Testing CI

.agents/skills/changelog

@@ -1 +0,0 @@
-../../.claude/skills/changelog

.agents/skills/cleanup

@@ -1 +0,0 @@
-../../.claude/skills/cleanup

.agents/skills/code-review

@@ -1 +0,0 @@
-../../.claude/skills/code-review

.agents/skills/docstring

@@ -1 +0,0 @@
-../../.claude/skills/docstring

.agents/skills/pr-description

@@ -1 +0,0 @@
-../../.claude/skills/pr-description

.agents/skills/pr-submit

@@ -1 +0,0 @@
-../../.claude/skills/pr-submit

.agents/skills/update-docs

@@ -1 +0,0 @@
-../../.claude/skills/update-docs

.claude/skills/cleanup/SKILL.md

@@ -1,8 +1,3 @@
----
-name: cleanup
-description: Review, refactor, document, and validate code changes in the current branch
----
-
 # Code Cleanup Skill
 
 The **Code Cleanup Skill** reviews, refactors, and documents code changes in your current branch, ensuring alignment with **Pipecat's architecture, coding standards, and example patterns**.

.github/workflows/coverage.yaml

@@ -42,7 +42,6 @@ jobs:
             --extra langchain \
             --extra livekit \
             --extra piper \
-            --extra runner \
             --extra sagemaker \
             --extra tracing \
             --extra websocket

.github/workflows/format.yaml

@@ -32,9 +32,7 @@ jobs:
         run: uv python install 3.12
 
       - name: Install development dependencies
-        # `--all-extras` (matching the dev setup in README.md) so pyright can
-        # resolve types from various optional dependencies.
-        run: uv sync --group dev --all-extras --no-extra gstreamer --no-extra local
+        run: uv sync --group dev --extra daily --extra tracing
 
       - name: Ruff formatter
         id: ruff-format

.github/workflows/tests.yaml

@@ -46,7 +46,6 @@ jobs:
             --extra langchain \
             --extra livekit \
             --extra piper \
-            --extra runner \
             --extra sagemaker \
             --extra tracing \
             --extra websocket

AGENTS.md

@@ -1,174 +0,0 @@
-# AGENTS.md
-
-This file provides guidance to AI coding agents when working with code in this repository.
-
-## Project Overview
-
-Pipecat is an open-source Python framework for building real-time voice and multimodal conversational AI agents. It orchestrates audio/video, AI services, transports, and conversation pipelines using a frame-based architecture.
-
-## Common Commands
-
-```bash
-# Setup development environment
-uv sync --group dev --all-extras --no-extra gstreamer --no-extra local
-
-# Install pre-commit hooks
-uv run pre-commit install
-
-# Run all tests
-uv run pytest
-
-# Run a single test file
-uv run pytest tests/test_name.py
-
-# Run a specific test
-uv run pytest tests/test_name.py::test_function_name
-
-# Preview changelog
-uv run towncrier build --draft --version Unreleased
-
-# Lint and format check
-uv run ruff check
-uv run ruff format --check
-
-# Update dependencies (after editing pyproject.toml)
-uv lock && uv sync
-```
-
-## Architecture
-
-### Frame-Based Pipeline Processing
-
-All data flows as **Frame** objects through a pipeline of **FrameProcessors**:
-
-```
-[Processor1] → [Processor2] → ... → [ProcessorN]
-```
-
-**Key components:**
-
-- **Frames** (`src/pipecat/frames/frames.py`): Data units (audio, text, video) and control signals. Flow DOWNSTREAM (input→output) or UPSTREAM (acknowledgments/errors).
-
-- **FrameProcessor** (`src/pipecat/processors/frame_processor.py`): Base processing unit. Each processor receives frames, processes them, and pushes results downstream.
-
-- **Pipeline** (`src/pipecat/pipeline/pipeline.py`): Chains processors together.
-
-- **ParallelPipeline** (`src/pipecat/pipeline/parallel_pipeline.py`): Runs multiple pipelines in parallel.
-
-- **Transports** (`src/pipecat/transports/`): Transports are frame processors used for external I/O layer (Daily WebRTC, LiveKit WebRTC, WebSocket, Local). Abstract interface via `BaseTransport`, `BaseInputTransport` and `BaseOutputTransport`.
-
-- **Pipeline Task (`src/pipecat/pipeline/task.py`)**: Runs and manages a pipeline. Pipeline tasks send the first frame, `StartFrame`, to the pipeline in order for processors to know they can start processing and pushing frames. Pipeline tasks internally create a pipeline with two additional processors, a source processor before the user-defined pipeline and a sink processor at the end. Those are used for multiple things: error handling, pipeline task level events, heartbeat monitoring, etc.
-
-- **Pipeline Runner (`src/pipecat/pipeline/runner.py`)**: High-level entry point for executing pipeline tasks. Handles signal management (SIGINT/SIGTERM) for graceful shutdown and optional garbage collection. Run a single pipeline task with `await runner.run(task)` or multiple concurrently with `await asyncio.gather(runner.run(task1), runner.run(task2))`.
-
-- **Services** (`src/pipecat/services/`): 60+ AI provider integrations (STT, TTS, LLM, etc.). Extend base classes: `AIService`, `LLMService`, `STTService`, `TTSService`, `VisionService`.
-
-- **Serializers** (`src/pipecat/serializers/`): Convert frames to/from wire formats for WebSocket transports. `FrameSerializer` base class defines `serialize()` and `deserialize()`. Telephony serializers (Twilio, Plivo, Vonage, Telnyx, Exotel, Genesys) handle provider-specific protocols and audio encoding (e.g., μ-law).
-
-- **RTVI** (`src/pipecat/processors/frameworks/rtvi.py`): Real-Time Voice Interface protocol bridging clients and the pipeline. `RTVIProcessor` handles incoming client messages (text input, audio, function call results). `RTVIObserver` converts pipeline frames to outgoing messages: user/bot speaking events, transcriptions, LLM/TTS lifecycle, function calls, metrics, and audio levels.
-
-- **Observers** (`src/pipecat/observers/`): Monitor frame flow without modifying the pipeline. Passed to `PipelineTask` via the `observers` parameter. Implement `on_process_frame()` and `on_push_frame()` callbacks.
-
-### Important Patterns
-
-- **Context Aggregation**: `LLMContext` accumulates messages for LLM calls; `UserResponse` aggregates user input
-
-- **Turn Management**: Turn management is done through `LLMUserAggregator` and
-  `LLMAssistantAggregator`, created with `LLMContextAggregatorPair`
-
-- **User turn strategies**: Detection of when the user starts and stops speaking is done via user turn start/stop strategies. They push `UserStartedSpeakingFrame` and `UserStoppedSpeakingFrame` respectively.
-
-- **Interruptions**: Interruptions are usually triggered by a user turn start strategy (e.g. `VADUserTurnStartStrategy`) but they can be triggered by other processors as well, in which case the user turn start strategies don't need to. An `InterruptionFrame` carries an optional `asyncio.Event` that is set when the frame reaches the pipeline sink. If a processor stops an `InterruptionFrame` from propagating downstream (i.e., doesn't push it), it **must** call `frame.complete()` to avoid stalling `push_interruption_task_frame_and_wait()` callers.
-
-- **Uninterruptible Frames**: These are frames that will not be removed from internal queues even if there's an interruption. For example, `EndFrame` and `StopFrame`.
-
-- **Events**: Most classes in Pipecat have `BaseObject` as the very base class. `BaseObject` has support for events. Events can run in the background in an async task (default) or synchronously (`sync=True`) if we want immediate action. Synchronous event handlers need to execute fast.
-
-- **Async Task Management**: Always use `self.create_task(coroutine, name)` instead of raw `asyncio.create_task()`. The `TaskManager` automatically tracks tasks and cleans them up on processor shutdown. Use `await self.cancel_task(task, timeout)` for cancellation.
-
-- **Error Handling**: Use `await self.push_error(msg, exception, fatal)` to push errors upstream. Services should use `fatal=False` (the default) so application code can handle errors and take action (e.g. switch to another service).
-
-### Key Directories
-
-| Directory                  | Purpose                                            |
-| -------------------------- | -------------------------------------------------- |
-| `src/pipecat/frames/`      | Frame definitions (100+ types)                     |
-| `src/pipecat/processors/`  | FrameProcessor base + aggregators, filters, audio  |
-| `src/pipecat/pipeline/`    | Pipeline orchestration                             |
-| `src/pipecat/services/`    | AI service integrations (60+ providers)            |
-| `src/pipecat/transports/`  | Transport layer (Daily, LiveKit, WebSocket, Local) |
-| `src/pipecat/serializers/` | Frame serialization for WebSocket protocols        |
-| `src/pipecat/observers/`   | Pipeline observers for monitoring frame flow       |
-| `src/pipecat/audio/`       | VAD, filters, mixers, turn detection, DTMF         |
-| `src/pipecat/turns/`       | User turn management                               |
-
-## Code Style
-
-- **Docstrings**: Google-style. Classes describe purpose; `__init__` has `Args:` section; dataclasses use `Parameters:` section.
-- **Deprecations**: Use the `.. deprecated:: <version>` Sphinx directive in docstrings (never inline tags like `[DEPRECATED]`), and pair it with a runtime `warnings.warn(..., DeprecationWarning)` at the call site. See `CONTRIBUTING.md` for full conventions.
-- **Linting**: Ruff (line length 100). Pre-commit hooks enforce formatting.
-- **Type hints**: Required for complex async code.
-- **Dataclass vs Pydantic**: Use `@dataclass` for frames and internal pipeline data (high-frequency, no validation needed). Use Pydantic `BaseModel` for configuration, parameters, metrics, and external API data (benefits from validation and serialization). Specifically:
-  - `@dataclass`: Frame types, context aggregator pairs, internal data containers
-  - `BaseModel`: Service `InputParams`, transport/VAD/turn params, metrics data, API request/response models, serializer params
-
-### Docstring Example
-
-```python
-class MyService(LLMService):
-    """Description of what the service does.
-
-    More detailed description.
-
-    Event handlers available:
-
-    - on_connected: Called when we are connected
-
-    Example::
-
-        @service.event_handler("on_connected")
-        async def on_connected(service, frame):
-            ...
-    """
-
-    def __init__(self, param1: str, **kwargs):
-        """Initialize the service.
-
-        Args:
-            param1: Description of param1.
-            **kwargs: Additional arguments passed to parent.
-        """
-        super().__init__(**kwargs)
-
-
-# Pydantic params class with a deprecated field
-class MyParams(BaseModel):
-    """Configuration parameters for MyService.
-
-    Parameters:
-        new_setting: Replacement for ``old_setting``.
-        old_setting: Legacy setting, no longer used.
-
-            .. deprecated:: 1.2.0
-                Use ``new_setting`` instead. Will be removed in 2.0.0.
-    """
-
-    new_setting: str = "default"
-    old_setting: str | None = None
-```
-
-## Service Implementation
-
-When adding a new service:
-
-1. Extend the appropriate base class (`STTService`, `TTSService`, `LLMService`, etc.)
-2. Implement required abstract methods
-3. Handle necessary frames
-4. By default, all frames should be pushed in the direction they came
-5. Push `ErrorFrame` on failures
-6. Add metrics tracking via `MetricsData` if relevant
-7. Follow the pattern of existing services in `src/pipecat/services/`
-
-## Testing
-
-Test utilities live in `src/pipecat/tests/utils.py`. Use `run_test()` to send frames through a pipeline and assert expected output frames in each direction. Use `SleepFrame(sleep=N)` to add delays between frames.

CLAUDE.md

@@ -1 +1,157 @@
-@AGENTS.md
+# CLAUDE.md
+
+This file provides guidance to Claude Code (claude.ai/code) when working with code in this repository.
+
+## Project Overview
+
+Pipecat is an open-source Python framework for building real-time voice and multimodal conversational AI agents. It orchestrates audio/video, AI services, transports, and conversation pipelines using a frame-based architecture.
+
+## Common Commands
+
+```bash
+# Setup development environment
+uv sync --group dev --all-extras --no-extra gstreamer
+
+# Install pre-commit hooks
+uv run pre-commit install
+
+# Run all tests
+uv run pytest
+
+# Run a single test file
+uv run pytest tests/test_name.py
+
+# Run a specific test
+uv run pytest tests/test_name.py::test_function_name
+
+# Preview changelog
+uv run towncrier build --draft --version Unreleased
+
+# Lint and format check
+uv run ruff check
+uv run ruff format --check
+
+# Update dependencies (after editing pyproject.toml)
+uv lock && uv sync
+```
+
+## Architecture
+
+### Frame-Based Pipeline Processing
+
+All data flows as **Frame** objects through a pipeline of **FrameProcessors**:
+
+```
+[Processor1] → [Processor2] → ... → [ProcessorN]
+```
+
+**Key components:**
+
+- **Frames** (`src/pipecat/frames/frames.py`): Data units (audio, text, video) and control signals. Flow DOWNSTREAM (input→output) or UPSTREAM (acknowledgments/errors).
+
+- **FrameProcessor** (`src/pipecat/processors/frame_processor.py`): Base processing unit. Each processor receives frames, processes them, and pushes results downstream.
+
+- **Pipeline** (`src/pipecat/pipeline/pipeline.py`): Chains processors together.
+
+- **ParallelPipeline** (`src/pipecat/pipeline/parallel_pipeline.py`): Runs multiple pipelines in parallel.
+
+- **Transports** (`src/pipecat/transports/`): Transports are frame processors used for external I/O layer (Daily WebRTC, LiveKit WebRTC, WebSocket, Local). Abstract interface via `BaseTransport`, `BaseInputTransport` and `BaseOutputTransport`.
+
+- **Pipeline Task (`src/pipecat/pipeline/task.py`)**: Runs and manages a pipeline. Pipeline tasks send the first frame, `StartFrame`, to the pipeline in order for processors to know they can start processing and pushing frames. Pipeline tasks internally create a pipeline with two additional processors, a source processor before the user-defined pipeline and a sink processor at the end. Those are used for multiple things: error handling, pipeline task level events, heartbeat monitoring, etc.
+
+- **Pipeline Runner (`src/pipecat/pipeline/runner.py`)**: High-level entry point for executing pipeline tasks. Handles signal management (SIGINT/SIGTERM) for graceful shutdown and optional garbage collection. Run a single pipeline task with `await runner.run(task)` or multiple concurrently with `await asyncio.gather(runner.run(task1), runner.run(task2))`.
+
+- **Services** (`src/pipecat/services/`): 60+ AI provider integrations (STT, TTS, LLM, etc.). Extend base classes: `AIService`, `LLMService`, `STTService`, `TTSService`, `VisionService`.
+
+- **Serializers** (`src/pipecat/serializers/`): Convert frames to/from wire formats for WebSocket transports. `FrameSerializer` base class defines `serialize()` and `deserialize()`. Telephony serializers (Twilio, Plivo, Vonage, Telnyx, Exotel, Genesys) handle provider-specific protocols and audio encoding (e.g., μ-law).
+
+- **RTVI** (`src/pipecat/processors/frameworks/rtvi.py`): Real-Time Voice Interface protocol bridging clients and the pipeline. `RTVIProcessor` handles incoming client messages (text input, audio, function call results). `RTVIObserver` converts pipeline frames to outgoing messages: user/bot speaking events, transcriptions, LLM/TTS lifecycle, function calls, metrics, and audio levels.
+
+- **Observers** (`src/pipecat/observers/`): Monitor frame flow without modifying the pipeline. Passed to `PipelineTask` via the `observers` parameter. Implement `on_process_frame()` and `on_push_frame()` callbacks.
+
+### Important Patterns
+
+- **Context Aggregation**: `LLMContext` accumulates messages for LLM calls; `UserResponse` aggregates user input
+
+- **Turn Management**: Turn management is done through `LLMUserAggregator` and
+  `LLMAssistantAggregator`, created with `LLMContextAggregatorPair`
+
+- **User turn strategies**: Detection of when the user starts and stops speaking is done via user turn start/stop strategies. They push `UserStartedSpeakingFrame` and `UserStoppedSpeakingFrame` respectively.
+
+- **Interruptions**: Interruptions are usually triggered by a user turn start strategy (e.g. `VADUserTurnStartStrategy`) but they can be triggered by other processors as well, in which case the user turn start strategies don't need to. An `InterruptionFrame` carries an optional `asyncio.Event` that is set when the frame reaches the pipeline sink. If a processor stops an `InterruptionFrame` from propagating downstream (i.e., doesn't push it), it **must** call `frame.complete()` to avoid stalling `push_interruption_task_frame_and_wait()` callers.
+
+- **Uninterruptible Frames**: These are frames that will not be removed from internal queues even if there's an interruption. For example, `EndFrame` and `StopFrame`.
+
+- **Events**: Most classes in Pipecat have `BaseObject` as the very base class. `BaseObject` has support for events. Events can run in the background in an async task (default) or synchronously (`sync=True`) if we want immediate action. Synchronous event handlers need to execute fast.
+
+- **Async Task Management**: Always use `self.create_task(coroutine, name)` instead of raw `asyncio.create_task()`. The `TaskManager` automatically tracks tasks and cleans them up on processor shutdown. Use `await self.cancel_task(task, timeout)` for cancellation.
+
+- **Error Handling**: Use `await self.push_error(msg, exception, fatal)` to push errors upstream. Services should use `fatal=False` (the default) so application code can handle errors and take action (e.g. switch to another service).
+
+### Key Directories
+
+| Directory                  | Purpose                                            |
+| -------------------------- | -------------------------------------------------- |
+| `src/pipecat/frames/`      | Frame definitions (100+ types)                     |
+| `src/pipecat/processors/`  | FrameProcessor base + aggregators, filters, audio  |
+| `src/pipecat/pipeline/`    | Pipeline orchestration                             |
+| `src/pipecat/services/`    | AI service integrations (60+ providers)            |
+| `src/pipecat/transports/`  | Transport layer (Daily, LiveKit, WebSocket, Local) |
+| `src/pipecat/serializers/` | Frame serialization for WebSocket protocols        |
+| `src/pipecat/observers/`   | Pipeline observers for monitoring frame flow       |
+| `src/pipecat/audio/`       | VAD, filters, mixers, turn detection, DTMF         |
+| `src/pipecat/turns/`       | User turn management                               |
+
+## Code Style
+
+- **Docstrings**: Google-style. Classes describe purpose; `__init__` has `Args:` section; dataclasses use `Parameters:` section.
+- **Linting**: Ruff (line length 100). Pre-commit hooks enforce formatting.
+- **Type hints**: Required for complex async code.
+- **Dataclass vs Pydantic**: Use `@dataclass` for frames and internal pipeline data (high-frequency, no validation needed). Use Pydantic `BaseModel` for configuration, parameters, metrics, and external API data (benefits from validation and serialization). Specifically:
+  - `@dataclass`: Frame types, context aggregator pairs, internal data containers
+  - `BaseModel`: Service `InputParams`, transport/VAD/turn params, metrics data, API request/response models, serializer params
+
+### Docstring Example
+
+```python
+class MyService(LLMService):
+    """Description of what the service does.
+
+    More detailed description.
+
+    Event handlers available:
+
+    - on_connected: Called when we are connected
+
+    Example::
+
+        @service.event_handler("on_connected")
+        async def on_connected(service, frame):
+            ...
+    """
+
+    def __init__(self, param1: str, **kwargs):
+        """Initialize the service.
+
+        Args:
+            param1: Description of param1.
+            **kwargs: Additional arguments passed to parent.
+        """
+        super().__init__(**kwargs)
+```
+
+## Service Implementation
+
+When adding a new service:
+
+1. Extend the appropriate base class (`STTService`, `TTSService`, `LLMService`, etc.)
+2. Implement required abstract methods
+3. Handle necessary frames
+4. By default, all frames should be pushed in the direction they came
+5. Push `ErrorFrame` on failures
+6. Add metrics tracking via `MetricsData` if relevant
+7. Follow the pattern of existing services in `src/pipecat/services/`
+
+## Testing
+
+Test utilities live in `src/pipecat/tests/utils.py`. Use `run_test()` to send frames through a pipeline and assert expected output frames in each direction. Use `SleepFrame(sleep=N)` to add delays between frames.

changelog/4390.added.md

@@ -1 +0,0 @@
-- Added a `max_buffer_delay_ms` constructor argument to `CartesiaTTSService` for controlling Cartesia's server-side text buffering. When unset, Pipecat picks a sensible default based on `text_aggregation_mode`: `0` in `SENTENCE` mode (custom buffering — avoids stacking client-side aggregation on top of Cartesia's default 3000ms server buffer) and unset in `TOKEN` mode (Cartesia's managed buffering applies). Pass an explicit value (0–5000ms) to override.

