Update python-compatibility workflow to include new user project check

.github/workflows/coverage.yaml

@@ -25,7 +25,7 @@ jobs:
           version: "latest"
 
       - name: Set up Python
-        run: uv python install 3.12
+        run: uv python install 3.10
 
       - name: Install system packages
         run: |

.github/workflows/generate-changelog.yml

@@ -1,174 +0,0 @@
-name: Generate Changelog for Release
-
-on:
-  workflow_dispatch:
-    inputs:
-      version:
-        description: "Release version (e.g., 0.0.97)"
-        required: true
-        type: string
-      date:
-        description: "Release date (YYYY-MM-DD format, defaults to today)"
-        required: false
-        type: string
-        default: ""
-
-permissions:
-  contents: write
-  pull-requests: write
-
-jobs:
-  generate-changelog:
-    runs-on: ubuntu-latest
-    steps:
-      - name: Checkout repository
-        uses: actions/checkout@v4
-        with:
-          fetch-depth: 0
-
-      - name: Set up Python
-        uses: actions/setup-python@v5
-        with:
-          python-version: "3.12"
-
-      - name: Install uv
-        uses: astral-sh/setup-uv@v4
-        with:
-          enable-cache: true
-
-      - name: Install dependencies
-        run: |
-          uv sync --group dev
-
-      - name: Set release date
-        id: set_date
-        run: |
-          if [ -z "${{ inputs.date }}" ]; then
-            RELEASE_DATE=$(date +%Y-%m-%d)
-            echo "Using today's date: $RELEASE_DATE"
-          else
-            RELEASE_DATE="${{ inputs.date }}"
-            echo "Using provided date: $RELEASE_DATE"
-          fi
-          echo "release_date=$RELEASE_DATE" >> $GITHUB_OUTPUT
-
-      - name: Validate inputs
-        run: |
-          # Validate version format (basic check)
-          if ! [[ "${{ inputs.version }}" =~ ^[0-9]+\.[0-9]+\.[0-9]+.*$ ]]; then
-            echo "Error: Version must be in format X.Y.Z (e.g., 0.0.97)"
-            exit 1
-          fi
-
-          # Validate date format if provided
-          if [ -n "${{ inputs.date }}" ]; then
-            if ! date -d "${{ inputs.date }}" >/dev/null 2>&1; then
-              # Try macOS date format
-              if ! date -j -f "%Y-%m-%d" "${{ inputs.date }}" >/dev/null 2>&1; then
-                echo "Error: Date must be in YYYY-MM-DD format (e.g., 2025-12-04)"
-                exit 1
-              fi
-            fi
-          fi
-
-      - name: Check for changelog fragments
-        id: check_fragments
-        run: |
-          FRAGMENT_COUNT=$(find changelog -name "*.md" ! -name "_template.md.j2" | wc -l | tr -d ' ')
-          echo "fragment_count=$FRAGMENT_COUNT" >> $GITHUB_OUTPUT
-
-          if [ "$FRAGMENT_COUNT" -eq "0" ]; then
-            echo "❌ Error: No changelog fragments found in changelog/"
-            echo ""
-            echo "Cannot create a release without changelog entries."
-            echo "Add changelog fragments to the changelog/ directory (e.g., 1234.added.md) and try again."
-            exit 1
-          fi
-
-          # Validate fragment types
-          VALID_TYPES="added changed deprecated removed fixed security"
-          INVALID_FRAGMENTS=""
-
-          for file in changelog/*.md; do
-            # Skip template
-            if [[ "$file" == "changelog/_template.md.j2" ]]; then
-              continue
-            fi
-            
-            # Extract type from filename (e.g., 1234.added.md -> added)
-            filename=$(basename "$file")
-            # Handle both 1234.added.md and 1234.added.2.md patterns
-            type=$(echo "$filename" | sed -E 's/^[0-9]+\.([a-z]+)(\.[0-9]+)?\.md$/\1/')
-            
-            # Check if type is valid
-            if ! echo "$VALID_TYPES" | grep -wq "$type"; then
-              INVALID_FRAGMENTS="$INVALID_FRAGMENTS\n  - $filename (type: '$type')"
-            fi
-          done
-
-          if [ -n "$INVALID_FRAGMENTS" ]; then
-            echo "❌ Error: Invalid changelog fragment types found:"
-            echo -e "$INVALID_FRAGMENTS"
-            echo ""
-            echo "Valid types are: $VALID_TYPES"
-            echo "Example: 1234.added.md, 5678.fixed.md"
-            exit 1
-          fi
-
-          echo "✓ Found $FRAGMENT_COUNT changelog fragment(s)"
-          echo "has_fragments=true" >> $GITHUB_OUTPUT
-
-      - name: Preview changelog
-        run: |
-          echo "## Preview of changelog for version ${{ inputs.version }}"
-          echo ""
-          uv run towncrier build --draft --version "${{ inputs.version }}" --date "${{ steps.set_date.outputs.release_date }}"
-
-      - name: Build changelog
-        run: |
-          uv run towncrier build --version "${{ inputs.version }}" --date "${{ steps.set_date.outputs.release_date }}" --yes
-
-      - name: Create Pull Request
-        uses: peter-evans/create-pull-request@v7
-        with:
-          token: ${{ secrets.GITHUB_TOKEN }}
-          commit-message: "Update changelog for version ${{ inputs.version }}"
-          title: "Release ${{ inputs.version }} - Changelog Update"
-          body: |
-            ## Changelog Update for Release ${{ inputs.version }}
-
-            This PR updates the CHANGELOG.md with all changes for version **${{ inputs.version }}**.
-
-            ### Summary
-            - **Version:** ${{ inputs.version }}
-            - **Date:** ${{ steps.set_date.outputs.release_date }}
-            - **Fragments processed:** ${{ steps.check_fragments.outputs.fragment_count }}
-
-            ### What this PR does
-            - ✅ Adds new release section to CHANGELOG.md
-            - ✅ Removes processed changelog fragments
-            - ✅ Ready to merge for release
-
-            ### Next Steps
-            1. Review the changelog entries below
-            2. Make any necessary edits to CHANGELOG.md if needed
-            3. Merge this PR
-            4. Continue with your release process
-
-            ---
-
-            <details>
-            <summary>📋 Preview of changes</summary>
-
-            The changelog has been updated with entries from the following fragments:
-
-            ```bash
-            ${{ steps.check_fragments.outputs.fragment_count }} fragments processed
-            ```
-
-            </details>
-          branch: changelog-${{ inputs.version }}
-          delete-branch: true
-          labels: |
-            changelog
-            release

.github/workflows/publish.yaml

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

.github/workflows/publish_test.yaml

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

.github/workflows/python-compatibility.yaml

@@ -9,14 +9,14 @@ on:
     paths: ['pyproject.toml']
 
 jobs:
-  test-compatibility:
+  test-dev-environment:
     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 }}
+    name: Dev Environment - Python ${{ matrix.python-version }}
     steps:
       - name: Checkout code
         uses: actions/checkout@v4
@@ -55,7 +55,69 @@ jobs:
             --no-extra moondream \
             --no-extra mlx-whisper
 
-      - name: Verify installation
+      - name: Verify dev installation
         run: |
           uv run python --version
-          uv run python -c "import pipecat; print('✅ Pipecat imports successfully')"
+          uv run python -c "import pipecat; print('✅ Dev environment - Pipecat imports successfully')"
+
+  test-user-experience:
+    runs-on: ubuntu-latest
+    strategy:
+      fail-fast: false
+      matrix:
+        python-version: ['3.10.18', '3.11.13', '3.12.11', '3.13.5']
+
+    name: User Experience - 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 }}
+
+      - name: Build local package
+        run: |
+          uv build
+
+      - name: Create test project
+        run: |
+          mkdir test-project
+          cd test-project
+          uv init --python ${{ matrix.python-version }}
+
+      - name: Test comprehensive extras with uv add (Python 3.10-3.12)
+        if: "!startsWith(matrix.python-version, '3.13.')"
+        run: |
+          cd test-project
+          # Use uv add with built wheel to leverage dependency management
+          uv add "../dist/pipecat_ai-"*".whl[anthropic,assemblyai,asyncai,aws,aws-nova-sonic,azure,cartesia,cerebras,deepseek,daily,deepgram,elevenlabs,fal,fireworks,fish,gladia,google,grok,groq,gstreamer,heygen,inworld,koala,langchain,livekit,lmnt,local,mcp,mem0,mlx-whisper,moondream,nim,neuphonic,noisereduce,openai,openpipe,openrouter,perplexity,playht,qwen,rime,riva,runner,sambanova,sentry,local-smart-turn,remote-smart-turn,silero,simli,soniox,soundfile,speechmatics,tavus,together,tracing,ultravox,webrtc,websocket,whisper]"
+
+      - name: Test Python 3.13 compatible extras with uv add
+        if: startsWith(matrix.python-version, '3.13.')
+        run: |
+          cd test-project
+          # Use uv add with built wheel and Python 3.13 compatible extras
+          uv add "../dist/pipecat_ai-"*".whl[anthropic,assemblyai,asyncai,aws,aws-nova-sonic,azure,cartesia,cerebras,deepseek,daily,deepgram,elevenlabs,fal,fireworks,fish,gladia,google,grok,groq,gstreamer,heygen,inworld,koala,langchain,livekit,lmnt,local,mcp,mem0,nim,neuphonic,noisereduce,openai,openpipe,openrouter,perplexity,playht,qwen,rime,riva,runner,sambanova,sentry,remote-smart-turn,silero,simli,soniox,soundfile,speechmatics,tavus,together,tracing,webrtc,websocket,whisper]"
+
+      - name: Verify user installation
+        run: |
+          cd test-project
+          uv run python --version
+          uv run python -c "import pipecat; print('✅ User experience - Pipecat imports successfully')"
+          # Test that basic functionality works
+          uv run python -c "from pipecat.pipeline.pipeline import Pipeline; print('✅ Pipeline import works')"

.github/workflows/sync-quickstart.yaml

@@ -23,12 +23,17 @@ jobs:
           token: ${{ secrets.QUICKSTART_SYNC_TOKEN }}
           path: quickstart-repo
 
-      - name: Sync files (excluding uv.lock and README.md)
+      - name: Sync files (excluding READMEs)
         run: |
-          # Copy all files except uv.lock and README.md
+          # Copy code files only, skip READMEs
+          cp examples/quickstart/bot.py quickstart-repo/
+          cp examples/quickstart/requirements.txt quickstart-repo/
+          cp examples/quickstart/env.example quickstart-repo/
+
+          # Copy any other files that aren't README.md
           find examples/quickstart -type f \
             -not -name "README.md" \
-            -not -name "uv.lock" \
+            -not -name "*.md" \
             -exec cp {} quickstart-repo/ \;
 
       - name: Commit and push changes

.github/workflows/tests.yaml

@@ -29,7 +29,7 @@ jobs:
           version: "latest"
 
       - name: Set up Python
-        run: uv python install 3.12
+        run: uv python install 3.10
 
       - name: Install system packages
         run: |

.github/workflows/update-lockfile.yaml

@@ -0,0 +1,42 @@
+name: Update lockfile
+
+on:
+  push:
+    paths:
+      - 'pyproject.toml'
+    branches:
+      - main
+  workflow_dispatch: # Allows manual triggering from GitHub UI
+
+jobs:
+  update-lockfile:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v4
+        with:
+          # This gives the workflow permission to push back to the repo
+          token: ${{ secrets.GITHUB_TOKEN }}
+
+      - name: Install uv
+        uses: astral-sh/setup-uv@v1
+
+      - name: Update lockfile
+        run: uv lock
+
+      - name: Check for changes
+        id: verify-changed-files
+        run: |
+          if [ -n "$(git status --porcelain)" ]; then
+            echo "changed=true" >> $GITHUB_OUTPUT
+          else
+            echo "changed=false" >> $GITHUB_OUTPUT
+          fi
+
+      - name: Commit lockfile
+        if: steps.verify-changed-files.outputs.changed == 'true'
+        run: |
+          git config --local user.email "action@github.com"  
+          git config --local user.name "GitHub Action"
+          git add uv.lock
+          git commit -m "chore: update uv.lock after dependency changes"
+          git push

COMMUNITY_INTEGRATIONS.md

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

CONTRIBUTING.md

@@ -1,9 +1,5 @@
 ## Contributing to Pipecat
 
-**Want to add a new service integration?**
-We encourage community-maintained integrations! Please see our [Community Integration Guide](COMMUNITY_INTEGRATIONS.md) for the process and requirements.
-
-**Want to contribute to Pipecat core?**
 We welcome contributions of all kinds! Your help is appreciated. Follow these steps to get involved:
 
 1. **Fork this repository**: Start by forking the Pipecat Documentation repository to your GitHub account.
@@ -17,138 +13,24 @@ We welcome contributions of all kinds! Your help is appreciated. Follow these st
    git checkout -b your-branch-name
    ```
 4. **Make your changes**: Edit or add files as necessary.
-5. **Add a changelog entry**: Create a changelog fragment file (see [Changelog Entries](#changelog-entries) below).
-6. **Test your changes**: Ensure that your changes look correct and follow the style set in the codebase.
-7. **Commit your changes**: Once you're satisfied with your changes, commit them with a meaningful message.
+5. **Test your changes**: Ensure that your changes look correct and follow the style set in the codebase.
+6. **Commit your changes**: Once you're satisfied with your changes, commit them with a meaningful message.
 
 ```bash
 git commit -m "Description of your changes"
 ```
 
-8. **Push your changes**: Push your branch to your forked repository.
+7. **Push your changes**: Push your branch to your forked repository.
 
 ```bash
 git push origin your-branch-name
 ```
 
-9. **Submit a Pull Request (PR)**: Open a PR from your forked repository to the main branch of this repo.
+8. **Submit a Pull Request (PR)**: Open a PR from your forked repository to the main branch of this repo.
    > Important: Describe the changes you've made clearly!
 
 Our maintainers will review your PR, and once everything is good, your contributions will be merged!
 
-## Changelog Entries
-
-Every pull request that makes a user-facing change should include a changelog entry. We use a changelog fragment system to avoid merge conflicts.
-
-### Creating a Changelog Fragment
-
-1. Create a new file in the `changelog/` directory with this naming pattern:
-
-   ```
-   <PR_number>.<type>.md
-   ```
-
-2. Choose the appropriate type:
-
-   - `added.md` - New features
-   - `changed.md` - Changes in existing functionality
-   - `deprecated.md` - Soon-to-be removed features
-   - `removed.md` - Removed features
-   - `fixed.md` - Bug fixes
-   - `security.md` - Security fixes
-
-3. Write your changelog entry as a Markdown bullet point. Include the `-` at the start:
-
-**Example files:**
-
-`changelog/1234.added.md`:
-
-```markdown
-- Added support for Anthropic Claude 3.5 Sonnet with improved streaming performance.
-```
-
-`changelog/5678.fixed.md`:
-
-```markdown
-- Fixed an issue where audio frames were dropped during high-load scenarios.
-```
-
-**For entries with nested bullets:**
-
-`changelog/1234.changed.md`:
-
-```markdown
-- Updated service configuration:
-
-  - Changed default timeout to 30 seconds
-  - Added retry logic for failed connections
-```
-
-### Multiple Changes in One PR
-
-**Different types of changes:** Create separate fragment files for each type:
-
-```
-changelog/1234.added.md
-changelog/1234.fixed.md
-```
-
-**Multiple changes of the same type:** Create numbered fragment files:
-
-```
-changelog/1234.changed.md
-changelog/1234.changed.2.md
-```
-
-**Related changes:** Use nested bullets in a single fragment:
-
-```markdown
-- Updated service configuration:
-
-  - Changed default timeout to 30 seconds
-  - Added retry logic for failed connections
-```
-
-**Rule of thumb:** One logical change per fragment file. If changes are unrelated, use separate files.
-
-### Preview Your Changes
-
-To see what your changelog entry will look like:
-
-```bash
-towncrier build --draft --version Unreleased
-```
-
-This won't modify any files, just show you a preview.
-
-### When to Skip Changelog Entries
-
-You can skip adding a changelog entry for:
-
-- Documentation-only changes
-- Internal refactoring with no user-facing impact
-- Test-only changes
-- CI/build configuration changes
-
-If you're unsure whether your change needs a changelog entry, ask in your PR!
-
-## 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

@@ -2,8 +2,7 @@
  <img alt="pipecat" width="300px" height="auto" src="https://raw.githubusercontent.com/pipecat-ai/pipecat/main/pipecat.png">
 </div></h1>
 
-[![PyPI](https://img.shields.io/pypi/v/pipecat-ai)](https://pypi.org/project/pipecat-ai) ![Tests](https://github.com/pipecat-ai/pipecat/actions/workflows/tests.yaml/badge.svg) [![codecov](https://codecov.io/gh/pipecat-ai/pipecat/graph/badge.svg?token=LNVUIVO4Y9)](https://codecov.io/gh/pipecat-ai/pipecat) [![Docs](https://img.shields.io/badge/Documentation-blue)](https://docs.pipecat.ai) [![Discord](https://img.shields.io/discord/1239284677165056021)](https://discord.gg/pipecat) [![Ask DeepWiki](https://deepwiki.com/badge.svg)](https://deepwiki.com/pipecat-ai/pipecat)
-[![](https://getmanta.ai/api/badges?text=Manta%20Graph&link=manta)](https://getmanta.ai/pipecat)
+[![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
 
@@ -20,6 +19,8 @@
 - **Business Agents** – customer intake, support bots, guided flows
 - **Complex Dialog Systems** – design logic with structured conversations
 
+🧭 Looking to build structured conversations? Check out [Pipecat Flows](https://github.com/pipecat-ai/pipecat-flows) for managing complex conversational states and transitions.
+
 ## 🧠 Why Pipecat?
 
