
     stt = GladiaSTTService(
         api_key=os.getenv("GLADIA_API_KEY", ""),
-        region=os.getenv("GLADIA_REGION"),
         params=GladiaInputParams(
             language_config=LanguageConfig(
                 languages=[Language.EN],
@@ -85,8 +74,8 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         },
     ]
 
-    context = LLMContext(messages)
-    context_aggregator = LLMContextAggregatorPair(context)
+    context = OpenAILLMContext(messages)
+    context_aggregator = llm.create_context_aggregator(context)
 
     pipeline = Pipeline(
         [
@@ -106,7 +95,6 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
             enable_metrics=True,
             enable_usage_metrics=True,
         ),
-        idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
     )
 
     @transport.event_handler("on_client_connected")
@@ -114,24 +102,18 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         logger.info(f"Client connected")
         # Kick off the conversation.
         messages.append({"role": "system", "content": "Please introduce yourself to the user."})
-        await task.queue_frames([LLMRunFrame()])
+        await task.queue_frames([context_aggregator.user().get_context_frame()])
 
     @transport.event_handler("on_client_disconnected")
     async def on_client_disconnected(transport, client):
         logger.info(f"Client disconnected")
         await task.cancel()
 
-    runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
+    runner = PipelineRunner(handle_sigint=handle_sigint)
     await runner.run(task)
 
 
-async def bot(runner_args: RunnerArguments):
-    """Main bot entry point compatible with Pipecat Cloud."""
-    transport = await create_transport(runner_args, transport_params)
-    await run_bot(transport, runner_args)
-
-
 if __name__ == "__main__":
-    from pipecat.runner.run import main
+    from pipecat.examples.run import main
 
-    main()
+    main(run_example, transport_params=transport_params)

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

@@ -4,30 +4,23 @@
 # SPDX-License-Identifier: BSD 2-Clause License
 #
 
-
+import argparse
 import os
 
 from dotenv import load_dotenv
 from loguru import logger
 
-from pipecat.audio.turn.smart_turn.base_smart_turn import SmartTurnParams
-from pipecat.audio.turn.smart_turn.local_smart_turn_v3 import LocalSmartTurnAnalyzerV3
 from pipecat.audio.vad.silero import SileroVADAnalyzer
-from pipecat.audio.vad.vad_analyzer import VADParams
-from pipecat.frames.frames import LLMRunFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.aggregators.llm_context import LLMContext
-from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
-from pipecat.runner.types import RunnerArguments
-from pipecat.runner.utils import create_transport
+from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
 from pipecat.services.deepgram.stt import DeepgramSTTService
 from pipecat.services.lmnt.tts import LmntTTSService
 from pipecat.services.openai.llm import OpenAILLMService
 from pipecat.transports.base_transport import BaseTransport, TransportParams
-from pipecat.transports.daily.transport import DailyParams
-from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
+from pipecat.transports.network.fastapi_websocket import FastAPIWebsocketParams
+from pipecat.transports.services.daily import DailyParams
 
 load_dotenv(override=True)
 
@@ -38,25 +31,22 @@ transport_params = {
     "daily": lambda: DailyParams(
         audio_in_enabled=True,
         audio_out_enabled=True,
-        vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
-        turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
+        vad_analyzer=SileroVADAnalyzer(),
     ),
     "twilio": lambda: FastAPIWebsocketParams(
         audio_in_enabled=True,
         audio_out_enabled=True,
-        vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
-        turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
+        vad_analyzer=SileroVADAnalyzer(),
     ),
     "webrtc": lambda: TransportParams(
         audio_in_enabled=True,
         audio_out_enabled=True,
-        vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
-        turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
+        vad_analyzer=SileroVADAnalyzer(),
     ),
 }
 
 
-async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
+async def run_example(transport: BaseTransport, _: argparse.Namespace, handle_sigint: bool):
     logger.info(f"Starting bot")
 
     stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
@@ -72,8 +62,8 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         },
     ]
 
-    context = LLMContext(messages)
-    context_aggregator = LLMContextAggregatorPair(context)
+    context = OpenAILLMContext(messages)
+    context_aggregator = llm.create_context_aggregator(context)
 
     pipeline = Pipeline(
         [
@@ -93,7 +83,6 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
             enable_metrics=True,
             enable_usage_metrics=True,
         ),
-        idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
     )
 
     @transport.event_handler("on_client_connected")
@@ -101,25 +90,19 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         logger.info(f"Client connected")
         # Kick off the conversation.
         messages.append({"role": "system", "content": "Please introduce yourself to the user."})
-        await task.queue_frames([LLMRunFrame()])
+        await task.queue_frames([context_aggregator.user().get_context_frame()])
 
     @transport.event_handler("on_client_disconnected")
     async def on_client_disconnected(transport, client):
         logger.info(f"Client disconnected")
         await task.cancel()
 
-    runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
+    runner = PipelineRunner(handle_sigint=handle_sigint)
 
     await runner.run(task)
 
 
-async def bot(runner_args: RunnerArguments):
-    """Main bot entry point compatible with Pipecat Cloud."""
-    transport = await create_transport(runner_args, transport_params)
-    await run_bot(transport, runner_args)
-
-
 if __name__ == "__main__":
-    from pipecat.runner.run import main
+    from pipecat.examples.run import main
 
-    main()
+    main(run_example, transport_params=transport_params)

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

@@ -4,31 +4,24 @@
 # SPDX-License-Identifier: BSD 2-Clause License
 #
 
-
+import argparse
 import os
 
 from dotenv import load_dotenv
 from loguru import logger
 
-from pipecat.audio.turn.smart_turn.base_smart_turn import SmartTurnParams
-from pipecat.audio.turn.smart_turn.local_smart_turn_v3 import LocalSmartTurnAnalyzerV3
 from pipecat.audio.vad.silero import SileroVADAnalyzer
-from pipecat.audio.vad.vad_analyzer import VADParams
-from pipecat.frames.frames import LLMRunFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.aggregators.llm_context import LLMContext
 from pipecat.processors.aggregators.llm_response import LLMUserAggregatorParams
-from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
-from pipecat.runner.types import RunnerArguments
-from pipecat.runner.utils import create_transport
+from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
 from pipecat.services.groq.llm import GroqLLMService
 from pipecat.services.groq.stt import GroqSTTService
 from pipecat.services.groq.tts import GroqTTSService
 from pipecat.transports.base_transport import BaseTransport, TransportParams
-from pipecat.transports.daily.transport import DailyParams
-from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
+from pipecat.transports.network.fastapi_websocket import FastAPIWebsocketParams
+from pipecat.transports.services.daily import DailyParams
 
 load_dotenv(override=True)
 
@@ -39,25 +32,22 @@ transport_params = {
     "daily": lambda: DailyParams(
         audio_in_enabled=True,
         audio_out_enabled=True,
-        vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
-        turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
+        vad_analyzer=SileroVADAnalyzer(),
     ),
     "twilio": lambda: FastAPIWebsocketParams(
         audio_in_enabled=True,
         audio_out_enabled=True,
-        vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
-        turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
+        vad_analyzer=SileroVADAnalyzer(),
     ),
     "webrtc": lambda: TransportParams(
         audio_in_enabled=True,
         audio_out_enabled=True,
-        vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
-        turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
+        vad_analyzer=SileroVADAnalyzer(),
     ),
 }
 
 
-async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
+async def run_example(transport: BaseTransport, _: argparse.Namespace, handle_sigint: bool):
     logger.info(f"Starting bot")
 
     stt = GroqSTTService(api_key=os.getenv("GROQ_API_KEY"))
@@ -75,8 +65,8 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         },
     ]
 
-    context = LLMContext(messages)
-    context_aggregator = LLMContextAggregatorPair(
+    context = OpenAILLMContext(messages)
+    context_aggregator = llm.create_context_aggregator(
         context, user_params=LLMUserAggregatorParams(aggregation_timeout=0.05)
     )
 
@@ -98,7 +88,6 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
             enable_metrics=True,
             enable_usage_metrics=True,
         ),
-        idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
     )
 
     @transport.event_handler("on_client_connected")
@@ -106,25 +95,19 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         logger.info(f"Client connected")
         # Kick off the conversation.
         messages.append({"role": "system", "content": "Please introduce yourself to the user."})
-        await task.queue_frames([LLMRunFrame()])
+        await task.queue_frames([context_aggregator.user().get_context_frame()])
 
     @transport.event_handler("on_client_disconnected")
     async def on_client_disconnected(transport, client):
         logger.info(f"Client disconnected")
         await task.cancel()
 
-    runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
+    runner = PipelineRunner(handle_sigint=handle_sigint)
 
     await runner.run(task)
 
 
-async def bot(runner_args: RunnerArguments):
-    """Main bot entry point compatible with Pipecat Cloud."""
-    transport = await create_transport(runner_args, transport_params)
-    await run_bot(transport, runner_args)
-
-
 if __name__ == "__main__":
-    from pipecat.runner.run import main
+    from pipecat.examples.run import main
 
-    main()
+    main(run_example, transport_params=transport_params)

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

@@ -1,177 +0,0 @@
-#
-# Copyright (c) 2024–2025, Daily
-#
-# SPDX-License-Identifier: BSD 2-Clause License
-#
-
-
-from dotenv import load_dotenv
-from loguru import logger
-
-from pipecat.audio.vad.silero import SileroVADAnalyzer
-from pipecat.frames.frames import LLMMessagesAppendFrame, LLMRunFrame
-from pipecat.pipeline.pipeline import Pipeline
-from pipecat.pipeline.runner import PipelineRunner
-from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.aggregators.llm_context import LLMContext
-from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
-from pipecat.processors.frameworks.strands_agents import StrandsAgentsProcessor
-from pipecat.runner.types import RunnerArguments
-from pipecat.runner.utils import create_transport
-from pipecat.services.aws.stt import AWSTranscribeSTTService
-from pipecat.services.aws.tts import AWSPollyTTSService
-from pipecat.transports.base_transport import BaseTransport, TransportParams
-from pipecat.transports.daily.transport import DailyParams
-from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
-
-# Strands agent setup
-try:
-    from strands import Agent, tool
-    from strands.models import BedrockModel
-except ImportError:
-    logger.warning("Strands not installed. Please install with: pip install strands-agents")
-    Agent = None
-    BedrockModel = None
-
-load_dotenv(override=True)
-
-# We store functions so objects (e.g. SileroVADAnalyzer) don't get
-# instantiated. The function will be called when the desired transport gets
-# selected.
-transport_params = {
-    "daily": lambda: DailyParams(
-        audio_in_enabled=True,
-        audio_out_enabled=True,
-        vad_analyzer=SileroVADAnalyzer(),
-    ),
-    "twilio": lambda: FastAPIWebsocketParams(
-        audio_in_enabled=True,
-        audio_out_enabled=True,
-        vad_analyzer=SileroVADAnalyzer(),
-    ),
-    "webrtc": lambda: TransportParams(
-        audio_in_enabled=True,
-        audio_out_enabled=True,
-        vad_analyzer=SileroVADAnalyzer(),
-    ),
-}
-
-
-def build_agent(model_id: str, max_tokens: int):
-    """Create and configure a Strands agent for NAB customer service coaching.
-
-    Args:
-        model_id: The AWS Bedrock model ID to use
-        max_tokens: Maximum tokens for the model
-
-    Returns:
-        Configured Strands Agent
-    """
-
-    @tool
-    def check_weather(location: str) -> str:
-        if location.lower() == "san francisco":
-            return "The weather in San Francisco is sunny and 30 degrees."
-        elif location.lower() == "sydney":
-            return "The weather in Sydney is cloudy and 20 degrees."
-        else:
-            return "I'm not sure about the weather in that location."
-
-    agent = Agent(
-        model=BedrockModel(
-            model_id=model_id,
-            max_tokens=max_tokens,
-        ),
-        tools=[check_weather],
-        system_prompt="You are a helpful assistant that can check the weather in a given location.",
-    )
-
-    return agent
-
-
-async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
-    logger.info(f"Starting bot")
-
-    stt = AWSTranscribeSTTService()
-
-    tts = AWSPollyTTSService(
-        region="us-west-2",  # only specific regions support generative TTS
-        voice_id="Joanna",
-        params=AWSPollyTTSService.InputParams(engine="generative", rate="1.1"),
-    )
-
-    # Create Strands agent processor
-    try:
-        agent = build_agent(model_id="us.anthropic.claude-3-5-haiku-20241022-v1:0", max_tokens=8000)
-        llm = StrandsAgentsProcessor(agent=agent)
-        logger.info("Successfully created Strands agent for NAB customer service coaching")
-    except Exception as e:
-        logger.error(f"Failed to create Strands agent: {e}")
-        raise ValueError(
-            "Unable to create Strands processor. Please ensure you have properly "
-            "installed strands-agents and configured your AWS credentials."
-        )
-
-    # Setup context aggregators for message handling
-    context = LLMContext()
-    context_aggregator = LLMContextAggregatorPair(context)
-
-    pipeline = Pipeline(
-        [
-            transport.input(),  # Transport user input
-            stt,  # Speech-to-text
-            context_aggregator.user(),  # User responses
-            llm,  # Strands Agents processor
-            tts,  # Text-to-speech
-            transport.output(),  # Transport bot output
-            context_aggregator.assistant(),  # Assistant spoken responses
-        ]
-    )
-
-    task = PipelineTask(
-        pipeline,
-        params=PipelineParams(
-            enable_metrics=True,
-            enable_usage_metrics=True,
-        ),
-        idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
-    )
-
-    @transport.event_handler("on_client_connected")
-    async def on_client_connected(transport, client):
-        logger.info(f"Client connected")
-        # Kick off the conversation.
-        await task.queue_frames(
-            [
-                LLMMessagesAppendFrame(
-                    messages=[
-                        {
-                            "role": "user",
-                            "content": f"Greet the user and introduce yourself.",
-                        }
-                    ],
-                    run_llm=True,
-                )
-            ]
-        )
-
-    @transport.event_handler("on_client_disconnected")
-    async def on_client_disconnected(transport, client):
-        logger.info(f"Client disconnected")
-        await task.cancel()
-
-    runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
-
-    await runner.run(task)
-
-
-async def bot(runner_args: RunnerArguments):
-    """Main bot entry point compatible with Pipecat Cloud."""
-    transport = await create_transport(runner_args, transport_params)
-    await run_bot(transport, runner_args)
-
-
-if __name__ == "__main__":
-    from pipecat.runner.run import main
-
-    main()

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

@@ -4,28 +4,22 @@
 # SPDX-License-Identifier: BSD 2-Clause License
 #
 
+import argparse
 
 from dotenv import load_dotenv
 from loguru import logger
 
-from pipecat.audio.turn.smart_turn.base_smart_turn import SmartTurnParams
-from pipecat.audio.turn.smart_turn.local_smart_turn_v3 import LocalSmartTurnAnalyzerV3
 from pipecat.audio.vad.silero import SileroVADAnalyzer
-from pipecat.audio.vad.vad_analyzer import VADParams
-from pipecat.frames.frames import LLMRunFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.aggregators.llm_context import LLMContext
-from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
-from pipecat.runner.types import RunnerArguments
-from pipecat.runner.utils import create_transport
+from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
 from pipecat.services.aws.llm import AWSBedrockLLMService
 from pipecat.services.aws.stt import AWSTranscribeSTTService
 from pipecat.services.aws.tts import AWSPollyTTSService
 from pipecat.transports.base_transport import BaseTransport, TransportParams
-from pipecat.transports.daily.transport import DailyParams
-from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
+from pipecat.transports.network.fastapi_websocket import FastAPIWebsocketParams
+from pipecat.transports.services.daily import DailyParams
 
 load_dotenv(override=True)
 
@@ -36,25 +30,22 @@ transport_params = {
     "daily": lambda: DailyParams(
         audio_in_enabled=True,
         audio_out_enabled=True,
-        vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
-        turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
+        vad_analyzer=SileroVADAnalyzer(),
     ),
     "twilio": lambda: FastAPIWebsocketParams(
         audio_in_enabled=True,
         audio_out_enabled=True,
-        vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
-        turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
+        vad_analyzer=SileroVADAnalyzer(),
     ),
     "webrtc": lambda: TransportParams(
         audio_in_enabled=True,
         audio_out_enabled=True,
-        vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
-        turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
+        vad_analyzer=SileroVADAnalyzer(),
     ),
 }
 
 
-async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
+async def run_example(transport: BaseTransport, _: argparse.Namespace, handle_sigint: bool):
     logger.info(f"Starting bot")
 
     stt = AWSTranscribeSTTService()
@@ -78,8 +69,8 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         },
     ]
 
-    context = LLMContext(messages)
-    context_aggregator = LLMContextAggregatorPair(context)
+    context = OpenAILLMContext(messages)
+    context_aggregator = llm.create_context_aggregator(context)
 
     pipeline = Pipeline(
         [
@@ -99,7 +90,6 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
             enable_metrics=True,
             enable_usage_metrics=True,
         ),
-        idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
     )
 
     @transport.event_handler("on_client_connected")
@@ -107,25 +97,19 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         logger.info(f"Client connected")
         # Kick off the conversation.
         messages.append({"role": "user", "content": "Please introduce yourself to the user."})
-        await task.queue_frames([LLMRunFrame()])
+        await task.queue_frames([context_aggregator.user().get_context_frame()])
 
     @transport.event_handler("on_client_disconnected")
     async def on_client_disconnected(transport, client):
         logger.info(f"Client disconnected")
         await task.cancel()
 
-    runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
+    runner = PipelineRunner(handle_sigint=handle_sigint)
 
     await runner.run(task)
 
 
-async def bot(runner_args: RunnerArguments):
-    """Main bot entry point compatible with Pipecat Cloud."""
-    transport = await create_transport(runner_args, transport_params)
-    await run_bot(transport, runner_args)
-
-
 if __name__ == "__main__":
-    from pipecat.runner.run import main
+    from pipecat.examples.run import main
 
-    main()
+    main(run_example, transport_params=transport_params)