changelog/4390.changed.2.md

@@ -1 +0,0 @@
-- Default `cartesia_version` for `CartesiaTTSService` bumped from `2025-04-16` to `2026-03-01`, matching `CartesiaHttpTTSService` and unlocking the `use_normalized_timestamps` and `max_buffer_delay_ms` fields.

changelog/4390.changed.md

@@ -1 +0,0 @@
-- ⚠️ `CartesiaTTSService` now sends `use_normalized_timestamps: true` instead of the deprecated `use_original_timestamps` field. Word timestamps now reflect what was actually spoken (post text-normalization and pronunciation-dictionary substitution), matching the convention Pipecat uses for ElevenLabs. This is a behavior change for `sonic-3` users, who were previously receiving timestamps tied to the input transcript.

changelog/4390.fixed.2.md

@@ -1 +0,0 @@
-- Fixed `CartesiaHttpTTSService` pushing two `ErrorFrame`s on a non-200 response — one with the API's error text and a second, less informative "Unknown error" frame from the outer exception handler. It now pushes a single frame that includes the HTTP status code and returns cleanly.

changelog/4390.fixed.3.md

@@ -1 +0,0 @@
-- Fixed Cartesia tag helpers (`SPELL`, `EMOTION_TAG`, `PAUSE_TAG`, `VOLUME_TAG`, `SPEED_TAG`) raising `TypeError` when called on an instance (e.g. `tts.SPELL("hi")`). They're now `@staticmethod` and callable from both the class and an instance.

changelog/4390.fixed.md

@@ -1 +0,0 @@
-- Fixed `CartesiaTTSService` surfacing `flush_done` messages from Cartesia as `ErrorFrame`s. The latest API emits a `flush_done` per transcript when server-side buffering is disabled; Pipecat now consumes them silently since each turn already has its own `context_id`.

changelog/4393.fixed.md

@@ -1 +0,0 @@
-- Fixed an issue where `LocalSmartTurnAnalyzerV3` was imported unconditionally for user turn stop strategies. It is now only imported when `default_user_turn_stop_strategies()` is called. This improves startup time and removes the `transformers` "PyTorch/TensorFlow/Flax not found" warning when the default stop strategies are not used.

changelog/4395.changed.md

@@ -1 +0,0 @@
-- Broadened `tool_resources` to `app_resources` for easy access not just in tool handlers but in other places like custom `FrameProcessor`s. Three changes: a rename (`tool_resources` → `app_resources`), a new `app_resources` property on `PipelineTask`, and a new `pipeline_task` property on `FrameProcessor`. Tool handlers now read `params.app_resources`; custom processors read `self.pipeline_task.app_resources`. The previous `tool_resources` aliases (on `PipelineTask`, `FunctionCallParams`, and `FrameProcessorSetup`) keep working but are deprecated as of 1.2.0 and emit `DeprecationWarning`s.

changelog/4397.changed.md

@@ -1 +0,0 @@
-- Lowered the per-message log in `SmallWebRTCInputTransport._handle_app_message` from `debug` to `trace`. App messages can be high-frequency and were noisy at debug level; set the loguru level to `TRACE` to see them again.

changelog/4400.added.md

@@ -1 +0,0 @@
-- Added a `mip_opt_out` constructor argument to `DeepgramTTSService` and `DeepgramHttpTTSService` so callers can opt out of the Deepgram Model Improvement Program. When set, the value is forwarded to Deepgram as a query parameter on the speak request. Defaults to `None`, which preserves the existing behavior. See https://dpgr.am/deepgram-mip for pricing implications before enabling.

changelog/4401.changed.md

@@ -1 +0,0 @@
-- Changed the default model for `GrokRealtimeLLMService` to `grok-voice-think-fast-1.0`, xAI's recommended Voice Agent model. The previous default of `grok-voice-fast-1.0` has been deprecated by xAI and is being removed.

changelog/4401.fixed.md

@@ -1 +0,0 @@
-- Fixed `GrokRealtimeLLMService` ignoring the configured model. The model was stored in `Settings` but never sent to xAI, so every session silently fell back to xAI's server-side default. The model is now passed via the `?model=` query parameter on the WebSocket URL as xAI's Voice Agent API requires.

changelog/4404.added.md

@@ -1 +0,0 @@
-- Added an opt-in `add_tool_change_messages` flag to the LLM aggregators (set via `LLMContextAggregatorPair(..., add_tool_change_messages=True)`) that appends a developer-role message to the context whenever `LLMSetToolsFrame` changes the set of advertised standard tools. Helps the LLM stay coherent across mid-conversation tool changes, mitigating several flavors of tool-call-related hallucination: calling tools that have been removed, avoiding tools that have been re-added, and hallucinating output (made-up answers or tool-call-shaped non-tool-calls) when tools are unavailable.

changelog/4405.added.2.md

@@ -1 +0,0 @@
-- Added `LLMTurnCompletionUserTurnStopStrategy` in `pipecat.turns.user_stop`. When installed, the strategy gates `on_user_turn_stopped` on a `UserTurnInferenceCompletedFrame` (a new fieldless system frame emitted by any component that can judge turn completeness — e.g. the `UserTurnCompletionLLMServiceMixin` on `✓`). A `finalization_timeout` provides a safety net if no completion frame ever arrives.

changelog/4405.added.3.md

@@ -1 +0,0 @@
-- Added `deferred(strategy)` and `DeferredUserTurnStopStrategy` in `pipecat.turns.user_stop`. Wraps a stop strategy so it fires only the inference-triggered event and suppresses `on_user_turn_stopped`, leaving finalization to another strategy in the chain such as `LLMTurnCompletionUserTurnStopStrategy`.

changelog/4405.added.4.md

@@ -1 +0,0 @@
-- Added `FilterIncompleteUserTurnStrategies` in `pipecat.turns.user_turn_strategies` — a `UserTurnStrategies` specialization that wraps the detector chain with `deferred(...)` and appends `LLMTurnCompletionUserTurnStopStrategy` as the finalizer. Common case: `user_turn_strategies=FilterIncompleteUserTurnStrategies()`. Pass `config=UserTurnCompletionConfig(...)` to customize timeouts and prompts.

changelog/4405.added.5.md

@@ -1 +0,0 @@
-- Added `ExternalUserTurnCompletionStopStrategy` in `pipecat.turns.user_stop` — a generic stop strategy that finalizes the user turn whenever a `UserTurnInferenceCompletedFrame` arrives, regardless of which component produced it. `LLMTurnCompletionUserTurnStopStrategy` now extends this base; future producers (Flux, custom end-of-turn classifiers, etc.) can use the base directly or subclass it to add producer-specific setup.

changelog/4405.added.md

@@ -1 +0,0 @@
-- Added `on_user_turn_inference_triggered`, a new event on the user turn controller, processor, aggregator and stop strategies that fires when a strategy has enough signal to start LLM inference. By default it fires together with `on_user_turn_stopped`; a gating strategy can fire only the inference-triggered event and defer finalization to a peer.

changelog/4405.deprecated.md

@@ -1 +0,0 @@
-- Deprecated `LLMUserAggregatorParams.filter_incomplete_user_turns`. Use `user_turn_strategies=FilterIncompleteUserTurnStrategies()` (or add `LLMTurnCompletionUserTurnStopStrategy` to a custom `user_turn_strategies.stop`) instead. Setting the legacy flag still works for one release: the aggregator emits a `DeprecationWarning` and rewires the strategies as if you had passed `FilterIncompleteUserTurnStrategies` directly.

changelog/4405.fixed.md

@@ -1 +0,0 @@
-- Fixed `on_user_turn_stopped` firing prematurely when `filter_incomplete_user_turns` was enabled. The event now fires only after the LLM confirms the user turn is complete (`✓`); previously the smart-turn detector's tentative stop was bubbling up before the LLM had a chance to veto it, causing observers, transcript appenders and UI indicators to receive an early — and sometimes duplicated — signal.

changelog/4407.added.md

@@ -1,6 +0,0 @@
-- Added first-class RTVI support for the UI Agent Protocol:
-  - Adds `ui-event`, `ui-snapshot`, and `ui-cancel-task` client-to-server messages, plus `ui-command` and `ui-task` server-to-client messages, with paired `*Data` / `*Message` pydantic models.
-  - Adds built-in command payload models for `Toast`, `Navigate`, `ScrollTo`, `Highlight`, `Focus`, `Click`, `SetInputValue`, and `SelectText`; matching default handlers live in `@pipecat-ai/client-react`.
-  - Adds `RTVIProcessor.on_ui_message` for inbound `ui-event`, `ui-snapshot`, and `ui-cancel-task` messages.
-  - Adds five UI pipeline frames, mirroring the `client-message` frame-and-event pattern: downstream code pushes `RTVIUICommandFrame` / `RTVIUITaskFrame` for the observer to wrap into outbound `UICommandMessage` / `UITaskMessage` envelopes, while the processor pushes inbound `RTVIUIEventFrame`, `RTVIUISnapshotFrame`, and `RTVIUICancelTaskFrame` alongside `on_ui_message`.
-  - Bumps the RTVI `PROTOCOL_VERSION` from `1.2.0` to `1.3.0`.

changelog/4414.fixed.md

@@ -1 +0,0 @@
-- Fixed `TTSSpeakFrame(append_to_context=True)` greetings sometimes splitting across two assistant messages in the LLM context and not surfacing in `on_assistant_turn_stopped`. The `LLMAssistantPushAggregationFrame` emitted at the end of a TTS context now carries a PTS just past the last word so it can't overtake clock-queued `TTSTextFrame`s in the transport's output, and `LLMAssistantAggregator` now triggers `on_assistant_turn_started`/`on_assistant_turn_stopped` when it receives the frame outside an LLM response cycle (restoring v0.0.104 behavior for greeting transcripts).

changelog/4415.fixed.md

@@ -1 +0,0 @@
-- Fixed `ElevenLabsTTSService` and `ElevenLabsHttpTTSService` producing merged words (e.g. `bookLook`) when using Flash models. Flash often splits sentences mid-stream into alignment chunks that begin with a real inter-word space, but the previous fix unconditionally stripped that space from every chunk. Leading spaces are now stripped only on the first alignment chunk of an utterance, so subsequent chunks correctly flush partial words across boundaries.

changelog/4416.added.md

@@ -1 +0,0 @@
-- AWS Transcribe STT, Polly TTS, Bedrock LLM, and the Bedrock AgentCore processor now resolve credentials via the standard boto3 provider chain (EC2 instance profiles, EKS pod roles / IRSA, ECS task roles, SSO, `~/.aws/credentials`) when explicit credentials and `AWS_*` environment variables are absent. Services running with IAM roles no longer need to export static credentials.

changelog/4416.fixed.md

@@ -1 +0,0 @@
-- Fixed AWS Polly TTS, Bedrock LLM, and the Bedrock AgentCore processor erroring out when only one of `AWS_ACCESS_KEY_ID` / `AWS_SECRET_ACCESS_KEY` was set in the environment. The half-populated kwargs are no longer forwarded to aioboto3; partial env-var configurations now fall through to the boto3 credential chain like fully-unset configurations do.

changelog/4417.security.md

@@ -1 +0,0 @@
-- Fixed a path traversal issue in the development runner's `/files/{filename:path}` download endpoint. Previously, when the runner was started with `--folder`, a request like `/files/..%2F..%2Fetc%2Fpasswd` could escape the configured folder because `%2F`-encoded separators bypassed Starlette's path normalisation. The endpoint now resolves the joined path and rejects any filename that escapes the allowed base with a 403, and also returns 404 (instead of an implicit `null` 200) when `--folder` is unset.

changelog/4422.changed.md

@@ -1 +0,0 @@
-- Changed the default Inworld TTS model from `inworld-tts-1.5-max` to `inworld-tts-2` (Realtime TTS-2) across `InworldHttpTTSService`, `InworldTTSService`, and the `InworldRealtimeLLMService` cascade. Existing users can pin the prior model explicitly via the `model`/`tts_model` argument; both `inworld-tts-1.5-max` and `inworld-tts-1.5-mini` remain valid model IDs.

changelog/4424.fixed.md

@@ -1 +0,0 @@
-- Fixed `ElevenLabsTTSService` and `ElevenLabsHttpTTSService` writing romanized/normalized text to the LLM context. With non-Latin input (e.g., Chinese), the assistant transcript was getting populated with pinyin (`Ni Hao !` instead of `你好！`), which then degraded subsequent LLM turns. The services now consume `alignment` by default and only switch to `normalizedAlignment` / `normalized_alignment` when `pronunciation_dictionary_locators` is configured (where `alignment` has overlapping restarts that produce duplicated/garbled words, per #4316). Both fields are read with preferred-with-fallback semantics since each is nullable per the API schema.

changelog/4426.added.md

@@ -1 +0,0 @@
-- Added `keyterms` support to ElevenLabs STT services so Scribe V2 callers can bias transcription for both file-based and realtime transcription.

changelog/4428.deprecated.md

@@ -1 +0,0 @@
-- Deprecated `ResampyResampler` in favor of `SOXRAudioResampler` (or the `create_file_resampler()` / `create_stream_resampler()` factories). Instantiating `ResampyResampler` now emits a `DeprecationWarning`. The class will be removed in Pipecat 2.0 along with the default `resampy` and `numba` dependencies.

changelog/4429.changed.md

@@ -1 +0,0 @@
-- Changed the default model for `GrokLLMService` from `grok-3` to `grok-4.20-non-reasoning`. xAI is retiring `grok-3` on May 15, 2026.

changelog/4430.added.md

@@ -1 +0,0 @@
-- Added `watchdog_min_timeout` parameter to `DeepgramFluxSTT` and `DeepgramFluxSageMakerSTT` (default `0.5` seconds) to control the minimum silence duration before the watchdog sends a silence packet to prevent dangling turns. The actual threshold is `max(chunk_duration * 2, watchdog_min_timeout)`, so it also adapts automatically to the audio chunk size in use.

changelog/4430.changed.md

@@ -1 +0,0 @@
-- `DeepgramFluxSTT` watchdog silence threshold is now dynamic: `max(chunk_duration * 2, watchdog_min_timeout)` instead of a fixed 500 ms. This prevents false silence injections when large audio chunks are sent at lower frequency.

changelog/4431.fixed.md

@@ -1 +0,0 @@
-- Fixed a deadlock in `TTSService` that could permanently stall pipeline processing when all three conditions occurred together: `pause_frame_processing=True`, an interruption arrived before any TTS audio was played, and an `UninterruptibleFrame` (e.g. `TTSUpdateSettingsFrame`, `FunctionCallResultFrame`) was in the processing queue at that moment. The process task would block on `__process_event.wait()` indefinitely because `BotStoppedSpeakingFrame` never arrives (no audio was played) and the interruption handler did not resume processing. Affects services using `pause_frame_processing=True` such as ElevenLabs, Rime, AsyncAI, Gradium, and ResembleAI.

changelog/4433.changed.md

@@ -1 +0,0 @@
-- `ElevenLabsTTSService` now sends `close_context` to the server as soon as the turn is complete (on `on_turn_context_completed`) rather than waiting until all audio has finished playing back. The `isFinal` message from ElevenLabs is now used to signal `TTSStoppedFrame` and clean up the audio context, improving turn transition timing.

changelog/4434.fixed.md

@@ -1 +0,0 @@
-- Fixed interruptions being delayed when a slow non-uninterruptible frame was processing and an uninterruptible frame was waiting in the queue. The bot would stall until the slow frame finished instead of cancelling it immediately on interruption.

changelog/4435.fixed.md

@@ -1 +0,0 @@
-- Fixed `TTSService` dropping uninterruptible frames (e.g. `FunctionCallResultFrame`) from its internal serialization queue when an interruption occurs. Previously, the queue was recreated on every interruption, silently discarding any queued frames. The queue is now reset instead of recreated, preserving uninterruptible frames so they are always delivered downstream.

changelog/4440.fixed.md

@@ -1 +0,0 @@
-- Fixed a race condition in the Daily transport that caused `AttributeError: 'NoneType' object has no attribute 'send_app_message'` when tearing down a pipeline. Both `DailyInputTransport` and `DailyOutputTransport` share the same `DailyTransportClient` and both call `cleanup()`, which was releasing the underlying `CallClient` on the first call — leaving the second caller with a `None` client.

changelog/4441.fixed.md

@@ -1 +0,0 @@
-- Restored `cancel_on_interruption=False` support for `AWSNovaSonicLLMService` and `OpenAIRealtimeLLMService`. These services previously honored the flag by simply not cancelling in-flight function calls on interruption; the introduction of the new async-tool mechanism (which threads started/intermediate/final messages through the LLM context) broke that path because the realtime services didn't know how to interpret those messages. Note that new-style streamed intermediate results (`FunctionCallResultProperties(is_final=False)`) are not supported on these realtime services. Similar fixes for other impacted realtime services are forthcoming.

changelog/4443.fixed.md

@@ -1 +0,0 @@
-- Fixed two misspelled Gemini TTS voice names in `GeminiTTSService.AVAILABLE_VOICES`.

changelog/4446.change.md

@@ -1 +0,0 @@
-- Updated `InworldHttpTTSService` and `InworldTTSService` to use PCM audio encoding by default, which returns audio bytes without headers.

changelog/4447.fixed.md

@@ -1 +0,0 @@
-- Extended the `cancel_on_interruption=False` regression fix to `GrokRealtimeLLMService`, `AzureRealtimeLLMService`, and `UltravoxRealtimeLLMService`. Grok and Azure use the same approach as in #4441 (each service detects async-tool messages in the LLM context and routes the final result to its formal tool-result channel; Azure inherits transitively from `OpenAIRealtimeLLMService`). Ultravox needed a different approach because its API freezes the conversation between `client_tool_invocation` and the matching `client_tool_result` — for async-registered functions it now ships a placeholder `client_tool_result` immediately when the function is invoked (to unfreeze the conversation), then injects the real result as user-side text once the tool finishes. Streamed intermediate results (`FunctionCallResultProperties(is_final=False)`) are still not supported on any of these realtime services. `GeminiLiveLLMService` and `InworldRealtimeLLMService` are excluded for now: Gemini Live's async-tool path needs deeper investigation, and Inworld appears to have a pre-existing problem with even simple tool calling on its Realtime API.

changelog/4448.added.md

@@ -1 +0,0 @@
-- Added `cancel_on_interruption=False` support for `GeminiLiveLLMService` on models that support Gemini's NON_BLOCKING tool mechanism (currently Gemini 2.x); the conversation now continues while the tool runs. On models that don't yet support NON_BLOCKING (Gemini 3.x), the service surfaces a one-time warning explaining the limitation. (Note: an intermittent 1008 error can occasionally fire on Gemini 2.5 during long-running tool calls; we auto-reconnect.)

changelog/4449.changed.md

@@ -1 +0,0 @@
-- Moved `create_task`, `cancel_task`, the `task_manager` property, and `setup(task_manager)` up from `FrameProcessor` to `BaseObject`. Custom `BaseObject` subclasses (turn strategies, controllers, etc.) now inherit these methods directly instead of reimplementing the task manager wiring. Owners propagate the task manager to their child `BaseObject`s via `await child.setup(task_manager)`.

changelog/4450.changed.md

@@ -1 +0,0 @@
-- Changed the default OpenAI Realtime input audio transcription model from `gpt-4o-transcribe` to `gpt-realtime-whisper` for both `OpenAIRealtimeSTTService` and `OpenAIRealtimeLLMService`. The new model does not accept the `prompt` parameter; if a prompt is supplied alongside `gpt-realtime-whisper`, it is dropped automatically and a warning is logged. To keep using prompt hints, explicitly pin `model="gpt-4o-transcribe"` (or `"gpt-4o-mini-transcribe"`).

changelog/4462.changed.md

@@ -1 +0,0 @@
-- Updated the default model for `CartesiaTTSService` and `CartesiaHttpTTSService` from `sonic-3` to `sonic-3.5`.

changelog/4464.added.2.md

@@ -1 +0,0 @@
-- Added NVIDIA Magpie TTS services via AWS SageMaker: `NvidiaSageMakerHTTPTTSService` (single HTTP invocation, streams raw PCM back) and `NvidiaSageMakerWebsocketTTSService` (persistent HTTP/2 bidi-stream with full interruption support via `InterruptibleTTSService`).

changelog/4464.added.md

@@ -1 +0,0 @@
-- Added `NvidiaSageMakerWebsocketSTTService` for streaming speech recognition using NVIDIA Nemotron ASR via an AWS SageMaker bidirectional-stream endpoint. Produces `InterimTranscriptionFrame` and `TranscriptionFrame` frames, is VAD-aware, and automatically reconnects on error.

changelog/4465.fixed.md

@@ -1 +0,0 @@
-- Fixed `OpenAIRealtimeLLMService` handling of multi-output-item responses (observed with `gpt-realtime-2`). A single response can now contain more than one audio item, and the first item's `audio.done` may arrive after the second item's deltas have started. Deltas still arrive strictly in playback order, so we continue to forward them as received (matching OpenAI's reference implementation). The fix removes spurious warnings, ensures truncation always targets the latest audio item, and emits a single bracketing `TTSStartedFrame`/`TTSStoppedFrame` pair per assistant turn (the Stopped is now pushed on `response.done`).

changelog/4470.added.md

@@ -1 +0,0 @@
-- Added support for `reasoning` configuration on `OpenAIRealtimeLLMService`, for use with reasoning-capable Realtime models such as `gpt-realtime-2`.

changelog/4472.changed.md

@@ -1 +0,0 @@
-- Changed the default model for `OpenAIRealtimeLLMService` from `gpt-realtime-1.5` to `gpt-realtime-2`.

changelog/4480.added.md

@@ -1 +0,0 @@
-- Added `wait_for_transcript_to_end_user_turn` on `LLMUserAggregatorParams` for pipelines where local turn detection drives a realtime service like Gemini Live. Set it to False to avoid unnecessary latency from transcript delay — the realtime service consumes user audio directly, so we don't need user transcripts in context before it can respond. The option makes it so that (1) turn strategies do not consider user transcripts, letting the user turn end sooner, and (2) user transcripts are then handled by the aggregator: a simple timer gives it time to gather those transcripts after the user turn ends, and once gathered, the aggregator emits a new `on_user_turn_message_finalized` event with the new user context message. The new event also fires in the default mode (coinciding with `on_user_turn_stopped`), so consumers that want the populated user transcript can subscribe to it uniformly. See `examples/realtime/realtime-gemini-live-local-vad.py` for the full pattern.

env.example

@@ -132,10 +132,6 @@ NOVITA_API_KEY=...
 
