Working on the 46 example

- Create new OpenAIAgentService that integrates OpenAI Agents SDK with Pipecat
- Support for agent loops, handoffs, guardrails, and session management
- Add streaming and non-streaming response modes
- Include comprehensive tool integration and error handling
- Add optional dependency for openai-agents package
- Create foundational examples showing basic usage and agent handoffs
- Add comprehensive tests with mocked dependencies
- Include detailed documentation and README

Key features:
- Real-time streaming responses compatible with Pipecat pipelines
- Agent handoffs for specialized task delegation
- Tool calling with automatic schema generation
- Input/output guardrails for safety and validation
- Session context management for conversation continuity
- Built-in tracing and monitoring integration

Examples:
- 45-openai-agent-basic.py: Basic agent with weather and trivia tools
- 46-openai-agent-handoffs.py: Multi-agent system with specialist handoffs

AGENTS.md

@@ -0,0 +1,285 @@
+# AGENTS.md
+
+## Project Overview
+
+Pipecat is an open-source Python framework for building real-time voice and multimodal conversational AI agents. The codebase is organized around a pipeline architecture where data flows through connected services (STT → LLM → TTS).
+
+## Development Environment Setup
+
+### Prerequisites
+- **Minimum Python Version:** 3.10
+- **Recommended Python Version:** 3.12
+- **Package Manager:** uv (recommended) or pip
+
+### Setup Commands
+
+```bash
+# Clone the repository
+git clone https://github.com/pipecat-ai/pipecat.git
+cd pipecat
+
+# Install dependencies with uv (recommended)
+uv sync --group dev --all-extras \
+  --no-extra gstreamer \
+  --no-extra krisp \
+  --no-extra local \
+  --no-extra ultravox
+
+# Or with pip
+pip install -e ".[dev]"
+
+# Install pre-commit hooks
+uv run pre-commit install
+
+# Set up environment variables
+cp env.example .env
+```
+
+## Build and Test Commands
+
+### Running Tests
+```bash
+# Run all tests
+uv run pytest
+
+# Run specific test file
+uv run pytest tests/test_name.py
+
+# Run tests with coverage
+uv run pytest --cov=pipecat --cov-report=html
+```
+
+### Code Quality
+```bash
+# Format code (required before commits)
+uv run ruff format
+
+# Lint code
+uv run ruff check
+
+# Type checking
+uv run mypy src/pipecat
+
+# Run pre-commit checks manually
+uv run pre-commit run --all-files
+```
+
+### Documentation
+```bash
+# Build API documentation
+cd docs/api
+./build-docs.sh
+
+# Build docs manually
+sphinx-build -b html . _build/html -W --keep-going
+```
+
+## Code Style Guidelines
+
+### Python Standards
+- **Formatting:** Strict PEP 8 via Ruff
+- **Docstrings:** Google-style format
+- **Type Hints:** Required for all public APIs
+- **Import Organization:** Automated via Ruff
+
+### Docstring Conventions
+- **Classes:** Describe purpose + `__init__` with complete `Args:` section
+- **Dataclasses:** Use `Parameters:` section, no `__init__` docstring
+- **Methods:** Include `Args:` and `Returns:` sections
+- **Properties:** Must have `Returns:` section
+- **Examples:** Use `Examples:` section with `::` syntax
+
+### File Organization
+```
+src/pipecat/           # Main package
+├── processors/        # Frame processors
+├── services/          # AI service integrations
+├── transports/        # Communication layers
+├── frames/            # Data frame definitions
+└── pipeline/          # Pipeline orchestration
+
+examples/foundational/ # Step-by-step tutorials
+tests/                 # Test suite
+```
+
+## Testing Instructions
+
+### Test Structure
+- **Unit Tests:** Test individual components in isolation
+- **Integration Tests:** Test service interactions
+- **Example Tests:** Validate foundational examples work
+
+### Adding Tests
+```bash
+# Test naming convention
+test_<component>_<functionality>.py
+
+# Run specific test pattern
+uv run pytest -k "test_pipeline"
+
+# Run with debugging
+uv run pytest -s -vv tests/test_name.py::test_function
+```
+
+### Pre-commit Requirements
+All commits must pass:
+- Ruff formatting
+- Ruff linting
+- Type checking
+- Basic test suite
+
+## Dependency Management
+
+### Using uv (Recommended)
+```bash
+# Add runtime dependency
+uv add package-name
+
+# Add optional dependency
+uv add --optional service package-name
+
+# Add development dependency
+uv add --group dev package-name
+
+# Update lockfile
+uv lock
+
+# Sync dependencies
+uv sync
+```
+
+### Important Notes
+- **Always commit both `pyproject.toml` and `uv.lock` together**
+- **Never manually edit `uv.lock`** - it's auto-generated
+- **Use extras for optional service dependencies** (e.g., `[openai]`, `[cartesia]`)
+
+## Project Structure Guidelines
+
+### Service Integration
+When adding new AI services:
+1. Create service class in `src/pipecat/services/<provider>/`
+2. Follow existing patterns (e.g., STTService, LLMService)
+3. Add to appropriate extras in `pyproject.toml`
+4. Include tests in `tests/`
+5. Add documentation examples
+
+### Frame Processing
+For custom processors:
+1. Inherit from `FrameProcessor`
+2. Implement `process_frame()` method. ALWAYS explicitly call `await super().process_frame(frame, direction)` at the top of this method.
+3. Handle frame direction (FrameDirection.UPSTREAM/DOWNSTREAM)
+4. Add proper type hints and docstrings
+
+### Transport Implementation
+For new transport layers:
+1. Inherit from `BaseTransport`
+2. Implement required abstract methods
+3. Handle connection lifecycle
+4. Support both input and output streams
+
+## Security Considerations
+
+### API Keys
+- **Never commit API keys** to the repository
+- **Use environment variables** for all secrets
+- **Reference `env.example`** for required variables
+- **Use `.env` files** for local development
+
+### Input Validation
+- **Validate all external inputs** (audio, text, API responses)
+- **Sanitize user data** before processing
+- **Handle rate limiting** for external services
+- **Implement proper timeout handling**
+
+## Performance Guidelines
+
+### Memory Management
+- **Clean up resources** in transport disconnection handlers
+- **Use async context managers** for service connections
+- **Implement proper frame lifecycle** management
+
+### Latency Optimization
+- **Choose appropriate STT services** for latency requirements
+- **Use streaming TTS** when possible
+- **Implement connection pooling** for HTTP services
+- **Consider WebRTC** for real-time applications
+
+## Common Patterns
+
+### Error Handling
+```python
+@transport.event_handler("on_error")
+async def on_error(transport, error):
+    logger.error(f"Transport error: {error}")
+
+    # Shutdown the pipeline
+    await task.queue_frame(EndFrame())
+ 
+```
+
+### Service Configuration
+```python
+# Use environment variables for configuration
+service = OpenAILLMService(
+    api_key=os.getenv("OPENAI_API_KEY", ""),
+    model="gpt-4o",
+    params={"temperature": 0.7}
+)
+```
+
+### Pipeline Assembly
+```python
+pipeline = Pipeline([
+    transport.input(),
+    stt_service,
+    context_aggregator.user(),
+    llm_service,
+    tts_service,
+    transport.output(),
+    context_aggregator.assistant(),
+])
+```
+
+## Commit and PR Guidelines
+
+### Commit Message Format
+```
+<type>(<scope>): <description>
+
+[optional body]
+
+[optional footer]
+```
+
+Types: `feat`, `fix`, `docs`, `style`, `refactor`, `test`, `chore`
+
+### PR Requirements
+- **All tests must pass**
+- **Code must be properly formatted** (Ruff)
+- **Include appropriate tests** for new functionality
+- **Update documentation** if needed
+- **Reference related issues** in description
+
+### Review Process
+1. Automated checks must pass
+2. Manual code review by maintainers
+3. Documentation review for user-facing changes
+4. Integration testing for service additions
+
+## Troubleshooting
+
+### Common Issues
+- **Import errors:** Run `uv sync` to ensure dependencies are installed
+- **Test failures:** Check environment variables in `.env`
+- **Format errors:** Run `uv run ruff format` before committing
+- **Type errors:** Ensure all public methods have type hints
+
+### Development Tips
+- **Use foundational examples** as starting points for testing
+- **Check existing services** for integration patterns
+- **Run tests frequently** during development
+- **Use IDE integration** for Ruff formatting
+
+### Getting Help
+- **Documentation:** [docs.pipecat.ai](https://docs.pipecat.ai)
+- **Issues:** [GitHub Issues](https://github.com/pipecat-ai/pipecat/issues)

CHANGELOG.md

@@ -5,14 +5,172 @@ All notable changes to **Pipecat** will be documented in this file.
 The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/),
 and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html).
 
+## [Unreleased]
+
+### Added
+
+- Added `on_pipeline_finished` event to `PipelineTask`. This event will get
+  fired when the pipeline is done running. This can be the result of a
+  `StopFrame`, `CancelFrame` or `EndFrame`.
+
+  ```python
+  @task.event_handler("on_pipeline_finished")
+  async def on_pipeline_finished(task: PipelineTask, frame: Frame):
+      ...
+  ```
+
+### Deprecated
+
+- `PipelineTask` events `on_pipeline_stopped`, `on_pipeline_ended` and
+  `on_pipeline_cancelled` are now deprecated. Use `on_pipeline_finished`
+  instead.
+
+### Fixed
+
+- Fixed an issue in `AudioBufferProcessor` where a recording is not created
+  when a bot speaks and user input is blocked.
+
+- Fixed a `FastAPIWebsocketTransport` and `SmallWebRTCTransport` issue where
+  `on_client_disconnected` would be triggered when the bot ends the
+  conversation. That is, `on_client_disconnected` should only be triggered when
+  the remote client actually disconnects.
+
+- Fixed an issue in `HeyGenVideoService` where the `BotStartedSpeakingFrame`
+  was blocked from moving through the Pipeline.
+
+## [0.0.85] - 2025-09-12
+
+### Added
+
+- `AzureSTTService` now pushes interim transcriptions.
+
+- Added `voice_cloning_key` to `GoogleTTSService` to support custom cloned
+  voices.
+
+- Added `speaking_rate` to `GoogleTTSService.InputParams` to control the
+  speaking rate.
+
+- Added a `speed` arg to `OpenAITTSService` to control the speed of the voice
+  response.
+
+- Added `FrameProcessor.push_interruption_task_frame_and_wait()`. Use this
+  method to programatically interrupt the bot from any part of the
+  pipeline. This guarantees that all the processors in the pipeline are
+  interrupted in order (from upstream to downstream). Internally, this works by
+  first pushing an `InterruptionTaskFrame` upstream until it reaches the
+  pipeline task. The pipeline task then generates an `InterruptionFrame`, which
+  flows downstream through all processors. Once the `InterruptionFrame` has
+  reaches the processor waiting for the interruption, the function returns and
+  execution continues after the call. Think of it as sending an upstream request
+  for interruption and waiting until the acknowledgment flows back downstream.
+
+- Added new base `TaskFrame` (which is a system frame). This is the base class
+  for all task frames (`EndTaskFrame`, `CancelTaskFrame`, etc.) that are meant
+  to be pushed upstream to reach the pipeline task.
+
+- Expanded support for universal `LLMContext` to the AWS Bedrock LLM service.
+  Using the universal `LLMContext` and associated `LLMContextAggregatorPair` is
+  a pre-requisite for using `LLMSwitcher` to switch between LLMs at runtime.
+
+- Added new fields to the development runner's `parse_telephony_websocket`
+  method in support of providing dynamic data to a bot.
+
+  - Twilio: Added a new `body` parameter, which parses the websocket message
+    for `customParameters`. Provide data via the `Parameter` nouns in your
+    TwiML to use this feature.
+  - Telnyx & Exotel: Both providers make the `to` and `from` phone numbers
+    available in the websocket messages. You can now access these numbers as
+    `call_data["to"]` and `call_data["from"]`.
+
+  Note: Each telephony provider offers different features. Refer to the
+  corresponding example in `pipecat-examples` to see how to pass custom data
+  to your bot.
+
+- Added `body` to the `WebsocketRunnerArguments` as an optional parameter.
+  Custom `body` information can be passed from the server into the bot file via
+  the `bot()` method using this new parameter.
+
+- Added video streaming support to `LiveKitTransport`.
+
+- Added `OpenAIRealtimeLLMService` and `AzureRealtimeLLMService` which provide
+  access to OpenAI Realtime.
+
+### Changed
+
+- `pipeline.tests.utils.run_test()` now allows passing `PipelineParams` instead
+  of individual parameters.
+
+### Removed
+
+- Remove `VisionImageRawFrame` in favor of context frames (`LLMContextFrame` or
+  `OpenAILLMContextFrame`).
+
+### Deprecated
+
+- `BotInterruptionFrame` is now deprecated, use `InterruptionTaskFrame` instead.
+
+- `StartInterruptionFrame` is now deprected, use `InterruptionFrame` instead.
+
+- Deprecate `VisionImageFrameAggregator` because `VisionImageRawFrame` has been
+  removed. See the `12*` examples for the new recommended replacement pattern.
+
+- `NoisereduceFilter` is now deprecated and will be removed in a future
+  version. Use other audio filters like `KrispFilter` or `AICFilter`.
+
+- Deprecated `OpenAIRealtimeBetaLLMService` and `AzureRealtimeBetaLLMService`.
+  Use `OpenAIRealtimeLLMService` and `AzureRealtimeLLMService`, respectively.
+  Each service will be removed in an upcoming version, 1.0.0.
+
+### Fixed
+
+- Fixed a `BaseOutputTransport` issue that caused incorrect detection of when
+  the bot stopped talking while using an audio mixer.
+
+- Fixed a `LiveKitTransport` issue where RTVI messages were not properly
+  encoded.
+
+- Add additional fixups to Mistral context messages to ensure they meet
+  Mistral-specific requirements, avoiding Mistral "invalid request" errors.
+
+- Fixed `DailyTransport` transcription handling to gracefully handle missing
+  `rawResponse` field in transcription messages, preventing KeyError crashes.
+
+## [0.0.84] - 2025-09-05
+
+### Added
+
+- Add the ability to send DTMF to `LiveKitTransport`.
+
+- Expanded support for universal `LLMContext` to the Anthropic LLM service.
+  Using the universal `LLMContext` and associated `LLMContextAggregatorPair` is
+  a pre-requisite for using `LLMSwitcher` to switch between LLMs at runtime.
+
+### Changed
+
+- Updated `daily-python` to 0.19.9.
+
+- Restored `DailyTransport`'s native DTMF support using Daily's `send_dtmf()`
+  method instead of generated audio tones.
+
+### Fixed
+
+- Fixed a `AWSBedrockLLMService` crash caused by an extra `await`.
+
+- Fixed a `OpenAIImageGenService` issue where it was not creating
+  `URLImageRawFrame` correctly.
+
 ## [0.0.83] - 2025-09-03
 
 ### Added
 
+- Added multilingual support for AsyncAI in `AsyncAITTSService` and `AsyncAIHttpTTSService`.
+
+  - New `languages`: `es`, `fr`, `de`, `it`.
+
 - Added new frames `InputTransportMessageUrgentFrame` and
   `DailyInputTransportMessageUrgentFrame` for transport messages received from
   external sources.
-  
+
 - Added `UserSpeakingFrame`. This will be sent upstream and downstream while VAD
   detects the user is speaking.
 
@@ -82,7 +240,8 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 
 - Added new config parameters to `GladiaSTTService`.
   - PreProcessingConfig > `audio_enhancer` to enhance audio quality.
-  - CustomVocabularyItem > `pronunciations` and `language` to specify special pronunciations and in which language it will be pronounced.
+  - CustomVocabularyItem > `pronunciations` and `language` to specify special
+    pronunciations and in which language it will be pronounced.
 
 ### Changed
 
@@ -101,7 +260,8 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 - `pipecat.frames.frames.KeypadEntry` is deprecated and has been moved to
   `pipecat.audio.dtmf.types.KeypadEntry`.
 
-- Updated `RimeTTSService`'s flush_audio message to conform with Rime's official API.
+- Updated `RimeTTSService`'s flush_audio message to conform with Rime's official
+  API.
 
 - Updated the default model for `CerebrasLLMService` to GPT-OSS-120B.
 

README.md

@@ -28,6 +28,41 @@
 - **Composable Pipelines**: Build complex behavior from modular components
 - **Real-Time**: Ultra-low latency interaction with different transports (e.g. WebSockets or WebRTC)
 
+## 📱 Client SDKs
+
+You can connect to Pipecat from any platform using our official SDKs:
+
+<table>
+  <tr>
+    <td>
+      <img src="https://cdn.jsdelivr.net/gh/devicons/devicon/icons/javascript/javascript-original.svg" width="40" height="40" alt="JavaScript"/>
+      <a href="https://docs.pipecat.ai/client/js/introduction">JavaScript</a>
+    </td>
+    <td>
+      <img src="https://cdn.jsdelivr.net/gh/devicons/devicon/icons/react/react-original.svg" width="40" height="40" alt="React"/>
+      <a href="https://docs.pipecat.ai/client/react/introduction">React</a>
+    </td>
+    <td>
+      <img src="https://cdn.jsdelivr.net/gh/devicons/devicon/icons/react/react-original.svg" width="40" height="40" alt="React Native"/>
+      <a href="https://docs.pipecat.ai/client/react-native/introduction">React Native</a>
+    </td>
+  </tr>
+  <tr>
+    <td>
+      <img src="https://cdn.jsdelivr.net/gh/devicons/devicon/icons/swift/swift-original.svg" width="40" height="40" alt="Swift"/>
+      <a href="https://docs.pipecat.ai/client/ios/introduction">Swift</a>
+    </td>
+    <td>
+      <img src="https://cdn.jsdelivr.net/gh/devicons/devicon/icons/kotlin/kotlin-original.svg" width="40" height="40" alt="Kotlin"/>
+      <a href="https://docs.pipecat.ai/client/android/introduction">Kotlin</a>
+    </td>
+    <td>
+      <img src="https://cdn.jsdelivr.net/gh/devicons/devicon/icons/cplusplus/cplusplus-original.svg" width="40" height="40" alt="JavaScript"/>
+      <a href="https://docs.pipecat.ai/client/c++/introduction">C++</a>
+    </td>
+  </tr>
+</table>
+
 ## 🎬 See it in action
 
 <p float="left">
@@ -38,17 +73,6 @@
     <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                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              |
@@ -62,7 +86,7 @@ You can connect to Pipecat from any platform using our official SDKs:
 | 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), [Noisereduce](https://docs.pipecat.ai/server/utilities/audio/noisereduce-filter)                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              |
+| Audio Processing    | [Silero VAD](https://docs.pipecat.ai/server/utilities/audio/silero-vad-analyzer), [Krisp](https://docs.pipecat.ai/server/utilities/audio/krisp-filter), [Koala](https://docs.pipecat.ai/server/utilities/audio/koala-filter), [ai-coustics](https://docs.pipecat.ai/server/utilities/audio/aic-filter)                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                |
 | Analytics & Metrics | [OpenTelemetry](https://docs.pipecat.ai/server/utilities/opentelemetry), [Sentry](https://docs.pipecat.ai/server/services/analytics/sentry)                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                           |
 
 📚 [View full services documentation →](https://docs.pipecat.ai/server/services/supported-services)
@@ -129,7 +153,11 @@ 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
+   uv sync --group dev --all-extras \
+     --no-extra gstreamer \
+     --no-extra krisp \
+     --no-extra local \
+     --no-extra ultravox # (ultravox not fully supported on macOS)
    ```
 
 3. Install the git pre-commit hooks:
@@ -138,23 +166,6 @@ 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

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

@@ -14,7 +14,7 @@ from loguru import logger
 
 from pipecat.audio.vad.silero import SileroVADAnalyzer
 from pipecat.frames.frames import (
-    BotInterruptionFrame,
+    InterruptionFrame,
     TextFrame,
     TranscriptionFrame,
     UserStartedSpeakingFrame,
@@ -115,7 +115,7 @@ async def main():
 
         await task.queue_frames(
             [
-                BotInterruptionFrame(),
+                InterruptionFrame(),
                 UserStartedSpeakingFrame(),
                 TranscriptionFrame(
                     user_id=participant_id,

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

@@ -36,7 +36,6 @@ load_dotenv(override=True)
 audiobuffer = AudioBufferProcessor(
     num_channels=2,  # 1 for mono, 2 for stereo (user left, bot right)
     enable_turn_audio=False,  # Enable per-turn audio recording
-    user_continuous_stream=True,  # User has continuous audio stream
 )
 
 

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

@@ -12,8 +12,8 @@ from dotenv import load_dotenv
 from loguru import logger
 
 from pipecat.frames.frames import (
+    InterruptionFrame,
     LLMRunFrame,
-    StartInterruptionFrame,
     UserStartedSpeakingFrame,
     UserStoppedSpeakingFrame,
 )
@@ -97,7 +97,7 @@ 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([StartInterruptionFrame(), UserStartedSpeakingFrame()])
+        await task.queue_frames([InterruptionFrame(), UserStartedSpeakingFrame()])
 
     @stt.event_handler("on_utterance_end")
     async def on_utterance_end(stt, *args, **kwargs):

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

@@ -16,10 +16,10 @@ from pipecat.audio.vad.silero import SileroVADAnalyzer
 from pipecat.frames.frames import (
     Frame,
     InputAudioRawFrame,
+    InterruptionFrame,
     LLMFullResponseEndFrame,
     LLMFullResponseStartFrame,
     LLMRunFrame,
-    StartInterruptionFrame,
     TextFrame,
     TranscriptionFrame,
     UserStartedSpeakingFrame,
@@ -93,9 +93,8 @@ 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(
-                self._user_context_aggregator.get_context_frame()
-            )
+            await self._user_context_aggregator.push_frame(LLMRunFrame())
+
         elif isinstance(frame, InputAudioRawFrame):
             if self._user_speaking:
                 self._audio_frames.append(frame)
@@ -151,7 +150,7 @@ class TranscriptExtractor(FrameProcessor):
         await self.push_frame(frame, direction)
 
 
-class TanscriptionContextFixup(FrameProcessor):
+class TranscriptionContextFixup(FrameProcessor):
     def __init__(self, context):
         super().__init__()
         self._context = context
@@ -182,9 +181,7 @@ class TanscriptionContextFixup(FrameProcessor):
 
         if isinstance(frame, MagicDemoTranscriptionFrame):
             self._transcript = frame.text
-        elif isinstance(frame, LLMFullResponseEndFrame) or isinstance(
-            frame, StartInterruptionFrame
-        ):
+        elif isinstance(frame, LLMFullResponseEndFrame) or isinstance(frame, InterruptionFrame):
             self.swap_user_audio()
             self.add_transcript_back_to_inference_output()
             self._transcript = ""
@@ -245,7 +242,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
     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 = TanscriptionContextFixup(context)
+    fixup_context_messages = TranscriptionContextFixup(context)
 
     pipeline = Pipeline(
         [

examples/foundational/12-describe-video.py

@@ -11,12 +11,19 @@ from dotenv import load_dotenv
 from loguru import logger
 
 from pipecat.audio.vad.silero import SileroVADAnalyzer
-from pipecat.frames.frames import Frame, TextFrame, TTSSpeakFrame, UserImageRequestFrame
+from pipecat.frames.frames import (
+    Frame,
+    LLMContextFrame,
+    TextFrame,
+    TTSSpeakFrame,
+    UserImageRawFrame,
+    UserImageRequestFrame,
+)
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineTask
+from pipecat.processors.aggregators.llm_context import LLMContext
 from pipecat.processors.aggregators.user_response import UserResponseAggregator
-from pipecat.processors.aggregators.vision_image_frame import VisionImageFrameAggregator
 from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
 from pipecat.runner.types import RunnerArguments
 from pipecat.runner.utils import (
@@ -34,6 +41,8 @@ load_dotenv(override=True)
 
 
 class UserImageRequester(FrameProcessor):
+    """Converts incoming text into requests for user images."""
+
     def __init__(self, participant_id: Optional[str] = None):
         super().__init__()
         self._participant_id = participant_id
@@ -46,9 +55,32 @@ class UserImageRequester(FrameProcessor):
 
         if self._participant_id and isinstance(frame, TextFrame):
             await self.push_frame(
-                UserImageRequestFrame(self._participant_id), FrameDirection.UPSTREAM
+                UserImageRequestFrame(self._participant_id, context=frame.text),
+                FrameDirection.UPSTREAM,
             )
-        await self.push_frame(frame, direction)
+        else:
+            await self.push_frame(frame, direction)
+
+
+class UserImageProcessor(FrameProcessor):
+    """Converts incoming user images into context frames."""
+
+    async def process_frame(self, frame: Frame, direction: FrameDirection):
+        await super().process_frame(frame, direction)
+
+        if isinstance(frame, UserImageRawFrame):
+            if frame.request and frame.request.context:
+                context = LLMContext()
+                context.add_image_frame_message(
+                    image=frame.image,
+                    text=frame.request.context,
+                    size=frame.size,
+                    format=frame.format,
+                )
+                frame = LLMContextFrame(context)
+                await self.push_frame(frame)
+        else:
+            await self.push_frame(frame, direction)
 
 
 # We store functions so objects (e.g. SileroVADAnalyzer) don't get
@@ -78,7 +110,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
     # Initialize the image requester without setting the participant ID yet
     image_requester = UserImageRequester()
 
-    vision_aggregator = VisionImageFrameAggregator()
+    image_processor = UserImageProcessor()
 
     # If you run into weird description, try with use_cpu=True
     moondream = MoondreamService()
@@ -96,7 +128,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
             stt,
             user_response,
             image_requester,
-            vision_aggregator,
+            image_processor,
             moondream,
             tts,
             transport.output(),
@@ -119,7 +151,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         image_requester.set_participant_id(client_id)
 
         # Welcome message
-        await task.queue_frame(TTSSpeakFrame("Hi there! Feel free to ask me what I see."))
+        await task.queue_frame(TTSSpeakFrame("Hi there! Feel free to ask me about what I see."))
 
     @transport.event_handler("on_client_disconnected")
     async def on_client_disconnected(transport, client):

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

@@ -11,12 +11,19 @@ from dotenv import load_dotenv
 from loguru import logger
 
 from pipecat.audio.vad.silero import SileroVADAnalyzer
-from pipecat.frames.frames import Frame, TextFrame, TTSSpeakFrame, UserImageRequestFrame
+from pipecat.frames.frames import (
+    Frame,
+    LLMContextFrame,
+    TextFrame,
+    TTSSpeakFrame,
+    UserImageRawFrame,
+    UserImageRequestFrame,
+)
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
+from pipecat.processors.aggregators.llm_context import LLMContext
 from pipecat.processors.aggregators.user_response import UserResponseAggregator
-from pipecat.processors.aggregators.vision_image_frame import VisionImageFrameAggregator
 from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
 from pipecat.runner.types import RunnerArguments
 from pipecat.runner.utils import (
@@ -34,6 +41,8 @@ load_dotenv(override=True)
 
 
 class UserImageRequester(FrameProcessor):
+    """Converts incoming text into requests for user images."""
+
     def __init__(self, participant_id: Optional[str] = None):
         super().__init__()
         self._participant_id = participant_id
@@ -46,9 +55,32 @@ class UserImageRequester(FrameProcessor):
 
         if self._participant_id and isinstance(frame, TextFrame):
             await self.push_frame(
-                UserImageRequestFrame(self._participant_id), FrameDirection.UPSTREAM
+                UserImageRequestFrame(self._participant_id, context=frame.text),
+                FrameDirection.UPSTREAM,
             )
-        await self.push_frame(frame, direction)
+        else:
+            await self.push_frame(frame, direction)
+
+
+class UserImageProcessor(FrameProcessor):
+    """Converts incoming user images into context frames."""
+
+    async def process_frame(self, frame: Frame, direction: FrameDirection):
+        await super().process_frame(frame, direction)
+
+        if isinstance(frame, UserImageRawFrame):
+            if frame.request and frame.request.context:
+                context = LLMContext()
+                context.add_image_frame_message(
+                    image=frame.image,
+                    text=frame.request.context,
+                    size=frame.size,
+                    format=frame.format,
+                )
+                frame = LLMContextFrame(context)
+                await self.push_frame(frame)
+        else:
+            await self.push_frame(frame, direction)
 
 
 # We store functions so objects (e.g. SileroVADAnalyzer) don't get
@@ -78,7 +110,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
     # Initialize the image requester without setting the participant ID yet
     image_requester = UserImageRequester()
 
-    vision_aggregator = VisionImageFrameAggregator()
+    image_processor = UserImageProcessor()
 
     stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
 
@@ -96,7 +128,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
             stt,
             user_response,
             image_requester,
-            vision_aggregator,
+            image_processor,
             google,
             tts,
             transport.output(),
@@ -123,7 +155,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         image_requester.set_participant_id(client_id)
 
         # Welcome message
-        await task.queue_frame(TTSSpeakFrame("Hi there! Feel free to ask me what I see."))
+        await task.queue_frame(TTSSpeakFrame("Hi there! Feel free to ask me about what I see."))
 
     @transport.event_handler("on_client_disconnected")
     async def on_client_disconnected(transport, client):

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

@@ -11,12 +11,19 @@ from dotenv import load_dotenv
 from loguru import logger
 
 from pipecat.audio.vad.silero import SileroVADAnalyzer
-from pipecat.frames.frames import Frame, TextFrame, TTSSpeakFrame, UserImageRequestFrame
+from pipecat.frames.frames import (
+    Frame,
+    LLMContextFrame,
+    TextFrame,
+    TTSSpeakFrame,
+    UserImageRawFrame,
+    UserImageRequestFrame,
+)
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
+from pipecat.processors.aggregators.llm_context import LLMContext
 from pipecat.processors.aggregators.user_response import UserResponseAggregator
-from pipecat.processors.aggregators.vision_image_frame import VisionImageFrameAggregator
 from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
 from pipecat.runner.types import RunnerArguments
 from pipecat.runner.utils import (
@@ -34,6 +41,8 @@ load_dotenv(override=True)
 
 
 class UserImageRequester(FrameProcessor):
+    """Converts incoming text into requests for user images."""
+
     def __init__(self, participant_id: Optional[str] = None):
         super().__init__()
         self._participant_id = participant_id
@@ -46,9 +55,32 @@ class UserImageRequester(FrameProcessor):
 
         if self._participant_id and isinstance(frame, TextFrame):
             await self.push_frame(
-                UserImageRequestFrame(self._participant_id), FrameDirection.UPSTREAM
+                UserImageRequestFrame(self._participant_id, context=frame.text),
+                FrameDirection.UPSTREAM,
             )
-        await self.push_frame(frame, direction)
+        else:
+            await self.push_frame(frame, direction)
+
+
+class UserImageProcessor(FrameProcessor):
+    """Converts incoming user images into context frames."""
+
+    async def process_frame(self, frame: Frame, direction: FrameDirection):
+        await super().process_frame(frame, direction)
+
+        if isinstance(frame, UserImageRawFrame):
+            if frame.request and frame.request.context:
+                context = LLMContext()
+                context.add_image_frame_message(
+                    image=frame.image,
+                    text=frame.request.context,
+                    size=frame.size,
+                    format=frame.format,
+                )
+                frame = LLMContextFrame(context)
+                await self.push_frame(frame)
+        else:
+            await self.push_frame(frame, direction)
 
 
 # We store functions so objects (e.g. SileroVADAnalyzer) don't get
@@ -78,7 +110,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
     # Initialize the image requester without setting the participant ID yet
     image_requester = UserImageRequester()
 
-    vision_aggregator = VisionImageFrameAggregator()
+    image_processor = UserImageProcessor()
 
     stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
 
@@ -96,7 +128,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
             stt,
             user_response,
             image_requester,
-            vision_aggregator,
+            image_processor,
             openai,
             tts,
             transport.output(),
@@ -123,7 +155,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         image_requester.set_participant_id(client_id)
 
         # Welcome message
-        await task.queue_frame(TTSSpeakFrame("Hi there! Feel free to ask me what I see."))
+        await task.queue_frame(TTSSpeakFrame("Hi there! Feel free to ask me about what I see."))
 