 - **Voice-first**: Integrates speech recognition, text-to-speech, and conversation handling
@@ -27,39 +28,6 @@
 - **Composable Pipelines**: Build complex behavior from modular components
 - **Real-Time**: Ultra-low latency interaction with different transports (e.g. WebSockets or WebRTC)
 
-## 🌐 Pipecat Ecosystem
-
-### 📱 Client SDKs
-
-Building client applications? You can connect to Pipecat from any platform using our official SDKs:
-
-<a href="https://docs.pipecat.ai/client/js/introduction">JavaScript</a> | <a href="https://docs.pipecat.ai/client/react/introduction">React</a> | <a href="https://docs.pipecat.ai/client/react-native/introduction">React Native</a> |
-<a href="https://docs.pipecat.ai/client/ios/introduction">Swift</a> | <a href="https://docs.pipecat.ai/client/android/introduction">Kotlin</a> | <a href="https://docs.pipecat.ai/client/c++/introduction">C++</a> | <a href="https://github.com/pipecat-ai/pipecat-esp32">ESP32</a>
-
-### 🧭 Structured conversations
-
-Looking to build structured conversations? Check out [Pipecat Flows](https://github.com/pipecat-ai/pipecat-flows) for managing complex conversational states and transitions.
-
-### 🪄 Beautiful UIs
-
-Want to build beautiful and engaging experiences? Checkout the [Voice UI Kit](https://github.com/pipecat-ai/voice-ui-kit), a collection of components, hooks and templates for building voice AI applications quickly.
-
-### 🛠️ Create and deploy projects
-
-Create a new project in under a minute with the [Pipecat CLI](https://github.com/pipecat-ai/pipecat-cli). Then use the CLI to monitor and deploy your agent to production.
-
-### 🔍 Debugging
-
-Looking for help debugging your pipeline and processors? Check out [Whisker](https://github.com/pipecat-ai/whisker), a real-time Pipecat debugger.
-
-### 🖥️ Terminal
-
-Love terminal applications? Check out [Tail](https://github.com/pipecat-ai/tail), a terminal dashboard for Pipecat.
-
-### 📺️ Pipecat TV Channel
-
-Catch new features, interviews, and how-tos on our [Pipecat TV](https://www.youtube.com/playlist?list=PLzU2zoMTQIHjqC3v4q2XVSR3hGSzwKFwH) channel.
-
 ## 🎬 See it in action
 
 <p float="left">
@@ -67,24 +35,35 @@ Catch new features, interviews, and how-tos on our [Pipecat TV](https://www.yout
     <a href="https://github.com/pipecat-ai/pipecat-examples/tree/main/storytelling-chatbot"><img src="https://raw.githubusercontent.com/pipecat-ai/pipecat-examples/main/storytelling-chatbot/image.png" width="400" /></a>
     <br/>
     <a href="https://github.com/pipecat-ai/pipecat-examples/tree/main/translation-chatbot"><img src="https://raw.githubusercontent.com/pipecat-ai/pipecat-examples/main/translation-chatbot/image.png" width="400" /></a>&nbsp;
-    <a href="https://github.com/pipecat-ai/pipecat/blob/main/examples/foundational/12-describe-video.py"><img src="https://github.com/pipecat-ai/pipecat/blob/main/examples/foundational/assets/moondream.png" width="400" /></a>
+    <a href="https://github.com/pipecat-ai/pipecat-examples/tree/main/moondream-chatbot"><img src="https://raw.githubusercontent.com/pipecat-ai/pipecat-examples/main/moondream-chatbot/image.png" width="400" /></a>
 </p>
 
+## 📱 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), [Gradium](https://docs.pipecat.ai/server/services/stt/gradium), [Groq (Whisper)](https://docs.pipecat.ai/server/services/stt/groq), [NVIDIA Riva](https://docs.pipecat.ai/server/services/stt/riva), [OpenAI (Whisper)](https://docs.pipecat.ai/server/services/stt/openai), [SambaNova (Whisper)](https://docs.pipecat.ai/server/services/stt/sambanova), [Sarvam](https://docs.pipecat.ai/server/services/stt/sarvam), [Soniox](https://docs.pipecat.ai/server/services/stt/soniox), [Speechmatics](https://docs.pipecat.ai/server/services/stt/speechmatics), [Ultravox](https://docs.pipecat.ai/server/services/stt/ultravox), [Whisper](https://docs.pipecat.ai/server/services/stt/whisper)                                                                                                                                                                                          |
-| LLMs                | [Anthropic](https://docs.pipecat.ai/server/services/llm/anthropic), [AWS](https://docs.pipecat.ai/server/services/llm/aws), [Azure](https://docs.pipecat.ai/server/services/llm/azure), [Cerebras](https://docs.pipecat.ai/server/services/llm/cerebras), [DeepSeek](https://docs.pipecat.ai/server/services/llm/deepseek), [Fireworks AI](https://docs.pipecat.ai/server/services/llm/fireworks), [Gemini](https://docs.pipecat.ai/server/services/llm/gemini), [Grok](https://docs.pipecat.ai/server/services/llm/grok), [Groq](https://docs.pipecat.ai/server/services/llm/groq), [Mistral](https://docs.pipecat.ai/server/services/llm/mistral), [NVIDIA NIM](https://docs.pipecat.ai/server/services/llm/nim), [Ollama](https://docs.pipecat.ai/server/services/llm/ollama), [OpenAI](https://docs.pipecat.ai/server/services/llm/openai), [OpenRouter](https://docs.pipecat.ai/server/services/llm/openrouter), [Perplexity](https://docs.pipecat.ai/server/services/llm/perplexity), [Qwen](https://docs.pipecat.ai/server/services/llm/qwen), [SambaNova](https://docs.pipecat.ai/server/services/llm/sambanova) [Together AI](https://docs.pipecat.ai/server/services/llm/together)                                                                                                                                                                                                                              |
-| Text-to-Speech      | [Async](https://docs.pipecat.ai/server/services/tts/asyncai), [AWS](https://docs.pipecat.ai/server/services/tts/aws), [Azure](https://docs.pipecat.ai/server/services/tts/azure), [Cartesia](https://docs.pipecat.ai/server/services/tts/cartesia), [Deepgram](https://docs.pipecat.ai/server/services/tts/deepgram), [ElevenLabs](https://docs.pipecat.ai/server/services/tts/elevenlabs), [Fish](https://docs.pipecat.ai/server/services/tts/fish), [Google](https://docs.pipecat.ai/server/services/tts/google), [Gradium](https://docs.pipecat.ai/server/services/tts/gradium), [Groq](https://docs.pipecat.ai/server/services/tts/groq), [Hume](https://docs.pipecat.ai/server/services/tts/hume), [Inworld](https://docs.pipecat.ai/server/services/tts/inworld), [LMNT](https://docs.pipecat.ai/server/services/tts/lmnt), [MiniMax](https://docs.pipecat.ai/server/services/tts/minimax), [Neuphonic](https://docs.pipecat.ai/server/services/tts/neuphonic), [NVIDIA Riva](https://docs.pipecat.ai/server/services/tts/riva), [OpenAI](https://docs.pipecat.ai/server/services/tts/openai), [Piper](https://docs.pipecat.ai/server/services/tts/piper), [PlayHT](https://docs.pipecat.ai/server/services/tts/playht), [Rime](https://docs.pipecat.ai/server/services/tts/rime), [Sarvam](https://docs.pipecat.ai/server/services/tts/sarvam), [Speechmatics](https://docs.pipecat.ai/server/services/tts/speechmatics), [XTTS](https://docs.pipecat.ai/server/services/tts/xtts) |
-| Speech-to-Speech    | [AWS Nova Sonic](https://docs.pipecat.ai/server/services/s2s/aws), [Gemini Multimodal Live](https://docs.pipecat.ai/server/services/s2s/gemini), [OpenAI Realtime](https://docs.pipecat.ai/server/services/s2s/openai)                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    |
-| Transport           | [Daily (WebRTC)](https://docs.pipecat.ai/server/services/transport/daily), [FastAPI Websocket](https://docs.pipecat.ai/server/services/transport/fastapi-websocket), [SmallWebRTCTransport](https://docs.pipecat.ai/server/services/transport/small-webrtc), [WebSocket Server](https://docs.pipecat.ai/server/services/transport/websocket-server), Local                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                |
-| Serializers         | [Plivo](https://docs.pipecat.ai/server/utilities/serializers/plivo), [Twilio](https://docs.pipecat.ai/server/utilities/serializers/twilio), [Telnyx](https://docs.pipecat.ai/server/utilities/serializers/telnyx)                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                         |
-| Video               | [HeyGen](https://docs.pipecat.ai/server/services/video/heygen), [Tavus](https://docs.pipecat.ai/server/services/video/tavus), [Simli](https://docs.pipecat.ai/server/services/video/simli)                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                |
-| Memory              | [mem0](https://docs.pipecat.ai/server/services/memory/mem0)                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                               |
-| Vision & Image      | [fal](https://docs.pipecat.ai/server/services/image-generation/fal), [Google Imagen](https://docs.pipecat.ai/server/services/image-generation/fal), [Moondream](https://docs.pipecat.ai/server/services/vision/moondream)                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                 |
-| Audio Processing    | [Silero VAD](https://docs.pipecat.ai/server/utilities/audio/silero-vad-analyzer), [Krisp](https://docs.pipecat.ai/server/utilities/audio/krisp-filter), [Koala](https://docs.pipecat.ai/server/utilities/audio/koala-filter), [ai-coustics](https://docs.pipecat.ai/server/utilities/audio/aic-filter)                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    |
-| Analytics & Metrics | [OpenTelemetry](https://docs.pipecat.ai/server/utilities/opentelemetry), [Sentry](https://docs.pipecat.ai/server/services/analytics/sentry)                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                               |
+| Category            | Services                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              |
+| ------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| Speech-to-Text      | [AssemblyAI](https://docs.pipecat.ai/server/services/stt/assemblyai), [AWS](https://docs.pipecat.ai/server/services/stt/aws), [Azure](https://docs.pipecat.ai/server/services/stt/azure), [Cartesia](https://docs.pipecat.ai/server/services/stt/cartesia), [Deepgram](https://docs.pipecat.ai/server/services/stt/deepgram), [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      | [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), [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)
 
@@ -135,8 +114,7 @@ You can get started with Pipecat running on your local machine, then move your a
 
 ### Prerequisites
 
-**Minimum Python Version:** 3.10
-**Recommended Python Version:** 3.12
+**Python Version:** 3.10+
 
 ### Setup Steps
 
@@ -150,11 +128,7 @@ You can get started with Pipecat running on your local machine, then move your a
 2. Install development and testing 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)
+   uv sync --group dev --all-extras --no-extra gstreamer --no-extra krisp --no-extra local
    ```
 
 3. Install the git pre-commit hooks:
@@ -163,6 +137,23 @@ You can get started with Pipecat running on your local machine, then move your a
    uv run pre-commit install
    ```
 
+### Python 3.13+ Compatibility
+
+Some features require PyTorch, which doesn't yet support Python 3.13+. Install using:
+
+```bash
+uv sync --group dev --all-extras \
+  --no-extra gstreamer \
+  --no-extra krisp \
+  --no-extra local \
+  --no-extra local-smart-turn \
+  --no-extra mlx-whisper \
+  --no-extra moondream \
+  --no-extra ultravox
+```
+
+> **Tip:** For full compatibility, use Python 3.12: `uv python pin 3.12`
+
 > **Note**: Some extras (local, gstreamer) require system dependencies. See documentation if you encounter build errors.
 