 # NVIDIA
 NVIDIA_API_KEY=...
-# For a full example of how to deploy to SageMaker, see:
-# https://github.com/pipecat-ai/pipecat-examples/tree/main/nvidia_sagemaker_example/deployment/aws-sagemaker-nvidia
-SAGEMAKER_ASR_ENDPOINT_NAME=...
-SAGEMAKER_MAGPIE_ENDPOINT_NAME=...
 
 # OpenAI
 OPENAI_API_KEY=...

examples/features/features-add-tool-change-messages.py

@@ -1,232 +0,0 @@
-#
-# Copyright (c) 2024-2026, Daily
-#
-# SPDX-License-Identifier: BSD 2-Clause License
-#
-
-"""Manual validation harness for the ``add_tool_change_messages`` feature.
-
-When tools change mid-conversation, LLMs can produce a few different
-flavors of tool-call-related hallucination:
-
-- **Forward hallucination** — calling a tool that has been removed.
-- **Negative hallucination** — refusing to call a tool that has been
-  re-added (because recent context is full of "I can't" responses).
-- **Hallucinated output when tools are unavailable** — making up an
-  answer rather than declining gracefully, or producing JSON that
-  *looks* like a tool call but is actually just an assistant text
-  response.
-
-The ``add_tool_change_messages`` feature mitigates these by appending a
-developer-role message to the conversation whenever ``LLMSetToolsFrame``
-changes the set of advertised tools, so the LLM stays in sync with what's
-actually available.
-
-This harness exercises all of those flavors by flipping the advertised
-tool set on a turn counter:
-
-    Phase 0 (turns 1–4):   weather tool ACTIVE — confirm baseline.
-    Phase 1 (turns 5–8):   tool REMOVED — keep asking for weather.
-    Phase 2 (turn 9+):     tool RE-ADDED — does the LLM call it again?
-
-Set ``ADD_TOOL_CHANGE_MESSAGES=0`` to disable the mitigation and see the
-unmitigated behavior. The default is ON so a fresh run shows the feature
-working.
-
-Defaults to Llama 3.1 8B Instruct via a locally-running Ollama —
-anecdotally one of the more hallucination-prone of the easily accessible
-models. Pull the model once with ``ollama pull llama3.1:8b`` and make
-sure ``ollama serve`` is running. Swap the LLM service to validate other
-providers.
-
-Run with::
-
-    uv run examples/features/features-add-tool-change-messages.py
-    ADD_TOOL_CHANGE_MESSAGES=0 uv run examples/features/features-add-tool-change-messages.py
-"""
-
-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, LLMSetToolsFrame
-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 NOT_GIVEN, LLMContext
-from pipecat.processors.aggregators.llm_response_universal import (
-    LLMContextAggregatorPair,
-    LLMUserAggregatorParams,
-)
-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.llm_service import FunctionCallParams
-from pipecat.services.ollama.llm import OLLamaLLMService
-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)
-
-# Default ON so a fresh run shows the feature working. Set to "0" to A/B
-# against the unmitigated behavior.
-ADD_TOOL_CHANGE_MESSAGES = os.environ.get("ADD_TOOL_CHANGE_MESSAGES", "1") == "1"
-
-
-async def fetch_weather_from_api(params: FunctionCallParams):
-    await params.result_callback({"conditions": "nice", "temperature": "75"})
-
-
-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 user's location.",
-        },
-    },
-    required=["location", "format"],
-)
-weather_tools = ToolsSchema(standard_tools=[weather_function])
-
-
-transport_params = {
-    "daily": lambda: DailyParams(audio_in_enabled=True, audio_out_enabled=True),
-    "twilio": lambda: FastAPIWebsocketParams(audio_in_enabled=True, audio_out_enabled=True),
-    "webrtc": lambda: TransportParams(audio_in_enabled=True, audio_out_enabled=True),
-}
-
-
-async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
-    logger.info(
-        f"Starting add_tool_change_messages demo bot "
-        f"(ADD_TOOL_CHANGE_MESSAGES={ADD_TOOL_CHANGE_MESSAGES})"
-    )
-
-    stt = DeepgramSTTService(api_key=os.environ["DEEPGRAM_API_KEY"])
-
-    tts = CartesiaTTSService(
-        api_key=os.environ["CARTESIA_API_KEY"],
-        settings=CartesiaTTSService.Settings(
-            voice="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
-        ),
-    )
-
-    llm = OLLamaLLMService(
-        settings=OLLamaLLMService.Settings(
-            # Llama 3.1 8B Instruct is anecdotally one of the more
-            # hallucination-prone of the easily accessible models — exactly
-            # what we want for this validation harness. Pull it with
-            # ``ollama pull llama3.1:8b`` and make sure ``ollama serve``
-            # is running.
-            model="llama3.1:8b",
-            system_instruction=(
-                "You are a helpful assistant in a voice conversation. Your responses "
-                "will be spoken aloud, so avoid emojis, bullet points, or other "
-                "formatting that can't be spoken. Respond briefly and naturally. "
-                "If the user asks for the current weather, use the `get_current_weather` "
-                "function if it's available. IMPORTANT: if you do not have access to the function, "
-                "say something along the lines of 'Sorry, I can't check the weather right now.'."
-            ),
-        ),
-    )
-    llm.register_function("get_current_weather", fetch_weather_from_api)
-
-    context = LLMContext(tools=weather_tools)
-    user_aggregator, assistant_aggregator = LLMContextAggregatorPair(
-        context,
-        user_params=LLMUserAggregatorParams(vad_analyzer=SileroVADAnalyzer()),
-        add_tool_change_messages=ADD_TOOL_CHANGE_MESSAGES,
-    )
-
-    pipeline = Pipeline(
-        [
-            transport.input(),
-            stt,
-            user_aggregator,
-            llm,
-            tts,
-            transport.output(),
-            assistant_aggregator,
-        ]
-    )
-
-    task = PipelineTask(
-        pipeline,
-        params=PipelineParams(enable_metrics=True, enable_usage_metrics=True),
-        idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
-    )
-
-    # Phase controller: roughly 4 turns per phase.
-    user_turn_count = 0
-    REMOVE_AT_TURN = 5  # tool gone for turn N onward
-    READD_AT_TURN = 9  # tool back for turn N onward
-
-    @user_aggregator.event_handler("on_user_turn_stopped")
-    async def on_user_turn_stopped(aggregator, strategy, message):
-        nonlocal user_turn_count
-        user_turn_count += 1
-        logger.info(f"=== User turn {user_turn_count} complete ===")
-
-        if user_turn_count == REMOVE_AT_TURN - 1:
-            logger.info(
-                "=== Phase 1: weather tool REMOVED. Keep asking about the weather "
-                "to exercise hallucination scenarios. ==="
-            )
-            await task.queue_frame(LLMSetToolsFrame(tools=NOT_GIVEN))
-        elif user_turn_count == READD_AT_TURN - 1:
-            logger.info(
-                "=== Phase 2: weather tool RE-ADDED. Ask for the weather again — "
-                "does the LLM call it, or keep refusing? (THIS IS THE TEST.) ==="
-            )
-            await task.queue_frame(LLMSetToolsFrame(tools=weather_tools))
-
-    @transport.event_handler("on_client_connected")
-    async def on_client_connected(transport, client):
-        logger.info("Client connected")
-        logger.info(
-            "=== Phase 0: weather tool ACTIVE. Ask for the weather a few times "
-            "to confirm it's working. ==="
-        )
-        context.add_message(
-            {
-                "role": "developer",
-                "content": (
-                    "Please introduce yourself briefly to the user, then invite them "
-                    "to ask about the weather."
-                ),
-            }
-        )
-        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/function-calling/function-calling-missing-handler.py

@@ -1,187 +0,0 @@
-#
-# Copyright (c) 2024-2026, Daily
-#
-# SPDX-License-Identifier: BSD 2-Clause License
-#
-
-"""Manual demonstration of the missing-handler (developer-error) recovery path.
-
-When a tool is advertised to the LLM via ``tools``/``LLMContext`` but
-the developer forgets to call ``llm.register_function(...)`` to wire up
-its handler, the LLM happily emits a tool call and then... nothing
-happens on the Pipecat side, leaving the conversation stuck.
-
-Pipecat's recovery path (``LLMService._missing_function_call_handler``)
-catches this case:
-
-- Logs a ``logger.error`` distinguishing **developer error** (tool advertised
-  but no handler registered) from a hallucination (tool not advertised),
-  pointing at the missing ``register_function`` call.
-- Returns a neutral terminal tool result
-  (``LLMService.MISSING_FUNCTION_CALL_MESSAGE_TEMPLATE``: "The function
-  `X` is not currently available.") so the call still terminates with a
-  normal tool result instead of leaving the conversation stuck.
-
-This example is **deliberately broken**: the weather schema is in
-``tools`` but ``register_function`` is *not* called. Ask the bot about
-the weather and observe:
-
-1. The LLM emits a tool call for ``get_current_weather``.
-2. ``logger.error`` fires with "advertised … but has no registered handler
-   — did you forget to call register_function()?"
-3. The terminal tool result is fed back to the LLM.
-4. The LLM responds in voice based on that result (typically something
-   like "the weather function isn't available right now").
-
-Uses the OpenAI LLM service with defaults. Swap to another provider to
-validate this behavior elsewhere.
-"""
-
-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,
-    LLMUserAggregatorParams,
-)
-from pipecat.runner.types import RunnerArguments
-from pipecat.runner.utils import create_transport
-from pipecat.services.cartesia.tts import CartesiaTTSService
-from pipecat.services.deepgram.stt import DeepgramSTTService
-from pipecat.services.openai.llm import OpenAILLMService
-from pipecat.transports.base_transport import BaseTransport, TransportParams
-from pipecat.transports.daily.transport import DailyParams
-from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
-
-load_dotenv(override=True)
-
-
-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 user's location.",
-        },
-    },
-    required=["location", "format"],
-)
-weather_tools = ToolsSchema(standard_tools=[weather_function])
-
-
-transport_params = {
-    "daily": lambda: DailyParams(audio_in_enabled=True, audio_out_enabled=True),
-    "twilio": lambda: FastAPIWebsocketParams(audio_in_enabled=True, audio_out_enabled=True),
-    "webrtc": lambda: TransportParams(audio_in_enabled=True, audio_out_enabled=True),
-}
-
-
-async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
-    logger.info("Starting missing-handler demo bot (no handler is registered on purpose)")
-
-    stt = DeepgramSTTService(api_key=os.environ["DEEPGRAM_API_KEY"])
-
-    tts = CartesiaTTSService(
-        api_key=os.environ["CARTESIA_API_KEY"],
-        settings=CartesiaTTSService.Settings(
-            voice="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
-        ),
-    )
-
-    llm = OpenAILLMService(
-        api_key=os.environ["OPENAI_API_KEY"],
-        settings=OpenAILLMService.Settings(
-            system_instruction=(
-                "You are a helpful assistant in a voice conversation. Your responses "
-                "will be spoken aloud, so avoid emojis, bullet points, or other "
-                "formatting that can't be spoken. Respond briefly and naturally. "
-                "Always use the get_current_weather function to answer questions "
-                "about the current weather."
-            ),
-        ),
-    )
-
-    # *** DELIBERATELY OMITTED ***
-    # The whole point of this example is to demonstrate the missing-handler
-    # recovery path. Re-add this line to wire the tool up correctly:
-    #
-    # llm.register_function("get_current_weather", fetch_weather_from_api)
-
-    context = LLMContext(tools=weather_tools)
-    user_aggregator, assistant_aggregator = LLMContextAggregatorPair(
-        context,
-        user_params=LLMUserAggregatorParams(vad_analyzer=SileroVADAnalyzer()),
-    )
-
-    pipeline = Pipeline(
-        [
-            transport.input(),
-            stt,
-            user_aggregator,
-            llm,
-            tts,
-            transport.output(),
-            assistant_aggregator,
-        ]
-    )
-
-    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("Client connected")
-        logger.info(
-            "=== Ask for the weather. Watch for a logger.error about the missing "
-            "handler, and listen for the LLM's response based on the recovery "
-            "message. ==="
-        )
-        context.add_message(
-            {
-                "role": "developer",
-                "content": (
-                    "Please introduce yourself briefly to the user, then invite "
-                    "them to ask about the weather."
-                ),
-            }
-        )
-        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/function-calling/function-calling-openai-async.py

@@ -29,7 +29,7 @@ from pipecat.runner.types import RunnerArguments
 from pipecat.runner.utils import create_transport
 from pipecat.services.llm_service import FunctionCallParams
 from pipecat.services.openai.llm import OpenAILLMService
-from pipecat.services.openai.stt import OpenAIRealtimeSTTService
+from pipecat.services.openai.stt import OpenAISTTService
 from pipecat.services.openai.tts import OpenAITTSService
 from pipecat.transports.base_transport import BaseTransport, TransportParams
 from pipecat.transports.daily.transport import DailyParams
@@ -69,7 +69,13 @@ transport_params = {
 async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
     logger.info(f"Starting bot")
 
-    stt = OpenAIRealtimeSTTService(api_key=os.environ["OPENAI_API_KEY"])
+    stt = OpenAISTTService(
+        api_key=os.environ["OPENAI_API_KEY"],
+        settings=OpenAISTTService.Settings(
+            model="gpt-4o-transcribe",
+            prompt="Expect words related weather, such as temperature and conditions. And restaurant names.",
+        ),
+    )
 
     tts = OpenAITTSService(
         api_key=os.environ["OPENAI_API_KEY"],

examples/function-calling/function-calling-openai.py

@@ -25,7 +25,7 @@ from pipecat.runner.types import RunnerArguments
 from pipecat.runner.utils import create_transport
 from pipecat.services.llm_service import FunctionCallParams
 from pipecat.services.openai.llm import OpenAILLMService
-from pipecat.services.openai.stt import OpenAIRealtimeSTTService
+from pipecat.services.openai.stt import OpenAISTTService
 from pipecat.services.openai.tts import OpenAITTSService
 from pipecat.transports.base_transport import BaseTransport, TransportParams
 from pipecat.transports.daily.transport import DailyParams
@@ -63,7 +63,13 @@ transport_params = {
 async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
     logger.info(f"Starting bot")
 
-    stt = OpenAIRealtimeSTTService(api_key=os.environ["OPENAI_API_KEY"])
+    stt = OpenAISTTService(
+        api_key=os.environ["OPENAI_API_KEY"],
+        settings=OpenAISTTService.Settings(
+            model="gpt-4o-transcribe",
+            prompt="Expect words related weather, such as temperature and conditions. And restaurant names.",
+        ),
+    )
 
     tts = OpenAITTSService(
         api_key=os.environ["OPENAI_API_KEY"],

examples/function-calling/function-calling-tool-resources.py

@@ -4,33 +4,23 @@
 # SPDX-License-Identifier: BSD 2-Clause License
 #
 
-"""Example demonstrating ``PipelineTask(app_resources=...)``.
+"""Example demonstrating ``PipelineTask(tool_resources=...)``.
 
-``app_resources`` is an application-defined bag of anything your
-application code may want to share across a session: database handles,
-HTTP clients, feature flags, per-user state, observability clients,
-in-memory caches — whatever fits your app. Pipecat passes it through
-untouched and exposes it as ``task.app_resources``, so any code with a
-handle on the task can read or mutate it.
+``tool_resources`` is an application-defined bag of anything you want every
+tool handler in a session to share by reference: database handles, HTTP
+clients, feature flags, per-user state, observability clients, in-memory
+caches — whatever fits your app. Pipecat passes it through untouched as
+``FunctionCallParams.tool_resources``.
 
-Two of the convenience aliases exercised below:
+This example uses a small ``ToolCallLogger`` as a stand-in for that "shared
+thing". A real app might just as easily pass a Postgres pool, a Redis
+client, a Stripe SDK instance, or any combination thereof. The mechanics
+shown here — construct once, hand to the task, read it from each handler,
+inspect it after the session — are the same regardless of what you put in.
 
-- Tool handlers read it from ``FunctionCallParams.app_resources``.
-- Custom ``FrameProcessor`` subclasses read it from
-  ``self.pipeline_task.app_resources``.
-
-This example uses two small loggers as stand-ins for that "shared thing":
-``ToolCallLogger`` (written from tool handlers) and
-``TranscriptionLogger`` (written from a custom ``FrameProcessor`` that
-sits in the pipeline). A real app might just as easily pass a Postgres
-pool, a Redis client, a Stripe SDK instance, or any combination thereof.
-The mechanics shown here — construct once, hand to the task, read it
-from each site, inspect it after the session — are the same regardless
-of what you put in.
-
-We bundle resources in a typed ``AppResources`` dataclass and cast back
-to it at each read site. Pipecat doesn't care what type you pass (a
-plain dict works too), but a typed container gives you autocomplete and
+We bundle resources in a typed ``SessionResources`` dataclass and cast back
+to it at the top of each handler. Pipecat doesn't care what type you pass
+(a plain dict works too), but a typed container gives you autocomplete and
 refactor safety instead of dict-by-string-key lookups.
 """
 
@@ -38,7 +28,7 @@ import json
 import os
 from collections.abc import Mapping
 from dataclasses import dataclass
-from datetime import UTC, datetime
+from datetime import UTC, datetime, timezone
 from typing import Any, cast
 
 from dotenv import load_dotenv
@@ -47,7 +37,7 @@ 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 Frame, LLMRunFrame, TranscriptionFrame, TTSSpeakFrame
+from pipecat.frames.frames import LLMRunFrame, TTSSpeakFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
@@ -56,7 +46,6 @@ from pipecat.processors.aggregators.llm_response_universal import (
     LLMContextAggregatorPair,
     LLMUserAggregatorParams,
 )
-from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
 from pipecat.runner.types import RunnerArguments
 from pipecat.runner.utils import create_transport
 from pipecat.services.cartesia.tts import CartesiaTTSService
@@ -97,80 +86,30 @@ class ToolCallLogger:
         return json.dumps(self._calls, indent=2)
 
 
-class TranscriptionLogger:
-    """Records final user transcriptions — written from a custom FrameProcessor."""
-
-    def __init__(self):
-        """Initialize the logger with an empty list of recorded transcriptions."""
-        self._entries: list[dict[str, Any]] = []
-
-    def log_transcription(self, text: str) -> None:
-        """Record a transcription.
-
-        Args:
-            text: The transcribed user utterance.
-        """
-        entry = {
-            "timestamp": datetime.now(UTC).isoformat(),
-            "text": text,
-        }
-        self._entries.append(entry)
-        logger.info(f"[TranscriptionLogger] {text!r}")
-
-    def dump(self) -> str:
-        """Return all recorded transcriptions as a JSON string."""
-        return json.dumps(self._entries, indent=2)
-
-
 @dataclass
-class AppResources:
-    """Typed container for everything the app shares across this session.
+class SessionResources:
+    """Typed container for everything the tool handlers in this session share.
 