     @transport.event_handler("on_client_disconnected")
     async def on_client_disconnected(transport, client):

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

@@ -11,12 +11,19 @@ from dotenv import load_dotenv
 from loguru import logger
 
 from pipecat.audio.vad.silero import SileroVADAnalyzer
-from pipecat.frames.frames import Frame, TextFrame, TTSSpeakFrame, UserImageRequestFrame
+from pipecat.frames.frames import (
+    Frame,
+    LLMContextFrame,
+    TextFrame,
+    TTSSpeakFrame,
+    UserImageRawFrame,
+    UserImageRequestFrame,
+)
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
+from pipecat.processors.aggregators.llm_context import LLMContext
 from pipecat.processors.aggregators.user_response import UserResponseAggregator
-from pipecat.processors.aggregators.vision_image_frame import VisionImageFrameAggregator
 from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
 from pipecat.runner.types import RunnerArguments
 from pipecat.runner.utils import (
@@ -34,6 +41,8 @@ load_dotenv(override=True)
 
 
 class UserImageRequester(FrameProcessor):
+    """Converts incoming text into requests for user images."""
+
     def __init__(self, participant_id: Optional[str] = None):
         super().__init__()
         self._participant_id = participant_id
@@ -46,9 +55,32 @@ class UserImageRequester(FrameProcessor):
 
         if self._participant_id and isinstance(frame, TextFrame):
             await self.push_frame(
-                UserImageRequestFrame(self._participant_id), FrameDirection.UPSTREAM
+                UserImageRequestFrame(self._participant_id, context=frame.text),
+                FrameDirection.UPSTREAM,
             )
-        await self.push_frame(frame, direction)
+        else:
+            await self.push_frame(frame, direction)
+
+
+class UserImageProcessor(FrameProcessor):
+    """Converts incoming user images into context frames."""
+
+    async def process_frame(self, frame: Frame, direction: FrameDirection):
+        await super().process_frame(frame, direction)
+
+        if isinstance(frame, UserImageRawFrame):
+            if frame.request and frame.request.context:
+                context = LLMContext()
+                context.add_image_frame_message(
+                    image=frame.image,
+                    text=frame.request.context,
+                    size=frame.size,
+                    format=frame.format,
+                )
+                frame = LLMContextFrame(context)
+                await self.push_frame(frame)
+        else:
+            await self.push_frame(frame, direction)
 
 
 # We store functions so objects (e.g. SileroVADAnalyzer) don't get
@@ -78,7 +110,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
     # Initialize the image requester without setting the participant ID yet
     image_requester = UserImageRequester()
 
-    vision_aggregator = VisionImageFrameAggregator()
+    image_processor = UserImageProcessor()
 
     stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
 
@@ -96,7 +128,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
             stt,
             user_response,
             image_requester,
-            vision_aggregator,
+            image_processor,
             anthropic,
             tts,
             transport.output(),
@@ -123,7 +155,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         image_requester.set_participant_id(client_id)
 
         # Welcome message
-        await task.queue_frame(TTSSpeakFrame("Hi there! Feel free to ask me what I see."))
+        await task.queue_frame(TTSSpeakFrame("Hi there! Feel free to ask me about what I see."))
 
     @transport.event_handler("on_client_disconnected")
     async def on_client_disconnected(transport, client):

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

@@ -0,0 +1,187 @@
+#
+# Copyright (c) 2024–2025, Daily
+#
+# SPDX-License-Identifier: BSD 2-Clause License
+#
+
+import os
+from typing import Optional
+
+from dotenv import load_dotenv
+from loguru import logger
+
+from pipecat.audio.vad.silero import SileroVADAnalyzer
+from pipecat.frames.frames import (
+    Frame,
+    LLMContextFrame,
+    TextFrame,
+    TTSSpeakFrame,
+    UserImageRawFrame,
+    UserImageRequestFrame,
+)
+from pipecat.pipeline.pipeline import Pipeline
+from pipecat.pipeline.runner import PipelineRunner
+from pipecat.pipeline.task import PipelineParams, PipelineTask
+from pipecat.processors.aggregators.llm_context import LLMContext
+from pipecat.processors.aggregators.user_response import UserResponseAggregator
+from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
+from pipecat.runner.types import RunnerArguments
+from pipecat.runner.utils import (
+    create_transport,
+    get_transport_client_id,
+    maybe_capture_participant_camera,
+)
+from pipecat.services.aws.llm import AWSBedrockLLMService
+from pipecat.services.cartesia.tts import CartesiaTTSService
+from pipecat.services.deepgram.stt import DeepgramSTTService
+from pipecat.transports.base_transport import BaseTransport, TransportParams
+from pipecat.transports.daily.transport import DailyParams
+
+load_dotenv(override=True)
+
+
+class UserImageRequester(FrameProcessor):
+    """Converts incoming text into requests for user images."""
+
+    def __init__(self, participant_id: Optional[str] = None):
+        super().__init__()
+        self._participant_id = participant_id
+
+    def set_participant_id(self, participant_id: str):
+        self._participant_id = participant_id
+
+    async def process_frame(self, frame: Frame, direction: FrameDirection):
+        await super().process_frame(frame, direction)
+
+        if self._participant_id and isinstance(frame, TextFrame):
+            await self.push_frame(
+                UserImageRequestFrame(self._participant_id, context=frame.text),
+                FrameDirection.UPSTREAM,
+            )
+        else:
+            await self.push_frame(frame, direction)
+
+
+class UserImageProcessor(FrameProcessor):
+    """Converts incoming user images into context frames."""
+
+    async def process_frame(self, frame: Frame, direction: FrameDirection):
+        await super().process_frame(frame, direction)
+
+        if isinstance(frame, UserImageRawFrame):
+            if frame.request and frame.request.context:
+                # Note: AWS Bedrock does not yet support the universal LLMContext
+                context = LLMContext()
+                context.add_image_frame_message(
+                    image=frame.image,
+                    text=frame.request.context,
+                    size=frame.size,
+                    format=frame.format,
+                )
+                frame = LLMContextFrame(context)
+                await self.push_frame(frame)
+        else:
+            await self.push_frame(frame, direction)
+
+
+# We store functions so objects (e.g. SileroVADAnalyzer) don't get
+# instantiated. The function will be called when the desired transport gets
+# selected.
+transport_params = {
+    "daily": lambda: DailyParams(
+        audio_in_enabled=True,
+        audio_out_enabled=True,
+        video_in_enabled=True,
+        vad_analyzer=SileroVADAnalyzer(),
+    ),
+    "webrtc": lambda: TransportParams(
+        audio_in_enabled=True,
+        audio_out_enabled=True,
+        video_in_enabled=True,
+        vad_analyzer=SileroVADAnalyzer(),
+    ),
+}
+
+
+async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
+    logger.info(f"Starting bot")
+
+    user_response = UserResponseAggregator()
+
+    # Initialize the image requester without setting the participant ID yet
+    image_requester = UserImageRequester()
+
+    image_processor = UserImageProcessor()
+
+    stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
+
+    # AWS for vision analysis
+    aws = AWSBedrockLLMService(
+        aws_region="us-west-2",
+        model="us.anthropic.claude-3-7-sonnet-20250219-v1:0",
+        # Note: usually, prefer providing latency="optimized" param.
+        # Here we can't because AWS Bedrock doesn't support it for Claude 3.7,
+        # which we need for image input.
+        params=AWSBedrockLLMService.InputParams(temperature=0.8),
+    )
+
+    tts = CartesiaTTSService(
+        api_key=os.getenv("CARTESIA_API_KEY"),
+        voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
+    )
+
+    pipeline = Pipeline(
+        [
+            transport.input(),
+            stt,
+            user_response,
+            image_requester,
+            image_processor,
+            aws,
+            tts,
+            transport.output(),
+        ]
+    )
+
+    task = PipelineTask(
+        pipeline,
+        params=PipelineParams(
+            enable_metrics=True,
+            enable_usage_metrics=True,
+        ),
+        idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
+    )
+
+    @transport.event_handler("on_client_connected")
+    async def on_client_connected(transport, client):
+        logger.info(f"Client connected: {client}")
+
+        await maybe_capture_participant_camera(transport, client)
+
+        # Set the participant ID in the image requester
+        client_id = get_transport_client_id(transport, client)
+        image_requester.set_participant_id(client_id)
+
+        # Welcome message
+        await task.queue_frame(TTSSpeakFrame("Hi there! Feel free to ask me about what I see."))
+
+    @transport.event_handler("on_client_disconnected")
+    async def on_client_disconnected(transport, client):
+        logger.info(f"Client disconnected")
+        await task.cancel()
+
+    runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
+
+    await runner.run(task)
+
+
+async def bot(runner_args: RunnerArguments):
+    """Main bot entry point compatible with Pipecat Cloud."""
+    transport = await create_transport(runner_args, transport_params)
+    await run_bot(transport, runner_args)
+
+
+if __name__ == "__main__":
+    from pipecat.runner.run import main
+
+    main()

examples/foundational/13-whisper-transcription.py

@@ -31,6 +31,9 @@ class TranscriptionLogger(FrameProcessor):
         if isinstance(frame, TranscriptionFrame):
             print(f"Transcription: {frame.text}")
 
+        # Push all frames through
+        await self.push_frame(frame, direction)
+
 
 # We store functions so objects (e.g. SileroVADAnalyzer) don't get
 # instantiated. The function will be called when the desired transport gets

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

@@ -32,6 +32,9 @@ class TranscriptionLogger(FrameProcessor):
         if isinstance(frame, TranscriptionFrame):
             print(f"Transcription: {frame.text}")
 
+        # Push all frames through
+        await self.push_frame(frame, direction)
+
 
 async def main():
     transport = LocalAudioTransport(

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

@@ -31,6 +31,9 @@ class TranscriptionLogger(FrameProcessor):
         if isinstance(frame, TranscriptionFrame):
             print(f"Transcription: {frame.text}")
 
+        # Push all frames through
+        await self.push_frame(frame, direction)
+
 
 # We store functions so objects (e.g. SileroVADAnalyzer) don't get
 # instantiated. The function will be called when the desired transport gets

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

@@ -31,6 +31,9 @@ class TranscriptionLogger(FrameProcessor):
         if isinstance(frame, TranscriptionFrame):
             print(f"Transcription: {frame.text}")
 
+        # Push all frames through
+        await self.push_frame(frame, direction)
+
 
 # We store functions so objects (e.g. SileroVADAnalyzer) don't get
 # instantiated. The function will be called when the desired transport gets

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

@@ -40,6 +40,9 @@ class TranscriptionLogger(FrameProcessor):
         elif isinstance(frame, TranslationFrame):
             print(f"Translation ({frame.language}): {frame.text}")
 
+        # Push all frames through
+        await self.push_frame(frame, direction)
+
 
 # We store functions so objects (e.g. SileroVADAnalyzer) don't get
 # instantiated. The function will be called when the desired transport gets

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

@@ -31,6 +31,9 @@ class TranscriptionLogger(FrameProcessor):
         if isinstance(frame, TranscriptionFrame):
             print(f"Transcription: {frame.text}")
 
+        # Push all frames through
+        await self.push_frame(frame, direction)
+
 
 # We store functions so objects (e.g. SileroVADAnalyzer) don't get
 # instantiated. The function will be called when the desired transport gets

examples/foundational/13e-whisper-mlx.py

@@ -52,6 +52,9 @@ class TranscriptionLogger(FrameProcessor):
         if isinstance(frame, TranscriptionFrame):
             self._last_transcription_time = time.time()
 
+        # Push all frames through
+        await self.push_frame(frame, direction)
+
 
 # We store functions so objects (e.g. SileroVADAnalyzer) don't get
 # instantiated. The function will be called when the desired transport gets

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

@@ -31,6 +31,9 @@ class TranscriptionLogger(FrameProcessor):
         if isinstance(frame, TranscriptionFrame):
             print(f"Transcription: {frame.text}")
 
+        # Push all frames through
+        await self.push_frame(frame, direction)
+
 
 # We store functions so objects (e.g. SileroVADAnalyzer) don't get
 # instantiated. The function will be called when the desired transport gets

examples/foundational/13g-sambanova-transcription.py

@@ -53,6 +53,9 @@ class TranscriptionLogger(FrameProcessor):
         if isinstance(frame, TranscriptionFrame):
             self._last_transcription_time = time.time()
 
+        # Push all frames through
+        await self.push_frame(frame, direction)
+
 
 # We store functions so objects (e.g. SileroVADAnalyzer) don't get
 # instantiated. The function will be called when the desired transport gets

examples/foundational/13h-speechmatics-transcription.py

@@ -32,6 +32,9 @@ class TranscriptionLogger(FrameProcessor):
         if isinstance(frame, TranscriptionFrame):
             print(f"Transcription: {frame.text}")
 
+        # Push all frames through
+        await self.push_frame(frame, direction)
+
 
 # We store functions so objects (e.g. SileroVADAnalyzer) don't get
 # instantiated. The function will be called when the desired transport gets

examples/foundational/13i-soniox-transcription.py

@@ -32,6 +32,9 @@ class TranscriptionLogger(FrameProcessor):
         if isinstance(frame, TranscriptionFrame):
             print(f"Transcription: {frame.text}")
 
+        # Push all frames through
+        await self.push_frame(frame, direction)
+
 
 transport_params = {
     "daily": lambda: DailyParams(

examples/foundational/13j-azure-transcription.py

@@ -32,6 +32,9 @@ class TranscriptionLogger(FrameProcessor):
         if isinstance(frame, TranscriptionFrame):
             print(f"Transcription: {frame.text}")
 
+        # Push all frames through
+        await self.push_frame(frame, direction)
+
 
 transport_params = {
     "daily": lambda: DailyParams(

examples/foundational/14aa-function-calling-aws-universal-context.py

@@ -0,0 +1,214 @@
+#
+# Copyright (c) 2024–2025, Daily
+#
+# SPDX-License-Identifier: BSD 2-Clause License
+#
+
+
+import asyncio
+import os
+
+from dotenv import load_dotenv
+from loguru import logger
+
+from pipecat.adapters.schemas.function_schema import FunctionSchema
+from pipecat.adapters.schemas.tools_schema import ToolsSchema
+from pipecat.audio.vad.silero import SileroVADAnalyzer
+from pipecat.frames.frames import LLMRunFrame
+from pipecat.pipeline.pipeline import Pipeline
+from pipecat.pipeline.runner import PipelineRunner
+from pipecat.pipeline.task import PipelineParams, PipelineTask
+from pipecat.processors.aggregators.llm_context import LLMContext
+from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
+from pipecat.runner.types import RunnerArguments
+from pipecat.runner.utils import (
+    create_transport,
+    get_transport_client_id,
+    maybe_capture_participant_camera,
+)
+from pipecat.services.aws.llm import AWSBedrockLLMService
+from pipecat.services.cartesia.tts import CartesiaTTSService
+from pipecat.services.deepgram.stt import DeepgramSTTService
+from pipecat.services.llm_service import FunctionCallParams
+from pipecat.transports.base_transport import BaseTransport, TransportParams
+from pipecat.transports.services.daily import DailyParams
+
+load_dotenv(override=True)
+
+
+# Global variable to store the client ID
+client_id = ""
+
+
+async def get_weather(params: FunctionCallParams):
+    location = params.arguments["location"]
+    await params.result_callback(f"The weather in {location} is currently 72 degrees and sunny.")
+
+
+async def get_image(params: FunctionCallParams):
+    question = params.arguments["question"]
+    logger.debug(f"Requesting image with user_id={client_id}, question={question}")
+
+    # Request the image frame
+    await params.llm.request_image_frame(
+        user_id=client_id,
+        function_name=params.function_name,
+        tool_call_id=params.tool_call_id,
+        text_content=question,
+    )
+
+    # Wait a short time for the frame to be processed
+    await asyncio.sleep(0.5)
+
+    # Return a result to complete the function call
+    await params.result_callback(
+        f"I've captured an image from your camera and I'm analyzing what you asked about: {question}"
+    )
+
+
+# We store functions so objects (e.g. SileroVADAnalyzer) don't get
+# instantiated. The function will be called when the desired transport gets
+# selected.
+transport_params = {
+    "daily": lambda: DailyParams(
+        audio_in_enabled=True,
+        audio_out_enabled=True,
+        video_in_enabled=True,
+        vad_analyzer=SileroVADAnalyzer(),
+    ),
+    "webrtc": lambda: TransportParams(
+        audio_in_enabled=True,
+        audio_out_enabled=True,
+        video_in_enabled=True,
+        vad_analyzer=SileroVADAnalyzer(),
+    ),
+}
+
+
+async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
+    logger.info(f"Starting bot")
+
+    stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
+
+    tts = CartesiaTTSService(
+        api_key=os.getenv("CARTESIA_API_KEY"),
+        voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
+    )
+
+    llm = AWSBedrockLLMService(
+        aws_region="us-west-2",
+        model="us.anthropic.claude-3-7-sonnet-20250219-v1:0",
+        # Note: usually, prefer providing latency="optimized" param.
+        # Here we can't because AWS Bedrock doesn't support it for Claude 3.7,
+        # which we need for image input.
+        params=AWSBedrockLLMService.InputParams(temperature=0.8),
+    )
+    llm.register_function("get_weather", get_weather)
+    llm.register_function("get_image", get_image)
+
+    weather_function = FunctionSchema(
+        name="get_weather",
+        description="Get the current weather",
+        properties={
+            "location": {
+                "type": "string",
+                "description": "The city and state, e.g. San Francisco, CA",
+            },
+        },
+        required=["location"],
+    )
+    get_image_function = FunctionSchema(
+        name="get_image",
+        description="Get an image from the video stream.",
+        properties={
+            "question": {
+                "type": "string",
+                "description": "The question that the user is asking about the image.",
+            }
+        },
+        required=["question"],
+    )
+    tools = ToolsSchema(standard_tools=[weather_function, get_image_function])
+
+    system_prompt = """\
+You are a helpful assistant who converses with a user and answers questions. Respond concisely to general questions.
+
+Your response will be turned into speech so use only simple words and punctuation.
+
+You have access to two tools: get_weather and get_image.
+
+You can respond to questions about the weather using the get_weather tool.
+
+You can answer questions about the user's video stream using the get_image tool. Some examples of phrases that \
+indicate you should use the get_image tool are:
+- What do you see?
+- What's in the video?
+- Can you describe the video?
+- Tell me about what you see.
+- Tell me something interesting about what you see.
+- What's happening in the video?
+
+If you need to use a tool, simply use the tool. Do not tell the user the tool you are using. Be brief and concise.
+    """
+
+    messages = [
+        {"role": "system", "content": system_prompt},
+        {"role": "user", "content": "Start the conversation by introducing yourself."},
+    ]
+
+    context = LLMContext(messages, tools)
+    context_aggregator = LLMContextAggregatorPair(context)
+
+    pipeline = Pipeline(
+        [
+            transport.input(),  # Transport user input
+            stt,  # STT
+            context_aggregator.user(),  # User speech to text
+            llm,  # LLM
+            tts,  # TTS
+            transport.output(),  # Transport bot output
+            context_aggregator.assistant(),  # Assistant spoken responses and tool context
+        ]
+    )
+
+    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: {client}")
+
+        await maybe_capture_participant_camera(transport, client)
+
+        global client_id
+        client_id = get_transport_client_id(transport, client)
+
+        # Kick off the conversation.
+        await task.queue_frames([LLMRunFrame()])
+
+    @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/14b-function-calling-anthropic-video.py

@@ -97,7 +97,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
     llm = AnthropicLLMService(
         api_key=os.getenv("ANTHROPIC_API_KEY"),
         model="claude-3-7-sonnet-latest",
-        enable_prompt_caching_beta=True,
+        params=AnthropicLLMService.InputParams(enable_prompt_caching=True),
     )
     llm.register_function("get_weather", get_weather)
     llm.register_function("get_image", get_image)

examples/foundational/14z-function-calling-anthropic-universal-context.py

@@ -0,0 +1,211 @@
+#
+# Copyright (c) 2024–2025, Daily
+#
+# SPDX-License-Identifier: BSD 2-Clause License
+#
+
+
+import asyncio
+import os
+
+from dotenv import load_dotenv
+from loguru import logger
+
+from pipecat.adapters.schemas.function_schema import FunctionSchema
+from pipecat.adapters.schemas.tools_schema import ToolsSchema
+from pipecat.audio.vad.silero import SileroVADAnalyzer
+from pipecat.frames.frames import LLMRunFrame
+from pipecat.pipeline.pipeline import Pipeline
+from pipecat.pipeline.runner import PipelineRunner
+from pipecat.pipeline.task import PipelineParams, PipelineTask
+from pipecat.processors.aggregators.llm_context import LLMContext
+from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
+from pipecat.runner.types import RunnerArguments
+from pipecat.runner.utils import (
+    create_transport,
+    get_transport_client_id,
+    maybe_capture_participant_camera,
+)
+from pipecat.services.anthropic.llm import AnthropicLLMService
+from pipecat.services.cartesia.tts import CartesiaTTSService
+from pipecat.services.deepgram.stt import DeepgramSTTService
+from pipecat.services.llm_service import FunctionCallParams
+from pipecat.transports.base_transport import BaseTransport, TransportParams
+from pipecat.transports.services.daily import DailyParams
+
+load_dotenv(override=True)
+
+
+# Global variable to store the client ID
+client_id = ""
+
+
+async def get_weather(params: FunctionCallParams):
+    location = params.arguments["location"]
+    await params.result_callback(f"The weather in {location} is currently 72 degrees and sunny.")
+
+
+async def get_image(params: FunctionCallParams):
+    question = params.arguments["question"]
+    logger.debug(f"Requesting image with user_id={client_id}, question={question}")
+
+    # Request the image frame
+    await params.llm.request_image_frame(
+        user_id=client_id,
+        function_name=params.function_name,
+        tool_call_id=params.tool_call_id,
+        text_content=question,
+    )
+
+    # Wait a short time for the frame to be processed
+    await asyncio.sleep(0.5)
+
+    # Return a result to complete the function call
+    await params.result_callback(
+        f"I've captured an image from your camera and I'm analyzing what you asked about: {question}"
+    )
+
+
+# We store functions so objects (e.g. SileroVADAnalyzer) don't get
+# instantiated. The function will be called when the desired transport gets
+# selected.
+transport_params = {
+    "daily": lambda: DailyParams(
+        audio_in_enabled=True,
+        audio_out_enabled=True,
+        video_in_enabled=True,
+        vad_analyzer=SileroVADAnalyzer(),
+    ),
+    "webrtc": lambda: TransportParams(
+        audio_in_enabled=True,
+        audio_out_enabled=True,
+        video_in_enabled=True,
+        vad_analyzer=SileroVADAnalyzer(),
+    ),
+}
+
+
+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 = AnthropicLLMService(
+        api_key=os.getenv("ANTHROPIC_API_KEY"),
+        model="claude-3-7-sonnet-latest",
+        params=AnthropicLLMService.InputParams(enable_prompt_caching=True),
+    )
+    llm.register_function("get_weather", get_weather)
+    llm.register_function("get_image", get_image)
+
+    weather_function = FunctionSchema(
+        name="get_weather",
+        description="Get the current weather",
+        properties={
+            "location": {
+                "type": "string",
+                "description": "The city and state, e.g. San Francisco, CA",
+            },
+        },
+        required=["location"],
+    )
+    get_image_function = FunctionSchema(
+        name="get_image",
+        description="Get an image from the video stream.",
+        properties={
+            "question": {
+                "type": "string",
+                "description": "The question that the user is asking about the image.",
+            }
+        },
+        required=["question"],
+    )
+    tools = ToolsSchema(standard_tools=[weather_function, get_image_function])
+
+    system_prompt = """\
+You are a helpful assistant who converses with a user and answers questions. Respond concisely to general questions.
+
+Your response will be turned into speech so use only simple words and punctuation.
+
+You have access to two tools: get_weather and get_image.
+
+You can respond to questions about the weather using the get_weather tool.
+
+You can answer questions about the user's video stream using the get_image tool. Some examples of phrases that \
+indicate you should use the get_image tool are:
+- What do you see?
+- What's in the video?
+- Can you describe the video?
+- Tell me about what you see.
+- Tell me something interesting about what you see.
+- What's happening in the video?
+
+If you need to use a tool, simply use the tool. Do not tell the user the tool you are using. Be brief and concise.
+    """
+
+    messages = [
+        {"role": "system", "content": system_prompt},
+        {"role": "user", "content": "Start the conversation by introducing yourself."},
+    ]
+
+    context = LLMContext(messages, tools)
+    context_aggregator = LLMContextAggregatorPair(context)
+
+    pipeline = Pipeline(
+        [
+            transport.input(),  # Transport user input
+            stt,  # STT
+            context_aggregator.user(),  # User speech to text
+            llm,  # LLM
+            tts,  # TTS
+            transport.output(),  # Transport bot output
+            context_aggregator.assistant(),  # Assistant spoken responses and tool context
+        ]
+    )
+
+    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: {client}")
+
+        await maybe_capture_participant_camera(transport, client)
+
+        global client_id
+        client_id = get_transport_client_id(transport, client)
+
+        # Kick off the conversation.
+        await task.queue_frames([LLMRunFrame()])
+
+    @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/19-openai-realtime.py

@@ -0,0 +1,228 @@
+#
+# Copyright (c) 2024–2025, Daily
+#
+# SPDX-License-Identifier: BSD 2-Clause License
+#
+
+
+import os
+from datetime import datetime
+
+from dotenv import load_dotenv
+from loguru import logger
+
+from pipecat.adapters.schemas.function_schema import FunctionSchema
+from pipecat.adapters.schemas.tools_schema import ToolsSchema
+from pipecat.audio.vad.silero import SileroVADAnalyzer
+from pipecat.frames.frames import LLMRunFrame, TranscriptionMessage
+from pipecat.observers.loggers.transcription_log_observer import TranscriptionLogObserver
+from pipecat.pipeline.pipeline import Pipeline
+from pipecat.pipeline.runner import PipelineRunner
+from pipecat.pipeline.task import PipelineParams, PipelineTask
+from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
+from pipecat.processors.transcript_processor import TranscriptProcessor
+from pipecat.runner.types import RunnerArguments
+from pipecat.runner.utils import create_transport
+from pipecat.services.llm_service import FunctionCallParams
+from pipecat.services.openai_realtime import (
+    InputAudioNoiseReduction,
+    InputAudioTranscription,
+    OpenAIRealtimeLLMService,
+    SemanticTurnDetection,
+    SessionProperties,
+)
+from pipecat.services.openai_realtime.events import AudioConfiguration, AudioInput
+from pipecat.transports.base_transport import BaseTransport, TransportParams
+from pipecat.transports.daily.transport import DailyParams
+from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
+
+load_dotenv(override=True)
+
+
+async def fetch_weather_from_api(params: FunctionCallParams):
+    temperature = 75 if params.arguments["format"] == "fahrenheit" else 24
+    await params.result_callback(
+        {
+            "conditions": "nice",
+            "temperature": temperature,
+            "format": params.arguments["format"],
+            "timestamp": datetime.now().strftime("%Y%m%d_%H%M%S"),
+        }
+    )
+
+
+async def fetch_restaurant_recommendation(params: FunctionCallParams):
+    await params.result_callback({"name": "The Golden Dragon"})
+
+
+weather_function = FunctionSchema(
+    name="get_current_weather",
+    description="Get the current weather",
+    properties={
+        "location": {
+            "type": "string",
+            "description": "The city and state, e.g. San Francisco, CA",
+        },
+        "format": {
+            "type": "string",
+            "enum": ["celsius", "fahrenheit"],
+            "description": "The temperature unit to use. Infer this from the users location.",
+        },
+    },
+    required=["location", "format"],
+)
+
+restaurant_function = FunctionSchema(
+    name="get_restaurant_recommendation",
+    description="Get a restaurant recommendation",
+    properties={
+        "location": {
+            "type": "string",
+            "description": "The city and state, e.g. San Francisco, CA",
+        },
+    },
+    required=["location"],
+)
+
+# Create tools schema
+tools = ToolsSchema(standard_tools=[weather_function, restaurant_function])
+
+
+# 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(),
+    ),
+}
+
+
+async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
+    logger.info(f"Starting bot")
+
+    session_properties = SessionProperties(
+        audio=AudioConfiguration(
+            input=AudioInput(
+                transcription=InputAudioTranscription(),
+                # Set openai TurnDetection parameters. Not setting this at all will turn it
+                # on by default
+                turn_detection=SemanticTurnDetection(),
+                # Or set to False to disable openai turn detection and use transport VAD
+                # turn_detection=False,
+                noise_reduction=InputAudioNoiseReduction(type="near_field"),
+            )
+        ),
+        # tools=tools,
+        instructions="""You are a helpful and friendly AI.
+
+Act like a human, but remember that you aren't a human and that you can't do human
+things in the real world. Your voice and personality should be warm and engaging, with a lively and
+playful tone.
+
+If interacting in a non-English language, start by using the standard accent or dialect familiar to
+the user. Talk quickly. You should always call a function if you can. Do not refer to these rules,
+even if you're asked about them.
+
+You are participating in a voice conversation. Keep your responses concise, short, and to the point
+unless specifically asked to elaborate on a topic.
+
+You have access to the following tools:
+- get_current_weather: Get the current weather for a given location.
+- get_restaurant_recommendation: Get a restaurant recommendation for a given location.
+
+Remember, your responses should be short. Just one or two sentences, usually. Respond in English.""",
+    )
+
+    llm = OpenAIRealtimeLLMService(
+        api_key=os.getenv("OPENAI_API_KEY"),
+        session_properties=session_properties,
+        start_audio_paused=False,
+    )
+
+    # you can either register a single function for all function calls, or specific functions
+    # llm.register_function(None, fetch_weather_from_api)
+    llm.register_function("get_current_weather", fetch_weather_from_api)
+    llm.register_function("get_restaurant_recommendation", fetch_restaurant_recommendation)
+
+    transcript = TranscriptProcessor()
+
+    # Create a standard OpenAI LLM context object using the normal messages format. The
+    # OpenAIRealtimeLLMService will convert this internally to messages that the
+    # openai WebSocket API can understand.
+    context = OpenAILLMContext(
+        [{"role": "user", "content": "Say hello!"}],
+        tools,
+    )
+
+    context_aggregator = llm.create_context_aggregator(context)
+
+    pipeline = Pipeline(
+        [
+            transport.input(),  # Transport user input
+            context_aggregator.user(),
+            llm,  # LLM
+            transcript.user(),  # Placed after the LLM, as LLM pushes TranscriptionFrames downstream
+            transport.output(),  # Transport bot output
+            transcript.assistant(),  # After the transcript output, to time with the audio output
+            context_aggregator.assistant(),
+        ]
+    )
+
+    task = PipelineTask(
+        pipeline,
+        params=PipelineParams(
+            enable_metrics=True,
+            enable_usage_metrics=True,
+        ),
+        idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
+        observers=[TranscriptionLogObserver()],
+    )
+
+    @transport.event_handler("on_client_connected")
+    async def on_client_connected(transport, client):
+        logger.info(f"Client connected")
+        # Kick off the conversation.
+        await task.queue_frames([LLMRunFrame()])
+
+    @transport.event_handler("on_client_disconnected")
+    async def on_client_disconnected(transport, client):
+        logger.info(f"Client disconnected")
+        await task.cancel()
+
+    # Register event handler for transcript updates
+    @transcript.event_handler("on_transcript_update")
+    async def on_transcript_update(processor, frame):
+        for msg in frame.messages:
+            if isinstance(msg, TranscriptionMessage):
+                timestamp = f"[{msg.timestamp}] " if msg.timestamp else ""
+                line = f"{timestamp}{msg.role}: {msg.content}"
+                logger.info(f"Transcript: {line}")
+
+    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/19a-azure-realtime.py