 ### Running tests
@@ -179,6 +170,54 @@ Run a specific test suite:
 uv run pytest tests/test_name.py
 ```
 
+### Setting up your editor
+
+This project uses strict [PEP 8](https://peps.python.org/pep-0008/) formatting via [Ruff](https://github.com/astral-sh/ruff).
+
+#### Emacs
+
+You can use [use-package](https://github.com/jwiegley/use-package) to install [emacs-lazy-ruff](https://github.com/christophermadsen/emacs-lazy-ruff) package and configure `ruff` arguments:
+
+```elisp
+(use-package lazy-ruff
+  :ensure t
+  :hook ((python-mode . lazy-ruff-mode))
+  :config
+  (setq lazy-ruff-format-command "ruff format")
+  (setq lazy-ruff-check-command "ruff check --select I"))
+```
+
+`ruff` was installed in the `venv` environment described before, so you should be able to use [pyvenv-auto](https://github.com/ryotaro612/pyvenv-auto) to automatically load that environment inside Emacs.
+
+```elisp
+(use-package pyvenv-auto
+  :ensure t
+  :defer t
+  :hook ((python-mode . pyvenv-auto-run)))
+```
+
+#### Visual Studio Code
+
+Install the
+[Ruff](https://marketplace.visualstudio.com/items?itemName=charliermarsh.ruff) extension. Then edit the user settings (_Ctrl-Shift-P_ `Open User Settings (JSON)`) and set it as the default Python formatter, and enable formatting on save:
+
+```json
+"[python]": {
+    "editor.defaultFormatter": "charliermarsh.ruff",
+    "editor.formatOnSave": true
+}
+```
+
+#### PyCharm
+
+`ruff` was installed in the `venv` environment described before, now to enable autoformatting on save, go to `File` -> `Settings` -> `Tools` -> `File Watchers` and add a new watcher with the following settings:
+
+1. **Name**: `Ruff formatter`
+2. **File type**: `Python`
+3. **Working directory**: `$ContentRoot$`
+4. **Arguments**: `format $FilePath$`
+5. **Program**: `$PyInterpreterDirectory$/ruff`
+
 ## 🤝 Contributing
 
 We welcome contributions from the community! Whether you're fixing bugs, improving documentation, or adding new features, here's how you can help:

SECURITY.md

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

changelog/_template.md.j2

@@ -1,16 +0,0 @@
-{% for section, _ in sections.items() %}
-{% if sections[section] %}
-{% for category, val in definitions.items() if category in sections[section]%}
-### {{ definitions[category]['name'] }}
-
-{% for text, values in sections[section][category].items() %}
-{{ text }}
-
-{% endfor %}
-{% endfor %}
-{% else %}
-No significant changes.
-
-{% endif %}
-{% endfor %}
-

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/conf.py

@@ -50,7 +50,6 @@ autodoc_mock_imports = [
     # Krisp - has build issues on some platforms
     "pipecat_ai_krisp",
     "krisp",
-    "krisp_audio",
     # System-specific GUI libraries
     "_tkinter",
     "tkinter",
@@ -119,6 +118,7 @@ def import_core_modules():
         "pipecat.observers",
         "pipecat.runner",
         "pipecat.serializers",
+        "pipecat.sync",
         "pipecat.transcriptions",
         "pipecat.utils",
     ]

docs/api/index.rst

@@ -21,7 +21,6 @@ Quick Links
    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>
@@ -30,6 +29,7 @@ Quick Links
    Runner <api/pipecat.runner>
    Serializers <api/pipecat.serializers>
    Services <api/pipecat.services>
+   Sync <api/pipecat.sync>
    Transcriptions <api/pipecat.transcriptions>
    Transports <api/pipecat.transports>
-   Utils <api/pipecat.utils>
+   Utils <api/pipecat.utils>

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.

env.example

@@ -1,12 +1,6 @@
-# AI-COUSTICS
-AICOUSTICS_LICENSE_KEY=...
-
 # Anthropic
 ANTHROPIC_API_KEY=...
 
-# Assembly AI
-ASSEMBLYAI_API_KEY=...
-
 # Async
 ASYNCAI_API_KEY=...
 ASYNCAI_VOICE_ID=...
@@ -24,19 +18,12 @@ AZURE_CHATGPT_API_KEY=...
 AZURE_CHATGPT_ENDPOINT=https://...
 AZURE_CHATGPT_MODEL=...
 
-AZURE_REALTIME_API_KEY=...
-AZURE_REALTIME_BASE_URL=...
-
 AZURE_DALLE_API_KEY=...
 AZURE_DALLE_ENDPOINT=https://...
 AZURE_DALLE_MODEL=...
 
 # Cartesia
 CARTESIA_API_KEY=...
-CARTESIA_VOICE_ID=...
-
-# Cerebras
-CEREBRAS_API_KEY=...
 
 # Daily
 DAILY_API_KEY=...
@@ -44,80 +31,37 @@ DAILY_SAMPLE_ROOM_URL=https://...
 
 # Deepgram
 DEEPGRAM_API_KEY=...
-SAGEMAKER_ENDPOINT_NAME=...
-
-# DeepSeek
-DEEPSEEK_API_KEY=...
 
 # ElevenLabs
 ELEVENLABS_API_KEY=...
 ELEVENLABS_VOICE_ID=...
 
+# Neuphonic
+NEUPHONIC_API_KEY=...
+
 # Fal
 FAL_KEY=...
 
 # Fireworks
 FIREWORKS_API_KEY=...
 
-# Fish Audio
-FISH_API_KEY=...
-
 # Gladia
 GLADIA_API_KEY=...
 GLADIA_REGION=...
 
 # Google
 GOOGLE_API_KEY=...
-GOOGLE_VERTEX_TEST_CREDENTIALS=...
 GOOGLE_CLOUD_PROJECT_ID=...
-GOOGLE_CLOUD_LOCATION=...
 GOOGLE_TEST_CREDENTIALS=...
-
-# Gradium
-GRAPDIUM_API_KEY=...
-
-# Grok
-GROK_API_KEY=...
-
-# Groq
-GROQ_API_KEY=...
-
-# Heygen
-HEYGEN_API_KEY=...
-
-# Hume
-HUME_API_KEY=...
-HUME_VOICE_ID=...
-
-# Inworld
-INWORLD_API_KEY=...
-
-# Krisp
-KRISP_MODEL_PATH=...
-
-# Krisp Viva
-KRISP_VIVA_MODEL_PATH=...
-
-# LiveKit
-LIVEKIT_API_KEY=...
-LIVEKIT_API_SECRET=...
+GOOGLE_VERTEX_TEST_CREDENTIALS=...
 
 # LMNT
 LMNT_API_KEY=...
 LMNT_VOICE_ID=...
 
-# MiniMax
-MINIMAX_API_KEY=...
-MINIMAX_GROUP_ID=...
-
-# Mistral
-MISTRAL_API_KEY=...
-
-# Neuphonic
-NEUPHONIC_API_KEY=...
-
-# NVIDIA
-NVIDIA_API_KEY=...
+# PlayHT
+PLAY_HT_USER_ID=...
+PLAY_HT_API_KEY=...
 
 # OpenAI
 OPENAI_API_KEY=...
@@ -125,73 +69,74 @@ OPENAI_API_KEY=...
 # OpenPipe
 OPENPIPE_API_KEY=...
 
-# OpenRouter
-OPENROUTER_API_KEY=...
-
-# Perplexity
-PERPLEXITY_API_KEY=...
-
-# Picovoice Koala
-KOALA_ACCESS_KEY=...
-
-# Piper
-PIPER_BASE_URL=...
-
-# PlayHT
-PLAYHT_USER_ID=...
-PLAYHT_API_KEY=...
-
-# Plivo
-PLIVO_AUTH_ID=...
-PLIVO_AUTH_TOKEN=...
-
-# Qwen
-QWEN_API_KEY=...
-
-# Rime
-RIME_API_KEY=...
-RIME_VOICE_ID=...
-
-# SambaNova
-SAMBANOVA_API_KEY=...
-
-# Sarvam AI
-SARVAM_API_KEY=...
-
-# Sentry
-SENTRY_DSN=...
+# Tavus
+TAVUS_API_KEY=...
+TAVUS_REPLICA_ID=...
+TAVUS_PERSONA_ID=...
 
 # Simli
 SIMLI_API_KEY=...
 SIMLI_FACE_ID=...
 
-# Smart turn
-LOCAL_SMART_TURN_MODEL_PATH=...
-FAL_SMART_TURN_API_KEY=...
+# Krisp
+KRISP_MODEL_PATH=...
 
-# Soniox
-SONIOX_API_KEY=...
+# DeepSeek
+DEEPSEEK_API_KEY=...
 
-# Speechmatics
-SPEECHMATICS_API_KEY=...
+# Groq
+GROQ_API_KEY=...
 
-# Tavus
-TAVUS_API_KEY=...
-TAVUS_REPLICA_ID=...
+# Grok
+GROK_API_KEY=...
 
-# Telnyx
-TELNYX_API_KEY=...
-TELNYX_ACCOUNT_SID=...
+# Inworld
+INWORLD_API_KEY=...
 
 # Together.ai
 TOGETHER_API_KEY=...
 
+# Cerebras
+CEREBRAS_API_KEY=...
+
+# Fish Audio
+FISH_API_KEY=...
+
+# Assembly AI
+ASSEMBLYAI_API_KEY=...
+
+# OpenRouter
+OPENROUTER_API_KEY=...
+
+# Piper
+PIPER_BASE_URL=...
+
+# Smart turn
+LOCAL_SMART_TURN_MODEL_PATH=...
+FAL_SMART_TURN_API_KEY=...
+
 # Twilio
 TWILIO_ACCOUNT_SID=...
 TWILIO_AUTH_TOKEN=...
 
-# WhatsApp
-WHATSAPP_TOKEN=...
-WHATSAPP_WEBHOOK_VERIFICATION_TOKEN=...
-WHATSAPP_PHONE_NUMBER_ID=...
-WHATSAPP_APP_SECRET=...
+# MiniMax
+MINIMAX_API_KEY=...
+MINIMAX_GROUP_ID=...
+
+# Sarvam AI
+SARVAM_API_KEY=...
+
+# Soniox
+SONIOX_API_KEY=
+
+# Speechmatics
+SPEECHMATICS_API_KEY=...
+
+# SambaNova
+SAMBANOVA_API_KEY=...
+
+# Sentry
+SENTRY_DSN=...
+
+# Heygen
+HEYGEN_API_KEY=...

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

@@ -18,8 +18,8 @@ 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)
 

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

@@ -18,8 +18,8 @@ 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)
 

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

@@ -17,8 +17,8 @@ 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)
 

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

@@ -11,13 +11,13 @@ import sys
 from dotenv import load_dotenv
 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)
 
@@ -50,7 +50,7 @@ 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

@@ -15,10 +15,10 @@ 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.nvidia.tts import NvidiaTTSService
+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)
 
@@ -36,7 +36,7 @@ transport_params = {
 async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
     logger.info(f"Starting bot")
 
-    tts = NvidiaTTSService(api_key=os.getenv("NVIDIA_API_KEY"))
+    tts = FastPitchTTSService(api_key=os.getenv("NVIDIA_API_KEY"))
 
     task = PipelineTask(
         Pipeline([tts, transport.output()]),

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

@@ -9,18 +9,21 @@ import os
 from dotenv import load_dotenv
 from loguru import logger
 
-from pipecat.frames.frames import EndFrame, LLMContextFrame
+from pipecat.frames.frames import EndFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineTask
-from pipecat.processors.aggregators.llm_context import LLMContext
+from pipecat.processors.aggregators.openai_llm_context import (
+    OpenAILLMContext,
+    OpenAILLMContextFrame,
+)
 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)
 
@@ -60,7 +63,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
     # 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([OpenAILLMContextFrame(OpenAILLMContext(messages)), EndFrame()])
 
     runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
 

examples/foundational/03-still-frame.py

@@ -18,7 +18,7 @@ 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)
 

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

@@ -17,7 +17,7 @@ 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)
 

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(),
         ),
     )
 
@@ -77,12 +71,12 @@ async def run_example(webrtc_connection: SmallWebRTCConnection):
     messages = [
         {
             "role": "system",
-            "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
+            "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
         },
     ]
 
-    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.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.aggregators.llm_context import LLMContext
-from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
+from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
 from pipecat.runner.daily import configure
 from pipecat.services.cartesia.tts import CartesiaTTSService
 from pipecat.services.openai.llm import OpenAILLMService
-from pipecat.transports.daily.transport import DailyParams, DailyTransport
+from pipecat.transports.services.daily import DailyLogLevel, DailyParams, DailyTransport
 
 load_dotenv(override=True)
 
@@ -45,10 +40,10 @@ 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)
 
         tts = CartesiaTTSService(
             api_key=os.getenv("CARTESIA_API_KEY"),
@@ -60,12 +55,12 @@ async def main():
         messages = [
             {
                 "role": "system",
-                "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
+                "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
             },
         ]
 
-        context = LLMContext(messages)
-        context_aggregator = LLMContextAggregatorPair(context)
+        context = OpenAILLMContext(messages)
+        context_aggregator = llm.create_context_aggregator(context)
 
         pipeline = Pipeline(
             [
@@ -91,7 +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

@@ -12,27 +12,23 @@ import sys
 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 (
-    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.processors.aggregators.openai_llm_context import OpenAILLMContext
 from pipecat.runner.livekit import configure
 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)
 
@@ -50,8 +46,7 @@ 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(),
         ),
     )
 
@@ -69,13 +64,13 @@ async def main():
             "role": "system",
             "content": "You are a helpful LLM in a WebRTC call. "
             "Your goal is to demonstrate your capabilities in a succinct way. "
-            "Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. "
+            "Your output will be converted to audio so don't include special characters in your answers. "
             "Respond to what the user said in a creative and helpful way.",
         },
     ]
 
-    context = LLMContext(messages)
-    context_aggregator = LLMContextAggregatorPair(context)
+    context = OpenAILLMContext(messages)
+    context_aggregator = llm.create_context_aggregator(context)
 
     pipeline = Pipeline(
         [
@@ -103,7 +98,7 @@ 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 +115,7 @@ async def main():
 
         await task.queue_frames(
             [
-                InterruptionFrame(),
+                BotInterruptionFrame(),
                 UserStartedSpeakingFrame(),
                 TranscriptionFrame(
                     user_id=participant_id,

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

@@ -14,7 +14,6 @@ from loguru import logger
 from pipecat.frames.frames import (
     DataFrame,
     Frame,
-    LLMContextFrame,
     LLMFullResponseStartFrame,
     TextFrame,
 )
@@ -22,8 +21,10 @@ 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.openai_llm_context import (
+    OpenAILLMContext,
+    OpenAILLMContextFrame,
+)
 from pipecat.processors.aggregators.sentence import SentenceAggregator
 from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
 from pipecat.runner.types import RunnerArguments
@@ -32,7 +33,7 @@ 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)
 
@@ -155,7 +156,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
                 }
             ]
             frames.append(MonthFrame(month=month))
-            frames.append(LLMContextFrame(LLMContext(messages)))
+            frames.append(OpenAILLMContextFrame(OpenAILLMContext(messages)))
 
         task = PipelineTask(
             pipeline,

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

@@ -15,7 +15,6 @@ from loguru import logger
 
 from pipecat.frames.frames import (
     Frame,
-    LLMContextFrame,
     OutputAudioRawFrame,
     TextFrame,
     TTSAudioRawFrame,
@@ -25,8 +24,10 @@ 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.openai_llm_context import (
+    OpenAILLMContext,
+    OpenAILLMContextFrame,
+)
 from pipecat.processors.aggregators.sentence import SentenceAggregator
 from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
 from pipecat.services.cartesia.tts import CartesiaHttpTTSService
@@ -139,7 +140,7 @@ async def main():
             )
 
             task = PipelineTask(pipeline)
-            await task.queue_frame(LLMContextFrame(LLMContext(messages)))
+            await task.queue_frame(OpenAILLMContextFrame(OpenAILLMContext(messages)))
             await task.stop_when_done()
 
             await runner.run(task)

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

@@ -9,11 +9,8 @@ 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,
@@ -23,8 +20,7 @@ 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
@@ -32,8 +28,8 @@ 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)
 
@@ -65,20 +61,17 @@ 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(),
     ),
 }
 
@@ -100,12 +93,12 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
     messages = [
         {
             "role": "system",
-            "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
+            "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
         },
     ]
 
-    context = LLMContext(messages)
-    context_aggregator = LLMContextAggregatorPair(context)
+    context = OpenAILLMContext(messages)
+    context_aggregator = llm.create_context_aggregator(context)
 
     pipeline = Pipeline(
         [
@@ -134,7 +127,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         logger.info(f"Client connected")
         # Kick off the conversation.
         messages.append({"role": "system", "content": "Please introduce yourself to the user."})
-        await task.queue_frames([LLMRunFrame()])
+        await task.queue_frames([context_aggregator.user().get_context_frame()])
 
     @transport.event_handler("on_client_disconnected")
     async def on_client_disconnected(transport, client):

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

@@ -10,22 +10,17 @@ 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
@@ -33,7 +28,7 @@ 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 +65,7 @@ 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 +78,7 @@ 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,
@@ -92,8 +86,7 @@ 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(),
     ),
 }
 
@@ -113,12 +106,12 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
     messages = [
         {
             "role": "system",
-            "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
+            "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
         },
     ]
 
-    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"),
@@ -151,7 +144,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
     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):

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

@@ -9,24 +9,19 @@ import os
 from dotenv import load_dotenv
 from loguru import logger
 
-from pipecat.audio.turn.smart_turn.base_smart_turn import SmartTurnParams
-from pipecat.audio.turn.smart_turn.local_smart_turn_v3 import LocalSmartTurnAnalyzerV3
 from pipecat.audio.vad.silero import SileroVADAnalyzer
-from pipecat.audio.vad.vad_analyzer import VADParams
-from pipecat.frames.frames import LLMRunFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.aggregators.llm_context import LLMContext
-from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
+from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
 from pipecat.runner.types import RunnerArguments
 from pipecat.runner.utils import create_transport
-from pipecat.services.cartesia.stt import CartesiaSTTService
 from pipecat.services.cartesia.tts import CartesiaHttpTTSService
+from pipecat.services.deepgram.stt import DeepgramSTTService
 from pipecat.services.openai.llm import OpenAILLMService
 from pipecat.transports.base_transport import BaseTransport, TransportParams
-from pipecat.transports.daily.transport import DailyParams
-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)
 
@@ -38,20 +33,17 @@ 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(),
     ),
 }
 
@@ -59,7 +51,7 @@ transport_params = {
 async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
     logger.info(f"Starting bot")
 
-    stt = CartesiaSTTService(api_key=os.getenv("CARTESIA_API_KEY"))
+    stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
 
     tts = CartesiaHttpTTSService(
         api_key=os.getenv("CARTESIA_API_KEY"),
@@ -71,12 +63,12 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
     messages = [
         {
             "role": "system",
-            "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
+            "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
         },
     ]
 
-    context = LLMContext(messages)
-    context_aggregator = LLMContextAggregatorPair(context)
+    context = OpenAILLMContext(messages)
+    context_aggregator = llm.create_context_aggregator(context)
 
     pipeline = Pipeline(
         [
@@ -104,7 +96,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         logger.info(f"Client connected")
         # Kick off the conversation.
         messages.append({"role": "system", "content": "Please introduce yourself to the user."})
-        await task.queue_frames([LLMRunFrame()])
+        await task.queue_frames([context_aggregator.user().get_context_frame()])
 
     @transport.event_handler("on_client_disconnected")
     async def on_client_disconnected(transport, client):

examples/foundational/07-interruptible.py

@@ -9,24 +9,19 @@ import os
 from dotenv import load_dotenv
 from loguru import logger
 
-from pipecat.audio.turn.smart_turn.base_smart_turn import SmartTurnParams
-from pipecat.audio.turn.smart_turn.local_smart_turn_v3 import LocalSmartTurnAnalyzerV3
 from pipecat.audio.vad.silero import SileroVADAnalyzer
-from pipecat.audio.vad.vad_analyzer import VADParams
-from pipecat.frames.frames import LLMRunFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.aggregators.llm_context import LLMContext
-from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
+from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
 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)
 
@@ -37,20 +32,17 @@ 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(),
     ),
 }
 
@@ -70,12 +62,12 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
     messages = [
         {
             "role": "system",
-            "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
+            "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
         },
     ]
 
-    context = LLMContext(messages)