     Add fields here as the app grows (e.g. ``db: AsyncConnection``,
-    ``http: httpx.AsyncClient``). Read sites ``cast()`` to this type to
-    get autocomplete and refactor safety:
-
-    - In tools: ``cast(AppResources, params.app_resources)``.
-    - In custom processors: ``cast(AppResources, self.pipeline_task.app_resources)``.
+    ``http: httpx.AsyncClient``). Handlers ``cast()`` ``params.tool_resources``
+    to this type to get autocomplete and refactor safety.
     """
 
     tool_call_logger: ToolCallLogger
-    transcription_logger: TranscriptionLogger
 
 
 async def fetch_weather_from_api(params: FunctionCallParams):
-    resources = cast(AppResources, params.app_resources)
+    resources = cast(SessionResources, params.tool_resources)
     resources.tool_call_logger.log_tool_call(params.function_name, params.arguments)
     await params.result_callback({"conditions": "nice", "temperature": "75"})
 
 
 async def fetch_restaurant_recommendation(params: FunctionCallParams):
-    resources = cast(AppResources, params.app_resources)
+    resources = cast(SessionResources, params.tool_resources)
     resources.tool_call_logger.log_tool_call(params.function_name, params.arguments)
     await params.result_callback({"name": "The Golden Dragon"})
 
 
-class TranscriptionLoggingProcessor(FrameProcessor):
-    """Logs each final user transcription into the shared app resources.
-
-    Demonstrates the second read site for ``app_resources``: any custom
-    ``FrameProcessor`` can reach the same bag every tool handler sees by
-    going through ``self.pipeline_task.app_resources``. ``pipeline_task``
-    is ``None`` until the task sets the processor up, so we guard against
-    that case.
-    """
-
-    async def process_frame(self, frame: Frame, direction: FrameDirection):
-        """Forward all frames; log final user transcriptions on the way through."""
-        await super().process_frame(frame, direction)
-
-        if isinstance(frame, TranscriptionFrame) and self.pipeline_task is not None:
-            resources = cast(AppResources, self.pipeline_task.app_resources)
-            resources.transcription_logger.log_transcription(frame.text)
-
-        await self.push_frame(frame, direction)
-
-
 # We use lambdas to defer transport parameter creation until the transport
 # type is selected at runtime.
 transport_params = {
@@ -264,7 +203,6 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         [
             transport.input(),
             stt,
-            TranscriptionLoggingProcessor(),
             user_aggregator,
             llm,
             tts,
@@ -273,14 +211,10 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         ]
     )
 
-    # Keep local handles so we can read collected state after the session
+    # Keep a local handle so we can read collected state after the session
     # ends; Pipecat never copies or clears the object.
     tool_call_logger = ToolCallLogger()
-    transcription_logger = TranscriptionLogger()
-    resources = AppResources(
-        tool_call_logger=tool_call_logger,
-        transcription_logger=transcription_logger,
-    )
+    resources = SessionResources(tool_call_logger=tool_call_logger)
 
     task = PipelineTask(
         pipeline,
@@ -289,7 +223,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
             enable_usage_metrics=True,
         ),
         idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
-        app_resources=resources,
+        tool_resources=resources,
     )
 
     @transport.event_handler("on_client_connected")
@@ -312,7 +246,6 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
 
     # The session has ended; read whatever state the handlers built up.
     logger.info(f"Tool calls logged during session:\n{tool_call_logger.dump()}")
-    logger.info(f"Transcriptions logged during session:\n{transcription_logger.dump()}")
 
 
 async def bot(runner_args: RunnerArguments):

examples/realtime/realtime-aws-nova-sonic-async-tool.py

@@ -1,184 +0,0 @@
-#
-# Copyright (c) 2024-2026, Daily
-#
-# SPDX-License-Identifier: BSD 2-Clause License
-#
-
-"""Example: async function call with the AWS Nova Sonic LLM service.
-
-The ``get_current_weather`` tool is registered with
-``cancel_on_interruption=False`` and simulates a slow API call (10s sleep).
-While the call is in flight the conversation continues; the result arrives
-later via the async-tool mechanism and is forwarded to Nova Sonic via the
-formal toolResult channel so the model can integrate it naturally into its
-next turn.
-"""
-
-import asyncio
-import os
-import random
-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.llm_context import LLMContext
-from pipecat.processors.aggregators.llm_response_universal import (
-    LLMContextAggregatorPair,
-    LLMUserAggregatorParams,
-)
-from pipecat.runner.types import RunnerArguments
-from pipecat.runner.utils import create_transport
-from pipecat.services.aws.nova_sonic.llm import AWSNovaSonicLLMService
-from pipecat.services.llm_service import FunctionCallParams
-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):
-    # Simulate a long-running API call so we can demonstrate that the
-    # conversation continues while the tool is in flight.
-    await asyncio.sleep(10)
-    temperature = (
-        random.randint(60, 85)
-        if params.arguments["format"] == "fahrenheit"
-        else random.randint(15, 30)
-    )
-    await params.result_callback(
-        {
-            "conditions": "nice",
-            "temperature": temperature,
-            "location": params.arguments["location"],
-            "format": params.arguments["format"],
-            "timestamp": datetime.now().strftime("%Y%m%d_%H%M%S"),
-        }
-    )
-
-
-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"],
-)
-
-tools = ToolsSchema(standard_tools=[weather_function])
-
-
-transport_params = {
-    "daily": lambda: DailyParams(
-        audio_in_enabled=True,
-        audio_out_enabled=True,
-    ),
-    "twilio": lambda: FastAPIWebsocketParams(
-        audio_in_enabled=True,
-        audio_out_enabled=True,
-    ),
-    "webrtc": lambda: TransportParams(
-        audio_in_enabled=True,
-        audio_out_enabled=True,
-    ),
-}
-
-
-async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
-    logger.info(f"Starting bot")
-
-    system_instruction = (
-        "You are a friendly assistant. The user and you will engage in a spoken "
-        "dialog exchanging the transcripts of a natural real-time conversation. "
-        "Keep your responses short, generally two or three sentences for chatty "
-        "scenarios. When the user asks for the weather, call get_current_weather. "
-        "While you wait for the result, keep chatting with the user. When the "
-        "result arrives, share it with the user naturally."
-    )
-
-    llm = AWSNovaSonicLLMService(
-        secret_access_key=os.environ["AWS_SECRET_ACCESS_KEY"],
-        access_key_id=os.environ["AWS_ACCESS_KEY_ID"],
-        region=os.environ["AWS_REGION"],
-        session_token=os.getenv("AWS_SESSION_TOKEN"),
-        settings=AWSNovaSonicLLMService.Settings(
-            voice="tiffany",
-            system_instruction=system_instruction,
-        ),
-    )
-
-    llm.register_function(
-        "get_current_weather",
-        fetch_weather_from_api,
-        cancel_on_interruption=False,
-    )
-
-    context = LLMContext(tools=tools)
-    user_aggregator, assistant_aggregator = LLMContextAggregatorPair(
-        context,
-        user_params=LLMUserAggregatorParams(vad_analyzer=SileroVADAnalyzer()),
-    )
-
-    pipeline = Pipeline(
-        [
-            transport.input(),
-            user_aggregator,
-            llm,
-            transport.output(),
-            assistant_aggregator,
-        ]
-    )
-
-    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")
-        context.add_message(
-            {"role": "developer", "content": "Please introduce yourself to the user."}
-        )
-        await task.queue_frames([LLMRunFrame()])
-
-    @transport.event_handler("on_client_disconnected")
-    async def on_client_disconnected(transport, client):
-        logger.info(f"Client disconnected")
-        await task.cancel()
-
-    runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
-    await runner.run(task)
-
-
-async def bot(runner_args: RunnerArguments):
-    """Main bot entry point compatible with Pipecat Cloud."""
-    transport = await create_transport(runner_args, transport_params)
-    await run_bot(transport, runner_args)
-
-
-if __name__ == "__main__":
-    from pipecat.runner.run import main
-
-    main()

examples/realtime/realtime-aws-nova-sonic.py

@@ -46,6 +46,11 @@ async def fetch_weather_from_api(params: FunctionCallParams):
         if params.arguments["format"] == "fahrenheit"
         else random.randint(15, 30)
     )
+    # Simulate a long network delay.
+    # You can continue chatting while waiting for this to complete.
+    # With Nova 2 Sonic (the default model), the assistant will respond
+    # appropriately once the function call is complete.
+    await asyncio.sleep(5)
     await params.result_callback(
         {
             "conditions": "nice",
@@ -145,7 +150,9 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
     # Register function for function calls
     # 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_current_weather", fetch_weather_from_api, cancel_on_interruption=False
+    )
 
     # Set up context and context management.
     context = LLMContext(tools=tools)

examples/realtime/realtime-azure-async-tool.py

@@ -1,195 +0,0 @@
-#
-# Copyright (c) 2024-2026, Daily
-#
-# SPDX-License-Identifier: BSD 2-Clause License
-#
-
-"""Example: async function call with the Azure Realtime LLM service.
-
-The ``get_current_weather`` tool is registered with
-``cancel_on_interruption=False`` and simulates a slow API call (10s sleep).
-While the call is in flight the conversation continues; the result arrives
-later via the async-tool mechanism and is forwarded to Azure Realtime as a
-``function_call_output`` so the model can integrate it naturally into its
-next turn.
-"""
-
-import asyncio
-import os
-import random
-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.llm_context import LLMContext
-from pipecat.processors.aggregators.llm_response_universal import (
-    LLMContextAggregatorPair,
-    LLMUserAggregatorParams,
-)
-from pipecat.runner.types import RunnerArguments
-from pipecat.runner.utils import create_transport
-from pipecat.services.azure.realtime.llm import AzureRealtimeLLMService
-from pipecat.services.llm_service import FunctionCallParams
-from pipecat.services.openai.realtime.events import (
-    AudioConfiguration,
-    AudioInput,
-    InputAudioTranscription,
-    SessionProperties,
-)
-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):
-    # Simulate a long-running API call so we can demonstrate that the
-    # conversation continues while the tool is in flight.
-    await asyncio.sleep(10)
-    temperature = (
-        random.randint(60, 85)
-        if params.arguments["format"] == "fahrenheit"
-        else random.randint(15, 30)
-    )
-    await params.result_callback(
-        {
-            "conditions": "nice",
-            "temperature": temperature,
-            "location": params.arguments["location"],
-            "format": params.arguments["format"],
-            "timestamp": datetime.now().strftime("%Y%m%d_%H%M%S"),
-        }
-    )
-
-
-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"],
-)
-
-tools = ToolsSchema(standard_tools=[weather_function])
-
-
-system_instruction = (
-    "You are a friendly assistant. The user and you will engage in a spoken "
-    "dialog exchanging the transcripts of a natural real-time conversation. "
-    "Keep your responses short, generally two or three sentences for chatty "
-    "scenarios. When the user asks for the weather, call get_current_weather. "
-    "While you wait for the result, keep chatting with the user. When the "
-    "result arrives, share it with the user naturally."
-)
-
-
-transport_params = {
-    "daily": lambda: DailyParams(
-        audio_in_enabled=True,
-        audio_out_enabled=True,
-    ),
-    "twilio": lambda: FastAPIWebsocketParams(
-        audio_in_enabled=True,
-        audio_out_enabled=True,
-    ),
-    "webrtc": lambda: TransportParams(
-        audio_in_enabled=True,
-        audio_out_enabled=True,
-    ),
-}
-
-
-async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
-    logger.info(f"Starting bot")
-
-    llm = AzureRealtimeLLMService(
-        api_key=os.environ["AZURE_REALTIME_API_KEY"],
-        base_url=os.environ["AZURE_REALTIME_BASE_URL"],
-        settings=AzureRealtimeLLMService.Settings(
-            system_instruction=system_instruction,
-            session_properties=SessionProperties(
-                audio=AudioConfiguration(
-                    input=AudioInput(
-                        transcription=InputAudioTranscription(model="whisper-1"),
-                    )
-                ),
-            ),
-        ),
-    )
-
-    llm.register_function(
-        "get_current_weather",
-        fetch_weather_from_api,
-        cancel_on_interruption=False,
-    )
-
-    context = LLMContext(tools=tools)
-    user_aggregator, assistant_aggregator = LLMContextAggregatorPair(
-        context,
-        user_params=LLMUserAggregatorParams(vad_analyzer=SileroVADAnalyzer()),
-    )
-
-    pipeline = Pipeline(
-        [
-            transport.input(),
-            user_aggregator,
-            llm,
-            transport.output(),
-            assistant_aggregator,
-        ]
-    )
-
-    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")
-        context.add_message(
-            {"role": "developer", "content": "Please introduce yourself to the user."}
-        )
-        await task.queue_frames([LLMRunFrame()])
-
-    @transport.event_handler("on_client_disconnected")
-    async def on_client_disconnected(transport, client):
-        logger.info(f"Client disconnected")
-        await task.cancel()
-
-    runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
-    await runner.run(task)
-
-
-async def bot(runner_args: RunnerArguments):
-    """Main bot entry point compatible with Pipecat Cloud."""
-    transport = await create_transport(runner_args, transport_params)
-    await run_bot(transport, runner_args)
-
-
-if __name__ == "__main__":
-    from pipecat.runner.run import main
-
-    main()

examples/realtime/realtime-gemini-live-function-calling.py

@@ -4,25 +4,15 @@
 # SPDX-License-Identifier: BSD 2-Clause License
 #
 
-"""Example: async function call with the Gemini Live LLM service.
 
-The ``get_current_weather`` tool is registered with
-``cancel_on_interruption=False`` and simulates a slow API call (10s sleep).
-While the call is in flight the conversation continues; the result arrives
-later via the async-tool mechanism and is forwarded to Gemini Live as a
-FunctionResponse so the model can integrate it naturally into its next turn.
-"""
-
-import asyncio
 import os
-import random
 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.adapters.schemas.tools_schema import AdapterType, ToolsSchema
 from pipecat.frames.frames import LLMRunFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
@@ -41,55 +31,33 @@ load_dotenv(override=True)
 
 
 async def fetch_weather_from_api(params: FunctionCallParams):
-    # Simulate a long-running API call so we can demonstrate that the
-    # conversation continues while the tool is in flight.
-    await asyncio.sleep(10)
-    temperature = (
-        random.randint(60, 85)
-        if params.arguments["format"] == "fahrenheit"
-        else random.randint(15, 30)
-    )
+    temperature = 75 if params.arguments["format"] == "fahrenheit" else 24
     await params.result_callback(
         {
             "conditions": "nice",
             "temperature": temperature,
-            "location": params.arguments["location"],
             "format": params.arguments["format"],
             "timestamp": datetime.now().strftime("%Y%m%d_%H%M%S"),
         }
     )
 
 
-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 user's location.",
-        },
-    },
-    required=["location", "format"],
-)
-
-tools = ToolsSchema(standard_tools=[weather_function])
+async def fetch_restaurant_recommendation(params: FunctionCallParams):
+    await params.result_callback({"name": "The Golden Dragon"})
 
 
-system_instruction = (
-    "You are a friendly assistant. The user and you will engage in a spoken "
-    "dialog exchanging the transcripts of a natural real-time conversation. "
-    "Keep your responses short, generally two or three sentences for chatty "
-    "scenarios. When the user asks for the weather, call get_current_weather. "
-    "While you wait for the result, keep chatting with the user. When the "
-    "result arrives, share it with the user naturally."
-)
+system_instruction = """
+You are a helpful assistant who can answer questions and use tools.
+
+You have three tools available to you:
+1. get_current_weather: Use this tool to get the current weather in a specific location.
+2. get_restaurant_recommendation: Use this tool to get a restaurant recommendation in a specific location.
+3. google_search: Use this tool to search the web for information.
+"""
 
 
+# We use lambdas to defer transport parameter creation until the transport
+# type is selected at runtime.
 transport_params = {
     "daily": lambda: DailyParams(
         audio_in_enabled=True,
@@ -109,6 +77,42 @@ transport_params = {
 async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
     logger.info(f"Starting bot")
 
+    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 user's 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"],
+    )
+    search_tool = {"google_search": {}}
+    # KNOWN ISSUE: If using GeminiVertexLiveLLMService, it appears
+    # you cannot use the "google_search" tool alongside other tools.
+    # See https://github.com/googleapis/python-genai/issues/941.
+    tools = ToolsSchema(
+        standard_tools=[weather_function, restaurant_function],
+        custom_tools={AdapterType.GEMINI: [search_tool]},
+    )
+
     llm = GeminiLiveLLMService(
         api_key=os.environ["GOOGLE_API_KEY"],
         settings=GeminiLiveLLMService.Settings(
@@ -117,12 +121,13 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         tools=tools,
     )
 
-    llm.register_function(
-        "get_current_weather",
-        fetch_weather_from_api,
-        cancel_on_interruption=False,
-    )
+    llm.register_function("get_current_weather", fetch_weather_from_api)
+    llm.register_function("get_restaurant_recommendation", fetch_restaurant_recommendation)
 
+    # You can provide the system instructions and tools in the context rather
+    # than as arguments to GeminiLiveLLMService, but note that doing so will
+    # trigger a (fast) reconnection when the GeminiLiveLLMService first
+    # receives the context (i.e. when we send the LLMRunFrame below).
     context = LLMContext()
     # Server-side VAD is enabled by default; no local VAD is added.
     user_aggregator, assistant_aggregator = LLMContextAggregatorPair(context)
@@ -149,6 +154,7 @@ 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")
+        # Kick off the conversation.
         context.add_message(
             {"role": "developer", "content": "Please introduce yourself to the user."}
         )
@@ -160,6 +166,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         await task.cancel()
 
     runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
+
     await runner.run(task)
 
 

examples/realtime/realtime-gemini-live-local-vad.py

@@ -20,7 +20,6 @@ from pipecat.processors.aggregators.llm_response_universal import (
     AssistantTurnStoppedMessage,
     LLMContextAggregatorPair,
     LLMUserAggregatorParams,
-    UserMessageFinalizedMessage,
     UserTurnStoppedMessage,
 )
 from pipecat.runner.types import RunnerArguments
@@ -71,25 +70,10 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
             },
         ],
     )
-    # `wait_for_transcript_to_end_user_turn=False` is the right setting
-    # for pipelines like this one — local turn detection driving a
-    # realtime service. It avoids unnecessary latency from transcript
-    # delay: the realtime service consumes user audio directly, so
-    # we don't need user transcripts in context before it can respond.
-    # With this option:
-    #
-    # - Turn strategies do not consider user transcripts, so the user
-    #   turn ends sooner.
-    # - User transcripts are handled by the aggregator: a simple
-    #   post-turn transcript wait gives them time to arrive after the
-    #   user turn ends, then the aggregator emits
-    #   `on_user_turn_message_finalized` with the new user context
-    #   message.
     user_aggregator, assistant_aggregator = LLMContextAggregatorPair(
         context,
         user_params=LLMUserAggregatorParams(
             vad_analyzer=SileroVADAnalyzer(),
-            wait_for_transcript_to_end_user_turn=False,
         ),
     )
 
@@ -123,23 +107,8 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         logger.info(f"Client disconnected")
         await task.cancel()
 