@@ -0,0 +1,221 @@
+#
+# Copyright (c) 2024–2025, Daily
+#
+# SPDX-License-Identifier: BSD 2-Clause License
+#
+
+
+import os
+from datetime import datetime
+
+from dotenv import load_dotenv
+from loguru import logger
+
+from pipecat.adapters.schemas.function_schema import FunctionSchema
+from pipecat.adapters.schemas.tools_schema import ToolsSchema
+from pipecat.audio.vad.silero import SileroVADAnalyzer
+from pipecat.frames.frames import LLMRunFrame
+from pipecat.pipeline.pipeline import Pipeline
+from pipecat.pipeline.runner import PipelineRunner
+from pipecat.pipeline.task import PipelineParams, PipelineTask
+from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
+from pipecat.runner.types import RunnerArguments
+from pipecat.runner.utils import create_transport
+from pipecat.services.llm_service import FunctionCallParams
+from pipecat.services.openai_realtime import (
+    AzureRealtimeLLMService,
+    InputAudioTranscription,
+    SessionProperties,
+)
+from pipecat.services.openai_realtime.events import AudioConfiguration, AudioInput
+from pipecat.transports.base_transport import BaseTransport, TransportParams
+from pipecat.transports.daily.transport import DailyParams
+from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
+
+load_dotenv(override=True)
+
+
+async def fetch_weather_from_api(params: FunctionCallParams):
+    temperature = 75 if params.arguments["format"] == "fahrenheit" else 24
+    await params.result_callback(
+        {
+            "conditions": "nice",
+            "temperature": temperature,
+            "format": params.arguments["format"],
+            "timestamp": datetime.now().strftime("%Y%m%d_%H%M%S"),
+        }
+    )
+
+
+async def fetch_restaurant_recommendation(params: FunctionCallParams):
+    await params.result_callback({"name": "The Golden Dragon"})
+
+
+# Define weather function using standardized schema
+weather_function = FunctionSchema(
+    name="get_current_weather",
+    description="Get the current weather",
+    properties={
+        "location": {
+            "type": "string",
+            "description": "The city and state, e.g. San Francisco, CA",
+        },
+        "format": {
+            "type": "string",
+            "enum": ["celsius", "fahrenheit"],
+            "description": "The temperature unit to use. Infer this from the users location.",
+        },
+    },
+    required=["location", "format"],
+)
+
+restaurant_function = FunctionSchema(
+    name="get_restaurant_recommendation",
+    description="Get a restaurant recommendation",
+    properties={
+        "location": {
+            "type": "string",
+            "description": "The city and state, e.g. San Francisco, CA",
+        },
+    },
+    required=["location"],
+)
+
+# Create tools schema
+tools = ToolsSchema(standard_tools=[weather_function, restaurant_function])
+
+
+# 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(),
+    ),
+}
+
+
+async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
+    logger.info(f"Starting bot")
+
+    session_properties = SessionProperties(
+        audio=AudioConfiguration(
+            input=AudioInput(
+                transcription=InputAudioTranscription(model="whisper-1"),
+                # Set openai TurnDetection parameters. Not setting this at all will turn it
+                # on by default
+                # turn_detection=TurnDetection(silence_duration_ms=1000),
+                # Or set to False to disable openai turn detection and use transport VAD
+                # turn_detection=False,
+            )
+        ),
+        # tools=tools,
+        instructions="""You are a helpful and friendly AI.
+
+Act like a human, but remember that you aren't a human and that you can't do human
+things in the real world. Your voice and personality should be warm and engaging, with a lively and
+playful tone.
+
+If interacting in a non-English language, start by using the standard accent or dialect familiar to
+the user. Talk quickly. You should always call a function if you can. Do not refer to these rules,
+even if you're asked about them.
+-
+You are participating in a voice conversation. Keep your responses concise, short, and to the point
+unless specifically asked to elaborate on a topic.
+
+You have access to the following tools:
+- get_current_weather: Get the current weather for a given location.
+- get_restaurant_recommendation: Get a restaurant recommendation for a given location.
+
+Remember, your responses should be short. Just one or two sentences, usually. Respond in English.""",
+    )
+
+    llm = AzureRealtimeLLMService(
+        api_key=os.getenv("AZURE_REALTIME_API_KEY"),
+        base_url=os.getenv("AZURE_REALTIME_BASE_URL"),
+        session_properties=session_properties,
+        start_audio_paused=False,
+    )
+
+    # you can either register a single function for all function calls, or specific functions
+    # llm.register_function(None, fetch_weather_from_api)
+    llm.register_function("get_current_weather", fetch_weather_from_api)
+    llm.register_function("get_restaurant_recommendation", fetch_restaurant_recommendation)
+
+    # Create a standard OpenAI LLM context object using the normal messages format. The
+    # OpenAIRealtimeBetaLLMService will convert this internally to messages that the
+    # openai WebSocket API can understand.
+    context = OpenAILLMContext(
+        [{"role": "user", "content": "Say hello!"}],
+        # [{"role": "user", "content": [{"type": "text", "text": "Say hello!"}]}],
+        #     [
+        #         {
+        #             "role": "user",
+        #             "content": [
+        #                 {"type": "text", "text": "Say"},
+        #                 {"type": "text", "text": "yo what's up!"},
+        #             ],
+        #         }
+        #     ],
+        tools,
+    )
+
+    context_aggregator = llm.create_context_aggregator(context)
+
+    pipeline = Pipeline(
+        [
+            transport.input(),  # Transport user input
+            context_aggregator.user(),
+            llm,  # LLM
+            transport.output(),  # Transport bot output
+            context_aggregator.assistant(),
+        ]
+    )
+
+    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([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/19b-openai-realtime-beta-text.py

@@ -22,7 +22,7 @@ from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
 from pipecat.processors.transcript_processor import TranscriptProcessor
 from pipecat.runner.types import RunnerArguments
 from pipecat.runner.utils import create_transport
-from pipecat.services.cartesia import CartesiaTTSService
+from pipecat.services.cartesia.tts import CartesiaTTSService
 from pipecat.services.llm_service import FunctionCallParams
 from pipecat.services.openai_realtime_beta import (
     InputAudioNoiseReduction,

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

@@ -0,0 +1,234 @@
+#
+# Copyright (c) 2024–2025, Daily
+#
+# SPDX-License-Identifier: BSD 2-Clause License
+#
+
+
+import os
+from datetime import datetime
+
+from dotenv import load_dotenv
+from loguru import logger
+
+from pipecat.adapters.schemas.function_schema import FunctionSchema
+from pipecat.adapters.schemas.tools_schema import ToolsSchema
+from pipecat.audio.vad.silero import SileroVADAnalyzer
+from pipecat.frames.frames import LLMRunFrame, TranscriptionMessage
+from pipecat.pipeline.pipeline import Pipeline
+from pipecat.pipeline.runner import PipelineRunner
+from pipecat.pipeline.task import PipelineParams, PipelineTask
+from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
+from pipecat.processors.transcript_processor import TranscriptProcessor
+from pipecat.runner.types import RunnerArguments
+from pipecat.runner.utils import create_transport
+from pipecat.services.cartesia import CartesiaTTSService
+from pipecat.services.llm_service import FunctionCallParams
+from pipecat.services.openai_realtime import (
+    InputAudioNoiseReduction,
+    InputAudioTranscription,
+    OpenAIRealtimeLLMService,
+    SemanticTurnDetection,
+    SessionProperties,
+)
+from pipecat.services.openai_realtime.events import AudioConfiguration, AudioInput
+from pipecat.transports.base_transport import BaseTransport, TransportParams
+from pipecat.transports.daily.transport import DailyParams
+from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
+
+load_dotenv(override=True)
+
+
+async def fetch_weather_from_api(params: FunctionCallParams):
+    temperature = 75 if params.arguments["format"] == "fahrenheit" else 24
+    await params.result_callback(
+        {
+            "conditions": "nice",
+            "temperature": temperature,
+            "format": params.arguments["format"],
+            "timestamp": datetime.now().strftime("%Y%m%d_%H%M%S"),
+        }
+    )
+
+
+async def fetch_restaurant_recommendation(params: FunctionCallParams):
+    await params.result_callback({"name": "The Golden Dragon"})
+
+
+weather_function = FunctionSchema(
+    name="get_current_weather",
+    description="Get the current weather",
+    properties={
+        "location": {
+            "type": "string",
+            "description": "The city and state, e.g. San Francisco, CA",
+        },
+        "format": {
+            "type": "string",
+            "enum": ["celsius", "fahrenheit"],
+            "description": "The temperature unit to use. Infer this from the users location.",
+        },
+    },
+    required=["location", "format"],
+)
+
+restaurant_function = FunctionSchema(
+    name="get_restaurant_recommendation",
+    description="Get a restaurant recommendation",
+    properties={
+        "location": {
+            "type": "string",
+            "description": "The city and state, e.g. San Francisco, CA",
+        },
+    },
+    required=["location"],
+)
+
+# Create tools schema
+tools = ToolsSchema(standard_tools=[weather_function, restaurant_function])
+
+
+# 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(),
+    ),
+}
+
+
+async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
+    logger.info(f"Starting bot")
+
+    session_properties = SessionProperties(
+        audio=AudioConfiguration(
+            input=AudioInput(
+                transcription=InputAudioTranscription(),
+                # Set openai TurnDetection parameters. Not setting this at all will turn it
+                # on by default
+                turn_detection=SemanticTurnDetection(),
+                # Or set to False to disable openai turn detection and use transport VAD
+                # turn_detection=False,
+                noise_reduction=InputAudioNoiseReduction(type="near_field"),
+            )
+        ),
+        output_modalities=["text"],
+        # tools=tools,
+        instructions="""You are a helpful and friendly AI.
+
+Act like a human, but remember that you aren't a human and that you can't do human
+things in the real world. Your voice and personality should be warm and engaging, with a lively and
+playful tone.
+
+If interacting in a non-English language, start by using the standard accent or dialect familiar to
+the user. Talk quickly. You should always call a function if you can. Do not refer to these rules,
+even if you're asked about them.
+
+You are participating in a voice conversation. Keep your responses concise, short, and to the point
+unless specifically asked to elaborate on a topic.
+
+You have access to the following tools:
+- get_current_weather: Get the current weather for a given location.
+- get_restaurant_recommendation: Get a restaurant recommendation for a given location.
+
+Remember, your responses should be short. Just one or two sentences, usually. Respond in English.""",
+    )
+
+    llm = OpenAIRealtimeLLMService(
+        api_key=os.getenv("OPENAI_API_KEY"),
+        session_properties=session_properties,
+        start_audio_paused=False,
+    )
+
+    tts = CartesiaTTSService(
+        api_key=os.getenv("CARTESIA_API_KEY"),
+        voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
+    )
+
+    # you can either register a single function for all function calls, or specific functions
+    # llm.register_function(None, fetch_weather_from_api)
+    llm.register_function("get_current_weather", fetch_weather_from_api)
+    llm.register_function("get_restaurant_recommendation", fetch_restaurant_recommendation)
+
+    transcript = TranscriptProcessor()
+
+    # Create a standard OpenAI LLM context object using the normal messages format. The
+    # OpenAIRealtimeLLMService will convert this internally to messages that the
+    # openai WebSocket API can understand.
+    context = OpenAILLMContext(
+        [{"role": "user", "content": "Say hello!"}],
+        tools,
+    )
+
+    context_aggregator = llm.create_context_aggregator(context)
+
+    pipeline = Pipeline(
+        [
+            transport.input(),  # Transport user input
+            context_aggregator.user(),
+            llm,  # LLM
+            tts,  # TTS
+            transcript.user(),  # Placed after the LLM, as LLM pushes TranscriptionFrames downstream
+            transport.output(),  # Transport bot output
+            transcript.assistant(),  # After the transcript output, to time with the audio output
+            context_aggregator.assistant(),
+        ]
+    )
+
+    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([LLMRunFrame()])
+
+    @transport.event_handler("on_client_disconnected")
+    async def on_client_disconnected(transport, client):
+        logger.info(f"Client disconnected")
+        await task.cancel()
+
+    # Register event handler for transcript updates
+    @transcript.event_handler("on_transcript_update")
+    async def on_transcript_update(processor, frame):
+        for msg in frame.messages:
+            if isinstance(msg, TranscriptionMessage):
+                timestamp = f"[{msg.timestamp}] " if msg.timestamp else ""
+                line = f"{timestamp}{msg.role}: {msg.content}"
+                logger.info(f"Transcript: {line}")
+
+    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/20b-persistent-context-openai-realtime-beta.py

@@ -0,0 +1,274 @@
+#
+# Copyright (c) 2024–2025, Daily
+#
+# SPDX-License-Identifier: BSD 2-Clause License
+#
+
+import asyncio
+import glob
+import json
+import os
+from datetime import datetime
+
+from dotenv import load_dotenv
+from loguru import logger
+
+from pipecat.audio.vad.silero import SileroVADAnalyzer
+from pipecat.frames.frames import LLMRunFrame
+from pipecat.pipeline.pipeline import Pipeline
+from pipecat.pipeline.runner import PipelineRunner
+from pipecat.pipeline.task import PipelineParams, PipelineTask
+from pipecat.processors.aggregators.openai_llm_context import (
+    OpenAILLMContext,
+)
+from pipecat.runner.types import RunnerArguments
+from pipecat.runner.utils import create_transport
+from pipecat.services.deepgram.stt import DeepgramSTTService
+from pipecat.services.llm_service import FunctionCallParams
+from pipecat.services.openai_realtime_beta import (
+    InputAudioTranscription,
+    OpenAIRealtimeBetaLLMService,
+    SessionProperties,
+    TurnDetection,
+)
+from pipecat.services.openai_realtime_beta.events import AudioConfiguration, AudioInput
+from pipecat.transports.base_transport import BaseTransport, TransportParams
+from pipecat.transports.daily.transport import DailyParams
+from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
+
+load_dotenv(override=True)
+
+BASE_FILENAME = "/tmp/pipecat_conversation_"
+
+
+async def fetch_weather_from_api(params: FunctionCallParams):
+    temperature = 75 if params.arguments["format"] == "fahrenheit" else 24
+    await params.result_callback(
+        {
+            "conditions": "nice",
+            "temperature": temperature,
+            "format": params.arguments["format"],
+            "timestamp": datetime.now().strftime("%Y%m%d_%H%M%S"),
+        }
+    )
+
+
+async def get_saved_conversation_filenames(params: FunctionCallParams):
+    # Construct the full pattern including the BASE_FILENAME
+    full_pattern = f"{BASE_FILENAME}*.json"
+
+    # Use glob to find all matching files
+    matching_files = glob.glob(full_pattern)
+    logger.debug(f"matching files: {matching_files}")
+
+    await params.result_callback({"filenames": matching_files})
+
+
+async def save_conversation(params: FunctionCallParams):
+    timestamp = datetime.now().strftime("%Y-%m-%d_%H:%M:%S")
+    filename = f"{BASE_FILENAME}{timestamp}.json"
+    logger.debug(
+        f"writing conversation to {filename}\n{json.dumps(params.context.messages, indent=4)}"
+    )
+    try:
+        with open(filename, "w") as file:
+            messages = params.context.get_messages_for_persistent_storage()
+            # remove the last message, which is the instruction we just gave to save the conversation
+            messages.pop()
+            json.dump(messages, file, indent=2)
+        await params.result_callback({"success": True})
+    except Exception as e:
+        await params.result_callback({"success": False, "error": str(e)})
+
+
+async def load_conversation(params: FunctionCallParams):
+    async def _reset():
+        filename = params.arguments["filename"]
+        logger.debug(f"loading conversation from {filename}")
+        try:
+            with open(filename, "r") as file:
+                params.context.set_messages(json.load(file))
+                await params.llm.reset_conversation()
+                await params.llm._create_response()
+        except Exception as e:
+            await params.result_callback({"success": False, "error": str(e)})
+
+    asyncio.create_task(_reset())
+
+
+tools = [
+    {
+        "type": "function",
+        "name": "get_current_weather",
+        "description": "Get the current weather",
+        "parameters": {
+            "type": "object",
+            "properties": {
+                "location": {
+                    "type": "string",
+                    "description": "The city and state, e.g. San Francisco, CA",
+                },
+                "format": {
+                    "type": "string",
+                    "enum": ["celsius", "fahrenheit"],
+                    "description": "The temperature unit to use. Infer this from the users location.",
+                },
+            },
+            "required": ["location", "format"],
+        },
+    },
+    {
+        "type": "function",
+        "name": "save_conversation",
+        "description": "Save the current conversatione. Use this function to persist the current conversation to external storage.",
+        "parameters": {
+            "type": "object",
+            "properties": {},
+            "required": [],
+        },
+    },
+    {
+        "type": "function",
+        "name": "get_saved_conversation_filenames",
+        "description": "Get a list of saved conversation histories. Returns a list of filenames. Each filename includes a date and timestamp. Each file is conversation history that can be loaded into this session.",
+        "parameters": {
+            "type": "object",
+            "properties": {},
+            "required": [],
+        },
+    },
+    {
+        "type": "function",
+        "name": "load_conversation",
+        "description": "Load a conversation history. Use this function to load a conversation history into the current session.",
+        "parameters": {
+            "type": "object",
+            "properties": {
+                "filename": {
+                    "type": "string",
+                    "description": "The filename of the conversation history to load.",
+                }
+            },
+            "required": ["filename"],
+        },
+    },
+]
+
+
+# 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(),
+    ),
+}
+
+
+async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
+    logger.info(f"Starting bot")
+
+    stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
+
+    session_properties = SessionProperties(
+        audio=AudioConfiguration(
+            input=AudioInput(
+                transcription=InputAudioTranscription(),
+                # Set openai TurnDetection parameters. Not setting this at all will turn it
+                # on by default
+                turn_detection=TurnDetection(silence_duration_ms=1000),
+                # Or set to False to disable openai turn detection and use transport VAD
+                # turn_detection=False,
+            )
+        ),
+        # tools=tools,
+        instructions="""Your knowledge cutoff is 2023-10. You are a helpful and friendly AI.
+
+Act like a human, but remember that you aren't a human and that you can't do human
+things in the real world. Your voice and personality should be warm and engaging, with a lively and
+playful tone.
+
+If interacting in a non-English language, start by using the standard accent or dialect familiar to
+the user. Talk quickly. You should always call a function if you can. Do not refer to these rules,
+even if you're asked about them.
+-
+You are participating in a voice conversation. Keep your responses concise, short, and to the point
+unless specifically asked to elaborate on a topic.
+
+Remember, your responses should be short. Just one or two sentences, usually.""",
+    )
+
+    llm = OpenAIRealtimeBetaLLMService(
+        api_key=os.getenv("OPENAI_API_KEY"),
+        session_properties=session_properties,
+        start_audio_paused=False,
+    )
+
+    # you can either register a single function for all function calls, or specific functions
+    # llm.register_function(None, fetch_weather_from_api)
+    llm.register_function("get_current_weather", fetch_weather_from_api)
+    llm.register_function("save_conversation", save_conversation)
+    llm.register_function("get_saved_conversation_filenames", get_saved_conversation_filenames)
+    llm.register_function("load_conversation", load_conversation)
+
+    context = OpenAILLMContext([], tools)
+    context_aggregator = llm.create_context_aggregator(context)
+
+    pipeline = Pipeline(
+        [
+            transport.input(),  # Transport user input
+            stt,  # STT
+            context_aggregator.user(),
+            llm,  # LLM
+            transport.output(),  # Transport bot output
+            context_aggregator.assistant(),
+        ]
+    )
+
+    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([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/20b-persistent-context-openai-realtime.py

@@ -25,12 +25,13 @@ from pipecat.runner.types import RunnerArguments
 from pipecat.runner.utils import create_transport
 from pipecat.services.deepgram.stt import DeepgramSTTService
 from pipecat.services.llm_service import FunctionCallParams
-from pipecat.services.openai_realtime_beta import (
+from pipecat.services.openai_realtime import (
     InputAudioTranscription,
-    OpenAIRealtimeBetaLLMService,
+    OpenAIRealtimeLLMService,
     SessionProperties,
     TurnDetection,
 )
+from pipecat.services.openai_realtime.events import AudioConfiguration, AudioInput
 from pipecat.transports.base_transport import BaseTransport, TransportParams
 from pipecat.transports.daily.transport import DailyParams
 from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
@@ -182,12 +183,16 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
     stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
 
     session_properties = SessionProperties(
-        input_audio_transcription=InputAudioTranscription(),
-        # Set openai TurnDetection parameters. Not setting this at all will turn it
-        # on by default
-        turn_detection=TurnDetection(silence_duration_ms=1000),
-        # Or set to False to disable openai turn detection and use transport VAD
-        # turn_detection=False,
+        audio=AudioConfiguration(
+            input=AudioInput(
+                transcription=InputAudioTranscription(),
+                # Set openai TurnDetection parameters. Not setting this at all will turn it
+                # on by default
+                turn_detection=TurnDetection(silence_duration_ms=1000),
+                # Or set to False to disable openai turn detection and use transport VAD
+                # turn_detection=False,
+            )
+        ),
         # tools=tools,
         instructions="""Your knowledge cutoff is 2023-10. You are a helpful and friendly AI.
 
@@ -205,7 +210,7 @@ unless specifically asked to elaborate on a topic.
 Remember, your responses should be short. Just one or two sentences, usually.""",
     )
 
-    llm = OpenAIRealtimeBetaLLMService(
+    llm = OpenAIRealtimeLLMService(
         api_key=os.getenv("OPENAI_API_KEY"),
         session_properties=session_properties,
         start_audio_paused=False,

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

@@ -18,9 +18,9 @@ from pipecat.frames.frames import (
     Frame,
     FunctionCallInProgressFrame,
     FunctionCallResultFrame,
+    InterruptionFrame,
     LLMRunFrame,
     StartFrame,
-    StartInterruptionFrame,
     SystemFrame,
     TextFrame,
     TranscriptionFrame,
@@ -144,7 +144,7 @@ class OutputGate(FrameProcessor):
                 await self._start()
             if isinstance(frame, (EndFrame, CancelFrame)):
                 await self._stop()
-            if isinstance(frame, StartInterruptionFrame):
+            if isinstance(frame, InterruptionFrame):
                 self._frames_buffer = []
                 self.close_gate()
             await self.push_frame(frame, direction)
@@ -232,7 +232,7 @@ class TurnDetectionLLM(Pipeline):
         async def pass_only_llm_trigger_frames(frame):
             return (
                 isinstance(frame, OpenAILLMContextFrame)
-                or isinstance(frame, StartInterruptionFrame)
+                or isinstance(frame, InterruptionFrame)
                 or isinstance(frame, FunctionCallInProgressFrame)
                 or isinstance(frame, FunctionCallResultFrame)
             )

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

@@ -18,9 +18,9 @@ from pipecat.frames.frames import (
     Frame,
     FunctionCallInProgressFrame,
     FunctionCallResultFrame,
+    InterruptionFrame,
     LLMRunFrame,
     StartFrame,
-    StartInterruptionFrame,
     SystemFrame,
     TextFrame,
     TranscriptionFrame,
@@ -347,7 +347,7 @@ class OutputGate(FrameProcessor):
                 await self._start()
             if isinstance(frame, (EndFrame, CancelFrame)):
                 await self._stop()
-            if isinstance(frame, StartInterruptionFrame):
+            if isinstance(frame, InterruptionFrame):
                 self._frames_buffer = []
                 self.close_gate()
             await self.push_frame(frame, direction)
@@ -426,7 +426,7 @@ class TurnDetectionLLM(Pipeline):
         async def pass_only_llm_trigger_frames(frame):
             return (
                 isinstance(frame, OpenAILLMContextFrame)
-                or isinstance(frame, StartInterruptionFrame)
+                or isinstance(frame, InterruptionFrame)
                 or isinstance(frame, FunctionCallInProgressFrame)
                 or isinstance(frame, FunctionCallResultFrame)
             )

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

@@ -20,10 +20,10 @@ from pipecat.frames.frames import (
     FunctionCallInProgressFrame,
     FunctionCallResultFrame,
     InputAudioRawFrame,
+    InterruptionFrame,
     LLMFullResponseStartFrame,
     LLMRunFrame,
     StartFrame,
-    StartInterruptionFrame,
     SystemFrame,
     TextFrame,
     TranscriptionFrame,
@@ -570,7 +570,7 @@ class OutputGate(FrameProcessor):
                 await self._start()
             if isinstance(frame, (EndFrame, CancelFrame)):
                 await self._stop()
-            if isinstance(frame, StartInterruptionFrame):
+            if isinstance(frame, InterruptionFrame):
                 self._frames_buffer = []
                 self.close_gate()
             await self.push_frame(frame, direction)

examples/foundational/30-observer.py

@@ -15,8 +15,8 @@ from pipecat.frames.frames import (
     BotStartedSpeakingFrame,
     BotStoppedSpeakingFrame,
     EndFrame,
+    InterruptionFrame,
     LLMRunFrame,
-    StartInterruptionFrame,
     TTSTextFrame,
     UserStartedSpeakingFrame,
 )
@@ -48,7 +48,7 @@ class CustomObserver(BaseObserver):
     """Observer to log interruptions and bot speaking events to the console.
 
     Logs all frame instances of:
-    - StartInterruptionFrame
+    - InterruptionFrame
     - BotStartedSpeakingFrame
     - BotStoppedSpeakingFrame
 
@@ -69,7 +69,7 @@ class CustomObserver(BaseObserver):
         # Create direction arrow
         arrow = "→" if direction == FrameDirection.DOWNSTREAM else "←"
 
-        if isinstance(frame, StartInterruptionFrame) and isinstance(src, BaseOutputTransport):
+        if isinstance(frame, InterruptionFrame) and isinstance(src, BaseOutputTransport):
             logger.info(f"⚡ INTERRUPTION START: {src} {arrow} {dst} at {time_sec:.2f}s")
         elif isinstance(frame, BotStartedSpeakingFrame):
             logger.info(f"🤖 BOT START SPEAKING: {src} {arrow} {dst} at {time_sec:.2f}s")

examples/foundational/38b-smart-turn-local.py

@@ -11,7 +11,7 @@ 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_v2 import LocalSmartTurnAnalyzerV2
+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
@@ -31,20 +31,7 @@ from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
 load_dotenv(override=True)
 
 # To use this locally, set the environment variable LOCAL_SMART_TURN_MODEL_PATH
-# to the path where the smart-turn repo is cloned.
-#
-# Example setup:
-#
-#   # Git LFS (Large File Storage)
-#   brew install git-lfs
-#   # Hugging Face uses LFS to store large model files, including .mlpackage
-#   git lfs install
-#   # Clone the repo with the smart_turn_classifier.mlpackage
-#   git clone https://huggingface.co/pipecat-ai/smart-turn-v2
-#
-# Then set the env variable:
-#   export LOCAL_SMART_TURN_MODEL_PATH=./smart-turn
-# or add it to your .env file
+# to the Smart Turn v3 ONNX model file.
 smart_turn_model_path = os.getenv("LOCAL_SMART_TURN_MODEL_PATH")
 
 # We store functions so objects (e.g. SileroVADAnalyzer) don't get
@@ -55,7 +42,7 @@ transport_params = {
         audio_in_enabled=True,
         audio_out_enabled=True,
         vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
-        turn_analyzer=LocalSmartTurnAnalyzerV2(
+        turn_analyzer=LocalSmartTurnAnalyzerV3(
             smart_turn_model_path=smart_turn_model_path, params=SmartTurnParams()
         ),
     ),
@@ -63,7 +50,7 @@ transport_params = {
         audio_in_enabled=True,
         audio_out_enabled=True,
         vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
-        turn_analyzer=LocalSmartTurnAnalyzerV2(
+        turn_analyzer=LocalSmartTurnAnalyzerV3(
             smart_turn_model_path=smart_turn_model_path, params=SmartTurnParams()
         ),
     ),
@@ -71,7 +58,7 @@ transport_params = {
         audio_in_enabled=True,
         audio_out_enabled=True,
         vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
-        turn_analyzer=LocalSmartTurnAnalyzerV2(
+        turn_analyzer=LocalSmartTurnAnalyzerV3(
             smart_turn_model_path=smart_turn_model_path, params=SmartTurnParams()
         ),
     ),

examples/foundational/43a-heygen-video-service.py

@@ -21,9 +21,10 @@ from pipecat.runner.utils import create_transport
 from pipecat.services.cartesia.tts import CartesiaTTSService
 from pipecat.services.deepgram.stt import DeepgramSTTService
 from pipecat.services.google.llm import GoogleLLMService
+from pipecat.services.heygen.api import AvatarQuality, NewSessionRequest
 from pipecat.services.heygen.video import HeyGenVideoService
 from pipecat.transports.base_transport import BaseTransport, TransportParams
-from pipecat.transports.daily.transport import DailyParams
+from pipecat.transports.daily.transport import DailyParams, DailyTransport
 
 load_dotenv(override=True)
 
@@ -38,6 +39,7 @@ transport_params = {
         video_out_is_live=True,
         video_out_width=1280,
         video_out_height=720,
+        video_out_bitrate=2_000_000,  # 2MBps
         vad_analyzer=SileroVADAnalyzer(),
     ),
     "webrtc": lambda: TransportParams(
@@ -64,7 +66,13 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
 
         llm = GoogleLLMService(api_key=os.getenv("GOOGLE_API_KEY"))
 