-    context_aggregator = LLMContextAggregatorPair(context)
+    context = OpenAILLMContext(messages)
+    context_aggregator = llm.create_context_aggregator(context)
 
     pipeline = Pipeline(
         [
@@ -103,7 +95,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         logger.info(f"Client connected")
         # Kick off the conversation.
         messages.append({"role": "system", "content": "Please introduce yourself to the user."})
-        await task.queue_frames([LLMRunFrame()])
+        await task.queue_frames([context_aggregator.user().get_context_frame()])
 
     @transport.event_handler("on_client_disconnected")
     async def on_client_disconnected(transport, client):

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

@@ -6,29 +6,26 @@
 
 import os
 
-import aiohttp
 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.processors.aggregators.openai_llm_context import OpenAILLMContext
 from pipecat.runner.types import RunnerArguments
 from pipecat.runner.utils import create_transport
+from pipecat.services.elevenlabs.tts import ElevenLabsTTSService
 from pipecat.services.openai.base_llm import BaseOpenAILLMService
 from pipecat.services.openai.llm import OpenAILLMService
 from pipecat.services.speechmatics.stt import SpeechmaticsSTTService
-from pipecat.services.speechmatics.tts import SpeechmaticsTTSService
 from pipecat.transcriptions.language import Language
 from pipecat.transports.base_transport import BaseTransport, TransportParams
-from pipecat.transports.daily.transport import DailyParams
-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)
 
@@ -52,127 +49,121 @@ transport_params = {
 
 
 async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
-    """Speechmatics STT and TTS Service Example
+    """Speechmatics STT Service Example
 
-    This example demonstrates using Speechmatics Speech-to-Text and Text-to-Speech services
-    with speaker diarization and intelligent speaker management. Key features:
+    This example demonstrates using Speechmatics Speech-to-Text service with speaker diarization and intelligent speaker management. Key features:
 
-    1. Speaker Diarization (STT)
+    1. Speaker Diarization
        - Automatically identifies and distinguishes between different speakers
        - First speaker is identified as 'S1', others get subsequent IDs
        - Uses `enable_diarization` parameter to manage speaker detection
 
-    2. Smart Speaker Control (STT)
+    2. Smart Speaker Control
        - `focus_speakers` parameter lets you target specific speakers (e.g. ["S1"])
        - Other speakers will be wrapped in PASSIVE tags
        - Only processes speech from focused speakers
        - Words from all speakers are wrapped with XML tags for clear speaker identification
        - Other speakers' speech only sent when focused speaker is active
 
-    3. Voice Activity Detection (STT)
+    3. Voice Activity Detection
        - Built-in VAD using `enable_vad` parameter
        - Remove `vad_analyzer` from `transport` config to use module's VAD
        - Emits speaker started/stopped events
 
-    4. Text-to-Speech (TTS)
-       - Low latency streaming audio synthesis
-       - Multiple voice options available including `sarah`, `theo`, and `megan`
-
-    5. Configuration Options
+    4. Configuration Options
        - `operating_point` parameter defaults to `ENHANCED` for optimal accuracy
        - Configurable `end_of_utterance_silence_trigger` (default 0.5s)
        - Customizable speaker formatting
        - Additional diarization settings available
 
-    For detailed information:
-    - STT: https://docs.speechmatics.com/rt-api-ref
-    - TTS: https://docs.speechmatics.com/text-to-speech/quickstart
+    For detailed information about operating points and configuration:
+    https://docs.speechmatics.com/rt-api-ref
     """
 
     logger.info(f"Starting bot")
-    async with aiohttp.ClientSession() as session:
-        stt = SpeechmaticsSTTService(
-            api_key=os.getenv("SPEECHMATICS_API_KEY"),
-            params=SpeechmaticsSTTService.InputParams(
-                language=Language.EN,
-                enable_vad=True,
-                enable_diarization=True,
-                focus_speakers=["S1"],
-                end_of_utterance_silence_trigger=0.5,
-                speaker_active_format="<{speaker_id}>{text}</{speaker_id}>",
-                speaker_passive_format="<PASSIVE><{speaker_id}>{text}</{speaker_id}></PASSIVE>",
+
+    stt = SpeechmaticsSTTService(
+        api_key=os.getenv("SPEECHMATICS_API_KEY"),
+        params=SpeechmaticsSTTService.InputParams(
+            language=Language.EN,
+            enable_vad=True,
+            enable_diarization=True,
+            focus_speakers=["S1"],
+            end_of_utterance_silence_trigger=0.5,
+            speaker_active_format="<{speaker_id}>{text}</{speaker_id}>",
+            speaker_passive_format="<PASSIVE><{speaker_id}>{text}</{speaker_id}></PASSIVE>",
+        ),
+    )
+
+    tts = ElevenLabsTTSService(
+        api_key=os.getenv("ELEVENLABS_API_KEY"),
+        voice_id=os.getenv("ELEVENLABS_VOICE_ID"),
+        model="eleven_turbo_v2_5",
+    )
+
+    llm = OpenAILLMService(
+        api_key=os.getenv("OPENAI_API_KEY"),
+        params=BaseOpenAILLMService.InputParams(temperature=0.75),
+    )
+
+    messages = [
+        {
+            "role": "system",
+            "content": (
+                "You are a helpful British assistant called Alfred. "
+                "Your goal is to demonstrate your capabilities in a succinct way. "
+                "Your output will be converted to audio so don't include special characters in your answers. "
+                "Always include punctuation in your responses. "
+                "Give very short replies - do not give longer replies unless strictly necessary. "
+                "Respond to what the user said in a concise, funny, creative and helpful way. "
+                "Use `<Sn/>` tags to identify different speakers - do not use tags in your replies. "
+                "Do not respond to speakers within `<PASSIVE/>` tags unless explicitly asked to. "
             ),
-        )
+        },
+    ]
 
-        tts = SpeechmaticsTTSService(
-            api_key=os.getenv("SPEECHMATICS_API_KEY"),
-            voice_id="sarah",
-            aiohttp_session=session,
-        )
+    context = OpenAILLMContext(messages)
+    context_aggregator = llm.create_context_aggregator(
+        context,
+        user_params=LLMUserAggregatorParams(aggregation_timeout=0.005),
+    )
 
-        llm = OpenAILLMService(
-            api_key=os.getenv("OPENAI_API_KEY"),
-            params=BaseOpenAILLMService.InputParams(temperature=0.75),
-        )
-
-        messages = [
-            {
-                "role": "system",
-                "content": (
-                    "You are a helpful British assistant called Sarah. "
-                    "Your goal is to demonstrate your capabilities in a succinct way. "
-                    "Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. "
-                    "Always include punctuation in your responses. "
-                    "Give very short replies - do not give longer replies unless strictly necessary. "
-                    "Respond to what the user said in a concise, funny, creative and helpful way. "
-                    "Use `<Sn/>` tags to identify different speakers - do not use tags in your replies. "
-                    "Do not respond to speakers within `<PASSIVE/>` tags unless explicitly asked to. "
-                ),
-            },
+    pipeline = Pipeline(
+        [
+            transport.input(),  # Transport user input
+            stt,
+            context_aggregator.user(),  # User responses
+            llm,  # LLM
+            tts,  # TTS
+            transport.output(),  # Transport bot output
+            context_aggregator.assistant(),  # Assistant spoken responses
         ]
+    )
 
-        context = LLMContext(messages)
-        context_aggregator = LLMContextAggregatorPair(
-            context,
-            user_params=LLMUserAggregatorParams(aggregation_timeout=0.005),
-        )
+    task = PipelineTask(
+        pipeline,
+        params=PipelineParams(
+            enable_metrics=True,
+            enable_usage_metrics=True,
+        ),
+        idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
+    )
 
-        pipeline = Pipeline(
-            [
-                transport.input(),  # Transport user input
-                stt,
-                context_aggregator.user(),  # User responses
-                llm,  # LLM
-                tts,  # TTS
-                transport.output(),  # Transport bot output
-                context_aggregator.assistant(),  # Assistant spoken responses
-            ]
-        )
+    @transport.event_handler("on_client_connected")
+    async def on_client_connected(transport, client):
+        logger.info(f"Client connected")
+        # Kick off the conversation.
+        messages.append({"role": "system", "content": "Say a short hello to the user."})
+        await task.queue_frames([context_aggregator.user().get_context_frame()])
 
-        task = PipelineTask(
-            pipeline,
-            params=PipelineParams(
-                enable_metrics=True,
-                enable_usage_metrics=True,
-            ),
-            idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
-        )
+    @transport.event_handler("on_client_disconnected")
+    async def on_client_disconnected(transport, client):
+        logger.info(f"Client disconnected")
+        await task.cancel()
 
-        @transport.event_handler("on_client_connected")
-        async def on_client_connected(transport, client):
-            logger.info(f"Client connected")
-            # Kick off the conversation.
-            messages.append({"role": "system", "content": "Say a short hello to the user."})
-            await task.queue_frames([LLMRunFrame()])
+    runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
 
-        @transport.event_handler("on_client_disconnected")
-        async def on_client_disconnected(transport, client):
-            logger.info(f"Client disconnected")
-            await task.cancel()
-
-        runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
-
-        await runner.run(task)
+    await runner.run(task)
 
 
 async def bot(runner_args: RunnerArguments):

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

@@ -6,33 +6,27 @@
 
 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 import (
     LLMUserAggregatorParams,
 )
-from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
+from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
 from pipecat.runner.types import RunnerArguments
 from pipecat.runner.utils import create_transport
+from pipecat.services.elevenlabs.tts import ElevenLabsTTSService
 from pipecat.services.openai.base_llm import BaseOpenAILLMService
 from pipecat.services.openai.llm import OpenAILLMService
 from pipecat.services.speechmatics.stt import SpeechmaticsSTTService
-from pipecat.services.speechmatics.tts import SpeechmaticsTTSService
 from pipecat.transcriptions.language import Language
 from pipecat.transports.base_transport import BaseTransport, TransportParams
-from pipecat.transports.daily.transport import DailyParams
-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)
 
@@ -43,125 +37,116 @@ 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):
-    """Run example using Speechmatics STT and TTS.
+    """Run example using Speechmatics STT.
 
-    This example demonstrates a complete Speechmatics integration with both Speech-to-Text
-    and Text-to-Speech services:
+    This example will use diarization within our STT service and output the words spoken by
+    each individual speaker and wrap them with XML tags for the LLM to process. Note the
+    instructions in the system context for the LLM. This greatly improves the conversation
+    experience by allowing the LLM to understand who is speaking in a multi-party call.
 
-    STT Features:
-    - Diarization to identify and distinguish between different speakers
-    - Words spoken by each speaker are wrapped with XML tags for LLM processing
-    - System context instructions help the LLM understand multi-party conversations
-    - ENHANCED operating point by default for optimal accuracy
+    By default, this example will use our ENHANCED operating point, which is optimized for
+    high accuracy. You can change this by setting the `operating_point` parameter to a different
+    value.
 
-    TTS Features:
-    - Low latency streaming audio synthesis
-    - Multiple voice options available including `sarah`, `theo`, and `megan`
-
-    For more information:
-    - STT: https://docs.speechmatics.com/rt-api-ref
-    - TTS: https://docs.speechmatics.com/text-to-speech/quickstart
+    For more information on operating points, see the Speechmatics documentation:
+    https://docs.speechmatics.com/rt-api-ref
     """
     logger.info(f"Starting bot")
 
-    async with aiohttp.ClientSession() as session:
-        stt = SpeechmaticsSTTService(
-            api_key=os.getenv("SPEECHMATICS_API_KEY"),
-            params=SpeechmaticsSTTService.InputParams(
-                language=Language.EN,
-                enable_diarization=True,
-                end_of_utterance_silence_trigger=0.5,
-                speaker_active_format="<{speaker_id}>{text}</{speaker_id}>",
+    stt = SpeechmaticsSTTService(
+        api_key=os.getenv("SPEECHMATICS_API_KEY"),
+        params=SpeechmaticsSTTService.InputParams(
+            language=Language.EN,
+            enable_diarization=True,
+            end_of_utterance_silence_trigger=0.5,
+            speaker_active_format="<{speaker_id}>{text}</{speaker_id}>",
+        ),
+    )
+
+    tts = ElevenLabsTTSService(
+        api_key=os.getenv("ELEVENLABS_API_KEY"),
+        voice_id=os.getenv("ELEVENLABS_VOICE_ID"),
+        model="eleven_turbo_v2_5",
+    )
+
+    llm = OpenAILLMService(
+        api_key=os.getenv("OPENAI_API_KEY"),
+        params=BaseOpenAILLMService.InputParams(temperature=0.75),
+    )
+
+    messages = [
+        {
+            "role": "system",
+            "content": (
+                "You are a helpful British assistant called Alfred. "
+                "Your goal is to demonstrate your capabilities in a succinct way. "
+                "Your output will be converted to audio so don't include special characters in your answers. "
+                "Always include punctuation in your responses. "
+                "Give very short replies - do not give longer replies unless strictly necessary. "
+                "Respond to what the user said in a concise, funny, creative and helpful way. "
+                "Use `<Sn/>` tags to identify different speakers - do not use tags in your replies."
             ),
-        )
+        },
+    ]
 
-        tts = SpeechmaticsTTSService(
-            api_key=os.getenv("SPEECHMATICS_API_KEY"),
-            voice_id="sarah",
-            aiohttp_session=session,
-        )
+    context = OpenAILLMContext(messages)
+    context_aggregator = llm.create_context_aggregator(
+        context,
+        user_params=LLMUserAggregatorParams(aggregation_timeout=0.005),
+    )
 
-        llm = OpenAILLMService(
-            api_key=os.getenv("OPENAI_API_KEY"),
-            params=BaseOpenAILLMService.InputParams(temperature=0.75),
-        )
-
-        messages = [
-            {
-                "role": "system",
-                "content": (
-                    "You are a helpful British assistant called Sarah. "
-                    "Your goal is to demonstrate your capabilities in a succinct way. "
-                    "Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. "
-                    "Always include punctuation in your responses. "
-                    "Give very short replies - do not give longer replies unless strictly necessary. "
-                    "Respond to what the user said in a concise, funny, creative and helpful way. "
-                    "Use `<Sn/>` tags to identify different speakers - do not use tags in your replies."
-                ),
-            },
+    pipeline = Pipeline(
+        [
+            transport.input(),  # Transport user input
+            stt,  # STT
+            context_aggregator.user(),  # User responses
+            llm,  # LLM
+            tts,  # TTS
+            transport.output(),  # Transport bot output
+            context_aggregator.assistant(),  # Assistant spoken responses
         ]
+    )
 
-        context = LLMContext(messages)
-        context_aggregator = LLMContextAggregatorPair(
-            context,
-            user_params=LLMUserAggregatorParams(aggregation_timeout=0.005),
-        )
+    task = PipelineTask(
+        pipeline,
+        params=PipelineParams(
+            enable_metrics=True,
+            enable_usage_metrics=True,
+        ),
+        idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
+    )
 
-        pipeline = Pipeline(
-            [
-                transport.input(),  # Transport user input
-                stt,  # STT
-                context_aggregator.user(),  # User responses
-                llm,  # LLM
-                tts,  # TTS
-                transport.output(),  # Transport bot output
-                context_aggregator.assistant(),  # Assistant spoken responses
-            ]
-        )
+    @transport.event_handler("on_client_connected")
+    async def on_client_connected(transport, client):
+        logger.info(f"Client connected")
+        # Kick off the conversation.
+        messages.append({"role": "system", "content": "Say a short hello to the user."})
+        await task.queue_frames([context_aggregator.user().get_context_frame()])
 
-        task = PipelineTask(
-            pipeline,
-            params=PipelineParams(
-                enable_metrics=True,
-                enable_usage_metrics=True,
-            ),
-            idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
-        )
+    @transport.event_handler("on_client_disconnected")
+    async def on_client_disconnected(transport, client):
+        logger.info(f"Client disconnected")
+        await task.cancel()
 
-        @transport.event_handler("on_client_connected")
-        async def on_client_connected(transport, client):
-            logger.info(f"Client connected")
-            # Kick off the conversation.
-            messages.append({"role": "system", "content": "Say a short hello to the user."})
-            await task.queue_frames([LLMRunFrame()])
+    runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
 
-        @transport.event_handler("on_client_disconnected")
-        async def on_client_disconnected(transport, client):
-            logger.info(f"Client disconnected")
-            await task.cancel()
-
-        runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
-
-        await runner.run(task)
+    await runner.run(task)
 
 
 async def bot(runner_args: RunnerArguments):

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

@@ -10,24 +10,19 @@ import os
 from dotenv import load_dotenv
 from loguru import logger
 
-from pipecat.audio.turn.smart_turn.base_smart_turn import SmartTurnParams
-from pipecat.audio.turn.smart_turn.local_smart_turn_v3 import LocalSmartTurnAnalyzerV3
 from pipecat.audio.vad.silero import SileroVADAnalyzer
-from pipecat.audio.vad.vad_analyzer import VADParams
-from pipecat.frames.frames import LLMRunFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.aggregators.llm_context import LLMContext
-from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
+from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
 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.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)
 
@@ -35,20 +30,17 @@ 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(),
     ),
 }
 
@@ -70,12 +62,12 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
     messages = [
         {
             "role": "system",
-            "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
+            "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
         },
     ]
 
-    context = LLMContext(messages)
-    context_aggregator = LLMContextAggregatorPair(context)
+    context = OpenAILLMContext(messages)
+    context_aggregator = llm.create_context_aggregator(context)
 
     pipeline = Pipeline(
         [
@@ -102,7 +94,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         logger.info(f"Client connected")