-    # `on_user_turn_stopped` fires at the end of the user turn. With
-    # `wait_for_transcript_to_end_user_turn=False`, no user
-    # transcripts have arrived yet at this point, so
-    # `message.content` is empty. Logged here to make the end-of-turn
-    # signal visible alongside the later finalization event.
     @user_aggregator.event_handler("on_user_turn_stopped")
     async def on_user_turn_stopped(aggregator, strategy, message: UserTurnStoppedMessage):
-        logger.info(f"User turn ended (strategy: {type(strategy).__name__})")
-
-    # `on_user_turn_message_finalized` fires when the user message has
-    # been finalized into the context. Here it fires later than
-    # `on_user_turn_stopped`, after the aggregator's post-turn
-    # transcript wait completes.
-    @user_aggregator.event_handler("on_user_turn_message_finalized")
-    async def on_user_turn_message_finalized(
-        aggregator, strategy, message: UserMessageFinalizedMessage
-    ):
         timestamp = f"[{message.timestamp}] " if message.timestamp else ""
         line = f"{timestamp}user: {message.content}"
         logger.info(f"Transcript: {line}")

examples/realtime/realtime-gemini-live.py

@@ -6,13 +6,10 @@
 
 
 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 AdapterType, ToolsSchema
 from pipecat.frames.frames import LLMRunFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
@@ -26,7 +23,6 @@ from pipecat.processors.aggregators.llm_response_universal import (
 from pipecat.runner.types import RunnerArguments
 from pipecat.runner.utils import create_transport
 from pipecat.services.google.gemini_live.llm import GeminiLiveLLMService
-from pipecat.services.llm_service import FunctionCallParams
 from pipecat.transports.base_transport import BaseTransport, TransportParams
 from pipecat.transports.daily.transport import DailyParams
 from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
@@ -34,32 +30,6 @@ 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"})
-
-
-system_instruction = """
-You are a helpful assistant who can answer questions and use tools.
-
-You have three tools available to you:
-1. get_current_weather: Use this tool to get the current weather in a specific location.
-2. get_restaurant_recommendation: Use this tool to get a restaurant recommendation in a specific location.
-3. google_search: Use this tool to search the web for information.
-"""
-
-
 # We use lambdas to defer transport parameter creation until the transport
 # type is selected at runtime.
 transport_params = {
@@ -81,55 +51,23 @@ transport_params = {
 async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
     logger.info(f"Starting bot")
 
-    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 user's 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"],
-    )
-    search_tool = {"google_search": {}}
-    # KNOWN ISSUE: If using GeminiVertexLiveLLMService, it appears
-    # you cannot use the "google_search" tool alongside other tools.
-    # See https://github.com/googleapis/python-genai/issues/941.
-    tools = ToolsSchema(
-        standard_tools=[weather_function, restaurant_function],
-        custom_tools={AdapterType.GEMINI: [search_tool]},
-    )
-
     llm = GeminiLiveLLMService(
         api_key=os.environ["GOOGLE_API_KEY"],
         settings=GeminiLiveLLMService.Settings(
-            system_instruction=system_instruction,
             voice="Aoede",  # Puck, Charon, Kore, Fenrir, Aoede
+            # system_instruction="Talk like a pirate."
         ),
-        tools=tools,
+        # inference_on_context_initialization=False,
     )
 
-    llm.register_function("get_current_weather", fetch_weather_from_api)
-    llm.register_function("get_restaurant_recommendation", fetch_restaurant_recommendation)
-
-    context = LLMContext()
+    context = LLMContext(
+        [
+            {
+                "role": "user",
+                "content": "Say hello. Then ask if I want to hear a joke.",
+            },
+        ],
+    )
     # Server-side VAD is enabled by default; no local VAD is added.
     user_aggregator, assistant_aggregator = LLMContextAggregatorPair(context)
 
@@ -156,9 +94,6 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
     async def on_client_connected(transport, client):
         logger.info(f"Client connected")
         # Kick off the conversation.
-        context.add_message(
-            {"role": "developer", "content": "Please introduce yourself to the user."}
-        )
         await task.queue_frames([LLMRunFrame()])
 
     @transport.event_handler("on_client_disconnected")

examples/realtime/realtime-grok-async-tool.py

@@ -1,179 +0,0 @@
-#
-# Copyright (c) 2024-2026, Daily
-#
-# SPDX-License-Identifier: BSD 2-Clause License
-#
-
-"""Example: async function call with the Grok Realtime LLM service.
-
-The ``get_current_weather`` tool is registered with
-``cancel_on_interruption=False`` and simulates a slow API call (10s sleep).
-While the call is in flight the conversation continues; the result arrives
-later via the async-tool mechanism and is forwarded to Grok Realtime as a
-``function_call_output`` so the model can integrate it naturally into its
-next turn.
-"""
-
-import asyncio
-import os
-import random
-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.frames.frames import LLMRunFrame
-from pipecat.pipeline.pipeline import Pipeline
-from pipecat.pipeline.runner import PipelineRunner
-from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.aggregators.llm_context import LLMContext
-from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
-from pipecat.runner.types import RunnerArguments
-from pipecat.runner.utils import create_transport
-from pipecat.services.llm_service import FunctionCallParams
-from pipecat.services.xai.realtime.events import SessionProperties
-from pipecat.services.xai.realtime.llm import GrokRealtimeLLMService
-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):
-    # Simulate a long-running API call so we can demonstrate that the
-    # conversation continues while the tool is in flight.
-    await asyncio.sleep(10)
-    temperature = (
-        random.randint(60, 85)
-        if params.arguments["format"] == "fahrenheit"
-        else random.randint(15, 30)
-    )
-    await params.result_callback(
-        {
-            "conditions": "nice",
-            "temperature": temperature,
-            "location": params.arguments["location"],
-            "format": params.arguments["format"],
-            "timestamp": datetime.now().strftime("%Y%m%d_%H%M%S"),
-        }
-    )
-
-
-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"],
-)
-
-tools = ToolsSchema(standard_tools=[weather_function])
-
-
-system_instruction = (
-    "You are a friendly assistant. The user and you will engage in a spoken "
-    "dialog exchanging the transcripts of a natural real-time conversation. "
-    "Keep your responses short, generally two or three sentences for chatty "
-    "scenarios. When the user asks for the weather, call get_current_weather. "
-    "While you wait for the result, keep chatting with the user. When the "
-    "result arrives, share it with the user naturally."
-)
-
-
-# Note: Grok has built-in server-side VAD, so we don't need local VAD.
-transport_params = {
-    "daily": lambda: DailyParams(
-        audio_in_enabled=True,
-        audio_out_enabled=True,
-    ),
-    "twilio": lambda: FastAPIWebsocketParams(
-        audio_in_enabled=True,
-        audio_out_enabled=True,
-    ),
-    "webrtc": lambda: TransportParams(
-        audio_in_enabled=True,
-        audio_out_enabled=True,
-    ),
-}
-
-
-async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
-    logger.info(f"Starting bot")
-
-    llm = GrokRealtimeLLMService(
-        api_key=os.environ["XAI_API_KEY"],
-        settings=GrokRealtimeLLMService.Settings(
-            system_instruction=system_instruction,
-            session_properties=SessionProperties(
-                voice="Ara",
-            ),
-        ),
-    )
-
-    llm.register_function(
-        "get_current_weather",
-        fetch_weather_from_api,
-        cancel_on_interruption=False,
-    )
-
-    context = LLMContext(tools=tools)
-    user_aggregator, assistant_aggregator = LLMContextAggregatorPair(context)
-
-    pipeline = Pipeline(
-        [
-            transport.input(),
-            user_aggregator,
-            llm,
-            transport.output(),
-            assistant_aggregator,
-        ]
-    )
-
-    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")
-        context.add_message(
-            {"role": "developer", "content": "Please introduce yourself to the user."}
-        )
-        await task.queue_frames([LLMRunFrame()])
-
-    @transport.event_handler("on_client_disconnected")
-    async def on_client_disconnected(transport, client):
-        logger.info(f"Client disconnected")
-        await task.cancel()
-
-    runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
-    await runner.run(task)
-
-
-async def bot(runner_args: RunnerArguments):
-    """Main bot entry point compatible with Pipecat Cloud."""
-    transport = await create_transport(runner_args, transport_params)
-    await run_bot(transport, runner_args)
-
-
-if __name__ == "__main__":
-    from pipecat.runner.run import main
-
-    main()

examples/realtime/realtime-openai-async-tool.py

@@ -1,198 +0,0 @@
-#
-# Copyright (c) 2024-2026, Daily
-#
-# SPDX-License-Identifier: BSD 2-Clause License
-#
-
-"""Example: async function call with the OpenAI Realtime LLM service.
-
-The ``get_current_weather`` tool is registered with
-``cancel_on_interruption=False`` and simulates a slow API call (10s sleep).
-While the call is in flight the conversation continues; the result arrives
-later via the async-tool mechanism and is forwarded to OpenAI Realtime as a
-``function_call_output`` so the model can integrate it naturally into its
-next turn.
-"""
-
-import asyncio
-import os
-import random
-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.llm_context import LLMContext
-from pipecat.processors.aggregators.llm_response_universal import (
-    LLMContextAggregatorPair,
-    LLMUserAggregatorParams,
-)
-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.events import (
-    AudioConfiguration,
-    AudioInput,
-    InputAudioNoiseReduction,
-    InputAudioTranscription,
-    SemanticTurnDetection,
-    SessionProperties,
-)
-from pipecat.services.openai.realtime.llm import OpenAIRealtimeLLMService
-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):
-    # Simulate a long-running API call so we can demonstrate that the
-    # conversation continues while the tool is in flight.
-    await asyncio.sleep(10)
-    temperature = (
-        random.randint(60, 85)
-        if params.arguments["format"] == "fahrenheit"
-        else random.randint(15, 30)
-    )
-    await params.result_callback(
-        {
-            "conditions": "nice",
-            "temperature": temperature,
-            "location": params.arguments["location"],
-            "format": params.arguments["format"],
-            "timestamp": datetime.now().strftime("%Y%m%d_%H%M%S"),
-        }
-    )
-
-
-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"],
-)
-
-tools = ToolsSchema(standard_tools=[weather_function])
-
-
-system_instruction = (
-    "You are a friendly assistant. The user and you will engage in a spoken "
-    "dialog exchanging the transcripts of a natural real-time conversation. "
-    "Keep your responses short, generally two or three sentences for chatty "
-    "scenarios. When the user asks for the weather, call get_current_weather. "
-    "While you wait for the result, keep chatting with the user. When the "
-    "result arrives, share it with the user naturally."
-)
-
-
-transport_params = {
-    "daily": lambda: DailyParams(
-        audio_in_enabled=True,
-        audio_out_enabled=True,
-    ),
-    "twilio": lambda: FastAPIWebsocketParams(
-        audio_in_enabled=True,
-        audio_out_enabled=True,
-    ),
-    "webrtc": lambda: TransportParams(
-        audio_in_enabled=True,
-        audio_out_enabled=True,
-    ),
-}
-
-
-async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
-    logger.info(f"Starting bot")
-
-    llm = OpenAIRealtimeLLMService(
-        api_key=os.environ["OPENAI_API_KEY"],
-        settings=OpenAIRealtimeLLMService.Settings(
-            system_instruction=system_instruction,
-            session_properties=SessionProperties(
-                audio=AudioConfiguration(
-                    input=AudioInput(
-                        transcription=InputAudioTranscription(),
-                        turn_detection=SemanticTurnDetection(),
-                        noise_reduction=InputAudioNoiseReduction(type="near_field"),
-                    )
-                ),
-            ),
-        ),
-    )
-
-    llm.register_function(
-        "get_current_weather",
-        fetch_weather_from_api,
-        cancel_on_interruption=False,
-    )
-
-    context = LLMContext(tools=tools)
-    user_aggregator, assistant_aggregator = LLMContextAggregatorPair(
-        context,
-        user_params=LLMUserAggregatorParams(vad_analyzer=SileroVADAnalyzer()),
-    )
-
-    pipeline = Pipeline(
-        [
-            transport.input(),
-            user_aggregator,
-            llm,
-            transport.output(),
-            assistant_aggregator,
-        ]
-    )
-
-    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")
-        context.add_message(
-            {"role": "developer", "content": "Please introduce yourself to the user."}
-        )
-        await task.queue_frames([LLMRunFrame()])
-
-    @transport.event_handler("on_client_disconnected")
-    async def on_client_disconnected(transport, client):
-        logger.info(f"Client disconnected")
-        await task.cancel()
-
-    runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
-    await runner.run(task)
-
-
-async def bot(runner_args: RunnerArguments):
-    """Main bot entry point compatible with Pipecat Cloud."""
-    transport = await create_transport(runner_args, transport_params)
-    await run_bot(transport, runner_args)
-
-
-if __name__ == "__main__":
-    from pipecat.runner.run import main
-
-    main()

examples/realtime/realtime-openai-local-vad.py

@@ -1,182 +0,0 @@
-#
-# Copyright (c) 2024-2026, Daily
-#
-# SPDX-License-Identifier: BSD 2-Clause License
-#
-
-
-import os
-
-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.llm_context import LLMContext
-from pipecat.processors.aggregators.llm_response_universal import (
-    AssistantTurnStoppedMessage,
-    LLMContextAggregatorPair,
-    LLMUserAggregatorParams,
-    UserMessageFinalizedMessage,
-    UserTurnStoppedMessage,
-)
-from pipecat.runner.types import RunnerArguments
-from pipecat.runner.utils import create_transport
-from pipecat.services.openai.realtime.events import (
-    AudioConfiguration,
-    AudioInput,
-    InputAudioTranscription,
-    SessionProperties,
-)
-from pipecat.services.openai.realtime.llm import OpenAIRealtimeLLMService
-from pipecat.transports.base_transport import BaseTransport, TransportParams
-from pipecat.transports.daily.transport import DailyParams
-from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
-
-load_dotenv(override=True)
-
-
-# We use lambdas to defer transport parameter creation until the transport
-# type is selected at runtime.
-transport_params = {
-    "daily": lambda: DailyParams(
-        audio_in_enabled=True,
-        audio_out_enabled=True,
-    ),
-    "twilio": lambda: FastAPIWebsocketParams(
-        audio_in_enabled=True,
-        audio_out_enabled=True,
-    ),
-    "webrtc": lambda: TransportParams(
-        audio_in_enabled=True,
-        audio_out_enabled=True,
-    ),
-}
-
-
-async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
-    logger.info(f"Starting bot")
-
-    # `turn_detection=False` disables OpenAI Realtime's server-side VAD,
-    # so this pipeline's local turn detection drives turn boundaries.
-    # The service then sends `input_audio_buffer.commit` +
-    # `response.create` when it sees `UserStoppedSpeakingFrame`.
-    llm = OpenAIRealtimeLLMService(
-        api_key=os.environ["OPENAI_API_KEY"],
-        settings=OpenAIRealtimeLLMService.Settings(
-            session_properties=SessionProperties(
-                audio=AudioConfiguration(
-                    input=AudioInput(
-                        transcription=InputAudioTranscription(),
-                        turn_detection=False,
-                    ),
-                ),
-            ),
-        ),
-    )
-
-    context = LLMContext(
-        [
-            {
-                "role": "developer",
-                "content": "Say hello. Then ask if I want to hear a joke.",
-            },
-        ],
-    )
-    # `wait_for_transcript_to_end_user_turn=False` is the right setting
-    # for pipelines like this one — local turn detection driving a
-    # realtime service. It avoids unnecessary latency from transcript
-    # delay: the realtime service consumes user audio directly, so
-    # we don't need user transcripts in context before it can respond.
-    # With this option:
-    #
-    # - Turn strategies do not consider user transcripts, so the user
-    #   turn ends sooner.
-    # - User transcripts are handled by the aggregator: a simple
-    #   post-turn transcript wait gives them time to arrive after the
-    #   user turn ends, then the aggregator emits
-    #   `on_user_turn_message_finalized` with the new user context
-    #   message.
-    user_aggregator, assistant_aggregator = LLMContextAggregatorPair(
-        context,
-        user_params=LLMUserAggregatorParams(
-            vad_analyzer=SileroVADAnalyzer(),
-            wait_for_transcript_to_end_user_turn=False,
-        ),
-    )
-
-    pipeline = Pipeline(
-        [
-            transport.input(),
-            user_aggregator,
-            llm,
-            transport.output(),
-            assistant_aggregator,
-        ]
-    )
-
-    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()
-
-    # `on_user_turn_stopped` fires at the end of the user turn. With
-    # `wait_for_transcript_to_end_user_turn=False`, no user
-    # transcripts have arrived yet at this point, so
-    # `message.content` is empty. Logged here to make the end-of-turn
-    # signal visible alongside the later finalization event.
-    @user_aggregator.event_handler("on_user_turn_stopped")
-    async def on_user_turn_stopped(aggregator, strategy, message: UserTurnStoppedMessage):
-        logger.info(f"User turn ended (strategy: {type(strategy).__name__})")
-
-    # `on_user_turn_message_finalized` fires when the user message has
-    # been finalized into the context. Here it fires later than
-    # `on_user_turn_stopped`, after the aggregator's post-turn
-    # transcript wait completes.
-    @user_aggregator.event_handler("on_user_turn_message_finalized")
-    async def on_user_turn_message_finalized(
-        aggregator, strategy, message: UserMessageFinalizedMessage
-    ):
-        timestamp = f"[{message.timestamp}] " if message.timestamp else ""
-        line = f"{timestamp}user: {message.content}"
-        logger.info(f"Transcript: {line}")
-
-    @assistant_aggregator.event_handler("on_assistant_turn_stopped")
-    async def on_assistant_turn_stopped(aggregator, message: AssistantTurnStoppedMessage):
-        timestamp = f"[{message.timestamp}] " if message.timestamp else ""
-        line = f"{timestamp}assistant: {message.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/realtime/realtime-openai.py

@@ -232,20 +232,6 @@ Remember, your responses should be short. Just one or two sentences, usually. Re
         #     [LLMUpdateSettingsFrame(settings=SessionProperties(tools=new_tools).model_dump())]
         # )
 
-        # Reasoning effort can be changed at runtime too. Only
-        # reasoning-capable Realtime models (e.g. gpt-realtime-2) support this.
-        # await task.queue_frames(
-        #     [
-        #         LLMUpdateSettingsFrame(
-        #             delta=OpenAIRealtimeLLMService.Settings(
-        #                 session_properties=SessionProperties(
-        #                     reasoning=Reasoning(effort="xhigh"),
-        #                 ),
-        #             )
-        #         )
-        #     ]
-        # )
-
     @transport.event_handler("on_client_disconnected")
     async def on_client_disconnected(transport, client):
         logger.info(f"Client disconnected")

examples/realtime/realtime-ultravox-async-tool.py

@@ -1,186 +0,0 @@
-#
-# Copyright (c) 2024-2026, Daily
-#
-# SPDX-License-Identifier: BSD 2-Clause License
-#
-
-"""Example: async function call with the Ultravox Realtime LLM service.
-
-The ``get_current_weather`` tool is registered with
-``cancel_on_interruption=False`` and simulates a slow API call (10s sleep).
-
-Ultravox's API freezes the conversation between ``client_tool_invocation``
-and the matching ``client_tool_result``, so the service ships a placeholder
-``client_tool_result`` immediately when an async-registered function is
-invoked (to unfreeze the conversation). When the real tool finishes, the
-actual result is injected as user-side text so the model picks it up.
-"""
-
-import asyncio
-import datetime
-import os
-import random
-
-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.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,
-    LLMUserAggregatorParams,
-)
-from pipecat.runner.types import RunnerArguments
-from pipecat.runner.utils import create_transport
-from pipecat.services.llm_service import FunctionCallParams
-from pipecat.services.ultravox.llm import OneShotInputParams, UltravoxRealtimeLLMService
-from pipecat.transports.base_transport import BaseTransport, TransportParams
-from pipecat.transports.daily.transport import DailyParams
-from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
-from pipecat.turns.user_stop import SpeechTimeoutUserTurnStopStrategy
-from pipecat.turns.user_turn_strategies import UserTurnStrategies
-
-load_dotenv(override=True)
-
-
-async def fetch_weather_from_api(params: FunctionCallParams):
-    # Simulate a long-running API call so we can demonstrate that the
-    # conversation continues while the tool is in flight.
-    await asyncio.sleep(10)
-    temperature = (
-        random.randint(60, 85)
-        if params.arguments["format"] == "fahrenheit"
-        else random.randint(15, 30)
-    )
-    await params.result_callback(
-        {
-            "conditions": "nice",
-            "temperature": temperature,
-            "location": params.arguments["location"],
-            "format": params.arguments["format"],
-            "timestamp": datetime.datetime.now().strftime("%Y%m%d_%H%M%S"),
-        }
-    )
-
-
-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"],
-)
-
-
-system_prompt = (
-    "You are a friendly assistant. The user and you will engage in a spoken "
-    "dialog exchanging the transcripts of a natural real-time conversation. "
-    "Keep your responses short, generally two or three sentences for chatty "
-    "scenarios. When the user asks for the weather, call get_current_weather. "
-    "While you wait for the result, keep chatting with the user. When the "
-    "result arrives, share it with the user naturally."
-)
-
-
-transport_params = {
-    "daily": lambda: DailyParams(
-        audio_in_enabled=True,
-        audio_out_enabled=True,
-    ),
-    "twilio": lambda: FastAPIWebsocketParams(
-        audio_in_enabled=True,
-        audio_out_enabled=True,
-    ),
-    "webrtc": lambda: TransportParams(
-        audio_in_enabled=True,
-        audio_out_enabled=True,
-    ),
-}
-
-
-async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
-    logger.info(f"Starting bot")
-
-    llm = UltravoxRealtimeLLMService(
-        params=OneShotInputParams(
-            api_key=os.environ["ULTRAVOX_API_KEY"],
-            system_prompt=system_prompt,
-            temperature=0.3,
-            max_duration=datetime.timedelta(minutes=3),
-        ),
-        one_shot_selected_tools=ToolsSchema(standard_tools=[weather_function]),
-    )
-
-    llm.register_function(
-        "get_current_weather",
-        fetch_weather_from_api,
-        cancel_on_interruption=False,
-    )
-
-    context = LLMContext([])
-    user_aggregator, assistant_aggregator = LLMContextAggregatorPair(
-        context,
-        user_params=LLMUserAggregatorParams(
-            user_turn_strategies=UserTurnStrategies(
-                stop=[SpeechTimeoutUserTurnStopStrategy()],
-            ),
-            vad_analyzer=SileroVADAnalyzer(),
-        ),
-    )
-
-    pipeline = Pipeline(
-        [
-            transport.input(),
-            user_aggregator,
-            llm,
-            transport.output(),
-            assistant_aggregator,
-        ]
-    )
-
-    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")
-
-    @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/transcription/transcription-openai.py