-        heyGen = HeyGenVideoService(api_key=os.getenv("HEYGEN_API_KEY"), session=session)
+        heyGen = HeyGenVideoService(
+            api_key=os.getenv("HEYGEN_API_KEY"),
+            session=session,
+            session_request=NewSessionRequest(
+                avatar_id="Shawn_Therapist_public", version="v2", quality=AvatarQuality.high
+            ),
+        )
 
         messages = [
             {
@@ -101,6 +109,18 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         @transport.event_handler("on_client_connected")
         async def on_client_connected(transport, client):
             logger.info(f"Client connected")
+            # Updating publishing settings to enable adaptive bitrate
+            if isinstance(transport, DailyTransport):
+                await transport.update_publishing(
+                    publishing_settings={
+                        "camera": {
+                            "sendSettings": {
+                                "allowAdaptiveLayers": True,
+                            }
+                        }
+                    }
+                )
+
             # Kick off the conversation.
             messages.append(
                 {

examples/foundational/45-openai-agent-basic.py

@@ -0,0 +1,205 @@
+#
+# Copyright (c) 2024–2025, Daily
+#
+# SPDX-License-Identifier: BSD 2-Clause License
+#
+
+"""
+Basic OpenAI Agent service example.
+
+This example demonstrates how to use the OpenAI Agents SDK within a Pipecat
+pipeline to create an interactive agent with tool calling capabilities.
+
+Requirements:
+- OpenAI API key
+- OpenAI Agents SDK: pip install openai-agents
+"""
+
+import os
+import random
+from typing import Any, List
+
+# Import agents SDK for tools and agent creation
+from agents import Agent, function_tool
+from dotenv import load_dotenv
+from loguru import logger
+from openai.types.chat import ChatCompletionMessageParam
+
+from pipecat.frames.frames import LLMRunFrame, TextFrame
+from pipecat.pipeline.pipeline import Pipeline
+from pipecat.pipeline.runner import PipelineRunner
+from pipecat.pipeline.task import PipelineTask
+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_agent.agent_service import OpenAIAgentService
+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)
+
+# Transport configuration
+transport_params = {
+    "daily": lambda: DailyParams(audio_out_enabled=True, audio_in_enabled=True),
+    "twilio": lambda: FastAPIWebsocketParams(audio_out_enabled=True, audio_in_enabled=True),
+    "webrtc": lambda: TransportParams(audio_out_enabled=True, audio_in_enabled=True),
+}
+
+
+@function_tool
+def get_weather(location: str) -> str:
+    """Get the current weather for a location.
+
+    Args:
+        location: The location to get weather for
+
+    Returns:
+        A weather description string
+    """
+    # Mock weather data - in real usage, integrate with weather API
+    weather_data = {
+        "San Francisco": "Foggy, 65°F",
+        "New York": "Sunny, 72°F",
+        "London": "Rainy, 59°F",
+        "Tokyo": "Partly cloudy, 68°F",
+    }
+    return weather_data.get(location, f"Weather data not available for {location}")
+
+
+@function_tool
+def get_random_fact() -> str:
+    """Get a random interesting fact.
+
+    Returns:
+        A random fact string
+    """
+    facts = [
+        "Honey never spoils. Archaeologists have found edible honey in ancient Egyptian tombs.",
+        "Octopuses have three hearts and blue blood.",
+        "The Great Wall of China isn't visible from space with the naked eye.",
+        "Bananas are berries, but strawberries aren't.",
+    ]
+    return random.choice(facts)
+
+
+def get_random_fact_tool():
+    """Example tool function for random facts."""
+
+    def get_random_fact() -> str:
+        """Get a random interesting fact.
+
+        Returns:
+            A random fact string.
+        """
+        facts = [
+            "Honey never spoils. Archaeologists have found edible honey in ancient Egyptian tombs.",
+            "A group of flamingos is called a 'flamboyance'.",
+            "Octopuses have three hearts and blue blood.",
+            "The Great Wall of China isn't visible from space with the naked eye.",
+            "Bananas are berries, but strawberries aren't.",
+        ]
+        return random.choice(facts)
+
+    return get_random_fact
+
+
+async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
+    logger.info("Starting OpenAI Agent bot")
+
+    # Set up STT for speech recognition
+    stt = DeepgramSTTService(
+        api_key=os.getenv("DEEPGRAM_API_KEY", ""),
+        model="nova-2",
+    )
+
+    # Set up TTS for voice output
+    tts = CartesiaTTSService(
+        api_key=os.getenv("CARTESIA_API_KEY", ""),
+        voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
+    )
+
+    # Create tools for the agent
+    tools: list[Any] = [
+        get_weather,
+        get_random_fact,
+    ]
+
+    # Create the agent with tools
+    agent = Agent(
+        name="Assistant",
+        instructions="""You are a helpful assistant with access to weather information and random facts. 
+        You can:
+        - Check weather for any location using the get_weather tool
+        - Share interesting facts using the get_random_fact tool
+        - Have natural conversations
+        
+        Be friendly, informative, and engaging in your responses.""",
+        tools=tools,
+    )
+
+    # Initialize the OpenAI Agent service with the pre-configured agent
+    agent_service = OpenAIAgentService(
+        agent=agent,
+        api_key=os.getenv("OPENAI_API_KEY"),
+        streaming=True,
+    )
+
+    # Set up conversation context with initial system message
+    messages: List[ChatCompletionMessageParam] = [
+        {
+            "role": "system",
+            "content": "You are a helpful assistant with access to weather information and random facts. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
+        },
+    ]
+
+    context = OpenAILLMContext(messages)
+    context_aggregator = agent_service.create_context_aggregator(context)
+
+    # Create the processing pipeline with context aggregators
+    pipeline = Pipeline(
+        [
+            transport.input(),  # Transport user input
+            stt,  # Speech to text
+            context_aggregator.user(),  # User responses
+            agent_service,  # OpenAI Agent processing
+            tts,  # Text to speech
+            transport.output(),  # Transport bot output
+            context_aggregator.assistant(),  # Assistant spoken responses
+        ]
+    )
+
+    task = PipelineTask(
+        pipeline,
+        idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
+    )
+
+    # Send an initial greeting when client connects
+    @transport.event_handler("on_client_connected")
+    async def on_client_connected(transport, client):
+        logger.info("Client connected, sending greeting")
+        # Kick off the conversation by adding system message and running LLM
+        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("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/46-openai-agent-handoffs.py

@@ -0,0 +1,276 @@
+#
+# Copyright (c) 2024–2025, Daily
+#
+# SPDX-License-Identifier: BSD 2-Clause License
+#
+
+"""
+Advanced OpenAI Agent service example with handoffs.
+
+This example demonstrates how to use multiple agents with handoffs in the
+OpenAI Agents SDK within a Pipecat pipeline, showcasing agent orchestration
+and specialization.
+
+Requirements:
+- OpenAI API key
+- OpenAI Agents SDK: pip install openai-agents
+"""
+
+import os
+import random
+from typing import Any, Dict, List
+
+from dotenv import load_dotenv
+from loguru import logger
+from openai.types.chat import ChatCompletionMessageParam
+
+from pipecat.frames.frames import LLMRunFrame, TextFrame
+from pipecat.pipeline.pipeline import Pipeline
+from pipecat.pipeline.runner import PipelineRunner
+from pipecat.pipeline.task import PipelineTask
+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_agent.agent_service import OpenAIAgentService
+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)
+
+# Transport configuration
+transport_params = {
+    "daily": lambda: DailyParams(audio_out_enabled=True, audio_in_enabled=True),
+    "twilio": lambda: FastAPIWebsocketParams(audio_out_enabled=True, audio_in_enabled=True),
+    "webrtc": lambda: TransportParams(audio_out_enabled=True, audio_in_enabled=True),
+}
+
+
+def create_weather_tools():
+    """Create weather-related tools."""
+
+    def get_weather(location: str) -> str:
+        """Get current weather for a location."""
+        conditions = ["sunny", "cloudy", "rainy", "snowy", "windy"]
+        temp = random.randint(-10, 35)
+        condition = random.choice(conditions)
+        return f"The weather in {location} is {condition} with a temperature of {temp}°C."
+
+    def get_forecast(location: str, days: int = 3) -> str:
+        """Get weather forecast for multiple days."""
+        forecast = []
+        for i in range(days):
+            conditions = ["sunny", "cloudy", "rainy", "snowy"]
+            temp = random.randint(-5, 30)
+            condition = random.choice(conditions)
+            day = "today" if i == 0 else f"in {i} day{'s' if i > 1 else ''}"
+            forecast.append(f"{day.capitalize()}: {condition}, {temp}°C")
+        return f"Weather forecast for {location}:\n" + "\n".join(forecast)
+
+    return [get_weather, get_forecast]
+
+
+def create_trivia_tools():
+    """Create trivia and fact tools."""
+
+    def get_random_fact() -> str:
+        """Get a random interesting fact."""
+        facts = [
+            "Honey never spoils. Archaeologists have found edible honey in ancient Egyptian tombs.",
+            "A group of flamingos is called a 'flamboyance'.",
+            "Octopuses have three hearts and blue blood.",
+            "The Great Wall of China isn't visible from space with the naked eye.",
+            "Bananas are berries, but strawberries aren't.",
+            "Wombat poop is cube-shaped.",
+            "A shrimp's heart is in its head.",
+            "It's impossible to hum while holding your nose.",
+        ]
+        return random.choice(facts)
+
+    def get_science_fact() -> str:
+        """Get a random science fact."""
+        facts = [
+            "The speed of light in a vacuum is approximately 299,792,458 meters per second.",
+            "DNA stands for Deoxyribonucleic Acid.",
+            "The human brain uses about 20% of the body's total energy.",
+            "There are more possible games of chess than atoms in the observable universe.",
+            "A single bolt of lightning contains enough energy to toast 100,000 slices of bread.",
+        ]
+        return random.choice(facts)
+
+    return [get_random_fact, get_science_fact]
+
+
+def create_math_tools():
+    """Create math calculation tools."""
+
+    def calculate(expression: str) -> str:
+        """Safely calculate a mathematical expression."""
+        try:
+            # Only allow basic math operations for safety
+            allowed_chars = set("0123456789+-*/.() ")
+            if not all(c in allowed_chars for c in expression):
+                return "Sorry, I can only calculate basic math expressions with +, -, *, /, and parentheses."
+
+            result = eval(expression)
+            return f"{expression} = {result}"
+        except Exception as e:
+            return f"Error calculating '{expression}': {str(e)}"
+
+    def generate_math_problem() -> str:
+        """Generate a random math problem."""
+        operations = ["+", "-", "*"]
+        a = random.randint(1, 20)
+        b = random.randint(1, 20)
+        op = random.choice(operations)
+
+        if op == "+":
+            answer = a + b
+        elif op == "-":
+            answer = a - b
+        else:  # multiplication
+            answer = a * b
+
+        return f"Here's a math problem for you: {a} {op} {b} = ?"
+
+    return [calculate, generate_math_problem]
+
+
+async def create_specialist_agents():
+    """Create specialized agents for different domains."""
+
+    # Weather specialist agent
+    weather_agent = OpenAIAgentService(
+        name="Weather Specialist",
+        instructions="""You are a weather specialist. You provide detailed weather information,
+        forecasts, and weather-related advice. Use your tools to get accurate weather data.
+        Be informative and helpful about weather conditions and what they might mean for
+        outdoor activities.""",
+        tools=create_weather_tools(),
+        api_key=os.getenv("OPENAI_API_KEY"),
+        streaming=True,
+    )
+
+    # Trivia specialist agent
+    trivia_agent = OpenAIAgentService(
+        name="Trivia Master",
+        instructions="""You are a trivia and facts specialist. You love sharing interesting
+        facts, trivia, and educational content. Use your tools to provide fascinating
+        information and engage users with fun facts. Make learning enjoyable!""",
+        tools=create_trivia_tools(),
+        api_key=os.getenv("OPENAI_API_KEY"),
+        streaming=True,
+    )
+
+    # Math specialist agent
+    math_agent = OpenAIAgentService(
+        name="Math Helper",
+        instructions="""You are a mathematics specialist. You help with calculations,
+        math problems, and mathematical concepts. Use your tools to solve problems
+        and generate practice questions. Make math accessible and fun!""",
+        tools=create_math_tools(),
+        api_key=os.getenv("OPENAI_API_KEY"),
+        streaming=True,
+    )
+
+    return weather_agent, trivia_agent, math_agent
+
+
+async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
+    logger.info("Starting OpenAI Agent bot with handoffs")
+
+    # Set up STT for speech recognition
+    stt = DeepgramSTTService(
+        api_key=os.getenv("DEEPGRAM_API_KEY", ""),
+        model="nova-2",
+    )
+
+    # Set up TTS for voice output
+    tts = CartesiaTTSService(
+        api_key=os.getenv("CARTESIA_API_KEY", ""),
+        voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
+    )
+
+    # Create specialist agents
+    weather_agent, trivia_agent, math_agent = await create_specialist_agents()
+
+    # Create the main triage agent that can hand off to specialists
+    triage_agent = OpenAIAgentService(
+        name="Assistant Coordinator",
+        instructions="""You are a helpful assistant coordinator. Your role is to understand
+        what the user needs and direct them to the right specialist:
+        
+        - For weather questions, forecasts, or outdoor activity planning -> Weather Specialist
+        - For interesting facts, trivia, or educational content -> Trivia Master  
+        - For calculations, math problems, or mathematical help -> Math Helper
+        
+        If the request doesn't clearly fit a specialist, you can handle general conversation
+        yourself. Always be friendly and explain when you're connecting them to a specialist.""",
+        handoffs=[weather_agent.agent, trivia_agent.agent, math_agent.agent],  # type: ignore
+        api_key=os.getenv("OPENAI_API_KEY"),
+        streaming=True,
+    )
+
+    # Set up conversation context with initial system message
+    messages: List[ChatCompletionMessageParam] = [
+        {
+            "role": "system",
+            "content": "You are a helpful assistant coordinator with access to weather information, trivia, and math tools. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
+        },
+    ]
+
+    context = OpenAILLMContext(messages)
+    context_aggregator = triage_agent.create_context_aggregator(context)
+
+    # Create the processing pipeline with context aggregators
+    pipeline = Pipeline(
+        [
+            transport.input(),  # Transport user input
+            stt,  # Speech to text
+            context_aggregator.user(),  # User responses
+            triage_agent,  # OpenAI Agent processing
+            tts,  # Text to speech
+            transport.output(),  # Transport bot output
+            context_aggregator.assistant(),  # Assistant spoken responses
+        ]
+    )
+
+    task = PipelineTask(
+        pipeline,
+        idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
+    )
+
+    # Send an initial greeting when client connects
+    @transport.event_handler("on_client_connected")
+    async def on_client_connected(transport, client):
+        logger.info("Client connected, sending greeting")
+        # Kick off the conversation by adding system message and running LLM
+        messages.append(
+            {
+                "role": "system",
+                "content": "Please introduce yourself to the user as an AI assistant coordinator who works with specialists for weather, trivia, and math topics.",
+            }
+        )
+        await task.queue_frames([LLMRunFrame()])
+
+    @transport.event_handler("on_client_disconnected")
+    async def on_client_disconnected(transport, client):
+        logger.info("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/quickstart/pyproject.toml

@@ -4,7 +4,7 @@ version = "0.1.0"
 description = "Quickstart example for building voice AI bots with Pipecat"
 requires-python = ">=3.10"
 dependencies = [
-    "pipecat-ai[webrtc,daily,silero,deepgram,openai,cartesia,runner]>=0.0.82",
+    "pipecat-ai[webrtc,daily,silero,deepgram,openai,cartesia,runner]>=0.0.83",
     "pipecatcloud>=0.2.4"
 ]
 

pyproject.toml

@@ -34,7 +34,7 @@ dependencies = [
     "pyloudnorm~=0.1.1",
     "resampy~=0.4.3",
     "soxr~=0.5.0",
-    "openai>=1.74.0,<=1.99.1",
+    "openai>=1.74.0,<2.0.0",
     # Pinning numba to resolve package dependencies
     "numba==0.61.2",
     "wait_for2>=0.4.1; python_version<'3.12'",
@@ -55,7 +55,7 @@ azure = [ "azure-cognitiveservices-speech~=1.42.0"]
 cartesia = [ "cartesia~=2.0.3", "websockets>=13.1,<15.0" ]
 cerebras = []
 deepseek = []
-daily = [ "daily-python~=0.19.8" ]
+daily = [ "daily-python~=0.19.9" ]
 deepgram = [ "deepgram-sdk~=4.7.0" ]
 elevenlabs = [ "websockets>=13.1,<15.0" ]
 fal = [ "fal-client~=0.5.9" ]
@@ -74,7 +74,7 @@ langchain = [ "langchain~=0.3.20", "langchain-community~=0.3.20", "langchain-ope
 livekit = [ "livekit~=0.22.0", "livekit-api~=0.8.2", "tenacity>=8.2.3,<10.0.0" ]
 lmnt = [ "websockets>=13.1,<15.0" ]
 local = [ "pyaudio~=0.2.14" ]
-mcp = [ "mcp[cli]~=1.9.4" ]
+mcp = [ "mcp[cli]>=1.11.0,<2.0.0" ]
 mem0 = [ "mem0ai~=0.1.94" ]
 mistral = []
 mlx-whisper = [ "mlx-whisper~=0.4.2" ]
@@ -83,7 +83,8 @@ nim = []
 neuphonic = [ "websockets>=13.1,<15.0" ]
 noisereduce = [ "noisereduce~=3.0.3" ]
 openai = [ "websockets>=13.1,<15.0" ]
-openpipe = [ "openpipe~=4.50.0" ]
+openai-agent = [ "openai-agents~=0.3.0" ]
+# openpipe = [ "openpipe~=4.50.0" ]  # Temporarily disabled due to openai version conflict
 openrouter = []
 perplexity = []
 playht = [ "websockets>=13.1,<15.0" ]
@@ -95,8 +96,9 @@ sambanova = []
 sarvam = [ "websockets>=13.1,<15.0" ]
 sentry = [ "sentry-sdk~=2.23.1" ]
 local-smart-turn = [ "coremltools>=8.0", "transformers", "torch>=2.5.0,<3", "torchaudio>=2.5.0,<3" ]
+local-smart-turn-v3 = [ "transformers", "torch>=2.5.0,<3", "torchaudio>=2.5.0,<3", "onnxruntime>=1.20.1, <2" ]
 remote-smart-turn = []
-silero = [ "onnxruntime~=1.20.1" ]
+silero = [ "onnxruntime>=1.20.1, <2" ]
 simli = [ "simli-ai~=0.1.10"]
 soniox = [ "websockets>=13.1,<15.0" ]
 soundfile = [ "soundfile~=0.13.0" ]
@@ -154,6 +156,7 @@ where = ["src"]
     "src/pipecat/audio/dtmf/dtmf-star.wav",
 ]
 "pipecat.services.aws_nova_sonic" = ["src/pipecat/services/aws_nova_sonic/ready.wav"]
+"pipecat.audio.turn.smart_turn.data" = ["src/pipecat/audio/turn/smart_turn/data/smart-turn-v3.0.onnx"]
 
 [tool.pytest.ini_options]
 addopts = "--verbose"

scripts/evals/eval.py

@@ -47,7 +47,7 @@ from pipecat.transports.daily.transport import DailyParams, DailyTransport
 SCRIPT_DIR = Path(__file__).resolve().parent
 
 PIPELINE_IDLE_TIMEOUT_SECS = 60
-EVAL_TIMEOUT_SECS = 90
+EVAL_TIMEOUT_SECS = 120
 
 EvalPrompt = str | Tuple[str, ImageFile]
 
@@ -266,8 +266,11 @@ async def run_eval_pipeline(
     elif isinstance(prompt, tuple):
         example_prompt, example_image = prompt
 
-    eval_prompt = f"The answer is correct if it's appropriate for the context and matches: {eval}."
-    common_system_prompt = f"Call the eval function with your assessment only if the user answers the question. {eval_prompt}"
+    eval_prompt = f"The answer is correct if it matches: {eval}."
+    common_system_prompt = (
+        "The user might say things other than the answer and that's allowed. "
+        f"You should only call the eval function with your assessment when the user actually answers the question. {eval_prompt}"
+    )
     if user_speaks_first:
         system_prompt = f"You are an LLM eval, be extremly brief. You will start the conversation by saying: '{example_prompt}'. {common_system_prompt}"
     else:

scripts/evals/run-release-evals.py

@@ -135,6 +135,25 @@ TESTS_14 = [
     ("14r-function-calling-aws.py", PROMPT_WEATHER, EVAL_WEATHER, BOT_SPEAKS_FIRST),
     ("14v-function-calling-openai.py", PROMPT_WEATHER, EVAL_WEATHER, BOT_SPEAKS_FIRST),
     ("14w-function-calling-mistral.py", PROMPT_WEATHER, EVAL_WEATHER, BOT_SPEAKS_FIRST),
+    ("14x-function-calling-universal-context.py", PROMPT_WEATHER, EVAL_WEATHER, BOT_SPEAKS_FIRST),
+    (
+        "14y-function-calling-google-universal-context.py",
+        PROMPT_WEATHER,
+        EVAL_WEATHER,
+        BOT_SPEAKS_FIRST,
+    ),
+    (
+        "14z-function-calling-anthropic-universal-context.py",
+        PROMPT_WEATHER,
+        EVAL_WEATHER,
+        BOT_SPEAKS_FIRST,
+    ),
+    (
+        "14aa-function-calling-aws-universal-context.py",
+        PROMPT_WEATHER,
+        EVAL_WEATHER,
+        BOT_SPEAKS_FIRST,
+    ),
     # Currently not working.
     # ("14c-function-calling-together.py", PROMPT_WEATHER, EVAL_WEATHER, BOT_SPEAKS_FIRST),
     # ("14l-function-calling-deepseek.py", PROMPT_WEATHER, EVAL_WEATHER, BOT_SPEAKS_FIRST),
@@ -148,6 +167,7 @@ TESTS_15 = [
 TESTS_19 = [
     ("19-openai-realtime-beta.py", PROMPT_WEATHER, EVAL_WEATHER, BOT_SPEAKS_FIRST),
     ("19a-azure-realtime-beta.py", PROMPT_WEATHER, EVAL_WEATHER, BOT_SPEAKS_FIRST),
+    ("19b-openai-realtime-text.py", PROMPT_WEATHER, EVAL_WEATHER, BOT_SPEAKS_FIRST),
     ("19b-openai-realtime-beta-text.py", PROMPT_WEATHER, EVAL_WEATHER, BOT_SPEAKS_FIRST),
 ]
 

src/pipecat/adapters/base_llm_adapter.py

@@ -16,7 +16,12 @@ from typing import Any, Dict, Generic, List, TypeVar
 from loguru import logger
 
 from pipecat.adapters.schemas.tools_schema import ToolsSchema
-from pipecat.processors.aggregators.llm_context import LLMContext, NotGiven
+from pipecat.processors.aggregators.llm_context import (
+    LLMContext,
+    LLMContextMessage,
+    LLMSpecificMessage,
+    NotGiven,
+)
 
 # Should be a TypedDict
 TLLMInvocationParams = TypeVar("TLLMInvocationParams", bound=dict[str, Any])
@@ -38,12 +43,23 @@ class BaseLLMAdapter(ABC, Generic[TLLMInvocationParams]):
     Subclasses must implement provider-specific conversion logic.
     """
 
+    @property
     @abstractmethod
-    def get_llm_invocation_params(self, context: LLMContext) -> TLLMInvocationParams:
+    def id_for_llm_specific_messages(self) -> str:
+        """Get the identifier used in LLMSpecificMessage instances for this LLM provider.
+
+        Returns:
+            The identifier string.
+        """
+        pass
+
+    @abstractmethod
+    def get_llm_invocation_params(self, context: LLMContext, **kwargs) -> TLLMInvocationParams:
         """Get provider-specific LLM invocation parameters from a universal LLM context.
 
         Args:
             context: The LLM context containing messages, tools, etc.
+            **kwargs: Additional provider-specific arguments that subclasses can use.
 
         Returns:
             Provider-specific parameters for invoking the LLM.
@@ -75,6 +91,28 @@ class BaseLLMAdapter(ABC, Generic[TLLMInvocationParams]):
         """
         pass
 
+    def create_llm_specific_message(self, message: Any) -> LLMSpecificMessage:
+        """Create an LLM-specific message (as opposed to a standard message) for use in an LLMContext.
+
+        Args:
+            message: The message content.
+
+        Returns:
+            A LLMSpecificMessage instance.
+        """
+        return LLMSpecificMessage(llm=self.id_for_llm_specific_messages, message=message)
+
+    def get_messages(self, context: LLMContext) -> List[LLMContextMessage]:
+        """Get messages from the LLM context, including standard and LLM-specific messages.
+
+        Args:
+            context: The LLM context containing messages.
+
+        Returns:
+            List of messages including standard and LLM-specific messages.
+        """
+        return context.get_messages(self.id_for_llm_specific_messages)
+
     def from_standard_tools(self, tools: Any) -> List[Any] | NotGiven:
         """Convert tools from standard format to provider format.
 

src/pipecat/adapters/services/anthropic_adapter.py

@@ -6,21 +6,33 @@
 
 """Anthropic LLM adapter for Pipecat."""
 
+import copy
+import json
+from dataclasses import dataclass
 from typing import Any, Dict, List, TypedDict
 
+from anthropic import NOT_GIVEN, NotGiven
+from anthropic.types.message_param import MessageParam
+from anthropic.types.tool_union_param import ToolUnionParam
+from loguru import logger
+
 from pipecat.adapters.base_llm_adapter import BaseLLMAdapter
 from pipecat.adapters.schemas.function_schema import FunctionSchema
 from pipecat.adapters.schemas.tools_schema import ToolsSchema
-from pipecat.processors.aggregators.llm_context import LLMContext
+from pipecat.processors.aggregators.llm_context import (
+    LLMContext,
+    LLMContextMessage,
+    LLMSpecificMessage,
+    LLMStandardMessage,
+)
 
 
 class AnthropicLLMInvocationParams(TypedDict):
-    """Context-based parameters for invoking Anthropic's LLM API.
+    """Context-based parameters for invoking Anthropic's LLM API."""
 
-    This is a placeholder until support for universal LLMContext machinery is added for Anthropic.
-    """
-
-    pass
+    system: str | NotGiven
+    messages: List[MessageParam]
+    tools: List[ToolUnionParam]
 
 
 class AnthropicLLMAdapter(BaseLLMAdapter[AnthropicLLMInvocationParams]):
@@ -30,33 +42,278 @@ class AnthropicLLMAdapter(BaseLLMAdapter[AnthropicLLMInvocationParams]):
     to the specific format required by Anthropic's Claude models for function calling.
     """
 
-    def get_llm_invocation_params(self, context: LLMContext) -> AnthropicLLMInvocationParams:
-        """Get Anthropic-specific LLM invocation parameters from a universal LLM context.
+    @property
+    def id_for_llm_specific_messages(self) -> str:
+        """Get the identifier used in LLMSpecificMessage instances for Anthropic."""
+        return "anthropic"
 
-        This is a placeholder until support for universal LLMContext machinery is added for Anthropic.
+    def get_llm_invocation_params(
+        self, context: LLMContext, enable_prompt_caching: bool
+    ) -> AnthropicLLMInvocationParams:
+        """Get Anthropic-specific LLM invocation parameters from a universal LLM context.
 
         Args:
             context: The LLM context containing messages, tools, etc.
+            enable_prompt_caching: Whether prompt caching should be enabled.
 
         Returns:
             Dictionary of parameters for invoking Anthropic's LLM API.
         """
-        raise NotImplementedError("Universal LLMContext is not yet supported for Anthropic.")
+        messages = self._from_universal_context_messages(self.get_messages(context))
+        return {
+            "system": messages.system,
+            "messages": (
+                self._with_cache_control_markers(messages.messages)
+                if enable_prompt_caching
+                else messages.messages
+            ),
+            # NOTE: LLMContext's tools are guaranteed to be a ToolsSchema (or NOT_GIVEN)
+            "tools": self.from_standard_tools(context.tools) or [],
+        }
 
-    def get_messages_for_logging(self, context) -> List[Dict[str, Any]]:
+    def get_messages_for_logging(self, context: LLMContext) -> List[Dict[str, Any]]:
         """Get messages from a universal LLM context in a format ready for logging about Anthropic.
 
         Removes or truncates sensitive data like image content for safe logging.
 
-        This is a placeholder until support for universal LLMContext machinery is added for Anthropic.
-
         Args:
             context: The LLM context containing messages.
 
         Returns:
             List of messages in a format ready for logging about Anthropic.
         """
-        raise NotImplementedError("Universal LLMContext is not yet supported for Anthropic.")
+        # Get messages in Anthropic's format
+        messages = self._from_universal_context_messages(self.get_messages(context)).messages
+
+        # Sanitize messages for logging
+        messages_for_logging = []
+        for message in messages:
+            msg = copy.deepcopy(message)
+            if "content" in msg:
+                if isinstance(msg["content"], list):
+                    for item in msg["content"]:
+                        if item["type"] == "image":
+                            item["source"]["data"] = "..."
+            messages_for_logging.append(msg)
+        return messages_for_logging
+
+    @dataclass
+    class ConvertedMessages:
+        """Container for Anthropic-formatted messages converted from universal context."""
+
+        messages: List[MessageParam]
+        system: str | NotGiven
+
+    def _from_universal_context_messages(
+        self, universal_context_messages: List[LLMContextMessage]
+    ) -> ConvertedMessages:
+        system = NOT_GIVEN
+        messages = []
+
+        # first, map messages using self._from_universal_context_message(m)
+        try:
+            messages = [self._from_universal_context_message(m) for m in universal_context_messages]
+        except Exception as e:
+            logger.error(f"Error mapping messages: {e}")
+
+        # See if we should pull the system message out of our messages list.
+        if messages and messages[0]["role"] == "system":
+            if len(messages) == 1:
+                # If we have only have a system message in the list, all we can really do
+                # without introducing too much magic is change the role to "user".
+                messages[0]["role"] = "user"
+            else:
+                # If we have more than one message, we'll pull the system message out of the
+                # list.
+                system = messages[0]["content"]
+                messages.pop(0)
+
+        # Convert any subsequent "system"-role messages to "user"-role
+        # messages, as Anthropic doesn't support system input messages.
+        for message in messages:
+            if message["role"] == "system":
+                message["role"] = "user"
+
+        # Merge consecutive messages with the same role.
+        i = 0
+        while i < len(messages) - 1:
+            current_message = messages[i]
+            next_message = messages[i + 1]
+            if current_message["role"] == next_message["role"]:
+                # Convert content to list of dictionaries if it's a string
+                if isinstance(current_message["content"], str):
+                    current_message["content"] = [
+                        {"type": "text", "text": current_message["content"]}
+                    ]
+                if isinstance(next_message["content"], str):
+                    next_message["content"] = [{"type": "text", "text": next_message["content"]}]
+                # Concatenate the content
+                current_message["content"].extend(next_message["content"])
+                # Remove the next message from the list
+                messages.pop(i + 1)
+            else:
+                i += 1
+
+        # Avoid empty content in messages
+        for message in messages:
+            if isinstance(message["content"], str) and message["content"] == "":
+                message["content"] = "(empty)"
+            elif isinstance(message["content"], list) and len(message["content"]) == 0:
+                message["content"] = [{"type": "text", "text": "(empty)"}]
+
+        return self.ConvertedMessages(messages=messages, system=system)
+
+    def _from_universal_context_message(self, message: LLMContextMessage) -> MessageParam:
+        if isinstance(message, LLMSpecificMessage):
+            return copy.deepcopy(message.message)
+        return self._from_standard_message(message)
+
+    def _from_standard_message(self, message: LLMStandardMessage) -> MessageParam:
+        """Convert standard universal context message to Anthropic format.
+
+        Handles conversion of text content, tool calls, and tool results.
+        Empty text content is converted to "(empty)".
+
+        Args:
+            message: Message in standard universal context format.
+
+        Returns:
+            Message in Anthropic format.
+
+        Examples:
+            Input standard format::
+
+                {
+                    "role": "assistant",
+                    "tool_calls": [
+                        {
+                            "id": "123",
+                            "function": {"name": "search", "arguments": '{"q": "test"}'}
+                        }
+                    ]
+                }
+
+            Output Anthropic format::
+
+                {
+                    "role": "assistant",
+                    "content": [
+                        {
+                            "type": "tool_use",
+                            "id": "123",
+                            "name": "search",
+                            "input": {"q": "test"}
+                        }
+                    ]
+                }
+        """
+        message = copy.deepcopy(message)
+        if message["role"] == "tool":
+            return {
+                "role": "user",
+                "content": [
+                    {
+                        "type": "tool_result",
+                        "tool_use_id": message["tool_call_id"],
+                        "content": message["content"],
+                    },
+                ],
+            }
+        if message.get("tool_calls"):
+            tc = message["tool_calls"]
+            ret = {"role": "assistant", "content": []}
+            for tool_call in tc:
+                function = tool_call["function"]
+                arguments = json.loads(function["arguments"])
+                new_tool_use = {
+                    "type": "tool_use",
+                    "id": tool_call["id"],
+                    "name": function["name"],
+                    "input": arguments,
+                }
+                ret["content"].append(new_tool_use)
+            return ret
+        content = message.get("content")
+        if isinstance(content, str):
+            # fix empty text
+            if content == "":
+                content = "(empty)"
+        elif isinstance(content, list):
+            for item in content:
+                # fix empty text
+                if item["type"] == "text" and item["text"] == "":
+                    item["text"] = "(empty)"
+                # handle image_url -> image conversion
+                if item["type"] == "image_url":
+                    item["type"] = "image"
+                    item["source"] = {
+                        "type": "base64",
+                        "media_type": "image/jpeg",
+                        "data": item["image_url"]["url"].split(",")[1],
+                    }
+                    del item["image_url"]
+            # In the case where there's a single image in the list (like what
+            # would result from a UserImageRawFrame), ensure that the image
+            # comes before text, as recommended by Anthropic docs
+            # (https://docs.anthropic.com/en/docs/build-with-claude/vision#example-one-image)
+            image_indices = [i for i, item in enumerate(content) if item["type"] == "image"]
+            text_indices = [i for i, item in enumerate(content) if item["type"] == "text"]
+            if len(image_indices) == 1 and text_indices:
+                img_idx = image_indices[0]
+                first_txt_idx = text_indices[0]
+                if img_idx > first_txt_idx:
+                    # Move image before the first text
+                    image_item = content.pop(img_idx)
+                    content.insert(first_txt_idx, image_item)
+
+        return message
+
+    def _with_cache_control_markers(self, messages: List[MessageParam]) -> List[MessageParam]:
+        """Add cache control markers to messages for prompt caching.
+
+        Args:
+            messages: List of messages in Anthropic format.
+
+        Returns:
+            List of messages with cache control markers added.
+        """
+
+        def add_cache_control_marker(message: MessageParam):
+            if isinstance(message["content"], str):
+                message["content"] = [{"type": "text", "text": message["content"]}]
+            message["content"][-1]["cache_control"] = {"type": "ephemeral"}
+
+        try:
+            # Add cache control markers to the most recent two user messages.
+            # - The marker at the most recent user message tells Anthropic to
+            #   cache the prompt up to that point.
+            # - The marker at the second-most-recent user message tells Anthropic
+            #   to look up the cached prompt that goes up to that point (the
+            #   point that *was* the last user message the previous turn).
+            # If we only added the marker to the last user message, we'd only
+            # ever be adding to the cache, never looking up from it.
+            # Why user messages? We're assuming that we're primarily running
+            # inference as soon as user turns come in. In Anthropic, turns
+            # strictly alternate between user and assistant.
+
+            messages_with_markers = copy.deepcopy(messages)
+
+            # Find the most recent two user messages
+            user_message_indices = []
+            for i in range(len(messages_with_markers) - 1, -1, -1):
+                if messages_with_markers[i]["role"] == "user":
+                    user_message_indices.append(i)
+                    if len(user_message_indices) == 2:
+                        break
+
+            # Add cache control markers to the identified user messages
+            for index in user_message_indices:
+                add_cache_control_marker(messages_with_markers[index])
+
+            return messages_with_markers
+        except Exception as e:
+            logger.error(f"Error adding cache control marker: {e}")
+            return messages_with_markers
 
     @staticmethod
     def _to_anthropic_function_format(function: FunctionSchema) -> Dict[str, Any]:

src/pipecat/adapters/services/aws_nova_sonic_adapter.py

@@ -31,6 +31,11 @@ class AWSNovaSonicLLMAdapter(BaseLLMAdapter[AWSNovaSonicLLMInvocationParams]):
     specific function-calling format, enabling tool use with Nova Sonic models.
     """
 
+    @property
+    def id_for_llm_specific_messages(self) -> str:
+        """Get the identifier used in LLMSpecificMessage instances for AWS Nova Sonic."""
+        raise NotImplementedError("Universal LLMContext is not yet supported for AWS Nova Sonic.")
+
     def get_llm_invocation_params(self, context: LLMContext) -> AWSNovaSonicLLMInvocationParams:
         """Get AWS Nova Sonic-specific LLM invocation parameters from a universal LLM context.
 

src/pipecat/adapters/services/bedrock_adapter.py

@@ -6,21 +6,33 @@
 
 """AWS Bedrock LLM adapter for Pipecat."""
 