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

@@ -1,171 +0,0 @@
-#
-# Copyright (c) 2024–2025, Daily
-#
-# SPDX-License-Identifier: BSD 2-Clause License
-#
-
-"""
-A conversational AI bot using Gemini for both LLM and TTS.
-
-This example demonstrates how to use Gemini's TTS capabilities with the new
-GeminiTTSService, which uses Gemini's TTS-specific models instead of Google Cloud TTS.
-
-Features showcased:
-- Gemini LLM for conversation
-- Gemini TTS with natural voice control
-- Support for different voice personalities
-- Style and tone control through natural language prompts
-
-Run with:
-    python examples/foundational/gemini-tts.py
-
-Make sure to set your environment variables:
-    export GOOGLE_API_KEY=your_api_key_here
-"""
-
-import os
-
-from dotenv import load_dotenv
-from loguru import logger
-
-from pipecat.audio.turn.smart_turn.base_smart_turn import SmartTurnParams
-from pipecat.audio.turn.smart_turn.local_smart_turn_v3 import LocalSmartTurnAnalyzerV3
-from pipecat.audio.vad.silero import SileroVADAnalyzer
-from pipecat.audio.vad.vad_analyzer import VADParams
-from pipecat.frames.frames import LLMRunFrame
-from pipecat.pipeline.pipeline import Pipeline
-from pipecat.pipeline.runner import PipelineRunner
-from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.aggregators.llm_context import LLMContext
-from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
-from pipecat.runner.types import RunnerArguments
-from pipecat.runner.utils import create_transport
-from pipecat.services.google.llm import GoogleLLMService
-from pipecat.services.google.stt import GoogleSTTService
-from pipecat.services.google.tts import GeminiTTSService
-from pipecat.transcriptions.language import Language
-from pipecat.transports.base_transport import BaseTransport, TransportParams
-from pipecat.transports.daily.transport import DailyParams
-from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
-
-load_dotenv(override=True)
-
-# We store functions so objects (e.g. SileroVADAnalyzer) don't get
-# instantiated. The function will be called when the desired transport gets
-# selected.
-transport_params = {
-    "daily": lambda: DailyParams(
-        audio_in_enabled=True,
-        audio_out_enabled=True,
-        vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
-        turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
-    ),
-    "twilio": lambda: FastAPIWebsocketParams(
-        audio_in_enabled=True,
-        audio_out_enabled=True,
-        vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
-        turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
-    ),
-    "webrtc": lambda: TransportParams(
-        audio_in_enabled=True,
-        audio_out_enabled=True,
-        vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
-        turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
-    ),
-}
-
-
-async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
-    logger.info(f"Starting bot with Gemini TTS")
-
-    stt = GoogleSTTService(
-        params=GoogleSTTService.InputParams(languages=Language.EN_US),
-        credentials=os.getenv("GOOGLE_TEST_CREDENTIALS"),
-    )
-
-    tts = GeminiTTSService(
-        api_key=os.getenv("GOOGLE_API_KEY"),
-        model="gemini-2.5-flash-preview-tts",  # TTS-specific model
-        voice_id="Charon",
-        params=GeminiTTSService.InputParams(language=Language.EN_US),
-    )
-
-    llm = GoogleLLMService(
-        api_key=os.getenv("GOOGLE_API_KEY"),
-        model="gemini-2.5-flash",
-    )
-
-    # System message that instructs the AI on how to speak
-    messages = [
-        {
-            "role": "system",
-            "content": """You are a helpful AI assistant in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way.
-
-            IMPORTANT: Since you're using Gemini TTS which supports natural voice control, you can include speaking instructions in your responses. For example:
-            - "Say cheerfully: Welcome to our conversation!"
-            - "Read this in a calm, professional tone: Here are the details you requested."
-            - "Speak in an excited whisper: I have some great news to share!"
-            - "Say slowly and clearly: Let me explain this step by step."
-
-            Feel free to use natural language instructions to control your voice style, tone, pace, and emotion. The TTS system will interpret these instructions and adjust the speech accordingly.
-
-            Your output will be converted to audio, so avoid special characters in your answers. Respond to what the user said in a creative and helpful way.""",
-        },
-    ]
-
-    context = LLMContext(messages)
-    context_aggregator = LLMContextAggregatorPair(context)
-
-    pipeline = Pipeline(
-        [
-            transport.input(),  # Transport user input
-            stt,  # STT
-            context_aggregator.user(),  # User responses
-            llm,  # LLM
-            tts,  # Gemini TTS
-            transport.output(),  # Transport bot output
-            context_aggregator.assistant(),  # Assistant spoken responses
-        ]
-    )
-
-    task = PipelineTask(
-        pipeline,
-        params=PipelineParams(
-            enable_metrics=True,
-            enable_usage_metrics=True,
-        ),
-        idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
-    )
-
-    @transport.event_handler("on_client_connected")
-    async def on_client_connected(transport, client):
-        logger.info(f"Client connected")
-        # Kick off the conversation with a styled introduction
-        messages.append(
-            {
-                "role": "system",
-                "content": "Say cheerfully and warmly: Hello! I'm your AI assistant powered by Gemini's new TTS technology. I can speak with different voices, tones, and styles. How can I help you today?",
-            }
-        )
-        await task.queue_frames([LLMRunFrame()])
-
-    @transport.event_handler("on_client_disconnected")
-    async def on_client_disconnected(transport, client):
-        logger.info(f"Client disconnected")
-        await task.cancel()
-
-    runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
-
-    await runner.run(task)
-
-
-async def bot(runner_args: RunnerArguments):
-    """Main bot entry point compatible with Pipecat Cloud."""
-    transport = await create_transport(runner_args, transport_params)
-    await run_bot(transport, runner_args)
-
-
-if __name__ == "__main__":
-    from pipecat.runner.run import main
-
-    main()

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

@@ -4,31 +4,24 @@
 # SPDX-License-Identifier: BSD 2-Clause License
 #
 
-
+import argparse
 import os
 
 from dotenv import load_dotenv
 from loguru import logger
 
-from pipecat.audio.turn.smart_turn.base_smart_turn import SmartTurnParams
-from pipecat.audio.turn.smart_turn.local_smart_turn_v3 import LocalSmartTurnAnalyzerV3
 from pipecat.audio.vad.silero import SileroVADAnalyzer
-from pipecat.audio.vad.vad_analyzer import VADParams
-from pipecat.frames.frames import LLMRunFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.aggregators.llm_context import LLMContext
-from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
-from pipecat.runner.types import RunnerArguments
-from pipecat.runner.utils import create_transport
+from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
 from pipecat.services.google.llm import GoogleLLMService
 from pipecat.services.google.stt import GoogleSTTService
 from pipecat.services.google.tts import GoogleTTSService
 from pipecat.transcriptions.language import Language
 from pipecat.transports.base_transport import BaseTransport, TransportParams
-from pipecat.transports.daily.transport import DailyParams
-from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
+from pipecat.transports.network.fastapi_websocket import FastAPIWebsocketParams
+from pipecat.transports.services.daily import DailyParams
 
 load_dotenv(override=True)
 
@@ -39,25 +32,22 @@ transport_params = {
     "daily": lambda: DailyParams(
         audio_in_enabled=True,
         audio_out_enabled=True,
-        vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
-        turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
+        vad_analyzer=SileroVADAnalyzer(),
     ),
     "twilio": lambda: FastAPIWebsocketParams(
         audio_in_enabled=True,
         audio_out_enabled=True,
-        vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
-        turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
+        vad_analyzer=SileroVADAnalyzer(),
     ),
     "webrtc": lambda: TransportParams(
         audio_in_enabled=True,
         audio_out_enabled=True,
-        vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
-        turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
+        vad_analyzer=SileroVADAnalyzer(),
     ),
 }
 
 
-async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
+async def run_example(transport: BaseTransport, _: argparse.Namespace, handle_sigint: bool):
     logger.info(f"Starting bot")
 
     stt = GoogleSTTService(
@@ -85,8 +75,8 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         },
     ]
 
-    context = LLMContext(messages)
-    context_aggregator = LLMContextAggregatorPair(context)
+    context = OpenAILLMContext(messages)
+    context_aggregator = llm.create_context_aggregator(context)
 
     pipeline = Pipeline(
         [
@@ -106,7 +96,6 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
             enable_metrics=True,
             enable_usage_metrics=True,
         ),
-        idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
     )
 
     @transport.event_handler("on_client_connected")
@@ -114,25 +103,19 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         logger.info(f"Client connected")
         # Kick off the conversation.
         messages.append({"role": "system", "content": "Please introduce yourself to the user."})
-        await task.queue_frames([LLMRunFrame()])
+        await task.queue_frames([context_aggregator.user().get_context_frame()])
 
     @transport.event_handler("on_client_disconnected")
     async def on_client_disconnected(transport, client):
         logger.info(f"Client disconnected")
         await task.cancel()
 
-    runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
+    runner = PipelineRunner(handle_sigint=handle_sigint)
 
     await runner.run(task)
 
 
-async def bot(runner_args: RunnerArguments):
-    """Main bot entry point compatible with Pipecat Cloud."""
-    transport = await create_transport(runner_args, transport_params)
-    await run_bot(transport, runner_args)
-
-
 if __name__ == "__main__":
-    from pipecat.runner.run import main
+    from pipecat.examples.run import main
 
-    main()
+    main(run_example, transport_params=transport_params)

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

@@ -4,30 +4,23 @@
 # SPDX-License-Identifier: BSD 2-Clause License
 #
 
-
+import argparse
 import os
 
 from dotenv import load_dotenv
 from loguru import logger
 
-from pipecat.audio.turn.smart_turn.base_smart_turn import SmartTurnParams
-from pipecat.audio.turn.smart_turn.local_smart_turn_v3 import LocalSmartTurnAnalyzerV3
 from pipecat.audio.vad.silero import SileroVADAnalyzer
-from pipecat.audio.vad.vad_analyzer import VADParams
-from pipecat.frames.frames import LLMRunFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.aggregators.llm_context import LLMContext
-from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
-from pipecat.runner.types import RunnerArguments
-from pipecat.runner.utils import create_transport
+from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
 from pipecat.services.assemblyai.stt import AssemblyAISTTService
 from pipecat.services.cartesia.tts import CartesiaTTSService
 from pipecat.services.openai.llm import OpenAILLMService
 from pipecat.transports.base_transport import BaseTransport, TransportParams
-from pipecat.transports.daily.transport import DailyParams
-from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
+from pipecat.transports.network.fastapi_websocket import FastAPIWebsocketParams
+from pipecat.transports.services.daily import DailyParams
 
 load_dotenv(override=True)
 
@@ -39,25 +32,22 @@ transport_params = {
     "daily": lambda: DailyParams(
         audio_in_enabled=True,
         audio_out_enabled=True,
-        vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
-        turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
+        vad_analyzer=SileroVADAnalyzer(),
     ),
     "twilio": lambda: FastAPIWebsocketParams(
         audio_in_enabled=True,
         audio_out_enabled=True,
-        vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
-        turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
+        vad_analyzer=SileroVADAnalyzer(),
     ),
     "webrtc": lambda: TransportParams(
         audio_in_enabled=True,
         audio_out_enabled=True,
-        vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
-        turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
+        vad_analyzer=SileroVADAnalyzer(),
     ),
 }
 
 
-async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
+async def run_example(transport: BaseTransport, _: argparse.Namespace, handle_sigint: bool):
     logger.info(f"Starting bot")
 
     stt = AssemblyAISTTService(
@@ -78,8 +68,8 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         },
     ]
 
-    context = LLMContext(messages)
-    context_aggregator = LLMContextAggregatorPair(context)
+    context = OpenAILLMContext(messages)
+    context_aggregator = llm.create_context_aggregator(context)
 
     pipeline = Pipeline(
         [
@@ -99,7 +89,6 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
             enable_metrics=True,
             enable_usage_metrics=True,
         ),
-        idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
     )
 
     @transport.event_handler("on_client_connected")
@@ -107,25 +96,19 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         logger.info(f"Client connected")
         # Kick off the conversation.
         messages.append({"role": "system", "content": "Please introduce yourself to the user."})
-        await task.queue_frames([LLMRunFrame()])
+        await task.queue_frames([context_aggregator.user().get_context_frame()])
 
     @transport.event_handler("on_client_disconnected")
     async def on_client_disconnected(transport, client):
         logger.info(f"Client disconnected")
         await task.cancel()
 
-    runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
+    runner = PipelineRunner(handle_sigint=handle_sigint)
 
     await runner.run(task)
 
 
-async def bot(runner_args: RunnerArguments):
-    """Main bot entry point compatible with Pipecat Cloud."""
-    transport = await create_transport(runner_args, transport_params)
-    await run_bot(transport, runner_args)
-
-
 if __name__ == "__main__":
-    from pipecat.runner.run import main
+    from pipecat.examples.run import main
 
-    main()
+    main(run_example, transport_params=transport_params)

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

@@ -4,31 +4,24 @@
 # SPDX-License-Identifier: BSD 2-Clause License
 #
 
-
+import argparse
 import os
 
 from dotenv import load_dotenv
 from loguru import logger
 
 from pipecat.audio.filters.krisp_filter import KrispFilter
-from pipecat.audio.turn.smart_turn.base_smart_turn import SmartTurnParams
-from pipecat.audio.turn.smart_turn.local_smart_turn_v3 import LocalSmartTurnAnalyzerV3
 from pipecat.audio.vad.silero import SileroVADAnalyzer
-from pipecat.audio.vad.vad_analyzer import VADParams
-from pipecat.frames.frames import LLMRunFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.aggregators.llm_context import LLMContext
-from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
-from pipecat.runner.types import RunnerArguments
-from pipecat.runner.utils import create_transport
+from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
 from pipecat.services.deepgram.stt import DeepgramSTTService
 from pipecat.services.deepgram.tts import DeepgramTTSService
 from pipecat.services.openai.llm import OpenAILLMService
 from pipecat.transports.base_transport import BaseTransport, TransportParams
-from pipecat.transports.daily.transport import DailyParams
-from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
+from pipecat.transports.network.fastapi_websocket import FastAPIWebsocketParams
+from pipecat.transports.services.daily import DailyParams
 
 load_dotenv(override=True)
 
@@ -39,28 +32,25 @@ transport_params = {
     "daily": lambda: DailyParams(
         audio_in_enabled=True,
         audio_out_enabled=True,
-        vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
-        turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
+        vad_analyzer=SileroVADAnalyzer(),
         audio_in_filter=KrispFilter(),
     ),
     "twilio": lambda: FastAPIWebsocketParams(
         audio_in_enabled=True,
         audio_out_enabled=True,
-        vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
-        turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
+        vad_analyzer=SileroVADAnalyzer(),
         audio_in_filter=KrispFilter(),
     ),
     "webrtc": lambda: TransportParams(
         audio_in_enabled=True,
         audio_out_enabled=True,
-        vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
-        turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
+        vad_analyzer=SileroVADAnalyzer(),
         audio_in_filter=KrispFilter(),
     ),
 }
 
 
-async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
+async def run_example(transport: BaseTransport, _: argparse.Namespace, handle_sigint: bool):
     logger.info(f"Starting bot")
 
     stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
@@ -76,8 +66,8 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         },
     ]
 
-    context = LLMContext(messages)
-    context_aggregator = LLMContextAggregatorPair(context)
+    context = OpenAILLMContext(messages)
+    context_aggregator = llm.create_context_aggregator(context)
 
     pipeline = Pipeline(
         [
@@ -97,7 +87,6 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
             enable_metrics=True,
             enable_usage_metrics=True,
         ),
-        idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
     )
 
     @transport.event_handler("on_client_connected")
@@ -105,25 +94,19 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         logger.info(f"Client connected")
         # Kick off the conversation.
         messages.append({"role": "system", "content": "Please introduce yourself to the user."})
-        await task.queue_frames([LLMRunFrame()])
+        await task.queue_frames([context_aggregator.user().get_context_frame()])
 
     @transport.event_handler("on_client_disconnected")
     async def on_client_disconnected(transport, client):
         logger.info(f"Client disconnected")
         await task.cancel()
 
-    runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
+    runner = PipelineRunner(handle_sigint=handle_sigint)
 
     await runner.run(task)
 
 
-async def bot(runner_args: RunnerArguments):
-    """Main bot entry point compatible with Pipecat Cloud."""
-    transport = await create_transport(runner_args, transport_params)
-    await run_bot(transport, runner_args)
-
-
 if __name__ == "__main__":
-    from pipecat.runner.run import main
+    from pipecat.examples.run import main
 
-    main()
+    main(run_example, transport_params=transport_params)

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

@@ -4,31 +4,24 @@
 # SPDX-License-Identifier: BSD 2-Clause License
 #
 
-
+import argparse
 import os
 
 import aiohttp
 from dotenv import load_dotenv
 from loguru import logger
 
-from pipecat.audio.turn.smart_turn.base_smart_turn import SmartTurnParams
-from pipecat.audio.turn.smart_turn.local_smart_turn_v3 import LocalSmartTurnAnalyzerV3
 from pipecat.audio.vad.silero import SileroVADAnalyzer
-from pipecat.audio.vad.vad_analyzer import VADParams
-from pipecat.frames.frames import LLMRunFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.aggregators.llm_context import LLMContext
-from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
-from pipecat.runner.types import RunnerArguments
-from pipecat.runner.utils import create_transport
+from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
 from pipecat.services.deepgram.stt import DeepgramSTTService
 from pipecat.services.openai.llm import OpenAILLMService
 from pipecat.services.rime.tts import RimeHttpTTSService
 from pipecat.transports.base_transport import BaseTransport, TransportParams
-from pipecat.transports.daily.transport import DailyParams
-from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
+from pipecat.transports.network.fastapi_websocket import FastAPIWebsocketParams
+from pipecat.transports.services.daily import DailyParams
 
 load_dotenv(override=True)
 
@@ -40,25 +33,22 @@ transport_params = {
     "daily": lambda: DailyParams(
         audio_in_enabled=True,
         audio_out_enabled=True,
-        vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
-        turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
+        vad_analyzer=SileroVADAnalyzer(),
     ),
     "twilio": lambda: FastAPIWebsocketParams(
         audio_in_enabled=True,
         audio_out_enabled=True,
-        vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
-        turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
+        vad_analyzer=SileroVADAnalyzer(),
     ),
     "webrtc": lambda: TransportParams(
         audio_in_enabled=True,
         audio_out_enabled=True,
-        vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
-        turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
+        vad_analyzer=SileroVADAnalyzer(),
     ),
 }
 
 
-async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
+async def run_example(transport: BaseTransport, _: argparse.Namespace, handle_sigint: bool):
     logger.info(f"Starting bot")
 
     # Create an HTTP session
@@ -81,8 +71,8 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
             },
         ]
 
-        context = LLMContext(messages)
-        context_aggregator = LLMContextAggregatorPair(context)
+        context = OpenAILLMContext(messages)
+        context_aggregator = llm.create_context_aggregator(context)
 
         pipeline = Pipeline(
             [
@@ -102,7 +92,6 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
                 enable_metrics=True,
                 enable_usage_metrics=True,
             ),
-            idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
         )
 
         @transport.event_handler("on_client_connected")
@@ -110,25 +99,19 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
             logger.info(f"Client connected")
             # Kick off the conversation.
             messages.append({"role": "system", "content": "Please introduce yourself to the user."})
-            await task.queue_frames([LLMRunFrame()])
+            await task.queue_frames([context_aggregator.user().get_context_frame()])
 
         @transport.event_handler("on_client_disconnected")
         async def on_client_disconnected(transport, client):
             logger.info(f"Client disconnected")
             await task.cancel()
 
-        runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
+        runner = PipelineRunner(handle_sigint=handle_sigint)
 
         await runner.run(task)
 
 
-async def bot(runner_args: RunnerArguments):
-    """Main bot entry point compatible with Pipecat Cloud."""
-    transport = await create_transport(runner_args, transport_params)
-    await run_bot(transport, runner_args)
-
-
 if __name__ == "__main__":
-    from pipecat.runner.run import main
+    from pipecat.examples.run import main
 
-    main()
+    main(run_example, transport_params=transport_params)

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

@@ -4,30 +4,23 @@
 # SPDX-License-Identifier: BSD 2-Clause License
 #
 
-
+import argparse
 import os
 
 from dotenv import load_dotenv
 from loguru import logger
 
-from pipecat.audio.turn.smart_turn.base_smart_turn import SmartTurnParams
-from pipecat.audio.turn.smart_turn.local_smart_turn_v3 import LocalSmartTurnAnalyzerV3
 from pipecat.audio.vad.silero import SileroVADAnalyzer
-from pipecat.audio.vad.vad_analyzer import VADParams
-from pipecat.frames.frames import LLMRunFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.aggregators.llm_context import LLMContext
-from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
-from pipecat.runner.types import RunnerArguments
-from pipecat.runner.utils import create_transport
+from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
 from pipecat.services.deepgram.stt import DeepgramSTTService
 from pipecat.services.openai.llm import OpenAILLMService
 from pipecat.services.rime.tts import RimeTTSService
 from pipecat.transports.base_transport import BaseTransport, TransportParams
-from pipecat.transports.daily.transport import DailyParams
-from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
+from pipecat.transports.network.fastapi_websocket import FastAPIWebsocketParams
+from pipecat.transports.services.daily import DailyParams
 
 load_dotenv(override=True)
 
@@ -38,25 +31,22 @@ transport_params = {
     "daily": lambda: DailyParams(
         audio_in_enabled=True,
         audio_out_enabled=True,
-        vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
-        turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
+        vad_analyzer=SileroVADAnalyzer(),
     ),
     "twilio": lambda: FastAPIWebsocketParams(
         audio_in_enabled=True,
         audio_out_enabled=True,
-        vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
-        turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
+        vad_analyzer=SileroVADAnalyzer(),
     ),
     "webrtc": lambda: TransportParams(
         audio_in_enabled=True,
         audio_out_enabled=True,
-        vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
-        turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
+        vad_analyzer=SileroVADAnalyzer(),
     ),
 }
 
 
-async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
+async def run_example(transport: BaseTransport, _: argparse.Namespace, handle_sigint: bool):
     logger.info(f"Starting bot")
 
     stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
@@ -75,8 +65,8 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         },
     ]
 
-    context = LLMContext(messages)
-    context_aggregator = LLMContextAggregatorPair(context)
+    context = OpenAILLMContext(messages)
+    context_aggregator = llm.create_context_aggregator(context)
 
     pipeline = Pipeline(
         [
@@ -96,7 +86,6 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
             enable_metrics=True,
             enable_usage_metrics=True,
         ),
-        idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
     )
 
     @transport.event_handler("on_client_connected")
@@ -104,25 +93,19 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         logger.info(f"Client connected")
         # Kick off the conversation.
         messages.append({"role": "system", "content": "Please introduce yourself to the user."})
-        await task.queue_frames([LLMRunFrame()])
+        await task.queue_frames([context_aggregator.user().get_context_frame()])
 
     @transport.event_handler("on_client_disconnected")
     async def on_client_disconnected(transport, client):
         logger.info(f"Client disconnected")
         await task.cancel()
 
-    runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
+    runner = PipelineRunner(handle_sigint=handle_sigint)
 
     await runner.run(task)
 
 
-async def bot(runner_args: RunnerArguments):
-    """Main bot entry point compatible with Pipecat Cloud."""
-    transport = await create_transport(runner_args, transport_params)
-    await run_bot(transport, runner_args)
-
-
 if __name__ == "__main__":
-    from pipecat.runner.run import main
+    from pipecat.examples.run import main
 
-    main()
+    main(run_example, transport_params=transport_params)

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

@@ -4,30 +4,23 @@
 # SPDX-License-Identifier: BSD 2-Clause License
 #
 
-
+import argparse
 import os
 
 from dotenv import load_dotenv
 from loguru import logger
 
-from pipecat.audio.turn.smart_turn.base_smart_turn import SmartTurnParams
-from pipecat.audio.turn.smart_turn.local_smart_turn_v3 import LocalSmartTurnAnalyzerV3
 from pipecat.audio.vad.silero import SileroVADAnalyzer
-from pipecat.audio.vad.vad_analyzer import VADParams
-from pipecat.frames.frames import LLMRunFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.aggregators.llm_context import LLMContext
-from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
-from pipecat.runner.types import RunnerArguments
-from pipecat.runner.utils import create_transport
+from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
 from pipecat.services.nim.llm import NimLLMService
 from pipecat.services.riva.stt import RivaSTTService
 from pipecat.services.riva.tts import RivaTTSService
 from pipecat.transports.base_transport import BaseTransport, TransportParams
-from pipecat.transports.daily.transport import DailyParams
-from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
+from pipecat.transports.network.fastapi_websocket import FastAPIWebsocketParams
+from pipecat.transports.services.daily import DailyParams
 
 load_dotenv(override=True)
 
@@ -38,25 +31,22 @@ transport_params = {
     "daily": lambda: DailyParams(
         audio_in_enabled=True,
         audio_out_enabled=True,
-        vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
-        turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
+        vad_analyzer=SileroVADAnalyzer(),
     ),
     "twilio": lambda: FastAPIWebsocketParams(
         audio_in_enabled=True,
         audio_out_enabled=True,
-        vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
-        turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
+        vad_analyzer=SileroVADAnalyzer(),
     ),
     "webrtc": lambda: TransportParams(
         audio_in_enabled=True,
         audio_out_enabled=True,
-        vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
-        turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
+        vad_analyzer=SileroVADAnalyzer(),
     ),
 }
 
 
-async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
+async def run_example(transport: BaseTransport, _: argparse.Namespace, handle_sigint: bool):
     logger.info(f"Starting bot")
 
     stt = RivaSTTService(api_key=os.getenv("NVIDIA_API_KEY"))
@@ -72,8 +62,8 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         },
     ]
 
-    context = LLMContext(messages)
-    context_aggregator = LLMContextAggregatorPair(context)
+    context = OpenAILLMContext(messages)
+    context_aggregator = llm.create_context_aggregator(context)
 
     pipeline = Pipeline(
         [
@@ -93,7 +83,6 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
             enable_metrics=True,
             enable_usage_metrics=True,
         ),
-        idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
     )
 
     @transport.event_handler("on_client_connected")
@@ -101,25 +90,19 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         logger.info(f"Client connected")
         # Kick off the conversation.
         messages.append({"role": "system", "content": "Please introduce yourself to the user."})
-        await task.queue_frames([LLMRunFrame()])
+        await task.queue_frames([context_aggregator.user().get_context_frame()])
 
     @transport.event_handler("on_client_disconnected")
     async def on_client_disconnected(transport, client):
         logger.info(f"Client disconnected")
         await task.cancel()
 
-    runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
+    runner = PipelineRunner(handle_sigint=handle_sigint)
 
     await runner.run(task)
 
 
-async def bot(runner_args: RunnerArguments):
-    """Main bot entry point compatible with Pipecat Cloud."""
-    transport = await create_transport(runner_args, transport_params)
-    await run_bot(transport, runner_args)
-
-
 if __name__ == "__main__":
-    from pipecat.runner.run import main
+    from pipecat.examples.run import main
 
-    main()
+    main(run_example, transport_params=transport_params)

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

@@ -4,7 +4,7 @@
 # SPDX-License-Identifier: BSD 2-Clause License
 #
 
-
+import argparse
 import os
 from dataclasses import dataclass
 
@@ -12,17 +12,13 @@ from dotenv import load_dotenv
 from google.genai.types import Content, Part
 from loguru import logger
 
-from pipecat.audio.turn.smart_turn.base_smart_turn import SmartTurnParams
-from pipecat.audio.turn.smart_turn.local_smart_turn_v3 import LocalSmartTurnAnalyzerV3
 from pipecat.audio.vad.silero import SileroVADAnalyzer
-from pipecat.audio.vad.vad_analyzer import VADParams
 from pipecat.frames.frames import (
     Frame,
     InputAudioRawFrame,
-    InterruptionFrame,
     LLMFullResponseEndFrame,
     LLMFullResponseStartFrame,
-    LLMRunFrame,
+    StartInterruptionFrame,
     TextFrame,
     TranscriptionFrame,
     UserStartedSpeakingFrame,
@@ -31,17 +27,14 @@ from pipecat.frames.frames import (
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.aggregators.llm_context import LLMContext
-from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
+from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
 from pipecat.processors.frame_processor import FrameProcessor
-from pipecat.runner.types import RunnerArguments
-from pipecat.runner.utils import create_transport
 from pipecat.services.google.llm import GoogleLLMService
 from pipecat.services.google.tts import GoogleTTSService
 from pipecat.transcriptions.language import Language
 from pipecat.transports.base_transport import BaseTransport, TransportParams
-from pipecat.transports.daily.transport import DailyParams
-from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
+from pipecat.transports.network.fastapi_websocket import FastAPIWebsocketParams
+from pipecat.transports.services.daily import DailyParams
 
 load_dotenv(override=True)
 
@@ -97,8 +90,9 @@ class UserAudioCollector(FrameProcessor):
         elif isinstance(frame, UserStoppedSpeakingFrame):
             self._user_speaking = False
             self._context.add_audio_frames_message(audio_frames=self._audio_frames)
-            await self._user_context_aggregator.push_frame(LLMRunFrame())
-
+            await self._user_context_aggregator.push_frame(
+                self._user_context_aggregator.get_context_frame()
+            )
         elif isinstance(frame, InputAudioRawFrame):
             if self._user_speaking:
                 self._audio_frames.append(frame)
@@ -154,7 +148,7 @@ class TranscriptExtractor(FrameProcessor):
         await self.push_frame(frame, direction)
 
 
-class TranscriptionContextFixup(FrameProcessor):
+class TanscriptionContextFixup(FrameProcessor):
     def __init__(self, context):
         super().__init__()
         self._context = context
@@ -185,7 +179,9 @@ class TranscriptionContextFixup(FrameProcessor):
 
         if isinstance(frame, MagicDemoTranscriptionFrame):
             self._transcript = frame.text
-        elif isinstance(frame, LLMFullResponseEndFrame) or isinstance(frame, InterruptionFrame):
+        elif isinstance(frame, LLMFullResponseEndFrame) or isinstance(
+            frame, StartInterruptionFrame
+        ):
             self.swap_user_audio()
             self.add_transcript_back_to_inference_output()
             self._transcript = ""
@@ -200,25 +196,22 @@ transport_params = {
     "daily": lambda: DailyParams(
         audio_in_enabled=True,
         audio_out_enabled=True,
-        vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
-        turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
+        vad_analyzer=SileroVADAnalyzer(),
     ),
     "twilio": lambda: FastAPIWebsocketParams(
         audio_in_enabled=True,
         audio_out_enabled=True,
-        vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
-        turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
+        vad_analyzer=SileroVADAnalyzer(),
     ),
     "webrtc": lambda: TransportParams(
         audio_in_enabled=True,
         audio_out_enabled=True,
-        vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
-        turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
+        vad_analyzer=SileroVADAnalyzer(),
     ),
 }
 
 
-async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
+async def run_example(transport: BaseTransport, _: argparse.Namespace, handle_sigint: bool):
     logger.info(f"Starting bot")
 
     llm = GoogleLLMService(
@@ -245,11 +238,11 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         },
     ]
 
-    context = LLMContext(messages)
-    context_aggregator = LLMContextAggregatorPair(context)
+    context = OpenAILLMContext(messages)
+    context_aggregator = llm.create_context_aggregator(context)
     audio_collector = UserAudioCollector(context, context_aggregator.user())
     pull_transcript_out_of_llm_output = TranscriptExtractor(context)
-    fixup_context_messages = TranscriptionContextFixup(context)
+    fixup_context_messages = TanscriptionContextFixup(context)
 
     pipeline = Pipeline(
         [
@@ -271,7 +264,6 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
             enable_metrics=True,
             enable_usage_metrics=True,
         ),
-        idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
     )
 
     @transport.event_handler("on_client_connected")
@@ -279,25 +271,19 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         logger.info(f"Client connected")
         # Kick off the conversation.
         messages.append({"role": "system", "content": "Please introduce yourself to the user."})
-        await task.queue_frames([LLMRunFrame()])
+        await task.queue_frames([context_aggregator.user().get_context_frame()])
 
     @transport.event_handler("on_client_disconnected")
     async def on_client_disconnected(transport, client):
         logger.info(f"Client disconnected")
         await task.cancel()
 
-    runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
+    runner = PipelineRunner(handle_sigint=handle_sigint)
 
     await runner.run(task)
 
 
-async def bot(runner_args: RunnerArguments):
-    """Main bot entry point compatible with Pipecat Cloud."""
-    transport = await create_transport(runner_args, transport_params)
-    await run_bot(transport, runner_args)
-
-
 if __name__ == "__main__":
-    from pipecat.runner.run import main
+    from pipecat.examples.run import main
 
-    main()
+    main(run_example, transport_params=transport_params)