@@ -49,7 +49,13 @@ transport_params = {
 async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
     logger.info(f"Starting bot")
 
-    stt = OpenAIRealtimeSTTService(api_key=os.environ["OPENAI_API_KEY"])
+    stt = OpenAIRealtimeSTTService(
+        api_key=os.environ["OPENAI_API_KEY"],
+        settings=OpenAIRealtimeSTTService.Settings(
+            model="gpt-4o-transcribe",
+            prompt="Expect words related to dogs, such as breed names.",
+        ),
+    )
 
     tl = TranscriptionLogger()
     vad_processor = VADProcessor(vad_analyzer=SileroVADAnalyzer())

examples/turn-management/turn-management-filter-incomplete-turns.py

@@ -10,7 +10,7 @@ Demonstrates LLM-based turn completion detection to suppress bot responses when
 the user was cut off mid-thought. The LLM outputs one of three markers:
 - ✓ (complete): User finished their thought, respond normally
 - ○ (incomplete short): User was cut off, wait ~5s then prompt
-- ◐ (incomplete long): User needs time to think, wait ~10s then prompt
+- ◐ (incomplete long): User needs time to think, wait ~15s then prompt
 
 When incomplete is detected, the bot's response is suppressed. After the timeout
 expires, the LLM is automatically prompted to re-engage the user.
@@ -41,7 +41,6 @@ from pipecat.services.openai.llm import OpenAILLMService
 from pipecat.transports.base_transport import BaseTransport, TransportParams
 from pipecat.transports.daily.transport import DailyParams
 from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
-from pipecat.turns.user_turn_strategies import FilterIncompleteUserTurnStrategies
 
 load_dotenv(override=True)
 
@@ -84,28 +83,23 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
     )
 
     context = LLMContext()
-    # `FilterIncompleteUserTurnStrategies` pairs the default detector
-    # chain with `LLMTurnCompletionUserTurnStopStrategy`: detectors
-    # trigger LLM inference but the public `on_user_turn_stopped` event
-    # fires only when the LLM confirms ✓. The LLM marks each response
-    # with one of:
-    # ✓ = complete (respond normally)
-    # ○ = incomplete short (wait 5s, then prompt)
-    # ◐ = incomplete long (wait 10s, then prompt)
     user_aggregator, assistant_aggregator = LLMContextAggregatorPair(
         context,
         user_params=LLMUserAggregatorParams(
             vad_analyzer=SileroVADAnalyzer(),
-            user_turn_strategies=FilterIncompleteUserTurnStrategies(
-                # Optional: customize turn completion behavior
-                # config=UserTurnCompletionConfig(
-                #     incomplete_short_timeout=5.0,
-                #     incomplete_long_timeout=10.0,
-                #     incomplete_short_prompt="Custom prompt...",
-                #     incomplete_long_prompt="Custom prompt...",
-                #     instructions="Custom turn completion instructions...",
-                # ),
-            ),
+            # Enable turn completion filtering - the LLM will output:
+            # ✓ = complete (respond normally)
+            # ○ = incomplete short (wait 5s, then prompt)
+            # ◐ = incomplete long (wait 15s, then prompt)
+            filter_incomplete_user_turns=True,
+            # Optional: customize turn completion behavior
+            # turn_completion_config=TurnCompletionConfig(
+            #     incomplete_short_timeout=5.0,
+            #     incomplete_long_timeout=15.0,
+            #     incomplete_short_prompt="Custom prompt...",
+            #     incomplete_long_prompt="Custom prompt...",
+            #     instructions="Custom turn completion instructions...",
+            # ),
         ),
     )
 

examples/voice/voice-nvidia-sagemaker.py

@@ -1,129 +0,0 @@
-#
-# Copyright (c) 2024-2026, Daily
-#
-# SPDX-License-Identifier: BSD 2-Clause License
-#
-# For a full example of how to deploy to SageMaker, see:
-# https://github.com/pipecat-ai/pipecat-examples/tree/main/nvidia_sagemaker_example/deployment/aws-sagemaker-nvidia
-
-import os
-
-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.llm_context import LLMContext
-from pipecat.processors.aggregators.llm_response_universal import (
-    LLMContextAggregatorPair,
-    LLMUserAggregatorParams,
-)
-from pipecat.runner.types import RunnerArguments
-from pipecat.runner.utils import create_transport
-from pipecat.services.nvidia.llm import NvidiaLLMService
-from pipecat.services.nvidia.sagemaker.stt import NvidiaSageMakerSTTService
-from pipecat.services.nvidia.sagemaker.tts import NvidiaSageMakerTTSService
-from pipecat.transports.base_transport import BaseTransport, TransportParams
-from pipecat.transports.daily.transport import DailyParams
-from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
-
-load_dotenv(override=True)
-
-# We use lambdas to defer transport parameter creation until the transport
-# type is selected at runtime.
-transport_params = {
-    "daily": lambda: DailyParams(
-        audio_in_enabled=True,
-        audio_out_enabled=True,
-    ),
-    "twilio": lambda: FastAPIWebsocketParams(
-        audio_in_enabled=True,
-        audio_out_enabled=True,
-    ),
-    "webrtc": lambda: TransportParams(
-        audio_in_enabled=True,
-        audio_out_enabled=True,
-    ),
-}
-
-
-async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
-    logger.info(f"Starting bot")
-
-    stt = NvidiaSageMakerSTTService(
-        endpoint_name=os.environ["SAGEMAKER_ASR_ENDPOINT_NAME"],
-        region=os.getenv("AWS_REGION", "us-west-2"),
-    )
-
-    llm = NvidiaLLMService(
-        api_key=os.environ["NVIDIA_API_KEY"],
-        settings=NvidiaLLMService.Settings(
-            model="meta/llama-3.3-70b-instruct",
-            system_instruction="You are a helpful assistant in a voice conversation. Your responses will be spoken aloud, so avoid emojis, bullet points, or other formatting that can't be spoken. Respond to what the user said in a creative, helpful, and brief way.",
-        ),
-    )
-
-    tts = NvidiaSageMakerTTSService(
-        endpoint_name=os.environ["SAGEMAKER_MAGPIE_ENDPOINT_NAME"],
-        region=os.getenv("AWS_REGION", "us-west-2"),
-    )
-
-    context = LLMContext()
-    user_aggregator, assistant_aggregator = LLMContextAggregatorPair(
-        context,
-        user_params=LLMUserAggregatorParams(vad_analyzer=SileroVADAnalyzer()),
-    )
-
-    pipeline = Pipeline(
-        [
-            transport.input(),  # Transport user input
-            stt,  # STT
-            user_aggregator,  # User responses
-            llm,  # LLM
-            tts,  # TTS
-            transport.output(),  # Transport bot output
-            assistant_aggregator,  # Assistant spoken responses
-        ]
-    )
-
-    task = PipelineTask(
-        pipeline,
-        params=PipelineParams(
-            enable_metrics=True,
-            enable_usage_metrics=True,
-        ),
-        idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
-    )
-
-    @transport.event_handler("on_client_connected")
-    async def on_client_connected(transport, client):
-        logger.info(f"Client connected")
-        # Kick off the conversation.
-        context.add_message(
-            {"role": "developer", "content": "Please introduce yourself to the user."}
-        )
-        await task.queue_frames([LLMRunFrame()])
-
-    @transport.event_handler("on_client_disconnected")
-    async def on_client_disconnected(transport, client):
-        logger.info(f"Client disconnected")
-        await task.cancel()
-
-    runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
-
-    await runner.run(task)
-
-
-async def bot(runner_args: RunnerArguments):
-    """Main bot entry point compatible with Pipecat Cloud."""
-    transport = await create_transport(runner_args, transport_params)
-    await run_bot(transport, runner_args)
-
-
-if __name__ == "__main__":
-    from pipecat.runner.run import main
-
-    main()

examples/voice/voice-openai.py

@@ -25,6 +25,7 @@ from pipecat.runner.utils import create_transport
 from pipecat.services.openai.llm import OpenAILLMService
 from pipecat.services.openai.stt import OpenAIRealtimeSTTService
 from pipecat.services.openai.tts import OpenAITTSService
+from pipecat.transcriptions.language import Language
 from pipecat.transports.base_transport import BaseTransport, TransportParams
 from pipecat.transports.daily.transport import DailyParams
 from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
@@ -52,7 +53,14 @@ transport_params = {
 async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
     logger.info(f"Starting bot")
 
-    stt = OpenAIRealtimeSTTService(api_key=os.environ["OPENAI_API_KEY"])
+    stt = OpenAIRealtimeSTTService(
+        api_key=os.environ["OPENAI_API_KEY"],
+        settings=OpenAIRealtimeSTTService.Settings(
+            model="gpt-4o-transcribe",
+            prompt="Expect words related to dogs, such as breed names.",
+            language=Language.EN,
+        ),
+    )
 
     tts = OpenAITTSService(
         api_key=os.environ["OPENAI_API_KEY"],
@@ -64,7 +72,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
     llm = OpenAILLMService(
         api_key=os.environ["OPENAI_API_KEY"],
         settings=OpenAILLMService.Settings(
-            system_instruction="You are a helpful assistant in a voice conversation. Your responses will be spoken aloud, so avoid emojis, bullet points, or other formatting that can't be spoken. Respond to what the user said in a creative, helpful, and brief way.",
+            system_instruction="You are very knowledgable about dogs. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
         ),
     )
 

pyrightconfig.json

@@ -6,54 +6,116 @@
   "exclude": ["**/*_pb2.py", "**/__pycache__"],
   "ignore": [
     "tests",
+    "src/pipecat/adapters/services/anthropic_adapter.py",
+    "src/pipecat/adapters/services/aws_nova_sonic_adapter.py",
+    "src/pipecat/adapters/services/bedrock_adapter.py",
+    "src/pipecat/adapters/services/gemini_adapter.py",
+    "src/pipecat/adapters/services/grok_realtime_adapter.py",
+    "src/pipecat/adapters/services/inworld_realtime_adapter.py",
+    "src/pipecat/adapters/services/open_ai_adapter.py",
+    "src/pipecat/adapters/services/open_ai_realtime_adapter.py",
+    "src/pipecat/adapters/services/open_ai_responses_adapter.py",
+    "src/pipecat/adapters/services/perplexity_adapter.py",
+    "src/pipecat/audio/dtmf/utils.py",
     "src/pipecat/audio/filters/aic_filter.py",
     "src/pipecat/audio/filters/krisp_viva_filter.py",
+    "src/pipecat/audio/filters/rnnoise_filter.py",
     "src/pipecat/audio/turn/smart_turn/local_smart_turn_v2.py",
     "src/pipecat/audio/turn/smart_turn/local_smart_turn_v3.py",
     "src/pipecat/audio/vad/silero.py",
+    "src/pipecat/processors/aggregators/llm_context.py",
     "src/pipecat/processors/aggregators/llm_response_universal.py",
     "src/pipecat/processors/frame_processor.py",
+    "src/pipecat/processors/frameworks/langchain.py",
     "src/pipecat/processors/frameworks/rtvi/observer.py",
+    "src/pipecat/processors/frameworks/rtvi/processor.py",
+    "src/pipecat/processors/frameworks/strands_agents.py",
     "src/pipecat/services/anthropic/llm.py",
+    "src/pipecat/services/assemblyai/stt.py",
+    "src/pipecat/services/aws/agent_core.py",
     "src/pipecat/services/aws/llm.py",
     "src/pipecat/services/aws/nova_sonic/llm.py",
     "src/pipecat/services/aws/sagemaker/bidi_client.py",
+    "src/pipecat/services/aws/stt.py",
+    "src/pipecat/services/aws/tts.py",
+    "src/pipecat/services/aws/utils.py",
+    "src/pipecat/services/azure/stt.py",
     "src/pipecat/services/azure/tts.py",
+    "src/pipecat/services/cartesia/stt.py",
+    "src/pipecat/services/cartesia/tts.py",
+    "src/pipecat/services/deepgram/flux/base.py",
     "src/pipecat/services/deepgram/flux/sagemaker/stt.py",
+    "src/pipecat/services/deepgram/flux/stt.py",
     "src/pipecat/services/deepgram/sagemaker/stt.py",
     "src/pipecat/services/deepgram/sagemaker/tts.py",
+    "src/pipecat/services/deepgram/tts.py",
+    "src/pipecat/services/elevenlabs/stt.py",
+    "src/pipecat/services/elevenlabs/tts.py",
+    "src/pipecat/services/fish/tts.py",
+    "src/pipecat/services/gladia/stt.py",
     "src/pipecat/services/google/gemini_live/llm.py",
+    "src/pipecat/services/google/gemini_live/vertex/llm.py",
+    "src/pipecat/services/google/image.py",
     "src/pipecat/services/google/llm.py",
     "src/pipecat/services/google/stt.py",
     "src/pipecat/services/google/tts.py",
+    "src/pipecat/services/gradium/stt.py",
+    "src/pipecat/services/groq/tts.py",
+    "src/pipecat/services/heygen/api_interactive_avatar.py",
+    "src/pipecat/services/heygen/base_api.py",
     "src/pipecat/services/heygen/client.py",
     "src/pipecat/services/heygen/video.py",
+    "src/pipecat/services/hume/tts.py",
     "src/pipecat/services/inworld/realtime/llm.py",
+    "src/pipecat/services/inworld/tts.py",
+    "src/pipecat/services/kokoro/tts.py",
     "src/pipecat/services/llm_service.py",
+    "src/pipecat/services/lmnt/tts.py",
     "src/pipecat/services/mem0/memory.py",
+    "src/pipecat/services/mistral/stt.py",
     "src/pipecat/services/mistral/tts.py",
+    "src/pipecat/services/moondream/vision.py",
+    "src/pipecat/services/neuphonic/tts.py",
     "src/pipecat/services/nvidia/stt.py",
     "src/pipecat/services/nvidia/tts.py",
     "src/pipecat/services/openai/base_llm.py",
+    "src/pipecat/services/openai/image.py",
+    "src/pipecat/services/openai/llm.py",
     "src/pipecat/services/openai/realtime/llm.py",
+    "src/pipecat/services/openai/responses/llm.py",
+    "src/pipecat/services/openai/stt.py",
+    "src/pipecat/services/openai/tts.py",
+    "src/pipecat/services/openrouter/llm.py",
+    "src/pipecat/services/piper/tts.py",
+    "src/pipecat/services/resembleai/tts.py",
     "src/pipecat/services/rime/tts.py",
     "src/pipecat/services/sambanova/llm.py",
     "src/pipecat/services/sarvam/stt.py",
+    "src/pipecat/services/sarvam/tts.py",
     "src/pipecat/services/simli/video.py",
+    "src/pipecat/services/smallest/tts.py",
+    "src/pipecat/services/soniox/stt.py",
     "src/pipecat/services/speechmatics/stt.py",
     "src/pipecat/services/stt_service.py",
     "src/pipecat/services/tavus/video.py",
     "src/pipecat/services/tts_service.py",
     "src/pipecat/services/ultravox/llm.py",
+    "src/pipecat/services/websocket_service.py",
     "src/pipecat/services/whisper/stt.py",
     "src/pipecat/services/xai/realtime/llm.py",
+    "src/pipecat/services/xtts/tts.py",
+    "src/pipecat/transports/base_output.py",
     "src/pipecat/transports/daily/transport.py",
+    "src/pipecat/transports/heygen/transport.py",
     "src/pipecat/transports/lemonslice/transport.py",
     "src/pipecat/transports/livekit/transport.py",
     "src/pipecat/transports/smallwebrtc/connection.py",
+    "src/pipecat/transports/smallwebrtc/request_handler.py",
     "src/pipecat/transports/smallwebrtc/transport.py",
     "src/pipecat/transports/tavus/transport.py",
-    "src/pipecat/transports/websocket/server.py"
+    "src/pipecat/transports/websocket/client.py",
+    "src/pipecat/transports/websocket/server.py",
+    "src/pipecat/transports/whatsapp/client.py"
   ],
   "reportMissingImports": false
 }

scripts/evals/run-release-evals.py

@@ -223,11 +223,12 @@ TESTS_REALTIME = [
     # ("realtime/realtime-azure.py", EVAL_WEATHER),
     ("realtime/realtime-openai-text.py", EVAL_WEATHER),
     ("realtime/realtime-openai-live-video.py", EVAL_VISION_CAMERA),
-    ("realtime/realtime-gemini-live.py", EVAL_WEATHER),
+    ("realtime/realtime-gemini-live.py", EVAL_SIMPLE_MATH),
     ("realtime/realtime-gemini-live-local-vad.py", EVAL_SIMPLE_MATH),
+    ("realtime/realtime-gemini-live-function-calling.py", EVAL_WEATHER),
     ("realtime/realtime-gemini-live-video.py", EVAL_VISION_CAMERA),
     ("realtime/realtime-gemini-live-google-search.py", EVAL_ONLINE_SEARCH),
-    ("realtime/realtime-gemini-live-vertex.py", EVAL_WEATHER),
+    ("realtime/realtime-gemini-live-vertex-function-calling.py", EVAL_WEATHER),
     ("realtime/realtime-aws-nova-sonic.py", EVAL_SIMPLE_MATH),
     ("realtime/realtime-ultravox.py", EVAL_ORDER),
     ("realtime/realtime-grok.py", EVAL_WEATHER),

src/pipecat/adapters/services/anthropic_adapter.py

@@ -9,7 +9,7 @@
 import copy
 import json
 from dataclasses import dataclass
-from typing import Any, TypedDict, TypeGuard, TypeVar, cast
+from typing import Any, TypedDict, TypeGuard, TypeVar
 
 from anthropic import NOT_GIVEN, NotGiven
 from anthropic.types.message_param import MessageParam
@@ -121,20 +121,16 @@ class AnthropicLLMAdapter(BaseLLMAdapter[AnthropicLLMInvocationParams]):
         messages = self._from_universal_context_messages(self.get_messages(context)).messages
 
         # Sanitize messages for logging
-        messages_for_logging: list[dict[str, Any]] = []
+        messages_for_logging = []
         for message in messages:
-            msg: dict[str, Any] = copy.deepcopy(dict(message))
-            content = msg.get("content")
-            if isinstance(content, list):
-                for item in content:
-                    if not isinstance(item, dict):
-                        continue
-                    if item.get("type") == "image":
-                        source = item.get("source")
-                        if isinstance(source, dict):
-                            source["data"] = "..."
-                    if item.get("type") == "thinking" and item.get("signature"):
-                        item["signature"] = "..."
+            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"] = "..."
+                        if item["type"] == "thinking" and item.get("signature"):
+                            item["signature"] = "..."
             messages_for_logging.append(msg)
         return messages_for_logging
 
@@ -189,13 +185,8 @@ class AnthropicLLMAdapter(BaseLLMAdapter[AnthropicLLMInvocationParams]):
                     ]
                 if isinstance(next_message["content"], str):
                     next_message["content"] = [{"type": "text", "text": next_message["content"]}]
-                # Concatenate the content. MessageParam types content as
-                # `str | Iterable[...]`, but this codebase assumes it's
-                # either a str or a list. The str case is handled above, so
-                # we assume that both are lists here.
-                cast(list[Any], current_message["content"]).extend(
-                    cast(list[Any], 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:
@@ -248,7 +239,7 @@ class AnthropicLLMAdapter(BaseLLMAdapter[AnthropicLLMInvocationParams]):
             }
 