-from typing import Any, Dict, List, TypedDict
+import base64
+import copy
+import json
+from dataclasses import dataclass
+from typing import Any, Dict, List, Literal, Optional, TypedDict
+
+from loguru import logger
 
 from pipecat.adapters.base_llm_adapter import BaseLLMAdapter
 from pipecat.adapters.schemas.function_schema import FunctionSchema
 from pipecat.adapters.schemas.tools_schema import ToolsSchema
-from pipecat.processors.aggregators.llm_context import LLMContext
+from pipecat.processors.aggregators.llm_context import (
+    LLMContext,
+    LLMContextMessage,
+    LLMContextToolChoice,
+    LLMSpecificMessage,
+    LLMStandardMessage,
+)
 
 
 class AWSBedrockLLMInvocationParams(TypedDict):
-    """Context-based parameters for invoking AWS Bedrock's LLM API.
+    """Context-based parameters for invoking AWS Bedrock's LLM API."""
 
-    This is a placeholder until support for universal LLMContext machinery is added for Bedrock.
-    """
-
-    pass
+    system: Optional[List[dict[str, Any]]]  # [{"text": "system message"}]
+    messages: List[dict[str, Any]]
+    tools: List[dict[str, Any]]
+    tool_choice: LLMContextToolChoice
 
 
 class AWSBedrockLLMAdapter(BaseLLMAdapter[AWSBedrockLLMInvocationParams]):
@@ -30,33 +42,244 @@ class AWSBedrockLLMAdapter(BaseLLMAdapter[AWSBedrockLLMInvocationParams]):
     into AWS Bedrock's expected tool format for function calling capabilities.
     """
 
+    @property
+    def id_for_llm_specific_messages(self) -> str:
+        """Get the identifier used in LLMSpecificMessage instances for AWS Bedrock."""
+        return "aws"
+
     def get_llm_invocation_params(self, context: LLMContext) -> AWSBedrockLLMInvocationParams:
         """Get AWS Bedrock-specific LLM invocation parameters from a universal LLM context.
 
-        This is a placeholder until support for universal LLMContext machinery is added for Bedrock.
-
         Args:
             context: The LLM context containing messages, tools, etc.
 
         Returns:
             Dictionary of parameters for invoking AWS Bedrock's LLM API.
         """
-        raise NotImplementedError("Universal LLMContext is not yet supported for AWS Bedrock.")
+        messages = self._from_universal_context_messages(self.get_messages(context))
+        return {
+            "system": messages.system,
+            "messages": messages.messages,
+            # NOTE: LLMContext's tools are guaranteed to be a ToolsSchema (or NOT_GIVEN)
+            "tools": self.from_standard_tools(context.tools) or [],
+            # To avoid refactoring in AWSBedrockLLMService, we just pass through tool_choice.
+            # Eventually (when we don't have to maintain the non-LLMContext code path) we should do
+            # the conversion to Bedrock's expected format here rather than in AWSBedrockLLMService.
+            "tool_choice": context.tool_choice,
+        }
 
     def get_messages_for_logging(self, context) -> List[Dict[str, Any]]:
         """Get messages from a universal LLM context in a format ready for logging about AWS Bedrock.
 
         Removes or truncates sensitive data like image content for safe logging.
 
-        This is a placeholder until support for universal LLMContext machinery is added for Bedrock.
-
         Args:
             context: The LLM context containing messages.
 
         Returns:
             List of messages in a format ready for logging about AWS Bedrock.
         """
-        raise NotImplementedError("Universal LLMContext is not yet supported for AWS Bedrock.")
+        # Get messages in Anthropic's format
+        messages = self._from_universal_context_messages(self.get_messages(context)).messages
+
+        # Sanitize messages for logging
+        messages_for_logging = []
+        for message in messages:
+            msg = copy.deepcopy(message)
+            if "content" in msg:
+                if isinstance(msg["content"], list):
+                    for item in msg["content"]:
+                        if item.get("image"):
+                            item["image"]["source"]["bytes"] = "..."
+            messages_for_logging.append(msg)
+        return messages_for_logging
+
+    @dataclass
+    class ConvertedMessages:
+        """Container for Anthropic-formatted messages converted from universal context."""
+
+        messages: List[dict[str, Any]]
+        system: Optional[str]
+
+    def _from_universal_context_messages(
+        self, universal_context_messages: List[LLMContextMessage]
+    ) -> ConvertedMessages:
+        system = None
+        messages = []
+
+        # first, map messages using self._from_universal_context_message(m)
+        try:
+            messages = [self._from_universal_context_message(m) for m in universal_context_messages]
+        except Exception as e:
+            logger.error(f"Error mapping messages: {e}")
+
+        # See if we should pull the system message out of our messages list
+        if messages and messages[0]["role"] == "system":
+            system = messages[0]["content"]
+            messages.pop(0)
+
+        # Convert any subsequent "system"-role messages to "user"-role
+        # messages, as AWS Bedrock doesn't support system input messages.
+        for message in messages:
+            if message["role"] == "system":
+                message["role"] = "user"
+
+        # Merge consecutive messages with the same role.
+        i = 0
+        while i < len(messages) - 1:
+            current_message = messages[i]
+            next_message = messages[i + 1]
+            if current_message["role"] == next_message["role"]:
+                # Convert content to list of dictionaries if it's a string
+                if isinstance(current_message["content"], str):
+                    current_message["content"] = [
+                        {"type": "text", "text": current_message["content"]}
+                    ]
+                if isinstance(next_message["content"], str):
+                    next_message["content"] = [{"type": "text", "text": next_message["content"]}]
+                # Concatenate the content
+                current_message["content"].extend(next_message["content"])
+                # Remove the next message from the list
+                messages.pop(i + 1)
+            else:
+                i += 1
+
+        # Avoid empty content in messages
+        for message in messages:
+            if isinstance(message["content"], str) and message["content"] == "":
+                message["content"] = "(empty)"
+            elif isinstance(message["content"], list) and len(message["content"]) == 0:
+                message["content"] = [{"type": "text", "text": "(empty)"}]
+
+        return self.ConvertedMessages(messages=messages, system=system)
+
+    def _from_universal_context_message(self, message: LLMContextMessage) -> dict[str, Any]:
+        if isinstance(message, LLMSpecificMessage):
+            return copy.deepcopy(message.message)
+        return self._from_standard_message(message)
+
+    def _from_standard_message(self, message: LLMStandardMessage) -> dict[str, Any]:
+        """Convert standard format message to AWS Bedrock format.
+
+        Handles conversion of text content, tool calls, and tool results.
+        Empty text content is converted to "(empty)".
+
+        Args:
+            message: Message in standard format.
+
+        Returns:
+            Message in AWS Bedrock format.
+
+        Examples:
+            Standard format input::
+
+                {
+                    "role": "assistant",
+                    "tool_calls": [
+                        {
+                            "id": "123",
+                            "function": {"name": "search", "arguments": '{"q": "test"}'}
+                        }
+                    ]
+                }
+
+            AWS Bedrock format output::
+
+                {
+                    "role": "assistant",
+                    "content": [
+                        {
+                            "toolUse": {
+                                "toolUseId": "123",
+                                "name": "search",
+                                "input": {"q": "test"}
+                            }
+                        }
+                    ]
+                }
+        """
+        message = copy.deepcopy(message)
+        if message["role"] == "tool":
+            # Try to parse the content as JSON if it looks like JSON
+            try:
+                if message["content"].strip().startswith("{") and message[
+                    "content"
+                ].strip().endswith("}"):
+                    content_json = json.loads(message["content"])
+                    tool_result_content = [{"json": content_json}]
+                else:
+                    tool_result_content = [{"text": message["content"]}]
+            except:
+                tool_result_content = [{"text": message["content"]}]
+
+            return {
+                "role": "user",
+                "content": [
+                    {
+                        "toolResult": {
+                            "toolUseId": message["tool_call_id"],
+                            "content": tool_result_content,
+                        },
+                    },
+                ],
+            }
+
+        if message.get("tool_calls"):
+            tc = message["tool_calls"]
+            ret = {"role": "assistant", "content": []}
+            for tool_call in tc:
+                function = tool_call["function"]
+                arguments = json.loads(function["arguments"])
+                new_tool_use = {
+                    "toolUse": {
+                        "toolUseId": tool_call["id"],
+                        "name": function["name"],
+                        "input": arguments,
+                    }
+                }
+                ret["content"].append(new_tool_use)
+            return ret
+
+        # Handle text content
+        content = message.get("content")
+        if isinstance(content, str):
+            if content == "":
+                return {"role": message["role"], "content": [{"text": "(empty)"}]}
+            else:
+                return {"role": message["role"], "content": [{"text": content}]}
+        elif isinstance(content, list):
+            new_content = []
+            for item in content:
+                # fix empty text
+                if item.get("type", "") == "text":
+                    text_content = item["text"] if item["text"] != "" else "(empty)"
+                    new_content.append({"text": text_content})
+                # handle image_url -> image conversion
+                if item["type"] == "image_url":
+                    new_item = {
+                        "image": {
+                            "format": "jpeg",
+                            "source": {
+                                "bytes": base64.b64decode(item["image_url"]["url"].split(",")[1])
+                            },
+                        }
+                    }
+                    new_content.append(new_item)
+            # In the case where there's a single image in the list (like what
+            # would result from a UserImageRawFrame), ensure that the image
+            # comes before text
+            image_indices = [i for i, item in enumerate(new_content) if "image" in item]
+            text_indices = [i for i, item in enumerate(new_content) if "text" in item]
+            if len(image_indices) == 1 and text_indices:
+                img_idx = image_indices[0]
+                first_txt_idx = text_indices[0]
+                if img_idx > first_txt_idx:
+                    # Move image before the first text
+                    image_item = new_content.pop(img_idx)
+                new_content.insert(first_txt_idx, image_item)
+            return {"role": message["role"], "content": new_content}
+
+        return message
 
     @staticmethod
     def _to_bedrock_function_format(function: FunctionSchema) -> Dict[str, Any]:

src/pipecat/adapters/services/gemini_adapter.py

@@ -54,6 +54,11 @@ class GeminiLLMAdapter(BaseLLMAdapter[GeminiLLMInvocationParams]):
     - Extracting and sanitizing messages from the LLM context for logging with Gemini.
     """
 
+    @property
+    def id_for_llm_specific_messages(self) -> str:
+        """Get the identifier used in LLMSpecificMessage instances for Google."""
+        return "google"
+
     def get_llm_invocation_params(self, context: LLMContext) -> GeminiLLMInvocationParams:
         """Get Gemini-specific LLM invocation parameters from a universal LLM context.
 
@@ -63,11 +68,11 @@ class GeminiLLMAdapter(BaseLLMAdapter[GeminiLLMInvocationParams]):
         Returns:
             Dictionary of parameters for Gemini's API.
         """
-        messages = self._from_universal_context_messages(self._get_messages(context))
+        messages = self._from_universal_context_messages(self.get_messages(context))
         return {
             "system_instruction": messages.system_instruction,
             "messages": messages.messages,
-            # NOTE; LLMContext's tools are guaranteed to be a ToolsSchema (or NOT_GIVEN)
+            # NOTE: LLMContext's tools are guaranteed to be a ToolsSchema (or NOT_GIVEN)
             "tools": self.from_standard_tools(context.tools),
         }
 
@@ -103,7 +108,7 @@ class GeminiLLMAdapter(BaseLLMAdapter[GeminiLLMInvocationParams]):
             List of messages in a format ready for logging about Gemini.
         """
         # Get messages in Gemini's format
-        messages = self._from_universal_context_messages(self._get_messages(context)).messages
+        messages = self._from_universal_context_messages(self.get_messages(context)).messages
 
         # Sanitize messages for logging
         messages_for_logging = []
@@ -119,9 +124,6 @@ class GeminiLLMAdapter(BaseLLMAdapter[GeminiLLMInvocationParams]):
             messages_for_logging.append(obj)
         return messages_for_logging
 
-    def _get_messages(self, context: LLMContext) -> List[LLMContextMessage]:
-        return context.get_messages("google")
-
     @dataclass
     class ConvertedMessages:
         """Container for Google-formatted messages converted from universal context."""
@@ -192,14 +194,14 @@ class GeminiLLMAdapter(BaseLLMAdapter[GeminiLLMInvocationParams]):
     def _from_standard_message(
         self, message: LLMStandardMessage, already_have_system_instruction: bool
     ) -> Content | str:
-        """Convert universal context message to Google Content object.
+        """Convert standard universal context message to Google Content object.
 
         Handles conversion of text, images, and function calls to Google's
         format.
         System instructions are returned as a plain string.
 
         Args:
-            message: Message in universal context format.
+            message: Message in standard universal context format.
             already_have_system_instruction: Whether we already have a system instruction
 
         Returns:
@@ -308,5 +310,4 @@ class GeminiLLMAdapter(BaseLLMAdapter[GeminiLLMInvocationParams]):
                     audio_bytes = base64.b64decode(input_audio["data"])
                     parts.append(Part(inline_data=Blob(mime_type="audio/wav", data=audio_bytes)))
 
-        message = Content(role=role, parts=parts)
-        return message
+        return Content(role=role, parts=parts)

src/pipecat/adapters/services/open_ai_adapter.py

@@ -24,6 +24,7 @@ from pipecat.processors.aggregators.llm_context import (
     LLMContext,
     LLMContextMessage,
     LLMContextToolChoice,
+    LLMSpecificMessage,
     NotGiven,
 )
 
@@ -47,6 +48,11 @@ class OpenAILLMAdapter(BaseLLMAdapter[OpenAILLMInvocationParams]):
     - Extracting and sanitizing messages from the LLM context for logging about OpenAI.
     """
 
+    @property
+    def id_for_llm_specific_messages(self) -> str:
+        """Get the identifier used in LLMSpecificMessage instances for OpenAI."""
+        return "openai"
+
     def get_llm_invocation_params(self, context: LLMContext) -> OpenAILLMInvocationParams:
         """Get OpenAI-specific LLM invocation parameters from a universal LLM context.
 
@@ -57,7 +63,7 @@ class OpenAILLMAdapter(BaseLLMAdapter[OpenAILLMInvocationParams]):
             Dictionary of parameters for OpenAI's ChatCompletion API.
         """
         return {
-            "messages": self._from_universal_context_messages(self._get_messages(context)),
+            "messages": self._from_universal_context_messages(self.get_messages(context)),
             # NOTE; LLMContext's tools are guaranteed to be a ToolsSchema (or NOT_GIVEN)
             "tools": self.from_standard_tools(context.tools),
             "tool_choice": context.tool_choice,
@@ -91,7 +97,7 @@ class OpenAILLMAdapter(BaseLLMAdapter[OpenAILLMInvocationParams]):
             List of messages in a format ready for logging about OpenAI.
         """
         msgs = []
-        for message in self._get_messages(context):
+        for message in self.get_messages(context):
             msg = copy.deepcopy(message)
             if "content" in msg:
                 if isinstance(msg["content"], list):
@@ -104,14 +110,18 @@ class OpenAILLMAdapter(BaseLLMAdapter[OpenAILLMInvocationParams]):
             msgs.append(msg)
         return msgs
 
-    def _get_messages(self, context: LLMContext) -> List[LLMContextMessage]:
-        return context.get_messages("openai")
-
     def _from_universal_context_messages(
         self, messages: List[LLMContextMessage]
     ) -> List[ChatCompletionMessageParam]:
-        # Just a pass-through: messages are already the right type
-        return messages
+        result = []
+        for message in messages:
+            if isinstance(message, LLMSpecificMessage):
+                # Extract the actual message content from LLMSpecificMessage
+                result.append(message.message)
+            else:
+                # Standard message, pass through unchanged
+                result.append(message)
+        return result
 
     def _from_standard_tool_choice(
         self, tool_choice: LLMContextToolChoice | NotGiven

src/pipecat/adapters/services/open_ai_realtime_adapter.py

@@ -30,6 +30,11 @@ class OpenAIRealtimeLLMAdapter(BaseLLMAdapter):
     OpenAI's Realtime API for function calling capabilities.
     """
 
+    @property
+    def id_for_llm_specific_messages(self) -> str:
+        """Get the identifier used in LLMSpecificMessage instances for OpenAI Realtime."""
+        raise NotImplementedError("Universal LLMContext is not yet supported for OpenAI Realtime.")
+
     def get_llm_invocation_params(self, context: LLMContext) -> OpenAIRealtimeLLMInvocationParams:
         """Get OpenAI Realtime-specific LLM invocation parameters from a universal LLM context.
 

src/pipecat/audio/filters/noisereduce_filter.py

@@ -33,6 +33,10 @@ class NoisereduceFilter(BaseAudioFilter):
     Applies spectral gating noise reduction algorithms to suppress background
     noise in audio streams. Uses the noisereduce library's default noise
     reduction parameters.
+
+    .. deprecated:: 0.0.85
+        `NoisereduceFilter` is deprecated and will be removed in a future version.
+        We recommend using other real-time audio filters like `KrispFilter` or `AICFilter`.
     """
 
     def __init__(self) -> None:
@@ -40,6 +44,17 @@ class NoisereduceFilter(BaseAudioFilter):
         self._filtering = True
         self._sample_rate = 0
 
+        import warnings
+
+        with warnings.catch_warnings():
+            warnings.simplefilter("always")
+            warnings.warn(
+                "`NoisereduceFilter` is deprecated. "
+                "Use other real-time audio filters like `KrispFilter` or `AICFilter`.",
+                DeprecationWarning,
+                stacklevel=2,
+            )
+
     async def start(self, sample_rate: int):
         """Initialize the filter with the transport's sample rate.
 

src/pipecat/audio/turn/smart_turn/local_smart_turn_v3.py

@@ -0,0 +1,124 @@
+#
+# Copyright (c) 2025, Daily
+#
+# SPDX-License-Identifier: BSD 2-Clause License
+#
+
+"""Local turn analyzer for on-device ML inference using the smart-turn-v3 model.
+
+This module provides a smart turn analyzer that uses an ONNX model for
+local end-of-turn detection without requiring network connectivity.
+"""
+
+from typing import Any, Dict, Optional
+
+import numpy as np
+from loguru import logger
+
+from pipecat.audio.turn.smart_turn.base_smart_turn import BaseSmartTurn
+
+try:
+    import onnxruntime as ort
+    from transformers import WhisperFeatureExtractor
+except ModuleNotFoundError as e:
+    logger.error(f"Exception: {e}")
+    logger.error(
+        "In order to use LocalSmartTurnAnalyzerV3, you need to `pip install pipecat-ai[local-smart-turn-v3]`."
+    )
+    raise Exception(f"Missing module: {e}")
+
+
+class LocalSmartTurnAnalyzerV3(BaseSmartTurn):
+    """Local turn analyzer using the smart-turn-v3 ONNX model.
+
+    Provides end-of-turn detection using locally-stored ONNX model,
+    enabling offline operation without network dependencies.
+    """
+
+    def __init__(self, *, smart_turn_model_path: Optional[str] = None, **kwargs):
+        """Initialize the local ONNX smart-turn-v3 analyzer.
+
+        Args:
+            smart_turn_model_path: Path to the ONNX model file. If this is not
+                set, the bundled smart-turn-v3.0 model will be used.
+            **kwargs: Additional arguments passed to BaseSmartTurn.
+        """
+        super().__init__(**kwargs)
+
+        logger.debug("Loading Local Smart Turn v3 model...")
+
+        if not smart_turn_model_path:
+            # Load bundled model
+            model_name = "smart-turn-v3.0.onnx"
+            package_path = "pipecat.audio.turn.smart_turn.data"
+
+            try:
+                import importlib_resources as impresources
+
+                smart_turn_model_path = str(impresources.files(package_path).joinpath(model_name))
+            except BaseException:
+                from importlib import resources as impresources
+
+                try:
+                    with impresources.path(package_path, model_name) as f:
+                        smart_turn_model_path = f
+                except BaseException:
+                    smart_turn_model_path = str(
+                        impresources.files(package_path).joinpath(model_name)
+                    )
+
+        so = ort.SessionOptions()
+        so.execution_mode = ort.ExecutionMode.ORT_SEQUENTIAL
+        so.inter_op_num_threads = 1
+        so.graph_optimization_level = ort.GraphOptimizationLevel.ORT_ENABLE_ALL
+
+        self._feature_extractor = WhisperFeatureExtractor(chunk_length=8)
+        self._session = ort.InferenceSession(smart_turn_model_path, sess_options=so)
+
+        logger.debug("Loaded Local Smart Turn v3")
+
+    async def _predict_endpoint(self, audio_array: np.ndarray) -> Dict[str, Any]:
+        """Predict end-of-turn using local ONNX model."""
+
+        def truncate_audio_to_last_n_seconds(audio_array, n_seconds=8, sample_rate=16000):
+            """Truncate audio to last n seconds or pad with zeros to meet n seconds."""
+            max_samples = n_seconds * sample_rate
+            if len(audio_array) > max_samples:
+                return audio_array[-max_samples:]
+            elif len(audio_array) < max_samples:
+                # Pad with zeros at the beginning
+                padding = max_samples - len(audio_array)
+                return np.pad(audio_array, (padding, 0), mode="constant", constant_values=0)
+            return audio_array
+
+        # Truncate to 8 seconds (keeping the end) or pad to 8 seconds
+        audio_array = truncate_audio_to_last_n_seconds(audio_array, n_seconds=8)
+
+        # Process audio using Whisper's feature extractor
+        inputs = self._feature_extractor(
+            audio_array,
+            sampling_rate=16000,
+            return_tensors="pt",
+            padding="max_length",
+            max_length=8 * 16000,
+            truncation=True,
+            do_normalize=True,
+        )
+
+        # Convert to numpy and ensure correct shape for ONNX
+        input_features = inputs.input_features.squeeze(0).numpy().astype(np.float32)
+        input_features = np.expand_dims(input_features, axis=0)  # Add batch dimension
+
+        # Run ONNX inference
+        outputs = self._session.run(None, {"input_features": input_features})
+
+        # Extract probability (ONNX model returns sigmoid probabilities)
+        probability = outputs[0][0].item()
+
+        # Make prediction (1 for Complete, 0 for Incomplete)
+        prediction = 1 if probability > 0.5 else 0
+
+        return {
+            "prediction": prediction,
+            "probability": probability,
+        }

src/pipecat/extensions/voicemail/voicemail_detector.py

@@ -21,7 +21,6 @@ from typing import List, Optional
 from loguru import logger
 
 from pipecat.frames.frames import (
-    BotInterruptionFrame,
     EndFrame,
     Frame,
     LLMFullResponseEndFrame,
@@ -360,7 +359,7 @@ class ClassificationProcessor(FrameProcessor):
             await self._voicemail_notifier.notify()  # Clear buffered TTS frames
 
             # Interrupt the current pipeline to stop any ongoing processing
-            await self.push_frame(BotInterruptionFrame(), FrameDirection.UPSTREAM)
+            await self.push_interruption_task_frame_and_wait()
 
             # Set the voicemail event to trigger the voicemail handler
             self._voicemail_event.clear()

src/pipecat/frames/frames.py

@@ -788,43 +788,6 @@ class FatalErrorFrame(ErrorFrame):
     fatal: bool = field(default=True, init=False)
 
 
-@dataclass
-class EndTaskFrame(SystemFrame):
-    """Frame to request graceful pipeline task closure.
-
-    This is used to notify the pipeline task that the pipeline should be
-    closed nicely (flushing all the queued frames) by pushing an EndFrame
-    downstream. This frame should be pushed upstream.
-    """
-
-    pass
-
-
-@dataclass
-class CancelTaskFrame(SystemFrame):
-    """Frame to request immediate pipeline task cancellation.
-
-    This is used to notify the pipeline task that the pipeline should be
-    stopped immediately by pushing a CancelFrame downstream. This frame
-    should be pushed upstream.
-    """
-
-    pass
-
-
-@dataclass
-class StopTaskFrame(SystemFrame):
-    """Frame to request pipeline task stop while keeping processors running.
-
-    This is used to notify the pipeline task that it should be stopped as
-    soon as possible (flushing all the queued frames) but that the pipeline
-    processors should be kept in a running state. This frame should be pushed
-    upstream.
-    """
-
-    pass
-
-
 @dataclass
 class FrameProcessorPauseUrgentFrame(SystemFrame):
     """Frame to pause frame processing immediately.
@@ -857,7 +820,7 @@ class FrameProcessorResumeUrgentFrame(SystemFrame):
 
 
 @dataclass
-class StartInterruptionFrame(SystemFrame):
+class InterruptionFrame(SystemFrame):
     """Frame indicating user started speaking (interruption detected).
 
     Emitted by the BaseInputTransport to indicate that a user has started
@@ -869,6 +832,34 @@ class StartInterruptionFrame(SystemFrame):
     pass
 
 
+@dataclass
+class StartInterruptionFrame(InterruptionFrame):
+    """Frame indicating user started speaking (interruption detected).
+
+    .. deprecated:: 0.0.85
+        This frame is deprecated and will be removed in a future version.
+        Instead, use `InterruptionFrame`.
+
+    Emitted by the BaseInputTransport to indicate that a user has started
+    speaking (i.e. is interrupting). This is similar to
+    UserStartedSpeakingFrame except that it should be pushed concurrently
+    with other frames (so the order is not guaranteed).
+    """
+
+    def __post_init__(self):
+        super().__post_init__()
+        import warnings
+
+        with warnings.catch_warnings():
+            warnings.simplefilter("always")
+            warnings.warn(
+                "StartInterruptionFrame is deprecated and will be removed in a future version. "
+                "Instead, use InterruptionFrame.",
+                DeprecationWarning,
+                stacklevel=2,
+            )
+
+
 @dataclass
 class UserStartedSpeakingFrame(SystemFrame):
     """Frame indicating user has started speaking.
@@ -944,20 +935,6 @@ class VADUserStoppedSpeakingFrame(SystemFrame):
     pass
 
 
-@dataclass
-class BotInterruptionFrame(SystemFrame):
-    """Frame indicating the bot should be interrupted.
-
-    Emitted when the bot should be interrupted. This will mainly cause the
-    same actions as if the user interrupted except that the
-    UserStartedSpeakingFrame and UserStoppedSpeakingFrame won't be generated.
-    This frame should be pushed upstreams. It results in the BaseInputTransport
-    starting an interruption by pushing a StartInterruptionFrame downstream.
-    """
-
-    pass
-
-
 @dataclass
 class BotStartedSpeakingFrame(SystemFrame):
     """Frame indicating the bot started speaking.
@@ -1253,23 +1230,6 @@ class UserImageRawFrame(InputImageRawFrame):
         return f"{self.name}(pts: {pts}, user: {self.user_id}, source: {self.transport_source}, size: {self.size}, format: {self.format}, request: {self.request})"
 
 
-@dataclass
-class VisionImageRawFrame(InputImageRawFrame):
-    """Image frame for vision/image analysis with associated text prompt.
-
-    An image with an associated text to ask for a description of it.
-
-    Parameters:
-        text: Optional text prompt describing what to analyze in the image.
-    """
-
-    text: Optional[str] = None
-
-    def __str__(self):
-        pts = format_pts(self.pts)
-        return f"{self.name}(pts: {pts}, text: [{self.text}], size: {self.size}, format: {self.format})"
-
-
 @dataclass
 class InputDTMFFrame(DTMFFrame, SystemFrame):
     """DTMF keypress input frame from transport."""
@@ -1306,6 +1266,103 @@ class SpeechControlParamsFrame(SystemFrame):
     turn_params: Optional[SmartTurnParams] = None
 
 
+#
+# Task frames
+#
+
+
+@dataclass
+class TaskFrame(SystemFrame):
+    """Base frame for task frames.
+
+    This is a base class for frames that are meant to be sent and handled
+    upstream by the pipeline task. This might result in a corresponding frame
+    sent downstream (e.g. `InterruptionTaskFrame` / `InterruptionFrame` or
+    `EndTaskFrame` / `EndFrame`).
+
+    """
+
+    pass
+
+
+@dataclass
+class EndTaskFrame(TaskFrame):
+    """Frame to request graceful pipeline task closure.
+
+    This is used to notify the pipeline task that the pipeline should be
+    closed nicely (flushing all the queued frames) by pushing an EndFrame
+    downstream. This frame should be pushed upstream.
+    """
+
+    pass
+
+
+@dataclass
+class CancelTaskFrame(TaskFrame):
+    """Frame to request immediate pipeline task cancellation.
+
+    This is used to notify the pipeline task that the pipeline should be
+    stopped immediately by pushing a CancelFrame downstream. This frame
+    should be pushed upstream.
+    """
+
+    pass
+
+
+@dataclass
+class StopTaskFrame(TaskFrame):
+    """Frame to request pipeline task stop while keeping processors running.
+
+    This is used to notify the pipeline task that it should be stopped as
+    soon as possible (flushing all the queued frames) but that the pipeline
+    processors should be kept in a running state. This frame should be pushed
+    upstream.
+    """
+
+    pass
+
+
+@dataclass
+class InterruptionTaskFrame(TaskFrame):
+    """Frame indicating the bot should be interrupted.
+
+    Emitted when the bot should be interrupted. This will mainly cause the
+    same actions as if the user interrupted except that the
+    UserStartedSpeakingFrame and UserStoppedSpeakingFrame won't be generated.
+    This frame should be pushed upstream.
+    """
+
+    pass
+
+
+@dataclass
+class BotInterruptionFrame(InterruptionTaskFrame):
+    """Frame indicating the bot should be interrupted.
+
+    .. deprecated:: 0.0.85
+        This frame is deprecated and will be removed in a future version.
+        Instead, use `InterruptionTaskFrame`.
+
+    Emitted when the bot should be interrupted. This will mainly cause the
+    same actions as if the user interrupted except that the
+    UserStartedSpeakingFrame and UserStoppedSpeakingFrame won't be generated.
+    This frame should be pushed upstream.
+    """
+
+    def __post_init__(self):
+        super().__post_init__()
+        import warnings
+
+        with warnings.catch_warnings():
+            warnings.simplefilter("always")
+            warnings.warn(
+                "BotInterruptionFrame is deprecated and will be removed in a future version. "
+                "Instead, use InterruptionTaskFrame.",
+                DeprecationWarning,
+                stacklevel=2,
+            )
+
+
 #
 # Control frames
 #

src/pipecat/observers/loggers/debug_log_observer.py

@@ -54,7 +54,7 @@ class DebugLogObserver(BaseObserver):
 
         Log frames with specific source/destination filters::
 
-            from pipecat.frames.frames import StartInterruptionFrame, UserStartedSpeakingFrame, LLMTextFrame
+            from pipecat.frames.frames import InterruptionFrame, UserStartedSpeakingFrame, LLMTextFrame
             from pipecat.observers.loggers.debug_log_observer import DebugLogObserver, FrameEndpoint
             from pipecat.transports.base_output import BaseOutputTransport
             from pipecat.services.stt_service import STTService
@@ -62,8 +62,8 @@ class DebugLogObserver(BaseObserver):
             observers=[
                 DebugLogObserver(
                     frame_types={
-                        # Only log StartInterruptionFrame when source is BaseOutputTransport
-                        StartInterruptionFrame: (BaseOutputTransport, FrameEndpoint.SOURCE),
+                        # Only log InterruptionFrame when source is BaseOutputTransport
+                        InterruptionFrame: (BaseOutputTransport, FrameEndpoint.SOURCE),
                         # Only log UserStartedSpeakingFrame when destination is STTService
                         UserStartedSpeakingFrame: (STTService, FrameEndpoint.DESTINATION),
                         # Log LLMTextFrame regardless of source or destination type

src/pipecat/pipeline/llm_switcher.py

@@ -30,25 +30,17 @@ class LLMSwitcher(ServiceSwitcher[StrategyType]):
         """Get the currently active LLM, if any."""
         return self.strategy.active_service
 
-    async def run_inference(
-        self, context: LLMContext, system_instruction: Optional[str] = None
-    ) -> Optional[str]:
+    async def run_inference(self, context: LLMContext) -> Optional[str]:
         """Run a one-shot, out-of-band (i.e. out-of-pipeline) inference with the given LLM context, using the currently active LLM.
 