         # Kick off the conversation.
         messages.append({"role": "system", "content": "Please introduce yourself to the user."})
-        await task.queue_frames([LLMRunFrame()])
+        await task.queue_frames([context_aggregator.user().get_context_frame()])
 
     @transport.event_handler("on_client_disconnected")
     async def on_client_disconnected(transport, client):

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

@@ -11,24 +11,19 @@ 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.processors.aggregators.openai_llm_context import OpenAILLMContext
 from pipecat.runner.types import RunnerArguments
 from pipecat.runner.utils import create_transport
 from pipecat.services.deepgram.stt import DeepgramSTTService
 from pipecat.services.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)
 
@@ -39,20 +34,17 @@ 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(),
     ),
 }
 
@@ -74,6 +66,9 @@ 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"))
@@ -81,12 +76,12 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         messages = [
             {
                 "role": "system",
-                "content": "You are very knowledgable about dogs. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
+                "content": "You are very knowledgable about dogs. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
             },
         ]
 
-        context = LLMContext(messages)
-        context_aggregator = LLMContextAggregatorPair(context)
+        context = OpenAILLMContext(messages)
+        context_aggregator = llm.create_context_aggregator(context)
 
         pipeline = Pipeline(
             [
@@ -114,7 +109,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
             logger.info(f"Client connected")
             # Kick off the conversation.
             messages.append({"role": "system", "content": "Please introduce yourself to the user."})
-            await task.queue_frames([LLMRunFrame()])
+            await task.queue_frames([context_aggregator.user().get_context_frame()])
 
         @transport.event_handler("on_client_disconnected")
         async def on_client_disconnected(transport, client):

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

@@ -11,24 +11,19 @@ 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.processors.aggregators.openai_llm_context import OpenAILLMContext
 from pipecat.runner.types import RunnerArguments
 from pipecat.runner.utils import create_transport
 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)
 
@@ -40,20 +35,17 @@ 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(),
     ),
 }
 
@@ -76,12 +68,12 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         messages = [
             {
                 "role": "system",
-                "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
+                "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
             },
         ]
 
-        context = LLMContext(messages)
-        context_aggregator = LLMContextAggregatorPair(context)
+        context = OpenAILLMContext(messages)
+        context_aggregator = llm.create_context_aggregator(context)
 
         pipeline = Pipeline(
             [
@@ -109,7 +101,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
             logger.info(f"Client connected")
             # Kick off the conversation.
             messages.append({"role": "system", "content": "Please introduce yourself to the user."})
-            await task.queue_frames([LLMRunFrame()])
+            await task.queue_frames([context_aggregator.user().get_context_frame()])
 
         @transport.event_handler("on_client_disconnected")
         async def on_client_disconnected(transport, client):

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

@@ -10,24 +10,19 @@ import os
 from dotenv import load_dotenv
 from loguru import logger
 
-from pipecat.audio.turn.smart_turn.base_smart_turn import SmartTurnParams
-from pipecat.audio.turn.smart_turn.local_smart_turn_v3 import LocalSmartTurnAnalyzerV3
 from pipecat.audio.vad.silero import SileroVADAnalyzer
-from pipecat.audio.vad.vad_analyzer import VADParams
-from pipecat.frames.frames import LLMRunFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.aggregators.llm_context import LLMContext
-from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
+from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
 from pipecat.runner.types import RunnerArguments
 from pipecat.runner.utils import create_transport
 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)
 
@@ -39,20 +34,17 @@ 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(),
     ),
 }
 
@@ -72,12 +64,12 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
     messages = [
         {
             "role": "system",
-            "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
+            "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
         },
     ]
 
-    context = LLMContext(messages)
-    context_aggregator = LLMContextAggregatorPair(context)
+    context = OpenAILLMContext(messages)
+    context_aggregator = llm.create_context_aggregator(context)
 
     pipeline = Pipeline(
         [
@@ -105,7 +97,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         logger.info(f"Client connected")
         # Kick off the conversation.
         messages.append({"role": "system", "content": "Please introduce yourself to the user."})
-        await task.queue_frames([LLMRunFrame()])
+        await task.queue_frames([context_aggregator.user().get_context_frame()])
 
     @transport.event_handler("on_client_disconnected")
     async def on_client_disconnected(transport, client):

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

@@ -1,174 +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.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=0.5,
-    )
-
-
-# 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: (
-        lambda aic: DailyParams(
-            audio_in_enabled=True,
-            audio_out_enabled=True,
-            vad_analyzer=aic.create_vad_analyzer(lookback_buffer_size=6.0, sensitivity=6.0),
-            turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
-            audio_in_filter=aic,
-        )
-    )(_create_aic_filter()),
-    "twilio": lambda: (
-        lambda aic: FastAPIWebsocketParams(
-            audio_in_enabled=True,
-            audio_out_enabled=True,
-            vad_analyzer=aic.create_vad_analyzer(lookback_buffer_size=6.0, sensitivity=6.0),
-            turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
-            audio_in_filter=aic,
-        )
-    )(_create_aic_filter()),
-    "webrtc": lambda: (
-        lambda aic: TransportParams(
-            audio_in_enabled=True,
-            audio_out_enabled=True,
-            vad_analyzer=aic.create_vad_analyzer(lookback_buffer_size=6.0, sensitivity=6.0),
-            turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
-            audio_in_filter=aic,
-        )
-    )(_create_aic_filter()),
-}
-
-
-async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
-    logger.info(f"Starting bot")
-
-    stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
-
-    tts = CartesiaTTSService(
-        api_key=os.getenv("CARTESIA_API_KEY"),
-        voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
-    )
-
-    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
-
-    messages = [
-        {
-            "role": "system",
-            "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
-        },
-    ]
-
-    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/07ae-interruptible-hume.py

@@ -1,153 +0,0 @@
-#
-# Copyright (c) 2024–2025, Daily
-#
-# SPDX-License-Identifier: BSD 2-Clause License
-#
-
-import os
-
-from dotenv import load_dotenv
-from loguru import logger
-
-from pipecat.audio.turn.smart_turn.base_smart_turn import SmartTurnParams
-from pipecat.audio.turn.smart_turn.local_smart_turn_v3 import LocalSmartTurnAnalyzerV3
-from pipecat.audio.vad.silero import SileroVADAnalyzer
-from pipecat.audio.vad.vad_analyzer import VADParams
-from pipecat.frames.frames import LLMRunFrame, TTSTextFrame
-from pipecat.observers.loggers.debug_log_observer import DebugLogObserver, FrameEndpoint
-from pipecat.pipeline.pipeline import Pipeline
-from pipecat.pipeline.runner import PipelineRunner
-from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.aggregators.llm_context import LLMContext
-from pipecat.processors.aggregators.llm_response_universal import (
-    LLMContextAggregatorPair,
-)
-from pipecat.processors.frameworks.rtvi import RTVIConfig, RTVIObserver, RTVIProcessor
-from pipecat.runner.types import RunnerArguments
-from pipecat.runner.utils import create_transport
-from pipecat.services.deepgram.stt import DeepgramSTTService
-from pipecat.services.hume.tts import HUME_SAMPLE_RATE, HumeTTSService
-from pipecat.services.openai.llm import OpenAILLMService
-from pipecat.transports.base_output import BaseOutputTransport
-from pipecat.transports.base_transport import BaseTransport, TransportParams
-from pipecat.transports.daily.transport import DailyParams
-from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
-
-load_dotenv(override=True)
-
-
-# We store functions so objects (e.g. SileroVADAnalyzer) don't get
-# instantiated. The function will be called when the desired transport gets
-# selected.
-transport_params = {
-    "daily": lambda: DailyParams(
-        audio_in_enabled=True,
-        audio_out_enabled=True,
-        vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
-        turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
-    ),
-    "twilio": lambda: FastAPIWebsocketParams(
-        audio_in_enabled=True,
-        audio_out_enabled=True,
-        vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
-        turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
-    ),
-    "webrtc": lambda: TransportParams(
-        audio_in_enabled=True,
-        audio_out_enabled=True,
-        vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
-        turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
-    ),
-}
-
-
-async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
-    logger.info(f"Starting bot")
-
-    stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
-
-    tts = HumeTTSService(
-        api_key=os.getenv("HUME_API_KEY"),
-        # Replace with your Hume voice ID
-        voice_id="f898a92e-685f-43fa-985b-a46920f0650b",
-    )
-
-    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
-
-    messages = [
-        {
-            "role": "system",
-            "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
-        },
-    ]
-
-    context = LLMContext(messages)
-    context_aggregator = LLMContextAggregatorPair(context)
-
-    rtvi = RTVIProcessor(config=RTVIConfig(config=[]))
-
-    pipeline = Pipeline(
-        [
-            transport.input(),  # Transport user input
-            rtvi,
-            stt,
-            context_aggregator.user(),  # User responses
-            llm,  # LLM
-            tts,  # TTS (HumeTTSService with word timestamps)
-            transport.output(),  # Transport bot output
-            context_aggregator.assistant(),  # Assistant spoken responses
-        ]
-    )
-
-    task = PipelineTask(
-        pipeline,
-        params=PipelineParams(
-            enable_metrics=True,
-            enable_usage_metrics=True,
-            audio_out_sample_rate=HUME_SAMPLE_RATE,
-        ),
-        idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
-        observers=[
-            RTVIObserver(rtvi),
-            DebugLogObserver(
-                frame_types={
-                    TTSTextFrame: (BaseOutputTransport, FrameEndpoint.SOURCE),
-                }
-            ),
-        ],
-    )
-
-    @rtvi.event_handler("on_client_ready")
-    async def on_client_ready(rtvi):
-        await rtvi.set_bot_ready()
-
-    @transport.event_handler("on_client_connected")
-    async def on_client_connected(transport, client):
-        logger.info(f"Client connected")
-        logger.info(
-            "💡 Word timestamps are enabled! Watch the console for TTSTextFrame logs showing each word with its PTS."
-        )
-        # Kick off the conversation.
-        messages.append({"role": "system", "content": "Please introduce yourself to the user."})
-        await task.queue_frames([LLMRunFrame()])
-
-    @transport.event_handler("on_client_disconnected")
-    async def on_client_disconnected(transport, client):
-        logger.info(f"Client disconnected")
-        await task.cancel()
-
-    runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
-
-    await runner.run(task)
-
-
-async def bot(runner_args: RunnerArguments):
-    """Main bot entry point compatible with Pipecat Cloud."""
-    transport = await create_transport(runner_args, transport_params)
-    await run_bot(transport, runner_args)
-
-
-if __name__ == "__main__":
-    from pipecat.runner.run import main
-
-    main()

examples/foundational/07af-interruptible-gradium.py

@@ -1,127 +0,0 @@
-#
-# Copyright (c) 2024–2025, Daily
-#
-# SPDX-License-Identifier: BSD 2-Clause License
-#
-
-import os
-
-from dotenv import load_dotenv
-from loguru import logger
-
-from pipecat.audio.turn.smart_turn.base_smart_turn import SmartTurnParams
-from pipecat.audio.turn.smart_turn.local_smart_turn_v3 import LocalSmartTurnAnalyzerV3
-from pipecat.audio.vad.silero import SileroVADAnalyzer
-from pipecat.audio.vad.vad_analyzer import VADParams
-from pipecat.frames.frames import LLMRunFrame
-from pipecat.pipeline.pipeline import Pipeline
-from pipecat.pipeline.runner import PipelineRunner
-from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.aggregators.llm_context import LLMContext
-from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
-from pipecat.runner.types import RunnerArguments
-from pipecat.runner.utils import create_transport
-from pipecat.services.gradium.stt import GradiumSTTService
-from pipecat.services.gradium.tts import GradiumTTSService
-from pipecat.services.openai.llm import OpenAILLMService
-from pipecat.transports.base_transport import BaseTransport, TransportParams
-from pipecat.transports.daily.transport import DailyParams
-from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
-
-load_dotenv(override=True)
-
-# We store functions so objects (e.g. SileroVADAnalyzer) don't get
-# instantiated. The function will be called when the desired transport gets
-# selected.
-transport_params = {
-    "daily": lambda: DailyParams(
-        audio_in_enabled=True,
-        audio_out_enabled=True,
-        vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
-        turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
-    ),
-    "twilio": lambda: FastAPIWebsocketParams(
-        audio_in_enabled=True,
-        audio_out_enabled=True,
-        vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
-        turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
-    ),
-    "webrtc": lambda: TransportParams(
-        audio_in_enabled=True,
-        audio_out_enabled=True,
-        vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
-        turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
-    ),
-}
-
-
-async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
-    logger.info(f"Starting bot")
-
-    stt = GradiumSTTService(api_key=os.getenv("GRADIUM_API_KEY"))
-
-    tts = GradiumTTSService(
-        api_key=os.getenv("GRADIUM_API_KEY"),
-        voice_id="YTpq7expH9539ERJ",
-    )
-
-    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
-
-    messages = [
-        {
-            "role": "system",
-            "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
-        },
-    ]
-
-    context = LLMContext(messages)
-    context_aggregator = LLMContextAggregatorPair(context)
-
-    pipeline = Pipeline(
-        [
-            transport.input(),  # Transport user input
-            stt,
-            context_aggregator.user(),  # User responses
-            llm,  # LLM
-            tts,  # TTS
-            transport.output(),  # Transport bot output
-            context_aggregator.assistant(),  # Assistant spoken responses
-        ]
-    )
-
-    task = PipelineTask(
-        pipeline,
-        params=PipelineParams(
-            enable_metrics=True,
-            enable_usage_metrics=True,
-        ),
-        idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
-    )
-
-    @transport.event_handler("on_client_connected")
-    async def on_client_connected(transport, client):
-        logger.info(f"Client connected")
-        # Kick off the conversation.
-        messages.append({"role": "system", "content": "Please introduce yourself to the user."})
-        await task.queue_frames([LLMRunFrame()])
-
-    @transport.event_handler("on_client_disconnected")
-    async def on_client_disconnected(transport, client):
-        logger.info(f"Client disconnected")
-        await task.cancel()
-
-    runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
-
-    await runner.run(task)
-
-
-async def bot(runner_args: RunnerArguments):
-    """Main bot entry point compatible with Pipecat Cloud."""
-    transport = await create_transport(runner_args, transport_params)
-    await run_bot(transport, runner_args)
-
-
-if __name__ == "__main__":
-    from pipecat.runner.run import main
-
-    main()

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

@@ -15,24 +15,26 @@ 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.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 (
+    LLMAssistantContextAggregator,
+    LLMUserContextAggregator,
+)
+from pipecat.processors.aggregators.openai_llm_context import (
+    OpenAILLMContext,
+)
 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)
 
@@ -53,20 +55,17 @@ 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(),
     ),
 }
 
@@ -101,18 +100,19 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
     )
     lc = LangchainProcessor(history_chain)
 
-    context = LLMContext()
-    context_aggregator = LLMContextAggregatorPair(context)
+    context = OpenAILLMContext()
+    tma_in = LLMUserContextAggregator(context=context)
+    tma_out = LLMAssistantContextAggregator(context=context)
 
     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
         ]
     )
 
@@ -129,7 +129,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
     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
+        # An `OpenAILLMContextFrame` 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."})]

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

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

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

@@ -1,137 +0,0 @@
-#
-# Copyright (c) 2024–2025, Daily
-#
-# SPDX-License-Identifier: BSD 2-Clause License
-#
-
-
-import os
-
-from dotenv import load_dotenv
-from loguru import logger
-
-from pipecat.audio.turn.smart_turn.base_smart_turn import SmartTurnParams
-from pipecat.audio.turn.smart_turn.local_smart_turn_v3 import LocalSmartTurnAnalyzerV3
-from pipecat.audio.vad.silero import SileroVADAnalyzer
-from pipecat.audio.vad.vad_analyzer import VADParams
-from pipecat.frames.frames import LLMRunFrame
-from pipecat.pipeline.pipeline import Pipeline
-from pipecat.pipeline.runner import PipelineRunner
-from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.aggregators.llm_context import LLMContext
-from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
-from pipecat.runner.types import RunnerArguments
-from pipecat.runner.utils import create_transport
-from pipecat.services.aws.llm import AWSBedrockLLMService
-from pipecat.services.deepgram.stt_sagemaker import DeepgramSageMakerSTTService
-from pipecat.services.deepgram.tts import DeepgramTTSService
-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")
-
-    # Initialize Deepgram SageMaker STT Service
-    # This requires:
-    # - AWS credentials configured (via environment variables or AWS CLI)
-    # - A deployed SageMaker endpoint with Deepgram model
-    stt = DeepgramSageMakerSTTService(
-        endpoint_name=os.getenv("SAGEMAKER_ENDPOINT_NAME"),
-        region=os.getenv("AWS_REGION"),
-    )
-
-    tts = DeepgramTTSService(api_key=os.getenv("DEEPGRAM_API_KEY"), voice="aura-2-andromeda-en")
-
-    llm = AWSBedrockLLMService(
-        aws_region=os.getenv("AWS_REGION"),
-        model="us.amazon.nova-pro-v1:0",
-        params=AWSBedrockLLMService.InputParams(temperature=0.8),
-    )
-
-    messages = [
-        {
-            "role": "system",
-            "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
-        },
-    ]
-
-    context = LLMContext(messages)
-    context_aggregator = LLMContextAggregatorPair(context)
-
-    pipeline = Pipeline(
-        [
-            transport.input(),  # Transport user input
-            stt,  # STT
-            context_aggregator.user(),  # User responses
-            llm,  # LLM
-            tts,  # TTS
-            transport.output(),  # Transport bot output
-            context_aggregator.assistant(),  # Assistant spoken responses
-        ]
-    )
-
-    task = PipelineTask(
-        pipeline,
-        params=PipelineParams(
-            enable_metrics=True,
-            enable_usage_metrics=True,
-        ),
-        idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
-    )
-
-    @transport.event_handler("on_client_connected")
-    async def on_client_connected(transport, client):
-        logger.info(f"Client connected")
-        # Kick off the conversation.
-        messages.append({"role": "system", "content": "Please introduce yourself to the user."})
-        await task.queue_frames([LLMRunFrame()])
-
-    @transport.event_handler("on_client_disconnected")
-    async def on_client_disconnected(transport, client):
-        logger.info(f"Client disconnected")
-        await task.cancel()
-
-    runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
-
-    await runner.run(task)
-
-
-async def bot(runner_args: RunnerArguments):
-    """Main bot entry point compatible with Pipecat Cloud."""
-    transport = await create_transport(runner_args, transport_params)
-    await run_bot(transport, runner_args)
-
-
-if __name__ == "__main__":
-    from pipecat.runner.run import main
-
-    main()

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

@@ -12,24 +12,23 @@ 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.processors.aggregators.openai_llm_context import OpenAILLMContext
 from pipecat.runner.types import RunnerArguments
 from pipecat.runner.utils import create_transport
 from pipecat.services.deepgram.stt import DeepgramSTTService
 from pipecat.services.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)
 
@@ -68,12 +67,12 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
     messages = [
         {
             "role": "system",
-            "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
+            "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
         },
     ]
 
-    context = LLMContext(messages)
-    context_aggregator = LLMContextAggregatorPair(context)
+    context = OpenAILLMContext(messages)
+    context_aggregator = llm.create_context_aggregator(context)
 
     pipeline = Pipeline(
         [
@@ -98,18 +97,18 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
 
     @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):

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

@@ -10,24 +10,19 @@ import os
 from dotenv import load_dotenv
 from loguru import logger
 
-from pipecat.audio.turn.smart_turn.base_smart_turn import SmartTurnParams
-from pipecat.audio.turn.smart_turn.local_smart_turn_v3 import LocalSmartTurnAnalyzerV3
 from pipecat.audio.vad.silero import SileroVADAnalyzer
-from pipecat.audio.vad.vad_analyzer import VADParams
-from pipecat.frames.frames import LLMRunFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.aggregators.llm_context import LLMContext
-from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
+from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
 from pipecat.runner.types import RunnerArguments
 from pipecat.runner.utils import create_transport
 from pipecat.services.deepgram.stt import DeepgramSTTService
 from pipecat.services.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)
 
@@ -39,20 +34,17 @@ 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(),
     ),
 }
 
@@ -69,12 +61,12 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
     messages = [
         {
             "role": "system",
-            "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
+            "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
         },
     ]
 
-    context = LLMContext(messages)
-    context_aggregator = LLMContextAggregatorPair(context)
+    context = OpenAILLMContext(messages)
+    context_aggregator = llm.create_context_aggregator(context)
 
     pipeline = Pipeline(
         [
@@ -102,7 +94,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         logger.info(f"Client connected")
         # Kick off the conversation.
         messages.append({"role": "system", "content": "Please introduce yourself to the user."})
-        await task.queue_frames([LLMRunFrame()])
+        await task.queue_frames([context_aggregator.user().get_context_frame()])
 
     @transport.event_handler("on_client_disconnected")
     async def on_client_disconnected(transport, client):

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

@@ -11,24 +11,19 @@ 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.processors.aggregators.openai_llm_context import OpenAILLMContext
 from pipecat.runner.types import RunnerArguments
 from pipecat.runner.utils import create_transport
-from pipecat.services.elevenlabs.stt import ElevenLabsSTTService
+from pipecat.services.deepgram.stt import DeepgramSTTService
 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)
 
@@ -40,20 +35,17 @@ 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(),
     ),
 }
 
@@ -63,10 +55,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
 
     # 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", ""),
@@ -79,12 +68,12 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         messages = [
             {
                 "role": "system",
-                "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
+                "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
             },
         ]
 
-        context = LLMContext(messages)
-        context_aggregator = LLMContextAggregatorPair(context)
+        context = OpenAILLMContext(messages)
+        context_aggregator = llm.create_context_aggregator(context)
 
         pipeline = Pipeline(
             [
@@ -112,7 +101,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
             logger.info(f"Client connected")
             # Kick off the conversation.
             messages.append({"role": "system", "content": "Please introduce yourself to the user."})
-            await task.queue_frames([LLMRunFrame()])
+            await task.queue_frames([context_aggregator.user().get_context_frame()])
 
         @transport.event_handler("on_client_disconnected")
         async def on_client_disconnected(transport, client):

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

@@ -10,24 +10,19 @@ import os
 from dotenv import load_dotenv
 from loguru import logger
 
-from pipecat.audio.turn.smart_turn.base_smart_turn import SmartTurnParams
-from pipecat.audio.turn.smart_turn.local_smart_turn_v3 import LocalSmartTurnAnalyzerV3
 from pipecat.audio.vad.silero import SileroVADAnalyzer
-from pipecat.audio.vad.vad_analyzer import VADParams
-from pipecat.frames.frames import LLMRunFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.aggregators.llm_context import LLMContext
-from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
+from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
 from pipecat.runner.types import RunnerArguments
 from pipecat.runner.utils import create_transport
-from pipecat.services.elevenlabs.stt import ElevenLabsRealtimeSTTService
+from pipecat.services.deepgram.stt import DeepgramSTTService
 from pipecat.services.elevenlabs.tts import ElevenLabsTTSService
 from pipecat.services.openai.llm import OpenAILLMService
 from pipecat.transports.base_transport import BaseTransport, TransportParams
-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)
 
@@ -39,20 +34,17 @@ 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(),
     ),
 }
 
@@ -60,7 +52,7 @@ transport_params = {
 async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
     logger.info(f"Starting bot")
 
-    stt = ElevenLabsRealtimeSTTService(api_key=os.getenv("ELEVENLABS_API_KEY"))
+    stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
 
     tts = ElevenLabsTTSService(
         api_key=os.getenv("ELEVENLABS_API_KEY", ""),
@@ -72,12 +64,12 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
     messages = [
         {
             "role": "system",
-            "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
+            "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
         },
     ]
 
-    context = LLMContext(messages)
-    context_aggregator = LLMContextAggregatorPair(context)
+    context = OpenAILLMContext(messages)
+    context_aggregator = llm.create_context_aggregator(context)
 
     pipeline = Pipeline(
         [
@@ -105,7 +97,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         logger.info(f"Client connected")
         # Kick off the conversation.
         messages.append({"role": "system", "content": "Please introduce yourself to the user."})
-        await task.queue_frames([LLMRunFrame()])
+        await task.queue_frames([context_aggregator.user().get_context_frame()])
 
     @transport.event_handler("on_client_disconnected")
     async def on_client_disconnected(transport, client):

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

@@ -10,24 +10,19 @@ import os
 from dotenv import load_dotenv
 from loguru import logger
 
-from pipecat.audio.turn.smart_turn.base_smart_turn import SmartTurnParams
-from pipecat.audio.turn.smart_turn.local_smart_turn_v3 import LocalSmartTurnAnalyzerV3
 from pipecat.audio.vad.silero import SileroVADAnalyzer
-from pipecat.audio.vad.vad_analyzer import VADParams
-from pipecat.frames.frames import LLMRunFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.aggregators.llm_context import LLMContext
-from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
+from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
 from pipecat.runner.types import RunnerArguments
 from pipecat.runner.utils import create_transport
 from pipecat.services.deepgram.stt import DeepgramSTTService
 from pipecat.services.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)
 
@@ -38,20 +33,17 @@ 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(),
     ),
 }
 
@@ -72,12 +64,12 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
     messages = [
         {
             "role": "system",
-            "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
+            "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
         },
     ]
 
-    context = LLMContext(messages)
-    context_aggregator = LLMContextAggregatorPair(context)
+    context = OpenAILLMContext(messages)
+    context_aggregator = llm.create_context_aggregator(context)
 
     pipeline = Pipeline(
         [
@@ -105,7 +97,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         logger.info(f"Client connected")
         # Kick off the conversation.
         messages.append({"role": "system", "content": "Please introduce yourself to the user."})
-        await task.queue_frames([LLMRunFrame()])
+        await task.queue_frames([context_aggregator.user().get_context_frame()])
 
     @transport.event_handler("on_client_disconnected")
     async def on_client_disconnected(transport, client):

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

@@ -10,16 +10,11 @@ import os
 from dotenv import load_dotenv
 from loguru import logger
 
-from pipecat.audio.turn.smart_turn.base_smart_turn import SmartTurnParams
-from pipecat.audio.turn.smart_turn.local_smart_turn_v3 import LocalSmartTurnAnalyzerV3
 from pipecat.audio.vad.silero import SileroVADAnalyzer
-from pipecat.audio.vad.vad_analyzer import VADParams
-from pipecat.frames.frames import LLMRunFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.aggregators.llm_context import LLMContext
-from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
+from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
 from pipecat.runner.types import RunnerArguments
 from pipecat.runner.utils import create_transport
 from pipecat.services.deepgram.stt import DeepgramSTTService
@@ -27,8 +22,8 @@ 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)
 
@@ -39,20 +34,17 @@ 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(),
     ),
 }
 
@@ -74,12 +66,12 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
     messages = [
         {
             "role": "system",
-            "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
+            "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
         },
     ]
 
-    context = LLMContext(messages)
-    context_aggregator = LLMContextAggregatorPair(context)
+    context = OpenAILLMContext(messages)
+    context_aggregator = llm.create_context_aggregator(context)
 
     pipeline = Pipeline(
         [
@@ -107,7 +99,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         logger.info(f"Client connected")
         # Kick off the conversation.
         messages.append({"role": "system", "content": "Please introduce yourself to the user."})
-        await task.queue_frames([LLMRunFrame()])
+        await task.queue_frames([context_aggregator.user().get_context_frame()])
 
     @transport.event_handler("on_client_disconnected")
     async def on_client_disconnected(transport, client):

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

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

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

@@ -10,24 +10,19 @@ import os
 from dotenv import load_dotenv
 from loguru import logger
 
-from pipecat.audio.turn.smart_turn.base_smart_turn import SmartTurnParams
-from pipecat.audio.turn.smart_turn.local_smart_turn_v3 import LocalSmartTurnAnalyzerV3
 from pipecat.audio.vad.silero import SileroVADAnalyzer
-from pipecat.audio.vad.vad_analyzer import VADParams
-from pipecat.frames.frames import LLMRunFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.aggregators.llm_context import LLMContext
-from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
+from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
 from pipecat.runner.types import RunnerArguments
 from pipecat.runner.utils import create_transport
 from pipecat.services.azure.llm import AzureLLMService
 from pipecat.services.azure.stt import AzureSTTService
 from pipecat.services.azure.tts import 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)
 
@@ -38,20 +33,17 @@ 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(),
     ),
 }
 
@@ -78,12 +70,12 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
     messages = [
         {
             "role": "system",
-            "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
+            "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
         },
     ]
 
-    context = LLMContext(messages)
-    context_aggregator = LLMContextAggregatorPair(context)
+    context = OpenAILLMContext(messages)
+    context_aggregator = llm.create_context_aggregator(context)
 
     pipeline = Pipeline(
         [
@@ -111,7 +103,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         logger.info(f"Client connected")
         # Kick off the conversation.
         messages.append({"role": "system", "content": "Please introduce yourself to the user."})
-        await task.queue_frames([LLMRunFrame()])
+        await task.queue_frames([context_aggregator.user().get_context_frame()])
 
     @transport.event_handler("on_client_disconnected")
     async def on_client_disconnected(transport, client):

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

@@ -10,24 +10,19 @@ import os
 from dotenv import load_dotenv
 from loguru import logger
 
-from pipecat.audio.turn.smart_turn.base_smart_turn import SmartTurnParams
-from pipecat.audio.turn.smart_turn.local_smart_turn_v3 import LocalSmartTurnAnalyzerV3
 from pipecat.audio.vad.silero import SileroVADAnalyzer
-from pipecat.audio.vad.vad_analyzer import VADParams
-from pipecat.frames.frames import LLMRunFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.aggregators.llm_context import LLMContext
-from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
+from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
 from pipecat.runner.types import RunnerArguments
 from pipecat.runner.utils import create_transport
 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)
 
@@ -38,20 +33,17 @@ 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(),
     ),
 }
 
@@ -72,12 +64,12 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
     messages = [
         {
             "role": "system",
-            "content": "You are very knowledgable about dogs. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
+            "content": "You are very knowledgable about dogs. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
         },
     ]
 
-    context = LLMContext(messages)
-    context_aggregator = LLMContextAggregatorPair(context)
+    context = OpenAILLMContext(messages)
+    context_aggregator = llm.create_context_aggregator(context)
 
     pipeline = Pipeline(
         [
@@ -106,7 +98,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         logger.info(f"Client connected")
         # Kick off the conversation.
         messages.append({"role": "system", "content": "Please introduce yourself to the user."})
-        await task.queue_frames([LLMRunFrame()])
+        await task.queue_frames([context_aggregator.user().get_context_frame()])
 
     @transport.event_handler("on_client_disconnected")
     async def on_client_disconnected(transport, client):

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

@@ -11,24 +11,19 @@ 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.processors.aggregators.openai_llm_context import OpenAILLMContext
 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.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)
 
@@ -39,20 +34,17 @@ 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(),
     ),
 }
 
@@ -77,12 +69,12 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
     messages = [
         {
             "role": "system",
-            "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
+            "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
         },
     ]
 
-    context = LLMContext(messages)
-    context_aggregator = LLMContextAggregatorPair(context)
+    context = OpenAILLMContext(messages)
+    context_aggregator = llm.create_context_aggregator(context)
 
     pipeline = Pipeline(
         [
@@ -110,7 +102,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         logger.info(f"Client connected")
         # Kick off the conversation.
         messages.append({"role": "system", "content": "Please introduce yourself to the user."})
-        await task.queue_frames([LLMRunFrame()])
+        await task.queue_frames([context_aggregator.user().get_context_frame()])
 
     @transport.event_handler("on_client_disconnected")
     async def on_client_disconnected(transport, client):

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

@@ -11,24 +11,19 @@ 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.processors.aggregators.openai_llm_context import OpenAILLMContext
 from pipecat.runner.types import RunnerArguments
 from pipecat.runner.utils import create_transport
 from pipecat.services.deepgram.stt import DeepgramSTTService
 from pipecat.services.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)
 
@@ -39,20 +34,17 @@ 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(),
     ),
 }
 
@@ -75,12 +67,12 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         messages = [
             {
                 "role": "system",
-                "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
+                "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
             },
         ]
 
-        context = LLMContext(messages)
-        context_aggregator = LLMContextAggregatorPair(context)
+        context = OpenAILLMContext(messages)
+        context_aggregator = llm.create_context_aggregator(context)
 
         pipeline = Pipeline(
             [
@@ -108,7 +100,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
             logger.info(f"Client connected")
             # Kick off the conversation.
             messages.append({"role": "system", "content": "Please introduce yourself to the user."})
-            await task.queue_frames([LLMRunFrame()])
+            await task.queue_frames([context_aggregator.user().get_context_frame()])
 
         @transport.event_handler("on_client_disconnected")
         async def on_client_disconnected(transport, client):

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

@@ -10,16 +10,11 @@ import os
 from dotenv import load_dotenv
 from loguru import logger
 
-from pipecat.audio.turn.smart_turn.base_smart_turn import SmartTurnParams
-from pipecat.audio.turn.smart_turn.local_smart_turn_v3 import LocalSmartTurnAnalyzerV3
 from pipecat.audio.vad.silero import SileroVADAnalyzer
-from pipecat.audio.vad.vad_analyzer import VADParams
-from pipecat.frames.frames import LLMRunFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.aggregators.llm_context import LLMContext
-from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
+from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
 from pipecat.runner.types import RunnerArguments
 from pipecat.runner.utils import create_transport
 from pipecat.services.cartesia.tts import CartesiaTTSService
@@ -28,8 +23,8 @@ 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)
 
@@ -40,20 +35,17 @@ 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(),
     ),
 }
 
@@ -81,12 +73,12 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
     messages = [
         {
             "role": "system",
-            "content": f"You are a helpful LLM. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
+            "content": f"You are a helpful LLM. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
         },
     ]
 
-    context = LLMContext(messages)
-    context_aggregator = LLMContextAggregatorPair(context)
+    context = OpenAILLMContext(messages)
+    context_aggregator = llm.create_context_aggregator(context)
 
     pipeline = Pipeline(
         [
@@ -114,7 +106,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         logger.info(f"Client connected")
         # Kick off the conversation.
         messages.append({"role": "system", "content": "Please introduce yourself to the user."})
-        await task.queue_frames([LLMRunFrame()])
+        await task.queue_frames([context_aggregator.user().get_context_frame()])
 
     @transport.event_handler("on_client_disconnected")
     async def on_client_disconnected(transport, client):

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

@@ -10,24 +10,19 @@ import os
 from dotenv import load_dotenv
 from loguru import logger
 
-from pipecat.audio.turn.smart_turn.base_smart_turn import SmartTurnParams
-from pipecat.audio.turn.smart_turn.local_smart_turn_v3 import LocalSmartTurnAnalyzerV3
 from pipecat.audio.vad.silero import SileroVADAnalyzer
-from pipecat.audio.vad.vad_analyzer import VADParams
-from pipecat.frames.frames import LLMRunFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.aggregators.llm_context import LLMContext
-from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
+from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
 from pipecat.runner.types import RunnerArguments
 from pipecat.runner.utils import create_transport
 from pipecat.services.deepgram.stt import DeepgramSTTService
 from pipecat.services.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)
 
@@ -38,20 +33,17 @@ 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(),
     ),
 }
 
@@ -68,12 +60,12 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
     messages = [
         {
             "role": "system",
-            "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
+            "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
         },
     ]
 
-    context = LLMContext(messages)
-    context_aggregator = LLMContextAggregatorPair(context)
+    context = OpenAILLMContext(messages)
+    context_aggregator = llm.create_context_aggregator(context)
 
     pipeline = Pipeline(
         [
@@ -101,7 +93,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         logger.info(f"Client connected")
         # Kick off the conversation.
         messages.append({"role": "system", "content": "Please introduce yourself to the user."})
-        await task.queue_frames([LLMRunFrame()])
+        await task.queue_frames([context_aggregator.user().get_context_frame()])
 
     @transport.event_handler("on_client_disconnected")
     async def on_client_disconnected(transport, client):

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

@@ -10,25 +10,20 @@ 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.processors.aggregators.openai_llm_context import OpenAILLMContext
 from pipecat.runner.types import RunnerArguments
 from pipecat.runner.utils import create_transport
 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)
 
@@ -39,20 +34,17 @@ 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(),
     ),
 }
 
@@ -71,12 +63,12 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
     messages = [
         {
             "role": "system",
-            "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
+            "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
         },
     ]
 
-    context = LLMContext(messages)
-    context_aggregator = LLMContextAggregatorPair(
+    context = OpenAILLMContext(messages)
+    context_aggregator = llm.create_context_aggregator(
         context, user_params=LLMUserAggregatorParams(aggregation_timeout=0.05)
     )
 
@@ -106,7 +98,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         logger.info(f"Client connected")
         # Kick off the conversation.
         messages.append({"role": "system", "content": "Please introduce yourself to the user."})
-        await task.queue_frames([LLMRunFrame()])
+        await task.queue_frames([context_aggregator.user().get_context_frame()])
 
     @transport.event_handler("on_client_disconnected")
     async def on_client_disconnected(transport, client):

examples/foundational/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

@@ -8,24 +8,19 @@
 from dotenv import load_dotenv
 from loguru import logger
 
-from pipecat.audio.turn.smart_turn.base_smart_turn import SmartTurnParams
-from pipecat.audio.turn.smart_turn.local_smart_turn_v3 import LocalSmartTurnAnalyzerV3
 from pipecat.audio.vad.silero import SileroVADAnalyzer
-from pipecat.audio.vad.vad_analyzer import VADParams
-from pipecat.frames.frames import LLMRunFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.aggregators.llm_context import LLMContext
-from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
+from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
 from pipecat.runner.types import RunnerArguments
 from pipecat.runner.utils import create_transport
 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)
 
@@ -36,20 +31,17 @@ 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(),
     ),
 }
 
@@ -67,19 +59,19 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
 
     llm = AWSBedrockLLMService(
         aws_region="us-west-2",
-        model="us.anthropic.claude-haiku-4-5-20251001-v1:0",
-        params=AWSBedrockLLMService.InputParams(temperature=0.8),
+        model="us.anthropic.claude-3-5-haiku-20241022-v1:0",
+        params=AWSBedrockLLMService.InputParams(temperature=0.8, latency="optimized"),
     )
 
     messages = [
         {
             "role": "system",
-            "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
+            "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
         },
     ]
 
-    context = LLMContext(messages)
-    context_aggregator = LLMContextAggregatorPair(context)
+    context = OpenAILLMContext(messages)
+    context_aggregator = llm.create_context_aggregator(context)
 
     pipeline = Pipeline(
         [
@@ -107,7 +99,7 @@ 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):

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

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

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

@@ -1,163 +0,0 @@
-#
-# Copyright (c) 2024–2025, Daily
-#
-# SPDX-License-Identifier: BSD 2-Clause License
-#
-
-
-import os
-
-from dotenv import load_dotenv
-from loguru import logger
-
-from pipecat.audio.turn.smart_turn.base_smart_turn import SmartTurnParams
-from pipecat.audio.turn.smart_turn.local_smart_turn_v3 import LocalSmartTurnAnalyzerV3
-from pipecat.audio.vad.silero import SileroVADAnalyzer
-from pipecat.audio.vad.vad_analyzer import VADParams
-from pipecat.frames.frames import LLMRunFrame
-from pipecat.pipeline.pipeline import Pipeline
-from pipecat.pipeline.runner import PipelineRunner
-from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.aggregators.llm_context import LLMContext
-from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
-from pipecat.runner.types import RunnerArguments
-from pipecat.runner.utils import create_transport
-from pipecat.services.google.llm import GoogleLLMService
-from pipecat.services.google.stt import GoogleSTTService
-from pipecat.services.google.tts import 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(
-        credentials=os.getenv("GOOGLE_TEST_CREDENTIALS"),
-        model="gemini-2.5-flash-tts",
-        voice_id="Charon",
-        params=GeminiTTSService.InputParams(
-            language=Language.EN_US,
-            prompt="You are a helpful AI assistant. Speak in a natural, conversational tone.",
-        ),
-    )
-
-    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: You're using Gemini TTS which supports expressive markup tags. You can use these tags in your responses:
-            - [sigh] - Insert a sigh sound
-            - [laughing] - Insert a laugh
-            - [uhm] - Insert a hesitation sound
-            - [whispering] - Speak the next part in a whisper
-            - [shouting] - Speak the next part louder
-            - [extremely fast] - Speak the next part very quickly
-            - [short pause], [medium pause], [long pause] - Add pauses for dramatic effect
-
-            Examples:
-            - "Well [sigh] that's a tricky question."
-            - "[laughing] That's a great joke!"
-            - "[whispering] Let me tell you a secret."
-            - "The answer is... [long pause] ...42!"
-
-            Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.""",
-        },
-    ]
-
-    context = LLMContext(messages)
-    context_aggregator = LLMContextAggregatorPair(context)
-
-    pipeline = Pipeline(
-        [
-            transport.input(),  # Transport user input
-            stt,  # STT
-            context_aggregator.user(),  # User responses
-            llm,  # LLM
-            tts,  # Gemini TTS
-            transport.output(),  # Transport bot output
-            context_aggregator.assistant(),  # Assistant spoken responses
-        ]
-    )
-
-    task = PipelineTask(
-        pipeline,
-        params=PipelineParams(
-            enable_metrics=True,
-            enable_usage_metrics=True,
-        ),
-        idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
-    )
-
-    @transport.event_handler("on_client_connected")
-    async def on_client_connected(transport, client):
-        logger.info(f"Client connected")
-        # Kick off the conversation
-        messages.append(
-            {
-                "role": "system",
-                "content": "Hello! I'm your AI assistant. I can help you with a variety of tasks. What would you like to know?",
-            }
-        )
-        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-http.py

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

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

@@ -10,16 +10,11 @@ import os
 from dotenv import load_dotenv
 from loguru import logger
 
-from pipecat.audio.turn.smart_turn.base_smart_turn import SmartTurnParams
-from pipecat.audio.turn.smart_turn.local_smart_turn_v3 import LocalSmartTurnAnalyzerV3
 from pipecat.audio.vad.silero import SileroVADAnalyzer
-from pipecat.audio.vad.vad_analyzer import VADParams
-from pipecat.frames.frames import LLMRunFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.aggregators.llm_context import LLMContext
-from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
+from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
 from pipecat.runner.types import RunnerArguments
 from pipecat.runner.utils import create_transport
 from pipecat.services.google.llm import GoogleLLMService
@@ -27,8 +22,8 @@ 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)
 
@@ -39,20 +34,17 @@ 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(),
     ),
 }
 
@@ -61,9 +53,8 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
     logger.info(f"Starting bot")
 
     stt = GoogleSTTService(
-        params=GoogleSTTService.InputParams(languages=Language.EN_US, model="chirp_3"),
+        params=GoogleSTTService.InputParams(languages=Language.EN_US),
         credentials=os.getenv("GOOGLE_TEST_CREDENTIALS"),
-        location="us",
     )
 
     tts = GoogleTTSService(
@@ -82,12 +73,12 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
     messages = [
         {
             "role": "system",
-            "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
+            "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
         },
     ]
 
-    context = LLMContext(messages)
-    context_aggregator = LLMContextAggregatorPair(context)
+    context = OpenAILLMContext(messages)
+    context_aggregator = llm.create_context_aggregator(context)
 
     pipeline = Pipeline(
         [
@@ -115,7 +106,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         logger.info(f"Client connected")
         # Kick off the conversation.
         messages.append({"role": "system", "content": "Please introduce yourself to the user."})
-        await task.queue_frames([LLMRunFrame()])
+        await task.queue_frames([context_aggregator.user().get_context_frame()])
 
     @transport.event_handler("on_client_disconnected")
     async def on_client_disconnected(transport, client):

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

@@ -10,24 +10,19 @@ import os
 from dotenv import load_dotenv
 from loguru import logger
 
-from pipecat.audio.turn.smart_turn.base_smart_turn import SmartTurnParams
-from pipecat.audio.turn.smart_turn.local_smart_turn_v3 import LocalSmartTurnAnalyzerV3
 from pipecat.audio.vad.silero import SileroVADAnalyzer
-from pipecat.audio.vad.vad_analyzer import VADParams
-from pipecat.frames.frames import LLMRunFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.aggregators.llm_context import LLMContext
-from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
+from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
 from pipecat.runner.types import RunnerArguments
 from pipecat.runner.utils import create_transport
 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)
 
@@ -39,20 +34,17 @@ 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(),
     ),
 }
 
@@ -74,12 +66,12 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
     messages = [
         {
             "role": "system",
-            "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
+            "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
         },
     ]
 
-    context = LLMContext(messages)
-    context_aggregator = LLMContextAggregatorPair(context)
+    context = OpenAILLMContext(messages)
+    context_aggregator = llm.create_context_aggregator(context)
 
     pipeline = Pipeline(
         [
@@ -107,7 +99,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         logger.info(f"Client connected")
         # Kick off the conversation.
         messages.append({"role": "system", "content": "Please introduce yourself to the user."})
-        await task.queue_frames([LLMRunFrame()])
+        await task.queue_frames([context_aggregator.user().get_context_frame()])
 
     @transport.event_handler("on_client_disconnected")
     async def on_client_disconnected(transport, client):

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

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

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

@@ -11,24 +11,19 @@ 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.processors.aggregators.openai_llm_context import OpenAILLMContext
 from pipecat.runner.types import RunnerArguments
 from pipecat.runner.utils import create_transport
 from pipecat.services.deepgram.stt import DeepgramSTTService
 from pipecat.services.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)
 
@@ -39,22 +34,19 @@ 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(),
     ),
 }
@@ -72,12 +64,12 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
     messages = [
         {
             "role": "system",
-            "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
+            "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
         },
     ]
 
-    context = LLMContext(messages)
-    context_aggregator = LLMContextAggregatorPair(context)
+    context = OpenAILLMContext(messages)
+    context_aggregator = llm.create_context_aggregator(context)
 
     pipeline = Pipeline(
         [
@@ -105,7 +97,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         logger.info(f"Client connected")
         # Kick off the conversation.
         messages.append({"role": "system", "content": "Please introduce yourself to the user."})
-        await task.queue_frames([LLMRunFrame()])
+        await task.queue_frames([context_aggregator.user().get_context_frame()])
 
     @transport.event_handler("on_client_disconnected")
     async def on_client_disconnected(transport, client):

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

@@ -11,24 +11,19 @@ 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.processors.aggregators.openai_llm_context import OpenAILLMContext
 from pipecat.runner.types import RunnerArguments
 from pipecat.runner.utils import create_transport
 from pipecat.services.deepgram.stt import DeepgramSTTService
 from pipecat.services.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)
 
@@ -40,20 +35,17 @@ 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(),
     ),
 }
 
@@ -77,12 +69,12 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         messages = [
             {
                 "role": "system",
-                "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
+                "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
             },
         ]
 
-        context = LLMContext(messages)
-        context_aggregator = LLMContextAggregatorPair(context)
+        context = OpenAILLMContext(messages)
+        context_aggregator = llm.create_context_aggregator(context)
 
         pipeline = Pipeline(
             [
@@ -110,7 +102,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
             logger.info(f"Client connected")
             # Kick off the conversation.
             messages.append({"role": "system", "content": "Please introduce yourself to the user."})
-            await task.queue_frames([LLMRunFrame()])
+            await task.queue_frames([context_aggregator.user().get_context_frame()])
 
         @transport.event_handler("on_client_disconnected")
         async def on_client_disconnected(transport, client):

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

@@ -10,24 +10,19 @@ import os
 from dotenv import load_dotenv
 from loguru import logger
 
-from pipecat.audio.turn.smart_turn.base_smart_turn import SmartTurnParams
-from pipecat.audio.turn.smart_turn.local_smart_turn_v3 import LocalSmartTurnAnalyzerV3
 from pipecat.audio.vad.silero import SileroVADAnalyzer
-from pipecat.audio.vad.vad_analyzer import VADParams
-from pipecat.frames.frames import LLMRunFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.aggregators.llm_context import LLMContext
-from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
+from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
 from pipecat.runner.types import RunnerArguments
 from pipecat.runner.utils import create_transport
 from pipecat.services.deepgram.stt import DeepgramSTTService
 from pipecat.services.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)
 
@@ -38,20 +33,17 @@ 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(),
     ),
 }
 
@@ -71,12 +63,12 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
     messages = [
         {
             "role": "system",
-            "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
+            "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
         },
     ]
 
-    context = LLMContext(messages)
-    context_aggregator = LLMContextAggregatorPair(context)
+    context = OpenAILLMContext(messages)
+    context_aggregator = llm.create_context_aggregator(context)
 
     pipeline = Pipeline(
         [
@@ -104,7 +96,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         logger.info(f"Client connected")
         # Kick off the conversation.
         messages.append({"role": "system", "content": "Please introduce yourself to the user."})
-        await task.queue_frames([LLMRunFrame()])
+        await task.queue_frames([context_aggregator.user().get_context_frame()])
 
     @transport.event_handler("on_client_disconnected")
     async def on_client_disconnected(transport, client):

examples/foundational/07r-interruptible-nvidia.py

@@ -1,127 +0,0 @@
-#
-# Copyright (c) 2024–2025, Daily
-#
-# SPDX-License-Identifier: BSD 2-Clause License
-#
-
-
-import os
-
-from dotenv import load_dotenv
-from loguru import logger
-
-from pipecat.audio.turn.smart_turn.base_smart_turn import SmartTurnParams
-from pipecat.audio.turn.smart_turn.local_smart_turn_v3 import LocalSmartTurnAnalyzerV3
-from pipecat.audio.vad.silero import SileroVADAnalyzer
-from pipecat.audio.vad.vad_analyzer import VADParams
-from pipecat.frames.frames import LLMRunFrame
-from pipecat.pipeline.pipeline import Pipeline
-from pipecat.pipeline.runner import PipelineRunner
-from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.aggregators.llm_context import LLMContext
-from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
-from pipecat.runner.types import RunnerArguments
-from pipecat.runner.utils import create_transport
-from pipecat.services.nvidia.llm import NvidiaLLMService
-from pipecat.services.nvidia.stt import NvidiaSTTService
-from pipecat.services.nvidia.tts import NvidiaTTSService
-from pipecat.transports.base_transport import BaseTransport, TransportParams
-from pipecat.transports.daily.transport import DailyParams
-from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
-
-load_dotenv(override=True)
-
-# We store functions so objects (e.g. SileroVADAnalyzer) don't get
-# instantiated. The function will be called when the desired transport gets
-# selected.
-transport_params = {
-    "daily": lambda: DailyParams(
-        audio_in_enabled=True,
-        audio_out_enabled=True,
-        vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
-        turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
-    ),
-    "twilio": lambda: FastAPIWebsocketParams(
-        audio_in_enabled=True,
-        audio_out_enabled=True,
-        vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
-        turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
-    ),
-    "webrtc": lambda: TransportParams(
-        audio_in_enabled=True,
-        audio_out_enabled=True,
-        vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
-        turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
-    ),
-}
-
-
-async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
-    logger.info(f"Starting bot")
-
-    stt = NvidiaSTTService(api_key=os.getenv("NVIDIA_API_KEY"))
-
-    llm = NvidiaLLMService(
-        api_key=os.getenv("NVIDIA_API_KEY"), model="meta/llama-3.1-405b-instruct"
-    )
-
-    tts = NvidiaTTSService(api_key=os.getenv("NVIDIA_API_KEY"))
-
-    messages = [
-        {
-            "role": "system",
-            "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
-        },
-    ]
-
-    context = LLMContext(messages)
-    context_aggregator = LLMContextAggregatorPair(context)
-
-    pipeline = Pipeline(
-        [
-            transport.input(),  # Transport user input
-            stt,  # STT
-            context_aggregator.user(),  # User responses
-            llm,  # LLM
-            tts,  # TTS
-            transport.output(),  # Transport bot output
-            context_aggregator.assistant(),  # Assistant spoken responses
-        ]
-    )
-
-    task = PipelineTask(
-        pipeline,
-        params=PipelineParams(
-            enable_metrics=True,
-            enable_usage_metrics=True,
-        ),
-        idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
-    )
-
-    @transport.event_handler("on_client_connected")
-    async def on_client_connected(transport, client):
-        logger.info(f"Client connected")
-        # Kick off the conversation.
-        messages.append({"role": "system", "content": "Please introduce yourself to the user."})
-        await task.queue_frames([LLMRunFrame()])
-
-    @transport.event_handler("on_client_disconnected")
-    async def on_client_disconnected(transport, client):
-        logger.info(f"Client disconnected")
-        await task.cancel()
-
-    runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
-
-    await runner.run(task)
-
-
-async def bot(runner_args: RunnerArguments):
-    """Main bot entry point compatible with Pipecat Cloud."""
-    transport = await create_transport(runner_args, transport_params)
-    await run_bot(transport, runner_args)
-
-
-if __name__ == "__main__":
-    from pipecat.runner.run import main
-
-    main()

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

@@ -10,26 +10,22 @@ import os
 from dotenv import load_dotenv
 from loguru import logger
 
-from pipecat.frames.frames import LLMRunFrame
+from pipecat.audio.vad.silero import SileroVADAnalyzer
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.aggregators.llm_response_universal import (
-    LLMContext,
-    LLMContextAggregatorPair,
-)
+from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
 from pipecat.runner.types import RunnerArguments
 from pipecat.runner.utils import create_transport
-from pipecat.services.deepgram.flux.stt import DeepgramFluxSTTService
-from pipecat.services.deepgram.tts import DeepgramTTSService
-from pipecat.services.openai.llm import OpenAILLMService
+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)
 
-
 # We store functions so objects (e.g. SileroVADAnalyzer) don't get
 # instantiated. The function will be called when the desired transport gets
 # selected.
@@ -37,14 +33,17 @@ 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(),
     ),
 }
 
@@ -52,24 +51,21 @@ transport_params = {
 async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
     logger.info(f"Starting bot")
 
-    stt = DeepgramFluxSTTService(
-        api_key=os.getenv("DEEPGRAM_API_KEY"),
-        params=DeepgramFluxSTTService.InputParams(min_confidence=0.3),
-    )
+    stt = RivaSTTService(api_key=os.getenv("NVIDIA_API_KEY"))
 
-    tts = DeepgramTTSService(api_key=os.getenv("DEEPGRAM_API_KEY"), voice="aura-2-andromeda-en")
+    llm = NimLLMService(api_key=os.getenv("NVIDIA_API_KEY"), model="meta/llama-3.1-405b-instruct")
 
-    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
+    tts = RivaTTSService(api_key=os.getenv("NVIDIA_API_KEY"))
 
     messages = [
         {
             "role": "system",
-            "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
+            "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)
+    context = OpenAILLMContext(messages)
+    context_aggregator = llm.create_context_aggregator(context)
 
     pipeline = Pipeline(
         [
@@ -97,17 +93,13 @@ 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()
 
-    @stt.event_handler("on_update")
-    async def on_deepgram_flux_update(stt, transcript):
-        logger.debug(f"On deeggram flux update: {transcript}")
-
     runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
 
     await runner.run(task)

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

@@ -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,
@@ -31,8 +27,7 @@ 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
@@ -40,8 +35,8 @@ 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)
 
@@ -53,7 +48,7 @@ You are a helpful LLM in a WebRTC call. Your goals are to be helpful and brief i
 You are expert at transcribing audio to text. You will receive a mixture of audio and text input. When
 asked to transcribe what the user said, output an exact, word-for-word transcription.
 
-Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points.
+Your output will be converted to audio so don't include special characters in your answers.
 
 Each time you answer, you should respond in three parts.
 
@@ -97,8 +92,9 @@ 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 +150,7 @@ 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 +181,9 @@ 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 = ""
@@ -200,20 +198,17 @@ 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(),
     ),
 }
 
@@ -245,11 +240,11 @@ 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(
         [
@@ -279,7 +274,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         logger.info(f"Client connected")
         # Kick off the conversation.
         messages.append({"role": "system", "content": "Please introduce yourself to the user."})
-        await task.queue_frames([LLMRunFrame()])
+        await task.queue_frames([context_aggregator.user().get_context_frame()])
 
     @transport.event_handler("on_client_disconnected")
     async def on_client_disconnected(transport, client):

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

@@ -10,24 +10,19 @@ import os
 from dotenv import load_dotenv
 from loguru import logger
 
-from pipecat.audio.turn.smart_turn.base_smart_turn import SmartTurnParams
-from pipecat.audio.turn.smart_turn.local_smart_turn_v3 import LocalSmartTurnAnalyzerV3
 from pipecat.audio.vad.silero import SileroVADAnalyzer
-from pipecat.audio.vad.vad_analyzer import VADParams
-from pipecat.frames.frames import LLMRunFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.aggregators.llm_context import LLMContext
-from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
+from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
 from pipecat.runner.types import RunnerArguments
 from pipecat.runner.utils import create_transport
 from pipecat.services.deepgram.stt import DeepgramSTTService
 from pipecat.services.fish.tts import FishAudioTTSService
 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)
 
@@ -39,20 +34,17 @@ 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(),
     ),
 }
 
@@ -72,12 +64,12 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
     messages = [
         {
             "role": "system",
-            "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
+            "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
         },
     ]
 
-    context = LLMContext(messages)
-    context_aggregator = LLMContextAggregatorPair(context)
+    context = OpenAILLMContext(messages)
+    context_aggregator = llm.create_context_aggregator(context)
 
     pipeline = Pipeline(
         [
@@ -105,7 +97,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         logger.info(f"Client connected")
         # Kick off the conversation.
         messages.append({"role": "system", "content": "Please introduce yourself to the user."})
-        await task.queue_frames([LLMRunFrame()])
+        await task.queue_frames([context_aggregator.user().get_context_frame()])
 
     @transport.event_handler("on_client_disconnected")
     async def on_client_disconnected(transport, client):

examples/foundational/07u-interruptible-ultravox.py

@@ -10,10 +10,7 @@ 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.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
@@ -22,8 +19,8 @@ from pipecat.runner.utils import create_transport
 from pipecat.services.cartesia.tts import CartesiaTTSService
 from pipecat.services.ultravox.stt import UltravoxSTTService
 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)
 
@@ -47,20 +44,17 @@ 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(),
     ),
 }
 

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

@@ -11,24 +11,19 @@ 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.processors.aggregators.openai_llm_context import OpenAILLMContext
 from pipecat.runner.types import RunnerArguments
 from pipecat.runner.utils import create_transport
 from pipecat.services.deepgram.stt import DeepgramSTTService
 from pipecat.services.neuphonic.tts import NeuphonicHttpTTSService
 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)
 
@@ -40,20 +35,17 @@ 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(),
     ),
 }
 
@@ -76,12 +68,12 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         messages = [
             {
                 "role": "system",
-                "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
+                "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
             },
         ]
 
-        context = LLMContext(messages)
-        context_aggregator = LLMContextAggregatorPair(context)
+        context = OpenAILLMContext(messages)
+        context_aggregator = llm.create_context_aggregator(context)
 
         pipeline = Pipeline(
             [
@@ -109,7 +101,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
             logger.info(f"Client connected")
             # Kick off the conversation.
             messages.append({"role": "system", "content": "Please introduce yourself to the user."})
-            await task.queue_frames([LLMRunFrame()])
+            await task.queue_frames([context_aggregator.user().get_context_frame()])
 
         @transport.event_handler("on_client_disconnected")
         async def on_client_disconnected(transport, client):

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

@@ -10,24 +10,19 @@ import os
 from dotenv import load_dotenv
 from loguru import logger
 
-from pipecat.audio.turn.smart_turn.base_smart_turn import SmartTurnParams
-from pipecat.audio.turn.smart_turn.local_smart_turn_v3 import LocalSmartTurnAnalyzerV3
 from pipecat.audio.vad.silero import SileroVADAnalyzer
-from pipecat.audio.vad.vad_analyzer import VADParams
-from pipecat.frames.frames import LLMRunFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.aggregators.llm_context import LLMContext
-from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
+from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
 from pipecat.runner.types import RunnerArguments
 from pipecat.runner.utils import create_transport
 from pipecat.services.deepgram.stt import DeepgramSTTService
 from pipecat.services.neuphonic.tts import NeuphonicTTSService
 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)
 
@@ -38,20 +33,17 @@ 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(),
     ),
 }
 
@@ -71,12 +63,12 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
     messages = [
         {
             "role": "system",
-            "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
+            "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
         },
     ]
 
-    context = LLMContext(messages)
-    context_aggregator = LLMContextAggregatorPair(context)
+    context = OpenAILLMContext(messages)
+    context_aggregator = llm.create_context_aggregator(context)
 
     pipeline = Pipeline(
         [
@@ -104,7 +96,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         logger.info(f"Client connected")
         # Kick off the conversation.
         messages.append({"role": "system", "content": "Please introduce yourself to the user."})
-        await task.queue_frames([LLMRunFrame()])
+        await task.queue_frames([context_aggregator.user().get_context_frame()])
 
     @transport.event_handler("on_client_disconnected")
     async def on_client_disconnected(transport, client):

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

@@ -10,24 +10,19 @@ import os
 from dotenv import load_dotenv
 from loguru import logger
 
-from pipecat.audio.turn.smart_turn.base_smart_turn import SmartTurnParams
-from pipecat.audio.turn.smart_turn.local_smart_turn_v3 import LocalSmartTurnAnalyzerV3
 from pipecat.audio.vad.silero import SileroVADAnalyzer
-from pipecat.audio.vad.vad_analyzer import VADParams
-from pipecat.frames.frames import LLMRunFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.aggregators.llm_context import LLMContext
-from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
+from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
 from pipecat.runner.types import RunnerArguments
 from pipecat.runner.utils import create_transport
 from pipecat.services.cartesia.tts import CartesiaTTSService
 from pipecat.services.fal.stt import FalSTTService
 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)
 
@@ -39,20 +34,17 @@ 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(),
     ),
 }
 
@@ -74,12 +66,12 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
     messages = [
         {
             "role": "system",
-            "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
+            "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
         },
     ]
 
-    context = LLMContext(messages)
-    context_aggregator = LLMContextAggregatorPair(context)
+    context = OpenAILLMContext(messages)
+    context_aggregator = llm.create_context_aggregator(context)
 
     pipeline = Pipeline(
         [
@@ -107,7 +99,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         logger.info(f"Client connected")
         # Kick off the conversation.
         messages.append({"role": "system", "content": "Please introduce yourself to the user."})
-        await task.queue_frames([LLMRunFrame()])
+        await task.queue_frames([context_aggregator.user().get_context_frame()])
 
     @transport.event_handler("on_client_disconnected")
     async def on_client_disconnected(transport, client):

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

@@ -11,16 +11,11 @@ import sys
 from dotenv import load_dotenv
 from loguru import logger
 
-from pipecat.audio.turn.smart_turn.base_smart_turn import SmartTurnParams
-from pipecat.audio.turn.smart_turn.local_smart_turn_v3 import LocalSmartTurnAnalyzerV3
 from pipecat.audio.vad.silero import SileroVADAnalyzer
-from pipecat.audio.vad.vad_analyzer import VADParams
-from pipecat.frames.frames import LLMRunFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.aggregators.llm_context import LLMContext
-from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
+from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
 from pipecat.runner.types import RunnerArguments
 from pipecat.runner.utils import create_transport
 from pipecat.services.cartesia.tts import CartesiaTTSService
@@ -39,8 +34,7 @@ async def main():
         LocalAudioTransportParams(
             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(),
         )
     )
 
@@ -56,12 +50,12 @@ async def main():
     messages = [
         {
             "role": "system",
-            "content": "You are a helpful LLM. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
+            "content": "You are a helpful LLM. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
         },
     ]
 
-    context = LLMContext(messages)
-    context_aggregator = LLMContextAggregatorPair(context)
+    context = OpenAILLMContext(messages)
+    context_aggregator = llm.create_context_aggregator(context)
 
     pipeline = Pipeline(
         [
@@ -84,7 +78,7 @@ async def main():
     )
 
     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()])
 
     runner = PipelineRunner()
 

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

@@ -11,16 +11,11 @@ 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.processors.aggregators.openai_llm_context import OpenAILLMContext
 from pipecat.runner.types import RunnerArguments
 from pipecat.runner.utils import create_transport
 from pipecat.services.deepgram.stt import DeepgramSTTService
@@ -28,8 +23,8 @@ from pipecat.services.minimax.tts import MiniMaxHttpTTSService
 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)
 
@@ -41,20 +36,17 @@ 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(),
     ),
 }
 
@@ -78,12 +70,12 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         messages = [
             {
                 "role": "system",
-                "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
+                "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
             },
         ]
 
-        context = LLMContext(messages)
-        context_aggregator = LLMContextAggregatorPair(context)
+        context = OpenAILLMContext(messages)
+        context_aggregator = llm.create_context_aggregator(context)
 
         pipeline = Pipeline(
             [
@@ -111,7 +103,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
             logger.info(f"Client connected")
             # Kick off the conversation.
             messages.append({"role": "system", "content": "Please introduce yourself to the user."})
-            await task.queue_frames([LLMRunFrame()])
+            await task.queue_frames([context_aggregator.user().get_context_frame()])
 
         @transport.event_handler("on_client_disconnected")
         async def on_client_disconnected(transport, client):