         # Fall back to assuming that the message is already in Anthropic format
-        return cast(MessageParam, copy.deepcopy(message.message))
+        return copy.deepcopy(message.message)
 
     def _from_standard_message(self, message: LLMStandardMessage) -> MessageParam:
         """Convert standard universal context message to Anthropic format.
@@ -289,26 +280,20 @@ class AnthropicLLMAdapter(BaseLLMAdapter[AnthropicLLMInvocationParams]):
                     ]
                 }
         """
-        # ChatCompletionMessageParam (input) and MessageParam (output) are
-        # different TypedDicts — work with the message as a plain dict for the
-        # transformations below and cast back to MessageParam at return sites.
-        msg = cast(dict[str, Any], copy.deepcopy(message))
-        if msg["role"] == "tool":
-            return cast(
-                MessageParam,
-                {
-                    "role": "user",
-                    "content": [
-                        {
-                            "type": "tool_result",
-                            "tool_use_id": msg["tool_call_id"],
-                            "content": msg["content"],
-                        },
-                    ],
-                },
-            )
-        if msg.get("tool_calls"):
-            tc = msg["tool_calls"]
+        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"]
@@ -320,8 +305,8 @@ class AnthropicLLMAdapter(BaseLLMAdapter[AnthropicLLMInvocationParams]):
                     "input": arguments,
                 }
                 ret["content"].append(new_tool_use)
-            return cast(MessageParam, ret)
-        content = msg.get("content")
+            return ret
+        content = message.get("content")
         if isinstance(content, str):
             # fix empty text
             if content == "":
@@ -369,7 +354,7 @@ class AnthropicLLMAdapter(BaseLLMAdapter[AnthropicLLMInvocationParams]):
                     image_item = content.pop(img_idx)
                     content.insert(first_txt_idx, image_item)
 
-        return cast(MessageParam, msg)
+        return message
 
     def _with_cache_control_markers(self, messages: list[MessageParam]) -> list[MessageParam]:
         """Add cache control markers to messages for prompt caching.
@@ -384,16 +369,7 @@ class AnthropicLLMAdapter(BaseLLMAdapter[AnthropicLLMInvocationParams]):
         def add_cache_control_marker(message: MessageParam):
             if isinstance(message["content"], str):
                 message["content"] = [{"type": "text", "text": message["content"]}]
-            # Assumptions on the next line:
-            #   - content is a list (str case handled above; this codebase only
-            #     ever constructs content as a str or a list)
-            #   - the list is non-empty (guaranteed by the empty-content
-            #     replacement in `_from_universal_context_messages`)
-            #   - the last item is a dict. The standard-message path enforces
-            #     this via TypedDicts (which are dicts at runtime); the
-            #     LLMSpecificMessage passthrough doesn't, but in practice
-            #     callers use dicts.
-            cast(list[Any], message["content"])[-1]["cache_control"] = {"type": "ephemeral"}
+            message["content"][-1]["cache_control"] = {"type": "ephemeral"}
 
         try:
             # Add cache control markers to the most recent two user messages.

src/pipecat/adapters/services/aws_nova_sonic_adapter.py

@@ -8,9 +8,9 @@
 
 import copy
 import json
-from dataclasses import asdict, dataclass
+from dataclasses import dataclass
 from enum import Enum
-from typing import Any, TypedDict, cast
+from typing import Any, TypedDict
 
 from loguru import logger
 
@@ -110,10 +110,7 @@ class AWSNovaSonicLLMAdapter(BaseLLMAdapter[AWSNovaSonicLLMInvocationParams]):
         Returns:
             List of messages in a format ready for logging about AWS Nova Sonic.
         """
-        return [
-            asdict(m)
-            for m in self._from_universal_context_messages(self.get_messages(context)).messages
-        ]
+        return self._from_universal_context_messages(self.get_messages(context)).messages
 
     @dataclass
     class ConvertedMessages:
@@ -126,27 +123,18 @@ class AWSNovaSonicLLMAdapter(BaseLLMAdapter[AWSNovaSonicLLMInvocationParams]):
         self, universal_context_messages: list[LLMContextMessage]
     ) -> ConvertedMessages:
         system_instruction = None
-        messages: list[AWSNovaSonicConversationHistoryMessage] = []
+        messages = []
 
         # Bail if there are no messages
         if not universal_context_messages:
-            return self.ConvertedMessages(messages=[])
+            return self.ConvertedMessages()
 
-        # NOTE: This adapter does not yet handle ``LLMSpecificMessage`` —
-        # those are filtered out below (the role-extraction and conversion
-        # logic only applies to standard message dicts). If/when this
-        # adapter grows a per-provider passthrough like the Anthropic
-        # adapter has, LLMSpecific items can flow through.
-        ucm: list[dict[str, Any]] = [
-            cast(dict[str, Any], m)
-            for m in copy.deepcopy(universal_context_messages)
-            if isinstance(m, dict)
-        ]
+        universal_context_messages = copy.deepcopy(universal_context_messages)
 
         # If we have a "system" message as our first message,
         # pull that out into "instruction"
-        if ucm and ucm[0].get("role") == "system":
-            system = ucm.pop(0)
+        if universal_context_messages[0].get("role") == "system":
+            system = universal_context_messages.pop(0)
             content = system.get("content")
             if isinstance(content, str):
                 system_instruction = content
@@ -157,21 +145,19 @@ class AWSNovaSonicLLMAdapter(BaseLLMAdapter[AWSNovaSonicLLMInvocationParams]):
 
         # Convert any remaining "system"/"developer" messages to "user",
         # as Nova Sonic only supports "user" and "assistant" in history.
-        for msg in ucm:
+        for msg in universal_context_messages:
             if msg.get("role") in ("system", "developer"):
                 msg["role"] = "user"
 
         # Process remaining messages to fill out conversation history.
-        for universal_context_message in ucm:
+        for universal_context_message in universal_context_messages:
             message = self._from_universal_context_message(universal_context_message)
             if message:
                 messages.append(message)
 
         return self.ConvertedMessages(messages=messages, system_instruction=system_instruction)
 
-    def _from_universal_context_message(
-        self, message: dict[str, Any]
-    ) -> AWSNovaSonicConversationHistoryMessage | None:
+    def _from_universal_context_message(self, message) -> AWSNovaSonicConversationHistoryMessage:
         """Convert standard message format to Nova Sonic format.
 
         Args:
@@ -181,18 +167,17 @@ class AWSNovaSonicLLMAdapter(BaseLLMAdapter[AWSNovaSonicLLMInvocationParams]):
             Nova Sonic conversation history message, or None if not convertible.
         """
         role = message.get("role")
-        if role == "user" or role == "assistant":
+        if message.get("role") == "user" or message.get("role") == "assistant":
             content = message.get("content")
-            if isinstance(content, list):
-                text_parts = []
-                for c in content:
+            if isinstance(message.get("content"), list):
+                content = ""
+                for c in message.get("content"):
                     if c.get("type") == "text":
-                        text_parts.append(c.get("text"))
+                        content += " " + c.get("text")
                     else:
                         logger.error(
                             f"Unhandled content type in context message: {c.get('type')} - {message}"
                         )
-                content = " ".join(t for t in text_parts if t)
             # There won't be content if this is an assistant tool call entry.
             # We're ignoring those since they can't be loaded into AWS Nova Sonic conversation
             # history

src/pipecat/adapters/services/bedrock_adapter.py

@@ -10,7 +10,7 @@ import base64
 import copy
 import json
 from dataclasses import dataclass
-from typing import Any, TypedDict, cast
+from typing import Any, TypedDict
 
 from loguru import logger
 
@@ -68,19 +68,16 @@ class AWSBedrockLLMAdapter(BaseLLMAdapter[AWSBedrockLLMInvocationParams]):
             system_instruction,
             discard_context_system=True,
         )
-        return cast(
-            AWSBedrockLLMInvocationParams,
-            {
-                "system": [{"text": effective_system}] if effective_system else None,
-                "messages": converted.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,
-            },
-        )
+        return {
+            "system": [{"text": effective_system}] if effective_system else None,
+            "messages": converted.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.
@@ -216,36 +213,35 @@ class AWSBedrockLLMAdapter(BaseLLMAdapter[AWSBedrockLLMInvocationParams]):
                     ]
                 }
         """
-        # ChatCompletionMessageParam (input) and the dict shape Bedrock expects
-        # are different — work with the deepcopied message as a plain dict for
-        # the transformations below.
-        msg = cast(dict[str, Any], copy.deepcopy(message))
-        if msg["role"] == "tool":
+        message = copy.deepcopy(message)
+        if message["role"] == "tool":
             # Try to parse the content as JSON if it looks like JSON
             try:
-                if msg["content"].strip().startswith("{") and msg["content"].strip().endswith("}"):
-                    content_json = json.loads(msg["content"])
+                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": msg["content"]}]
+                    tool_result_content = [{"text": message["content"]}]
             except (json.JSONDecodeError, ValueError, AttributeError):
-                tool_result_content = [{"text": msg["content"]}]
+                tool_result_content = [{"text": message["content"]}]
 
             return {
                 "role": "user",
                 "content": [
                     {
                         "toolResult": {
-                            "toolUseId": msg["tool_call_id"],
+                            "toolUseId": message["tool_call_id"],
                             "content": tool_result_content,
                         },
                     },
                 ],
             }
 
-        if msg.get("tool_calls"):
-            tc = msg["tool_calls"]
-            ret: dict[str, Any] = {"role": "assistant", "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"])
@@ -260,12 +256,12 @@ class AWSBedrockLLMAdapter(BaseLLMAdapter[AWSBedrockLLMInvocationParams]):
             return ret
 
         # Handle text content
-        content = msg.get("content")
+        content = message.get("content")
         if isinstance(content, str):
             if content == "":
-                return {"role": msg["role"], "content": [{"text": "(empty)"}]}
+                return {"role": message["role"], "content": [{"text": "(empty)"}]}
             else:
-                return {"role": msg["role"], "content": [{"text": content}]}
+                return {"role": message["role"], "content": [{"text": content}]}
         elif isinstance(content, list):
             new_content = []
             for item in content:
@@ -304,9 +300,9 @@ class AWSBedrockLLMAdapter(BaseLLMAdapter[AWSBedrockLLMInvocationParams]):
                     # Move image before the first text
                     image_item = new_content.pop(img_idx)
                 new_content.insert(first_txt_idx, image_item)
-            return {"role": msg["role"], "content": new_content}
+            return {"role": message["role"], "content": new_content}
 
-        return msg
+        return message
 
     @staticmethod
     def _to_bedrock_function_format(function: FunctionSchema) -> dict[str, Any]:

src/pipecat/adapters/services/gemini_adapter.py

@@ -9,7 +9,7 @@
 import base64
 import json
 from dataclasses import dataclass, field
-from typing import Any, TypedDict, cast
+from typing import Any, TypedDict
 
 from loguru import logger
 from openai import NotGiven
@@ -139,36 +139,6 @@ class GeminiLLMAdapter(BaseLLMAdapter[GeminiLLMInvocationParams]):
 
         return formatted_standard_tools + custom_gemini_tools
 
-    @staticmethod
-    def to_function_response_dict(content: Any) -> dict[str, Any]:
-        """Convert a tool-result content value to Gemini's FunctionResponse.response shape.
-
-        Gemini's ``FunctionResponse.response`` field requires a dict, so
-        non-dict values (e.g. plain strings, JSON-encoded scalars, or
-        sentinel strings like ``"COMPLETED"`` used when a function returned
-        no value) are wrapped as ``{"value": <value>}``. JSON strings that
-        decode to a dict are passed through as-is.
-
-        Args:
-            content: The tool-result content. Typically the JSON-encoded
-                return value of a function, but can also be a plain string
-                (e.g. ``"COMPLETED"``) or already-parsed dict.
-
-        Returns:
-            A dict suitable for ``FunctionResponse.response``.
-        """
-        if isinstance(content, dict):
-            return content
-        if not isinstance(content, str):
-            return {"value": content}
-        try:
-            decoded = json.loads(content)
-        except (json.JSONDecodeError, ValueError):
-            return {"value": content}
-        if isinstance(decoded, dict):
-            return decoded
-        return {"value": decoded}
-
     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 Gemini.
 
@@ -184,12 +154,9 @@ class GeminiLLMAdapter(BaseLLMAdapter[GeminiLLMInvocationParams]):
         messages = self._from_universal_context_messages(self.get_messages(context)).messages
 
         # Sanitize messages for logging
-        messages_for_logging: list[dict[str, Any]] = []
+        messages_for_logging = []
         for message in messages:
-            # `to_json_dict()` returns `dict[str, object]`; treat as a plain
-            # dict for the value indexing/mutation below. The broad `except`
-            # below is the safety net if any item isn't shaped as expected.
-            obj: dict[str, Any] = cast(dict[str, Any], message.to_json_dict())
+            obj = message.to_json_dict()
             try:
                 if "parts" in obj:
                     for part in obj["parts"]:
@@ -307,8 +274,7 @@ class GeminiLLMAdapter(BaseLLMAdapter[GeminiLLMInvocationParams]):
         # Check if we only have function-related messages (no regular text)
         effective_system = extracted_system or system_instruction
         has_regular_messages = any(
-            msg.parts is not None
-            and len(msg.parts) == 1
+            len(msg.parts) == 1
             and getattr(msg.parts[0], "text", None)
             and not getattr(msg.parts[0], "function_call", None)
             and not getattr(msg.parts[0], "function_response", None)
@@ -380,11 +346,8 @@ class GeminiLLMAdapter(BaseLLMAdapter[GeminiLLMInvocationParams]):
                     parts=[Part(function_call=FunctionCall(name="search", args={"query": "test"}))]
                 )
         """
-        # ChatCompletionMessageParam (a union of TypedDicts) doesn't allow
-        # the dict-style key access used below; treat it as a plain dict.
-        msg = cast(dict[str, Any], message)
-        role = msg["role"]
-        content = msg.get("content", [])
+        role = message["role"]
+        content = message.get("content", [])
 
         # Convert non-initial system/developer messages to user role,
         # as Gemini doesn't support these as input messages.
@@ -396,8 +359,8 @@ class GeminiLLMAdapter(BaseLLMAdapter[GeminiLLMInvocationParams]):
         parts = []
         tool_call_id_to_name_mapping = {}
 
-        if msg.get("tool_calls"):
-            for tc in msg["tool_calls"]:
+        if message.get("tool_calls"):
+            for tc in message["tool_calls"]:
                 id = tc["id"]
                 name = tc["function"]["name"]
                 tool_call_id_to_name_mapping[id] = name
@@ -412,10 +375,19 @@ class GeminiLLMAdapter(BaseLLMAdapter[GeminiLLMInvocationParams]):
                 )
         elif role == "tool":
             role = "user"
-            response_dict = self.to_function_response_dict(msg["content"])
+            try:
+                response = json.loads(message["content"])
+                if isinstance(response, dict):
+                    response_dict = response
+                else:
+                    response_dict = {"value": response}
+            except Exception as e:
+                # Response might not be JSON-deserializable.
+                # This occurs with a UserImageFrame, for example, where we get a plain "COMPLETED" string.
+                response_dict = {"value": message["content"]}
 
             # Get function name from mapping using tool_call_id, or fallback
-            tool_call_id = msg.get("tool_call_id")
+            tool_call_id = message.get("tool_call_id")
             function_name = "tool_call_result"  # Default fallback
             if tool_call_id and tool_call_id in params.tool_call_id_to_name_mapping:
                 function_name = params.tool_call_id_to_name_mapping[tool_call_id]
@@ -519,7 +491,7 @@ class GeminiLLMAdapter(BaseLLMAdapter[GeminiLLMInvocationParams]):
 
         def is_tool_call_message(msg: Content) -> bool:
             """Check if message contains only function_call parts."""
-            return bool(
+            return (
                 msg.role == "model"
                 and msg.parts
                 and all(getattr(part, "function_call", None) for part in msg.parts)
@@ -527,8 +499,6 @@ class GeminiLLMAdapter(BaseLLMAdapter[GeminiLLMInvocationParams]):
 
         def message_has_thought_signature(msg: Content) -> bool:
             """Check if any part in the message has a thought_signature."""
-            if msg.parts is None:
-                return False
             return any(getattr(part, "thought_signature", None) for part in msg.parts)
 
         merged_messages = []
@@ -594,8 +564,6 @@ class GeminiLLMAdapter(BaseLLMAdapter[GeminiLLMInvocationParams]):
         logger.debug(f"Thought signatures to apply: {len(thought_signature_dicts)}")
         for ts in thought_signature_dicts:
             bookmark = ts.get("bookmark")
-            if bookmark is None:
-                continue
             if bookmark.get("function_call"):
                 logger.trace(f" - To function call: {bookmark['function_call']}")
             elif bookmark.get("text"):
@@ -697,8 +665,6 @@ class GeminiLLMAdapter(BaseLLMAdapter[GeminiLLMInvocationParams]):
         if (
             hasattr(part, "inline_data")
             and part.inline_data
-            and part.inline_data.data is not None
-            and bookmark_inline_data.data is not None
             # Comparing length should be good enough for matching inline data,
             # especially since we're already matching thought signatures in
             # strict message order. Comparing actual data is expensive.

src/pipecat/adapters/services/grok_realtime_adapter.py

@@ -13,7 +13,7 @@ Grok's Voice Agent API.
 import copy
 import json
 from dataclasses import dataclass
-from typing import Any, TypedDict, cast
+from typing import Any, TypedDict
 
 from loguru import logger
 
@@ -85,10 +85,7 @@ class GrokRealtimeLLMAdapter(BaseLLMAdapter):
         Returns:
             List of messages with sensitive data redacted.
         """
-        return cast(
-            list[dict[str, Any]],
-            self.get_messages(context, truncate_large_values=True),
-        )
+        return self.get_messages(context, truncate_large_values=True)
 
     @dataclass
     class ConvertedMessages:
@@ -114,20 +111,11 @@ class GrokRealtimeLLMAdapter(BaseLLMAdapter):
         if not universal_context_messages:
             return self.ConvertedMessages(messages=[])
 
-        # NOTE: This adapter does not yet handle ``LLMSpecificMessage`` —
-        # those are filtered out below. Other adapters (e.g. Anthropic)
-        # dispatch LLMSpecific items through a per-provider passthrough.
-        # The pack-into-single-text-message strategy here doesn't compose
-        # with opaque per-provider payloads.
-        messages: list[dict[str, Any]] = [
-            cast(dict[str, Any], m)
-            for m in copy.deepcopy(universal_context_messages)
-            if isinstance(m, dict)
-        ]
+        messages = copy.deepcopy(universal_context_messages)
         system_instruction = None
 
         # Extract system message as session instructions
-        if messages and messages[0].get("role") == "system":
+        if messages[0].get("role") == "system":
             system = messages.pop(0)
             content = system.get("content")
             if isinstance(content, str):
@@ -145,9 +133,7 @@ class GrokRealtimeLLMAdapter(BaseLLMAdapter):
         # Single user message can be sent normally
         if len(messages) == 1 and messages[0].get("role") == "user":
             return self.ConvertedMessages(
-                messages=[
-                    self._from_universal_context_message(cast(LLMContextMessage, messages[0]))
-                ],
+                messages=[self._from_universal_context_message(messages[0])],
                 system_instruction=system_instruction,
             )
 
@@ -195,29 +181,26 @@ class GrokRealtimeLLMAdapter(BaseLLMAdapter):
         Returns:
             ConversationItem formatted for Grok Realtime API.
         """
-        # NOTE: ``LLMSpecificMessage`` is not yet handled here — see the
-        # corresponding note in `_from_universal_context_messages`.
-        msg = cast(dict[str, Any], message)
-        if msg.get("role") == "user":
-            content = msg.get("content")
+        if message.get("role") == "user":
+            content = message.get("content")
             if isinstance(content, list):
-                text_parts = []
+                text_content = ""
                 for c in content:
                     if c.get("type") == "text":
-                        text_parts.append(c.get("text"))
+                        text_content += " " + c.get("text")
                     else:
                         logger.error(
-                            f"Unhandled content type in context message: {c.get('type')} - {msg}"
+                            f"Unhandled content type in context message: {c.get('type')} - {message}"
                         )
-                content = " ".join(t for t in text_parts if t).strip()
+                content = text_content.strip()
             return events.ConversationItem(
                 role="user",
                 type="message",
                 content=[events.ItemContent(type="input_text", text=content)],
             )
 