         Args:
             context: The LLM context containing conversation history.
-            system_instruction: Optional system instruction to guide the LLM's
-              behavior. You could also (again, optionally) provide a system
-              instruction directly in the context. If both are provided, the
-              one in the context takes precedence.
 
         Returns:
             The LLM's response as a string, or None if no response is generated.
         """
         if self.active_llm:
-            return await self.active_llm.run_inference(
-                context=context, system_instruction=system_instruction
-            )
+            return await self.active_llm.run_inference(context=context)
         return None
 
     def register_function(

src/pipecat/pipeline/task.py

@@ -32,6 +32,8 @@ from pipecat.frames.frames import (
     Frame,
     HeartbeatFrame,
     InputAudioRawFrame,
+    InterruptionFrame,
+    InterruptionTaskFrame,
     MetricsFrame,
     StartFrame,
     StopFrame,
@@ -113,9 +115,28 @@ class PipelineTask(BasePipelineTask):
     - on_frame_reached_downstream: Called when downstream frames reach the sink
     - on_idle_timeout: Called when pipeline is idle beyond timeout threshold
     - on_pipeline_started: Called when pipeline starts with StartFrame
-    - on_pipeline_stopped: Called when pipeline stops with StopFrame
-    - on_pipeline_ended: Called when pipeline ends with EndFrame
-    - on_pipeline_cancelled: Called when pipeline is cancelled
+    - on_pipeline_stopped: [deprecated] Called when pipeline stops with StopFrame
+
+            .. deprecated:: 0.0.86
+                Use `on_pipeline_finished` instead.
+
+    - on_pipeline_ended: [deprecated] Called when pipeline ends with EndFrame
+
+            .. deprecated:: 0.0.86
+                Use `on_pipeline_finished` instead.
+
+    - on_pipeline_cancelled: [deprecated] Called when pipeline is cancelled with CancelFrame
+
+            .. deprecated:: 0.0.86
+                Use `on_pipeline_finished` instead.
+
+    - on_pipeline_finished: Called after the pipeline has reached any terminal state.
+          This includes:
+              - StopFrame: pipeline was stopped (processors keep connections open)
+              - EndFrame: pipeline ended normally
+              - CancelFrame: pipeline was cancelled
+          Use this event for cleanup, logging, or post-processing tasks. Users can inspect
+          the frame if they need to handle specific cases.
 
     Example::
 
@@ -126,6 +147,10 @@ class PipelineTask(BasePipelineTask):
         @task.event_handler("on_idle_timeout")
         async def on_pipeline_idle_timeout(task):
             ...
+
+        @task.event_handler("on_pipeline_finished")
+        async def on_pipeline_finished(task, frame):
+            ...
     """
 
     def __init__(
@@ -262,6 +287,7 @@ class PipelineTask(BasePipelineTask):
         self._register_event_handler("on_pipeline_stopped")
         self._register_event_handler("on_pipeline_ended")
         self._register_event_handler("on_pipeline_cancelled")
+        self._register_event_handler("on_pipeline_finished")
 
     @property
     def params(self) -> PipelineParams:
@@ -290,6 +316,27 @@ class PipelineTask(BasePipelineTask):
         """
         return self._turn_trace_observer
 
+    def event_handler(self, event_name: str):
+        """Decorator for registering event handlers.
+
+        Args:
+            event_name: The name of the event to handle.
+
+        Returns:
+            The decorator function that registers the handler.
+        """
+        if event_name in ["on_pipeline_stopped", "on_pipeline_ended", "on_pipeline_cancelled"]:
+            import warnings
+
+            with warnings.catch_warnings():
+                warnings.simplefilter("always")
+                warnings.warn(
+                    f"Event '{event_name}' is deprecated, use 'on_pipeline_finished' instead.",
+                    DeprecationWarning,
+                )
+
+        return super().event_handler(event_name)
+
     def add_observer(self, observer: BaseObserver):
         """Add an observer to monitor pipeline execution.
 
@@ -532,6 +579,7 @@ class PipelineTask(BasePipelineTask):
                 )
             finally:
                 await self._call_event_handler("on_pipeline_cancelled", frame)
+                await self._call_event_handler("on_pipeline_finished", frame)
 
         logger.debug(f"{self}: Closing. Waiting for {frame} to reach the end of the pipeline...")
 
@@ -627,13 +675,23 @@ class PipelineTask(BasePipelineTask):
 
         if isinstance(frame, EndTaskFrame):
             # Tell the task we should end nicely.
+            logger.debug(f"{self}: received end task frame {frame}")
             await self.queue_frame(EndFrame())
         elif isinstance(frame, CancelTaskFrame):
             # Tell the task we should end right away.
+            logger.debug(f"{self}: received cancel task frame {frame}")
             await self.queue_frame(CancelFrame())
         elif isinstance(frame, StopTaskFrame):
             # Tell the task we should stop nicely.
+            logger.debug(f"{self}: received stop task frame {frame}")
             await self.queue_frame(StopFrame())
+        elif isinstance(frame, InterruptionTaskFrame):
+            # Tell the task we should interrupt the pipeline. Note that we are
+            # bypassing the push queue and directly queue into the
+            # pipeline. This is in case the push task is blocked waiting for a
+            # pipeline-ending frame to finish traversing the pipeline.
+            logger.debug(f"{self}: received interruption task frame {frame}")
+            await self._pipeline.queue_frame(InterruptionFrame())
         elif isinstance(frame, ErrorFrame):
             if frame.fatal:
                 logger.error(f"A fatal error occurred: {frame}")
@@ -642,7 +700,7 @@ class PipelineTask(BasePipelineTask):
                 # Tell the task we should stop.
                 await self.queue_frame(StopTaskFrame())
             else:
-                logger.warning(f"Something went wrong: {frame}")
+                logger.warning(f"{self}: Something went wrong: {frame}")
 
     async def _sink_push_frame(self, frame: Frame, direction: FrameDirection):
         """Process frames coming downstream from the pipeline.
@@ -669,9 +727,11 @@ class PipelineTask(BasePipelineTask):
             self._pipeline_start_event.set()
         elif isinstance(frame, EndFrame):
             await self._call_event_handler("on_pipeline_ended", frame)
+            await self._call_event_handler("on_pipeline_finished", frame)
             self._pipeline_end_event.set()
         elif isinstance(frame, StopFrame):
             await self._call_event_handler("on_pipeline_stopped", frame)
+            await self._call_event_handler("on_pipeline_finished", frame)
             self._pipeline_end_event.set()
         elif isinstance(frame, CancelFrame):
             self._pipeline_end_event.set()

src/pipecat/processors/aggregators/dtmf_aggregator.py

@@ -16,7 +16,6 @@ from typing import Optional
 
 from pipecat.audio.dtmf.types import KeypadEntry
 from pipecat.frames.frames import (
-    BotInterruptionFrame,
     CancelFrame,
     EndFrame,
     Frame,
@@ -24,7 +23,7 @@ from pipecat.frames.frames import (
     StartFrame,
     TranscriptionFrame,
 )
-from pipecat.processors.frame_processor import FrameDirection, FrameProcessor, FrameProcessorSetup
+from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
 from pipecat.utils.time import time_now_iso8601
 
 
@@ -105,7 +104,7 @@ class DTMFAggregator(FrameProcessor):
 
         # For first digit, schedule interruption.
         if is_first_digit:
-            await self.push_frame(BotInterruptionFrame(), FrameDirection.UPSTREAM)
+            await self.push_interruption_task_frame_and_wait()
 
         # Check for immediate flush conditions
         if frame.button == self._termination_digit:

src/pipecat/processors/aggregators/llm_response.py

@@ -22,7 +22,6 @@ from pipecat.audio.interruptions.base_interruption_strategy import BaseInterrupt
 from pipecat.audio.turn.smart_turn.base_smart_turn import SmartTurnParams
 from pipecat.audio.vad.vad_analyzer import VADParams
 from pipecat.frames.frames import (
-    BotInterruptionFrame,
     BotStartedSpeakingFrame,
     BotStoppedSpeakingFrame,
     CancelFrame,
@@ -36,6 +35,7 @@ from pipecat.frames.frames import (
     FunctionCallsStartedFrame,
     InputAudioRawFrame,
     InterimTranscriptionFrame,
+    InterruptionFrame,
     LLMFullResponseEndFrame,
     LLMFullResponseStartFrame,
     LLMMessagesAppendFrame,
@@ -48,7 +48,6 @@ from pipecat.frames.frames import (
     OpenAILLMContextAssistantTimestampFrame,
     SpeechControlParamsFrame,
     StartFrame,
-    StartInterruptionFrame,
     TextFrame,
     TranscriptionFrame,
     UserImageRawFrame,
@@ -138,7 +137,7 @@ class LLMFullResponseAggregator(FrameProcessor):
         """
         await super().process_frame(frame, direction)
 
-        if isinstance(frame, StartInterruptionFrame):
+        if isinstance(frame, InterruptionFrame):
             await self._call_event_handler("on_completion", self._aggregation, False)
             self._aggregation = ""
             self._started = False
@@ -532,9 +531,9 @@ class LLMUserContextAggregator(LLMContextResponseAggregator):
 
                 if should_interrupt:
                     logger.debug(
-                        "Interruption conditions met - pushing BotInterruptionFrame and aggregation"
+                        "Interruption conditions met - pushing interruption and aggregation"
                     )
-                    await self.push_frame(BotInterruptionFrame(), FrameDirection.UPSTREAM)
+                    await self.push_interruption_task_frame_and_wait()
                     await self._process_aggregation()
                 else:
                     logger.debug("Interruption conditions not met - not pushing aggregation")
@@ -838,7 +837,7 @@ class LLMAssistantContextAggregator(LLMContextResponseAggregator):
         """
         await super().process_frame(frame, direction)
 
-        if isinstance(frame, StartInterruptionFrame):
+        if isinstance(frame, InterruptionFrame):
             await self._handle_interruptions(frame)
             await self.push_frame(frame, direction)
         elif isinstance(frame, LLMFullResponseStartFrame):
@@ -904,7 +903,7 @@ class LLMAssistantContextAggregator(LLMContextResponseAggregator):
         if frame.run_llm:
             await self.push_context_frame(FrameDirection.UPSTREAM)
 
-    async def _handle_interruptions(self, frame: StartInterruptionFrame):
+    async def _handle_interruptions(self, frame: InterruptionFrame):
         await self.push_aggregation()
         self._started = 0
         await self.reset()

src/pipecat/processors/aggregators/llm_response_universal.py

@@ -13,7 +13,6 @@ LLM processing, and text-to-speech components in conversational AI pipelines.
 
 import asyncio
 import json
-from dataclasses import dataclass
 from typing import Any, Dict, List, Literal, Optional, Set
 
 from loguru import logger
@@ -23,7 +22,6 @@ from pipecat.audio.interruptions.base_interruption_strategy import BaseInterrupt
 from pipecat.audio.turn.smart_turn.base_smart_turn import SmartTurnParams
 from pipecat.audio.vad.vad_analyzer import VADParams
 from pipecat.frames.frames import (
-    BotInterruptionFrame,
     BotStartedSpeakingFrame,
     BotStoppedSpeakingFrame,
     CancelFrame,
@@ -37,6 +35,7 @@ from pipecat.frames.frames import (
     FunctionCallsStartedFrame,
     InputAudioRawFrame,
     InterimTranscriptionFrame,
+    InterruptionFrame,
     LLMContextAssistantTimestampFrame,
     LLMContextFrame,
     LLMFullResponseEndFrame,
@@ -48,7 +47,6 @@ from pipecat.frames.frames import (
     LLMSetToolsFrame,
     SpeechControlParamsFrame,
     StartFrame,
-    StartInterruptionFrame,
     TextFrame,
     TranscriptionFrame,
     UserImageRawFrame,
@@ -311,9 +309,9 @@ class LLMUserAggregator(LLMContextAggregator):
 
                 if should_interrupt:
                     logger.debug(
-                        "Interruption conditions met - pushing BotInterruptionFrame and aggregation"
+                        "Interruption conditions met - pushing interruption and aggregation"
                     )
-                    await self.push_frame(BotInterruptionFrame(), FrameDirection.UPSTREAM)
+                    await self.push_interruption_task_frame_and_wait()
                     await self._process_aggregation()
                 else:
                     logger.debug("Interruption conditions not met - not pushing aggregation")
@@ -579,7 +577,7 @@ class LLMAssistantAggregator(LLMContextAggregator):
         """
         await super().process_frame(frame, direction)
 
-        if isinstance(frame, StartInterruptionFrame):
+        if isinstance(frame, InterruptionFrame):
             await self._handle_interruptions(frame)
             await self.push_frame(frame, direction)
         elif isinstance(frame, LLMFullResponseStartFrame):
@@ -645,7 +643,7 @@ class LLMAssistantAggregator(LLMContextAggregator):
         if frame.run_llm:
             await self.push_context_frame(FrameDirection.UPSTREAM)
 
-    async def _handle_interruptions(self, frame: StartInterruptionFrame):
+    async def _handle_interruptions(self, frame: InterruptionFrame):
         await self._push_aggregation()
         self._started = 0
         await self.reset()

src/pipecat/processors/aggregators/vision_image_frame.py

@@ -10,13 +10,22 @@ This module provides frame aggregation functionality to combine text and image
 frames into vision frames for multimodal processing.
 """
 
-from pipecat.frames.frames import Frame, InputImageRawFrame, TextFrame, VisionImageRawFrame
+from pipecat.frames.frames import Frame, InputImageRawFrame, TextFrame
+from pipecat.processors.aggregators.openai_llm_context import (
+    OpenAILLMContext,
+    OpenAILLMContextFrame,
+)
 from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
 
 
 class VisionImageFrameAggregator(FrameProcessor):
     """Aggregates consecutive text and image frames into vision frames.
 
+    .. deprecated:: 0.0.85
+        VisionImageRawFrame has been removed in favor of context frames
+        (LLMContextFrame or OpenAILLMContextFrame), so this aggregator is not
+        needed anymore. See the 12* examples for the new recommended pattern.
+
     This aggregator waits for a consecutive TextFrame and an InputImageRawFrame.
     After the InputImageRawFrame arrives it will output a VisionImageRawFrame
     combining both the text and image data for multimodal processing.
@@ -28,6 +37,17 @@ class VisionImageFrameAggregator(FrameProcessor):
         The aggregator starts with no cached text, waiting for the first
         TextFrame to arrive before it can create vision frames.
         """
+        import warnings
+
+        warnings.warn(
+            "VisionImageFrameAggregator is deprecated. "
+            "VisionImageRawFrame has been removed in favor of context frames "
+            "(LLMContextFrame or OpenAILLMContextFrame), so this aggregator is "
+            "not needed anymore. See the 12* examples for the new recommended "
+            "pattern.",
+            DeprecationWarning,
+            stacklevel=2,
+        )
         super().__init__()
         self._describe_text = None
 
@@ -47,12 +67,14 @@ class VisionImageFrameAggregator(FrameProcessor):
             self._describe_text = frame.text
         elif isinstance(frame, InputImageRawFrame):
             if self._describe_text:
-                frame = VisionImageRawFrame(
+                context = OpenAILLMContext()
+                context.add_image_frame_message(
                     text=self._describe_text,
                     image=frame.image,
                     size=frame.size,
                     format=frame.format,
                 )
+                frame = OpenAILLMContextFrame(context)
                 await self.push_frame(frame)
                 self._describe_text = None
         else:

src/pipecat/processors/audio/audio_buffer_processor.py

@@ -137,12 +137,12 @@ class AudioBufferProcessor(FrameProcessor):
         return self._num_channels
 
     def has_audio(self) -> bool:
-        """Check if both user and bot audio buffers contain data.
+        """Check if either user or bot audio buffers contain data.
 
         Returns:
-            True if both buffers contain audio data.
+            True if either buffer contains audio data.
         """
-        return self._buffer_has_audio(self._user_audio_buffer) and self._buffer_has_audio(
+        return self._buffer_has_audio(self._user_audio_buffer) or self._buffer_has_audio(
             self._bot_audio_buffer
         )
 

src/pipecat/processors/filters/stt_mute_filter.py

@@ -25,8 +25,8 @@ from pipecat.frames.frames import (
     FunctionCallResultFrame,
     InputAudioRawFrame,
     InterimTranscriptionFrame,
+    InterruptionFrame,
     StartFrame,
-    StartInterruptionFrame,
     STTMuteFrame,
     TranscriptionFrame,
     UserStartedSpeakingFrame,
@@ -204,7 +204,7 @@ class STTMuteFilter(FrameProcessor):
         if isinstance(
             frame,
             (
-                StartInterruptionFrame,
+                InterruptionFrame,
                 VADUserStartedSpeakingFrame,
                 VADUserStoppedSpeakingFrame,
                 UserStartedSpeakingFrame,

src/pipecat/processors/frame_processor.py

@@ -28,8 +28,9 @@ from pipecat.frames.frames import (
     FrameProcessorPauseUrgentFrame,
     FrameProcessorResumeFrame,
     FrameProcessorResumeUrgentFrame,
+    InterruptionFrame,
+    InterruptionTaskFrame,
     StartFrame,
-    StartInterruptionFrame,
     SystemFrame,
 )
 from pipecat.metrics.metrics import LLMTokenUsage, MetricsData
@@ -219,6 +220,9 @@ class FrameProcessor(BaseObject):
         self.__process_event: Optional[asyncio.Event] = None
         self.__process_frame_task: Optional[asyncio.Task] = None
 
+        self._wait_for_interruption = False
+        self._wait_interruption_event = asyncio.Event()
+
     @property
     def id(self) -> int:
         """Get the unique identifier for this processor.
@@ -542,6 +546,14 @@ class FrameProcessor(BaseObject):
         if self._cancelling:
             return
 
+        # If we are waiting for an interruption we will bypass all queued system
+        # frames and we will process the frame right away. This is because a
+        # previous system frame might be waiting for the interruption frame and
+        # it's blocking the input task.
+        if self._wait_for_interruption and isinstance(frame, InterruptionFrame):
+            await self.__process_frame(frame, direction, callback)
+            return
+
         if self._enable_direct_mode:
             await self.__process_frame(frame, direction, callback)
         else:
@@ -588,7 +600,7 @@ class FrameProcessor(BaseObject):
 
         if isinstance(frame, StartFrame):
             await self.__start(frame)
-        elif isinstance(frame, StartInterruptionFrame):
+        elif isinstance(frame, InterruptionFrame):
             await self._start_interruption()
             await self.stop_all_metrics()
         elif isinstance(frame, CancelFrame):
@@ -620,6 +632,32 @@ class FrameProcessor(BaseObject):
 
         await self.__internal_push_frame(frame, direction)
 
+        if isinstance(frame, InterruptionFrame):
+            self._wait_interruption_event.set()
+
+    async def push_interruption_task_frame_and_wait(self):
+        """Push an interruption task frame upstream and wait for the interruption.
+
+        This function sends an `InterruptionTaskFrame` upstream to the pipeline
+        task and waits to receive the corresponding `InterruptionFrame`. When
+        the function finishes it is guaranteed that the `InterruptionFrame` has
+        been pushed downstream.
+        """
+        self._wait_for_interruption = True
+
+        await self.push_frame(InterruptionTaskFrame(), FrameDirection.UPSTREAM)
+
+        # Wait for an `InterruptionFrame` to come to this processor and be
+        # pushed. Take a look at `push_frame()` to see how we first push the
+        # `InterruptionFrame` and then we set the event in order to maintain
+        # frame ordering.
+        await self._wait_interruption_event.wait()
+
+        # Clean the event.
+        self._wait_interruption_event.clear()
+
+        self._wait_for_interruption = False
+
     async def __start(self, frame: StartFrame):
         """Handle the start frame to initialize processor state.
 
@@ -669,20 +707,22 @@ class FrameProcessor(BaseObject):
     async def _start_interruption(self):
         """Start handling an interruption by cancelling current tasks."""
         try:
-            # Cancel the process task. This will stop processing queued frames.
-            await self.__cancel_process_task()
+            if self._wait_for_interruption:
+                # If we get here we know the process task was just waiting for
+                # an interruption (push_interruption_task_frame_and_wait()), so
+                # we can't cancel the task because it might still need to do
+                # more things (e.g. pushing a frame after the
+                # interruption). Instead we just drain the queue because this is
+                # an interruption.
+                self.__reset_process_task()
+            else:
+                # Cancel and re-create the process task including the queue.
+                await self.__cancel_process_task()
+                self.__create_process_task()
         except Exception as e:
             logger.exception(f"Uncaught exception in {self} when handling _start_interruption: {e}")
             await self.push_error(ErrorFrame(str(e)))
 
-        # Create a new process queue and task.
-        self.__create_process_task()
-
-    async def _stop_interruption(self):
-        """Stop handling an interruption."""
-        # Nothing to do right now.
-        pass
-
     async def __internal_push_frame(self, frame: Frame, direction: FrameDirection):
         """Internal method to push frames to adjacent processors.
 
@@ -764,6 +804,17 @@ class FrameProcessor(BaseObject):
             self.__process_queue = asyncio.Queue()
             self.__process_frame_task = self.create_task(self.__process_frame_task_handler())
 
+    def __reset_process_task(self):
+        """Reset non-system frame processing task."""
+        if self._enable_direct_mode:
+            return
+
+        self.__should_block_frames = False
+        self.__process_event = asyncio.Event()
+        while not self.__process_queue.empty():
+            self.__process_queue.get_nowait()
+            self.__process_queue.task_done()
+
     async def __cancel_process_task(self):
         """Cancel the non-system frame processing task."""
         if self.__process_frame_task:

src/pipecat/processors/frameworks/rtvi.py

@@ -30,7 +30,6 @@ from loguru import logger
 from pydantic import BaseModel, Field, PrivateAttr, ValidationError
 
 from pipecat.frames.frames import (
-    BotInterruptionFrame,
     BotStartedSpeakingFrame,
     BotStoppedSpeakingFrame,
     CancelFrame,
@@ -1206,7 +1205,7 @@ class RTVIProcessor(FrameProcessor):
 
     async def interrupt_bot(self):
         """Send a bot interruption frame upstream."""
-        await self.push_frame(BotInterruptionFrame(), FrameDirection.UPSTREAM)
+        await self.push_interruption_task_frame_and_wait()
 
     async def send_server_message(self, data: Any):
         """Send a server message to the client."""

src/pipecat/processors/transcript_processor.py

@@ -19,7 +19,7 @@ from pipecat.frames.frames import (
     CancelFrame,
     EndFrame,
     Frame,
-    StartInterruptionFrame,
+    InterruptionFrame,
     TranscriptionFrame,
     TranscriptionMessage,
     TranscriptionUpdateFrame,
@@ -86,7 +86,7 @@ class AssistantTranscriptProcessor(BaseTranscriptProcessor):
     transcript messages. Utterances are completed when:
 
     - The bot stops speaking (BotStoppedSpeakingFrame)
-    - The bot is interrupted (StartInterruptionFrame)
+    - The bot is interrupted (InterruptionFrame)
     - The pipeline ends (EndFrame)
     """
 
@@ -185,7 +185,7 @@ class AssistantTranscriptProcessor(BaseTranscriptProcessor):
 
         - TTSTextFrame: Aggregates text for current utterance
         - BotStoppedSpeakingFrame: Completes current utterance
-        - StartInterruptionFrame: Completes current utterance due to interruption
+        - InterruptionFrame: Completes current utterance due to interruption
         - EndFrame: Completes current utterance at pipeline end
         - CancelFrame: Completes current utterance due to cancellation
 
@@ -195,7 +195,7 @@ class AssistantTranscriptProcessor(BaseTranscriptProcessor):
         """
         await super().process_frame(frame, direction)
 
-        if isinstance(frame, (StartInterruptionFrame, CancelFrame)):
+        if isinstance(frame, (InterruptionFrame, CancelFrame)):
             # Push frame first otherwise our emitted transcription update frame
             # might get cleaned up.
             await self.push_frame(frame, direction)

src/pipecat/runner/types.py

@@ -51,9 +51,11 @@ class WebSocketRunnerArguments(RunnerArguments):
 
     Parameters:
         websocket: WebSocket connection for audio streaming
+        body: Additional request data
     """
 
     websocket: WebSocket
+    body: Optional[Any] = field(default_factory=dict)
 
 
 @dataclass

src/pipecat/runner/utils.py

@@ -99,16 +99,35 @@ async def parse_telephony_websocket(websocket: WebSocket):
         tuple: (transport_type: str, call_data: dict)
 
         call_data contains provider-specific fields:
-        - Twilio: {"stream_id": str, "call_id": str}
-        - Telnyx: {"stream_id": str, "call_control_id": str, "outbound_encoding": str}
-        - Plivo: {"stream_id": str, "call_id": str}
-        - Exotel: {"stream_id": str, "call_id": str, "account_sid": str}
+        - Twilio: {
+            "stream_id": str,
+            "call_id": str,
+            "body": dict
+        }
+        - Telnyx: {
+            "stream_id": str,
+            "call_control_id": str,
+            "outbound_encoding": str,
+            "from": str,
+            "to": str,
+        }
+        - Plivo: {
+            "stream_id": str,
+            "call_id": str,
+        }
+        - Exotel: {
+            "stream_id": str,
+            "call_id": str,
+            "account_sid": str,
+            "from": str,
+            "to": str,
+        }
 
     Example usage::
 
         transport_type, call_data = await parse_telephony_websocket(websocket)
-        if transport_type == "telnyx":
-            outbound_encoding = call_data["outbound_encoding"]
+        if transport_type == "twilio":
+            user_id = call_data["body"]["user_id"]
     """
     # Read first two messages
     start_data = websocket.iter_text()
@@ -151,9 +170,12 @@ async def parse_telephony_websocket(websocket: WebSocket):
         # Extract provider-specific data
         if transport_type == "twilio":
             start_data = call_data_raw.get("start", {})
+            body_data = start_data.get("customParameters", {})
             call_data = {
                 "stream_id": start_data.get("streamSid"),
                 "call_id": start_data.get("callSid"),
+                # All custom parameters
+                "body": body_data,
             }
 
         elif transport_type == "telnyx":
@@ -163,6 +185,8 @@ async def parse_telephony_websocket(websocket: WebSocket):
                 "outbound_encoding": call_data_raw.get("start", {})
                 .get("media_format", {})
                 .get("encoding"),
+                "from": call_data_raw.get("start", {}).get("from", ""),
+                "to": call_data_raw.get("start", {}).get("to", ""),
             }
 
         elif transport_type == "plivo":
@@ -178,6 +202,8 @@ async def parse_telephony_websocket(websocket: WebSocket):
                 "stream_id": start_data.get("stream_sid"),
                 "call_id": start_data.get("call_sid"),
                 "account_sid": start_data.get("account_sid"),
+                "from": start_data.get("from", ""),
+                "to": start_data.get("to", ""),
             }
 
         else:

src/pipecat/serializers/exotel.py

@@ -20,8 +20,8 @@ from pipecat.frames.frames import (
     Frame,
     InputAudioRawFrame,
     InputDTMFFrame,
+    InterruptionFrame,
     StartFrame,
-    StartInterruptionFrame,
     TransportMessageFrame,
     TransportMessageUrgentFrame,
 )
@@ -98,7 +98,7 @@ class ExotelFrameSerializer(FrameSerializer):
         Returns:
             Serialized data as string or bytes, or None if the frame isn't handled.
         """
-        if isinstance(frame, StartInterruptionFrame):
+        if isinstance(frame, InterruptionFrame):
             answer = {"event": "clear", "streamSid": self._stream_sid}
             return json.dumps(answer)
         elif isinstance(frame, AudioRawFrame):

src/pipecat/serializers/plivo.py

@@ -22,8 +22,8 @@ from pipecat.frames.frames import (
     Frame,
     InputAudioRawFrame,
     InputDTMFFrame,
+    InterruptionFrame,
     StartFrame,
-    StartInterruptionFrame,
     TransportMessageFrame,
     TransportMessageUrgentFrame,
 )
@@ -122,7 +122,7 @@ class PlivoFrameSerializer(FrameSerializer):
             self._hangup_attempted = True
             await self._hang_up_call()
             return None
-        elif isinstance(frame, StartInterruptionFrame):
+        elif isinstance(frame, InterruptionFrame):
             answer = {"event": "clearAudio", "streamId": self._stream_id}
             return json.dumps(answer)
         elif isinstance(frame, AudioRawFrame):

src/pipecat/serializers/telnyx.py

@@ -29,8 +29,8 @@ from pipecat.frames.frames import (
     Frame,
     InputAudioRawFrame,
     InputDTMFFrame,
+    InterruptionFrame,
     StartFrame,
-    StartInterruptionFrame,
 )
 from pipecat.serializers.base_serializer import FrameSerializer, FrameSerializerType
 
@@ -137,7 +137,7 @@ class TelnyxFrameSerializer(FrameSerializer):
             self._hangup_attempted = True
             await self._hang_up_call()
             return None
-        elif isinstance(frame, StartInterruptionFrame):
+        elif isinstance(frame, InterruptionFrame):
             answer = {"event": "clear"}
             return json.dumps(answer)
         elif isinstance(frame, AudioRawFrame):

src/pipecat/serializers/twilio.py

@@ -22,8 +22,8 @@ from pipecat.frames.frames import (
     Frame,
     InputAudioRawFrame,
     InputDTMFFrame,
+    InterruptionFrame,
     StartFrame,
-    StartInterruptionFrame,
     TransportMessageFrame,
     TransportMessageUrgentFrame,
 )
@@ -122,7 +122,7 @@ class TwilioFrameSerializer(FrameSerializer):
             self._hangup_attempted = True
             await self._hang_up_call()
             return None
-        elif isinstance(frame, StartInterruptionFrame):
+        elif isinstance(frame, InterruptionFrame):
             answer = {"event": "clear", "streamSid": self._stream_sid}
             return json.dumps(answer)
         elif isinstance(frame, AudioRawFrame):

src/pipecat/services/anthropic/llm.py

@@ -24,7 +24,10 @@ from loguru import logger
 from PIL import Image
 from pydantic import BaseModel, Field
 
-from pipecat.adapters.services.anthropic_adapter import AnthropicLLMAdapter
+from pipecat.adapters.services.anthropic_adapter import (
+    AnthropicLLMAdapter,
+    AnthropicLLMInvocationParams,
+)
 from pipecat.frames.frames import (
     ErrorFrame,
     Frame,
@@ -39,7 +42,6 @@ from pipecat.frames.frames import (
     LLMTextFrame,
     LLMUpdateSettingsFrame,
     UserImageRawFrame,
-    VisionImageRawFrame,
 )
 from pipecat.metrics.metrics import LLMTokenUsage
 from pipecat.processors.aggregators.llm_context import LLMContext
@@ -112,7 +114,12 @@ class AnthropicLLMService(LLMService):
         """Input parameters for Anthropic model inference.
 
         Parameters:
-            enable_prompt_caching_beta: Whether to enable beta prompt caching feature.
+            enable_prompt_caching: Whether to enable the prompt caching feature.
+            enable_prompt_caching_beta (deprecated): Whether to enable the beta prompt caching feature.
+
+                .. deprecated:: 0.0.84
+                    Use the `enable_prompt_caching` parameter instead.
+
             max_tokens: Maximum tokens to generate. Must be at least 1.
             temperature: Sampling temperature between 0.0 and 1.0.
             top_k: Top-k sampling parameter.
@@ -120,13 +127,26 @@ class AnthropicLLMService(LLMService):
             extra: Additional parameters to pass to the API.
         """
 
-        enable_prompt_caching_beta: Optional[bool] = False
+        enable_prompt_caching: Optional[bool] = None
+        enable_prompt_caching_beta: Optional[bool] = None
         max_tokens: Optional[int] = Field(default_factory=lambda: 4096, ge=1)
         temperature: Optional[float] = Field(default_factory=lambda: NOT_GIVEN, ge=0.0, le=1.0)
         top_k: Optional[int] = Field(default_factory=lambda: NOT_GIVEN, ge=0)
         top_p: Optional[float] = Field(default_factory=lambda: NOT_GIVEN, ge=0.0, le=1.0)
         extra: Optional[Dict[str, Any]] = Field(default_factory=dict)
 
+        def model_post_init(self, __context):
+            """Post-initialization to handle deprecated parameters."""
+            if self.enable_prompt_caching_beta is not None:
+                import warnings
+
+                warnings.simplefilter("always")
+                warnings.warn(
+                    "enable_prompt_caching_beta is deprecated. Use enable_prompt_caching instead.",
+                    DeprecationWarning,
+                    stacklevel=2,
+                )
+
     def __init__(
         self,
         *,
@@ -159,7 +179,15 @@ class AnthropicLLMService(LLMService):
         self._retry_on_timeout = retry_on_timeout
         self._settings = {
             "max_tokens": params.max_tokens,
-            "enable_prompt_caching_beta": params.enable_prompt_caching_beta or False,
+            "enable_prompt_caching": (
+                params.enable_prompt_caching
+                if params.enable_prompt_caching is not None
+                else (
+                    params.enable_prompt_caching_beta
+                    if params.enable_prompt_caching_beta is not None
+                    else False
+                )
+            ),
             "temperature": params.temperature,
             "top_k": params.top_k,
             "top_p": params.top_p,
@@ -199,34 +227,28 @@ class AnthropicLLMService(LLMService):
             response = await api_call(**params)
             return response
 
-    async def run_inference(
-        self, context: LLMContext | OpenAILLMContext, system_instruction: Optional[str] = None
-    ) -> Optional[str]:
+    async def run_inference(self, context: LLMContext | OpenAILLMContext) -> Optional[str]:
         """Run a one-shot, out-of-band (i.e. out-of-pipeline) inference with the given LLM context.
 
         Args:
             context: The LLM context containing conversation history.
-            system_instruction: Optional system instruction to guide the LLM's
-              behavior. You could also (again, optionally) provide a system
-              instruction directly in the context. If both are provided, the
-              one in the context takes precedence.
 
         Returns:
             The LLM's response as a string, or None if no response is generated.
         """
         messages = []
-        system = []
+        system = NOT_GIVEN
         if isinstance(context, LLMContext):
-            # Future code will be something like this:
-            # adapter = self.get_llm_adapter()
-            # params: AnthropicLLMInvocationParams = adapter.get_llm_invocation_params(context)
-            # messages = params["messages"]
-            # system = params["system_instruction"]
-            raise NotImplementedError("Universal LLMContext is not yet supported for Anthropic.")
+            adapter: AnthropicLLMAdapter = self.get_llm_adapter()
+            params = adapter.get_llm_invocation_params(
+                context, enable_prompt_caching=self._settings["enable_prompt_caching"]
+            )
+            messages = params["messages"]
+            system = params["system"]
         else:
             context = AnthropicLLMContext.upgrade_to_anthropic(context)
             messages = context.messages
-            system = getattr(context, "system", None) or system_instruction
+            system = getattr(context, "system", NOT_GIVEN)
 
         # LLM completion
         response = await self._client.messages.create(
@@ -239,15 +261,6 @@ class AnthropicLLMService(LLMService):
 
         return response.content[0].text
 
-    @property
-    def enable_prompt_caching_beta(self) -> bool:
-        """Check if prompt caching beta feature is enabled.
-
-        Returns:
-            True if prompt caching is enabled.
-        """
-        return self._enable_prompt_caching_beta
-
     def create_context_aggregator(
         self,
         context: OpenAILLMContext,
@@ -277,8 +290,31 @@ class AnthropicLLMService(LLMService):
         assistant = AnthropicAssistantContextAggregator(context, params=assistant_params)
         return AnthropicContextAggregatorPair(_user=user, _assistant=assistant)
 
+    def _get_llm_invocation_params(
+        self, context: OpenAILLMContext | LLMContext
+    ) -> AnthropicLLMInvocationParams:
+        # Universal LLMContext
+        if isinstance(context, LLMContext):
+            adapter: AnthropicLLMAdapter = self.get_llm_adapter()
+            params = adapter.get_llm_invocation_params(
+                context, enable_prompt_caching=self._settings["enable_prompt_caching"]
+            )
+            return params
+
+        # Anthropic-specific context
+        messages = (
+            context.get_messages_with_cache_control_markers()
+            if self._settings["enable_prompt_caching"]
+            else context.messages
+        )
+        return AnthropicLLMInvocationParams(
+            system=context.system,
+            messages=messages,
+            tools=context.tools or [],
+        )
+
     @traced_llm
-    async def _process_context(self, context: OpenAILLMContext):
+    async def _process_context(self, context: OpenAILLMContext | LLMContext):
         # Usage tracking. We track the usage reported by Anthropic in prompt_tokens and
         # completion_tokens. We also estimate the completion tokens from output text
         # and use that estimate if we are interrupted, because we almost certainly won't
@@ -294,24 +330,22 @@ class AnthropicLLMService(LLMService):
             await self.push_frame(LLMFullResponseStartFrame())
             await self.start_processing_metrics()
 
+            params_from_context = self._get_llm_invocation_params(context)
+
+            if isinstance(context, LLMContext):
+                adapter = self.get_llm_adapter()
+                context_type_for_logging = "universal"
+                messages_for_logging = adapter.get_messages_for_logging(context)
+            else:
+                context_type_for_logging = "LLM-specific"
+                messages_for_logging = context.get_messages_for_logging()
             logger.debug(
-                f"{self}: Generating chat [{context.system}] | {context.get_messages_for_logging()}"
+                f"{self}: Generating chat from {context_type_for_logging} context [{params_from_context['system']}] | {messages_for_logging}"
             )
 
-            messages = context.messages
-            if self._settings["enable_prompt_caching_beta"]:
-                messages = context.get_messages_with_cache_control_markers()
-
-            api_call = self._client.messages.create
-            if self._settings["enable_prompt_caching_beta"]:
-                api_call = self._client.beta.prompt_caching.messages.create
-
             await self.start_ttfb_metrics()
 
             params = {
-                "tools": context.tools or [],
-                "system": context.system,
-                "messages": messages,
                 "model": self.model_name,
                 "max_tokens": self._settings["max_tokens"],
                 "stream": True,
@@ -320,9 +354,12 @@ class AnthropicLLMService(LLMService):
                 "top_p": self._settings["top_p"],
             }
 
+            # Messages, system, tools
+            params.update(params_from_context)
+
             params.update(self._settings["extra"])
 
-            response = await self._create_message_stream(api_call, params)
+            response = await self._create_message_stream(self._client.messages.create, params)
 
             await self.stop_ttfb_metrics()
 
@@ -405,7 +442,10 @@ class AnthropicLLMService(LLMService):
                         prompt_tokens + cache_creation_input_tokens + cache_read_input_tokens
                     )
                     if total_input_tokens >= 1024:
-                        context.turns_above_cache_threshold += 1
+                        if hasattr(
+                            context, "turns_above_cache_threshold"
+                        ):  # LLMContext doesn't have this attribute
+                            context.turns_above_cache_threshold += 1
 
             await self.run_function_calls(function_calls)
 
@@ -451,20 +491,14 @@ class AnthropicLLMService(LLMService):
         if isinstance(frame, OpenAILLMContextFrame):
             context: "AnthropicLLMContext" = AnthropicLLMContext.upgrade_to_anthropic(frame.context)
         elif isinstance(frame, LLMContextFrame):
-            raise NotImplementedError("Universal LLMContext is not yet supported for Anthropic.")
+            context = frame.context
         elif isinstance(frame, LLMMessagesFrame):
             context = AnthropicLLMContext.from_messages(frame.messages)
-        elif isinstance(frame, VisionImageRawFrame):
-            # This is only useful in very simple pipelines because it creates
-            # a new context. Generally we want a context manager to catch
-            # UserImageRawFrames coming through the pipeline and add them
-            # to the context.
-            context = AnthropicLLMContext.from_image_frame(frame)
         elif isinstance(frame, LLMUpdateSettingsFrame):
             await self._update_settings(frame.settings)
         elif isinstance(frame, LLMEnablePromptCachingFrame):
             logger.debug(f"Setting enable prompt caching to: [{frame.enable}]")
-            self._settings["enable_prompt_caching_beta"] = frame.enable
+            self._settings["enable_prompt_caching"] = frame.enable
         else:
             await self.push_frame(frame, direction)
 
@@ -585,22 +619,6 @@ class AnthropicLLMContext(OpenAILLMContext):
         self._restructure_from_openai_messages()
         return self
 
-    @classmethod
-    def from_image_frame(cls, frame: VisionImageRawFrame) -> "AnthropicLLMContext":
-        """Create context from a vision image frame.
-
-        Args:
-            frame: The vision image frame to process.
-
-        Returns:
-            New Anthropic context with the image message.
-        """
-        context = cls()
-        context.add_image_frame_message(
-            format=frame.format, size=frame.size, image=frame.image, text=frame.text
-        )
-        return context
-
     def set_messages(self, messages: List):
         """Set the messages list and reset cache tracking.
 

src/pipecat/services/asyncai/tts.py

@@ -20,8 +20,8 @@ from pipecat.frames.frames import (
     EndFrame,
     ErrorFrame,
     Frame,
+    InterruptionFrame,
     StartFrame,
-    StartInterruptionFrame,
     TTSAudioRawFrame,
     TTSStartedFrame,
     TTSStoppedFrame,
@@ -52,6 +52,10 @@ def language_to_async_language(language: Language) -> Optional[str]:
     """
     BASE_LANGUAGES = {
         Language.EN: "en",
+        Language.FR: "fr",
+        Language.ES: "es",
+        Language.DE: "de",
+        Language.IT: "it",
     }
 
     result = BASE_LANGUAGES.get(language)
@@ -271,7 +275,7 @@ class AsyncAITTSService(InterruptibleTTSService):
             direction: The direction to push the frame.
         """
         await super().push_frame(frame, direction)
-        if isinstance(frame, (TTSStoppedFrame, StartInterruptionFrame)):
+        if isinstance(frame, (TTSStoppedFrame, InterruptionFrame)):
             self._started = False
 
     async def _receive_messages(self):

src/pipecat/services/aws/llm.py

@@ -25,7 +25,10 @@ from loguru import logger
 from PIL import Image
 from pydantic import BaseModel, Field
 
-from pipecat.adapters.services.bedrock_adapter import AWSBedrockLLMAdapter
+from pipecat.adapters.services.bedrock_adapter import (
+    AWSBedrockLLMAdapter,
+    AWSBedrockLLMInvocationParams,
+)
 from pipecat.frames.frames import (
     Frame,
     FunctionCallCancelFrame,
@@ -39,7 +42,6 @@ from pipecat.frames.frames import (
     LLMTextFrame,
     LLMUpdateSettingsFrame,
     UserImageRawFrame,
-    VisionImageRawFrame,
 )
 from pipecat.metrics.metrics import LLMTokenUsage
 from pipecat.processors.aggregators.llm_context import LLMContext
@@ -180,22 +182,6 @@ class AWSBedrockLLMContext(OpenAILLMContext):
         self._restructure_from_openai_messages()
         return self
 
-    @classmethod
-    def from_image_frame(cls, frame: VisionImageRawFrame) -> "AWSBedrockLLMContext":
-        """Create AWS Bedrock context from vision image frame.
-
-        Args:
-            frame: The vision image frame to convert.
-
-        Returns:
-            New AWS Bedrock LLM context instance.
-        """
-        context = cls()
-        context.add_image_frame_message(
-            format=frame.format, size=frame.size, image=frame.image, text=frame.text
-        )
-        return context
-
     def set_messages(self, messages: List):
         """Set the messages list and restructure for Bedrock format.
 
@@ -399,9 +385,33 @@ class AWSBedrockLLMContext(OpenAILLMContext):
         elif isinstance(content, list):
             new_content = []
             for item in content:
+                # fix empty text
                 if item.get("type", "") == "text":
                     text_content = item["text"] if item["text"] != "" else "(empty)"
                     new_content.append({"text": text_content})
+                # handle image_url -> image conversion
+                if item["type"] == "image_url":
+                    new_item = {
+                        "image": {
+                            "format": "jpeg",
+                            "source": {
+                                "bytes": base64.b64decode(item["image_url"]["url"].split(",")[1])
+                            },
+                        }
+                    }
+                    new_content.append(new_item)
+            # In the case where there's a single image in the list (like what
+            # would result from a UserImageRawFrame), ensure that the image
+            # comes before text
+            image_indices = [i for i, item in enumerate(new_content) if "image" in item]
+            text_indices = [i for i, item in enumerate(new_content) if "text" in item]
+            if len(image_indices) == 1 and text_indices:
+                img_idx = image_indices[0]
+                first_txt_idx = text_indices[0]
+                if img_idx > first_txt_idx:
+                    # Move image before the first text
+                    image_item = new_content.pop(img_idx)
+                new_content.insert(first_txt_idx, image_item)
             return {"role": message["role"], "content": new_content}
 
         return message
@@ -569,7 +579,7 @@ class AWSBedrockLLMContext(OpenAILLMContext):
                 if isinstance(msg["content"], list):
                     for item in msg["content"]:
                         if item.get("image"):
-                            item["source"]["bytes"] = "..."
+                            item["image"]["source"]["bytes"] = "..."
             msgs.append(msg)
         return msgs
 
@@ -792,79 +802,64 @@ class AWSBedrockLLMService(LLMService):
         """
         return True
 
-    async def run_inference(
-        self, context: LLMContext | OpenAILLMContext, system_instruction: Optional[str] = None
-    ) -> Optional[str]:
+    async def run_inference(self, context: LLMContext | OpenAILLMContext) -> Optional[str]:
         """Run a one-shot, out-of-band (i.e. out-of-pipeline) inference with the given LLM context.
 
         Args:
             context: The LLM context containing conversation history.
-            system_instruction: Optional system instruction to guide the LLM's
-              behavior. You could also (again, optionally) provide a system
-              instruction directly in the context. If both are provided, the
-              one in the context takes precedence.
 
         Returns:
             The LLM's response as a string, or None if no response is generated.
         """
-        try:
-            messages = []
-            system = []
-            if isinstance(context, LLMContext):
-                # Future code will be something like this:
-                # adapter = self.get_llm_adapter()
-                # params: AWSBedrockLLMInvocationParams = adapter.get_llm_invocation_params(context)
-                # messages = params["messages"]
-                # system = params["system_instruction"]
-                raise NotImplementedError(
-                    "Universal LLMContext is not yet supported for AWS Bedrock."
-                )
-            else:
-                context = AWSBedrockLLMContext.upgrade_to_bedrock(context)
-                messages = context.messages
-                system = getattr(context, "system", None) or system_instruction
+        messages = []
+        system = []
+        if isinstance(context, LLMContext):
+            adapter: AWSBedrockLLMAdapter = self.get_llm_adapter()
+            params: AWSBedrockLLMInvocationParams = adapter.get_llm_invocation_params(context)
+            messages = params["messages"]
+            system = params["system"]  # [{"text": "system message"}]
+        else:
+            context = AWSBedrockLLMContext.upgrade_to_bedrock(context)
+            messages = context.messages
+            system = getattr(context, "system", None)  # [{"text": "system message"}]
 
-            # Determine if we're using Claude or Nova based on model ID
-            model_id = self.model_name
+        # Determine if we're using Claude or Nova based on model ID
+        model_id = self.model_name
 
-            # Prepare request parameters
-            request_params = {
-                "modelId": model_id,
-                "messages": messages,
-                "inferenceConfig": {
-                    "maxTokens": 8192,
-                    "temperature": 0.7,
-                    "topP": 0.9,
-                },
-            }
+        # Prepare request parameters
+        request_params = {
+            "modelId": model_id,
+            "messages": messages,
+            "inferenceConfig": {
+                "maxTokens": 8192,
+                "temperature": 0.7,
+                "topP": 0.9,
+            },
+        }
 
-            if system:
-                request_params["system"] = [{"text": system}]
+        if system:
+            request_params["system"] = system
 
-            async with self._aws_session.client(
-                service_name="bedrock-runtime", **self._aws_params
-            ) as client:
-                # Call Bedrock without streaming
-                response = await client.converse(**request_params)
+        async with self._aws_session.client(
+            service_name="bedrock-runtime", **self._aws_params
+        ) as client:
+            # Call Bedrock without streaming
+            response = await client.converse(**request_params)
 
-                # Extract the response text
-                if (
-                    "output" in response
-                    and "message" in response["output"]
-                    and "content" in response["output"]["message"]
-                ):
-                    content = response["output"]["message"]["content"]
-                    if isinstance(content, list):
-                        for item in content:
-                            if item.get("text"):
-                                return item["text"]
-                    elif isinstance(content, str):
-                        return content
+            # Extract the response text
+            if (
+                "output" in response
+                and "message" in response["output"]
+                and "content" in response["output"]["message"]
+            ):
+                content = response["output"]["message"]["content"]
+                if isinstance(content, list):
+                    for item in content:
+                        if item.get("text"):
+                            return item["text"]
+                elif isinstance(content, str):
+                    return content
 
-                return None
-
-        except Exception as e:
-            logger.error(f"Bedrock summary generation failed: {e}", exc_info=True)
             return None
 
     async def _create_converse_stream(self, client, request_params):
@@ -880,7 +875,7 @@ class AWSBedrockLLMService(LLMService):
         if self._retry_on_timeout:
             try:
                 response = await asyncio.wait_for(
-                    await client.converse_stream(**request_params), timeout=self._retry_timeout_secs
+                    client.converse_stream(**request_params), timeout=self._retry_timeout_secs
                 )
                 return response
             except (ReadTimeoutError, asyncio.TimeoutError) as e:
@@ -939,8 +934,25 @@ class AWSBedrockLLMService(LLMService):
             }
         }
 
+    def _get_llm_invocation_params(
+        self, context: OpenAILLMContext | LLMContext
+    ) -> AWSBedrockLLMInvocationParams:
+        # Universal LLMContext
+        if isinstance(context, LLMContext):
+            adapter: AWSBedrockLLMAdapter = self.get_llm_adapter()
+            params = adapter.get_llm_invocation_params(context)
+            return params
+
+        # AWS Bedrock-specific context
+        return AWSBedrockLLMInvocationParams(
+            system=getattr(context, "system", None),
+            messages=context.messages,
+            tools=context.tools or [],
+            tool_choice=context.tool_choice,
+        )
+
     @traced_llm
-    async def _process_context(self, context: AWSBedrockLLMContext):
+    async def _process_context(self, context: AWSBedrockLLMContext | LLMContext):
         # Usage tracking
         prompt_tokens = 0
         completion_tokens = 0
@@ -957,6 +969,12 @@ class AWSBedrockLLMService(LLMService):
 
             await self.start_ttfb_metrics()
 
+            params_from_context = self._get_llm_invocation_params(context)
+            messages = params_from_context["messages"]
+            system = params_from_context["system"]
+            tools = params_from_context["tools"]
+            tool_choice = params_from_context["tool_choice"]
+
             # Set up inference config
             inference_config = {
                 "maxTokens": self._settings["max_tokens"],
@@ -967,17 +985,18 @@ class AWSBedrockLLMService(LLMService):
             # Prepare request parameters
             request_params = {
                 "modelId": self.model_name,
-                "messages": context.messages,
+                "messages": messages,
                 "inferenceConfig": inference_config,
                 "additionalModelRequestFields": self._settings["additional_model_request_fields"],
             }
 
             # Add system message
-            request_params["system"] = context.system
+            if system:
+                request_params["system"] = system
 
             # Check if messages contain tool use or tool result content blocks
             has_tool_content = False
-            for message in context.messages:
+            for message in messages:
                 if isinstance(message.get("content"), list):
                     for content_item in message["content"]:
                         if "toolUse" in content_item or "toolResult" in content_item:
@@ -987,7 +1006,6 @@ class AWSBedrockLLMService(LLMService):
                     break
 
             # Handle tools: use current tools, or no-op if tool content exists but no current tools
-            tools = context.tools or []
             if has_tool_content and not tools:
                 tools = [self._create_no_op_tool()]
                 using_noop_tool = True
@@ -996,17 +1014,15 @@ class AWSBedrockLLMService(LLMService):
                 tool_config = {"tools": tools}
 
                 # Only add tool_choice if we have real tools (not just no-op)
-                if not using_noop_tool and context.tool_choice:
-                    if context.tool_choice == "auto":
+                if not using_noop_tool and tool_choice:
+                    if tool_choice == "auto":
                         tool_config["toolChoice"] = {"auto": {}}
-                    elif context.tool_choice == "none":
+                    elif tool_choice == "none":
                         # Skip adding toolChoice for "none"
                         pass
-                    elif (
-                        isinstance(context.tool_choice, dict) and "function" in context.tool_choice
-                    ):
+                    elif isinstance(tool_choice, dict) and "function" in tool_choice:
                         tool_config["toolChoice"] = {
-                            "tool": {"name": context.tool_choice["function"]["name"]}
+                            "tool": {"name": tool_choice["function"]["name"]}
                         }
 
                 request_params["toolConfig"] = tool_config
@@ -1015,7 +1031,17 @@ class AWSBedrockLLMService(LLMService):
             if self._settings["latency"] in ["standard", "optimized"]:
                 request_params["performanceConfig"] = {"latency": self._settings["latency"]}
 
-            logger.debug(f"Calling AWS Bedrock model with: {request_params}")
+            # Log request params with messages redacted for logging
+            if isinstance(context, LLMContext):
+                adapter = self.get_llm_adapter()
+                context_type_for_logging = "universal"
+                messages_for_logging = adapter.get_messages_for_logging(context)
+            else:
+                context_type_for_logging = "LLM-specific"
+                messages_for_logging = context.get_messages_for_logging()
+            logger.debug(
+                f"{self}: Generating chat from {context_type_for_logging} context [{system}] | {messages_for_logging}"
+            )
 
             async with self._aws_session.client(
                 service_name="bedrock-runtime", **self._aws_params
@@ -1123,15 +1149,9 @@ class AWSBedrockLLMService(LLMService):
         if isinstance(frame, OpenAILLMContextFrame):
             context = AWSBedrockLLMContext.upgrade_to_bedrock(frame.context)
         if isinstance(frame, LLMContextFrame):
-            raise NotImplementedError("Universal LLMContext is not yet supported for AWS Bedrock.")
+            context = frame.context
         elif isinstance(frame, LLMMessagesFrame):
             context = AWSBedrockLLMContext.from_messages(frame.messages)
-        elif isinstance(frame, VisionImageRawFrame):
-            # This is only useful in very simple pipelines because it creates
-            # a new context. Generally we want a context manager to catch
-            # UserImageRawFrames coming through the pipeline and add them
-            # to the context.
-            context = AWSBedrockLLMContext.from_image_frame(frame)
         elif isinstance(frame, LLMUpdateSettingsFrame):
             await self._update_settings(frame.settings)
         else:

src/pipecat/services/aws_nova_sonic/aws.py

@@ -247,13 +247,14 @@ class AWSNovaSonicLLMService(LLMService):
         self._ready_to_send_context = False
         self._handling_bot_stopped_speaking = False
         self._triggering_assistant_response = False
-        self._assistant_response_trigger_audio: Optional[bytes] = (
-            None  # Not cleared on _disconnect()
-        )
         self._disconnecting = False
         self._connected_time: Optional[float] = None
         self._wants_connection = False
 
+        file_path = files("pipecat.services.aws_nova_sonic").joinpath("ready.wav")
+        with wave.open(file_path.open("rb"), "rb") as wav_file:
+            self._assistant_response_trigger_audio = wav_file.readframes(wav_file.getnframes())
+
     #
     # standard AIService frame handling
     #
@@ -1099,20 +1100,13 @@ class AWSNovaSonicLLMService(LLMService):
 
         self._triggering_assistant_response = True
 
-        # Read audio bytes, if we don't already have them cached
-        if not self._assistant_response_trigger_audio:
-            file_path = files("pipecat.services.aws_nova_sonic").joinpath("ready.wav")
-            with wave.open(file_path.open("rb"), "rb") as wav_file:
-                self._assistant_response_trigger_audio = wav_file.readframes(wav_file.getnframes())
-
         # Send the trigger audio, if we're fully connected and set up
-        if self._connected_time is not None:
+        if self._connected_time:
             await self._send_assistant_response_trigger()
 
     async def _send_assistant_response_trigger(self):
-        if (
-            not self._assistant_response_trigger_audio or self._connected_time is None
-        ):  # should never happen
+        if not self._connected_time:
+            # should never happen
             return
 
         try:

src/pipecat/services/aws_nova_sonic/context.py

@@ -21,13 +21,13 @@ from pipecat.frames.frames import (
     DataFrame,
     Frame,
     FunctionCallResultFrame,
+    InterruptionFrame,
     LLMFullResponseEndFrame,
     LLMFullResponseStartFrame,
     LLMMessagesAppendFrame,
     LLMMessagesUpdateFrame,
     LLMSetToolChoiceFrame,
     LLMSetToolsFrame,
-    StartInterruptionFrame,
     TextFrame,
     UserImageRawFrame,
 )
@@ -306,7 +306,7 @@ class AWSNovaSonicAssistantContextAggregator(OpenAIAssistantContextAggregator):
         if isinstance(
             frame,
             (
-                StartInterruptionFrame,
+                InterruptionFrame,
                 LLMFullResponseStartFrame,
                 LLMFullResponseEndFrame,
                 TextFrame,

src/pipecat/services/azure/stt.py

@@ -19,6 +19,7 @@ from pipecat.frames.frames import (
     CancelFrame,
     EndFrame,
     Frame,
+    InterimTranscriptionFrame,
     StartFrame,
     TranscriptionFrame,
 )
@@ -140,6 +141,7 @@ class AzureSTTService(STTService):
         self._speech_recognizer = SpeechRecognizer(
             speech_config=self._speech_config, audio_config=audio_config
         )
+        self._speech_recognizer.recognizing.connect(self._on_handle_recognizing)
         self._speech_recognizer.recognized.connect(self._on_handle_recognized)
         self._speech_recognizer.start_continuous_recognition_async()
 
@@ -197,3 +199,15 @@ class AzureSTTService(STTService):
                 self._handle_transcription(event.result.text, True, language), self.get_event_loop()
             )
             asyncio.run_coroutine_threadsafe(self.push_frame(frame), self.get_event_loop())
+
+    def _on_handle_recognizing(self, event):
+        if event.result.reason == ResultReason.RecognizingSpeech and len(event.result.text) > 0:
+            language = getattr(event.result, "language", None) or self._settings.get("language")
+            frame = InterimTranscriptionFrame(
+                event.result.text,
+                self._user_id,
+                time_now_iso8601(),
+                language,
+                result=event,
+            )
+            asyncio.run_coroutine_threadsafe(self.push_frame(frame), self.get_event_loop())

src/pipecat/services/cartesia/tts.py

@@ -20,8 +20,8 @@ from pipecat.frames.frames import (
     EndFrame,
     ErrorFrame,
     Frame,
+    InterruptionFrame,
     StartFrame,
-    StartInterruptionFrame,
     TTSAudioRawFrame,
     TTSStartedFrame,
     TTSStoppedFrame,
@@ -371,7 +371,7 @@ class CartesiaTTSService(AudioContextWordTTSService):
             return self._websocket
         raise Exception("Websocket not connected")
 
-    async def _handle_interruption(self, frame: StartInterruptionFrame, direction: FrameDirection):
+    async def _handle_interruption(self, frame: InterruptionFrame, direction: FrameDirection):
         await super()._handle_interruption(frame, direction)
         await self.stop_all_metrics()
         if self._context_id:

src/pipecat/services/elevenlabs/tts.py

@@ -25,9 +25,9 @@ from pipecat.frames.frames import (
     EndFrame,
     ErrorFrame,
     Frame,
+    InterruptionFrame,
     LLMFullResponseEndFrame,
     StartFrame,
-    StartInterruptionFrame,
     TTSAudioRawFrame,
     TTSStartedFrame,
     TTSStoppedFrame,
@@ -460,7 +460,7 @@ class ElevenLabsTTSService(AudioContextWordTTSService):
             direction: The direction to push the frame.
         """
         await super().push_frame(frame, direction)
-        if isinstance(frame, (TTSStoppedFrame, StartInterruptionFrame)):
+        if isinstance(frame, (TTSStoppedFrame, InterruptionFrame)):
             self._started = False
             if isinstance(frame, TTSStoppedFrame):
                 await self.add_word_timestamps([("Reset", 0)])
@@ -549,7 +549,7 @@ class ElevenLabsTTSService(AudioContextWordTTSService):
             return self._websocket
         raise Exception("Websocket not connected")
 
-    async def _handle_interruption(self, frame: StartInterruptionFrame, direction: FrameDirection):
+    async def _handle_interruption(self, frame: InterruptionFrame, direction: FrameDirection):
         """Handle interruption by closing the current context."""
         await super()._handle_interruption(frame, direction)
 
@@ -558,7 +558,7 @@ class ElevenLabsTTSService(AudioContextWordTTSService):
             logger.trace(f"Closing context {self._context_id} due to interruption")
             try:
                 # ElevenLabs requires that Pipecat manages the contexts and closes them
-                # when they're not longer in use. Since a StartInterruptionFrame is pushed
+                # when they're not longer in use. Since an InterruptionFrame is pushed
                 # every time the user speaks, we'll use this as a trigger to close the context
                 # and reset the state.
                 # Note: We do not need to call remove_audio_context here, as the context is
@@ -856,7 +856,7 @@ class ElevenLabsHttpTTSService(WordTTSService):
             direction: The direction to push the frame.
         """
         await super().push_frame(frame, direction)
-        if isinstance(frame, (StartInterruptionFrame, TTSStoppedFrame)):
+        if isinstance(frame, (InterruptionFrame, TTSStoppedFrame)):
             # Reset timing on interruption or stop
             self._reset_state()
 

src/pipecat/services/fish/tts.py

@@ -21,8 +21,8 @@ from pipecat.frames.frames import (
     EndFrame,
     ErrorFrame,
     Frame,
+    InterruptionFrame,
     StartFrame,
-    StartInterruptionFrame,
     TTSAudioRawFrame,
     TTSStartedFrame,
     TTSStoppedFrame,
@@ -259,7 +259,7 @@ class FishAudioTTSService(InterruptibleTTSService):
             return self._websocket
         raise Exception("Websocket not connected")
 
-    async def _handle_interruption(self, frame: StartInterruptionFrame, direction: FrameDirection):
+    async def _handle_interruption(self, frame: InterruptionFrame, direction: FrameDirection):
         await super()._handle_interruption(frame, direction)
         await self.stop_all_metrics()
         self._request_id = None

src/pipecat/services/gemini_multimodal_live/gemini.py

@@ -33,6 +33,8 @@ from pipecat.frames.frames import (
     InputAudioRawFrame,
     InputImageRawFrame,
     InputTextRawFrame,
+    InterruptionFrame,
+    LLMContextFrame,
     LLMFullResponseEndFrame,
     LLMFullResponseStartFrame,
     LLMMessagesAppendFrame,
@@ -40,7 +42,6 @@ from pipecat.frames.frames import (
     LLMTextFrame,
     LLMUpdateSettingsFrame,
     StartFrame,
-    StartInterruptionFrame,
     TranscriptionFrame,
     TTSAudioRawFrame,
     TTSStartedFrame,
@@ -738,6 +739,10 @@ class GeminiMultimodalLiveLLMService(LLMService):
                 # Support just one tool call per context frame for now
                 tool_result_message = context.messages[-1]
                 await self._tool_result(tool_result_message)
+        elif isinstance(frame, LLMContextFrame):
+            raise NotImplementedError(
+                "Universal LLMContext is not yet supported for Gemini Multimodal Live."
+            )
         elif isinstance(frame, InputTextRawFrame):
             await self._send_user_text(frame.text)
             await self.push_frame(frame, direction)
@@ -747,7 +752,7 @@ class GeminiMultimodalLiveLLMService(LLMService):
         elif isinstance(frame, InputImageRawFrame):
             await self._send_user_video(frame)
             await self.push_frame(frame, direction)
-        elif isinstance(frame, StartInterruptionFrame):
+        elif isinstance(frame, InterruptionFrame):
             await self._handle_interruption()
             await self.push_frame(frame, direction)
         elif isinstance(frame, UserStartedSpeakingFrame):

src/pipecat/services/google/llm.py

@@ -36,7 +36,6 @@ from pipecat.frames.frames import (
     LLMTextFrame,
     LLMUpdateSettingsFrame,
     UserImageRawFrame,
-    VisionImageRawFrame,
 )
 from pipecat.metrics.metrics import LLMTokenUsage
 from pipecat.processors.aggregators.llm_context import LLMContext
@@ -733,17 +732,11 @@ class GoogleLLMService(LLMService):
     def _create_client(self, api_key: str, http_options: Optional[HttpOptions] = None):
         self._client = genai.Client(api_key=api_key, http_options=http_options)
 
-    async def run_inference(
-        self, context: LLMContext | OpenAILLMContext, system_instruction: Optional[str] = None
-    ) -> Optional[str]:
+    async def run_inference(self, context: LLMContext | OpenAILLMContext) -> Optional[str]:
         """Run a one-shot, out-of-band (i.e. out-of-pipeline) inference with the given LLM context.
 
         Args:
             context: The LLM context containing conversation history.
-            system_instruction: Optional system instruction to guide the LLM's
-              behavior. You could also (again, optionally) provide a system
-              instruction directly in the context. If both are provided, the
-              one in the context takes precedence.
 
         Returns:
             The LLM's response as a string, or None if no response is generated.
@@ -758,7 +751,7 @@ class GoogleLLMService(LLMService):
         else:
             context = GoogleLLMContext.upgrade_to_google(context)
             messages = context.messages
-            system = getattr(context, "system_message", None) or system_instruction
+            system = getattr(context, "system_message", None)
 
         generation_config = GenerateContentConfig(system_instruction=system)
 
@@ -858,8 +851,7 @@ class GoogleLLMService(LLMService):
         self, context: OpenAILLMContext
     ) -> AsyncIterator[GenerateContentResponse]:
         logger.debug(
-            # f"{self}: Generating chat [{self._system_instruction}] | {context.get_messages_for_logging()}"
-            f"{self}: Generating chat from OpenAI context {context.get_messages_for_logging()}"
+            f"{self}: Generating chat from LLM-specific context [{context.system_message}] | {context.get_messages_for_logging()}"
         )
 
         params = GeminiLLMInvocationParams(
@@ -874,13 +866,12 @@ class GoogleLLMService(LLMService):
         self, context: LLMContext
     ) -> AsyncIterator[GenerateContentResponse]:
         adapter = self.get_llm_adapter()
-        logger.debug(
-            # f"{self}: Generating chat [{self._system_instruction}] | {context.get_messages_for_logging()}"
-            f"{self}: Generating chat from universal context {adapter.get_messages_for_logging(context)}"
-        )
-
         params: GeminiLLMInvocationParams = adapter.get_llm_invocation_params(context)
 
+        logger.debug(
+            f"{self}: Generating chat from universal context [{params['system_instruction']}] | {adapter.get_messages_for_logging(context)}"
+        )
+
         return await self._stream_content(params)
 
     @traced_llm
@@ -1021,15 +1012,6 @@ class GoogleLLMService(LLMService):
             # NOTE: LLMMessagesFrame is deprecated, so we don't support the newer universal
             # LLMContext with it
             context = GoogleLLMContext(frame.messages)
-        elif isinstance(frame, VisionImageRawFrame):
-            # This is only useful in very simple pipelines because it creates
-            # a new context. Generally we want a context manager to catch
-            # UserImageRawFrames coming through the pipeline and add them
-            # to the context.
-            context = GoogleLLMContext()
-            context.add_image_frame_message(
-                format=frame.format, size=frame.size, image=frame.image, text=frame.text
-            )
         elif isinstance(frame, LLMUpdateSettingsFrame):
             await self._update_settings(frame.settings)
         else:

src/pipecat/services/google/tts.py

@@ -500,9 +500,11 @@ class GoogleTTSService(TTSService):
 
         Parameters:
             language: Language for synthesis. Defaults to English.
+            speaking_rate: The speaking rate, in the range [0.25, 4.0].
         """
 
         language: Optional[Language] = Language.EN
+        speaking_rate: Optional[float] = None
 
     def __init__(
         self,
@@ -510,6 +512,7 @@ class GoogleTTSService(TTSService):
         credentials: Optional[str] = None,
         credentials_path: Optional[str] = None,
         voice_id: str = "en-US-Chirp3-HD-Charon",
+        voice_cloning_key: Optional[str] = None,
         sample_rate: Optional[int] = None,
         params: InputParams = InputParams(),
         **kwargs,
@@ -520,6 +523,7 @@ class GoogleTTSService(TTSService):
             credentials: JSON string containing Google Cloud service account credentials.
             credentials_path: Path to Google Cloud service account JSON file.
             voice_id: Google TTS voice identifier (e.g., "en-US-Chirp3-HD-Charon").
+            voice_cloning_key: The voice cloning key for Chirp 3 custom voices.
             sample_rate: Audio sample rate in Hz. If None, uses default.
             params: Language configuration parameters.
             **kwargs: Additional arguments passed to parent TTSService.
@@ -532,8 +536,10 @@ class GoogleTTSService(TTSService):
             "language": self.language_to_service_language(params.language)
             if params.language
             else "en-US",
+            "speaking_rate": params.speaking_rate,
         }
         self.set_voice(voice_id)
+        self._voice_cloning_key = voice_cloning_key
         self._client: texttospeech_v1.TextToSpeechAsyncClient = self._create_client(
             credentials, credentials_path
         )
@@ -600,15 +606,24 @@ class GoogleTTSService(TTSService):
         try:
             await self.start_ttfb_metrics()
 
-            voice = texttospeech_v1.VoiceSelectionParams(
-                language_code=self._settings["language"], name=self._voice_id
-            )
+            if self._voice_cloning_key:
+                voice_clone_params = texttospeech_v1.VoiceCloneParams(
+                    voice_cloning_key=self._voice_cloning_key
+                )
+                voice = texttospeech_v1.VoiceSelectionParams(
+                    language_code=self._settings["language"], voice_clone=voice_clone_params
+                )
+            else:
+                voice = texttospeech_v1.VoiceSelectionParams(
+                    language_code=self._settings["language"], name=self._voice_id
+                )
 
             streaming_config = texttospeech_v1.StreamingSynthesizeConfig(
                 voice=voice,
                 streaming_audio_config=texttospeech_v1.StreamingAudioConfig(
                     audio_encoding=texttospeech_v1.AudioEncoding.PCM,
                     sample_rate_hertz=self.sample_rate,
+                    speaking_rate=self._settings["speaking_rate"],
                 ),
             )
             config_request = texttospeech_v1.StreamingSynthesizeRequest(

src/pipecat/services/heygen/video.py

@@ -240,6 +240,7 @@ class HeyGenVideoService(AIService):
             # As soon as we receive actual audio, the base output transport will create a
             # BotStartedSpeakingFrame, which we can use as a signal for the TTFB metrics.
             await self.stop_ttfb_metrics()
+            await self.push_frame(frame, direction)
         else:
             await self.push_frame(frame, direction)
 

src/pipecat/services/llm_service.py

@@ -36,15 +36,15 @@ from pipecat.frames.frames import (
     FunctionCallResultFrame,
     FunctionCallResultProperties,
     FunctionCallsStartedFrame,
+    InterruptionFrame,
     LLMConfigureOutputFrame,
     LLMFullResponseEndFrame,
     LLMFullResponseStartFrame,
     LLMTextFrame,
     StartFrame,
-    StartInterruptionFrame,
     UserImageRequestFrame,
 )
-from pipecat.processors.aggregators.llm_context import LLMContext
+from pipecat.processors.aggregators.llm_context import LLMContext, LLMSpecificMessage
 from pipecat.processors.aggregators.llm_response import (
     LLMAssistantAggregatorParams,
     LLMUserAggregatorParams,
@@ -195,18 +195,24 @@ class LLMService(AIService):
         """
         return self._adapter
 
-    async def run_inference(
-        self, context: LLMContext | OpenAILLMContext, system_instruction: Optional[str] = None
-    ) -> Optional[str]:
+    def create_llm_specific_message(self, message: Any) -> LLMSpecificMessage:
+        """Create an LLM-specific message (as opposed to a standard message) for use in an LLMContext.
+
+        Args:
+            message: The message content.
+
+        Returns:
+            A LLMSpecificMessage instance.
+        """
+        return self.get_llm_adapter().create_llm_specific_message(message)
+
+    async def run_inference(self, context: LLMContext | OpenAILLMContext) -> Optional[str]:
         """Run a one-shot, out-of-band (i.e. out-of-pipeline) inference with the given LLM context.
 
         Must be implemented by subclasses.
 
         Args:
             context: The LLM context containing conversation history.
-            system_instruction: Optional system instruction to guide the LLM's
-              behavior. You could also (again, optionally) provide a system
-              instruction directly in the context.
 
         Returns:
             The LLM's response as a string, or None if no response is generated.
@@ -274,7 +280,7 @@ class LLMService(AIService):
         """
         await super().process_frame(frame, direction)
 
-        if isinstance(frame, StartInterruptionFrame):
+        if isinstance(frame, InterruptionFrame):
             await self._handle_interruptions(frame)
         elif isinstance(frame, LLMConfigureOutputFrame):
             self._skip_tts = frame.skip_tts
@@ -291,7 +297,7 @@ class LLMService(AIService):
 
         await super().push_frame(frame, direction)
 
-    async def _handle_interruptions(self, _: StartInterruptionFrame):
+    async def _handle_interruptions(self, _: InterruptionFrame):
         for function_name, entry in self._functions.items():
             if entry.cancel_on_interruption:
                 await self._cancel_function_call(function_name)

src/pipecat/services/lmnt/tts.py

@@ -16,8 +16,8 @@ from pipecat.frames.frames import (
     EndFrame,
     ErrorFrame,
     Frame,
+    InterruptionFrame,
     StartFrame,
-    StartInterruptionFrame,
     TTSAudioRawFrame,
     TTSStartedFrame,
     TTSStoppedFrame,
@@ -180,7 +180,7 @@ class LmntTTSService(InterruptibleTTSService):
             direction: The direction to push the frame.
         """
         await super().push_frame(frame, direction)
-        if isinstance(frame, (TTSStoppedFrame, StartInterruptionFrame)):
+        if isinstance(frame, (TTSStoppedFrame, InterruptionFrame)):
             self._started = False
 
     async def _connect(self):

src/pipecat/services/mistral/llm.py

@@ -57,16 +57,18 @@ class MistralLLMService(OpenAILLMService):
         logger.debug(f"Creating Mistral client with api {base_url}")
         return super().create_client(api_key, base_url, **kwargs)
 
-    def _apply_mistral_assistant_prefix(
+    def _apply_mistral_fixups(
         self, messages: List[ChatCompletionMessageParam]
     ) -> List[ChatCompletionMessageParam]:
-        """Apply Mistral's assistant message prefix requirement.
+        """Apply fixups to messages to meet Mistral-specific requirements.
 
-        Mistral requires assistant messages to have prefix=True when they
-        are the final message in a conversation. According to Mistral's API:
-        - Assistant messages with prefix=True MUST be the last message
-        - Only add prefix=True to the final assistant message when needed
-        - This allows assistant messages to be accepted as the last message
+        1. A "tool"-role message must be followed by an assistant message.
+
+        2. "system"-role messages must only appear at the start of a
+           conversation.
+
+        3. Assistant messages must have prefix=True when they are the final
+           message in a conversation (but at no other point).
 
         Args:
             messages: The original list of messages.
@@ -80,6 +82,25 @@ class MistralLLMService(OpenAILLMService):
         # Create a copy to avoid modifying the original
         fixed_messages = [dict(msg) for msg in messages]
 
+        # Ensure all tool responses are followed by an assistant message
+        assistant_insert_indices = []
+        for i, msg in enumerate(fixed_messages):
+            if msg.get("role") == "tool":
+                # If this is the last message or the next message is not assistant
+                if i == len(fixed_messages) - 1 or fixed_messages[i + 1].get("role") != "assistant":
+                    assistant_insert_indices.append(i + 1)
+        for idx in reversed(assistant_insert_indices):
+            fixed_messages.insert(idx, {"role": "assistant", "content": " "})
+
+        # Convert any "system" messages that aren't at the start (i.e., after the initial contiguous block) to "user"
+        first_non_system_idx = next(
+            (i for i, msg in enumerate(fixed_messages) if msg.get("role") != "system"),
+            len(fixed_messages),
+        )
+        for i, msg in enumerate(fixed_messages):
+            if msg.get("role") == "system" and i >= first_non_system_idx:
+                msg["role"] = "user"
+
         # Get the last message
         last_message = fixed_messages[-1]
 
@@ -158,7 +179,7 @@ class MistralLLMService(OpenAILLMService):
         - Core completion settings
         """
         # Apply Mistral's assistant prefix requirement for API compatibility
-        fixed_messages = self._apply_mistral_assistant_prefix(params_from_context["messages"])
+        fixed_messages = self._apply_mistral_fixups(params_from_context["messages"])
 
         params = {
             "model": self.model_name,

src/pipecat/services/moondream/vision.py

@@ -11,17 +11,20 @@ for image analysis and description generation.
 """
 
 import asyncio
-from typing import AsyncGenerator
+import base64
+from io import BytesIO
+from typing import AsyncGenerator, Optional
 
 from loguru import logger
 from PIL import Image
 
-from pipecat.frames.frames import ErrorFrame, Frame, TextFrame, VisionImageRawFrame
+from pipecat.frames.frames import ErrorFrame, Frame, TextFrame
+from pipecat.processors.aggregators.llm_context import LLMContext
 from pipecat.services.vision_service import VisionService
 
 try:
     import torch
-    from transformers import AutoModelForCausalLM, AutoTokenizer
+    from transformers import AutoModelForCausalLM
 except ModuleNotFoundError as e:
     logger.error(f"Exception: {e}")
     logger.error("In order to use Moondream, you need to `pip install pipecat-ai[moondream]`.")
@@ -94,11 +97,11 @@ class MoondreamService(VisionService):
 
         logger.debug("Loaded Moondream model")
 
-    async def run_vision(self, frame: VisionImageRawFrame) -> AsyncGenerator[Frame, None]:
+    async def run_vision(self, context: LLMContext) -> AsyncGenerator[Frame, None]:
         """Analyze an image and generate a description.
 
         Args:
-            frame: Vision frame containing the image data and optional question text.
+            context: The context to process, containing image data.
 
         Yields:
             Frame: TextFrame containing the generated image description, or ErrorFrame
@@ -109,22 +112,45 @@ class MoondreamService(VisionService):
             yield ErrorFrame("Moondream model not available")
             return
 
-        logger.debug(f"Analyzing image: {frame}")
+        image_bytes = None
+        text = None
+        try:
+            messages = context.get_messages()
+            last_message = messages[-1]
+            last_message_content = last_message.get("content")
 
-        def get_image_description(frame: VisionImageRawFrame):
-            """Generate description for the given image frame.
+            for item in last_message_content:
+                if isinstance(item, dict):
+                    if (
+                        "image_url" in item
+                        and isinstance(item["image_url"], dict)
+                        and item["image_url"].get("url")
+                    ):
+                        image_bytes = base64.b64decode(item["image_url"]["url"].split(",")[1])
+                    elif "text" in item and isinstance(item["text"], str):
+                        text = item["text"]
 
-            Args:
-                frame: Vision frame containing image data and question.
+        except Exception as e:
+            logger.error(f"Exception during image extraction: {e}")
+            yield ErrorFrame("Failed to extract image from context")
+            return
 
-            Returns:
-                str: Generated description of the image.
-            """
-            image = Image.frombytes(frame.format, frame.size, frame.image)
+        if not image_bytes:
+            logger.error("No image found in context")
+            yield ErrorFrame("No image found in context")
+            return
+
+        logger.debug(
+            f"Analyzing image (bytes length: {len(image_bytes) if image_bytes else 'None'})"
+        )
+
+        def get_image_description(bytes: bytes, text: Optional[str]) -> str:
+            image_buffer = BytesIO(bytes)
+            image = Image.open(image_buffer)
             image_embeds = self._model.encode_image(image)
-            description = self._model.query(image_embeds, frame.text)["answer"]
+            description = self._model.query(image_embeds, text)["answer"]
             return description
 
-        description = await asyncio.to_thread(get_image_description, frame)
+        description = await asyncio.to_thread(get_image_description, image_bytes, text)
 
         yield TextFrame(text=description)

src/pipecat/services/neuphonic/tts.py

@@ -25,9 +25,9 @@ from pipecat.frames.frames import (
     EndFrame,
     ErrorFrame,
     Frame,
+    InterruptionFrame,
     LLMFullResponseEndFrame,
     StartFrame,
-    StartInterruptionFrame,
     TTSAudioRawFrame,
     TTSSpeakFrame,
     TTSStartedFrame,
@@ -224,7 +224,7 @@ class NeuphonicTTSService(InterruptibleTTSService):
             direction: The direction to push the frame.
         """
         await super().push_frame(frame, direction)
-        if isinstance(frame, (TTSStoppedFrame, StartInterruptionFrame)):
+        if isinstance(frame, (TTSStoppedFrame, InterruptionFrame)):
             self._started = False
 
     async def process_frame(self, frame: Frame, direction: FrameDirection):

src/pipecat/services/openai/base_llm.py

@@ -32,7 +32,6 @@ from pipecat.frames.frames import (
     LLMMessagesFrame,
     LLMTextFrame,
     LLMUpdateSettingsFrame,
-    VisionImageRawFrame,
 )
 from pipecat.metrics.metrics import LLMTokenUsage
 from pipecat.processors.aggregators.llm_context import LLMContext
@@ -245,16 +244,11 @@ class BaseOpenAILLMService(LLMService):
         params.update(self._settings["extra"])
         return params
 
-    async def run_inference(
-        self, context: LLMContext | OpenAILLMContext, system_instruction: Optional[str] = None
-    ) -> Optional[str]:
+    async def run_inference(self, context: LLMContext | OpenAILLMContext) -> Optional[str]:
         """Run a one-shot, out-of-band (i.e. out-of-pipeline) inference with the given LLM context.
 
         Args:
             context: The LLM context containing conversation history.
-            system_instruction: Optional system instruction to guide the LLM's
-              behavior. You could also (again, optionally) provide a system
-              instruction directly in the context.
 
         Returns:
             The LLM's response as a string, or None if no response is generated.
@@ -279,7 +273,7 @@ class BaseOpenAILLMService(LLMService):
         self, context: OpenAILLMContext
     ) -> AsyncStream[ChatCompletionChunk]:
         logger.debug(
-            f"{self}: Generating chat from OpenAI context {context.get_messages_for_logging()}"
+            f"{self}: Generating chat from LLM-specific context {context.get_messages_for_logging()}"
         )
 
         messages: List[ChatCompletionMessageParam] = context.get_messages()
@@ -423,8 +417,8 @@ class BaseOpenAILLMService(LLMService):
         """Process frames for LLM completion requests.
 
         Handles OpenAILLMContextFrame, LLMContextFrame, LLMMessagesFrame,
-        VisionImageRawFrame, and LLMUpdateSettingsFrame to trigger LLM
-        completions and manage settings.
+        and LLMUpdateSettingsFrame to trigger LLM completions and manage
+        settings.
 
         Args:
             frame: The frame to process.
@@ -443,16 +437,6 @@ class BaseOpenAILLMService(LLMService):
             # NOTE: LLMMessagesFrame is deprecated, so we don't support the newer universal
             # LLMContext with it
             context = OpenAILLMContext.from_messages(frame.messages)
-        elif isinstance(frame, VisionImageRawFrame):
-            # This is only useful in very simple pipelines because it creates
-            # a new context. Generally we want a context manager to catch
-            # UserImageRawFrames coming through the pipeline and add them
-            # to the context.
-            # TODO: support the newer universal LLMContext with a VisionImageRawFrame equivalent?
-            context = OpenAILLMContext()
-            context.add_image_frame_message(
-                format=frame.format, size=frame.size, image=frame.image, text=frame.text
-            )
         elif isinstance(frame, LLMUpdateSettingsFrame):
             await self._update_settings(frame.settings)
         else:

src/pipecat/services/openai/image.py

@@ -84,5 +84,10 @@ class OpenAIImageGenService(ImageGenService):
         async with self._aiohttp_session.get(image_url) as response:
             image_stream = io.BytesIO(await response.content.read())
             image = Image.open(image_stream)
-            frame = URLImageRawFrame(image_url, image.tobytes(), image.size, image.format)
+            frame = URLImageRawFrame(
+                image=image.tobytes(),
+                size=image.size,
+                format=image.format,
+                url=image_url,
+            )
             yield frame

src/pipecat/services/openai/tts.py

@@ -64,6 +64,7 @@ class OpenAITTSService(TTSService):
         model: str = "gpt-4o-mini-tts",
         sample_rate: Optional[int] = None,
         instructions: Optional[str] = None,
+        speed: Optional[float] = None,
         **kwargs,
     ):
         """Initialize OpenAI TTS service.
@@ -75,6 +76,7 @@ class OpenAITTSService(TTSService):
             model: TTS model to use. Defaults to "gpt-4o-mini-tts".
             sample_rate: Output audio sample rate in Hz. If None, uses OpenAI's default 24kHz.
             instructions: Optional instructions to guide voice synthesis behavior.
+            speed: Voice speed control (0.25 to 4.0, default 1.0).
             **kwargs: Additional keyword arguments passed to TTSService.
         """
         if sample_rate and sample_rate != self.OPENAI_SAMPLE_RATE:
@@ -84,6 +86,7 @@ class OpenAITTSService(TTSService):
             )
         super().__init__(sample_rate=sample_rate, **kwargs)
 
+        self._speed = speed
         self.set_model_name(model)
         self.set_voice(voice)
         self._instructions = instructions
@@ -133,17 +136,22 @@ class OpenAITTSService(TTSService):
         try:
             await self.start_ttfb_metrics()
 
-            # Setup extra body parameters
-            extra_body = {}
+            # Setup API parameters
+            create_params = {
+                "input": text,
+                "model": self.model_name,
+                "voice": VALID_VOICES[self._voice_id],
+                "response_format": "pcm",
+            }
+
             if self._instructions:
-                extra_body["instructions"] = self._instructions
+                create_params["instructions"] = self._instructions
+
+            if self._speed:
+                create_params["speed"] = self._speed
 
             async with self._client.audio.speech.with_streaming_response.create(
-                input=text,
-                model=self.model_name,
-                voice=VALID_VOICES[self._voice_id],
-                response_format="pcm",
-                extra_body=extra_body,
+                **create_params
             ) as r:
                 if r.status_code != 200:
                     error = await r.text()

src/pipecat/services/openai_agent/README.md

@@ -0,0 +1,209 @@
+# OpenAI Agents SDK Integration
+
+This service integrates the [OpenAI Agents SDK](https://openai.github.io/openai-agents-python/) with Pipecat, enabling powerful agentic workflows with features like:
+
+- **Agent loops** with tool calling and response streaming
+- **Handoffs** between specialized agents  
+- **Guardrails** for input/output validation
+- **Sessions** with automatic conversation history
+- **Built-in tracing** and monitoring
+
+## Installation
+
+Install the OpenAI Agents SDK dependency:
+
+```bash
+pip install "pipecat-ai[openai-agent]"
+# or
+uv add "pipecat-ai[openai-agent]"
+```
+
+## Basic Usage
+
+```python
+from pipecat.services.openai_agent import OpenAIAgentService
+
+# Create a simple agent
+agent_service = OpenAIAgentService(
+    name="Assistant",
+    instructions="You are a helpful assistant.",
+    api_key=os.getenv("OPENAI_API_KEY"),
+    streaming=True,
+)
+
+# Use in a pipeline
+pipeline = Pipeline([
+    transport.input(),
+    stt,
+    agent_service,
+    tts,
+    transport.output(),
+])
+```
+
+## Features
+
+### Tool Integration
+
+```python
+def get_weather(location: str) -> str:
+    """Get weather for a location."""
+    return f"Weather in {location}: sunny, 22°C"
+
+agent_service = OpenAIAgentService(
+    name="Weather Assistant",
+    instructions="Help users with weather information.",
+    tools=[get_weather],
+    api_key=os.getenv("OPENAI_API_KEY"),
+)
+```
+
+### Agent Handoffs
+
+```python
+# Create specialized agents
+weather_agent = OpenAIAgentService(
+    name="Weather Specialist",
+    instructions="Provide weather information and forecasts.",
+    tools=[get_weather, get_forecast],
+)
+
+trivia_agent = OpenAIAgentService(
+    name="Trivia Master", 
+    instructions="Share interesting facts and trivia.",
+    tools=[get_random_fact],
+)
+
+# Create coordinator that can hand off to specialists
+coordinator = OpenAIAgentService(
+    name="Coordinator",
+    instructions="Route users to the right specialist.",
+    handoffs=[weather_agent.agent, trivia_agent.agent],
+)
+```
+
+### Guardrails
+
+```python
+from agents import InputGuardrail, GuardrailFunctionOutput
+
+async def content_filter(ctx, agent, input_data):
+    # Check input for appropriate content
+    if is_inappropriate(input_data):
+        return GuardrailFunctionOutput(
+            tripwire_triggered=True,
+            output_info="Content not allowed"
+        )
+    return GuardrailFunctionOutput(tripwire_triggered=False)
+
+agent_service = OpenAIAgentService(
+    name="Safe Assistant",
+    instructions="You are a helpful and safe assistant.",
+    input_guardrails=[InputGuardrail(guardrail_function=content_filter)],
+)
+```
+
+### Session Management
+
+```python
+agent_service = OpenAIAgentService(
+    name="Personal Assistant",
+    instructions="Remember user preferences and context.",
+    session_config={
+        "user_id": "user_123",
+        "memory_enabled": True,
+    }
+)
+
+# Update session context dynamically
+agent_service.update_session_context({
+    "user_preferences": {"language": "en", "style": "formal"}
+})
+```
+
+## Configuration Options
+
+### Basic Parameters
+
+- `name`: Agent identifier for handoffs and tracing
+- `instructions`: System prompt defining agent behavior  
+- `api_key`: OpenAI API key (or use `OPENAI_API_KEY` env var)
+- `streaming`: Enable real-time token streaming (default: True)
+
+### Advanced Configuration
+
+- `tools`: List of callable functions for the agent to use
+- `handoffs`: List of other agents this agent can transfer to
+- `input_guardrails`: Input validation and filtering
+- `output_guardrails`: Output validation and filtering  
+- `model_config`: Model settings (model, temperature, etc.)
+- `session_config`: Session and memory configuration
+
+### Model Configuration
+
+```python
+agent_service = OpenAIAgentService(
+    name="Precise Assistant",
+    instructions="Provide accurate, concise responses.",
+    model_config={
+        "model": "gpt-4o",
+        "temperature": 0.1,
+        "max_tokens": 150,
+    }
+)
+```
+
+## Examples
+
+See the foundational examples:
+
+- [`45-openai-agent-basic.py`](../examples/foundational/45-openai-agent-basic.py) - Basic agent with tools
+- [`46-openai-agent-handoffs.py`](../examples/foundational/46-openai-agent-handoffs.py) - Multi-agent system with handoffs
+
+## Methods
+
+### Core Methods
+
+- `update_agent_config()` - Update instructions and model settings
+- `add_tool()` - Add new tools dynamically
+- `add_handoff_agent()` - Add handoff destinations
+- `get_session_context()` - Get current session state
+- `update_session_context()` - Update session variables
+
+### Lifecycle Methods
+
+Inherited from `AIService`:
+- `start()` - Initialize the agent
+- `stop()` - Clean up resources
+- `cancel()` - Cancel ongoing operations
+
+## Integration with Pipecat
+
+The service processes `TextFrame` inputs and generates:
+- `LLMFullResponseStartFrame` - Response beginning
+- `LLMTextFrame` - Streaming text tokens (if streaming enabled)
+- `LLMFullResponseEndFrame` - Response completion
+
+This integrates seamlessly with Pipecat's conversation pipeline and context aggregators.
+
+## Error Handling
+
+The service includes robust error handling for:
+- Missing API keys or SDK installation
+- Agent processing failures  
+- Network connectivity issues
+- Malformed tool responses
+
+Errors are emitted as `ErrorFrame` objects in the pipeline.
+
+## Requirements
+
+- OpenAI API key
+- `openai-agents` package
+- Python 3.10+
+
+## Limitations
+
+- Currently supports OpenAI models only (via Agents SDK)
+- Handoffs work within individual requests (no cross-request state)
+- Real-time voice features require additional setup

src/pipecat/services/openai_agent/__init__.py

@@ -0,0 +1,11 @@
+#
+# Copyright (c) 2024–2025, Daily
+#
+# SPDX-License-Identifier: BSD 2-Clause License
+#
+
+"""OpenAI Agents SDK service for Pipecat integration."""
+
+from .agent_service import OpenAIAgentService
+
+__all__ = ["OpenAIAgentService"]