-        if msg.get("role") == "assistant" and msg.get("tool_calls"):
-            tc = msg["tool_calls"][0]
+        if message.get("role") == "assistant" and message.get("tool_calls"):
+            tc = message.get("tool_calls")[0]
             return events.ConversationItem(
                 type="function_call",
                 call_id=tc["id"],
@@ -225,7 +208,7 @@ class GrokRealtimeLLMAdapter(BaseLLMAdapter):
                 arguments=tc["function"]["arguments"],
             )
 
-        raise ValueError(f"Unhandled message type in _from_universal_context_message: {msg}")
+        logger.error(f"Unhandled message type in _from_universal_context_message: {message}")
 
     @staticmethod
     def _to_grok_function_format(function: FunctionSchema) -> dict[str, Any]:

src/pipecat/adapters/services/inworld_realtime_adapter.py

@@ -13,7 +13,7 @@ Inworld's Realtime API.
 import copy
 import json
 from dataclasses import dataclass
-from typing import Any, TypedDict, cast
+from typing import Any, TypedDict
 
 from loguru import logger
 
@@ -85,10 +85,7 @@ class InworldRealtimeLLMAdapter(BaseLLMAdapter):
         Returns:
             List of messages with sensitive data redacted.
         """
-        return cast(
-            list[dict[str, Any]],
-            self.get_messages(context, truncate_large_values=True),
-        )
+        return self.get_messages(context, truncate_large_values=True)
 
     @dataclass
     class ConvertedMessages:
@@ -114,20 +111,11 @@ class InworldRealtimeLLMAdapter(BaseLLMAdapter):
         if not universal_context_messages:
             return self.ConvertedMessages(messages=[])
 
-        # NOTE: This adapter does not yet handle ``LLMSpecificMessage`` —
-        # those are filtered out below. Other adapters (e.g. Anthropic)
-        # dispatch LLMSpecific items through a per-provider passthrough.
-        # The pack-into-single-text-message strategy here doesn't compose
-        # with opaque per-provider payloads.
-        messages: list[dict[str, Any]] = [
-            cast(dict[str, Any], m)
-            for m in copy.deepcopy(universal_context_messages)
-            if isinstance(m, dict)
-        ]
+        messages = copy.deepcopy(universal_context_messages)
         system_instruction = None
 
         # Extract system message as session instructions
-        if messages and messages[0].get("role") == "system":
+        if messages[0].get("role") == "system":
             system = messages.pop(0)
             content = system.get("content")
             if isinstance(content, str):
@@ -145,9 +133,7 @@ class InworldRealtimeLLMAdapter(BaseLLMAdapter):
         # Single user message can be sent normally
         if len(messages) == 1 and messages[0].get("role") == "user":
             return self.ConvertedMessages(
-                messages=[
-                    self._from_universal_context_message(cast(LLMContextMessage, messages[0]))
-                ],
+                messages=[self._from_universal_context_message(messages[0])],
                 system_instruction=system_instruction,
             )
 
@@ -195,29 +181,26 @@ class InworldRealtimeLLMAdapter(BaseLLMAdapter):
         Returns:
             ConversationItem formatted for Inworld Realtime API.
         """
-        # NOTE: ``LLMSpecificMessage`` is not yet handled here — see the
-        # corresponding note in `_from_universal_context_messages`.
-        msg = cast(dict[str, Any], message)
-        if msg.get("role") == "user":
-            content = msg.get("content")
+        if message.get("role") == "user":
+            content = message.get("content")
             if isinstance(content, list):
-                text_parts = []
+                text_content = ""
                 for c in content:
                     if c.get("type") == "text":
-                        text_parts.append(c.get("text"))
+                        text_content += " " + c.get("text")
                     else:
                         logger.error(
-                            f"Unhandled content type in context message: {c.get('type')} - {msg}"
+                            f"Unhandled content type in context message: {c.get('type')} - {message}"
                         )
-                content = " ".join(t for t in text_parts if t).strip()
+                content = text_content.strip()
             return events.ConversationItem(
                 role="user",
                 type="message",
                 content=[events.ItemContent(type="input_text", text=content)],
             )
 
-        if msg.get("role") == "assistant" and msg.get("tool_calls"):
-            tc = msg["tool_calls"][0]
+        if message.get("role") == "assistant" and message.get("tool_calls"):
+            tc = message.get("tool_calls")[0]
             return events.ConversationItem(
                 type="function_call",
                 call_id=tc["id"],
@@ -225,7 +208,7 @@ class InworldRealtimeLLMAdapter(BaseLLMAdapter):
                 arguments=tc["function"]["arguments"],
             )
 
-        raise ValueError(f"Unhandled message type in _from_universal_context_message: {msg}")
+        logger.error(f"Unhandled message type in _from_universal_context_message: {message}")
 
     @staticmethod
     def _to_inworld_function_format(function: FunctionSchema) -> dict[str, Any]:

src/pipecat/adapters/services/open_ai_adapter.py

@@ -127,15 +127,12 @@ class OpenAILLMAdapter(BaseLLMAdapter[OpenAILLMInvocationParams]):
         )
 
         if system_instruction:
-            # Detect initial system message for warning purposes (don't extract).
-            # ChatCompletionMessageParam.content is `str | Iterable[...]`; we
-            # only forward it for warning purposes, so coerce non-strings to
-            # None — the resolver handles None.
-            initial_content: str | None = None
-            if messages and messages[0].get("role") == "system":
-                raw_content = messages[0].get("content", "")
-                if isinstance(raw_content, str):
-                    initial_content = raw_content
+            # Detect initial system message for warning purposes (don't extract)
+            initial_content = (
+                messages[0].get("content", "")
+                if messages and messages[0].get("role") == "system"
+                else None
+            )
             self._resolve_system_instruction(
                 initial_content,
                 system_instruction,
@@ -143,15 +140,12 @@ class OpenAILLMAdapter(BaseLLMAdapter[OpenAILLMInvocationParams]):
             )
             messages = [{"role": "system", "content": system_instruction}] + messages
 
-        return cast(
-            OpenAILLMInvocationParams,
-            {
-                "messages": messages,
-                # NOTE; LLMContext's tools are guaranteed to be a ToolsSchema (or NOT_GIVEN)
-                "tools": self.from_standard_tools(context.tools),
-                "tool_choice": _openai_from_llm_context_tool_choice(context.tool_choice),
-            },
-        )
+        return {
+            "messages": messages,
+            # NOTE; LLMContext's tools are guaranteed to be a ToolsSchema (or NOT_GIVEN)
+            "tools": self.from_standard_tools(context.tools),
+            "tool_choice": _openai_from_llm_context_tool_choice(context.tool_choice),
+        }
 
     def to_provider_tools_format(self, tools_schema: ToolsSchema) -> list[ChatCompletionToolParam]:
         """Convert function schemas to OpenAI's function-calling format.
@@ -164,19 +158,13 @@ class OpenAILLMAdapter(BaseLLMAdapter[OpenAILLMInvocationParams]):
             with ChatCompletion API.
         """
         functions_schema = tools_schema.standard_tools
-        # `function=...` expects a `FunctionDefinition` TypedDict; the dict
-        # produced by `to_default_dict()` is structurally compatible. Cast at
-        # the boundary.
-        formatted_standard_tools: list[ChatCompletionToolParam] = [
-            ChatCompletionToolParam(type="function", function=cast(Any, func.to_default_dict()))
+        formatted_standard_tools = [
+            ChatCompletionToolParam(type="function", function=func.to_default_dict())
             for func in functions_schema
         ]
-        custom_openai_tools: list[ChatCompletionToolParam] = []
+        custom_openai_tools = []
         if tools_schema.custom_tools:
-            custom_openai_tools = cast(
-                list[ChatCompletionToolParam],
-                tools_schema.custom_tools.get(AdapterType.OPENAI, []),
-            )
+            custom_openai_tools = tools_schema.custom_tools.get(AdapterType.OPENAI, [])
         return formatted_standard_tools + custom_openai_tools
 
     def get_messages_for_logging(self, context: LLMContext) -> list[dict[str, Any]]:
@@ -190,10 +178,7 @@ class OpenAILLMAdapter(BaseLLMAdapter[OpenAILLMInvocationParams]):
         Returns:
             List of messages in a format ready for logging about OpenAI.
         """
-        return cast(
-            list[dict[str, Any]],
-            self.get_messages(context, truncate_large_values=True),
-        )
+        return self.get_messages(context, truncate_large_values=True)
 
     def _from_universal_context_messages(
         self,

src/pipecat/adapters/services/open_ai_realtime_adapter.py

@@ -9,7 +9,7 @@
 import copy
 import json
 from dataclasses import dataclass
-from typing import Any, TypedDict, cast
+from typing import Any, TypedDict
 
 from loguru import logger
 
@@ -81,7 +81,7 @@ class OpenAIRealtimeLLMAdapter(BaseLLMAdapter):
         Returns:
             List of messages in a format ready for logging about OpenAI Realtime.
         """
-        return cast(list[dict[str, Any]], self.get_messages(context, truncate_large_values=True))
+        return self.get_messages(context, truncate_large_values=True)
 
     @dataclass
     class ConvertedMessages:
@@ -101,24 +101,12 @@ class OpenAIRealtimeLLMAdapter(BaseLLMAdapter):
         if not universal_context_messages:
             return self.ConvertedMessages(messages=[])
 
-        # NOTE: This adapter does not yet handle ``LLMSpecificMessage`` — those
-        # are filtered out below. Other adapters (e.g. Anthropic) dispatch
-        # LLMSpecific items through a per-provider passthrough. For OpenAI
-        # Realtime, the strategy here packs a multi-message history into a
-        # single text message (see comment further down), which doesn't
-        # compose with opaque per-provider payloads. If/when this adapter
-        # adopts the per-message strategy, LLMSpecific items can flow
-        # through `_from_universal_context_message` like in other adapters.
-        messages: list[dict[str, Any]] = [
-            cast(dict[str, Any], m)
-            for m in copy.deepcopy(universal_context_messages)
-            if isinstance(m, dict)
-        ]
+        messages = copy.deepcopy(universal_context_messages)
         system_instruction = None
 
         # If we have a "system" message as our first message,
         # pull that out into session "instructions"
-        if messages and messages[0].get("role") == "system":
+        if messages[0].get("role") == "system":
             system = messages.pop(0)
             content = system.get("content")
             if isinstance(content, str):
@@ -136,9 +124,7 @@ class OpenAIRealtimeLLMAdapter(BaseLLMAdapter):
         # If we have just a single "user" item, we can just send it normally
         if len(messages) == 1 and messages[0].get("role") == "user":
             return self.ConvertedMessages(
-                messages=[
-                    self._from_universal_context_message(cast(LLMContextMessage, messages[0]))
-                ],
+                messages=[self._from_universal_context_message(messages[0])],
                 system_instruction=system_instruction,
             )
 
@@ -156,18 +142,18 @@ class OpenAIRealtimeLLMAdapter(BaseLLMAdapter):
 
         return self.ConvertedMessages(
             messages=[
-                events.ConversationItem(
-                    role="user",
-                    type="message",
-                    content=[
-                        events.ItemContent(
-                            type="input_text",
-                            text="\n\n".join(
+                {
+                    "role": "user",
+                    "type": "message",
+                    "content": [
+                        {
+                            "type": "input_text",
+                            "text": "\n\n".join(
                                 [intro_text, json.dumps(messages, indent=2), trailing_text]
                             ),
-                        )
+                        }
                     ],
-                )
+                }
             ],
             system_instruction=system_instruction,
         )
@@ -175,34 +161,31 @@ class OpenAIRealtimeLLMAdapter(BaseLLMAdapter):
     def _from_universal_context_message(
         self, message: LLMContextMessage
     ) -> events.ConversationItem:
-        # NOTE: ``LLMSpecificMessage`` is not yet handled here — see the
-        # corresponding note in `_from_universal_context_messages`.
-        msg = cast(dict[str, Any], message)
-        if msg.get("role") == "user":
-            content = msg.get("content")
-            if isinstance(content, list):
+        if message.get("role") == "user":
+            content = message.get("content")
+            if isinstance(message.get("content"), list):
                 content = ""
-                for c in msg.get("content", []):
+                for c in message.get("content"):
                     if c.get("type") == "text":
                         content += " " + c.get("text")
                     else:
                         logger.error(
-                            f"Unhandled content type in context message: {c.get('type')} - {msg}"
+                            f"Unhandled content type in context message: {c.get('type')} - {message}"
                         )
             return events.ConversationItem(
                 role="user",
                 type="message",
                 content=[events.ItemContent(type="input_text", text=content)],
             )
-        if msg.get("role") == "assistant" and msg.get("tool_calls"):
-            tc = msg["tool_calls"][0]
+        if message.get("role") == "assistant" and message.get("tool_calls"):
+            tc = message.get("tool_calls")[0]
             return events.ConversationItem(
                 type="function_call",
                 call_id=tc["id"],
                 name=tc["function"]["name"],
                 arguments=tc["function"]["arguments"],
             )
-        raise ValueError(f"Unhandled message type in _from_universal_context_message: {msg}")
+        logger.error(f"Unhandled message type in _from_universal_context_message: {message}")
 
     @staticmethod
     def _to_openai_realtime_function_format(function: FunctionSchema) -> dict[str, Any]:

src/pipecat/adapters/services/open_ai_responses_adapter.py

@@ -6,7 +6,7 @@
 
 """OpenAI Responses API adapter for Pipecat."""
 
-from typing import Any, Required, TypedDict, cast
+from typing import Any, TypedDict
 
 from openai._types import NotGiven as OpenAINotGiven
 from openai.types.responses import FunctionToolParam, ResponseInputItemParam, ToolParam
@@ -23,10 +23,8 @@ from pipecat.processors.aggregators.llm_context import (
 class OpenAIResponsesLLMInvocationParams(TypedDict, total=False):
     """Context-based parameters for invoking OpenAI Responses API."""
 
-    # `input` and `tools` are always populated by `get_llm_invocation_params`;
-    # `instructions` is only set when a system instruction is present.
-    input: Required[list[ResponseInputItemParam]]
-    tools: Required[list[ToolParam] | OpenAINotGiven]
+    input: list[ResponseInputItemParam]
+    tools: list[ToolParam] | OpenAINotGiven
     instructions: str
 
 
@@ -66,11 +64,8 @@ class OpenAIResponsesLLMAdapter(BaseLLMAdapter[OpenAIResponsesLLMInvocationParam
         if system_instruction and messages:
             first_msg = messages[0] if not isinstance(messages[0], LLMSpecificMessage) else None
             if first_msg and first_msg.get("role") == "system":
-                # `content` is `str | Iterable[...]`; we only forward it for
-                # warning purposes. Coerce non-strings to None.
-                first_content = first_msg.get("content", "")
                 self._resolve_system_instruction(
-                    first_content if isinstance(first_content, str) else None,
+                    first_msg.get("content", ""),
                     system_instruction,
                     discard_context_system=False,
                 )
@@ -148,10 +143,7 @@ class OpenAIResponsesLLMAdapter(BaseLLMAdapter[OpenAIResponsesLLMInvocationParam
         Returns:
             List of messages in a format ready for logging.
         """
-        return cast(
-            list[dict[str, Any]],
-            self.get_messages(context, truncate_large_values=True),
-        )
+        return self.get_messages(context, truncate_large_values=True)
 
     def _convert_messages_to_input(
         self, messages: list[LLMContextMessage]
@@ -177,15 +169,13 @@ class OpenAIResponsesLLMAdapter(BaseLLMAdapter[OpenAIResponsesLLMInvocationParam
                 content = message.get("content", "")
                 if isinstance(content, list):
                     content = self._convert_multimodal_content(content)
-                result.append(
-                    cast(ResponseInputItemParam, {"role": "developer", "content": content})
-                )
+                result.append({"role": "developer", "content": content})
 
             elif role == "user":
                 content = message.get("content", "")
                 if isinstance(content, list):
                     content = self._convert_multimodal_content(content)
-                result.append(cast(ResponseInputItemParam, {"role": "user", "content": content}))
+                result.append({"role": "user", "content": content})
 
             elif role == "assistant":
                 tool_calls = message.get("tool_calls")
@@ -204,9 +194,7 @@ class OpenAIResponsesLLMAdapter(BaseLLMAdapter[OpenAIResponsesLLMInvocationParam
                     content = message.get("content", "")
                     if isinstance(content, list):
                         content = self._convert_multimodal_content(content)
-                    result.append(
-                        cast(ResponseInputItemParam, {"role": "assistant", "content": content})
-                    )
+                    result.append({"role": "assistant", "content": content})
 
             elif role == "tool":
                 content = message.get("content", "")

src/pipecat/adapters/services/perplexity_adapter.py

@@ -28,7 +28,6 @@ the messages are sent to Perplexity's API.
 """
 
 import copy
-from typing import Any, cast
 
 from openai.types.chat import ChatCompletionMessageParam
 
@@ -117,11 +116,7 @@ class PerplexityLLMAdapter(OpenAILLMAdapter):
         if not messages:
             return messages
 
-        # ChatCompletionMessageParam is a union of TypedDicts; the
-        # transformations below mutate by key/index in ways those TypedDicts
-        # don't permit. Work against a plain-dict view for the duration of
-        # the transformation and cast back at the return site.
-        msgs: list[dict[str, Any]] = cast(list[dict[str, Any]], copy.deepcopy(messages))
+        messages = copy.deepcopy(messages)
 
         # Note: "developer" → "user" conversion is handled by the parent adapter
         # via the convert_developer_to_user parameter.
@@ -130,10 +125,10 @@ class PerplexityLLMAdapter(OpenAILLMAdapter):
         # Perplexity allows system messages at the start, but rejects them
         # after any non-system message.
         in_initial_system_block = True
-        for i in range(len(msgs)):
-            if msgs[i].get("role") == "system":
+        for i in range(len(messages)):
+            if messages[i].get("role") == "system":
                 if not in_initial_system_block:
-                    msgs[i]["role"] = "user"
+                    messages[i]["role"] = "user"
             else:
                 in_initial_system_block = False
 
@@ -142,9 +137,9 @@ class PerplexityLLMAdapter(OpenAILLMAdapter):
         # messages that violate Perplexity's strict alternation requirement.
         # Skip consecutive system messages at the start — Perplexity allows those.
         i = 0
-        while i < len(msgs) - 1:
-            current = msgs[i]
-            next_msg = msgs[i + 1]
+        while i < len(messages) - 1:
+            current = messages[i]
+            next_msg = messages[i + 1]
             if current["role"] == next_msg["role"] == "system":
                 # Perplexity allows multiple initial system messages, don't merge
                 i += 1
@@ -159,7 +154,7 @@ class PerplexityLLMAdapter(OpenAILLMAdapter):
                     next_msg.get("content"), list
                 ):
                     current["content"].extend(next_msg["content"])
-                msgs.pop(i + 1)
+                messages.pop(i + 1)
             else:
                 i += 1
 
@@ -167,7 +162,7 @@ class PerplexityLLMAdapter(OpenAILLMAdapter):
         # Perplexity requires the last message to be "user" or "tool".
         # OpenAI appears to silently ignore trailing assistant messages
         # server-side, so removing them preserves equivalent behavior.
-        while msgs and msgs[-1].get("role") == "assistant":
-            msgs.pop()
+        while messages and messages[-1].get("role") == "assistant":
+            messages.pop()
 
-        return cast(list[ChatCompletionMessageParam], msgs)
+        return messages

src/pipecat/audio/dtmf/utils.py

@@ -14,7 +14,7 @@ in-memory after first load to improve performance on subsequent accesses.
 import asyncio
 import io
 import wave
-from importlib.resources import as_file, files
+from importlib.resources import files
 
 import aiofiles
 
@@ -52,12 +52,10 @@ async def load_dtmf_audio(button: KeypadEntry, *, sample_rate: int = 8000) -> by
             __DTMF_RESAMPLER__ = create_file_resampler()
 
         dtmf_file_name = __DTMF_FILE_NAME.get(button, f"dtmf-{button.value}.wav")
-        # `as_file` materialises the resource as a real filesystem `Path`,
-        # which aiofiles can open. (For installed packages this is just the
-        # bundled file; for zipped distributions it would extract to a temp.)
-        with as_file(files("pipecat.audio.dtmf").joinpath(dtmf_file_name)) as dtmf_file_path:
-            async with aiofiles.open(dtmf_file_path, "rb") as f:
-                data = await f.read()
+        dtmf_file_path = files("pipecat.audio.dtmf").joinpath(dtmf_file_name)
+
+        async with aiofiles.open(dtmf_file_path, "rb") as f:
+            data = await f.read()
 
         with io.BytesIO(data) as buffer:
             with wave.open(buffer, "rb") as wf: